diff --git a/.gitignore b/.gitignore index ae44b9a..8619775 100644 --- a/.gitignore +++ b/.gitignore @@ -20,6 +20,12 @@ results.csv # Compiled python modules. *.pyc +# results folders in feature_importance +feature_importance/**/results/* + +# gene data files ending with .csv +feature_importance/gene-data/*.csv + # Setuptools distribution folder. /dist/ diff --git a/feature_importance/compile-jupyter/compile_pdf.sh b/feature_importance/compile-jupyter/compile_pdf.sh index 95e41f5..fdbebcd 100755 --- a/feature_importance/compile-jupyter/compile_pdf.sh +++ b/feature_importance/compile-jupyter/compile_pdf.sh @@ -73,9 +73,15 @@ temp_path="${path%.ipynb}-temp.ipynb" # add title and author metadata python3 compile_helper.py add_metadata "$path" "$title" "${authors[@]}" -# convert to pdf -jupyter nbconvert --execute --no-input --to pdf "$temp_path" \ - --output "$output_name" +# use the following to convert to pdf by re-running the file +# jupyter nbconvert --execute --no-input --to pdf "$temp_path" \ +# --output "$output_name" + +# use the following to convert to pdf without re-running the file +# jupyter nbconvert --no-input --to pdf "$temp_path" --output "$output_name" + +# use the following to convert to pdf including code +jupyter nbconvert --execute --to pdf "$temp_path" --output "$output_name" # delete temporary file python3 compile_helper.py delete_temp "$path" diff --git a/feature_importance/correlation-bias/correlation-runner.sh b/feature_importance/correlation-bias/correlation-runner.sh new file mode 100644 index 0000000..fe8b5c5 --- /dev/null +++ b/feature_importance/correlation-bias/correlation-runner.sh @@ -0,0 +1,14 @@ +#!/bin/bash + +slurm_script="correlation.sh" + +for rep in {1..50} +do + for pve in {0.1,0.4} + do + for rho in {0.5,0.6,0.7,0.8,0.9,0.99} + do + sbatch $slurm_script $rep $pve $rho # Submit SLURM job using the specified script + done + done +done \ No newline at end of file diff --git a/feature_importance/correlation-bias/correlation.ipynb b/feature_importance/correlation-bias/correlation.ipynb new file mode 100644 index 0000000..ea27e66 --- /dev/null +++ b/feature_importance/correlation-bias/correlation.ipynb @@ -0,0 +1,215 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import math\n", + "from matplotlib import font_manager" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "methods = ['shap', 'signed_normalized_l2_avg', 'signed_normalized_l2_noavg',\n", + " 'signed_nonnormalized_l2_avg', 'signed_nonnormalized_l2_noavg',\n", + " 'nonl2_avg', 'nonl2_noavg', 'l2_ranking', 'nonl2_ranking',\n", + " 'normalized_l2_ranking', 'baseline', 'mdi']\n", + "pves = [0.1, 0.4]\n", + "rhos = [0.5, 0.6, 0.7, 0.8, 0.9, 0.99]\n", + "mean_results = {}\n", + "sd_results = {}\n", + "for method in methods:\n", + " group_results = {}\n", + " sd_group_results = {}\n", + " sig_mat = np.zeros((len(pves), len(rhos)))\n", + " c_nsig_mat = np.zeros((len(pves), len(rhos)))\n", + " nsig_mat = np.zeros((len(pves), len(rhos)))\n", + " sig_sd = np.zeros((len(pves), len(rhos)))\n", + " c_nsig_sd = np.zeros((len(pves), len(rhos)))\n", + " nsig_sd = np.zeros((len(pves), len(rhos)))\n", + " for pve_idx in range(len(pves)):\n", + " for rho_idx in range(len(rhos)):\n", + " rankings = np.zeros((250, 100, 50)) # 250 samples, 100 features, 50 seeds\n", + " for seed in range(1, 51):\n", + " if method == 'mdi':\n", + " ranking = pd.read_csv(f\"results/pve{pves[pve_idx]}/rho{rhos[rho_idx]}/seed{seed}/rankings/{method}.csv\").to_numpy()\n", + " # repeat ranking 250 times on axis 0\n", + " ranking = np.repeat(ranking.T, rankings.shape[0], axis=0)\n", + " rankings[:, :, seed-1] = ranking\n", + " else:\n", + " rankings[:, :, seed-1] = pd.read_csv(f\"results/pve{pves[pve_idx]}/rho{rhos[rho_idx]}/seed{seed}/rankings/{method}.csv\").to_numpy()\n", + " # print(np.mean(rankings, axis = 0).shape)\n", + " sds = np.std(np.mean(rankings, axis = 0), axis=1)/math.sqrt(rankings.shape[0])\n", + " # print(sds.shape)\n", + " rankings = np.mean(rankings, axis=2)\n", + " # average first six columns in rankings\n", + " sig_mat[pve_idx, rho_idx] = np.mean(rankings[:, :6])\n", + " sig_sd[pve_idx, rho_idx] = np.mean(sds[:6])\n", + " # average features 7-50 in rankings\n", + " c_nsig_mat[pve_idx, rho_idx] = np.mean(rankings[:, 6:50])\n", + " c_nsig_sd[pve_idx, rho_idx] = np.mean(sds[6:50])\n", + " # average features 51-100 in rankings\n", + " nsig_mat[pve_idx, rho_idx] = np.mean(rankings[:, 50:])\n", + " nsig_sd[pve_idx, rho_idx] = np.mean(sds[50:])\n", + " group_results['sig'] = sig_mat\n", + " group_results['c_nsig'] = c_nsig_mat\n", + " group_results['nsig'] = nsig_mat\n", + " sd_group_results['sig'] = sig_sd\n", + " sd_group_results['c_nsig'] = c_nsig_sd\n", + " sd_group_results['nsig'] = nsig_sd\n", + " sd_results[method] = sd_group_results\n", + " mean_results[method] = group_results" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "viz_methods = ['shap', 'nonl2_ranking', 'mdi']\n", + "titles = {'shap': 'TreeSHAP', 'nonl2_ranking': 'LMDI+', 'mdi': 'MDI'}\n", + "for pve_idx in range(len(pves)): \n", + " for method in viz_methods:\n", + " # plot results, where each group is a separate line on the plot\n", + " plt.plot(mean_results[method]['sig'][pve_idx, :], marker = \"o\",\n", + " label=\"Signal\", color='forestgreen')\n", + " plt.fill_between(range(len(rhos)),\n", + " mean_results[method]['sig'][pve_idx, :] - \\\n", + " sd_results[method]['sig'][pve_idx, :],\n", + " mean_results[method]['sig'][pve_idx, :] + \\\n", + " sd_results[method]['sig'][pve_idx, :],\n", + " color='forestgreen', alpha=0.3)\n", + " plt.plot(mean_results[method]['c_nsig'][pve_idx, :], marker = \"o\",\n", + " label=\"Cor. Non-Signal\", color='#ADEBB3')\n", + " plt.fill_between(range(len(rhos)),\n", + " mean_results[method]['c_nsig'][pve_idx, :] - \\\n", + " sd_results[method]['c_nsig'][pve_idx, :],\n", + " mean_results[method]['c_nsig'][pve_idx, :] + \\\n", + " sd_results[method]['c_nsig'][pve_idx, :],\n", + " color='#ADEBB3', alpha=0.3)\n", + " plt.plot(mean_results[method]['nsig'][pve_idx, :], marker = \"o\",\n", + " label=\"Non-Signal\", color='red')\n", + " plt.fill_between(range(len(rhos)),\n", + " mean_results[method]['nsig'][pve_idx, :] - \\\n", + " sd_results[method]['nsig'][pve_idx, :],\n", + " mean_results[method]['nsig'][pve_idx, :] + \\\n", + " sd_results[method]['nsig'][pve_idx, :],\n", + " color = 'red', alpha=0.3)\n", + " # x-axis ticks should be rho values\n", + " plt.xticks(range(len(rhos)), rhos)\n", + " plt.xlabel(\"Correlation\", fontsize=18)\n", + " # make xaxis label big\n", + " plt.tick_params(axis='both', labelsize=15)\n", + " # y-axis label should be Average Ranking\n", + " plt.ylabel(\"Average Ranking\", fontsize=18)\n", + " plt.title(titles[method], fontsize=20, fontweight='bold')\n", + " # set y-axis limits from 15-70\n", + " plt.ylim(10, 70)\n", + " # put legend to the right of the plot\n", + " # plt.legend()\n", + " title_font_properties = font_manager.FontProperties(weight='bold', size=18)\n", + " plt.legend(title = \"Feature Group\", loc = \"upper left\", bbox_to_anchor=(1,0.7), fontsize=15, title_fontproperties = title_font_properties)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mdi", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/feature_importance/correlation-bias/correlation.sh b/feature_importance/correlation-bias/correlation.sh new file mode 100644 index 0000000..fc5ad6a --- /dev/null +++ b/feature_importance/correlation-bias/correlation.sh @@ -0,0 +1,10 @@ +#!/bin/bash +#SBATCH --cpus-per-task=8 + +njobs=8 + +source activate mdi +command="correlation_pipeline.py --seed ${1} --pve ${2} --rho ${3} --njobs $njobs" + +# Execute the command +python $command \ No newline at end of file diff --git a/feature_importance/correlation-bias/correlation_pipeline.py b/feature_importance/correlation-bias/correlation_pipeline.py new file mode 100644 index 0000000..858e45c --- /dev/null +++ b/feature_importance/correlation-bias/correlation_pipeline.py @@ -0,0 +1,305 @@ +# imports from imodels +from imodels import get_clean_dataset +from imodels.tree.rf_plus.rf_plus.rf_plus_models import \ + RandomForestPlusRegressor, RandomForestPlusClassifier +from imodels.tree.rf_plus.feature_importance.rfplus_explainer import \ + RFPlusMDI, AloRFPlusMDI +from simulations_util import partial_linear_lss_model + +# imports from sklearn +from sklearn.model_selection import train_test_split +from sklearn.metrics import roc_auc_score, average_precision_score, f1_score, \ + accuracy_score, r2_score, f1_score, log_loss, root_mean_squared_error +from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier +from sklearn.linear_model import LogisticRegression, LinearRegression +from sklearn.preprocessing import OneHotEncoder, LabelEncoder +from sklearn.impute import SimpleImputer + +# parallelization imports +from joblib import Parallel, delayed + +# timing imports +import time + +# other data science imports +import numpy as np +import pandas as pd +import shap +from ucimlrepo import fetch_ucirepo + +# i/o imports +import argparse +import os +from os.path import join as oj + +# global variable for classification/regression status +TASK = None + +def simulate_data(rho, pve, seed): + + np.random.seed(seed) + + n = 250 # number of samples + p1 = 50 # number of correlated features + p2 = 50 # number of uncorrelated features + + # create the covariance matrix for the first block (correlated features) + Sigma_1 = np.full((p1, p1), rho) # matrix filled with rho + np.fill_diagonal(Sigma_1, 1) # set diagonal elements to 1 + + # create the covariance matrix for the second block (uncorrelated features) + Sigma_2 = np.eye(p2) # identity matrix for independent features + + # create the full covariance matrix by combining the two blocks + # using np.zeros to initialize the off-diagonal blocks + Sigma = np.block([ + [Sigma_1, np.zeros((p1, p2))], # Correlated features with independent features (zero covariance) + [np.zeros((p2, p1)), Sigma_2] # Independent features (identity covariance) + ]) + + # mean vector (zero mean) + mu = np.zeros(100) + + # draw X from the multivariate normal distribution + X = np.random.multivariate_normal(mu, Sigma, size = n) + + y = partial_linear_lss_model(X=X, s=2, m=3, r=2, tau=0, beta=1, heritability=pve) + + return X, y + +def split_data(X, y, test_size, seed): + # split data into train and test sets + X_train, X_test, y_train, y_test = train_test_split(X, y, + test_size=test_size, + random_state=seed) + return X_train, X_test, y_train, y_test + +def fit_models(X_train, y_train): + # fit models + if TASK == "classification": + rf = RandomForestClassifier(n_estimators = 100, min_samples_leaf=3, + max_features = "sqrt", random_state=42) + rf.fit(X_train, y_train) + rf_plus = RandomForestPlusClassifier(rf_model=rf) + rf_plus.fit(X_train, y_train) + elif TASK == "regression": + rf = RandomForestRegressor(n_estimators = 100, min_samples_leaf=5, + max_features = 0.33, random_state=42) + rf.fit(X_train, y_train) + rf_plus = RandomForestPlusRegressor(rf_model=rf) + rf_plus.fit(X_train, y_train) + else: + raise ValueError("Task must be either 'classification' or 'regression'.") + return rf, rf_plus + +def get_shap(X, shap_explainer): + if TASK == "classification": + # the shap values are an array of shape + # (# of samples, # of features, # of classes), and in this binary + # classification case, we want the shap values for the positive class. + # check_additivity=False is used to speed up computation. + shap_values = \ + shap_explainer.shap_values(X, check_additivity=False)[:, :, 1] + else: + # check_additivity=False is used to speed up computation. + shap_values = shap_explainer.shap_values(X, check_additivity=False) + # get the rankings of the shap values. negative absolute value is taken + # because np.argsort sorts from smallest to largest. + shap_rankings = np.argsort(-np.abs(shap_values), axis = 1) + return shap_values, shap_rankings + +def get_lmdi(X, y, lmdi_explainer, l2norm, sign, normalize, leaf_average, ranking=False): + # get feature importances + lmdi = lmdi_explainer.explain_linear_partial(X, y, l2norm=l2norm, sign=sign, + normalize=normalize, + leaf_average=leaf_average, + ranking=ranking) + mdi_rankings = lmdi_explainer.get_rankings(np.abs(lmdi)) + return lmdi, mdi_rankings + +if __name__ == '__main__': + + # start time + start = time.time() + + TASK = "regression" + + # store command-line arguments + parser = argparse.ArgumentParser() + parser.add_argument('--seed', type=int, default=None) + parser.add_argument('--rho', type=float, default=None) + parser.add_argument('--pve', type=float, default=None) + parser.add_argument('--njobs', type=int, default=1) + args = parser.parse_args() + + # convert namespace to a dictionary + args_dict = vars(args) + + # assign the arguments to variables + seed = args_dict['seed'] + rho = args_dict['rho'] + pve = args_dict['pve'] + njobs = args_dict['njobs'] + + X_train, y_train = simulate_data(rho, pve, seed) + + # end time + end = time.time() + + # print progress message + print(f"Progress Message 1/5: Obtained data with PVE = {pve}, rho = {rho}, and seed = {seed}.") + print(f"Step #1 took {end-start} seconds.") + + # start time + start = time.time() + + # fit the prediction models + rf, rf_plus = fit_models(X_train, y_train) + + # fit baseline model + rf_plus_baseline = RandomForestPlusRegressor(rf_model=rf, + include_raw=False, fit_on="inbag", + prediction_model=LinearRegression()) + rf_plus_baseline.fit(X_train, y_train) + + # end time + end = time.time() + + print(f"Progress Message 2/5: RF and RF+ models fit.") + print(f"Step #2 took {end-start} seconds.") + + # start time + start = time.time() + + # obtain shap feature importances + # shap_explainer = shap.TreeExplainer(rf) + # shap_values, shap_rankings = get_shap(X_train, shap_explainer) + + # end time + end = time.time() + + print(f"Progress Message 3/5: SHAP values/rankings obtained.") + print(f"Step #3 took {end-start} seconds.") + + # start time + start = time.time() + + # obtain lmdi feature importances + # lmdi_explainer_signed_normalized_l2_avg = AloRFPlusMDI(rf_plus, mode = "only_k") + # lmdi_explainer_signed_normalized_l2_noavg = AloRFPlusMDI(rf_plus, mode = "only_k") + # lmdi_explainer_signed_nonnormalized_l2_avg = AloRFPlusMDI(rf_plus, mode = "only_k") + # lmdi_explainer_signed_nonnormalized_l2_noavg = AloRFPlusMDI(rf_plus, mode = "only_k") + # lmdi_explainer_nonl2_avg = AloRFPlusMDI(rf_plus, mode = "only_k") + # lmdi_explainer_nonl2_noavg = AloRFPlusMDI(rf_plus, mode = "only_k") + # lmdi_explainer_l2_ranking = AloRFPlusMDI(rf_plus, mode = "only_k") + # lmdi_explainer_nonl2_ranking = AloRFPlusMDI(rf_plus, mode = "only_k") + # lmdi_explainer_normalized_l2_ranking = AloRFPlusMDI(rf_plus, mode = "only_k") + # lmdi_explainer_nonnormalized_l2_ranking = AloRFPlusMDI(rf_plus, mode = "only_k") + # lmdi_baseline_explainer = RFPlusMDI(rf_plus_baseline, mode = "only_k", evaluate_on = "inbag") + # lmdi_values_signed_normalized_l2_avg, \ + # lmdi_rankings_signed_normalized_l2_avg = \ + # get_lmdi(X_train, y_train, lmdi_explainer_signed_normalized_l2_avg, + # l2norm=True, sign=True, normalize=True, leaf_average=True) + # lmdi_values_signed_normalized_l2_noavg, \ + # lmdi_rankings_signed_normalized_l2_noavg = \ + # get_lmdi(X_train, y_train,lmdi_explainer_signed_normalized_l2_noavg, + # l2norm=True, sign=True, normalize=True, leaf_average=False) + # lmdi_values_signed_nonnormalized_l2_avg, \ + # lmdi_rankings_signed_nonnormalized_l2_avg = \ + # get_lmdi(X_train,y_train,lmdi_explainer_signed_nonnormalized_l2_avg, + # l2norm=True, sign=True, normalize=False, leaf_average=True) + # lmdi_values_signed_nonnormalized_l2_noavg, \ + # lmdi_rankings_signed_nonnormalized_l2_noavg = \ + # get_lmdi(X_train, y_train, + # lmdi_explainer_signed_nonnormalized_l2_noavg, l2norm=True, + # sign=True, normalize=False, leaf_average=False) + # lmdi_values_nonl2_avg, lmdi_rankings_nonl2_avg = \ + # get_lmdi(X_train, y_train, lmdi_explainer_nonl2_avg, l2norm=False, + # sign=False, normalize=False, leaf_average=True) + # lmdi_values_nonl2_noavg, lmdi_rankings_nonl2_noavg = \ + # get_lmdi(X_train, y_train, lmdi_explainer_nonl2_noavg, l2norm=False, + # sign=False, normalize=False, leaf_average=False) + # lmdi_values_l2_ranking, lmdi_rankings_l2_ranking = \ + # get_lmdi(X_train, y_train, lmdi_explainer_l2_ranking, l2norm=True, + # sign=False, normalize=False, leaf_average=False, ranking=True) + # lmdi_values_nonl2_ranking, lmdi_rankings_nonl2_ranking = \ + # get_lmdi(X_train, y_train, lmdi_explainer_nonl2_ranking, l2norm=False, + # sign=False, normalize=False, leaf_average=False, ranking=True) + # lmdi_values_normalized_l2_ranking, lmdi_rankings_normalized_l2_ranking = \ + # get_lmdi(X_train, y_train, lmdi_explainer_normalized_l2_ranking, l2norm=True, + # sign=False, normalize=True, leaf_average=False, ranking=True) + # lmdi_values_baseline, lmdi_rankings_baseline = \ + # get_lmdi(X_train, y_train, lmdi_baseline_explainer, l2norm=False, + # sign=False, normalize=False, leaf_average=False) + + # # create storage for iteration purposes + # lfi_values = \ + # {'shap': shap_values, + # 'signed_normalized_l2_avg': lmdi_values_signed_normalized_l2_avg, + # 'signed_normalized_l2_noavg': lmdi_values_signed_normalized_l2_noavg, + # 'signed_nonnormalized_l2_avg': lmdi_values_signed_nonnormalized_l2_avg, + # 'signed_nonnormalized_l2_noavg': + # lmdi_values_signed_nonnormalized_l2_noavg, + # 'nonl2_avg': lmdi_values_nonl2_avg, + # 'nonl2_noavg': lmdi_values_nonl2_noavg, + # 'l2_ranking': lmdi_values_l2_ranking, + # 'nonl2_ranking': lmdi_values_nonl2_ranking, + # 'normalized_l2_ranking': lmdi_values_normalized_l2_ranking, + # 'baseline': lmdi_values_baseline} + # lfi_rankings = \ + # {'shap': shap_rankings, + # 'signed_normalized_l2_avg': lmdi_rankings_signed_normalized_l2_avg, + # 'signed_normalized_l2_noavg': lmdi_rankings_signed_normalized_l2_noavg, + # 'signed_nonnormalized_l2_avg': lmdi_rankings_signed_nonnormalized_l2_avg, + # 'signed_nonnormalized_l2_noavg': + # lmdi_rankings_signed_nonnormalized_l2_noavg, + # 'nonl2_avg': lmdi_rankings_nonl2_avg, + # 'nonl2_noavg': lmdi_rankings_nonl2_noavg, + # 'l2_ranking': lmdi_rankings_l2_ranking, + # 'nonl2_ranking': lmdi_rankings_nonl2_ranking, + # 'normalized_l2_ranking': lmdi_values_normalized_l2_ranking, + # 'baseline': lmdi_rankings_baseline} + + # get mdi importances from rf + mdi_values = rf.feature_importances_ + mdi_rankings = np.argsort(-np.abs(mdi_values)) + + # create storage for iteration purposes + lfi_values = \ + {'mdi': mdi_values} + + lfi_rankings = \ + {'mdi': mdi_rankings} + + # end time + end = time.time() + + print(f"Progress Message 4/5: LMDI+ values/rankings obtained.") + print(f"Step #4 took {end-start} seconds.") + + result_dir = oj(os.path.dirname(os.path.realpath(__file__)), + f'results/pve{pve}/rho{rho}/seed{seed}') + + # get result dataframes + for method, values in lfi_values.items(): + df = pd.DataFrame(values) + directory = oj(result_dir, + f'values') + if not os.path.exists(directory): + os.makedirs(directory) + df.to_csv(oj(directory, f'{method}.csv'), + index=False) + for method, rankings in lfi_rankings.items(): + df = pd.DataFrame(rankings) + directory = oj(result_dir, + f'rankings') + if not os.path.exists(directory): + os.makedirs(directory) + df.to_csv(oj(directory, f'{method}.csv'), + index=False) + + # end time + end = time.time() + + print(f"Progress Message 5/5: Results saved to {result_dir}.") + print(f"Step #5 took {end-start} seconds.") \ No newline at end of file diff --git a/feature_importance/correlation-bias/simulations_util.py b/feature_importance/correlation-bias/simulations_util.py new file mode 100644 index 0000000..c516f97 --- /dev/null +++ b/feature_importance/correlation-bias/simulations_util.py @@ -0,0 +1,1156 @@ +import numpy as np +import pandas as pd +import random +from scipy.linalg import toeplitz +import warnings +import math +import imodels +import openml + +def sample_real_data_X(source=None, data_name=None, task_id=None, seed=4307, normalize=False, sample_row_n=None): + np.random.seed(seed) + if source == "imodels": + X, _, _ = imodels.get_clean_dataset(data_name) + elif source == "openml": + task = openml.tasks.get_task(task_id) + dataset_id = task.dataset_id + dataset = openml.datasets.get_dataset(dataset_id) + X, _, _, _ = dataset.get_data(target=dataset.default_target_attribute, dataset_format="array") + if normalize: + X = (X - X.mean()) / X.std() + if sample_row_n is not None: + keep_idx = np.random.choice(X.shape[0], sample_row_n, replace=False) + X = X[keep_idx, :] + return X + + +def sample_real_data_y(X=None, source=None, data_name=None, task_id=None, + seed=4307, sample_row_n=None, return_support=True): + np.random.seed(seed) + if source == "imodels": + _, y, _ = imodels.get_clean_dataset(data_name) + elif source == "openml": + task = openml.tasks.get_task(task_id) + dataset_id = task.dataset_id + dataset = openml.datasets.get_dataset(dataset_id) + _, y, _, _ = dataset.get_data(target=dataset.default_target_attribute,dataset_format="array") + if sample_row_n is not None: + keep_idx = np.random.choice(y.shape[0], sample_row_n, replace=False) + y = y[keep_idx, :] + if return_support: + return y, np.ones(y.shape), None + return y + +def sample_real_X(fpath=None, X=None, seed=None, normalize=True, + sample_row_n=None, sample_col_n=None, permute_col=True, + signal_features=None, n_signal_features=None, permute_nonsignal_col=None): + """ + :param fpath: path to X data + :param X: data matrix + :param seed: random seed + :param normalize: boolean; whether or not to normalize columns in data to mean 0 and variance 1 + :param sample_row_n: number of samples to subset; default keeps all rows + :param sample_col_n: number of features to subset; default keeps all columns + :param permute_col: boolean; whether or not to permute the columns + :param signal_features: list of features to use as signal features + :param n_signal_features: number of signal features; required if permute_nonsignal_col is not None + :param permute_nonsignal_col: how to permute the nonsignal features; must be one of + [None, "block", "indep", "augment"], where None performs no permutation, "block" performs the permutation + row-wise, "indep" permutes each nonsignal feature column independently, "augment" augments the signal features + with the row-permuted X matrix. + :return: + """ + assert permute_nonsignal_col in [None, "block", "indep", "augment"] + if X is None: + X = pd.read_csv(fpath) + if normalize: + X = (X - X.mean()) / X.std() + if seed is not None: + np.random.seed(seed) + if permute_col: + X = X[np.random.permutation(X.columns)] + if sample_row_n is not None: + keep_idx = np.random.choice(X.shape[0], sample_row_n, replace=False) + X = X.iloc[keep_idx, :] + if sample_col_n is not None: + if signal_features is None: + X = X.sample(n=sample_col_n, replace=False, axis=1) + else: + rand_features = np.random.choice([col for col in X.columns if col not in signal_features], + sample_col_n - len(signal_features), replace=False) + X = X[signal_features + list(rand_features)] + if signal_features is not None: + X = X[signal_features + [col for col in X.columns if col not in signal_features]] + if permute_nonsignal_col is not None: + assert n_signal_features is not None + if permute_nonsignal_col == "block": + X = np.hstack([X.iloc[:, :n_signal_features].to_numpy(), + X.iloc[np.random.permutation(X.shape[0]), n_signal_features:].to_numpy()]) + X = pd.DataFrame(X) + elif permute_nonsignal_col == "indep": + for j in range(n_signal_features, X.shape[1]): + X.iloc[:, j] = np.random.permutation(X.iloc[:, j]) + elif permute_nonsignal_col == "augment": + X = np.hstack([X.iloc[:, :n_signal_features].to_numpy(), + X.iloc[np.random.permutation(X.shape[0]), :].to_numpy()]) + X = IndexedArray(pd.DataFrame(X).to_numpy(), index=keep_idx) + return X + return X.to_numpy() + + +def sample_normal_X(n, d, mean=0, scale=1, corr=0, Sigma=None): + """ + Sample X with iid normal entries + :param n: + :param d: + :param mean: + :param scale: + :param corr: + :param Sigma: + :return: + """ + if Sigma is not None: + if np.isscalar(mean): + mean = np.repeat(mean, d) + X = np.random.multivariate_normal(mean, Sigma, size=n) + elif corr == 0: + X = np.random.normal(mean, scale, size=(n, d)) + else: + Sigma = np.zeros((d, d)) + corr + np.fill_diagonal(Sigma, 1) + if np.isscalar(mean): + mean = np.repeat(mean, d) + X = np.random.multivariate_normal(mean, Sigma, size=n) + return X + + +def sample_normal_X_subgroups(n, d, mean, scale): + """ + :param n: Number of samples + :param d: Number of features + :param mean: Nested list of mean of normal distribution for each subgroup + :param scale: Nested ist of scale of normal distribution for each subgroup + :return: + """ + assert len(mean[0]) == len(scale[0]) == d + num_groups = len(mean) + result = [] + group_size = n // num_groups + for i in range(num_groups): + X = np.zeros((group_size, d)) + for j in range(d): + X[:, j] = np.random.normal(mean[i][j], scale[i][j], size=group_size) + result.append(X) + return np.vstack(result) + +def sample_block_cor_X(n, d, rho, n_blocks, mean=0): + """ + Sample X from N(mean, Sigma) where Sigma is a block diagonal covariance matrix + :param n: number of samples + :param d: number of features + :param rho: correlation or vector of correlations + :param n_blocks: number of blocks + :param mean: mean of normal distribution + :return: + """ + Sigma = np.zeros((d, d)) + block_size = d // n_blocks + if np.isscalar(rho): + rho = np.repeat(rho, n_blocks) + for i in range(n_blocks): + start = i * block_size + end = (i + 1) * block_size + if i == (n_blocks - 1): + end = d + Sigma[start:end, start:end] = rho[i] + np.fill_diagonal(Sigma, 1) + X = sample_normal_X(n=n, d=d, mean=mean, Sigma=Sigma) + return X + + +def sample_X(support, X_fun, **kwargs): + """ + Wrapper around dgp function for X that reorders columns so support features are in front + :param support: + :param X_fun: + :param kwargs: + :return: + """ + X = X_fun(**kwargs) + for i in range(X.shape[1]): + if i not in support: + support.append(i) + X[:] = X[:, support] + return X + + +def generate_coef(beta, s): + if isinstance(beta, int) or isinstance(beta, float): + beta = np.repeat(beta, repeats=s) + return beta + + +def corrupt_leverage(x_train, y_train, mean_shift, corrupt_quantile, mode="normal"): + assert mode in ["normal", "constant"] + if mean_shift is None: + return y_train + ranked_rows = np.apply_along_axis(np.linalg.norm, axis=1, arr=x_train).argsort().argsort() + low_idx = np.where(ranked_rows < round(corrupt_quantile * len(y_train)))[0] + hi_idx = np.where(ranked_rows >= (len(y_train) - round(corrupt_quantile * len(y_train))))[0] + if mode == "normal": + hi_corrupted = np.random.normal(mean_shift, 1, size=len(hi_idx)) + low_corrupted = np.random.normal(-mean_shift, 1, size=len(low_idx)) + elif mode == "constant": + hi_corrupted = mean_shift + low_corrupted = -mean_shift + y_train[hi_idx] = hi_corrupted + y_train[low_idx] = low_corrupted + return y_train + + +def linear_model(X, sigma, s, beta, heritability=None, snr=None, error_fun=None, + frac_corrupt=None, corrupt_how='permute', corrupt_size=None, + corrupt_mean=None, return_support=False): + """ + This method is used to crete responses from a linear model with hard sparsity + Parameters: + X: X matrix + s: sparsity + beta: coefficient vector. If beta not a vector, then assumed a constant + sigma: s.d. of added noise + Returns: + numpy array of shape (n) + """ + n, p = X.shape + def create_y(x, s, beta): + linear_term = 0 + for j in range(s): + linear_term += x[j] * beta[j] + return linear_term + + beta = generate_coef(beta, s) + y_train = np.array([create_y(X[i, :], s, beta) for i in range(len(X))]) + if heritability is not None: + sigma = (np.var(y_train) * ((1.0 - heritability) / heritability)) ** 0.5 + if snr is not None: + sigma = (np.var(y_train) / snr) ** 0.5 + if error_fun is None: + error_fun = np.random.randn + if frac_corrupt is None and corrupt_size is None: + y_train = y_train + sigma * error_fun(n) + else: + if frac_corrupt is None: + frac_corrupt = 0 + num_corrupt = int(np.floor(frac_corrupt*len(y_train))) + corrupt_indices = random.sample([*range(len(y_train))], k=num_corrupt) + if corrupt_how == 'permute': + corrupt_array = y_train[corrupt_indices] + corrupt_array = random.sample(list(corrupt_array), len(corrupt_array)) + for i,index in enumerate(corrupt_indices): + y_train[index] = corrupt_array[i] + y_train = y_train + sigma * error_fun(n) + elif corrupt_how == 'cauchy': + for i in range(len(y_train)): + if i in corrupt_indices: + y_train[i] = y_train[i] + sigma*np.random.standard_cauchy() + else: + y_train[i] = y_train[i] + sigma*error_fun() + elif corrupt_how == "leverage_constant": + if isinstance(corrupt_size, int): + corrupt_quantile = corrupt_size / n + else: + corrupt_quantile = corrupt_size + y_train = y_train + sigma * error_fun(n) + corrupt_idx = np.random.choice(range(s, p), size=1) + y_train = corrupt_leverage(X[:, corrupt_idx], y_train, mean_shift=corrupt_mean, corrupt_quantile=corrupt_quantile, mode="constant") + elif corrupt_how == "leverage_normal": + if isinstance(corrupt_size, int): + corrupt_quantile = corrupt_size / n + else: + corrupt_quantile = corrupt_size + y_train = y_train + sigma * error_fun(n) + corrupt_idx = np.random.choice(range(s, p), size=1) + y_train = corrupt_leverage(X[:, corrupt_idx], y_train, mean_shift=corrupt_mean, corrupt_quantile=corrupt_quantile, mode="normal") + + if return_support: + support = np.concatenate((np.ones(s), np.zeros(X.shape[1] - s))) + return y_train, support, beta + else: + return y_train + + +def linear_model_two_groups(X, sigma, s, beta, heritability=None, snr=None, error_fun=None, + frac_corrupt=None, corrupt_how='permute', corrupt_size=None, + corrupt_mean=None, return_support=False): + """ + This method is used to crete responses for two groups from a linear model with hard sparsity + Parameters: + X: X matrix + s: sparsity + beta: coefficient vector. If beta not a vector, then assumed a constant + sigma: s.d. of added noise + Returns: + numpy array of shape (n) + """ + n, p = X.shape + + ### Update start for local MDI+ + def create_y(x, s, beta, group_index): + assert group_index in [0, 1] + linear_term = 0 + start = group_index * s + for j in range(s): + linear_term += x[start+j] * beta[j] + return linear_term + + # Generate two coefficient vectors for each subgroup + beta_group1 = generate_coef(beta, s) + beta_group2 = generate_coef(beta, s) + # Generate two response vectors for each subgroup + y_train_group1 = np.array([create_y(X[i, :], s, beta_group1, group_index=0) for i in range(n//2)]) + y_train_group2 = np.array([create_y(X[i, :], s, beta_group2, group_index=1) for i in range(n//2, n)]) + y_train = np.concatenate((y_train_group1, y_train_group2)) + ### Update for local MDI+ done + + if heritability is not None: + sigma = (np.var(y_train) * ((1.0 - heritability) / heritability)) ** 0.5 + if snr is not None: + sigma = (np.var(y_train) / snr) ** 0.5 + if error_fun is None: + error_fun = np.random.randn + if frac_corrupt is None and corrupt_size is None: + y_train = y_train + sigma * error_fun(n) + else: + if frac_corrupt is None: + frac_corrupt = 0 + num_corrupt = int(np.floor(frac_corrupt*len(y_train))) + corrupt_indices = random.sample([*range(len(y_train))], k=num_corrupt) + if corrupt_how == 'permute': + corrupt_array = y_train[corrupt_indices] + corrupt_array = random.sample(list(corrupt_array), len(corrupt_array)) + for i,index in enumerate(corrupt_indices): + y_train[index] = corrupt_array[i] + y_train = y_train + sigma * error_fun(n) + elif corrupt_how == 'cauchy': + for i in range(len(y_train)): + if i in corrupt_indices: + y_train[i] = y_train[i] + sigma*np.random.standard_cauchy() + else: + y_train[i] = y_train[i] + sigma*error_fun() + elif corrupt_how == "leverage_constant": + if isinstance(corrupt_size, int): + corrupt_quantile = corrupt_size / n + else: + corrupt_quantile = corrupt_size + y_train = y_train + sigma * error_fun(n) + corrupt_idx = np.random.choice(range(s, p), size=1) + y_train = corrupt_leverage(X[:, corrupt_idx], y_train, mean_shift=corrupt_mean, corrupt_quantile=corrupt_quantile, mode="constant") + elif corrupt_how == "leverage_normal": + if isinstance(corrupt_size, int): + corrupt_quantile = corrupt_size / n + else: + corrupt_quantile = corrupt_size + y_train = y_train + sigma * error_fun(n) + corrupt_idx = np.random.choice(range(s, p), size=1) + y_train = corrupt_leverage(X[:, corrupt_idx], y_train, mean_shift=corrupt_mean, corrupt_quantile=corrupt_quantile, mode="normal") + + ### Update start for local MDI+ + if return_support: + support_group1 = np.concatenate((np.ones(s), np.zeros(X.shape[1] - s))) + support_group2 = np.concatenate((np.zeros(s), np.ones(s), np.zeros(X.shape[1] - 2*s))) + return y_train, support_group1, support_group2, beta_group1, beta_group2 + else: + return y_train + ### Update for local MDI+ done + +def lss_model(X, sigma, m, r, tau, beta, heritability=None, snr=None, error_fun=None, min_active=None, + frac_corrupt=None, corrupt_how='permute', corrupt_size=None, corrupt_mean=None, + return_support=False): + """ + This method creates response from an LSS model + + X: data matrix + m: number of interaction terms + r: max order of interaction + tau: threshold + sigma: standard deviation of noise + beta: coefficient vector. If beta not a vector, then assumed a constant + + :return + y_train: numpy array of shape (n) + """ + n, p = X.shape + assert p >= m * r # Cannot have more interactions * size than the dimension + + def lss_func(x, beta): + x_bool = (x - tau) > 0 + y = 0 + for j in range(m): + lss_term_components = x_bool[j * r:j * r + r] + lss_term = int(all(lss_term_components)) + y += lss_term * beta[j] + return y + + def lss_vector_fun(x, beta): + x_bool = (x - tau) > 0 + y = 0 + max_iter = 100 + features = np.arange(p) + support_idx = [] + for j in range(m): + cnt = 0 + while True: + int_features = np.random.choice(features, size=r, replace=False) + lss_term_components = x_bool[:, int_features] + lss_term = np.apply_along_axis(all, 1, lss_term_components) + cnt += 1 + if np.mean(lss_term) >= min_active or cnt > max_iter: + y += lss_term * beta[j] + features = list(set(features).difference(set(int_features))) + support_idx.append(int_features) + if cnt > max_iter: + warnings.warn("Could not find interaction {} with min active >= {}".format(j, min_active)) + break + support_idx = np.stack(support_idx).ravel() + support = np.zeros(p) + for j in support_idx: + support[j] = 1 + return y, support + + beta = generate_coef(beta, m) + if tau == 'median': + tau = np.median(X,axis = 0) + + if min_active is None: + y_train = np.array([lss_func(X[i, :], beta) for i in range(n)]) + support = np.concatenate((np.ones(m * r), np.zeros(X.shape[1] - (m * r)))) + else: + y_train, support = lss_vector_fun(X, beta) + + if heritability is not None: + sigma = (np.var(y_train) * ((1.0 - heritability) / heritability)) ** 0.5 + if snr is not None: + sigma = (np.var(y_train) / snr) ** 0.5 + if error_fun is None: + error_fun = np.random.randn + + if frac_corrupt is None and corrupt_size is None: + y_train = y_train + sigma * error_fun(n) + else: + if frac_corrupt is None: + frac_corrupt = 0 + num_corrupt = int(np.floor(frac_corrupt*len(y_train))) + corrupt_indices = random.sample([*range(len(y_train))], k=num_corrupt) + if corrupt_how == 'permute': + corrupt_array = y_train[corrupt_indices] + corrupt_array = random.sample(list(corrupt_array), len(corrupt_array)) + for i,index in enumerate(corrupt_indices): + y_train[index] = corrupt_array[i] + y_train = y_train + sigma * error_fun(n) + elif corrupt_how == 'cauchy': + for i in range(len(y_train)): + if i in corrupt_indices: + y_train[i] = y_train[i] + sigma*np.random.standard_cauchy() + else: + y_train[i] = y_train[i] + sigma*error_fun() + elif corrupt_how == "leverage_constant": + if isinstance(corrupt_size, int): + corrupt_quantile = corrupt_size / n + else: + corrupt_quantile = corrupt_size + y_train = y_train + sigma * error_fun(n) + corrupt_idx = np.random.choice(range(m*r, p), size=1) + y_train = corrupt_leverage(X[:, corrupt_idx], y_train, mean_shift=corrupt_mean, corrupt_quantile=corrupt_quantile, mode="constant") + elif corrupt_how == "leverage_normal": + if isinstance(corrupt_size, int): + corrupt_quantile = corrupt_size / n + else: + corrupt_quantile = corrupt_size + y_train = y_train + sigma * error_fun(n) + corrupt_idx = np.random.choice(range(m*r, p), size=1) + y_train = corrupt_leverage(X[:, corrupt_idx], y_train, mean_shift=corrupt_mean, corrupt_quantile=corrupt_quantile, mode="normal") + + if return_support: + return y_train, support, beta + else: + return y_train + + +def partial_linear_lss_model(X, s, m, r, tau, beta, heritability=None, snr=None, error_fun=None, + min_active=None, frac_corrupt=None, corrupt_how='permute', corrupt_size=None, + corrupt_mean=None, diagnostics=False, return_support=False): + """ + This method creates response from an linear + lss model + + X: data matrix + m: number of interaction terms + r: max order of interaction + s: denotes number of linear terms in EACH interaction term + tau: threshold + sigma: standard deviation of noise + beta: coefficient vector. If beta not a vector, then assumed a constant + + :return + y_train: numpy array of shape (n) + """ + n, p = X.shape + assert p >= m * r # Cannot have more interactions * size than the dimension + assert s <= r + + def partial_linear_func(x,s,beta): + y = 0.0 + count = 0 + for j in range(m): + for i in range(s): + y += beta[count]*x[j*r+i] + count += 1 + return y + + + def lss_func(x, beta): + x_bool = (x - tau) > 0 + y = 0 + for j in range(m): + lss_term_components = x_bool[j * r:j * r + r] + lss_term = int(all(lss_term_components)) + y += lss_term * beta[j] + return y + + def lss_vector_fun(x, beta, beta_linear): + x_bool = (x - tau) > 0 + y = 0 + max_iter = 100 + features = np.arange(p) + support_idx = [] + for j in range(m): + cnt = 0 + while True: + int_features = np.concatenate( + [np.arange(j*r, j*r+s), np.random.choice(features, size=r-s, replace=False)] + ) + lss_term_components = x_bool[:, int_features] + lss_term = np.apply_along_axis(all, 1, lss_term_components) + cnt += 1 + if np.mean(lss_term) >= min_active or cnt > max_iter: + norm_constant = sum(np.var(x[:, (j*r):(j*r+s)], axis=0) * beta_linear[(j*s):((j+1)*s)]**2) + relative_beta = beta[j] / sum(beta_linear[(j*s):((j+1)*s)]) + y += lss_term * relative_beta * np.sqrt(norm_constant) / np.std(lss_term) + features = list(set(features).difference(set(int_features))) + support_idx.append(int_features) + if cnt > max_iter: + warnings.warn("Could not find interaction {} with min active >= {}".format(j, min_active)) + break + support_idx = np.stack(support_idx).ravel() + support = np.zeros(p) + for j in support_idx: + support[j] = 1 + return y, support + + beta_lss = generate_coef(beta, m) + beta_linear = generate_coef(beta, s*m) + if tau == 'median': + tau = np.median(X,axis = 0) + + y_train_linear = np.array([partial_linear_func(X[i, :],s,beta_linear ) for i in range(n)]) + if min_active is None: + y_train_lss = np.array([lss_func(X[i, :], beta_lss) for i in range(n)]) + support = np.concatenate((np.ones(max(m * r, s)), np.zeros(X.shape[1] - max((m * r), s)))) + else: + y_train_lss, support = lss_vector_fun(X, beta_lss, beta_linear) + y_train = np.array([y_train_linear[i] + y_train_lss[i] for i in range(n)]) + if heritability is not None: + sigma = (np.var(y_train) * ((1.0 - heritability) / heritability)) ** 0.5 + if snr is not None: + sigma = (np.var(y_train) / snr) ** 0.5 + if error_fun is None: + error_fun = np.random.randn + + if frac_corrupt is None and corrupt_size is None: + y_train = y_train + sigma * error_fun(n) + else: + if frac_corrupt is None: + frac_corrupt = 0 + num_corrupt = int(np.floor(frac_corrupt*len(y_train))) + corrupt_indices = random.sample([*range(len(y_train))], k=num_corrupt) + if corrupt_how == 'permute': + corrupt_array = y_train[corrupt_indices] + corrupt_array = random.sample(list(corrupt_array), len(corrupt_array)) + for i,index in enumerate(corrupt_indices): + y_train[index] = corrupt_array[i] + y_train = y_train + sigma * error_fun(n) + elif corrupt_how == 'cauchy': + for i in range(len(y_train)): + if i in corrupt_indices: + y_train[i] = y_train[i] + sigma*np.random.standard_cauchy() + else: + y_train[i] = y_train[i] + sigma*error_fun() + elif corrupt_how == "leverage_constant": + if isinstance(corrupt_size, int): + corrupt_quantile = corrupt_size / n + else: + corrupt_quantile = corrupt_size + y_train = y_train + sigma * error_fun(n) + corrupt_idx = np.random.choice(range(max(m*r, s), p), size=1) + y_train = corrupt_leverage(X[:, corrupt_idx], y_train, mean_shift=corrupt_mean, corrupt_quantile=corrupt_quantile, mode="constant") + elif corrupt_how == "leverage_normal": + if isinstance(corrupt_size, int): + corrupt_quantile = corrupt_size / n + else: + corrupt_quantile = corrupt_size + y_train = y_train + sigma * error_fun(n) + corrupt_idx = np.random.choice(range(max(m*r, s), p), size=1) + y_train = corrupt_leverage(X[:, corrupt_idx], y_train, mean_shift=corrupt_mean, corrupt_quantile=corrupt_quantile, mode="normal") + + if return_support: + return y_train, support, beta_lss + elif diagnostics: + return y_train, y_train_linear, y_train_lss + else: + return y_train + + +def hierarchical_poly(X, sigma=None, m=1, r=1, beta=1, heritability=None, snr=None, + frac_corrupt=None, corrupt_how='permute', corrupt_size=None, + corrupt_mean=None, error_fun=None, return_support=False): + """ + This method creates response from an Linear + LSS model + + X: data matrix + m: number of interaction terms + r: max order of interaction + s: sparsity + sigma: standard deviation of noise + beta: coefficient vector. If beta not a vector, then assumed a constant + + :return + y_train: numpy array of shape (n) + """ + + n, p = X.shape + assert p >= m * r + + def reg_func(x, beta): + y = 0 + for i in range(m): + hier_term = 1.0 + for j in range(r): + hier_term += x[i * r + j] * hier_term + y += hier_term * beta[i] + return y + + beta = generate_coef(beta, m) + y_train = np.array([reg_func(X[i, :], beta) for i in range(n)]) + if heritability is not None: + sigma = (np.var(y_train) * ((1.0 - heritability) / heritability)) ** 0.5 + if snr is not None: + sigma = (np.var(y_train) / snr) ** 0.5 + if error_fun is None: + error_fun = np.random.randn + + if frac_corrupt is None and corrupt_size is None: + y_train = y_train + sigma * error_fun(n) + else: + if frac_corrupt is None: + frac_corrupt = 0 + num_corrupt = int(np.floor(frac_corrupt*len(y_train))) + corrupt_indices = random.sample([*range(len(y_train))], k=num_corrupt) + if corrupt_how == 'permute': + corrupt_array = y_train[corrupt_indices] + corrupt_array = random.sample(list(corrupt_array), len(corrupt_array)) + for i,index in enumerate(corrupt_indices): + y_train[index] = corrupt_array[i] + y_train = y_train + sigma * error_fun(n) + elif corrupt_how == 'cauchy': + for i in range(len(y_train)): + if i in corrupt_indices: + y_train[i] = y_train[i] + sigma*np.random.standard_cauchy() + else: + y_train[i] = y_train[i] + sigma*error_fun() + elif corrupt_how == "leverage_constant": + if isinstance(corrupt_size, int): + corrupt_quantile = corrupt_size / n + else: + corrupt_quantile = corrupt_size + y_train = y_train + sigma * error_fun(n) + corrupt_idx = np.random.choice(range(m*r, p), size=1) + y_train = corrupt_leverage(X[:, corrupt_idx], y_train, mean_shift=corrupt_mean, corrupt_quantile=corrupt_quantile, mode="constant") + elif corrupt_how == "leverage_normal": + if isinstance(corrupt_size, int): + corrupt_quantile = corrupt_size / n + else: + corrupt_quantile = corrupt_size + y_train = y_train + sigma * error_fun(n) + corrupt_idx = np.random.choice(range(m*r, p), size=1) + y_train = corrupt_leverage(X[:, corrupt_idx], y_train, mean_shift=corrupt_mean, corrupt_quantile=corrupt_quantile, mode="normal") + + if return_support: + support = np.concatenate((np.ones(m * r), np.zeros(X.shape[1] - (m * r)))) + return y_train, support, beta + else: + return y_train + + +def logistic_model(X, s, beta=None, beta_grid=np.logspace(-4, 4, 100), heritability=None, + frac_label_corruption=None, return_support=False): + """ + This method is used to create responses from a sum of squares model with hard sparsity + Parameters: + X: X matrix + s: sparsity + beta: coefficient vector. If beta not a vector, then assumed a constant + Returns: + numpy array of shape (n) + """ + + def create_prob(x, beta): + linear_term = 0 + for j in range(len(beta)): + linear_term += x[j] * beta[j] + prob = 1 / (1 + np.exp(-linear_term)) + return prob + + def create_y(x, beta): + linear_term = 0 + for j in range(len(beta)): + linear_term += x[j] * beta[j] + prob = 1 / (1 + np.exp(-linear_term)) + return (np.random.uniform(size=1) < prob) * 1 + + if heritability is None: + beta = generate_coef(beta, s) + y_train = np.array([create_y(X[i, :], beta) for i in range(len(X))]).ravel() + else: + # find beta to get desired heritability via adaptive grid search within eps=0.01 + y_train, beta, heritability, hdict = logistic_heritability_search(X, heritability, s, create_prob, beta_grid) + + if frac_label_corruption is None: + y_train = y_train + else: + corrupt_indices = np.random.choice(np.arange(len(y_train)), size=math.ceil(frac_label_corruption*len(y_train))) + y_train[corrupt_indices] = 1 - y_train[corrupt_indices] + if return_support: + support = np.concatenate((np.ones(s), np.zeros(X.shape[1] - s))) + return y_train, support, beta + else: + return y_train + + +def logistic_lss_model(X, m, r, tau, beta=None, heritability=None, beta_grid=np.logspace(-4, 4, 100), + min_active=None, frac_label_corruption=None, return_support=False): + """ + This method is used to create responses from a logistic model model with lss + X: X matrix + s: sparsity + beta: coefficient vector. If beta not a vector, then assumed a constant + Returns: + numpy array of shape (n) + """ + n, p = X.shape + + def lss_prob_func(x, beta): + x_bool = (x - tau) > 0 + y = 0 + for j in range(m): + lss_term_components = x_bool[j * r:j * r + r] + lss_term = int(all(lss_term_components)) + y += lss_term * beta[j] + prob = 1 / (1 + np.exp(-y)) + return prob + + def lss_func(x, beta): + x_bool = (x - tau) > 0 + y = 0 + for j in range(m): + lss_term_components = x_bool[j * r:j * r + r] + lss_term = int(all(lss_term_components)) + y += lss_term * beta[j] + prob = 1 / (1 + np.exp(-y)) + return (np.random.uniform(size=1) < prob) * 1 + + def lss_vector_fun(x, beta): + x_bool = (x - tau) > 0 + y = 0 + max_iter = 100 + features = np.arange(p) + support_idx = [] + for j in range(m): + cnt = 0 + while True: + int_features = np.random.choice(features, size=r, replace=False) + lss_term_components = x_bool[:, int_features] + lss_term = np.apply_along_axis(all, 1, lss_term_components) + cnt += 1 + if np.mean(lss_term) >= min_active or cnt > max_iter: + y += lss_term * beta[j] + features = list(set(features).difference(set(int_features))) + support_idx.append(int_features) + if cnt > max_iter: + warnings.warn("Could not find interaction {} with min active >= {}".format(j, min_active)) + break + prob = 1 / (1 + np.exp(-y)) + y = (np.random.uniform(size=n) < prob) * 1 + support_idx = np.stack(support_idx).ravel() + support = np.zeros(p) + for j in support_idx: + support[j] = 1 + return y, support + + if tau == 'median': + tau = np.median(X,axis = 0) + + if heritability is None: + beta = generate_coef(beta, m) + if min_active is None: + y_train = np.array([lss_func(X[i, :], beta) for i in range(n)]).ravel() + support = np.concatenate((np.ones(m * r), np.zeros(X.shape[1] - (m * r)))) + else: + y_train, support = lss_vector_fun(X, beta) + y_train = y_train.ravel() + else: + if min_active is not None: + raise ValueError("Cannot set heritability and min_active at the same time.") + # find beta to get desired heritability via adaptive grid search within eps=0.01 (need to jitter beta to reach higher signals) + y_train, beta, heritability, hdict = logistic_heritability_search(X, heritability, m, lss_prob_func, beta_grid, jitter_beta=True) + support = np.concatenate((np.ones(m * r), np.zeros(X.shape[1] - (m * r)))) + + if frac_label_corruption is None: + y_train = y_train + else: + corrupt_indices = np.random.choice(np.arange(len(y_train)), size=math.ceil(frac_label_corruption*len(y_train))) + y_train[corrupt_indices] = 1 - y_train[corrupt_indices] + + if return_support: + return y_train, support, beta + else: + return y_train + + +def logistic_partial_linear_lss_model(X, s, m, r, tau, beta=None, heritability=None, beta_grid=np.logspace(-4, 4, 100), + min_active=None, frac_label_corruption=None, return_support=False): + """ + This method is used to create responses from a logistic model model with lss + X: X matrix + s: sparsity + beta: coefficient vector. If beta not a vector, then assumed a constant + Returns: + numpy array of shape (n) + """ + n, p = X.shape + assert p >= m * r + + def partial_linear_func(x,s,beta): + y = 0.0 + count = 0 + for j in range(m): + for i in range(s): + y += beta[count]*x[j*r+i] + count += 1 + return y + + def lss_func(x, beta): + x_bool = (x - tau) > 0 + y = 0 + for j in range(m): + lss_term_components = x_bool[j * r:j * r + r] + lss_term = int(all(lss_term_components)) + y += lss_term * beta[j] + return y + + def logistic_link_func(y): + prob = 1 / (1 + np.exp(-y)) + return (np.random.uniform(size=1) < prob) * 1 + + def logistic_prob_func(y): + prob = 1 / (1 + np.exp(-y)) + return prob + + def lss_vector_fun(x, beta, beta_linear): + x_bool = (x - tau) > 0 + y = 0 + max_iter = 100 + features = np.arange(p) + support_idx = [] + for j in range(m): + cnt = 0 + while True: + int_features = np.concatenate( + [np.arange(j*r, j*r+s), np.random.choice(features, size=r-s, replace=False)] + ) + lss_term_components = x_bool[:, int_features] + lss_term = np.apply_along_axis(all, 1, lss_term_components) + cnt += 1 + if np.mean(lss_term) >= min_active or cnt > max_iter: + norm_constant = sum(np.var(x[:, (j*r):(j*r+s)], axis=0) * beta_linear[(j*s):((j+1)*s)]**2) + relative_beta = beta[j] / sum(beta_linear[(j*s):((j+1)*s)]) + y += lss_term * relative_beta * np.sqrt(norm_constant) / np.std(lss_term) + features = list(set(features).difference(set(int_features))) + support_idx.append(int_features) + if cnt > max_iter: + warnings.warn("Could not find interaction {} with min active >= {}".format(j, min_active)) + break + support_idx = np.stack(support_idx).ravel() + support = np.zeros(p) + for j in support_idx: + support[j] = 1 + return y, support + + if tau == 'median': + tau = np.median(X,axis = 0) + + if heritability is None: + beta_lss = generate_coef(beta, m) + beta_linear = generate_coef(beta, s*m) + + y_train_linear = np.array([partial_linear_func(X[i, :],s,beta_linear ) for i in range(n)]) + if min_active is None: + y_train_lss = np.array([lss_func(X[i, :], beta_lss) for i in range(n)]) + support = np.concatenate((np.ones(m * r), np.zeros(X.shape[1] - (m * r)))) + else: + y_train_lss, support = lss_vector_fun(X, beta_lss, beta_linear) + y_train = np.array([y_train_linear[i] + y_train_lss[i] for i in range(n)]) + y_train = np.array([logistic_link_func(y_train[i]) for i in range(n)]) + else: + if min_active is not None: + raise ValueError("Cannot set heritability and min_active at the same time.") + # find beta to get desired heritability via adaptive grid search within eps=0.01 + eps = 0.01 + max_iter = 1000 + pves = {} + for idx, beta in enumerate(beta_grid): + beta_lss_vec = generate_coef(beta, m) + beta_linear_vec = generate_coef(beta, s*m) + + y_train_linear = np.array([partial_linear_func(X[i, :], s, beta_linear_vec) for i in range(n)]) + y_train_lss = np.array([lss_func(X[i, :], beta_lss_vec) for i in range(n)]) + y_train_sum = np.array([y_train_linear[i] + y_train_lss[i] for i in range(n)]) + prob_train = np.array([logistic_prob_func(y_train_sum[i]) for i in range(n)]).ravel() + np.random.seed(idx) + y_train = (np.random.uniform(size=len(prob_train)) < prob_train) * 1 + pve = np.var(prob_train) / np.var(y_train) + pves[(idx, beta)] = pve + + (idx, beta), pve = min(pves.items(), key=lambda x: abs(x[1] - heritability)) + beta_lss_vec = generate_coef(beta, m) + beta_linear_vec = generate_coef(beta, s*m) + + y_train_linear = np.array([partial_linear_func(X[i, :], s, beta_linear_vec) for i in range(n)]) + y_train_lss = np.array([lss_func(X[i, :], beta_lss_vec) for i in range(n)]) + y_train_sum = np.array([y_train_linear[i] + y_train_lss[i] for i in range(n)]) + + prob_train = np.array([logistic_prob_func(y_train_sum[i]) for i in range(n)]).ravel() + np.random.seed(idx) + y_train = (np.random.uniform(size=len(prob_train)) < prob_train) * 1 + if pve > heritability: + min_beta = beta_grid[idx-1] + max_beta = beta + else: + min_beta = beta + max_beta = beta_grid[idx+1] + cur_beta = (min_beta + max_beta) / 2 + iter = 1 + while np.abs(pve - heritability) > eps: + beta_lss_vec = generate_coef(cur_beta, m) + beta_linear_vec = generate_coef(cur_beta, s*m) + + y_train_linear = np.array([partial_linear_func(X[i, :], s, beta_linear_vec) for i in range(n)]) + y_train_lss = np.array([lss_func(X[i, :], beta_lss_vec) for i in range(n)]) + y_train_sum = np.array([y_train_linear[i] + y_train_lss[i] for i in range(n)]) + + prob_train = np.array([logistic_prob_func(y_train_sum[i]) for i in range(n)]).ravel() + np.random.seed(iter + len(beta_grid)) + y_train = (np.random.uniform(size=len(prob_train)) < prob_train) * 1 + pve = np.var(prob_train) / np.var(y_train) + pves[(iter + len(beta_grid), cur_beta)] = pve + if pve > heritability: + max_beta = cur_beta + else: + min_beta = cur_beta + beta = cur_beta + cur_beta = (min_beta + max_beta) / 2 + iter += 1 + if iter > max_iter: + (idx, cur_beta), pve = min(pves.items(), key=lambda x: abs(x[1] - heritability)) + beta_lss_vec = generate_coef(cur_beta, m) + beta_linear_vec = generate_coef(cur_beta, s*m) + + y_train_linear = np.array([partial_linear_func(X[i, :], s, beta_linear_vec) for i in range(n)]) + y_train_lss = np.array([lss_func(X[i, :], beta_lss_vec) for i in range(n)]) + y_train_sum = np.array([y_train_linear[i] + y_train_lss[i] for i in range(n)]) + + prob_train = np.array([logistic_prob_func(y_train_sum[i]) for i in range(n)]).ravel() + np.random.seed(idx) + y_train = (np.random.uniform(size=len(prob_train)) < prob_train) * 1 + pve = np.var(prob_train) / np.var(y_train) + beta = cur_beta + break + support = np.concatenate((np.ones(m * r), np.zeros(X.shape[1] - (m * r)))) + + if frac_label_corruption is None: + y_train = y_train + else: + corrupt_indices = np.random.choice(np.arange(len(y_train)), size=math.ceil(frac_label_corruption*len(y_train))) + y_train[corrupt_indices] = 1 - y_train[corrupt_indices] + + y_train = y_train.ravel() + + if return_support: + return y_train, support, beta + else: + return y_train + + +def logistic_hier_model(X, m, r, beta=None, heritability=None, beta_grid=np.logspace(-4, 4, 100), + frac_label_corruption=None, return_support=False): + + n, p = X.shape + assert p >= m * r + + def reg_func(x, beta): + y = 0 + for i in range(m): + hier_term = 1.0 + for j in range(r): + hier_term += x[i * r + j] * hier_term + y += hier_term * beta[i] + return y + + def logistic_link_func(y): + prob = 1 / (1 + np.exp(-y)) + return (np.random.uniform(size=1) < prob) * 1 + + def prob_func(x, beta): + y = 0 + for i in range(m): + hier_term = 1.0 + for j in range(r): + hier_term += x[i * r + j] * hier_term + y += hier_term * beta[i] + return 1 / (1 + np.exp(-y)) + + if heritability is None: + beta = generate_coef(beta, m) + y_train = np.array([reg_func(X[i, :], beta) for i in range(n)]) + y_train = np.array([logistic_link_func(y_train[i]) for i in range(n)]) + else: + # find beta to get desired heritability via adaptive grid search within eps=0.01 + y_train, beta, heritability, hdict = logistic_heritability_search(X, heritability, m, prob_func, beta_grid) + + if frac_label_corruption is None: + y_train = y_train + else: + corrupt_indices = np.random.choice(np.arange(len(y_train)), size=math.ceil(frac_label_corruption*len(y_train))) + y_train[corrupt_indices] = 1 - y_train[corrupt_indices] + y_train = y_train.ravel() + + if return_support: + support = np.concatenate((np.ones(m * r), np.zeros(X.shape[1] - (m * r)))) + return y_train, support, beta + else: + return y_train + + +def logistic_heritability_search(X, heritability, s, prob_fun, beta_grid=np.logspace(-4, 4, 100), + eps=0.01, max_iter=1000, jitter_beta=False, return_pve=True): + pves = {} + + # first search over beta grid + for idx, beta in enumerate(beta_grid): + np.random.seed(idx) + beta_vec = generate_coef(beta, s) + if jitter_beta: + beta_vec = beta_vec + np.random.uniform(-1e-4, 1e-4, beta_vec.shape) + prob_train = np.array([prob_fun(X[i, :], beta_vec) for i in range(len(X))]).ravel() + y_train = (np.random.uniform(size=len(prob_train)) < prob_train) * 1 + pve = np.var(prob_train) / np.var(y_train) + pves[(idx, beta)] = pve + + # find beta with heritability closest to desired heritability + (idx, beta), pve = min(pves.items(), key=lambda x: abs(x[1] - heritability)) + np.random.seed(idx) + beta_vec = generate_coef(beta, s) + if jitter_beta: + beta_vec = beta_vec + np.random.uniform(-1e-4, 1e-4, beta_vec.shape) + prob_train = np.array([prob_fun(X[i, :], beta_vec) for i in range(len(X))]).ravel() + y_train = (np.random.uniform(size=len(prob_train)) < prob_train) * 1 + + # search nearby beta to get closer to desired heritability + if pve > heritability: + min_beta = beta_grid[idx-1] + max_beta = beta + else: + min_beta = beta + max_beta = beta_grid[idx+1] + cur_beta = (min_beta + max_beta) / 2 + iter = 1 + while np.abs(pve - heritability) > eps: + np.random.seed(iter + len(beta_grid)) + beta_vec = generate_coef(cur_beta, s) + if jitter_beta: + beta_vec = beta_vec + np.random.uniform(-1e-4, 1e-4, beta_vec.shape) + prob_train = np.array([prob_fun(X[i, :], beta_vec) for i in range(len(X))]).ravel() + y_train = (np.random.uniform(size=len(prob_train)) < prob_train) * 1 + pve = np.var(prob_train) / np.var(y_train) + pves[(iter + len(beta_grid), cur_beta)] = pve + if pve > heritability: + max_beta = cur_beta + else: + min_beta = cur_beta + cur_beta = (min_beta + max_beta) / 2 + beta = beta_vec + iter += 1 + if iter > max_iter: + (idx, cur_beta), pve = min(pves.items(), key=lambda x: abs(x[1] - heritability)) + np.random.seed(idx) + beta_vec = generate_coef(cur_beta, s) + if jitter_beta: + beta_vec = beta_vec + np.random.uniform(-1e-4, 1e-4, beta_vec.shape) + prob_train = np.array([prob_fun(X[i, :], beta_vec) for i in range(len(X))]).ravel() + y_train = (np.random.uniform(size=len(prob_train)) < prob_train) * 1 + pve = np.var(prob_train) / np.var(y_train) + beta = beta_vec + break + + if return_pve: + return y_train, beta, pve, pves + else: + return y_train, beta + + +def entropy_X(n, scale=False): + x1 = np.random.choice([0, 1], (n, 1), replace=True) + x2 = np.random.normal(0, 1, (n, 1)) + x3 = np.random.choice(np.arange(4), (n, 1), replace=True) + x4 = np.random.choice(np.arange(10), (n, 1), replace=True) + x5 = np.random.choice(np.arange(20), (n, 1), replace=True) + X = np.concatenate((x1, x2, x3, x4, x5), axis=1) + if scale: + X = (X - X.mean()) / X.std() + return X + + +def entropy_y(X, c=3, return_support=False): + if any(X[:, 0] < 0): + x = (X[:, 0] > 0) * 1 + else: + x = X[:, 0] + prob = ((c - 2) * x + 1) / c + y = (np.random.uniform(size=len(prob)) < prob) * 1 + if return_support: + support = np.array([0, 1, 0, 0, 0]) + beta = None + return y, support, beta + else: + return y + + +class IndexedArray(np.ndarray): + def __new__(cls, input_array, index=None): + obj = np.asarray(input_array).view(cls) + obj.index = index + return obj + + def __array_finalize__(self, obj): + if obj is None: + return + self.index = getattr(obj, 'index', None) + diff --git a/feature_importance/entropy-bias/entropy-runner.sh b/feature_importance/entropy-bias/entropy-runner.sh new file mode 100644 index 0000000..5dd36ed --- /dev/null +++ b/feature_importance/entropy-bias/entropy-runner.sh @@ -0,0 +1,16 @@ +#!/bin/bash + +slurm_script="entropy.sh" +types=("classification" "regression") + + +for rep in {1..50} +do + for value in {50,100,250,500,1000} + do + for task in "${types[@]}" + do + sbatch $slurm_script $rep $task $value # Submit SLURM job using the specified script + done + done +done \ No newline at end of file diff --git a/feature_importance/entropy-bias/entropy.ipynb b/feature_importance/entropy-bias/entropy.ipynb new file mode 100644 index 0000000..990428d --- /dev/null +++ b/feature_importance/entropy-bias/entropy.ipynb @@ -0,0 +1,129 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import math\n", + "from matplotlib import font_manager" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "methods = ['shap', 'signed_normalized_l2_avg', 'signed_normalized_l2_noavg',\n", + " 'signed_nonnormalized_l2_avg', 'signed_nonnormalized_l2_noavg',\n", + " 'nonl2_avg', 'nonl2_noavg', 'l2_ranking', 'nonl2_ranking',\n", + " 'normalized_l2_ranking', 'baseline', 'mdi']\n", + "tasks = [\"regression\", \"classification\"]\n", + "use_test = False\n", + "n_samples = [50, 100, 250, 500, 1000]\n", + "mean_results = {}\n", + "for method in methods:\n", + " x1_mat = np.zeros((len(tasks), len(n_samples)))\n", + " for task_idx in range(len(tasks)):\n", + " for n_idx in range(len(n_samples)):\n", + " if method == \"mdi\":\n", + " rankings = np.zeros((5, 50)) # 5 features, 50 seeds\n", + " for seed in range(1, 51):\n", + " rankings[:, seed-1] = pd.read_csv(f\"results/{tasks[task_idx]}/n{n_samples[n_idx]}/seed{seed}/rankings/mdi.csv\").to_numpy().reshape(-1)\n", + " rankings = np.mean(rankings, axis=1)\n", + " x1_mat[task_idx, n_idx] = rankings[0]\n", + " else:\n", + " rankings = np.zeros((n_samples[n_idx], 5, 50)) # n_samples[n_idx] samples, 5 features, 50 seeds\n", + " for seed in range(1, 51):\n", + " rankings[:, :, seed-1] = pd.read_csv(f\"results/{tasks[task_idx]}/n{n_samples[n_idx]}/seed{seed}/rankings/{method}.csv\").to_numpy()\n", + " rankings = np.mean(rankings, axis=2)\n", + " # average first columns in rankings\n", + " x1_mat[task_idx, n_idx] = np.mean(rankings[:, 0])\n", + " mean_results[method] = x1_mat" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "viz_methods = ['shap', 'nonl2_noavg', 'mdi']\n", + "titles = {'shap': 'TreeSHAP', 'nonl2_noavg': 'LMDI+', 'mdi': 'MDI'}\n", + "title = [\"Regression\", \"Classification\"]\n", + "test = [\"Train\", \"Test\"]\n", + "colors = {'shap': 'orange', 'nonl2_noavg': 'black', 'mdi': 'purple'}\n", + "for task_idx in range(len(tasks)): \n", + " for method in viz_methods:\n", + " # plot results, where each group is a separate line on the plot\n", + " plt.plot(mean_results[method][task_idx, :] + 1, marker = \"o\",\n", + " label=f\"{titles[method]}\", color = colors[method])\n", + " # x-axis ticks should be rho values\n", + " plt.xticks(range(len(n_samples)), n_samples)\n", + " plt.yticks(range(1, 6), range(1, 6))\n", + " plt.xlabel(\"Sample Size\", fontsize=18)\n", + " # make xaxis label big\n", + " plt.tick_params(axis='both', labelsize=15)\n", + " # y-axis label should be Average Ranking\n", + " plt.ylabel(\"Average Rank of X1\", fontsize=18)\n", + " plt.title(f\"{title[task_idx]} ({test[int(use_test)]})\", fontsize=20, fontweight='bold')\n", + " plt.legend(fontsize=15)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mdi", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/feature_importance/entropy-bias/entropy.sh b/feature_importance/entropy-bias/entropy.sh new file mode 100644 index 0000000..02be0a4 --- /dev/null +++ b/feature_importance/entropy-bias/entropy.sh @@ -0,0 +1,11 @@ +#!/bin/bash +#SBATCH --cpus-per-task=8 + +njobs=8 +test=1 + +source activate mdi +command="entropy_pipeline.py --seed ${1} --task ${2} --n ${3} --test $test --njobs $njobs" + +# Execute the command +python $command \ No newline at end of file diff --git a/feature_importance/entropy-bias/entropy_pipeline.py b/feature_importance/entropy-bias/entropy_pipeline.py new file mode 100644 index 0000000..ccbf826 --- /dev/null +++ b/feature_importance/entropy-bias/entropy_pipeline.py @@ -0,0 +1,343 @@ +# imports from imodels +from imodels import get_clean_dataset +from imodels.tree.rf_plus.rf_plus.rf_plus_models import \ + RandomForestPlusRegressor, RandomForestPlusClassifier +from imodels.tree.rf_plus.feature_importance.rfplus_explainer import \ + RFPlusMDI, AloRFPlusMDI + +# imports from sklearn +from sklearn.model_selection import train_test_split +from sklearn.metrics import roc_auc_score, average_precision_score, f1_score, \ + accuracy_score, r2_score, f1_score, log_loss, root_mean_squared_error +from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier +from sklearn.linear_model import LogisticRegression, LinearRegression +from sklearn.preprocessing import OneHotEncoder, LabelEncoder +from sklearn.impute import SimpleImputer + +# parallelization imports +from joblib import Parallel, delayed + +# timing imports +import time + +# other data science imports +import numpy as np +import pandas as pd +import shap +from ucimlrepo import fetch_ucirepo + +# i/o imports +import argparse +import os +from os.path import join as oj + +# global variable for classification/regression status +TASK = None + +def simulate_data(n, seed): + np.random.seed(seed) + + # get X1 from Bernoulli(0.5) + X1 = np.random.binomial(1, 0.5, size=(n,)) + # get X2 from N(0, 1) + X2 = np.random.normal(size=(n,)) + # get X3 from uniform discrete distribution with four categories + X3 = np.random.randint(1, 4, size=(n,)) + # get X4 from uniform discrete distribution with ten categories + X4 = np.random.randint(1, 10, size=(n,)) + # get X5 from uniform discrete distribution with twenty categories + X5 = np.random.randint(1, 20, size=(n,)) + + # combine the features + X = np.column_stack((X1, X2, X3, X4, X5)) + + # create y from X1 and noise + if TASK == "regression": + # y = X1 + N(0, sigma^2) where sigma^2 chosen to achieve PVE = 0.1 + heritability = 0.1 + sigma = (np.var(X1) * ((1.0 - heritability) / heritability)) ** 0.5 + epsilon = np.random.randn(n) + y = X1 + sigma * epsilon + elif TASK == "classification": + # y has probability (1+x1)/3 of being 1 + y = np.random.binomial(1, (1 + X1) / 3, size=(n,)) + + return X, y + +def split_data(X, y, test_size, seed): + # split data into train and test sets + X_train, X_test, y_train, y_test = train_test_split(X, y, + test_size=test_size, + random_state=seed) + return X_train, X_test, y_train, y_test + +def fit_models(X_train, y_train): + # fit models + if TASK == "classification": + rf = RandomForestClassifier(n_estimators = 100, min_samples_leaf=3, + max_features = "sqrt", random_state=42) + rf.fit(X_train, y_train) + rf_plus = RandomForestPlusClassifier(rf_model=rf) + rf_plus.fit(X_train, y_train) + elif TASK == "regression": + rf = RandomForestRegressor(n_estimators = 100, min_samples_leaf=5, + max_features = 0.33, random_state=42) + rf.fit(X_train, y_train) + rf_plus = RandomForestPlusRegressor(rf_model=rf) + rf_plus.fit(X_train, y_train) + else: + raise ValueError("Task must be either 'classification' or 'regression'.") + return rf, rf_plus + +def get_shap(X, shap_explainer): + if TASK == "classification": + # the shap values are an array of shape + # (# of samples, # of features, # of classes), and in this binary + # classification case, we want the shap values for the positive class. + # check_additivity=False is used to speed up computation. + shap_values = \ + shap_explainer.shap_values(X, check_additivity=False)[:, :, 1] + else: + # check_additivity=False is used to speed up computation. + shap_values = shap_explainer.shap_values(X, check_additivity=False) + # get the rankings of the shap values. negative absolute value is taken + # because np.argsort sorts from smallest to largest. + shap_rankings = np.argsort(-np.abs(shap_values), axis = 1) + return shap_values, shap_rankings + +def get_lmdi(X, y, lmdi_explainer, l2norm, sign, normalize, leaf_average, ranking=False): + # get feature importances + lmdi = lmdi_explainer.explain_linear_partial(X, y, l2norm=l2norm, sign=sign, + normalize=normalize, + leaf_average=leaf_average, + ranking=ranking) + mdi_rankings = lmdi_explainer.get_rankings(np.abs(lmdi)) + return lmdi, mdi_rankings + +if __name__ == '__main__': + + # start time + start = time.time() + + # store command-line arguments + parser = argparse.ArgumentParser() + parser.add_argument('--seed', type=int, default=None) + parser.add_argument('--n', type=int, default=None) + parser.add_argument('--task', type=str, default=None) + parser.add_argument('--test', type=int, default=0) + parser.add_argument('--njobs', type=int, default=1) + args = parser.parse_args() + + # convert namespace to a dictionary + args_dict = vars(args) + + # assign the arguments to variables + seed = args_dict['seed'] + n = args_dict['n'] + TASK = args_dict['task'] + use_test = bool(args_dict['test']) + njobs = args_dict['njobs'] + + train_size = 500 + X, y = simulate_data(500 + n, seed) + # since data is simulated, we can split it in a deterministic way + X_train = X[:train_size] + y_train = y[:train_size] + X_test = X[train_size:] + y_test = y[train_size:] + + # end time + end = time.time() + + # print progress message + print(f"Progress Message 1/5: Obtained {TASK} data with n = {n}.") + print(f"Step #1 took {end-start} seconds.") + + # start time + start = time.time() + + # fit the prediction models + rf, rf_plus = fit_models(X_train, y_train) + + # fit baseline model + if TASK == "classification": + rf_plus_baseline = RandomForestPlusClassifier(rf_model=rf, + include_raw=False, fit_on="inbag", + prediction_model=LogisticRegression()) + elif TASK == "regression": + rf_plus_baseline = RandomForestPlusRegressor(rf_model=rf, + include_raw=False, fit_on="inbag", + prediction_model=LinearRegression()) + rf_plus_baseline.fit(X_train, y_train) + + # end time + end = time.time() + + print(f"Progress Message 2/5: RF and RF+ models fit.") + print(f"Step #2 took {end-start} seconds.") + + # start time + start = time.time() + + # obtain shap feature importances + shap_explainer = shap.TreeExplainer(rf) + shap_values, shap_rankings = get_shap(X_train, shap_explainer) + + # end time + end = time.time() + + print(f"Progress Message 3/5: SHAP values/rankings obtained.") + print(f"Step #3 took {end-start} seconds.") + + # start time + start = time.time() + + # obtain lmdi feature importances + lmdi_explainer_signed_normalized_l2_avg = AloRFPlusMDI(rf_plus, mode = "only_k") + lmdi_explainer_signed_normalized_l2_noavg = AloRFPlusMDI(rf_plus, mode = "only_k") + lmdi_explainer_signed_nonnormalized_l2_avg = AloRFPlusMDI(rf_plus, mode = "only_k") + lmdi_explainer_signed_nonnormalized_l2_noavg = AloRFPlusMDI(rf_plus, mode = "only_k") + lmdi_explainer_nonl2_avg = AloRFPlusMDI(rf_plus, mode = "only_k") + lmdi_explainer_nonl2_noavg = AloRFPlusMDI(rf_plus, mode = "only_k") + lmdi_explainer_l2_ranking = AloRFPlusMDI(rf_plus, mode = "only_k") + lmdi_explainer_nonl2_ranking = AloRFPlusMDI(rf_plus, mode = "only_k") + lmdi_explainer_normalized_l2_ranking = AloRFPlusMDI(rf_plus, mode = "only_k") + lmdi_explainer_nonnormalized_l2_ranking = AloRFPlusMDI(rf_plus, mode = "only_k") + lmdi_baseline_explainer = RFPlusMDI(rf_plus_baseline, mode = "only_k", evaluate_on = "inbag") + lmdi_values_signed_normalized_l2_avg, \ + lmdi_rankings_signed_normalized_l2_avg = \ + get_lmdi(X_train, y_train, lmdi_explainer_signed_normalized_l2_avg, + l2norm=True, sign=True, normalize=True, leaf_average=True) + lmdi_values_signed_normalized_l2_noavg, \ + lmdi_rankings_signed_normalized_l2_noavg = \ + get_lmdi(X_train, y_train,lmdi_explainer_signed_normalized_l2_noavg, + l2norm=True, sign=True, normalize=True, leaf_average=False) + lmdi_values_signed_nonnormalized_l2_avg, \ + lmdi_rankings_signed_nonnormalized_l2_avg = \ + get_lmdi(X_train,y_train,lmdi_explainer_signed_nonnormalized_l2_avg, + l2norm=True, sign=True, normalize=False, leaf_average=True) + lmdi_values_signed_nonnormalized_l2_noavg, \ + lmdi_rankings_signed_nonnormalized_l2_noavg = \ + get_lmdi(X_train, y_train, + lmdi_explainer_signed_nonnormalized_l2_noavg, l2norm=True, + sign=True, normalize=False, leaf_average=False) + lmdi_values_nonl2_avg, lmdi_rankings_nonl2_avg = \ + get_lmdi(X_train, y_train, lmdi_explainer_nonl2_avg, l2norm=False, + sign=False, normalize=False, leaf_average=True) + lmdi_values_nonl2_noavg, lmdi_rankings_nonl2_noavg = \ + get_lmdi(X_train, y_train, lmdi_explainer_nonl2_noavg, l2norm=False, + sign=False, normalize=False, leaf_average=False) + lmdi_values_l2_ranking, lmdi_rankings_l2_ranking = \ + get_lmdi(X_train, y_train, lmdi_explainer_l2_ranking, l2norm=True, + sign=False, normalize=False, leaf_average=False, ranking=True) + lmdi_values_nonl2_ranking, lmdi_rankings_nonl2_ranking = \ + get_lmdi(X_train, y_train, lmdi_explainer_nonl2_ranking, l2norm=False, + sign=False, normalize=False, leaf_average=False, ranking=True) + lmdi_values_normalized_l2_ranking, lmdi_rankings_normalized_l2_ranking = \ + get_lmdi(X_train, y_train, lmdi_explainer_normalized_l2_ranking, l2norm=True, + sign=False, normalize=True, leaf_average=False, ranking=True) + lmdi_values_baseline, lmdi_rankings_baseline = \ + get_lmdi(X_train, y_train, lmdi_baseline_explainer, l2norm=False, + sign=False, normalize=False, leaf_average=False) + if use_test: + lmdi_values_signed_normalized_l2_avg, \ + lmdi_rankings_signed_normalized_l2_avg = \ + get_lmdi(X_test, None, lmdi_explainer_signed_normalized_l2_avg, l2norm=True, sign=True, + normalize=True, leaf_average=True) + lmdi_values_signed_normalized_l2_noavg, \ + lmdi_rankings_signed_normalized_l2_noavg = \ + get_lmdi(X_test, None, lmdi_explainer_signed_normalized_l2_noavg, l2norm=True, sign=True, + normalize=True, leaf_average=False) + lmdi_values_signed_nonnormalized_l2_avg, \ + lmdi_rankings_signed_nonnormalized_l2_avg = \ + get_lmdi(X_test, None, lmdi_explainer_signed_nonnormalized_l2_avg, l2norm=True, sign=True, + normalize=False, leaf_average=True) + lmdi_values_signed_nonnormalized_l2_noavg, \ + lmdi_rankings_signed_nonnormalized_l2_noavg = \ + get_lmdi(X_test, None, lmdi_explainer_signed_nonnormalized_l2_noavg, l2norm=True, sign=True, + normalize=False, leaf_average=False) + lmdi_values_nonl2_avg, lmdi_rankings_nonl2_avg = \ + get_lmdi(X_test, None, lmdi_explainer_nonl2_avg, l2norm=False, sign=False, + normalize=False, leaf_average=True) + lmdi_values_nonl2_noavg, lmdi_rankings_nonl2_noavg = \ + get_lmdi(X_test, None, lmdi_explainer_nonl2_noavg, l2norm=False, sign=False, + normalize=False, leaf_average=False) + lmdi_values_l2_ranking, lmdi_rankings_l2_ranking = \ + get_lmdi(X_test, None, lmdi_explainer_l2_ranking, l2norm=True, + sign=False, normalize=False, leaf_average=False, ranking=True) + lmdi_values_nonl2_ranking, lmdi_rankings_nonl2_ranking = \ + get_lmdi(X_test, None, lmdi_explainer_nonl2_ranking, l2norm=False, + sign=False, normalize=False, leaf_average=False, ranking=True) + lmdi_values_normalized_l2_ranking, lmdi_rankings_normalized_l2_ranking = \ + get_lmdi(X_test, None, lmdi_explainer_l2_ranking, l2norm=True, + sign=False, normalize=True, leaf_average=False, ranking=True) + lmdi_values_baseline, lmdi_rankings_baseline = \ + get_lmdi(X_test, None, lmdi_baseline_explainer, l2norm=True, sign=False, + normalize=False, leaf_average=False) + + # get mdi importances from rf + mdi_values = rf.feature_importances_ + mdi_rankings = np.argsort(-np.abs(mdi_values)) + + # create storage for iteration purposes + lfi_values = \ + {'shap': shap_values, + 'signed_normalized_l2_avg': lmdi_values_signed_normalized_l2_avg, + 'signed_normalized_l2_noavg': lmdi_values_signed_normalized_l2_noavg, + 'signed_nonnormalized_l2_avg': lmdi_values_signed_nonnormalized_l2_avg, + 'signed_nonnormalized_l2_noavg': + lmdi_values_signed_nonnormalized_l2_noavg, + 'nonl2_avg': lmdi_values_nonl2_avg, + 'nonl2_noavg': lmdi_values_nonl2_noavg, + 'l2_ranking': lmdi_values_l2_ranking, + 'nonl2_ranking': lmdi_values_nonl2_ranking, + 'normalized_l2_ranking': lmdi_values_normalized_l2_ranking, + 'baseline': lmdi_values_baseline, + 'mdi': mdi_values} + lfi_rankings = \ + {'shap': shap_rankings, + 'signed_normalized_l2_avg': lmdi_rankings_signed_normalized_l2_avg, + 'signed_normalized_l2_noavg': lmdi_rankings_signed_normalized_l2_noavg, + 'signed_nonnormalized_l2_avg': lmdi_rankings_signed_nonnormalized_l2_avg, + 'signed_nonnormalized_l2_noavg': + lmdi_rankings_signed_nonnormalized_l2_noavg, + 'nonl2_avg': lmdi_rankings_nonl2_avg, + 'nonl2_noavg': lmdi_rankings_nonl2_noavg, + 'l2_ranking': lmdi_rankings_l2_ranking, + 'nonl2_ranking': lmdi_rankings_nonl2_ranking, + 'normalized_l2_ranking': lmdi_values_normalized_l2_ranking, + 'baseline': lmdi_rankings_baseline, + 'mdi': mdi_rankings} + + # end time + end = time.time() + + print(f"Progress Message 4/5: LMDI+ values/rankings obtained.") + print(f"Step #4 took {end-start} seconds.") + + result_dir = oj(os.path.dirname(os.path.realpath(__file__)), + f'results/{TASK}/test{int(use_test)}/n{n}/seed{seed}') + + # get result dataframes + for method, values in lfi_values.items(): + df = pd.DataFrame(values) + directory = oj(result_dir, + f'values') + if not os.path.exists(directory): + os.makedirs(directory) + df.to_csv(oj(directory, f'{method}.csv'), + index=False) + for method, rankings in lfi_rankings.items(): + df = pd.DataFrame(rankings) + directory = oj(result_dir, + f'rankings') + if not os.path.exists(directory): + os.makedirs(directory) + df.to_csv(oj(directory, f'{method}.csv'), + index=False) + + # end time + end = time.time() + + print(f"Progress Message 5/5: Results saved to {result_dir}.") + print(f"Step #5 took {end-start} seconds.") \ No newline at end of file diff --git a/feature_importance/entropy-bias/lfi_methods.py b/feature_importance/entropy-bias/lfi_methods.py new file mode 100644 index 0000000..ada2806 --- /dev/null +++ b/feature_importance/entropy-bias/lfi_methods.py @@ -0,0 +1,1595 @@ +import os +import sys +import pandas as pd +import numpy as np +import sklearn.base +from sklearn.base import RegressorMixin, ClassifierMixin +from sklearn.metrics import mean_squared_error, log_loss +from functools import reduce + +import shap +import lime +import lime.lime_tabular +from imodels.tree.rf_plus.rf_plus.rf_plus_models import RandomForestPlusRegressor, RandomForestPlusClassifier +from sklearn.ensemble import RandomForestRegressor +from imodels.tree.rf_plus.feature_importance.rfplus_explainer import * +from sklearn.metrics import r2_score, mean_absolute_error, accuracy_score, roc_auc_score, mean_squared_error + +def tree_shap_evaluation_RF_retrain(X_train, y_train, fit=None, mode="absolute"): + """ + Compute average treeshap value across observations. + Larger absolute values indicate more important features. + :param X: design matrix + :param y: response + :param fit: fitted model of interest (tree-based) + :return: dataframe of shape: (n_samples, n_features) + """ + explainer = shap.TreeExplainer(fit) + local_fi_score_train = explainer.shap_values(X_train, check_additivity=False) + if sklearn.base.is_classifier(fit): + if mode == "absolute": + return np.abs(local_fi_score_train[:,:,1]) + if mode == "absolute": + return np.abs(local_fi_score_train) + + +def lime_evaluation_RF_retrain(X_train, y_train, fit=None, mode="absolute"): + result = np.zeros((X_train.shape[0], X_train.shape[1])) + if sklearn.base.is_classifier(fit): + task = "classification" + else: + task = "regression" + if task == "classification": + explainer = lime.lime_tabular.LimeTabularExplainer(X_train,verbose=False,mode=task) + num_features = X_train.shape[1] + for i in range(X_train.shape[0]): + exp = explainer.explain_instance(X_train[i,:], fit.predict_proba, num_features=num_features) + original_feature_importance = exp.as_map()[1] + sorted_feature_importance = sorted(original_feature_importance,key = lambda x: x[0]) + for j in range(num_features): + result[i,j] = sorted_feature_importance[j][1] #abs(sorted_feature_importance[j][1]) + elif task == "regression": + explainer = lime.lime_tabular.LimeTabularExplainer(X_train,verbose=False,mode=task) + num_features = X_train.shape[1] + for i in range(X_train.shape[0]): + exp = explainer.explain_instance(X_train[i,:], fit.predict, num_features=num_features) + original_feature_importance = exp.as_map()[1] + sorted_feature_importance = sorted(original_feature_importance,key = lambda x: x[0]) + for j in range(num_features): + result[i,j] = sorted_feature_importance[j][1] + if mode == "absolute": + lime_values = np.abs(result) + return lime_values + + +def LFI_evaluation_RFPlus_inbag_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = RFPlusMDI(fit, mode = 'only_k', evaluate_on="inbag") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train) + if mode == "absolute": + return np.abs(local_fi_score_train) + + +def LFI_evaluation_RFPlus_oob_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="oob") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train) + if mode == "absolute": + return np.abs(local_fi_score_train) + +def LFI_evaluation_RFPlus_all_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="all") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train) + if mode == "absolute": + return np.abs(local_fi_score_train) + + +def LFI_evaluation_RFPlus_inbag_error_metric_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = RFPlusMDI(fit, mode = 'keep_rest_zero', evaluate_on="inbag") + local_fi_score_train = rf_plus_mdi.explain_linear_partial_error_metric(X=X_train, y=y_train) + if mode == "absolute": + return np.abs(local_fi_score_train) + + +def LFI_evaluation_RFPlus_oob_error_metric_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, mode = 'keep_rest_zero', evaluate_on="oob") + local_fi_score_train = rf_plus_mdi.explain_linear_partial_error_metric(X=X_train, y=y_train) + if mode == "absolute": + return np.abs(local_fi_score_train) + +def LFI_evaluation_RFPlus_all_error_metric_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, mode = 'keep_rest_zero', evaluate_on="all") + local_fi_score_train = rf_plus_mdi.explain_linear_partial_error_metric(X=X_train, y=y_train) + if mode == "absolute": + return np.abs(local_fi_score_train) + + +def LFI_evaluation_RFPlus_inbag_error_metric_average_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = RFPlusMDI(fit, mode = 'keep_rest_zero', evaluate_on="inbag") + local_fi_score_train = rf_plus_mdi.explain_linear_partial_error_metric(X=X_train, y=y_train, leaf_average=True) + if mode == "absolute": + return np.abs(local_fi_score_train) + + +def LFI_evaluation_RFPlus_oob_error_metric_average_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, mode = 'keep_rest_zero', evaluate_on="oob") + local_fi_score_train = rf_plus_mdi.explain_linear_partial_error_metric(X=X_train, y=y_train, leaf_average=True) + if mode == "absolute": + return np.abs(local_fi_score_train) + +def LFI_evaluation_RFPlus_all_error_metric_average_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, mode = 'keep_rest_zero', evaluate_on="all") + local_fi_score_train = rf_plus_mdi.explain_linear_partial_error_metric(X=X_train, y=y_train, leaf_average=True) + if mode == "absolute": + return np.abs(local_fi_score_train) + + +def LFI_evaluation_RFPlus_inbag_error_metric_ranking_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = RFPlusMDI(fit, mode = 'keep_rest_zero', evaluate_on="inbag") + local_fi_score_train = rf_plus_mdi.explain_linear_partial_error_metric(X=X_train, y=y_train, ranking=True) + if mode == "absolute": + return np.abs(local_fi_score_train) + + +def LFI_evaluation_RFPlus_oob_error_metric_ranking_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, mode = 'keep_rest_zero', evaluate_on="oob") + local_fi_score_train = rf_plus_mdi.explain_linear_partial_error_metric(X=X_train, y=y_train, ranking=True) + if mode == "absolute": + return np.abs(local_fi_score_train) + +def LFI_evaluation_RFPlus_all_error_metric_ranking_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, mode = 'keep_rest_zero', evaluate_on="all") + local_fi_score_train = rf_plus_mdi.explain_linear_partial_error_metric(X=X_train, y=y_train, ranking=True) + if mode == "absolute": + return np.abs(local_fi_score_train) + + +def LFI_evaluation_RFPlus_inbag_average_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = RFPlusMDI(fit, mode = 'only_k', evaluate_on="inbag") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, leaf_average = True) + if mode == "absolute": + return np.abs(local_fi_score_train) + + +def LFI_evaluation_RFPlus_oob_average_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="oob") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, leaf_average = True) + if mode == "absolute": + return np.abs(local_fi_score_train) + +def LFI_evaluation_RFPlus_all_average_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="all") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, leaf_average = True) + if mode == "absolute": + return np.abs(local_fi_score_train) + + +def LFI_evaluation_RFPlus_inbag_ranking_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = RFPlusMDI(fit, mode = 'only_k', evaluate_on="inbag") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, ranking = True) + if mode == "absolute": + return np.abs(local_fi_score_train) + + +def LFI_evaluation_RFPlus_oob_ranking_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="oob") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, ranking = True) + if mode == "absolute": + return np.abs(local_fi_score_train) + +def LFI_evaluation_RFPlus_all_ranking_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="all") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, ranking = True) + if mode == "absolute": + return np.abs(local_fi_score_train) + +def LFI_evaluation_RFPlus_inbag_ranking_ridge_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = RFPlusMDI(fit, mode = 'only_k', evaluate_on="inbag") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, ranking = True) + if mode == "absolute": + return np.abs(local_fi_score_train) + + +def LFI_evaluation_RFPlus_oob_ranking_ridge_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = RFPlusMDI(fit, mode = 'only_k', evaluate_on="oob") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, ranking = True) + if mode == "absolute": + return np.abs(local_fi_score_train) + +def LFI_evaluation_RFPlus_all_ranking_ridge_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = RFPlusMDI(fit, mode = 'only_k', evaluate_on="all") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, ranking = True) + if mode == "absolute": + return np.abs(local_fi_score_train) + + + +# def LFI_evaluation_RFPlus_inbag_l2_norm_sign_retrain(X_train, y_train, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = RFPlusMDI(fit, mode = 'only_k', evaluate_on="inbag") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=True) +# if mode == "absolute": +# return np.abs(local_fi_score_train) + + +# def LFI_evaluation_RFPlus_oob_l2_norm_sign_retrain(X_train, y_train, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="oob") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=True) +# if mode == "absolute": +# return np.abs(local_fi_score_train) + +# def LFI_evaluation_RFPlus_all_l2_norm_sign_retrain(X_train, y_train, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="all") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=True) +# if mode == "absolute": +# return np.abs(local_fi_score_train) + + +def LFI_evaluation_RFPlus_inbag_l2_norm_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = RFPlusMDI(fit, mode = 'only_k', evaluate_on="inbag") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=False) + if mode == "absolute": + return np.abs(local_fi_score_train) + + +def LFI_evaluation_RFPlus_oob_l2_norm_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="oob") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=False) + if mode == "absolute": + return np.abs(local_fi_score_train) + +def LFI_evaluation_RFPlus_all_l2_norm_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="all") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=False) + if mode == "absolute": + return np.abs(local_fi_score_train) + +def LFI_evaluation_RFPlus_inbag_l2_norm_average_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = RFPlusMDI(fit, mode = 'only_k', evaluate_on="inbag") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=False, leaf_average= True) + if mode == "absolute": + return np.abs(local_fi_score_train) + +def LFI_evaluation_RFPlus_oob_l2_norm_average_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="oob") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=False, leaf_average= True) + if mode == "absolute": + return np.abs(local_fi_score_train) + +def LFI_evaluation_RFPlus_all_l2_norm_average_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="all") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=False, leaf_average= True) + if mode == "absolute": + return np.abs(local_fi_score_train) + +def LFI_evaluation_RFPlus_inbag_l2_norm_ranking_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = RFPlusMDI(fit, mode = 'only_k', evaluate_on="inbag") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=False, ranking=True) + if mode == "absolute": + return np.abs(local_fi_score_train) + + +def LFI_evaluation_RFPlus_oob_l2_norm_ranking_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="oob") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=False, ranking=True) + if mode == "absolute": + return np.abs(local_fi_score_train) + +def LFI_evaluation_RFPlus_all_l2_norm_ranking_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="all") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=False, ranking=True) + if mode == "absolute": + return np.abs(local_fi_score_train) + + + + + + + + + + + + + + + + + +#### Baseline Methods +def random(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute", train_only=False): + local_fi_score_train = np.random.randn(*X_train.shape) + local_fi_score_train_subset = np.random.randn(*X_train_subset.shape) + local_fi_score_test = np.random.randn(*X_test.shape) + local_fi_score_test_subset = np.random.randn(*X_test_subset.shape) + if mode == "absolute": + return np.abs(local_fi_score_train), np.abs(local_fi_score_train_subset), np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) + else: + return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset + # local_fi_score_train_subset = feature_importance_mask(local_fi_score_train_subset, local_fi_score_train_subset, mode, mask_to = "zero") + # local_fi_score_test = feature_importance_mask(local_fi_score_test, local_fi_score_test, mode, mask_to = "zero") + # local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset, mode, mask_to = "zero") + # return local_fi_score_train, np.abs(local_fi_score_train_subset), np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) + + +def tree_shap_evaluation_RF(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute", train_only=False): + """ + Compute average treeshap value across observations. + Larger absolute values indicate more important features. + :param X: design matrix + :param y: response + :param fit: fitted model of interest (tree-based) + :return: dataframe of shape: (n_samples, n_features) + """ + explainer = shap.TreeExplainer(fit) + local_fi_score_train = explainer.shap_values(X_train, check_additivity=False) + local_fi_score_train_subset = explainer.shap_values(X_train_subset, check_additivity=False) + local_fi_score_test = explainer.shap_values(X_test, check_additivity=False) + local_fi_score_test_subset = explainer.shap_values(X_test_subset, check_additivity=False) + if sklearn.base.is_classifier(fit): + if mode == "absolute": + #return None, np.sum(np.abs(local_fi_score_train_subset),axis=-1), np.sum(np.abs(local_fi_score_test),axis=-1), np.sum(np.abs(local_fi_score_test_subset),axis=-1) + return np.abs(local_fi_score_train[:,:,1]), np.abs(local_fi_score_train_subset[:,:,1]), np.abs(local_fi_score_test[:,:,1]), np.abs(local_fi_score_test_subset[:,:,1]) + else: + return local_fi_score_train[:,:,1], local_fi_score_train_subset[:,:,1], local_fi_score_test[:,:,1], local_fi_score_test_subset[:,:,1] + else: + if mode == "absolute": + return np.abs(local_fi_score_train), np.abs(local_fi_score_train_subset), np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) + else: + # local_fi_score_train_subset = feature_importance_mask(local_fi_score_train_subset, local_fi_score_train_subset, mode, mask_to = "zero") + # local_fi_score_test = feature_importance_mask(local_fi_score_test, local_fi_score_test, mode, mask_to = "zero") + # local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset, mode, mask_to = "zero") + return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset + + +def lime_evaluation_RF(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute", train_only=False): + if train_only: + result = np.zeros((X_train.shape[0], X_train.shape[1])) + if sklearn.base.is_classifier(fit): + task = "classification" + else: + task = "regression" + + if task == "classification": + explainer = lime.lime_tabular.LimeTabularExplainer(X_train,verbose=False,mode=task) + num_features = X_train.shape[1] + for i in range(X_train.shape[0]): + exp = explainer.explain_instance(X_train[i,:], fit.predict_proba, num_features=num_features) + original_feature_importance = exp.as_map()[1] + sorted_feature_importance = sorted(original_feature_importance,key = lambda x: x[0]) + for j in range(num_features): + result[i,j] = sorted_feature_importance[j][1] #abs(sorted_feature_importance[j][1]) + elif task == "regression": + explainer = lime.lime_tabular.LimeTabularExplainer(X_train,verbose=False,mode=task) + num_features = X_train.shape[1] + for i in range(X_train.shape[0]): + exp = explainer.explain_instance(X_train[i,:], fit.predict, num_features=num_features) + original_feature_importance = exp.as_map()[1] + sorted_feature_importance = sorted(original_feature_importance,key = lambda x: x[0]) + for j in range(num_features): + result[i,j] = sorted_feature_importance[j][1] + if mode == "absolute": + lime_values = np.abs(result) + else: + lime_values = result + + return lime_values, None, None, None + + + +# def kernel_shap_evaluation_RF_plus(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_kernel_shap = RFPlusKernelSHAP(fit) +# local_fi_score_train = None +# local_fi_score_train_subset = rf_plus_kernel_shap.explain(X_train=X_train, X_test=X_train_subset) +# local_fi_score_test = None +# local_fi_score_test_subset = rf_plus_kernel_shap.explain(X_train=X_train, X_test=X_test_subset) +# if sklearn.base.is_classifier(fit): +# if mode == "absolute": +# #return None, np.sum(np.abs(local_fi_score_train_subset),axis=-1), np.sum(np.abs(local_fi_score_test),axis=-1), np.sum(np.abs(local_fi_score_test_subset),axis=-1) +# return None, np.abs(local_fi_score_train_subset[:,:,1]), None, np.abs(local_fi_score_test_subset[:,:,1]) +# else: +# return None, local_fi_score_train_subset[:,:,1], None, local_fi_score_test_subset[:,:,1] +# else: +# if mode == "absolute": +# return None, np.abs(local_fi_score_train_subset), None, np.abs(local_fi_score_test_subset) +# else: +# # local_fi_score_train_subset = feature_importance_mask(local_fi_score_train_subset, local_fi_score_train_subset, mode, mask_to = "zero") +# # local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset, mode, mask_to = "zero") +# return None, local_fi_score_train_subset, None, local_fi_score_test_subset + + +# def lime_evaluation_RF_plus(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_lime = RFPlusLime(fit) +# local_fi_score_train = None +# local_fi_score_train_subset = rf_plus_lime.explain(X_train=X_train, X_test=X_train_subset).values +# local_fi_score_test = None +# local_fi_score_test_subset = rf_plus_lime.explain(X_train=X_train, X_test=X_test_subset).values +# if mode == "absolute": +# return None, np.abs(local_fi_score_train_subset), None, np.abs(local_fi_score_test_subset) +# else: +# return None, local_fi_score_train_subset, None, local_fi_score_test_subset +# # local_fi_score_train_subset = feature_importance_mask(local_fi_score_train_subset, local_fi_score_train_subset, mode, mask_to = "zero") +# # local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset, mode, mask_to = "zero") +# # return local_fi_score_train, np.abs(local_fi_score_train_subset), local_fi_score_test, np.abs(local_fi_score_test_subset) + + + + + + + + + + + +### Feature Importance Methods for RF+ + +# def LFI_evaluation_RFPlus_inbag(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = RFPlusMDI(fit, evaluate_on="inbag") +# local_fi_score_train = np.abs(rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train)) +# local_fi_score_train_subset = None +# local_fi_score_test = np.abs(rf_plus_mdi.explain_linear_partial(X=X_test, y=None)) +# local_fi_score_test_subset = np.abs(rf_plus_mdi.explain_linear_partial(X=X_test_subset, y=None)) +# return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset + + +def LFI_evaluation_RFPlus_oob(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute", train_only=False): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="oob") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train) + local_fi_score_train_subset = None + local_fi_score_test = rf_plus_mdi.explain_linear_partial(X=X_test, y=None) + local_fi_score_test_subset = rf_plus_mdi.explain_linear_partial(X=X_test_subset, y=None) + if mode == "absolute": + return np.abs(local_fi_score_train), None, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) + else: + return local_fi_score_train, None, local_fi_score_test, local_fi_score_test_subset + + +def LFI_evaluation_RFPlus_all(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute", train_only=False): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="all") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train) + local_fi_score_train_subset = None + local_fi_score_test = rf_plus_mdi.explain_linear_partial(X=X_test, y=None) + local_fi_score_test_subset = rf_plus_mdi.explain_linear_partial(X=X_test_subset, y=None) + if mode == "absolute": + return np.abs(local_fi_score_train), None, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) + else: + return local_fi_score_train, None, local_fi_score_test, local_fi_score_test_subset + + + +def LFI_evaluation_RFPlus_oob_error_metric(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute", train_only=False): + assert train_only == True + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="oob") + local_fi_score_train = rf_plus_mdi.explain(X=X_train, y=y_train)[0] + if mode == "absolute": + return np.abs(local_fi_score_train), None, None, None + else: + return local_fi_score_train, None, None, None + +def LFI_evaluation_RFPlus_all_error_metric(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute", train_only=False): + assert train_only == True + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="all") + local_fi_score_train = rf_plus_mdi.explain(X=X_train, y=y_train)[0] + if mode == "absolute": + return np.abs(local_fi_score_train), None, None, None + else: + return local_fi_score_train, None, None, None + + +# ##### Average Leaf +# def LFI_evaluation_RFPlus_inbag_avg_leaf(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = RFPlusMDI(fit, evaluate_on="inbag") +# local_fi_score_train = np.abs(rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, leaf_average=True)) +# local_fi_score_train_subset = None +# local_fi_score_test = np.abs(rf_plus_mdi.explain_linear_partial(X=X_test, y=None,leaf_average=True)) +# local_fi_score_test_subset = np.abs(rf_plus_mdi.explain_linear_partial(X=X_test_subset, y=None, leaf_average=True)) +# return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset + + +# def LFI_evaluation_RFPlus_oob_avg_leaf(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="oob") +# local_fi_score_train = np.abs(rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train,leaf_average=True)) +# local_fi_score_train_subset = None +# local_fi_score_test = np.abs(rf_plus_mdi.explain_linear_partial(X=X_test, y=None,leaf_average=True)) +# local_fi_score_test_subset = np.abs(rf_plus_mdi.explain_linear_partial(X=X_test_subset, y=None,leaf_average=True)) +# return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset + + +# def LFI_evaluation_RFPlus_all_avg_leaf(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="all") +# local_fi_score_train = np.abs(rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train,leaf_average=True)) +# local_fi_score_train_subset = None +# local_fi_score_test = np.abs(rf_plus_mdi.explain_linear_partial(X=X_test, y=None,leaf_average=True)) +# local_fi_score_test_subset = np.abs(rf_plus_mdi.explain_linear_partial(X=X_test_subset, y=None,leaf_average=True)) +# return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset + + + +##### l2 norm with sign +# def LFI_evaluation_RFPlus_inbag_l2_norm_sign(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = RFPlusMDI(fit, evaluate_on="inbag") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=True) +# local_fi_score_train_subset = None +# local_fi_score_test = rf_plus_mdi.explain_linear_partial(X=X_test, y=None, l2norm=True, sign=True) +# local_fi_score_test_subset = rf_plus_mdi.explain_linear_partial(X=X_test_subset, y=None, l2norm=True, sign=True) +# if mode == "absolute": +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) +# else: +# return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset + + +def LFI_evaluation_RFPlus_oob_l2_norm_sign(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute", train_only=False): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="oob") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=True) + local_fi_score_train_subset = None + local_fi_score_test = rf_plus_mdi.explain_linear_partial(X=X_test, y=None, l2norm=True, sign=True) + local_fi_score_test_subset = rf_plus_mdi.explain_linear_partial(X=X_test_subset, y=None, l2norm=True, sign=True) + if mode == "absolute": + return np.abs(local_fi_score_train), None, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) + else: + return local_fi_score_train, None, local_fi_score_test, local_fi_score_test_subset + + +def LFI_evaluation_RFPlus_all_l2_norm_sign(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute", train_only=False): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="all") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=True) + local_fi_score_train_subset = None + local_fi_score_test = rf_plus_mdi.explain_linear_partial(X=X_test, y=None, l2norm=True, sign=True) + local_fi_score_test_subset = rf_plus_mdi.explain_linear_partial(X=X_test_subset, y=None, l2norm=True, sign=True) + if mode == "absolute": + return np.abs(local_fi_score_train), None, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) + else: + return local_fi_score_train, None, local_fi_score_test, local_fi_score_test_subset + + +# ##### l2 norm +# def LFI_evaluation_RFPlus_inbag_l2_norm(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = RFPlusMDI(fit, evaluate_on="inbag") +# local_fi_score_train = np.abs(rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True)) +# local_fi_score_train_subset = None +# local_fi_score_test = np.abs(rf_plus_mdi.explain_linear_partial(X=X_test, y=None, l2norm=True)) +# local_fi_score_test_subset = np.abs(rf_plus_mdi.explain_linear_partial(X=X_test_subset, y=None, l2norm=True)) +# return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset + + +# def LFI_evaluation_RFPlus_oob_l2_norm(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="oob") +# local_fi_score_train = np.abs(rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True)) +# local_fi_score_train_subset = None +# local_fi_score_test = np.abs(rf_plus_mdi.explain_linear_partial(X=X_test, y=None, l2norm=True)) +# local_fi_score_test_subset = np.abs(rf_plus_mdi.explain_linear_partial(X=X_test_subset, y=None, l2norm=True)) +# return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset + + +# def LFI_evaluation_RFPlus_all_l2_norm(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="all") +# local_fi_score_train = np.abs(rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True)) +# local_fi_score_train_subset = None +# local_fi_score_test = np.abs(rf_plus_mdi.explain_linear_partial(X=X_test, y=None, l2norm=True)) +# local_fi_score_test_subset = np.abs(rf_plus_mdi.explain_linear_partial(X=X_test_subset, y=None, l2norm=True)) +# return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset + +# ##### Average Leaf and l2 norm +# def LFI_evaluation_RFPlus_inbag_l2_norm_avg_leaf(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = RFPlusMDI(fit, evaluate_on="inbag") +# local_fi_score_train = np.abs(rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, leaf_average=True)) +# local_fi_score_train_subset = None +# local_fi_score_test = np.abs(rf_plus_mdi.explain_linear_partial(X=X_test, y=None, l2norm=True ,leaf_average=True)) +# local_fi_score_test_subset = np.abs(rf_plus_mdi.explain_linear_partial(X=X_test_subset, y=None, l2norm=True ,leaf_average=True)) +# return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset + + +# def LFI_evaluation_RFPlus_oob_l2_norm_avg_leaf(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="oob") +# local_fi_score_train = np.abs(rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, leaf_average=True)) +# local_fi_score_train_subset = None +# local_fi_score_test = np.abs(rf_plus_mdi.explain_linear_partial(X=X_test, y=None, l2norm=True, leaf_average=True)) +# local_fi_score_test_subset = np.abs(rf_plus_mdi.explain_linear_partial(X=X_test_subset, y=None, l2norm=True, leaf_average=True)) +# return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset + + +# def LFI_evaluation_RFPlus_all_l2_norm_avg_leaf(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="all") +# local_fi_score_train = np.abs(rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, leaf_average=True)) +# local_fi_score_train_subset = None +# local_fi_score_test = np.abs(rf_plus_mdi.explain_linear_partial(X=X_test, y=None, l2norm=True, leaf_average=True)) +# local_fi_score_test_subset = np.abs(rf_plus_mdi.explain_linear_partial(X=X_test_subset, y=None, l2norm=True, leaf_average=True)) +# return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset + + + + + +# ### Feature Importance Methods for RF+ avg leaf +# def LFI_evaluation_RFPlus_inbag_avg_leaf(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi_train = RFPlusMDI(fit, evaluate_on="inbag") +# rf_plus_mdi_test = RFPlusMDI(fit, evaluate_on="all") +# local_fi_score_train = np.abs(rf_plus_mdi_train.explain(X=X_train, y=y_train, leaf_average=True)[0]) +# local_fi_score_train_subset = None +# local_fi_score_test = np.abs(rf_plus_mdi_test.explain(X=X_test, y=None, leaf_average=True)[0]) +# local_fi_score_test_subset = np.abs(rf_plus_mdi_test.explain(X=X_test_subset, y=None, leaf_average=True)[0]) +# if mode != "absolute": +# local_fi_score_train_mask = rf_plus_mdi_train.explain_subtract_intercept(X=X_train, y=y_train) +# local_fi_score_test_mask = rf_plus_mdi_test.explain_subtract_intercept(X=X_test, y=None) +# local_fi_score_test_subset_mask = rf_plus_mdi_test.explain_subtract_intercept(X=X_test_subset, y=None) +# local_fi_score_train = feature_importance_mask(local_fi_score_train, local_fi_score_train_mask, mode, mask_to = "inf") +# local_fi_score_test = feature_importance_mask(local_fi_score_test, local_fi_score_test_mask, mode, mask_to = "inf") +# local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset_mask, mode, mask_to = "inf") +# return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset + + +# def LFI_evaluation_RFPlus_oob_avg_leaf(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi_train = AloRFPlusMDI(fit, evaluate_on="oob") +# rf_plus_mdi_test = AloRFPlusMDI(fit, evaluate_on="all") +# local_fi_score_train = np.abs(rf_plus_mdi_train.explain(X=X_train, y=y_train, leaf_average=True)[0]) +# local_fi_score_train_subset = None +# local_fi_score_test = np.abs(rf_plus_mdi_test.explain(X=X_test, y=None, leaf_average=True)[0]) +# local_fi_score_test_subset = np.abs(rf_plus_mdi_test.explain(X=X_test_subset, y=None, leaf_average=True)[0]) +# if mode != "absolute": +# local_fi_score_train_mask = rf_plus_mdi_train.explain_subtract_intercept(X=X_train, y=y_train) +# local_fi_score_test_mask = rf_plus_mdi_test.explain_subtract_intercept(X=X_test, y=None) +# local_fi_score_test_subset_mask = rf_plus_mdi_test.explain_subtract_intercept(X=X_test_subset, y=None) +# local_fi_score_train = feature_importance_mask(local_fi_score_train, local_fi_score_train_mask, mode, mask_to = "inf") +# local_fi_score_test = feature_importance_mask(local_fi_score_test, local_fi_score_test_mask, mode, mask_to = "inf") +# local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset_mask, mode, mask_to = "inf") +# return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset + + +# def LFI_evaluation_RFPlus_all_avg_leaf(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="all") +# local_fi_score_train = np.abs(rf_plus_mdi.explain(X=X_train, y=y_train, leaf_average=True)[0]) +# local_fi_score_train_subset = None +# local_fi_score_test = np.abs(rf_plus_mdi.explain(X=X_test, y=None, leaf_average=True)[0]) +# local_fi_score_test_subset = np.abs(rf_plus_mdi.explain(X=X_test_subset, y=None, leaf_average=True)[0]) +# if mode != "absolute": +# local_fi_score_train_mask = rf_plus_mdi.explain_subtract_intercept(X=X_train, y=y_train) +# local_fi_score_test_mask = rf_plus_mdi.explain_subtract_intercept(X=X_test, y=None) +# local_fi_score_test_subset_mask = rf_plus_mdi.explain_subtract_intercept(X=X_test_subset, y=None) +# local_fi_score_train = feature_importance_mask(local_fi_score_train, local_fi_score_train_mask, mode, mask_to = "inf") +# local_fi_score_test = feature_importance_mask(local_fi_score_test, local_fi_score_test_mask, mode, mask_to = "inf") +# local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset_mask, mode, mask_to = "inf") +# return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset + + +# ### No intercept +# def LFI_evaluation_RFPlus_inbag_subtract_intercept(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi_train = RFPlusMDI(fit, evaluate_on="inbag") +# rf_plus_mdi_test = RFPlusMDI(fit, evaluate_on="all") +# local_fi_score_train = rf_plus_mdi_train.explain_subtract_intercept(X=X_train, y=y_train) +# local_fi_score_train_subset = None +# local_fi_score_test = rf_plus_mdi_test.explain_subtract_intercept(X=X_test, y=None) +# local_fi_score_test_subset = rf_plus_mdi_test.explain_subtract_intercept(X=X_test_subset, y=None) +# if mode == "absolute": +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) +# else: +# local_fi_score_train = feature_importance_mask(local_fi_score_train, local_fi_score_train, mode, mask_to = "zero") +# local_fi_score_test = feature_importance_mask(local_fi_score_test, local_fi_score_test, mode, mask_to = "zero") +# local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset, mode, mask_to = "zero") +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) + + +# def LFI_evaluation_RFPlus_oob_subtract_intercept(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi_train = AloRFPlusMDI(fit, evaluate_on="oob") +# rf_plus_mdi_test = AloRFPlusMDI(fit, evaluate_on="all") +# local_fi_score_train = rf_plus_mdi_train.explain_subtract_intercept(X=X_train, y=y_train) +# local_fi_score_train_subset = None +# local_fi_score_test = rf_plus_mdi_test.explain_subtract_intercept(X=X_test, y=None) +# local_fi_score_test_subset = rf_plus_mdi_test.explain_subtract_intercept(X=X_test_subset, y=None) +# if mode == "absolute": +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) +# else: +# local_fi_score_train = feature_importance_mask(local_fi_score_train, local_fi_score_train, mode, mask_to = "zero") +# local_fi_score_test = feature_importance_mask(local_fi_score_test, local_fi_score_test, mode, mask_to = "zero") +# local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset, mode, mask_to = "zero") +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) + + + +# def LFI_evaluation_RFPlus_all_subtract_intercept(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="all") +# local_fi_score_train = rf_plus_mdi.explain_subtract_intercept(X=X_train, y=y_train) +# local_fi_score_train_subset = None +# local_fi_score_test = rf_plus_mdi.explain_subtract_intercept(X=X_test, y=None) +# local_fi_score_test_subset = rf_plus_mdi.explain_subtract_intercept(X=X_test_subset, y=None) +# if mode == "absolute": +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) +# else: +# local_fi_score_train = feature_importance_mask(local_fi_score_train, local_fi_score_train, mode, mask_to = "zero") +# local_fi_score_test = feature_importance_mask(local_fi_score_test, local_fi_score_test, mode, mask_to = "zero") +# local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset, mode, mask_to = "zero") +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) + + + + + +# ### No intercept and average leaf +# def LFI_evaluation_RFPlus_inbag_subtract_intercept_avg_leaf(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi_train = RFPlusMDI(fit, evaluate_on="inbag") +# rf_plus_mdi_test = RFPlusMDI(fit, evaluate_on="all") +# local_fi_score_train = rf_plus_mdi_train.explain_subtract_intercept(X=X_train, y=y_train, leaf_average=True) +# local_fi_score_train_subset = None +# local_fi_score_test = rf_plus_mdi_test.explain_subtract_intercept(X=X_test, y=None, leaf_average=True) +# local_fi_score_test_subset = rf_plus_mdi_test.explain_subtract_intercept(X=X_test_subset, y=None, leaf_average=True) +# if mode == "absolute": +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) +# else: +# local_fi_score_train = feature_importance_mask(local_fi_score_train, local_fi_score_train, mode, mask_to = "zero") +# local_fi_score_test = feature_importance_mask(local_fi_score_test, local_fi_score_test, mode, mask_to = "zero") +# local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset, mode, mask_to = "zero") +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) + + +# def LFI_evaluation_RFPlus_oob_subtract_intercept_avg_leaf(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi_train = AloRFPlusMDI(fit, evaluate_on="oob") +# rf_plus_mdi_test = AloRFPlusMDI(fit, evaluate_on="all") +# local_fi_score_train = rf_plus_mdi_train.explain_subtract_intercept(X=X_train, y=y_train, leaf_average=True) +# local_fi_score_train_subset = None +# local_fi_score_test = rf_plus_mdi_test.explain_subtract_intercept(X=X_test, y=None, leaf_average=True) +# local_fi_score_test_subset = rf_plus_mdi_test.explain_subtract_intercept(X=X_test_subset, y=None, leaf_average=True) +# if mode == "absolute": +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) +# else: +# local_fi_score_train = feature_importance_mask(local_fi_score_train, local_fi_score_train, mode, mask_to = "zero") +# local_fi_score_test = feature_importance_mask(local_fi_score_test, local_fi_score_test, mode, mask_to = "zero") +# local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset, mode, mask_to = "zero") +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) + + + +# def LFI_evaluation_RFPlus_all_subtract_intercept_avg_leaf(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="all") +# local_fi_score_train = rf_plus_mdi.explain_subtract_intercept(X=X_train, y=y_train, leaf_average=True) +# local_fi_score_train_subset = None +# local_fi_score_test = rf_plus_mdi.explain_subtract_intercept(X=X_test, y=None, leaf_average=True) +# local_fi_score_test_subset = rf_plus_mdi.explain_subtract_intercept(X=X_test_subset, y=None, leaf_average=True) +# if mode == "absolute": +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) +# else: +# local_fi_score_train = feature_importance_mask(local_fi_score_train, local_fi_score_train, mode, mask_to = "zero") +# local_fi_score_test = feature_importance_mask(local_fi_score_test, local_fi_score_test, mode, mask_to = "zero") +# local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset, mode, mask_to = "zero") +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) + + + + + + +# ### Subtract train mean +# def LFI_evaluation_RFPlus_inbag_subtract_train_mean(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi_train = RFPlusMDI(fit, evaluate_on="inbag") +# rf_plus_mdi_test = RFPlusMDI(fit, evaluate_on="all") +# constant = np.mean(y_train) +# local_fi_score_train = rf_plus_mdi_train.explain_subtract_constant(X=X_train,constant=constant, y=y_train) +# local_fi_score_train_subset = None +# local_fi_score_test = rf_plus_mdi_test.explain_subtract_constant(X=X_test,constant=constant, y=None) +# local_fi_score_test_subset = rf_plus_mdi_test.explain_subtract_constant(X=X_test_subset,constant=constant, y=None) +# if mode == "absolute": +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) +# else: +# local_fi_score_train = feature_importance_mask(local_fi_score_train, local_fi_score_train, mode, mask_to = "zero") +# local_fi_score_test = feature_importance_mask(local_fi_score_test, local_fi_score_test, mode, mask_to = "zero") +# local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset, mode, mask_to = "zero") +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) + + +# def LFI_evaluation_RFPlus_oob_subtract_train_mean(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi_train = AloRFPlusMDI(fit, evaluate_on="oob") +# rf_plus_mdi_test = AloRFPlusMDI(fit, evaluate_on="all") +# constant = np.mean(y_train) +# local_fi_score_train = rf_plus_mdi_train.explain_subtract_constant(X=X_train, constant=constant,y=y_train) +# local_fi_score_train_subset = None +# local_fi_score_test = rf_plus_mdi_test.explain_subtract_constant(X=X_test,constant=constant, y=None) +# local_fi_score_test_subset = rf_plus_mdi_test.explain_subtract_constant(X=X_test_subset, constant=constant,y=None) +# if mode == "absolute": +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) +# else: +# local_fi_score_train = feature_importance_mask(local_fi_score_train, local_fi_score_train, mode, mask_to = "zero") +# local_fi_score_test = feature_importance_mask(local_fi_score_test, local_fi_score_test, mode, mask_to = "zero") +# local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset, mode, mask_to = "zero") +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) + + + +# def LFI_evaluation_RFPlus_all_subtract_train_mean(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="all") +# constant = np.mean(y_train) +# local_fi_score_train = rf_plus_mdi.explain_subtract_constant(X=X_train,constant=constant, y=y_train) +# local_fi_score_train_subset = None +# local_fi_score_test = rf_plus_mdi.explain_subtract_constant(X=X_test,constant=constant, y=None) +# local_fi_score_test_subset = rf_plus_mdi.explain_subtract_constant(X=X_test_subset,constant=constant, y=None) +# if mode == "absolute": +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) +# else: +# local_fi_score_train = feature_importance_mask(local_fi_score_train, local_fi_score_train, mode, mask_to = "zero") +# local_fi_score_test = feature_importance_mask(local_fi_score_test, local_fi_score_test, mode, mask_to = "zero") +# local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset, mode, mask_to = "zero") +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) + + +# ### subtract pred mean +# def LFI_evaluation_RFPlus_inbag_subtract_pred_mean(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi_train = RFPlusMDI(fit, evaluate_on="inbag") +# rf_plus_mdi_test = RFPlusMDI(fit, evaluate_on="all") +# constant = np.mean(y_train_pred) +# local_fi_score_train = rf_plus_mdi_train.explain_subtract_constant(X=X_train,constant=constant, y=y_train) +# local_fi_score_train_subset = None +# local_fi_score_test = rf_plus_mdi_test.explain_subtract_constant(X=X_test,constant=constant, y=None) +# local_fi_score_test_subset = rf_plus_mdi_test.explain_subtract_constant(X=X_test_subset,constant=constant, y=None) +# if mode == "absolute": +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) +# else: +# local_fi_score_train = feature_importance_mask(local_fi_score_train, local_fi_score_train, mode, mask_to = "zero") +# local_fi_score_test = feature_importance_mask(local_fi_score_test, local_fi_score_test, mode, mask_to = "zero") +# local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset, mode, mask_to = "zero") +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) + + +# def LFI_evaluation_RFPlus_oob_subtract_pred_mean(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi_train = AloRFPlusMDI(fit, evaluate_on="oob") +# rf_plus_mdi_test = AloRFPlusMDI(fit, evaluate_on="all") +# constant = np.mean(y_train_pred) +# local_fi_score_train = rf_plus_mdi_train.explain_subtract_constant(X=X_train, constant=constant,y=y_train) +# local_fi_score_train_subset = None +# local_fi_score_test = rf_plus_mdi_test.explain_subtract_constant(X=X_test,constant=constant, y=None) +# local_fi_score_test_subset = rf_plus_mdi_test.explain_subtract_constant(X=X_test_subset, constant=constant,y=None) +# if mode == "absolute": +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) +# else: +# local_fi_score_train = feature_importance_mask(local_fi_score_train, local_fi_score_train, mode, mask_to = "zero") +# local_fi_score_test = feature_importance_mask(local_fi_score_test, local_fi_score_test, mode, mask_to = "zero") +# local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset, mode, mask_to = "zero") +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) + + + +# def LFI_evaluation_RFPlus_all_subtract_pred_mean(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="all") +# constant = np.mean(y_train_pred) +# local_fi_score_train = rf_plus_mdi.explain_subtract_constant(X=X_train,constant=constant, y=y_train) +# local_fi_score_train_subset = None +# local_fi_score_test = rf_plus_mdi.explain_subtract_constant(X=X_test,constant=constant, y=None) +# local_fi_score_test_subset = rf_plus_mdi.explain_subtract_constant(X=X_test_subset,constant=constant, y=None) +# if mode == "absolute": +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) +# else: +# local_fi_score_train = feature_importance_mask(local_fi_score_train, local_fi_score_train, mode, mask_to = "zero") +# local_fi_score_test = feature_importance_mask(local_fi_score_test, local_fi_score_test, mode, mask_to = "zero") +# local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset, mode, mask_to = "zero") +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) + + +### +# def LFI_evaluation_RFPlus_inbag_2(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# assert mode == "absolute" +# rf_plus_mdi_train = RFPlusMDI(fit, evaluate_on="inbag") +# rf_plus_mdi_test = RFPlusMDI(fit, evaluate_on="all") +# local_fi_score_train = np.abs(rf_plus_mdi_train.explain(X=X_train, y=y_train)[1]) +# local_fi_score_train_subset = None +# local_fi_score_test = np.abs(rf_plus_mdi_test.explain(X=X_test, y=None)[1]) +# local_fi_score_test_subset = np.abs(rf_plus_mdi_test.explain(X=X_test_subset, y=None)[1]) +# return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset + + +# def LFI_evaluation_RFPlus_oob_2(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# assert mode == "absolute" +# rf_plus_mdi_train = AloRFPlusMDI(fit, evaluate_on="oob") +# rf_plus_mdi_test = AloRFPlusMDI(fit, evaluate_on="all") +# local_fi_score_train = np.abs(rf_plus_mdi_train.explain(X=X_train, y=y_train)[1]) +# local_fi_score_train_subset = None +# local_fi_score_test = np.abs(rf_plus_mdi_test.explain(X=X_test, y=None)[1]) +# local_fi_score_test_subset = np.abs(rf_plus_mdi_test.explain(X=X_test_subset, y=None)[1]) +# return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset + + + +# def LFI_evaluation_RFPlus_all_2(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# assert mode == "absolute" +# rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="all") +# local_fi_score_train = np.abs(rf_plus_mdi.explain(X=X_train, y=y_train)[1]) +# local_fi_score_train_subset = None +# local_fi_score_test = np.abs(rf_plus_mdi.explain(X=X_test, y=None)[1]) +# local_fi_score_test_subset = np.abs(rf_plus_mdi.explain(X=X_test_subset, y=None)[1]) +# return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset + + +# def LFI_evaluation_oracle_RF_plus(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# subsets = [(None, None),(None, None),(X_test, y_test), (X_test_subset, y_test_subset)] +# result_tables = [] +# rf_plus_mdi = RFPlusMDI(fit, evaluate_on="all") + +# for X_data, y_data in subsets: +# if isinstance(X_data, np.ndarray): +# local_feature_importances, partial_preds = rf_plus_mdi.explain(X=X_data, y=y_data) +# abs_local_feature_importances = np.abs(local_feature_importances) +# result_tables.append(abs_local_feature_importances) +# else: +# result_tables.append(None) + +# return tuple(result_tables) + +# def fast_r2_score(y_true, y_pred, multiclass=False): +# """ +# Evaluates the r-squared value between the observed and estimated responses. +# Equivalent to sklearn.metrics.r2_score but without the robust error +# checking, thus leading to a much faster implementation (at the cost of +# this error checking). For multi-class responses, returns the mean +# r-squared value across each column in the response matrix. + +# Parameters +# ---------- +# y_true: array-like of shape (n_samples, n_targets) +# Observed responses. +# y_pred: array-like of shape (n_samples, n_targets) +# Predicted responses. +# multiclass: bool +# Whether or not the responses are multi-class. + +# Returns +# ------- +# Scalar quantity, measuring the r-squared value. +# """ +# numerator = ((y_true - y_pred) ** 2).sum(axis=0, dtype=np.float64) +# denominator = ((y_true - np.mean(y_true, axis=0)) ** 2). \ +# sum(axis=0, dtype=np.float64) +# if multiclass: +# return np.mean(1 - numerator / denominator) +# else: +# return 1 - numerator / denominator + + +# def neg_log_loss(y_true, y_pred): +# """ +# Evaluates the negative log-loss between the observed and +# predicted responses. + +# Parameters +# ---------- +# y_true: array-like of shape (n_samples, n_targets) +# Observed responses. +# y_pred: array-like of shape (n_samples, n_targets) +# Predicted probabilies. + +# Returns +# ------- +# Scalar quantity, measuring the negative log-loss value. +# """ +# return -log_loss(y_true, y_pred) + +# def neg_mae(y_true, y_pred): +# return -mean_absolute_error(y_true, y_pred) + +# def partial_preds_to_scores(partial_preds, y_test, scoring_fn): +# scores = [] +# for k in range(partial_preds.shape[1]): +# y_pred = partial_preds[:,k] +# scores.append(scoring_fn(y_test, y_pred)) +# return scores + +# def LFI_global_MDI_plus_RF_Plus(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None): +# if isinstance(fit, RandomForestPlusRegressor): +# scoring_fn = fast_r2_score +# elif isinstance(fit, RandomForestPlusClassifier): +# scoring_fn = neg_log_loss +# test_classification_scoring_fn = neg_mae +# y_test_subset_hat = fit.predict(X_test_subset) +# y_test_hat = fit.predict(X_test) +# subsets = [(X_train, y_train, y_train),(None, None, None),(X_test, None, y_test_hat), (X_test_subset, None, y_test_subset_hat)] +# result_tables = [] +# rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="all") + +# for X_data, y_data, y_hat in subsets: +# if isinstance(X_data, np.ndarray): +# if isinstance(fit, RandomForestPlusClassifier) and (np.array_equal(X_data, X_test) or np.array_equal(X_data, X_test_subset)): +# local_feature_importances, partial_preds = rf_plus_mdi.explain(X=X_data, y=y_data) +# scores = partial_preds_to_scores(partial_preds, y_hat, test_classification_scoring_fn) +# result_tables.append(np.tile(scores, (X_data.shape[0], 1))) +# else: +# local_feature_importances, partial_preds = rf_plus_mdi.explain(X=X_data, y=y_data) +# scores = partial_preds_to_scores(partial_preds, y_hat, scoring_fn) +# result_tables.append(np.tile(scores, (X_data.shape[0], 1))) +# else: +# result_tables.append(None) + +# return tuple(result_tables) + + + +# ########## Pos_Neg +# # Feature Importance Methods for RF +# def tree_shap_evaluation_RF_pos_neg(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None): +# """ +# Compute average treeshap value across observations. +# Larger absolute values indicate more important features. +# :param X: design matrix +# :param y: response +# :param fit: fitted model of interest (tree-based) +# :return: dataframe of shape: (n_samples, n_features) +# """ +# def add_abs(a, b): +# return abs(a) + abs(b) + +# subsets = [(None, None), (X_train_subset, None), (X_test, None), (X_test_subset, None)] +# result_tables = [] + +# explainer = shap.TreeExplainer(fit) + +# for X_data, _ in subsets: +# if isinstance(X_data, np.ndarray): +# shap_values = explainer.shap_values(X_data, check_additivity=False) +# if sklearn.base.is_classifier(fit): +# # Shape values are returned as a list of arrays, one for each class +# #results = np.sum(np.abs(shap_values), axis=-1) +# results = np.sum(shap_values, axis=-1) +# else: +# results = shap_values + +# result_tables.append(results) +# result_tables.append(np.abs(results)) +# else: +# result_tables.append(None) +# result_tables.append(None) +# return tuple(result_tables) + +# # Feature Importance Methods for RF+ +# def LFI_evaluation_RFPlus_inbag_pos_neg(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# subsets = [(X_train, y_train), (None, None), (X_test, None), (X_test_subset, None)] +# result_tables = [] + +# for X_data, y_data in subsets: +# if isinstance(X_data, np.ndarray): +# if np.array_equal(X_data, X_train): +# rf_plus_mdi = RFPlusMDI(fit, evaluate_on="inbag") +# else: +# rf_plus_mdi = RFPlusMDI(fit, evaluate_on="all") +# partial_preds_subtract_intercept = rf_plus_mdi.explain_subtract_intercept(X=X_data, y=y_data) +# local_feature_importances, _ = rf_plus_mdi.explain(X=X_data, y=y_data) +# abs_local_feature_importances = np.abs(local_feature_importances) +# result_tables.append(partial_preds_subtract_intercept) +# result_tables.append(abs_local_feature_importances) +# else: +# result_tables.append(None) +# result_tables.append(None) +# return tuple(result_tables) + +# def LFI_evaluation_RFPlus_oob_pos_neg(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# subsets = [(X_train, y_train), (None, None), (X_test, None), (X_test_subset, None)] +# result_tables = [] + +# for X_data, y_data in subsets: +# if isinstance(X_data, np.ndarray): +# if np.array_equal(X_data, X_train): +# rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="oob") +# else: +# rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="all") +# partial_preds_subtract_intercept = rf_plus_mdi.explain_subtract_intercept(X=X_data, y=y_data) +# local_feature_importances, _ = rf_plus_mdi.explain(X=X_data, y=y_data) +# abs_local_feature_importances = np.abs(local_feature_importances) +# result_tables.append(partial_preds_subtract_intercept) +# result_tables.append(abs_local_feature_importances) +# else: +# result_tables.append(None) +# result_tables.append(None) +# return tuple(result_tables) + +# def LFI_evaluation_RFPlus_all_pos_neg(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# subsets = [(X_train, y_train), (None, None), (X_test, None), (X_test_subset, None)] +# result_tables = [] +# rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="all") + +# for X_data, y_data in subsets: +# if isinstance(X_data, np.ndarray): +# partial_preds_subtract_intercept = rf_plus_mdi.explain_subtract_intercept(X=X_data, y=y_data) +# local_feature_importances, _ = rf_plus_mdi.explain(X=X_data, y=y_data) +# abs_local_feature_importances = np.abs(local_feature_importances) +# result_tables.append(partial_preds_subtract_intercept) +# result_tables.append(abs_local_feature_importances) +# else: +# result_tables.append(None) +# result_tables.append(None) + +# return tuple(result_tables) + +# def lime_evaluation_RF_plus_pos_neg(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# subsets = [(None, None), (X_train_subset, None), (None, None), (X_test_subset, None)] +# result_tables = [] + +# for X_data, _ in subsets: +# if isinstance(X_data, np.ndarray): +# rf_plus_lime = RFPlusLime(fit) +# lime_values = rf_plus_lime.explain(X_train=X_train, X_test=X_data).values +# result_tables.append(lime_values) +# result_tables.append(np.abs(lime_values)) +# else: +# result_tables.append(None) +# result_tables.append(None) + +# return tuple(result_tables) + + +# def kernel_shap_evaluation_RF_plus_pos_neg(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# subsets = [(None, None), (X_train_subset, None), (None, None), (X_test_subset, None)] +# result_tables = [] + +# for X_data, _ in subsets: +# if isinstance(X_data, np.ndarray): +# rf_plus_kernel_shap = RFPlusKernelSHAP(fit) +# kernel_shap_scores = rf_plus_kernel_shap.explain(X_train=X_train, X_test=X_data) +# result_tables.append(kernel_shap_scores) +# result_tables.append(np.abs(kernel_shap_scores)) +# else: +# result_tables.append(None) +# result_tables.append(None) + +# return tuple(result_tables) + + + + + + + + + + + + + + + + +# result_table = pd.DataFrame(kernel_shap_scores, columns=[f'Feature_{i}' for i in range(num_features)]) +# result_tables.append(result_table) + +# def MDI_local_sub_stumps(X, y, fit, scoring_fns="auto", return_stability_scores=False, **kwargs): +# """ +# Compute local MDI importance for each feature and sample. +# :param X: design matrix +# :param y: response +# :param fit: fitted model of interest (tree-based) +# :return: dataframe of shape: (n_samples, n_features) + +# """ +# num_samples, num_features = X.shape +# if isinstance(fit, RegressorMixin): +# RFPlus = RandomForestPlusRegressor +# elif isinstance(fit, ClassifierMixin): +# RFPlus = RandomForestPlusClassifier +# else: +# raise ValueError("Unknown task.") +# rf_plus_model = RFPlus(rf_model=fit, **kwargs) +# rf_plus_model.fit(X, y) + +# try: +# mdi_plus_scores = rf_plus_model.get_mdi_plus_scores(X=X, y=y, local_scoring_fns=mean_squared_error, version = "sub", lfi=False)["local"].values +# if return_stability_scores: +# raise NotImplementedError +# stability_scores = rf_plus_model.get_mdi_plus_stability_scores(B=25) +# except ValueError as e: +# if str(e) == 'Transformer representation was empty for all trees.': +# mdi_plus_scores = np.zeros((num_samples, num_features)) +# stability_scores = None +# else: +# raise +# result_table = pd.DataFrame(mdi_plus_scores, columns=[f'Feature_{i}' for i in range(num_features)]) + +# return result_table + +# def MDI_local_all_stumps(X, y, fit, scoring_fns="auto", return_stability_scores=False, **kwargs): +# """ +# Wrapper around MDI+ object to get feature importance scores + +# :param X: ndarray of shape (n_samples, n_features) +# The covariate matrix. If a pd.DataFrame object is supplied, then +# the column names are used in the output +# :param y: ndarray of shape (n_samples, n_targets) +# The observed responses. +# :param rf_model: scikit-learn random forest object or None +# The RF model to be used for interpretation. If None, then a new +# RandomForestRegressor or RandomForestClassifier is instantiated. +# :param kwargs: additional arguments to pass to +# RandomForestPlusRegressor or RandomForestPlusClassifier class. +# :return: dataframe - [Var, Importance] +# Var: variable name +# Importance: MDI+ score +# """ +# num_samples, num_features = X.shape +# if isinstance(fit, RegressorMixin): +# RFPlus = RandomForestPlusRegressor +# elif isinstance(fit, ClassifierMixin): +# RFPlus = RandomForestPlusClassifier +# else: +# raise ValueError("Unknown task.") +# rf_plus_model = RFPlus(rf_model=fit, **kwargs) +# rf_plus_model.fit(X, y) + +# try: +# mdi_plus_scores = rf_plus_model.get_mdi_plus_scores(X=X, y=y, local_scoring_fns=mean_squared_error, version = "all", lfi=False)["local"].values +# if return_stability_scores: +# raise NotImplementedError +# stability_scores = rf_plus_model.get_mdi_plus_stability_scores(B=25) +# except ValueError as e: +# if str(e) == 'Transformer representation was empty for all trees.': +# mdi_plus_scores = np.zeros((num_samples, num_features)) +# stability_scores = None +# else: +# raise +# result_table = pd.DataFrame(mdi_plus_scores, columns=[f'Feature_{i}' for i in range(num_features)]) + +# return result_table + + +# def LFI_absolute_sum(X, y, fit, scoring_fns="auto", return_stability_scores=False, **kwargs): +# num_samples, num_features = X.shape +# if isinstance(fit, RegressorMixin): +# RFPlus = RandomForestPlusRegressor +# elif isinstance(fit, ClassifierMixin): +# RFPlus = RandomForestPlusClassifier +# else: +# raise ValueError("Unknown task.") +# rf_plus_model = RFPlus(rf_model=fit, **kwargs) +# rf_plus_model.fit(X, y) + +# try: +# mdi_plus_scores = rf_plus_model.get_mdi_plus_scores(X=X, y=y, lfi=True, lfi_abs="outside")["lfi"].values +# if return_stability_scores: +# raise NotImplementedError +# stability_scores = rf_plus_model.get_mdi_plus_stability_scores(B=25) +# except ValueError as e: +# if str(e) == 'Transformer representation was empty for all trees.': +# mdi_plus_scores = np.zeros((num_samples, num_features)) +# stability_scores = None +# else: +# raise +# result_table = pd.DataFrame(mdi_plus_scores, columns=[f'Feature_{i}' for i in range(num_features)]) + +# return result_table + +# def lime_local(X, y, fit): +# """ +# Compute LIME local importance for each feature and sample. +# Larger values indicate more important features. +# :param X: design matrix +# :param y: response +# :param fit: fitted model of interest (tree-based) +# :return: dataframe of shape: (n_samples, n_features) + +# """ + +# np.random.seed(1) +# num_samples, num_features = X.shape +# result = np.zeros((num_samples, num_features)) +# explainer = lime.lime_tabular.LimeTabularExplainer(X, verbose=False, mode='regression') +# for i in range(num_samples): +# exp = explainer.explain_instance(X[i], fit.predict, num_features=num_features) +# original_feature_importance = exp.as_map()[1] +# sorted_feature_importance = sorted(original_feature_importance, key=lambda x: x[0]) +# for j in range(num_features): +# result[i,j] = abs(sorted_feature_importance[j][1]) +# # Convert the array to a DataFrame +# result_table = pd.DataFrame(result, columns=[f'Feature_{i}' for i in range(num_features)]) + +# return result_table + +# def tree_shap_local(X, y, fit): +# """ +# Compute average treeshap value across observations. +# Larger absolute values indicate more important features. +# :param X: design matrix +# :param y: response +# :param fit: fitted model of interest (tree-based) +# :return: dataframe of shape: (n_samples, n_features) +# """ +# explainer = shap.TreeExplainer(fit) +# shap_values = explainer.shap_values(X, check_additivity=False) +# if sklearn.base.is_classifier(fit): +# # Shape values are returned as a list of arrays, one for each class +# def add_abs(a, b): +# return abs(a) + abs(b) +# results = np.sum(np.abs(shap_values),axis=-1) +# else: +# results = abs(shap_values) +# result_table = pd.DataFrame(results, columns=[f'Feature_{i}' for i in range(X.shape[1])]) + +# return result_table + +# def permutation_local(X, y, fit, num_permutations=100): +# """ +# Compute local permutation importance for each feature and sample. +# Larger values indicate more important features. +# :param X: design matrix +# :param y: response +# :param fit: fitted model of interest (tree-based) +# :num_permutations: Number of permutations for each feature (default is 100) +# :return: dataframe of shape: (n_samples, n_features) +# """ + +# # Get the number of samples and features +# num_samples, num_features = X.shape + +# # Initialize array to store local permutation importance +# lpi = np.zeros((num_samples, num_features)) + +# # For each feature +# for k in range(num_features): +# # Permute X_k num_permutations times +# for b in range(num_permutations): +# X_permuted = X.copy() +# X_permuted[:, k] = np.random.permutation(X[:, k]) + +# # Feed permuted data through the fitted model +# y_pred_permuted = fit.predict(X_permuted) + +# # Calculate MSE for each sample +# for i in range(num_samples): +# lpi[i, k] += (y[i]-y_pred_permuted[i])**2 + +# lpi /= num_permutations + +# # Convert the array to a DataFrame +# result_table = pd.DataFrame(lpi, columns=[f'Feature_{i}' for i in range(num_features)]) + +# return result_table + + +# def MDI_local_sub_stumps_evaluate(X_train, y_train, X_test, y_test, fit, scoring_fns="auto", return_stability_scores=False, **kwargs): +# """ +# Compute local MDI importance for each feature and sample. +# :param X: design matrix +# :param y: response +# :param fit: fitted model of interest (tree-based) +# :return: dataframe of shape: (n_samples, n_features) + +# """ +# num_samples, num_features = X_test.shape +# if isinstance(fit, RegressorMixin): +# RFPlus = RandomForestPlusRegressor +# elif isinstance(fit, ClassifierMixin): +# RFPlus = RandomForestPlusClassifier +# else: +# raise ValueError("Unknown task.") +# rf_plus_model = RFPlus(rf_model=fit, **kwargs) +# rf_plus_model.fit(X_train, y_train) + +# try: +# mdi_plus_scores = rf_plus_model.get_mdi_plus_scores(X=X_test, y=y_test, local_scoring_fns=scoring_fns, version = "sub", lfi=False, sample_split=None)["local"].values +# if return_stability_scores: +# raise NotImplementedError +# stability_scores = rf_plus_model.get_mdi_plus_stability_scores(B=25) +# except ValueError as e: +# if str(e) == 'Transformer representation was empty for all trees.': +# mdi_plus_scores = np.zeros((num_samples, num_features)) +# stability_scores = None +# else: +# raise +# result_table = pd.DataFrame(mdi_plus_scores, columns=[f'Feature_{i}' for i in range(num_features)]) + +# return result_table + +# def lime_local(X_train, y_train, X_test, y_test, fit): +# """ +# Compute LIME local importance for each feature and sample. +# Larger values indicate more important features. +# :param X: design matrix +# :param y: response +# :param fit: fitted model of interest (tree-based) +# :return: dataframe of shape: (n_samples, n_features) + +# """ +# if isinstance(fit, RegressorMixin): +# mode='regression' +# elif isinstance(fit, ClassifierMixin): +# mode='classification' +# np.random.seed(1) +# num_samples, num_features = X_test.shape +# result = np.zeros((num_samples, num_features)) +# explainer = lime.lime_tabular.LimeTabularExplainer(X_train, verbose=False, mode=mode) +# for i in range(num_samples): +# exp = explainer.explain_instance(X_test[i], fit.predict, num_features=num_features) +# original_feature_importance = exp.as_map()[1] +# sorted_feature_importance = sorted(original_feature_importance, key=lambda x: x[0]) +# for j in range(num_features): +# result[i,j] = abs(sorted_feature_importance[j][1]) +# # Convert the array to a DataFrame +# result_table = pd.DataFrame(result, columns=[f'Feature_{i}' for i in range(num_features)]) + +# return result_table + +# def MDI_local_all_stumps_evaluate(X_train, y_train, X_test, y_test, fit, scoring_fns="auto", return_stability_scores=False, **kwargs): +# """ +# Wrapper around MDI+ object to get feature importance scores + +# :param X: ndarray of shape (n_samples, n_features) +# The covariate matrix. If a pd.DataFrame object is supplied, then +# the column names are used in the output +# :param y: ndarray of shape (n_samples, n_targets) +# The observed responses. +# :param rf_model: scikit-learn random forest object or None +# The RF model to be used for interpretation. If None, then a new +# RandomForestRegressor or RandomForestClassifier is instantiated. +# :param kwargs: additional arguments to pass to +# RandomForestPlusRegressor or RandomForestPlusClassifier class. +# :return: dataframe - [Var, Importance] +# Var: variable name +# Importance: MDI+ score +# """ +# num_samples, num_features = X_test.shape +# if isinstance(fit, RegressorMixin): +# RFPlus = RandomForestPlusRegressor +# elif isinstance(fit, ClassifierMixin): +# RFPlus = RandomForestPlusClassifier +# else: +# raise ValueError("Unknown task.") +# rf_plus_model = RFPlus(rf_model=fit, **kwargs) +# rf_plus_model.fit(X_train, y_train) + +# try: +# mdi_plus_scores = rf_plus_model.get_mdi_plus_scores(X=X_test, y=y_test, local_scoring_fns=scoring_fns, version = "all", lfi=False, sample_split=None)["local"].values +# if return_stability_scores: +# raise NotImplementedError +# stability_scores = rf_plus_model.get_mdi_plus_stability_scores(B=25) +# except ValueError as e: +# if str(e) == 'Transformer representation was empty for all trees.': +# mdi_plus_scores = np.zeros((num_samples, num_features)) +# stability_scores = None +# else: +# raise +# result_table = pd.DataFrame(mdi_plus_scores, columns=[f'Feature_{i}' for i in range(num_features)]) + +# return result_table + +# def LFI_ablation_test_evaluation(X_train, y_train, X_test, y_test, fit, scoring_fns="auto", return_stability_scores=False, **kwargs): +# num_samples, num_features = X_test.shape +# if isinstance(fit, RegressorMixin): +# RFPlus = RandomForestPlusRegressor +# elif isinstance(fit, ClassifierMixin): +# RFPlus = RandomForestPlusClassifier +# else: +# raise ValueError("Unknown task.") +# rf_plus_model = RFPlus(rf_model=fit, **kwargs) +# rf_plus_model.fit(X_train, y_train) + +# try: +# mdi_plus_scores = rf_plus_model.get_mdi_plus_scores(X=X_test, y=y_test, lfi=True, lfi_abs="none", sample_split=None, train_or_test = "test")["lfi"].values +# mdi_plus_scores = np.abs(mdi_plus_scores) +# if return_stability_scores: +# raise NotImplementedError +# stability_scores = rf_plus_model.get_mdi_plus_stability_scores(B=25) +# except ValueError as e: +# if str(e) == 'Transformer representation was empty for all trees.': +# mdi_plus_scores = np.zeros((num_samples, num_features)) +# stability_scores = None +# else: +# raise +# result_table = pd.DataFrame(mdi_plus_scores, columns=[f'Feature_{i}' for i in range(num_features)]) + +# return result_table + +######################## Considering not using these methods +# def LFI_sum_absolute_evaluate(X_train, y_train, X_test, y_test, fit, scoring_fns="auto", return_stability_scores=False, **kwargs): +# num_samples, num_features = X_test.shape +# if isinstance(fit, RegressorMixin): +# RFPlus = RandomForestPlusRegressor +# elif isinstance(fit, ClassifierMixin): +# RFPlus = RandomForestPlusClassifier +# else: +# raise ValueError("Unknown task.") +# rf_plus_model = RFPlus(rf_model=fit, **kwargs) +# rf_plus_model.fit(X_train, y_train) + +# try: +# mdi_plus_scores = rf_plus_model.get_mdi_plus_scores(X=X_test, y=y_test, lfi=True, lfi_abs="inside", sample_split=None)["lfi"].values +# if return_stability_scores: +# raise NotImplementedError +# stability_scores = rf_plus_model.get_mdi_plus_stability_scores(B=25) +# except ValueError as e: +# if str(e) == 'Transformer representation was empty for all trees.': +# mdi_plus_scores = np.zeros((num_samples, num_features)) +# stability_scores = None +# else: +# raise +# result_table = pd.DataFrame(mdi_plus_scores, columns=[f'Feature_{i}' for i in range(num_features)]) + +# return result_table + +# def LFI_sum_absolute(X, y, fit, scoring_fns="auto", return_stability_scores=False, **kwargs): +# num_samples, num_features = X.shape +# if isinstance(fit, RegressorMixin): +# RFPlus = RandomForestPlusRegressor +# elif isinstance(fit, ClassifierMixin): +# RFPlus = RandomForestPlusClassifier +# else: +# raise ValueError("Unknown task.") +# rf_plus_model = RFPlus(rf_model=fit, **kwargs) +# rf_plus_model.fit(X, y) + +# try: +# mdi_plus_scores = rf_plus_model.get_mdi_plus_scores(X=X, y=y, lfi=True, lfi_abs="inside")["lfi"].values +# if return_stability_scores: +# raise NotImplementedError +# stability_scores = rf_plus_model.get_mdi_plus_stability_scores(B=25) +# except ValueError as e: +# if str(e) == 'Transformer representation was empty for all trees.': +# mdi_plus_scores = np.zeros((num_samples, num_features)) +# stability_scores = None +# else: +# raise +# result_table = pd.DataFrame(mdi_plus_scores, columns=[f'Feature_{i}' for i in range(num_features)]) + +# return result_table \ No newline at end of file diff --git a/feature_importance/gene-data/analyze_importance.ipynb b/feature_importance/gene-data/analyze_importance.ipynb new file mode 100644 index 0000000..6034f6c --- /dev/null +++ b/feature_importance/gene-data/analyze_importance.ipynb @@ -0,0 +1,604 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# read in data\n", + "raw_genotype = pd.read_csv(\"/scratch/users/omer_ronen/mutemb_esm/X_k_5_ilvm_oh.csv\")\n", + "phenotype = pd.read_csv(\"/scratch/users/omer_ronen/mutemb_esm/y_ilvm_oh.csv\")\n", + "# save column names\n", + "colnames = raw_genotype.columns\n", + "# make genotype a numpy array\n", + "raw_genotype = raw_genotype.to_numpy()\n", + "# make phenotype a 1D numpy array\n", + "phenotype = phenotype.to_numpy().reshape(-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# obtain same train-test split as the experiment, so that gender/age are aligned\n", + "raw_genotype_train, raw_genotype_test, phenotype_train, phenotype_test = \\\n", + " train_test_split(raw_genotype, phenotype, test_size = 0.3,\n", + " random_state = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Do `frame.T.groupby(...)` without axis instead.\n", + " lmdi_importances = np.abs(lmdi_importances.iloc[:,:-12]).groupby(feature_names, axis=1).max()\n" + ] + } + ], + "source": [ + "# Extract the feature name (part before the first underscore)\n", + "feature_names = lmdi_importances.iloc[:,:-12].columns.str.split('_').str[0]\n", + "\n", + "# Group by feature name and sum the columns\n", + "lmdi_importances = np.abs(lmdi_importances.iloc[:,:-12]).groupby(feature_names, axis=1).max()\n", + "\n", + "# get new column names\n", + "new_colnames = lmdi_importances.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# get importances for men and women\n", + "lmdi_importances_men = lmdi_importances[raw_genotype_train[:,-12]==1]\n", + "lmdi_importances_women = lmdi_importances[raw_genotype_train[:,-12]==0]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# get importances for \"old\" and \"young\" (above/below 0 on standardardized age)\n", + "lmdi_importances_old = lmdi_importances[raw_genotype_train[:,-11] > 0]\n", + "lmdi_importances_young = lmdi_importances[raw_genotype_train[:,-11] <= 0]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# get importances for gender and age\n", + "lmdi_importances_old_men = lmdi_importances[(raw_genotype_train[:,-12]==1) & (raw_genotype_train[:,-11] > 0)]\n", + "lmdi_importances_old_women = lmdi_importances[(raw_genotype_train[:,-12]==0) & (raw_genotype_train[:,-11] > 0)]\n", + "lmdi_importances_young_men = lmdi_importances[(raw_genotype_train[:,-12]==1) & (raw_genotype_train[:,-11] <= 0)]\n", + "lmdi_importances_young_women = lmdi_importances[(raw_genotype_train[:,-12]==0) & (raw_genotype_train[:,-11] <= 0)]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# get rankings\n", + "lmdi_rankings = lmdi_importances.shape[1]-np.argsort(np.abs(lmdi_importances), axis = 1)\n", + "\n", + "# rankings for gender\n", + "lmdi_rankings_men = lmdi_importances_men.shape[1]-np.argsort(np.abs(lmdi_importances_men), axis = 1)\n", + "lmdi_rankings_women = lmdi_importances_women.shape[1]-np.argsort(np.abs(lmdi_importances_women), axis = 1)\n", + "\n", + "# rankings for age\n", + "lmdi_rankings_old = lmdi_importances_old.shape[1]-np.argsort(np.abs(lmdi_importances_old), axis = 1)\n", + "lmdi_rankings_young = lmdi_importances_young.shape[1]-np.argsort(np.abs(lmdi_importances_young), axis = 1)\n", + "\n", + "# rankings for age & gender\n", + "lmdi_rankings_old_men = lmdi_importances_old_men.shape[1]-np.argsort(np.abs(lmdi_importances_old_men), axis = 1)\n", + "lmdi_rankings_old_women = lmdi_importances_old_women.shape[1]-np.argsort(np.abs(lmdi_importances_old_women), axis = 1)\n", + "lmdi_rankings_young_men = lmdi_importances_young_men.shape[1]-np.argsort(np.abs(lmdi_importances_young_men), axis = 1)\n", + "lmdi_rankings_young_women = lmdi_importances_young_women.shape[1]-np.argsort(np.abs(lmdi_importances_young_women), axis = 1)\n", + "\n", + "# make them dataframes with the same column names\n", + "lmdi_rankings = pd.DataFrame(lmdi_rankings, columns = new_colnames)\n", + "lmdi_rankings_men = pd.DataFrame(lmdi_rankings_men, columns = new_colnames)\n", + "lmdi_rankings_women = pd.DataFrame(lmdi_rankings_women, columns = new_colnames)\n", + "lmdi_rankings_old = pd.DataFrame(lmdi_rankings_old, columns = new_colnames)\n", + "lmdi_rankings_young = pd.DataFrame(lmdi_rankings_young, columns = new_colnames)\n", + "lmdi_rankings_old_men = pd.DataFrame(lmdi_rankings_old_men, columns = new_colnames)\n", + "lmdi_rankings_old_women = pd.DataFrame(lmdi_rankings_old_women, columns = new_colnames)\n", + "lmdi_rankings_young_men = pd.DataFrame(lmdi_rankings_young_men, columns = new_colnames)\n", + "lmdi_rankings_young_women = pd.DataFrame(lmdi_rankings_young_women, columns = new_colnames)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Most Important Genes (Overall): Average LMDI+ Score\n", + "ENSG00000113594 1.873875e-04\n", + "ENSG00000130159 1.523576e-05\n", + "ENSG00000100934 2.936840e-06\n", + "ENSG00000244694 2.784408e-07\n", + "ENSG00000134551 4.023462e-08\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "# get the most important genes\n", + "print(\"Most Important Genes (Overall): Average LMDI+ Score\")\n", + "print(np.abs(lmdi_importances).mean(axis=0).sort_values(ascending=False))\n", + "top_genes_overall = np.abs(lmdi_importances).mean(axis=0).sort_values(ascending=False).index" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Most Important Genes (Men): Average LMDI+ Score\n", + "ENSG00000113594 1.370554e-04\n", + "ENSG00000130159 2.251469e-05\n", + "ENSG00000244694 5.239777e-07\n", + "ENSG00000100934 2.701864e-07\n", + "ENSG00000134551 7.464378e-08\n", + "dtype: float64\n", + "Most Important Genes (Women): Average LMDI+ Score\n", + "ENSG00000113594 2.325798e-04\n", + "ENSG00000130159 8.700128e-06\n", + "ENSG00000100934 5.331184e-06\n", + "ENSG00000244694 5.797731e-08\n", + "ENSG00000134551 9.339212e-09\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "# get the most important genes\n", + "print(\"Most Important Genes (Men): Average LMDI+ Score\")\n", + "print(np.abs(lmdi_importances_men).mean(axis=0).sort_values(ascending=False))\n", + "top_genes_men = np.abs(lmdi_importances_men).mean(axis=0).sort_values(ascending=False).index\n", + "print(\"Most Important Genes (Women): Average LMDI+ Score\")\n", + "print(np.abs(lmdi_importances_women).mean(axis=0).sort_values(ascending=False))\n", + "top_genes_women = np.abs(lmdi_importances_women).mean(axis=0).sort_values(ascending=False).index" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Most Important Genes (Old): Average LMDI+ Score\n", + "ENSG00000113594 1.959623e-04\n", + "ENSG00000130159 1.909861e-05\n", + "ENSG00000100934 2.530044e-06\n", + "ENSG00000244694 4.256678e-07\n", + "ENSG00000134551 7.643922e-08\n", + "dtype: float64\n", + "Most Important Genes (Young): Average LMDI+ Score\n", + "ENSG00000113594 1.798357e-04\n", + "ENSG00000130159 1.183378e-05\n", + "ENSG00000100934 3.295102e-06\n", + "ENSG00000244694 1.487792e-07\n", + "ENSG00000134551 8.349538e-09\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "# get the most important genes\n", + "print(\"Most Important Genes (Old): Average LMDI+ Score\")\n", + "print(np.abs(lmdi_importances_old).mean(axis=0).sort_values(ascending=False))\n", + "print(\"Most Important Genes (Young): Average LMDI+ Score\")\n", + "print(np.abs(lmdi_importances_young).mean(axis=0).sort_values(ascending=False))\n", + "top_genes_old = np.abs(lmdi_importances_old).mean(axis=0).sort_values(ascending=False).index\n", + "top_genes_young = np.abs(lmdi_importances_young).mean(axis=0).sort_values(ascending=False).index" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Most Important Genes (Old Men): Average LMDI+ Score\n", + "ENSG00000113594 1.265393e-04\n", + "ENSG00000130159 3.102749e-05\n", + "ENSG00000244694 7.651274e-07\n", + "ENSG00000134551 1.383567e-07\n", + "ENSG00000100934 6.396098e-08\n", + "dtype: float64\n", + "Most Important Genes (Old Women): Average LMDI+ Score\n", + "ENSG00000113594 2.711184e-04\n", + "ENSG00000130159 6.184622e-06\n", + "ENSG00000100934 5.199781e-06\n", + "ENSG00000244694 5.817504e-08\n", + "ENSG00000134551 9.408447e-09\n", + "dtype: float64\n", + "Most Important Genes (Young Men): Average LMDI+ Score\n", + "ENSG00000113594 1.482011e-04\n", + "ENSG00000130159 1.349215e-05\n", + "ENSG00000100934 4.887603e-07\n", + "ENSG00000244694 2.683884e-07\n", + "ENSG00000134551 7.115780e-09\n", + "dtype: float64\n", + "Most Important Genes (Young Women): Average LMDI+ Score\n", + "ENSG00000113594 2.038902e-04\n", + "ENSG00000130159 1.057277e-05\n", + "ENSG00000100934 5.429005e-06\n", + "ENSG00000244694 5.783012e-08\n", + "ENSG00000134551 9.287670e-09\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "# get the most important genes\n", + "print(\"Most Important Genes (Old Men): Average LMDI+ Score\")\n", + "print(np.abs(lmdi_importances_old_men).mean(axis=0).sort_values(ascending=False))\n", + "print(\"Most Important Genes (Old Women): Average LMDI+ Score\")\n", + "print(np.abs(lmdi_importances_old_women).mean(axis=0).sort_values(ascending=False))\n", + "print(\"Most Important Genes (Young Men): Average LMDI+ Score\")\n", + "print(np.abs(lmdi_importances_young_men).mean(axis=0).sort_values(ascending=False))\n", + "print(\"Most Important Genes (Young Women): Average LMDI+ Score\")\n", + "print(np.abs(lmdi_importances_young_women).mean(axis=0).sort_values(ascending=False))\n", + "top_genes_old_men = np.abs(lmdi_importances_old_men).mean(axis=0).sort_values(ascending=False).index\n", + "top_genes_old_women = np.abs(lmdi_importances_old_women).mean(axis=0).sort_values(ascending=False).index\n", + "top_genes_young_men = np.abs(lmdi_importances_young_men).mean(axis=0).sort_values(ascending=False).index\n", + "top_genes_young_women = np.abs(lmdi_importances_young_women).mean(axis=0).sort_values(ascending=False).index" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There is a 50.00% Overlap of Top Genes (Men and Women). The intersection is \n", + "['ENSG00000100934' 'ENSG00000113594' 'ENSG00000130159' 'ENSG00000134551'\n", + " 'ENSG00000244694'].\n", + "There is a 50.00% Overlap of Top Genes (Old and Young). The intersection is \n", + "['ENSG00000100934' 'ENSG00000113594' 'ENSG00000130159' 'ENSG00000134551'\n", + " 'ENSG00000244694'].\n", + "There is a 50.00% Overlap of Top Genes (Age x Gender). The intersection is \n", + "['ENSG00000100934' 'ENSG00000113594' 'ENSG00000130159' 'ENSG00000134551'\n", + " 'ENSG00000244694'].\n" + ] + } + ], + "source": [ + "# check the rankings overlap for top genes\n", + "overlap_gender = np.intersect1d(top_genes_men, top_genes_women)\n", + "overlap_age = np.intersect1d(top_genes_old, top_genes_young)\n", + "overlap_age_gender = np.intersect1d(np.intersect1d(top_genes_old_men,\n", + " top_genes_old_women),\n", + " np.intersect1d(top_genes_young_men,\n", + " top_genes_young_women))\n", + "print(f\"There is a {overlap_gender.shape[0]/10.0*100:.2f}% \" +\n", + " \"Overlap of Top Genes (Men and Women). \" +\n", + " f\"The intersection is \\n{overlap_gender}.\")\n", + "print(f\"There is a {overlap_age.shape[0]/10.0*100:.2f}% \" + \n", + " \"Overlap of Top Genes (Old and Young). \" +\n", + " f\"The intersection is \\n{overlap_age}.\")\n", + "print(f\"There is a {overlap_age_gender.shape[0]/10.0*100:.2f}% \" +\n", + " \"Overlap of Top Genes (Age x Gender). \" +\n", + " f\"The intersection is \\n{overlap_age_gender}.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot histogram of total average importance scores for genes\n", + "plt.hist(np.abs(lmdi_importances.iloc[:,:-12]).mean(axis=0), bins = 50)\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mdi", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/feature_importance/gene-data/analyze_importance_full_embedding.ipynb b/feature_importance/gene-data/analyze_importance_full_embedding.ipynb new file mode 100644 index 0000000..3ceeec9 --- /dev/null +++ b/feature_importance/gene-data/analyze_importance_full_embedding.ipynb @@ -0,0 +1,929 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# read in data\n", + "embedding_genotype = pd.read_csv(\"/scratch/users/omer_ronen/mutemb_esm/X_k_5_ilvm_esm_prod_pppl_full_ptv.csv\")\n", + "phenotype = pd.read_csv(\"/scratch/users/omer_ronen/mutemb_esm/y_ilvm_oh.csv\")\n", + "# save column names\n", + "colnames = embedding_genotype.columns\n", + "# make genotype a numpy array\n", + "embedding_genotype = embedding_genotype.to_numpy()\n", + "# make phenotype a 1D numpy array\n", + "phenotype = phenotype.to_numpy().reshape(-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# obtain same train-test split as the experiment, so that gender/age are aligned\n", + "embedding_genotype_train, embedding_genotype_test, phenotype_train, phenotype_test = \\\n", + " train_test_split(embedding_genotype, phenotype, test_size = 0.3,\n", + " random_state = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot histogram of genotype column -11\n", + "plt.hist(embedding_genotype_train[:, -11], bins = 10)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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"lmdi_importances_young = lmdi_importances[embedding_genotype_train[:,-11] <= 0]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# get importances for gender and age\n", + "lmdi_importances_old_men = lmdi_importances[(embedding_genotype_train[:,-12]==1) & (embedding_genotype_train[:,-11] > 0)]\n", + "lmdi_importances_old_women = lmdi_importances[(embedding_genotype_train[:,-12]==0) & (embedding_genotype_train[:,-11] > 0)]\n", + "lmdi_importances_young_men = lmdi_importances[(embedding_genotype_train[:,-12]==1) & (embedding_genotype_train[:,-11] <= 0)]\n", + "lmdi_importances_young_women = lmdi_importances[(embedding_genotype_train[:,-12]==0) & (embedding_genotype_train[:,-11] <= 0)]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# get rankings\n", + "lmdi_rankings = lmdi_importances.shape[1]-np.argsort(np.abs(lmdi_importances), axis = 1)\n", + "\n", + "# rankings for gender\n", + "lmdi_rankings_men = lmdi_importances_men.shape[1]-np.argsort(np.abs(lmdi_importances_men), axis = 1)\n", + "lmdi_rankings_women = lmdi_importances_women.shape[1]-np.argsort(np.abs(lmdi_importances_women), axis = 1)\n", + "\n", + "# rankings for age\n", + "lmdi_rankings_old = lmdi_importances_old.shape[1]-np.argsort(np.abs(lmdi_importances_old), axis = 1)\n", + "lmdi_rankings_young = lmdi_importances_young.shape[1]-np.argsort(np.abs(lmdi_importances_young), axis = 1)\n", + "\n", + "# rankings for age & gender\n", + "lmdi_rankings_old_men = lmdi_importances_old_men.shape[1]-np.argsort(np.abs(lmdi_importances_old_men), axis = 1)\n", + "lmdi_rankings_old_women = lmdi_importances_old_women.shape[1]-np.argsort(np.abs(lmdi_importances_old_women), axis = 1)\n", + "lmdi_rankings_young_men = lmdi_importances_young_men.shape[1]-np.argsort(np.abs(lmdi_importances_young_men), axis = 1)\n", + "lmdi_rankings_young_women = lmdi_importances_young_women.shape[1]-np.argsort(np.abs(lmdi_importances_young_women), axis = 1)\n", + "\n", + "# make them dataframes with the same column names\n", + "lmdi_rankings = pd.DataFrame(lmdi_rankings, columns = new_colnames)\n", + "lmdi_rankings_men = pd.DataFrame(lmdi_rankings_men, columns = new_colnames)\n", + "lmdi_rankings_women = pd.DataFrame(lmdi_rankings_women, columns = new_colnames)\n", + "lmdi_rankings_old = pd.DataFrame(lmdi_rankings_old, columns = new_colnames)\n", + "lmdi_rankings_young = pd.DataFrame(lmdi_rankings_young, columns = new_colnames)\n", + "lmdi_rankings_old_men = pd.DataFrame(lmdi_rankings_old_men, columns = new_colnames)\n", + "lmdi_rankings_old_women = pd.DataFrame(lmdi_rankings_old_women, columns = new_colnames)\n", + "lmdi_rankings_young_men = pd.DataFrame(lmdi_rankings_young_men, columns = new_colnames)\n", + "lmdi_rankings_young_women = pd.DataFrame(lmdi_rankings_young_women, columns = new_colnames)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Most Important Genes (Overall): Average LMDI+ Score\n", + "0 9.559041e-01\n", + "1 1.502168e-02\n", + "4 3.527430e-03\n", + "3 2.611389e-03\n", + "5 1.097138e-04\n", + "9 5.616715e-05\n", + "6 3.458527e-05\n", + "2 8.351031e-06\n", + "10 6.252818e-06\n", + "8 1.510368e-06\n", + "ENSG00000155657 1.503399e-06\n", + "7 7.398582e-07\n", + "ENSG00000185294 2.841375e-07\n", + "ENSG00000136319 4.059637e-08\n", + "11 0.000000e+00\n", + "ENSG00000117791 0.000000e+00\n", + "ENSG00000177464 0.000000e+00\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "# get the most important genes\n", + "print(\"Most Important Genes (Overall): Average LMDI+ Score\")\n", + "print(np.abs(lmdi_importances).mean(axis=0).sort_values(ascending=False))\n", + "top_genes_overall = np.abs(lmdi_importances).mean(axis=0).sort_values(ascending=False).index" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Most Important Genes (Men): Average LMDI+ Score\n", + "0 9.344040e-01\n", + "1 3.100540e-02\n", + "4 5.972581e-03\n", + "3 4.454992e-03\n", + "5 1.106660e-04\n", + "9 8.625243e-05\n", + "6 6.811428e-05\n", + "2 7.514162e-06\n", + "10 4.377979e-06\n", + "ENSG00000155657 1.514999e-06\n", + "8 9.687162e-07\n", + "7 4.912088e-07\n", + "ENSG00000136319 5.315755e-08\n", + "ENSG00000185294 3.919621e-08\n", + "11 0.000000e+00\n", + "ENSG00000117791 0.000000e+00\n", + "ENSG00000177464 0.000000e+00\n", + "dtype: float64\n", + "Most Important Genes (Women): Average LMDI+ Score\n", + "0 9.752086e-01\n", + "4 1.331971e-03\n", + "3 9.560494e-04\n", + "1 6.701582e-04\n", + "5 1.088589e-04\n", + "9 2.915409e-05\n", + "2 9.102442e-06\n", + "10 7.936204e-06\n", + "6 4.480131e-06\n", + "8 1.996708e-06\n", + "ENSG00000155657 1.492983e-06\n", + "7 9.631164e-07\n", + "ENSG00000185294 5.040661e-07\n", + "ENSG00000136319 2.931790e-08\n", + "11 0.000000e+00\n", + "ENSG00000117791 0.000000e+00\n", + "ENSG00000177464 0.000000e+00\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "# get the most important genes\n", + "print(\"Most Important Genes (Men): Average LMDI+ Score\")\n", + "print(np.abs(lmdi_importances_men).mean(axis=0).sort_values(ascending=False))\n", + "top_genes_men = np.abs(lmdi_importances_men).mean(axis=0).sort_values(ascending=False).index\n", + "print(\"Most Important Genes (Women): Average LMDI+ Score\")\n", + "print(np.abs(lmdi_importances_women).mean(axis=0).sort_values(ascending=False))\n", + "top_genes_women = np.abs(lmdi_importances_women).mean(axis=0).sort_values(ascending=False).index" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Most Important Genes (Old): Average LMDI+ Score\n", + "0 9.626220e-01\n", + "1 1.375187e-02\n", + "4 2.394050e-03\n", + "3 1.730518e-03\n", + "5 1.165286e-04\n", + "6 5.496523e-05\n", + "9 3.414627e-05\n", + "10 6.625175e-06\n", + "2 3.622961e-06\n", + "ENSG00000155657 1.323754e-06\n", + "8 1.297000e-06\n", + "7 4.793677e-07\n", + "ENSG00000185294 2.328852e-07\n", + "ENSG00000136319 3.635320e-08\n", + "11 0.000000e+00\n", + "ENSG00000117791 0.000000e+00\n", + "ENSG00000177464 0.000000e+00\n", + "dtype: float64\n", + "Most Important Genes (Young): Average LMDI+ Score\n", + "0 9.499876e-01\n", + "1 1.613999e-02\n", + "4 4.525588e-03\n", + "3 3.387165e-03\n", + "5 1.037121e-04\n", + "9 7.556075e-05\n", + "6 1.663681e-05\n", + "2 1.251500e-05\n", + "10 5.924886e-06\n", + "8 1.698279e-06\n", + "ENSG00000155657 1.661610e-06\n", + "7 9.692700e-07\n", + "ENSG00000185294 3.292749e-07\n", + "ENSG00000136319 4.433329e-08\n", + "11 0.000000e+00\n", + "ENSG00000117791 0.000000e+00\n", + "ENSG00000177464 0.000000e+00\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "# get the most important genes\n", + "print(\"Most Important Genes (Old): Average LMDI+ Score\")\n", + "print(np.abs(lmdi_importances_old).mean(axis=0).sort_values(ascending=False))\n", + "print(\"Most Important Genes (Young): Average LMDI+ Score\")\n", + "print(np.abs(lmdi_importances_young).mean(axis=0).sort_values(ascending=False))\n", + "top_genes_old = np.abs(lmdi_importances_old).mean(axis=0).sort_values(ascending=False).index\n", + "top_genes_young = np.abs(lmdi_importances_young).mean(axis=0).sort_values(ascending=False).index" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Most Important Genes (Old Men): Average LMDI+ Score\n", + "0 9.496213e-01\n", + "1 2.586072e-02\n", + "4 3.643885e-03\n", + "3 2.769448e-03\n", + "5 1.187600e-04\n", + "6 1.017926e-04\n", + "9 4.249313e-05\n", + "10 4.770579e-06\n", + "2 1.946116e-06\n", + "ENSG00000155657 1.217336e-06\n", + "8 6.742712e-07\n", + "7 3.627751e-07\n", + "ENSG00000136319 5.890921e-08\n", + "ENSG00000185294 5.716680e-08\n", + "11 0.000000e+00\n", + "ENSG00000117791 0.000000e+00\n", + "ENSG00000177464 0.000000e+00\n", + "dtype: float64\n", + "Most Important Genes (Old Women): Average LMDI+ Score\n", + "0 9.766963e-01\n", + "4 1.041001e-03\n", + "1 6.430414e-04\n", + "3 6.057923e-04\n", + "5 1.141129e-04\n", + "9 2.511012e-05\n", + "10 8.632926e-06\n", + "2 5.438283e-06\n", + "6 4.270740e-06\n", + "8 1.971155e-06\n", + "ENSG00000155657 1.438960e-06\n", + "7 6.055887e-07\n", + "ENSG00000185294 4.231148e-07\n", + "ENSG00000136319 1.193448e-08\n", + "11 0.000000e+00\n", + "ENSG00000117791 0.000000e+00\n", + "ENSG00000177464 0.000000e+00\n", + "dtype: float64\n", + "Most Important Genes (Young Men): Average LMDI+ Score\n", + "0 9.182754e-01\n", + "1 3.645814e-02\n", + "4 8.440715e-03\n", + "3 6.241465e-03\n", + "9 1.326320e-04\n", + "5 1.020873e-04\n", + "6 3.241932e-05\n", + "2 1.341561e-05\n", + "10 3.961870e-06\n", + "ENSG00000155657 1.830485e-06\n", + "8 1.280792e-06\n", + "7 6.273328e-07\n", + "ENSG00000136319 4.706149e-08\n", + "ENSG00000185294 2.014956e-08\n", + "11 0.000000e+00\n", + "ENSG00000117791 0.000000e+00\n", + "ENSG00000177464 0.000000e+00\n", + "dtype: float64\n", + "Most Important Genes (Young Women): Average LMDI+ Score\n", + "0 9.741012e-01\n", + "4 1.548580e-03\n", + "3 1.216795e-03\n", + "1 6.903451e-04\n", + "5 1.049476e-04\n", + "9 3.216459e-05\n", + "2 1.183019e-05\n", + "10 7.417537e-06\n", + "6 4.636011e-06\n", + "8 2.015731e-06\n", + "ENSG00000155657 1.533200e-06\n", + "7 1.229274e-06\n", + "ENSG00000185294 5.643294e-07\n", + "ENSG00000136319 4.225881e-08\n", + "11 0.000000e+00\n", + "ENSG00000117791 0.000000e+00\n", + "ENSG00000177464 0.000000e+00\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "# get the most important genes\n", + "print(\"Most Important Genes (Old Men): Average LMDI+ Score\")\n", + "print(np.abs(lmdi_importances_old_men).mean(axis=0).sort_values(ascending=False))\n", + "print(\"Most Important Genes (Old Women): Average LMDI+ Score\")\n", + "print(np.abs(lmdi_importances_old_women).mean(axis=0).sort_values(ascending=False))\n", + "print(\"Most Important Genes (Young Men): Average LMDI+ Score\")\n", + "print(np.abs(lmdi_importances_young_men).mean(axis=0).sort_values(ascending=False))\n", + "print(\"Most Important Genes (Young Women): Average LMDI+ Score\")\n", + "print(np.abs(lmdi_importances_young_women).mean(axis=0).sort_values(ascending=False))\n", + "top_genes_old_men = np.abs(lmdi_importances_old_men).mean(axis=0).sort_values(ascending=False).index\n", + "top_genes_old_women = np.abs(lmdi_importances_old_women).mean(axis=0).sort_values(ascending=False).index\n", + "top_genes_young_men = np.abs(lmdi_importances_young_men).mean(axis=0).sort_values(ascending=False).index\n", + "top_genes_young_women = np.abs(lmdi_importances_young_women).mean(axis=0).sort_values(ascending=False).index" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There is a 170.00% Overlap of Top Genes (Men and Women). The intersection is \n", + "['0' '1' '10' '11' '2' '3' '4' '5' '6' '7' '8' '9' 'ENSG00000117791'\n", + " 'ENSG00000136319' 'ENSG00000155657' 'ENSG00000177464' 'ENSG00000185294'].\n", + "There is a 170.00% Overlap of Top Genes (Old and Young). The intersection is \n", + "['0' '1' '10' '11' '2' '3' '4' '5' '6' '7' '8' '9' 'ENSG00000117791'\n", + " 'ENSG00000136319' 'ENSG00000155657' 'ENSG00000177464' 'ENSG00000185294'].\n", + "There is a 170.00% Overlap of Top Genes (Age x Gender). The intersection is \n", + "['0' '1' '10' '11' '2' '3' '4' '5' '6' '7' '8' '9' 'ENSG00000117791'\n", + " 'ENSG00000136319' 'ENSG00000155657' 'ENSG00000177464' 'ENSG00000185294'].\n" + ] + } + ], + "source": [ + "# check the rankings overlap for top genes\n", + "overlap_gender = np.intersect1d(top_genes_men, top_genes_women)\n", + "overlap_age = np.intersect1d(top_genes_old, top_genes_young)\n", + "overlap_age_gender = np.intersect1d(np.intersect1d(top_genes_old_men,\n", + " top_genes_old_women),\n", + " np.intersect1d(top_genes_young_men,\n", + " top_genes_young_women))\n", + "print(f\"There is a {overlap_gender.shape[0]/10.0*100:.2f}% \" +\n", + " \"Overlap of Top Genes (Men and Women). \" +\n", + " f\"The intersection is \\n{overlap_gender}.\")\n", + "print(f\"There is a {overlap_age.shape[0]/10.0*100:.2f}% \" + \n", + " \"Overlap of Top Genes (Old and Young). \" +\n", + " f\"The intersection is \\n{overlap_age}.\")\n", + "print(f\"There is a {overlap_age_gender.shape[0]/10.0*100:.2f}% \" +\n", + " \"Overlap of Top Genes (Age x Gender). \" +\n", + " f\"The intersection is \\n{overlap_age_gender}.\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mdi", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/feature_importance/gene-data/analyze_importance_large.ipynb b/feature_importance/gene-data/analyze_importance_large.ipynb new file mode 100644 index 0000000..3eb2387 --- /dev/null +++ b/feature_importance/gene-data/analyze_importance_large.ipynb @@ -0,0 +1,506 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "# read in data\n", + "embedding_genotype = pd.read_csv(\"/scratch/users/omer_ronen/mutemb_esm/X_k_5_ilvm_esm_prod_pppl_full_ptv.csv\")\n", + "phenotype = pd.read_csv(\"/scratch/users/omer_ronen/mutemb_esm/y_ilvm_oh.csv\")\n", + "# save column names\n", + "colnames = embedding_genotype.columns\n", + "# make genotype a numpy array\n", + "embedding_genotype = embedding_genotype.to_numpy()\n", + "# make phenotype a 1D numpy array\n", + "phenotype = phenotype.to_numpy().reshape(-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "# obtain same train-test split as the experiment, so that gender/age are aligned\n", + "embedding_genotype_train, embedding_genotype_test, phenotype_train, phenotype_test = \\\n", + " train_test_split(embedding_genotype, phenotype, test_size = 0.3,\n", + " random_state = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot histogram of genotype column -11\n", + "plt.hist(embedding_genotype_train[:, -11], bins = 10)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1.99648074e-04, 0.00000000e+00, 1.14263550e-04, ...,\n", + " 4.84040126e-04, 1.48835961e-05, 0.00000000e+00])" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# read in results\n", + "lmdi_importances = pd.read_csv('lmdi_plus_importance_scores_subset_embedding_data.csv',\n", + " header=None)\n", + "mdi_importances = pd.read_csv('mdi_importance_scores_subset_embedding_data.csv', header = None)\n", + "mdi_importances = mdi_importances.to_numpy().reshape(-1)\n", + "mdi_importances" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "# subset the genotype data to only include the top 1000 features\n", + "top_1000_features = np.argsort(mdi_importances)[::-1][:1000]\n", + "top_1000_colnames = colnames[top_1000_features]\n", + "embedding_genotype_train_subset = embedding_genotype_train[:, top_1000_features]\n", + "embedding_genotype_test_subset = embedding_genotype_test[:, top_1000_features]\n", + "embedding_genotype_train_subset = pd.DataFrame(embedding_genotype_train_subset,\n", + " columns = top_1000_colnames)\n", + "embedding_genotype_test_subset = pd.DataFrame(embedding_genotype_test_subset,\n", + " columns=top_1000_colnames)\n", + "lmdi_importances.columns = top_1000_colnames" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_517434/925260568.py:5: FutureWarning: DataFrame.groupby with axis=1 is deprecated. Do `frame.T.groupby(...)` without axis instead.\n", + " lmdi_importances = np.abs(lmdi_importances).groupby(feature_names, axis=1).max()\n" + ] + } + ], + "source": [ + "# Extract the feature name (part before the first underscore)\n", + "feature_names = lmdi_importances.columns.str.split('_').str[0]\n", + "\n", + "# Group by feature name and sum the columns\n", + "lmdi_importances = np.abs(lmdi_importances).groupby(feature_names, axis=1).max()\n", + "\n", + "# get new column names\n", + "new_colnames = lmdi_importances.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "# get importances for men and women\n", + "lmdi_importances_men = lmdi_importances[embedding_genotype_train[:,-12]==1]\n", + "lmdi_importances_women = lmdi_importances[embedding_genotype_train[:,-12]==0]" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "# get importances for \"old\" and \"young\" (above/below 0 on standardardized age)\n", + "lmdi_importances_old = lmdi_importances[embedding_genotype_train[:,-11] > 0]\n", + "lmdi_importances_young = lmdi_importances[embedding_genotype_train[:,-11] <= 0]" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "# get importances for gender and age\n", + "lmdi_importances_old_men = lmdi_importances[(embedding_genotype_train[:,-12]==1) & (embedding_genotype_train[:,-11] > 0)]\n", + "lmdi_importances_old_women = lmdi_importances[(embedding_genotype_train[:,-12]==0) & (embedding_genotype_train[:,-11] > 0)]\n", + "lmdi_importances_young_men = lmdi_importances[(embedding_genotype_train[:,-12]==1) & (embedding_genotype_train[:,-11] <= 0)]\n", + "lmdi_importances_young_women = lmdi_importances[(embedding_genotype_train[:,-12]==0) & (embedding_genotype_train[:,-11] <= 0)]" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "# get rankings\n", + "lmdi_rankings = lmdi_importances.shape[1]-np.argsort(np.abs(lmdi_importances), axis = 1)\n", + "\n", + "# rankings for gender\n", + "lmdi_rankings_men = lmdi_importances_men.shape[1]-np.argsort(np.abs(lmdi_importances_men), axis = 1)\n", + "lmdi_rankings_women = lmdi_importances_women.shape[1]-np.argsort(np.abs(lmdi_importances_women), axis = 1)\n", + "\n", + "# rankings for age\n", + "lmdi_rankings_old = lmdi_importances_old.shape[1]-np.argsort(np.abs(lmdi_importances_old), axis = 1)\n", + "lmdi_rankings_young = lmdi_importances_young.shape[1]-np.argsort(np.abs(lmdi_importances_young), axis = 1)\n", + "\n", + "# rankings for age & gender\n", + "lmdi_rankings_old_men = lmdi_importances_old_men.shape[1]-np.argsort(np.abs(lmdi_importances_old_men), axis = 1)\n", + "lmdi_rankings_old_women = lmdi_importances_old_women.shape[1]-np.argsort(np.abs(lmdi_importances_old_women), axis = 1)\n", + "lmdi_rankings_young_men = lmdi_importances_young_men.shape[1]-np.argsort(np.abs(lmdi_importances_young_men), axis = 1)\n", + "lmdi_rankings_young_women = lmdi_importances_young_women.shape[1]-np.argsort(np.abs(lmdi_importances_young_women), axis = 1)\n", + "\n", + "# make them dataframes with the same column names\n", + "lmdi_rankings = pd.DataFrame(lmdi_rankings, columns = new_colnames)\n", + "lmdi_rankings_men = pd.DataFrame(lmdi_rankings_men, columns = new_colnames)\n", + "lmdi_rankings_women = pd.DataFrame(lmdi_rankings_women, columns = new_colnames)\n", + "lmdi_rankings_old = pd.DataFrame(lmdi_rankings_old, columns = new_colnames)\n", + "lmdi_rankings_young = pd.DataFrame(lmdi_rankings_young, columns = new_colnames)\n", + "lmdi_rankings_old_men = pd.DataFrame(lmdi_rankings_old_men, columns = new_colnames)\n", + "lmdi_rankings_old_women = pd.DataFrame(lmdi_rankings_old_women, columns = new_colnames)\n", + "lmdi_rankings_young_men = pd.DataFrame(lmdi_rankings_young_men, columns = new_colnames)\n", + "lmdi_rankings_young_women = pd.DataFrame(lmdi_rankings_young_women, columns = new_colnames)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Most Important Genes (Overall): Average LMDI+ Score\n", + "0 0.955753\n", + "1 0.015032\n", + "ENSG00000155657 0.004813\n", + "4 0.003532\n", + "3 0.002636\n", + "5 0.000115\n", + "ENSG00000185294 0.000102\n", + "9 0.000058\n", + "6 0.000036\n", + "7 0.000011\n", + "10 0.000007\n", + "ENSG00000136319 0.000003\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "# get the most important genes\n", + "print(\"Most Important Genes (Overall): Average LMDI+ Score\")\n", + "print(np.abs(lmdi_importances).mean(axis=0).sort_values(ascending=False))\n", + "top_genes_overall = np.abs(lmdi_importances).mean(axis=0).sort_values(ascending=False).index" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Most Important Genes (Men): Average LMDI+ Score\n", + "0 0.934312\n", + "1 0.031034\n", + "4 0.005957\n", + "ENSG00000155657 0.005181\n", + "3 0.004467\n", + "5 0.000116\n", + "9 0.000090\n", + "6 0.000070\n", + "ENSG00000185294 0.000014\n", + "7 0.000007\n", + "10 0.000005\n", + "ENSG00000136319 0.000003\n", + "dtype: float64\n", + "Most Important Genes (Women): Average LMDI+ Score\n", + "0 0.975004\n", + "ENSG00000155657 0.004482\n", + "4 0.001354\n", + "3 0.000992\n", + "1 0.000665\n", + "ENSG00000185294 0.000180\n", + "5 0.000114\n", + "9 0.000029\n", + "7 0.000014\n", + "10 0.000009\n", + "6 0.000005\n", + "ENSG00000136319 0.000002\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "# get the most important genes\n", + "print(\"Most Important Genes (Men): Average LMDI+ Score\")\n", + "print(np.abs(lmdi_importances_men).mean(axis=0).sort_values(ascending=False))\n", + "top_genes_men = np.abs(lmdi_importances_men).mean(axis=0).sort_values(ascending=False).index\n", + "print(\"Most Important Genes (Women): Average LMDI+ Score\")\n", + "print(np.abs(lmdi_importances_women).mean(axis=0).sort_values(ascending=False))\n", + "top_genes_women = np.abs(lmdi_importances_women).mean(axis=0).sort_values(ascending=False).index" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Most Important Genes (Old): Average LMDI+ Score\n", + "0 0.962496\n", + "1 0.013763\n", + "ENSG00000155657 0.004509\n", + "4 0.002401\n", + "3 0.001739\n", + "5 0.000120\n", + "ENSG00000185294 0.000076\n", + "6 0.000058\n", + "9 0.000036\n", + "10 0.000007\n", + "7 0.000004\n", + "ENSG00000136319 0.000002\n", + "dtype: float64\n", + "Most Important Genes (Young): Average LMDI+ Score\n", + "0 0.949814\n", + "1 0.016150\n", + "ENSG00000155657 0.005080\n", + "4 0.004527\n", + "3 0.003426\n", + "ENSG00000185294 0.000124\n", + "5 0.000110\n", + "9 0.000078\n", + "7 0.000017\n", + "6 0.000017\n", + "10 0.000006\n", + "ENSG00000136319 0.000003\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "# get the most important genes\n", + "print(\"Most Important Genes (Old): Average LMDI+ Score\")\n", + "print(np.abs(lmdi_importances_old).mean(axis=0).sort_values(ascending=False))\n", + "print(\"Most Important Genes (Young): Average LMDI+ Score\")\n", + "print(np.abs(lmdi_importances_young).mean(axis=0).sort_values(ascending=False))\n", + "top_genes_old = np.abs(lmdi_importances_old).mean(axis=0).sort_values(ascending=False).index\n", + "top_genes_young = np.abs(lmdi_importances_young).mean(axis=0).sort_values(ascending=False).index" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Most Important Genes (Old Men): Average LMDI+ Score\n", + "0 0.949527\n", + "1 0.025887\n", + "ENSG00000155657 0.004783\n", + "4 0.003643\n", + "3 0.002769\n", + "5 0.000124\n", + "6 0.000107\n", + "9 0.000045\n", + "ENSG00000185294 0.000008\n", + "10 0.000005\n", + "ENSG00000136319 0.000004\n", + "7 0.000001\n", + "dtype: float64\n", + "Most Important Genes (Old Women): Average LMDI+ Score\n", + "0 9.765353e-01\n", + "ENSG00000155657 4.212876e-03\n", + "4 1.055802e-03\n", + "1 6.382022e-04\n", + "3 6.236623e-04\n", + "ENSG00000185294 1.504904e-04\n", + "5 1.157747e-04\n", + "9 2.526012e-05\n", + "10 9.122975e-06\n", + "7 6.016167e-06\n", + "6 4.670115e-06\n", + "ENSG00000136319 1.985748e-07\n", + "dtype: float64\n", + "Most Important Genes (Young Men): Average LMDI+ Score\n", + "0 0.918186\n", + "1 0.036488\n", + "4 0.008410\n", + "3 0.006266\n", + "ENSG00000155657 0.005603\n", + "9 0.000138\n", + "5 0.000108\n", + "6 0.000032\n", + "ENSG00000185294 0.000021\n", + "7 0.000014\n", + "10 0.000004\n", + "ENSG00000136319 0.000002\n", + "dtype: float64\n", + "Most Important Genes (Young Women): Average LMDI+ Score\n", + "0 0.973864\n", + "ENSG00000155657 0.004682\n", + "4 0.001575\n", + "3 0.001267\n", + "1 0.000685\n", + "ENSG00000185294 0.000203\n", + "5 0.000112\n", + "9 0.000032\n", + "7 0.000019\n", + "10 0.000008\n", + "6 0.000005\n", + "ENSG00000136319 0.000004\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "# get the most important genes\n", + "print(\"Most Important Genes (Old Men): Average LMDI+ Score\")\n", + "print(np.abs(lmdi_importances_old_men).mean(axis=0).sort_values(ascending=False))\n", + "print(\"Most Important Genes (Old Women): Average LMDI+ Score\")\n", + "print(np.abs(lmdi_importances_old_women).mean(axis=0).sort_values(ascending=False))\n", + "print(\"Most Important Genes (Young Men): Average LMDI+ Score\")\n", + "print(np.abs(lmdi_importances_young_men).mean(axis=0).sort_values(ascending=False))\n", + "print(\"Most Important Genes (Young Women): Average LMDI+ Score\")\n", + "print(np.abs(lmdi_importances_young_women).mean(axis=0).sort_values(ascending=False))\n", + "top_genes_old_men = np.abs(lmdi_importances_old_men).mean(axis=0).sort_values(ascending=False).index\n", + "top_genes_old_women = np.abs(lmdi_importances_old_women).mean(axis=0).sort_values(ascending=False).index\n", + "top_genes_young_men = np.abs(lmdi_importances_young_men).mean(axis=0).sort_values(ascending=False).index\n", + "top_genes_young_women = np.abs(lmdi_importances_young_women).mean(axis=0).sort_values(ascending=False).index" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There is a 120.00% Overlap of Top Genes (Men and Women). The intersection is \n", + "['0' '1' '10' '3' '4' '5' '6' '7' '9' 'ENSG00000136319' 'ENSG00000155657'\n", + " 'ENSG00000185294'].\n", + "There is a 120.00% Overlap of Top Genes (Old and Young). The intersection is \n", + "['0' '1' '10' '3' '4' '5' '6' '7' '9' 'ENSG00000136319' 'ENSG00000155657'\n", + " 'ENSG00000185294'].\n", + "There is a 120.00% Overlap of Top Genes (Age x Gender). The intersection is \n", + "['0' '1' '10' '3' '4' '5' '6' '7' '9' 'ENSG00000136319' 'ENSG00000155657'\n", + " 'ENSG00000185294'].\n" + ] + } + ], + "source": [ + "# check the rankings overlap for top genes\n", + "overlap_gender = np.intersect1d(top_genes_men, top_genes_women)\n", + "overlap_age = np.intersect1d(top_genes_old, top_genes_young)\n", + "overlap_age_gender = np.intersect1d(np.intersect1d(top_genes_old_men,\n", + " top_genes_old_women),\n", + " np.intersect1d(top_genes_young_men,\n", + " top_genes_young_women))\n", + "print(f\"There is a {overlap_gender.shape[0]/10.0*100:.2f}% \" +\n", + " \"Overlap of Top Genes (Men and Women). \" +\n", + " f\"The intersection is \\n{overlap_gender}.\")\n", + "print(f\"There is a {overlap_age.shape[0]/10.0*100:.2f}% \" + \n", + " \"Overlap of Top Genes (Old and Young). \" +\n", + " f\"The intersection is \\n{overlap_age}.\")\n", + "print(f\"There is a {overlap_age_gender.shape[0]/10.0*100:.2f}% \" +\n", + " \"Overlap of Top Genes (Age x Gender). \" +\n", + " f\"The intersection is \\n{overlap_age_gender}.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot histogram of total average importance scores for genes\n", + "plt.hist(np.abs(lmdi_importances.iloc[:,:-12]).mean(axis=0), bins = 50)\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mdi", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/feature_importance/gene-data/gene_importance.py b/feature_importance/gene-data/gene_importance.py new file mode 100644 index 0000000..71c897a --- /dev/null +++ b/feature_importance/gene-data/gene_importance.py @@ -0,0 +1,96 @@ +import numpy as np +import pandas as pd +from sklearn.model_selection import train_test_split +from sklearn.ensemble import RandomForestRegressor +from sklearn.linear_model import Ridge +from imodels.tree.rf_plus.rf_plus.rf_plus_models import RandomForestPlusRegressor +from imodels.tree.rf_plus.feature_importance.rfplus_explainer import RFPlusMDI + +if __name__ == '__main__': + + # read in data + genotype = pd.read_csv("/scratch/users/omer_ronen/mutemb_esm/X_k_5_ilvm_esm_prod_pppl_full_ptv.csv") + # genotype = pd.read_csv("/scratch/users/omer_ronen/mutemb_esm/X_k_5_ilvm_oh.csv") + phenotype = pd.read_csv('/scratch/users/omer_ronen/mutemb_esm/y_ilvm_oh.csv') + # make genotype a numpy array + genotype = genotype.to_numpy() + # make phenotype a 1D numpy array + phenotype = phenotype.to_numpy().reshape(-1) + + # train-test split + genotype_train, genotype_test, phenotype_train, phenotype_test = \ + train_test_split(genotype, phenotype, test_size = 0.3, + random_state = 1) + + print("Data split") + + # fit random forest model + rf = RandomForestRegressor(n_estimators = 100, max_depth=5, random_state = 42) + rf.fit(genotype_train, phenotype_train) + + print("RF fitted") + + # predict on test set + phenotype_pred = rf.predict(genotype_test) + + print("Predictions obtained") + + # calculate correlation for pred-check + cor = np.corrcoef(phenotype_test, phenotype_pred)[0,1] + + print("Correlation calculated: ", cor) + + # get MDI feature importance scores + importances = rf.feature_importances_ + + print("Importance scores obtained") + + # write importances to a file + np.savetxt('mdi_importance_scores_subset_embedding_data.csv', importances, delimiter = ',') + + print("Importance scores written to mdi_importance_scores_subset_embedding_data.csv") + + # subset the genotype data to only include the top 1000 features + top_1000_features = np.argsort(importances)[::-1][:1000] + genotype_train_subset = genotype_train[:, top_1000_features] + genotype_test_subset = genotype_test[:, top_1000_features] + + # fit new rf on subset + rf_subset = RandomForestRegressor(n_estimators = 100, max_depth=5, random_state = 42) + rf_subset.fit(genotype_train_subset, phenotype_train) + + print("Subset RF fitted") + + # predict on test set + phenotype_subset_pred = rf_subset.predict(genotype_test_subset) + + print("Subset predictions obtained") + + # calculate correlation for pred-check + subset_cor = np.corrcoef(phenotype_test, phenotype_subset_pred)[0,1] + + print("Subset correlation calculated: ", subset_cor) + + # get MDI feature importance scores + subset_importances = rf_subset.feature_importances_ + + print("Subset importance scores obtained") + + # write importances to a file + np.savetxt('mdi_importance_scores_subset_rf_embedding_data.csv', importances, delimiter = ',') + + print("Importance scores written to mdi_importance_scores_subset_rf_embedding_data.csv") + + # fit rf+ + rf_plus = RandomForestPlusRegressor(rf_model=rf_subset, prediction_model=Ridge()) + rf_plus.fit(genotype_train_subset, phenotype_train) + + # get lmdi+ + lmdi_explainer = RFPlusMDI(rf_plus) + lmdi_importances = lmdi_explainer.explain_linear_partial(X=genotype_train_subset, y=phenotype_train, l2norm=True, sign=True, normalize=True, njobs=-1) + + # write lmdi+ importances to a file + np.savetxt('lmdi_plus_importance_scores_subset_embedding_data.csv', lmdi_importances, delimiter = ',') + + print("Importance scores written to lmdi_plus_importance_scores_subset_embedding_data.csv") + \ No newline at end of file diff --git a/feature_importance/gene-data/importance.sh b/feature_importance/gene-data/importance.sh new file mode 100644 index 0000000..bca0801 --- /dev/null +++ b/feature_importance/gene-data/importance.sh @@ -0,0 +1,11 @@ +#!/bin/bash +#SBATCH --mail-user=zachrewolinski@berkeley.edu +#SBATCH --mail-type=ALL +#SBATCH --cpus-per-task=32 +#SBATCH --exclusive + +source activate mdi +command="gene_importance.py" + +# Execute the command +python $command \ No newline at end of file diff --git a/feature_importance/gene-data/pca-analysis.ipynb b/feature_importance/gene-data/pca-analysis.ipynb new file mode 100644 index 0000000..d3d507f --- /dev/null +++ b/feature_importance/gene-data/pca-analysis.ipynb @@ -0,0 +1,257 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# imodels imports\n", + "from imodels import get_clean_dataset\n", + "from imodels.tree.rf_plus.rf_plus.rf_plus_models import RandomForestPlusClassifier, RandomForestPlusRegressor\n", + "from imodels.tree.rf_plus.feature_importance.rfplus_explainer import AloRFPlusMDI, RFPlusMDI\n", + "\n", + "# sklearn imports\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n", + "from sklearn.linear_model import Ridge\n", + "from sklearn.metrics import r2_score, root_mean_squared_error\n", + "\n", + "# other important libraries\n", + "import numpy as np\n", + "import pandas as pd\n", + "import xgboost as xgb\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# pca stuff\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.preprocessing import StandardScaler" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# genotype = pd.read_csv(\"/scratch/users/omer_ronen/mutemb_esm/X_k_5_ilvm_oh.csv\")\n", + "genotype = pd.read_csv(\"/scratch/users/omer_ronen/mutemb_esm/X_k_5_ilvm_esm_prod_pppl_full_ptv.csv\")\n", + "phenotype = pd.read_csv('/scratch/users/omer_ronen/mutemb_esm/y_ilvm_oh.csv')\n", + "# make genotype a numpy array\n", + "genotype = genotype.to_numpy()\n", + "# get last twelve features in genotype\n", + "limited_genotype = genotype[:,-12:]\n", + "# make phenotype a 1D numpy array\n", + "phenotype = phenotype.to_numpy().reshape(-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "scaler = StandardScaler()\n", + "genotype_scaled = scaler.fit_transform(genotype)\n", + "\n", + "# Step 2: Perform PCA\n", + "pca = PCA(n_components=5000) # We reduce the data to 2 principal components\n", + "genotype_pca = pca.fit_transform(genotype_scaled)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(22542, 4270)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# get point where explained_variance_ratio is zero\n", + "zero_variance = np.where(np.isclose(pca.explained_variance_ratio_, 0))\n", + "genotype_pca = genotype_pca[:,:zero_variance[0][0]]" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Step 3: Explained Variance Ratio (Proportion of variance explained by each component)\n", + "explained_variance_ratio = pca.explained_variance_ratio_[:zero_variance[0][0]]\n", + "\n", + "# Step 4: Create the Scree Plot\n", + "plt.figure(figsize=(8, 6))\n", + "plt.plot(range(1, len(explained_variance_ratio) + 1), explained_variance_ratio, marker='o', linestyle='--')\n", + "plt.title('Scree Plot - Proportion of Variance Explained')\n", + "plt.xlabel('Principal Component')\n", + "plt.ylabel('Proportion of Variance Explained')\n", + "plt.xticks(range(1, len(explained_variance_ratio) + 1))\n", + "plt.grid(True)\n", + "plt.show()\n", + "\n", + "# Step 5: (Optional) Plot cumulative explained variance\n", + "cumulative_variance = np.cumsum(explained_variance_ratio)\n", + "\n", + "plt.figure(figsize=(8, 6))\n", + "plt.plot(range(1, len(cumulative_variance) + 1), cumulative_variance, marker='o', color='green', linestyle='-')\n", + "plt.title('Cumulative Explained Variance')\n", + "plt.xlabel('Principal Component')\n", + "plt.ylabel('Cumulative Proportion of Variance Explained')\n", + "plt.xticks(range(1, len(cumulative_variance) + 1))\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Full Model Correlation: 0.507616414984057 +/- 0.0\n", + "Limited Model Correlation: 0.6205696145882396 +/- 0.0\n", + "Minimum Difference in Correlation: -0.1129531996041826\n", + "Min of Full Correlation - Max of Limited Correlation: -0.1129531996041826\n", + "Full Model R2: 0.25602762362888865 +/- 0.0\n", + "Limited Model R2: 0.3839563913017795 +/- 0.0\n", + "Minimum Difference in R2: -0.12792876767289085\n", + "Min of Full R2 - Max of Limited R2: -0.12792876767289085\n", + "Full Model RMSE: 7.346221858814532 +/- 0.0\n", + "Limited Model RMSE: 6.684845324765358 +/- 0.0\n", + "Minimum Difference in RMSE: 0.6613765340491744\n", + "Min of Full RMSE - Max of Limited RMSE: 0.6613765340491744\n" + ] + } + ], + "source": [ + "cor_limited = []\n", + "cor_full = []\n", + "r2_limited = []\n", + "r2_full = []\n", + "rmse_limited = []\n", + "rmse_full = []\n", + "\n", + "for i in range(1):\n", + " # train-test split\n", + " genotype_train, genotype_test, limited_genotype_train, \\\n", + " limited_genotype_test, phenotype_train, phenotype_test = \\\n", + " train_test_split(genotype_pca, limited_genotype, phenotype, test_size = 0.3,\n", + " random_state = i)\n", + " # fit xgboost on full and limited models\n", + " params = {\n", + " 'objective': 'reg:squarederror', # Use 'reg:squarederror' for regression\n", + " 'max_depth': 5,\n", + " 'eta': 0.1,\n", + " 'eval_metric': 'logloss'\n", + " }\n", + " limited_dtrain = xgb.DMatrix(limited_genotype_train, label=phenotype_train)\n", + " full_dtrain = xgb.DMatrix(genotype_train, label=phenotype_train)\n", + " # train the model\n", + " limited_model = xgb.train(params, limited_dtrain, num_boost_round=100)\n", + " full_model = xgb.train(params, full_dtrain, num_boost_round=100)\n", + " # predict on test set\n", + " limited_pred = limited_model.predict(xgb.DMatrix(limited_genotype_test))\n", + " full_pred = full_model.predict(xgb.DMatrix(genotype_test))\n", + " # calculate correlation for pred-check\n", + " cor_full.append(np.corrcoef(phenotype_test, full_pred)[0,1])\n", + " cor_limited.append(np.corrcoef(phenotype_test, limited_pred)[0,1])\n", + " r2_full.append(r2_score(phenotype_test, full_pred))\n", + " r2_limited.append(r2_score(phenotype_test, limited_pred))\n", + " rmse_full.append(root_mean_squared_error(phenotype_test, full_pred))\n", + " rmse_limited.append(root_mean_squared_error(phenotype_test, limited_pred))\n", + "\n", + "cor_full = np.array(cor_full)\n", + "cor_limited = np.array(cor_limited)\n", + "r2_full = np.array(r2_full)\n", + "r2_limited = np.array(r2_limited)\n", + "rmse_full = np.array(rmse_full)\n", + "rmse_limited = np.array(rmse_limited)\n", + "# get means and sds of each\n", + "mean_cor_full = np.mean(cor_full)\n", + "mean_cor_limited = np.mean(cor_limited)\n", + "sd_cor_full = np.std(cor_full)\n", + "sd_cor_limited = np.std(cor_limited)\n", + "mean_r2_full = np.mean(r2_full)\n", + "mean_r2_limited = np.mean(r2_limited)\n", + "sd_r2_full = np.std(r2_full)\n", + "sd_r2_limited = np.std(r2_limited)\n", + "mean_rmse_full = np.mean(rmse_full)\n", + "mean_rmse_limited = np.mean(rmse_limited)\n", + "sd_rmse_full = np.std(rmse_full)\n", + "sd_rmse_limited = np.std(rmse_limited)\n", + "\n", + "print(\"Full Model Correlation: \", mean_cor_full, \" +/- \", sd_cor_full)\n", + "print(\"Limited Model Correlation: \", mean_cor_limited, \" +/- \", sd_cor_limited)\n", + "print(\"Minimum Difference in Correlation: \", np.min(cor_full - cor_limited))\n", + "print(\"Min of Full Correlation - Max of Limited Correlation: \", np.min(cor_full) - np.max(cor_limited))\n", + "\n", + "print(\"Full Model R2: \", mean_r2_full, \" +/- \", sd_r2_full)\n", + "print(\"Limited Model R2: \", mean_r2_limited, \" +/- \", sd_r2_limited)\n", + "print(\"Minimum Difference in R2: \", np.min(r2_full - r2_limited))\n", + "print(\"Min of Full R2 - Max of Limited R2: \", np.min(r2_full) - np.max(r2_limited))\n", + "\n", + "print(\"Full Model RMSE: \", mean_rmse_full, \" +/- \", sd_rmse_full)\n", + "print(\"Limited Model RMSE: \", mean_rmse_limited, \" +/- \", sd_rmse_limited)\n", + "print(\"Minimum Difference in RMSE: \", np.min(rmse_full - rmse_limited))\n", + "print(\"Min of Full RMSE - Max of Limited RMSE: \", np.min(rmse_full) - np.max(rmse_limited))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mdi", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/feature_importance/gene-data/sandbox.ipynb b/feature_importance/gene-data/sandbox.ipynb new file mode 100644 index 0000000..69831db --- /dev/null +++ b/feature_importance/gene-data/sandbox.ipynb @@ -0,0 +1,816 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# imodels imports\n", + "from imodels import get_clean_dataset\n", + "from imodels.tree.rf_plus.rf_plus.rf_plus_models import RandomForestPlusClassifier, RandomForestPlusRegressor\n", + "from imodels.tree.rf_plus.feature_importance.rfplus_explainer import AloRFPlusMDI, RFPlusMDI\n", + "\n", + "# sklearn imports\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n", + "from sklearn.linear_model import Ridge\n", + "from sklearn.metrics import r2_score, root_mean_squared_error\n", + "\n", + "# other important libraries\n", + "import numpy as np\n", + "import pandas as pd\n", + "import xgboost as xgb" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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"... ... ... ... ... \n", + "22537 2.062718 -5.997158 ... -11.4531 \n", + "22538 3.813261 -3.097620 ... 4.4830 \n", + "22539 4.529860 -29.523612 ... -13.1914 \n", + "22540 -0.651454 1.425238 ... -9.1427 \n", + "22541 1.774870 -5.201490 ... -11.9419 \n", + "\n", + " 3 4 5 6 7 8 9 \\\n", + "0 6.34158 -0.149284 3.079470 -2.65142 -1.422720 2.519350 -1.354790 \n", + "1 6.91117 -1.549940 -1.794670 -4.96364 -3.814100 5.289460 -3.050990 \n", + "2 5.19352 -2.030970 1.857810 -1.04143 -2.310750 -1.988220 -1.379210 \n", + "3 4.66729 -1.694440 6.084100 1.51288 -2.057870 0.984135 -1.844740 \n", + "4 1.19131 -0.691440 2.401740 -5.41882 0.195783 0.113224 -2.276820 \n", + "... ... ... ... ... ... ... ... \n", + "22537 3.87597 -1.390360 4.543550 -2.97738 -0.566625 -0.319262 0.345175 \n", + "22538 3.87090 5.963360 -20.854400 2.56826 2.250370 12.044400 1.293590 \n", + "22539 3.42565 -1.183190 3.156370 -1.35358 -0.036006 -1.449080 -1.900950 \n", + "22540 3.15598 -2.184210 -3.060840 -7.26838 -3.319090 -2.294830 -4.789090 \n", + "22541 4.29505 -0.666773 -0.128824 -6.73571 -0.693877 -0.388256 -1.155780 \n", + "\n", + " 10 11 \n", + "0 -0.105849 5.789740 \n", + "1 -4.269630 -0.530574 \n", + "2 5.147300 -2.660630 \n", + "3 -2.955540 2.409810 \n", + "4 7.193060 6.534780 \n", + "... ... ... \n", + "22537 -5.943890 1.634150 \n", + "22538 -3.276760 -12.048100 \n", + "22539 3.274970 -0.588565 \n", + "22540 3.248110 2.456600 \n", + "22541 4.287650 -0.327399 \n", + "\n", + "[22542 rows x 15137 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "genotype = pd.read_csv(\"/scratch/users/omer_ronen/mutemb_esm/X_k_5_ilvm_esm_prod_pppl_full_ptv.csv\")\n", + "genotype" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Full Model Correlation: 0.6307915897423984\n", + "Limited Model Correlation: 0.6302596772658053\n" + ] + } + ], + "source": [ + "genotype = pd.read_csv(\"/scratch/users/omer_ronen/mutemb_esm/X_k_5_ilvm_oh.csv\")\n", + "# genotype = pd.read_csv(\"/scratch/users/omer_ronen/mutemb_esm/X_k_5_ilvm_esm_prod_pppl_full_ptv.csv\")\n", + "phenotype = pd.read_csv('/scratch/users/omer_ronen/mutemb_esm/y_ilvm_oh.csv')\n", + "# make genotype a numpy array\n", + "genotype = genotype.to_numpy()\n", + "# get last twelve features in genotype\n", + "limited_genotype = genotype[:,-12:]\n", + "# make phenotype a 1D numpy array\n", + "phenotype = phenotype.to_numpy().reshape(-1)\n", + "\n", + "# train-test split\n", + "genotype_train, genotype_test, limited_genotype_train, \\\n", + "limited_genotype_test, phenotype_train, phenotype_test = \\\n", + " train_test_split(genotype, limited_genotype, phenotype, test_size = 0.3,\n", + " random_state = 1)\n", + " \n", + "# fit random forest model\n", + "rf = RandomForestRegressor(n_estimators = 100, max_depth=5, random_state = 42)\n", + "rf.fit(genotype_train, phenotype_train)\n", + "rf_limited = RandomForestRegressor(n_estimators = 100, max_depth=5, random_state = 42)\n", + "rf_limited.fit(limited_genotype_train, phenotype_train)\n", + "\n", + "# predict on test set\n", + "phenotype_pred = rf.predict(genotype_test)\n", + "phenotype_pred_limited = rf_limited.predict(limited_genotype_test)\n", + "\n", + "# calculate correlation for pred-check\n", + "cor = np.corrcoef(phenotype_test, phenotype_pred)[0,1]\n", + "cor_limited = np.corrcoef(phenotype_test, phenotype_pred_limited)[0,1]\n", + "\n", + "print(\"Full Model Correlation: \", cor)\n", + "print(\"Limited Model Correlation: \", cor_limited)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Full Model Correlation: 0.6283474015852735 +/- 0.004913913615241869\n", + "Limited Model Correlation: 0.6282689917860866 +/- 0.004970445344235337\n", + "Minimum Difference in Correlation: -0.0005021615925988598\n", + "Min of Full Correlation - Max of Limited Correlation: -0.01750065169358539\n", + "Full Model R2: 0.39437944691740767 +/- 0.006239891379850461\n", + "Limited Model R2: 0.3942764052299147 +/- 0.006313129672735268\n", + "Minimum Difference in R2: -0.00068856590086952\n", + "Min of Full R2 - Max of Limited R2: -0.022201831451174714\n", + "Full Model RMSE: 6.643785953975964 +/- 0.0812864149692228\n", + "Limited Model RMSE: 6.644335398139373 +/- 0.08032920158349648\n", + "Minimum Difference in RMSE: -0.0043769474032338\n", + "Min of Full RMSE - Max of Limited RMSE: -0.2641286077247198\n" + ] + } + ], + "source": [ + "genotype = pd.read_csv(\"/scratch/users/omer_ronen/mutemb_esm/X_k_5_ilvm_oh.csv\")\n", + "# genotype = pd.read_csv(\"/scratch/users/omer_ronen/mutemb_esm/X_k_5_ilvm_esm_prod_pppl_full_ptv.csv\")\n", + "phenotype = pd.read_csv('/scratch/users/omer_ronen/mutemb_esm/y_ilvm_oh.csv')\n", + "# make genotype a numpy array\n", + "genotype = genotype.to_numpy()\n", + "# get last twelve features in genotype\n", + "limited_genotype = genotype[:,-12:]\n", + "# make phenotype a 1D numpy array\n", + "phenotype = phenotype.to_numpy().reshape(-1)\n", + "\n", + "cor_limited = []\n", + "cor_full = []\n", + "r2_limited = []\n", + "r2_full = []\n", + "rmse_limited = []\n", + "rmse_full = []\n", + "\n", + "for i in range(20):\n", + " # train-test split\n", + " genotype_train, genotype_test, limited_genotype_train, \\\n", + " limited_genotype_test, phenotype_train, phenotype_test = \\\n", + " train_test_split(genotype, limited_genotype, phenotype, test_size = 0.3,\n", + " random_state = i)\n", + " # fit random forest model\n", + " rf = RandomForestRegressor(n_estimators = 100, max_depth=5, random_state = 42)\n", + " rf.fit(genotype_train, phenotype_train)\n", + " rf_limited = RandomForestRegressor(n_estimators = 100, max_depth=5, random_state = 42)\n", + " rf_limited.fit(limited_genotype_train, phenotype_train)\n", + "\n", + " # predict on test set\n", + " phenotype_pred = rf.predict(genotype_test)\n", + " phenotype_pred_limited = rf_limited.predict(limited_genotype_test)\n", + "\n", + " # calculate correlation for pred-check\n", + " cor_full.append(np.corrcoef(phenotype_test, phenotype_pred)[0,1])\n", + " cor_limited.append(np.corrcoef(phenotype_test, phenotype_pred_limited)[0,1])\n", + " r2_full.append(r2_score(phenotype_test, phenotype_pred))\n", + " r2_limited.append(r2_score(phenotype_test, phenotype_pred_limited))\n", + " rmse_full.append(root_mean_squared_error(phenotype_test, phenotype_pred))\n", + " rmse_limited.append(root_mean_squared_error(phenotype_test, phenotype_pred_limited))\n", + " \n", + "cor_full = np.array(cor_full)\n", + "cor_limited = np.array(cor_limited)\n", + "r2_full = np.array(r2_full)\n", + "r2_limited = np.array(r2_limited)\n", + "rmse_full = np.array(rmse_full)\n", + "rmse_limited = np.array(rmse_limited)\n", + "# get means and sds of each\n", + "mean_cor_full = np.mean(cor_full)\n", + "mean_cor_limited = np.mean(cor_limited)\n", + "sd_cor_full = np.std(cor_full)\n", + "sd_cor_limited = np.std(cor_limited)\n", + "mean_r2_full = np.mean(r2_full)\n", + "mean_r2_limited = np.mean(r2_limited)\n", + "sd_r2_full = np.std(r2_full)\n", + "sd_r2_limited = np.std(r2_limited)\n", + "mean_rmse_full = np.mean(rmse_full)\n", + "mean_rmse_limited = np.mean(rmse_limited)\n", + "sd_rmse_full = np.std(rmse_full)\n", + "sd_rmse_limited = np.std(rmse_limited)\n", + "\n", + "print(\"Full Model Correlation: \", mean_cor_full, \" +/- \", sd_cor_full)\n", + "print(\"Limited Model Correlation: \", mean_cor_limited, \" +/- \", sd_cor_limited)\n", + "print(\"Minimum Difference in Correlation: \", np.min(cor_full - cor_limited))\n", + "print(\"Min of Full Correlation - Max of Limited Correlation: \", np.min(cor_full) - np.max(cor_limited))\n", + "\n", + "print(\"Full Model R2: \", mean_r2_full, \" +/- \", sd_r2_full)\n", + "print(\"Limited Model R2: \", mean_r2_limited, \" +/- \", sd_r2_limited)\n", + "print(\"Minimum Difference in R2: \", np.min(r2_full - r2_limited))\n", + "print(\"Min of Full R2 - Max of Limited R2: \", np.min(r2_full) - np.max(r2_limited))\n", + "\n", + "print(\"Full Model RMSE: \", mean_rmse_full, \" +/- \", sd_rmse_full)\n", + "print(\"Limited Model RMSE: \", mean_rmse_limited, \" +/- \", sd_rmse_limited)\n", + "print(\"Minimum Difference in RMSE: \", np.min(rmse_full - rmse_limited))\n", + "print(\"Min of Full RMSE - Max of Limited RMSE: \", np.min(rmse_full) - np.max(rmse_limited))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# genotype = pd.read_csv(\"/scratch/users/omer_ronen/mutemb_esm/X_k_5_ilvm_oh.csv\")\n", + "# genotype = pd.read_csv(\"/scratch/users/omer_ronen/mutemb_esm/X_k_5_ilvm_esm_prod_pppl_full_ptv.csv\")\n", + "phenotype = pd.read_csv('/scratch/users/omer_ronen/mutemb_esm/y_ilvm_oh.csv')\n", + "# make genotype a numpy array\n", + "genotype = genotype.to_numpy()\n", + "# get last twelve features in genotype\n", + "limited_genotype = genotype[:,-12:]\n", + "# make phenotype a 1D numpy array\n", + "phenotype = phenotype.to_numpy().reshape(-1)\n", + "\n", + "cor_limited = []\n", + "cor_full = []\n", + "r2_limited = []\n", + "r2_full = []\n", + "rmse_limited = []\n", + "rmse_full = []\n", + "\n", + "for i in range(1):\n", + " # train-test split\n", + " genotype_train, genotype_test, limited_genotype_train, \\\n", + " limited_genotype_test, phenotype_train, phenotype_test = \\\n", + " train_test_split(genotype, limited_genotype, phenotype, test_size = 0.3,\n", + " random_state = i)\n", + " # fit xgboost on full and limited models\n", + " params = {\n", + " 'objective': 'reg:squarederror', # Use 'reg:squarederror' for regression\n", + " 'max_depth': 5,\n", + " 'eta': 0.1,\n", + " 'eval_metric': 'logloss'\n", + " }\n", + " limited_dtrain = xgb.DMatrix(limited_genotype_train, label=phenotype_train)\n", + " full_dtrain = xgb.DMatrix(genotype_train, label=phenotype_train)\n", + " # train the model\n", + " limited_model = xgb.train(params, limited_dtrain, num_boost_round=100)\n", + " full_model = xgb.train(params, full_dtrain, num_boost_round=100)\n", + " # predict on test set\n", + " limited_pred = limited_model.predict(xgb.DMatrix(limited_genotype_test))\n", + " full_pred = full_model.predict(xgb.DMatrix(genotype_test))\n", + " # calculate correlation for pred-check\n", + " cor_full.append(np.corrcoef(phenotype_test, full_pred)[0,1])\n", + " cor_limited.append(np.corrcoef(phenotype_test, limited_pred)[0,1])\n", + " r2_full.append(r2_score(phenotype_test, full_pred))\n", + " r2_limited.append(r2_score(phenotype_test, limited_pred))\n", + " rmse_full.append(root_mean_squared_error(phenotype_test, full_pred))\n", + " rmse_limited.append(root_mean_squared_error(phenotype_test, limited_pred))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Full Model Correlation: 0.6169481551089033 +/- 0.0\n", + "Limited Model Correlation: 0.6205696145882396 +/- 0.0\n", + "Minimum Difference in Correlation: -0.003621459479336231\n", + "Min of Full Correlation - Max of Limited Correlation: -0.003621459479336231\n", + "Full Model R2: 0.37968110494361884 +/- 0.0\n", + "Limited Model R2: 0.3839563913017795 +/- 0.0\n", + "Minimum Difference in R2: -0.004275286358160657\n", + "Min of Full R2 - Max of Limited R2: -0.004275286358160657\n", + "Full Model RMSE: 6.708001326981556 +/- 0.0\n", + "Limited Model RMSE: 6.684845324765358 +/- 0.0\n", + "Minimum Difference in RMSE: 0.02315600221619807\n", + "Min of Full RMSE - Max of Limited RMSE: 0.02315600221619807\n" + ] + } + ], + "source": [ + "cor_full = np.array(cor_full)\n", + "cor_limited = np.array(cor_limited)\n", + "r2_full = np.array(r2_full)\n", + "r2_limited = np.array(r2_limited)\n", + "rmse_full = np.array(rmse_full)\n", + "rmse_limited = np.array(rmse_limited)\n", + "# get means and sds of each\n", + "mean_cor_full = np.mean(cor_full)\n", + "mean_cor_limited = np.mean(cor_limited)\n", + "sd_cor_full = np.std(cor_full)\n", + "sd_cor_limited = np.std(cor_limited)\n", + "mean_r2_full = np.mean(r2_full)\n", + "mean_r2_limited = np.mean(r2_limited)\n", + "sd_r2_full = np.std(r2_full)\n", + "sd_r2_limited = np.std(r2_limited)\n", + "mean_rmse_full = np.mean(rmse_full)\n", + "mean_rmse_limited = np.mean(rmse_limited)\n", + "sd_rmse_full = np.std(rmse_full)\n", + "sd_rmse_limited = np.std(rmse_limited)\n", + "\n", + "print(\"Full Model Correlation: \", mean_cor_full, \" +/- \", sd_cor_full)\n", + "print(\"Limited Model Correlation: \", mean_cor_limited, \" +/- \", sd_cor_limited)\n", + "print(\"Minimum Difference in Correlation: \", np.min(cor_full - cor_limited))\n", + "print(\"Min of Full Correlation - Max of Limited Correlation: \", np.min(cor_full) - np.max(cor_limited))\n", + "\n", + "print(\"Full Model R2: \", mean_r2_full, \" +/- \", sd_r2_full)\n", + "print(\"Limited Model R2: \", mean_r2_limited, \" +/- \", sd_r2_limited)\n", + "print(\"Minimum Difference in R2: \", np.min(r2_full - r2_limited))\n", + "print(\"Min of Full R2 - Max of Limited R2: \", np.min(r2_full) - np.max(r2_limited))\n", + "\n", + "print(\"Full Model RMSE: \", mean_rmse_full, \" +/- \", sd_rmse_full)\n", + "print(\"Limited Model RMSE: \", mean_rmse_limited, \" +/- \", sd_rmse_limited)\n", + "print(\"Minimum Difference in RMSE: \", np.min(rmse_full - rmse_limited))\n", + "print(\"Min of Full RMSE - Max of Limited RMSE: \", np.min(rmse_full) - np.max(rmse_limited))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "R^2 on Test Data: 0.39809163318237584\n" + ] + } + ], + "source": [ + "# train a rf regressor\n", + "rf = RandomForestRegressor(n_estimators=100, max_depth=5, random_state=1)\n", + "rf.fit(X_train, y_train)\n", + "rf_pred = rf.predict(X_test)\n", + "print(f'R^2 on Test Data: {r2_score(y_test, rf_pred)}')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 16.4s\n", + "[Parallel(n_jobs=-1)]: Done 184 tasks | elapsed: 1.1min\n", + "[Parallel(n_jobs=-1)]: Done 434 tasks | elapsed: 2.5min\n", + "[Parallel(n_jobs=-1)]: Done 784 tasks | elapsed: 4.4min\n", + "[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed: 5.6min finished\n" + ] + } + ], + "source": [ + "# fit rf+\n", + "rf_plus = RandomForestPlusRegressor(rf_model = rf, prediction_model = Ridge())\n", + "rf_plus.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[5], line 4\u001b[0m\n\u001b[1;32m 2\u001b[0m lmdi_explainer \u001b[38;5;241m=\u001b[39m RFPlusMDI(rf_plus)\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# calculate feature importance\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m lmdi_values \u001b[38;5;241m=\u001b[39m \u001b[43mlmdi_explainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexplain_linear_partial\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43ml2norm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msign\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnormalize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/research/imodels/imodels/tree/rf_plus/feature_importance/rfplus_explainer.py:273\u001b[0m, in \u001b[0;36mRFPlusMDI.explain_linear_partial\u001b[0;34m(self, X, y, leaf_average, l2norm, sign, njobs, normalize)\u001b[0m\n\u001b[1;32m 270\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_leafs_in_test_samples_time \u001b[38;5;241m=\u001b[39m end_get_leafs_in_test_samples \u001b[38;5;241m-\u001b[39m start_get_leafs_in_test_samples\n\u001b[1;32m 272\u001b[0m \u001b[38;5;66;03m# all_tree_LFI_scores has shape X.shape[0], X.shape[1], num_trees \u001b[39;00m\n\u001b[0;32m--> 273\u001b[0m all_tree_LFI_scores \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_LFI_subtract_intercept\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mleaf_average\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43ml2norm\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msign\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnjobs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnormalize\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 275\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(all_tree_LFI_scores\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]):\n\u001b[1;32m 276\u001b[0m ith_partial_preds \u001b[38;5;241m=\u001b[39m all_tree_LFI_scores[:,:,i]\n", + "File \u001b[0;32m~/research/imodels/imodels/tree/rf_plus/feature_importance/rfplus_explainer.py:416\u001b[0m, in \u001b[0;36mRFPlusMDI._get_LFI_subtract_intercept\u001b[0;34m(self, X, y, leaf_average, l2norm, sign, njobs, normalize)\u001b[0m\n\u001b[1;32m 413\u001b[0m ith_partial_preds \u001b[38;5;241m=\u001b[39m tree_explainer\u001b[38;5;241m.\u001b[39mpredict_partial_subtract_intercept(blocked_data_ith_tree, l2norm\u001b[38;5;241m=\u001b[39ml2norm,\n\u001b[1;32m 414\u001b[0m sign\u001b[38;5;241m=\u001b[39msign, sigmoid\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, normalize\u001b[38;5;241m=\u001b[39mnormalize, njobs\u001b[38;5;241m=\u001b[39mnjobs)\n\u001b[1;32m 415\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 416\u001b[0m ith_partial_preds \u001b[38;5;241m=\u001b[39m \u001b[43mtree_explainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict_partial_subtract_intercept\u001b[49m\u001b[43m(\u001b[49m\u001b[43mblocked_data_ith_tree\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43ml2norm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43ml2norm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 417\u001b[0m \u001b[43m \u001b[49m\u001b[43msign\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msign\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnormalize\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mnormalize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnjobs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnjobs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 418\u001b[0m ith_partial_preds \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([ith_partial_preds[j] \u001b[38;5;28;01mfor\u001b[39;00m j \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(X\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m])])\u001b[38;5;241m.\u001b[39mT\n\u001b[1;32m 419\u001b[0m LFIs[:,:,i] \u001b[38;5;241m=\u001b[39m ith_partial_preds\n", + "File \u001b[0;32m~/research/imodels/imodels/tree/rf_plus/feature_importance/ppms/ppms.py:87\u001b[0m, in \u001b[0;36mMDIPlusGenericRegressorPPM.predict_partial_subtract_intercept\u001b[0;34m(self, blocked_data, l2norm, sign, normalize, njobs)\u001b[0m\n\u001b[1;32m 85\u001b[0m start_partial_preds \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m 86\u001b[0m \u001b[38;5;66;03m# delayed makes sure that predictions get arranged in the correct order\u001b[39;00m\n\u001b[0;32m---> 87\u001b[0m partial_preds \u001b[38;5;241m=\u001b[39m \u001b[43mParallel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnjobs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdelayed\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpredict_wrapper\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43mk\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 88\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mrange\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mn_blocks\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 89\u001b[0m end_partial_preds \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m 91\u001b[0m \u001b[38;5;66;03m# parse through the outputs of the parallel data structure\u001b[39;00m\n", + "File \u001b[0;32m/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/joblib/parallel.py:1863\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m 1861\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_sequential_output(iterable)\n\u001b[1;32m 1862\u001b[0m \u001b[38;5;28mnext\u001b[39m(output)\n\u001b[0;32m-> 1863\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m output \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreturn_generator \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1865\u001b[0m \u001b[38;5;66;03m# Let's create an ID that uniquely identifies the current call. If the\u001b[39;00m\n\u001b[1;32m 1866\u001b[0m \u001b[38;5;66;03m# call is interrupted early and that the same instance is immediately\u001b[39;00m\n\u001b[1;32m 1867\u001b[0m \u001b[38;5;66;03m# re-used, this id will be used to prevent workers that were\u001b[39;00m\n\u001b[1;32m 1868\u001b[0m \u001b[38;5;66;03m# concurrently finalizing a task from the previous call to run the\u001b[39;00m\n\u001b[1;32m 1869\u001b[0m \u001b[38;5;66;03m# callback.\u001b[39;00m\n\u001b[1;32m 1870\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n", + "File \u001b[0;32m/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/joblib/parallel.py:1792\u001b[0m, in \u001b[0;36mParallel._get_sequential_output\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m 1790\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_dispatched_batches \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m 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\u001b[0;36mMDIPlusGenericRegressorPPM.predict_partial_subtract_intercept..predict_wrapper\u001b[0;34m(k)\u001b[0m\n\u001b[1;32m 77\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpredict_wrapper\u001b[39m(k):\n\u001b[1;32m 78\u001b[0m start_pred_k \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[0;32m---> 79\u001b[0m predict_k \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict_partial_k_subtract_intercept\u001b[49m\u001b[43m(\u001b[49m\u001b[43mblocked_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 80\u001b[0m \u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43ml2norm\u001b[49m\u001b[43m,\u001b[49m\u001b[43msign\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 81\u001b[0m \u001b[43m \u001b[49m\u001b[43mnormalize\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 82\u001b[0m end_time_k \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m 83\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m predict_k, end_time_k \u001b[38;5;241m-\u001b[39m start_pred_k\n", + "File \u001b[0;32m~/research/imodels/imodels/tree/rf_plus/feature_importance/ppms/ppms.py:146\u001b[0m, in \u001b[0;36mMDIPlusGenericRegressorPPM.predict_partial_k_subtract_intercept\u001b[0;34m(self, blocked_data, k, l2norm, sign, normalize)\u001b[0m\n\u001b[1;32m 131\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpredict_partial_k_subtract_intercept\u001b[39m(\u001b[38;5;28mself\u001b[39m, blocked_data, k, l2norm,\n\u001b[1;32m 132\u001b[0m sign, normalize):\n\u001b[1;32m 133\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 134\u001b[0m \u001b[38;5;124;03m Gets the partial predictions for an individual feature k, omitting the\u001b[39;00m\n\u001b[1;32m 135\u001b[0m \u001b[38;5;124;03m regression intercept in the predictions for the model.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 144\u001b[0m \u001b[38;5;124;03m dict: mapping of feature index to partial predictions.\u001b[39;00m\n\u001b[1;32m 145\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 146\u001b[0m modified_data \u001b[38;5;241m=\u001b[39m \u001b[43mblocked_data\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_modified_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43monly_k\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 147\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (sign \u001b[38;5;129;01mor\u001b[39;00m normalize) \u001b[38;5;129;01mand\u001b[39;00m (\u001b[38;5;129;01mnot\u001b[39;00m l2norm):\n\u001b[1;32m 148\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWarning: sign and normalize only work with l2norm=True.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/research/imodels/imodels/tree/rf_plus/data_transformations/block_transformers.py:160\u001b[0m, in \u001b[0;36mBlockPartitionedData.get_modified_data\u001b[0;34m(self, k, mode)\u001b[0m\n\u001b[1;32m 140\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_modified_data\u001b[39m(\u001b[38;5;28mself\u001b[39m, k, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkeep_k\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 141\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 142\u001b[0m \u001b[38;5;124;03m Modify the data by either imputing the mean of each feature in block k\u001b[39;00m\n\u001b[1;32m 143\u001b[0m \u001b[38;5;124;03m (keep_rest) or imputing the mean of each feature not in block k\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 158\u001b[0m \u001b[38;5;124;03m blocks together\u001b[39;00m\n\u001b[1;32m 159\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 160\u001b[0m modified_blocks \u001b[38;5;241m=\u001b[39m [np\u001b[38;5;241m.\u001b[39mouter(np\u001b[38;5;241m.\u001b[39mones(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_samples), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_means[i])\n\u001b[1;32m 161\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_blocks)]\n\u001b[1;32m 162\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkeep_k\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 163\u001b[0m data_blocks \u001b[38;5;241m=\u001b[39m \\\n\u001b[1;32m 164\u001b[0m [\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data_blocks[i] \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;241m==\u001b[39m k \u001b[38;5;28;01melse\u001b[39;00m modified_blocks[i] \u001b[38;5;28;01mfor\u001b[39;00m\n\u001b[1;32m 165\u001b[0m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_blocks)]\n", + "File \u001b[0;32m~/research/imodels/imodels/tree/rf_plus/data_transformations/block_transformers.py:160\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 140\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_modified_data\u001b[39m(\u001b[38;5;28mself\u001b[39m, k, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkeep_k\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 141\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 142\u001b[0m \u001b[38;5;124;03m Modify the data by either imputing the mean of each feature in block k\u001b[39;00m\n\u001b[1;32m 143\u001b[0m \u001b[38;5;124;03m (keep_rest) or imputing the mean of each feature not in block k\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 158\u001b[0m \u001b[38;5;124;03m blocks together\u001b[39;00m\n\u001b[1;32m 159\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 160\u001b[0m modified_blocks \u001b[38;5;241m=\u001b[39m [np\u001b[38;5;241m.\u001b[39mouter(\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mones\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_samples\u001b[49m\u001b[43m)\u001b[49m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_means[i])\n\u001b[1;32m 161\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_blocks)]\n\u001b[1;32m 162\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkeep_k\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 163\u001b[0m data_blocks \u001b[38;5;241m=\u001b[39m \\\n\u001b[1;32m 164\u001b[0m [\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data_blocks[i] \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;241m==\u001b[39m k \u001b[38;5;28;01melse\u001b[39;00m modified_blocks[i] \u001b[38;5;28;01mfor\u001b[39;00m\n\u001b[1;32m 165\u001b[0m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_blocks)]\n", + "File \u001b[0;32m/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/numpy/core/numeric.py:192\u001b[0m, in \u001b[0;36mones\u001b[0;34m(shape, dtype, order, like)\u001b[0m\n\u001b[1;32m 189\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _ones_with_like(like, shape, dtype\u001b[38;5;241m=\u001b[39mdtype, order\u001b[38;5;241m=\u001b[39morder)\n\u001b[1;32m 191\u001b[0m a \u001b[38;5;241m=\u001b[39m empty(shape, dtype, order)\n\u001b[0;32m--> 192\u001b[0m \u001b[43mmultiarray\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcopyto\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcasting\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43munsafe\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 193\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m a\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "# create an explainer\n", + "lmdi_explainer = RFPlusMDI(rf_plus)\n", + "# calculate feature importance\n", + "lmdi_values = lmdi_explainer.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=True, normalize=True)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mdi", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/feature_importance/gene-data/test_access.ipynb b/feature_importance/gene-data/test_access.ipynb new file mode 100644 index 0000000..450a5dc --- /dev/null +++ b/feature_importance/gene-data/test_access.ipynb @@ -0,0 +1,726 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(22542, 365)\n" + ] + } + ], + "source": [ + "genotype = pd.read_csv(\"/scratch/users/omer_ronen/mutemb_esm/X_k_5_ilvm_oh.csv\")\n", + "print(genotype.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(22542, 1)\n" + ] + } + ], + "source": [ + "phenotype = pd.read_csv('/scratch/users/omer_ronen/mutemb_esm/y_ilvm_oh.csv')\n", + "print(phenotype.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "# remove first column which is the index\n", + "# genotype = genotype.iloc[:,1:]\n", + "# phenotype = phenotype.iloc[:,1:]\n", + "\n", + "# make phenotype a 1D numpy array\n", + "phenotype = phenotype.to_numpy().reshape(-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "# train-test split\n", + "genotype_train, genotype_test, phenotype_train, phenotype_test = \\\n", + " train_test_split(genotype, phenotype, test_size = 0.3, random_state = 42)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "44.925074823624456\n", + "46.61814275853355\n" + ] + } + ], + "source": [ + "# get average of phenotype for genotype['0']==0 and genotype['0']==1\n", + "phenotype_0 = phenotype_train[genotype_train['0']==0]\n", + "phenotype_1 = phenotype_train[genotype_train['0']==1]\n", + "print(np.mean(phenotype_0))\n", + "print(np.mean(phenotype_1))" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1. , 0.12068478],\n", + " [0.12068478, 1. ]])" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# create array of np.mean(phenotype_0) if genotype['0']==0 and np.mean(phenotype_1) if genotype['0']==1\n", + "phenotype_test_pred = np.where(genotype_test['0']==0, np.mean(phenotype_0), np.mean(phenotype_1))\n", + "# calculate correlation between phenotype_train and phenotype_train_pred\n", + "np.corrcoef(phenotype_test, phenotype_test_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
RandomForestRegressor(random_state=42)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "RandomForestRegressor(random_state=42)" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# fit random forest model\n", + "rf = RandomForestRegressor(n_estimators = 100, random_state = 42)\n", + "rf.fit(genotype_train, phenotype_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "# predict on test set\n", + "phenotype_pred = rf.predict(genotype_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.43609822112817503" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# calculate correlation for pred-check\n", + "np.corrcoef(phenotype_pred, phenotype_test_pred)[0,1]" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1. , 0.05106382],\n", + " [0.05106382, 1. ]])" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.corrcoef(phenotype_test, phenotype_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "# get MDI feature importance scores\n", + "importances = rf.feature_importances_" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0.0, 0.0918999839333947]" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# get max and min feature importance scores\n", + "max_imp = np.max(importances)\n", + "min_imp = np.min(importances)\n", + "[min_imp, max_imp]" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "549" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# get number of features with nonzero importance\n", + "num_nonzero = np.sum(importances > 0)\n", + "num_nonzero" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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F3nPlypWqqqrSXXfdpdLSUrW3t+vVV1/Vzp07r89ZAgCAYS3qYKmqqtKJEye0du1aeb1eFRQUqL29PXRh7ZEjR5SYeH7hpri4WE1NTVq9erVqa2s1efJktba2Kj8/PzTmgQceUGNjo+rq6vTYY4/ptttu00svvaQ777zzOpwiAAAY7qJ+DotFPIcFAIDh5z/2HBYAAIBYIFgAAIB5BAsAADCPYAEAAOYRLAAAwDyCBQAAmEewAAAA8wgWAABgHsECAADMI1gAAIB5BAsAADCPYAEAAOYRLAAAwDyCBQAAmEewAAAA8wgWAABgHsECAADMI1gAAIB5BAsAADCPYAEAAOYRLAAAwDyCBQAAmEewAAAA8wgWAABgHsECAADMI1gAAIB5BAsAADCPYAEAAOYRLAAAwDyCBQAAmEewAAAA8wgWAABgHsECAADMI1gAAIB5BAsAADCPYAEAAOYRLAAAwDyCBQAAmEewAAAA8wgWAABgHsECAADMI1gAAIB5BAsAADCPYAEAAOYRLAAAwDyCBQAAmEewAAAA8wgWAABgHsECAADMI1gAAIB5BAsAADCPYAEAAOYRLAAAwDyCBQAAmEewAAAA8wgWAABgHsECAADMI1gAAIB5BAsAADCPYAEAAOYRLAAAwDyCBQAAmHdVwdLQ0KCcnBylpqaqsLBQu3fvvuT45uZmTZkyRampqXK5XNq+fftFxz7yyCNKSEjQhg0brmZqAAAgDkUdLFu2bFF1dbXWrVun7u5uTZs2TeXl5err64s4vqurS/Pnz9eSJUu0b98+ud1uud1u9fT0XDD25Zdf1q5du5SdnR39mQAAgLgVdbA8++yz+s53vqPFixdr6tSpamxs1OjRo/WrX/0q4vjnnntO99xzj1auXKnbb79dP/7xj/XVr35VGzduDBv30UcfadmyZfrtb3+rm2666erOBgAAxKWoguXs2bPau3evysrKzr9BYqLKysrk8XgiHuPxeMLGS1J5eXnY+EAgoIcfflgrV67Ul7/85cvO48yZM/L5fGEvAAAQv6IKlpMnT8rv9ysjIyNse0ZGhrxeb8RjvF7vZcf/7Gc/U1JSkh577LErmkddXZ3S09NDL6fTGc1pAACAYSbmdwnt3btXzz33nF544QUlJCRc0TE1NTXq7+8PvY4ePfofniUAAIilqIJl3Lhxcjgc6u3tDdve29urzMzMiMdkZmZecnxnZ6f6+vo0ceJEJSUlKSkpSR9++KFWrFihnJyciO+ZkpKitLS0sBcAAIhfUQVLcnKypk+fro6OjtC2QCCgjo4OFRUVRTymqKgobLwk7dixIzT+4Ycf1ttvv639+/eHXtnZ2Vq5cqV+//vfR3s+AAAgDiVFe0B1dbUWLVqkGTNmaNasWdqwYYMGBwe1ePFiSdLChQs1fvx41dXVSZKWL1+uOXPmqL6+XvPmzdPmzZu1Z88ebdq0SZI0duxYjR07NuzvuOmmm5SZmanbbrvtWs8PAADEgaiDpaqqSidOnNDatWvl9XpVUFCg9vb20IW1R44cUWLi+YWb4uJiNTU1afXq1aqtrdXkyZPV2tqq/Pz863cWAAAgriUEg8FgrCdxrXw+n9LT09Xf38/1LAAADBPRfH7H/C4hAACAyyFYAACAeQQLAAAwj2ABAADmESwAAMA8ggUAAJhHsAAAAPMIFgAAYB7BAgAAzCNYAACAeQQLAAAwj2ABAADmESwAAMA8ggUAAJhHsAAAAPMIFgAAYB7BAgAAzCNYAACAeQQLAAAwj2ABAADmESwAAMA8ggUAAJhHsAAAAPMIFgAAYB7BAgAAzCNYAACAeQQLAAAwj2ABAADmESwAAMA8ggUAAJhHsAAAAPMIFgAAYB7BAgAAzCNYAACAeQQLAAAwj2ABAADmESwAAMA8ggUAAJhHsAAAAPMIFgAAYB7BAgAAzCNYAACAeQQLAAAwj2ABAADmESwAAMA8ggUAAJhHsAAAAPMIFgAAYB7BAgAAzCNYAACAeQQLAAAwj2ABAADmESwAAMA8ggUAAJhHsAAAAPMIFgAAYB7BAgAAzCNYAACAeQQLAAAwj2ABAADmESwAAMA8ggUAAJh3VcHS0NCgnJwcpaamqrCwULt3777k+ObmZk2ZMkWpqalyuVzavn17aN+5c+f05JNPyuVyacyYMcrOztbChQt17Nixq5kaAACIQ1EHy5YtW1RdXa1169apu7tb06ZNU3l5ufr6+iKO7+rq0vz587VkyRLt27dPbrdbbrdbPT09kqRTp06pu7tba9asUXd3t1paWvTuu+/qvvvuu7YzAwAAcSMhGAwGozmgsLBQM2fO1MaNGyVJgUBATqdTy5Yt06pVqy4YX1VVpcHBQbW1tYW2zZ49WwUFBWpsbIz4d/zlL3/RrFmz9OGHH2rixImXnZPP51N6err6+/uVlpYWzekAAIAYiebzO6oVlrNnz2rv3r0qKys7/waJiSorK5PH44l4jMfjCRsvSeXl5RcdL0n9/f1KSEjQF77whYj7z5w5I5/PF/YCAADxK6pgOXnypPx+vzIyMsK2Z2RkyOv1RjzG6/VGNf6zzz7Tk08+qfnz51+0turq6pSenh56OZ3OaE4DAAAMM6buEjp37py+9a1vKRgM6he/+MVFx9XU1Ki/vz/0Onr06A2cJQAAuNGSohk8btw4ORwO9fb2hm3v7e1VZmZmxGMyMzOvaPxQrHz44Yd6/fXXL/ldVkpKilJSUqKZOgAAGMaiWmFJTk7W9OnT1dHREdoWCATU0dGhoqKiiMcUFRWFjZekHTt2hI0fipWDBw/qtdde09ixY6OZFgAAiHNRrbBIUnV1tRYtWqQZM2Zo1qxZ2rBhgwYHB7V48WJJ0sKFCzV+/HjV1dVJkpYvX645c+aovr5e8+bN0+bNm7Vnzx5t2rRJ0j9jpbKyUt3d3Wpra5Pf7w9d33LLLbcoOTn5ep0rAAAYpqIOlqqqKp04cUJr166V1+tVQUGB2tvbQxfWHjlyRImJ5xduiouL1dTUpNWrV6u2tlaTJ09Wa2ur8vPzJUkfffSRtm3bJkkqKCgI+7veeOMNzZ079ypPDQAAxIuon8NiEc9hAQBg+PmPPYcFAAAgFggWAABgHsECAADMI1gAAIB5BAsAADCPYAEAAOYRLAAAwDyCBQAAmEewAAAA8wgWAABgHsECAADMI1gAAIB5BAsAADCPYAEAAOYRLAAAwDyCBQAAmEewAAAA8wgWAABgHsECAADMI1gAAIB5BAsAADCPYAEAAOYRLAAAwDyCBQAAmEewAAAA85JiPQEAuBi/36/Ozk4dP35cWVlZKikpkcPhiPW0AMQAKywATGppaVFeXp5KS0u1YMEClZaWKi8vTy0tLbGeGoAYIFgAmNPS0qLKykq5XC55PB4NDAzI4/HI5XKpsrKSaAFGoIRgMBiM9SSulc/nU3p6uvr7+5WWlhbr6QC4Bn6/X3l5eXK5XGptbVVi4vn/rgoEAnK73erp6dHBgwf5eggY5qL5/GaFBYApnZ2dOnz4sGpra8NiRZISExNVU1OjQ4cOqbOzM0YzBBALBAsAU44fPy5Jys/Pj7h/aPvQOAAjA8ECwJSsrCxJUk9PT8T9Q9uHxgEYGQgWAKaUlJQoJydHTz/9tAKBQNi+QCCguro65ebmqqSkJEYzBBALBAsAUxwOh+rr69XW1ia32x12l5Db7VZbW5vWr1/PBbfACMOD4wCYU1FRoa1bt2rFihUqLi4Obc/NzdXWrVtVUVERw9kBiAVuawZgFk+6BeJbNJ/frLAAMMvhcGju3LmxngYAA7iGBQAAmEewAAAA8wgWAABgHsECAADMI1gAAIB5BAsAADCPYAEAAOYRLAAAwDyCBQAAmEewAAAA8wgWAABgHsECAADMI1gAAIB5BAsAADCPYAEAAOYRLAAAwDyCBQAAmEewAAAA8wgWAABgHsECwKzTp09r6dKlKi8v19KlS3X69OlYTwlAjCQEg8FgrCdxrXw+n9LT09Xf36+0tLRYTwfAdeB2u/XKK69csP3+++9Xa2vrjZ8QgOsums9vVlgAmDMUK8nJybr77rv10EMP6e6771ZycrJeeeUVud3uWE8RwA3GCgsAU06fPq3Ro0fL4XDI7/dfsH9o+6lTpzRq1KgYzBDA9cIKC4Bha+XKlZIUMVb+dfvQOAAjA8ECwJS//e1v13UcgPhwVcHS0NCgnJwcpaamqrCwULt3777k+ObmZk2ZMkWpqalyuVzavn172P5gMKi1a9cqKytLo0aNUllZmQ4ePHg1UwMwzH3yySfXdRyA+BB1sGzZskXV1dVat26duru7NW3aNJWXl6uvry/i+K6uLs2fP19LlizRvn375Ha75Xa71dPTExrzzDPP6Oc//7kaGxv11ltvacyYMSovL9dnn3129WcGYFjav3//dR0HID5EfdFtYWGhZs6cqY0bN0qSAoGAnE6nli1bplWrVl0wvqqqSoODg2prawttmz17tgoKCtTY2KhgMKjs7GytWLFCP/zhDyVJ/f39ysjI0AsvvKAHH3zwsnPiolsgfiQkJIT98xNPPKElS5bol7/8pZ555pmwfXFwzwAwokXz+Z0UzRufPXtWe/fuVU1NTWhbYmKiysrK5PF4Ih7j8XhUXV0dtq28vDz0HIVDhw7J6/WqrKwstD89PV2FhYXyeDwRg+XMmTM6c+ZM6J99Pl80pwHgIk4eP6rOl395Xd7r1KlBvf/+B1Ef978ywxd+d/x6vXb8en3Eff/n0W9e1dxuvfV/avToMVd17L8aPz5bs+59SEoefc3vBeDSogqWkydPyu/3KyMjI2x7RkbGRS+A83q9Ecd7vd7Q/qFtFxvz7+rq6vSjH/0omqkDuAKdL/9SD/T93+v3hhmXH/Lv1n7vf0Qx+rXo/wJJ+vT/v65Vn3Tov76o3GL3dXgzAJcSVbBYUVNTE7Zq4/P55HQ6YzgjID6UPLBEL798fd7raldYonmK7dU+QO66rrDM+No1vw+Ay4sqWMaNGyeHw6He3t6w7b29vcrMzIx4TGZm5iXHD/1vb2+vsrKywsYUFBREfM+UlBSlpKREM3UAV2BcllMP/Pf/jukcuo9HfiT/v7v//vu19hcv3YAZAbAgqruEkpOTNX36dHV0dIS2BQIBdXR0qKioKOIxRUVFYeMlaceOHaHxubm5yszMDBvj8/n01ltvXfQ9AcSvK11h4feEgJEl6q+EqqurtWjRIs2YMUOzZs3Shg0bNDg4qMWLF0uSFi5cqPHjx6uurk6StHz5cs2ZM0f19fWaN2+eNm/erD179mjTpk2S/nlHwOOPP66nnnpKkydPVm5urtasWaPs7Gx+LwQYoYLB4AV3C/37fgAjS9TBUlVVpRMnTmjt2rXyer0qKChQe3t76KLZI0eOKDHx/MJNcXGxmpqatHr1atXW1mry5MlqbW1Vfn5+aMwTTzyhwcFBffe739Unn3yiO++8U+3t7UpNTb0OpwhgOAoGgxf8YjO/1AyMXPz4IQAAiAl+/BAAAMQVggUAAJhHsAAAAPMIFgAAYB7BAgAAzCNYAACAeQQLAAAwj2ABAADmESwAAMC8qB/Nb9HQw3p9Pl+MZwIAAK7U0Of2lTx0Py6CZWBgQJLkdDpjPBMAABCtgYEBpaenX3JMXPyWUCAQ0LFjx3TzzTdf8hdeAQw/Pp9PTqdTR48e5bfCgDgTDAY1MDCg7OzssB9OjiQuggVA/OLHTQFIXHQLAACGAYIFAACYR7AAMC0lJUXr1q1TSkpKrKcCIIa4hgUAAJjHCgsAADCPYAEAAOYRLAAAwDyCBQAAmEewADDrj3/8o77xjW8oOztbCQkJam1tjfWUAMQIwQLArMHBQU2bNk0NDQ2xngqAGIuLHz8EEJ/uvfde3XvvvbGeBgADWGEBAADmESwAAMA8ggUAAJhHsAAAAPMIFgAAYB53CQEw69NPP9V7770X+udDhw5p//79uuWWWzRx4sQYzgzAjcavNQMwa+fOnSotLb1g+6JFi/TCCy/c+AkBiBmCBQAAmMc1LAAAwDyCBQAAmEewAAAA8wgWAABgHsECAADMI1gAAIB5BAsAADCPYAEAAOYRLAAAwDyCBQAAmEewAAAA8wgWAABg3v8DSTOoIZTeX0IAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot importances in boxplot\n", + "plt.boxplot(importances)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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ArEbsAAAAqxE7AADAasQOAACwWlSxs2LFCl199dVyu91yu93y+/3auHGjc/3EiROqqKhQenq6Ro4cqZKSErW1tUXcR0tLi4qLi5WSkqKMjAw9+OCDOnnyZP+sBgAA4DRRxc4VV1yhJUuWqKGhQbt27dItt9yiO+64Q01NTZKk+fPn69VXX9X69eu1detWtba2asaMGc7tT506peLiYnV1dWn79u1as2aNVq9erYULF/bvqgAAAD4TZ4wxF3IHaWlpeuKJJ3TnnXfq8ssv19q1a3XnnXdKkj744ANNmDBBgUBABQUF2rhxo775zW+qtbVVXq9XkrRy5Uo99NBDOnz4sJKSks7pMcPhsDwej7LmvaR4V4pzft+S4gtZCgAAGEC9379DoZDcbvegPe55v2bn1KlTWrdunY4fPy6/36+GhgZ1d3ersLDQGTN+/HhlZ2crEAhIkgKBgCZOnOiEjiQVFRUpHA47zw4BAAD0p4Rob7B79275/X6dOHFCI0eO1Msvv6zc3Fw1NjYqKSlJqampEeO9Xq+CwaAkKRgMRoRO7/Xea2fT2dmpzs5O5+twOBzttAEAwCUq6md2vvKVr6ixsVH19fWaM2eOysrKtGfPnoGYm6O6uloej8c5srKyBvTxAACAPaKOnaSkJF155ZXKy8tTdXW1Jk2apH/6p3+Sz+dTV1eXOjo6Isa3tbXJ5/NJknw+3xnvzur9undMX6qqqhQKhZxj//790U4bAABcoi74c3Z6enrU2dmpvLw8JSYmqq6uzrnW3NyslpYW+f1+SZLf79fu3bvV3t7ujKmtrZXb7VZubu5ZH8Plcjlvd+89AAAAzkVUr9mpqqrStGnTlJ2draNHj2rt2rX67//+b23evFkej0fl5eWqrKxUWlqa3G637rvvPvn9fhUUFEiSpk6dqtzcXM2cOVNLly5VMBjUggULVFFRIZfLNSALBAAAl7aoYqe9vV3f+c53dOjQIXk8Hl199dXavHmz/vIv/1KS9NRTTyk+Pl4lJSXq7OxUUVGRli9f7tx+2LBhqqmp0Zw5c+T3+zVixAiVlZVp8eLF/bsqAACAz1zw5+zEAp+zAwDA0DPkPmcHAABgKCB2AACA1YgdAABgNWIHAABYjdgBAABWI3YAAIDViB0AAGA1YgcAAFiN2AEAAFYjdgAAgNWIHQAAYDViBwAAWI3YAQAAViN2AACA1YgdAABgNWIHAABYjdgBAABWI3YAAIDViB0AAGA1YgcAAFiN2AEAAFYjdgAAgNWIHQAAYDViBwAAWI3YAQAAViN2AACA1YgdAABgNWIHAABYjdgBAABWI3YAAIDViB0AAGA1YgcAAFiN2AEAAFYjdgAAgNWIHQAAYDViBwAAWI3YAQAAViN2AACA1YgdAABgNWIHAABYjdgBAABWI3YAAIDViB0AAGA1YgcAAFgtqtiprq7W1772NY0aNUoZGRmaPn26mpubI8acOHFCFRUVSk9P18iRI1VSUqK2traIMS0tLSouLlZKSooyMjL04IMP6uTJkxe+GgAAgNNEFTtbt25VRUWFduzYodraWnV3d2vq1Kk6fvy4M2b+/Pl69dVXtX79em3dulWtra2aMWOGc/3UqVMqLi5WV1eXtm/frjVr1mj16tVauHBh/60KAADgM3HGGHO+Nz58+LAyMjK0detW/cVf/IVCoZAuv/xyrV27Vnfeeack6YMPPtCECRMUCARUUFCgjRs36pvf/KZaW1vl9XolSStXrtRDDz2kw4cPKykp6XMfNxwOy+PxKGveS4p3pTjn9y0pPt+lAACAAdb7/TsUCsntdg/a417Qa3ZCoZAkKS0tTZLU0NCg7u5uFRYWOmPGjx+v7OxsBQIBSVIgENDEiROd0JGkoqIihcNhNTU19fk4nZ2dCofDEQcAAMC5OO/Y6enp0bx583TjjTfqqquukiQFg0ElJSUpNTU1YqzX61UwGHTG/HHo9F7vvdaX6upqeTwe58jKyjrfaQMAgEvMecdORUWF3nvvPa1bt64/59OnqqoqhUIh59i/f/+APyYAALBDwvncaO7cuaqpqdG2bdt0xRVXOOd9Pp+6urrU0dER8exOW1ubfD6fM+att96KuL/ed2v1jjmdy+WSy+U6n6kCAIBLXFTP7BhjNHfuXL388svasmWLcnJyIq7n5eUpMTFRdXV1zrnm5ma1tLTI7/dLkvx+v3bv3q329nZnTG1trdxut3Jzcy9kLQAAAGeI6pmdiooKrV27Vq+88opGjRrlvMbG4/EoOTlZHo9H5eXlqqysVFpamtxut+677z75/X4VFBRIkqZOnarc3FzNnDlTS5cuVTAY1IIFC1RRUcGzNwAAoN9FFTsrVqyQJN18880R51etWqV77rlHkvTUU08pPj5eJSUl6uzsVFFRkZYvX+6MHTZsmGpqajRnzhz5/X6NGDFCZWVlWrx48YWtBAAAoA8X9Dk7scLn7AAAMPQMyc/ZAQAAuNgROwAAwGrEDgAAsBqxAwAArEbsAAAAqxE7AADAasQOAACwGrEDAACsRuwAAACrETsAAMBqxA4AALAasQMAAKxG7AAAAKsROwAAwGrEDgAAsBqxAwAArEbsAAAAqxE7AADAasQOAACwGrEDAACsRuwAAACrETsAAMBqxA4AALAasQMAAKxG7AAAAKsROwAAwGrEDgAAsBqxAwAArEbsAAAAqxE7AADAasQOAACwGrEDAACsRuwAAACrETsAAMBqxA4AALAasQMAAKxG7AAAAKsROwAAwGrEDgAAsBqxAwAArEbsAAAAqxE7AADAasQOAACwGrEDAACsFnXsbNu2TbfffrsyMzMVFxenX/7ylxHXjTFauHChxowZo+TkZBUWFuqjjz6KGHPkyBGVlpbK7XYrNTVV5eXlOnbs2AUtBAAAoC9Rx87x48c1adIkPfvss31eX7p0qZYtW6aVK1eqvr5eI0aMUFFRkU6cOOGMKS0tVVNTk2pra1VTU6Nt27Zp9uzZ578KAACAs4gzxpjzvnFcnF5++WVNnz5d0qfP6mRmZur+++/XAw88IEkKhULyer1avXq17rrrLr3//vvKzc3Vzp07NXnyZEnSpk2bdNttt+nAgQPKzMz83McNh8PyeDzKmveS4l0pzvl9S4rPdykAAGCA9X7/DoVCcrvdg/a4/fqanb179yoYDKqwsNA55/F4lJ+fr0AgIEkKBAJKTU11QkeSCgsLFR8fr/r6+j7vt7OzU+FwOOIAAAA4F/0aO8FgUJLk9Xojznu9XudaMBhURkZGxPWEhASlpaU5Y05XXV0tj8fjHFlZWf05bQAAYLEh8W6sqqoqhUIh59i/f3+spwQAAIaIfo0dn88nSWpra4s439bW5lzz+Xxqb2+PuH7y5EkdOXLEGXM6l8slt9sdcQAAAJyLfo2dnJwc+Xw+1dXVOefC4bDq6+vl9/slSX6/Xx0dHWpoaHDGbNmyRT09PcrPz+/P6QAAACgh2hscO3ZMH3/8sfP13r171djYqLS0NGVnZ2vevHn6yU9+onHjxiknJ0ePPPKIMjMznXdsTZgwQbfeeqtmzZqllStXqru7W3PnztVdd911Tu/EAgAAiEbUsbNr1y594xvfcL6urKyUJJWVlWn16tX60Y9+pOPHj2v27Nnq6OjQTTfdpE2bNmn48OHObV544QXNnTtXU6ZMUXx8vEpKSrRs2bJ+WA4AAECkC/qcnVjhc3YAABh6rPicHQAAgIsNsQMAAKxG7AAAAKsROwAAwGrEDgAAsBqxAwAArEbsAAAAqxE7AADAasQOAACwGrEDAACsRuwAAACrETsAAMBqxA4AALAasQMAAKxG7AAAAKsROwAAwGrEDgAAsBqxAwAArEbsAAAAqxE7AADAasQOAACwGrEDAACsRuwAAACrETsAAMBqxA4AALAasQMAAKxG7AAAAKsROwAAwGrEDgAAsBqxAwAArEbsAAAAqxE7AADAasQOAACwGrEDAACsRuwAAACrETsAAMBqCbGeQH/64sMbIr7et6Q4RjMBAAAXC57ZAQAAViN2AACA1YgdAABgNWIHAABYjdgBAABWI3YAAIDViB0AAGC1mH7OzrPPPqsnnnhCwWBQkyZN0jPPPKPrr7++3+7/9M/dkfjsHQAALjUxe2bnxRdfVGVlpRYtWqS3335bkyZNUlFRkdrb22M1JQAAYKGYPbPz5JNPatasWfrud78rSVq5cqU2bNign//853r44YdjNa0+neszRHyCMwAAF5+YxE5XV5caGhpUVVXlnIuPj1dhYaECgcAZ4zs7O9XZ2el8HQqFJEk9nb+L+rGz568/jxmfKRwOn3Hu9Pn0NWYwXbVo8xnn3nusKAYzAQDgD98XjTGD+rgxiZ3f/va3OnXqlLxeb8R5r9erDz744Izx1dXVeuyxx844f3DFPQM1xc/lebp/xgy2i3FOAIBLyyeffCKPxzNojzck/iLQqqoqVVZWOl93dHRo7NixamlpGdT/WDhTOBxWVlaW9u/fL7fbHevpXNLYi4sL+3HxYC8uHqFQSNnZ2UpLSxvUx41J7Fx22WUaNmyY2traIs63tbXJ5/OdMd7lcsnlcp1x3uPx8D/uRcLtdrMXFwn24uLCflw82IuLR3z84L4/KibvxkpKSlJeXp7q6uqccz09Paqrq5Pf74/FlAAAgKVi9mOsyspKlZWVafLkybr++uv19NNP6/jx4867swAAAPpDzGLnb/7mb3T48GEtXLhQwWBQ11xzjTZt2nTGi5b74nK5tGjRoj5/tIXBxV5cPNiLiwv7cfFgLy4esdqLODPY7/8CAAAYRPzdWAAAwGrEDgAAsBqxAwAArEbsAAAAq8Ukdp599ll98Ytf1PDhw5Wfn6+33nrrT45fv369xo8fr+HDh2vixIl67bXXIq4bY7Rw4UKNGTNGycnJKiws1EcffRQx5siRIyotLZXb7VZqaqrKy8t17Nixfl/bUDPYe7Fv3z6Vl5crJydHycnJ+rM/+zMtWrRIXV1dA7K+oSYWvzZ6dXZ26pprrlFcXJwaGxv7a0lDVqz2YsOGDcrPz1dycrJGjx6t6dOn9+eyhqRY7MWHH36oO+64Q5dddpncbrduuukm/dd//Ve/r22o6e+9+I//+A9NnTpV6enpZ/2958SJE6qoqFB6erpGjhypkpKSMz6U+HOZQbZu3TqTlJRkfv7zn5umpiYza9Ysk5qaatra2voc/+abb5phw4aZpUuXmj179pgFCxaYxMREs3v3bmfMkiVLjMfjMb/85S/Nu+++a/7qr/7K5OTkmN///vfOmFtvvdVMmjTJ7Nixw7zxxhvmyiuvNHffffeAr/diFou92Lhxo7nnnnvM5s2bza9//WvzyiuvmIyMDHP//fcPypovZrH6tdHrBz/4gZk2bZqRZN55552BWuaQEKu9+Pd//3czevRos2LFCtPc3GyamprMiy++OODrvZjFai/GjRtnbrvtNvPuu++aDz/80Hz/+983KSkp5tChQwO+5ovVQOzFL37xC/PYY4+Z559//qy/99x7770mKyvL1NXVmV27dpmCggJzww03RDX3QY+d66+/3lRUVDhfnzp1ymRmZprq6uo+x3/72982xcXFEefy8/PN9773PWOMMT09Pcbn85knnnjCud7R0WFcLpf5t3/7N2OMMXv27DGSzM6dO50xGzduNHFxcebgwYP9trahJhZ70ZelS5eanJycC1mKFWK5H6+99poZP368aWpqInZMbPaiu7vbfOELXzD//M//3N/LGdJisReHDx82ksy2bducMeFw2EgytbW1/ba2oaa/9+KP7d27t8/fezo6OkxiYqJZv369c+799983kkwgEDjnuQ/qj7G6urrU0NCgwsJC51x8fLwKCwsVCAT6vE0gEIgYL0lFRUXO+L179yoYDEaM8Xg8ys/Pd8YEAgGlpqZq8uTJzpjCwkLFx8ervr6+39Y3lMRqL/oSCoUG/S+Fu9jEcj/a2to0a9Ys/cu//ItSUlL6c1lDUqz24u2339bBgwcVHx+va6+9VmPGjNG0adP03nvv9fcSh4xY7UV6erq+8pWv6Be/+IWOHz+ukydP6qc//akyMjKUl5fX38scEgZiL85FQ0ODuru7I+5n/Pjxys7Ojup+BjV2fvvb3+rUqVNnfEqy1+tVMBjs8zbBYPBPju/95+eNycjIiLiekJCgtLS0sz6u7WK1F6f7+OOP9cwzz+h73/veea3DFrHaD2OM7rnnHt17770Rfxi4lMVqL37zm99Ikh599FEtWLBANTU1Gj16tG6++WYdOXLkwhc2BMVqL+Li4vT666/rnXfe0ahRozR8+HA9+eST2rRpk0aPHt0vaxtqBmIvzkUwGFRSUpJSU1Mv6H54NxZi5uDBg7r11lv1rW99S7NmzYr1dC5JzzzzjI4ePaqqqqpYT+WS19PTI0n6u7/7O5WUlCgvL0+rVq1SXFyc1q9fH+PZXVqMMaqoqFBGRobeeOMNvfXWW5o+fbpuv/12HTp0KNbTw3kY1Ni57LLLNGzYsDNeRd3W1iafz9fnbXw+358c3/vPzxvT3t4ecf3kyZM6cuTIWR/XdrHai16tra36xje+oRtuuEHPPffcBa3FBrHajy1btigQCMjlcikhIUFXXnmlJGny5MkqKyu78IUNQbHaizFjxkiScnNznesul0tf+tKX1NLScgErGrpi+euipqZG69at04033qjrrrtOy5cvV3JystasWdMvaxtqBmIvzoXP51NXV5c6Ojou6H4GNXaSkpKUl5enuro651xPT4/q6urk9/v7vI3f748YL0m1tbXO+JycHPl8vogx4XBY9fX1zhi/36+Ojg41NDQ4Y7Zs2aKenh7l5+f32/qGkljthfTpMzo333yz8yfX+HieYIzVfixbtkzvvvuuGhsb1djY6Lwt9MUXX9Tf//3f9+sah4pY7UVeXp5cLpeam5udMd3d3dq3b5/Gjh3bb+sbSmK1F7/73e8k6Yzfm+Lj451n4C41A7EX5yIvL0+JiYkR99Pc3KyWlpao7icmbz13uVxm9erVZs+ePWb27NkmNTXVBINBY4wxM2fONA8//LAz/s033zQJCQnmH/7hH8z7779vFi1a1OfbCFNTU80rr7xi/vd//9fccccdfb71/NprrzX19fXmf/7nf8y4ceN463kM9uLAgQPmyiuvNFOmTDEHDhwwhw4dco5LXax+bfyxs70j4lITq7344Q9/aL7whS+YzZs3mw8++MCUl5ebjIwMc+TIkcFb/EUmFntx+PBhk56ebmbMmGEaGxtNc3OzeeCBB0xiYqJpbGwc3P8AF5GB2ItPPvnEvPPOO2bDhg1Gklm3bp155513Ir4n3HvvvSY7O9ts2bLF7Nq1y/j9fuP3+6Oa+6DHjjHGPPPMMyY7O9skJSWZ66+/3uzYscO59vWvf92UlZVFjH/ppZfMl7/8ZZOUlGS++tWvmg0bNkRc7+npMY888ojxer3G5XKZKVOmmObm5ogxn3zyibn77rvNyJEjjdvtNt/97nfN0aNHB2yNQ8Vg78WqVauMpD4PxObXxh8jdv4gFnvR1dVl7r//fpORkWFGjRplCgsLzXvvvTdgaxwqYrEXO3fuNFOnTjVpaWlm1KhRpqCgwLz22msDtsahor/34mzfExYtWuSM+f3vf2++//3vm9GjR5uUlBTz13/911H/ATnOGGPO/XkgAACAoYUXSwAAAKsROwAAwGrEDgAAsBqxAwAArEbsAAAAqxE7AADAasQOAACwGrEDAACsRuwAAACrETsAAMBqxA4AALAasQMAAKz2/0IVk6fov0FcAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot importance in histogram, zoom in on the left tail\n", + "plt.hist(importances, bins = 1000)\n", + "plt.xlim(0, 0.01)\n", + "plt.show()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mdi", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/feature_importance/ranking_importance_local_sims.ipynb b/feature_importance/ranking_importance_local_sims.ipynb index 7913ef0..a1048dd 100644 --- a/feature_importance/ranking_importance_local_sims.ipynb +++ b/feature_importance/ranking_importance_local_sims.ipynb @@ -331,7 +331,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.0" + "version": "3.1.undefined" }, "orig_nbformat": 4 }, diff --git a/feature_importance/runtime.sh b/feature_importance/runtime.sh new file mode 100644 index 0000000..6e23fb6 --- /dev/null +++ b/feature_importance/runtime.sh @@ -0,0 +1,12 @@ +#!/bin/bash +#SBATCH --mail-user=zachrewolinski@berkeley.edu +#SBATCH --mail-type=ALL +#SBATCH --cpus-per-task=24 + + +source activate mdi +# command="runtime_test.py --n_samples 40000 --n_features 10000 --lfi_method linear_partial --rep 1" +command="runtime_test.py --n_samples ${1} --n_features ${2} --lfi_method ${3} --rep ${4}" + +# Execute the command +python $command \ No newline at end of file diff --git a/feature_importance/runtime_analysis.ipynb b/feature_importance/runtime_analysis.ipynb new file mode 100644 index 0000000..930665a --- /dev/null +++ b/feature_importance/runtime_analysis.ipynb @@ -0,0 +1,692 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "
" + ], + "text/plain": [ + " n_samples n_features check_data_time fit_rf_time fit_forest_time \\\n", + "0 1000 10 3.274944e-08 0.000112 0.003239 \n", + "1 1000 50 8.165836e-08 0.000351 0.003318 \n", + "2 1000 100 1.583497e-07 0.000663 0.003614 \n", + "3 1000 200 6.971425e-07 0.001287 0.004140 \n", + "4 1000 500 2.399981e-06 0.004005 0.005772 \n", + "5 10000 10 2.017617e-07 0.001196 0.003794 \n", + "6 10000 50 2.492534e-06 0.005879 0.004132 \n", + "7 10000 100 1.949999e-06 0.010032 0.004272 \n", + "8 10000 200 2.960066e-06 0.020960 0.004617 \n", + "9 10000 500 6.414718e-06 0.043432 0.005975 \n", + "10 10000 1000 1.552731e-05 0.142733 0.008915 \n", + "11 10000 2000 2.416829e-05 0.171418 0.010738 \n", + "12 50000 10 2.226366e-06 0.005441 0.005168 \n", + "13 50000 50 3.472037e-06 0.028065 0.006712 \n", + "14 50000 100 7.003877e-06 0.057445 0.008191 \n", + "15 50000 200 1.345075e-05 0.129197 0.010147 \n", + "16 50000 500 3.156460e-05 0.292437 0.018987 \n", + "17 50000 1000 6.334417e-05 0.529514 0.027970 \n", + "18 50000 2000 1.459683e-04 1.636215 0.057337 \n", + "\n", + " init_transformer_time get_transformed_data_time \\\n", + "0 4.284769e-07 0.000008 \n", + "1 4.073577e-07 0.000019 \n", + "2 3.601578e-07 0.000025 \n", + "3 3.399733e-07 0.000043 \n", + "4 3.950000e-07 0.000121 \n", + "5 4.754202e-07 0.000031 \n", + "6 5.299386e-07 0.000065 \n", + "7 5.242099e-07 0.000079 \n", + "8 5.289826e-07 0.000105 \n", + "9 4.795071e-07 0.000204 \n", + "10 5.673461e-07 0.000436 \n", + "11 4.667123e-07 0.000715 \n", + "12 6.758200e-07 0.000338 \n", + "13 6.770912e-07 0.000591 \n", + "14 7.404963e-07 0.000812 \n", + "15 7.357789e-07 0.001193 \n", + "16 8.764866e-07 0.002987 \n", + "17 7.781221e-07 0.005029 \n", + "18 8.055511e-07 0.011303 \n", + "\n", + " fit_prediction_model_time average_tree_time init_ppm_time \\\n", + "0 0.000004 0.000013 0.000006 \n", + "1 0.000008 0.000027 0.000005 \n", + "2 0.000006 0.000032 0.000007 \n", + "3 0.000005 0.000049 0.000006 \n", + "4 0.000006 0.000128 0.000005 \n", + "5 0.000015 0.000048 0.000007 \n", + "6 0.000054 0.000121 0.000007 \n", + "7 0.000062 0.000142 0.000007 \n", + "8 0.000057 0.000164 0.000006 \n", + "9 0.000050 0.000256 0.000005 \n", + "10 0.000054 0.000491 0.000009 \n", + "11 0.000043 0.000760 0.000009 \n", + "12 0.000101 0.000445 0.000007 \n", + "13 0.000217 0.000814 0.000008 \n", + "14 0.000327 0.001144 0.000007 \n", + "15 0.000364 0.001563 0.000008 \n", + "16 0.000488 0.003482 0.000007 \n", + "17 0.000439 0.005474 0.000009 \n", + "18 0.000438 0.011747 0.000010 \n", + "\n", + " get_leafs_in_test_samples_time partial_predictions_time \\\n", + "0 1.125866e-09 0.001869 \n", + "1 1.192093e-09 0.011720 \n", + "2 1.258320e-09 0.027868 \n", + "3 1.125866e-09 0.087310 \n", + "4 1.158979e-09 0.809124 \n", + "5 1.357661e-09 0.008754 \n", + "6 3.808075e-09 0.079885 \n", + "7 1.688798e-09 0.095768 \n", + "8 7.483694e-09 0.199680 \n", + "9 3.311369e-09 1.065729 \n", + "10 2.043115e-08 4.788533 \n", + "11 7.980400e-09 12.313502 \n", + "12 3.145801e-09 0.015718 \n", + "13 4.768372e-09 0.103509 \n", + "14 2.516641e-09 0.371323 \n", + "15 1.056327e-08 0.989804 \n", + "16 2.172258e-08 3.573767 \n", + "17 1.175536e-08 7.754951 \n", + "18 1.549688e-06 27.865931 \n", + "\n", + " mean_partial_pred_time_per_estimator \\\n", + "0 0.000016 \n", + "1 0.000111 \n", + "2 0.000267 \n", + "3 0.000846 \n", + "4 0.008013 \n", + "5 0.000075 \n", + "6 0.000731 \n", + "7 0.000882 \n", + "8 0.001765 \n", + "9 0.009956 \n", + "10 0.045933 \n", + "11 0.120610 \n", + "12 0.000101 \n", + "13 0.000825 \n", + "14 0.003198 \n", + "15 0.008594 \n", + "16 0.032371 \n", + "17 0.071461 \n", + "18 0.260831 \n", + "\n", + " mean_partial_pred_k_time_per_estimator leaf_average_time get_lfi_time \n", + "0 4.111603e-07 0.000068 0.001938 \n", + "1 9.948801e-07 0.000086 0.011806 \n", + "2 1.448899e-06 0.000115 0.027983 \n", + "3 2.323120e-06 0.000184 0.087494 \n", + "4 5.209711e-06 0.000480 0.809604 \n", + "5 4.300915e-06 0.000231 0.008986 \n", + "6 8.065332e-06 0.000584 0.080469 \n", + "7 8.562051e-06 0.000931 0.096699 \n", + "8 9.232622e-06 0.001977 0.201657 \n", + "9 1.185042e-05 0.005109 1.070838 \n", + "10 1.535963e-05 0.013853 4.802387 \n", + "11 1.563723e-05 0.019892 12.333395 \n", + "12 2.364585e-05 0.000886 0.016604 \n", + "13 2.921565e-05 0.003258 0.106768 \n", + "14 2.871819e-05 0.005283 0.376606 \n", + "15 3.201343e-05 0.012191 1.001995 \n", + "16 3.608210e-05 0.027138 3.600906 \n", + "17 3.999868e-05 0.080971 7.835925 \n", + "18 6.547023e-05 1.083715 28.949908 " + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "import glob\n", + "import os\n", + "import matplotlib.pyplot as plt\n", + "\n", + "file_pattern = os.path.join(\"results\", 'new_time_results*.csv')\n", + "file_list = glob.glob(file_pattern)\n", + "\n", + "dataframes = [pd.read_csv(file) for file in file_list]\n", + "combined_df = pd.concat(dataframes, ignore_index=True)\n", + "# remove the 'method' column\n", + "combined_df = combined_df.drop(columns=['method'])\n", + "# order combined ef by n_samples then n_features\n", + "combined_df = combined_df.sort_values(by=['n_samples', 'n_features'])\n", + "# average dataframe by n_samples and n_features\n", + "combined_df = combined_df.groupby(['n_samples', 'n_features']).mean().reset_index()\n", + "# convert units from seconds to hours in all columns that have the word 'time'\n", + "time_cols = [col for col in combined_df.columns if 'time' in col]\n", + "combined_df[time_cols] = combined_df[time_cols] / 3600\n", + "combined_df\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# for each n_samples, plot n_features vs total_time\n", + "# plot should be scatterplot connected by lines\n", + "for n_samples in combined_df['n_samples'].unique():\n", + " df = combined_df[combined_df['n_samples'] == n_samples]\n", + " plt.plot(df['n_features'], df['fit_forest_time'], linestyle = '-', marker = \"o\", color = 'blue')\n", + " plt.plot(df['n_features'], df['average_tree_time'], linestyle = '-', color = 'red',marker = \"o\")\n", + " plt.plot(df['n_features'], df['average_tree_time']*100, linestyle = '-', color = 'green',marker = \"o\")\n", + " plt.plot(df['n_features'], df['average_tree_time']*12.5, linestyle = '-', color = 'orange',marker = \"o\")\n", + " plt.xlabel('# of Features')\n", + " plt.ylabel('Time (Hours)')\n", + " plt.title(f'Fitting RF+ with {n_samples} Samples')\n", + " # create a legend that breaks it down by color\n", + " plt.legend(['Fit RF+ in Parallel', 'Fit Single Tree', 'Fit RF+ Sequentially', 'Perfect Parallelization'])\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# for each n_samples, plot n_features vs total_time\n", + "# plot should be scatterplot connected by lines\n", + "for n_samples in combined_df['n_samples'].unique():\n", + " df = combined_df[combined_df['n_samples'] == n_samples]\n", + " plt.plot(df['n_features'], df['partial_predictions_time'], linestyle = '-', marker = \"o\")\n", + " plt.xlabel('# of Features')\n", + " plt.ylabel('Time (Hours)')\n", + " plt.title(f'Obtaining Partial Predictions for {n_samples} Samples')\n", + " plt.show()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mdi", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/feature_importance/runtime_loop.sh b/feature_importance/runtime_loop.sh new file mode 100644 index 0000000..7ede132 --- /dev/null +++ b/feature_importance/runtime_loop.sh @@ -0,0 +1,29 @@ +#!/bin/bash + +# define bash file +slurm_script="runtime.sh" + +# define arrays for n and p +# n_values=(1000 10000 50000 100000 150000 174368) +n_values=(1000 10000 50000) +p_values=(10 50 100 200 500 1000 2000 5000 10000) +#lfi_methods=("linear_partial" "r2") +lfi_methods=("linear_partial") + +# iterate over two reps +for rep in {1..2}; do + # iterate over each lfi method + for lfi_method in "${lfi_methods[@]}"; do + # iterate over each value of n + for n in "${n_values[@]}"; do + # iterate over each value of p + for p in "${p_values[@]}"; do + # check if n is greater than p + if [ "$n" -gt "$p" ]; then + # run the bash file with the current combination of n and p + sbatch $slurm_script $n $p $lfi_method $rep + fi + done + done + done +done \ No newline at end of file diff --git a/feature_importance/runtime_test.py b/feature_importance/runtime_test.py new file mode 100644 index 0000000..bb8cfb1 --- /dev/null +++ b/feature_importance/runtime_test.py @@ -0,0 +1,100 @@ +import numpy as np +import pandas as pd +import time +from sklearn.ensemble import RandomForestRegressor +from sklearn.linear_model import SGDRegressor +from sklearn.model_selection import train_test_split +from imodels.tree.rf_plus.rf_plus.rf_plus_models import RandomForestPlusRegressor +from imodels.tree.rf_plus.feature_importance.rfplus_explainer import RFPlusMDI +import argparse +import os +from os.path import join as oj + +def run_experiment(n_samples, n_features, lfi_method): + + # X = np.load("X.npy") + # y = np.load("y.npy") + + # create dataframe to store time results + time_results = pd.DataFrame(columns=['n_samples', 'n_features', 'method', + 'check_data_time', 'fit_rf_time', 'fit_forest_time', + 'init_transformer_time', 'get_transformed_data_time', 'fit_prediction_model_time', + 'average_tree_time', 'init_ppm_time', 'get_leafs_in_test_samples_time', + 'partial_predictions_time', 'mean_partial_pred_time_per_estimator', + 'mean_partial_pred_k_time_per_estimator', 'leaf_average_time', + 'get_lfi_time']) + time_row = [] + + # put n_samples and n_features in time_results + time_row.append(n_samples) + time_row.append(n_features) + time_row.append(lfi_method) + + # generate normally distributed data with n_samples and n_features + X = np.random.normal(size=(n_samples, n_features)) + # make y the sum of half of the features plus noise + y = np.sum(X[:, :n_features//2], axis=1) + np.random.normal(size=n_samples) + + # initialize RF model + rf = RandomForestRegressor(n_estimators = 100, max_depth = 5, min_samples_leaf = 5, max_features = 0.33, random_state = 42) + + # fit RF+ model + rf_plus = RandomForestPlusRegressor(rf_model=rf, prediction_model=SGDRegressor(alpha = 0.001)) + rf_plus.fit(X, y) + + # get feature importance + rf_plus_explainer = RFPlusMDI(rf_plus) + if lfi_method == "linear_partial": + lmdi = rf_plus_explainer.explain_linear_partial(X, y, leaf_average = True, l2norm=True, njobs = -1) + else: + lmdi = rf_plus_explainer.explain_r2(X, y, l2norm=True) + + time_row.append(rf_plus.check_data_time) + time_row.append(rf_plus.fit_rf_time) + time_row.append(rf_plus.fit_forest_time) + time_row = time_row + list(rf_plus.fit_trees_time.mean()) + time_row.append(rf_plus_explainer.init_ppm_time) + time_row.append(rf_plus_explainer.get_leafs_in_test_samples_time) + time_row.append(rf_plus_explainer.partial_predictions_time) + lst = list() + lst2 = list() + for explainer in rf_plus_explainer.tree_explainers: + lst.append(explainer._total_partial_preds_time) + lst2.append(explainer._partial_preds_time) + time_row.append(np.mean(np.array(lst))) + time_row.append(np.mean(np.array(lst2))) + time_row.append(rf_plus_explainer.leaf_average_time) + time_row.append(rf_plus_explainer.get_lfi_time) + + # append time_row to time_results + time_results.loc[0] = time_row + + return time_results + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + default_dir = os.getenv("SCRATCH") + if default_dir is not None: + default_dir = oj(default_dir, "feature_importance", "results") + else: + default_dir = oj(os.path.dirname(os.path.realpath(__file__)), 'results') + parser.add_argument('--n_samples', type=int, default=None) + parser.add_argument('--n_features', type=int, default=None) + parser.add_argument('--lfi_method', type=str, default=None) + parser.add_argument('--rep', type=int, default=None) + args = parser.parse_args() + + # Convert Namespace to a dictionary + args_dict = vars(args) + + # Assign each key-value pair to a variable + n_samples = args_dict['n_samples'] + n_features = args_dict['n_features'] + lfi_method = args_dict['lfi_method'] + rep = args_dict['rep'] + print("Running time experiment getting LFI using", lfi_method, "with", n_samples, "rows and", n_features, "features for the", rep, "th time.") + time_results = run_experiment(n_samples, n_features, lfi_method) + time_results.to_csv(oj(default_dir, f'new_time_results_n{n_samples}_p{n_features}_method_{lfi_method}_rep{rep}.csv'), index=False) + print("Ran experiment successfully!") + + \ No newline at end of file diff --git a/feature_importance/subgroup/compas.ipynb b/feature_importance/subgroup/compas.ipynb deleted file mode 100644 index e39a446..0000000 --- a/feature_importance/subgroup/compas.ipynb +++ /dev/null @@ -1,708 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# import required packages\n", - "import shutup\n", - "# shutup.please() # deprecation warnings are driving me mad\n", - "from imodels import get_clean_dataset\n", - "import numpy as np\n", - "import pandas as pd\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import r2_score\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "from imodels.tree.rf_plus.rf_plus.rf_plus_models import RandomForestPlusClassifier\n", - "from imodels.tree.rf_plus.feature_importance.rfplus_explainer import AloRFPlusMDI\n", - "from subgroup_detection import detect_subgroups, compute_rbo_matrix\n", - "import matplotlib.pyplot as plt \n", - "import seaborn as sns" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 1, 1, ..., 0, 0, 0])" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# get pre-cleaned compas dataset from imodels\n", - "X, y, feature_names = get_clean_dataset('compas_two_year_clean', data_source='imodels')\n", - "X = pd.DataFrame(X, columns=feature_names)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# we add an id column to the dataset to track potential subgroups\n", - "X['id'] = np.arange(X.shape[0])\n", - "\n", - "# the propublica study narrowed the dataset to only African-American and\n", - "# Caucasian defendants, and doing so keeps the vast majority of the data,\n", - "# so we will do the same.\n", - "y = y[(X['race:African-American'] == 1) | (X['race:Caucasian'] == 1)]\n", - "X = X[(X['race:African-American'] == 1) | (X['race:Caucasian'] == 1)]\n", - "\n", - "# now that we have narrowed the dataset, we should remove the one-hot encodings\n", - "# of variables that are consistently zero, such as the other ethnicities.\n", - "# we also drop age because the binned 'age category' is preferred here.\n", - "X = X.drop([\"race:Asian\", \"race:Hispanic\", \"race:Native_American\",\n", - " \"race:Other\", \"age\"], axis = 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# split data into training and testing sets\n", - "# we won't actually use the test set here though, since 'discovery' would be\n", - "# a post-hoc analysis in real life\n", - "# proportion of training data is small so rf+ can fit without taking hours\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,\n", - " random_state=42)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total Proportion of Recidivism: 0.5015157256536567\n", - "Proportion of Recidivism in Training Data: 0.5064970221981592\n", - "Total Proportion of African-American Defendants: 0.6015536\n", - "Proportion of African-American Defendants in Training Data: 0.5979968\n", - "Total Proportion of Caucasian Defendants: 0.39844638\n", - "Proportion of Caucasian Defendants in Training Data: 0.40200326\n", - "Total Proportion of Male Defendants: 0.80466086\n", - "Proportion of Male Defendants in Training Data: 0.80915\n", - "Total Proportion of Female Defendants: 0.19533914\n", - "Proportion of Female Defendants in Training Data: 0.19085003\n" - ] - } - ], - "source": [ - "print(\"Total Proportion of Recidivism:\", y.mean())\n", - "print(\"Proportion of Recidivism in Training Data:\", y_train.mean())\n", - "print(\"Total Proportion of African-American Defendants:\",\n", - " X[\"race:African-American\"].mean())\n", - "print(\"Proportion of African-American Defendants in Training Data:\",\n", - " X_train[\"race:African-American\"].mean())\n", - "print(\"Total Proportion of Caucasian Defendants:\",\n", - " X[\"race:Caucasian\"].mean())\n", - "print(\"Proportion of Caucasian Defendants in Training Data:\",\n", - " X_train[\"race:Caucasian\"].mean())\n", - "print(\"Total Proportion of Male Defendants:\",\n", - " X[\"sex:Male\"].mean())\n", - "print(\"Proportion of Male Defendants in Training Data:\",\n", - " X_train[\"sex:Male\"].mean())\n", - "print(\"Total Proportion of Female Defendants:\",\n", - " X[\"sex:Female\"].mean())\n", - "print(\"Proportion of Female Defendants in Training Data:\",\n", - " X_train[\"sex:Female\"].mean())" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 7.8min\n", - "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 21.1min finished\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RF+ Test Set Accuracy: 0.6199494949494949\n" - ] - } - ], - "source": [ - "# fit RF+ model\n", - "rf = RandomForestClassifier(n_estimators=100, random_state=1)\n", - "rf_plus = RandomForestPlusClassifier(rf)\n", - "rf_plus.fit(X_train, y_train)\n", - "y_pred = rf_plus.predict(X_test)\n", - "\n", - "# compute accuracy on the test set\n", - "accuracy = np.mean(y_pred == y_test)\n", - "print(f'RF+ Test Set Accuracy: {accuracy}')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# get Local MDI+ feature importances\n", - "mdi_explainer = AloRFPlusMDI(rf_plus,evaluate_on='oob')\n", - "mdi, partial_preds = mdi_explainer.explain(np.asarray(X_train), y_train)\n", - "mdi_rankings = mdi_explainer.get_rankings(mdi)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# detect subgroups by calculating pairwise rbo matrix\n", - "# and feeding it to hierarchical clustering\n", - "rbo_matrix = compute_rbo_matrix(mdi_rankings, p = 0.9)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/IPython/core/pylabtools.py:77: DeprecationWarning: backend2gui is deprecated since IPython 8.24, backends are managed in matplotlib and can be externally registered.\n", - " warnings.warn(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/IPython/core/pylabtools.py:77: DeprecationWarning: backend2gui is deprecated since IPython 8.24, backends are managed in matplotlib and can be externally registered.\n", - " warnings.warn(\n", - "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/IPython/core/pylabtools.py:77: DeprecationWarning: backend2gui is deprecated since IPython 8.24, backends are managed in matplotlib and can be externally registered.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "clusters = detect_subgroups(rbo_matrix, linkage_method = 'ward')" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2 0.51164\n", - "1 0.48836\n", - "Name: proportion, dtype: float64\n" - ] - } - ], - "source": [ - "# add clusters as column to X_train\n", - "X_train['cluster'] = clusters\n", - "\n", - "# proportion of observations in each cluster\n", - "cluster_proportions = X_train['cluster'].value_counts(normalize=True)\n", - "# remove name from cluster_proportions\n", - "cluster_proportions.index.name = None\n", - "print(cluster_proportions)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Cluster Proportions for Women:\n", - "1 0.52766\n", - "2 0.47234\n", - "Name: proportion, dtype: float64\n", - "Cluster Proportions for Men:\n", - "2 0.52091\n", - "1 0.47909\n", - "Name: proportion, dtype: float64\n" - ] - } - ], - "source": [ - "# calculate proportions of each cluster by gender\n", - "prop_women = X_train[X_train['sex:Female'] == 1]['cluster'].value_counts(normalize=True)\n", - "print(\"Cluster Proportions for Women:\")\n", - "prop_women.index.name = None\n", - "print(prop_women)\n", - "\n", - "prop_men = X_train[X_train['sex:Male'] == 1]['cluster'].value_counts(normalize=True)\n", - "print(\"Cluster Proportions for Men:\")\n", - "prop_men.index.name = None\n", - "print(prop_men)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Cluster Proportions for African-Americans:\n", - "2 0.507922\n", - "1 0.492078\n", - "Name: proportion, dtype: float64\n", - "Cluster Proportions for Caucasians:\n", - "2 0.517172\n", - "1 0.482828\n", - "Name: proportion, dtype: float64\n" - ] - } - ], - "source": [ - "# calculate proportions of each cluster by race/ethnicity\n", - "prop_aa = X_train[X_train['race:African-American'] == 1]['cluster'].value_counts(normalize=True)\n", - "print(\"Cluster Proportions for African-Americans:\")\n", - "prop_aa.index.name = None\n", - "print(prop_aa)\n", - "\n", - "prop_cau = X_train[X_train['race:Caucasian'] == 1]['cluster'].value_counts(normalize=True)\n", - "print(\"Cluster Proportions for Caucasians:\")\n", - "prop_cau.index.name = None\n", - "print(prop_cau)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Cluster Proportions for Recidivists\n", - "2 0.587921\n", - "1 0.412079\n", - "Name: proportion, dtype: float64\n", - "Cluster Proportions for Non-Recidivists\n", - "1 0.566648\n", - "2 0.433352\n", - "Name: proportion, dtype: float64\n" - ] - } - ], - "source": [ - "# calculate proportions of each cluster by actual recidivism\n", - "prop_recid = X_train[y_train == 1]['cluster'].value_counts(normalize=True)\n", - "print(\"Cluster Proportions for Recidivists\")\n", - "prop_recid.index.name = None\n", - "print(prop_recid)\n", - "\n", - "prop_no_recid = X_train[y_train == 0]['cluster'].value_counts(normalize=True)\n", - "print(\"Cluster Proportions for Non-Recidivists\")\n", - "prop_no_recid.index.name = None\n", - "print(prop_no_recid)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "def permutation_test(df, variable, num_permutations=5000):\n", - " # calculate the observed difference in means\n", - " df = df.copy()\n", - " var_true= df[df[variable] == 1]\n", - " prop_cluster1 = var_true[var_true[\"cluster\"]==1].shape[0]/var_true.shape[0]\n", - " prop_cluster2 = var_true[var_true[\"cluster\"]==2].shape[0]/var_true.shape[0]\n", - " observed_diff = prop_cluster1 - prop_cluster2\n", - " \n", - " # get number of observations in cluster1\n", - " cluster1 = df[df[\"cluster\"]==1].shape[0]\n", - " \n", - " # perform permutations\n", - " perm_diffs = []\n", - " for _ in range(num_permutations):\n", - " dfarr = np.asarray(df)\n", - " np.random.shuffle(np.asarray(dfarr))\n", - " dfarr[:cluster1,-1] = 1\n", - " dfarr[cluster1:,-1] = 2\n", - " dfarr = pd.DataFrame(dfarr, columns=df.columns)\n", - " var_true= dfarr[dfarr[variable] == 1]\n", - " prop_cluster1 = var_true[var_true[\"cluster\"]==1].shape[0]/var_true.shape[0]\n", - " prop_cluster2 = var_true[var_true[\"cluster\"]==2].shape[0]/var_true.shape[0]\n", - " perm_diff = prop_cluster1 - prop_cluster2\n", - " perm_diffs.append(perm_diff)\n", - " \n", - " # calculate the p-value\n", - " perm_diffs = np.array(perm_diffs)\n", - " p_value = np.mean(np.abs(perm_diffs) >= np.abs(observed_diff))\n", - " \n", - " return observed_diff, p_value\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Permutation Results for race:African-American\n", - "Observed Difference in Clusters: -0.015844273426889988\n", - "Permutation-Test p-Value: 0.7282\n" - ] - } - ], - "source": [ - "observed_diff, p_value = permutation_test(X_train, \"race:African-American\")\n", - "print(\"Permutation Results for race:African-American\")\n", - "print(\"Observed Difference in Clusters:\", observed_diff)\n", - "print(\"Permutation-Test p-Value:\", p_value)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Permutation Results for race:Caucasian\n", - "Observed Difference in Clusters: -0.03434343434343434\n", - "Permutation-Test p-Value: 0.2958\n" - ] - } - ], - "source": [ - "observed_diff, p_value = permutation_test(X_train, \"race:Caucasian\")\n", - "print(\"Permutation Results for race:Caucasian\")\n", - "print(\"Observed Difference in Clusters:\", observed_diff)\n", - "print(\"Permutation-Test p-Value:\", p_value)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Permutation Results for sex:Female\n", - "Observed Difference in Clusters: 0.05531914893617018\n", - "Permutation-Test p-Value: 0.1894\n" - ] - } - ], - "source": [ - "observed_diff, p_value = permutation_test(X_train, \"sex:Female\")\n", - "print(\"Permutation Results for sex:Female\")\n", - "print(\"Observed Difference in Clusters:\", observed_diff)\n", - "print(\"Permutation-Test p-Value:\", p_value)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Permutation Results for sex:Male\n", - "Observed Difference in Clusters: -0.04182000669120112\n", - "Permutation-Test p-Value: 0.012\n" - ] - } - ], - "source": [ - "observed_diff, p_value = permutation_test(X_train, \"sex:Male\")\n", - "print(\"Permutation Results for sex:Male\")\n", - "print(\"Observed Difference in Clusters:\", observed_diff)\n", - "print(\"Permutation-Test p-Value:\", p_value)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Permutation Results for Felony Charge\n", - "Observed Difference in Clusters: -0.03700657894736842\n", - "Permutation-Test p-Value: 0.1398\n" - ] - } - ], - "source": [ - "observed_diff, p_value = permutation_test(X_train, \"c_charge_degree:F\")\n", - "print(\"Permutation Results for Felony Charge\")\n", - "print(\"Observed Difference in Clusters:\", observed_diff)\n", - "print(\"Permutation-Test p-Value:\", p_value)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Permutation Results for Misdemeanor Charge\n", - "Observed Difference in Clusters: 0.003169572107765417\n", - "Permutation-Test p-Value: 0.9496\n" - ] - } - ], - "source": [ - "observed_diff, p_value = permutation_test(X_train, \"c_charge_degree:M\")\n", - "print(\"Permutation Results for Misdemeanor Charge\")\n", - "print(\"Observed Difference in Clusters:\", observed_diff)\n", - "print(\"Permutation-Test p-Value:\", p_value)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "cluster\n", - "1 14.08592\n", - "2 15.72963\n", - "Name: c_jail_time, dtype: float32" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# get average jail time in each cluster\n", - "avg_jail_time = X_train.groupby('cluster')['c_jail_time'].mean()\n", - "avg_jail_time" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Permutation Results for Recidivism Outcome\n", - "Observed Difference in Clusters: -0.17584179583110632\n", - "Permutation-Test p-Value: 0.0\n" - ] - } - ], - "source": [ - "X_train_with_y = X_train.copy()\n", - "X_train_with_y[\"y\"] = y_train\n", - "observed_diff, p_value = permutation_test(X_train_with_y, \"y\")\n", - "print(\"Permutation Results for Recidivism Outcome\")\n", - "print(\"Observed Difference in Clusters:\", observed_diff)\n", - "print(\"Permutation-Test p-Value:\", p_value)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], - "source": [ - "# Sort the DataFrame based on cluster membership\n", - "kept_features = list(range(1,11)) + [11] + list(range(15, len(feature_names)))\n", - "kept_features = np.asarray(feature_names)[kept_features]\n", - "mdi_copy = pd.DataFrame(mdi, columns=kept_features).copy()\n", - "mdi\n", - "mdi_copy['cluster'] = clusters\n", - "df_sorted = mdi_copy.sort_values('cluster').reset_index(drop=True)\n", - "\n", - "# Drop the cluster column for the heatmap\n", - "data_sorted = df_sorted.drop('cluster', axis=1)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(10, 8))\n", - "sns.heatmap(data_sorted, cmap='viridis', cbar=True)\n", - "\n", - "# Add dotted horizontal lines to separate clusters\n", - "current_cluster = df_sorted['cluster'].iloc[0]\n", - "for idx in range(1, len(df_sorted)):\n", - " if df_sorted['cluster'].iloc[idx] != current_cluster:\n", - " plt.axhline(idx, color='red', linestyle='--')\n", - " current_cluster = df_sorted['cluster'].iloc[idx]\n", - "\n", - "plt.title('Heatmap with Cluster Separators')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Number of Priors in Cluster 1: 4.2267184\n", - "Average Number of Priors in Cluster 2: 2.7962964\n" - ] - } - ], - "source": [ - "# get average number of priors (priors_count) in cluster 1 and cluster 2\n", - "avg_priors_cluster1 = X_train[X_train['cluster'] == 1]['priors_count'].mean()\n", - "avg_priors_cluster2 = X_train[X_train['cluster'] == 2]['priors_count'].mean()\n", - "print(\"Average Number of Priors in Cluster 1:\", avg_priors_cluster1)\n", - "print(\"Average Number of Priors in Cluster 2:\", avg_priors_cluster2)" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Number of Priors for Recidivists: 4.91876\n", - "Average Number of Priors for Non-Recidivists: 2.0334613\n" - ] - } - ], - "source": [ - "# get average number of priors for those who recidivated and those who did not\n", - "avg_priors_recid = X_train[y_train == 1]['priors_count'].mean()\n", - "avg_priors_no_recid = X_train[y_train == 0]['priors_count'].mean()\n", - "print(\"Average Number of Priors for Recidivists:\", avg_priors_recid)\n", - "print(\"Average Number of Priors for Non-Recidivists:\", avg_priors_no_recid)" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Proportion of Cluster 1 that Recidivated: 0.42738359201773835\n", - "Proportion of Cluster 2 that Recidivated: 0.582010582010582\n" - ] - } - ], - "source": [ - "# get proportion of cluster 1 that recidivated and cluster 2 that recidivated\n", - "prop_recid_cluster1 = X_train[(X_train['cluster'] == 1) & (y_train == 1)].shape[0] / X_train[X_train['cluster'] == 1].shape[0]\n", - "prop_recid_cluster2 = X_train[(X_train['cluster'] == 2) & (y_train == 1)].shape[0] / X_train[X_train['cluster'] == 2].shape[0]\n", - "print(\"Proportion of Cluster 1 that Recidivated:\", prop_recid_cluster1)\n", - "print(\"Proportion of Cluster 2 that Recidivated:\", prop_recid_cluster2)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "mdi", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.14" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/feature_importance/subgroup/current/agglomerative_subgroups.py b/feature_importance/subgroup/current/agglomerative_subgroups.py new file mode 100644 index 0000000..3b64128 --- /dev/null +++ b/feature_importance/subgroup/current/agglomerative_subgroups.py @@ -0,0 +1,709 @@ +# import required packages +from imodels import get_clean_dataset +import numpy as np +import pandas as pd +from sklearn.model_selection import train_test_split +from sklearn.metrics import roc_auc_score, average_precision_score, f1_score, \ + accuracy_score, r2_score, f1_score, log_loss, root_mean_squared_error +from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier +from imodels.tree.rf_plus.rf_plus.rf_plus_models import RandomForestPlusRegressor, RandomForestPlusClassifier +from imodels.tree.rf_plus.feature_importance.rfplus_explainer import RFPlusMDI, AloRFPlusMDI +import shap +from subgroup_detection import * +import warnings +from sklearn.linear_model import RidgeCV, LogisticRegressionCV, LogisticRegression, LinearRegression +import argparse +import os +from os.path import join as oj +import scipy.cluster.hierarchy as sch +from scipy.cluster.hierarchy import fcluster, cut_tree +from scipy import cluster +from scipy.spatial.distance import squareform +import time +from joblib import Parallel, delayed +from ucimlrepo import fetch_ucirepo +from sklearn.preprocessing import OneHotEncoder, LabelEncoder +from sklearn.impute import SimpleImputer + +# global variable for classification/regression status +TASK = None + +def preprocessing_data_X(X): + categorical_cols = X.select_dtypes(include=["object", "category"]).columns + numerical_cols = X.select_dtypes(include=["number"]).columns + if X[numerical_cols].isnull().any().any(): + num_imputer = SimpleImputer(strategy="mean") + X[numerical_cols] = num_imputer.fit_transform(X[numerical_cols]) + if len(categorical_cols) > 0 and X[categorical_cols].isnull().any().any(): + # Convert categorical columns to string to ensure consistent types + X[categorical_cols] = X[categorical_cols].astype(str) + cat_imputer = SimpleImputer(strategy="most_frequent") + X[categorical_cols] = cat_imputer.fit_transform(X[categorical_cols]) + if len(categorical_cols) > 0: + encoder = OneHotEncoder(handle_unknown="ignore", sparse_output=False) + X_categorical = encoder.fit_transform(X[categorical_cols]) + X_categorical_df = pd.DataFrame( + X_categorical, + columns=encoder.get_feature_names_out(categorical_cols), + index=X.index + ) + X = pd.concat([X[numerical_cols], X_categorical_df], axis=1) + else: + X = X[numerical_cols] + X = X.to_numpy() + if X.shape[0]>2000: + X = X[:2000,:] + return X + +def preprocessing_data_y(y): + if y.to_numpy().shape[1] > 1: + y = y.iloc[:, 0].to_numpy().flatten() + else: + y = y.to_numpy().flatten() + if y.shape[0]>2000: + y = y[:2000] + + if np.all(np.vectorize(isinstance)(y, str)): + encoder = LabelEncoder() + y = encoder.fit_transform(y) + return y + +def get_parkinsons_dataset(): + # fetch dataset + parkinsons = fetch_ucirepo(id=189) + + # data (as pandas dataframes) + X = parkinsons.data.features + y = parkinsons.data.targets + cols = X.columns + + X = preprocessing_data_X(X) + y = preprocessing_data_y(y) + + return X, y, cols + +def get_performance_data(): + performance = fetch_ucirepo(id=320) + X = performance.data.features + y = performance.data.targets + cols = X.columns + + X = preprocessing_data_X(X) + y = preprocessing_data_y(y) + + return X, y, cols + +def get_temperature_data(): + temperature = fetch_ucirepo(id=925) + X = temperature.data.features + y = temperature.data.targets + cols = X.columns + + X = preprocessing_data_X(X) + y = preprocessing_data_y(y) + + return X, y, cols + +def get_ccle_data(): + + X = pd.read_csv('X_ccle_rnaseq_PD-0325901_top500.csv') + y = pd.read_csv('y_ccle_rnaseq_PD-0325901.csv') + cols = X.columns + X = X.to_numpy() + y = y.to_numpy().flatten() + return X, y, cols + +def get_adult_dataset(num_samples): + + # fetch dataset + adult = fetch_ucirepo(id=2) + + # data (as pandas dataframes) + X = adult.data.features + y = adult.data.targets + + X = X.dropna() + + # drop the same ones in y, which is a dataframe + y = y.loc[X.index] + + # one hot encode adult dataset + X_encoded = pd.get_dummies(X, drop_first=True) + + # convert y to 1 (>50K) and 0 (<=50K) + y = y.replace({'<=50K' : 0, '<=50K.' : 0, '>50K' : 1, ">50K." : 1}) + y = y['income'] + + # replace trues and falses in X_encoded with 1s and 0s + X_encoded = X_encoded.replace({True : 1, False : 0}) + + # return the first num_samples samples, make all return values numpy arrays + return X_encoded.iloc[:num_samples].values, y.iloc[:num_samples].values, X_encoded.columns + +def split_data(X, y, seed): + # split data into train and test sets + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, + random_state=seed) + return X_train, X_test, y_train, y_test + +def fit_models(X_train, y_train): + # fit models + if TASK == "classification": + rf = RandomForestClassifier(n_estimators=100, min_samples_leaf=5, + random_state=42) + rf.fit(X_train, y_train) + # rf_plus = RandomForestPlusClassifier(rf_model=rf, + # prediction_model=LogisticRegressionCV()) + rf_plus = RandomForestPlusClassifier(rf_model=rf) + rf_plus.fit(X_train, y_train) + elif TASK == "regression": + rf = RandomForestRegressor(n_estimators=100, min_samples_leaf=5, + random_state=42) + rf.fit(X_train, y_train) + # rf_plus = RandomForestPlusRegressor(rf_model=rf, + # prediction_model=RidgeCV()) + rf_plus = RandomForestPlusRegressor(rf_model=rf) + rf_plus.fit(X_train, y_train) + else: + raise ValueError("Task must be either 'classification' or 'regression'.") + return rf, rf_plus + +def get_shap(X, shap_explainer): + if TASK == "classification": + # the shap values are an array of shape + # (# of samples, # of features, # of classes), and in this binary + # classification case, we want the shap values for the positive class. + # check_additivity=False is used to speed up computation. + shap_values = \ + shap_explainer.shap_values(X, check_additivity=False)[:, :, 1] + else: + # check_additivity=False is used to speed up computation. + shap_values = shap_explainer.shap_values(X, check_additivity=False) + # get the rankings of the shap values. negative absolute value is taken + # because np.argsort sorts from smallest to largest. + shap_rankings = np.argsort(-np.abs(shap_values), axis = 1) + return shap_values, shap_rankings + +def get_lmdi(X, y, lmdi_explainer, l2norm, sign, normalize, leaf_average, ranking=False): + # get feature importances + lmdi = lmdi_explainer.explain_linear_partial(X, y, l2norm=l2norm, sign=sign, + normalize=normalize, + leaf_average=leaf_average, + ranking=ranking) + mdi_rankings = lmdi_explainer.get_rankings(np.abs(lmdi)) + return lmdi, mdi_rankings + +if __name__ == '__main__': + + # start time + start = time.time() + + # store command-line arguments + parser = argparse.ArgumentParser() + parser.add_argument('--seed', type=int, default=None) + parser.add_argument('--datasource', type=str, default=None) + parser.add_argument('--dataname', type=str, default=None) + parser.add_argument('--use_test', type=int, default=0) + parser.add_argument('--njobs', type=int, default=1) + args = parser.parse_args() + + # convert namespace to a dictionary + args_dict = vars(args) + + # assign the arguments to variables + seed = args_dict['seed'] + datasource = args_dict['datasource'] + dataname = args_dict['dataname'] + use_test = bool(args_dict['use_test']) # convert from 0/1 to boolean + njobs = args_dict['njobs'] + + # if the datasource is openml, we need to make the dataname an integer + if datasource == "openml": + dataname = int(dataname) + + # if the datasource is a file, we need to read the file rather than call + # get_clean_dataset + if datasource == "function": + if dataname == "adult": + X, y, feature_names = get_adult_dataset(5000) + elif dataname == "parkinsons": + X, y, feature_names = get_parkinsons_dataset() + elif dataname == "performance": + X, y, feature_names = get_performance_data() + elif dataname == "temperature": + X, y, feature_names = get_temperature_data() + elif dataname == "ccle": + X, y, feature_names = get_ccle_data() + else: + raise ValueError("Unknown function dataset.") + else: + # obtain data + X, y, feature_names = get_clean_dataset(dataname, data_source = datasource) + # if y is not a float (abalone), convert it + if y.dtype != np.float64: + y = y.astype(np.float64) + + # end time + end = time.time() + + # print progress message + print(f"Progress Message 1/15: Obtained {dataname} from {datasource}.") + print(f"Step #1 took {end-start} seconds.") + + # start time + start = time.time() + + # check if task is regression or classification + if len(np.unique(y)) == 2: + TASK = 'classification' + else: + TASK = 'regression' + # convert y to float, if it is not already (ints will cause errors) + y = y.astype(float) + + # end time + end = time.time() + + print(f"Progress Message 2/15: Task is identified as {TASK}.") + print(f"Step #2 took {end-start} seconds.") + + # start time + start = time.time() + + # split data + X_train, X_test, y_train, y_test = split_data(X, y, seed) + + # end time + end = time.time() + + print(f"Training Data Shape: {X_train.shape}") + print(f"Testing Data Shape: {X_test.shape}") + + print(f"Progress Message 3/15: Data split with seed {seed}.") + print(f"Step #3 took {end-start} seconds.") + + # start time + start = time.time() + + # fit the prediction models + rf, rf_plus = fit_models(X_train, y_train) + + # fit baseline model + if TASK == "classification": + rf_plus_baseline = RandomForestPlusClassifier(rf_model=rf, include_raw=False, fit_on="inbag", prediction_model=LogisticRegression()) + elif TASK == "regression": + rf_plus_baseline = RandomForestPlusRegressor(rf_model=rf, include_raw=False, fit_on="inbag", prediction_model=LinearRegression()) + rf_plus_baseline.fit(X_train, y_train) + + # end time + end = time.time() + + print(f"Progress Message 4/15: RF and RF+ models fit.") + print(f"Step #4 took {end-start} seconds.") + + # start time + start = time.time() + + # obtain shap feature importances + shap_explainer = shap.TreeExplainer(rf) + if use_test: + shap_values, shap_rankings = get_shap(X_test, shap_explainer) + else: + shap_values, shap_rankings = get_shap(X_train, shap_explainer) + + # end time + end = time.time() + + print(f"Progress Message 5/15: SHAP values/rankings obtained.") + print(f"Step #5 took {end-start} seconds.") + + # start time + start = time.time() + + # obtain lmdi feature importances + lmdi_explainer_signed_normalized_l2_avg = AloRFPlusMDI(rf_plus, mode = "only_k") + lmdi_explainer_signed_normalized_l2_noavg = AloRFPlusMDI(rf_plus, mode = "only_k") + lmdi_explainer_signed_nonnormalized_l2_avg = AloRFPlusMDI(rf_plus, mode = "only_k") + lmdi_explainer_signed_nonnormalized_l2_noavg = AloRFPlusMDI(rf_plus, mode = "only_k") + lmdi_explainer_nonl2_avg = AloRFPlusMDI(rf_plus, mode = "only_k") + lmdi_explainer_nonl2_noavg = AloRFPlusMDI(rf_plus, mode = "only_k") + lmdi_explainer_l2_ranking = AloRFPlusMDI(rf_plus, mode = "only_k") + lmdi_explainer_nonl2_ranking = AloRFPlusMDI(rf_plus, mode = "only_k") + lmdi_explainer_normalized_l2_ranking = AloRFPlusMDI(rf_plus, mode = "only_k") + lmdi_explainer_nonnormalized_l2_ranking = AloRFPlusMDI(rf_plus, mode = "only_k") + lmdi_baseline_explainer = RFPlusMDI(rf_plus_baseline, mode = "only_k", evaluate_on = "inbag") + lmdi_values_signed_normalized_l2_avg, \ + lmdi_rankings_signed_normalized_l2_avg = \ + get_lmdi(X_train, y_train, lmdi_explainer_signed_normalized_l2_avg, + l2norm=True, sign=True, normalize=True, leaf_average=True) + lmdi_values_signed_normalized_l2_noavg, \ + lmdi_rankings_signed_normalized_l2_noavg = \ + get_lmdi(X_train, y_train,lmdi_explainer_signed_normalized_l2_noavg, + l2norm=True, sign=True, normalize=True, leaf_average=False) + lmdi_values_signed_nonnormalized_l2_avg, \ + lmdi_rankings_signed_nonnormalized_l2_avg = \ + get_lmdi(X_train,y_train,lmdi_explainer_signed_nonnormalized_l2_avg, + l2norm=True, sign=True, normalize=False, leaf_average=True) + lmdi_values_signed_nonnormalized_l2_noavg, \ + lmdi_rankings_signed_nonnormalized_l2_noavg = \ + get_lmdi(X_train, y_train, + lmdi_explainer_signed_nonnormalized_l2_noavg, l2norm=True, + sign=True, normalize=False, leaf_average=False) + lmdi_values_nonl2_avg, lmdi_rankings_nonl2_avg = \ + get_lmdi(X_train, y_train, lmdi_explainer_nonl2_avg, l2norm=False, + sign=False, normalize=False, leaf_average=True) + lmdi_values_nonl2_noavg, lmdi_rankings_nonl2_noavg = \ + get_lmdi(X_train, y_train, lmdi_explainer_nonl2_noavg, l2norm=False, + sign=False, normalize=False, leaf_average=False) + lmdi_values_l2_ranking, lmdi_rankings_l2_ranking = \ + get_lmdi(X_train, y_train, lmdi_explainer_l2_ranking, l2norm=True, + sign=False, normalize=False, leaf_average=False, ranking=True) + lmdi_values_nonl2_ranking, lmdi_rankings_nonl2_ranking = \ + get_lmdi(X_train, y_train, lmdi_explainer_nonl2_ranking, l2norm=False, + sign=False, normalize=False, leaf_average=False, ranking=True) + lmdi_values_normalized_l2_ranking, lmdi_rankings_normalized_l2_ranking = \ + get_lmdi(X_train, y_train, lmdi_explainer_normalized_l2_ranking, l2norm=True, + sign=False, normalize=True, leaf_average=False, ranking=True) + lmdi_values_baseline, lmdi_rankings_baseline = \ + get_lmdi(X_train, y_train, lmdi_baseline_explainer, l2norm=False, + sign=False, normalize=False, leaf_average=False) + if use_test: + lmdi_values_signed_normalized_l2_avg, \ + lmdi_rankings_signed_normalized_l2_avg = \ + get_lmdi(X_test, None, lmdi_explainer_signed_normalized_l2_avg, l2norm=True, sign=True, + normalize=True, leaf_average=True) + lmdi_values_signed_normalized_l2_noavg, \ + lmdi_rankings_signed_normalized_l2_noavg = \ + get_lmdi(X_test, None, lmdi_explainer_signed_normalized_l2_noavg, l2norm=True, sign=True, + normalize=True, leaf_average=False) + lmdi_values_signed_nonnormalized_l2_avg, \ + lmdi_rankings_signed_nonnormalized_l2_avg = \ + get_lmdi(X_test, None, lmdi_explainer_signed_nonnormalized_l2_avg, l2norm=True, sign=True, + normalize=False, leaf_average=True) + lmdi_values_signed_nonnormalized_l2_noavg, \ + lmdi_rankings_signed_nonnormalized_l2_noavg = \ + get_lmdi(X_test, None, lmdi_explainer_signed_nonnormalized_l2_noavg, l2norm=True, sign=True, + normalize=False, leaf_average=False) + lmdi_values_nonl2_avg, lmdi_rankings_nonl2_avg = \ + get_lmdi(X_test, None, lmdi_explainer_nonl2_avg, l2norm=False, sign=False, + normalize=False, leaf_average=True) + lmdi_values_nonl2_noavg, lmdi_rankings_nonl2_noavg = \ + get_lmdi(X_test, None, lmdi_explainer_nonl2_noavg, l2norm=False, sign=False, + normalize=False, leaf_average=False) + lmdi_values_l2_ranking, lmdi_rankings_l2_ranking = \ + get_lmdi(X_test, None, lmdi_explainer_l2_ranking, l2norm=True, + sign=False, normalize=False, leaf_average=False, ranking=True) + lmdi_values_nonl2_ranking, lmdi_rankings_nonl2_ranking = \ + get_lmdi(X_test, None, lmdi_explainer_nonl2_ranking, l2norm=False, + sign=False, normalize=False, leaf_average=False, ranking=True) + lmdi_values_normalized_l2_ranking, lmdi_rankings_normalized_l2_ranking = \ + get_lmdi(X_test, None, lmdi_explainer_l2_ranking, l2norm=True, + sign=False, normalize=True, leaf_average=False, ranking=True) + lmdi_values_baseline, lmdi_rankings_baseline = \ + get_lmdi(X_test, None, lmdi_baseline_explainer, l2norm=True, sign=False, + normalize=False, leaf_average=False) + + # end time + end = time.time() + + print(f"Progress Message 6/15: LMDI+ values/rankings obtained.") + print(f"Step #6 took {end-start} seconds.") + + # start time + start = time.time() + + # create storage for iteration purposes + lfi_values = \ + {'shap': shap_values, + 'signed_normalized_l2_avg': lmdi_values_signed_normalized_l2_avg, + 'signed_normalized_l2_noavg': lmdi_values_signed_normalized_l2_noavg, + 'signed_nonnormalized_l2_avg': lmdi_values_signed_nonnormalized_l2_avg, + 'signed_nonnormalized_l2_noavg': + lmdi_values_signed_nonnormalized_l2_noavg, + 'nonl2_avg': lmdi_values_nonl2_avg, + 'nonl2_noavg': lmdi_values_nonl2_noavg, + 'l2_ranking': lmdi_values_l2_ranking, + 'nonl2_ranking': lmdi_values_nonl2_ranking, + 'normalized_l2_ranking': lmdi_values_normalized_l2_ranking, + # 'normalized_nonl2_ranking': lmdi_values_normalized_nonl2_ranking, + 'baseline': lmdi_values_baseline} + lfi_rankings = \ + {'shap': shap_rankings, + 'signed_normalized_l2_avg': lmdi_rankings_signed_normalized_l2_avg, + 'signed_normalized_l2_noavg': lmdi_rankings_signed_normalized_l2_noavg, + 'signed_nonnormalized_l2_avg': lmdi_rankings_signed_nonnormalized_l2_avg, + 'signed_nonnormalized_l2_noavg': + lmdi_rankings_signed_nonnormalized_l2_noavg, + 'nonl2_avg': lmdi_rankings_nonl2_avg, + 'nonl2_noavg': lmdi_rankings_nonl2_noavg, + 'l2_ranking': lmdi_rankings_l2_ranking, + 'nonl2_ranking': lmdi_rankings_nonl2_ranking, + # 'normalized_l2_ranking': lmdi_values_normalized_l2_ranking, + # 'normalized_nonl2_ranking': lmdi_values_normalized_nonl2_ranking, + 'baseline': lmdi_rankings_baseline} + + # get rbo matrices for rankings + # rbo_matrices = {} + # for method, ranking in lfi_rankings.items(): + # for p in [0.1, 0.3, 0.5, 0.7, 0.9]: + # rbo_matrices[method + "_" + str(p)] = \ + # compute_rbo_matrix(ranking, 'distance', p=p) + + # def compute_rbo_for_method_and_p(method, ranking, p): + # """ + # Helper function to compute the RBO matrix for a given method and p value. + # """ + # # print("method:") + # # print(method) + # return (method + "_" + str(p), compute_rbo_matrix(ranking, 'distance', p=p)) + + # # parallelize the computation of RBO matrices + # rbo_matrices = dict(Parallel(n_jobs=njobs)( + # delayed(compute_rbo_for_method_and_p)(method, ranking, p) + # for method, ranking in lfi_rankings.items() + # for p in [0.1, 0.3, 0.5, 0.7, 0.9] + # )) + + # end time + end = time.time() + + print(f"Progress Message 7/15: RBO matrices computed.") + print(f"Step #7 took {end-start} seconds.") + + # start time + start = time.time() + + # get linkages for values + values_linkage = {} + for method, values in lfi_values.items(): + # values_linkage[method] = sch.linkage(values, method="ward") + values_linkage[method] = cluster.hierarchy.ward(values) + + # end time + end = time.time() + + print(f"Progress Message 8/15: Linkages for values computed.") + print(f"Step #8 took {end-start} seconds.") + + # start time + start = time.time() + + # # get linkages for rankings + # rankings_linkage = {} + # for method, rbo_mat in rbo_matrices.items(): + # # rankings_linkage[method] = sch.linkage(squareform(rbo_mat), + # # method="ward") + # rankings_linkage[method] = cluster.hierarchy.ward(squareform(rbo_mat)) + + # end time + end = time.time() + + print(f"Progress Message 9/15: Linkages for rankings computed.") + print(f"Step #9 took {end-start} seconds.") + + # start time + start = time.time() + + # get clusters for values + value_clusters = {} + for method, link in values_linkage.items(): + # maximum number of clusters is the number of unique feature importances + max_num_clusters = np.unique(lfi_values[method], axis = 0).shape[0] + print(f"The Number of Unique Values (Maximum # of Clusters) for {method} is {max_num_clusters}.") + num_cluster_map = {} + for num_clusters in np.arange(1, max_num_clusters + 1): + # num_cluster_map[num_clusters] = fcluster(link, num_clusters, + # criterion = "maxclust") + num_cluster_map[num_clusters] = cut_tree(link, n_clusters=num_clusters).flatten() + value_clusters[method] = num_cluster_map + + # end time + end = time.time() + + print(f"Progress Message 10/15: Clusters for values computed.") + print(f"Step #10 took {end-start} seconds.") + + # start time + start = time.time() + + # # get clusters for rankings + # ranking_clusters = {} + # for method, link in rankings_linkage.items(): + # # maximum number of clusters is the number of unique rankings + # max_num_clusters = np.unique(rbo_matrices[method], axis = 0).shape[0] + # print(f"The Number of Unique Rankings (Maximum # of Clusters) for {method} is {max_num_clusters}.") + # num_cluster_map = {} + # for num_clusters in np.arange(1, max_num_clusters + 1): + # # num_cluster_map[num_clusters] = fcluster(link, num_clusters, + # # criterion = "maxclust") + # num_cluster_map[num_clusters] = cut_tree(link, n_clusters=num_clusters).flatten() + # ranking_clusters[method] = num_cluster_map + + # end time + end = time.time() + + print(f"Progress Message 11/15: Clusters for rankings computed.") + print(f"Step #11 took {end-start} seconds.") + + # start time + start = time.time() + + # get predictions and performance metrics for each methods clusters + if TASK == "classification": + metrics = {"AUROC": roc_auc_score, "AUPRC": average_precision_score, + "F1": f1_score, "Accuracy": accuracy_score, + "R^2": r2_score, "Cross-Entropy": log_loss} + elif TASK == "regression": + metrics = {"R^2": r2_score, "RMSE": root_mean_squared_error} + + # maps method to future dataframe (dict for now) where the columns of the + # dataframe are the metrics and the rows are the number of clusters, and + # the values are the performance of the method on the metric for the number + # of clusters. + # method_values_results = {} + # for method, cluster_map in value_clusters.items(): + # metric_results = {} + # max_num_clusters = np.max(list(cluster_map.keys())) + # metric_results["nclust"] = np.arange(1, max_num_clusters + 1) + # for metric, metric_func in metrics.items(): + # cluster_results = np.repeat(np.nan, max_num_clusters) + # for num_clusters, clusters in cluster_map.items(): + # cluster_predictions = np.repeat(np.nan, len(clusters)) + # cluster_truths = np.repeat(np.nan, len(clusters)) + # for i in range(num_clusters): + # cluster_indices = np.where(clusters == i + 1)[0] + # if y_train[cluster_indices].shape[0] == 0: + # continue + # cluster_predictions[cluster_indices] = \ + # np.mean(y_train[cluster_indices]) + # if metric in ["Accuracy", "F1"]: + # cluster_predictions[cluster_indices] = \ + # cluster_predictions[cluster_indices] > 0.5 + # cluster_truths[cluster_indices] = y_train[cluster_indices] + # cluster_results[num_clusters-1] = metric_func(cluster_truths, + # cluster_predictions) + # metric_results[metric] = cluster_results + # method_values_results[method] = metric_results + + def evaluate_method(method, cluster_map, metrics, y_data): + metric_results = {} + max_num_clusters = np.max(list(cluster_map.keys())) + metric_results["nclust"] = np.arange(1, max_num_clusters + 1) + for metric, metric_func in metrics.items(): + cluster_results = np.repeat(np.nan, max_num_clusters) + for num_clusters, clusters in cluster_map.items(): + cluster_predictions = np.repeat(np.nan, len(clusters)) + cluster_truths = np.repeat(np.nan, len(clusters)) + for i in range(num_clusters): + cluster_indices = np.where(clusters == i)[0] + if y_data[cluster_indices].shape[0] == 0: + print("ERROR: Empty cluster!") + continue + cluster_predictions[cluster_indices] = \ + np.mean(y_data[cluster_indices]) + if metric in ["Accuracy", "F1"]: + cluster_predictions[cluster_indices] = \ + cluster_predictions[cluster_indices] > 0.5 + cluster_truths[cluster_indices] = y_data[cluster_indices] + cluster_results[num_clusters-1] = metric_func(cluster_truths, + cluster_predictions) + metric_results[metric] = cluster_results + return method, metric_results + + if use_test: + method_values_results = dict(Parallel(n_jobs=njobs)( + delayed(evaluate_method)(method, cluster_map, metrics, y_test) + for method, cluster_map in value_clusters.items())) + else: + method_values_results = dict(Parallel(n_jobs=njobs)( + delayed(evaluate_method)(method, cluster_map, metrics, y_train) + for method, cluster_map in value_clusters.items())) + + # end time + end = time.time() + + print(f"Progress Message 12/15: Performance metrics computed for values.") + print(f"Step #12 took {end-start} seconds.") + + # start time + start = time.time() + + # maps method to future dataframe (dict for now) where the columns of the + # dataframe are the metrics and the rows are the number of clusters, and + # the values are the performance of the method on the metric for the number + # of clusters. + # method_rankings_results = {} + # for method, cluster_map in ranking_clusters.items(): + # metric_results = {} + # max_num_clusters = np.max(list(cluster_map.keys())) + # metric_results["nclust"] = np.arange(1, max_num_clusters + 1) + # for metric, metric_func in metrics.items(): + # cluster_results = np.repeat(np.nan, max_num_clusters) + # for num_clusters, clusters in cluster_map.items(): + # cluster_predictions = np.repeat(np.nan, len(clusters)) + # cluster_truths = np.repeat(np.nan, len(clusters)) + # for i in range(num_clusters): + # cluster_indices = np.where(clusters == i + 1)[0] + # if y_train[cluster_indices].shape[0] == 0: + # continue + # cluster_predictions[cluster_indices] = \ + # np.mean(y_train[cluster_indices]) + # if metric in ["Accuracy", "F1"]: + # cluster_predictions[cluster_indices] = \ + # cluster_predictions[cluster_indices] > 0.5 + # cluster_truths[cluster_indices] = y_train[cluster_indices] + # cluster_results[num_clusters-1] = metric_func(cluster_truths, + # cluster_predictions) + # metric_results[metric] = cluster_results + # method_rankings_results[method] = metric_results + + # if use_test: + # method_rankings_results = dict(Parallel(n_jobs=njobs)( + # delayed(evaluate_method)(method, cluster_map, metrics, y_test) + # for method, cluster_map in ranking_clusters.items())) + # else: + # method_rankings_results = dict(Parallel(n_jobs=njobs)( + # delayed(evaluate_method)(method, cluster_map, metrics, y_train) + # for method, cluster_map in ranking_clusters.items())) + + # end time + end = time.time() + + print(f"Progress Message 13/15: Performance metrics computed for rankings.") + print(f"Step #13 took {end-start} seconds.") + + # start time + start = time.time() + + if use_test: + result_dir = oj(os.path.dirname(os.path.realpath(__file__)), 'results/test_data') + else: + result_dir = oj(os.path.dirname(os.path.realpath(__file__)), 'results/train_data') + + # get result dataframes + for method, metric_results in method_values_results.items(): + df = pd.DataFrame(metric_results) + df.to_csv(oj(result_dir, + f'{datasource}_{dataname}_seed{seed}_{method}_values.csv'), + index=False) + + # end time + end = time.time() + + print(f"Progress Message 14/15: Value results saved to {result_dir}.") + print(f"Step #14 took {end-start} seconds.") + + # start time + start = time.time() + + # for method, metric_results in method_rankings_results.items(): + # df = pd.DataFrame(metric_results) + # df.to_csv(oj(result_dir, + # f'{datasource}_{dataname}_seed{seed}_{method}_ranking.csv'), + # index=False) + + # end time + end = time.time() + + print(f"Progress Message 15/15: Ranking results saved to {result_dir}.") + print(f"Step #15 took {end-start} seconds.") \ No newline at end of file diff --git a/feature_importance/subgroup/current/sandbox.ipynb b/feature_importance/subgroup/current/sandbox.ipynb new file mode 100644 index 0000000..b38c56c --- /dev/null +++ b/feature_importance/subgroup/current/sandbox.ipynb @@ -0,0 +1,17385 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "import numpy as np\n", + "import pandas as pd\n", + "from subgroup_detection import *\n", + "from subgroup_experiment import *\n", + "from agglomerative_subgroups import get_parkinsons_dataset, get_ccle_data, \\\n", + " get_performance_data, get_temperature_data\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LogisticRegression, LinearRegression\n", + "from imodels.tree.rf_plus.rf_plus.rf_plus_models import RandomForestPlusRegressor, RandomForestPlusClassifier\n", + "from imodels.tree.rf_plus.feature_importance.rfplus_explainer import RFPlusMDI, AloRFPlusMDI\n", + "from scipy import cluster\n", + "from scipy.cluster.hierarchy import fcluster, cut_tree\n", + "from sklearn.metrics import roc_auc_score, average_precision_score, f1_score, \\\n", + " accuracy_score, r2_score, f1_score, log_loss, root_mean_squared_error\n", + "from imodels import get_clean_dataset\n", + "import openml\n", + "from ucimlrepo import fetch_ucirepo" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "ids = [361247, 361243, 361242, 361251, 361253, 361260, 361259, 361256, 361254, 361622]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3577: FutureWarning: Starting from Version 0.15.0 `download_splits` will default to ``False`` instead of ``True`` and be independent from `download_data`. To disable this message until version 0.15 explicitly set `download_splits` to a bool.\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/openml/tasks/functions.py:442: FutureWarning: Starting from Version 0.15 `download_data`, `download_qualities`, and `download_features_meta_data` will all be ``False`` instead of ``True`` by default to enable lazy loading. To disable this message until version 0.15 explicitly set `download_data`, `download_qualities`, and `download_features_meta_data` to a bool while calling `get_dataset`.\n", + " dataset = get_dataset(task.dataset_id, *dataset_args, **get_dataset_kwargs)\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/openml/tasks/task.py:150: FutureWarning: Starting from Version 0.15 `download_data`, `download_qualities`, and `download_features_meta_data` will all be ``False`` instead of ``True`` by default to enable lazy loading. To disable this message until version 0.15 explicitly set `download_data`, `download_qualities`, and `download_features_meta_data` to a bool while calling `get_dataset`.\n", + " return datasets.get_dataset(self.dataset_id)\n", + "/tmp/ipykernel_1562095/4221774038.py:3: FutureWarning: Support for `dataset_format='array'` will be removed in 0.15,start using `dataset_format='dataframe' to ensure your code will continue to work. You can use the dataframe's `to_numpy` function to continue using numpy arrays.\n", + " X, y, _, cols = dataset.get_data(target=dataset.default_target_attribute,dataset_format=\"array\")\n" + ] + } + ], + "source": [ + "task = openml.tasks.get_task(ids[0])\n", + "dataset = task.get_dataset()\n", + "X, y, _, cols = dataset.get_data(target=dataset.default_target_attribute,dataset_format=\"array\")\n", + "# set X to first 2000 rows of X\n", + "X = X[:2000]\n", + "y = y[:2000]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# get the data\n", + "# X, y, cols = get_clean_dataset(dataset_name = ids[1], data_source='openml')\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,\n", + " random_state=42)\n", + "# discern if the task is classification or regression\n", + "if len(np.unique(y)) == 2:\n", + " task = 'classification'\n", + "else:\n", + " task = 'regression'\n", + " # convert y to float, if it is not already (ints will cause errors)\n", + " y = y.astype(float)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def fit_models(X_train, y_train, task):\n", + " # fit models\n", + " if task == \"classification\":\n", + " rf = RandomForestClassifier(n_estimators=100, min_samples_leaf=3,\n", + " max_features='sqrt', random_state=42)\n", + " rf.fit(X_train, y_train)\n", + " rf_plus_baseline = RandomForestPlusClassifier(rf_model=rf,\n", + " include_raw=False, fit_on=\"inbag\",\n", + " prediction_model=LogisticRegression())\n", + " rf_plus_baseline.fit(X_train, y_train)\n", + " rf_plus = RandomForestPlusClassifier(rf_model=rf)\n", + " rf_plus.fit(X_train, y_train)\n", + " elif task == \"regression\":\n", + " rf = RandomForestRegressor(n_estimators=100, min_samples_leaf=5,\n", + " max_features=0.33, random_state=42)\n", + " rf.fit(X_train, y_train)\n", + " rf_plus_baseline = RandomForestPlusRegressor(rf_model=rf,\n", + " include_raw=False, fit_on=\"inbag\",\n", + " prediction_model=LinearRegression())\n", + " rf_plus_baseline.fit(X_train, y_train)\n", + " rf_plus = RandomForestPlusRegressor(rf_model=rf)\n", + " rf_plus.fit(X_train, y_train)\n", + " else:\n", + " raise ValueError(\"Task must be either 'classification' or 'regression'.\")\n", + " return rf, rf_plus_baseline, rf_plus" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 20.3s\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 25.1s finished\n", + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 76 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 76 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 85 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 76 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 83 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 76 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 72 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 76 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 82 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 70 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 83 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 83 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 81 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 79 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 81 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 76 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 70 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 76 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 81 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 84 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 82 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 82 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 83 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 83 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 81 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 83 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 79 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 66 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 79 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 76 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 79 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 83 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 93 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 67 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 76 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 82 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 83 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 67 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 68 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 88 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 86 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 84 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 82 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 69 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 68 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 79 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 84 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 83 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 82 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 66 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 86 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 79 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 69 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 76 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 72 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 70 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 86 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 81 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 79 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 66 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 72 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 79 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 79 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 79 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 68 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 81 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 69 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 82 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 79 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 84 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 84 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 72 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 69 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 83 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 79 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 76 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 68 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 79 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 79 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 72 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 82 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 69 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 76 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 72 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 76 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 82 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 76 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 85 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 70 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 79 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 81 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 83 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 76 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 72 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 69 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 76 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 79 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 76 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 69 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 97 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 70 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 70 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 70 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 79 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 72 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 70 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 68 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 82 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 72 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 70 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 68 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 79 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 70 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 97 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 81 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 67 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 96 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 69 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 68 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 94 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 79 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 70 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 68 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 97 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 67 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 68 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 87 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 81 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 67 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 70 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 79 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 79 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 5.7min\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 70 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 69 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 87 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 81 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 79 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 76 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 76 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 86 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 72 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 69 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 79 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 88 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 85 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 76 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 79 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 83 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 88 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 79 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 70 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 83 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 79 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 76 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 84 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 86 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 82 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 70 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 81 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 86 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 79 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 76 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 72 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 72 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 69 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 79 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 76 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 70 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 79 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 76 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 81 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 81 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 69 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 85 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 72 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 82 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 79 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 69 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 86 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 83 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 88 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 87 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 69 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 69 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 76 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 81 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 70 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 76 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 72 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 76 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 83 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 81 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 81 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 82 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 81 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 72 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 72 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 81 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 83 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 84 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 76 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 90 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 72 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 92 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 82 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 89 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 79 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 82 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 79 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 81 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 83 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 79 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 81 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 72 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 72 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 72 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 86 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 79 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 67 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 87 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 72 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 72 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 76 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 69 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 68 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 70 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 68 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 67 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 68 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 70 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 72 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 68 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 69 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 76 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 69 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 70 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 69 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 76 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 79 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 76 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 68 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 81 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 69 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 81 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 67 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 82 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 84 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 76 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 82 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 72 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 72 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 81 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 79 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 79 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 72 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 84 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 82 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 69 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 83 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 81 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 70 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 70 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 81 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 67 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 86 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 70 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 72 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 83 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 68 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 81 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 70 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 76 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 70 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 76 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 72 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 82 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 70 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 69 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 69 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 72 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 79 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 84 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 69 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 82 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 70 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 76 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 70 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 84 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 72 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 81 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 69 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 88 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 69 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 83 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 84 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 69 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 88 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 88 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 70 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 70 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 70 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 72 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 69 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 77 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 92 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 83 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 72 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 83 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 80 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 88 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 70 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 72 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 78 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 83 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 83 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 70 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 85 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 70 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 72 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 92 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 76 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 74 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 72 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 84 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 72 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 69 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 71 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 75 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 69 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 73 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 70 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 70 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 15.3min finished\n" + ] + } + ], + "source": [ + "rf, rf_plus_baseline, rf_plus = fit_models(X_train, y_train, task)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def get_shap(X, shap_explainer, task):\n", + " if task == \"classification\":\n", + " # the shap values are an array of shape\n", + " # (# of samples, # of features, # of classes), and in this binary\n", + " # classification case, we want the shap values for the positive class.\n", + " # check_additivity=False is used to speed up computation.\n", + " shap_values = \\\n", + " shap_explainer.shap_values(X, check_additivity=False)[:, :, 1]\n", + " else:\n", + " # check_additivity=False is used to speed up computation.\n", + " shap_values = shap_explainer.shap_values(X, check_additivity=False)\n", + " # get the rankings of the shap values. negative absolute value is taken\n", + " # because np.argsort sorts from smallest to largest.\n", + " shap_rankings = np.argsort(-np.abs(shap_values), axis = 1)\n", + " return shap_values, shap_rankings" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "shap_explainer = shap.TreeExplainer(rf)\n", + "shap_values, shap_rankings = get_shap(X_train, shap_explainer, task)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def get_lmdi(X, y, lmdi_explainer, l2norm, sign, normalize, leaf_average, ranking=False):\n", + " # get feature importances\n", + " lmdi = lmdi_explainer.explain_linear_partial(X, y, l2norm=l2norm, sign=sign,\n", + " normalize=normalize,\n", + " leaf_average=leaf_average,\n", + " ranking=ranking)\n", + " mdi_rankings = lmdi_explainer.get_rankings(np.abs(lmdi))\n", + " return lmdi, mdi_rankings" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "lmdi_explainer_signed_normalized_l2_avg = AloRFPlusMDI(rf_plus, mode = \"only_k\")\n", + "lmdi_explainer_signed_normalized_l2_noavg = AloRFPlusMDI(rf_plus, mode = \"only_k\")\n", + "lmdi_explainer_signed_nonnormalized_l2_avg = AloRFPlusMDI(rf_plus, mode = \"only_k\")\n", + "lmdi_explainer_signed_nonnormalized_l2_noavg = AloRFPlusMDI(rf_plus, mode = \"only_k\")\n", + "lmdi_explainer_nonl2_avg = AloRFPlusMDI(rf_plus, mode = \"only_k\")\n", + "lmdi_explainer_nonl2_noavg = AloRFPlusMDI(rf_plus, mode = \"only_k\")\n", + "lmdi_explainer_l2_ranking = AloRFPlusMDI(rf_plus, mode = \"only_k\")\n", + "lmdi_explainer_nonl2_ranking = AloRFPlusMDI(rf_plus, mode = \"only_k\")\n", + "lmdi_explainer_l2_ranking_nonloo = RFPlusMDI(rf_plus, mode = \"only_k\")\n", + "lmdi_explainer_nonl2_ranking_nonloo = RFPlusMDI(rf_plus, mode = \"only_k\")\n", + "lmdi_explainer_normalized_l2_ranking = AloRFPlusMDI(rf_plus, mode = \"only_k\")\n", + "lmdi_explainer_nonnormalized_l2_ranking = AloRFPlusMDI(rf_plus, mode = \"only_k\")\n", + "lmdi_baseline_explainer = RFPlusMDI(rf_plus_baseline, mode = \"only_k\", evaluate_on = \"inbag\")\n", + "lmdi_values_signed_normalized_l2_avg, \\\n", + " lmdi_rankings_signed_normalized_l2_avg = \\\n", + " get_lmdi(X_train, y_train, lmdi_explainer_signed_normalized_l2_avg,\n", + " l2norm=True, sign=True, normalize=True, leaf_average=True)\n", + "lmdi_values_signed_normalized_l2_noavg, \\\n", + " lmdi_rankings_signed_normalized_l2_noavg = \\\n", + " get_lmdi(X_train, y_train,lmdi_explainer_signed_normalized_l2_noavg,\n", + " l2norm=True, sign=True, normalize=True, leaf_average=False)\n", + "lmdi_values_signed_nonnormalized_l2_avg, \\\n", + " lmdi_rankings_signed_nonnormalized_l2_avg = \\\n", + " get_lmdi(X_train,y_train,lmdi_explainer_signed_nonnormalized_l2_avg,\n", + " l2norm=True, sign=True, normalize=False, leaf_average=True)\n", + "lmdi_values_signed_nonnormalized_l2_noavg, \\\n", + " lmdi_rankings_signed_nonnormalized_l2_noavg = \\\n", + " get_lmdi(X_train, y_train,\n", + " lmdi_explainer_signed_nonnormalized_l2_noavg, l2norm=True,\n", + " sign=True, normalize=False, leaf_average=False)\n", + "lmdi_values_nonl2_avg, lmdi_rankings_nonl2_avg = \\\n", + " get_lmdi(X_train, y_train, lmdi_explainer_nonl2_avg, l2norm=False,\n", + " sign=False, normalize=False, leaf_average=True)\n", + "lmdi_values_nonl2_noavg, lmdi_rankings_nonl2_noavg = \\\n", + " get_lmdi(X_train, y_train, lmdi_explainer_nonl2_noavg, l2norm=False,\n", + " sign=False, normalize=False, leaf_average=False)\n", + "lmdi_values_l2_ranking, lmdi_rankings_l2_ranking = \\\n", + " get_lmdi(X_train, y_train, lmdi_explainer_l2_ranking, l2norm=True,\n", + " sign=False, normalize=False, leaf_average=False, ranking=True)\n", + "lmdi_values_nonl2_ranking, lmdi_rankings_nonl2_ranking = \\\n", + " get_lmdi(X_train, y_train, lmdi_explainer_nonl2_ranking, l2norm=False,\n", + " sign=False, normalize=False, leaf_average=False, ranking=True)\n", + "lmdi_values_l2_ranking_nonloo, lmdi_rankings_l2_ranking_nonloo = \\\n", + " get_lmdi(X_train, y_train, lmdi_explainer_l2_ranking_nonloo, l2norm=True,\n", + " sign=False, normalize=False, leaf_average=False, ranking=True)\n", + "lmdi_values_nonl2_ranking_nonloo, lmdi_rankings_nonl2_ranking_nonloo = \\\n", + " get_lmdi(X_train, y_train, lmdi_explainer_nonl2_ranking_nonloo, l2norm=False,\n", + " sign=False, normalize=False, leaf_average=False, ranking=True)\n", + "lmdi_values_normalized_l2_ranking, lmdi_rankings_normalized_l2_ranking = \\\n", + " get_lmdi(X_train, y_train, lmdi_explainer_normalized_l2_ranking, l2norm=True,\n", + " sign=False, normalize=True, leaf_average=False, ranking=True)\n", + "lmdi_values_baseline, lmdi_rankings_baseline = \\\n", + " get_lmdi(X_train, y_train, lmdi_baseline_explainer, l2norm=False,\n", + " sign=False, normalize=False, leaf_average=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# create storage for iteration purposes\n", + "lfi_values = \\\n", + " {'shap': shap_values,\n", + " # 'signed_normalized_l2_avg': lmdi_values_signed_normalized_l2_avg,\n", + " # 'signed_normalized_l2_noavg': lmdi_values_signed_normalized_l2_noavg,\n", + " 'signed_nonnormalized_l2_avg': lmdi_values_signed_nonnormalized_l2_avg,\n", + " 'signed_nonnormalized_l2_noavg':\n", + " lmdi_values_signed_nonnormalized_l2_noavg,\n", + " 'nonl2_avg': lmdi_values_nonl2_avg,\n", + " 'nonl2_noavg': lmdi_values_nonl2_noavg,\n", + " 'l2_ranking': lmdi_values_l2_ranking,\n", + " 'nonl2_ranking': lmdi_values_nonl2_ranking,\n", + " 'l2_ranking_nonloo': lmdi_values_l2_ranking_nonloo,\n", + " 'nonl2_ranking_nonloo': lmdi_values_nonl2_ranking_nonloo,\n", + " # 'normalized_l2_ranking': lmdi_values_normalized_l2_ranking,\n", + " # 'normalized_nonl2_ranking': lmdi_values_normalized_nonl2_ranking,\n", + " 'baseline': lmdi_values_baseline}\n", + "lfi_rankings = \\\n", + " {'shap': shap_rankings,\n", + " 'signed_normalized_l2_avg': lmdi_rankings_signed_normalized_l2_avg,\n", + " 'signed_normalized_l2_noavg': lmdi_rankings_signed_normalized_l2_noavg,\n", + " 'signed_nonnormalized_l2_avg': lmdi_rankings_signed_nonnormalized_l2_avg,\n", + " 'signed_nonnormalized_l2_noavg':\n", + " lmdi_rankings_signed_nonnormalized_l2_noavg,\n", + " 'nonl2_avg': lmdi_rankings_nonl2_avg,\n", + " 'nonl2_noavg': lmdi_rankings_nonl2_noavg,\n", + " 'l2_ranking': lmdi_rankings_l2_ranking,\n", + " 'nonl2_ranking': lmdi_rankings_nonl2_ranking,\n", + " # 'normalized_l2_ranking': lmdi_values_normalized_l2_ranking,\n", + " # 'normalized_nonl2_ranking': lmdi_values_normalized_nonl2_ranking,\n", + " 'baseline': lmdi_rankings_baseline}" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "test_shap_values, test_shap_rankings = get_shap(X_test, shap_explainer, task)\n", + "test_lmdi_values_signed_normalized_l2_avg, \\\n", + " test_lmdi_rankings_signed_normalized_l2_avg = \\\n", + " get_lmdi(X_test, None, lmdi_explainer_signed_normalized_l2_avg,\n", + " l2norm=True, sign=True, normalize=True, leaf_average=True)\n", + "test_lmdi_values_signed_normalized_l2_noavg, \\\n", + " test_lmdi_rankings_signed_normalized_l2_noavg = \\\n", + " get_lmdi(X_test, None,lmdi_explainer_signed_normalized_l2_noavg,\n", + " l2norm=True, sign=True, normalize=True, leaf_average=False)\n", + "test_lmdi_values_signed_nonnormalized_l2_avg, \\\n", + " test_lmdi_rankings_signed_nonnormalized_l2_avg = \\\n", + " get_lmdi(X_test, None,lmdi_explainer_signed_nonnormalized_l2_avg,\n", + " l2norm=True, sign=True, normalize=False, leaf_average=True)\n", + "test_lmdi_values_signed_nonnormalized_l2_noavg, \\\n", + " test_lmdi_rankings_signed_nonnormalized_l2_noavg = \\\n", + " get_lmdi(X_test, None,\n", + " lmdi_explainer_signed_nonnormalized_l2_noavg, l2norm=True,\n", + " sign=True, normalize=False, leaf_average=False)\n", + "test_lmdi_values_nonl2_avg, test_lmdi_rankings_nonl2_avg = \\\n", + " get_lmdi(X_test, None, lmdi_explainer_nonl2_avg, l2norm=False,\n", + " sign=False, normalize=False, leaf_average=True)\n", + "test_lmdi_values_nonl2_noavg, test_lmdi_rankings_nonl2_noavg = \\\n", + " get_lmdi(X_test, None, lmdi_explainer_nonl2_noavg, l2norm=False,\n", + " sign=False, normalize=False, leaf_average=False)\n", + "test_lmdi_values_l2_ranking, test_lmdi_rankings_l2_ranking = \\\n", + " get_lmdi(X_test, None, lmdi_explainer_l2_ranking, l2norm=True,\n", + " sign=False, normalize=False, leaf_average=False, ranking=True)\n", + "test_lmdi_values_nonl2_ranking, test_lmdi_rankings_nonl2_ranking = \\\n", + " get_lmdi(X_test, None, lmdi_explainer_nonl2_ranking, l2norm=False,\n", + " sign=False, normalize=False, leaf_average=False, ranking=True)\n", + "test_lmdi_values_l2_ranking_nonloo, test_lmdi_rankings_l2_ranking_nonloo = \\\n", + " get_lmdi(X_test, None, lmdi_explainer_l2_ranking_nonloo, l2norm=True,\n", + " sign=False, normalize=False, leaf_average=False, ranking=True)\n", + "test_lmdi_values_nonl2_ranking_nonloo, test_lmdi_rankings_nonl2_ranking_nonloo = \\\n", + " get_lmdi(X_test, None, lmdi_explainer_nonl2_ranking_nonloo, l2norm=False,\n", + " sign=False, normalize=False, leaf_average=False, ranking=True)\n", + "test_lmdi_values_normalized_l2_ranking, test_lmdi_rankings_normalized_l2_ranking = \\\n", + " get_lmdi(X_test, None, lmdi_explainer_normalized_l2_ranking, l2norm=True,\n", + " sign=False, normalize=True, leaf_average=False, ranking=True)\n", + "test_lmdi_values_baseline, test_lmdi_rankings_baseline = \\\n", + " get_lmdi(X_test, None, lmdi_baseline_explainer, l2norm=False,\n", + " sign=False, normalize=False, leaf_average=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# create storage for iteration purposes\n", + "test_lfi_values = \\\n", + " {'shap': test_shap_values,\n", + " # 'signed_normalized_l2_avg': test_lmdi_values_signed_normalized_l2_avg,\n", + " # 'signed_normalized_l2_noavg': test_lmdi_values_signed_normalized_l2_noavg,\n", + " 'signed_nonnormalized_l2_avg': test_lmdi_values_signed_nonnormalized_l2_avg,\n", + " 'signed_nonnormalized_l2_noavg':\n", + " test_lmdi_values_signed_nonnormalized_l2_noavg,\n", + " 'nonl2_avg': test_lmdi_values_nonl2_avg,\n", + " 'nonl2_noavg': test_lmdi_values_nonl2_noavg,\n", + " 'l2_ranking': test_lmdi_values_l2_ranking,\n", + " 'nonl2_ranking': test_lmdi_values_nonl2_ranking,\n", + " 'l2_ranking_nonloo': test_lmdi_values_l2_ranking_nonloo,\n", + " 'nonl2_ranking_nonloo': test_lmdi_values_nonl2_ranking_nonloo,\n", + " # 'normalized_l2_ranking': test_lmdi_values_normalized_l2_ranking,\n", + " # 'normalized_nonl2_ranking': lmdi_values_normalized_nonl2_ranking,\n", + " 'baseline': test_lmdi_values_baseline}\n", + "test_lfi_rankings = \\\n", + " {'shap': test_shap_rankings,\n", + " 'signed_normalized_l2_avg': test_lmdi_rankings_signed_normalized_l2_avg,\n", + " 'signed_normalized_l2_noavg': test_lmdi_rankings_signed_normalized_l2_noavg,\n", + " 'signed_nonnormalized_l2_avg': test_lmdi_rankings_signed_nonnormalized_l2_avg,\n", + " 'signed_nonnormalized_l2_noavg':\n", + " test_lmdi_rankings_signed_nonnormalized_l2_noavg,\n", + " 'nonl2_avg': test_lmdi_rankings_nonl2_avg,\n", + " 'nonl2_noavg': test_lmdi_rankings_nonl2_noavg,\n", + " 'l2_ranking': test_lmdi_rankings_l2_ranking,\n", + " 'nonl2_ranking': test_lmdi_rankings_nonl2_ranking,\n", + " # 'normalized_l2_ranking': lmdi_values_normalized_l2_ranking,\n", + " # 'normalized_nonl2_ranking': lmdi_values_normalized_nonl2_ranking,\n", + " 'baseline': test_lmdi_rankings_baseline}" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "values_linkage = {}\n", + "for method, values in lfi_values.items():\n", + " # values_linkage[method] = sch.linkage(values, method=\"ward\")\n", + " values_linkage[method] = cluster.hierarchy.ward(values)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# get clusters for values\n", + "value_clusters = {}\n", + "for method, link in values_linkage.items():\n", + " num_cluster_map = {}\n", + " for num_clusters in range(2, 11):\n", + " # num_cluster_map[num_clusters] = fcluster(link, num_clusters,\n", + " # criterion = \"maxclust\")\n", + " num_cluster_map[num_clusters] = cut_tree(link, n_clusters=num_clusters).flatten()\n", + " value_clusters[method] = num_cluster_map" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# for each method, for each number of clusters, get the clusters and compute their centroids\n", + "value_centroids = {}\n", + "for method, clusters in value_clusters.items():\n", + " num_cluster_centroids = {}\n", + " for num_clusters, cluster_labels in clusters.items():\n", + " centroids = np.zeros((num_clusters, X.shape[1]))\n", + " for cluster_num in range(num_clusters):\n", + " cluster_indices = np.where(cluster_labels == cluster_num)[0]\n", + " cluster_values = lfi_values[method][cluster_indices]\n", + " centroids[cluster_num] = np.mean(cluster_values, axis = 0)\n", + " num_cluster_centroids[num_clusters] = centroids\n", + " value_centroids[method] = num_cluster_centroids" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# for each method, for its test values, assign the test values to the closest centroid\n", + "test_value_clusters = {}\n", + "for method, centroids in value_centroids.items():\n", + " num_cluster_map = {}\n", + " for num_clusters, centroid_values in centroids.items():\n", + " test_clusters = np.zeros(len(test_lfi_values[method]))\n", + " for i, test_value in enumerate(test_lfi_values[method]):\n", + " distances = np.linalg.norm(centroid_values - test_value, axis = 1)\n", + " test_clusters[i] = np.argmin(distances)\n", + " num_cluster_map[num_clusters] = test_clusters\n", + " test_value_clusters[method] = num_cluster_map" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# using original data, split train and test set into clusters according to each method\n", + "train_clusters = {}\n", + "for method, clusters in value_clusters.items():\n", + " num_cluster_map = {}\n", + " for num_clusters, cluster_labels in clusters.items():\n", + " cluster_map = {}\n", + " for cluster_num in range(num_clusters):\n", + " cluster_indices = np.where(cluster_labels == cluster_num)[0]\n", + " cluster_map[cluster_num] = cluster_indices\n", + " num_cluster_map[num_clusters] = cluster_map\n", + " train_clusters[method] = num_cluster_map\n", + "test_clusters = {}\n", + "for method, clusters in test_value_clusters.items():\n", + " num_cluster_map = {}\n", + " for num_clusters, cluster_labels in clusters.items():\n", + " cluster_map = {}\n", + " for cluster_num in range(num_clusters):\n", + " cluster_indices = np.where(cluster_labels == cluster_num)[0]\n", + " cluster_map[cluster_num] = cluster_indices\n", + " num_cluster_map[num_clusters] = cluster_map\n", + " test_clusters[method] = num_cluster_map" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'shap': {2: {0: array([ 0, 1, 2, ..., 1396, 1398, 1399]),\n", + " 1: array([ 4, 14, 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1381, 1383, 1385, 1386, 1387,\n", + " 1389, 1390, 1392, 1396, 1398, 1399])},\n", + " 4: {0: array([ 0, 1, 2, 3, 6, 12, 17, 18, 19, 26, 51,\n", + " 52, 54, 58, 59, 67, 70, 78, 86, 94, 105, 111,\n", + " 121, 126, 131, 133, 150, 154, 166, 172, 182, 190, 192,\n", + " 199, 205, 206, 207, 209, 216, 228, 229, 231, 244, 253,\n", + " 260, 265, 266, 278, 288, 292, 300, 301, 302, 312, 323,\n", + " 324, 325, 335, 338, 339, 344, 370, 371, 380, 384, 388,\n", + " 391, 394, 404, 419, 421, 430, 435, 442, 450, 453, 460,\n", + " 473, 484, 486, 491, 494, 496, 499, 505, 515, 517, 521,\n", + " 534, 560, 561, 581, 582, 598, 599, 601, 612, 618, 629,\n", + " 631, 657, 668, 669, 670, 674, 678, 684, 686, 700, 702,\n", + " 703, 704, 711, 719, 722, 732, 733, 744, 747, 756, 759,\n", + " 763, 779, 780, 783, 787, 793, 797, 802, 804, 810, 815,\n", + " 816, 823, 828, 832, 840, 844, 852, 858, 864, 869, 872,\n", + " 873, 876, 877, 879, 884, 885, 888, 889, 899, 904, 905,\n", + " 907, 910, 919, 920, 935, 952, 957, 968, 981, 983, 990,\n", + " 991, 992, 993, 999, 1013, 1015, 1022, 1029, 1030, 1033, 1053,\n", + " 1068, 1073, 1094, 1100, 1117, 1121, 1122, 1123, 1129, 1146, 1154,\n", + " 1169, 1170, 1171, 1182, 1186, 1198, 1200, 1208, 1211, 1212, 1218,\n", + " 1221, 1230, 1237, 1239, 1268, 1282, 1292, 1296, 1301, 1308, 1314,\n", + " 1319, 1322, 1327, 1336, 1341, 1344, 1345, 1352, 1361, 1362, 1363,\n", + " 1368, 1370, 1371, 1373, 1376, 1384, 1388, 1394]),\n", + " 1: array([ 4, 14, 24, 33, 34, 50, 57, 62, 69, 71, 72,\n", + " 73, 75, 81, 83, 84, 85, 91, 95, 100, 117, 125,\n", + " 134, 136, 152, 157, 160, 161, 171, 173, 176, 180, 183,\n", + " 185, 188, 196, 198, 203, 204, 217, 223, 224, 225, 237,\n", + " 243, 256, 270, 279, 287, 293, 308, 313, 317, 319, 320,\n", + " 321, 326, 330, 333, 345, 349, 356, 358, 359, 364, 367,\n", + " 377, 379, 382, 387, 395, 406, 410, 415, 417, 440, 441,\n", + " 443, 447, 451, 457, 458, 466, 469, 470, 479, 481, 498,\n", + " 506, 514, 516, 519, 523, 524, 525, 531, 538, 541, 549,\n", + " 566, 568, 571, 576, 577, 584, 585, 586, 588, 593, 595,\n", + " 596, 600, 610, 613, 621, 625, 626, 632, 638, 642, 646,\n", + " 649, 651, 653, 656, 659, 660, 663, 671, 679, 681, 693,\n", + " 706, 713, 721, 723, 724, 729, 734, 735, 736, 738, 745,\n", + " 749, 751, 752, 754, 755, 771, 781, 784, 791, 794, 795,\n", + " 800, 801, 805, 807, 808, 813, 822, 826, 827, 829, 854,\n", + " 855, 866, 870, 881, 892, 894, 897, 898, 900, 902, 925,\n", + " 927, 930, 945, 948, 949, 958, 959, 963, 976, 977, 988,\n", + " 995, 998, 1004, 1012, 1018, 1024, 1035, 1049, 1051, 1055, 1058,\n", + " 1059, 1066, 1067, 1072, 1074, 1077, 1078, 1081, 1087, 1089, 1090,\n", + " 1091, 1093, 1096, 1097, 1098, 1099, 1104, 1107, 1112, 1114, 1116,\n", + " 1120, 1128, 1133, 1136, 1139, 1140, 1143, 1147, 1149, 1152, 1156,\n", + " 1159, 1167, 1176, 1178, 1181, 1187, 1189, 1194, 1205, 1207, 1213,\n", + " 1215, 1216, 1222, 1229, 1233, 1235, 1238, 1242, 1248, 1250, 1256,\n", + " 1257, 1260, 1263, 1271, 1276, 1281, 1285, 1287, 1288, 1290, 1293,\n", + " 1295, 1300, 1302, 1304, 1307, 1310, 1313, 1325, 1331, 1338, 1340,\n", + " 1343, 1347, 1356, 1357, 1365, 1366, 1382, 1391, 1393, 1395, 1397]),\n", + " 2: array([ 5, 7, 8, 9, 10, 11, 13, 16, 21, 23, 25,\n", + " 27, 28, 29, 30, 31, 32, 35, 36, 37, 38, 39,\n", + " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 53,\n", + " 55, 56, 60, 61, 63, 64, 66, 68, 74, 77, 79,\n", + " 80, 82, 87, 88, 89, 90, 92, 93, 96, 97, 98,\n", + " 99, 102, 103, 104, 106, 107, 108, 109, 110, 112, 113,\n", + " 114, 115, 116, 118, 120, 122, 124, 127, 128, 130, 132,\n", + " 135, 138, 139, 140, 141, 142, 143, 144, 145, 146, 148,\n", + " 149, 151, 153, 155, 156, 158, 159, 162, 163, 164, 165,\n", + " 167, 168, 169, 170, 174, 175, 177, 178, 179, 181, 184,\n", + " 186, 187, 189, 191, 193, 194, 195, 197, 200, 201, 202,\n", + " 208, 210, 211, 212, 213, 214, 218, 219, 220, 221, 222,\n", + " 227, 232, 233, 234, 235, 236, 238, 239, 240, 241, 245,\n", + " 246, 247, 248, 249, 250, 252, 254, 255, 257, 258, 259,\n", + " 261, 262, 264, 267, 268, 269, 271, 272, 273, 274, 276,\n", + " 277, 280, 281, 282, 283, 284, 285, 286, 289, 290, 291,\n", + " 294, 295, 296, 297, 298, 299, 303, 304, 305, 306, 307,\n", + " 310, 311, 314, 315, 316, 322, 327, 329, 331, 332, 334,\n", + " 336, 340, 341, 342, 343, 346, 347, 348, 350, 351, 352,\n", + " 353, 354, 357, 360, 361, 363, 365, 366, 368, 369, 372,\n", + " 373, 374, 375, 376, 378, 381, 383, 385, 386, 389, 390,\n", + " 392, 393, 396, 397, 398, 399, 400, 401, 402, 405, 407,\n", + " 408, 409, 411, 412, 414, 416, 418, 420, 422, 423, 424,\n", + " 425, 426, 427, 428, 429, 431, 433, 434, 436, 437, 438,\n", + " 439, 444, 445, 446, 448, 449, 452, 454, 455, 456, 459,\n", + " 462, 463, 464, 465, 467, 468, 471, 472, 474, 475, 476,\n", + " 477, 478, 480, 482, 483, 485, 487, 488, 489, 490, 492,\n", + " 495, 497, 500, 501, 502, 503, 504, 507, 508, 509, 510,\n", + " 511, 512, 513, 518, 520, 522, 526, 527, 528, 529, 530,\n", + " 532, 533, 535, 536, 537, 539, 540, 543, 544, 545, 546,\n", + " 547, 548, 550, 551, 552, 553, 554, 555, 556, 558, 559,\n", + " 563, 564, 565, 567, 569, 570, 572, 573, 574, 575, 578,\n", + " 579, 580, 583, 589, 590, 591, 592, 594, 602, 603, 604,\n", + " 605, 606, 607, 608, 609, 611, 614, 615, 616, 617, 619,\n", + " 620, 622, 623, 624, 627, 628, 633, 634, 635, 636, 637,\n", + " 639, 640, 643, 644, 645, 647, 648, 650, 652, 654, 658,\n", + " 661, 662, 665, 666, 667, 672, 673, 675, 676, 677, 680,\n", + " 682, 683, 685, 687, 688, 689, 690, 691, 692, 694, 695,\n", + " 696, 697, 698, 699, 705, 707, 708, 709, 710, 712, 714,\n", + " 715, 716, 717, 718, 720, 726, 727, 728, 730, 731, 739,\n", + " 740, 741, 742, 743, 746, 748, 750, 753, 757, 758, 760,\n", + " 761, 762, 764, 765, 766, 767, 768, 769, 772, 774, 775,\n", + " 776, 777, 778, 782, 785, 786, 788, 789, 790, 792, 796,\n", + " 798, 799, 803, 811, 812, 814, 817, 818, 819, 824, 825,\n", + " 830, 831, 833, 834, 835, 837, 838, 839, 841, 842, 843,\n", + " 845, 846, 847, 848, 849, 850, 853, 856, 857, 860, 861,\n", + " 862, 863, 865, 867, 868, 871, 874, 875, 878, 880, 882,\n", + " 883, 886, 887, 890, 891, 896, 901, 903, 906, 908, 909,\n", + " 911, 912, 913, 914, 916, 917, 918, 921, 922, 923, 924,\n", + " 926, 928, 929, 931, 932, 933, 934, 936, 937, 938, 939,\n", + " 940, 941, 942, 943, 944, 946, 947, 950, 951, 953, 954,\n", + " 955, 956, 960, 961, 962, 964, 965, 966, 970, 971, 972,\n", + " 973, 974, 975, 979, 980, 984, 985, 986, 987, 989, 994,\n", + " 996, 997, 1000, 1001, 1002, 1003, 1005, 1006, 1008, 1009, 1010,\n", + " 1011, 1014, 1016, 1017, 1019, 1020, 1021, 1023, 1025, 1026, 1027,\n", + " 1028, 1031, 1034, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043,\n", + " 1044, 1045, 1046, 1047, 1048, 1050, 1052, 1054, 1056, 1057, 1060,\n", + " 1061, 1062, 1063, 1064, 1065, 1069, 1070, 1071, 1075, 1079, 1080,\n", + " 1082, 1083, 1084, 1085, 1086, 1088, 1092, 1095, 1101, 1102, 1103,\n", + " 1106, 1108, 1109, 1110, 1111, 1113, 1115, 1118, 1119, 1124, 1125,\n", + " 1126, 1127, 1130, 1131, 1132, 1134, 1135, 1137, 1138, 1141, 1142,\n", + " 1144, 1145, 1148, 1150, 1155, 1157, 1158, 1160, 1161, 1162, 1163,\n", + " 1164, 1165, 1166, 1168, 1172, 1173, 1174, 1175, 1177, 1179, 1180,\n", + " 1183, 1184, 1185, 1188, 1190, 1192, 1193, 1195, 1196, 1197, 1199,\n", + " 1201, 1202, 1203, 1204, 1206, 1209, 1210, 1214, 1217, 1219, 1220,\n", + " 1224, 1225, 1226, 1227, 1228, 1231, 1232, 1234, 1236, 1240, 1241,\n", + " 1243, 1244, 1245, 1246, 1247, 1249, 1251, 1252, 1253, 1254, 1255,\n", + " 1258, 1259, 1261, 1262, 1264, 1265, 1266, 1267, 1269, 1270, 1272,\n", + " 1273, 1274, 1275, 1277, 1278, 1279, 1280, 1283, 1286, 1289, 1291,\n", + " 1294, 1298, 1299, 1303, 1305, 1306, 1309, 1311, 1312, 1315, 1316,\n", + " 1317, 1318, 1320, 1321, 1323, 1324, 1326, 1328, 1329, 1330, 1332,\n", + " 1333, 1334, 1335, 1337, 1339, 1342, 1346, 1348, 1349, 1350, 1351,\n", + " 1353, 1355, 1358, 1359, 1364, 1367, 1369, 1372, 1374, 1375, 1378,\n", + " 1380, 1383, 1385, 1386, 1387, 1389, 1390, 1392, 1398, 1399]),\n", + " 3: array([ 15, 20, 22, 65, 76, 101, 119, 123, 129, 137, 147,\n", + " 215, 226, 230, 242, 251, 263, 275, 309, 318, 328, 337,\n", + " 355, 362, 403, 413, 432, 461, 493, 542, 557, 562, 587,\n", + " 597, 630, 641, 655, 664, 701, 725, 737, 770, 773, 806,\n", + " 809, 820, 821, 836, 851, 859, 893, 895, 915, 967, 969,\n", + " 978, 982, 1007, 1032, 1076, 1105, 1151, 1153, 1191, 1223, 1284,\n", + " 1297, 1354, 1360, 1377, 1379, 1381, 1396])},\n", + " 5: {0: array([ 0, 1, 2, 3, 6, 12, 17, 18, 19, 26, 51,\n", + " 52, 54, 58, 59, 67, 70, 78, 86, 94, 105, 111,\n", + " 121, 126, 131, 133, 150, 154, 166, 172, 182, 190, 192,\n", + " 199, 205, 206, 207, 209, 216, 228, 229, 231, 244, 253,\n", + " 260, 265, 266, 278, 288, 292, 300, 301, 302, 312, 323,\n", + " 324, 325, 335, 338, 339, 344, 370, 371, 380, 384, 388,\n", + " 391, 394, 404, 419, 421, 430, 435, 442, 450, 453, 460,\n", + " 473, 484, 486, 491, 494, 496, 499, 505, 515, 517, 521,\n", + " 534, 560, 561, 581, 582, 598, 599, 601, 612, 618, 629,\n", + " 631, 657, 668, 669, 670, 674, 678, 684, 686, 700, 702,\n", + " 703, 704, 711, 719, 722, 732, 733, 744, 747, 756, 759,\n", + " 763, 779, 780, 783, 787, 793, 797, 802, 804, 810, 815,\n", + " 816, 823, 828, 832, 840, 844, 852, 858, 864, 869, 872,\n", + " 873, 876, 877, 879, 884, 885, 888, 889, 899, 904, 905,\n", + " 907, 910, 919, 920, 935, 952, 957, 968, 981, 983, 990,\n", + " 991, 992, 993, 999, 1013, 1015, 1022, 1029, 1030, 1033, 1053,\n", + " 1068, 1073, 1094, 1100, 1117, 1121, 1122, 1123, 1129, 1146, 1154,\n", + " 1169, 1170, 1171, 1182, 1186, 1198, 1200, 1208, 1211, 1212, 1218,\n", + " 1221, 1230, 1237, 1239, 1268, 1282, 1292, 1296, 1301, 1308, 1314,\n", + " 1319, 1322, 1327, 1336, 1341, 1344, 1345, 1352, 1361, 1362, 1363,\n", + " 1368, 1370, 1371, 1373, 1376, 1384, 1388, 1394]),\n", + " 1: array([ 4, 14, 24, 33, 34, 50, 57, 69, 72, 73, 81,\n", + " 83, 84, 85, 91, 95, 117, 125, 134, 136, 152, 157,\n", + " 160, 161, 171, 173, 180, 183, 185, 188, 196, 198, 203,\n", + " 204, 217, 223, 224, 225, 237, 243, 256, 270, 279, 287,\n", + " 293, 313, 317, 319, 320, 321, 330, 345, 349, 356, 358,\n", + " 359, 364, 367, 377, 379, 382, 387, 395, 406, 410, 415,\n", + " 417, 440, 447, 451, 457, 458, 466, 469, 470, 479, 506,\n", + " 514, 516, 519, 523, 524, 525, 531, 538, 541, 549, 568,\n", + " 571, 576, 577, 584, 585, 588, 593, 596, 600, 613, 621,\n", + " 625, 626, 632, 638, 642, 646, 649, 651, 653, 656, 659,\n", + " 660, 663, 671, 679, 681, 693, 706, 713, 721, 723, 724,\n", + " 729, 734, 735, 736, 745, 749, 751, 752, 755, 771, 781,\n", + " 784, 791, 794, 795, 800, 801, 805, 807, 808, 813, 822,\n", + " 826, 827, 829, 854, 855, 870, 881, 892, 894, 897, 898,\n", + " 900, 902, 925, 927, 930, 945, 948, 958, 959, 976, 977,\n", + " 988, 998, 1004, 1024, 1035, 1049, 1051, 1055, 1058, 1066, 1067,\n", + " 1072, 1074, 1077, 1087, 1089, 1090, 1091, 1093, 1096, 1097, 1098,\n", + " 1099, 1104, 1107, 1112, 1114, 1116, 1120, 1128, 1133, 1139, 1143,\n", + " 1147, 1149, 1152, 1159, 1167, 1178, 1181, 1189, 1194, 1205, 1213,\n", + " 1215, 1216, 1222, 1229, 1233, 1235, 1238, 1256, 1257, 1260, 1271,\n", + " 1276, 1281, 1285, 1287, 1290, 1293, 1295, 1300, 1302, 1304, 1313,\n", + " 1325, 1331, 1338, 1343, 1347, 1356, 1357, 1365, 1366, 1382, 1391,\n", + " 1393, 1397]),\n", + " 2: array([ 5, 7, 8, 9, 10, 11, 13, 16, 21, 23, 25,\n", + " 27, 28, 29, 30, 31, 32, 35, 36, 37, 38, 39,\n", + " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 53,\n", + " 55, 56, 60, 61, 63, 64, 66, 68, 74, 77, 79,\n", + " 80, 82, 87, 88, 89, 90, 92, 93, 96, 97, 98,\n", + " 99, 102, 103, 104, 106, 107, 108, 109, 110, 112, 113,\n", + " 114, 115, 116, 118, 120, 122, 124, 127, 128, 130, 132,\n", + " 135, 138, 139, 140, 141, 142, 143, 144, 145, 146, 148,\n", + " 149, 151, 153, 155, 156, 158, 159, 162, 163, 164, 165,\n", + " 167, 168, 169, 170, 174, 175, 177, 178, 179, 181, 184,\n", + " 186, 187, 189, 191, 193, 194, 195, 197, 200, 201, 202,\n", + " 208, 210, 211, 212, 213, 214, 218, 219, 220, 221, 222,\n", + " 227, 232, 233, 234, 235, 236, 238, 239, 240, 241, 245,\n", + " 246, 247, 248, 249, 250, 252, 254, 255, 257, 258, 259,\n", + " 261, 262, 264, 267, 268, 269, 271, 272, 273, 274, 276,\n", + " 277, 280, 281, 282, 283, 284, 285, 286, 289, 290, 291,\n", + " 294, 295, 296, 297, 298, 299, 303, 304, 305, 306, 307,\n", + " 310, 311, 314, 315, 316, 322, 327, 329, 331, 332, 334,\n", + " 336, 340, 341, 342, 343, 346, 347, 348, 350, 351, 352,\n", + " 353, 354, 357, 360, 361, 363, 365, 366, 368, 369, 372,\n", + " 373, 374, 375, 376, 378, 381, 383, 385, 386, 389, 390,\n", + " 392, 393, 396, 397, 398, 399, 400, 401, 402, 405, 407,\n", + " 408, 409, 411, 412, 414, 416, 418, 420, 422, 423, 424,\n", + " 425, 426, 427, 428, 429, 431, 433, 434, 436, 437, 438,\n", + " 439, 444, 445, 446, 448, 449, 452, 454, 455, 456, 459,\n", + " 462, 463, 464, 465, 467, 468, 471, 472, 474, 475, 476,\n", + " 477, 478, 480, 482, 483, 485, 487, 488, 489, 490, 492,\n", + " 495, 497, 500, 501, 502, 503, 504, 507, 508, 509, 510,\n", + " 511, 512, 513, 518, 520, 522, 526, 527, 528, 529, 530,\n", + " 532, 533, 535, 536, 537, 539, 540, 543, 544, 545, 546,\n", + " 547, 548, 550, 551, 552, 553, 554, 555, 556, 558, 559,\n", + " 563, 564, 565, 567, 569, 570, 572, 573, 574, 575, 578,\n", + " 579, 580, 583, 589, 590, 591, 592, 594, 602, 603, 604,\n", + " 605, 606, 607, 608, 609, 611, 614, 615, 616, 617, 619,\n", + " 620, 622, 623, 624, 627, 628, 633, 634, 635, 636, 637,\n", + " 639, 640, 643, 644, 645, 647, 648, 650, 652, 654, 658,\n", + " 661, 662, 665, 666, 667, 672, 673, 675, 676, 677, 680,\n", + " 682, 683, 685, 687, 688, 689, 690, 691, 692, 694, 695,\n", + " 696, 697, 698, 699, 705, 707, 708, 709, 710, 712, 714,\n", + " 715, 716, 717, 718, 720, 726, 727, 728, 730, 731, 739,\n", + " 740, 741, 742, 743, 746, 748, 750, 753, 757, 758, 760,\n", + " 761, 762, 764, 765, 766, 767, 768, 769, 772, 774, 775,\n", + " 776, 777, 778, 782, 785, 786, 788, 789, 790, 792, 796,\n", + " 798, 799, 803, 811, 812, 814, 817, 818, 819, 824, 825,\n", + " 830, 831, 833, 834, 835, 837, 838, 839, 841, 842, 843,\n", + " 845, 846, 847, 848, 849, 850, 853, 856, 857, 860, 861,\n", + " 862, 863, 865, 867, 868, 871, 874, 875, 878, 880, 882,\n", + " 883, 886, 887, 890, 891, 896, 901, 903, 906, 908, 909,\n", + " 911, 912, 913, 914, 916, 917, 918, 921, 922, 923, 924,\n", + " 926, 928, 929, 931, 932, 933, 934, 936, 937, 938, 939,\n", + " 940, 941, 942, 943, 944, 946, 947, 950, 951, 953, 954,\n", + " 955, 956, 960, 961, 962, 964, 965, 966, 970, 971, 972,\n", + " 973, 974, 975, 979, 980, 984, 985, 986, 987, 989, 994,\n", + " 996, 997, 1000, 1001, 1002, 1003, 1005, 1006, 1008, 1009, 1010,\n", + " 1011, 1014, 1016, 1017, 1019, 1020, 1021, 1023, 1025, 1026, 1027,\n", + " 1028, 1031, 1034, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043,\n", + " 1044, 1045, 1046, 1047, 1048, 1050, 1052, 1054, 1056, 1057, 1060,\n", + " 1061, 1062, 1063, 1064, 1065, 1069, 1070, 1071, 1075, 1079, 1080,\n", + " 1082, 1083, 1084, 1085, 1086, 1088, 1092, 1095, 1101, 1102, 1103,\n", + " 1106, 1108, 1109, 1110, 1111, 1113, 1115, 1118, 1119, 1124, 1125,\n", + " 1126, 1127, 1130, 1131, 1132, 1134, 1135, 1137, 1138, 1141, 1142,\n", + " 1144, 1145, 1148, 1150, 1155, 1157, 1158, 1160, 1161, 1162, 1163,\n", + " 1164, 1165, 1166, 1168, 1172, 1173, 1174, 1175, 1177, 1179, 1180,\n", + " 1183, 1184, 1185, 1188, 1190, 1192, 1193, 1195, 1196, 1197, 1199,\n", + " 1201, 1202, 1203, 1204, 1206, 1209, 1210, 1214, 1217, 1219, 1220,\n", + " 1224, 1225, 1226, 1227, 1228, 1231, 1232, 1234, 1236, 1240, 1241,\n", + " 1243, 1244, 1245, 1246, 1247, 1249, 1251, 1252, 1253, 1254, 1255,\n", + " 1258, 1259, 1261, 1262, 1264, 1265, 1266, 1267, 1269, 1270, 1272,\n", + " 1273, 1274, 1275, 1277, 1278, 1279, 1280, 1283, 1286, 1289, 1291,\n", + " 1294, 1298, 1299, 1303, 1305, 1306, 1309, 1311, 1312, 1315, 1316,\n", + " 1317, 1318, 1320, 1321, 1323, 1324, 1326, 1328, 1329, 1330, 1332,\n", + " 1333, 1334, 1335, 1337, 1339, 1342, 1346, 1348, 1349, 1350, 1351,\n", + " 1353, 1355, 1358, 1359, 1364, 1367, 1369, 1372, 1374, 1375, 1378,\n", + " 1380, 1383, 1385, 1386, 1387, 1389, 1390, 1392, 1398, 1399]),\n", + " 3: array([ 15, 20, 22, 65, 76, 101, 119, 123, 129, 137, 147,\n", + " 215, 226, 230, 242, 251, 263, 275, 309, 318, 328, 337,\n", + " 355, 362, 403, 413, 432, 461, 493, 542, 557, 562, 587,\n", + " 597, 630, 641, 655, 664, 701, 725, 737, 770, 773, 806,\n", + " 809, 820, 821, 836, 851, 859, 893, 895, 915, 967, 969,\n", + " 978, 982, 1007, 1032, 1076, 1105, 1151, 1153, 1191, 1223, 1284,\n", + " 1297, 1354, 1360, 1377, 1379, 1381, 1396]),\n", + " 4: array([ 62, 71, 75, 100, 176, 308, 326, 333, 441, 443, 481,\n", + " 498, 566, 586, 595, 610, 738, 754, 866, 949, 963, 995,\n", + " 1012, 1018, 1059, 1078, 1081, 1136, 1140, 1156, 1176, 1187, 1207,\n", + " 1242, 1248, 1250, 1263, 1288, 1307, 1310, 1340, 1395])},\n", + " 6: {0: array([ 0, 1, 2, 3, 6, 12, 17, 18, 19, 26, 51,\n", + " 52, 54, 58, 59, 67, 70, 78, 86, 94, 105, 111,\n", + " 121, 126, 131, 133, 150, 154, 166, 172, 182, 190, 192,\n", + " 199, 205, 206, 207, 209, 216, 228, 229, 231, 244, 253,\n", + " 260, 265, 266, 278, 288, 292, 300, 301, 302, 312, 323,\n", + " 324, 325, 335, 338, 339, 344, 370, 371, 380, 384, 388,\n", + " 391, 394, 404, 419, 421, 430, 435, 442, 450, 453, 460,\n", + " 473, 484, 486, 491, 494, 496, 499, 505, 515, 517, 521,\n", + " 534, 560, 561, 581, 582, 598, 599, 601, 612, 618, 629,\n", + " 631, 657, 668, 669, 670, 674, 678, 684, 686, 700, 702,\n", + " 703, 704, 711, 719, 722, 732, 733, 744, 747, 756, 759,\n", + " 763, 779, 780, 783, 787, 793, 797, 802, 804, 810, 815,\n", + " 816, 823, 828, 832, 840, 844, 852, 858, 864, 869, 872,\n", + " 873, 876, 877, 879, 884, 885, 888, 889, 899, 904, 905,\n", + " 907, 910, 919, 920, 935, 952, 957, 968, 981, 983, 990,\n", + " 991, 992, 993, 999, 1013, 1015, 1022, 1029, 1030, 1033, 1053,\n", + " 1068, 1073, 1094, 1100, 1117, 1121, 1122, 1123, 1129, 1146, 1154,\n", + " 1169, 1170, 1171, 1182, 1186, 1198, 1200, 1208, 1211, 1212, 1218,\n", + " 1221, 1230, 1237, 1239, 1268, 1282, 1292, 1296, 1301, 1308, 1314,\n", + " 1319, 1322, 1327, 1336, 1341, 1344, 1345, 1352, 1361, 1362, 1363,\n", + " 1368, 1370, 1371, 1373, 1376, 1384, 1388, 1394]),\n", + " 1: array([ 4, 14, 24, 33, 34, 50, 57, 69, 72, 73, 81,\n", + " 83, 84, 85, 91, 95, 117, 125, 134, 136, 152, 157,\n", + " 160, 161, 171, 173, 180, 183, 185, 188, 196, 198, 203,\n", + " 204, 217, 223, 224, 225, 237, 243, 256, 270, 279, 287,\n", + " 293, 313, 317, 319, 320, 321, 330, 345, 349, 356, 358,\n", + " 359, 364, 367, 377, 379, 382, 387, 395, 406, 410, 415,\n", + " 417, 440, 447, 451, 457, 458, 466, 469, 470, 479, 506,\n", + " 514, 516, 519, 523, 524, 525, 531, 538, 541, 549, 568,\n", + " 571, 576, 577, 584, 585, 588, 593, 596, 600, 613, 621,\n", + " 625, 626, 632, 638, 642, 646, 649, 651, 653, 656, 659,\n", + " 660, 663, 671, 679, 681, 693, 706, 713, 721, 723, 724,\n", + " 729, 734, 735, 736, 745, 749, 751, 752, 755, 771, 781,\n", + " 784, 791, 794, 795, 800, 801, 805, 807, 808, 813, 822,\n", + " 826, 827, 829, 854, 855, 870, 881, 892, 894, 897, 898,\n", + " 900, 902, 925, 927, 930, 945, 948, 958, 959, 976, 977,\n", + " 988, 998, 1004, 1024, 1035, 1049, 1051, 1055, 1058, 1066, 1067,\n", + " 1072, 1074, 1077, 1087, 1089, 1090, 1091, 1093, 1096, 1097, 1098,\n", + " 1099, 1104, 1107, 1112, 1114, 1116, 1120, 1128, 1133, 1139, 1143,\n", + " 1147, 1149, 1152, 1159, 1167, 1178, 1181, 1189, 1194, 1205, 1213,\n", + " 1215, 1216, 1222, 1229, 1233, 1235, 1238, 1256, 1257, 1260, 1271,\n", + " 1276, 1281, 1285, 1287, 1290, 1293, 1295, 1300, 1302, 1304, 1313,\n", + " 1325, 1331, 1338, 1343, 1347, 1356, 1357, 1365, 1366, 1382, 1391,\n", + " 1393, 1397]),\n", + " 2: array([ 5, 7, 8, 9, 10, 11, 16, 21, 23, 25, 32,\n", + " 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,\n", + " 46, 47, 53, 55, 56, 60, 61, 63, 66, 68, 77,\n", + " 79, 87, 88, 89, 90, 92, 93, 96, 98, 103, 104,\n", + " 106, 107, 108, 109, 110, 112, 113, 115, 116, 120, 127,\n", + " 130, 132, 135, 138, 139, 141, 143, 146, 148, 149, 151,\n", + " 153, 155, 156, 158, 163, 164, 165, 167, 168, 169, 175,\n", + " 179, 181, 184, 186, 187, 189, 191, 193, 194, 197, 200,\n", + " 208, 210, 211, 214, 220, 221, 222, 232, 233, 234, 235,\n", + " 236, 238, 239, 240, 241, 245, 246, 247, 248, 249, 250,\n", + " 252, 254, 257, 258, 259, 261, 264, 268, 269, 273, 276,\n", + " 277, 280, 281, 282, 284, 285, 286, 295, 297, 299, 303,\n", + " 307, 311, 314, 316, 329, 336, 340, 342, 343, 346, 347,\n", + " 348, 350, 351, 352, 354, 361, 363, 369, 374, 375, 376,\n", + " 386, 389, 390, 392, 393, 396, 397, 399, 400, 405, 408,\n", + " 409, 411, 412, 414, 416, 418, 424, 425, 427, 429, 437,\n", + " 438, 439, 449, 452, 454, 456, 459, 464, 465, 467, 468,\n", + " 474, 477, 478, 480, 487, 488, 489, 490, 492, 495, 497,\n", + " 501, 502, 504, 507, 509, 511, 512, 513, 522, 526, 529,\n", + " 532, 533, 535, 536, 537, 543, 544, 546, 550, 552, 553,\n", + " 554, 555, 556, 558, 559, 563, 564, 565, 570, 572, 573,\n", + " 575, 578, 583, 589, 590, 602, 603, 605, 606, 608, 609,\n", + " 616, 619, 620, 623, 624, 628, 633, 634, 635, 636, 640,\n", + " 643, 645, 654, 661, 662, 665, 667, 672, 673, 675, 680,\n", + " 682, 683, 687, 688, 690, 691, 692, 694, 696, 697, 698,\n", + " 699, 705, 707, 708, 709, 710, 712, 717, 718, 720, 726,\n", + " 727, 728, 730, 739, 740, 741, 743, 746, 748, 750, 753,\n", + " 758, 760, 764, 765, 766, 768, 769, 774, 776, 778, 786,\n", + " 788, 790, 798, 803, 811, 814, 817, 818, 819, 824, 825,\n", + " 830, 831, 833, 834, 835, 837, 838, 839, 842, 845, 847,\n", + " 849, 850, 856, 857, 860, 861, 862, 863, 865, 867, 868,\n", + " 871, 874, 875, 878, 880, 882, 887, 896, 901, 903, 906,\n", + " 908, 911, 922, 928, 929, 931, 932, 934, 936, 937, 938,\n", + " 939, 941, 942, 943, 946, 947, 950, 953, 954, 955, 956,\n", + " 960, 961, 962, 964, 966, 970, 971, 972, 973, 975, 980,\n", + " 984, 985, 989, 994, 996, 997, 1000, 1001, 1002, 1003, 1005,\n", + " 1006, 1008, 1010, 1011, 1014, 1016, 1017, 1019, 1020, 1021, 1025,\n", + " 1026, 1027, 1028, 1034, 1036, 1037, 1039, 1040, 1042, 1043, 1045,\n", + " 1046, 1047, 1048, 1050, 1056, 1057, 1060, 1061, 1063, 1070, 1079,\n", + " 1080, 1082, 1083, 1084, 1086, 1101, 1103, 1106, 1110, 1111, 1113,\n", + " 1115, 1118, 1119, 1124, 1125, 1127, 1131, 1132, 1134, 1135, 1137,\n", + " 1138, 1141, 1142, 1144, 1145, 1150, 1157, 1158, 1161, 1163, 1164,\n", + " 1165, 1166, 1168, 1172, 1173, 1174, 1175, 1179, 1180, 1183, 1185,\n", + " 1188, 1190, 1193, 1195, 1197, 1199, 1201, 1202, 1209, 1210, 1217,\n", + " 1219, 1220, 1224, 1225, 1227, 1228, 1231, 1240, 1241, 1243, 1244,\n", + " 1245, 1246, 1249, 1251, 1253, 1254, 1255, 1258, 1262, 1264, 1265,\n", + " 1270, 1273, 1275, 1277, 1278, 1279, 1280, 1283, 1286, 1289, 1294,\n", + " 1298, 1306, 1309, 1312, 1316, 1317, 1318, 1321, 1323, 1333, 1334,\n", + " 1335, 1337, 1342, 1348, 1349, 1350, 1353, 1355, 1358, 1359, 1369,\n", + " 1372, 1380, 1383, 1385, 1387, 1389, 1390, 1392, 1399]),\n", + " 3: array([ 13, 27, 28, 29, 30, 31, 48, 49, 64, 74, 80,\n", + " 82, 97, 99, 102, 114, 118, 122, 124, 128, 140, 142,\n", + " 144, 145, 159, 162, 170, 174, 177, 178, 195, 201, 202,\n", + " 212, 213, 218, 219, 227, 255, 262, 267, 271, 272, 274,\n", + " 283, 289, 290, 291, 294, 296, 298, 304, 305, 306, 310,\n", + " 315, 322, 327, 331, 332, 334, 341, 353, 357, 360, 365,\n", + " 366, 368, 372, 373, 378, 381, 383, 385, 398, 401, 402,\n", + " 407, 420, 422, 423, 426, 428, 431, 433, 434, 436, 444,\n", + " 445, 446, 448, 455, 462, 463, 471, 472, 475, 476, 482,\n", + " 483, 485, 500, 503, 508, 510, 518, 520, 527, 528, 530,\n", + " 539, 540, 545, 547, 548, 551, 567, 569, 574, 579, 580,\n", + " 591, 592, 594, 604, 607, 611, 614, 615, 617, 622, 627,\n", + " 637, 639, 644, 647, 648, 650, 652, 658, 666, 676, 677,\n", + " 685, 689, 695, 714, 715, 716, 731, 742, 757, 761, 762,\n", + " 767, 772, 775, 777, 782, 785, 789, 792, 796, 799, 812,\n", + " 841, 843, 846, 848, 853, 883, 886, 890, 891, 909, 912,\n", + " 913, 914, 916, 917, 918, 921, 923, 924, 926, 933, 940,\n", + " 944, 951, 965, 974, 979, 986, 987, 1009, 1023, 1031, 1038,\n", + " 1041, 1044, 1052, 1054, 1062, 1064, 1065, 1069, 1071, 1075, 1085,\n", + " 1088, 1092, 1095, 1102, 1108, 1109, 1126, 1130, 1148, 1155, 1160,\n", + " 1162, 1177, 1184, 1192, 1196, 1203, 1204, 1206, 1214, 1226, 1232,\n", + " 1234, 1236, 1247, 1252, 1259, 1261, 1266, 1267, 1269, 1272, 1274,\n", + " 1291, 1299, 1303, 1305, 1311, 1315, 1320, 1324, 1326, 1328, 1329,\n", + " 1330, 1332, 1339, 1346, 1351, 1364, 1367, 1374, 1375, 1378, 1386,\n", + " 1398]),\n", + " 4: array([ 15, 20, 22, 65, 76, 101, 119, 123, 129, 137, 147,\n", + " 215, 226, 230, 242, 251, 263, 275, 309, 318, 328, 337,\n", + " 355, 362, 403, 413, 432, 461, 493, 542, 557, 562, 587,\n", + " 597, 630, 641, 655, 664, 701, 725, 737, 770, 773, 806,\n", + " 809, 820, 821, 836, 851, 859, 893, 895, 915, 967, 969,\n", + " 978, 982, 1007, 1032, 1076, 1105, 1151, 1153, 1191, 1223, 1284,\n", + " 1297, 1354, 1360, 1377, 1379, 1381, 1396]),\n", + " 5: array([ 62, 71, 75, 100, 176, 308, 326, 333, 441, 443, 481,\n", + " 498, 566, 586, 595, 610, 738, 754, 866, 949, 963, 995,\n", + " 1012, 1018, 1059, 1078, 1081, 1136, 1140, 1156, 1176, 1187, 1207,\n", + " 1242, 1248, 1250, 1263, 1288, 1307, 1310, 1340, 1395])},\n", + " 7: {0: array([ 0, 1, 2, 3, 12, 17, 18, 19, 26, 51, 52,\n", + " 54, 58, 59, 67, 70, 78, 86, 94, 105, 111, 121,\n", + " 126, 131, 133, 150, 154, 166, 172, 182, 190, 192, 199,\n", + " 205, 206, 207, 209, 216, 228, 229, 231, 244, 253, 260,\n", + " 265, 266, 278, 288, 300, 301, 302, 312, 323, 324, 325,\n", + " 335, 338, 344, 370, 371, 380, 384, 388, 391, 394, 404,\n", + " 419, 421, 430, 435, 442, 450, 453, 460, 473, 484, 486,\n", + " 491, 494, 496, 499, 505, 515, 517, 521, 534, 560, 561,\n", + " 581, 582, 598, 601, 612, 618, 629, 631, 657, 668, 669,\n", + " 674, 684, 686, 700, 702, 703, 704, 711, 722, 732, 733,\n", + " 744, 747, 756, 759, 779, 780, 787, 793, 797, 802, 804,\n", + " 810, 815, 823, 828, 832, 840, 844, 852, 858, 864, 869,\n", + " 872, 873, 876, 879, 884, 885, 889, 899, 904, 905, 907,\n", + " 910, 919, 920, 952, 957, 968, 981, 983, 990, 991, 992,\n", + " 1013, 1029, 1030, 1053, 1068, 1073, 1100, 1117, 1121, 1122, 1129,\n", + " 1146, 1154, 1169, 1170, 1171, 1182, 1186, 1198, 1200, 1208, 1212,\n", + " 1218, 1221, 1230, 1237, 1239, 1268, 1282, 1292, 1296, 1301, 1308,\n", + " 1314, 1319, 1322, 1327, 1336, 1341, 1344, 1345, 1352, 1361, 1362,\n", + " 1363, 1368, 1370, 1371, 1373, 1376, 1384, 1388, 1394]),\n", + " 1: array([ 4, 14, 24, 33, 34, 50, 57, 69, 72, 73, 81,\n", + " 83, 84, 85, 91, 95, 117, 125, 134, 136, 152, 157,\n", + " 160, 161, 171, 173, 180, 183, 185, 188, 196, 198, 203,\n", + " 204, 217, 223, 224, 225, 237, 243, 256, 270, 279, 287,\n", + " 293, 313, 317, 319, 320, 321, 330, 345, 349, 356, 358,\n", + " 359, 364, 367, 377, 379, 382, 387, 395, 406, 410, 415,\n", + " 417, 440, 447, 451, 457, 458, 466, 469, 470, 479, 506,\n", + " 514, 516, 519, 523, 524, 525, 531, 538, 541, 549, 568,\n", + " 571, 576, 577, 584, 585, 588, 593, 596, 600, 613, 621,\n", + " 625, 626, 632, 638, 642, 646, 649, 651, 653, 656, 659,\n", + " 660, 663, 671, 679, 681, 693, 706, 713, 721, 723, 724,\n", + " 729, 734, 735, 736, 745, 749, 751, 752, 755, 771, 781,\n", + " 784, 791, 794, 795, 800, 801, 805, 807, 808, 813, 822,\n", + " 826, 827, 829, 854, 855, 870, 881, 892, 894, 897, 898,\n", + " 900, 902, 925, 927, 930, 945, 948, 958, 959, 976, 977,\n", + " 988, 998, 1004, 1024, 1035, 1049, 1051, 1055, 1058, 1066, 1067,\n", + " 1072, 1074, 1077, 1087, 1089, 1090, 1091, 1093, 1096, 1097, 1098,\n", + " 1099, 1104, 1107, 1112, 1114, 1116, 1120, 1128, 1133, 1139, 1143,\n", + " 1147, 1149, 1152, 1159, 1167, 1178, 1181, 1189, 1194, 1205, 1213,\n", + " 1215, 1216, 1222, 1229, 1233, 1235, 1238, 1256, 1257, 1260, 1271,\n", + " 1276, 1281, 1285, 1287, 1290, 1293, 1295, 1300, 1302, 1304, 1313,\n", + " 1325, 1331, 1338, 1343, 1347, 1356, 1357, 1365, 1366, 1382, 1391,\n", + " 1393, 1397]),\n", + " 2: array([ 5, 7, 8, 9, 10, 11, 16, 21, 23, 25, 32,\n", + " 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,\n", + " 46, 47, 53, 55, 56, 60, 61, 63, 66, 68, 77,\n", + " 79, 87, 88, 89, 90, 92, 93, 96, 98, 103, 104,\n", + " 106, 107, 108, 109, 110, 112, 113, 115, 116, 120, 127,\n", + " 130, 132, 135, 138, 139, 141, 143, 146, 148, 149, 151,\n", + " 153, 155, 156, 158, 163, 164, 165, 167, 168, 169, 175,\n", + " 179, 181, 184, 186, 187, 189, 191, 193, 194, 197, 200,\n", + " 208, 210, 211, 214, 220, 221, 222, 232, 233, 234, 235,\n", + " 236, 238, 239, 240, 241, 245, 246, 247, 248, 249, 250,\n", + " 252, 254, 257, 258, 259, 261, 264, 268, 269, 273, 276,\n", + " 277, 280, 281, 282, 284, 285, 286, 295, 297, 299, 303,\n", + " 307, 311, 314, 316, 329, 336, 340, 342, 343, 346, 347,\n", + " 348, 350, 351, 352, 354, 361, 363, 369, 374, 375, 376,\n", + " 386, 389, 390, 392, 393, 396, 397, 399, 400, 405, 408,\n", + " 409, 411, 412, 414, 416, 418, 424, 425, 427, 429, 437,\n", + " 438, 439, 449, 452, 454, 456, 459, 464, 465, 467, 468,\n", + " 474, 477, 478, 480, 487, 488, 489, 490, 492, 495, 497,\n", + " 501, 502, 504, 507, 509, 511, 512, 513, 522, 526, 529,\n", + " 532, 533, 535, 536, 537, 543, 544, 546, 550, 552, 553,\n", + " 554, 555, 556, 558, 559, 563, 564, 565, 570, 572, 573,\n", + " 575, 578, 583, 589, 590, 602, 603, 605, 606, 608, 609,\n", + " 616, 619, 620, 623, 624, 628, 633, 634, 635, 636, 640,\n", + " 643, 645, 654, 661, 662, 665, 667, 672, 673, 675, 680,\n", + " 682, 683, 687, 688, 690, 691, 692, 694, 696, 697, 698,\n", + " 699, 705, 707, 708, 709, 710, 712, 717, 718, 720, 726,\n", + " 727, 728, 730, 739, 740, 741, 743, 746, 748, 750, 753,\n", + " 758, 760, 764, 765, 766, 768, 769, 774, 776, 778, 786,\n", + " 788, 790, 798, 803, 811, 814, 817, 818, 819, 824, 825,\n", + " 830, 831, 833, 834, 835, 837, 838, 839, 842, 845, 847,\n", + " 849, 850, 856, 857, 860, 861, 862, 863, 865, 867, 868,\n", + " 871, 874, 875, 878, 880, 882, 887, 896, 901, 903, 906,\n", + " 908, 911, 922, 928, 929, 931, 932, 934, 936, 937, 938,\n", + " 939, 941, 942, 943, 946, 947, 950, 953, 954, 955, 956,\n", + " 960, 961, 962, 964, 966, 970, 971, 972, 973, 975, 980,\n", + " 984, 985, 989, 994, 996, 997, 1000, 1001, 1002, 1003, 1005,\n", + " 1006, 1008, 1010, 1011, 1014, 1016, 1017, 1019, 1020, 1021, 1025,\n", + " 1026, 1027, 1028, 1034, 1036, 1037, 1039, 1040, 1042, 1043, 1045,\n", + " 1046, 1047, 1048, 1050, 1056, 1057, 1060, 1061, 1063, 1070, 1079,\n", + " 1080, 1082, 1083, 1084, 1086, 1101, 1103, 1106, 1110, 1111, 1113,\n", + " 1115, 1118, 1119, 1124, 1125, 1127, 1131, 1132, 1134, 1135, 1137,\n", + " 1138, 1141, 1142, 1144, 1145, 1150, 1157, 1158, 1161, 1163, 1164,\n", + " 1165, 1166, 1168, 1172, 1173, 1174, 1175, 1179, 1180, 1183, 1185,\n", + " 1188, 1190, 1193, 1195, 1197, 1199, 1201, 1202, 1209, 1210, 1217,\n", + " 1219, 1220, 1224, 1225, 1227, 1228, 1231, 1240, 1241, 1243, 1244,\n", + " 1245, 1246, 1249, 1251, 1253, 1254, 1255, 1258, 1262, 1264, 1265,\n", + " 1270, 1273, 1275, 1277, 1278, 1279, 1280, 1283, 1286, 1289, 1294,\n", + " 1298, 1306, 1309, 1312, 1316, 1317, 1318, 1321, 1323, 1333, 1334,\n", + " 1335, 1337, 1342, 1348, 1349, 1350, 1353, 1355, 1358, 1359, 1369,\n", + " 1372, 1380, 1383, 1385, 1387, 1389, 1390, 1392, 1399]),\n", + " 3: array([ 6, 292, 339, 599, 670, 678, 719, 763, 783, 816, 877,\n", + " 888, 935, 993, 999, 1015, 1022, 1033, 1094, 1123, 1211]),\n", + " 4: array([ 13, 27, 28, 29, 30, 31, 48, 49, 64, 74, 80,\n", + " 82, 97, 99, 102, 114, 118, 122, 124, 128, 140, 142,\n", + " 144, 145, 159, 162, 170, 174, 177, 178, 195, 201, 202,\n", + " 212, 213, 218, 219, 227, 255, 262, 267, 271, 272, 274,\n", + " 283, 289, 290, 291, 294, 296, 298, 304, 305, 306, 310,\n", + " 315, 322, 327, 331, 332, 334, 341, 353, 357, 360, 365,\n", + " 366, 368, 372, 373, 378, 381, 383, 385, 398, 401, 402,\n", + " 407, 420, 422, 423, 426, 428, 431, 433, 434, 436, 444,\n", + " 445, 446, 448, 455, 462, 463, 471, 472, 475, 476, 482,\n", + " 483, 485, 500, 503, 508, 510, 518, 520, 527, 528, 530,\n", + " 539, 540, 545, 547, 548, 551, 567, 569, 574, 579, 580,\n", + " 591, 592, 594, 604, 607, 611, 614, 615, 617, 622, 627,\n", + " 637, 639, 644, 647, 648, 650, 652, 658, 666, 676, 677,\n", + " 685, 689, 695, 714, 715, 716, 731, 742, 757, 761, 762,\n", + " 767, 772, 775, 777, 782, 785, 789, 792, 796, 799, 812,\n", + " 841, 843, 846, 848, 853, 883, 886, 890, 891, 909, 912,\n", + " 913, 914, 916, 917, 918, 921, 923, 924, 926, 933, 940,\n", + " 944, 951, 965, 974, 979, 986, 987, 1009, 1023, 1031, 1038,\n", + " 1041, 1044, 1052, 1054, 1062, 1064, 1065, 1069, 1071, 1075, 1085,\n", + " 1088, 1092, 1095, 1102, 1108, 1109, 1126, 1130, 1148, 1155, 1160,\n", + " 1162, 1177, 1184, 1192, 1196, 1203, 1204, 1206, 1214, 1226, 1232,\n", + " 1234, 1236, 1247, 1252, 1259, 1261, 1266, 1267, 1269, 1272, 1274,\n", + " 1291, 1299, 1303, 1305, 1311, 1315, 1320, 1324, 1326, 1328, 1329,\n", + " 1330, 1332, 1339, 1346, 1351, 1364, 1367, 1374, 1375, 1378, 1386,\n", + " 1398]),\n", + " 5: array([ 15, 20, 22, 65, 76, 101, 119, 123, 129, 137, 147,\n", + " 215, 226, 230, 242, 251, 263, 275, 309, 318, 328, 337,\n", + " 355, 362, 403, 413, 432, 461, 493, 542, 557, 562, 587,\n", + " 597, 630, 641, 655, 664, 701, 725, 737, 770, 773, 806,\n", + " 809, 820, 821, 836, 851, 859, 893, 895, 915, 967, 969,\n", + " 978, 982, 1007, 1032, 1076, 1105, 1151, 1153, 1191, 1223, 1284,\n", + " 1297, 1354, 1360, 1377, 1379, 1381, 1396]),\n", + " 6: array([ 62, 71, 75, 100, 176, 308, 326, 333, 441, 443, 481,\n", + " 498, 566, 586, 595, 610, 738, 754, 866, 949, 963, 995,\n", + " 1012, 1018, 1059, 1078, 1081, 1136, 1140, 1156, 1176, 1187, 1207,\n", + " 1242, 1248, 1250, 1263, 1288, 1307, 1310, 1340, 1395])},\n", + " 8: {0: array([ 0, 1, 2, 3, 12, 17, 18, 19, 26, 51, 52,\n", + " 54, 58, 59, 67, 70, 78, 86, 94, 105, 111, 121,\n", + " 126, 131, 133, 150, 154, 166, 172, 182, 190, 192, 199,\n", + " 205, 206, 207, 209, 216, 228, 229, 231, 244, 253, 260,\n", + " 265, 266, 278, 288, 300, 301, 302, 312, 323, 324, 325,\n", + " 335, 338, 344, 370, 371, 380, 384, 388, 391, 394, 404,\n", + " 419, 421, 430, 435, 442, 450, 453, 460, 473, 484, 486,\n", + " 491, 494, 496, 499, 505, 515, 517, 521, 534, 560, 561,\n", + " 581, 582, 598, 601, 612, 618, 629, 631, 657, 668, 669,\n", + " 674, 684, 686, 700, 702, 703, 704, 711, 722, 732, 733,\n", + " 744, 747, 756, 759, 779, 780, 787, 793, 797, 802, 804,\n", + " 810, 815, 823, 828, 832, 840, 844, 852, 858, 864, 869,\n", + " 872, 873, 876, 879, 884, 885, 889, 899, 904, 905, 907,\n", + " 910, 919, 920, 952, 957, 968, 981, 983, 990, 991, 992,\n", + " 1013, 1029, 1030, 1053, 1068, 1073, 1100, 1117, 1121, 1122, 1129,\n", + " 1146, 1154, 1169, 1170, 1171, 1182, 1186, 1198, 1200, 1208, 1212,\n", + " 1218, 1221, 1230, 1237, 1239, 1268, 1282, 1292, 1296, 1301, 1308,\n", + " 1314, 1319, 1322, 1327, 1336, 1341, 1344, 1345, 1352, 1361, 1362,\n", + " 1363, 1368, 1370, 1371, 1373, 1376, 1384, 1388, 1394]),\n", + " 1: array([ 4, 14, 24, 33, 34, 50, 57, 69, 72, 73, 81,\n", + " 83, 84, 85, 91, 95, 117, 125, 134, 136, 152, 157,\n", + " 160, 161, 171, 173, 180, 183, 185, 188, 196, 198, 203,\n", + " 204, 217, 223, 224, 225, 237, 243, 256, 270, 279, 287,\n", + " 293, 313, 317, 319, 320, 321, 330, 345, 349, 356, 358,\n", + " 359, 364, 367, 377, 379, 382, 387, 395, 406, 410, 415,\n", + " 417, 440, 447, 451, 457, 458, 466, 469, 470, 479, 506,\n", + " 514, 516, 519, 523, 524, 525, 531, 538, 541, 549, 568,\n", + " 571, 576, 577, 584, 585, 588, 593, 596, 600, 613, 621,\n", + " 625, 626, 632, 638, 642, 646, 649, 651, 653, 656, 659,\n", + " 660, 663, 671, 679, 681, 693, 706, 713, 721, 723, 724,\n", + " 729, 734, 735, 736, 745, 749, 751, 752, 755, 771, 781,\n", + " 784, 791, 794, 795, 800, 801, 805, 807, 808, 813, 822,\n", + " 826, 827, 829, 854, 855, 870, 881, 892, 894, 897, 898,\n", + " 900, 902, 925, 927, 930, 945, 948, 958, 959, 976, 977,\n", + " 988, 998, 1004, 1024, 1035, 1049, 1051, 1055, 1058, 1066, 1067,\n", + " 1072, 1074, 1077, 1087, 1089, 1090, 1091, 1093, 1096, 1097, 1098,\n", + " 1099, 1104, 1107, 1112, 1114, 1116, 1120, 1128, 1133, 1139, 1143,\n", + " 1147, 1149, 1152, 1159, 1167, 1178, 1181, 1189, 1194, 1205, 1213,\n", + " 1215, 1216, 1222, 1229, 1233, 1235, 1238, 1256, 1257, 1260, 1271,\n", + " 1276, 1281, 1285, 1287, 1290, 1293, 1295, 1300, 1302, 1304, 1313,\n", + " 1325, 1331, 1338, 1343, 1347, 1356, 1357, 1365, 1366, 1382, 1391,\n", + " 1393, 1397]),\n", + " 2: array([ 5, 7, 8, 9, 21, 25, 32, 36, 38, 42, 43,\n", + " 46, 47, 56, 60, 66, 68, 77, 79, 87, 92, 108,\n", + " 112, 115, 120, 130, 135, 138, 139, 141, 143, 146, 148,\n", + " 149, 153, 158, 164, 167, 169, 175, 179, 189, 194, 197,\n", + " 200, 211, 214, 221, 222, 232, 235, 236, 240, 241, 245,\n", + " 246, 247, 248, 249, 252, 254, 259, 261, 264, 269, 273,\n", + " 277, 280, 282, 299, 307, 311, 314, 316, 340, 346, 347,\n", + " 352, 354, 361, 363, 369, 386, 389, 392, 393, 397, 400,\n", + " 408, 411, 412, 416, 418, 425, 427, 429, 449, 452, 454,\n", + " 459, 464, 465, 467, 468, 477, 480, 489, 490, 492, 495,\n", + " 501, 507, 509, 512, 522, 529, 532, 533, 535, 537, 543,\n", + " 544, 546, 550, 552, 554, 558, 564, 572, 589, 602, 603,\n", + " 606, 616, 620, 624, 628, 633, 634, 635, 640, 643, 645,\n", + " 654, 662, 673, 675, 680, 682, 683, 688, 690, 691, 696,\n", + " 697, 698, 707, 709, 710, 717, 718, 728, 730, 739, 740,\n", + " 741, 743, 746, 748, 750, 764, 766, 768, 769, 774, 798,\n", + " 803, 811, 814, 818, 819, 824, 825, 830, 831, 835, 837,\n", + " 839, 842, 849, 856, 857, 861, 862, 863, 867, 868, 871,\n", + " 874, 887, 896, 901, 908, 911, 928, 934, 936, 937, 938,\n", + " 939, 941, 942, 943, 947, 950, 953, 960, 961, 962, 964,\n", + " 970, 971, 975, 980, 984, 985, 996, 997, 1001, 1003, 1005,\n", + " 1006, 1011, 1021, 1025, 1026, 1028, 1037, 1039, 1042, 1045, 1046,\n", + " 1047, 1048, 1057, 1060, 1063, 1080, 1083, 1084, 1103, 1110, 1113,\n", + " 1119, 1124, 1131, 1135, 1137, 1138, 1150, 1157, 1158, 1161, 1163,\n", + " 1164, 1165, 1166, 1173, 1174, 1175, 1179, 1180, 1183, 1185, 1193,\n", + " 1195, 1199, 1201, 1202, 1209, 1210, 1219, 1220, 1225, 1227, 1231,\n", + " 1240, 1241, 1244, 1253, 1255, 1258, 1264, 1273, 1278, 1279, 1283,\n", + " 1286, 1289, 1306, 1309, 1316, 1321, 1334, 1337, 1342, 1348, 1349,\n", + " 1355, 1358, 1369, 1372, 1380, 1387, 1390]),\n", + " 3: array([ 6, 292, 339, 599, 670, 678, 719, 763, 783, 816, 877,\n", + " 888, 935, 993, 999, 1015, 1022, 1033, 1094, 1123, 1211]),\n", + " 4: array([ 10, 11, 16, 23, 35, 37, 39, 40, 41, 44, 45,\n", + " 53, 55, 61, 63, 88, 89, 90, 93, 96, 98, 103,\n", + " 104, 106, 107, 109, 110, 113, 116, 127, 132, 151, 155,\n", + " 156, 163, 165, 168, 181, 184, 186, 187, 191, 193, 208,\n", + " 210, 220, 233, 234, 238, 239, 250, 257, 258, 268, 276,\n", + " 281, 284, 285, 286, 295, 297, 303, 329, 336, 342, 343,\n", + " 348, 350, 351, 374, 375, 376, 390, 396, 399, 405, 409,\n", + " 414, 424, 437, 438, 439, 456, 474, 478, 487, 488, 497,\n", + " 502, 504, 511, 513, 526, 536, 553, 555, 556, 559, 563,\n", + " 565, 570, 573, 575, 578, 583, 590, 605, 608, 609, 619,\n", + " 623, 636, 661, 665, 667, 672, 687, 692, 694, 699, 705,\n", + " 708, 712, 720, 726, 727, 753, 758, 760, 765, 776, 778,\n", + " 786, 788, 790, 817, 833, 834, 838, 845, 847, 850, 860,\n", + " 865, 875, 878, 880, 882, 903, 906, 922, 929, 931, 932,\n", + " 946, 954, 955, 956, 966, 972, 973, 989, 994, 1000, 1002,\n", + " 1008, 1010, 1014, 1016, 1017, 1019, 1020, 1027, 1034, 1036, 1040,\n", + " 1043, 1050, 1056, 1061, 1070, 1079, 1082, 1086, 1101, 1106, 1111,\n", + " 1115, 1118, 1125, 1127, 1132, 1134, 1141, 1142, 1144, 1145, 1168,\n", + " 1172, 1188, 1190, 1197, 1217, 1224, 1228, 1243, 1245, 1246, 1249,\n", + " 1251, 1254, 1262, 1265, 1270, 1275, 1277, 1280, 1294, 1298, 1312,\n", + " 1317, 1318, 1323, 1333, 1335, 1350, 1353, 1359, 1383, 1385, 1389,\n", + " 1392, 1399]),\n", + " 5: array([ 13, 27, 28, 29, 30, 31, 48, 49, 64, 74, 80,\n", + " 82, 97, 99, 102, 114, 118, 122, 124, 128, 140, 142,\n", + " 144, 145, 159, 162, 170, 174, 177, 178, 195, 201, 202,\n", + " 212, 213, 218, 219, 227, 255, 262, 267, 271, 272, 274,\n", + " 283, 289, 290, 291, 294, 296, 298, 304, 305, 306, 310,\n", + " 315, 322, 327, 331, 332, 334, 341, 353, 357, 360, 365,\n", + " 366, 368, 372, 373, 378, 381, 383, 385, 398, 401, 402,\n", + " 407, 420, 422, 423, 426, 428, 431, 433, 434, 436, 444,\n", + " 445, 446, 448, 455, 462, 463, 471, 472, 475, 476, 482,\n", + " 483, 485, 500, 503, 508, 510, 518, 520, 527, 528, 530,\n", + " 539, 540, 545, 547, 548, 551, 567, 569, 574, 579, 580,\n", + " 591, 592, 594, 604, 607, 611, 614, 615, 617, 622, 627,\n", + " 637, 639, 644, 647, 648, 650, 652, 658, 666, 676, 677,\n", + " 685, 689, 695, 714, 715, 716, 731, 742, 757, 761, 762,\n", + " 767, 772, 775, 777, 782, 785, 789, 792, 796, 799, 812,\n", + " 841, 843, 846, 848, 853, 883, 886, 890, 891, 909, 912,\n", + " 913, 914, 916, 917, 918, 921, 923, 924, 926, 933, 940,\n", + " 944, 951, 965, 974, 979, 986, 987, 1009, 1023, 1031, 1038,\n", + " 1041, 1044, 1052, 1054, 1062, 1064, 1065, 1069, 1071, 1075, 1085,\n", + " 1088, 1092, 1095, 1102, 1108, 1109, 1126, 1130, 1148, 1155, 1160,\n", + " 1162, 1177, 1184, 1192, 1196, 1203, 1204, 1206, 1214, 1226, 1232,\n", + " 1234, 1236, 1247, 1252, 1259, 1261, 1266, 1267, 1269, 1272, 1274,\n", + " 1291, 1299, 1303, 1305, 1311, 1315, 1320, 1324, 1326, 1328, 1329,\n", + " 1330, 1332, 1339, 1346, 1351, 1364, 1367, 1374, 1375, 1378, 1386,\n", + " 1398]),\n", + " 6: array([ 15, 20, 22, 65, 76, 101, 119, 123, 129, 137, 147,\n", + " 215, 226, 230, 242, 251, 263, 275, 309, 318, 328, 337,\n", + " 355, 362, 403, 413, 432, 461, 493, 542, 557, 562, 587,\n", + " 597, 630, 641, 655, 664, 701, 725, 737, 770, 773, 806,\n", + " 809, 820, 821, 836, 851, 859, 893, 895, 915, 967, 969,\n", + " 978, 982, 1007, 1032, 1076, 1105, 1151, 1153, 1191, 1223, 1284,\n", + " 1297, 1354, 1360, 1377, 1379, 1381, 1396]),\n", + " 7: array([ 62, 71, 75, 100, 176, 308, 326, 333, 441, 443, 481,\n", + " 498, 566, 586, 595, 610, 738, 754, 866, 949, 963, 995,\n", + " 1012, 1018, 1059, 1078, 1081, 1136, 1140, 1156, 1176, 1187, 1207,\n", + " 1242, 1248, 1250, 1263, 1288, 1307, 1310, 1340, 1395])},\n", + " 9: {0: array([ 0, 1, 2, 3, 12, 17, 18, 19, 26, 51, 52,\n", + " 54, 58, 59, 67, 70, 78, 86, 94, 105, 111, 121,\n", + " 126, 131, 133, 150, 154, 166, 172, 182, 190, 192, 199,\n", + " 205, 206, 207, 209, 216, 228, 229, 231, 244, 253, 260,\n", + " 265, 266, 278, 288, 300, 301, 302, 312, 323, 324, 325,\n", + " 335, 338, 344, 370, 371, 380, 384, 388, 391, 394, 404,\n", + " 419, 421, 430, 435, 442, 450, 453, 460, 473, 484, 486,\n", + " 491, 494, 496, 499, 505, 515, 517, 521, 534, 560, 561,\n", + " 581, 582, 598, 601, 612, 618, 629, 631, 657, 668, 669,\n", + " 674, 684, 686, 700, 702, 703, 704, 711, 722, 732, 733,\n", + " 744, 747, 756, 759, 779, 780, 787, 793, 797, 802, 804,\n", + " 810, 815, 823, 828, 832, 840, 844, 852, 858, 864, 869,\n", + " 872, 873, 876, 879, 884, 885, 889, 899, 904, 905, 907,\n", + " 910, 919, 920, 952, 957, 968, 981, 983, 990, 991, 992,\n", + " 1013, 1029, 1030, 1053, 1068, 1073, 1100, 1117, 1121, 1122, 1129,\n", + " 1146, 1154, 1169, 1170, 1171, 1182, 1186, 1198, 1200, 1208, 1212,\n", + " 1218, 1221, 1230, 1237, 1239, 1268, 1282, 1292, 1296, 1301, 1308,\n", + " 1314, 1319, 1322, 1327, 1336, 1341, 1344, 1345, 1352, 1361, 1362,\n", + " 1363, 1368, 1370, 1371, 1373, 1376, 1384, 1388, 1394]),\n", + " 1: array([ 4, 14, 24, 33, 34, 50, 57, 69, 72, 73, 81,\n", + " 83, 84, 85, 91, 95, 117, 125, 134, 136, 152, 157,\n", + " 160, 161, 171, 173, 180, 183, 185, 188, 196, 198, 203,\n", + " 204, 217, 223, 224, 225, 237, 243, 256, 270, 279, 287,\n", + " 293, 313, 317, 319, 320, 321, 330, 345, 349, 356, 358,\n", + " 359, 364, 367, 377, 379, 382, 387, 395, 406, 410, 415,\n", + " 417, 440, 447, 451, 457, 458, 466, 469, 470, 479, 506,\n", + " 514, 516, 519, 523, 524, 525, 531, 538, 541, 549, 568,\n", + " 571, 576, 577, 584, 585, 588, 593, 596, 600, 613, 621,\n", + " 625, 626, 632, 638, 642, 646, 649, 651, 653, 656, 659,\n", + " 660, 663, 671, 679, 681, 693, 706, 713, 721, 723, 724,\n", + " 729, 734, 735, 736, 745, 749, 751, 752, 755, 771, 781,\n", + " 784, 791, 794, 795, 800, 801, 805, 807, 808, 813, 822,\n", + " 826, 827, 829, 854, 855, 870, 881, 892, 894, 897, 898,\n", + " 900, 902, 925, 927, 930, 945, 948, 958, 959, 976, 977,\n", + " 988, 998, 1004, 1024, 1035, 1049, 1051, 1055, 1058, 1066, 1067,\n", + " 1072, 1074, 1077, 1087, 1089, 1090, 1091, 1093, 1096, 1097, 1098,\n", + " 1099, 1104, 1107, 1112, 1114, 1116, 1120, 1128, 1133, 1139, 1143,\n", + " 1147, 1149, 1152, 1159, 1167, 1178, 1181, 1189, 1194, 1205, 1213,\n", + " 1215, 1216, 1222, 1229, 1233, 1235, 1238, 1256, 1257, 1260, 1271,\n", + " 1276, 1281, 1285, 1287, 1290, 1293, 1295, 1300, 1302, 1304, 1313,\n", + " 1325, 1331, 1338, 1343, 1347, 1356, 1357, 1365, 1366, 1382, 1391,\n", + " 1393, 1397]),\n", + " 2: array([ 5, 7, 8, 9, 21, 25, 32, 36, 38, 42, 43,\n", + " 46, 47, 56, 60, 66, 68, 77, 79, 87, 92, 108,\n", + " 112, 115, 120, 130, 135, 138, 139, 141, 143, 146, 148,\n", + " 149, 153, 158, 164, 167, 169, 175, 179, 189, 194, 197,\n", + " 200, 211, 214, 221, 222, 232, 235, 236, 240, 241, 245,\n", + " 246, 247, 248, 249, 252, 254, 259, 261, 264, 269, 273,\n", + " 277, 280, 282, 299, 307, 311, 314, 316, 340, 346, 347,\n", + " 352, 354, 361, 363, 369, 386, 389, 392, 393, 397, 400,\n", + " 408, 411, 412, 416, 418, 425, 427, 429, 449, 452, 454,\n", + " 459, 464, 465, 467, 468, 477, 480, 489, 490, 492, 495,\n", + " 501, 507, 509, 512, 522, 529, 532, 533, 535, 537, 543,\n", + " 544, 546, 550, 552, 554, 558, 564, 572, 589, 602, 603,\n", + " 606, 616, 620, 624, 628, 633, 634, 635, 640, 643, 645,\n", + " 654, 662, 673, 675, 680, 682, 683, 688, 690, 691, 696,\n", + " 697, 698, 707, 709, 710, 717, 718, 728, 730, 739, 740,\n", + " 741, 743, 746, 748, 750, 764, 766, 768, 769, 774, 798,\n", + " 803, 811, 814, 818, 819, 824, 825, 830, 831, 835, 837,\n", + " 839, 842, 849, 856, 857, 861, 862, 863, 867, 868, 871,\n", + " 874, 887, 896, 901, 908, 911, 928, 934, 936, 937, 938,\n", + " 939, 941, 942, 943, 947, 950, 953, 960, 961, 962, 964,\n", + " 970, 971, 975, 980, 984, 985, 996, 997, 1001, 1003, 1005,\n", + " 1006, 1011, 1021, 1025, 1026, 1028, 1037, 1039, 1042, 1045, 1046,\n", + " 1047, 1048, 1057, 1060, 1063, 1080, 1083, 1084, 1103, 1110, 1113,\n", + " 1119, 1124, 1131, 1135, 1137, 1138, 1150, 1157, 1158, 1161, 1163,\n", + " 1164, 1165, 1166, 1173, 1174, 1175, 1179, 1180, 1183, 1185, 1193,\n", + " 1195, 1199, 1201, 1202, 1209, 1210, 1219, 1220, 1225, 1227, 1231,\n", + " 1240, 1241, 1244, 1253, 1255, 1258, 1264, 1273, 1278, 1279, 1283,\n", + " 1286, 1289, 1306, 1309, 1316, 1321, 1334, 1337, 1342, 1348, 1349,\n", + " 1355, 1358, 1369, 1372, 1380, 1387, 1390]),\n", + " 3: array([ 6, 292, 339, 599, 670, 678, 719, 763, 783, 816, 877,\n", + " 888, 935, 993, 999, 1015, 1022, 1033, 1094, 1123, 1211]),\n", + " 4: array([ 10, 11, 16, 23, 35, 37, 39, 40, 41, 44, 45,\n", + " 53, 55, 61, 63, 88, 89, 90, 93, 96, 98, 103,\n", + " 104, 106, 107, 109, 110, 113, 116, 127, 132, 151, 155,\n", + " 156, 163, 165, 168, 181, 184, 186, 187, 191, 193, 208,\n", + " 210, 220, 233, 234, 238, 239, 250, 257, 258, 268, 276,\n", + " 281, 284, 285, 286, 295, 297, 303, 329, 336, 342, 343,\n", + " 348, 350, 351, 374, 375, 376, 390, 396, 399, 405, 409,\n", + " 414, 424, 437, 438, 439, 456, 474, 478, 487, 488, 497,\n", + " 502, 504, 511, 513, 526, 536, 553, 555, 556, 559, 563,\n", + " 565, 570, 573, 575, 578, 583, 590, 605, 608, 609, 619,\n", + " 623, 636, 661, 665, 667, 672, 687, 692, 694, 699, 705,\n", + " 708, 712, 720, 726, 727, 753, 758, 760, 765, 776, 778,\n", + " 786, 788, 790, 817, 833, 834, 838, 845, 847, 850, 860,\n", + " 865, 875, 878, 880, 882, 903, 906, 922, 929, 931, 932,\n", + " 946, 954, 955, 956, 966, 972, 973, 989, 994, 1000, 1002,\n", + " 1008, 1010, 1014, 1016, 1017, 1019, 1020, 1027, 1034, 1036, 1040,\n", + " 1043, 1050, 1056, 1061, 1070, 1079, 1082, 1086, 1101, 1106, 1111,\n", + " 1115, 1118, 1125, 1127, 1132, 1134, 1141, 1142, 1144, 1145, 1168,\n", + " 1172, 1188, 1190, 1197, 1217, 1224, 1228, 1243, 1245, 1246, 1249,\n", + " 1251, 1254, 1262, 1265, 1270, 1275, 1277, 1280, 1294, 1298, 1312,\n", + " 1317, 1318, 1323, 1333, 1335, 1350, 1353, 1359, 1383, 1385, 1389,\n", + " 1392, 1399]),\n", + " 5: array([ 13, 27, 28, 29, 30, 31, 48, 49, 64, 74, 80,\n", + " 82, 97, 99, 102, 114, 118, 122, 124, 128, 140, 142,\n", + " 144, 145, 159, 162, 170, 174, 177, 178, 195, 201, 202,\n", + " 212, 213, 218, 219, 227, 255, 262, 267, 271, 272, 274,\n", + " 283, 289, 290, 291, 294, 296, 298, 304, 305, 306, 310,\n", + " 315, 322, 327, 331, 332, 334, 341, 353, 357, 360, 365,\n", + " 366, 368, 372, 373, 378, 381, 383, 385, 398, 401, 402,\n", + " 407, 420, 422, 423, 426, 428, 431, 433, 434, 436, 444,\n", + " 445, 446, 448, 455, 462, 463, 471, 472, 475, 476, 482,\n", + " 483, 485, 500, 503, 508, 510, 518, 520, 527, 528, 530,\n", + " 539, 540, 545, 547, 548, 551, 567, 569, 574, 579, 580,\n", + " 591, 592, 594, 604, 607, 611, 614, 615, 617, 622, 627,\n", + " 637, 639, 644, 647, 648, 650, 652, 658, 666, 676, 677,\n", + " 685, 689, 695, 714, 715, 716, 731, 742, 757, 761, 762,\n", + " 767, 772, 775, 777, 782, 785, 789, 792, 796, 799, 812,\n", + " 841, 843, 846, 848, 853, 883, 886, 890, 891, 909, 912,\n", + " 913, 914, 916, 917, 918, 921, 923, 924, 926, 933, 940,\n", + " 944, 951, 965, 974, 979, 986, 987, 1009, 1023, 1031, 1038,\n", + " 1041, 1044, 1052, 1054, 1062, 1064, 1065, 1069, 1071, 1075, 1085,\n", + " 1088, 1092, 1095, 1102, 1108, 1109, 1126, 1130, 1148, 1155, 1160,\n", + " 1162, 1177, 1184, 1192, 1196, 1203, 1204, 1206, 1214, 1226, 1232,\n", + " 1234, 1236, 1247, 1252, 1259, 1261, 1266, 1267, 1269, 1272, 1274,\n", + " 1291, 1299, 1303, 1305, 1311, 1315, 1320, 1324, 1326, 1328, 1329,\n", + " 1330, 1332, 1339, 1346, 1351, 1364, 1367, 1374, 1375, 1378, 1386,\n", + " 1398]),\n", + " 6: array([ 15, 20, 22, 65, 119, 137, 147, 215, 230, 242, 309,\n", + " 318, 328, 337, 355, 432, 461, 493, 542, 562, 587, 597,\n", + " 701, 770, 806, 809, 821, 836, 859, 895, 915, 978, 1032,\n", + " 1151, 1223, 1354, 1360, 1377, 1379, 1381]),\n", + " 7: array([ 62, 71, 75, 100, 176, 308, 326, 333, 441, 443, 481,\n", + " 498, 566, 586, 595, 610, 738, 754, 866, 949, 963, 995,\n", + " 1012, 1018, 1059, 1078, 1081, 1136, 1140, 1156, 1176, 1187, 1207,\n", + " 1242, 1248, 1250, 1263, 1288, 1307, 1310, 1340, 1395]),\n", + " 8: array([ 76, 101, 123, 129, 226, 251, 263, 275, 362, 403, 413,\n", + " 557, 630, 641, 655, 664, 725, 737, 773, 820, 851, 893,\n", + " 967, 969, 982, 1007, 1076, 1105, 1153, 1191, 1284, 1297, 1396])},\n", + " 10: {0: array([ 0, 1, 2, 3, 12, 17, 18, 19, 26, 51, 52,\n", + " 54, 58, 59, 67, 70, 78, 86, 94, 105, 111, 121,\n", + " 126, 131, 133, 150, 154, 166, 172, 182, 190, 192, 199,\n", + " 205, 206, 207, 209, 216, 228, 229, 231, 244, 253, 260,\n", + " 265, 266, 278, 288, 300, 301, 302, 312, 323, 324, 325,\n", + " 335, 338, 344, 370, 371, 380, 384, 388, 391, 394, 404,\n", + " 419, 421, 430, 435, 442, 450, 453, 460, 473, 484, 486,\n", + " 491, 494, 496, 499, 505, 515, 517, 521, 534, 560, 561,\n", + " 581, 582, 598, 601, 612, 618, 629, 631, 657, 668, 669,\n", + " 674, 684, 686, 700, 702, 703, 704, 711, 722, 732, 733,\n", + " 744, 747, 756, 759, 779, 780, 787, 793, 797, 802, 804,\n", + " 810, 815, 823, 828, 832, 840, 844, 852, 858, 864, 869,\n", + " 872, 873, 876, 879, 884, 885, 889, 899, 904, 905, 907,\n", + " 910, 919, 920, 952, 957, 968, 981, 983, 990, 991, 992,\n", + " 1013, 1029, 1030, 1053, 1068, 1073, 1100, 1117, 1121, 1122, 1129,\n", + " 1146, 1154, 1169, 1170, 1171, 1182, 1186, 1198, 1200, 1208, 1212,\n", + " 1218, 1221, 1230, 1237, 1239, 1268, 1282, 1292, 1296, 1301, 1308,\n", + " 1314, 1319, 1322, 1327, 1336, 1341, 1344, 1345, 1352, 1361, 1362,\n", + " 1363, 1368, 1370, 1371, 1373, 1376, 1384, 1388, 1394]),\n", + " 1: array([ 4, 14, 24, 33, 34, 50, 57, 69, 72, 73, 81,\n", + " 83, 84, 85, 91, 95, 117, 125, 134, 136, 152, 157,\n", + " 160, 161, 171, 173, 180, 183, 185, 188, 196, 198, 203,\n", + " 204, 217, 223, 224, 225, 237, 243, 256, 270, 279, 287,\n", + " 293, 313, 317, 319, 320, 321, 330, 345, 349, 356, 358,\n", + " 359, 364, 367, 377, 379, 382, 387, 395, 406, 410, 415,\n", + " 417, 440, 447, 451, 457, 458, 466, 469, 470, 479, 506,\n", + " 514, 516, 519, 523, 524, 525, 531, 538, 541, 549, 568,\n", + " 571, 576, 577, 584, 585, 588, 593, 596, 600, 613, 621,\n", + " 625, 626, 632, 638, 642, 646, 649, 651, 653, 656, 659,\n", + " 660, 663, 671, 679, 681, 693, 706, 713, 721, 723, 724,\n", + " 729, 734, 735, 736, 745, 749, 751, 752, 755, 771, 781,\n", + " 784, 791, 794, 795, 800, 801, 805, 807, 808, 813, 822,\n", + " 826, 827, 829, 854, 855, 870, 881, 892, 894, 897, 898,\n", + " 900, 902, 925, 927, 930, 945, 948, 958, 959, 976, 977,\n", + " 988, 998, 1004, 1024, 1035, 1049, 1051, 1055, 1058, 1066, 1067,\n", + " 1072, 1074, 1077, 1087, 1089, 1090, 1091, 1093, 1096, 1097, 1098,\n", + " 1099, 1104, 1107, 1112, 1114, 1116, 1120, 1128, 1133, 1139, 1143,\n", + " 1147, 1149, 1152, 1159, 1167, 1178, 1181, 1189, 1194, 1205, 1213,\n", + " 1215, 1216, 1222, 1229, 1233, 1235, 1238, 1256, 1257, 1260, 1271,\n", + " 1276, 1281, 1285, 1287, 1290, 1293, 1295, 1300, 1302, 1304, 1313,\n", + " 1325, 1331, 1338, 1343, 1347, 1356, 1357, 1365, 1366, 1382, 1391,\n", + " 1393, 1397]),\n", + " 2: array([ 5, 32, 38, 46, 66, 92, 108, 139, 141, 143, 148,\n", + " 149, 158, 167, 175, 179, 197, 211, 232, 252, 264, 273,\n", + " 277, 340, 354, 386, 400, 412, 429, 449, 459, 467, 468,\n", + " 480, 489, 501, 509, 512, 543, 552, 564, 620, 624, 634,\n", + " 643, 645, 675, 690, 707, 709, 718, 769, 774, 798, 803,\n", + " 839, 867, 896, 938, 939, 942, 943, 947, 961, 964, 997,\n", + " 1011, 1021, 1028, 1084, 1103, 1113, 1119, 1135, 1137, 1163, 1164,\n", + " 1165, 1201, 1209, 1244, 1253, 1264, 1289, 1321, 1337, 1355, 1372]),\n", + " 3: array([ 6, 292, 339, 599, 670, 678, 719, 763, 783, 816, 877,\n", + " 888, 935, 993, 999, 1015, 1022, 1033, 1094, 1123, 1211]),\n", + " 4: array([ 7, 8, 9, 21, 25, 36, 42, 43, 47, 56, 60,\n", + " 68, 77, 79, 87, 112, 115, 120, 130, 135, 138, 146,\n", + " 153, 164, 169, 189, 194, 200, 214, 221, 222, 235, 236,\n", + " 240, 241, 245, 246, 247, 248, 249, 254, 259, 261, 269,\n", + " 280, 282, 299, 307, 311, 314, 316, 346, 347, 352, 361,\n", + " 363, 369, 389, 392, 393, 397, 408, 411, 416, 418, 425,\n", + " 427, 452, 454, 464, 465, 477, 490, 492, 495, 507, 522,\n", + " 529, 532, 533, 535, 537, 544, 546, 550, 554, 558, 572,\n", + " 589, 602, 603, 606, 616, 628, 633, 635, 640, 654, 662,\n", + " 673, 680, 682, 683, 688, 691, 696, 697, 698, 710, 717,\n", + " 728, 730, 739, 740, 741, 743, 746, 748, 750, 764, 766,\n", + " 768, 811, 814, 818, 819, 824, 825, 830, 831, 835, 837,\n", + " 842, 849, 856, 857, 861, 862, 863, 868, 871, 874, 887,\n", + " 901, 908, 911, 928, 934, 936, 937, 941, 950, 953, 960,\n", + " 962, 970, 971, 975, 980, 984, 985, 996, 1001, 1003, 1005,\n", + " 1006, 1025, 1026, 1037, 1039, 1042, 1045, 1046, 1047, 1048, 1057,\n", + " 1060, 1063, 1080, 1083, 1110, 1124, 1131, 1138, 1150, 1157, 1158,\n", + " 1161, 1166, 1173, 1174, 1175, 1179, 1180, 1183, 1185, 1193, 1195,\n", + " 1199, 1202, 1210, 1219, 1220, 1225, 1227, 1231, 1240, 1241, 1255,\n", + " 1258, 1273, 1278, 1279, 1283, 1286, 1306, 1309, 1316, 1334, 1342,\n", + " 1348, 1349, 1358, 1369, 1380, 1387, 1390]),\n", + " 5: array([ 10, 11, 16, 23, 35, 37, 39, 40, 41, 44, 45,\n", + " 53, 55, 61, 63, 88, 89, 90, 93, 96, 98, 103,\n", + " 104, 106, 107, 109, 110, 113, 116, 127, 132, 151, 155,\n", + " 156, 163, 165, 168, 181, 184, 186, 187, 191, 193, 208,\n", + " 210, 220, 233, 234, 238, 239, 250, 257, 258, 268, 276,\n", + " 281, 284, 285, 286, 295, 297, 303, 329, 336, 342, 343,\n", + " 348, 350, 351, 374, 375, 376, 390, 396, 399, 405, 409,\n", + " 414, 424, 437, 438, 439, 456, 474, 478, 487, 488, 497,\n", + " 502, 504, 511, 513, 526, 536, 553, 555, 556, 559, 563,\n", + " 565, 570, 573, 575, 578, 583, 590, 605, 608, 609, 619,\n", + " 623, 636, 661, 665, 667, 672, 687, 692, 694, 699, 705,\n", + " 708, 712, 720, 726, 727, 753, 758, 760, 765, 776, 778,\n", + " 786, 788, 790, 817, 833, 834, 838, 845, 847, 850, 860,\n", + " 865, 875, 878, 880, 882, 903, 906, 922, 929, 931, 932,\n", + " 946, 954, 955, 956, 966, 972, 973, 989, 994, 1000, 1002,\n", + " 1008, 1010, 1014, 1016, 1017, 1019, 1020, 1027, 1034, 1036, 1040,\n", + " 1043, 1050, 1056, 1061, 1070, 1079, 1082, 1086, 1101, 1106, 1111,\n", + " 1115, 1118, 1125, 1127, 1132, 1134, 1141, 1142, 1144, 1145, 1168,\n", + " 1172, 1188, 1190, 1197, 1217, 1224, 1228, 1243, 1245, 1246, 1249,\n", + " 1251, 1254, 1262, 1265, 1270, 1275, 1277, 1280, 1294, 1298, 1312,\n", + " 1317, 1318, 1323, 1333, 1335, 1350, 1353, 1359, 1383, 1385, 1389,\n", + " 1392, 1399]),\n", + " 6: array([ 13, 27, 28, 29, 30, 31, 48, 49, 64, 74, 80,\n", + " 82, 97, 99, 102, 114, 118, 122, 124, 128, 140, 142,\n", + " 144, 145, 159, 162, 170, 174, 177, 178, 195, 201, 202,\n", + " 212, 213, 218, 219, 227, 255, 262, 267, 271, 272, 274,\n", + " 283, 289, 290, 291, 294, 296, 298, 304, 305, 306, 310,\n", + " 315, 322, 327, 331, 332, 334, 341, 353, 357, 360, 365,\n", + " 366, 368, 372, 373, 378, 381, 383, 385, 398, 401, 402,\n", + " 407, 420, 422, 423, 426, 428, 431, 433, 434, 436, 444,\n", + " 445, 446, 448, 455, 462, 463, 471, 472, 475, 476, 482,\n", + " 483, 485, 500, 503, 508, 510, 518, 520, 527, 528, 530,\n", + " 539, 540, 545, 547, 548, 551, 567, 569, 574, 579, 580,\n", + " 591, 592, 594, 604, 607, 611, 614, 615, 617, 622, 627,\n", + " 637, 639, 644, 647, 648, 650, 652, 658, 666, 676, 677,\n", + " 685, 689, 695, 714, 715, 716, 731, 742, 757, 761, 762,\n", + " 767, 772, 775, 777, 782, 785, 789, 792, 796, 799, 812,\n", + " 841, 843, 846, 848, 853, 883, 886, 890, 891, 909, 912,\n", + " 913, 914, 916, 917, 918, 921, 923, 924, 926, 933, 940,\n", + " 944, 951, 965, 974, 979, 986, 987, 1009, 1023, 1031, 1038,\n", + " 1041, 1044, 1052, 1054, 1062, 1064, 1065, 1069, 1071, 1075, 1085,\n", + " 1088, 1092, 1095, 1102, 1108, 1109, 1126, 1130, 1148, 1155, 1160,\n", + " 1162, 1177, 1184, 1192, 1196, 1203, 1204, 1206, 1214, 1226, 1232,\n", + " 1234, 1236, 1247, 1252, 1259, 1261, 1266, 1267, 1269, 1272, 1274,\n", + " 1291, 1299, 1303, 1305, 1311, 1315, 1320, 1324, 1326, 1328, 1329,\n", + " 1330, 1332, 1339, 1346, 1351, 1364, 1367, 1374, 1375, 1378, 1386,\n", + " 1398]),\n", + " 7: array([ 15, 20, 22, 65, 119, 137, 147, 215, 230, 242, 309,\n", + " 318, 328, 337, 355, 432, 461, 493, 542, 562, 587, 597,\n", + " 701, 770, 806, 809, 821, 836, 859, 895, 915, 978, 1032,\n", + " 1151, 1223, 1354, 1360, 1377, 1379, 1381]),\n", + " 8: array([ 62, 71, 75, 100, 176, 308, 326, 333, 441, 443, 481,\n", + " 498, 566, 586, 595, 610, 738, 754, 866, 949, 963, 995,\n", + " 1012, 1018, 1059, 1078, 1081, 1136, 1140, 1156, 1176, 1187, 1207,\n", + " 1242, 1248, 1250, 1263, 1288, 1307, 1310, 1340, 1395]),\n", + " 9: array([ 76, 101, 123, 129, 226, 251, 263, 275, 362, 403, 413,\n", + " 557, 630, 641, 655, 664, 725, 737, 773, 820, 851, 893,\n", + " 967, 969, 982, 1007, 1076, 1105, 1153, 1191, 1284, 1297, 1396])}},\n", + " 'signed_nonnormalized_l2_avg': {2: {0: array([ 0, 2, 6, 8, 19, 20, 21, 22, 47, 52, 54,\n", + " 55, 59, 61, 63, 65, 68, 77, 78, 88, 89, 93,\n", + " 96, 98, 101, 104, 105, 109, 112, 113, 115, 119, 120,\n", + " 121, 123, 126, 127, 131, 133, 137, 147, 150, 151, 153,\n", + " 154, 155, 181, 182, 184, 187, 194, 199, 205, 208, 209,\n", + " 226, 228, 229, 230, 231, 239, 254, 258, 260, 261, 266,\n", + " 268, 275, 276, 280, 282, 284, 292, 295, 297, 299, 309,\n", + " 316, 323, 328, 336, 337, 338, 339, 342, 348, 351, 355,\n", + " 362, 363, 370, 380, 389, 391, 392, 399, 404, 409, 413,\n", + " 432, 435, 450, 454, 474, 484, 488, 494, 497, 515, 521,\n", + " 526, 542, 553, 559, 561, 562, 563, 573, 578, 587, 590,\n", + " 597, 599, 601, 605, 606, 619, 628, 631, 640, 641, 655,\n", + " 661, 665, 670, 672, 678, 680, 683, 686, 691, 692, 696,\n", + " 699, 701, 702, 708, 717, 719, 720, 726, 733, 737, 740,\n", + " 744, 748, 753, 759, 763, 770, 773, 783, 786, 787, 793,\n", + " 797, 802, 804, 806, 809, 810, 815, 816, 818, 821, 825,\n", + " 836, 838, 859, 864, 868, 873, 875, 877, 879, 880, 882,\n", + " 885, 888, 889, 895, 901, 907, 910, 915, 920, 928, 931,\n", + " 934, 935, 937, 952, 955, 957, 967, 973, 975, 978, 980,\n", + " 983, 993, 999, 1000, 1005, 1007, 1014, 1017, 1020, 1022, 1030,\n", + " 1032, 1033, 1039, 1040, 1047, 1048, 1050, 1056, 1068, 1073, 1094,\n", + " 1106, 1110, 1115, 1117, 1118, 1122, 1123, 1124, 1125, 1127, 1129,\n", + " 1134, 1138, 1141, 1142, 1153, 1172, 1183, 1190, 1191, 1208, 1211,\n", + " 1221, 1223, 1240, 1241, 1246, 1249, 1251, 1254, 1255, 1262, 1265,\n", + " 1275, 1283, 1284, 1286, 1292, 1294, 1296, 1298, 1308, 1312, 1314,\n", + " 1318, 1327, 1342, 1344, 1348, 1350, 1353, 1354, 1358, 1360, 1361,\n", + " 1362, 1377, 1379, 1381, 1383, 1387, 1389, 1394]),\n", + " 1: array([ 1, 3, 4, ..., 1397, 1398, 1399])},\n", + " 3: {0: array([ 0, 2, 20, 22, 54, 61, 65, 89, 101, 105, 112,\n", + " 119, 123, 127, 133, 137, 147, 154, 182, 187, 205, 208,\n", + " 226, 230, 268, 309, 323, 328, 337, 355, 362, 380, 391,\n", + " 413, 432, 435, 484, 488, 515, 542, 562, 587, 590, 597,\n", + " 601, 641, 661, 692, 701, 733, 737, 744, 770, 773, 793,\n", + " 806, 809, 810, 821, 836, 859, 864, 879, 882, 895, 907,\n", + " 915, 920, 931, 952, 955, 957, 967, 978, 1007, 1017, 1032,\n", + " 1068, 1115, 1117, 1125, 1127, 1129, 1153, 1190, 1191, 1208, 1223,\n", + " 1246, 1284, 1294, 1298, 1308, 1318, 1327, 1344, 1348, 1350, 1354,\n", + " 1360, 1362, 1377, 1379, 1381, 1389]),\n", + " 1: array([ 1, 3, 4, ..., 1397, 1398, 1399]),\n", + " 2: array([ 6, 8, 19, 21, 47, 52, 55, 59, 63, 68, 77,\n", + " 78, 88, 93, 96, 98, 104, 109, 113, 115, 120, 121,\n", + " 126, 131, 150, 151, 153, 155, 181, 184, 194, 199, 209,\n", + " 228, 229, 231, 239, 254, 258, 260, 261, 266, 275, 276,\n", + " 280, 282, 284, 292, 295, 297, 299, 316, 336, 338, 339,\n", + " 342, 348, 351, 363, 370, 389, 392, 399, 404, 409, 450,\n", + " 454, 474, 494, 497, 521, 526, 553, 559, 561, 563, 573,\n", + " 578, 599, 605, 606, 619, 628, 631, 640, 655, 665, 670,\n", + " 672, 678, 680, 683, 686, 691, 696, 699, 702, 708, 717,\n", + " 719, 720, 726, 740, 748, 753, 759, 763, 783, 786, 787,\n", + " 797, 802, 804, 815, 816, 818, 825, 838, 868, 873, 875,\n", + " 877, 880, 885, 888, 889, 901, 910, 928, 934, 935, 937,\n", + " 973, 975, 980, 983, 993, 999, 1000, 1005, 1014, 1020, 1022,\n", + " 1030, 1033, 1039, 1040, 1047, 1048, 1050, 1056, 1073, 1094, 1106,\n", + " 1110, 1118, 1122, 1123, 1124, 1134, 1138, 1141, 1142, 1172, 1183,\n", + " 1211, 1221, 1240, 1241, 1249, 1251, 1254, 1255, 1262, 1265, 1275,\n", + " 1283, 1286, 1292, 1296, 1312, 1314, 1342, 1353, 1358, 1361, 1383,\n", + " 1387, 1394])},\n", + " 4: {0: array([ 0, 2, 20, 22, 54, 61, 65, 89, 101, 105, 112,\n", + " 119, 123, 127, 133, 137, 147, 154, 182, 187, 205, 208,\n", + " 226, 230, 268, 309, 323, 328, 337, 355, 362, 380, 391,\n", + " 413, 432, 435, 484, 488, 515, 542, 562, 587, 590, 597,\n", + " 601, 641, 661, 692, 701, 733, 737, 744, 770, 773, 793,\n", + " 806, 809, 810, 821, 836, 859, 864, 879, 882, 895, 907,\n", + " 915, 920, 931, 952, 955, 957, 967, 978, 1007, 1017, 1032,\n", + " 1068, 1115, 1117, 1125, 1127, 1129, 1153, 1190, 1191, 1208, 1223,\n", + " 1246, 1284, 1294, 1298, 1308, 1318, 1327, 1344, 1348, 1350, 1354,\n", + " 1360, 1362, 1377, 1379, 1381, 1389]),\n", + " 1: array([ 1, 3, 5, 7, 9, 10, 11, 12, 13, 15, 16,\n", + " 17, 18, 23, 25, 26, 27, 28, 29, 30, 31, 32,\n", + " 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44,\n", + " 45, 46, 48, 49, 51, 53, 56, 58, 60, 64, 66,\n", + " 67, 70, 74, 76, 79, 80, 82, 86, 87, 90, 92,\n", + " 94, 97, 99, 102, 103, 106, 107, 108, 110, 111, 114,\n", + " 116, 118, 122, 124, 128, 129, 130, 132, 135, 138, 139,\n", + " 140, 141, 142, 143, 144, 145, 146, 148, 149, 156, 158,\n", + " 159, 162, 163, 164, 165, 166, 167, 168, 169, 170, 172,\n", + " 174, 175, 177, 178, 179, 186, 189, 190, 191, 192, 193,\n", + " 195, 197, 200, 201, 202, 206, 207, 210, 211, 212, 213,\n", + " 214, 215, 216, 218, 219, 220, 221, 222, 227, 232, 233,\n", + " 234, 235, 236, 238, 240, 241, 242, 244, 245, 246, 247,\n", + " 248, 249, 250, 251, 252, 253, 255, 257, 259, 262, 263,\n", + " 264, 265, 267, 269, 271, 272, 273, 274, 277, 278, 281,\n", + " 283, 285, 286, 288, 289, 290, 291, 294, 296, 298, 300,\n", + " 301, 302, 303, 304, 305, 306, 307, 310, 311, 312, 314,\n", + " 315, 318, 322, 324, 325, 327, 329, 331, 332, 334, 335,\n", + " 340, 341, 343, 344, 346, 347, 350, 352, 353, 354, 357,\n", + " 360, 361, 365, 366, 368, 369, 371, 372, 373, 374, 375,\n", + " 376, 378, 381, 383, 384, 385, 386, 388, 390, 393, 394,\n", + " 396, 397, 398, 400, 401, 402, 403, 405, 407, 408, 411,\n", + " 412, 414, 416, 418, 419, 420, 421, 422, 423, 424, 425,\n", + " 426, 427, 428, 429, 430, 431, 433, 434, 436, 437, 438,\n", + " 439, 442, 444, 445, 446, 448, 449, 452, 453, 455, 456,\n", + " 459, 460, 461, 462, 463, 464, 465, 467, 468, 471, 472,\n", + " 473, 475, 476, 477, 478, 480, 482, 483, 485, 486, 487,\n", + " 489, 490, 491, 492, 493, 495, 496, 499, 500, 501, 502,\n", + " 503, 504, 505, 507, 508, 509, 510, 511, 512, 513, 517,\n", + " 518, 520, 522, 527, 528, 529, 530, 532, 533, 534, 535,\n", + " 536, 537, 539, 540, 543, 544, 545, 546, 547, 548, 550,\n", + " 551, 552, 554, 555, 556, 557, 558, 560, 564, 565, 567,\n", + " 569, 570, 572, 574, 575, 579, 580, 581, 582, 583, 589,\n", + " 591, 592, 594, 598, 602, 603, 604, 607, 608, 609, 611,\n", + " 612, 614, 615, 616, 617, 618, 620, 622, 623, 624, 627,\n", + " 629, 630, 633, 634, 635, 636, 637, 639, 643, 644, 645,\n", + " 647, 648, 650, 652, 654, 657, 658, 662, 664, 666, 667,\n", + " 668, 669, 673, 674, 675, 676, 677, 682, 684, 685, 687,\n", + " 688, 689, 690, 694, 695, 697, 698, 700, 703, 704, 705,\n", + " 707, 709, 710, 711, 712, 714, 715, 716, 718, 722, 725,\n", + " 727, 728, 730, 731, 732, 739, 741, 742, 743, 746, 747,\n", + " 750, 756, 757, 758, 760, 761, 762, 764, 765, 766, 767,\n", + " 768, 769, 772, 774, 775, 776, 777, 778, 779, 780, 782,\n", + " 785, 788, 789, 790, 792, 796, 798, 799, 803, 811, 812,\n", + " 814, 817, 819, 820, 823, 824, 828, 830, 831, 832, 833,\n", + " 834, 835, 837, 839, 840, 841, 842, 843, 844, 845, 846,\n", + " 847, 848, 849, 850, 851, 852, 853, 856, 857, 858, 860,\n", + " 861, 862, 863, 865, 867, 869, 871, 872, 874, 876, 878,\n", + " 883, 884, 886, 887, 890, 891, 893, 896, 899, 903, 904,\n", + " 905, 906, 908, 909, 911, 912, 913, 914, 916, 917, 918,\n", + " 919, 921, 922, 923, 924, 926, 929, 932, 933, 936, 938,\n", + " 939, 940, 941, 942, 943, 944, 946, 947, 950, 951, 953,\n", + " 954, 956, 960, 961, 962, 964, 965, 966, 968, 969, 970,\n", + " 971, 972, 974, 979, 981, 982, 984, 985, 986, 987, 989,\n", + " 990, 991, 992, 994, 996, 997, 1001, 1002, 1003, 1006, 1008,\n", + " 1009, 1010, 1011, 1013, 1015, 1016, 1019, 1021, 1023, 1025, 1026,\n", + " 1027, 1028, 1029, 1031, 1034, 1036, 1037, 1038, 1041, 1042, 1043,\n", + " 1044, 1045, 1046, 1052, 1053, 1054, 1057, 1060, 1061, 1062, 1063,\n", + " 1064, 1065, 1069, 1070, 1071, 1075, 1076, 1079, 1080, 1082, 1083,\n", + " 1084, 1085, 1086, 1088, 1092, 1095, 1100, 1101, 1102, 1103, 1105,\n", + " 1108, 1109, 1111, 1113, 1119, 1121, 1126, 1130, 1131, 1132, 1135,\n", + " 1137, 1144, 1145, 1146, 1148, 1150, 1151, 1154, 1155, 1157, 1158,\n", + " 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1168, 1169, 1170, 1171,\n", + " 1173, 1174, 1175, 1177, 1179, 1180, 1182, 1184, 1185, 1186, 1188,\n", + " 1192, 1193, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203,\n", + " 1204, 1206, 1209, 1210, 1212, 1214, 1217, 1218, 1219, 1220, 1224,\n", + " 1225, 1226, 1227, 1228, 1230, 1231, 1232, 1234, 1236, 1237, 1239,\n", + " 1243, 1244, 1245, 1247, 1252, 1253, 1258, 1259, 1261, 1264, 1266,\n", + " 1267, 1268, 1269, 1270, 1272, 1273, 1274, 1277, 1278, 1279, 1280,\n", + " 1282, 1289, 1291, 1297, 1299, 1301, 1303, 1305, 1306, 1309, 1311,\n", + " 1315, 1316, 1317, 1319, 1320, 1321, 1322, 1323, 1324, 1326, 1328,\n", + " 1329, 1330, 1332, 1333, 1334, 1335, 1336, 1337, 1339, 1341, 1345,\n", + " 1346, 1349, 1351, 1352, 1355, 1359, 1363, 1364, 1367, 1368, 1369,\n", + " 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1378, 1380, 1384, 1385,\n", + " 1386, 1388, 1390, 1392, 1396, 1398, 1399]),\n", + " 2: array([ 4, 14, 24, 33, 50, 57, 62, 69, 71, 72, 73,\n", + " 75, 81, 83, 84, 85, 91, 95, 100, 117, 125, 134,\n", + " 136, 152, 157, 160, 161, 171, 173, 176, 180, 183, 185,\n", + " 188, 196, 198, 203, 204, 217, 223, 224, 225, 237, 243,\n", + " 256, 270, 279, 287, 293, 308, 313, 317, 319, 320, 321,\n", + " 326, 330, 333, 345, 349, 356, 358, 359, 364, 367, 377,\n", + " 379, 382, 387, 395, 406, 410, 415, 417, 440, 441, 443,\n", + " 447, 451, 457, 458, 466, 469, 470, 479, 481, 498, 506,\n", + " 514, 516, 519, 523, 524, 525, 531, 538, 541, 549, 566,\n", + " 568, 571, 576, 577, 584, 585, 586, 588, 593, 595, 596,\n", + " 600, 610, 613, 621, 625, 626, 632, 638, 642, 646, 649,\n", + " 651, 653, 656, 659, 660, 663, 671, 679, 681, 693, 706,\n", + " 713, 721, 723, 724, 729, 734, 735, 736, 738, 745, 749,\n", + " 751, 752, 754, 755, 771, 781, 784, 791, 794, 795, 800,\n", + " 801, 805, 807, 808, 813, 822, 826, 827, 829, 854, 855,\n", + " 866, 870, 881, 892, 894, 897, 898, 900, 902, 925, 927,\n", + " 930, 945, 948, 949, 958, 959, 963, 976, 977, 988, 995,\n", + " 998, 1004, 1012, 1018, 1024, 1035, 1049, 1051, 1055, 1058, 1059,\n", + " 1066, 1067, 1072, 1074, 1077, 1078, 1081, 1087, 1089, 1090, 1091,\n", + " 1093, 1096, 1097, 1098, 1099, 1104, 1107, 1112, 1114, 1116, 1120,\n", + " 1128, 1133, 1136, 1139, 1140, 1143, 1147, 1149, 1152, 1156, 1159,\n", + " 1167, 1176, 1178, 1181, 1187, 1189, 1194, 1205, 1207, 1213, 1215,\n", + " 1216, 1222, 1229, 1233, 1235, 1238, 1242, 1248, 1250, 1256, 1257,\n", + " 1260, 1263, 1271, 1276, 1281, 1285, 1287, 1288, 1290, 1293, 1295,\n", + " 1300, 1302, 1304, 1307, 1310, 1313, 1325, 1331, 1338, 1340, 1343,\n", + " 1347, 1356, 1357, 1365, 1366, 1382, 1391, 1393, 1395, 1397]),\n", + " 3: array([ 6, 8, 19, 21, 47, 52, 55, 59, 63, 68, 77,\n", + " 78, 88, 93, 96, 98, 104, 109, 113, 115, 120, 121,\n", + " 126, 131, 150, 151, 153, 155, 181, 184, 194, 199, 209,\n", + " 228, 229, 231, 239, 254, 258, 260, 261, 266, 275, 276,\n", + " 280, 282, 284, 292, 295, 297, 299, 316, 336, 338, 339,\n", + " 342, 348, 351, 363, 370, 389, 392, 399, 404, 409, 450,\n", + " 454, 474, 494, 497, 521, 526, 553, 559, 561, 563, 573,\n", + " 578, 599, 605, 606, 619, 628, 631, 640, 655, 665, 670,\n", + " 672, 678, 680, 683, 686, 691, 696, 699, 702, 708, 717,\n", + " 719, 720, 726, 740, 748, 753, 759, 763, 783, 786, 787,\n", + " 797, 802, 804, 815, 816, 818, 825, 838, 868, 873, 875,\n", + " 877, 880, 885, 888, 889, 901, 910, 928, 934, 935, 937,\n", + " 973, 975, 980, 983, 993, 999, 1000, 1005, 1014, 1020, 1022,\n", + " 1030, 1033, 1039, 1040, 1047, 1048, 1050, 1056, 1073, 1094, 1106,\n", + " 1110, 1118, 1122, 1123, 1124, 1134, 1138, 1141, 1142, 1172, 1183,\n", + " 1211, 1221, 1240, 1241, 1249, 1251, 1254, 1255, 1262, 1265, 1275,\n", + " 1283, 1286, 1292, 1296, 1312, 1314, 1342, 1353, 1358, 1361, 1383,\n", + " 1387, 1394])},\n", + " 5: {0: array([ 0, 2, 20, 22, 54, 61, 65, 89, 101, 105, 112,\n", + " 119, 123, 127, 133, 137, 147, 154, 182, 187, 205, 208,\n", + " 226, 230, 268, 309, 323, 328, 337, 355, 362, 380, 391,\n", + " 413, 432, 435, 484, 488, 515, 542, 562, 587, 590, 597,\n", + " 601, 641, 661, 692, 701, 733, 737, 744, 770, 773, 793,\n", + " 806, 809, 810, 821, 836, 859, 864, 879, 882, 895, 907,\n", + " 915, 920, 931, 952, 955, 957, 967, 978, 1007, 1017, 1032,\n", + " 1068, 1115, 1117, 1125, 1127, 1129, 1153, 1190, 1191, 1208, 1223,\n", + " 1246, 1284, 1294, 1298, 1308, 1318, 1327, 1344, 1348, 1350, 1354,\n", + " 1360, 1362, 1377, 1379, 1381, 1389]),\n", + " 1: array([ 1, 3, 5, 7, 9, 12, 13, 17, 18, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 34, 36, 38, 42, 46,\n", + " 48, 49, 51, 56, 58, 60, 64, 66, 67, 70, 74,\n", + " 80, 82, 86, 87, 90, 92, 94, 97, 99, 102, 108,\n", + " 111, 114, 118, 122, 124, 128, 130, 132, 135, 138, 139,\n", + " 140, 141, 142, 143, 144, 145, 148, 149, 158, 159, 162,\n", + " 166, 167, 169, 170, 172, 174, 175, 177, 178, 179, 189,\n", + " 190, 192, 195, 197, 200, 201, 202, 206, 207, 211, 212,\n", + " 213, 214, 216, 218, 219, 221, 222, 227, 232, 235, 236,\n", + " 240, 241, 244, 245, 246, 247, 249, 252, 253, 255, 259,\n", + " 262, 264, 265, 267, 271, 272, 273, 274, 277, 278, 283,\n", + " 288, 289, 290, 291, 294, 296, 298, 300, 301, 302, 304,\n", + " 305, 306, 307, 310, 312, 314, 315, 322, 324, 325, 327,\n", + " 331, 332, 334, 335, 340, 341, 344, 352, 353, 354, 357,\n", + " 361, 365, 366, 368, 369, 371, 372, 373, 378, 381, 383,\n", + " 384, 385, 386, 388, 390, 394, 397, 398, 400, 401, 402,\n", + " 407, 408, 411, 412, 416, 418, 419, 420, 421, 422, 423,\n", + " 425, 426, 427, 428, 429, 430, 431, 433, 434, 436, 442,\n", + " 444, 445, 446, 448, 449, 452, 453, 455, 459, 460, 462,\n", + " 463, 464, 465, 467, 468, 471, 472, 473, 475, 476, 480,\n", + " 482, 483, 485, 486, 489, 490, 491, 492, 495, 496, 499,\n", + " 500, 501, 503, 505, 508, 509, 510, 512, 517, 518, 520,\n", + " 522, 527, 528, 529, 530, 532, 533, 534, 535, 537, 539,\n", + " 540, 543, 545, 547, 548, 550, 551, 552, 554, 558, 560,\n", + " 564, 567, 569, 572, 574, 579, 580, 581, 582, 589, 591,\n", + " 592, 594, 598, 602, 603, 604, 607, 611, 612, 614, 615,\n", + " 616, 617, 618, 620, 622, 624, 627, 629, 633, 634, 635,\n", + " 637, 639, 643, 644, 645, 647, 648, 650, 652, 657, 658,\n", + " 662, 666, 668, 673, 674, 675, 676, 677, 682, 684, 685,\n", + " 688, 689, 690, 695, 697, 703, 704, 707, 709, 711, 714,\n", + " 715, 716, 718, 728, 730, 731, 732, 741, 742, 743, 747,\n", + " 750, 756, 757, 761, 762, 764, 766, 767, 769, 772, 774,\n", + " 775, 777, 779, 780, 782, 785, 789, 792, 796, 798, 799,\n", + " 803, 811, 812, 814, 819, 823, 824, 828, 830, 831, 832,\n", + " 835, 837, 839, 840, 841, 843, 844, 846, 848, 849, 852,\n", + " 853, 856, 857, 861, 867, 869, 871, 872, 874, 876, 883,\n", + " 884, 886, 887, 890, 891, 896, 899, 905, 908, 909, 912,\n", + " 913, 914, 916, 917, 918, 919, 921, 923, 924, 926, 933,\n", + " 938, 939, 940, 941, 942, 943, 944, 947, 950, 951, 953,\n", + " 960, 961, 962, 964, 965, 968, 971, 974, 979, 984, 985,\n", + " 986, 987, 990, 991, 992, 996, 997, 1001, 1003, 1006, 1009,\n", + " 1011, 1013, 1021, 1023, 1025, 1026, 1028, 1029, 1031, 1037, 1038,\n", + " 1041, 1042, 1044, 1045, 1046, 1052, 1053, 1054, 1060, 1062, 1063,\n", + " 1064, 1065, 1069, 1071, 1075, 1080, 1083, 1084, 1085, 1088, 1092,\n", + " 1095, 1100, 1102, 1103, 1108, 1109, 1113, 1119, 1121, 1126, 1130,\n", + " 1131, 1135, 1137, 1146, 1148, 1154, 1155, 1158, 1160, 1161, 1162,\n", + " 1163, 1164, 1165, 1166, 1169, 1170, 1171, 1173, 1177, 1179, 1180,\n", + " 1182, 1184, 1185, 1192, 1193, 1195, 1196, 1198, 1200, 1201, 1203,\n", + " 1204, 1206, 1209, 1210, 1212, 1214, 1218, 1219, 1220, 1225, 1226,\n", + " 1227, 1230, 1231, 1232, 1234, 1236, 1237, 1239, 1244, 1247, 1252,\n", + " 1253, 1258, 1259, 1261, 1264, 1266, 1267, 1269, 1272, 1273, 1274,\n", + " 1278, 1279, 1282, 1289, 1291, 1299, 1301, 1303, 1305, 1306, 1311,\n", + " 1315, 1316, 1320, 1321, 1322, 1324, 1326, 1328, 1329, 1330, 1332,\n", + " 1334, 1336, 1337, 1339, 1341, 1345, 1346, 1351, 1355, 1363, 1364,\n", + " 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1378,\n", + " 1380, 1384, 1386, 1388, 1390, 1398]),\n", + " 2: array([ 4, 14, 24, 33, 50, 57, 62, 69, 71, 72, 73,\n", + " 75, 81, 83, 84, 85, 91, 95, 100, 117, 125, 134,\n", + " 136, 152, 157, 160, 161, 171, 173, 176, 180, 183, 185,\n", + " 188, 196, 198, 203, 204, 217, 223, 224, 225, 237, 243,\n", + " 256, 270, 279, 287, 293, 308, 313, 317, 319, 320, 321,\n", + " 326, 330, 333, 345, 349, 356, 358, 359, 364, 367, 377,\n", + " 379, 382, 387, 395, 406, 410, 415, 417, 440, 441, 443,\n", + " 447, 451, 457, 458, 466, 469, 470, 479, 481, 498, 506,\n", + " 514, 516, 519, 523, 524, 525, 531, 538, 541, 549, 566,\n", + " 568, 571, 576, 577, 584, 585, 586, 588, 593, 595, 596,\n", + " 600, 610, 613, 621, 625, 626, 632, 638, 642, 646, 649,\n", + " 651, 653, 656, 659, 660, 663, 671, 679, 681, 693, 706,\n", + " 713, 721, 723, 724, 729, 734, 735, 736, 738, 745, 749,\n", + " 751, 752, 754, 755, 771, 781, 784, 791, 794, 795, 800,\n", + " 801, 805, 807, 808, 813, 822, 826, 827, 829, 854, 855,\n", + " 866, 870, 881, 892, 894, 897, 898, 900, 902, 925, 927,\n", + " 930, 945, 948, 949, 958, 959, 963, 976, 977, 988, 995,\n", + " 998, 1004, 1012, 1018, 1024, 1035, 1049, 1051, 1055, 1058, 1059,\n", + " 1066, 1067, 1072, 1074, 1077, 1078, 1081, 1087, 1089, 1090, 1091,\n", + " 1093, 1096, 1097, 1098, 1099, 1104, 1107, 1112, 1114, 1116, 1120,\n", + " 1128, 1133, 1136, 1139, 1140, 1143, 1147, 1149, 1152, 1156, 1159,\n", + " 1167, 1176, 1178, 1181, 1187, 1189, 1194, 1205, 1207, 1213, 1215,\n", + " 1216, 1222, 1229, 1233, 1235, 1238, 1242, 1248, 1250, 1256, 1257,\n", + " 1260, 1263, 1271, 1276, 1281, 1285, 1287, 1288, 1290, 1293, 1295,\n", + " 1300, 1302, 1304, 1307, 1310, 1313, 1325, 1331, 1338, 1340, 1343,\n", + " 1347, 1356, 1357, 1365, 1366, 1382, 1391, 1393, 1395, 1397]),\n", + " 3: array([ 6, 8, 19, 21, 47, 52, 55, 59, 63, 68, 77,\n", + " 78, 88, 93, 96, 98, 104, 109, 113, 115, 120, 121,\n", + " 126, 131, 150, 151, 153, 155, 181, 184, 194, 199, 209,\n", + " 228, 229, 231, 239, 254, 258, 260, 261, 266, 275, 276,\n", + " 280, 282, 284, 292, 295, 297, 299, 316, 336, 338, 339,\n", + " 342, 348, 351, 363, 370, 389, 392, 399, 404, 409, 450,\n", + " 454, 474, 494, 497, 521, 526, 553, 559, 561, 563, 573,\n", + " 578, 599, 605, 606, 619, 628, 631, 640, 655, 665, 670,\n", + " 672, 678, 680, 683, 686, 691, 696, 699, 702, 708, 717,\n", + " 719, 720, 726, 740, 748, 753, 759, 763, 783, 786, 787,\n", + " 797, 802, 804, 815, 816, 818, 825, 838, 868, 873, 875,\n", + " 877, 880, 885, 888, 889, 901, 910, 928, 934, 935, 937,\n", + " 973, 975, 980, 983, 993, 999, 1000, 1005, 1014, 1020, 1022,\n", + " 1030, 1033, 1039, 1040, 1047, 1048, 1050, 1056, 1073, 1094, 1106,\n", + " 1110, 1118, 1122, 1123, 1124, 1134, 1138, 1141, 1142, 1172, 1183,\n", + " 1211, 1221, 1240, 1241, 1249, 1251, 1254, 1255, 1262, 1265, 1275,\n", + " 1283, 1286, 1292, 1296, 1312, 1314, 1342, 1353, 1358, 1361, 1383,\n", + " 1387, 1394]),\n", + " 4: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 41, 43,\n", + " 44, 45, 53, 76, 79, 103, 106, 107, 110, 116, 129,\n", + " 146, 156, 163, 164, 165, 168, 186, 191, 193, 210, 215,\n", + " 220, 233, 234, 238, 242, 248, 250, 251, 257, 263, 269,\n", + " 281, 285, 286, 303, 311, 318, 329, 343, 346, 347, 350,\n", + " 360, 374, 375, 376, 393, 396, 403, 405, 414, 424, 437,\n", + " 438, 439, 456, 461, 477, 478, 487, 493, 502, 504, 507,\n", + " 511, 513, 536, 544, 546, 555, 556, 557, 565, 570, 575,\n", + " 583, 608, 609, 623, 630, 636, 654, 664, 667, 669, 687,\n", + " 694, 698, 700, 705, 710, 712, 722, 725, 727, 739, 746,\n", + " 758, 760, 765, 768, 776, 778, 788, 790, 817, 820, 833,\n", + " 834, 842, 845, 847, 850, 851, 858, 860, 862, 863, 865,\n", + " 878, 893, 903, 904, 906, 911, 922, 929, 932, 936, 946,\n", + " 954, 956, 966, 969, 970, 972, 981, 982, 989, 994, 1002,\n", + " 1008, 1010, 1015, 1016, 1019, 1027, 1034, 1036, 1043, 1057, 1061,\n", + " 1070, 1076, 1079, 1082, 1086, 1101, 1105, 1111, 1132, 1144, 1145,\n", + " 1150, 1151, 1157, 1168, 1174, 1175, 1186, 1188, 1197, 1199, 1202,\n", + " 1217, 1224, 1228, 1243, 1245, 1268, 1270, 1277, 1280, 1297, 1309,\n", + " 1317, 1319, 1323, 1333, 1335, 1349, 1352, 1359, 1385, 1392, 1396,\n", + " 1399])},\n", + " 6: {0: array([ 0, 2, 20, 22, 54, 61, 65, 89, 101, 105, 112,\n", + " 119, 123, 127, 133, 137, 147, 154, 182, 187, 205, 208,\n", + " 226, 230, 268, 309, 323, 328, 337, 355, 362, 380, 391,\n", + " 413, 432, 435, 484, 488, 515, 542, 562, 587, 590, 597,\n", + " 601, 641, 661, 692, 701, 733, 737, 744, 770, 773, 793,\n", + " 806, 809, 810, 821, 836, 859, 864, 879, 882, 895, 907,\n", + " 915, 920, 931, 952, 955, 957, 967, 978, 1007, 1017, 1032,\n", + " 1068, 1115, 1117, 1125, 1127, 1129, 1153, 1190, 1191, 1208, 1223,\n", + " 1246, 1284, 1294, 1298, 1308, 1318, 1327, 1344, 1348, 1350, 1354,\n", + " 1360, 1362, 1377, 1379, 1381, 1389]),\n", + " 1: array([ 1, 3, 5, 7, 9, 12, 13, 17, 18, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 34, 36, 38, 42, 46,\n", + " 48, 49, 51, 56, 58, 60, 64, 66, 67, 70, 74,\n", + " 80, 82, 86, 87, 90, 92, 94, 97, 99, 102, 108,\n", + " 111, 114, 118, 122, 124, 128, 130, 132, 135, 138, 139,\n", + " 140, 141, 142, 143, 144, 145, 148, 149, 158, 159, 162,\n", + " 166, 167, 169, 170, 172, 174, 175, 177, 178, 179, 189,\n", + " 190, 192, 195, 197, 200, 201, 202, 206, 207, 211, 212,\n", + " 213, 214, 216, 218, 219, 221, 222, 227, 232, 235, 236,\n", + " 240, 241, 244, 245, 246, 247, 249, 252, 253, 255, 259,\n", + " 262, 264, 265, 267, 271, 272, 273, 274, 277, 278, 283,\n", + " 288, 289, 290, 291, 294, 296, 298, 300, 301, 302, 304,\n", + " 305, 306, 307, 310, 312, 314, 315, 322, 324, 325, 327,\n", + " 331, 332, 334, 335, 340, 341, 344, 352, 353, 354, 357,\n", + " 361, 365, 366, 368, 369, 371, 372, 373, 378, 381, 383,\n", + " 384, 385, 386, 388, 390, 394, 397, 398, 400, 401, 402,\n", + " 407, 408, 411, 412, 416, 418, 419, 420, 421, 422, 423,\n", + " 425, 426, 427, 428, 429, 430, 431, 433, 434, 436, 442,\n", + " 444, 445, 446, 448, 449, 452, 453, 455, 459, 460, 462,\n", + " 463, 464, 465, 467, 468, 471, 472, 473, 475, 476, 480,\n", + " 482, 483, 485, 486, 489, 490, 491, 492, 495, 496, 499,\n", + " 500, 501, 503, 505, 508, 509, 510, 512, 517, 518, 520,\n", + " 522, 527, 528, 529, 530, 532, 533, 534, 535, 537, 539,\n", + " 540, 543, 545, 547, 548, 550, 551, 552, 554, 558, 560,\n", + " 564, 567, 569, 572, 574, 579, 580, 581, 582, 589, 591,\n", + " 592, 594, 598, 602, 603, 604, 607, 611, 612, 614, 615,\n", + " 616, 617, 618, 620, 622, 624, 627, 629, 633, 634, 635,\n", + " 637, 639, 643, 644, 645, 647, 648, 650, 652, 657, 658,\n", + " 662, 666, 668, 673, 674, 675, 676, 677, 682, 684, 685,\n", + " 688, 689, 690, 695, 697, 703, 704, 707, 709, 711, 714,\n", + " 715, 716, 718, 728, 730, 731, 732, 741, 742, 743, 747,\n", + " 750, 756, 757, 761, 762, 764, 766, 767, 769, 772, 774,\n", + " 775, 777, 779, 780, 782, 785, 789, 792, 796, 798, 799,\n", + " 803, 811, 812, 814, 819, 823, 824, 828, 830, 831, 832,\n", + " 835, 837, 839, 840, 841, 843, 844, 846, 848, 849, 852,\n", + " 853, 856, 857, 861, 867, 869, 871, 872, 874, 876, 883,\n", + " 884, 886, 887, 890, 891, 896, 899, 905, 908, 909, 912,\n", + " 913, 914, 916, 917, 918, 919, 921, 923, 924, 926, 933,\n", + " 938, 939, 940, 941, 942, 943, 944, 947, 950, 951, 953,\n", + " 960, 961, 962, 964, 965, 968, 971, 974, 979, 984, 985,\n", + " 986, 987, 990, 991, 992, 996, 997, 1001, 1003, 1006, 1009,\n", + " 1011, 1013, 1021, 1023, 1025, 1026, 1028, 1029, 1031, 1037, 1038,\n", + " 1041, 1042, 1044, 1045, 1046, 1052, 1053, 1054, 1060, 1062, 1063,\n", + " 1064, 1065, 1069, 1071, 1075, 1080, 1083, 1084, 1085, 1088, 1092,\n", + " 1095, 1100, 1102, 1103, 1108, 1109, 1113, 1119, 1121, 1126, 1130,\n", + " 1131, 1135, 1137, 1146, 1148, 1154, 1155, 1158, 1160, 1161, 1162,\n", + " 1163, 1164, 1165, 1166, 1169, 1170, 1171, 1173, 1177, 1179, 1180,\n", + " 1182, 1184, 1185, 1192, 1193, 1195, 1196, 1198, 1200, 1201, 1203,\n", + " 1204, 1206, 1209, 1210, 1212, 1214, 1218, 1219, 1220, 1225, 1226,\n", + " 1227, 1230, 1231, 1232, 1234, 1236, 1237, 1239, 1244, 1247, 1252,\n", + " 1253, 1258, 1259, 1261, 1264, 1266, 1267, 1269, 1272, 1273, 1274,\n", + " 1278, 1279, 1282, 1289, 1291, 1299, 1301, 1303, 1305, 1306, 1311,\n", + " 1315, 1316, 1320, 1321, 1322, 1324, 1326, 1328, 1329, 1330, 1332,\n", + " 1334, 1336, 1337, 1339, 1341, 1345, 1346, 1351, 1355, 1363, 1364,\n", + " 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1378,\n", + " 1380, 1384, 1386, 1388, 1390, 1398]),\n", + " 2: array([ 4, 14, 24, 33, 50, 57, 62, 69, 71, 72, 73,\n", + " 75, 81, 83, 84, 85, 91, 95, 100, 117, 125, 134,\n", + " 136, 152, 157, 160, 161, 171, 173, 176, 183, 185, 198,\n", + " 203, 204, 217, 224, 225, 237, 243, 256, 270, 287, 293,\n", + " 308, 313, 317, 319, 320, 321, 326, 330, 333, 345, 356,\n", + " 358, 359, 364, 367, 377, 379, 382, 387, 395, 406, 410,\n", + " 415, 417, 440, 441, 443, 447, 451, 457, 458, 466, 469,\n", + " 470, 479, 481, 498, 506, 514, 516, 519, 523, 524, 525,\n", + " 531, 538, 541, 549, 566, 568, 571, 577, 584, 585, 586,\n", + " 588, 593, 595, 596, 600, 610, 613, 621, 625, 626, 632,\n", + " 638, 642, 646, 649, 651, 653, 656, 659, 660, 663, 679,\n", + " 681, 693, 706, 713, 721, 723, 729, 734, 735, 738, 745,\n", + " 749, 751, 752, 754, 755, 771, 781, 784, 791, 795, 800,\n", + " 801, 805, 807, 808, 813, 822, 826, 827, 829, 854, 855,\n", + " 866, 870, 881, 892, 894, 897, 898, 900, 902, 927, 930,\n", + " 945, 948, 949, 958, 959, 963, 976, 977, 988, 995, 998,\n", + " 1004, 1012, 1018, 1024, 1035, 1049, 1051, 1055, 1058, 1059, 1066,\n", + " 1067, 1072, 1074, 1077, 1078, 1081, 1087, 1089, 1090, 1091, 1093,\n", + " 1096, 1097, 1098, 1099, 1104, 1107, 1112, 1114, 1116, 1120, 1128,\n", + " 1133, 1136, 1139, 1140, 1143, 1149, 1152, 1156, 1159, 1167, 1176,\n", + " 1178, 1181, 1187, 1189, 1194, 1205, 1207, 1213, 1216, 1222, 1229,\n", + " 1233, 1235, 1238, 1242, 1248, 1250, 1256, 1257, 1260, 1263, 1271,\n", + " 1276, 1281, 1285, 1287, 1288, 1290, 1293, 1295, 1300, 1302, 1304,\n", + " 1307, 1310, 1313, 1325, 1331, 1338, 1340, 1343, 1347, 1356, 1357,\n", + " 1365, 1366, 1382, 1391, 1393, 1395, 1397]),\n", + " 3: array([ 6, 8, 19, 21, 47, 52, 55, 59, 63, 68, 77,\n", + " 78, 88, 93, 96, 98, 104, 109, 113, 115, 120, 121,\n", + " 126, 131, 150, 151, 153, 155, 181, 184, 194, 199, 209,\n", + " 228, 229, 231, 239, 254, 258, 260, 261, 266, 275, 276,\n", + " 280, 282, 284, 292, 295, 297, 299, 316, 336, 338, 339,\n", + " 342, 348, 351, 363, 370, 389, 392, 399, 404, 409, 450,\n", + " 454, 474, 494, 497, 521, 526, 553, 559, 561, 563, 573,\n", + " 578, 599, 605, 606, 619, 628, 631, 640, 655, 665, 670,\n", + " 672, 678, 680, 683, 686, 691, 696, 699, 702, 708, 717,\n", + " 719, 720, 726, 740, 748, 753, 759, 763, 783, 786, 787,\n", + " 797, 802, 804, 815, 816, 818, 825, 838, 868, 873, 875,\n", + " 877, 880, 885, 888, 889, 901, 910, 928, 934, 935, 937,\n", + " 973, 975, 980, 983, 993, 999, 1000, 1005, 1014, 1020, 1022,\n", + " 1030, 1033, 1039, 1040, 1047, 1048, 1050, 1056, 1073, 1094, 1106,\n", + " 1110, 1118, 1122, 1123, 1124, 1134, 1138, 1141, 1142, 1172, 1183,\n", + " 1211, 1221, 1240, 1241, 1249, 1251, 1254, 1255, 1262, 1265, 1275,\n", + " 1283, 1286, 1292, 1296, 1312, 1314, 1342, 1353, 1358, 1361, 1383,\n", + " 1387, 1394]),\n", + " 4: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 41, 43,\n", + " 44, 45, 53, 76, 79, 103, 106, 107, 110, 116, 129,\n", + " 146, 156, 163, 164, 165, 168, 186, 191, 193, 210, 215,\n", + " 220, 233, 234, 238, 242, 248, 250, 251, 257, 263, 269,\n", + " 281, 285, 286, 303, 311, 318, 329, 343, 346, 347, 350,\n", + " 360, 374, 375, 376, 393, 396, 403, 405, 414, 424, 437,\n", + " 438, 439, 456, 461, 477, 478, 487, 493, 502, 504, 507,\n", + " 511, 513, 536, 544, 546, 555, 556, 557, 565, 570, 575,\n", + " 583, 608, 609, 623, 630, 636, 654, 664, 667, 669, 687,\n", + " 694, 698, 700, 705, 710, 712, 722, 725, 727, 739, 746,\n", + " 758, 760, 765, 768, 776, 778, 788, 790, 817, 820, 833,\n", + " 834, 842, 845, 847, 850, 851, 858, 860, 862, 863, 865,\n", + " 878, 893, 903, 904, 906, 911, 922, 929, 932, 936, 946,\n", + " 954, 956, 966, 969, 970, 972, 981, 982, 989, 994, 1002,\n", + " 1008, 1010, 1015, 1016, 1019, 1027, 1034, 1036, 1043, 1057, 1061,\n", + " 1070, 1076, 1079, 1082, 1086, 1101, 1105, 1111, 1132, 1144, 1145,\n", + " 1150, 1151, 1157, 1168, 1174, 1175, 1186, 1188, 1197, 1199, 1202,\n", + " 1217, 1224, 1228, 1243, 1245, 1268, 1270, 1277, 1280, 1297, 1309,\n", + " 1317, 1319, 1323, 1333, 1335, 1349, 1352, 1359, 1385, 1392, 1396,\n", + " 1399]),\n", + " 5: array([ 180, 188, 196, 223, 279, 349, 576, 671, 724, 736, 794,\n", + " 925, 1147, 1215])},\n", + " 7: {0: array([ 0, 2, 20, 22, 54, 61, 65, 89, 101, 105, 112,\n", + " 119, 123, 127, 133, 137, 147, 154, 182, 187, 205, 208,\n", + " 226, 230, 268, 309, 323, 328, 337, 355, 362, 380, 391,\n", + " 413, 432, 435, 484, 488, 515, 542, 562, 587, 590, 597,\n", + " 601, 641, 661, 692, 701, 733, 737, 744, 770, 773, 793,\n", + " 806, 809, 810, 821, 836, 859, 864, 879, 882, 895, 907,\n", + " 915, 920, 931, 952, 955, 957, 967, 978, 1007, 1017, 1032,\n", + " 1068, 1115, 1117, 1125, 1127, 1129, 1153, 1190, 1191, 1208, 1223,\n", + " 1246, 1284, 1294, 1298, 1308, 1318, 1327, 1344, 1348, 1350, 1354,\n", + " 1360, 1362, 1377, 1379, 1381, 1389]),\n", + " 1: array([ 1, 3, 5, 7, 9, 12, 13, 17, 18, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 34, 36, 38, 42, 46,\n", + " 48, 49, 51, 56, 58, 60, 64, 66, 67, 70, 74,\n", + " 80, 82, 86, 87, 90, 92, 94, 97, 99, 102, 108,\n", + " 111, 114, 118, 122, 124, 128, 130, 132, 135, 138, 139,\n", + " 140, 141, 142, 143, 144, 145, 148, 149, 158, 159, 162,\n", + " 166, 167, 169, 170, 172, 174, 175, 177, 178, 179, 189,\n", + " 190, 192, 195, 197, 200, 201, 202, 206, 207, 211, 212,\n", + " 213, 214, 216, 218, 219, 221, 222, 227, 232, 235, 236,\n", + " 240, 241, 244, 245, 246, 247, 249, 252, 253, 255, 259,\n", + " 262, 264, 265, 267, 271, 272, 273, 274, 277, 278, 283,\n", + " 288, 289, 290, 291, 294, 296, 298, 300, 301, 302, 304,\n", + " 305, 306, 307, 310, 312, 314, 315, 322, 324, 325, 327,\n", + " 331, 332, 334, 335, 340, 341, 344, 352, 353, 354, 357,\n", + " 361, 365, 366, 368, 369, 371, 372, 373, 378, 381, 383,\n", + " 384, 385, 386, 388, 390, 394, 397, 398, 400, 401, 402,\n", + " 407, 408, 411, 412, 416, 418, 419, 420, 421, 422, 423,\n", + " 425, 426, 427, 428, 429, 430, 431, 433, 434, 436, 442,\n", + " 444, 445, 446, 448, 449, 452, 453, 455, 459, 460, 462,\n", + " 463, 464, 465, 467, 468, 471, 472, 473, 475, 476, 480,\n", + " 482, 483, 485, 486, 489, 490, 491, 492, 495, 496, 499,\n", + " 500, 501, 503, 505, 508, 509, 510, 512, 517, 518, 520,\n", + " 522, 527, 528, 529, 530, 532, 533, 534, 535, 537, 539,\n", + " 540, 543, 545, 547, 548, 550, 551, 552, 554, 558, 560,\n", + " 564, 567, 569, 572, 574, 579, 580, 581, 582, 589, 591,\n", + " 592, 594, 598, 602, 603, 604, 607, 611, 612, 614, 615,\n", + " 616, 617, 618, 620, 622, 624, 627, 629, 633, 634, 635,\n", + " 637, 639, 643, 644, 645, 647, 648, 650, 652, 657, 658,\n", + " 662, 666, 668, 673, 674, 675, 676, 677, 682, 684, 685,\n", + " 688, 689, 690, 695, 697, 703, 704, 707, 709, 711, 714,\n", + " 715, 716, 718, 728, 730, 731, 732, 741, 742, 743, 747,\n", + " 750, 756, 757, 761, 762, 764, 766, 767, 769, 772, 774,\n", + " 775, 777, 779, 780, 782, 785, 789, 792, 796, 798, 799,\n", + " 803, 811, 812, 814, 819, 823, 824, 828, 830, 831, 832,\n", + " 835, 837, 839, 840, 841, 843, 844, 846, 848, 849, 852,\n", + " 853, 856, 857, 861, 867, 869, 871, 872, 874, 876, 883,\n", + " 884, 886, 887, 890, 891, 896, 899, 905, 908, 909, 912,\n", + " 913, 914, 916, 917, 918, 919, 921, 923, 924, 926, 933,\n", + " 938, 939, 940, 941, 942, 943, 944, 947, 950, 951, 953,\n", + " 960, 961, 962, 964, 965, 968, 971, 974, 979, 984, 985,\n", + " 986, 987, 990, 991, 992, 996, 997, 1001, 1003, 1006, 1009,\n", + " 1011, 1013, 1021, 1023, 1025, 1026, 1028, 1029, 1031, 1037, 1038,\n", + " 1041, 1042, 1044, 1045, 1046, 1052, 1053, 1054, 1060, 1062, 1063,\n", + " 1064, 1065, 1069, 1071, 1075, 1080, 1083, 1084, 1085, 1088, 1092,\n", + " 1095, 1100, 1102, 1103, 1108, 1109, 1113, 1119, 1121, 1126, 1130,\n", + " 1131, 1135, 1137, 1146, 1148, 1154, 1155, 1158, 1160, 1161, 1162,\n", + " 1163, 1164, 1165, 1166, 1169, 1170, 1171, 1173, 1177, 1179, 1180,\n", + " 1182, 1184, 1185, 1192, 1193, 1195, 1196, 1198, 1200, 1201, 1203,\n", + " 1204, 1206, 1209, 1210, 1212, 1214, 1218, 1219, 1220, 1225, 1226,\n", + " 1227, 1230, 1231, 1232, 1234, 1236, 1237, 1239, 1244, 1247, 1252,\n", + " 1253, 1258, 1259, 1261, 1264, 1266, 1267, 1269, 1272, 1273, 1274,\n", + " 1278, 1279, 1282, 1289, 1291, 1299, 1301, 1303, 1305, 1306, 1311,\n", + " 1315, 1316, 1320, 1321, 1322, 1324, 1326, 1328, 1329, 1330, 1332,\n", + " 1334, 1336, 1337, 1339, 1341, 1345, 1346, 1351, 1355, 1363, 1364,\n", + " 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1378,\n", + " 1380, 1384, 1386, 1388, 1390, 1398]),\n", + " 2: array([ 4, 14, 24, 33, 50, 57, 72, 73, 81, 83, 84,\n", + " 85, 91, 95, 117, 125, 134, 136, 152, 157, 160, 161,\n", + " 171, 183, 185, 198, 203, 204, 217, 224, 225, 237, 243,\n", + " 256, 270, 287, 293, 313, 320, 321, 345, 356, 358, 364,\n", + " 367, 379, 382, 387, 406, 410, 417, 440, 447, 457, 458,\n", + " 466, 470, 479, 506, 514, 519, 523, 524, 525, 538, 541,\n", + " 549, 568, 577, 588, 593, 596, 600, 613, 621, 625, 626,\n", + " 632, 638, 646, 653, 656, 660, 663, 693, 706, 713, 721,\n", + " 723, 734, 735, 745, 749, 752, 755, 771, 781, 784, 801,\n", + " 805, 807, 813, 822, 827, 829, 854, 855, 881, 892, 894,\n", + " 897, 900, 927, 930, 945, 948, 959, 976, 977, 988, 998,\n", + " 1004, 1035, 1049, 1051, 1058, 1066, 1067, 1072, 1074, 1077, 1087,\n", + " 1089, 1091, 1096, 1097, 1098, 1099, 1104, 1107, 1112, 1120, 1128,\n", + " 1133, 1139, 1143, 1149, 1152, 1159, 1167, 1189, 1194, 1205, 1213,\n", + " 1216, 1222, 1229, 1235, 1238, 1256, 1257, 1276, 1281, 1285, 1287,\n", + " 1290, 1293, 1295, 1300, 1302, 1304, 1325, 1331, 1338, 1347, 1356,\n", + " 1357, 1365, 1366, 1382, 1391, 1393, 1397]),\n", + " 3: array([ 6, 8, 19, 21, 47, 52, 55, 59, 63, 68, 77,\n", + " 78, 88, 93, 96, 98, 104, 109, 113, 115, 120, 121,\n", + " 126, 131, 150, 151, 153, 155, 181, 184, 194, 199, 209,\n", + " 228, 229, 231, 239, 254, 258, 260, 261, 266, 275, 276,\n", + " 280, 282, 284, 292, 295, 297, 299, 316, 336, 338, 339,\n", + " 342, 348, 351, 363, 370, 389, 392, 399, 404, 409, 450,\n", + " 454, 474, 494, 497, 521, 526, 553, 559, 561, 563, 573,\n", + " 578, 599, 605, 606, 619, 628, 631, 640, 655, 665, 670,\n", + " 672, 678, 680, 683, 686, 691, 696, 699, 702, 708, 717,\n", + " 719, 720, 726, 740, 748, 753, 759, 763, 783, 786, 787,\n", + " 797, 802, 804, 815, 816, 818, 825, 838, 868, 873, 875,\n", + " 877, 880, 885, 888, 889, 901, 910, 928, 934, 935, 937,\n", + " 973, 975, 980, 983, 993, 999, 1000, 1005, 1014, 1020, 1022,\n", + " 1030, 1033, 1039, 1040, 1047, 1048, 1050, 1056, 1073, 1094, 1106,\n", + " 1110, 1118, 1122, 1123, 1124, 1134, 1138, 1141, 1142, 1172, 1183,\n", + " 1211, 1221, 1240, 1241, 1249, 1251, 1254, 1255, 1262, 1265, 1275,\n", + " 1283, 1286, 1292, 1296, 1312, 1314, 1342, 1353, 1358, 1361, 1383,\n", + " 1387, 1394]),\n", + " 4: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 41, 43,\n", + " 44, 45, 53, 76, 79, 103, 106, 107, 110, 116, 129,\n", + " 146, 156, 163, 164, 165, 168, 186, 191, 193, 210, 215,\n", + " 220, 233, 234, 238, 242, 248, 250, 251, 257, 263, 269,\n", + " 281, 285, 286, 303, 311, 318, 329, 343, 346, 347, 350,\n", + " 360, 374, 375, 376, 393, 396, 403, 405, 414, 424, 437,\n", + " 438, 439, 456, 461, 477, 478, 487, 493, 502, 504, 507,\n", + " 511, 513, 536, 544, 546, 555, 556, 557, 565, 570, 575,\n", + " 583, 608, 609, 623, 630, 636, 654, 664, 667, 669, 687,\n", + " 694, 698, 700, 705, 710, 712, 722, 725, 727, 739, 746,\n", + " 758, 760, 765, 768, 776, 778, 788, 790, 817, 820, 833,\n", + " 834, 842, 845, 847, 850, 851, 858, 860, 862, 863, 865,\n", + " 878, 893, 903, 904, 906, 911, 922, 929, 932, 936, 946,\n", + " 954, 956, 966, 969, 970, 972, 981, 982, 989, 994, 1002,\n", + " 1008, 1010, 1015, 1016, 1019, 1027, 1034, 1036, 1043, 1057, 1061,\n", + " 1070, 1076, 1079, 1082, 1086, 1101, 1105, 1111, 1132, 1144, 1145,\n", + " 1150, 1151, 1157, 1168, 1174, 1175, 1186, 1188, 1197, 1199, 1202,\n", + " 1217, 1224, 1228, 1243, 1245, 1268, 1270, 1277, 1280, 1297, 1309,\n", + " 1317, 1319, 1323, 1333, 1335, 1349, 1352, 1359, 1385, 1392, 1396,\n", + " 1399]),\n", + " 5: array([ 62, 69, 71, 75, 100, 173, 176, 308, 317, 319, 326,\n", + " 330, 333, 359, 377, 395, 415, 441, 443, 451, 469, 481,\n", + " 498, 516, 531, 566, 571, 584, 585, 586, 595, 610, 642,\n", + " 649, 651, 659, 679, 681, 729, 738, 751, 754, 791, 795,\n", + " 800, 808, 826, 866, 870, 898, 902, 949, 958, 963, 995,\n", + " 1012, 1018, 1024, 1055, 1059, 1078, 1081, 1090, 1093, 1114, 1116,\n", + " 1136, 1140, 1156, 1176, 1178, 1181, 1187, 1207, 1233, 1242, 1248,\n", + " 1250, 1260, 1263, 1271, 1288, 1307, 1310, 1313, 1340, 1343, 1395]),\n", + " 6: array([ 180, 188, 196, 223, 279, 349, 576, 671, 724, 736, 794,\n", + " 925, 1147, 1215])},\n", + " 8: {0: array([ 0, 101, 123, 133, 182, 205, 226, 323, 362, 380, 413,\n", + " 515, 601, 641, 737, 773, 810, 907, 920, 957, 1007, 1153,\n", + " 1191, 1284, 1308, 1327, 1344]),\n", + " 1: array([ 1, 3, 5, 7, 9, 12, 13, 17, 18, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 34, 36, 38, 42, 46,\n", + " 48, 49, 51, 56, 58, 60, 64, 66, 67, 70, 74,\n", + " 80, 82, 86, 87, 90, 92, 94, 97, 99, 102, 108,\n", + " 111, 114, 118, 122, 124, 128, 130, 132, 135, 138, 139,\n", + " 140, 141, 142, 143, 144, 145, 148, 149, 158, 159, 162,\n", + " 166, 167, 169, 170, 172, 174, 175, 177, 178, 179, 189,\n", + " 190, 192, 195, 197, 200, 201, 202, 206, 207, 211, 212,\n", + " 213, 214, 216, 218, 219, 221, 222, 227, 232, 235, 236,\n", + " 240, 241, 244, 245, 246, 247, 249, 252, 253, 255, 259,\n", + " 262, 264, 265, 267, 271, 272, 273, 274, 277, 278, 283,\n", + " 288, 289, 290, 291, 294, 296, 298, 300, 301, 302, 304,\n", + " 305, 306, 307, 310, 312, 314, 315, 322, 324, 325, 327,\n", + " 331, 332, 334, 335, 340, 341, 344, 352, 353, 354, 357,\n", + " 361, 365, 366, 368, 369, 371, 372, 373, 378, 381, 383,\n", + " 384, 385, 386, 388, 390, 394, 397, 398, 400, 401, 402,\n", + " 407, 408, 411, 412, 416, 418, 419, 420, 421, 422, 423,\n", + " 425, 426, 427, 428, 429, 430, 431, 433, 434, 436, 442,\n", + " 444, 445, 446, 448, 449, 452, 453, 455, 459, 460, 462,\n", + " 463, 464, 465, 467, 468, 471, 472, 473, 475, 476, 480,\n", + " 482, 483, 485, 486, 489, 490, 491, 492, 495, 496, 499,\n", + " 500, 501, 503, 505, 508, 509, 510, 512, 517, 518, 520,\n", + " 522, 527, 528, 529, 530, 532, 533, 534, 535, 537, 539,\n", + " 540, 543, 545, 547, 548, 550, 551, 552, 554, 558, 560,\n", + " 564, 567, 569, 572, 574, 579, 580, 581, 582, 589, 591,\n", + " 592, 594, 598, 602, 603, 604, 607, 611, 612, 614, 615,\n", + " 616, 617, 618, 620, 622, 624, 627, 629, 633, 634, 635,\n", + " 637, 639, 643, 644, 645, 647, 648, 650, 652, 657, 658,\n", + " 662, 666, 668, 673, 674, 675, 676, 677, 682, 684, 685,\n", + " 688, 689, 690, 695, 697, 703, 704, 707, 709, 711, 714,\n", + " 715, 716, 718, 728, 730, 731, 732, 741, 742, 743, 747,\n", + " 750, 756, 757, 761, 762, 764, 766, 767, 769, 772, 774,\n", + " 775, 777, 779, 780, 782, 785, 789, 792, 796, 798, 799,\n", + " 803, 811, 812, 814, 819, 823, 824, 828, 830, 831, 832,\n", + " 835, 837, 839, 840, 841, 843, 844, 846, 848, 849, 852,\n", + " 853, 856, 857, 861, 867, 869, 871, 872, 874, 876, 883,\n", + " 884, 886, 887, 890, 891, 896, 899, 905, 908, 909, 912,\n", + " 913, 914, 916, 917, 918, 919, 921, 923, 924, 926, 933,\n", + " 938, 939, 940, 941, 942, 943, 944, 947, 950, 951, 953,\n", + " 960, 961, 962, 964, 965, 968, 971, 974, 979, 984, 985,\n", + " 986, 987, 990, 991, 992, 996, 997, 1001, 1003, 1006, 1009,\n", + " 1011, 1013, 1021, 1023, 1025, 1026, 1028, 1029, 1031, 1037, 1038,\n", + " 1041, 1042, 1044, 1045, 1046, 1052, 1053, 1054, 1060, 1062, 1063,\n", + " 1064, 1065, 1069, 1071, 1075, 1080, 1083, 1084, 1085, 1088, 1092,\n", + " 1095, 1100, 1102, 1103, 1108, 1109, 1113, 1119, 1121, 1126, 1130,\n", + " 1131, 1135, 1137, 1146, 1148, 1154, 1155, 1158, 1160, 1161, 1162,\n", + " 1163, 1164, 1165, 1166, 1169, 1170, 1171, 1173, 1177, 1179, 1180,\n", + " 1182, 1184, 1185, 1192, 1193, 1195, 1196, 1198, 1200, 1201, 1203,\n", + " 1204, 1206, 1209, 1210, 1212, 1214, 1218, 1219, 1220, 1225, 1226,\n", + " 1227, 1230, 1231, 1232, 1234, 1236, 1237, 1239, 1244, 1247, 1252,\n", + " 1253, 1258, 1259, 1261, 1264, 1266, 1267, 1269, 1272, 1273, 1274,\n", + " 1278, 1279, 1282, 1289, 1291, 1299, 1301, 1303, 1305, 1306, 1311,\n", + " 1315, 1316, 1320, 1321, 1322, 1324, 1326, 1328, 1329, 1330, 1332,\n", + " 1334, 1336, 1337, 1339, 1341, 1345, 1346, 1351, 1355, 1363, 1364,\n", + " 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1378,\n", + " 1380, 1384, 1386, 1388, 1390, 1398]),\n", + " 2: array([ 2, 20, 22, 54, 61, 65, 89, 105, 112, 119, 127,\n", + " 137, 147, 154, 187, 208, 230, 268, 309, 328, 337, 355,\n", + " 391, 432, 435, 484, 488, 542, 562, 587, 590, 597, 661,\n", + " 692, 701, 733, 744, 770, 793, 806, 809, 821, 836, 859,\n", + " 864, 879, 882, 895, 915, 931, 952, 955, 967, 978, 1017,\n", + " 1032, 1068, 1115, 1117, 1125, 1127, 1129, 1190, 1208, 1223, 1246,\n", + " 1294, 1298, 1318, 1348, 1350, 1354, 1360, 1362, 1377, 1379, 1381,\n", + " 1389]),\n", + " 3: array([ 4, 14, 24, 33, 50, 57, 72, 73, 81, 83, 84,\n", + " 85, 91, 95, 117, 125, 134, 136, 152, 157, 160, 161,\n", + " 171, 183, 185, 198, 203, 204, 217, 224, 225, 237, 243,\n", + " 256, 270, 287, 293, 313, 320, 321, 345, 356, 358, 364,\n", + " 367, 379, 382, 387, 406, 410, 417, 440, 447, 457, 458,\n", + " 466, 470, 479, 506, 514, 519, 523, 524, 525, 538, 541,\n", + " 549, 568, 577, 588, 593, 596, 600, 613, 621, 625, 626,\n", + " 632, 638, 646, 653, 656, 660, 663, 693, 706, 713, 721,\n", + " 723, 734, 735, 745, 749, 752, 755, 771, 781, 784, 801,\n", + " 805, 807, 813, 822, 827, 829, 854, 855, 881, 892, 894,\n", + " 897, 900, 927, 930, 945, 948, 959, 976, 977, 988, 998,\n", + " 1004, 1035, 1049, 1051, 1058, 1066, 1067, 1072, 1074, 1077, 1087,\n", + " 1089, 1091, 1096, 1097, 1098, 1099, 1104, 1107, 1112, 1120, 1128,\n", + " 1133, 1139, 1143, 1149, 1152, 1159, 1167, 1189, 1194, 1205, 1213,\n", + " 1216, 1222, 1229, 1235, 1238, 1256, 1257, 1276, 1281, 1285, 1287,\n", + " 1290, 1293, 1295, 1300, 1302, 1304, 1325, 1331, 1338, 1347, 1356,\n", + " 1357, 1365, 1366, 1382, 1391, 1393, 1397]),\n", + " 4: array([ 6, 8, 19, 21, 47, 52, 55, 59, 63, 68, 77,\n", + " 78, 88, 93, 96, 98, 104, 109, 113, 115, 120, 121,\n", + " 126, 131, 150, 151, 153, 155, 181, 184, 194, 199, 209,\n", + " 228, 229, 231, 239, 254, 258, 260, 261, 266, 275, 276,\n", + " 280, 282, 284, 292, 295, 297, 299, 316, 336, 338, 339,\n", + " 342, 348, 351, 363, 370, 389, 392, 399, 404, 409, 450,\n", + " 454, 474, 494, 497, 521, 526, 553, 559, 561, 563, 573,\n", + " 578, 599, 605, 606, 619, 628, 631, 640, 655, 665, 670,\n", + " 672, 678, 680, 683, 686, 691, 696, 699, 702, 708, 717,\n", + " 719, 720, 726, 740, 748, 753, 759, 763, 783, 786, 787,\n", + " 797, 802, 804, 815, 816, 818, 825, 838, 868, 873, 875,\n", + " 877, 880, 885, 888, 889, 901, 910, 928, 934, 935, 937,\n", + " 973, 975, 980, 983, 993, 999, 1000, 1005, 1014, 1020, 1022,\n", + " 1030, 1033, 1039, 1040, 1047, 1048, 1050, 1056, 1073, 1094, 1106,\n", + " 1110, 1118, 1122, 1123, 1124, 1134, 1138, 1141, 1142, 1172, 1183,\n", + " 1211, 1221, 1240, 1241, 1249, 1251, 1254, 1255, 1262, 1265, 1275,\n", + " 1283, 1286, 1292, 1296, 1312, 1314, 1342, 1353, 1358, 1361, 1383,\n", + " 1387, 1394]),\n", + " 5: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 41, 43,\n", + " 44, 45, 53, 76, 79, 103, 106, 107, 110, 116, 129,\n", + " 146, 156, 163, 164, 165, 168, 186, 191, 193, 210, 215,\n", + " 220, 233, 234, 238, 242, 248, 250, 251, 257, 263, 269,\n", + " 281, 285, 286, 303, 311, 318, 329, 343, 346, 347, 350,\n", + " 360, 374, 375, 376, 393, 396, 403, 405, 414, 424, 437,\n", + " 438, 439, 456, 461, 477, 478, 487, 493, 502, 504, 507,\n", + " 511, 513, 536, 544, 546, 555, 556, 557, 565, 570, 575,\n", + " 583, 608, 609, 623, 630, 636, 654, 664, 667, 669, 687,\n", + " 694, 698, 700, 705, 710, 712, 722, 725, 727, 739, 746,\n", + " 758, 760, 765, 768, 776, 778, 788, 790, 817, 820, 833,\n", + " 834, 842, 845, 847, 850, 851, 858, 860, 862, 863, 865,\n", + " 878, 893, 903, 904, 906, 911, 922, 929, 932, 936, 946,\n", + " 954, 956, 966, 969, 970, 972, 981, 982, 989, 994, 1002,\n", + " 1008, 1010, 1015, 1016, 1019, 1027, 1034, 1036, 1043, 1057, 1061,\n", + " 1070, 1076, 1079, 1082, 1086, 1101, 1105, 1111, 1132, 1144, 1145,\n", + " 1150, 1151, 1157, 1168, 1174, 1175, 1186, 1188, 1197, 1199, 1202,\n", + " 1217, 1224, 1228, 1243, 1245, 1268, 1270, 1277, 1280, 1297, 1309,\n", + " 1317, 1319, 1323, 1333, 1335, 1349, 1352, 1359, 1385, 1392, 1396,\n", + " 1399]),\n", + " 6: array([ 62, 69, 71, 75, 100, 173, 176, 308, 317, 319, 326,\n", + " 330, 333, 359, 377, 395, 415, 441, 443, 451, 469, 481,\n", + " 498, 516, 531, 566, 571, 584, 585, 586, 595, 610, 642,\n", + " 649, 651, 659, 679, 681, 729, 738, 751, 754, 791, 795,\n", + " 800, 808, 826, 866, 870, 898, 902, 949, 958, 963, 995,\n", + " 1012, 1018, 1024, 1055, 1059, 1078, 1081, 1090, 1093, 1114, 1116,\n", + " 1136, 1140, 1156, 1176, 1178, 1181, 1187, 1207, 1233, 1242, 1248,\n", + " 1250, 1260, 1263, 1271, 1288, 1307, 1310, 1313, 1340, 1343, 1395]),\n", + " 7: array([ 180, 188, 196, 223, 279, 349, 576, 671, 724, 736, 794,\n", + " 925, 1147, 1215])},\n", + " 9: {0: array([ 0, 101, 123, 133, 182, 205, 226, 323, 362, 380, 413,\n", + " 515, 601, 641, 737, 773, 810, 907, 920, 957, 1007, 1153,\n", + " 1191, 1284, 1308, 1327, 1344]),\n", + " 1: array([ 1, 3, 5, 7, 9, 12, 13, 17, 18, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 34, 36, 38, 42, 46,\n", + " 48, 49, 51, 56, 58, 60, 64, 66, 67, 70, 74,\n", + " 80, 82, 86, 87, 90, 92, 94, 97, 99, 102, 108,\n", + " 111, 114, 118, 122, 124, 128, 130, 132, 135, 138, 139,\n", + " 140, 141, 142, 143, 144, 145, 148, 149, 158, 159, 162,\n", + " 166, 167, 169, 170, 172, 174, 175, 177, 178, 179, 189,\n", + " 190, 192, 195, 197, 200, 201, 202, 206, 207, 211, 212,\n", + " 213, 214, 216, 218, 219, 221, 222, 227, 232, 235, 236,\n", + " 240, 241, 244, 245, 246, 247, 249, 252, 253, 255, 259,\n", + " 262, 264, 265, 267, 271, 272, 273, 274, 277, 278, 283,\n", + " 288, 289, 290, 291, 294, 296, 298, 300, 301, 302, 304,\n", + " 305, 306, 307, 310, 312, 314, 315, 322, 324, 325, 327,\n", + " 331, 332, 334, 335, 340, 341, 344, 352, 353, 354, 357,\n", + " 361, 365, 366, 368, 369, 371, 372, 373, 378, 381, 383,\n", + " 384, 385, 386, 388, 390, 394, 397, 398, 400, 401, 402,\n", + " 407, 408, 411, 412, 416, 418, 419, 420, 421, 422, 423,\n", + " 425, 426, 427, 428, 429, 430, 431, 433, 434, 436, 442,\n", + " 444, 445, 446, 448, 449, 452, 453, 455, 459, 460, 462,\n", + " 463, 464, 465, 467, 468, 471, 472, 473, 475, 476, 480,\n", + " 482, 483, 485, 486, 489, 490, 491, 492, 495, 496, 499,\n", + " 500, 501, 503, 505, 508, 509, 510, 512, 517, 518, 520,\n", + " 522, 527, 528, 529, 530, 532, 533, 534, 535, 537, 539,\n", + " 540, 543, 545, 547, 548, 550, 551, 552, 554, 558, 560,\n", + " 564, 567, 569, 572, 574, 579, 580, 581, 582, 589, 591,\n", + " 592, 594, 598, 602, 603, 604, 607, 611, 612, 614, 615,\n", + " 616, 617, 618, 620, 622, 624, 627, 629, 633, 634, 635,\n", + " 637, 639, 643, 644, 645, 647, 648, 650, 652, 657, 658,\n", + " 662, 666, 668, 673, 674, 675, 676, 677, 682, 684, 685,\n", + " 688, 689, 690, 695, 697, 703, 704, 707, 709, 711, 714,\n", + " 715, 716, 718, 728, 730, 731, 732, 741, 742, 743, 747,\n", + " 750, 756, 757, 761, 762, 764, 766, 767, 769, 772, 774,\n", + " 775, 777, 779, 780, 782, 785, 789, 792, 796, 798, 799,\n", + " 803, 811, 812, 814, 819, 823, 824, 828, 830, 831, 832,\n", + " 835, 837, 839, 840, 841, 843, 844, 846, 848, 849, 852,\n", + " 853, 856, 857, 861, 867, 869, 871, 872, 874, 876, 883,\n", + " 884, 886, 887, 890, 891, 896, 899, 905, 908, 909, 912,\n", + " 913, 914, 916, 917, 918, 919, 921, 923, 924, 926, 933,\n", + " 938, 939, 940, 941, 942, 943, 944, 947, 950, 951, 953,\n", + " 960, 961, 962, 964, 965, 968, 971, 974, 979, 984, 985,\n", + " 986, 987, 990, 991, 992, 996, 997, 1001, 1003, 1006, 1009,\n", + " 1011, 1013, 1021, 1023, 1025, 1026, 1028, 1029, 1031, 1037, 1038,\n", + " 1041, 1042, 1044, 1045, 1046, 1052, 1053, 1054, 1060, 1062, 1063,\n", + " 1064, 1065, 1069, 1071, 1075, 1080, 1083, 1084, 1085, 1088, 1092,\n", + " 1095, 1100, 1102, 1103, 1108, 1109, 1113, 1119, 1121, 1126, 1130,\n", + " 1131, 1135, 1137, 1146, 1148, 1154, 1155, 1158, 1160, 1161, 1162,\n", + " 1163, 1164, 1165, 1166, 1169, 1170, 1171, 1173, 1177, 1179, 1180,\n", + " 1182, 1184, 1185, 1192, 1193, 1195, 1196, 1198, 1200, 1201, 1203,\n", + " 1204, 1206, 1209, 1210, 1212, 1214, 1218, 1219, 1220, 1225, 1226,\n", + " 1227, 1230, 1231, 1232, 1234, 1236, 1237, 1239, 1244, 1247, 1252,\n", + " 1253, 1258, 1259, 1261, 1264, 1266, 1267, 1269, 1272, 1273, 1274,\n", + " 1278, 1279, 1282, 1289, 1291, 1299, 1301, 1303, 1305, 1306, 1311,\n", + " 1315, 1316, 1320, 1321, 1322, 1324, 1326, 1328, 1329, 1330, 1332,\n", + " 1334, 1336, 1337, 1339, 1341, 1345, 1346, 1351, 1355, 1363, 1364,\n", + " 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1378,\n", + " 1380, 1384, 1386, 1388, 1390, 1398]),\n", + " 2: array([ 2, 20, 22, 54, 61, 65, 89, 105, 112, 119, 127,\n", + " 137, 147, 154, 187, 208, 230, 268, 309, 328, 337, 355,\n", + " 391, 432, 435, 484, 488, 542, 562, 587, 590, 597, 661,\n", + " 692, 701, 733, 744, 770, 793, 806, 809, 821, 836, 859,\n", + " 864, 879, 882, 895, 915, 931, 952, 955, 967, 978, 1017,\n", + " 1032, 1068, 1115, 1117, 1125, 1127, 1129, 1190, 1208, 1223, 1246,\n", + " 1294, 1298, 1318, 1348, 1350, 1354, 1360, 1362, 1377, 1379, 1381,\n", + " 1389]),\n", + " 3: array([ 4, 14, 24, 33, 50, 57, 72, 73, 81, 83, 84,\n", + " 85, 91, 95, 117, 125, 134, 136, 152, 157, 160, 161,\n", + " 171, 183, 185, 198, 203, 204, 217, 224, 225, 237, 243,\n", + " 256, 270, 287, 293, 313, 320, 321, 345, 356, 358, 364,\n", + " 367, 379, 382, 387, 406, 410, 417, 440, 447, 457, 458,\n", + " 466, 470, 479, 506, 514, 519, 523, 524, 525, 538, 541,\n", + " 549, 568, 577, 588, 593, 596, 600, 613, 621, 625, 626,\n", + " 632, 638, 646, 653, 656, 660, 663, 693, 706, 713, 721,\n", + " 723, 734, 735, 745, 749, 752, 755, 771, 781, 784, 801,\n", + " 805, 807, 813, 822, 827, 829, 854, 855, 881, 892, 894,\n", + " 897, 900, 927, 930, 945, 948, 959, 976, 977, 988, 998,\n", + " 1004, 1035, 1049, 1051, 1058, 1066, 1067, 1072, 1074, 1077, 1087,\n", + " 1089, 1091, 1096, 1097, 1098, 1099, 1104, 1107, 1112, 1120, 1128,\n", + " 1133, 1139, 1143, 1149, 1152, 1159, 1167, 1189, 1194, 1205, 1213,\n", + " 1216, 1222, 1229, 1235, 1238, 1256, 1257, 1276, 1281, 1285, 1287,\n", + " 1290, 1293, 1295, 1300, 1302, 1304, 1325, 1331, 1338, 1347, 1356,\n", + " 1357, 1365, 1366, 1382, 1391, 1393, 1397]),\n", + " 4: array([ 6, 52, 78, 121, 126, 131, 209, 229, 260, 266, 275,\n", + " 284, 292, 339, 342, 370, 409, 450, 561, 599, 606, 631,\n", + " 655, 665, 670, 678, 702, 719, 759, 763, 783, 787, 804,\n", + " 815, 816, 877, 888, 889, 935, 993, 999, 1022, 1033, 1073,\n", + " 1094, 1106, 1123, 1211, 1221, 1292, 1314, 1361, 1394]),\n", + " 5: array([ 8, 19, 21, 47, 55, 59, 63, 68, 77, 88, 93,\n", + " 96, 98, 104, 109, 113, 115, 120, 150, 151, 153, 155,\n", + " 181, 184, 194, 199, 228, 231, 239, 254, 258, 261, 276,\n", + " 280, 282, 295, 297, 299, 316, 336, 338, 348, 351, 363,\n", + " 389, 392, 399, 404, 454, 474, 494, 497, 521, 526, 553,\n", + " 559, 563, 573, 578, 605, 619, 628, 640, 672, 680, 683,\n", + " 686, 691, 696, 699, 708, 717, 720, 726, 740, 748, 753,\n", + " 786, 797, 802, 818, 825, 838, 868, 873, 875, 880, 885,\n", + " 901, 910, 928, 934, 937, 973, 975, 980, 983, 1000, 1005,\n", + " 1014, 1020, 1030, 1039, 1040, 1047, 1048, 1050, 1056, 1110, 1118,\n", + " 1122, 1124, 1134, 1138, 1141, 1142, 1172, 1183, 1240, 1241, 1249,\n", + " 1251, 1254, 1255, 1262, 1265, 1275, 1283, 1286, 1296, 1312, 1342,\n", + " 1353, 1358, 1383, 1387]),\n", + " 6: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 41, 43,\n", + " 44, 45, 53, 76, 79, 103, 106, 107, 110, 116, 129,\n", + " 146, 156, 163, 164, 165, 168, 186, 191, 193, 210, 215,\n", + " 220, 233, 234, 238, 242, 248, 250, 251, 257, 263, 269,\n", + " 281, 285, 286, 303, 311, 318, 329, 343, 346, 347, 350,\n", + " 360, 374, 375, 376, 393, 396, 403, 405, 414, 424, 437,\n", + " 438, 439, 456, 461, 477, 478, 487, 493, 502, 504, 507,\n", + " 511, 513, 536, 544, 546, 555, 556, 557, 565, 570, 575,\n", + " 583, 608, 609, 623, 630, 636, 654, 664, 667, 669, 687,\n", + " 694, 698, 700, 705, 710, 712, 722, 725, 727, 739, 746,\n", + " 758, 760, 765, 768, 776, 778, 788, 790, 817, 820, 833,\n", + " 834, 842, 845, 847, 850, 851, 858, 860, 862, 863, 865,\n", + " 878, 893, 903, 904, 906, 911, 922, 929, 932, 936, 946,\n", + " 954, 956, 966, 969, 970, 972, 981, 982, 989, 994, 1002,\n", + " 1008, 1010, 1015, 1016, 1019, 1027, 1034, 1036, 1043, 1057, 1061,\n", + " 1070, 1076, 1079, 1082, 1086, 1101, 1105, 1111, 1132, 1144, 1145,\n", + " 1150, 1151, 1157, 1168, 1174, 1175, 1186, 1188, 1197, 1199, 1202,\n", + " 1217, 1224, 1228, 1243, 1245, 1268, 1270, 1277, 1280, 1297, 1309,\n", + " 1317, 1319, 1323, 1333, 1335, 1349, 1352, 1359, 1385, 1392, 1396,\n", + " 1399]),\n", + " 7: array([ 62, 69, 71, 75, 100, 173, 176, 308, 317, 319, 326,\n", + " 330, 333, 359, 377, 395, 415, 441, 443, 451, 469, 481,\n", + " 498, 516, 531, 566, 571, 584, 585, 586, 595, 610, 642,\n", + " 649, 651, 659, 679, 681, 729, 738, 751, 754, 791, 795,\n", + " 800, 808, 826, 866, 870, 898, 902, 949, 958, 963, 995,\n", + " 1012, 1018, 1024, 1055, 1059, 1078, 1081, 1090, 1093, 1114, 1116,\n", + " 1136, 1140, 1156, 1176, 1178, 1181, 1187, 1207, 1233, 1242, 1248,\n", + " 1250, 1260, 1263, 1271, 1288, 1307, 1310, 1313, 1340, 1343, 1395]),\n", + " 8: array([ 180, 188, 196, 223, 279, 349, 576, 671, 724, 736, 794,\n", + " 925, 1147, 1215])},\n", + " 10: {0: array([ 0, 101, 123, 133, 182, 205, 226, 323, 362, 380, 413,\n", + " 515, 601, 641, 737, 773, 810, 907, 920, 957, 1007, 1153,\n", + " 1191, 1284, 1308, 1327, 1344]),\n", + " 1: array([ 1, 3, 5, 7, 9, 12, 13, 17, 18, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 34, 36, 38, 42, 46,\n", + " 48, 49, 51, 56, 58, 60, 64, 66, 67, 70, 74,\n", + " 80, 82, 86, 87, 90, 92, 94, 97, 99, 102, 108,\n", + " 111, 114, 118, 122, 124, 128, 130, 132, 135, 138, 139,\n", + " 140, 141, 142, 143, 144, 145, 148, 149, 158, 159, 162,\n", + " 166, 167, 169, 170, 172, 174, 175, 177, 178, 179, 189,\n", + " 190, 192, 195, 197, 200, 201, 202, 206, 207, 211, 212,\n", + " 213, 214, 216, 218, 219, 221, 222, 227, 232, 235, 236,\n", + " 240, 241, 244, 245, 246, 247, 249, 252, 253, 255, 259,\n", + " 262, 264, 265, 267, 271, 272, 273, 274, 277, 278, 283,\n", + " 288, 289, 290, 291, 294, 296, 298, 300, 301, 302, 304,\n", + " 305, 306, 307, 310, 312, 314, 315, 322, 324, 325, 327,\n", + " 331, 332, 334, 335, 340, 341, 344, 352, 353, 354, 357,\n", + " 361, 365, 366, 368, 369, 371, 372, 373, 378, 381, 383,\n", + " 384, 385, 386, 388, 390, 394, 397, 398, 400, 401, 402,\n", + " 407, 408, 411, 412, 416, 418, 419, 420, 421, 422, 423,\n", + " 425, 426, 427, 428, 429, 430, 431, 433, 434, 436, 442,\n", + " 444, 445, 446, 448, 449, 452, 453, 455, 459, 460, 462,\n", + " 463, 464, 465, 467, 468, 471, 472, 473, 475, 476, 480,\n", + " 482, 483, 485, 486, 489, 490, 491, 492, 495, 496, 499,\n", + " 500, 501, 503, 505, 508, 509, 510, 512, 517, 518, 520,\n", + " 522, 527, 528, 529, 530, 532, 533, 534, 535, 537, 539,\n", + " 540, 543, 545, 547, 548, 550, 551, 552, 554, 558, 560,\n", + " 564, 567, 569, 572, 574, 579, 580, 581, 582, 589, 591,\n", + " 592, 594, 598, 602, 603, 604, 607, 611, 612, 614, 615,\n", + " 616, 617, 618, 620, 622, 624, 627, 629, 633, 634, 635,\n", + " 637, 639, 643, 644, 645, 647, 648, 650, 652, 657, 658,\n", + " 662, 666, 668, 673, 674, 675, 676, 677, 682, 684, 685,\n", + " 688, 689, 690, 695, 697, 703, 704, 707, 709, 711, 714,\n", + " 715, 716, 718, 728, 730, 731, 732, 741, 742, 743, 747,\n", + " 750, 756, 757, 761, 762, 764, 766, 767, 769, 772, 774,\n", + " 775, 777, 779, 780, 782, 785, 789, 792, 796, 798, 799,\n", + " 803, 811, 812, 814, 819, 823, 824, 828, 830, 831, 832,\n", + " 835, 837, 839, 840, 841, 843, 844, 846, 848, 849, 852,\n", + " 853, 856, 857, 861, 867, 869, 871, 872, 874, 876, 883,\n", + " 884, 886, 887, 890, 891, 896, 899, 905, 908, 909, 912,\n", + " 913, 914, 916, 917, 918, 919, 921, 923, 924, 926, 933,\n", + " 938, 939, 940, 941, 942, 943, 944, 947, 950, 951, 953,\n", + " 960, 961, 962, 964, 965, 968, 971, 974, 979, 984, 985,\n", + " 986, 987, 990, 991, 992, 996, 997, 1001, 1003, 1006, 1009,\n", + " 1011, 1013, 1021, 1023, 1025, 1026, 1028, 1029, 1031, 1037, 1038,\n", + " 1041, 1042, 1044, 1045, 1046, 1052, 1053, 1054, 1060, 1062, 1063,\n", + " 1064, 1065, 1069, 1071, 1075, 1080, 1083, 1084, 1085, 1088, 1092,\n", + " 1095, 1100, 1102, 1103, 1108, 1109, 1113, 1119, 1121, 1126, 1130,\n", + " 1131, 1135, 1137, 1146, 1148, 1154, 1155, 1158, 1160, 1161, 1162,\n", + " 1163, 1164, 1165, 1166, 1169, 1170, 1171, 1173, 1177, 1179, 1180,\n", + " 1182, 1184, 1185, 1192, 1193, 1195, 1196, 1198, 1200, 1201, 1203,\n", + " 1204, 1206, 1209, 1210, 1212, 1214, 1218, 1219, 1220, 1225, 1226,\n", + " 1227, 1230, 1231, 1232, 1234, 1236, 1237, 1239, 1244, 1247, 1252,\n", + " 1253, 1258, 1259, 1261, 1264, 1266, 1267, 1269, 1272, 1273, 1274,\n", + " 1278, 1279, 1282, 1289, 1291, 1299, 1301, 1303, 1305, 1306, 1311,\n", + " 1315, 1316, 1320, 1321, 1322, 1324, 1326, 1328, 1329, 1330, 1332,\n", + " 1334, 1336, 1337, 1339, 1341, 1345, 1346, 1351, 1355, 1363, 1364,\n", + " 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1378,\n", + " 1380, 1384, 1386, 1388, 1390, 1398]),\n", + " 2: array([ 2, 20, 22, 54, 61, 65, 89, 105, 112, 119, 127,\n", + " 137, 147, 154, 187, 208, 230, 268, 309, 328, 337, 355,\n", + " 391, 432, 435, 484, 488, 542, 562, 587, 590, 597, 661,\n", + " 692, 701, 733, 744, 770, 793, 806, 809, 821, 836, 859,\n", + " 864, 879, 882, 895, 915, 931, 952, 955, 967, 978, 1017,\n", + " 1032, 1068, 1115, 1117, 1125, 1127, 1129, 1190, 1208, 1223, 1246,\n", + " 1294, 1298, 1318, 1348, 1350, 1354, 1360, 1362, 1377, 1379, 1381,\n", + " 1389]),\n", + " 3: array([ 4, 14, 24, 33, 50, 57, 72, 73, 81, 83, 84,\n", + " 85, 91, 95, 117, 125, 134, 136, 152, 157, 160, 161,\n", + " 171, 183, 185, 198, 203, 204, 217, 224, 225, 237, 243,\n", + " 256, 270, 287, 293, 313, 320, 321, 345, 356, 358, 364,\n", + " 367, 379, 382, 387, 406, 410, 417, 440, 447, 457, 458,\n", + " 466, 470, 479, 506, 514, 519, 523, 524, 525, 538, 541,\n", + " 549, 568, 577, 588, 593, 596, 600, 613, 621, 625, 626,\n", + " 632, 638, 646, 653, 656, 660, 663, 693, 706, 713, 721,\n", + " 723, 734, 735, 745, 749, 752, 755, 771, 781, 784, 801,\n", + " 805, 807, 813, 822, 827, 829, 854, 855, 881, 892, 894,\n", + " 897, 900, 927, 930, 945, 948, 959, 976, 977, 988, 998,\n", + " 1004, 1035, 1049, 1051, 1058, 1066, 1067, 1072, 1074, 1077, 1087,\n", + " 1089, 1091, 1096, 1097, 1098, 1099, 1104, 1107, 1112, 1120, 1128,\n", + " 1133, 1139, 1143, 1149, 1152, 1159, 1167, 1189, 1194, 1205, 1213,\n", + " 1216, 1222, 1229, 1235, 1238, 1256, 1257, 1276, 1281, 1285, 1287,\n", + " 1290, 1293, 1295, 1300, 1302, 1304, 1325, 1331, 1338, 1347, 1356,\n", + " 1357, 1365, 1366, 1382, 1391, 1393, 1397]),\n", + " 4: array([ 6, 52, 78, 121, 126, 131, 209, 229, 260, 266, 275,\n", + " 284, 292, 339, 342, 370, 409, 450, 561, 599, 606, 631,\n", + " 655, 665, 670, 678, 702, 719, 759, 763, 783, 787, 804,\n", + " 815, 816, 877, 888, 889, 935, 993, 999, 1022, 1033, 1073,\n", + " 1094, 1106, 1123, 1211, 1221, 1292, 1314, 1361, 1394]),\n", + " 5: array([ 8, 19, 21, 47, 55, 59, 63, 68, 77, 88, 93,\n", + " 96, 98, 104, 109, 113, 115, 120, 150, 151, 153, 155,\n", + " 181, 184, 194, 199, 228, 231, 239, 254, 258, 261, 276,\n", + " 280, 282, 295, 297, 299, 316, 336, 338, 348, 351, 363,\n", + " 389, 392, 399, 404, 454, 474, 494, 497, 521, 526, 553,\n", + " 559, 563, 573, 578, 605, 619, 628, 640, 672, 680, 683,\n", + " 686, 691, 696, 699, 708, 717, 720, 726, 740, 748, 753,\n", + " 786, 797, 802, 818, 825, 838, 868, 873, 875, 880, 885,\n", + " 901, 910, 928, 934, 937, 973, 975, 980, 983, 1000, 1005,\n", + " 1014, 1020, 1030, 1039, 1040, 1047, 1048, 1050, 1056, 1110, 1118,\n", + " 1122, 1124, 1134, 1138, 1141, 1142, 1172, 1183, 1240, 1241, 1249,\n", + " 1251, 1254, 1255, 1262, 1265, 1275, 1283, 1286, 1296, 1312, 1342,\n", + " 1353, 1358, 1383, 1387]),\n", + " 6: array([ 10, 11, 16, 23, 35, 37, 39, 41, 43, 44, 45,\n", + " 53, 76, 79, 103, 106, 107, 110, 129, 146, 156, 163,\n", + " 164, 165, 168, 186, 193, 210, 220, 233, 234, 238, 248,\n", + " 251, 257, 263, 269, 281, 285, 286, 303, 311, 343, 346,\n", + " 347, 350, 360, 375, 376, 393, 396, 403, 405, 414, 424,\n", + " 437, 439, 456, 477, 478, 487, 502, 504, 507, 511, 513,\n", + " 536, 544, 546, 555, 556, 557, 565, 570, 575, 583, 608,\n", + " 609, 623, 630, 636, 654, 664, 667, 669, 687, 694, 698,\n", + " 700, 705, 710, 712, 722, 725, 727, 739, 746, 758, 760,\n", + " 765, 768, 776, 778, 790, 817, 820, 833, 834, 842, 845,\n", + " 850, 851, 858, 860, 862, 863, 865, 878, 893, 903, 904,\n", + " 906, 911, 922, 929, 932, 936, 946, 954, 969, 970, 972,\n", + " 981, 982, 989, 994, 1002, 1008, 1010, 1015, 1019, 1027, 1034,\n", + " 1036, 1043, 1057, 1070, 1076, 1079, 1082, 1086, 1101, 1105, 1111,\n", + " 1132, 1144, 1145, 1150, 1157, 1168, 1174, 1175, 1186, 1202, 1217,\n", + " 1228, 1243, 1245, 1268, 1277, 1280, 1297, 1309, 1317, 1319, 1323,\n", + " 1333, 1335, 1349, 1352, 1359, 1385, 1392, 1396, 1399]),\n", + " 7: array([ 15, 40, 116, 191, 215, 242, 250, 318, 329, 374, 438,\n", + " 461, 493, 788, 847, 956, 966, 1016, 1061, 1151, 1188, 1197,\n", + " 1199, 1224, 1270]),\n", + " 8: array([ 62, 69, 71, 75, 100, 173, 176, 308, 317, 319, 326,\n", + " 330, 333, 359, 377, 395, 415, 441, 443, 451, 469, 481,\n", + " 498, 516, 531, 566, 571, 584, 585, 586, 595, 610, 642,\n", + " 649, 651, 659, 679, 681, 729, 738, 751, 754, 791, 795,\n", + " 800, 808, 826, 866, 870, 898, 902, 949, 958, 963, 995,\n", + " 1012, 1018, 1024, 1055, 1059, 1078, 1081, 1090, 1093, 1114, 1116,\n", + " 1136, 1140, 1156, 1176, 1178, 1181, 1187, 1207, 1233, 1242, 1248,\n", + " 1250, 1260, 1263, 1271, 1288, 1307, 1310, 1313, 1340, 1343, 1395]),\n", + " 9: array([ 180, 188, 196, 223, 279, 349, 576, 671, 724, 736, 794,\n", + " 925, 1147, 1215])}},\n", + " 'signed_nonnormalized_l2_noavg': {2: {0: array([ 0, 2, 6, 8, 19, 20, 21, 22, 47, 52, 54,\n", + " 55, 59, 61, 63, 65, 68, 77, 78, 88, 89, 93,\n", + " 96, 98, 101, 104, 105, 109, 112, 113, 115, 119, 120,\n", + " 121, 123, 127, 131, 133, 137, 147, 150, 151, 153, 154,\n", + " 155, 181, 182, 184, 187, 194, 199, 205, 208, 209, 226,\n", + " 228, 229, 230, 231, 239, 254, 258, 261, 268, 275, 276,\n", + " 280, 282, 284, 295, 297, 299, 309, 316, 323, 328, 336,\n", + " 337, 338, 339, 342, 348, 351, 355, 362, 363, 380, 389,\n", + " 391, 392, 399, 404, 413, 432, 435, 450, 454, 474, 484,\n", + " 488, 494, 497, 515, 521, 526, 542, 553, 559, 561, 562,\n", + " 563, 573, 578, 587, 590, 597, 601, 605, 606, 619, 628,\n", + " 640, 641, 655, 661, 672, 680, 683, 686, 691, 692, 696,\n", + " 699, 701, 702, 708, 717, 719, 720, 726, 733, 737, 740,\n", + " 744, 748, 753, 763, 770, 773, 786, 793, 797, 802, 804,\n", + " 806, 809, 810, 815, 816, 818, 821, 825, 836, 838, 858,\n", + " 859, 864, 868, 873, 875, 879, 880, 882, 885, 895, 901,\n", + " 907, 910, 915, 920, 928, 931, 934, 937, 952, 955, 957,\n", + " 967, 973, 975, 978, 980, 983, 993, 999, 1000, 1005, 1007,\n", + " 1014, 1017, 1020, 1022, 1030, 1032, 1039, 1040, 1047, 1048, 1050,\n", + " 1056, 1068, 1073, 1094, 1106, 1110, 1115, 1117, 1118, 1122, 1123,\n", + " 1124, 1125, 1127, 1129, 1134, 1138, 1141, 1142, 1153, 1172, 1183,\n", + " 1190, 1191, 1208, 1223, 1240, 1241, 1245, 1246, 1249, 1251, 1254,\n", + " 1255, 1262, 1265, 1275, 1283, 1284, 1286, 1294, 1296, 1298, 1308,\n", + " 1312, 1314, 1318, 1319, 1327, 1342, 1344, 1348, 1350, 1353, 1354,\n", + " 1358, 1360, 1361, 1362, 1377, 1379, 1381, 1383, 1387, 1389, 1394]),\n", + " 1: array([ 1, 3, 4, ..., 1397, 1398, 1399])},\n", + " 3: {0: array([ 0, 2, 6, 20, 22, 54, 61, 65, 78, 89, 101,\n", + " 105, 112, 119, 123, 127, 131, 133, 137, 147, 154, 182,\n", + " 187, 205, 208, 209, 226, 229, 230, 268, 275, 284, 309,\n", + " 323, 328, 337, 339, 342, 355, 362, 380, 391, 413, 432,\n", + " 435, 450, 484, 488, 515, 542, 562, 587, 590, 597, 601,\n", + " 641, 655, 661, 692, 701, 702, 719, 733, 737, 744, 763,\n", + " 770, 773, 793, 804, 806, 809, 810, 816, 821, 836, 859,\n", + " 864, 879, 882, 895, 907, 915, 920, 931, 952, 955, 957,\n", + " 967, 978, 993, 999, 1007, 1017, 1022, 1032, 1068, 1094, 1106,\n", + " 1115, 1117, 1123, 1125, 1127, 1129, 1153, 1190, 1191, 1208, 1223,\n", + " 1246, 1284, 1294, 1298, 1308, 1318, 1327, 1344, 1348, 1350, 1354,\n", + " 1360, 1362, 1377, 1379, 1381, 1389]),\n", + " 1: array([ 1, 3, 4, ..., 1397, 1398, 1399]),\n", + " 2: array([ 8, 19, 21, 47, 52, 55, 59, 63, 68, 77, 88,\n", + " 93, 96, 98, 104, 109, 113, 115, 120, 121, 150, 151,\n", + " 153, 155, 181, 184, 194, 199, 228, 231, 239, 254, 258,\n", + " 261, 276, 280, 282, 295, 297, 299, 316, 336, 338, 348,\n", + " 351, 363, 389, 392, 399, 404, 454, 474, 494, 497, 521,\n", + " 526, 553, 559, 561, 563, 573, 578, 605, 606, 619, 628,\n", + " 640, 672, 680, 683, 686, 691, 696, 699, 708, 717, 720,\n", + " 726, 740, 748, 753, 786, 797, 802, 815, 818, 825, 838,\n", + " 858, 868, 873, 875, 880, 885, 901, 910, 928, 934, 937,\n", + " 973, 975, 980, 983, 1000, 1005, 1014, 1020, 1030, 1039, 1040,\n", + " 1047, 1048, 1050, 1056, 1073, 1110, 1118, 1122, 1124, 1134, 1138,\n", + " 1141, 1142, 1172, 1183, 1240, 1241, 1245, 1249, 1251, 1254, 1255,\n", + " 1262, 1265, 1275, 1283, 1286, 1296, 1312, 1314, 1319, 1342, 1353,\n", + " 1358, 1361, 1383, 1387, 1394])},\n", + " 4: {0: array([ 0, 2, 6, 20, 22, 54, 61, 65, 78, 89, 101,\n", + " 105, 112, 119, 123, 127, 131, 133, 137, 147, 154, 182,\n", + " 187, 205, 208, 209, 226, 229, 230, 268, 275, 284, 309,\n", + " 323, 328, 337, 339, 342, 355, 362, 380, 391, 413, 432,\n", + " 435, 450, 484, 488, 515, 542, 562, 587, 590, 597, 601,\n", + " 641, 655, 661, 692, 701, 702, 719, 733, 737, 744, 763,\n", + " 770, 773, 793, 804, 806, 809, 810, 816, 821, 836, 859,\n", + " 864, 879, 882, 895, 907, 915, 920, 931, 952, 955, 957,\n", + " 967, 978, 993, 999, 1007, 1017, 1022, 1032, 1068, 1094, 1106,\n", + " 1115, 1117, 1123, 1125, 1127, 1129, 1153, 1190, 1191, 1208, 1223,\n", + " 1246, 1284, 1294, 1298, 1308, 1318, 1327, 1344, 1348, 1350, 1354,\n", + " 1360, 1362, 1377, 1379, 1381, 1389]),\n", + " 1: array([ 1, 3, 5, 7, 9, 10, 11, 12, 13, 15, 16,\n", + " 17, 18, 23, 25, 26, 27, 28, 29, 30, 31, 32,\n", + " 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,\n", + " 46, 48, 49, 51, 53, 56, 58, 60, 64, 66, 67,\n", + " 70, 74, 76, 79, 80, 82, 86, 87, 90, 92, 94,\n", + " 97, 99, 102, 103, 106, 107, 108, 110, 111, 114, 116,\n", + " 118, 122, 124, 126, 128, 129, 130, 132, 135, 138, 139,\n", + " 140, 141, 142, 143, 144, 145, 146, 148, 149, 156, 158,\n", + " 159, 162, 163, 164, 165, 166, 167, 168, 169, 170, 172,\n", + " 174, 175, 177, 178, 179, 186, 189, 190, 191, 192, 193,\n", + " 195, 197, 200, 201, 202, 206, 207, 210, 211, 212, 213,\n", + " 214, 215, 216, 218, 219, 220, 221, 222, 227, 232, 233,\n", + " 234, 235, 236, 238, 240, 241, 242, 244, 245, 246, 247,\n", + " 248, 249, 250, 251, 252, 253, 255, 257, 259, 260, 262,\n", + " 263, 264, 265, 266, 267, 269, 271, 272, 273, 274, 277,\n", + " 278, 281, 283, 285, 286, 288, 289, 290, 291, 292, 294,\n", + " 296, 298, 300, 301, 302, 304, 305, 306, 307, 310, 311,\n", + " 312, 314, 315, 318, 322, 324, 325, 327, 329, 330, 331,\n", + " 332, 334, 335, 340, 341, 343, 344, 346, 347, 350, 352,\n", + " 353, 354, 357, 359, 361, 365, 366, 368, 369, 370, 371,\n", + " 372, 373, 374, 375, 376, 378, 381, 383, 384, 385, 386,\n", + " 388, 390, 393, 394, 396, 397, 398, 400, 401, 402, 403,\n", + " 405, 407, 408, 409, 411, 412, 414, 416, 418, 419, 420,\n", + " 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431,\n", + " 433, 434, 436, 437, 438, 439, 442, 444, 445, 446, 448,\n", + " 449, 451, 452, 453, 455, 456, 459, 460, 461, 462, 463,\n", + " 464, 465, 467, 468, 471, 472, 473, 475, 476, 477, 478,\n", + " 480, 482, 483, 485, 486, 487, 489, 490, 491, 492, 493,\n", + " 495, 496, 499, 500, 501, 502, 503, 504, 505, 507, 508,\n", + " 509, 510, 511, 512, 513, 517, 518, 520, 522, 527, 528,\n", + " 529, 530, 532, 533, 534, 535, 536, 537, 539, 540, 543,\n", + " 544, 545, 546, 547, 548, 550, 551, 552, 554, 555, 556,\n", + " 557, 558, 560, 564, 565, 567, 569, 570, 571, 572, 574,\n", + " 575, 579, 580, 581, 582, 583, 585, 589, 591, 592, 594,\n", + " 596, 598, 599, 602, 603, 604, 607, 608, 609, 611, 612,\n", + " 614, 615, 616, 617, 618, 620, 622, 623, 624, 627, 629,\n", + " 630, 631, 633, 634, 635, 636, 637, 639, 643, 644, 645,\n", + " 647, 648, 650, 652, 654, 657, 658, 662, 664, 665, 666,\n", + " 667, 668, 669, 670, 673, 674, 675, 676, 677, 678, 682,\n", + " 684, 687, 688, 689, 690, 694, 695, 697, 698, 700, 703,\n", + " 704, 705, 707, 709, 710, 711, 712, 714, 715, 716, 718,\n", + " 722, 725, 727, 728, 730, 731, 732, 739, 741, 742, 743,\n", + " 746, 747, 750, 756, 757, 758, 759, 760, 761, 762, 764,\n", + " 765, 766, 767, 768, 769, 772, 774, 775, 776, 777, 778,\n", + " 779, 780, 782, 783, 785, 787, 788, 789, 790, 791, 792,\n", + " 795, 796, 798, 799, 803, 811, 812, 814, 817, 819, 820,\n", + " 823, 824, 826, 828, 830, 831, 832, 833, 834, 835, 837,\n", + " 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849,\n", + " 850, 851, 852, 853, 856, 857, 860, 861, 862, 863, 865,\n", + " 867, 869, 870, 871, 872, 874, 876, 877, 878, 883, 884,\n", + " 886, 887, 888, 889, 890, 891, 893, 896, 899, 902, 903,\n", + " 904, 905, 906, 908, 909, 911, 912, 913, 914, 916, 917,\n", + " 918, 919, 921, 922, 923, 924, 926, 929, 932, 933, 935,\n", + " 936, 938, 939, 940, 941, 942, 943, 944, 946, 947, 950,\n", + " 951, 953, 954, 956, 960, 961, 962, 963, 964, 965, 966,\n", + " 968, 969, 970, 971, 972, 974, 979, 981, 982, 984, 985,\n", + " 986, 987, 989, 990, 991, 992, 994, 996, 997, 1001, 1002,\n", + " 1003, 1006, 1008, 1009, 1010, 1011, 1013, 1015, 1016, 1019, 1021,\n", + " 1023, 1025, 1026, 1027, 1028, 1029, 1031, 1033, 1034, 1036, 1037,\n", + " 1038, 1041, 1042, 1043, 1044, 1045, 1046, 1052, 1053, 1054, 1057,\n", + " 1060, 1061, 1062, 1063, 1064, 1065, 1069, 1070, 1071, 1075, 1076,\n", + " 1079, 1080, 1082, 1083, 1084, 1085, 1086, 1088, 1090, 1092, 1095,\n", + " 1100, 1101, 1102, 1103, 1105, 1108, 1109, 1111, 1113, 1116, 1119,\n", + " 1121, 1126, 1130, 1131, 1132, 1135, 1136, 1137, 1144, 1145, 1146,\n", + " 1148, 1150, 1151, 1154, 1155, 1157, 1158, 1160, 1161, 1162, 1163,\n", + " 1164, 1165, 1166, 1168, 1169, 1170, 1171, 1173, 1174, 1175, 1177,\n", + " 1179, 1180, 1181, 1182, 1184, 1185, 1186, 1188, 1192, 1193, 1195,\n", + " 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1206, 1209,\n", + " 1210, 1211, 1212, 1214, 1217, 1218, 1219, 1220, 1221, 1224, 1225,\n", + " 1226, 1227, 1228, 1230, 1231, 1232, 1233, 1234, 1236, 1237, 1239,\n", + " 1243, 1244, 1247, 1252, 1253, 1258, 1259, 1261, 1264, 1266, 1267,\n", + " 1268, 1269, 1270, 1272, 1273, 1274, 1277, 1278, 1279, 1280, 1282,\n", + " 1289, 1291, 1292, 1297, 1299, 1301, 1303, 1305, 1306, 1309, 1311,\n", + " 1315, 1316, 1317, 1320, 1321, 1322, 1323, 1324, 1326, 1328, 1329,\n", + " 1330, 1332, 1333, 1334, 1335, 1336, 1337, 1339, 1341, 1345, 1346,\n", + " 1349, 1351, 1352, 1355, 1359, 1363, 1364, 1367, 1368, 1369, 1370,\n", + " 1371, 1372, 1373, 1374, 1375, 1376, 1378, 1380, 1384, 1385, 1386,\n", + " 1388, 1390, 1392, 1396, 1398, 1399]),\n", + " 2: array([ 4, 14, 24, 33, 34, 50, 57, 62, 69, 71, 72,\n", + " 73, 75, 81, 83, 84, 85, 91, 95, 100, 117, 125,\n", + " 134, 136, 152, 157, 160, 161, 171, 173, 176, 180, 183,\n", + " 185, 188, 196, 198, 203, 204, 217, 223, 224, 225, 237,\n", + " 243, 256, 270, 279, 287, 293, 303, 308, 313, 317, 319,\n", + " 320, 321, 326, 333, 345, 349, 356, 358, 360, 364, 367,\n", + " 377, 379, 382, 387, 395, 406, 410, 415, 417, 440, 441,\n", + " 443, 447, 457, 458, 466, 469, 470, 479, 481, 498, 506,\n", + " 514, 516, 519, 523, 524, 525, 531, 538, 541, 549, 566,\n", + " 568, 576, 577, 584, 586, 588, 593, 595, 600, 610, 613,\n", + " 621, 625, 626, 632, 638, 642, 646, 649, 651, 653, 656,\n", + " 659, 660, 663, 671, 679, 681, 685, 693, 706, 713, 721,\n", + " 723, 724, 729, 734, 735, 736, 738, 745, 749, 751, 752,\n", + " 754, 755, 771, 781, 784, 794, 800, 801, 805, 807, 808,\n", + " 813, 822, 827, 829, 854, 855, 866, 881, 892, 894, 897,\n", + " 898, 900, 925, 927, 930, 945, 948, 949, 958, 959, 976,\n", + " 977, 988, 995, 998, 1004, 1012, 1018, 1024, 1035, 1049, 1051,\n", + " 1055, 1058, 1059, 1066, 1067, 1072, 1074, 1077, 1078, 1081, 1087,\n", + " 1089, 1091, 1093, 1096, 1097, 1098, 1099, 1104, 1107, 1112, 1114,\n", + " 1120, 1128, 1133, 1139, 1140, 1143, 1147, 1149, 1152, 1156, 1159,\n", + " 1167, 1176, 1178, 1187, 1189, 1194, 1205, 1207, 1213, 1215, 1216,\n", + " 1222, 1229, 1235, 1238, 1242, 1248, 1250, 1256, 1257, 1260, 1263,\n", + " 1271, 1276, 1281, 1285, 1287, 1288, 1290, 1293, 1295, 1300, 1302,\n", + " 1304, 1307, 1310, 1313, 1325, 1331, 1338, 1340, 1343, 1347, 1356,\n", + " 1357, 1365, 1366, 1382, 1391, 1393, 1395, 1397]),\n", + " 3: array([ 8, 19, 21, 47, 52, 55, 59, 63, 68, 77, 88,\n", + " 93, 96, 98, 104, 109, 113, 115, 120, 121, 150, 151,\n", + " 153, 155, 181, 184, 194, 199, 228, 231, 239, 254, 258,\n", + " 261, 276, 280, 282, 295, 297, 299, 316, 336, 338, 348,\n", + " 351, 363, 389, 392, 399, 404, 454, 474, 494, 497, 521,\n", + " 526, 553, 559, 561, 563, 573, 578, 605, 606, 619, 628,\n", + " 640, 672, 680, 683, 686, 691, 696, 699, 708, 717, 720,\n", + " 726, 740, 748, 753, 786, 797, 802, 815, 818, 825, 838,\n", + " 858, 868, 873, 875, 880, 885, 901, 910, 928, 934, 937,\n", + " 973, 975, 980, 983, 1000, 1005, 1014, 1020, 1030, 1039, 1040,\n", + " 1047, 1048, 1050, 1056, 1073, 1110, 1118, 1122, 1124, 1134, 1138,\n", + " 1141, 1142, 1172, 1183, 1240, 1241, 1245, 1249, 1251, 1254, 1255,\n", + " 1262, 1265, 1275, 1283, 1286, 1296, 1312, 1314, 1319, 1342, 1353,\n", + " 1358, 1361, 1383, 1387, 1394])},\n", + " 5: {0: array([ 0, 6, 20, 22, 54, 61, 65, 78, 89, 112, 119,\n", + " 127, 131, 137, 147, 154, 187, 205, 208, 209, 226, 229,\n", + " 230, 275, 284, 323, 328, 337, 339, 342, 355, 391, 413,\n", + " 432, 450, 484, 488, 515, 542, 562, 587, 590, 597, 601,\n", + " 655, 661, 692, 701, 719, 737, 744, 763, 770, 793, 804,\n", + " 806, 809, 810, 816, 821, 836, 859, 895, 907, 920, 967,\n", + " 978, 993, 999, 1017, 1022, 1032, 1068, 1094, 1106, 1115, 1117,\n", + " 1123, 1125, 1127, 1129, 1190, 1223, 1246, 1294, 1318, 1344, 1354,\n", + " 1360, 1362, 1377, 1379, 1381]),\n", + " 1: array([ 1, 3, 5, 7, 9, 10, 11, 12, 13, 15, 16,\n", + " 17, 18, 23, 25, 26, 27, 28, 29, 30, 31, 32,\n", + " 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,\n", + " 46, 48, 49, 51, 53, 56, 58, 60, 64, 66, 67,\n", + " 70, 74, 76, 79, 80, 82, 86, 87, 90, 92, 94,\n", + " 97, 99, 102, 103, 106, 107, 108, 110, 111, 114, 116,\n", + " 118, 122, 124, 126, 128, 129, 130, 132, 135, 138, 139,\n", + " 140, 141, 142, 143, 144, 145, 146, 148, 149, 156, 158,\n", + " 159, 162, 163, 164, 165, 166, 167, 168, 169, 170, 172,\n", + " 174, 175, 177, 178, 179, 186, 189, 190, 191, 192, 193,\n", + " 195, 197, 200, 201, 202, 206, 207, 210, 211, 212, 213,\n", + " 214, 215, 216, 218, 219, 220, 221, 222, 227, 232, 233,\n", + " 234, 235, 236, 238, 240, 241, 242, 244, 245, 246, 247,\n", + " 248, 249, 250, 251, 252, 253, 255, 257, 259, 260, 262,\n", + " 263, 264, 265, 266, 267, 269, 271, 272, 273, 274, 277,\n", + " 278, 281, 283, 285, 286, 288, 289, 290, 291, 292, 294,\n", + " 296, 298, 300, 301, 302, 304, 305, 306, 307, 310, 311,\n", + " 312, 314, 315, 318, 322, 324, 325, 327, 329, 330, 331,\n", + " 332, 334, 335, 340, 341, 343, 344, 346, 347, 350, 352,\n", + " 353, 354, 357, 359, 361, 365, 366, 368, 369, 370, 371,\n", + " 372, 373, 374, 375, 376, 378, 381, 383, 384, 385, 386,\n", + " 388, 390, 393, 394, 396, 397, 398, 400, 401, 402, 403,\n", + " 405, 407, 408, 409, 411, 412, 414, 416, 418, 419, 420,\n", + " 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431,\n", + " 433, 434, 436, 437, 438, 439, 442, 444, 445, 446, 448,\n", + " 449, 451, 452, 453, 455, 456, 459, 460, 461, 462, 463,\n", + " 464, 465, 467, 468, 471, 472, 473, 475, 476, 477, 478,\n", + " 480, 482, 483, 485, 486, 487, 489, 490, 491, 492, 493,\n", + " 495, 496, 499, 500, 501, 502, 503, 504, 505, 507, 508,\n", + " 509, 510, 511, 512, 513, 517, 518, 520, 522, 527, 528,\n", + " 529, 530, 532, 533, 534, 535, 536, 537, 539, 540, 543,\n", + " 544, 545, 546, 547, 548, 550, 551, 552, 554, 555, 556,\n", + " 557, 558, 560, 564, 565, 567, 569, 570, 571, 572, 574,\n", + " 575, 579, 580, 581, 582, 583, 585, 589, 591, 592, 594,\n", + " 596, 598, 599, 602, 603, 604, 607, 608, 609, 611, 612,\n", + " 614, 615, 616, 617, 618, 620, 622, 623, 624, 627, 629,\n", + " 630, 631, 633, 634, 635, 636, 637, 639, 643, 644, 645,\n", + " 647, 648, 650, 652, 654, 657, 658, 662, 664, 665, 666,\n", + " 667, 668, 669, 670, 673, 674, 675, 676, 677, 678, 682,\n", + " 684, 687, 688, 689, 690, 694, 695, 697, 698, 700, 703,\n", + " 704, 705, 707, 709, 710, 711, 712, 714, 715, 716, 718,\n", + " 722, 725, 727, 728, 730, 731, 732, 739, 741, 742, 743,\n", + " 746, 747, 750, 756, 757, 758, 759, 760, 761, 762, 764,\n", + " 765, 766, 767, 768, 769, 772, 774, 775, 776, 777, 778,\n", + " 779, 780, 782, 783, 785, 787, 788, 789, 790, 791, 792,\n", + " 795, 796, 798, 799, 803, 811, 812, 814, 817, 819, 820,\n", + " 823, 824, 826, 828, 830, 831, 832, 833, 834, 835, 837,\n", + " 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849,\n", + " 850, 851, 852, 853, 856, 857, 860, 861, 862, 863, 865,\n", + " 867, 869, 870, 871, 872, 874, 876, 877, 878, 883, 884,\n", + " 886, 887, 888, 889, 890, 891, 893, 896, 899, 902, 903,\n", + " 904, 905, 906, 908, 909, 911, 912, 913, 914, 916, 917,\n", + " 918, 919, 921, 922, 923, 924, 926, 929, 932, 933, 935,\n", + " 936, 938, 939, 940, 941, 942, 943, 944, 946, 947, 950,\n", + " 951, 953, 954, 956, 960, 961, 962, 963, 964, 965, 966,\n", + " 968, 969, 970, 971, 972, 974, 979, 981, 982, 984, 985,\n", + " 986, 987, 989, 990, 991, 992, 994, 996, 997, 1001, 1002,\n", + " 1003, 1006, 1008, 1009, 1010, 1011, 1013, 1015, 1016, 1019, 1021,\n", + " 1023, 1025, 1026, 1027, 1028, 1029, 1031, 1033, 1034, 1036, 1037,\n", + " 1038, 1041, 1042, 1043, 1044, 1045, 1046, 1052, 1053, 1054, 1057,\n", + " 1060, 1061, 1062, 1063, 1064, 1065, 1069, 1070, 1071, 1075, 1076,\n", + " 1079, 1080, 1082, 1083, 1084, 1085, 1086, 1088, 1090, 1092, 1095,\n", + " 1100, 1101, 1102, 1103, 1105, 1108, 1109, 1111, 1113, 1116, 1119,\n", + " 1121, 1126, 1130, 1131, 1132, 1135, 1136, 1137, 1144, 1145, 1146,\n", + " 1148, 1150, 1151, 1154, 1155, 1157, 1158, 1160, 1161, 1162, 1163,\n", + " 1164, 1165, 1166, 1168, 1169, 1170, 1171, 1173, 1174, 1175, 1177,\n", + " 1179, 1180, 1181, 1182, 1184, 1185, 1186, 1188, 1192, 1193, 1195,\n", + " 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1206, 1209,\n", + " 1210, 1211, 1212, 1214, 1217, 1218, 1219, 1220, 1221, 1224, 1225,\n", + " 1226, 1227, 1228, 1230, 1231, 1232, 1233, 1234, 1236, 1237, 1239,\n", + " 1243, 1244, 1247, 1252, 1253, 1258, 1259, 1261, 1264, 1266, 1267,\n", + " 1268, 1269, 1270, 1272, 1273, 1274, 1277, 1278, 1279, 1280, 1282,\n", + " 1289, 1291, 1292, 1297, 1299, 1301, 1303, 1305, 1306, 1309, 1311,\n", + " 1315, 1316, 1317, 1320, 1321, 1322, 1323, 1324, 1326, 1328, 1329,\n", + " 1330, 1332, 1333, 1334, 1335, 1336, 1337, 1339, 1341, 1345, 1346,\n", + " 1349, 1351, 1352, 1355, 1359, 1363, 1364, 1367, 1368, 1369, 1370,\n", + " 1371, 1372, 1373, 1374, 1375, 1376, 1378, 1380, 1384, 1385, 1386,\n", + " 1388, 1390, 1392, 1396, 1398, 1399]),\n", + " 2: array([ 2, 101, 105, 123, 133, 182, 268, 309, 362, 380, 435,\n", + " 641, 702, 733, 773, 864, 879, 882, 915, 931, 952, 955,\n", + " 957, 1007, 1153, 1191, 1208, 1284, 1298, 1308, 1327, 1348, 1350,\n", + " 1389]),\n", + " 3: array([ 4, 14, 24, 33, 34, 50, 57, 62, 69, 71, 72,\n", + " 73, 75, 81, 83, 84, 85, 91, 95, 100, 117, 125,\n", + " 134, 136, 152, 157, 160, 161, 171, 173, 176, 180, 183,\n", + " 185, 188, 196, 198, 203, 204, 217, 223, 224, 225, 237,\n", + " 243, 256, 270, 279, 287, 293, 303, 308, 313, 317, 319,\n", + " 320, 321, 326, 333, 345, 349, 356, 358, 360, 364, 367,\n", + " 377, 379, 382, 387, 395, 406, 410, 415, 417, 440, 441,\n", + " 443, 447, 457, 458, 466, 469, 470, 479, 481, 498, 506,\n", + " 514, 516, 519, 523, 524, 525, 531, 538, 541, 549, 566,\n", + " 568, 576, 577, 584, 586, 588, 593, 595, 600, 610, 613,\n", + " 621, 625, 626, 632, 638, 642, 646, 649, 651, 653, 656,\n", + " 659, 660, 663, 671, 679, 681, 685, 693, 706, 713, 721,\n", + " 723, 724, 729, 734, 735, 736, 738, 745, 749, 751, 752,\n", + " 754, 755, 771, 781, 784, 794, 800, 801, 805, 807, 808,\n", + " 813, 822, 827, 829, 854, 855, 866, 881, 892, 894, 897,\n", + " 898, 900, 925, 927, 930, 945, 948, 949, 958, 959, 976,\n", + " 977, 988, 995, 998, 1004, 1012, 1018, 1024, 1035, 1049, 1051,\n", + " 1055, 1058, 1059, 1066, 1067, 1072, 1074, 1077, 1078, 1081, 1087,\n", + " 1089, 1091, 1093, 1096, 1097, 1098, 1099, 1104, 1107, 1112, 1114,\n", + " 1120, 1128, 1133, 1139, 1140, 1143, 1147, 1149, 1152, 1156, 1159,\n", + " 1167, 1176, 1178, 1187, 1189, 1194, 1205, 1207, 1213, 1215, 1216,\n", + " 1222, 1229, 1235, 1238, 1242, 1248, 1250, 1256, 1257, 1260, 1263,\n", + " 1271, 1276, 1281, 1285, 1287, 1288, 1290, 1293, 1295, 1300, 1302,\n", + " 1304, 1307, 1310, 1313, 1325, 1331, 1338, 1340, 1343, 1347, 1356,\n", + " 1357, 1365, 1366, 1382, 1391, 1393, 1395, 1397]),\n", + " 4: array([ 8, 19, 21, 47, 52, 55, 59, 63, 68, 77, 88,\n", + " 93, 96, 98, 104, 109, 113, 115, 120, 121, 150, 151,\n", + " 153, 155, 181, 184, 194, 199, 228, 231, 239, 254, 258,\n", + " 261, 276, 280, 282, 295, 297, 299, 316, 336, 338, 348,\n", + " 351, 363, 389, 392, 399, 404, 454, 474, 494, 497, 521,\n", + " 526, 553, 559, 561, 563, 573, 578, 605, 606, 619, 628,\n", + " 640, 672, 680, 683, 686, 691, 696, 699, 708, 717, 720,\n", + " 726, 740, 748, 753, 786, 797, 802, 815, 818, 825, 838,\n", + " 858, 868, 873, 875, 880, 885, 901, 910, 928, 934, 937,\n", + " 973, 975, 980, 983, 1000, 1005, 1014, 1020, 1030, 1039, 1040,\n", + " 1047, 1048, 1050, 1056, 1073, 1110, 1118, 1122, 1124, 1134, 1138,\n", + " 1141, 1142, 1172, 1183, 1240, 1241, 1245, 1249, 1251, 1254, 1255,\n", + " 1262, 1265, 1275, 1283, 1286, 1296, 1312, 1314, 1319, 1342, 1353,\n", + " 1358, 1361, 1383, 1387, 1394])},\n", + " 6: {0: array([ 0, 6, 20, 22, 54, 61, 65, 78, 89, 112, 119,\n", + " 127, 131, 137, 147, 154, 187, 205, 208, 209, 226, 229,\n", + " 230, 275, 284, 323, 328, 337, 339, 342, 355, 391, 413,\n", + " 432, 450, 484, 488, 515, 542, 562, 587, 590, 597, 601,\n", + " 655, 661, 692, 701, 719, 737, 744, 763, 770, 793, 804,\n", + " 806, 809, 810, 816, 821, 836, 859, 895, 907, 920, 967,\n", + " 978, 993, 999, 1017, 1022, 1032, 1068, 1094, 1106, 1115, 1117,\n", + " 1123, 1125, 1127, 1129, 1190, 1223, 1246, 1294, 1318, 1344, 1354,\n", + " 1360, 1362, 1377, 1379, 1381]),\n", + " 1: array([ 1, 3, 5, 7, 9, 10, 11, 12, 13, 15, 16,\n", + " 17, 18, 23, 25, 26, 27, 28, 29, 30, 31, 32,\n", + " 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,\n", + " 46, 48, 49, 51, 53, 56, 58, 60, 64, 66, 67,\n", + " 70, 74, 76, 79, 80, 82, 86, 87, 90, 92, 94,\n", + " 97, 99, 102, 103, 106, 107, 108, 110, 111, 114, 116,\n", + " 118, 122, 124, 126, 128, 129, 130, 132, 135, 138, 139,\n", + " 140, 141, 142, 143, 144, 145, 146, 148, 149, 156, 158,\n", + " 159, 162, 163, 164, 165, 166, 167, 168, 169, 170, 172,\n", + " 174, 175, 177, 178, 179, 186, 189, 190, 191, 192, 193,\n", + " 195, 197, 200, 201, 202, 206, 207, 210, 211, 212, 213,\n", + " 214, 215, 216, 218, 219, 220, 221, 222, 227, 232, 233,\n", + " 234, 235, 236, 238, 240, 241, 242, 244, 245, 246, 247,\n", + " 248, 249, 250, 251, 252, 253, 255, 257, 259, 260, 262,\n", + " 263, 264, 265, 266, 267, 269, 271, 272, 273, 274, 277,\n", + " 278, 281, 283, 285, 286, 288, 289, 290, 291, 292, 294,\n", + " 296, 298, 300, 301, 302, 304, 305, 306, 307, 310, 311,\n", + " 312, 314, 315, 318, 322, 324, 325, 327, 329, 330, 331,\n", + " 332, 334, 335, 340, 341, 343, 344, 346, 347, 350, 352,\n", + " 353, 354, 357, 359, 361, 365, 366, 368, 369, 370, 371,\n", + " 372, 373, 374, 375, 376, 378, 381, 383, 384, 385, 386,\n", + " 388, 390, 393, 394, 396, 397, 398, 400, 401, 402, 403,\n", + " 405, 407, 408, 409, 411, 412, 414, 416, 418, 419, 420,\n", + " 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431,\n", + " 433, 434, 436, 437, 438, 439, 442, 444, 445, 446, 448,\n", + " 449, 451, 452, 453, 455, 456, 459, 460, 461, 462, 463,\n", + " 464, 465, 467, 468, 471, 472, 473, 475, 476, 477, 478,\n", + " 480, 482, 483, 485, 486, 487, 489, 490, 491, 492, 493,\n", + " 495, 496, 499, 500, 501, 502, 503, 504, 505, 507, 508,\n", + " 509, 510, 511, 512, 513, 517, 518, 520, 522, 527, 528,\n", + " 529, 530, 532, 533, 534, 535, 536, 537, 539, 540, 543,\n", + " 544, 545, 546, 547, 548, 550, 551, 552, 554, 555, 556,\n", + " 557, 558, 560, 564, 565, 567, 569, 570, 571, 572, 574,\n", + " 575, 579, 580, 581, 582, 583, 585, 589, 591, 592, 594,\n", + " 596, 598, 599, 602, 603, 604, 607, 608, 609, 611, 612,\n", + " 614, 615, 616, 617, 618, 620, 622, 623, 624, 627, 629,\n", + " 630, 631, 633, 634, 635, 636, 637, 639, 643, 644, 645,\n", + " 647, 648, 650, 652, 654, 657, 658, 662, 664, 665, 666,\n", + " 667, 668, 669, 670, 673, 674, 675, 676, 677, 678, 682,\n", + " 684, 687, 688, 689, 690, 694, 695, 697, 698, 700, 703,\n", + " 704, 705, 707, 709, 710, 711, 712, 714, 715, 716, 718,\n", + " 722, 725, 727, 728, 730, 731, 732, 739, 741, 742, 743,\n", + " 746, 747, 750, 756, 757, 758, 759, 760, 761, 762, 764,\n", + " 765, 766, 767, 768, 769, 772, 774, 775, 776, 777, 778,\n", + " 779, 780, 782, 783, 785, 787, 788, 789, 790, 791, 792,\n", + " 795, 796, 798, 799, 803, 811, 812, 814, 817, 819, 820,\n", + " 823, 824, 826, 828, 830, 831, 832, 833, 834, 835, 837,\n", + " 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849,\n", + " 850, 851, 852, 853, 856, 857, 860, 861, 862, 863, 865,\n", + " 867, 869, 870, 871, 872, 874, 876, 877, 878, 883, 884,\n", + " 886, 887, 888, 889, 890, 891, 893, 896, 899, 902, 903,\n", + " 904, 905, 906, 908, 909, 911, 912, 913, 914, 916, 917,\n", + " 918, 919, 921, 922, 923, 924, 926, 929, 932, 933, 935,\n", + " 936, 938, 939, 940, 941, 942, 943, 944, 946, 947, 950,\n", + " 951, 953, 954, 956, 960, 961, 962, 963, 964, 965, 966,\n", + " 968, 969, 970, 971, 972, 974, 979, 981, 982, 984, 985,\n", + " 986, 987, 989, 990, 991, 992, 994, 996, 997, 1001, 1002,\n", + " 1003, 1006, 1008, 1009, 1010, 1011, 1013, 1015, 1016, 1019, 1021,\n", + " 1023, 1025, 1026, 1027, 1028, 1029, 1031, 1033, 1034, 1036, 1037,\n", + " 1038, 1041, 1042, 1043, 1044, 1045, 1046, 1052, 1053, 1054, 1057,\n", + " 1060, 1061, 1062, 1063, 1064, 1065, 1069, 1070, 1071, 1075, 1076,\n", + " 1079, 1080, 1082, 1083, 1084, 1085, 1086, 1088, 1090, 1092, 1095,\n", + " 1100, 1101, 1102, 1103, 1105, 1108, 1109, 1111, 1113, 1116, 1119,\n", + " 1121, 1126, 1130, 1131, 1132, 1135, 1136, 1137, 1144, 1145, 1146,\n", + " 1148, 1150, 1151, 1154, 1155, 1157, 1158, 1160, 1161, 1162, 1163,\n", + " 1164, 1165, 1166, 1168, 1169, 1170, 1171, 1173, 1174, 1175, 1177,\n", + " 1179, 1180, 1181, 1182, 1184, 1185, 1186, 1188, 1192, 1193, 1195,\n", + " 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1206, 1209,\n", + " 1210, 1211, 1212, 1214, 1217, 1218, 1219, 1220, 1221, 1224, 1225,\n", + " 1226, 1227, 1228, 1230, 1231, 1232, 1233, 1234, 1236, 1237, 1239,\n", + " 1243, 1244, 1247, 1252, 1253, 1258, 1259, 1261, 1264, 1266, 1267,\n", + " 1268, 1269, 1270, 1272, 1273, 1274, 1277, 1278, 1279, 1280, 1282,\n", + " 1289, 1291, 1292, 1297, 1299, 1301, 1303, 1305, 1306, 1309, 1311,\n", + " 1315, 1316, 1317, 1320, 1321, 1322, 1323, 1324, 1326, 1328, 1329,\n", + " 1330, 1332, 1333, 1334, 1335, 1336, 1337, 1339, 1341, 1345, 1346,\n", + " 1349, 1351, 1352, 1355, 1359, 1363, 1364, 1367, 1368, 1369, 1370,\n", + " 1371, 1372, 1373, 1374, 1375, 1376, 1378, 1380, 1384, 1385, 1386,\n", + " 1388, 1390, 1392, 1396, 1398, 1399]),\n", + " 2: array([ 2, 101, 105, 123, 133, 182, 268, 309, 362, 380, 435,\n", + " 641, 733, 773, 864, 879, 882, 915, 931, 952, 955, 957,\n", + " 1007, 1153, 1191, 1208, 1284, 1298, 1308, 1327, 1348, 1350, 1389]),\n", + " 3: array([ 4, 14, 24, 33, 34, 50, 57, 62, 69, 71, 72,\n", + " 73, 75, 81, 83, 84, 85, 91, 95, 100, 117, 125,\n", + " 134, 136, 152, 157, 160, 161, 171, 173, 176, 180, 183,\n", + " 185, 188, 196, 198, 203, 204, 217, 223, 224, 225, 237,\n", + " 243, 256, 270, 279, 287, 293, 303, 308, 313, 317, 319,\n", + " 320, 321, 326, 333, 345, 349, 356, 358, 360, 364, 367,\n", + " 377, 379, 382, 387, 395, 406, 410, 415, 417, 440, 441,\n", + " 443, 447, 457, 458, 466, 469, 470, 479, 481, 498, 506,\n", + " 514, 516, 519, 523, 524, 525, 531, 538, 541, 549, 566,\n", + " 568, 576, 577, 584, 586, 588, 593, 595, 600, 610, 613,\n", + " 621, 625, 626, 632, 638, 642, 646, 649, 651, 653, 656,\n", + " 659, 660, 663, 671, 679, 681, 685, 693, 706, 713, 721,\n", + " 723, 724, 729, 734, 735, 736, 738, 745, 749, 751, 752,\n", + " 754, 755, 771, 781, 784, 794, 800, 801, 805, 807, 808,\n", + " 813, 822, 827, 829, 854, 855, 866, 881, 892, 894, 897,\n", + " 898, 900, 925, 927, 930, 945, 948, 949, 958, 959, 976,\n", + " 977, 988, 995, 998, 1004, 1012, 1018, 1024, 1035, 1049, 1051,\n", + " 1055, 1058, 1059, 1066, 1067, 1072, 1074, 1077, 1078, 1081, 1087,\n", + " 1089, 1091, 1093, 1096, 1097, 1098, 1099, 1104, 1107, 1112, 1114,\n", + " 1120, 1128, 1133, 1139, 1140, 1143, 1147, 1149, 1152, 1156, 1159,\n", + " 1167, 1176, 1178, 1187, 1189, 1194, 1205, 1207, 1213, 1215, 1216,\n", + " 1222, 1229, 1235, 1238, 1242, 1248, 1250, 1256, 1257, 1260, 1263,\n", + " 1271, 1276, 1281, 1285, 1287, 1288, 1290, 1293, 1295, 1300, 1302,\n", + " 1304, 1307, 1310, 1313, 1325, 1331, 1338, 1340, 1343, 1347, 1356,\n", + " 1357, 1365, 1366, 1382, 1391, 1393, 1395, 1397]),\n", + " 4: array([ 8, 19, 21, 47, 52, 55, 59, 63, 68, 77, 88,\n", + " 93, 96, 98, 104, 109, 113, 115, 120, 121, 150, 151,\n", + " 153, 155, 181, 184, 194, 199, 228, 231, 239, 254, 258,\n", + " 261, 276, 280, 282, 295, 297, 299, 316, 336, 338, 348,\n", + " 351, 363, 389, 392, 399, 404, 454, 474, 494, 497, 521,\n", + " 526, 553, 559, 561, 563, 573, 578, 605, 606, 619, 628,\n", + " 640, 672, 680, 683, 686, 691, 696, 699, 708, 717, 720,\n", + " 726, 740, 748, 753, 786, 797, 802, 815, 818, 825, 838,\n", + " 858, 868, 873, 875, 880, 885, 901, 910, 928, 934, 937,\n", + " 973, 975, 980, 983, 1000, 1005, 1014, 1020, 1030, 1039, 1040,\n", + " 1047, 1048, 1050, 1056, 1073, 1110, 1118, 1122, 1124, 1134, 1138,\n", + " 1141, 1142, 1172, 1183, 1240, 1241, 1245, 1249, 1251, 1254, 1255,\n", + " 1262, 1265, 1275, 1283, 1286, 1296, 1312, 1314, 1319, 1342, 1353,\n", + " 1358, 1361, 1383, 1387, 1394]),\n", + " 5: array([702])},\n", + " 7: {0: array([ 0, 6, 20, 22, 54, 61, 65, 78, 89, 112, 119,\n", + " 127, 131, 137, 147, 154, 187, 205, 208, 209, 226, 229,\n", + " 230, 275, 284, 323, 328, 337, 339, 342, 355, 391, 413,\n", + " 432, 450, 484, 488, 515, 542, 562, 587, 590, 597, 601,\n", + " 655, 661, 692, 701, 719, 737, 744, 763, 770, 793, 804,\n", + " 806, 809, 810, 816, 821, 836, 859, 895, 907, 920, 967,\n", + " 978, 993, 999, 1017, 1022, 1032, 1068, 1094, 1106, 1115, 1117,\n", + " 1123, 1125, 1127, 1129, 1190, 1223, 1246, 1294, 1318, 1344, 1354,\n", + " 1360, 1362, 1377, 1379, 1381]),\n", + " 1: array([ 1, 3, 5, 7, 9, 12, 13, 17, 18, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 36, 38, 41, 42, 46,\n", + " 48, 49, 51, 56, 58, 60, 64, 66, 67, 70, 74,\n", + " 80, 82, 86, 87, 90, 92, 94, 97, 99, 102, 108,\n", + " 111, 114, 118, 122, 124, 126, 128, 130, 132, 135, 138,\n", + " 139, 140, 141, 142, 143, 144, 145, 148, 149, 158, 159,\n", + " 162, 166, 167, 169, 170, 172, 174, 175, 177, 178, 179,\n", + " 189, 190, 192, 195, 197, 200, 201, 202, 206, 207, 211,\n", + " 212, 213, 214, 216, 218, 219, 221, 222, 227, 232, 235,\n", + " 236, 238, 240, 241, 244, 245, 246, 247, 249, 252, 253,\n", + " 255, 259, 260, 262, 264, 265, 266, 267, 271, 272, 273,\n", + " 274, 277, 278, 283, 288, 289, 290, 291, 292, 294, 296,\n", + " 298, 300, 301, 302, 304, 305, 306, 307, 310, 312, 314,\n", + " 315, 322, 324, 325, 327, 331, 332, 334, 335, 340, 341,\n", + " 344, 352, 353, 354, 357, 361, 365, 366, 368, 369, 370,\n", + " 371, 372, 373, 376, 378, 381, 383, 384, 385, 386, 388,\n", + " 390, 394, 397, 398, 400, 401, 402, 407, 408, 409, 411,\n", + " 412, 414, 416, 418, 419, 420, 421, 422, 423, 425, 426,\n", + " 427, 428, 429, 430, 431, 433, 434, 436, 442, 444, 445,\n", + " 446, 448, 449, 452, 453, 455, 459, 460, 462, 463, 464,\n", + " 465, 467, 468, 471, 472, 475, 476, 480, 482, 483, 485,\n", + " 486, 489, 490, 491, 492, 495, 496, 499, 500, 501, 503,\n", + " 505, 508, 509, 510, 512, 517, 518, 520, 522, 527, 528,\n", + " 529, 530, 533, 534, 535, 536, 537, 539, 540, 543, 545,\n", + " 547, 548, 550, 551, 552, 554, 558, 560, 564, 567, 569,\n", + " 570, 572, 574, 579, 580, 581, 582, 589, 591, 592, 594,\n", + " 596, 598, 599, 602, 603, 604, 607, 611, 612, 614, 615,\n", + " 616, 617, 618, 620, 622, 624, 627, 629, 631, 633, 634,\n", + " 635, 637, 639, 643, 644, 645, 647, 648, 650, 652, 657,\n", + " 658, 662, 665, 666, 668, 670, 673, 674, 675, 676, 677,\n", + " 678, 682, 684, 687, 688, 689, 690, 695, 697, 700, 703,\n", + " 704, 707, 709, 711, 714, 715, 716, 718, 728, 730, 731,\n", + " 732, 741, 742, 743, 747, 750, 756, 757, 759, 761, 762,\n", + " 764, 765, 766, 767, 769, 772, 774, 775, 777, 779, 780,\n", + " 782, 783, 785, 787, 789, 792, 796, 798, 799, 803, 811,\n", + " 812, 814, 819, 823, 824, 828, 830, 831, 832, 835, 837,\n", + " 839, 840, 841, 843, 844, 846, 848, 849, 853, 856, 857,\n", + " 861, 867, 869, 871, 872, 874, 876, 877, 883, 884, 886,\n", + " 887, 888, 889, 890, 891, 896, 899, 904, 905, 908, 909,\n", + " 912, 913, 914, 916, 917, 918, 919, 921, 923, 924, 926,\n", + " 929, 932, 933, 935, 938, 939, 940, 941, 942, 943, 944,\n", + " 947, 950, 951, 953, 960, 961, 962, 964, 965, 968, 971,\n", + " 974, 979, 984, 985, 986, 987, 990, 991, 992, 996, 997,\n", + " 1001, 1003, 1006, 1009, 1010, 1011, 1013, 1021, 1023, 1025, 1026,\n", + " 1028, 1029, 1031, 1033, 1037, 1038, 1041, 1042, 1044, 1045, 1046,\n", + " 1052, 1053, 1054, 1060, 1062, 1063, 1064, 1065, 1069, 1071, 1075,\n", + " 1080, 1083, 1084, 1085, 1088, 1092, 1095, 1100, 1102, 1103, 1108,\n", + " 1109, 1113, 1119, 1121, 1126, 1130, 1131, 1135, 1137, 1146, 1148,\n", + " 1154, 1155, 1158, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1169,\n", + " 1170, 1171, 1173, 1177, 1179, 1180, 1182, 1184, 1185, 1192, 1193,\n", + " 1195, 1196, 1198, 1200, 1201, 1203, 1204, 1206, 1209, 1210, 1211,\n", + " 1212, 1214, 1218, 1219, 1220, 1221, 1225, 1226, 1227, 1230, 1231,\n", + " 1232, 1234, 1236, 1237, 1239, 1244, 1247, 1252, 1253, 1258, 1259,\n", + " 1261, 1264, 1266, 1267, 1269, 1272, 1273, 1274, 1278, 1279, 1282,\n", + " 1289, 1291, 1292, 1299, 1301, 1303, 1305, 1306, 1311, 1315, 1316,\n", + " 1317, 1320, 1321, 1322, 1324, 1326, 1328, 1329, 1330, 1332, 1334,\n", + " 1336, 1337, 1339, 1341, 1345, 1346, 1351, 1352, 1355, 1363, 1364,\n", + " 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1378,\n", + " 1380, 1384, 1385, 1386, 1388, 1390, 1398]),\n", + " 2: array([ 2, 101, 105, 123, 133, 182, 268, 309, 362, 380, 435,\n", + " 641, 733, 773, 864, 879, 882, 915, 931, 952, 955, 957,\n", + " 1007, 1153, 1191, 1208, 1284, 1298, 1308, 1327, 1348, 1350, 1389]),\n", + " 3: array([ 4, 14, 24, 33, 34, 50, 57, 62, 69, 71, 72,\n", + " 73, 75, 81, 83, 84, 85, 91, 95, 100, 117, 125,\n", + " 134, 136, 152, 157, 160, 161, 171, 173, 176, 180, 183,\n", + " 185, 188, 196, 198, 203, 204, 217, 223, 224, 225, 237,\n", + " 243, 256, 270, 279, 287, 293, 303, 308, 313, 317, 319,\n", + " 320, 321, 326, 333, 345, 349, 356, 358, 360, 364, 367,\n", + " 377, 379, 382, 387, 395, 406, 410, 415, 417, 440, 441,\n", + " 443, 447, 457, 458, 466, 469, 470, 479, 481, 498, 506,\n", + " 514, 516, 519, 523, 524, 525, 531, 538, 541, 549, 566,\n", + " 568, 576, 577, 584, 586, 588, 593, 595, 600, 610, 613,\n", + " 621, 625, 626, 632, 638, 642, 646, 649, 651, 653, 656,\n", + " 659, 660, 663, 671, 679, 681, 685, 693, 706, 713, 721,\n", + " 723, 724, 729, 734, 735, 736, 738, 745, 749, 751, 752,\n", + " 754, 755, 771, 781, 784, 794, 800, 801, 805, 807, 808,\n", + " 813, 822, 827, 829, 854, 855, 866, 881, 892, 894, 897,\n", + " 898, 900, 925, 927, 930, 945, 948, 949, 958, 959, 976,\n", + " 977, 988, 995, 998, 1004, 1012, 1018, 1024, 1035, 1049, 1051,\n", + " 1055, 1058, 1059, 1066, 1067, 1072, 1074, 1077, 1078, 1081, 1087,\n", + " 1089, 1091, 1093, 1096, 1097, 1098, 1099, 1104, 1107, 1112, 1114,\n", + " 1120, 1128, 1133, 1139, 1140, 1143, 1147, 1149, 1152, 1156, 1159,\n", + " 1167, 1176, 1178, 1187, 1189, 1194, 1205, 1207, 1213, 1215, 1216,\n", + " 1222, 1229, 1235, 1238, 1242, 1248, 1250, 1256, 1257, 1260, 1263,\n", + " 1271, 1276, 1281, 1285, 1287, 1288, 1290, 1293, 1295, 1300, 1302,\n", + " 1304, 1307, 1310, 1313, 1325, 1331, 1338, 1340, 1343, 1347, 1356,\n", + " 1357, 1365, 1366, 1382, 1391, 1393, 1395, 1397]),\n", + " 4: array([ 8, 19, 21, 47, 52, 55, 59, 63, 68, 77, 88,\n", + " 93, 96, 98, 104, 109, 113, 115, 120, 121, 150, 151,\n", + " 153, 155, 181, 184, 194, 199, 228, 231, 239, 254, 258,\n", + " 261, 276, 280, 282, 295, 297, 299, 316, 336, 338, 348,\n", + " 351, 363, 389, 392, 399, 404, 454, 474, 494, 497, 521,\n", + " 526, 553, 559, 561, 563, 573, 578, 605, 606, 619, 628,\n", + " 640, 672, 680, 683, 686, 691, 696, 699, 708, 717, 720,\n", + " 726, 740, 748, 753, 786, 797, 802, 815, 818, 825, 838,\n", + " 858, 868, 873, 875, 880, 885, 901, 910, 928, 934, 937,\n", + " 973, 975, 980, 983, 1000, 1005, 1014, 1020, 1030, 1039, 1040,\n", + " 1047, 1048, 1050, 1056, 1073, 1110, 1118, 1122, 1124, 1134, 1138,\n", + " 1141, 1142, 1172, 1183, 1240, 1241, 1245, 1249, 1251, 1254, 1255,\n", + " 1262, 1265, 1275, 1283, 1286, 1296, 1312, 1314, 1319, 1342, 1353,\n", + " 1358, 1361, 1383, 1387, 1394]),\n", + " 5: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 43, 44,\n", + " 45, 53, 76, 79, 103, 106, 107, 110, 116, 129, 146,\n", + " 156, 163, 164, 165, 168, 186, 191, 193, 210, 215, 220,\n", + " 233, 234, 242, 248, 250, 251, 257, 263, 269, 281, 285,\n", + " 286, 311, 318, 329, 330, 343, 346, 347, 350, 359, 374,\n", + " 375, 393, 396, 403, 405, 424, 437, 438, 439, 451, 456,\n", + " 461, 473, 477, 478, 487, 493, 502, 504, 507, 511, 513,\n", + " 532, 544, 546, 555, 556, 557, 565, 571, 575, 583, 585,\n", + " 608, 609, 623, 630, 636, 654, 664, 667, 669, 694, 698,\n", + " 705, 710, 712, 722, 725, 727, 739, 746, 758, 760, 768,\n", + " 776, 778, 788, 790, 791, 795, 817, 820, 826, 833, 834,\n", + " 842, 845, 847, 850, 851, 852, 860, 862, 863, 865, 870,\n", + " 878, 893, 902, 903, 906, 911, 922, 936, 946, 954, 956,\n", + " 963, 966, 969, 970, 972, 981, 982, 989, 994, 1002, 1008,\n", + " 1015, 1016, 1019, 1027, 1034, 1036, 1043, 1057, 1061, 1070, 1076,\n", + " 1079, 1082, 1086, 1090, 1101, 1105, 1111, 1116, 1132, 1136, 1144,\n", + " 1145, 1150, 1151, 1157, 1168, 1174, 1175, 1181, 1186, 1188, 1197,\n", + " 1199, 1202, 1217, 1224, 1228, 1233, 1243, 1268, 1270, 1277, 1280,\n", + " 1297, 1309, 1323, 1333, 1335, 1349, 1359, 1392, 1396, 1399]),\n", + " 6: array([702])},\n", + " 8: {0: array([ 0, 6, 20, 22, 54, 61, 65, 78, 89, 112, 119,\n", + " 127, 131, 137, 147, 154, 187, 205, 208, 209, 226, 229,\n", + " 230, 275, 284, 323, 328, 337, 339, 342, 355, 391, 413,\n", + " 432, 450, 484, 488, 515, 542, 562, 587, 590, 597, 601,\n", + " 655, 661, 692, 701, 719, 737, 744, 763, 770, 793, 804,\n", + " 806, 809, 810, 816, 821, 836, 859, 895, 907, 920, 967,\n", + " 978, 993, 999, 1017, 1022, 1032, 1068, 1094, 1106, 1115, 1117,\n", + " 1123, 1125, 1127, 1129, 1190, 1223, 1246, 1294, 1318, 1344, 1354,\n", + " 1360, 1362, 1377, 1379, 1381]),\n", + " 1: array([ 1, 3, 5, 7, 9, 12, 13, 17, 18, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 36, 38, 41, 42, 46,\n", + " 48, 49, 51, 56, 58, 60, 64, 66, 67, 70, 74,\n", + " 80, 82, 86, 87, 90, 92, 94, 97, 99, 102, 108,\n", + " 111, 114, 118, 122, 124, 126, 128, 130, 132, 135, 138,\n", + " 139, 140, 141, 142, 143, 144, 145, 148, 149, 158, 159,\n", + " 162, 166, 167, 169, 170, 172, 174, 175, 177, 178, 179,\n", + " 189, 190, 192, 195, 197, 200, 201, 202, 206, 207, 211,\n", + " 212, 213, 214, 216, 218, 219, 221, 222, 227, 232, 235,\n", + " 236, 238, 240, 241, 244, 245, 246, 247, 249, 252, 253,\n", + " 255, 259, 260, 262, 264, 265, 266, 267, 271, 272, 273,\n", + " 274, 277, 278, 283, 288, 289, 290, 291, 292, 294, 296,\n", + " 298, 300, 301, 302, 304, 305, 306, 307, 310, 312, 314,\n", + " 315, 322, 324, 325, 327, 331, 332, 334, 335, 340, 341,\n", + " 344, 352, 353, 354, 357, 361, 365, 366, 368, 369, 370,\n", + " 371, 372, 373, 376, 378, 381, 383, 384, 385, 386, 388,\n", + " 390, 394, 397, 398, 400, 401, 402, 407, 408, 409, 411,\n", + " 412, 414, 416, 418, 419, 420, 421, 422, 423, 425, 426,\n", + " 427, 428, 429, 430, 431, 433, 434, 436, 442, 444, 445,\n", + " 446, 448, 449, 452, 453, 455, 459, 460, 462, 463, 464,\n", + " 465, 467, 468, 471, 472, 475, 476, 480, 482, 483, 485,\n", + " 486, 489, 490, 491, 492, 495, 496, 499, 500, 501, 503,\n", + " 505, 508, 509, 510, 512, 517, 518, 520, 522, 527, 528,\n", + " 529, 530, 533, 534, 535, 536, 537, 539, 540, 543, 545,\n", + " 547, 548, 550, 551, 552, 554, 558, 560, 564, 567, 569,\n", + " 570, 572, 574, 579, 580, 581, 582, 589, 591, 592, 594,\n", + " 596, 598, 599, 602, 603, 604, 607, 611, 612, 614, 615,\n", + " 616, 617, 618, 620, 622, 624, 627, 629, 631, 633, 634,\n", + " 635, 637, 639, 643, 644, 645, 647, 648, 650, 652, 657,\n", + " 658, 662, 665, 666, 668, 670, 673, 674, 675, 676, 677,\n", + " 678, 682, 684, 687, 688, 689, 690, 695, 697, 700, 703,\n", + " 704, 707, 709, 711, 714, 715, 716, 718, 728, 730, 731,\n", + " 732, 741, 742, 743, 747, 750, 756, 757, 759, 761, 762,\n", + " 764, 765, 766, 767, 769, 772, 774, 775, 777, 779, 780,\n", + " 782, 783, 785, 787, 789, 792, 796, 798, 799, 803, 811,\n", + " 812, 814, 819, 823, 824, 828, 830, 831, 832, 835, 837,\n", + " 839, 840, 841, 843, 844, 846, 848, 849, 853, 856, 857,\n", + " 861, 867, 869, 871, 872, 874, 876, 877, 883, 884, 886,\n", + " 887, 888, 889, 890, 891, 896, 899, 904, 905, 908, 909,\n", + " 912, 913, 914, 916, 917, 918, 919, 921, 923, 924, 926,\n", + " 929, 932, 933, 935, 938, 939, 940, 941, 942, 943, 944,\n", + " 947, 950, 951, 953, 960, 961, 962, 964, 965, 968, 971,\n", + " 974, 979, 984, 985, 986, 987, 990, 991, 992, 996, 997,\n", + " 1001, 1003, 1006, 1009, 1010, 1011, 1013, 1021, 1023, 1025, 1026,\n", + " 1028, 1029, 1031, 1033, 1037, 1038, 1041, 1042, 1044, 1045, 1046,\n", + " 1052, 1053, 1054, 1060, 1062, 1063, 1064, 1065, 1069, 1071, 1075,\n", + " 1080, 1083, 1084, 1085, 1088, 1092, 1095, 1100, 1102, 1103, 1108,\n", + " 1109, 1113, 1119, 1121, 1126, 1130, 1131, 1135, 1137, 1146, 1148,\n", + " 1154, 1155, 1158, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1169,\n", + " 1170, 1171, 1173, 1177, 1179, 1180, 1182, 1184, 1185, 1192, 1193,\n", + " 1195, 1196, 1198, 1200, 1201, 1203, 1204, 1206, 1209, 1210, 1211,\n", + " 1212, 1214, 1218, 1219, 1220, 1221, 1225, 1226, 1227, 1230, 1231,\n", + " 1232, 1234, 1236, 1237, 1239, 1244, 1247, 1252, 1253, 1258, 1259,\n", + " 1261, 1264, 1266, 1267, 1269, 1272, 1273, 1274, 1278, 1279, 1282,\n", + " 1289, 1291, 1292, 1299, 1301, 1303, 1305, 1306, 1311, 1315, 1316,\n", + " 1317, 1320, 1321, 1322, 1324, 1326, 1328, 1329, 1330, 1332, 1334,\n", + " 1336, 1337, 1339, 1341, 1345, 1346, 1351, 1352, 1355, 1363, 1364,\n", + " 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1378,\n", + " 1380, 1384, 1385, 1386, 1388, 1390, 1398]),\n", + " 2: array([ 2, 101, 105, 123, 133, 182, 268, 309, 362, 380, 435,\n", + " 641, 733, 773, 864, 879, 882, 915, 931, 952, 955, 957,\n", + " 1007, 1153, 1191, 1208, 1284, 1298, 1308, 1327, 1348, 1350, 1389]),\n", + " 3: array([ 4, 14, 24, 33, 34, 50, 57, 62, 69, 71, 72,\n", + " 73, 75, 81, 83, 84, 85, 91, 95, 100, 117, 125,\n", + " 134, 136, 152, 157, 160, 161, 171, 173, 176, 183, 185,\n", + " 198, 203, 204, 217, 224, 225, 237, 243, 256, 270, 287,\n", + " 293, 303, 308, 313, 317, 319, 320, 321, 326, 333, 345,\n", + " 356, 358, 360, 364, 367, 377, 379, 382, 387, 395, 406,\n", + " 410, 415, 417, 440, 441, 443, 447, 457, 458, 466, 469,\n", + " 470, 479, 481, 498, 506, 514, 516, 519, 523, 524, 525,\n", + " 531, 538, 541, 549, 566, 568, 577, 584, 586, 588, 593,\n", + " 595, 600, 610, 613, 621, 625, 626, 632, 638, 642, 646,\n", + " 649, 651, 653, 656, 659, 660, 663, 679, 681, 685, 693,\n", + " 706, 713, 721, 723, 729, 734, 735, 738, 745, 749, 751,\n", + " 752, 754, 755, 771, 781, 784, 800, 801, 805, 807, 808,\n", + " 813, 822, 829, 854, 855, 866, 881, 892, 894, 897, 898,\n", + " 900, 927, 930, 945, 948, 949, 958, 959, 976, 977, 988,\n", + " 995, 998, 1004, 1012, 1018, 1024, 1035, 1049, 1051, 1055, 1058,\n", + " 1059, 1066, 1067, 1072, 1074, 1077, 1078, 1081, 1087, 1089, 1091,\n", + " 1093, 1096, 1097, 1098, 1099, 1104, 1107, 1112, 1114, 1120, 1128,\n", + " 1133, 1139, 1140, 1143, 1149, 1152, 1156, 1159, 1167, 1176, 1178,\n", + " 1187, 1189, 1194, 1205, 1207, 1213, 1216, 1222, 1229, 1238, 1242,\n", + " 1248, 1250, 1256, 1257, 1260, 1263, 1271, 1276, 1281, 1285, 1287,\n", + " 1288, 1290, 1293, 1295, 1300, 1302, 1304, 1307, 1310, 1313, 1325,\n", + " 1331, 1338, 1340, 1343, 1347, 1356, 1357, 1365, 1366, 1382, 1391,\n", + " 1393, 1395, 1397]),\n", + " 4: array([ 8, 19, 21, 47, 52, 55, 59, 63, 68, 77, 88,\n", + " 93, 96, 98, 104, 109, 113, 115, 120, 121, 150, 151,\n", + " 153, 155, 181, 184, 194, 199, 228, 231, 239, 254, 258,\n", + " 261, 276, 280, 282, 295, 297, 299, 316, 336, 338, 348,\n", + " 351, 363, 389, 392, 399, 404, 454, 474, 494, 497, 521,\n", + " 526, 553, 559, 561, 563, 573, 578, 605, 606, 619, 628,\n", + " 640, 672, 680, 683, 686, 691, 696, 699, 708, 717, 720,\n", + " 726, 740, 748, 753, 786, 797, 802, 815, 818, 825, 838,\n", + " 858, 868, 873, 875, 880, 885, 901, 910, 928, 934, 937,\n", + " 973, 975, 980, 983, 1000, 1005, 1014, 1020, 1030, 1039, 1040,\n", + " 1047, 1048, 1050, 1056, 1073, 1110, 1118, 1122, 1124, 1134, 1138,\n", + " 1141, 1142, 1172, 1183, 1240, 1241, 1245, 1249, 1251, 1254, 1255,\n", + " 1262, 1265, 1275, 1283, 1286, 1296, 1312, 1314, 1319, 1342, 1353,\n", + " 1358, 1361, 1383, 1387, 1394]),\n", + " 5: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 43, 44,\n", + " 45, 53, 76, 79, 103, 106, 107, 110, 116, 129, 146,\n", + " 156, 163, 164, 165, 168, 186, 191, 193, 210, 215, 220,\n", + " 233, 234, 242, 248, 250, 251, 257, 263, 269, 281, 285,\n", + " 286, 311, 318, 329, 330, 343, 346, 347, 350, 359, 374,\n", + " 375, 393, 396, 403, 405, 424, 437, 438, 439, 451, 456,\n", + " 461, 473, 477, 478, 487, 493, 502, 504, 507, 511, 513,\n", + " 532, 544, 546, 555, 556, 557, 565, 571, 575, 583, 585,\n", + " 608, 609, 623, 630, 636, 654, 664, 667, 669, 694, 698,\n", + " 705, 710, 712, 722, 725, 727, 739, 746, 758, 760, 768,\n", + " 776, 778, 788, 790, 791, 795, 817, 820, 826, 833, 834,\n", + " 842, 845, 847, 850, 851, 852, 860, 862, 863, 865, 870,\n", + " 878, 893, 902, 903, 906, 911, 922, 936, 946, 954, 956,\n", + " 963, 966, 969, 970, 972, 981, 982, 989, 994, 1002, 1008,\n", + " 1015, 1016, 1019, 1027, 1034, 1036, 1043, 1057, 1061, 1070, 1076,\n", + " 1079, 1082, 1086, 1090, 1101, 1105, 1111, 1116, 1132, 1136, 1144,\n", + " 1145, 1150, 1151, 1157, 1168, 1174, 1175, 1181, 1186, 1188, 1197,\n", + " 1199, 1202, 1217, 1224, 1228, 1233, 1243, 1268, 1270, 1277, 1280,\n", + " 1297, 1309, 1323, 1333, 1335, 1349, 1359, 1392, 1396, 1399]),\n", + " 6: array([ 180, 188, 196, 223, 279, 349, 576, 671, 724, 736, 794,\n", + " 827, 925, 1147, 1215, 1235]),\n", + " 7: array([702])},\n", + " 9: {0: array([ 0, 78, 205, 226, 275, 323, 413, 515, 601, 655, 737,\n", + " 744, 793, 810, 907, 920, 1117, 1344, 1362]),\n", + " 1: array([ 1, 3, 5, 7, 9, 12, 13, 17, 18, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 36, 38, 41, 42, 46,\n", + " 48, 49, 51, 56, 58, 60, 64, 66, 67, 70, 74,\n", + " 80, 82, 86, 87, 90, 92, 94, 97, 99, 102, 108,\n", + " 111, 114, 118, 122, 124, 126, 128, 130, 132, 135, 138,\n", + " 139, 140, 141, 142, 143, 144, 145, 148, 149, 158, 159,\n", + " 162, 166, 167, 169, 170, 172, 174, 175, 177, 178, 179,\n", + " 189, 190, 192, 195, 197, 200, 201, 202, 206, 207, 211,\n", + " 212, 213, 214, 216, 218, 219, 221, 222, 227, 232, 235,\n", + " 236, 238, 240, 241, 244, 245, 246, 247, 249, 252, 253,\n", + " 255, 259, 260, 262, 264, 265, 266, 267, 271, 272, 273,\n", + " 274, 277, 278, 283, 288, 289, 290, 291, 292, 294, 296,\n", + " 298, 300, 301, 302, 304, 305, 306, 307, 310, 312, 314,\n", + " 315, 322, 324, 325, 327, 331, 332, 334, 335, 340, 341,\n", + " 344, 352, 353, 354, 357, 361, 365, 366, 368, 369, 370,\n", + " 371, 372, 373, 376, 378, 381, 383, 384, 385, 386, 388,\n", + " 390, 394, 397, 398, 400, 401, 402, 407, 408, 409, 411,\n", + " 412, 414, 416, 418, 419, 420, 421, 422, 423, 425, 426,\n", + " 427, 428, 429, 430, 431, 433, 434, 436, 442, 444, 445,\n", + " 446, 448, 449, 452, 453, 455, 459, 460, 462, 463, 464,\n", + " 465, 467, 468, 471, 472, 475, 476, 480, 482, 483, 485,\n", + " 486, 489, 490, 491, 492, 495, 496, 499, 500, 501, 503,\n", + " 505, 508, 509, 510, 512, 517, 518, 520, 522, 527, 528,\n", + " 529, 530, 533, 534, 535, 536, 537, 539, 540, 543, 545,\n", + " 547, 548, 550, 551, 552, 554, 558, 560, 564, 567, 569,\n", + " 570, 572, 574, 579, 580, 581, 582, 589, 591, 592, 594,\n", + " 596, 598, 599, 602, 603, 604, 607, 611, 612, 614, 615,\n", + " 616, 617, 618, 620, 622, 624, 627, 629, 631, 633, 634,\n", + " 635, 637, 639, 643, 644, 645, 647, 648, 650, 652, 657,\n", + " 658, 662, 665, 666, 668, 670, 673, 674, 675, 676, 677,\n", + " 678, 682, 684, 687, 688, 689, 690, 695, 697, 700, 703,\n", + " 704, 707, 709, 711, 714, 715, 716, 718, 728, 730, 731,\n", + " 732, 741, 742, 743, 747, 750, 756, 757, 759, 761, 762,\n", + " 764, 765, 766, 767, 769, 772, 774, 775, 777, 779, 780,\n", + " 782, 783, 785, 787, 789, 792, 796, 798, 799, 803, 811,\n", + " 812, 814, 819, 823, 824, 828, 830, 831, 832, 835, 837,\n", + " 839, 840, 841, 843, 844, 846, 848, 849, 853, 856, 857,\n", + " 861, 867, 869, 871, 872, 874, 876, 877, 883, 884, 886,\n", + " 887, 888, 889, 890, 891, 896, 899, 904, 905, 908, 909,\n", + " 912, 913, 914, 916, 917, 918, 919, 921, 923, 924, 926,\n", + " 929, 932, 933, 935, 938, 939, 940, 941, 942, 943, 944,\n", + " 947, 950, 951, 953, 960, 961, 962, 964, 965, 968, 971,\n", + " 974, 979, 984, 985, 986, 987, 990, 991, 992, 996, 997,\n", + " 1001, 1003, 1006, 1009, 1010, 1011, 1013, 1021, 1023, 1025, 1026,\n", + " 1028, 1029, 1031, 1033, 1037, 1038, 1041, 1042, 1044, 1045, 1046,\n", + " 1052, 1053, 1054, 1060, 1062, 1063, 1064, 1065, 1069, 1071, 1075,\n", + " 1080, 1083, 1084, 1085, 1088, 1092, 1095, 1100, 1102, 1103, 1108,\n", + " 1109, 1113, 1119, 1121, 1126, 1130, 1131, 1135, 1137, 1146, 1148,\n", + " 1154, 1155, 1158, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1169,\n", + " 1170, 1171, 1173, 1177, 1179, 1180, 1182, 1184, 1185, 1192, 1193,\n", + " 1195, 1196, 1198, 1200, 1201, 1203, 1204, 1206, 1209, 1210, 1211,\n", + " 1212, 1214, 1218, 1219, 1220, 1221, 1225, 1226, 1227, 1230, 1231,\n", + " 1232, 1234, 1236, 1237, 1239, 1244, 1247, 1252, 1253, 1258, 1259,\n", + " 1261, 1264, 1266, 1267, 1269, 1272, 1273, 1274, 1278, 1279, 1282,\n", + " 1289, 1291, 1292, 1299, 1301, 1303, 1305, 1306, 1311, 1315, 1316,\n", + " 1317, 1320, 1321, 1322, 1324, 1326, 1328, 1329, 1330, 1332, 1334,\n", + " 1336, 1337, 1339, 1341, 1345, 1346, 1351, 1352, 1355, 1363, 1364,\n", + " 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1378,\n", + " 1380, 1384, 1385, 1386, 1388, 1390, 1398]),\n", + " 2: array([ 2, 101, 105, 123, 133, 182, 268, 309, 362, 380, 435,\n", + " 641, 733, 773, 864, 879, 882, 915, 931, 952, 955, 957,\n", + " 1007, 1153, 1191, 1208, 1284, 1298, 1308, 1327, 1348, 1350, 1389]),\n", + " 3: array([ 4, 14, 24, 33, 34, 50, 57, 62, 69, 71, 72,\n", + " 73, 75, 81, 83, 84, 85, 91, 95, 100, 117, 125,\n", + " 134, 136, 152, 157, 160, 161, 171, 173, 176, 183, 185,\n", + " 198, 203, 204, 217, 224, 225, 237, 243, 256, 270, 287,\n", + " 293, 303, 308, 313, 317, 319, 320, 321, 326, 333, 345,\n", + " 356, 358, 360, 364, 367, 377, 379, 382, 387, 395, 406,\n", + " 410, 415, 417, 440, 441, 443, 447, 457, 458, 466, 469,\n", + " 470, 479, 481, 498, 506, 514, 516, 519, 523, 524, 525,\n", + " 531, 538, 541, 549, 566, 568, 577, 584, 586, 588, 593,\n", + " 595, 600, 610, 613, 621, 625, 626, 632, 638, 642, 646,\n", + " 649, 651, 653, 656, 659, 660, 663, 679, 681, 685, 693,\n", + " 706, 713, 721, 723, 729, 734, 735, 738, 745, 749, 751,\n", + " 752, 754, 755, 771, 781, 784, 800, 801, 805, 807, 808,\n", + " 813, 822, 829, 854, 855, 866, 881, 892, 894, 897, 898,\n", + " 900, 927, 930, 945, 948, 949, 958, 959, 976, 977, 988,\n", + " 995, 998, 1004, 1012, 1018, 1024, 1035, 1049, 1051, 1055, 1058,\n", + " 1059, 1066, 1067, 1072, 1074, 1077, 1078, 1081, 1087, 1089, 1091,\n", + " 1093, 1096, 1097, 1098, 1099, 1104, 1107, 1112, 1114, 1120, 1128,\n", + " 1133, 1139, 1140, 1143, 1149, 1152, 1156, 1159, 1167, 1176, 1178,\n", + " 1187, 1189, 1194, 1205, 1207, 1213, 1216, 1222, 1229, 1238, 1242,\n", + " 1248, 1250, 1256, 1257, 1260, 1263, 1271, 1276, 1281, 1285, 1287,\n", + " 1288, 1290, 1293, 1295, 1300, 1302, 1304, 1307, 1310, 1313, 1325,\n", + " 1331, 1338, 1340, 1343, 1347, 1356, 1357, 1365, 1366, 1382, 1391,\n", + " 1393, 1395, 1397]),\n", + " 4: array([ 6, 20, 22, 54, 61, 65, 89, 112, 119, 127, 131,\n", + " 137, 147, 154, 187, 208, 209, 229, 230, 284, 328, 337,\n", + " 339, 342, 355, 391, 432, 450, 484, 488, 542, 562, 587,\n", + " 590, 597, 661, 692, 701, 719, 763, 770, 804, 806, 809,\n", + " 816, 821, 836, 859, 895, 967, 978, 993, 999, 1017, 1022,\n", + " 1032, 1068, 1094, 1106, 1115, 1123, 1125, 1127, 1129, 1190, 1223,\n", + " 1246, 1294, 1318, 1354, 1360, 1377, 1379, 1381]),\n", + " 5: array([ 8, 19, 21, 47, 52, 55, 59, 63, 68, 77, 88,\n", + " 93, 96, 98, 104, 109, 113, 115, 120, 121, 150, 151,\n", + " 153, 155, 181, 184, 194, 199, 228, 231, 239, 254, 258,\n", + " 261, 276, 280, 282, 295, 297, 299, 316, 336, 338, 348,\n", + " 351, 363, 389, 392, 399, 404, 454, 474, 494, 497, 521,\n", + " 526, 553, 559, 561, 563, 573, 578, 605, 606, 619, 628,\n", + " 640, 672, 680, 683, 686, 691, 696, 699, 708, 717, 720,\n", + " 726, 740, 748, 753, 786, 797, 802, 815, 818, 825, 838,\n", + " 858, 868, 873, 875, 880, 885, 901, 910, 928, 934, 937,\n", + " 973, 975, 980, 983, 1000, 1005, 1014, 1020, 1030, 1039, 1040,\n", + " 1047, 1048, 1050, 1056, 1073, 1110, 1118, 1122, 1124, 1134, 1138,\n", + " 1141, 1142, 1172, 1183, 1240, 1241, 1245, 1249, 1251, 1254, 1255,\n", + " 1262, 1265, 1275, 1283, 1286, 1296, 1312, 1314, 1319, 1342, 1353,\n", + " 1358, 1361, 1383, 1387, 1394]),\n", + " 6: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 43, 44,\n", + " 45, 53, 76, 79, 103, 106, 107, 110, 116, 129, 146,\n", + " 156, 163, 164, 165, 168, 186, 191, 193, 210, 215, 220,\n", + " 233, 234, 242, 248, 250, 251, 257, 263, 269, 281, 285,\n", + " 286, 311, 318, 329, 330, 343, 346, 347, 350, 359, 374,\n", + " 375, 393, 396, 403, 405, 424, 437, 438, 439, 451, 456,\n", + " 461, 473, 477, 478, 487, 493, 502, 504, 507, 511, 513,\n", + " 532, 544, 546, 555, 556, 557, 565, 571, 575, 583, 585,\n", + " 608, 609, 623, 630, 636, 654, 664, 667, 669, 694, 698,\n", + " 705, 710, 712, 722, 725, 727, 739, 746, 758, 760, 768,\n", + " 776, 778, 788, 790, 791, 795, 817, 820, 826, 833, 834,\n", + " 842, 845, 847, 850, 851, 852, 860, 862, 863, 865, 870,\n", + " 878, 893, 902, 903, 906, 911, 922, 936, 946, 954, 956,\n", + " 963, 966, 969, 970, 972, 981, 982, 989, 994, 1002, 1008,\n", + " 1015, 1016, 1019, 1027, 1034, 1036, 1043, 1057, 1061, 1070, 1076,\n", + " 1079, 1082, 1086, 1090, 1101, 1105, 1111, 1116, 1132, 1136, 1144,\n", + " 1145, 1150, 1151, 1157, 1168, 1174, 1175, 1181, 1186, 1188, 1197,\n", + " 1199, 1202, 1217, 1224, 1228, 1233, 1243, 1268, 1270, 1277, 1280,\n", + " 1297, 1309, 1323, 1333, 1335, 1349, 1359, 1392, 1396, 1399]),\n", + " 7: array([ 180, 188, 196, 223, 279, 349, 576, 671, 724, 736, 794,\n", + " 827, 925, 1147, 1215, 1235]),\n", + " 8: array([702])},\n", + " 10: {0: array([ 0, 78, 205, 226, 275, 323, 413, 515, 601, 655, 737,\n", + " 744, 793, 810, 907, 920, 1117, 1344, 1362]),\n", + " 1: array([ 1, 3, 5, 7, 9, 12, 13, 17, 18, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 36, 38, 41, 42, 46,\n", + " 48, 49, 51, 56, 58, 60, 64, 66, 67, 70, 74,\n", + " 80, 82, 86, 87, 90, 92, 94, 97, 99, 102, 108,\n", + " 111, 114, 118, 122, 124, 126, 128, 130, 132, 135, 138,\n", + " 139, 140, 141, 142, 143, 144, 145, 148, 149, 158, 159,\n", + " 162, 166, 167, 169, 170, 172, 174, 175, 177, 178, 179,\n", + " 189, 190, 192, 195, 197, 200, 201, 202, 206, 207, 211,\n", + " 212, 213, 214, 216, 218, 219, 221, 222, 227, 232, 235,\n", + " 236, 238, 240, 241, 244, 245, 246, 247, 249, 252, 253,\n", + " 255, 259, 260, 262, 264, 265, 266, 267, 271, 272, 273,\n", + " 274, 277, 278, 283, 288, 289, 290, 291, 292, 294, 296,\n", + " 298, 300, 301, 302, 304, 305, 306, 307, 310, 312, 314,\n", + " 315, 322, 324, 325, 327, 331, 332, 334, 335, 340, 341,\n", + " 344, 352, 353, 354, 357, 361, 365, 366, 368, 369, 370,\n", + " 371, 372, 373, 376, 378, 381, 383, 384, 385, 386, 388,\n", + " 390, 394, 397, 398, 400, 401, 402, 407, 408, 409, 411,\n", + " 412, 414, 416, 418, 419, 420, 421, 422, 423, 425, 426,\n", + " 427, 428, 429, 430, 431, 433, 434, 436, 442, 444, 445,\n", + " 446, 448, 449, 452, 453, 455, 459, 460, 462, 463, 464,\n", + " 465, 467, 468, 471, 472, 475, 476, 480, 482, 483, 485,\n", + " 486, 489, 490, 491, 492, 495, 496, 499, 500, 501, 503,\n", + " 505, 508, 509, 510, 512, 517, 518, 520, 522, 527, 528,\n", + " 529, 530, 533, 534, 535, 536, 537, 539, 540, 543, 545,\n", + " 547, 548, 550, 551, 552, 554, 558, 560, 564, 567, 569,\n", + " 570, 572, 574, 579, 580, 581, 582, 589, 591, 592, 594,\n", + " 596, 598, 599, 602, 603, 604, 607, 611, 612, 614, 615,\n", + " 616, 617, 618, 620, 622, 624, 627, 629, 631, 633, 634,\n", + " 635, 637, 639, 643, 644, 645, 647, 648, 650, 652, 657,\n", + " 658, 662, 665, 666, 668, 670, 673, 674, 675, 676, 677,\n", + " 678, 682, 684, 687, 688, 689, 690, 695, 697, 700, 703,\n", + " 704, 707, 709, 711, 714, 715, 716, 718, 728, 730, 731,\n", + " 732, 741, 742, 743, 747, 750, 756, 757, 759, 761, 762,\n", + " 764, 765, 766, 767, 769, 772, 774, 775, 777, 779, 780,\n", + " 782, 783, 785, 787, 789, 792, 796, 798, 799, 803, 811,\n", + " 812, 814, 819, 823, 824, 828, 830, 831, 832, 835, 837,\n", + " 839, 840, 841, 843, 844, 846, 848, 849, 853, 856, 857,\n", + " 861, 867, 869, 871, 872, 874, 876, 877, 883, 884, 886,\n", + " 887, 888, 889, 890, 891, 896, 899, 904, 905, 908, 909,\n", + " 912, 913, 914, 916, 917, 918, 919, 921, 923, 924, 926,\n", + " 929, 932, 933, 935, 938, 939, 940, 941, 942, 943, 944,\n", + " 947, 950, 951, 953, 960, 961, 962, 964, 965, 968, 971,\n", + " 974, 979, 984, 985, 986, 987, 990, 991, 992, 996, 997,\n", + " 1001, 1003, 1006, 1009, 1010, 1011, 1013, 1021, 1023, 1025, 1026,\n", + " 1028, 1029, 1031, 1033, 1037, 1038, 1041, 1042, 1044, 1045, 1046,\n", + " 1052, 1053, 1054, 1060, 1062, 1063, 1064, 1065, 1069, 1071, 1075,\n", + " 1080, 1083, 1084, 1085, 1088, 1092, 1095, 1100, 1102, 1103, 1108,\n", + " 1109, 1113, 1119, 1121, 1126, 1130, 1131, 1135, 1137, 1146, 1148,\n", + " 1154, 1155, 1158, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1169,\n", + " 1170, 1171, 1173, 1177, 1179, 1180, 1182, 1184, 1185, 1192, 1193,\n", + " 1195, 1196, 1198, 1200, 1201, 1203, 1204, 1206, 1209, 1210, 1211,\n", + " 1212, 1214, 1218, 1219, 1220, 1221, 1225, 1226, 1227, 1230, 1231,\n", + " 1232, 1234, 1236, 1237, 1239, 1244, 1247, 1252, 1253, 1258, 1259,\n", + " 1261, 1264, 1266, 1267, 1269, 1272, 1273, 1274, 1278, 1279, 1282,\n", + " 1289, 1291, 1292, 1299, 1301, 1303, 1305, 1306, 1311, 1315, 1316,\n", + " 1317, 1320, 1321, 1322, 1324, 1326, 1328, 1329, 1330, 1332, 1334,\n", + " 1336, 1337, 1339, 1341, 1345, 1346, 1351, 1352, 1355, 1363, 1364,\n", + " 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1378,\n", + " 1380, 1384, 1385, 1386, 1388, 1390, 1398]),\n", + " 2: array([ 2, 101, 105, 123, 133, 182, 268, 309, 362, 380, 435,\n", + " 641, 733, 773, 864, 879, 882, 915, 931, 952, 955, 957,\n", + " 1007, 1153, 1191, 1208, 1284, 1298, 1308, 1327, 1348, 1350, 1389]),\n", + " 3: array([ 4, 14, 24, 33, 34, 50, 57, 62, 69, 71, 72,\n", + " 73, 75, 81, 83, 84, 85, 91, 95, 100, 117, 125,\n", + " 134, 136, 152, 157, 160, 161, 171, 173, 176, 183, 185,\n", + " 198, 203, 204, 217, 224, 225, 237, 243, 256, 270, 287,\n", + " 293, 303, 308, 313, 317, 319, 320, 321, 326, 333, 345,\n", + " 356, 358, 360, 364, 367, 377, 379, 382, 387, 395, 406,\n", + " 410, 415, 417, 440, 441, 443, 447, 457, 458, 466, 469,\n", + " 470, 479, 481, 498, 506, 514, 516, 519, 523, 524, 525,\n", + " 531, 538, 541, 549, 566, 568, 577, 584, 586, 588, 593,\n", + " 595, 600, 610, 613, 621, 625, 626, 632, 638, 642, 646,\n", + " 649, 651, 653, 656, 659, 660, 663, 679, 681, 685, 693,\n", + " 706, 713, 721, 723, 729, 734, 735, 738, 745, 749, 751,\n", + " 752, 754, 755, 771, 781, 784, 800, 801, 805, 807, 808,\n", + " 813, 822, 829, 854, 855, 866, 881, 892, 894, 897, 898,\n", + " 900, 927, 930, 945, 948, 949, 958, 959, 976, 977, 988,\n", + " 995, 998, 1004, 1012, 1018, 1024, 1035, 1049, 1051, 1055, 1058,\n", + " 1059, 1066, 1067, 1072, 1074, 1077, 1078, 1081, 1087, 1089, 1091,\n", + " 1093, 1096, 1097, 1098, 1099, 1104, 1107, 1112, 1114, 1120, 1128,\n", + " 1133, 1139, 1140, 1143, 1149, 1152, 1156, 1159, 1167, 1176, 1178,\n", + " 1187, 1189, 1194, 1205, 1207, 1213, 1216, 1222, 1229, 1238, 1242,\n", + " 1248, 1250, 1256, 1257, 1260, 1263, 1271, 1276, 1281, 1285, 1287,\n", + " 1288, 1290, 1293, 1295, 1300, 1302, 1304, 1307, 1310, 1313, 1325,\n", + " 1331, 1338, 1340, 1343, 1347, 1356, 1357, 1365, 1366, 1382, 1391,\n", + " 1393, 1395, 1397]),\n", + " 4: array([ 6, 20, 22, 54, 61, 65, 89, 112, 119, 127, 131,\n", + " 137, 147, 154, 187, 208, 209, 229, 230, 284, 328, 337,\n", + " 339, 342, 355, 391, 432, 450, 484, 488, 542, 562, 587,\n", + " 590, 597, 661, 692, 701, 719, 763, 770, 804, 806, 809,\n", + " 816, 821, 836, 859, 895, 967, 978, 993, 999, 1017, 1022,\n", + " 1032, 1068, 1094, 1106, 1115, 1123, 1125, 1127, 1129, 1190, 1223,\n", + " 1246, 1294, 1318, 1354, 1360, 1377, 1379, 1381]),\n", + " 5: array([ 8, 19, 21, 47, 52, 55, 59, 63, 68, 77, 88,\n", + " 93, 96, 98, 104, 109, 113, 115, 120, 121, 150, 151,\n", + " 153, 155, 181, 184, 194, 199, 228, 231, 239, 254, 258,\n", + " 261, 276, 280, 282, 295, 297, 299, 316, 336, 338, 348,\n", + " 351, 363, 389, 392, 399, 404, 454, 474, 494, 497, 521,\n", + " 526, 553, 559, 561, 563, 573, 578, 605, 606, 619, 628,\n", + " 640, 672, 680, 683, 686, 691, 696, 699, 708, 717, 720,\n", + " 726, 740, 748, 753, 786, 797, 802, 815, 818, 825, 838,\n", + " 858, 868, 873, 875, 880, 885, 901, 910, 928, 934, 937,\n", + " 973, 975, 980, 983, 1000, 1005, 1014, 1020, 1030, 1039, 1040,\n", + " 1047, 1048, 1050, 1056, 1073, 1110, 1118, 1122, 1124, 1134, 1138,\n", + " 1141, 1142, 1172, 1183, 1240, 1241, 1245, 1249, 1251, 1254, 1255,\n", + " 1262, 1265, 1275, 1283, 1286, 1296, 1312, 1314, 1319, 1342, 1353,\n", + " 1358, 1361, 1383, 1387, 1394]),\n", + " 6: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 43, 44,\n", + " 45, 53, 76, 79, 103, 106, 107, 110, 116, 129, 146,\n", + " 156, 163, 164, 165, 168, 186, 191, 193, 210, 215, 220,\n", + " 233, 234, 242, 248, 250, 251, 257, 263, 269, 281, 285,\n", + " 286, 311, 318, 329, 330, 343, 346, 347, 350, 359, 374,\n", + " 375, 393, 396, 403, 405, 424, 437, 438, 439, 451, 456,\n", + " 461, 473, 477, 478, 487, 493, 502, 504, 507, 511, 513,\n", + " 532, 544, 546, 555, 556, 557, 565, 571, 575, 583, 585,\n", + " 608, 609, 623, 630, 636, 654, 664, 667, 669, 694, 698,\n", + " 705, 710, 712, 722, 725, 727, 739, 746, 758, 760, 768,\n", + " 776, 778, 788, 790, 791, 795, 817, 820, 826, 833, 834,\n", + " 842, 845, 847, 850, 851, 852, 860, 862, 863, 865, 870,\n", + " 878, 893, 902, 903, 906, 911, 922, 936, 946, 954, 956,\n", + " 963, 966, 969, 970, 972, 981, 982, 989, 994, 1002, 1008,\n", + " 1015, 1016, 1019, 1027, 1034, 1036, 1043, 1057, 1061, 1070, 1076,\n", + " 1079, 1082, 1086, 1090, 1101, 1105, 1111, 1116, 1132, 1136, 1144,\n", + " 1145, 1150, 1151, 1157, 1168, 1174, 1175, 1181, 1186, 1188, 1197,\n", + " 1199, 1202, 1217, 1224, 1228, 1233, 1243, 1268, 1270, 1277, 1280,\n", + " 1297, 1309, 1323, 1333, 1335, 1349, 1359, 1392, 1396, 1399]),\n", + " 7: array([ 180, 188, 196, 223, 279, 349, 576, 671, 724, 736, 794,\n", + " 827, 925, 1147, 1215]),\n", + " 8: array([702]),\n", + " 9: array([1235])}},\n", + " 'nonl2_avg': {2: {0: array([ 0, 2, 6, 8, 19, 20, 21, 22, 41, 47, 52,\n", + " 54, 55, 59, 61, 63, 65, 68, 77, 78, 79, 88,\n", + " 89, 93, 96, 98, 101, 104, 105, 109, 112, 113, 115,\n", + " 119, 120, 121, 123, 126, 127, 131, 133, 137, 147, 150,\n", + " 151, 153, 154, 155, 181, 182, 184, 187, 194, 199, 205,\n", + " 208, 209, 226, 228, 229, 230, 231, 238, 239, 254, 258,\n", + " 260, 261, 266, 268, 275, 276, 280, 282, 284, 292, 295,\n", + " 297, 299, 303, 309, 316, 323, 328, 336, 337, 338, 339,\n", + " 342, 348, 351, 355, 360, 362, 363, 370, 376, 380, 389,\n", + " 391, 392, 399, 404, 409, 413, 414, 432, 435, 450, 454,\n", + " 474, 484, 488, 494, 497, 515, 517, 521, 526, 536, 542,\n", + " 553, 559, 561, 562, 563, 570, 573, 578, 587, 590, 597,\n", + " 599, 601, 605, 606, 619, 628, 631, 640, 641, 655, 661,\n", + " 665, 670, 672, 678, 680, 683, 686, 687, 691, 692, 696,\n", + " 699, 701, 702, 708, 717, 719, 720, 726, 733, 737, 740,\n", + " 744, 748, 753, 759, 763, 765, 770, 773, 783, 786, 787,\n", + " 793, 797, 802, 804, 806, 809, 810, 815, 816, 818, 821,\n", + " 825, 836, 838, 858, 859, 864, 868, 873, 875, 877, 879,\n", + " 880, 882, 885, 888, 889, 895, 901, 904, 907, 910, 915,\n", + " 920, 928, 929, 931, 932, 934, 935, 937, 952, 955, 957,\n", + " 967, 973, 975, 978, 980, 983, 993, 999, 1000, 1005, 1007,\n", + " 1010, 1014, 1017, 1020, 1022, 1030, 1032, 1033, 1039, 1040, 1047,\n", + " 1048, 1050, 1056, 1068, 1073, 1094, 1106, 1110, 1115, 1117, 1118,\n", + " 1122, 1123, 1124, 1125, 1127, 1129, 1134, 1138, 1141, 1142, 1153,\n", + " 1172, 1183, 1190, 1191, 1208, 1211, 1221, 1223, 1240, 1241, 1245,\n", + " 1246, 1249, 1251, 1254, 1255, 1262, 1265, 1275, 1283, 1284, 1286,\n", + " 1292, 1294, 1296, 1298, 1308, 1312, 1314, 1317, 1318, 1319, 1327,\n", + " 1342, 1344, 1348, 1350, 1352, 1353, 1354, 1358, 1360, 1361, 1362,\n", + " 1377, 1379, 1381, 1383, 1385, 1387, 1389, 1394]),\n", + " 1: array([ 1, 3, 4, ..., 1397, 1398, 1399])},\n", + " 3: {0: array([ 0, 2, 6, 8, 19, 20, 21, 22, 41, 47, 52,\n", + " 54, 55, 59, 61, 63, 65, 68, 77, 78, 79, 88,\n", + " 89, 93, 96, 98, 101, 104, 105, 109, 112, 113, 115,\n", + " 119, 120, 121, 123, 126, 127, 131, 133, 137, 147, 150,\n", + " 151, 153, 154, 155, 181, 182, 184, 187, 194, 199, 205,\n", + " 208, 209, 226, 228, 229, 230, 231, 238, 239, 254, 258,\n", + " 260, 261, 266, 268, 275, 276, 280, 282, 284, 292, 295,\n", + " 297, 299, 303, 309, 316, 323, 328, 336, 337, 338, 339,\n", + " 342, 348, 351, 355, 360, 362, 363, 370, 376, 380, 389,\n", + " 391, 392, 399, 404, 409, 413, 414, 432, 435, 450, 454,\n", + " 474, 484, 488, 494, 497, 515, 517, 521, 526, 536, 542,\n", + " 553, 559, 561, 562, 563, 570, 573, 578, 587, 590, 597,\n", + " 599, 601, 605, 606, 619, 628, 631, 640, 641, 655, 661,\n", + " 665, 670, 672, 678, 680, 683, 686, 687, 691, 692, 696,\n", + " 699, 701, 702, 708, 717, 719, 720, 726, 733, 737, 740,\n", + " 744, 748, 753, 759, 763, 765, 770, 773, 783, 786, 787,\n", + " 793, 797, 802, 804, 806, 809, 810, 815, 816, 818, 821,\n", + " 825, 836, 838, 858, 859, 864, 868, 873, 875, 877, 879,\n", + " 880, 882, 885, 888, 889, 895, 901, 904, 907, 910, 915,\n", + " 920, 928, 929, 931, 932, 934, 935, 937, 952, 955, 957,\n", + " 967, 973, 975, 978, 980, 983, 993, 999, 1000, 1005, 1007,\n", + " 1010, 1014, 1017, 1020, 1022, 1030, 1032, 1033, 1039, 1040, 1047,\n", + " 1048, 1050, 1056, 1068, 1073, 1094, 1106, 1110, 1115, 1117, 1118,\n", + " 1122, 1123, 1124, 1125, 1127, 1129, 1134, 1138, 1141, 1142, 1153,\n", + " 1172, 1183, 1190, 1191, 1208, 1211, 1221, 1223, 1240, 1241, 1245,\n", + " 1246, 1249, 1251, 1254, 1255, 1262, 1265, 1275, 1283, 1284, 1286,\n", + " 1292, 1294, 1296, 1298, 1308, 1312, 1314, 1317, 1318, 1319, 1327,\n", + " 1342, 1344, 1348, 1350, 1352, 1353, 1354, 1358, 1360, 1361, 1362,\n", + " 1377, 1379, 1381, 1383, 1385, 1387, 1389, 1394]),\n", + " 1: array([ 1, 3, 4, 5, 7, 9, 12, 13, 14, 17, 18,\n", + " 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n", + " 36, 38, 42, 46, 48, 49, 50, 51, 56, 57, 58,\n", + " 60, 64, 66, 67, 70, 72, 73, 74, 80, 81, 82,\n", + " 83, 84, 85, 86, 87, 90, 91, 92, 94, 95, 97,\n", + " 99, 102, 108, 111, 114, 117, 118, 122, 124, 125, 128,\n", + " 130, 132, 134, 135, 136, 138, 139, 140, 141, 142, 143,\n", + " 144, 145, 146, 148, 149, 152, 157, 158, 159, 160, 161,\n", + " 162, 166, 167, 169, 170, 171, 172, 174, 175, 177, 178,\n", + " 179, 180, 183, 185, 188, 189, 190, 192, 195, 196, 197,\n", + " 198, 200, 201, 202, 203, 204, 206, 207, 211, 212, 213,\n", + " 214, 216, 217, 218, 219, 221, 222, 223, 224, 225, 227,\n", + " 232, 235, 236, 237, 240, 241, 243, 244, 245, 246, 247,\n", + " 249, 252, 253, 255, 256, 259, 262, 264, 265, 267, 270,\n", + " 271, 272, 273, 274, 277, 278, 279, 283, 287, 288, 289,\n", + " 290, 291, 293, 294, 296, 298, 300, 301, 302, 304, 305,\n", + " 306, 307, 310, 312, 313, 314, 315, 320, 321, 322, 324,\n", + " 325, 327, 331, 332, 334, 335, 340, 341, 344, 345, 349,\n", + " 352, 353, 354, 356, 357, 358, 361, 364, 365, 366, 367,\n", + " 368, 369, 371, 372, 373, 378, 379, 381, 382, 383, 384,\n", + " 385, 386, 387, 388, 390, 394, 395, 397, 398, 400, 401,\n", + " 402, 406, 407, 408, 410, 411, 412, 416, 417, 418, 419,\n", + " 420, 421, 422, 423, 425, 426, 427, 428, 429, 430, 431,\n", + " 433, 434, 436, 440, 442, 444, 445, 446, 447, 448, 449,\n", + " 452, 453, 455, 457, 458, 459, 460, 462, 463, 464, 465,\n", + " 466, 467, 468, 470, 471, 472, 473, 475, 476, 479, 480,\n", + " 482, 483, 485, 486, 489, 490, 491, 492, 495, 496, 499,\n", + " 500, 501, 503, 505, 506, 508, 509, 510, 512, 514, 518,\n", + " 519, 520, 522, 523, 524, 525, 527, 528, 529, 530, 533,\n", + " 534, 535, 537, 538, 539, 540, 541, 543, 545, 547, 548,\n", + " 549, 550, 551, 552, 554, 558, 560, 564, 567, 568, 569,\n", + " 572, 574, 576, 577, 579, 580, 581, 582, 588, 589, 591,\n", + " 592, 593, 594, 596, 598, 600, 602, 603, 604, 607, 611,\n", + " 612, 613, 614, 615, 616, 617, 618, 620, 621, 622, 624,\n", + " 625, 626, 627, 629, 632, 633, 634, 635, 637, 638, 639,\n", + " 643, 644, 645, 646, 647, 648, 650, 652, 653, 656, 657,\n", + " 658, 660, 662, 663, 666, 668, 671, 673, 674, 675, 676,\n", + " 677, 682, 684, 685, 688, 689, 690, 693, 695, 697, 700,\n", + " 703, 704, 706, 707, 709, 711, 713, 714, 715, 716, 718,\n", + " 721, 723, 724, 728, 730, 731, 732, 734, 735, 736, 741,\n", + " 742, 743, 745, 747, 749, 750, 752, 755, 756, 757, 761,\n", + " 762, 764, 766, 767, 769, 771, 772, 774, 775, 777, 779,\n", + " 780, 781, 782, 784, 785, 789, 792, 794, 796, 798, 799,\n", + " 801, 803, 805, 807, 811, 812, 813, 814, 819, 822, 823,\n", + " 824, 827, 828, 829, 830, 831, 832, 835, 837, 839, 840,\n", + " 841, 843, 844, 846, 848, 849, 852, 853, 854, 855, 856,\n", + " 857, 861, 867, 869, 871, 872, 874, 876, 881, 883, 884,\n", + " 886, 887, 890, 891, 892, 894, 896, 897, 899, 900, 905,\n", + " 908, 909, 912, 913, 914, 916, 917, 918, 919, 921, 923,\n", + " 924, 925, 926, 927, 930, 933, 938, 939, 940, 941, 942,\n", + " 943, 944, 945, 947, 948, 950, 951, 953, 959, 960, 961,\n", + " 962, 964, 965, 968, 971, 974, 976, 977, 979, 984, 985,\n", + " 986, 987, 988, 990, 991, 992, 996, 997, 998, 1001, 1003,\n", + " 1004, 1006, 1009, 1011, 1013, 1021, 1023, 1025, 1026, 1028, 1029,\n", + " 1031, 1035, 1037, 1038, 1041, 1042, 1044, 1045, 1046, 1049, 1051,\n", + " 1052, 1053, 1054, 1058, 1060, 1062, 1063, 1064, 1065, 1066, 1067,\n", + " 1069, 1071, 1072, 1074, 1075, 1077, 1080, 1083, 1084, 1085, 1087,\n", + " 1088, 1089, 1091, 1092, 1095, 1096, 1097, 1098, 1099, 1100, 1102,\n", + " 1103, 1104, 1107, 1108, 1109, 1112, 1113, 1119, 1120, 1121, 1126,\n", + " 1128, 1130, 1131, 1133, 1135, 1137, 1139, 1143, 1146, 1147, 1148,\n", + " 1149, 1152, 1154, 1155, 1158, 1159, 1160, 1161, 1162, 1163, 1164,\n", + " 1165, 1166, 1167, 1169, 1170, 1171, 1173, 1177, 1179, 1180, 1182,\n", + " 1184, 1185, 1189, 1192, 1193, 1194, 1195, 1196, 1198, 1200, 1201,\n", + " 1203, 1204, 1205, 1206, 1209, 1210, 1212, 1213, 1214, 1215, 1216,\n", + " 1218, 1219, 1220, 1222, 1225, 1226, 1227, 1229, 1230, 1231, 1232,\n", + " 1234, 1235, 1236, 1237, 1238, 1239, 1244, 1247, 1252, 1253, 1256,\n", + " 1257, 1258, 1259, 1261, 1264, 1266, 1267, 1269, 1272, 1273, 1274,\n", + " 1276, 1278, 1279, 1281, 1282, 1285, 1287, 1289, 1290, 1291, 1293,\n", + " 1295, 1299, 1300, 1301, 1302, 1303, 1304, 1305, 1306, 1311, 1315,\n", + " 1316, 1320, 1321, 1322, 1324, 1325, 1326, 1328, 1329, 1330, 1331,\n", + " 1332, 1334, 1336, 1337, 1338, 1339, 1341, 1345, 1346, 1347, 1351,\n", + " 1355, 1356, 1357, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370,\n", + " 1371, 1372, 1373, 1374, 1375, 1376, 1378, 1380, 1382, 1384, 1386,\n", + " 1388, 1390, 1391, 1393, 1397, 1398]),\n", + " 2: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 43, 44,\n", + " 45, 53, 62, 69, 71, 75, 76, 100, 103, 106, 107,\n", + " 110, 116, 129, 156, 163, 164, 165, 168, 173, 176, 186,\n", + " 191, 193, 210, 215, 220, 233, 234, 242, 248, 250, 251,\n", + " 257, 263, 269, 281, 285, 286, 308, 311, 317, 318, 319,\n", + " 326, 329, 330, 333, 343, 346, 347, 350, 359, 374, 375,\n", + " 377, 393, 396, 403, 405, 415, 424, 437, 438, 439, 441,\n", + " 443, 451, 456, 461, 469, 477, 478, 481, 487, 493, 498,\n", + " 502, 504, 507, 511, 513, 516, 531, 532, 544, 546, 555,\n", + " 556, 557, 565, 566, 571, 575, 583, 584, 585, 586, 595,\n", + " 608, 609, 610, 623, 630, 636, 642, 649, 651, 654, 659,\n", + " 664, 667, 669, 679, 681, 694, 698, 705, 710, 712, 722,\n", + " 725, 727, 729, 738, 739, 746, 751, 754, 758, 760, 768,\n", + " 776, 778, 788, 790, 791, 795, 800, 808, 817, 820, 826,\n", + " 833, 834, 842, 845, 847, 850, 851, 860, 862, 863, 865,\n", + " 866, 870, 878, 893, 898, 902, 903, 906, 911, 922, 936,\n", + " 946, 949, 954, 956, 958, 963, 966, 969, 970, 972, 981,\n", + " 982, 989, 994, 995, 1002, 1008, 1012, 1015, 1016, 1018, 1019,\n", + " 1024, 1027, 1034, 1036, 1043, 1055, 1057, 1059, 1061, 1070, 1076,\n", + " 1078, 1079, 1081, 1082, 1086, 1090, 1093, 1101, 1105, 1111, 1114,\n", + " 1116, 1132, 1136, 1140, 1144, 1145, 1150, 1151, 1156, 1157, 1168,\n", + " 1174, 1175, 1176, 1178, 1181, 1186, 1187, 1188, 1197, 1199, 1202,\n", + " 1207, 1217, 1224, 1228, 1233, 1242, 1243, 1248, 1250, 1260, 1263,\n", + " 1268, 1270, 1271, 1277, 1280, 1288, 1297, 1307, 1309, 1310, 1313,\n", + " 1323, 1333, 1335, 1340, 1343, 1349, 1359, 1392, 1395, 1396, 1399])},\n", + " 4: {0: array([ 0, 2, 6, 8, 19, 20, 21, 22, 41, 47, 52,\n", + " 54, 55, 59, 61, 63, 65, 68, 77, 78, 79, 88,\n", + " 89, 93, 96, 98, 101, 104, 105, 109, 112, 113, 115,\n", + " 119, 120, 121, 123, 126, 127, 131, 133, 137, 147, 150,\n", + " 151, 153, 154, 155, 181, 182, 184, 187, 194, 199, 205,\n", + " 208, 209, 226, 228, 229, 230, 231, 238, 239, 254, 258,\n", + " 260, 261, 266, 268, 275, 276, 280, 282, 284, 292, 295,\n", + " 297, 299, 303, 309, 316, 323, 328, 336, 337, 338, 339,\n", + " 342, 348, 351, 355, 360, 362, 363, 370, 376, 380, 389,\n", + " 391, 392, 399, 404, 409, 413, 414, 432, 435, 450, 454,\n", + " 474, 484, 488, 494, 497, 515, 517, 521, 526, 536, 542,\n", + " 553, 559, 561, 562, 563, 570, 573, 578, 587, 590, 597,\n", + " 599, 601, 605, 606, 619, 628, 631, 640, 641, 655, 661,\n", + " 665, 670, 672, 678, 680, 683, 686, 687, 691, 692, 696,\n", + " 699, 701, 702, 708, 717, 719, 720, 726, 733, 737, 740,\n", + " 744, 748, 753, 759, 763, 765, 770, 773, 783, 786, 787,\n", + " 793, 797, 802, 804, 806, 809, 810, 815, 816, 818, 821,\n", + " 825, 836, 838, 858, 859, 864, 868, 873, 875, 877, 879,\n", + " 880, 882, 885, 888, 889, 895, 901, 904, 907, 910, 915,\n", + " 920, 928, 929, 931, 932, 934, 935, 937, 952, 955, 957,\n", + " 967, 973, 975, 978, 980, 983, 993, 999, 1000, 1005, 1007,\n", + " 1010, 1014, 1017, 1020, 1022, 1030, 1032, 1033, 1039, 1040, 1047,\n", + " 1048, 1050, 1056, 1068, 1073, 1094, 1106, 1110, 1115, 1117, 1118,\n", + " 1122, 1123, 1124, 1125, 1127, 1129, 1134, 1138, 1141, 1142, 1153,\n", + " 1172, 1183, 1190, 1191, 1208, 1211, 1221, 1223, 1240, 1241, 1245,\n", + " 1246, 1249, 1251, 1254, 1255, 1262, 1265, 1275, 1283, 1284, 1286,\n", + " 1292, 1294, 1296, 1298, 1308, 1312, 1314, 1317, 1318, 1319, 1327,\n", + " 1342, 1344, 1348, 1350, 1352, 1353, 1354, 1358, 1360, 1361, 1362,\n", + " 1377, 1379, 1381, 1383, 1385, 1387, 1389, 1394]),\n", + " 1: array([ 1, 3, 5, 7, 9, 12, 13, 17, 18, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 36, 38, 42, 46, 48,\n", + " 49, 51, 56, 58, 60, 64, 66, 67, 70, 74, 80,\n", + " 82, 86, 87, 90, 92, 94, 97, 99, 102, 108, 111,\n", + " 114, 118, 122, 124, 128, 130, 132, 135, 138, 139, 140,\n", + " 141, 142, 143, 144, 145, 146, 148, 149, 158, 159, 162,\n", + " 166, 167, 169, 170, 172, 174, 175, 177, 178, 179, 189,\n", + " 190, 192, 195, 197, 200, 201, 202, 206, 207, 211, 212,\n", + " 213, 214, 216, 218, 219, 221, 222, 227, 232, 235, 236,\n", + " 240, 241, 244, 245, 246, 247, 249, 252, 253, 255, 259,\n", + " 262, 264, 265, 267, 271, 272, 273, 274, 277, 278, 283,\n", + " 288, 289, 290, 291, 294, 296, 298, 300, 301, 302, 304,\n", + " 305, 306, 307, 310, 312, 314, 315, 322, 324, 325, 327,\n", + " 331, 332, 334, 335, 340, 341, 344, 352, 353, 354, 357,\n", + " 361, 365, 366, 368, 369, 371, 372, 373, 378, 381, 383,\n", + " 384, 385, 386, 388, 390, 394, 397, 398, 400, 401, 402,\n", + " 407, 408, 411, 412, 416, 418, 419, 420, 421, 422, 423,\n", + " 425, 426, 427, 428, 429, 430, 431, 433, 434, 436, 442,\n", + " 444, 445, 446, 448, 449, 452, 453, 455, 459, 460, 462,\n", + " 463, 464, 465, 467, 468, 471, 472, 473, 475, 476, 480,\n", + " 482, 483, 485, 486, 489, 490, 491, 492, 495, 496, 499,\n", + " 500, 501, 503, 505, 508, 509, 510, 512, 518, 520, 522,\n", + " 527, 528, 529, 530, 533, 534, 535, 537, 539, 540, 543,\n", + " 545, 547, 548, 550, 551, 552, 554, 558, 560, 564, 567,\n", + " 569, 572, 574, 579, 580, 581, 582, 589, 591, 592, 594,\n", + " 598, 602, 603, 604, 607, 611, 612, 614, 615, 616, 617,\n", + " 618, 620, 622, 624, 627, 629, 633, 634, 635, 637, 639,\n", + " 643, 644, 645, 647, 648, 650, 652, 657, 658, 662, 666,\n", + " 668, 673, 674, 675, 676, 677, 682, 684, 685, 688, 689,\n", + " 690, 695, 697, 700, 703, 704, 707, 709, 711, 714, 715,\n", + " 716, 718, 728, 730, 731, 732, 741, 742, 743, 747, 750,\n", + " 756, 757, 761, 762, 764, 766, 767, 769, 772, 774, 775,\n", + " 777, 779, 780, 782, 785, 789, 792, 796, 798, 799, 803,\n", + " 811, 812, 814, 819, 823, 824, 828, 830, 831, 832, 835,\n", + " 837, 839, 840, 841, 843, 844, 846, 848, 849, 852, 853,\n", + " 856, 857, 861, 867, 869, 871, 872, 874, 876, 883, 884,\n", + " 886, 887, 890, 891, 896, 899, 905, 908, 909, 912, 913,\n", + " 914, 916, 917, 918, 919, 921, 923, 924, 926, 933, 938,\n", + " 939, 940, 941, 942, 943, 944, 947, 950, 951, 953, 960,\n", + " 961, 962, 964, 965, 968, 971, 974, 979, 984, 985, 986,\n", + " 987, 990, 991, 992, 996, 997, 1001, 1003, 1006, 1009, 1011,\n", + " 1013, 1021, 1023, 1025, 1026, 1028, 1029, 1031, 1037, 1038, 1041,\n", + " 1042, 1044, 1045, 1046, 1052, 1053, 1054, 1060, 1062, 1063, 1064,\n", + " 1065, 1069, 1071, 1075, 1080, 1083, 1084, 1085, 1088, 1092, 1095,\n", + " 1100, 1102, 1103, 1108, 1109, 1113, 1119, 1121, 1126, 1130, 1131,\n", + " 1135, 1137, 1146, 1148, 1154, 1155, 1158, 1160, 1161, 1162, 1163,\n", + " 1164, 1165, 1166, 1169, 1170, 1171, 1173, 1177, 1179, 1180, 1182,\n", + " 1184, 1185, 1192, 1193, 1195, 1196, 1198, 1200, 1201, 1203, 1204,\n", + " 1206, 1209, 1210, 1212, 1214, 1218, 1219, 1220, 1225, 1226, 1227,\n", + " 1230, 1231, 1232, 1234, 1236, 1237, 1239, 1244, 1247, 1252, 1253,\n", + " 1258, 1259, 1261, 1264, 1266, 1267, 1269, 1272, 1273, 1274, 1278,\n", + " 1279, 1282, 1289, 1291, 1299, 1301, 1303, 1305, 1306, 1311, 1315,\n", + " 1316, 1320, 1321, 1322, 1324, 1326, 1328, 1329, 1330, 1332, 1334,\n", + " 1336, 1337, 1339, 1341, 1345, 1346, 1351, 1355, 1363, 1364, 1367,\n", + " 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1378, 1380,\n", + " 1384, 1386, 1388, 1390, 1398]),\n", + " 2: array([ 4, 14, 24, 33, 34, 50, 57, 72, 73, 81, 83,\n", + " 84, 85, 91, 95, 117, 125, 134, 136, 152, 157, 160,\n", + " 161, 171, 180, 183, 185, 188, 196, 198, 203, 204, 217,\n", + " 223, 224, 225, 237, 243, 256, 270, 279, 287, 293, 313,\n", + " 320, 321, 345, 349, 356, 358, 364, 367, 379, 382, 387,\n", + " 395, 406, 410, 417, 440, 447, 457, 458, 466, 470, 479,\n", + " 506, 514, 519, 523, 524, 525, 538, 541, 549, 568, 576,\n", + " 577, 588, 593, 596, 600, 613, 621, 625, 626, 632, 638,\n", + " 646, 653, 656, 660, 663, 671, 693, 706, 713, 721, 723,\n", + " 724, 734, 735, 736, 745, 749, 752, 755, 771, 781, 784,\n", + " 794, 801, 805, 807, 813, 822, 827, 829, 854, 855, 881,\n", + " 892, 894, 897, 900, 925, 927, 930, 945, 948, 959, 976,\n", + " 977, 988, 998, 1004, 1035, 1049, 1051, 1058, 1066, 1067, 1072,\n", + " 1074, 1077, 1087, 1089, 1091, 1096, 1097, 1098, 1099, 1104, 1107,\n", + " 1112, 1120, 1128, 1133, 1139, 1143, 1147, 1149, 1152, 1159, 1167,\n", + " 1189, 1194, 1205, 1213, 1215, 1216, 1222, 1229, 1235, 1238, 1256,\n", + " 1257, 1276, 1281, 1285, 1287, 1290, 1293, 1295, 1300, 1302, 1304,\n", + " 1325, 1331, 1338, 1347, 1356, 1357, 1365, 1366, 1382, 1391, 1393,\n", + " 1397]),\n", + " 3: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 43, 44,\n", + " 45, 53, 62, 69, 71, 75, 76, 100, 103, 106, 107,\n", + " 110, 116, 129, 156, 163, 164, 165, 168, 173, 176, 186,\n", + " 191, 193, 210, 215, 220, 233, 234, 242, 248, 250, 251,\n", + " 257, 263, 269, 281, 285, 286, 308, 311, 317, 318, 319,\n", + " 326, 329, 330, 333, 343, 346, 347, 350, 359, 374, 375,\n", + " 377, 393, 396, 403, 405, 415, 424, 437, 438, 439, 441,\n", + " 443, 451, 456, 461, 469, 477, 478, 481, 487, 493, 498,\n", + " 502, 504, 507, 511, 513, 516, 531, 532, 544, 546, 555,\n", + " 556, 557, 565, 566, 571, 575, 583, 584, 585, 586, 595,\n", + " 608, 609, 610, 623, 630, 636, 642, 649, 651, 654, 659,\n", + " 664, 667, 669, 679, 681, 694, 698, 705, 710, 712, 722,\n", + " 725, 727, 729, 738, 739, 746, 751, 754, 758, 760, 768,\n", + " 776, 778, 788, 790, 791, 795, 800, 808, 817, 820, 826,\n", + " 833, 834, 842, 845, 847, 850, 851, 860, 862, 863, 865,\n", + " 866, 870, 878, 893, 898, 902, 903, 906, 911, 922, 936,\n", + " 946, 949, 954, 956, 958, 963, 966, 969, 970, 972, 981,\n", + " 982, 989, 994, 995, 1002, 1008, 1012, 1015, 1016, 1018, 1019,\n", + " 1024, 1027, 1034, 1036, 1043, 1055, 1057, 1059, 1061, 1070, 1076,\n", + " 1078, 1079, 1081, 1082, 1086, 1090, 1093, 1101, 1105, 1111, 1114,\n", + " 1116, 1132, 1136, 1140, 1144, 1145, 1150, 1151, 1156, 1157, 1168,\n", + " 1174, 1175, 1176, 1178, 1181, 1186, 1187, 1188, 1197, 1199, 1202,\n", + " 1207, 1217, 1224, 1228, 1233, 1242, 1243, 1248, 1250, 1260, 1263,\n", + " 1268, 1270, 1271, 1277, 1280, 1288, 1297, 1307, 1309, 1310, 1313,\n", + " 1323, 1333, 1335, 1340, 1343, 1349, 1359, 1392, 1395, 1396, 1399])},\n", + " 5: {0: array([ 0, 2, 20, 22, 54, 61, 65, 89, 101, 105, 112,\n", + " 119, 123, 127, 131, 133, 137, 147, 154, 182, 187, 205,\n", + " 208, 209, 226, 229, 230, 268, 275, 284, 309, 323, 328,\n", + " 337, 342, 355, 362, 380, 391, 413, 432, 435, 450, 484,\n", + " 488, 515, 542, 562, 587, 590, 597, 601, 641, 655, 661,\n", + " 692, 701, 733, 737, 744, 770, 773, 793, 806, 809, 810,\n", + " 821, 836, 859, 864, 879, 882, 895, 907, 915, 920, 931,\n", + " 952, 955, 957, 967, 978, 1007, 1017, 1032, 1068, 1115, 1117,\n", + " 1125, 1127, 1129, 1153, 1190, 1191, 1208, 1223, 1246, 1284, 1294,\n", + " 1298, 1308, 1318, 1327, 1344, 1348, 1350, 1354, 1360, 1362, 1377,\n", + " 1379, 1381, 1389, 1394]),\n", + " 1: array([ 1, 3, 5, 7, 9, 12, 13, 17, 18, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 36, 38, 42, 46, 48,\n", + " 49, 51, 56, 58, 60, 64, 66, 67, 70, 74, 80,\n", + " 82, 86, 87, 90, 92, 94, 97, 99, 102, 108, 111,\n", + " 114, 118, 122, 124, 128, 130, 132, 135, 138, 139, 140,\n", + " 141, 142, 143, 144, 145, 146, 148, 149, 158, 159, 162,\n", + " 166, 167, 169, 170, 172, 174, 175, 177, 178, 179, 189,\n", + " 190, 192, 195, 197, 200, 201, 202, 206, 207, 211, 212,\n", + " 213, 214, 216, 218, 219, 221, 222, 227, 232, 235, 236,\n", + " 240, 241, 244, 245, 246, 247, 249, 252, 253, 255, 259,\n", + " 262, 264, 265, 267, 271, 272, 273, 274, 277, 278, 283,\n", + " 288, 289, 290, 291, 294, 296, 298, 300, 301, 302, 304,\n", + " 305, 306, 307, 310, 312, 314, 315, 322, 324, 325, 327,\n", + " 331, 332, 334, 335, 340, 341, 344, 352, 353, 354, 357,\n", + " 361, 365, 366, 368, 369, 371, 372, 373, 378, 381, 383,\n", + " 384, 385, 386, 388, 390, 394, 397, 398, 400, 401, 402,\n", + " 407, 408, 411, 412, 416, 418, 419, 420, 421, 422, 423,\n", + " 425, 426, 427, 428, 429, 430, 431, 433, 434, 436, 442,\n", + " 444, 445, 446, 448, 449, 452, 453, 455, 459, 460, 462,\n", + " 463, 464, 465, 467, 468, 471, 472, 473, 475, 476, 480,\n", + " 482, 483, 485, 486, 489, 490, 491, 492, 495, 496, 499,\n", + " 500, 501, 503, 505, 508, 509, 510, 512, 518, 520, 522,\n", + " 527, 528, 529, 530, 533, 534, 535, 537, 539, 540, 543,\n", + " 545, 547, 548, 550, 551, 552, 554, 558, 560, 564, 567,\n", + " 569, 572, 574, 579, 580, 581, 582, 589, 591, 592, 594,\n", + " 598, 602, 603, 604, 607, 611, 612, 614, 615, 616, 617,\n", + " 618, 620, 622, 624, 627, 629, 633, 634, 635, 637, 639,\n", + " 643, 644, 645, 647, 648, 650, 652, 657, 658, 662, 666,\n", + " 668, 673, 674, 675, 676, 677, 682, 684, 685, 688, 689,\n", + " 690, 695, 697, 700, 703, 704, 707, 709, 711, 714, 715,\n", + " 716, 718, 728, 730, 731, 732, 741, 742, 743, 747, 750,\n", + " 756, 757, 761, 762, 764, 766, 767, 769, 772, 774, 775,\n", + " 777, 779, 780, 782, 785, 789, 792, 796, 798, 799, 803,\n", + " 811, 812, 814, 819, 823, 824, 828, 830, 831, 832, 835,\n", + " 837, 839, 840, 841, 843, 844, 846, 848, 849, 852, 853,\n", + " 856, 857, 861, 867, 869, 871, 872, 874, 876, 883, 884,\n", + " 886, 887, 890, 891, 896, 899, 905, 908, 909, 912, 913,\n", + " 914, 916, 917, 918, 919, 921, 923, 924, 926, 933, 938,\n", + " 939, 940, 941, 942, 943, 944, 947, 950, 951, 953, 960,\n", + " 961, 962, 964, 965, 968, 971, 974, 979, 984, 985, 986,\n", + " 987, 990, 991, 992, 996, 997, 1001, 1003, 1006, 1009, 1011,\n", + " 1013, 1021, 1023, 1025, 1026, 1028, 1029, 1031, 1037, 1038, 1041,\n", + " 1042, 1044, 1045, 1046, 1052, 1053, 1054, 1060, 1062, 1063, 1064,\n", + " 1065, 1069, 1071, 1075, 1080, 1083, 1084, 1085, 1088, 1092, 1095,\n", + " 1100, 1102, 1103, 1108, 1109, 1113, 1119, 1121, 1126, 1130, 1131,\n", + " 1135, 1137, 1146, 1148, 1154, 1155, 1158, 1160, 1161, 1162, 1163,\n", + " 1164, 1165, 1166, 1169, 1170, 1171, 1173, 1177, 1179, 1180, 1182,\n", + " 1184, 1185, 1192, 1193, 1195, 1196, 1198, 1200, 1201, 1203, 1204,\n", + " 1206, 1209, 1210, 1212, 1214, 1218, 1219, 1220, 1225, 1226, 1227,\n", + " 1230, 1231, 1232, 1234, 1236, 1237, 1239, 1244, 1247, 1252, 1253,\n", + " 1258, 1259, 1261, 1264, 1266, 1267, 1269, 1272, 1273, 1274, 1278,\n", + " 1279, 1282, 1289, 1291, 1299, 1301, 1303, 1305, 1306, 1311, 1315,\n", + " 1316, 1320, 1321, 1322, 1324, 1326, 1328, 1329, 1330, 1332, 1334,\n", + " 1336, 1337, 1339, 1341, 1345, 1346, 1351, 1355, 1363, 1364, 1367,\n", + " 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1378, 1380,\n", + " 1384, 1386, 1388, 1390, 1398]),\n", + " 2: array([ 4, 14, 24, 33, 34, 50, 57, 72, 73, 81, 83,\n", + " 84, 85, 91, 95, 117, 125, 134, 136, 152, 157, 160,\n", + " 161, 171, 180, 183, 185, 188, 196, 198, 203, 204, 217,\n", + " 223, 224, 225, 237, 243, 256, 270, 279, 287, 293, 313,\n", + " 320, 321, 345, 349, 356, 358, 364, 367, 379, 382, 387,\n", + " 395, 406, 410, 417, 440, 447, 457, 458, 466, 470, 479,\n", + " 506, 514, 519, 523, 524, 525, 538, 541, 549, 568, 576,\n", + " 577, 588, 593, 596, 600, 613, 621, 625, 626, 632, 638,\n", + " 646, 653, 656, 660, 663, 671, 693, 706, 713, 721, 723,\n", + " 724, 734, 735, 736, 745, 749, 752, 755, 771, 781, 784,\n", + " 794, 801, 805, 807, 813, 822, 827, 829, 854, 855, 881,\n", + " 892, 894, 897, 900, 925, 927, 930, 945, 948, 959, 976,\n", + " 977, 988, 998, 1004, 1035, 1049, 1051, 1058, 1066, 1067, 1072,\n", + " 1074, 1077, 1087, 1089, 1091, 1096, 1097, 1098, 1099, 1104, 1107,\n", + " 1112, 1120, 1128, 1133, 1139, 1143, 1147, 1149, 1152, 1159, 1167,\n", + " 1189, 1194, 1205, 1213, 1215, 1216, 1222, 1229, 1235, 1238, 1256,\n", + " 1257, 1276, 1281, 1285, 1287, 1290, 1293, 1295, 1300, 1302, 1304,\n", + " 1325, 1331, 1338, 1347, 1356, 1357, 1365, 1366, 1382, 1391, 1393,\n", + " 1397]),\n", + " 3: array([ 6, 8, 19, 21, 41, 47, 52, 55, 59, 63, 68,\n", + " 77, 78, 79, 88, 93, 96, 98, 104, 109, 113, 115,\n", + " 120, 121, 126, 150, 151, 153, 155, 181, 184, 194, 199,\n", + " 228, 231, 238, 239, 254, 258, 260, 261, 266, 276, 280,\n", + " 282, 292, 295, 297, 299, 303, 316, 336, 338, 339, 348,\n", + " 351, 360, 363, 370, 376, 389, 392, 399, 404, 409, 414,\n", + " 454, 474, 494, 497, 517, 521, 526, 536, 553, 559, 561,\n", + " 563, 570, 573, 578, 599, 605, 606, 619, 628, 631, 640,\n", + " 665, 670, 672, 678, 680, 683, 686, 687, 691, 696, 699,\n", + " 702, 708, 717, 719, 720, 726, 740, 748, 753, 759, 763,\n", + " 765, 783, 786, 787, 797, 802, 804, 815, 816, 818, 825,\n", + " 838, 858, 868, 873, 875, 877, 880, 885, 888, 889, 901,\n", + " 904, 910, 928, 929, 932, 934, 935, 937, 973, 975, 980,\n", + " 983, 993, 999, 1000, 1005, 1010, 1014, 1020, 1022, 1030, 1033,\n", + " 1039, 1040, 1047, 1048, 1050, 1056, 1073, 1094, 1106, 1110, 1118,\n", + " 1122, 1123, 1124, 1134, 1138, 1141, 1142, 1172, 1183, 1211, 1221,\n", + " 1240, 1241, 1245, 1249, 1251, 1254, 1255, 1262, 1265, 1275, 1283,\n", + " 1286, 1292, 1296, 1312, 1314, 1317, 1319, 1342, 1352, 1353, 1358,\n", + " 1361, 1383, 1385, 1387]),\n", + " 4: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 43, 44,\n", + " 45, 53, 62, 69, 71, 75, 76, 100, 103, 106, 107,\n", + " 110, 116, 129, 156, 163, 164, 165, 168, 173, 176, 186,\n", + " 191, 193, 210, 215, 220, 233, 234, 242, 248, 250, 251,\n", + " 257, 263, 269, 281, 285, 286, 308, 311, 317, 318, 319,\n", + " 326, 329, 330, 333, 343, 346, 347, 350, 359, 374, 375,\n", + " 377, 393, 396, 403, 405, 415, 424, 437, 438, 439, 441,\n", + " 443, 451, 456, 461, 469, 477, 478, 481, 487, 493, 498,\n", + " 502, 504, 507, 511, 513, 516, 531, 532, 544, 546, 555,\n", + " 556, 557, 565, 566, 571, 575, 583, 584, 585, 586, 595,\n", + " 608, 609, 610, 623, 630, 636, 642, 649, 651, 654, 659,\n", + " 664, 667, 669, 679, 681, 694, 698, 705, 710, 712, 722,\n", + " 725, 727, 729, 738, 739, 746, 751, 754, 758, 760, 768,\n", + " 776, 778, 788, 790, 791, 795, 800, 808, 817, 820, 826,\n", + " 833, 834, 842, 845, 847, 850, 851, 860, 862, 863, 865,\n", + " 866, 870, 878, 893, 898, 902, 903, 906, 911, 922, 936,\n", + " 946, 949, 954, 956, 958, 963, 966, 969, 970, 972, 981,\n", + " 982, 989, 994, 995, 1002, 1008, 1012, 1015, 1016, 1018, 1019,\n", + " 1024, 1027, 1034, 1036, 1043, 1055, 1057, 1059, 1061, 1070, 1076,\n", + " 1078, 1079, 1081, 1082, 1086, 1090, 1093, 1101, 1105, 1111, 1114,\n", + " 1116, 1132, 1136, 1140, 1144, 1145, 1150, 1151, 1156, 1157, 1168,\n", + " 1174, 1175, 1176, 1178, 1181, 1186, 1187, 1188, 1197, 1199, 1202,\n", + " 1207, 1217, 1224, 1228, 1233, 1242, 1243, 1248, 1250, 1260, 1263,\n", + " 1268, 1270, 1271, 1277, 1280, 1288, 1297, 1307, 1309, 1310, 1313,\n", + " 1323, 1333, 1335, 1340, 1343, 1349, 1359, 1392, 1395, 1396, 1399])},\n", + " 6: {0: array([ 0, 2, 20, 22, 54, 61, 65, 89, 101, 105, 112,\n", + " 119, 123, 127, 131, 133, 137, 147, 154, 182, 187, 205,\n", + " 208, 209, 226, 229, 230, 268, 275, 284, 309, 323, 328,\n", + " 337, 342, 355, 362, 380, 391, 413, 432, 435, 450, 484,\n", + " 488, 515, 542, 562, 587, 590, 597, 601, 641, 655, 661,\n", + " 692, 701, 733, 737, 744, 770, 773, 793, 806, 809, 810,\n", + " 821, 836, 859, 864, 879, 882, 895, 907, 915, 920, 931,\n", + " 952, 955, 957, 967, 978, 1007, 1017, 1032, 1068, 1115, 1117,\n", + " 1125, 1127, 1129, 1153, 1190, 1191, 1208, 1223, 1246, 1284, 1294,\n", + " 1298, 1308, 1318, 1327, 1344, 1348, 1350, 1354, 1360, 1362, 1377,\n", + " 1379, 1381, 1389, 1394]),\n", + " 1: array([ 1, 3, 5, 7, 9, 12, 13, 17, 18, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 36, 38, 42, 46, 48,\n", + " 49, 51, 56, 58, 60, 64, 66, 67, 70, 74, 80,\n", + " 82, 86, 87, 90, 92, 94, 97, 99, 102, 108, 111,\n", + " 114, 118, 122, 124, 128, 130, 132, 135, 138, 139, 140,\n", + " 141, 142, 143, 144, 145, 146, 148, 149, 158, 159, 162,\n", + " 166, 167, 169, 170, 172, 174, 175, 177, 178, 179, 189,\n", + " 190, 192, 195, 197, 200, 201, 202, 206, 207, 211, 212,\n", + " 213, 214, 216, 218, 219, 221, 222, 227, 232, 235, 236,\n", + " 240, 241, 244, 245, 246, 247, 249, 252, 253, 255, 259,\n", + " 262, 264, 265, 267, 271, 272, 273, 274, 277, 278, 283,\n", + " 288, 289, 290, 291, 294, 296, 298, 300, 301, 302, 304,\n", + " 305, 306, 307, 310, 312, 314, 315, 322, 324, 325, 327,\n", + " 331, 332, 334, 335, 340, 341, 344, 352, 353, 354, 357,\n", + " 361, 365, 366, 368, 369, 371, 372, 373, 378, 381, 383,\n", + " 384, 385, 386, 388, 390, 394, 397, 398, 400, 401, 402,\n", + " 407, 408, 411, 412, 416, 418, 419, 420, 421, 422, 423,\n", + " 425, 426, 427, 428, 429, 430, 431, 433, 434, 436, 442,\n", + " 444, 445, 446, 448, 449, 452, 453, 455, 459, 460, 462,\n", + " 463, 464, 465, 467, 468, 471, 472, 473, 475, 476, 480,\n", + " 482, 483, 485, 486, 489, 490, 491, 492, 495, 496, 499,\n", + " 500, 501, 503, 505, 508, 509, 510, 512, 518, 520, 522,\n", + " 527, 528, 529, 530, 533, 534, 535, 537, 539, 540, 543,\n", + " 545, 547, 548, 550, 551, 552, 554, 558, 560, 564, 567,\n", + " 569, 572, 574, 579, 580, 581, 582, 589, 591, 592, 594,\n", + " 598, 602, 603, 604, 607, 611, 612, 614, 615, 616, 617,\n", + " 618, 620, 622, 624, 627, 629, 633, 634, 635, 637, 639,\n", + " 643, 644, 645, 647, 648, 650, 652, 657, 658, 662, 666,\n", + " 668, 673, 674, 675, 676, 677, 682, 684, 685, 688, 689,\n", + " 690, 695, 697, 700, 703, 704, 707, 709, 711, 714, 715,\n", + " 716, 718, 728, 730, 731, 732, 741, 742, 743, 747, 750,\n", + " 756, 757, 761, 762, 764, 766, 767, 769, 772, 774, 775,\n", + " 777, 779, 780, 782, 785, 789, 792, 796, 798, 799, 803,\n", + " 811, 812, 814, 819, 823, 824, 828, 830, 831, 832, 835,\n", + " 837, 839, 840, 841, 843, 844, 846, 848, 849, 852, 853,\n", + " 856, 857, 861, 867, 869, 871, 872, 874, 876, 883, 884,\n", + " 886, 887, 890, 891, 896, 899, 905, 908, 909, 912, 913,\n", + " 914, 916, 917, 918, 919, 921, 923, 924, 926, 933, 938,\n", + " 939, 940, 941, 942, 943, 944, 947, 950, 951, 953, 960,\n", + " 961, 962, 964, 965, 968, 971, 974, 979, 984, 985, 986,\n", + " 987, 990, 991, 992, 996, 997, 1001, 1003, 1006, 1009, 1011,\n", + " 1013, 1021, 1023, 1025, 1026, 1028, 1029, 1031, 1037, 1038, 1041,\n", + " 1042, 1044, 1045, 1046, 1052, 1053, 1054, 1060, 1062, 1063, 1064,\n", + " 1065, 1069, 1071, 1075, 1080, 1083, 1084, 1085, 1088, 1092, 1095,\n", + " 1100, 1102, 1103, 1108, 1109, 1113, 1119, 1121, 1126, 1130, 1131,\n", + " 1135, 1137, 1146, 1148, 1154, 1155, 1158, 1160, 1161, 1162, 1163,\n", + " 1164, 1165, 1166, 1169, 1170, 1171, 1173, 1177, 1179, 1180, 1182,\n", + " 1184, 1185, 1192, 1193, 1195, 1196, 1198, 1200, 1201, 1203, 1204,\n", + " 1206, 1209, 1210, 1212, 1214, 1218, 1219, 1220, 1225, 1226, 1227,\n", + " 1230, 1231, 1232, 1234, 1236, 1237, 1239, 1244, 1247, 1252, 1253,\n", + " 1258, 1259, 1261, 1264, 1266, 1267, 1269, 1272, 1273, 1274, 1278,\n", + " 1279, 1282, 1289, 1291, 1299, 1301, 1303, 1305, 1306, 1311, 1315,\n", + " 1316, 1320, 1321, 1322, 1324, 1326, 1328, 1329, 1330, 1332, 1334,\n", + " 1336, 1337, 1339, 1341, 1345, 1346, 1351, 1355, 1363, 1364, 1367,\n", + " 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1378, 1380,\n", + " 1384, 1386, 1388, 1390, 1398]),\n", + " 2: array([ 4, 14, 24, 33, 34, 50, 57, 72, 73, 81, 83,\n", + " 84, 85, 91, 95, 117, 125, 134, 136, 152, 157, 160,\n", + " 161, 171, 180, 183, 185, 188, 196, 198, 203, 204, 217,\n", + " 223, 224, 225, 237, 243, 256, 270, 279, 287, 293, 313,\n", + " 320, 321, 345, 349, 356, 358, 364, 367, 379, 382, 387,\n", + " 395, 406, 410, 417, 440, 447, 457, 458, 466, 470, 479,\n", + " 506, 514, 519, 523, 524, 525, 538, 541, 549, 568, 576,\n", + " 577, 588, 593, 596, 600, 613, 621, 625, 626, 632, 638,\n", + " 646, 653, 656, 660, 663, 671, 693, 706, 713, 721, 723,\n", + " 724, 734, 735, 736, 745, 749, 752, 755, 771, 781, 784,\n", + " 794, 801, 805, 807, 813, 822, 827, 829, 854, 855, 881,\n", + " 892, 894, 897, 900, 925, 927, 930, 945, 948, 959, 976,\n", + " 977, 988, 998, 1004, 1035, 1049, 1051, 1058, 1066, 1067, 1072,\n", + " 1074, 1077, 1087, 1089, 1091, 1096, 1097, 1098, 1099, 1104, 1107,\n", + " 1112, 1120, 1128, 1133, 1139, 1143, 1147, 1149, 1152, 1159, 1167,\n", + " 1189, 1194, 1205, 1213, 1215, 1216, 1222, 1229, 1235, 1238, 1256,\n", + " 1257, 1276, 1281, 1285, 1287, 1290, 1293, 1295, 1300, 1302, 1304,\n", + " 1325, 1331, 1338, 1347, 1356, 1357, 1365, 1366, 1382, 1391, 1393,\n", + " 1397]),\n", + " 3: array([ 6, 8, 19, 21, 41, 47, 52, 55, 59, 63, 68,\n", + " 77, 78, 79, 88, 93, 96, 98, 104, 109, 113, 115,\n", + " 120, 121, 126, 150, 151, 153, 155, 181, 184, 194, 199,\n", + " 228, 231, 238, 239, 254, 258, 260, 261, 266, 276, 280,\n", + " 282, 292, 295, 297, 299, 303, 316, 336, 338, 339, 348,\n", + " 351, 360, 363, 370, 376, 389, 392, 399, 404, 409, 414,\n", + " 454, 474, 494, 497, 517, 521, 526, 536, 553, 559, 561,\n", + " 563, 570, 573, 578, 599, 605, 606, 619, 628, 631, 640,\n", + " 665, 670, 672, 678, 680, 683, 686, 687, 691, 696, 699,\n", + " 702, 708, 717, 719, 720, 726, 740, 748, 753, 759, 763,\n", + " 765, 783, 786, 787, 797, 802, 804, 815, 816, 818, 825,\n", + " 838, 858, 868, 873, 875, 877, 880, 885, 888, 889, 901,\n", + " 904, 910, 928, 929, 932, 934, 935, 937, 973, 975, 980,\n", + " 983, 993, 999, 1000, 1005, 1010, 1014, 1020, 1022, 1030, 1033,\n", + " 1039, 1040, 1047, 1048, 1050, 1056, 1073, 1094, 1106, 1110, 1118,\n", + " 1122, 1123, 1124, 1134, 1138, 1141, 1142, 1172, 1183, 1211, 1221,\n", + " 1240, 1241, 1245, 1249, 1251, 1254, 1255, 1262, 1265, 1275, 1283,\n", + " 1286, 1292, 1296, 1312, 1314, 1317, 1319, 1342, 1352, 1353, 1358,\n", + " 1361, 1383, 1385, 1387]),\n", + " 4: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 43, 44,\n", + " 45, 53, 76, 103, 106, 107, 110, 116, 129, 156, 163,\n", + " 164, 165, 168, 186, 191, 193, 210, 215, 220, 233, 234,\n", + " 242, 248, 250, 251, 257, 263, 269, 281, 285, 286, 311,\n", + " 318, 329, 343, 346, 347, 350, 374, 375, 393, 396, 403,\n", + " 405, 424, 437, 438, 439, 456, 461, 477, 478, 487, 493,\n", + " 502, 504, 507, 511, 513, 532, 544, 546, 555, 556, 557,\n", + " 565, 575, 583, 608, 609, 623, 630, 636, 654, 664, 667,\n", + " 669, 694, 698, 705, 710, 712, 722, 725, 727, 739, 746,\n", + " 758, 760, 768, 776, 778, 788, 790, 817, 820, 833, 834,\n", + " 842, 845, 847, 850, 851, 860, 862, 863, 865, 878, 893,\n", + " 903, 906, 911, 922, 936, 946, 954, 956, 966, 969, 970,\n", + " 972, 981, 982, 989, 994, 1002, 1008, 1015, 1016, 1019, 1027,\n", + " 1034, 1036, 1043, 1057, 1061, 1070, 1076, 1079, 1082, 1086, 1101,\n", + " 1105, 1111, 1132, 1144, 1145, 1150, 1151, 1157, 1168, 1174, 1175,\n", + " 1186, 1188, 1197, 1199, 1202, 1217, 1224, 1228, 1243, 1268, 1270,\n", + " 1277, 1280, 1297, 1309, 1323, 1333, 1335, 1349, 1359, 1392, 1396,\n", + " 1399]),\n", + " 5: array([ 62, 69, 71, 75, 100, 173, 176, 308, 317, 319, 326,\n", + " 330, 333, 359, 377, 415, 441, 443, 451, 469, 481, 498,\n", + " 516, 531, 566, 571, 584, 585, 586, 595, 610, 642, 649,\n", + " 651, 659, 679, 681, 729, 738, 751, 754, 791, 795, 800,\n", + " 808, 826, 866, 870, 898, 902, 949, 958, 963, 995, 1012,\n", + " 1018, 1024, 1055, 1059, 1078, 1081, 1090, 1093, 1114, 1116, 1136,\n", + " 1140, 1156, 1176, 1178, 1181, 1187, 1207, 1233, 1242, 1248, 1250,\n", + " 1260, 1263, 1271, 1288, 1307, 1310, 1313, 1340, 1343, 1395])},\n", + " 7: {0: array([ 0, 2, 20, 22, 54, 61, 65, 89, 101, 105, 112,\n", + " 119, 123, 127, 131, 133, 137, 147, 154, 182, 187, 205,\n", + " 208, 209, 226, 229, 230, 268, 275, 284, 309, 323, 328,\n", + " 337, 342, 355, 362, 380, 391, 413, 432, 435, 450, 484,\n", + " 488, 515, 542, 562, 587, 590, 597, 601, 641, 655, 661,\n", + " 692, 701, 733, 737, 744, 770, 773, 793, 806, 809, 810,\n", + " 821, 836, 859, 864, 879, 882, 895, 907, 915, 920, 931,\n", + " 952, 955, 957, 967, 978, 1007, 1017, 1032, 1068, 1115, 1117,\n", + " 1125, 1127, 1129, 1153, 1190, 1191, 1208, 1223, 1246, 1284, 1294,\n", + " 1298, 1308, 1318, 1327, 1344, 1348, 1350, 1354, 1360, 1362, 1377,\n", + " 1379, 1381, 1389, 1394]),\n", + " 1: array([ 1, 3, 5, 7, 9, 12, 13, 17, 18, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 36, 38, 42, 46, 48,\n", + " 49, 51, 56, 58, 60, 64, 66, 67, 70, 74, 80,\n", + " 82, 86, 87, 90, 92, 94, 97, 99, 102, 108, 111,\n", + " 114, 118, 122, 124, 128, 130, 132, 135, 138, 139, 140,\n", + " 141, 142, 143, 144, 145, 146, 148, 149, 158, 159, 162,\n", + " 166, 167, 169, 170, 172, 174, 175, 177, 178, 179, 189,\n", + " 190, 192, 195, 197, 200, 201, 202, 206, 207, 211, 212,\n", + " 213, 214, 216, 218, 219, 221, 222, 227, 232, 235, 236,\n", + " 240, 241, 244, 245, 246, 247, 249, 252, 253, 255, 259,\n", + " 262, 264, 265, 267, 271, 272, 273, 274, 277, 278, 283,\n", + " 288, 289, 290, 291, 294, 296, 298, 300, 301, 302, 304,\n", + " 305, 306, 307, 310, 312, 314, 315, 322, 324, 325, 327,\n", + " 331, 332, 334, 335, 340, 341, 344, 352, 353, 354, 357,\n", + " 361, 365, 366, 368, 369, 371, 372, 373, 378, 381, 383,\n", + " 384, 385, 386, 388, 390, 394, 397, 398, 400, 401, 402,\n", + " 407, 408, 411, 412, 416, 418, 419, 420, 421, 422, 423,\n", + " 425, 426, 427, 428, 429, 430, 431, 433, 434, 436, 442,\n", + " 444, 445, 446, 448, 449, 452, 453, 455, 459, 460, 462,\n", + " 463, 464, 465, 467, 468, 471, 472, 473, 475, 476, 480,\n", + " 482, 483, 485, 486, 489, 490, 491, 492, 495, 496, 499,\n", + " 500, 501, 503, 505, 508, 509, 510, 512, 518, 520, 522,\n", + " 527, 528, 529, 530, 533, 534, 535, 537, 539, 540, 543,\n", + " 545, 547, 548, 550, 551, 552, 554, 558, 560, 564, 567,\n", + " 569, 572, 574, 579, 580, 581, 582, 589, 591, 592, 594,\n", + " 598, 602, 603, 604, 607, 611, 612, 614, 615, 616, 617,\n", + " 618, 620, 622, 624, 627, 629, 633, 634, 635, 637, 639,\n", + " 643, 644, 645, 647, 648, 650, 652, 657, 658, 662, 666,\n", + " 668, 673, 674, 675, 676, 677, 682, 684, 685, 688, 689,\n", + " 690, 695, 697, 700, 703, 704, 707, 709, 711, 714, 715,\n", + " 716, 718, 728, 730, 731, 732, 741, 742, 743, 747, 750,\n", + " 756, 757, 761, 762, 764, 766, 767, 769, 772, 774, 775,\n", + " 777, 779, 780, 782, 785, 789, 792, 796, 798, 799, 803,\n", + " 811, 812, 814, 819, 823, 824, 828, 830, 831, 832, 835,\n", + " 837, 839, 840, 841, 843, 844, 846, 848, 849, 852, 853,\n", + " 856, 857, 861, 867, 869, 871, 872, 874, 876, 883, 884,\n", + " 886, 887, 890, 891, 896, 899, 905, 908, 909, 912, 913,\n", + " 914, 916, 917, 918, 919, 921, 923, 924, 926, 933, 938,\n", + " 939, 940, 941, 942, 943, 944, 947, 950, 951, 953, 960,\n", + " 961, 962, 964, 965, 968, 971, 974, 979, 984, 985, 986,\n", + " 987, 990, 991, 992, 996, 997, 1001, 1003, 1006, 1009, 1011,\n", + " 1013, 1021, 1023, 1025, 1026, 1028, 1029, 1031, 1037, 1038, 1041,\n", + " 1042, 1044, 1045, 1046, 1052, 1053, 1054, 1060, 1062, 1063, 1064,\n", + " 1065, 1069, 1071, 1075, 1080, 1083, 1084, 1085, 1088, 1092, 1095,\n", + " 1100, 1102, 1103, 1108, 1109, 1113, 1119, 1121, 1126, 1130, 1131,\n", + " 1135, 1137, 1146, 1148, 1154, 1155, 1158, 1160, 1161, 1162, 1163,\n", + " 1164, 1165, 1166, 1169, 1170, 1171, 1173, 1177, 1179, 1180, 1182,\n", + " 1184, 1185, 1192, 1193, 1195, 1196, 1198, 1200, 1201, 1203, 1204,\n", + " 1206, 1209, 1210, 1212, 1214, 1218, 1219, 1220, 1225, 1226, 1227,\n", + " 1230, 1231, 1232, 1234, 1236, 1237, 1239, 1244, 1247, 1252, 1253,\n", + " 1258, 1259, 1261, 1264, 1266, 1267, 1269, 1272, 1273, 1274, 1278,\n", + " 1279, 1282, 1289, 1291, 1299, 1301, 1303, 1305, 1306, 1311, 1315,\n", + " 1316, 1320, 1321, 1322, 1324, 1326, 1328, 1329, 1330, 1332, 1334,\n", + " 1336, 1337, 1339, 1341, 1345, 1346, 1351, 1355, 1363, 1364, 1367,\n", + " 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1378, 1380,\n", + " 1384, 1386, 1388, 1390, 1398]),\n", + " 2: array([ 4, 14, 24, 33, 34, 50, 57, 72, 73, 81, 83,\n", + " 84, 85, 91, 95, 117, 125, 134, 136, 152, 157, 160,\n", + " 161, 171, 180, 183, 185, 188, 196, 198, 203, 204, 217,\n", + " 223, 224, 225, 237, 243, 256, 270, 279, 287, 293, 313,\n", + " 320, 321, 345, 349, 356, 358, 364, 367, 379, 382, 387,\n", + " 395, 406, 410, 417, 440, 447, 457, 458, 466, 470, 479,\n", + " 506, 514, 519, 523, 524, 525, 538, 541, 549, 568, 576,\n", + " 577, 588, 593, 596, 600, 613, 621, 625, 626, 632, 638,\n", + " 646, 653, 656, 660, 663, 671, 693, 706, 713, 721, 723,\n", + " 724, 734, 735, 736, 745, 749, 752, 755, 771, 781, 784,\n", + " 794, 801, 805, 807, 813, 822, 827, 829, 854, 855, 881,\n", + " 892, 894, 897, 900, 925, 927, 930, 945, 948, 959, 976,\n", + " 977, 988, 998, 1004, 1035, 1049, 1051, 1058, 1066, 1067, 1072,\n", + " 1074, 1077, 1087, 1089, 1091, 1096, 1097, 1098, 1099, 1104, 1107,\n", + " 1112, 1120, 1128, 1133, 1139, 1143, 1147, 1149, 1152, 1159, 1167,\n", + " 1189, 1194, 1205, 1213, 1215, 1216, 1222, 1229, 1235, 1238, 1256,\n", + " 1257, 1276, 1281, 1285, 1287, 1290, 1293, 1295, 1300, 1302, 1304,\n", + " 1325, 1331, 1338, 1347, 1356, 1357, 1365, 1366, 1382, 1391, 1393,\n", + " 1397]),\n", + " 3: array([ 6, 41, 78, 79, 126, 238, 260, 266, 292, 303, 339,\n", + " 360, 370, 376, 414, 517, 536, 570, 599, 631, 665, 670,\n", + " 678, 687, 702, 719, 759, 763, 765, 783, 787, 816, 858,\n", + " 877, 888, 889, 904, 929, 932, 935, 993, 999, 1010, 1022,\n", + " 1033, 1094, 1123, 1211, 1221, 1317, 1352, 1385]),\n", + " 4: array([ 8, 19, 21, 47, 52, 55, 59, 63, 68, 77, 88,\n", + " 93, 96, 98, 104, 109, 113, 115, 120, 121, 150, 151,\n", + " 153, 155, 181, 184, 194, 199, 228, 231, 239, 254, 258,\n", + " 261, 276, 280, 282, 295, 297, 299, 316, 336, 338, 348,\n", + " 351, 363, 389, 392, 399, 404, 409, 454, 474, 494, 497,\n", + " 521, 526, 553, 559, 561, 563, 573, 578, 605, 606, 619,\n", + " 628, 640, 672, 680, 683, 686, 691, 696, 699, 708, 717,\n", + " 720, 726, 740, 748, 753, 786, 797, 802, 804, 815, 818,\n", + " 825, 838, 868, 873, 875, 880, 885, 901, 910, 928, 934,\n", + " 937, 973, 975, 980, 983, 1000, 1005, 1014, 1020, 1030, 1039,\n", + " 1040, 1047, 1048, 1050, 1056, 1073, 1106, 1110, 1118, 1122, 1124,\n", + " 1134, 1138, 1141, 1142, 1172, 1183, 1240, 1241, 1245, 1249, 1251,\n", + " 1254, 1255, 1262, 1265, 1275, 1283, 1286, 1292, 1296, 1312, 1314,\n", + " 1319, 1342, 1353, 1358, 1361, 1383, 1387]),\n", + " 5: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 43, 44,\n", + " 45, 53, 76, 103, 106, 107, 110, 116, 129, 156, 163,\n", + " 164, 165, 168, 186, 191, 193, 210, 215, 220, 233, 234,\n", + " 242, 248, 250, 251, 257, 263, 269, 281, 285, 286, 311,\n", + " 318, 329, 343, 346, 347, 350, 374, 375, 393, 396, 403,\n", + " 405, 424, 437, 438, 439, 456, 461, 477, 478, 487, 493,\n", + " 502, 504, 507, 511, 513, 532, 544, 546, 555, 556, 557,\n", + " 565, 575, 583, 608, 609, 623, 630, 636, 654, 664, 667,\n", + " 669, 694, 698, 705, 710, 712, 722, 725, 727, 739, 746,\n", + " 758, 760, 768, 776, 778, 788, 790, 817, 820, 833, 834,\n", + " 842, 845, 847, 850, 851, 860, 862, 863, 865, 878, 893,\n", + " 903, 906, 911, 922, 936, 946, 954, 956, 966, 969, 970,\n", + " 972, 981, 982, 989, 994, 1002, 1008, 1015, 1016, 1019, 1027,\n", + " 1034, 1036, 1043, 1057, 1061, 1070, 1076, 1079, 1082, 1086, 1101,\n", + " 1105, 1111, 1132, 1144, 1145, 1150, 1151, 1157, 1168, 1174, 1175,\n", + " 1186, 1188, 1197, 1199, 1202, 1217, 1224, 1228, 1243, 1268, 1270,\n", + " 1277, 1280, 1297, 1309, 1323, 1333, 1335, 1349, 1359, 1392, 1396,\n", + " 1399]),\n", + " 6: array([ 62, 69, 71, 75, 100, 173, 176, 308, 317, 319, 326,\n", + " 330, 333, 359, 377, 415, 441, 443, 451, 469, 481, 498,\n", + " 516, 531, 566, 571, 584, 585, 586, 595, 610, 642, 649,\n", + " 651, 659, 679, 681, 729, 738, 751, 754, 791, 795, 800,\n", + " 808, 826, 866, 870, 898, 902, 949, 958, 963, 995, 1012,\n", + " 1018, 1024, 1055, 1059, 1078, 1081, 1090, 1093, 1114, 1116, 1136,\n", + " 1140, 1156, 1176, 1178, 1181, 1187, 1207, 1233, 1242, 1248, 1250,\n", + " 1260, 1263, 1271, 1288, 1307, 1310, 1313, 1340, 1343, 1395])},\n", + " 8: {0: array([ 0, 2, 20, 22, 54, 61, 65, 89, 101, 105, 112,\n", + " 119, 123, 127, 131, 133, 137, 147, 154, 182, 187, 205,\n", + " 208, 209, 226, 229, 230, 268, 275, 284, 309, 323, 328,\n", + " 337, 342, 355, 362, 380, 391, 413, 432, 435, 450, 484,\n", + " 488, 515, 542, 562, 587, 590, 597, 601, 641, 655, 661,\n", + " 692, 701, 733, 737, 744, 770, 773, 793, 806, 809, 810,\n", + " 821, 836, 859, 864, 879, 882, 895, 907, 915, 920, 931,\n", + " 952, 955, 957, 967, 978, 1007, 1017, 1032, 1068, 1115, 1117,\n", + " 1125, 1127, 1129, 1153, 1190, 1191, 1208, 1223, 1246, 1284, 1294,\n", + " 1298, 1308, 1318, 1327, 1344, 1348, 1350, 1354, 1360, 1362, 1377,\n", + " 1379, 1381, 1389, 1394]),\n", + " 1: array([ 1, 3, 13, 26, 27, 28, 29, 30, 31, 48, 49,\n", + " 58, 74, 80, 82, 92, 97, 99, 102, 114, 118, 122,\n", + " 124, 128, 140, 142, 144, 145, 159, 162, 170, 174, 178,\n", + " 190, 195, 202, 212, 213, 216, 218, 219, 227, 232, 253,\n", + " 255, 262, 267, 271, 272, 283, 289, 290, 291, 294, 296,\n", + " 298, 304, 305, 306, 315, 322, 331, 332, 334, 335, 341,\n", + " 344, 353, 357, 365, 366, 368, 371, 372, 373, 378, 381,\n", + " 383, 385, 388, 398, 401, 402, 407, 420, 422, 423, 426,\n", + " 428, 431, 433, 434, 436, 445, 446, 448, 455, 463, 471,\n", + " 472, 475, 476, 482, 483, 485, 491, 496, 500, 508, 510,\n", + " 518, 520, 527, 528, 530, 540, 545, 547, 551, 567, 569,\n", + " 574, 579, 591, 592, 594, 604, 607, 611, 614, 615, 617,\n", + " 622, 627, 629, 637, 644, 648, 650, 652, 657, 658, 666,\n", + " 676, 677, 689, 714, 731, 742, 747, 757, 761, 762, 767,\n", + " 772, 775, 777, 780, 785, 789, 792, 796, 799, 812, 823,\n", + " 828, 841, 843, 844, 848, 853, 876, 883, 886, 890, 891,\n", + " 899, 905, 909, 912, 913, 914, 916, 917, 918, 919, 921,\n", + " 924, 926, 933, 940, 944, 951, 965, 968, 974, 979, 986,\n", + " 1009, 1023, 1031, 1038, 1041, 1044, 1052, 1054, 1062, 1065, 1069,\n", + " 1071, 1075, 1085, 1088, 1092, 1102, 1108, 1109, 1126, 1148, 1155,\n", + " 1160, 1162, 1177, 1184, 1192, 1196, 1203, 1204, 1206, 1214, 1230,\n", + " 1232, 1234, 1236, 1247, 1252, 1259, 1261, 1266, 1269, 1272, 1274,\n", + " 1291, 1299, 1301, 1303, 1305, 1320, 1324, 1326, 1329, 1330, 1332,\n", + " 1339, 1351, 1364, 1367, 1370, 1374, 1376, 1378, 1386, 1388, 1398]),\n", + " 2: array([ 4, 14, 24, 33, 34, 50, 57, 72, 73, 81, 83,\n", + " 84, 85, 91, 95, 117, 125, 134, 136, 152, 157, 160,\n", + " 161, 171, 180, 183, 185, 188, 196, 198, 203, 204, 217,\n", + " 223, 224, 225, 237, 243, 256, 270, 279, 287, 293, 313,\n", + " 320, 321, 345, 349, 356, 358, 364, 367, 379, 382, 387,\n", + " 395, 406, 410, 417, 440, 447, 457, 458, 466, 470, 479,\n", + " 506, 514, 519, 523, 524, 525, 538, 541, 549, 568, 576,\n", + " 577, 588, 593, 596, 600, 613, 621, 625, 626, 632, 638,\n", + " 646, 653, 656, 660, 663, 671, 693, 706, 713, 721, 723,\n", + " 724, 734, 735, 736, 745, 749, 752, 755, 771, 781, 784,\n", + " 794, 801, 805, 807, 813, 822, 827, 829, 854, 855, 881,\n", + " 892, 894, 897, 900, 925, 927, 930, 945, 948, 959, 976,\n", + " 977, 988, 998, 1004, 1035, 1049, 1051, 1058, 1066, 1067, 1072,\n", + " 1074, 1077, 1087, 1089, 1091, 1096, 1097, 1098, 1099, 1104, 1107,\n", + " 1112, 1120, 1128, 1133, 1139, 1143, 1147, 1149, 1152, 1159, 1167,\n", + " 1189, 1194, 1205, 1213, 1215, 1216, 1222, 1229, 1235, 1238, 1256,\n", + " 1257, 1276, 1281, 1285, 1287, 1290, 1293, 1295, 1300, 1302, 1304,\n", + " 1325, 1331, 1338, 1347, 1356, 1357, 1365, 1366, 1382, 1391, 1393,\n", + " 1397]),\n", + " 3: array([ 5, 7, 9, 12, 17, 18, 25, 32, 36, 38, 42,\n", + " 46, 51, 56, 60, 64, 66, 67, 70, 86, 87, 90,\n", + " 94, 108, 111, 130, 132, 135, 138, 139, 141, 143, 146,\n", + " 148, 149, 158, 166, 167, 169, 172, 175, 177, 179, 189,\n", + " 192, 197, 200, 201, 206, 207, 211, 214, 221, 222, 235,\n", + " 236, 240, 241, 244, 245, 246, 247, 249, 252, 259, 264,\n", + " 265, 273, 274, 277, 278, 288, 300, 301, 302, 307, 310,\n", + " 312, 314, 324, 325, 327, 340, 352, 354, 361, 369, 384,\n", + " 386, 390, 394, 397, 400, 408, 411, 412, 416, 418, 419,\n", + " 421, 425, 427, 429, 430, 442, 444, 449, 452, 453, 459,\n", + " 460, 462, 464, 465, 467, 468, 473, 480, 486, 489, 490,\n", + " 492, 495, 499, 501, 503, 505, 509, 512, 522, 529, 533,\n", + " 534, 535, 537, 539, 543, 548, 550, 552, 554, 558, 560,\n", + " 564, 572, 580, 581, 582, 589, 598, 602, 603, 612, 616,\n", + " 618, 620, 624, 633, 634, 635, 639, 643, 645, 647, 662,\n", + " 668, 673, 674, 675, 682, 684, 685, 688, 690, 695, 697,\n", + " 700, 703, 704, 707, 709, 711, 715, 716, 718, 728, 730,\n", + " 732, 741, 743, 750, 756, 764, 766, 769, 774, 779, 782,\n", + " 798, 803, 811, 814, 819, 824, 830, 831, 832, 835, 837,\n", + " 839, 840, 846, 849, 852, 856, 857, 861, 867, 869, 871,\n", + " 872, 874, 884, 887, 896, 908, 923, 938, 939, 941, 942,\n", + " 943, 947, 950, 953, 960, 961, 962, 964, 971, 984, 985,\n", + " 987, 990, 991, 992, 996, 997, 1001, 1003, 1006, 1011, 1013,\n", + " 1021, 1025, 1026, 1028, 1029, 1037, 1042, 1045, 1046, 1053, 1060,\n", + " 1063, 1064, 1080, 1083, 1084, 1095, 1100, 1103, 1113, 1119, 1121,\n", + " 1130, 1131, 1135, 1137, 1146, 1154, 1158, 1161, 1163, 1164, 1165,\n", + " 1166, 1169, 1170, 1171, 1173, 1179, 1180, 1182, 1185, 1193, 1195,\n", + " 1198, 1200, 1201, 1209, 1210, 1212, 1218, 1219, 1220, 1225, 1226,\n", + " 1227, 1231, 1237, 1239, 1244, 1253, 1258, 1264, 1267, 1273, 1278,\n", + " 1279, 1282, 1289, 1306, 1311, 1315, 1316, 1321, 1322, 1328, 1334,\n", + " 1336, 1337, 1341, 1345, 1346, 1355, 1363, 1368, 1369, 1371, 1372,\n", + " 1373, 1375, 1380, 1384, 1390]),\n", + " 4: array([ 6, 41, 78, 79, 126, 238, 260, 266, 292, 303, 339,\n", + " 360, 370, 376, 414, 517, 536, 570, 599, 631, 665, 670,\n", + " 678, 687, 702, 719, 759, 763, 765, 783, 787, 816, 858,\n", + " 877, 888, 889, 904, 929, 932, 935, 993, 999, 1010, 1022,\n", + " 1033, 1094, 1123, 1211, 1221, 1317, 1352, 1385]),\n", + " 5: array([ 8, 19, 21, 47, 52, 55, 59, 63, 68, 77, 88,\n", + " 93, 96, 98, 104, 109, 113, 115, 120, 121, 150, 151,\n", + " 153, 155, 181, 184, 194, 199, 228, 231, 239, 254, 258,\n", + " 261, 276, 280, 282, 295, 297, 299, 316, 336, 338, 348,\n", + " 351, 363, 389, 392, 399, 404, 409, 454, 474, 494, 497,\n", + " 521, 526, 553, 559, 561, 563, 573, 578, 605, 606, 619,\n", + " 628, 640, 672, 680, 683, 686, 691, 696, 699, 708, 717,\n", + " 720, 726, 740, 748, 753, 786, 797, 802, 804, 815, 818,\n", + " 825, 838, 868, 873, 875, 880, 885, 901, 910, 928, 934,\n", + " 937, 973, 975, 980, 983, 1000, 1005, 1014, 1020, 1030, 1039,\n", + " 1040, 1047, 1048, 1050, 1056, 1073, 1106, 1110, 1118, 1122, 1124,\n", + " 1134, 1138, 1141, 1142, 1172, 1183, 1240, 1241, 1245, 1249, 1251,\n", + " 1254, 1255, 1262, 1265, 1275, 1283, 1286, 1292, 1296, 1312, 1314,\n", + " 1319, 1342, 1353, 1358, 1361, 1383, 1387]),\n", + " 6: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 43, 44,\n", + " 45, 53, 76, 103, 106, 107, 110, 116, 129, 156, 163,\n", + " 164, 165, 168, 186, 191, 193, 210, 215, 220, 233, 234,\n", + " 242, 248, 250, 251, 257, 263, 269, 281, 285, 286, 311,\n", + " 318, 329, 343, 346, 347, 350, 374, 375, 393, 396, 403,\n", + " 405, 424, 437, 438, 439, 456, 461, 477, 478, 487, 493,\n", + " 502, 504, 507, 511, 513, 532, 544, 546, 555, 556, 557,\n", + " 565, 575, 583, 608, 609, 623, 630, 636, 654, 664, 667,\n", + " 669, 694, 698, 705, 710, 712, 722, 725, 727, 739, 746,\n", + " 758, 760, 768, 776, 778, 788, 790, 817, 820, 833, 834,\n", + " 842, 845, 847, 850, 851, 860, 862, 863, 865, 878, 893,\n", + " 903, 906, 911, 922, 936, 946, 954, 956, 966, 969, 970,\n", + " 972, 981, 982, 989, 994, 1002, 1008, 1015, 1016, 1019, 1027,\n", + " 1034, 1036, 1043, 1057, 1061, 1070, 1076, 1079, 1082, 1086, 1101,\n", + " 1105, 1111, 1132, 1144, 1145, 1150, 1151, 1157, 1168, 1174, 1175,\n", + " 1186, 1188, 1197, 1199, 1202, 1217, 1224, 1228, 1243, 1268, 1270,\n", + " 1277, 1280, 1297, 1309, 1323, 1333, 1335, 1349, 1359, 1392, 1396,\n", + " 1399]),\n", + " 7: array([ 62, 69, 71, 75, 100, 173, 176, 308, 317, 319, 326,\n", + " 330, 333, 359, 377, 415, 441, 443, 451, 469, 481, 498,\n", + " 516, 531, 566, 571, 584, 585, 586, 595, 610, 642, 649,\n", + " 651, 659, 679, 681, 729, 738, 751, 754, 791, 795, 800,\n", + " 808, 826, 866, 870, 898, 902, 949, 958, 963, 995, 1012,\n", + " 1018, 1024, 1055, 1059, 1078, 1081, 1090, 1093, 1114, 1116, 1136,\n", + " 1140, 1156, 1176, 1178, 1181, 1187, 1207, 1233, 1242, 1248, 1250,\n", + " 1260, 1263, 1271, 1288, 1307, 1310, 1313, 1340, 1343, 1395])},\n", + " 9: {0: array([ 0, 2, 20, 22, 54, 61, 65, 89, 101, 105, 112,\n", + " 119, 123, 127, 131, 133, 137, 147, 154, 182, 187, 205,\n", + " 208, 209, 226, 229, 230, 268, 275, 284, 309, 323, 328,\n", + " 337, 342, 355, 362, 380, 391, 413, 432, 435, 450, 484,\n", + " 488, 515, 542, 562, 587, 590, 597, 601, 641, 655, 661,\n", + " 692, 701, 733, 737, 744, 770, 773, 793, 806, 809, 810,\n", + " 821, 836, 859, 864, 879, 882, 895, 907, 915, 920, 931,\n", + " 952, 955, 957, 967, 978, 1007, 1017, 1032, 1068, 1115, 1117,\n", + " 1125, 1127, 1129, 1153, 1190, 1191, 1208, 1223, 1246, 1284, 1294,\n", + " 1298, 1308, 1318, 1327, 1344, 1348, 1350, 1354, 1360, 1362, 1377,\n", + " 1379, 1381, 1389, 1394]),\n", + " 1: array([ 1, 3, 13, 26, 27, 28, 29, 30, 31, 48, 49,\n", + " 58, 74, 80, 82, 92, 97, 99, 102, 114, 118, 122,\n", + " 124, 128, 140, 142, 144, 145, 159, 162, 170, 174, 178,\n", + " 190, 195, 202, 212, 213, 216, 218, 219, 227, 232, 253,\n", + " 255, 262, 267, 271, 272, 283, 289, 290, 291, 294, 296,\n", + " 298, 304, 305, 306, 315, 322, 331, 332, 334, 335, 341,\n", + " 344, 353, 357, 365, 366, 368, 371, 372, 373, 378, 381,\n", + " 383, 385, 388, 398, 401, 402, 407, 420, 422, 423, 426,\n", + " 428, 431, 433, 434, 436, 445, 446, 448, 455, 463, 471,\n", + " 472, 475, 476, 482, 483, 485, 491, 496, 500, 508, 510,\n", + " 518, 520, 527, 528, 530, 540, 545, 547, 551, 567, 569,\n", + " 574, 579, 591, 592, 594, 604, 607, 611, 614, 615, 617,\n", + " 622, 627, 629, 637, 644, 648, 650, 652, 657, 658, 666,\n", + " 676, 677, 689, 714, 731, 742, 747, 757, 761, 762, 767,\n", + " 772, 775, 777, 780, 785, 789, 792, 796, 799, 812, 823,\n", + " 828, 841, 843, 844, 848, 853, 876, 883, 886, 890, 891,\n", + " 899, 905, 909, 912, 913, 914, 916, 917, 918, 919, 921,\n", + " 924, 926, 933, 940, 944, 951, 965, 968, 974, 979, 986,\n", + " 1009, 1023, 1031, 1038, 1041, 1044, 1052, 1054, 1062, 1065, 1069,\n", + " 1071, 1075, 1085, 1088, 1092, 1102, 1108, 1109, 1126, 1148, 1155,\n", + " 1160, 1162, 1177, 1184, 1192, 1196, 1203, 1204, 1206, 1214, 1230,\n", + " 1232, 1234, 1236, 1247, 1252, 1259, 1261, 1266, 1269, 1272, 1274,\n", + " 1291, 1299, 1301, 1303, 1305, 1320, 1324, 1326, 1329, 1330, 1332,\n", + " 1339, 1351, 1364, 1367, 1370, 1374, 1376, 1378, 1386, 1388, 1398]),\n", + " 2: array([ 4, 14, 24, 33, 34, 50, 57, 72, 73, 81, 83,\n", + " 84, 85, 91, 95, 117, 125, 134, 136, 152, 157, 160,\n", + " 161, 171, 183, 185, 198, 203, 204, 217, 224, 225, 237,\n", + " 243, 256, 270, 287, 293, 313, 320, 321, 345, 356, 358,\n", + " 364, 367, 379, 382, 387, 395, 406, 410, 417, 440, 447,\n", + " 457, 458, 466, 470, 479, 506, 514, 519, 523, 524, 525,\n", + " 538, 541, 549, 568, 577, 588, 593, 596, 600, 613, 621,\n", + " 625, 626, 632, 638, 646, 653, 656, 660, 663, 693, 706,\n", + " 713, 721, 723, 734, 735, 745, 749, 752, 755, 771, 781,\n", + " 784, 801, 805, 807, 813, 822, 827, 829, 854, 855, 881,\n", + " 892, 894, 897, 900, 927, 930, 945, 948, 959, 976, 977,\n", + " 988, 998, 1004, 1035, 1049, 1051, 1058, 1066, 1067, 1072, 1074,\n", + " 1077, 1087, 1089, 1091, 1096, 1097, 1098, 1099, 1104, 1107, 1112,\n", + " 1120, 1128, 1133, 1139, 1143, 1149, 1152, 1159, 1167, 1189, 1194,\n", + " 1205, 1213, 1216, 1222, 1229, 1235, 1238, 1256, 1257, 1276, 1281,\n", + " 1285, 1287, 1290, 1293, 1295, 1300, 1302, 1304, 1325, 1331, 1338,\n", + " 1347, 1356, 1357, 1365, 1366, 1382, 1391, 1393, 1397]),\n", + " 3: array([ 5, 7, 9, 12, 17, 18, 25, 32, 36, 38, 42,\n", + " 46, 51, 56, 60, 64, 66, 67, 70, 86, 87, 90,\n", + " 94, 108, 111, 130, 132, 135, 138, 139, 141, 143, 146,\n", + " 148, 149, 158, 166, 167, 169, 172, 175, 177, 179, 189,\n", + " 192, 197, 200, 201, 206, 207, 211, 214, 221, 222, 235,\n", + " 236, 240, 241, 244, 245, 246, 247, 249, 252, 259, 264,\n", + " 265, 273, 274, 277, 278, 288, 300, 301, 302, 307, 310,\n", + " 312, 314, 324, 325, 327, 340, 352, 354, 361, 369, 384,\n", + " 386, 390, 394, 397, 400, 408, 411, 412, 416, 418, 419,\n", + " 421, 425, 427, 429, 430, 442, 444, 449, 452, 453, 459,\n", + " 460, 462, 464, 465, 467, 468, 473, 480, 486, 489, 490,\n", + " 492, 495, 499, 501, 503, 505, 509, 512, 522, 529, 533,\n", + " 534, 535, 537, 539, 543, 548, 550, 552, 554, 558, 560,\n", + " 564, 572, 580, 581, 582, 589, 598, 602, 603, 612, 616,\n", + " 618, 620, 624, 633, 634, 635, 639, 643, 645, 647, 662,\n", + " 668, 673, 674, 675, 682, 684, 685, 688, 690, 695, 697,\n", + " 700, 703, 704, 707, 709, 711, 715, 716, 718, 728, 730,\n", + " 732, 741, 743, 750, 756, 764, 766, 769, 774, 779, 782,\n", + " 798, 803, 811, 814, 819, 824, 830, 831, 832, 835, 837,\n", + " 839, 840, 846, 849, 852, 856, 857, 861, 867, 869, 871,\n", + " 872, 874, 884, 887, 896, 908, 923, 938, 939, 941, 942,\n", + " 943, 947, 950, 953, 960, 961, 962, 964, 971, 984, 985,\n", + " 987, 990, 991, 992, 996, 997, 1001, 1003, 1006, 1011, 1013,\n", + " 1021, 1025, 1026, 1028, 1029, 1037, 1042, 1045, 1046, 1053, 1060,\n", + " 1063, 1064, 1080, 1083, 1084, 1095, 1100, 1103, 1113, 1119, 1121,\n", + " 1130, 1131, 1135, 1137, 1146, 1154, 1158, 1161, 1163, 1164, 1165,\n", + " 1166, 1169, 1170, 1171, 1173, 1179, 1180, 1182, 1185, 1193, 1195,\n", + " 1198, 1200, 1201, 1209, 1210, 1212, 1218, 1219, 1220, 1225, 1226,\n", + " 1227, 1231, 1237, 1239, 1244, 1253, 1258, 1264, 1267, 1273, 1278,\n", + " 1279, 1282, 1289, 1306, 1311, 1315, 1316, 1321, 1322, 1328, 1334,\n", + " 1336, 1337, 1341, 1345, 1346, 1355, 1363, 1368, 1369, 1371, 1372,\n", + " 1373, 1375, 1380, 1384, 1390]),\n", + " 4: array([ 6, 41, 78, 79, 126, 238, 260, 266, 292, 303, 339,\n", + " 360, 370, 376, 414, 517, 536, 570, 599, 631, 665, 670,\n", + " 678, 687, 702, 719, 759, 763, 765, 783, 787, 816, 858,\n", + " 877, 888, 889, 904, 929, 932, 935, 993, 999, 1010, 1022,\n", + " 1033, 1094, 1123, 1211, 1221, 1317, 1352, 1385]),\n", + " 5: array([ 8, 19, 21, 47, 52, 55, 59, 63, 68, 77, 88,\n", + " 93, 96, 98, 104, 109, 113, 115, 120, 121, 150, 151,\n", + " 153, 155, 181, 184, 194, 199, 228, 231, 239, 254, 258,\n", + " 261, 276, 280, 282, 295, 297, 299, 316, 336, 338, 348,\n", + " 351, 363, 389, 392, 399, 404, 409, 454, 474, 494, 497,\n", + " 521, 526, 553, 559, 561, 563, 573, 578, 605, 606, 619,\n", + " 628, 640, 672, 680, 683, 686, 691, 696, 699, 708, 717,\n", + " 720, 726, 740, 748, 753, 786, 797, 802, 804, 815, 818,\n", + " 825, 838, 868, 873, 875, 880, 885, 901, 910, 928, 934,\n", + " 937, 973, 975, 980, 983, 1000, 1005, 1014, 1020, 1030, 1039,\n", + " 1040, 1047, 1048, 1050, 1056, 1073, 1106, 1110, 1118, 1122, 1124,\n", + " 1134, 1138, 1141, 1142, 1172, 1183, 1240, 1241, 1245, 1249, 1251,\n", + " 1254, 1255, 1262, 1265, 1275, 1283, 1286, 1292, 1296, 1312, 1314,\n", + " 1319, 1342, 1353, 1358, 1361, 1383, 1387]),\n", + " 6: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 43, 44,\n", + " 45, 53, 76, 103, 106, 107, 110, 116, 129, 156, 163,\n", + " 164, 165, 168, 186, 191, 193, 210, 215, 220, 233, 234,\n", + " 242, 248, 250, 251, 257, 263, 269, 281, 285, 286, 311,\n", + " 318, 329, 343, 346, 347, 350, 374, 375, 393, 396, 403,\n", + " 405, 424, 437, 438, 439, 456, 461, 477, 478, 487, 493,\n", + " 502, 504, 507, 511, 513, 532, 544, 546, 555, 556, 557,\n", + " 565, 575, 583, 608, 609, 623, 630, 636, 654, 664, 667,\n", + " 669, 694, 698, 705, 710, 712, 722, 725, 727, 739, 746,\n", + " 758, 760, 768, 776, 778, 788, 790, 817, 820, 833, 834,\n", + " 842, 845, 847, 850, 851, 860, 862, 863, 865, 878, 893,\n", + " 903, 906, 911, 922, 936, 946, 954, 956, 966, 969, 970,\n", + " 972, 981, 982, 989, 994, 1002, 1008, 1015, 1016, 1019, 1027,\n", + " 1034, 1036, 1043, 1057, 1061, 1070, 1076, 1079, 1082, 1086, 1101,\n", + " 1105, 1111, 1132, 1144, 1145, 1150, 1151, 1157, 1168, 1174, 1175,\n", + " 1186, 1188, 1197, 1199, 1202, 1217, 1224, 1228, 1243, 1268, 1270,\n", + " 1277, 1280, 1297, 1309, 1323, 1333, 1335, 1349, 1359, 1392, 1396,\n", + " 1399]),\n", + " 7: array([ 62, 69, 71, 75, 100, 173, 176, 308, 317, 319, 326,\n", + " 330, 333, 359, 377, 415, 441, 443, 451, 469, 481, 498,\n", + " 516, 531, 566, 571, 584, 585, 586, 595, 610, 642, 649,\n", + " 651, 659, 679, 681, 729, 738, 751, 754, 791, 795, 800,\n", + " 808, 826, 866, 870, 898, 902, 949, 958, 963, 995, 1012,\n", + " 1018, 1024, 1055, 1059, 1078, 1081, 1090, 1093, 1114, 1116, 1136,\n", + " 1140, 1156, 1176, 1178, 1181, 1187, 1207, 1233, 1242, 1248, 1250,\n", + " 1260, 1263, 1271, 1288, 1307, 1310, 1313, 1340, 1343, 1395]),\n", + " 8: array([ 180, 188, 196, 223, 279, 349, 576, 671, 724, 736, 794,\n", + " 925, 1147, 1215])},\n", + " 10: {0: array([ 0, 2, 20, 22, 54, 61, 65, 89, 101, 105, 112,\n", + " 119, 123, 127, 131, 133, 137, 147, 154, 182, 187, 205,\n", + " 208, 209, 226, 229, 230, 268, 275, 284, 309, 323, 328,\n", + " 337, 342, 355, 362, 380, 391, 413, 432, 435, 450, 484,\n", + " 488, 515, 542, 562, 587, 590, 597, 601, 641, 655, 661,\n", + " 692, 701, 733, 737, 744, 770, 773, 793, 806, 809, 810,\n", + " 821, 836, 859, 864, 879, 882, 895, 907, 915, 920, 931,\n", + " 952, 955, 957, 967, 978, 1007, 1017, 1032, 1068, 1115, 1117,\n", + " 1125, 1127, 1129, 1153, 1190, 1191, 1208, 1223, 1246, 1284, 1294,\n", + " 1298, 1308, 1318, 1327, 1344, 1348, 1350, 1354, 1360, 1362, 1377,\n", + " 1379, 1381, 1389, 1394]),\n", + " 1: array([ 1, 3, 13, 26, 27, 28, 29, 30, 31, 48, 49,\n", + " 58, 74, 80, 82, 92, 97, 99, 102, 114, 118, 122,\n", + " 124, 128, 140, 142, 144, 145, 159, 162, 170, 174, 178,\n", + " 190, 195, 202, 212, 213, 216, 218, 219, 227, 232, 253,\n", + " 255, 262, 267, 271, 272, 283, 289, 290, 291, 294, 296,\n", + " 298, 304, 305, 306, 315, 322, 331, 332, 334, 335, 341,\n", + " 344, 353, 357, 365, 366, 368, 371, 372, 373, 378, 381,\n", + " 383, 385, 388, 398, 401, 402, 407, 420, 422, 423, 426,\n", + " 428, 431, 433, 434, 436, 445, 446, 448, 455, 463, 471,\n", + " 472, 475, 476, 482, 483, 485, 491, 496, 500, 508, 510,\n", + " 518, 520, 527, 528, 530, 540, 545, 547, 551, 567, 569,\n", + " 574, 579, 591, 592, 594, 604, 607, 611, 614, 615, 617,\n", + " 622, 627, 629, 637, 644, 648, 650, 652, 657, 658, 666,\n", + " 676, 677, 689, 714, 731, 742, 747, 757, 761, 762, 767,\n", + " 772, 775, 777, 780, 785, 789, 792, 796, 799, 812, 823,\n", + " 828, 841, 843, 844, 848, 853, 876, 883, 886, 890, 891,\n", + " 899, 905, 909, 912, 913, 914, 916, 917, 918, 919, 921,\n", + " 924, 926, 933, 940, 944, 951, 965, 968, 974, 979, 986,\n", + " 1009, 1023, 1031, 1038, 1041, 1044, 1052, 1054, 1062, 1065, 1069,\n", + " 1071, 1075, 1085, 1088, 1092, 1102, 1108, 1109, 1126, 1148, 1155,\n", + " 1160, 1162, 1177, 1184, 1192, 1196, 1203, 1204, 1206, 1214, 1230,\n", + " 1232, 1234, 1236, 1247, 1252, 1259, 1261, 1266, 1269, 1272, 1274,\n", + " 1291, 1299, 1301, 1303, 1305, 1320, 1324, 1326, 1329, 1330, 1332,\n", + " 1339, 1351, 1364, 1367, 1370, 1374, 1376, 1378, 1386, 1388, 1398]),\n", + " 2: array([ 4, 14, 24, 33, 34, 50, 57, 72, 73, 81, 83,\n", + " 84, 85, 91, 95, 117, 125, 134, 136, 152, 157, 160,\n", + " 161, 171, 183, 185, 198, 203, 204, 217, 224, 225, 237,\n", + " 243, 256, 270, 287, 293, 313, 320, 321, 345, 356, 358,\n", + " 364, 367, 379, 382, 387, 395, 406, 410, 417, 440, 447,\n", + " 457, 458, 466, 470, 479, 506, 514, 519, 523, 524, 525,\n", + " 538, 541, 549, 568, 577, 588, 593, 596, 600, 613, 621,\n", + " 625, 626, 632, 638, 646, 653, 656, 660, 663, 693, 706,\n", + " 713, 721, 723, 734, 735, 745, 749, 752, 755, 771, 781,\n", + " 784, 801, 805, 807, 813, 822, 827, 829, 854, 855, 881,\n", + " 892, 894, 897, 900, 927, 930, 945, 948, 959, 976, 977,\n", + " 988, 998, 1004, 1035, 1049, 1051, 1058, 1066, 1067, 1072, 1074,\n", + " 1077, 1087, 1089, 1091, 1096, 1097, 1098, 1099, 1104, 1107, 1112,\n", + " 1120, 1128, 1133, 1139, 1143, 1149, 1152, 1159, 1167, 1189, 1194,\n", + " 1205, 1213, 1216, 1222, 1229, 1235, 1238, 1256, 1257, 1276, 1281,\n", + " 1285, 1287, 1290, 1293, 1295, 1300, 1302, 1304, 1325, 1331, 1338,\n", + " 1347, 1356, 1357, 1365, 1366, 1382, 1391, 1393, 1397]),\n", + " 3: array([ 5, 7, 9, 12, 17, 18, 25, 32, 36, 38, 42,\n", + " 46, 51, 56, 60, 64, 66, 67, 70, 86, 87, 90,\n", + " 94, 108, 111, 130, 132, 135, 138, 139, 141, 143, 146,\n", + " 148, 149, 158, 166, 167, 169, 172, 175, 177, 179, 189,\n", + " 192, 197, 200, 201, 206, 207, 211, 214, 221, 222, 235,\n", + " 236, 240, 241, 244, 245, 246, 247, 249, 252, 259, 264,\n", + " 265, 273, 274, 277, 278, 288, 300, 301, 302, 307, 310,\n", + " 312, 314, 324, 325, 327, 340, 352, 354, 361, 369, 384,\n", + " 386, 390, 394, 397, 400, 408, 411, 412, 416, 418, 419,\n", + " 421, 425, 427, 429, 430, 442, 444, 449, 452, 453, 459,\n", + " 460, 462, 464, 465, 467, 468, 473, 480, 486, 489, 490,\n", + " 492, 495, 499, 501, 503, 505, 509, 512, 522, 529, 533,\n", + " 534, 535, 537, 539, 543, 548, 550, 552, 554, 558, 560,\n", + " 564, 572, 580, 581, 582, 589, 598, 602, 603, 612, 616,\n", + " 618, 620, 624, 633, 634, 635, 639, 643, 645, 647, 662,\n", + " 668, 673, 674, 675, 682, 684, 685, 688, 690, 695, 697,\n", + " 700, 703, 704, 707, 709, 711, 715, 716, 718, 728, 730,\n", + " 732, 741, 743, 750, 756, 764, 766, 769, 774, 779, 782,\n", + " 798, 803, 811, 814, 819, 824, 830, 831, 832, 835, 837,\n", + " 839, 840, 846, 849, 852, 856, 857, 861, 867, 869, 871,\n", + " 872, 874, 884, 887, 896, 908, 923, 938, 939, 941, 942,\n", + " 943, 947, 950, 953, 960, 961, 962, 964, 971, 984, 985,\n", + " 987, 990, 991, 992, 996, 997, 1001, 1003, 1006, 1011, 1013,\n", + " 1021, 1025, 1026, 1028, 1029, 1037, 1042, 1045, 1046, 1053, 1060,\n", + " 1063, 1064, 1080, 1083, 1084, 1095, 1100, 1103, 1113, 1119, 1121,\n", + " 1130, 1131, 1135, 1137, 1146, 1154, 1158, 1161, 1163, 1164, 1165,\n", + " 1166, 1169, 1170, 1171, 1173, 1179, 1180, 1182, 1185, 1193, 1195,\n", + " 1198, 1200, 1201, 1209, 1210, 1212, 1218, 1219, 1220, 1225, 1226,\n", + " 1227, 1231, 1237, 1239, 1244, 1253, 1258, 1264, 1267, 1273, 1278,\n", + " 1279, 1282, 1289, 1306, 1311, 1315, 1316, 1321, 1322, 1328, 1334,\n", + " 1336, 1337, 1341, 1345, 1346, 1355, 1363, 1368, 1369, 1371, 1372,\n", + " 1373, 1375, 1380, 1384, 1390]),\n", + " 4: array([ 6, 41, 78, 79, 126, 238, 260, 266, 292, 303, 339,\n", + " 360, 370, 376, 414, 517, 536, 570, 599, 631, 665, 670,\n", + " 678, 687, 702, 719, 759, 763, 765, 783, 787, 816, 858,\n", + " 877, 888, 889, 904, 929, 932, 935, 993, 999, 1010, 1022,\n", + " 1033, 1094, 1123, 1211, 1221, 1317, 1352, 1385]),\n", + " 5: array([ 8, 19, 21, 47, 52, 55, 59, 63, 68, 77, 88,\n", + " 93, 96, 98, 104, 109, 113, 115, 120, 121, 150, 151,\n", + " 153, 155, 181, 184, 194, 199, 228, 231, 239, 254, 258,\n", + " 261, 276, 280, 282, 295, 297, 299, 316, 336, 338, 348,\n", + " 351, 363, 389, 392, 399, 404, 409, 454, 474, 494, 497,\n", + " 521, 526, 553, 559, 561, 563, 573, 578, 605, 606, 619,\n", + " 628, 640, 672, 680, 683, 686, 691, 696, 699, 708, 717,\n", + " 720, 726, 740, 748, 753, 786, 797, 802, 804, 815, 818,\n", + " 825, 838, 868, 873, 875, 880, 885, 901, 910, 928, 934,\n", + " 937, 973, 975, 980, 983, 1000, 1005, 1014, 1020, 1030, 1039,\n", + " 1040, 1047, 1048, 1050, 1056, 1073, 1106, 1110, 1118, 1122, 1124,\n", + " 1134, 1138, 1141, 1142, 1172, 1183, 1240, 1241, 1245, 1249, 1251,\n", + " 1254, 1255, 1262, 1265, 1275, 1283, 1286, 1292, 1296, 1312, 1314,\n", + " 1319, 1342, 1353, 1358, 1361, 1383, 1387]),\n", + " 6: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 43, 44,\n", + " 45, 53, 76, 103, 106, 107, 110, 116, 129, 156, 163,\n", + " 164, 165, 168, 186, 191, 193, 210, 215, 220, 233, 234,\n", + " 242, 248, 250, 251, 257, 263, 269, 281, 285, 286, 311,\n", + " 318, 329, 343, 346, 347, 350, 374, 375, 393, 396, 403,\n", + " 405, 424, 437, 438, 439, 456, 461, 477, 478, 487, 493,\n", + " 502, 504, 507, 511, 513, 532, 544, 546, 555, 556, 557,\n", + " 565, 575, 583, 608, 609, 623, 630, 636, 654, 664, 667,\n", + " 669, 694, 698, 705, 710, 712, 722, 725, 727, 739, 746,\n", + " 758, 760, 768, 776, 778, 788, 790, 817, 820, 833, 834,\n", + " 842, 845, 847, 850, 851, 860, 862, 863, 865, 878, 893,\n", + " 903, 906, 911, 922, 936, 946, 954, 956, 966, 969, 970,\n", + " 972, 981, 982, 989, 994, 1002, 1008, 1015, 1016, 1019, 1027,\n", + " 1034, 1036, 1043, 1057, 1061, 1070, 1076, 1079, 1082, 1086, 1101,\n", + " 1105, 1111, 1132, 1144, 1145, 1150, 1151, 1157, 1168, 1174, 1175,\n", + " 1186, 1188, 1197, 1199, 1202, 1217, 1224, 1228, 1243, 1268, 1270,\n", + " 1277, 1280, 1297, 1309, 1323, 1333, 1335, 1349, 1359, 1392, 1396,\n", + " 1399]),\n", + " 7: array([ 62, 308, 443, 595, 610, 866, 995, 1059, 1176, 1187, 1207,\n", + " 1250, 1263, 1288, 1310]),\n", + " 8: array([ 69, 71, 75, 100, 173, 176, 317, 319, 326, 330, 333,\n", + " 359, 377, 415, 441, 451, 469, 481, 498, 516, 531, 566,\n", + " 571, 584, 585, 586, 642, 649, 651, 659, 679, 681, 729,\n", + " 738, 751, 754, 791, 795, 800, 808, 826, 870, 898, 902,\n", + " 949, 958, 963, 1012, 1018, 1024, 1055, 1078, 1081, 1090, 1093,\n", + " 1114, 1116, 1136, 1140, 1156, 1178, 1181, 1233, 1242, 1248, 1260,\n", + " 1271, 1307, 1313, 1340, 1343, 1395]),\n", + " 9: array([ 180, 188, 196, 223, 279, 349, 576, 671, 724, 736, 794,\n", + " 925, 1147, 1215])}},\n", + " 'nonl2_noavg': {2: {0: array([ 0, 2, 6, 8, 19, 20, 21, 22, 41, 47, 52,\n", + " 54, 55, 59, 61, 63, 65, 68, 77, 78, 88, 89,\n", + " 93, 96, 98, 101, 104, 105, 109, 112, 113, 115, 119,\n", + " 120, 121, 123, 126, 127, 131, 133, 137, 147, 150, 151,\n", + " 153, 154, 155, 181, 182, 184, 187, 194, 199, 205, 208,\n", + " 209, 226, 228, 229, 230, 231, 239, 254, 258, 260, 261,\n", + " 266, 268, 275, 276, 280, 282, 284, 292, 295, 297, 299,\n", + " 309, 316, 323, 328, 336, 337, 338, 339, 342, 348, 351,\n", + " 355, 362, 363, 370, 380, 389, 391, 392, 399, 404, 409,\n", + " 413, 432, 435, 450, 454, 474, 484, 488, 494, 497, 515,\n", + " 521, 526, 542, 553, 559, 561, 562, 563, 570, 573, 578,\n", + " 587, 590, 597, 599, 601, 605, 606, 619, 628, 631, 640,\n", + " 641, 655, 661, 665, 670, 672, 678, 680, 683, 686, 687,\n", + " 691, 692, 696, 699, 701, 702, 708, 717, 719, 720, 726,\n", + " 733, 737, 740, 744, 748, 753, 759, 763, 765, 770, 773,\n", + " 783, 786, 787, 793, 797, 802, 804, 806, 809, 810, 815,\n", + " 816, 818, 821, 825, 836, 838, 858, 859, 864, 868, 873,\n", + " 875, 879, 880, 882, 885, 888, 889, 895, 901, 904, 907,\n", + " 910, 915, 920, 928, 931, 932, 934, 935, 937, 952, 955,\n", + " 957, 967, 973, 975, 978, 980, 983, 993, 999, 1000, 1005,\n", + " 1007, 1010, 1014, 1017, 1020, 1022, 1030, 1032, 1033, 1039, 1040,\n", + " 1047, 1048, 1050, 1056, 1068, 1073, 1094, 1106, 1110, 1115, 1117,\n", + " 1118, 1122, 1123, 1124, 1125, 1127, 1129, 1134, 1138, 1141, 1142,\n", + " 1153, 1172, 1183, 1190, 1191, 1208, 1211, 1221, 1223, 1240, 1241,\n", + " 1245, 1246, 1249, 1251, 1254, 1255, 1262, 1265, 1275, 1283, 1284,\n", + " 1286, 1292, 1294, 1296, 1298, 1308, 1312, 1314, 1317, 1318, 1319,\n", + " 1327, 1342, 1344, 1348, 1350, 1352, 1353, 1354, 1358, 1360, 1361,\n", + " 1362, 1377, 1379, 1381, 1383, 1387, 1389, 1394]),\n", + " 1: array([ 1, 3, 4, ..., 1397, 1398, 1399])},\n", + " 3: {0: array([ 0, 2, 6, 8, 19, 20, 21, 22, 41, 47, 52,\n", + " 54, 55, 59, 61, 63, 65, 68, 77, 78, 88, 89,\n", + " 93, 96, 98, 101, 104, 105, 109, 112, 113, 115, 119,\n", + " 120, 121, 123, 126, 127, 131, 133, 137, 147, 150, 151,\n", + " 153, 154, 155, 181, 182, 184, 187, 194, 199, 205, 208,\n", + " 209, 226, 228, 229, 230, 231, 239, 254, 258, 260, 261,\n", + " 266, 268, 275, 276, 280, 282, 284, 292, 295, 297, 299,\n", + " 309, 316, 323, 328, 336, 337, 338, 339, 342, 348, 351,\n", + " 355, 362, 363, 370, 380, 389, 391, 392, 399, 404, 409,\n", + " 413, 432, 435, 450, 454, 474, 484, 488, 494, 497, 515,\n", + " 521, 526, 542, 553, 559, 561, 562, 563, 570, 573, 578,\n", + " 587, 590, 597, 599, 601, 605, 606, 619, 628, 631, 640,\n", + " 641, 655, 661, 665, 670, 672, 678, 680, 683, 686, 687,\n", + " 691, 692, 696, 699, 701, 702, 708, 717, 719, 720, 726,\n", + " 733, 737, 740, 744, 748, 753, 759, 763, 765, 770, 773,\n", + " 783, 786, 787, 793, 797, 802, 804, 806, 809, 810, 815,\n", + " 816, 818, 821, 825, 836, 838, 858, 859, 864, 868, 873,\n", + " 875, 879, 880, 882, 885, 888, 889, 895, 901, 904, 907,\n", + " 910, 915, 920, 928, 931, 932, 934, 935, 937, 952, 955,\n", + " 957, 967, 973, 975, 978, 980, 983, 993, 999, 1000, 1005,\n", + " 1007, 1010, 1014, 1017, 1020, 1022, 1030, 1032, 1033, 1039, 1040,\n", + " 1047, 1048, 1050, 1056, 1068, 1073, 1094, 1106, 1110, 1115, 1117,\n", + " 1118, 1122, 1123, 1124, 1125, 1127, 1129, 1134, 1138, 1141, 1142,\n", + " 1153, 1172, 1183, 1190, 1191, 1208, 1211, 1221, 1223, 1240, 1241,\n", + " 1245, 1246, 1249, 1251, 1254, 1255, 1262, 1265, 1275, 1283, 1284,\n", + " 1286, 1292, 1294, 1296, 1298, 1308, 1312, 1314, 1317, 1318, 1319,\n", + " 1327, 1342, 1344, 1348, 1350, 1352, 1353, 1354, 1358, 1360, 1361,\n", + " 1362, 1377, 1379, 1381, 1383, 1387, 1389, 1394]),\n", + " 1: array([ 1, 3, 4, 5, 7, 9, 12, 13, 14, 17, 18,\n", + " 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n", + " 36, 38, 42, 46, 48, 49, 50, 51, 56, 57, 58,\n", + " 60, 64, 66, 67, 70, 72, 73, 74, 80, 81, 82,\n", + " 83, 84, 85, 86, 87, 90, 91, 92, 94, 95, 97,\n", + " 99, 102, 108, 111, 114, 117, 118, 122, 124, 125, 128,\n", + " 130, 132, 134, 135, 136, 138, 139, 140, 141, 142, 143,\n", + " 144, 145, 146, 148, 149, 152, 157, 158, 159, 160, 161,\n", + " 162, 166, 167, 169, 170, 171, 172, 173, 174, 175, 177,\n", + " 178, 179, 180, 183, 185, 188, 189, 190, 192, 195, 196,\n", + " 197, 198, 200, 201, 202, 203, 204, 206, 207, 211, 212,\n", + " 213, 214, 216, 217, 218, 219, 221, 222, 223, 224, 225,\n", + " 227, 232, 235, 236, 237, 238, 240, 241, 243, 244, 245,\n", + " 246, 247, 249, 252, 253, 255, 256, 259, 262, 264, 265,\n", + " 267, 270, 271, 272, 273, 274, 277, 278, 279, 283, 287,\n", + " 288, 289, 290, 291, 293, 294, 296, 298, 300, 301, 302,\n", + " 303, 304, 305, 306, 307, 310, 312, 313, 314, 315, 320,\n", + " 321, 322, 324, 325, 327, 331, 332, 334, 335, 340, 341,\n", + " 344, 345, 349, 352, 353, 354, 356, 357, 358, 360, 361,\n", + " 364, 365, 366, 367, 368, 369, 371, 372, 373, 376, 378,\n", + " 379, 381, 382, 383, 384, 385, 386, 387, 388, 390, 394,\n", + " 395, 397, 398, 400, 401, 402, 406, 407, 408, 410, 411,\n", + " 412, 414, 416, 417, 418, 419, 420, 421, 422, 423, 425,\n", + " 426, 427, 428, 429, 430, 431, 433, 434, 436, 440, 442,\n", + " 444, 445, 446, 447, 448, 449, 452, 453, 455, 457, 458,\n", + " 459, 460, 462, 463, 464, 465, 466, 467, 468, 470, 471,\n", + " 472, 475, 476, 479, 480, 482, 483, 485, 486, 489, 490,\n", + " 491, 492, 495, 496, 499, 500, 501, 503, 505, 506, 508,\n", + " 509, 510, 512, 514, 518, 519, 520, 522, 523, 524, 525,\n", + " 527, 528, 529, 530, 533, 534, 535, 537, 538, 539, 540,\n", + " 541, 543, 545, 547, 548, 549, 550, 551, 552, 554, 558,\n", + " 560, 564, 567, 568, 569, 572, 574, 576, 577, 579, 580,\n", + " 581, 582, 588, 589, 591, 592, 593, 594, 596, 598, 600,\n", + " 602, 603, 604, 607, 611, 612, 613, 614, 615, 616, 617,\n", + " 618, 620, 621, 622, 624, 625, 626, 627, 629, 632, 633,\n", + " 634, 635, 637, 638, 639, 643, 644, 645, 646, 647, 648,\n", + " 650, 652, 653, 656, 657, 658, 660, 662, 663, 666, 668,\n", + " 671, 673, 674, 675, 676, 677, 681, 682, 684, 685, 688,\n", + " 689, 690, 693, 695, 697, 700, 703, 704, 706, 707, 709,\n", + " 711, 713, 714, 715, 716, 718, 721, 723, 724, 728, 730,\n", + " 731, 732, 734, 735, 736, 741, 742, 743, 745, 747, 749,\n", + " 750, 752, 755, 756, 757, 761, 762, 764, 766, 767, 769,\n", + " 771, 772, 774, 775, 777, 779, 780, 781, 782, 784, 785,\n", + " 789, 792, 794, 796, 798, 799, 801, 803, 805, 807, 811,\n", + " 812, 813, 814, 819, 822, 823, 824, 827, 828, 829, 830,\n", + " 831, 832, 835, 837, 839, 840, 841, 843, 844, 846, 848,\n", + " 849, 853, 854, 855, 856, 857, 861, 867, 869, 871, 872,\n", + " 874, 876, 881, 883, 884, 886, 887, 890, 891, 892, 894,\n", + " 896, 897, 899, 900, 905, 908, 909, 912, 913, 914, 916,\n", + " 917, 918, 919, 921, 923, 924, 925, 926, 927, 929, 930,\n", + " 933, 938, 939, 940, 941, 942, 943, 944, 945, 947, 948,\n", + " 950, 951, 953, 959, 960, 961, 962, 964, 965, 968, 971,\n", + " 974, 976, 977, 979, 984, 985, 986, 987, 988, 990, 991,\n", + " 992, 996, 997, 998, 1001, 1003, 1004, 1006, 1009, 1011, 1013,\n", + " 1021, 1023, 1025, 1026, 1028, 1029, 1031, 1035, 1037, 1038, 1041,\n", + " 1042, 1044, 1045, 1046, 1049, 1051, 1052, 1053, 1054, 1058, 1060,\n", + " 1062, 1063, 1064, 1065, 1066, 1067, 1069, 1071, 1072, 1074, 1075,\n", + " 1077, 1080, 1083, 1084, 1085, 1087, 1088, 1089, 1091, 1092, 1095,\n", + " 1096, 1097, 1098, 1099, 1100, 1102, 1103, 1104, 1107, 1108, 1109,\n", + " 1112, 1113, 1119, 1120, 1121, 1126, 1128, 1130, 1131, 1133, 1135,\n", + " 1137, 1139, 1143, 1146, 1147, 1148, 1149, 1152, 1154, 1155, 1158,\n", + " 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1169, 1170,\n", + " 1171, 1173, 1177, 1179, 1180, 1182, 1184, 1185, 1189, 1192, 1193,\n", + " 1194, 1195, 1196, 1198, 1200, 1201, 1203, 1204, 1205, 1206, 1209,\n", + " 1210, 1212, 1213, 1214, 1215, 1216, 1218, 1219, 1220, 1222, 1225,\n", + " 1226, 1227, 1229, 1230, 1231, 1232, 1234, 1235, 1236, 1237, 1238,\n", + " 1239, 1244, 1247, 1252, 1253, 1256, 1257, 1258, 1259, 1261, 1264,\n", + " 1266, 1267, 1269, 1272, 1273, 1274, 1276, 1278, 1279, 1281, 1282,\n", + " 1285, 1287, 1289, 1290, 1291, 1293, 1295, 1299, 1300, 1301, 1302,\n", + " 1303, 1304, 1305, 1306, 1311, 1315, 1316, 1320, 1321, 1322, 1324,\n", + " 1325, 1326, 1328, 1329, 1330, 1331, 1332, 1334, 1336, 1337, 1338,\n", + " 1339, 1341, 1345, 1346, 1347, 1351, 1355, 1356, 1357, 1363, 1364,\n", + " 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1376,\n", + " 1378, 1380, 1382, 1384, 1385, 1386, 1388, 1390, 1391, 1393, 1397,\n", + " 1398]),\n", + " 2: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 43, 44,\n", + " 45, 53, 62, 69, 71, 75, 76, 79, 100, 103, 106,\n", + " 107, 110, 116, 129, 156, 163, 164, 165, 168, 176, 186,\n", + " 191, 193, 210, 215, 220, 233, 234, 242, 248, 250, 251,\n", + " 257, 263, 269, 281, 285, 286, 308, 311, 317, 318, 319,\n", + " 326, 329, 330, 333, 343, 346, 347, 350, 359, 374, 375,\n", + " 377, 393, 396, 403, 405, 415, 424, 437, 438, 439, 441,\n", + " 443, 451, 456, 461, 469, 473, 477, 478, 481, 487, 493,\n", + " 498, 502, 504, 507, 511, 513, 516, 517, 531, 532, 536,\n", + " 544, 546, 555, 556, 557, 565, 566, 571, 575, 583, 584,\n", + " 585, 586, 595, 608, 609, 610, 623, 630, 636, 642, 649,\n", + " 651, 654, 659, 664, 667, 669, 679, 694, 698, 705, 710,\n", + " 712, 722, 725, 727, 729, 738, 739, 746, 751, 754, 758,\n", + " 760, 768, 776, 778, 788, 790, 791, 795, 800, 808, 817,\n", + " 820, 826, 833, 834, 842, 845, 847, 850, 851, 852, 860,\n", + " 862, 863, 865, 866, 870, 877, 878, 893, 898, 902, 903,\n", + " 906, 911, 922, 936, 946, 949, 954, 956, 958, 963, 966,\n", + " 969, 970, 972, 981, 982, 989, 994, 995, 1002, 1008, 1012,\n", + " 1015, 1016, 1018, 1019, 1024, 1027, 1034, 1036, 1043, 1055, 1057,\n", + " 1059, 1061, 1070, 1076, 1078, 1079, 1081, 1082, 1086, 1090, 1093,\n", + " 1101, 1105, 1111, 1114, 1116, 1132, 1136, 1140, 1144, 1145, 1150,\n", + " 1151, 1156, 1157, 1168, 1174, 1175, 1176, 1178, 1181, 1186, 1187,\n", + " 1188, 1197, 1199, 1202, 1207, 1217, 1224, 1228, 1233, 1242, 1243,\n", + " 1248, 1250, 1260, 1263, 1268, 1270, 1271, 1277, 1280, 1288, 1297,\n", + " 1307, 1309, 1310, 1313, 1323, 1333, 1335, 1340, 1343, 1349, 1359,\n", + " 1375, 1392, 1395, 1396, 1399])},\n", + " 4: {0: array([ 0, 2, 6, 8, 19, 20, 21, 22, 41, 47, 52,\n", + " 54, 55, 59, 61, 63, 65, 68, 77, 78, 88, 89,\n", + " 93, 96, 98, 101, 104, 105, 109, 112, 113, 115, 119,\n", + " 120, 121, 123, 126, 127, 131, 133, 137, 147, 150, 151,\n", + " 153, 154, 155, 181, 182, 184, 187, 194, 199, 205, 208,\n", + " 209, 226, 228, 229, 230, 231, 239, 254, 258, 260, 261,\n", + " 266, 268, 275, 276, 280, 282, 284, 292, 295, 297, 299,\n", + " 309, 316, 323, 328, 336, 337, 338, 339, 342, 348, 351,\n", + " 355, 362, 363, 370, 380, 389, 391, 392, 399, 404, 409,\n", + " 413, 432, 435, 450, 454, 474, 484, 488, 494, 497, 515,\n", + " 521, 526, 542, 553, 559, 561, 562, 563, 570, 573, 578,\n", + " 587, 590, 597, 599, 601, 605, 606, 619, 628, 631, 640,\n", + " 641, 655, 661, 665, 670, 672, 678, 680, 683, 686, 687,\n", + " 691, 692, 696, 699, 701, 702, 708, 717, 719, 720, 726,\n", + " 733, 737, 740, 744, 748, 753, 759, 763, 765, 770, 773,\n", + " 783, 786, 787, 793, 797, 802, 804, 806, 809, 810, 815,\n", + " 816, 818, 821, 825, 836, 838, 858, 859, 864, 868, 873,\n", + " 875, 879, 880, 882, 885, 888, 889, 895, 901, 904, 907,\n", + " 910, 915, 920, 928, 931, 932, 934, 935, 937, 952, 955,\n", + " 957, 967, 973, 975, 978, 980, 983, 993, 999, 1000, 1005,\n", + " 1007, 1010, 1014, 1017, 1020, 1022, 1030, 1032, 1033, 1039, 1040,\n", + " 1047, 1048, 1050, 1056, 1068, 1073, 1094, 1106, 1110, 1115, 1117,\n", + " 1118, 1122, 1123, 1124, 1125, 1127, 1129, 1134, 1138, 1141, 1142,\n", + " 1153, 1172, 1183, 1190, 1191, 1208, 1211, 1221, 1223, 1240, 1241,\n", + " 1245, 1246, 1249, 1251, 1254, 1255, 1262, 1265, 1275, 1283, 1284,\n", + " 1286, 1292, 1294, 1296, 1298, 1308, 1312, 1314, 1317, 1318, 1319,\n", + " 1327, 1342, 1344, 1348, 1350, 1352, 1353, 1354, 1358, 1360, 1361,\n", + " 1362, 1377, 1379, 1381, 1383, 1387, 1389, 1394]),\n", + " 1: array([ 1, 3, 5, 7, 9, 12, 13, 17, 18, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 36, 38, 42, 46, 48,\n", + " 49, 51, 56, 58, 60, 64, 66, 67, 70, 74, 80,\n", + " 82, 86, 87, 90, 92, 94, 97, 99, 102, 108, 111,\n", + " 114, 118, 122, 124, 128, 130, 132, 135, 138, 139, 140,\n", + " 141, 142, 143, 144, 145, 146, 148, 149, 158, 159, 162,\n", + " 166, 167, 169, 170, 172, 173, 174, 175, 177, 178, 179,\n", + " 189, 190, 192, 195, 197, 200, 201, 202, 206, 207, 211,\n", + " 212, 213, 214, 216, 218, 219, 221, 222, 227, 232, 235,\n", + " 236, 238, 240, 241, 244, 245, 246, 247, 249, 252, 253,\n", + " 255, 259, 262, 264, 265, 267, 271, 272, 273, 274, 277,\n", + " 278, 283, 288, 289, 290, 291, 294, 296, 298, 300, 301,\n", + " 302, 303, 304, 305, 306, 307, 310, 312, 314, 315, 322,\n", + " 324, 325, 327, 331, 332, 334, 335, 340, 341, 344, 352,\n", + " 353, 354, 357, 360, 361, 364, 365, 366, 368, 369, 371,\n", + " 372, 373, 376, 378, 381, 383, 384, 385, 386, 388, 390,\n", + " 394, 397, 398, 400, 401, 402, 407, 408, 411, 412, 414,\n", + " 416, 418, 419, 420, 421, 422, 423, 425, 426, 427, 428,\n", + " 429, 430, 431, 433, 434, 436, 442, 444, 445, 446, 448,\n", + " 449, 452, 453, 455, 459, 460, 462, 463, 464, 465, 467,\n", + " 468, 471, 472, 475, 476, 480, 482, 483, 485, 486, 489,\n", + " 490, 491, 492, 495, 496, 499, 500, 501, 503, 505, 508,\n", + " 509, 510, 512, 518, 520, 522, 527, 528, 529, 530, 533,\n", + " 534, 535, 537, 539, 540, 543, 545, 547, 548, 550, 551,\n", + " 552, 554, 558, 560, 564, 567, 569, 572, 574, 579, 580,\n", + " 581, 582, 589, 591, 592, 594, 596, 598, 602, 603, 604,\n", + " 607, 611, 612, 614, 615, 616, 617, 618, 620, 622, 624,\n", + " 627, 629, 633, 634, 635, 637, 639, 643, 644, 645, 647,\n", + " 648, 650, 652, 657, 658, 662, 666, 668, 673, 674, 675,\n", + " 676, 677, 681, 682, 684, 685, 688, 689, 690, 695, 697,\n", + " 700, 703, 704, 707, 709, 711, 714, 715, 716, 718, 728,\n", + " 730, 731, 732, 741, 742, 743, 747, 750, 756, 757, 761,\n", + " 762, 764, 766, 767, 769, 772, 774, 775, 777, 779, 780,\n", + " 782, 785, 789, 792, 796, 798, 799, 803, 811, 812, 814,\n", + " 819, 823, 824, 828, 830, 831, 832, 835, 837, 839, 840,\n", + " 841, 843, 844, 846, 848, 849, 853, 856, 857, 861, 867,\n", + " 869, 871, 872, 874, 876, 883, 884, 886, 887, 890, 891,\n", + " 896, 899, 905, 908, 909, 912, 913, 914, 916, 917, 918,\n", + " 919, 921, 923, 924, 926, 929, 933, 938, 939, 940, 941,\n", + " 942, 943, 944, 947, 950, 951, 953, 960, 961, 962, 964,\n", + " 965, 968, 971, 974, 979, 984, 985, 986, 987, 990, 991,\n", + " 992, 996, 997, 1001, 1003, 1006, 1009, 1011, 1013, 1021, 1023,\n", + " 1025, 1026, 1028, 1029, 1031, 1037, 1038, 1041, 1042, 1044, 1045,\n", + " 1046, 1052, 1053, 1054, 1060, 1062, 1063, 1064, 1065, 1069, 1071,\n", + " 1075, 1080, 1083, 1084, 1085, 1088, 1092, 1095, 1100, 1102, 1103,\n", + " 1108, 1109, 1113, 1119, 1121, 1126, 1130, 1131, 1135, 1137, 1146,\n", + " 1148, 1154, 1155, 1158, 1160, 1161, 1162, 1163, 1164, 1165, 1166,\n", + " 1169, 1170, 1171, 1173, 1177, 1179, 1180, 1182, 1184, 1185, 1192,\n", + " 1193, 1195, 1196, 1198, 1200, 1201, 1203, 1204, 1206, 1209, 1210,\n", + " 1212, 1214, 1218, 1219, 1220, 1225, 1226, 1227, 1230, 1231, 1232,\n", + " 1234, 1236, 1237, 1239, 1244, 1247, 1252, 1253, 1258, 1259, 1261,\n", + " 1264, 1266, 1267, 1269, 1272, 1273, 1274, 1278, 1279, 1282, 1289,\n", + " 1291, 1299, 1301, 1303, 1305, 1306, 1311, 1315, 1316, 1320, 1321,\n", + " 1322, 1324, 1326, 1328, 1329, 1330, 1332, 1334, 1336, 1337, 1339,\n", + " 1341, 1345, 1346, 1351, 1355, 1363, 1364, 1367, 1368, 1369, 1370,\n", + " 1371, 1372, 1373, 1374, 1376, 1378, 1380, 1384, 1385, 1386, 1388,\n", + " 1390, 1398]),\n", + " 2: array([ 4, 14, 24, 33, 34, 50, 57, 72, 73, 81, 83,\n", + " 84, 85, 91, 95, 117, 125, 134, 136, 152, 157, 160,\n", + " 161, 171, 180, 183, 185, 188, 196, 198, 203, 204, 217,\n", + " 223, 224, 225, 237, 243, 256, 270, 279, 287, 293, 313,\n", + " 320, 321, 345, 349, 356, 358, 367, 379, 382, 387, 395,\n", + " 406, 410, 417, 440, 447, 457, 458, 466, 470, 479, 506,\n", + " 514, 519, 523, 524, 525, 538, 541, 549, 568, 576, 577,\n", + " 588, 593, 600, 613, 621, 625, 626, 632, 638, 646, 653,\n", + " 656, 660, 663, 671, 693, 706, 713, 721, 723, 724, 734,\n", + " 735, 736, 745, 749, 752, 755, 771, 781, 784, 794, 801,\n", + " 805, 807, 813, 822, 827, 829, 854, 855, 881, 892, 894,\n", + " 897, 900, 925, 927, 930, 945, 948, 959, 976, 977, 988,\n", + " 998, 1004, 1035, 1049, 1051, 1058, 1066, 1067, 1072, 1074, 1077,\n", + " 1087, 1089, 1091, 1096, 1097, 1098, 1099, 1104, 1107, 1112, 1120,\n", + " 1128, 1133, 1139, 1143, 1147, 1149, 1152, 1159, 1167, 1189, 1194,\n", + " 1205, 1213, 1215, 1216, 1222, 1229, 1235, 1238, 1256, 1257, 1276,\n", + " 1281, 1285, 1287, 1290, 1293, 1295, 1300, 1302, 1304, 1325, 1331,\n", + " 1338, 1347, 1356, 1357, 1365, 1366, 1382, 1391, 1393, 1397]),\n", + " 3: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 43, 44,\n", + " 45, 53, 62, 69, 71, 75, 76, 79, 100, 103, 106,\n", + " 107, 110, 116, 129, 156, 163, 164, 165, 168, 176, 186,\n", + " 191, 193, 210, 215, 220, 233, 234, 242, 248, 250, 251,\n", + " 257, 263, 269, 281, 285, 286, 308, 311, 317, 318, 319,\n", + " 326, 329, 330, 333, 343, 346, 347, 350, 359, 374, 375,\n", + " 377, 393, 396, 403, 405, 415, 424, 437, 438, 439, 441,\n", + " 443, 451, 456, 461, 469, 473, 477, 478, 481, 487, 493,\n", + " 498, 502, 504, 507, 511, 513, 516, 517, 531, 532, 536,\n", + " 544, 546, 555, 556, 557, 565, 566, 571, 575, 583, 584,\n", + " 585, 586, 595, 608, 609, 610, 623, 630, 636, 642, 649,\n", + " 651, 654, 659, 664, 667, 669, 679, 694, 698, 705, 710,\n", + " 712, 722, 725, 727, 729, 738, 739, 746, 751, 754, 758,\n", + " 760, 768, 776, 778, 788, 790, 791, 795, 800, 808, 817,\n", + " 820, 826, 833, 834, 842, 845, 847, 850, 851, 852, 860,\n", + " 862, 863, 865, 866, 870, 877, 878, 893, 898, 902, 903,\n", + " 906, 911, 922, 936, 946, 949, 954, 956, 958, 963, 966,\n", + " 969, 970, 972, 981, 982, 989, 994, 995, 1002, 1008, 1012,\n", + " 1015, 1016, 1018, 1019, 1024, 1027, 1034, 1036, 1043, 1055, 1057,\n", + " 1059, 1061, 1070, 1076, 1078, 1079, 1081, 1082, 1086, 1090, 1093,\n", + " 1101, 1105, 1111, 1114, 1116, 1132, 1136, 1140, 1144, 1145, 1150,\n", + " 1151, 1156, 1157, 1168, 1174, 1175, 1176, 1178, 1181, 1186, 1187,\n", + " 1188, 1197, 1199, 1202, 1207, 1217, 1224, 1228, 1233, 1242, 1243,\n", + " 1248, 1250, 1260, 1263, 1268, 1270, 1271, 1277, 1280, 1288, 1297,\n", + " 1307, 1309, 1310, 1313, 1323, 1333, 1335, 1340, 1343, 1349, 1359,\n", + " 1375, 1392, 1395, 1396, 1399])},\n", + " 5: {0: array([ 0, 2, 20, 22, 54, 61, 65, 78, 89, 101, 105,\n", + " 112, 119, 123, 127, 133, 137, 147, 154, 182, 187, 205,\n", + " 208, 226, 230, 268, 275, 284, 309, 323, 328, 337, 342,\n", + " 355, 362, 380, 391, 413, 432, 435, 484, 488, 515, 542,\n", + " 562, 587, 590, 597, 601, 641, 655, 661, 692, 701, 719,\n", + " 733, 737, 744, 770, 773, 793, 806, 809, 810, 821, 836,\n", + " 859, 864, 879, 882, 895, 907, 915, 920, 931, 952, 955,\n", + " 957, 967, 978, 993, 999, 1007, 1017, 1032, 1068, 1094, 1115,\n", + " 1117, 1123, 1125, 1127, 1153, 1190, 1191, 1208, 1223, 1246, 1284,\n", + " 1294, 1298, 1308, 1318, 1327, 1344, 1348, 1350, 1354, 1360, 1362,\n", + " 1377, 1379, 1381, 1389]),\n", + " 1: array([ 1, 3, 5, 7, 9, 12, 13, 17, 18, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 36, 38, 42, 46, 48,\n", + " 49, 51, 56, 58, 60, 64, 66, 67, 70, 74, 80,\n", + " 82, 86, 87, 90, 92, 94, 97, 99, 102, 108, 111,\n", + " 114, 118, 122, 124, 128, 130, 132, 135, 138, 139, 140,\n", + " 141, 142, 143, 144, 145, 146, 148, 149, 158, 159, 162,\n", + " 166, 167, 169, 170, 172, 173, 174, 175, 177, 178, 179,\n", + " 189, 190, 192, 195, 197, 200, 201, 202, 206, 207, 211,\n", + " 212, 213, 214, 216, 218, 219, 221, 222, 227, 232, 235,\n", + " 236, 238, 240, 241, 244, 245, 246, 247, 249, 252, 253,\n", + " 255, 259, 262, 264, 265, 267, 271, 272, 273, 274, 277,\n", + " 278, 283, 288, 289, 290, 291, 294, 296, 298, 300, 301,\n", + " 302, 303, 304, 305, 306, 307, 310, 312, 314, 315, 322,\n", + " 324, 325, 327, 331, 332, 334, 335, 340, 341, 344, 352,\n", + " 353, 354, 357, 360, 361, 364, 365, 366, 368, 369, 371,\n", + " 372, 373, 376, 378, 381, 383, 384, 385, 386, 388, 390,\n", + " 394, 397, 398, 400, 401, 402, 407, 408, 411, 412, 414,\n", + " 416, 418, 419, 420, 421, 422, 423, 425, 426, 427, 428,\n", + " 429, 430, 431, 433, 434, 436, 442, 444, 445, 446, 448,\n", + " 449, 452, 453, 455, 459, 460, 462, 463, 464, 465, 467,\n", + " 468, 471, 472, 475, 476, 480, 482, 483, 485, 486, 489,\n", + " 490, 491, 492, 495, 496, 499, 500, 501, 503, 505, 508,\n", + " 509, 510, 512, 518, 520, 522, 527, 528, 529, 530, 533,\n", + " 534, 535, 537, 539, 540, 543, 545, 547, 548, 550, 551,\n", + " 552, 554, 558, 560, 564, 567, 569, 572, 574, 579, 580,\n", + " 581, 582, 589, 591, 592, 594, 596, 598, 602, 603, 604,\n", + " 607, 611, 612, 614, 615, 616, 617, 618, 620, 622, 624,\n", + " 627, 629, 633, 634, 635, 637, 639, 643, 644, 645, 647,\n", + " 648, 650, 652, 657, 658, 662, 666, 668, 673, 674, 675,\n", + " 676, 677, 681, 682, 684, 685, 688, 689, 690, 695, 697,\n", + " 700, 703, 704, 707, 709, 711, 714, 715, 716, 718, 728,\n", + " 730, 731, 732, 741, 742, 743, 747, 750, 756, 757, 761,\n", + " 762, 764, 766, 767, 769, 772, 774, 775, 777, 779, 780,\n", + " 782, 785, 789, 792, 796, 798, 799, 803, 811, 812, 814,\n", + " 819, 823, 824, 828, 830, 831, 832, 835, 837, 839, 840,\n", + " 841, 843, 844, 846, 848, 849, 853, 856, 857, 861, 867,\n", + " 869, 871, 872, 874, 876, 883, 884, 886, 887, 890, 891,\n", + " 896, 899, 905, 908, 909, 912, 913, 914, 916, 917, 918,\n", + " 919, 921, 923, 924, 926, 929, 933, 938, 939, 940, 941,\n", + " 942, 943, 944, 947, 950, 951, 953, 960, 961, 962, 964,\n", + " 965, 968, 971, 974, 979, 984, 985, 986, 987, 990, 991,\n", + " 992, 996, 997, 1001, 1003, 1006, 1009, 1011, 1013, 1021, 1023,\n", + " 1025, 1026, 1028, 1029, 1031, 1037, 1038, 1041, 1042, 1044, 1045,\n", + " 1046, 1052, 1053, 1054, 1060, 1062, 1063, 1064, 1065, 1069, 1071,\n", + " 1075, 1080, 1083, 1084, 1085, 1088, 1092, 1095, 1100, 1102, 1103,\n", + " 1108, 1109, 1113, 1119, 1121, 1126, 1130, 1131, 1135, 1137, 1146,\n", + " 1148, 1154, 1155, 1158, 1160, 1161, 1162, 1163, 1164, 1165, 1166,\n", + " 1169, 1170, 1171, 1173, 1177, 1179, 1180, 1182, 1184, 1185, 1192,\n", + " 1193, 1195, 1196, 1198, 1200, 1201, 1203, 1204, 1206, 1209, 1210,\n", + " 1212, 1214, 1218, 1219, 1220, 1225, 1226, 1227, 1230, 1231, 1232,\n", + " 1234, 1236, 1237, 1239, 1244, 1247, 1252, 1253, 1258, 1259, 1261,\n", + " 1264, 1266, 1267, 1269, 1272, 1273, 1274, 1278, 1279, 1282, 1289,\n", + " 1291, 1299, 1301, 1303, 1305, 1306, 1311, 1315, 1316, 1320, 1321,\n", + " 1322, 1324, 1326, 1328, 1329, 1330, 1332, 1334, 1336, 1337, 1339,\n", + " 1341, 1345, 1346, 1351, 1355, 1363, 1364, 1367, 1368, 1369, 1370,\n", + " 1371, 1372, 1373, 1374, 1376, 1378, 1380, 1384, 1385, 1386, 1388,\n", + " 1390, 1398]),\n", + " 2: array([ 4, 14, 24, 33, 34, 50, 57, 72, 73, 81, 83,\n", + " 84, 85, 91, 95, 117, 125, 134, 136, 152, 157, 160,\n", + " 161, 171, 180, 183, 185, 188, 196, 198, 203, 204, 217,\n", + " 223, 224, 225, 237, 243, 256, 270, 279, 287, 293, 313,\n", + " 320, 321, 345, 349, 356, 358, 367, 379, 382, 387, 395,\n", + " 406, 410, 417, 440, 447, 457, 458, 466, 470, 479, 506,\n", + " 514, 519, 523, 524, 525, 538, 541, 549, 568, 576, 577,\n", + " 588, 593, 600, 613, 621, 625, 626, 632, 638, 646, 653,\n", + " 656, 660, 663, 671, 693, 706, 713, 721, 723, 724, 734,\n", + " 735, 736, 745, 749, 752, 755, 771, 781, 784, 794, 801,\n", + " 805, 807, 813, 822, 827, 829, 854, 855, 881, 892, 894,\n", + " 897, 900, 925, 927, 930, 945, 948, 959, 976, 977, 988,\n", + " 998, 1004, 1035, 1049, 1051, 1058, 1066, 1067, 1072, 1074, 1077,\n", + " 1087, 1089, 1091, 1096, 1097, 1098, 1099, 1104, 1107, 1112, 1120,\n", + " 1128, 1133, 1139, 1143, 1147, 1149, 1152, 1159, 1167, 1189, 1194,\n", + " 1205, 1213, 1215, 1216, 1222, 1229, 1235, 1238, 1256, 1257, 1276,\n", + " 1281, 1285, 1287, 1290, 1293, 1295, 1300, 1302, 1304, 1325, 1331,\n", + " 1338, 1347, 1356, 1357, 1365, 1366, 1382, 1391, 1393, 1397]),\n", + " 3: array([ 6, 8, 19, 21, 41, 47, 52, 55, 59, 63, 68,\n", + " 77, 88, 93, 96, 98, 104, 109, 113, 115, 120, 121,\n", + " 126, 131, 150, 151, 153, 155, 181, 184, 194, 199, 209,\n", + " 228, 229, 231, 239, 254, 258, 260, 261, 266, 276, 280,\n", + " 282, 292, 295, 297, 299, 316, 336, 338, 339, 348, 351,\n", + " 363, 370, 389, 392, 399, 404, 409, 450, 454, 474, 494,\n", + " 497, 521, 526, 553, 559, 561, 563, 570, 573, 578, 599,\n", + " 605, 606, 619, 628, 631, 640, 665, 670, 672, 678, 680,\n", + " 683, 686, 687, 691, 696, 699, 702, 708, 717, 720, 726,\n", + " 740, 748, 753, 759, 763, 765, 783, 786, 787, 797, 802,\n", + " 804, 815, 816, 818, 825, 838, 858, 868, 873, 875, 880,\n", + " 885, 888, 889, 901, 904, 910, 928, 932, 934, 935, 937,\n", + " 973, 975, 980, 983, 1000, 1005, 1010, 1014, 1020, 1022, 1030,\n", + " 1033, 1039, 1040, 1047, 1048, 1050, 1056, 1073, 1106, 1110, 1118,\n", + " 1122, 1124, 1129, 1134, 1138, 1141, 1142, 1172, 1183, 1211, 1221,\n", + " 1240, 1241, 1245, 1249, 1251, 1254, 1255, 1262, 1265, 1275, 1283,\n", + " 1286, 1292, 1296, 1312, 1314, 1317, 1319, 1342, 1352, 1353, 1358,\n", + " 1361, 1383, 1387, 1394]),\n", + " 4: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 43, 44,\n", + " 45, 53, 62, 69, 71, 75, 76, 79, 100, 103, 106,\n", + " 107, 110, 116, 129, 156, 163, 164, 165, 168, 176, 186,\n", + " 191, 193, 210, 215, 220, 233, 234, 242, 248, 250, 251,\n", + " 257, 263, 269, 281, 285, 286, 308, 311, 317, 318, 319,\n", + " 326, 329, 330, 333, 343, 346, 347, 350, 359, 374, 375,\n", + " 377, 393, 396, 403, 405, 415, 424, 437, 438, 439, 441,\n", + " 443, 451, 456, 461, 469, 473, 477, 478, 481, 487, 493,\n", + " 498, 502, 504, 507, 511, 513, 516, 517, 531, 532, 536,\n", + " 544, 546, 555, 556, 557, 565, 566, 571, 575, 583, 584,\n", + " 585, 586, 595, 608, 609, 610, 623, 630, 636, 642, 649,\n", + " 651, 654, 659, 664, 667, 669, 679, 694, 698, 705, 710,\n", + " 712, 722, 725, 727, 729, 738, 739, 746, 751, 754, 758,\n", + " 760, 768, 776, 778, 788, 790, 791, 795, 800, 808, 817,\n", + " 820, 826, 833, 834, 842, 845, 847, 850, 851, 852, 860,\n", + " 862, 863, 865, 866, 870, 877, 878, 893, 898, 902, 903,\n", + " 906, 911, 922, 936, 946, 949, 954, 956, 958, 963, 966,\n", + " 969, 970, 972, 981, 982, 989, 994, 995, 1002, 1008, 1012,\n", + " 1015, 1016, 1018, 1019, 1024, 1027, 1034, 1036, 1043, 1055, 1057,\n", + " 1059, 1061, 1070, 1076, 1078, 1079, 1081, 1082, 1086, 1090, 1093,\n", + " 1101, 1105, 1111, 1114, 1116, 1132, 1136, 1140, 1144, 1145, 1150,\n", + " 1151, 1156, 1157, 1168, 1174, 1175, 1176, 1178, 1181, 1186, 1187,\n", + " 1188, 1197, 1199, 1202, 1207, 1217, 1224, 1228, 1233, 1242, 1243,\n", + " 1248, 1250, 1260, 1263, 1268, 1270, 1271, 1277, 1280, 1288, 1297,\n", + " 1307, 1309, 1310, 1313, 1323, 1333, 1335, 1340, 1343, 1349, 1359,\n", + " 1375, 1392, 1395, 1396, 1399])},\n", + " 6: {0: array([ 0, 2, 20, 22, 54, 61, 65, 78, 89, 101, 105,\n", + " 112, 119, 123, 127, 133, 137, 147, 154, 182, 187, 205,\n", + " 208, 226, 230, 268, 275, 284, 309, 323, 328, 337, 342,\n", + " 355, 362, 380, 391, 413, 432, 435, 484, 488, 515, 542,\n", + " 562, 587, 590, 597, 601, 641, 655, 661, 692, 701, 719,\n", + " 733, 737, 744, 770, 773, 793, 806, 809, 810, 821, 836,\n", + " 859, 864, 879, 882, 895, 907, 915, 920, 931, 952, 955,\n", + " 957, 967, 978, 993, 999, 1007, 1017, 1032, 1068, 1094, 1115,\n", + " 1117, 1123, 1125, 1127, 1153, 1190, 1191, 1208, 1223, 1246, 1284,\n", + " 1294, 1298, 1308, 1318, 1327, 1344, 1348, 1350, 1354, 1360, 1362,\n", + " 1377, 1379, 1381, 1389]),\n", + " 1: array([ 1, 3, 5, 7, 9, 12, 13, 17, 18, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 36, 38, 42, 46, 48,\n", + " 49, 51, 56, 58, 60, 64, 66, 67, 70, 74, 80,\n", + " 82, 86, 87, 90, 92, 94, 97, 99, 102, 108, 111,\n", + " 114, 118, 122, 124, 128, 130, 132, 135, 138, 139, 140,\n", + " 141, 142, 143, 144, 145, 146, 148, 149, 158, 159, 162,\n", + " 166, 167, 169, 170, 172, 173, 174, 175, 177, 178, 179,\n", + " 189, 190, 192, 195, 197, 200, 201, 202, 206, 207, 211,\n", + " 212, 213, 214, 216, 218, 219, 221, 222, 227, 232, 235,\n", + " 236, 238, 240, 241, 244, 245, 246, 247, 249, 252, 253,\n", + " 255, 259, 262, 264, 265, 267, 271, 272, 273, 274, 277,\n", + " 278, 283, 288, 289, 290, 291, 294, 296, 298, 300, 301,\n", + " 302, 303, 304, 305, 306, 307, 310, 312, 314, 315, 322,\n", + " 324, 325, 327, 331, 332, 334, 335, 340, 341, 344, 352,\n", + " 353, 354, 357, 360, 361, 364, 365, 366, 368, 369, 371,\n", + " 372, 373, 376, 378, 381, 383, 384, 385, 386, 388, 390,\n", + " 394, 397, 398, 400, 401, 402, 407, 408, 411, 412, 414,\n", + " 416, 418, 419, 420, 421, 422, 423, 425, 426, 427, 428,\n", + " 429, 430, 431, 433, 434, 436, 442, 444, 445, 446, 448,\n", + " 449, 452, 453, 455, 459, 460, 462, 463, 464, 465, 467,\n", + " 468, 471, 472, 475, 476, 480, 482, 483, 485, 486, 489,\n", + " 490, 491, 492, 495, 496, 499, 500, 501, 503, 505, 508,\n", + " 509, 510, 512, 518, 520, 522, 527, 528, 529, 530, 533,\n", + " 534, 535, 537, 539, 540, 543, 545, 547, 548, 550, 551,\n", + " 552, 554, 558, 560, 564, 567, 569, 572, 574, 579, 580,\n", + " 581, 582, 589, 591, 592, 594, 596, 598, 602, 603, 604,\n", + " 607, 611, 612, 614, 615, 616, 617, 618, 620, 622, 624,\n", + " 627, 629, 633, 634, 635, 637, 639, 643, 644, 645, 647,\n", + " 648, 650, 652, 657, 658, 662, 666, 668, 673, 674, 675,\n", + " 676, 677, 681, 682, 684, 685, 688, 689, 690, 695, 697,\n", + " 700, 703, 704, 707, 709, 711, 714, 715, 716, 718, 728,\n", + " 730, 731, 732, 741, 742, 743, 747, 750, 756, 757, 761,\n", + " 762, 764, 766, 767, 769, 772, 774, 775, 777, 779, 780,\n", + " 782, 785, 789, 792, 796, 798, 799, 803, 811, 812, 814,\n", + " 819, 823, 824, 828, 830, 831, 832, 835, 837, 839, 840,\n", + " 841, 843, 844, 846, 848, 849, 853, 856, 857, 861, 867,\n", + " 869, 871, 872, 874, 876, 883, 884, 886, 887, 890, 891,\n", + " 896, 899, 905, 908, 909, 912, 913, 914, 916, 917, 918,\n", + " 919, 921, 923, 924, 926, 929, 933, 938, 939, 940, 941,\n", + " 942, 943, 944, 947, 950, 951, 953, 960, 961, 962, 964,\n", + " 965, 968, 971, 974, 979, 984, 985, 986, 987, 990, 991,\n", + " 992, 996, 997, 1001, 1003, 1006, 1009, 1011, 1013, 1021, 1023,\n", + " 1025, 1026, 1028, 1029, 1031, 1037, 1038, 1041, 1042, 1044, 1045,\n", + " 1046, 1052, 1053, 1054, 1060, 1062, 1063, 1064, 1065, 1069, 1071,\n", + " 1075, 1080, 1083, 1084, 1085, 1088, 1092, 1095, 1100, 1102, 1103,\n", + " 1108, 1109, 1113, 1119, 1121, 1126, 1130, 1131, 1135, 1137, 1146,\n", + " 1148, 1154, 1155, 1158, 1160, 1161, 1162, 1163, 1164, 1165, 1166,\n", + " 1169, 1170, 1171, 1173, 1177, 1179, 1180, 1182, 1184, 1185, 1192,\n", + " 1193, 1195, 1196, 1198, 1200, 1201, 1203, 1204, 1206, 1209, 1210,\n", + " 1212, 1214, 1218, 1219, 1220, 1225, 1226, 1227, 1230, 1231, 1232,\n", + " 1234, 1236, 1237, 1239, 1244, 1247, 1252, 1253, 1258, 1259, 1261,\n", + " 1264, 1266, 1267, 1269, 1272, 1273, 1274, 1278, 1279, 1282, 1289,\n", + " 1291, 1299, 1301, 1303, 1305, 1306, 1311, 1315, 1316, 1320, 1321,\n", + " 1322, 1324, 1326, 1328, 1329, 1330, 1332, 1334, 1336, 1337, 1339,\n", + " 1341, 1345, 1346, 1351, 1355, 1363, 1364, 1367, 1368, 1369, 1370,\n", + " 1371, 1372, 1373, 1374, 1376, 1378, 1380, 1384, 1385, 1386, 1388,\n", + " 1390, 1398]),\n", + " 2: array([ 4, 14, 24, 33, 34, 50, 57, 72, 73, 81, 83,\n", + " 84, 85, 91, 95, 117, 125, 134, 136, 152, 157, 160,\n", + " 161, 171, 180, 183, 185, 188, 196, 198, 203, 204, 217,\n", + " 223, 224, 225, 237, 243, 256, 270, 279, 287, 293, 313,\n", + " 320, 321, 345, 349, 356, 358, 367, 379, 382, 387, 395,\n", + " 406, 410, 417, 440, 447, 457, 458, 466, 470, 479, 506,\n", + " 514, 519, 523, 524, 525, 538, 541, 549, 568, 576, 577,\n", + " 588, 593, 600, 613, 621, 625, 626, 632, 638, 646, 653,\n", + " 656, 660, 663, 671, 693, 706, 713, 721, 723, 724, 734,\n", + " 735, 736, 745, 749, 752, 755, 771, 781, 784, 794, 801,\n", + " 805, 807, 813, 822, 827, 829, 854, 855, 881, 892, 894,\n", + " 897, 900, 925, 927, 930, 945, 948, 959, 976, 977, 988,\n", + " 998, 1004, 1035, 1049, 1051, 1058, 1066, 1067, 1072, 1074, 1077,\n", + " 1087, 1089, 1091, 1096, 1097, 1098, 1099, 1104, 1107, 1112, 1120,\n", + " 1128, 1133, 1139, 1143, 1147, 1149, 1152, 1159, 1167, 1189, 1194,\n", + " 1205, 1213, 1215, 1216, 1222, 1229, 1235, 1238, 1256, 1257, 1276,\n", + " 1281, 1285, 1287, 1290, 1293, 1295, 1300, 1302, 1304, 1325, 1331,\n", + " 1338, 1347, 1356, 1357, 1365, 1366, 1382, 1391, 1393, 1397]),\n", + " 3: array([ 6, 8, 19, 21, 41, 47, 52, 55, 59, 63, 68,\n", + " 77, 88, 93, 96, 98, 104, 109, 113, 115, 120, 121,\n", + " 126, 131, 150, 151, 153, 155, 181, 184, 194, 199, 209,\n", + " 228, 229, 231, 239, 254, 258, 260, 261, 266, 276, 280,\n", + " 282, 292, 295, 297, 299, 316, 336, 338, 339, 348, 351,\n", + " 363, 370, 389, 392, 399, 404, 409, 450, 454, 474, 494,\n", + " 497, 521, 526, 553, 559, 561, 563, 570, 573, 578, 599,\n", + " 605, 606, 619, 628, 631, 640, 665, 670, 672, 678, 680,\n", + " 683, 686, 687, 691, 696, 699, 702, 708, 717, 720, 726,\n", + " 740, 748, 753, 759, 763, 765, 783, 786, 787, 797, 802,\n", + " 804, 815, 816, 818, 825, 838, 858, 868, 873, 875, 880,\n", + " 885, 888, 889, 901, 904, 910, 928, 932, 934, 935, 937,\n", + " 973, 975, 980, 983, 1000, 1005, 1010, 1014, 1020, 1022, 1030,\n", + " 1033, 1039, 1040, 1047, 1048, 1050, 1056, 1073, 1106, 1110, 1118,\n", + " 1122, 1124, 1129, 1134, 1138, 1141, 1142, 1172, 1183, 1211, 1221,\n", + " 1240, 1241, 1245, 1249, 1251, 1254, 1255, 1262, 1265, 1275, 1283,\n", + " 1286, 1292, 1296, 1312, 1314, 1317, 1319, 1342, 1352, 1353, 1358,\n", + " 1361, 1383, 1387, 1394]),\n", + " 4: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 43, 44,\n", + " 45, 53, 76, 79, 103, 106, 107, 110, 116, 129, 156,\n", + " 163, 164, 165, 168, 186, 191, 193, 210, 215, 220, 233,\n", + " 234, 242, 248, 250, 251, 257, 263, 269, 281, 285, 286,\n", + " 311, 318, 329, 343, 346, 347, 350, 374, 375, 393, 396,\n", + " 403, 405, 424, 437, 438, 439, 456, 461, 473, 477, 478,\n", + " 487, 493, 502, 504, 507, 511, 513, 517, 532, 536, 544,\n", + " 546, 555, 556, 557, 565, 575, 583, 608, 609, 623, 630,\n", + " 636, 654, 664, 667, 669, 694, 698, 705, 710, 712, 722,\n", + " 725, 727, 739, 746, 758, 760, 768, 776, 778, 788, 790,\n", + " 800, 817, 820, 833, 834, 842, 845, 847, 850, 851, 852,\n", + " 860, 862, 863, 865, 877, 878, 893, 903, 906, 911, 922,\n", + " 936, 946, 954, 956, 963, 966, 969, 970, 972, 981, 982,\n", + " 989, 994, 1002, 1008, 1015, 1016, 1019, 1027, 1034, 1036, 1043,\n", + " 1057, 1061, 1070, 1076, 1079, 1082, 1086, 1101, 1105, 1111, 1132,\n", + " 1136, 1144, 1145, 1150, 1151, 1157, 1168, 1174, 1175, 1186, 1188,\n", + " 1197, 1199, 1202, 1217, 1224, 1228, 1243, 1268, 1270, 1277, 1280,\n", + " 1297, 1309, 1323, 1333, 1335, 1349, 1359, 1375, 1392, 1396, 1399]),\n", + " 5: array([ 62, 69, 71, 75, 100, 176, 308, 317, 319, 326, 330,\n", + " 333, 359, 377, 415, 441, 443, 451, 469, 481, 498, 516,\n", + " 531, 566, 571, 584, 585, 586, 595, 610, 642, 649, 651,\n", + " 659, 679, 729, 738, 751, 754, 791, 795, 808, 826, 866,\n", + " 870, 898, 902, 949, 958, 995, 1012, 1018, 1024, 1055, 1059,\n", + " 1078, 1081, 1090, 1093, 1114, 1116, 1140, 1156, 1176, 1178, 1181,\n", + " 1187, 1207, 1233, 1242, 1248, 1250, 1260, 1263, 1271, 1288, 1307,\n", + " 1310, 1313, 1340, 1343, 1395])},\n", + " 7: {0: array([ 0, 2, 20, 22, 54, 61, 65, 78, 89, 101, 105,\n", + " 112, 119, 123, 127, 133, 137, 147, 154, 182, 187, 205,\n", + " 208, 226, 230, 268, 275, 284, 309, 323, 328, 337, 342,\n", + " 355, 362, 380, 391, 413, 432, 435, 484, 488, 515, 542,\n", + " 562, 587, 590, 597, 601, 641, 655, 661, 692, 701, 719,\n", + " 733, 737, 744, 770, 773, 793, 806, 809, 810, 821, 836,\n", + " 859, 864, 879, 882, 895, 907, 915, 920, 931, 952, 955,\n", + " 957, 967, 978, 993, 999, 1007, 1017, 1032, 1068, 1094, 1115,\n", + " 1117, 1123, 1125, 1127, 1153, 1190, 1191, 1208, 1223, 1246, 1284,\n", + " 1294, 1298, 1308, 1318, 1327, 1344, 1348, 1350, 1354, 1360, 1362,\n", + " 1377, 1379, 1381, 1389]),\n", + " 1: array([ 1, 3, 5, 7, 9, 12, 13, 17, 18, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 36, 38, 42, 46, 48,\n", + " 49, 51, 56, 58, 60, 64, 66, 67, 70, 74, 80,\n", + " 82, 86, 87, 90, 92, 94, 97, 99, 102, 108, 111,\n", + " 114, 118, 122, 124, 128, 130, 132, 135, 138, 139, 140,\n", + " 141, 142, 143, 144, 145, 146, 148, 149, 158, 159, 162,\n", + " 166, 167, 169, 170, 172, 173, 174, 175, 177, 178, 179,\n", + " 189, 190, 192, 195, 197, 200, 201, 202, 206, 207, 211,\n", + " 212, 213, 214, 216, 218, 219, 221, 222, 227, 232, 235,\n", + " 236, 238, 240, 241, 244, 245, 246, 247, 249, 252, 253,\n", + " 255, 259, 262, 264, 265, 267, 271, 272, 273, 274, 277,\n", + " 278, 283, 288, 289, 290, 291, 294, 296, 298, 300, 301,\n", + " 302, 303, 304, 305, 306, 307, 310, 312, 314, 315, 322,\n", + " 324, 325, 327, 331, 332, 334, 335, 340, 341, 344, 352,\n", + " 353, 354, 357, 360, 361, 364, 365, 366, 368, 369, 371,\n", + " 372, 373, 376, 378, 381, 383, 384, 385, 386, 388, 390,\n", + " 394, 397, 398, 400, 401, 402, 407, 408, 411, 412, 414,\n", + " 416, 418, 419, 420, 421, 422, 423, 425, 426, 427, 428,\n", + " 429, 430, 431, 433, 434, 436, 442, 444, 445, 446, 448,\n", + " 449, 452, 453, 455, 459, 460, 462, 463, 464, 465, 467,\n", + " 468, 471, 472, 475, 476, 480, 482, 483, 485, 486, 489,\n", + " 490, 491, 492, 495, 496, 499, 500, 501, 503, 505, 508,\n", + " 509, 510, 512, 518, 520, 522, 527, 528, 529, 530, 533,\n", + " 534, 535, 537, 539, 540, 543, 545, 547, 548, 550, 551,\n", + " 552, 554, 558, 560, 564, 567, 569, 572, 574, 579, 580,\n", + " 581, 582, 589, 591, 592, 594, 596, 598, 602, 603, 604,\n", + " 607, 611, 612, 614, 615, 616, 617, 618, 620, 622, 624,\n", + " 627, 629, 633, 634, 635, 637, 639, 643, 644, 645, 647,\n", + " 648, 650, 652, 657, 658, 662, 666, 668, 673, 674, 675,\n", + " 676, 677, 681, 682, 684, 685, 688, 689, 690, 695, 697,\n", + " 700, 703, 704, 707, 709, 711, 714, 715, 716, 718, 728,\n", + " 730, 731, 732, 741, 742, 743, 747, 750, 756, 757, 761,\n", + " 762, 764, 766, 767, 769, 772, 774, 775, 777, 779, 780,\n", + " 782, 785, 789, 792, 796, 798, 799, 803, 811, 812, 814,\n", + " 819, 823, 824, 828, 830, 831, 832, 835, 837, 839, 840,\n", + " 841, 843, 844, 846, 848, 849, 853, 856, 857, 861, 867,\n", + " 869, 871, 872, 874, 876, 883, 884, 886, 887, 890, 891,\n", + " 896, 899, 905, 908, 909, 912, 913, 914, 916, 917, 918,\n", + " 919, 921, 923, 924, 926, 929, 933, 938, 939, 940, 941,\n", + " 942, 943, 944, 947, 950, 951, 953, 960, 961, 962, 964,\n", + " 965, 968, 971, 974, 979, 984, 985, 986, 987, 990, 991,\n", + " 992, 996, 997, 1001, 1003, 1006, 1009, 1011, 1013, 1021, 1023,\n", + " 1025, 1026, 1028, 1029, 1031, 1037, 1038, 1041, 1042, 1044, 1045,\n", + " 1046, 1052, 1053, 1054, 1060, 1062, 1063, 1064, 1065, 1069, 1071,\n", + " 1075, 1080, 1083, 1084, 1085, 1088, 1092, 1095, 1100, 1102, 1103,\n", + " 1108, 1109, 1113, 1119, 1121, 1126, 1130, 1131, 1135, 1137, 1146,\n", + " 1148, 1154, 1155, 1158, 1160, 1161, 1162, 1163, 1164, 1165, 1166,\n", + " 1169, 1170, 1171, 1173, 1177, 1179, 1180, 1182, 1184, 1185, 1192,\n", + " 1193, 1195, 1196, 1198, 1200, 1201, 1203, 1204, 1206, 1209, 1210,\n", + " 1212, 1214, 1218, 1219, 1220, 1225, 1226, 1227, 1230, 1231, 1232,\n", + " 1234, 1236, 1237, 1239, 1244, 1247, 1252, 1253, 1258, 1259, 1261,\n", + " 1264, 1266, 1267, 1269, 1272, 1273, 1274, 1278, 1279, 1282, 1289,\n", + " 1291, 1299, 1301, 1303, 1305, 1306, 1311, 1315, 1316, 1320, 1321,\n", + " 1322, 1324, 1326, 1328, 1329, 1330, 1332, 1334, 1336, 1337, 1339,\n", + " 1341, 1345, 1346, 1351, 1355, 1363, 1364, 1367, 1368, 1369, 1370,\n", + " 1371, 1372, 1373, 1374, 1376, 1378, 1380, 1384, 1385, 1386, 1388,\n", + " 1390, 1398]),\n", + " 2: array([ 4, 14, 24, 33, 34, 50, 57, 72, 73, 81, 83,\n", + " 84, 85, 91, 95, 117, 125, 134, 136, 152, 157, 160,\n", + " 161, 171, 180, 183, 185, 188, 196, 198, 203, 204, 217,\n", + " 223, 224, 225, 237, 243, 256, 270, 279, 287, 293, 313,\n", + " 320, 321, 345, 349, 356, 358, 367, 379, 382, 387, 395,\n", + " 406, 410, 417, 440, 447, 457, 458, 466, 470, 479, 506,\n", + " 514, 519, 523, 524, 525, 538, 541, 549, 568, 576, 577,\n", + " 588, 593, 600, 613, 621, 625, 626, 632, 638, 646, 653,\n", + " 656, 660, 663, 671, 693, 706, 713, 721, 723, 724, 734,\n", + " 735, 736, 745, 749, 752, 755, 771, 781, 784, 794, 801,\n", + " 805, 807, 813, 822, 827, 829, 854, 855, 881, 892, 894,\n", + " 897, 900, 925, 927, 930, 945, 948, 959, 976, 977, 988,\n", + " 998, 1004, 1035, 1049, 1051, 1058, 1066, 1067, 1072, 1074, 1077,\n", + " 1087, 1089, 1091, 1096, 1097, 1098, 1099, 1104, 1107, 1112, 1120,\n", + " 1128, 1133, 1139, 1143, 1147, 1149, 1152, 1159, 1167, 1189, 1194,\n", + " 1205, 1213, 1215, 1216, 1222, 1229, 1235, 1238, 1256, 1257, 1276,\n", + " 1281, 1285, 1287, 1290, 1293, 1295, 1300, 1302, 1304, 1325, 1331,\n", + " 1338, 1347, 1356, 1357, 1365, 1366, 1382, 1391, 1393, 1397]),\n", + " 3: array([ 6, 41, 126, 131, 209, 229, 260, 266, 292, 339, 370,\n", + " 450, 570, 599, 631, 665, 670, 678, 687, 759, 763, 765,\n", + " 783, 787, 804, 816, 888, 889, 904, 932, 935, 1010, 1022,\n", + " 1033, 1129, 1211, 1221, 1292, 1317, 1352]),\n", + " 4: array([ 8, 19, 21, 47, 52, 55, 59, 63, 68, 77, 88,\n", + " 93, 96, 98, 104, 109, 113, 115, 120, 121, 150, 151,\n", + " 153, 155, 181, 184, 194, 199, 228, 231, 239, 254, 258,\n", + " 261, 276, 280, 282, 295, 297, 299, 316, 336, 338, 348,\n", + " 351, 363, 389, 392, 399, 404, 409, 454, 474, 494, 497,\n", + " 521, 526, 553, 559, 561, 563, 573, 578, 605, 606, 619,\n", + " 628, 640, 672, 680, 683, 686, 691, 696, 699, 702, 708,\n", + " 717, 720, 726, 740, 748, 753, 786, 797, 802, 815, 818,\n", + " 825, 838, 858, 868, 873, 875, 880, 885, 901, 910, 928,\n", + " 934, 937, 973, 975, 980, 983, 1000, 1005, 1014, 1020, 1030,\n", + " 1039, 1040, 1047, 1048, 1050, 1056, 1073, 1106, 1110, 1118, 1122,\n", + " 1124, 1134, 1138, 1141, 1142, 1172, 1183, 1240, 1241, 1245, 1249,\n", + " 1251, 1254, 1255, 1262, 1265, 1275, 1283, 1286, 1296, 1312, 1314,\n", + " 1319, 1342, 1353, 1358, 1361, 1383, 1387, 1394]),\n", + " 5: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 43, 44,\n", + " 45, 53, 76, 79, 103, 106, 107, 110, 116, 129, 156,\n", + " 163, 164, 165, 168, 186, 191, 193, 210, 215, 220, 233,\n", + " 234, 242, 248, 250, 251, 257, 263, 269, 281, 285, 286,\n", + " 311, 318, 329, 343, 346, 347, 350, 374, 375, 393, 396,\n", + " 403, 405, 424, 437, 438, 439, 456, 461, 473, 477, 478,\n", + " 487, 493, 502, 504, 507, 511, 513, 517, 532, 536, 544,\n", + " 546, 555, 556, 557, 565, 575, 583, 608, 609, 623, 630,\n", + " 636, 654, 664, 667, 669, 694, 698, 705, 710, 712, 722,\n", + " 725, 727, 739, 746, 758, 760, 768, 776, 778, 788, 790,\n", + " 800, 817, 820, 833, 834, 842, 845, 847, 850, 851, 852,\n", + " 860, 862, 863, 865, 877, 878, 893, 903, 906, 911, 922,\n", + " 936, 946, 954, 956, 963, 966, 969, 970, 972, 981, 982,\n", + " 989, 994, 1002, 1008, 1015, 1016, 1019, 1027, 1034, 1036, 1043,\n", + " 1057, 1061, 1070, 1076, 1079, 1082, 1086, 1101, 1105, 1111, 1132,\n", + " 1136, 1144, 1145, 1150, 1151, 1157, 1168, 1174, 1175, 1186, 1188,\n", + " 1197, 1199, 1202, 1217, 1224, 1228, 1243, 1268, 1270, 1277, 1280,\n", + " 1297, 1309, 1323, 1333, 1335, 1349, 1359, 1375, 1392, 1396, 1399]),\n", + " 6: array([ 62, 69, 71, 75, 100, 176, 308, 317, 319, 326, 330,\n", + " 333, 359, 377, 415, 441, 443, 451, 469, 481, 498, 516,\n", + " 531, 566, 571, 584, 585, 586, 595, 610, 642, 649, 651,\n", + " 659, 679, 729, 738, 751, 754, 791, 795, 808, 826, 866,\n", + " 870, 898, 902, 949, 958, 995, 1012, 1018, 1024, 1055, 1059,\n", + " 1078, 1081, 1090, 1093, 1114, 1116, 1140, 1156, 1176, 1178, 1181,\n", + " 1187, 1207, 1233, 1242, 1248, 1250, 1260, 1263, 1271, 1288, 1307,\n", + " 1310, 1313, 1340, 1343, 1395])},\n", + " 8: {0: array([ 0, 2, 20, 22, 54, 61, 65, 78, 89, 101, 105,\n", + " 112, 119, 123, 127, 133, 137, 147, 154, 182, 187, 205,\n", + " 208, 226, 230, 268, 275, 284, 309, 323, 328, 337, 342,\n", + " 355, 362, 380, 391, 413, 432, 435, 484, 488, 515, 542,\n", + " 562, 587, 590, 597, 601, 641, 655, 661, 692, 701, 719,\n", + " 733, 737, 744, 770, 773, 793, 806, 809, 810, 821, 836,\n", + " 859, 864, 879, 882, 895, 907, 915, 920, 931, 952, 955,\n", + " 957, 967, 978, 993, 999, 1007, 1017, 1032, 1068, 1094, 1115,\n", + " 1117, 1123, 1125, 1127, 1153, 1190, 1191, 1208, 1223, 1246, 1284,\n", + " 1294, 1298, 1308, 1318, 1327, 1344, 1348, 1350, 1354, 1360, 1362,\n", + " 1377, 1379, 1381, 1389]),\n", + " 1: array([ 1, 3, 5, 7, 9, 12, 13, 17, 18, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 36, 38, 42, 46, 48,\n", + " 49, 51, 56, 58, 60, 64, 66, 67, 70, 74, 80,\n", + " 82, 86, 87, 90, 92, 94, 97, 99, 102, 108, 111,\n", + " 114, 118, 122, 124, 128, 130, 132, 135, 138, 139, 140,\n", + " 141, 142, 143, 144, 145, 146, 148, 149, 158, 159, 162,\n", + " 166, 167, 169, 170, 172, 173, 174, 175, 177, 178, 179,\n", + " 189, 190, 192, 195, 197, 200, 201, 202, 206, 207, 211,\n", + " 212, 213, 214, 216, 218, 219, 221, 222, 227, 232, 235,\n", + " 236, 238, 240, 241, 244, 245, 246, 247, 249, 252, 253,\n", + " 255, 259, 262, 264, 265, 267, 271, 272, 273, 274, 277,\n", + " 278, 283, 288, 289, 290, 291, 294, 296, 298, 300, 301,\n", + " 302, 303, 304, 305, 306, 307, 310, 312, 314, 315, 322,\n", + " 324, 325, 327, 331, 332, 334, 335, 340, 341, 344, 352,\n", + " 353, 354, 357, 360, 361, 364, 365, 366, 368, 369, 371,\n", + " 372, 373, 376, 378, 381, 383, 384, 385, 386, 388, 390,\n", + " 394, 397, 398, 400, 401, 402, 407, 408, 411, 412, 414,\n", + " 416, 418, 419, 420, 421, 422, 423, 425, 426, 427, 428,\n", + " 429, 430, 431, 433, 434, 436, 442, 444, 445, 446, 448,\n", + " 449, 452, 453, 455, 459, 460, 462, 463, 464, 465, 467,\n", + " 468, 471, 472, 475, 476, 480, 482, 483, 485, 486, 489,\n", + " 490, 491, 492, 495, 496, 499, 500, 501, 503, 505, 508,\n", + " 509, 510, 512, 518, 520, 522, 527, 528, 529, 530, 533,\n", + " 534, 535, 537, 539, 540, 543, 545, 547, 548, 550, 551,\n", + " 552, 554, 558, 560, 564, 567, 569, 572, 574, 579, 580,\n", + " 581, 582, 589, 591, 592, 594, 596, 598, 602, 603, 604,\n", + " 607, 611, 612, 614, 615, 616, 617, 618, 620, 622, 624,\n", + " 627, 629, 633, 634, 635, 637, 639, 643, 644, 645, 647,\n", + " 648, 650, 652, 657, 658, 662, 666, 668, 673, 674, 675,\n", + " 676, 677, 681, 682, 684, 685, 688, 689, 690, 695, 697,\n", + " 700, 703, 704, 707, 709, 711, 714, 715, 716, 718, 728,\n", + " 730, 731, 732, 741, 742, 743, 747, 750, 756, 757, 761,\n", + " 762, 764, 766, 767, 769, 772, 774, 775, 777, 779, 780,\n", + " 782, 785, 789, 792, 796, 798, 799, 803, 811, 812, 814,\n", + " 819, 823, 824, 828, 830, 831, 832, 835, 837, 839, 840,\n", + " 841, 843, 844, 846, 848, 849, 853, 856, 857, 861, 867,\n", + " 869, 871, 872, 874, 876, 883, 884, 886, 887, 890, 891,\n", + " 896, 899, 905, 908, 909, 912, 913, 914, 916, 917, 918,\n", + " 919, 921, 923, 924, 926, 929, 933, 938, 939, 940, 941,\n", + " 942, 943, 944, 947, 950, 951, 953, 960, 961, 962, 964,\n", + " 965, 968, 971, 974, 979, 984, 985, 986, 987, 990, 991,\n", + " 992, 996, 997, 1001, 1003, 1006, 1009, 1011, 1013, 1021, 1023,\n", + " 1025, 1026, 1028, 1029, 1031, 1037, 1038, 1041, 1042, 1044, 1045,\n", + " 1046, 1052, 1053, 1054, 1060, 1062, 1063, 1064, 1065, 1069, 1071,\n", + " 1075, 1080, 1083, 1084, 1085, 1088, 1092, 1095, 1100, 1102, 1103,\n", + " 1108, 1109, 1113, 1119, 1121, 1126, 1130, 1131, 1135, 1137, 1146,\n", + " 1148, 1154, 1155, 1158, 1160, 1161, 1162, 1163, 1164, 1165, 1166,\n", + " 1169, 1170, 1171, 1173, 1177, 1179, 1180, 1182, 1184, 1185, 1192,\n", + " 1193, 1195, 1196, 1198, 1200, 1201, 1203, 1204, 1206, 1209, 1210,\n", + " 1212, 1214, 1218, 1219, 1220, 1225, 1226, 1227, 1230, 1231, 1232,\n", + " 1234, 1236, 1237, 1239, 1244, 1247, 1252, 1253, 1258, 1259, 1261,\n", + " 1264, 1266, 1267, 1269, 1272, 1273, 1274, 1278, 1279, 1282, 1289,\n", + " 1291, 1299, 1301, 1303, 1305, 1306, 1311, 1315, 1316, 1320, 1321,\n", + " 1322, 1324, 1326, 1328, 1329, 1330, 1332, 1334, 1336, 1337, 1339,\n", + " 1341, 1345, 1346, 1351, 1355, 1363, 1364, 1367, 1368, 1369, 1370,\n", + " 1371, 1372, 1373, 1374, 1376, 1378, 1380, 1384, 1385, 1386, 1388,\n", + " 1390, 1398]),\n", + " 2: array([ 4, 14, 24, 33, 34, 50, 57, 72, 73, 81, 83,\n", + " 84, 85, 91, 95, 117, 125, 134, 136, 152, 157, 160,\n", + " 161, 171, 183, 185, 198, 203, 204, 217, 224, 225, 237,\n", + " 243, 256, 270, 287, 293, 313, 320, 321, 345, 356, 358,\n", + " 367, 379, 382, 387, 395, 406, 410, 417, 440, 447, 457,\n", + " 458, 466, 470, 479, 506, 514, 519, 523, 524, 525, 538,\n", + " 541, 549, 568, 577, 588, 593, 600, 613, 621, 625, 626,\n", + " 632, 638, 646, 653, 656, 660, 663, 693, 706, 713, 721,\n", + " 723, 734, 735, 745, 749, 752, 755, 771, 781, 784, 801,\n", + " 805, 807, 813, 822, 827, 829, 854, 855, 881, 892, 894,\n", + " 897, 900, 927, 930, 945, 948, 959, 976, 977, 988, 998,\n", + " 1004, 1035, 1049, 1051, 1058, 1066, 1067, 1072, 1074, 1077, 1087,\n", + " 1089, 1091, 1096, 1097, 1098, 1099, 1104, 1107, 1112, 1120, 1128,\n", + " 1133, 1139, 1143, 1149, 1152, 1159, 1167, 1189, 1194, 1205, 1213,\n", + " 1216, 1222, 1229, 1235, 1238, 1256, 1257, 1276, 1281, 1285, 1287,\n", + " 1290, 1293, 1295, 1300, 1302, 1304, 1325, 1331, 1338, 1347, 1356,\n", + " 1357, 1365, 1366, 1382, 1391, 1393, 1397]),\n", + " 3: array([ 6, 41, 126, 131, 209, 229, 260, 266, 292, 339, 370,\n", + " 450, 570, 599, 631, 665, 670, 678, 687, 759, 763, 765,\n", + " 783, 787, 804, 816, 888, 889, 904, 932, 935, 1010, 1022,\n", + " 1033, 1129, 1211, 1221, 1292, 1317, 1352]),\n", + " 4: array([ 8, 19, 21, 47, 52, 55, 59, 63, 68, 77, 88,\n", + " 93, 96, 98, 104, 109, 113, 115, 120, 121, 150, 151,\n", + " 153, 155, 181, 184, 194, 199, 228, 231, 239, 254, 258,\n", + " 261, 276, 280, 282, 295, 297, 299, 316, 336, 338, 348,\n", + " 351, 363, 389, 392, 399, 404, 409, 454, 474, 494, 497,\n", + " 521, 526, 553, 559, 561, 563, 573, 578, 605, 606, 619,\n", + " 628, 640, 672, 680, 683, 686, 691, 696, 699, 702, 708,\n", + " 717, 720, 726, 740, 748, 753, 786, 797, 802, 815, 818,\n", + " 825, 838, 858, 868, 873, 875, 880, 885, 901, 910, 928,\n", + " 934, 937, 973, 975, 980, 983, 1000, 1005, 1014, 1020, 1030,\n", + " 1039, 1040, 1047, 1048, 1050, 1056, 1073, 1106, 1110, 1118, 1122,\n", + " 1124, 1134, 1138, 1141, 1142, 1172, 1183, 1240, 1241, 1245, 1249,\n", + " 1251, 1254, 1255, 1262, 1265, 1275, 1283, 1286, 1296, 1312, 1314,\n", + " 1319, 1342, 1353, 1358, 1361, 1383, 1387, 1394]),\n", + " 5: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 43, 44,\n", + " 45, 53, 76, 79, 103, 106, 107, 110, 116, 129, 156,\n", + " 163, 164, 165, 168, 186, 191, 193, 210, 215, 220, 233,\n", + " 234, 242, 248, 250, 251, 257, 263, 269, 281, 285, 286,\n", + " 311, 318, 329, 343, 346, 347, 350, 374, 375, 393, 396,\n", + " 403, 405, 424, 437, 438, 439, 456, 461, 473, 477, 478,\n", + " 487, 493, 502, 504, 507, 511, 513, 517, 532, 536, 544,\n", + " 546, 555, 556, 557, 565, 575, 583, 608, 609, 623, 630,\n", + " 636, 654, 664, 667, 669, 694, 698, 705, 710, 712, 722,\n", + " 725, 727, 739, 746, 758, 760, 768, 776, 778, 788, 790,\n", + " 800, 817, 820, 833, 834, 842, 845, 847, 850, 851, 852,\n", + " 860, 862, 863, 865, 877, 878, 893, 903, 906, 911, 922,\n", + " 936, 946, 954, 956, 963, 966, 969, 970, 972, 981, 982,\n", + " 989, 994, 1002, 1008, 1015, 1016, 1019, 1027, 1034, 1036, 1043,\n", + " 1057, 1061, 1070, 1076, 1079, 1082, 1086, 1101, 1105, 1111, 1132,\n", + " 1136, 1144, 1145, 1150, 1151, 1157, 1168, 1174, 1175, 1186, 1188,\n", + " 1197, 1199, 1202, 1217, 1224, 1228, 1243, 1268, 1270, 1277, 1280,\n", + " 1297, 1309, 1323, 1333, 1335, 1349, 1359, 1375, 1392, 1396, 1399]),\n", + " 6: array([ 62, 69, 71, 75, 100, 176, 308, 317, 319, 326, 330,\n", + " 333, 359, 377, 415, 441, 443, 451, 469, 481, 498, 516,\n", + " 531, 566, 571, 584, 585, 586, 595, 610, 642, 649, 651,\n", + " 659, 679, 729, 738, 751, 754, 791, 795, 808, 826, 866,\n", + " 870, 898, 902, 949, 958, 995, 1012, 1018, 1024, 1055, 1059,\n", + " 1078, 1081, 1090, 1093, 1114, 1116, 1140, 1156, 1176, 1178, 1181,\n", + " 1187, 1207, 1233, 1242, 1248, 1250, 1260, 1263, 1271, 1288, 1307,\n", + " 1310, 1313, 1340, 1343, 1395]),\n", + " 7: array([ 180, 188, 196, 223, 279, 349, 576, 671, 724, 736, 794,\n", + " 925, 1147, 1215])},\n", + " 9: {0: array([ 0, 2, 20, 22, 54, 61, 65, 78, 89, 101, 105,\n", + " 112, 119, 123, 127, 133, 137, 147, 154, 182, 187, 205,\n", + " 208, 226, 230, 268, 275, 284, 309, 323, 328, 337, 342,\n", + " 355, 362, 380, 391, 413, 432, 435, 484, 488, 515, 542,\n", + " 562, 587, 590, 597, 601, 641, 655, 661, 692, 701, 719,\n", + " 733, 737, 744, 770, 773, 793, 806, 809, 810, 821, 836,\n", + " 859, 864, 879, 882, 895, 907, 915, 920, 931, 952, 955,\n", + " 957, 967, 978, 993, 999, 1007, 1017, 1032, 1068, 1094, 1115,\n", + " 1117, 1123, 1125, 1127, 1153, 1190, 1191, 1208, 1223, 1246, 1284,\n", + " 1294, 1298, 1308, 1318, 1327, 1344, 1348, 1350, 1354, 1360, 1362,\n", + " 1377, 1379, 1381, 1389]),\n", + " 1: array([ 1, 3, 13, 25, 26, 27, 28, 29, 30, 31, 48,\n", + " 49, 58, 64, 74, 80, 82, 86, 90, 97, 99, 102,\n", + " 114, 118, 122, 124, 128, 132, 140, 142, 144, 145, 159,\n", + " 162, 166, 170, 173, 174, 177, 178, 190, 195, 201, 202,\n", + " 212, 213, 216, 218, 219, 227, 232, 253, 255, 262, 267,\n", + " 271, 272, 283, 289, 290, 291, 294, 296, 298, 303, 304,\n", + " 305, 306, 310, 315, 322, 331, 332, 334, 335, 341, 344,\n", + " 353, 357, 360, 364, 365, 366, 368, 371, 372, 373, 378,\n", + " 381, 383, 385, 388, 390, 398, 401, 402, 407, 420, 422,\n", + " 423, 426, 428, 430, 431, 433, 434, 436, 444, 445, 446,\n", + " 448, 455, 462, 463, 471, 472, 475, 476, 482, 483, 485,\n", + " 490, 491, 496, 500, 503, 508, 510, 518, 520, 527, 528,\n", + " 530, 540, 543, 545, 547, 551, 567, 569, 574, 579, 591,\n", + " 592, 594, 596, 604, 607, 611, 614, 615, 617, 622, 627,\n", + " 629, 637, 639, 644, 647, 648, 650, 652, 657, 658, 666,\n", + " 673, 676, 677, 681, 685, 689, 695, 714, 715, 716, 731,\n", + " 742, 747, 757, 761, 762, 767, 772, 775, 777, 779, 780,\n", + " 782, 785, 789, 792, 796, 799, 812, 823, 828, 840, 841,\n", + " 843, 844, 846, 848, 853, 876, 883, 886, 890, 891, 899,\n", + " 905, 909, 912, 913, 914, 916, 917, 918, 919, 921, 923,\n", + " 924, 926, 933, 940, 944, 951, 965, 968, 974, 979, 986,\n", + " 990, 1009, 1023, 1029, 1031, 1038, 1041, 1044, 1052, 1054, 1062,\n", + " 1064, 1065, 1069, 1071, 1075, 1085, 1088, 1092, 1095, 1102, 1108,\n", + " 1109, 1126, 1148, 1155, 1158, 1160, 1162, 1177, 1184, 1192, 1196,\n", + " 1203, 1204, 1206, 1212, 1214, 1226, 1230, 1232, 1234, 1236, 1239,\n", + " 1247, 1252, 1259, 1261, 1266, 1269, 1272, 1274, 1291, 1299, 1301,\n", + " 1303, 1305, 1311, 1320, 1322, 1324, 1326, 1328, 1329, 1330, 1332,\n", + " 1339, 1351, 1364, 1367, 1370, 1374, 1376, 1378, 1386, 1388, 1398]),\n", + " 2: array([ 4, 14, 24, 33, 34, 50, 57, 72, 73, 81, 83,\n", + " 84, 85, 91, 95, 117, 125, 134, 136, 152, 157, 160,\n", + " 161, 171, 183, 185, 198, 203, 204, 217, 224, 225, 237,\n", + " 243, 256, 270, 287, 293, 313, 320, 321, 345, 356, 358,\n", + " 367, 379, 382, 387, 395, 406, 410, 417, 440, 447, 457,\n", + " 458, 466, 470, 479, 506, 514, 519, 523, 524, 525, 538,\n", + " 541, 549, 568, 577, 588, 593, 600, 613, 621, 625, 626,\n", + " 632, 638, 646, 653, 656, 660, 663, 693, 706, 713, 721,\n", + " 723, 734, 735, 745, 749, 752, 755, 771, 781, 784, 801,\n", + " 805, 807, 813, 822, 827, 829, 854, 855, 881, 892, 894,\n", + " 897, 900, 927, 930, 945, 948, 959, 976, 977, 988, 998,\n", + " 1004, 1035, 1049, 1051, 1058, 1066, 1067, 1072, 1074, 1077, 1087,\n", + " 1089, 1091, 1096, 1097, 1098, 1099, 1104, 1107, 1112, 1120, 1128,\n", + " 1133, 1139, 1143, 1149, 1152, 1159, 1167, 1189, 1194, 1205, 1213,\n", + " 1216, 1222, 1229, 1235, 1238, 1256, 1257, 1276, 1281, 1285, 1287,\n", + " 1290, 1293, 1295, 1300, 1302, 1304, 1325, 1331, 1338, 1347, 1356,\n", + " 1357, 1365, 1366, 1382, 1391, 1393, 1397]),\n", + " 3: array([ 5, 7, 9, 12, 17, 18, 32, 36, 38, 42, 46,\n", + " 51, 56, 60, 66, 67, 70, 87, 92, 94, 108, 111,\n", + " 130, 135, 138, 139, 141, 143, 146, 148, 149, 158, 167,\n", + " 169, 172, 175, 179, 189, 192, 197, 200, 206, 207, 211,\n", + " 214, 221, 222, 235, 236, 238, 240, 241, 244, 245, 246,\n", + " 247, 249, 252, 259, 264, 265, 273, 274, 277, 278, 288,\n", + " 300, 301, 302, 307, 312, 314, 324, 325, 327, 340, 352,\n", + " 354, 361, 369, 376, 384, 386, 394, 397, 400, 408, 411,\n", + " 412, 414, 416, 418, 419, 421, 425, 427, 429, 442, 449,\n", + " 452, 453, 459, 460, 464, 465, 467, 468, 480, 486, 489,\n", + " 492, 495, 499, 501, 505, 509, 512, 522, 529, 533, 534,\n", + " 535, 537, 539, 548, 550, 552, 554, 558, 560, 564, 572,\n", + " 580, 581, 582, 589, 598, 602, 603, 612, 616, 618, 620,\n", + " 624, 633, 634, 635, 643, 645, 662, 668, 674, 675, 682,\n", + " 684, 688, 690, 697, 700, 703, 704, 707, 709, 711, 718,\n", + " 728, 730, 732, 741, 743, 750, 756, 764, 766, 769, 774,\n", + " 798, 803, 811, 814, 819, 824, 830, 831, 832, 835, 837,\n", + " 839, 849, 856, 857, 861, 867, 869, 871, 872, 874, 884,\n", + " 887, 896, 908, 929, 938, 939, 941, 942, 943, 947, 950,\n", + " 953, 960, 961, 962, 964, 971, 984, 985, 987, 991, 992,\n", + " 996, 997, 1001, 1003, 1006, 1011, 1013, 1021, 1025, 1026, 1028,\n", + " 1037, 1042, 1045, 1046, 1053, 1060, 1063, 1080, 1083, 1084, 1100,\n", + " 1103, 1113, 1119, 1121, 1130, 1131, 1135, 1137, 1146, 1154, 1161,\n", + " 1163, 1164, 1165, 1166, 1169, 1170, 1171, 1173, 1179, 1180, 1182,\n", + " 1185, 1193, 1195, 1198, 1200, 1201, 1209, 1210, 1218, 1219, 1220,\n", + " 1225, 1227, 1231, 1237, 1244, 1253, 1258, 1264, 1267, 1273, 1278,\n", + " 1279, 1282, 1289, 1306, 1315, 1316, 1321, 1334, 1336, 1337, 1341,\n", + " 1345, 1346, 1355, 1363, 1368, 1369, 1371, 1372, 1373, 1380, 1384,\n", + " 1385, 1390]),\n", + " 4: array([ 6, 41, 126, 131, 209, 229, 260, 266, 292, 339, 370,\n", + " 450, 570, 599, 631, 665, 670, 678, 687, 759, 763, 765,\n", + " 783, 787, 804, 816, 888, 889, 904, 932, 935, 1010, 1022,\n", + " 1033, 1129, 1211, 1221, 1292, 1317, 1352]),\n", + " 5: array([ 8, 19, 21, 47, 52, 55, 59, 63, 68, 77, 88,\n", + " 93, 96, 98, 104, 109, 113, 115, 120, 121, 150, 151,\n", + " 153, 155, 181, 184, 194, 199, 228, 231, 239, 254, 258,\n", + " 261, 276, 280, 282, 295, 297, 299, 316, 336, 338, 348,\n", + " 351, 363, 389, 392, 399, 404, 409, 454, 474, 494, 497,\n", + " 521, 526, 553, 559, 561, 563, 573, 578, 605, 606, 619,\n", + " 628, 640, 672, 680, 683, 686, 691, 696, 699, 702, 708,\n", + " 717, 720, 726, 740, 748, 753, 786, 797, 802, 815, 818,\n", + " 825, 838, 858, 868, 873, 875, 880, 885, 901, 910, 928,\n", + " 934, 937, 973, 975, 980, 983, 1000, 1005, 1014, 1020, 1030,\n", + " 1039, 1040, 1047, 1048, 1050, 1056, 1073, 1106, 1110, 1118, 1122,\n", + " 1124, 1134, 1138, 1141, 1142, 1172, 1183, 1240, 1241, 1245, 1249,\n", + " 1251, 1254, 1255, 1262, 1265, 1275, 1283, 1286, 1296, 1312, 1314,\n", + " 1319, 1342, 1353, 1358, 1361, 1383, 1387, 1394]),\n", + " 6: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 43, 44,\n", + " 45, 53, 76, 79, 103, 106, 107, 110, 116, 129, 156,\n", + " 163, 164, 165, 168, 186, 191, 193, 210, 215, 220, 233,\n", + " 234, 242, 248, 250, 251, 257, 263, 269, 281, 285, 286,\n", + " 311, 318, 329, 343, 346, 347, 350, 374, 375, 393, 396,\n", + " 403, 405, 424, 437, 438, 439, 456, 461, 473, 477, 478,\n", + " 487, 493, 502, 504, 507, 511, 513, 517, 532, 536, 544,\n", + " 546, 555, 556, 557, 565, 575, 583, 608, 609, 623, 630,\n", + " 636, 654, 664, 667, 669, 694, 698, 705, 710, 712, 722,\n", + " 725, 727, 739, 746, 758, 760, 768, 776, 778, 788, 790,\n", + " 800, 817, 820, 833, 834, 842, 845, 847, 850, 851, 852,\n", + " 860, 862, 863, 865, 877, 878, 893, 903, 906, 911, 922,\n", + " 936, 946, 954, 956, 963, 966, 969, 970, 972, 981, 982,\n", + " 989, 994, 1002, 1008, 1015, 1016, 1019, 1027, 1034, 1036, 1043,\n", + " 1057, 1061, 1070, 1076, 1079, 1082, 1086, 1101, 1105, 1111, 1132,\n", + " 1136, 1144, 1145, 1150, 1151, 1157, 1168, 1174, 1175, 1186, 1188,\n", + " 1197, 1199, 1202, 1217, 1224, 1228, 1243, 1268, 1270, 1277, 1280,\n", + " 1297, 1309, 1323, 1333, 1335, 1349, 1359, 1375, 1392, 1396, 1399]),\n", + " 7: array([ 62, 69, 71, 75, 100, 176, 308, 317, 319, 326, 330,\n", + " 333, 359, 377, 415, 441, 443, 451, 469, 481, 498, 516,\n", + " 531, 566, 571, 584, 585, 586, 595, 610, 642, 649, 651,\n", + " 659, 679, 729, 738, 751, 754, 791, 795, 808, 826, 866,\n", + " 870, 898, 902, 949, 958, 995, 1012, 1018, 1024, 1055, 1059,\n", + " 1078, 1081, 1090, 1093, 1114, 1116, 1140, 1156, 1176, 1178, 1181,\n", + " 1187, 1207, 1233, 1242, 1248, 1250, 1260, 1263, 1271, 1288, 1307,\n", + " 1310, 1313, 1340, 1343, 1395]),\n", + " 8: array([ 180, 188, 196, 223, 279, 349, 576, 671, 724, 736, 794,\n", + " 925, 1147, 1215])},\n", + " 10: {0: array([ 0, 20, 22, 54, 65, 112, 119, 137, 147, 154, 205,\n", + " 230, 309, 323, 328, 337, 355, 391, 432, 484, 515, 542,\n", + " 562, 587, 597, 601, 701, 744, 770, 793, 806, 809, 810,\n", + " 821, 836, 859, 879, 895, 907, 920, 952, 978, 999, 1032,\n", + " 1068, 1117, 1123, 1223, 1344, 1354, 1360, 1362, 1377, 1379, 1381]),\n", + " 1: array([ 1, 3, 13, 25, 26, 27, 28, 29, 30, 31, 48,\n", + " 49, 58, 64, 74, 80, 82, 86, 90, 97, 99, 102,\n", + " 114, 118, 122, 124, 128, 132, 140, 142, 144, 145, 159,\n", + " 162, 166, 170, 173, 174, 177, 178, 190, 195, 201, 202,\n", + " 212, 213, 216, 218, 219, 227, 232, 253, 255, 262, 267,\n", + " 271, 272, 283, 289, 290, 291, 294, 296, 298, 303, 304,\n", + " 305, 306, 310, 315, 322, 331, 332, 334, 335, 341, 344,\n", + " 353, 357, 360, 364, 365, 366, 368, 371, 372, 373, 378,\n", + " 381, 383, 385, 388, 390, 398, 401, 402, 407, 420, 422,\n", + " 423, 426, 428, 430, 431, 433, 434, 436, 444, 445, 446,\n", + " 448, 455, 462, 463, 471, 472, 475, 476, 482, 483, 485,\n", + " 490, 491, 496, 500, 503, 508, 510, 518, 520, 527, 528,\n", + " 530, 540, 543, 545, 547, 551, 567, 569, 574, 579, 591,\n", + " 592, 594, 596, 604, 607, 611, 614, 615, 617, 622, 627,\n", + " 629, 637, 639, 644, 647, 648, 650, 652, 657, 658, 666,\n", + " 673, 676, 677, 681, 685, 689, 695, 714, 715, 716, 731,\n", + " 742, 747, 757, 761, 762, 767, 772, 775, 777, 779, 780,\n", + " 782, 785, 789, 792, 796, 799, 812, 823, 828, 840, 841,\n", + " 843, 844, 846, 848, 853, 876, 883, 886, 890, 891, 899,\n", + " 905, 909, 912, 913, 914, 916, 917, 918, 919, 921, 923,\n", + " 924, 926, 933, 940, 944, 951, 965, 968, 974, 979, 986,\n", + " 990, 1009, 1023, 1029, 1031, 1038, 1041, 1044, 1052, 1054, 1062,\n", + " 1064, 1065, 1069, 1071, 1075, 1085, 1088, 1092, 1095, 1102, 1108,\n", + " 1109, 1126, 1148, 1155, 1158, 1160, 1162, 1177, 1184, 1192, 1196,\n", + " 1203, 1204, 1206, 1212, 1214, 1226, 1230, 1232, 1234, 1236, 1239,\n", + " 1247, 1252, 1259, 1261, 1266, 1269, 1272, 1274, 1291, 1299, 1301,\n", + " 1303, 1305, 1311, 1320, 1322, 1324, 1326, 1328, 1329, 1330, 1332,\n", + " 1339, 1351, 1364, 1367, 1370, 1374, 1376, 1378, 1386, 1388, 1398]),\n", + " 2: array([ 2, 61, 78, 89, 101, 105, 123, 127, 133, 182, 187,\n", + " 208, 226, 268, 275, 284, 342, 362, 380, 413, 435, 488,\n", + " 590, 641, 655, 661, 692, 719, 733, 737, 773, 864, 882,\n", + " 915, 931, 955, 957, 967, 993, 1007, 1017, 1094, 1115, 1125,\n", + " 1127, 1153, 1190, 1191, 1208, 1246, 1284, 1294, 1298, 1308, 1318,\n", + " 1327, 1348, 1350, 1389]),\n", + " 3: array([ 4, 14, 24, 33, 34, 50, 57, 72, 73, 81, 83,\n", + " 84, 85, 91, 95, 117, 125, 134, 136, 152, 157, 160,\n", + " 161, 171, 183, 185, 198, 203, 204, 217, 224, 225, 237,\n", + " 243, 256, 270, 287, 293, 313, 320, 321, 345, 356, 358,\n", + " 367, 379, 382, 387, 395, 406, 410, 417, 440, 447, 457,\n", + " 458, 466, 470, 479, 506, 514, 519, 523, 524, 525, 538,\n", + " 541, 549, 568, 577, 588, 593, 600, 613, 621, 625, 626,\n", + " 632, 638, 646, 653, 656, 660, 663, 693, 706, 713, 721,\n", + " 723, 734, 735, 745, 749, 752, 755, 771, 781, 784, 801,\n", + " 805, 807, 813, 822, 827, 829, 854, 855, 881, 892, 894,\n", + " 897, 900, 927, 930, 945, 948, 959, 976, 977, 988, 998,\n", + " 1004, 1035, 1049, 1051, 1058, 1066, 1067, 1072, 1074, 1077, 1087,\n", + " 1089, 1091, 1096, 1097, 1098, 1099, 1104, 1107, 1112, 1120, 1128,\n", + " 1133, 1139, 1143, 1149, 1152, 1159, 1167, 1189, 1194, 1205, 1213,\n", + " 1216, 1222, 1229, 1235, 1238, 1256, 1257, 1276, 1281, 1285, 1287,\n", + " 1290, 1293, 1295, 1300, 1302, 1304, 1325, 1331, 1338, 1347, 1356,\n", + " 1357, 1365, 1366, 1382, 1391, 1393, 1397]),\n", + " 4: array([ 5, 7, 9, 12, 17, 18, 32, 36, 38, 42, 46,\n", + " 51, 56, 60, 66, 67, 70, 87, 92, 94, 108, 111,\n", + " 130, 135, 138, 139, 141, 143, 146, 148, 149, 158, 167,\n", + " 169, 172, 175, 179, 189, 192, 197, 200, 206, 207, 211,\n", + " 214, 221, 222, 235, 236, 238, 240, 241, 244, 245, 246,\n", + " 247, 249, 252, 259, 264, 265, 273, 274, 277, 278, 288,\n", + " 300, 301, 302, 307, 312, 314, 324, 325, 327, 340, 352,\n", + " 354, 361, 369, 376, 384, 386, 394, 397, 400, 408, 411,\n", + " 412, 414, 416, 418, 419, 421, 425, 427, 429, 442, 449,\n", + " 452, 453, 459, 460, 464, 465, 467, 468, 480, 486, 489,\n", + " 492, 495, 499, 501, 505, 509, 512, 522, 529, 533, 534,\n", + " 535, 537, 539, 548, 550, 552, 554, 558, 560, 564, 572,\n", + " 580, 581, 582, 589, 598, 602, 603, 612, 616, 618, 620,\n", + " 624, 633, 634, 635, 643, 645, 662, 668, 674, 675, 682,\n", + " 684, 688, 690, 697, 700, 703, 704, 707, 709, 711, 718,\n", + " 728, 730, 732, 741, 743, 750, 756, 764, 766, 769, 774,\n", + " 798, 803, 811, 814, 819, 824, 830, 831, 832, 835, 837,\n", + " 839, 849, 856, 857, 861, 867, 869, 871, 872, 874, 884,\n", + " 887, 896, 908, 929, 938, 939, 941, 942, 943, 947, 950,\n", + " 953, 960, 961, 962, 964, 971, 984, 985, 987, 991, 992,\n", + " 996, 997, 1001, 1003, 1006, 1011, 1013, 1021, 1025, 1026, 1028,\n", + " 1037, 1042, 1045, 1046, 1053, 1060, 1063, 1080, 1083, 1084, 1100,\n", + " 1103, 1113, 1119, 1121, 1130, 1131, 1135, 1137, 1146, 1154, 1161,\n", + " 1163, 1164, 1165, 1166, 1169, 1170, 1171, 1173, 1179, 1180, 1182,\n", + " 1185, 1193, 1195, 1198, 1200, 1201, 1209, 1210, 1218, 1219, 1220,\n", + " 1225, 1227, 1231, 1237, 1244, 1253, 1258, 1264, 1267, 1273, 1278,\n", + " 1279, 1282, 1289, 1306, 1315, 1316, 1321, 1334, 1336, 1337, 1341,\n", + " 1345, 1346, 1355, 1363, 1368, 1369, 1371, 1372, 1373, 1380, 1384,\n", + " 1385, 1390]),\n", + " 5: array([ 6, 41, 126, 131, 209, 229, 260, 266, 292, 339, 370,\n", + " 450, 570, 599, 631, 665, 670, 678, 687, 759, 763, 765,\n", + " 783, 787, 804, 816, 888, 889, 904, 932, 935, 1010, 1022,\n", + " 1033, 1129, 1211, 1221, 1292, 1317, 1352]),\n", + " 6: array([ 8, 19, 21, 47, 52, 55, 59, 63, 68, 77, 88,\n", + " 93, 96, 98, 104, 109, 113, 115, 120, 121, 150, 151,\n", + " 153, 155, 181, 184, 194, 199, 228, 231, 239, 254, 258,\n", + " 261, 276, 280, 282, 295, 297, 299, 316, 336, 338, 348,\n", + " 351, 363, 389, 392, 399, 404, 409, 454, 474, 494, 497,\n", + " 521, 526, 553, 559, 561, 563, 573, 578, 605, 606, 619,\n", + " 628, 640, 672, 680, 683, 686, 691, 696, 699, 702, 708,\n", + " 717, 720, 726, 740, 748, 753, 786, 797, 802, 815, 818,\n", + " 825, 838, 858, 868, 873, 875, 880, 885, 901, 910, 928,\n", + " 934, 937, 973, 975, 980, 983, 1000, 1005, 1014, 1020, 1030,\n", + " 1039, 1040, 1047, 1048, 1050, 1056, 1073, 1106, 1110, 1118, 1122,\n", + " 1124, 1134, 1138, 1141, 1142, 1172, 1183, 1240, 1241, 1245, 1249,\n", + " 1251, 1254, 1255, 1262, 1265, 1275, 1283, 1286, 1296, 1312, 1314,\n", + " 1319, 1342, 1353, 1358, 1361, 1383, 1387, 1394]),\n", + " 7: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 43, 44,\n", + " 45, 53, 76, 79, 103, 106, 107, 110, 116, 129, 156,\n", + " 163, 164, 165, 168, 186, 191, 193, 210, 215, 220, 233,\n", + " 234, 242, 248, 250, 251, 257, 263, 269, 281, 285, 286,\n", + " 311, 318, 329, 343, 346, 347, 350, 374, 375, 393, 396,\n", + " 403, 405, 424, 437, 438, 439, 456, 461, 473, 477, 478,\n", + " 487, 493, 502, 504, 507, 511, 513, 517, 532, 536, 544,\n", + " 546, 555, 556, 557, 565, 575, 583, 608, 609, 623, 630,\n", + " 636, 654, 664, 667, 669, 694, 698, 705, 710, 712, 722,\n", + " 725, 727, 739, 746, 758, 760, 768, 776, 778, 788, 790,\n", + " 800, 817, 820, 833, 834, 842, 845, 847, 850, 851, 852,\n", + " 860, 862, 863, 865, 877, 878, 893, 903, 906, 911, 922,\n", + " 936, 946, 954, 956, 963, 966, 969, 970, 972, 981, 982,\n", + " 989, 994, 1002, 1008, 1015, 1016, 1019, 1027, 1034, 1036, 1043,\n", + " 1057, 1061, 1070, 1076, 1079, 1082, 1086, 1101, 1105, 1111, 1132,\n", + " 1136, 1144, 1145, 1150, 1151, 1157, 1168, 1174, 1175, 1186, 1188,\n", + " 1197, 1199, 1202, 1217, 1224, 1228, 1243, 1268, 1270, 1277, 1280,\n", + " 1297, 1309, 1323, 1333, 1335, 1349, 1359, 1375, 1392, 1396, 1399]),\n", + " 8: array([ 62, 69, 71, 75, 100, 176, 308, 317, 319, 326, 330,\n", + " 333, 359, 377, 415, 441, 443, 451, 469, 481, 498, 516,\n", + " 531, 566, 571, 584, 585, 586, 595, 610, 642, 649, 651,\n", + " 659, 679, 729, 738, 751, 754, 791, 795, 808, 826, 866,\n", + " 870, 898, 902, 949, 958, 995, 1012, 1018, 1024, 1055, 1059,\n", + " 1078, 1081, 1090, 1093, 1114, 1116, 1140, 1156, 1176, 1178, 1181,\n", + " 1187, 1207, 1233, 1242, 1248, 1250, 1260, 1263, 1271, 1288, 1307,\n", + " 1310, 1313, 1340, 1343, 1395]),\n", + " 9: array([ 180, 188, 196, 223, 279, 349, 576, 671, 724, 736, 794,\n", + " 925, 1147, 1215])}},\n", + " 'l2_ranking': {2: {0: array([ 0, 2, 6, 8, 19, 20, 21, 22, 47, 52, 54,\n", + " 55, 59, 61, 63, 65, 68, 77, 78, 88, 89, 93,\n", + " 98, 101, 104, 105, 109, 112, 115, 119, 120, 121, 123,\n", + " 127, 131, 133, 137, 147, 150, 151, 153, 154, 155, 181,\n", + " 182, 184, 187, 194, 199, 205, 208, 209, 226, 228, 229,\n", + " 230, 231, 239, 254, 258, 261, 268, 275, 280, 282, 284,\n", + " 292, 295, 297, 299, 309, 316, 323, 328, 336, 337, 338,\n", + " 339, 342, 348, 351, 355, 362, 363, 380, 389, 391, 392,\n", + " 399, 404, 413, 432, 435, 450, 454, 474, 484, 488, 494,\n", + " 497, 515, 521, 526, 542, 553, 559, 561, 562, 563, 573,\n", + " 578, 587, 590, 596, 597, 599, 601, 605, 606, 619, 628,\n", + " 640, 641, 655, 661, 670, 672, 678, 680, 683, 686, 691,\n", + " 692, 696, 699, 701, 702, 708, 717, 719, 720, 726, 733,\n", + " 737, 740, 744, 748, 763, 770, 773, 783, 786, 793, 797,\n", + " 802, 804, 806, 809, 810, 815, 816, 818, 821, 825, 836,\n", + " 838, 858, 859, 864, 868, 873, 875, 879, 880, 882, 885,\n", + " 888, 895, 901, 907, 910, 915, 920, 928, 931, 934, 935,\n", + " 937, 952, 955, 957, 967, 973, 975, 978, 980, 983, 993,\n", + " 999, 1000, 1005, 1007, 1014, 1017, 1020, 1022, 1030, 1032, 1033,\n", + " 1039, 1040, 1047, 1048, 1050, 1056, 1068, 1073, 1094, 1106, 1110,\n", + " 1115, 1117, 1118, 1122, 1123, 1124, 1125, 1127, 1129, 1134, 1138,\n", + " 1141, 1142, 1153, 1172, 1183, 1190, 1191, 1208, 1211, 1223, 1240,\n", + " 1241, 1245, 1246, 1249, 1251, 1254, 1255, 1262, 1265, 1275, 1283,\n", + " 1284, 1286, 1294, 1296, 1298, 1308, 1312, 1314, 1318, 1319, 1327,\n", + " 1342, 1344, 1348, 1350, 1353, 1354, 1358, 1360, 1361, 1362, 1377,\n", + " 1379, 1381, 1383, 1387, 1389, 1394]),\n", + " 1: array([ 1, 3, 4, ..., 1397, 1398, 1399])},\n", + " 3: {0: array([ 0, 2, 20, 22, 54, 61, 65, 78, 89, 101, 105,\n", + " 112, 119, 123, 127, 131, 133, 137, 147, 154, 182, 187,\n", + " 205, 208, 209, 226, 229, 230, 268, 275, 284, 309, 323,\n", + " 328, 337, 342, 355, 362, 380, 391, 413, 432, 435, 450,\n", + " 484, 488, 515, 542, 562, 587, 590, 596, 597, 601, 641,\n", + " 655, 661, 692, 701, 702, 719, 733, 737, 744, 770, 773,\n", + " 793, 804, 806, 809, 810, 821, 836, 859, 864, 879, 882,\n", + " 895, 907, 915, 920, 931, 952, 955, 957, 967, 978, 993,\n", + " 999, 1007, 1017, 1032, 1068, 1094, 1106, 1115, 1117, 1123, 1125,\n", + " 1127, 1129, 1153, 1190, 1191, 1208, 1223, 1246, 1284, 1294, 1298,\n", + " 1308, 1318, 1327, 1344, 1348, 1350, 1354, 1360, 1362, 1377, 1379,\n", + " 1381, 1389]),\n", + " 1: array([ 1, 3, 4, ..., 1397, 1398, 1399]),\n", + " 2: array([ 6, 8, 19, 21, 47, 52, 55, 59, 63, 68, 77,\n", + " 88, 93, 98, 104, 109, 115, 120, 121, 150, 151, 153,\n", + " 155, 181, 184, 194, 199, 228, 231, 239, 254, 258, 261,\n", + " 280, 282, 292, 295, 297, 299, 316, 336, 338, 339, 348,\n", + " 351, 363, 389, 392, 399, 404, 454, 474, 494, 497, 521,\n", + " 526, 553, 559, 561, 563, 573, 578, 599, 605, 606, 619,\n", + " 628, 640, 670, 672, 678, 680, 683, 686, 691, 696, 699,\n", + " 708, 717, 720, 726, 740, 748, 763, 783, 786, 797, 802,\n", + " 815, 816, 818, 825, 838, 858, 868, 873, 875, 880, 885,\n", + " 888, 901, 910, 928, 934, 935, 937, 973, 975, 980, 983,\n", + " 1000, 1005, 1014, 1020, 1022, 1030, 1033, 1039, 1040, 1047, 1048,\n", + " 1050, 1056, 1073, 1110, 1118, 1122, 1124, 1134, 1138, 1141, 1142,\n", + " 1172, 1183, 1211, 1240, 1241, 1245, 1249, 1251, 1254, 1255, 1262,\n", + " 1265, 1275, 1283, 1286, 1296, 1312, 1314, 1319, 1342, 1353, 1358,\n", + " 1361, 1383, 1387, 1394])},\n", + " 4: {0: array([ 0, 2, 20, 22, 54, 61, 65, 78, 89, 101, 105,\n", + " 112, 119, 123, 127, 131, 133, 137, 147, 154, 182, 187,\n", + " 205, 208, 209, 226, 229, 230, 268, 275, 284, 309, 323,\n", + " 328, 337, 342, 355, 362, 380, 391, 413, 432, 435, 450,\n", + " 484, 488, 515, 542, 562, 587, 590, 596, 597, 601, 641,\n", + " 655, 661, 692, 701, 702, 719, 733, 737, 744, 770, 773,\n", + " 793, 804, 806, 809, 810, 821, 836, 859, 864, 879, 882,\n", + " 895, 907, 915, 920, 931, 952, 955, 957, 967, 978, 993,\n", + " 999, 1007, 1017, 1032, 1068, 1094, 1106, 1115, 1117, 1123, 1125,\n", + " 1127, 1129, 1153, 1190, 1191, 1208, 1223, 1246, 1284, 1294, 1298,\n", + " 1308, 1318, 1327, 1344, 1348, 1350, 1354, 1360, 1362, 1377, 1379,\n", + " 1381, 1389]),\n", + " 1: array([ 1, 3, 5, 7, 9, 10, 11, 12, 13, 15, 16,\n", + " 17, 18, 23, 25, 26, 27, 28, 29, 30, 31, 32,\n", + " 35, 36, 37, 38, 39, 41, 42, 43, 44, 45, 46,\n", + " 48, 49, 51, 53, 56, 58, 60, 64, 66, 67, 70,\n", + " 74, 76, 80, 82, 86, 87, 90, 92, 94, 97, 99,\n", + " 102, 103, 106, 107, 108, 110, 111, 114, 118, 122, 124,\n", + " 128, 129, 130, 132, 135, 138, 139, 140, 141, 142, 143,\n", + " 144, 145, 146, 148, 149, 156, 158, 159, 162, 163, 164,\n", + " 165, 166, 167, 168, 169, 170, 172, 174, 175, 177, 178,\n", + " 179, 186, 189, 190, 192, 193, 195, 197, 200, 201, 202,\n", + " 206, 207, 210, 211, 212, 213, 214, 216, 218, 219, 220,\n", + " 221, 222, 227, 232, 233, 234, 235, 236, 238, 240, 241,\n", + " 242, 244, 245, 246, 247, 248, 249, 251, 252, 253, 255,\n", + " 257, 259, 262, 263, 264, 265, 267, 269, 271, 272, 273,\n", + " 274, 277, 278, 281, 283, 285, 286, 288, 289, 290, 291,\n", + " 294, 296, 298, 300, 301, 302, 304, 305, 306, 307, 310,\n", + " 311, 312, 314, 315, 318, 322, 324, 325, 327, 331, 332,\n", + " 334, 335, 340, 341, 343, 344, 346, 347, 350, 352, 353,\n", + " 354, 357, 361, 365, 366, 368, 369, 371, 372, 373, 375,\n", + " 376, 378, 381, 383, 384, 385, 386, 388, 390, 393, 394,\n", + " 396, 397, 398, 400, 401, 402, 403, 405, 407, 408, 409,\n", + " 411, 412, 414, 416, 418, 419, 420, 421, 422, 423, 424,\n", + " 425, 426, 427, 428, 429, 430, 431, 433, 434, 436, 437,\n", + " 439, 442, 444, 445, 446, 448, 449, 452, 453, 455, 456,\n", + " 459, 460, 461, 462, 463, 464, 465, 467, 468, 471, 472,\n", + " 473, 475, 476, 477, 478, 480, 482, 483, 485, 486, 487,\n", + " 489, 490, 491, 492, 493, 495, 496, 499, 500, 501, 502,\n", + " 503, 504, 505, 507, 508, 509, 510, 511, 512, 513, 517,\n", + " 518, 520, 522, 527, 528, 529, 530, 532, 533, 534, 535,\n", + " 536, 537, 539, 540, 543, 544, 545, 546, 547, 548, 550,\n", + " 551, 552, 554, 555, 556, 557, 558, 560, 564, 565, 567,\n", + " 569, 570, 572, 574, 575, 579, 580, 581, 582, 583, 589,\n", + " 591, 592, 594, 598, 602, 603, 604, 607, 608, 609, 611,\n", + " 612, 614, 615, 616, 617, 618, 620, 622, 623, 624, 627,\n", + " 629, 630, 633, 634, 635, 636, 637, 639, 643, 644, 645,\n", + " 647, 648, 650, 652, 654, 657, 658, 662, 664, 665, 666,\n", + " 667, 668, 669, 673, 674, 675, 676, 677, 682, 684, 687,\n", + " 688, 689, 690, 694, 695, 697, 698, 700, 703, 704, 705,\n", + " 707, 709, 710, 711, 712, 714, 715, 716, 718, 725, 727,\n", + " 728, 730, 731, 732, 739, 741, 742, 743, 746, 747, 750,\n", + " 756, 757, 758, 759, 760, 761, 762, 764, 765, 766, 767,\n", + " 768, 769, 772, 774, 775, 776, 777, 778, 779, 780, 782,\n", + " 785, 789, 790, 792, 796, 798, 799, 803, 811, 812, 814,\n", + " 817, 819, 820, 823, 824, 828, 830, 831, 832, 833, 834,\n", + " 835, 837, 839, 840, 841, 842, 843, 844, 845, 846, 848,\n", + " 849, 850, 851, 852, 853, 856, 857, 860, 861, 862, 863,\n", + " 865, 867, 869, 871, 872, 874, 876, 878, 883, 884, 886,\n", + " 887, 890, 891, 893, 896, 899, 903, 904, 905, 906, 908,\n", + " 909, 911, 912, 913, 914, 916, 917, 918, 919, 921, 922,\n", + " 923, 924, 926, 929, 932, 933, 936, 938, 939, 940, 941,\n", + " 942, 943, 944, 946, 947, 950, 951, 953, 954, 960, 961,\n", + " 962, 964, 965, 968, 969, 970, 971, 972, 974, 979, 982,\n", + " 984, 985, 986, 987, 989, 990, 991, 992, 994, 996, 997,\n", + " 1001, 1002, 1003, 1006, 1008, 1009, 1010, 1011, 1013, 1019, 1021,\n", + " 1023, 1025, 1026, 1027, 1028, 1029, 1031, 1034, 1036, 1037, 1038,\n", + " 1041, 1042, 1043, 1044, 1045, 1046, 1052, 1053, 1054, 1057, 1060,\n", + " 1062, 1063, 1064, 1065, 1069, 1070, 1071, 1075, 1076, 1079, 1080,\n", + " 1082, 1083, 1084, 1085, 1086, 1088, 1092, 1095, 1100, 1101, 1102,\n", + " 1103, 1105, 1108, 1109, 1111, 1113, 1119, 1121, 1126, 1130, 1131,\n", + " 1132, 1135, 1137, 1144, 1145, 1146, 1148, 1150, 1151, 1154, 1155,\n", + " 1157, 1158, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1168, 1169,\n", + " 1170, 1171, 1173, 1174, 1175, 1177, 1179, 1180, 1182, 1184, 1185,\n", + " 1186, 1192, 1193, 1195, 1196, 1198, 1199, 1200, 1201, 1202, 1203,\n", + " 1204, 1206, 1209, 1210, 1212, 1214, 1217, 1218, 1219, 1220, 1225,\n", + " 1226, 1227, 1228, 1230, 1231, 1232, 1234, 1236, 1237, 1239, 1243,\n", + " 1244, 1247, 1252, 1253, 1258, 1259, 1261, 1264, 1266, 1267, 1268,\n", + " 1269, 1272, 1273, 1274, 1277, 1278, 1279, 1280, 1282, 1289, 1291,\n", + " 1292, 1297, 1299, 1301, 1303, 1305, 1306, 1309, 1311, 1315, 1316,\n", + " 1317, 1320, 1321, 1322, 1323, 1324, 1326, 1328, 1329, 1330, 1332,\n", + " 1333, 1334, 1335, 1336, 1337, 1339, 1341, 1345, 1346, 1349, 1351,\n", + " 1355, 1359, 1363, 1364, 1367, 1368, 1369, 1370, 1371, 1372, 1373,\n", + " 1374, 1376, 1378, 1380, 1384, 1385, 1386, 1388, 1390, 1392, 1396,\n", + " 1398, 1399]),\n", + " 2: array([ 4, 14, 24, 33, 34, 40, 50, 57, 62, 69, 71,\n", + " 72, 73, 75, 79, 81, 83, 84, 85, 91, 95, 96,\n", + " 100, 113, 116, 117, 125, 126, 134, 136, 152, 157, 160,\n", + " 161, 171, 173, 176, 180, 183, 185, 188, 191, 196, 198,\n", + " 203, 204, 215, 217, 223, 224, 225, 237, 243, 250, 256,\n", + " 260, 266, 270, 276, 279, 287, 293, 303, 308, 313, 317,\n", + " 319, 320, 321, 326, 329, 330, 333, 345, 349, 356, 358,\n", + " 359, 360, 364, 367, 370, 374, 377, 379, 382, 387, 395,\n", + " 406, 410, 415, 417, 438, 440, 441, 443, 447, 451, 457,\n", + " 458, 466, 469, 470, 479, 481, 498, 506, 514, 516, 519,\n", + " 523, 524, 525, 531, 538, 541, 549, 566, 568, 571, 576,\n", + " 577, 584, 585, 586, 588, 593, 595, 600, 610, 613, 621,\n", + " 625, 626, 631, 632, 638, 642, 646, 649, 651, 653, 656,\n", + " 659, 660, 663, 671, 679, 681, 685, 693, 706, 713, 721,\n", + " 722, 723, 724, 729, 734, 735, 736, 738, 745, 749, 751,\n", + " 752, 753, 754, 755, 771, 781, 784, 787, 788, 791, 794,\n", + " 795, 800, 801, 805, 807, 808, 813, 822, 826, 827, 829,\n", + " 847, 854, 855, 866, 870, 877, 881, 889, 892, 894, 897,\n", + " 898, 900, 902, 925, 927, 930, 945, 948, 949, 956, 958,\n", + " 959, 963, 966, 976, 977, 981, 988, 995, 998, 1004, 1012,\n", + " 1015, 1016, 1018, 1024, 1035, 1049, 1051, 1055, 1058, 1059, 1061,\n", + " 1066, 1067, 1072, 1074, 1077, 1078, 1081, 1087, 1089, 1090, 1091,\n", + " 1093, 1096, 1097, 1098, 1099, 1104, 1107, 1112, 1114, 1116, 1120,\n", + " 1128, 1133, 1136, 1139, 1140, 1143, 1147, 1149, 1152, 1156, 1159,\n", + " 1167, 1176, 1178, 1181, 1187, 1188, 1189, 1194, 1197, 1205, 1207,\n", + " 1213, 1215, 1216, 1221, 1222, 1224, 1229, 1233, 1235, 1238, 1242,\n", + " 1248, 1250, 1256, 1257, 1260, 1263, 1270, 1271, 1276, 1281, 1285,\n", + " 1287, 1288, 1290, 1293, 1295, 1300, 1302, 1304, 1307, 1310, 1313,\n", + " 1325, 1331, 1338, 1340, 1343, 1347, 1352, 1356, 1357, 1365, 1366,\n", + " 1375, 1382, 1391, 1393, 1395, 1397]),\n", + " 3: array([ 6, 8, 19, 21, 47, 52, 55, 59, 63, 68, 77,\n", + " 88, 93, 98, 104, 109, 115, 120, 121, 150, 151, 153,\n", + " 155, 181, 184, 194, 199, 228, 231, 239, 254, 258, 261,\n", + " 280, 282, 292, 295, 297, 299, 316, 336, 338, 339, 348,\n", + " 351, 363, 389, 392, 399, 404, 454, 474, 494, 497, 521,\n", + " 526, 553, 559, 561, 563, 573, 578, 599, 605, 606, 619,\n", + " 628, 640, 670, 672, 678, 680, 683, 686, 691, 696, 699,\n", + " 708, 717, 720, 726, 740, 748, 763, 783, 786, 797, 802,\n", + " 815, 816, 818, 825, 838, 858, 868, 873, 875, 880, 885,\n", + " 888, 901, 910, 928, 934, 935, 937, 973, 975, 980, 983,\n", + " 1000, 1005, 1014, 1020, 1022, 1030, 1033, 1039, 1040, 1047, 1048,\n", + " 1050, 1056, 1073, 1110, 1118, 1122, 1124, 1134, 1138, 1141, 1142,\n", + " 1172, 1183, 1211, 1240, 1241, 1245, 1249, 1251, 1254, 1255, 1262,\n", + " 1265, 1275, 1283, 1286, 1296, 1312, 1314, 1319, 1342, 1353, 1358,\n", + " 1361, 1383, 1387, 1394])},\n", + " 5: {0: array([ 0, 2, 20, 22, 54, 61, 65, 78, 89, 101, 105,\n", + " 112, 119, 123, 127, 131, 133, 137, 147, 154, 182, 187,\n", + " 205, 208, 209, 226, 229, 230, 268, 275, 284, 309, 323,\n", + " 328, 337, 342, 355, 362, 380, 391, 413, 432, 435, 450,\n", + " 484, 488, 515, 542, 562, 587, 590, 596, 597, 601, 641,\n", + " 655, 661, 692, 701, 719, 733, 737, 744, 770, 773, 793,\n", + " 804, 806, 809, 810, 821, 836, 859, 864, 879, 882, 895,\n", + " 907, 915, 920, 931, 952, 955, 957, 967, 978, 993, 999,\n", + " 1007, 1017, 1032, 1068, 1094, 1106, 1115, 1117, 1123, 1125, 1127,\n", + " 1129, 1153, 1190, 1191, 1208, 1223, 1246, 1284, 1294, 1298, 1308,\n", + " 1318, 1327, 1344, 1348, 1350, 1354, 1360, 1362, 1377, 1379, 1381,\n", + " 1389]),\n", + " 1: array([ 1, 3, 5, 7, 9, 10, 11, 12, 13, 15, 16,\n", + " 17, 18, 23, 25, 26, 27, 28, 29, 30, 31, 32,\n", + " 35, 36, 37, 38, 39, 41, 42, 43, 44, 45, 46,\n", + " 48, 49, 51, 53, 56, 58, 60, 64, 66, 67, 70,\n", + " 74, 76, 80, 82, 86, 87, 90, 92, 94, 97, 99,\n", + " 102, 103, 106, 107, 108, 110, 111, 114, 118, 122, 124,\n", + " 128, 129, 130, 132, 135, 138, 139, 140, 141, 142, 143,\n", + " 144, 145, 146, 148, 149, 156, 158, 159, 162, 163, 164,\n", + " 165, 166, 167, 168, 169, 170, 172, 174, 175, 177, 178,\n", + " 179, 186, 189, 190, 192, 193, 195, 197, 200, 201, 202,\n", + " 206, 207, 210, 211, 212, 213, 214, 216, 218, 219, 220,\n", + " 221, 222, 227, 232, 233, 234, 235, 236, 238, 240, 241,\n", + " 242, 244, 245, 246, 247, 248, 249, 251, 252, 253, 255,\n", + " 257, 259, 262, 263, 264, 265, 267, 269, 271, 272, 273,\n", + " 274, 277, 278, 281, 283, 285, 286, 288, 289, 290, 291,\n", + " 294, 296, 298, 300, 301, 302, 304, 305, 306, 307, 310,\n", + " 311, 312, 314, 315, 318, 322, 324, 325, 327, 331, 332,\n", + " 334, 335, 340, 341, 343, 344, 346, 347, 350, 352, 353,\n", + " 354, 357, 361, 365, 366, 368, 369, 371, 372, 373, 375,\n", + " 376, 378, 381, 383, 384, 385, 386, 388, 390, 393, 394,\n", + " 396, 397, 398, 400, 401, 402, 403, 405, 407, 408, 409,\n", + " 411, 412, 414, 416, 418, 419, 420, 421, 422, 423, 424,\n", + " 425, 426, 427, 428, 429, 430, 431, 433, 434, 436, 437,\n", + " 439, 442, 444, 445, 446, 448, 449, 452, 453, 455, 456,\n", + " 459, 460, 461, 462, 463, 464, 465, 467, 468, 471, 472,\n", + " 473, 475, 476, 477, 478, 480, 482, 483, 485, 486, 487,\n", + " 489, 490, 491, 492, 493, 495, 496, 499, 500, 501, 502,\n", + " 503, 504, 505, 507, 508, 509, 510, 511, 512, 513, 517,\n", + " 518, 520, 522, 527, 528, 529, 530, 532, 533, 534, 535,\n", + " 536, 537, 539, 540, 543, 544, 545, 546, 547, 548, 550,\n", + " 551, 552, 554, 555, 556, 557, 558, 560, 564, 565, 567,\n", + " 569, 570, 572, 574, 575, 579, 580, 581, 582, 583, 589,\n", + " 591, 592, 594, 598, 602, 603, 604, 607, 608, 609, 611,\n", + " 612, 614, 615, 616, 617, 618, 620, 622, 623, 624, 627,\n", + " 629, 630, 633, 634, 635, 636, 637, 639, 643, 644, 645,\n", + " 647, 648, 650, 652, 654, 657, 658, 662, 664, 665, 666,\n", + " 667, 668, 669, 673, 674, 675, 676, 677, 682, 684, 687,\n", + " 688, 689, 690, 694, 695, 697, 698, 700, 703, 704, 705,\n", + " 707, 709, 710, 711, 712, 714, 715, 716, 718, 725, 727,\n", + " 728, 730, 731, 732, 739, 741, 742, 743, 746, 747, 750,\n", + " 756, 757, 758, 759, 760, 761, 762, 764, 765, 766, 767,\n", + " 768, 769, 772, 774, 775, 776, 777, 778, 779, 780, 782,\n", + " 785, 789, 790, 792, 796, 798, 799, 803, 811, 812, 814,\n", + " 817, 819, 820, 823, 824, 828, 830, 831, 832, 833, 834,\n", + " 835, 837, 839, 840, 841, 842, 843, 844, 845, 846, 848,\n", + " 849, 850, 851, 852, 853, 856, 857, 860, 861, 862, 863,\n", + " 865, 867, 869, 871, 872, 874, 876, 878, 883, 884, 886,\n", + " 887, 890, 891, 893, 896, 899, 903, 904, 905, 906, 908,\n", + " 909, 911, 912, 913, 914, 916, 917, 918, 919, 921, 922,\n", + " 923, 924, 926, 929, 932, 933, 936, 938, 939, 940, 941,\n", + " 942, 943, 944, 946, 947, 950, 951, 953, 954, 960, 961,\n", + " 962, 964, 965, 968, 969, 970, 971, 972, 974, 979, 982,\n", + " 984, 985, 986, 987, 989, 990, 991, 992, 994, 996, 997,\n", + " 1001, 1002, 1003, 1006, 1008, 1009, 1010, 1011, 1013, 1019, 1021,\n", + " 1023, 1025, 1026, 1027, 1028, 1029, 1031, 1034, 1036, 1037, 1038,\n", + " 1041, 1042, 1043, 1044, 1045, 1046, 1052, 1053, 1054, 1057, 1060,\n", + " 1062, 1063, 1064, 1065, 1069, 1070, 1071, 1075, 1076, 1079, 1080,\n", + " 1082, 1083, 1084, 1085, 1086, 1088, 1092, 1095, 1100, 1101, 1102,\n", + " 1103, 1105, 1108, 1109, 1111, 1113, 1119, 1121, 1126, 1130, 1131,\n", + " 1132, 1135, 1137, 1144, 1145, 1146, 1148, 1150, 1151, 1154, 1155,\n", + " 1157, 1158, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1168, 1169,\n", + " 1170, 1171, 1173, 1174, 1175, 1177, 1179, 1180, 1182, 1184, 1185,\n", + " 1186, 1192, 1193, 1195, 1196, 1198, 1199, 1200, 1201, 1202, 1203,\n", + " 1204, 1206, 1209, 1210, 1212, 1214, 1217, 1218, 1219, 1220, 1225,\n", + " 1226, 1227, 1228, 1230, 1231, 1232, 1234, 1236, 1237, 1239, 1243,\n", + " 1244, 1247, 1252, 1253, 1258, 1259, 1261, 1264, 1266, 1267, 1268,\n", + " 1269, 1272, 1273, 1274, 1277, 1278, 1279, 1280, 1282, 1289, 1291,\n", + " 1292, 1297, 1299, 1301, 1303, 1305, 1306, 1309, 1311, 1315, 1316,\n", + " 1317, 1320, 1321, 1322, 1323, 1324, 1326, 1328, 1329, 1330, 1332,\n", + " 1333, 1334, 1335, 1336, 1337, 1339, 1341, 1345, 1346, 1349, 1351,\n", + " 1355, 1359, 1363, 1364, 1367, 1368, 1369, 1370, 1371, 1372, 1373,\n", + " 1374, 1376, 1378, 1380, 1384, 1385, 1386, 1388, 1390, 1392, 1396,\n", + " 1398, 1399]),\n", + " 2: array([ 4, 14, 24, 33, 34, 40, 50, 57, 62, 69, 71,\n", + " 72, 73, 75, 79, 81, 83, 84, 85, 91, 95, 96,\n", + " 100, 113, 116, 117, 125, 126, 134, 136, 152, 157, 160,\n", + " 161, 171, 173, 176, 180, 183, 185, 188, 191, 196, 198,\n", + " 203, 204, 215, 217, 223, 224, 225, 237, 243, 250, 256,\n", + " 260, 266, 270, 276, 279, 287, 293, 303, 308, 313, 317,\n", + " 319, 320, 321, 326, 329, 330, 333, 345, 349, 356, 358,\n", + " 359, 360, 364, 367, 370, 374, 377, 379, 382, 387, 395,\n", + " 406, 410, 415, 417, 438, 440, 441, 443, 447, 451, 457,\n", + " 458, 466, 469, 470, 479, 481, 498, 506, 514, 516, 519,\n", + " 523, 524, 525, 531, 538, 541, 549, 566, 568, 571, 576,\n", + " 577, 584, 585, 586, 588, 593, 595, 600, 610, 613, 621,\n", + " 625, 626, 631, 632, 638, 642, 646, 649, 651, 653, 656,\n", + " 659, 660, 663, 671, 679, 681, 685, 693, 706, 713, 721,\n", + " 722, 723, 724, 729, 734, 735, 736, 738, 745, 749, 751,\n", + " 752, 753, 754, 755, 771, 781, 784, 787, 788, 791, 794,\n", + " 795, 800, 801, 805, 807, 808, 813, 822, 826, 827, 829,\n", + " 847, 854, 855, 866, 870, 877, 881, 889, 892, 894, 897,\n", + " 898, 900, 902, 925, 927, 930, 945, 948, 949, 956, 958,\n", + " 959, 963, 966, 976, 977, 981, 988, 995, 998, 1004, 1012,\n", + " 1015, 1016, 1018, 1024, 1035, 1049, 1051, 1055, 1058, 1059, 1061,\n", + " 1066, 1067, 1072, 1074, 1077, 1078, 1081, 1087, 1089, 1090, 1091,\n", + " 1093, 1096, 1097, 1098, 1099, 1104, 1107, 1112, 1114, 1116, 1120,\n", + " 1128, 1133, 1136, 1139, 1140, 1143, 1147, 1149, 1152, 1156, 1159,\n", + " 1167, 1176, 1178, 1181, 1187, 1188, 1189, 1194, 1197, 1205, 1207,\n", + " 1213, 1215, 1216, 1221, 1222, 1224, 1229, 1233, 1235, 1238, 1242,\n", + " 1248, 1250, 1256, 1257, 1260, 1263, 1270, 1271, 1276, 1281, 1285,\n", + " 1287, 1288, 1290, 1293, 1295, 1300, 1302, 1304, 1307, 1310, 1313,\n", + " 1325, 1331, 1338, 1340, 1343, 1347, 1352, 1356, 1357, 1365, 1366,\n", + " 1375, 1382, 1391, 1393, 1395, 1397]),\n", + " 3: array([ 6, 8, 19, 21, 47, 52, 55, 59, 63, 68, 77,\n", + " 88, 93, 98, 104, 109, 115, 120, 121, 150, 151, 153,\n", + " 155, 181, 184, 194, 199, 228, 231, 239, 254, 258, 261,\n", + " 280, 282, 292, 295, 297, 299, 316, 336, 338, 339, 348,\n", + " 351, 363, 389, 392, 399, 404, 454, 474, 494, 497, 521,\n", + " 526, 553, 559, 561, 563, 573, 578, 599, 605, 606, 619,\n", + " 628, 640, 670, 672, 678, 680, 683, 686, 691, 696, 699,\n", + " 708, 717, 720, 726, 740, 748, 763, 783, 786, 797, 802,\n", + " 815, 816, 818, 825, 838, 858, 868, 873, 875, 880, 885,\n", + " 888, 901, 910, 928, 934, 935, 937, 973, 975, 980, 983,\n", + " 1000, 1005, 1014, 1020, 1022, 1030, 1033, 1039, 1040, 1047, 1048,\n", + " 1050, 1056, 1073, 1110, 1118, 1122, 1124, 1134, 1138, 1141, 1142,\n", + " 1172, 1183, 1211, 1240, 1241, 1245, 1249, 1251, 1254, 1255, 1262,\n", + " 1265, 1275, 1283, 1286, 1296, 1312, 1314, 1319, 1342, 1353, 1358,\n", + " 1361, 1383, 1387, 1394]),\n", + " 4: array([702])},\n", + " 6: {0: array([ 0, 2, 20, 22, 54, 61, 65, 78, 89, 101, 105,\n", + " 112, 119, 123, 127, 131, 133, 137, 147, 154, 182, 187,\n", + " 205, 208, 209, 226, 229, 230, 268, 275, 284, 309, 323,\n", + " 328, 337, 342, 355, 362, 380, 391, 413, 432, 435, 450,\n", + " 484, 488, 515, 542, 562, 587, 590, 596, 597, 601, 641,\n", + " 655, 661, 692, 701, 719, 733, 737, 744, 770, 773, 793,\n", + " 804, 806, 809, 810, 821, 836, 859, 864, 879, 882, 895,\n", + " 907, 915, 920, 931, 952, 955, 957, 967, 978, 993, 999,\n", + " 1007, 1017, 1032, 1068, 1094, 1106, 1115, 1117, 1123, 1125, 1127,\n", + " 1129, 1153, 1190, 1191, 1208, 1223, 1246, 1284, 1294, 1298, 1308,\n", + " 1318, 1327, 1344, 1348, 1350, 1354, 1360, 1362, 1377, 1379, 1381,\n", + " 1389]),\n", + " 1: array([ 1, 3, 5, 7, 9, 10, 11, 12, 13, 15, 16,\n", + " 17, 18, 23, 25, 26, 27, 28, 29, 30, 31, 32,\n", + " 35, 36, 37, 38, 39, 41, 42, 43, 44, 45, 46,\n", + " 48, 49, 51, 53, 56, 58, 60, 64, 66, 67, 70,\n", + " 74, 76, 80, 82, 86, 87, 90, 92, 94, 97, 99,\n", + " 102, 103, 106, 107, 108, 110, 111, 114, 118, 122, 124,\n", + " 128, 129, 130, 132, 135, 138, 139, 140, 141, 142, 143,\n", + " 144, 145, 146, 148, 149, 156, 158, 159, 162, 163, 164,\n", + " 165, 166, 167, 168, 169, 170, 172, 174, 175, 177, 178,\n", + " 179, 186, 189, 190, 192, 193, 195, 197, 200, 201, 202,\n", + " 206, 207, 210, 211, 212, 213, 214, 216, 218, 219, 220,\n", + " 221, 222, 227, 232, 233, 234, 235, 236, 238, 240, 241,\n", + " 242, 244, 245, 246, 247, 248, 249, 251, 252, 253, 255,\n", + " 257, 259, 262, 263, 264, 265, 267, 269, 271, 272, 273,\n", + " 274, 277, 278, 281, 283, 285, 286, 288, 289, 290, 291,\n", + " 294, 296, 298, 300, 301, 302, 304, 305, 306, 307, 310,\n", + " 311, 312, 314, 315, 318, 322, 324, 325, 327, 331, 332,\n", + " 334, 335, 340, 341, 343, 344, 346, 347, 350, 352, 353,\n", + " 354, 357, 361, 365, 366, 368, 369, 371, 372, 373, 375,\n", + " 376, 378, 381, 383, 384, 385, 386, 388, 390, 393, 394,\n", + " 396, 397, 398, 400, 401, 402, 403, 405, 407, 408, 409,\n", + " 411, 412, 414, 416, 418, 419, 420, 421, 422, 423, 424,\n", + " 425, 426, 427, 428, 429, 430, 431, 433, 434, 436, 437,\n", + " 439, 442, 444, 445, 446, 448, 449, 452, 453, 455, 456,\n", + " 459, 460, 461, 462, 463, 464, 465, 467, 468, 471, 472,\n", + " 473, 475, 476, 477, 478, 480, 482, 483, 485, 486, 487,\n", + " 489, 490, 491, 492, 493, 495, 496, 499, 500, 501, 502,\n", + " 503, 504, 505, 507, 508, 509, 510, 511, 512, 513, 517,\n", + " 518, 520, 522, 527, 528, 529, 530, 532, 533, 534, 535,\n", + " 536, 537, 539, 540, 543, 544, 545, 546, 547, 548, 550,\n", + " 551, 552, 554, 555, 556, 557, 558, 560, 564, 565, 567,\n", + " 569, 570, 572, 574, 575, 579, 580, 581, 582, 583, 589,\n", + " 591, 592, 594, 598, 602, 603, 604, 607, 608, 609, 611,\n", + " 612, 614, 615, 616, 617, 618, 620, 622, 623, 624, 627,\n", + " 629, 630, 633, 634, 635, 636, 637, 639, 643, 644, 645,\n", + " 647, 648, 650, 652, 654, 657, 658, 662, 664, 665, 666,\n", + " 667, 668, 669, 673, 674, 675, 676, 677, 682, 684, 687,\n", + " 688, 689, 690, 694, 695, 697, 698, 700, 703, 704, 705,\n", + " 707, 709, 710, 711, 712, 714, 715, 716, 718, 725, 727,\n", + " 728, 730, 731, 732, 739, 741, 742, 743, 746, 747, 750,\n", + " 756, 757, 758, 759, 760, 761, 762, 764, 765, 766, 767,\n", + " 768, 769, 772, 774, 775, 776, 777, 778, 779, 780, 782,\n", + " 785, 789, 790, 792, 796, 798, 799, 803, 811, 812, 814,\n", + " 817, 819, 820, 823, 824, 828, 830, 831, 832, 833, 834,\n", + " 835, 837, 839, 840, 841, 842, 843, 844, 845, 846, 848,\n", + " 849, 850, 851, 852, 853, 856, 857, 860, 861, 862, 863,\n", + " 865, 867, 869, 871, 872, 874, 876, 878, 883, 884, 886,\n", + " 887, 890, 891, 893, 896, 899, 903, 904, 905, 906, 908,\n", + " 909, 911, 912, 913, 914, 916, 917, 918, 919, 921, 922,\n", + " 923, 924, 926, 929, 932, 933, 936, 938, 939, 940, 941,\n", + " 942, 943, 944, 946, 947, 950, 951, 953, 954, 960, 961,\n", + " 962, 964, 965, 968, 969, 970, 971, 972, 974, 979, 982,\n", + " 984, 985, 986, 987, 989, 990, 991, 992, 994, 996, 997,\n", + " 1001, 1002, 1003, 1006, 1008, 1009, 1010, 1011, 1013, 1019, 1021,\n", + " 1023, 1025, 1026, 1027, 1028, 1029, 1031, 1034, 1036, 1037, 1038,\n", + " 1041, 1042, 1043, 1044, 1045, 1046, 1052, 1053, 1054, 1057, 1060,\n", + " 1062, 1063, 1064, 1065, 1069, 1070, 1071, 1075, 1076, 1079, 1080,\n", + " 1082, 1083, 1084, 1085, 1086, 1088, 1092, 1095, 1100, 1101, 1102,\n", + " 1103, 1105, 1108, 1109, 1111, 1113, 1119, 1121, 1126, 1130, 1131,\n", + " 1132, 1135, 1137, 1144, 1145, 1146, 1148, 1150, 1151, 1154, 1155,\n", + " 1157, 1158, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1168, 1169,\n", + " 1170, 1171, 1173, 1174, 1175, 1177, 1179, 1180, 1182, 1184, 1185,\n", + " 1186, 1192, 1193, 1195, 1196, 1198, 1199, 1200, 1201, 1202, 1203,\n", + " 1204, 1206, 1209, 1210, 1212, 1214, 1217, 1218, 1219, 1220, 1225,\n", + " 1226, 1227, 1228, 1230, 1231, 1232, 1234, 1236, 1237, 1239, 1243,\n", + " 1244, 1247, 1252, 1253, 1258, 1259, 1261, 1264, 1266, 1267, 1268,\n", + " 1269, 1272, 1273, 1274, 1277, 1278, 1279, 1280, 1282, 1289, 1291,\n", + " 1292, 1297, 1299, 1301, 1303, 1305, 1306, 1309, 1311, 1315, 1316,\n", + " 1317, 1320, 1321, 1322, 1323, 1324, 1326, 1328, 1329, 1330, 1332,\n", + " 1333, 1334, 1335, 1336, 1337, 1339, 1341, 1345, 1346, 1349, 1351,\n", + " 1355, 1359, 1363, 1364, 1367, 1368, 1369, 1370, 1371, 1372, 1373,\n", + " 1374, 1376, 1378, 1380, 1384, 1385, 1386, 1388, 1390, 1392, 1396,\n", + " 1398, 1399]),\n", + " 2: array([ 4, 14, 24, 33, 34, 40, 50, 57, 62, 69, 71,\n", + " 72, 73, 75, 79, 81, 83, 84, 85, 91, 95, 96,\n", + " 100, 113, 116, 117, 125, 126, 134, 136, 152, 157, 160,\n", + " 161, 171, 173, 176, 183, 185, 191, 198, 203, 204, 215,\n", + " 217, 224, 225, 237, 243, 250, 256, 260, 266, 270, 276,\n", + " 287, 293, 303, 308, 313, 317, 319, 320, 321, 326, 329,\n", + " 330, 333, 345, 356, 358, 359, 360, 364, 367, 370, 374,\n", + " 377, 379, 382, 387, 395, 406, 410, 415, 417, 438, 440,\n", + " 441, 443, 447, 451, 457, 458, 466, 469, 470, 479, 481,\n", + " 498, 506, 514, 516, 519, 523, 524, 525, 531, 538, 541,\n", + " 549, 566, 568, 571, 577, 584, 585, 586, 588, 593, 595,\n", + " 600, 610, 613, 621, 625, 626, 631, 632, 638, 642, 646,\n", + " 649, 651, 653, 656, 659, 660, 663, 679, 681, 685, 693,\n", + " 706, 713, 721, 722, 723, 729, 734, 735, 738, 745, 749,\n", + " 751, 752, 753, 754, 755, 771, 781, 784, 787, 788, 791,\n", + " 795, 800, 801, 805, 807, 808, 813, 822, 826, 829, 847,\n", + " 854, 855, 866, 870, 877, 881, 889, 892, 894, 897, 898,\n", + " 900, 902, 927, 930, 945, 948, 949, 956, 958, 959, 963,\n", + " 966, 976, 977, 981, 988, 995, 998, 1004, 1012, 1015, 1016,\n", + " 1018, 1024, 1035, 1049, 1051, 1055, 1058, 1059, 1061, 1066, 1067,\n", + " 1072, 1074, 1077, 1078, 1081, 1087, 1089, 1090, 1091, 1093, 1096,\n", + " 1097, 1098, 1099, 1104, 1107, 1112, 1114, 1116, 1120, 1128, 1133,\n", + " 1136, 1139, 1140, 1143, 1149, 1152, 1156, 1159, 1167, 1176, 1178,\n", + " 1181, 1187, 1188, 1189, 1194, 1197, 1205, 1207, 1213, 1216, 1221,\n", + " 1222, 1224, 1229, 1233, 1235, 1238, 1242, 1248, 1250, 1256, 1257,\n", + " 1260, 1263, 1270, 1271, 1276, 1281, 1285, 1287, 1288, 1290, 1293,\n", + " 1295, 1300, 1302, 1304, 1307, 1310, 1313, 1325, 1331, 1338, 1340,\n", + " 1343, 1347, 1352, 1356, 1357, 1365, 1366, 1375, 1382, 1391, 1393,\n", + " 1395, 1397]),\n", + " 3: array([ 6, 8, 19, 21, 47, 52, 55, 59, 63, 68, 77,\n", + " 88, 93, 98, 104, 109, 115, 120, 121, 150, 151, 153,\n", + " 155, 181, 184, 194, 199, 228, 231, 239, 254, 258, 261,\n", + " 280, 282, 292, 295, 297, 299, 316, 336, 338, 339, 348,\n", + " 351, 363, 389, 392, 399, 404, 454, 474, 494, 497, 521,\n", + " 526, 553, 559, 561, 563, 573, 578, 599, 605, 606, 619,\n", + " 628, 640, 670, 672, 678, 680, 683, 686, 691, 696, 699,\n", + " 708, 717, 720, 726, 740, 748, 763, 783, 786, 797, 802,\n", + " 815, 816, 818, 825, 838, 858, 868, 873, 875, 880, 885,\n", + " 888, 901, 910, 928, 934, 935, 937, 973, 975, 980, 983,\n", + " 1000, 1005, 1014, 1020, 1022, 1030, 1033, 1039, 1040, 1047, 1048,\n", + " 1050, 1056, 1073, 1110, 1118, 1122, 1124, 1134, 1138, 1141, 1142,\n", + " 1172, 1183, 1211, 1240, 1241, 1245, 1249, 1251, 1254, 1255, 1262,\n", + " 1265, 1275, 1283, 1286, 1296, 1312, 1314, 1319, 1342, 1353, 1358,\n", + " 1361, 1383, 1387, 1394]),\n", + " 4: array([ 180, 188, 196, 223, 279, 349, 576, 671, 724, 736, 794,\n", + " 827, 925, 1147, 1215]),\n", + " 5: array([702])},\n", + " 7: {0: array([ 0, 2, 78, 101, 105, 123, 133, 182, 205, 226, 268,\n", + " 275, 309, 323, 362, 380, 413, 435, 515, 601, 641, 655,\n", + " 733, 737, 744, 773, 793, 810, 864, 879, 907, 915, 920,\n", + " 952, 957, 1007, 1117, 1153, 1191, 1208, 1284, 1298, 1308, 1327,\n", + " 1344, 1348, 1362]),\n", + " 1: array([ 1, 3, 5, 7, 9, 10, 11, 12, 13, 15, 16,\n", + " 17, 18, 23, 25, 26, 27, 28, 29, 30, 31, 32,\n", + " 35, 36, 37, 38, 39, 41, 42, 43, 44, 45, 46,\n", + " 48, 49, 51, 53, 56, 58, 60, 64, 66, 67, 70,\n", + " 74, 76, 80, 82, 86, 87, 90, 92, 94, 97, 99,\n", + " 102, 103, 106, 107, 108, 110, 111, 114, 118, 122, 124,\n", + " 128, 129, 130, 132, 135, 138, 139, 140, 141, 142, 143,\n", + " 144, 145, 146, 148, 149, 156, 158, 159, 162, 163, 164,\n", + " 165, 166, 167, 168, 169, 170, 172, 174, 175, 177, 178,\n", + " 179, 186, 189, 190, 192, 193, 195, 197, 200, 201, 202,\n", + " 206, 207, 210, 211, 212, 213, 214, 216, 218, 219, 220,\n", + " 221, 222, 227, 232, 233, 234, 235, 236, 238, 240, 241,\n", + " 242, 244, 245, 246, 247, 248, 249, 251, 252, 253, 255,\n", + " 257, 259, 262, 263, 264, 265, 267, 269, 271, 272, 273,\n", + " 274, 277, 278, 281, 283, 285, 286, 288, 289, 290, 291,\n", + " 294, 296, 298, 300, 301, 302, 304, 305, 306, 307, 310,\n", + " 311, 312, 314, 315, 318, 322, 324, 325, 327, 331, 332,\n", + " 334, 335, 340, 341, 343, 344, 346, 347, 350, 352, 353,\n", + " 354, 357, 361, 365, 366, 368, 369, 371, 372, 373, 375,\n", + " 376, 378, 381, 383, 384, 385, 386, 388, 390, 393, 394,\n", + " 396, 397, 398, 400, 401, 402, 403, 405, 407, 408, 409,\n", + " 411, 412, 414, 416, 418, 419, 420, 421, 422, 423, 424,\n", + " 425, 426, 427, 428, 429, 430, 431, 433, 434, 436, 437,\n", + " 439, 442, 444, 445, 446, 448, 449, 452, 453, 455, 456,\n", + " 459, 460, 461, 462, 463, 464, 465, 467, 468, 471, 472,\n", + " 473, 475, 476, 477, 478, 480, 482, 483, 485, 486, 487,\n", + " 489, 490, 491, 492, 493, 495, 496, 499, 500, 501, 502,\n", + " 503, 504, 505, 507, 508, 509, 510, 511, 512, 513, 517,\n", + " 518, 520, 522, 527, 528, 529, 530, 532, 533, 534, 535,\n", + " 536, 537, 539, 540, 543, 544, 545, 546, 547, 548, 550,\n", + " 551, 552, 554, 555, 556, 557, 558, 560, 564, 565, 567,\n", + " 569, 570, 572, 574, 575, 579, 580, 581, 582, 583, 589,\n", + " 591, 592, 594, 598, 602, 603, 604, 607, 608, 609, 611,\n", + " 612, 614, 615, 616, 617, 618, 620, 622, 623, 624, 627,\n", + " 629, 630, 633, 634, 635, 636, 637, 639, 643, 644, 645,\n", + " 647, 648, 650, 652, 654, 657, 658, 662, 664, 665, 666,\n", + " 667, 668, 669, 673, 674, 675, 676, 677, 682, 684, 687,\n", + " 688, 689, 690, 694, 695, 697, 698, 700, 703, 704, 705,\n", + " 707, 709, 710, 711, 712, 714, 715, 716, 718, 725, 727,\n", + " 728, 730, 731, 732, 739, 741, 742, 743, 746, 747, 750,\n", + " 756, 757, 758, 759, 760, 761, 762, 764, 765, 766, 767,\n", + " 768, 769, 772, 774, 775, 776, 777, 778, 779, 780, 782,\n", + " 785, 789, 790, 792, 796, 798, 799, 803, 811, 812, 814,\n", + " 817, 819, 820, 823, 824, 828, 830, 831, 832, 833, 834,\n", + " 835, 837, 839, 840, 841, 842, 843, 844, 845, 846, 848,\n", + " 849, 850, 851, 852, 853, 856, 857, 860, 861, 862, 863,\n", + " 865, 867, 869, 871, 872, 874, 876, 878, 883, 884, 886,\n", + " 887, 890, 891, 893, 896, 899, 903, 904, 905, 906, 908,\n", + " 909, 911, 912, 913, 914, 916, 917, 918, 919, 921, 922,\n", + " 923, 924, 926, 929, 932, 933, 936, 938, 939, 940, 941,\n", + " 942, 943, 944, 946, 947, 950, 951, 953, 954, 960, 961,\n", + " 962, 964, 965, 968, 969, 970, 971, 972, 974, 979, 982,\n", + " 984, 985, 986, 987, 989, 990, 991, 992, 994, 996, 997,\n", + " 1001, 1002, 1003, 1006, 1008, 1009, 1010, 1011, 1013, 1019, 1021,\n", + " 1023, 1025, 1026, 1027, 1028, 1029, 1031, 1034, 1036, 1037, 1038,\n", + " 1041, 1042, 1043, 1044, 1045, 1046, 1052, 1053, 1054, 1057, 1060,\n", + " 1062, 1063, 1064, 1065, 1069, 1070, 1071, 1075, 1076, 1079, 1080,\n", + " 1082, 1083, 1084, 1085, 1086, 1088, 1092, 1095, 1100, 1101, 1102,\n", + " 1103, 1105, 1108, 1109, 1111, 1113, 1119, 1121, 1126, 1130, 1131,\n", + " 1132, 1135, 1137, 1144, 1145, 1146, 1148, 1150, 1151, 1154, 1155,\n", + " 1157, 1158, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1168, 1169,\n", + " 1170, 1171, 1173, 1174, 1175, 1177, 1179, 1180, 1182, 1184, 1185,\n", + " 1186, 1192, 1193, 1195, 1196, 1198, 1199, 1200, 1201, 1202, 1203,\n", + " 1204, 1206, 1209, 1210, 1212, 1214, 1217, 1218, 1219, 1220, 1225,\n", + " 1226, 1227, 1228, 1230, 1231, 1232, 1234, 1236, 1237, 1239, 1243,\n", + " 1244, 1247, 1252, 1253, 1258, 1259, 1261, 1264, 1266, 1267, 1268,\n", + " 1269, 1272, 1273, 1274, 1277, 1278, 1279, 1280, 1282, 1289, 1291,\n", + " 1292, 1297, 1299, 1301, 1303, 1305, 1306, 1309, 1311, 1315, 1316,\n", + " 1317, 1320, 1321, 1322, 1323, 1324, 1326, 1328, 1329, 1330, 1332,\n", + " 1333, 1334, 1335, 1336, 1337, 1339, 1341, 1345, 1346, 1349, 1351,\n", + " 1355, 1359, 1363, 1364, 1367, 1368, 1369, 1370, 1371, 1372, 1373,\n", + " 1374, 1376, 1378, 1380, 1384, 1385, 1386, 1388, 1390, 1392, 1396,\n", + " 1398, 1399]),\n", + " 2: array([ 4, 14, 24, 33, 34, 40, 50, 57, 62, 69, 71,\n", + " 72, 73, 75, 79, 81, 83, 84, 85, 91, 95, 96,\n", + " 100, 113, 116, 117, 125, 126, 134, 136, 152, 157, 160,\n", + " 161, 171, 173, 176, 183, 185, 191, 198, 203, 204, 215,\n", + " 217, 224, 225, 237, 243, 250, 256, 260, 266, 270, 276,\n", + " 287, 293, 303, 308, 313, 317, 319, 320, 321, 326, 329,\n", + " 330, 333, 345, 356, 358, 359, 360, 364, 367, 370, 374,\n", + " 377, 379, 382, 387, 395, 406, 410, 415, 417, 438, 440,\n", + " 441, 443, 447, 451, 457, 458, 466, 469, 470, 479, 481,\n", + " 498, 506, 514, 516, 519, 523, 524, 525, 531, 538, 541,\n", + " 549, 566, 568, 571, 577, 584, 585, 586, 588, 593, 595,\n", + " 600, 610, 613, 621, 625, 626, 631, 632, 638, 642, 646,\n", + " 649, 651, 653, 656, 659, 660, 663, 679, 681, 685, 693,\n", + " 706, 713, 721, 722, 723, 729, 734, 735, 738, 745, 749,\n", + " 751, 752, 753, 754, 755, 771, 781, 784, 787, 788, 791,\n", + " 795, 800, 801, 805, 807, 808, 813, 822, 826, 829, 847,\n", + " 854, 855, 866, 870, 877, 881, 889, 892, 894, 897, 898,\n", + " 900, 902, 927, 930, 945, 948, 949, 956, 958, 959, 963,\n", + " 966, 976, 977, 981, 988, 995, 998, 1004, 1012, 1015, 1016,\n", + " 1018, 1024, 1035, 1049, 1051, 1055, 1058, 1059, 1061, 1066, 1067,\n", + " 1072, 1074, 1077, 1078, 1081, 1087, 1089, 1090, 1091, 1093, 1096,\n", + " 1097, 1098, 1099, 1104, 1107, 1112, 1114, 1116, 1120, 1128, 1133,\n", + " 1136, 1139, 1140, 1143, 1149, 1152, 1156, 1159, 1167, 1176, 1178,\n", + " 1181, 1187, 1188, 1189, 1194, 1197, 1205, 1207, 1213, 1216, 1221,\n", + " 1222, 1224, 1229, 1233, 1235, 1238, 1242, 1248, 1250, 1256, 1257,\n", + " 1260, 1263, 1270, 1271, 1276, 1281, 1285, 1287, 1288, 1290, 1293,\n", + " 1295, 1300, 1302, 1304, 1307, 1310, 1313, 1325, 1331, 1338, 1340,\n", + " 1343, 1347, 1352, 1356, 1357, 1365, 1366, 1375, 1382, 1391, 1393,\n", + " 1395, 1397]),\n", + " 3: array([ 6, 8, 19, 21, 47, 52, 55, 59, 63, 68, 77,\n", + " 88, 93, 98, 104, 109, 115, 120, 121, 150, 151, 153,\n", + " 155, 181, 184, 194, 199, 228, 231, 239, 254, 258, 261,\n", + " 280, 282, 292, 295, 297, 299, 316, 336, 338, 339, 348,\n", + " 351, 363, 389, 392, 399, 404, 454, 474, 494, 497, 521,\n", + " 526, 553, 559, 561, 563, 573, 578, 599, 605, 606, 619,\n", + " 628, 640, 670, 672, 678, 680, 683, 686, 691, 696, 699,\n", + " 708, 717, 720, 726, 740, 748, 763, 783, 786, 797, 802,\n", + " 815, 816, 818, 825, 838, 858, 868, 873, 875, 880, 885,\n", + " 888, 901, 910, 928, 934, 935, 937, 973, 975, 980, 983,\n", + " 1000, 1005, 1014, 1020, 1022, 1030, 1033, 1039, 1040, 1047, 1048,\n", + " 1050, 1056, 1073, 1110, 1118, 1122, 1124, 1134, 1138, 1141, 1142,\n", + " 1172, 1183, 1211, 1240, 1241, 1245, 1249, 1251, 1254, 1255, 1262,\n", + " 1265, 1275, 1283, 1286, 1296, 1312, 1314, 1319, 1342, 1353, 1358,\n", + " 1361, 1383, 1387, 1394]),\n", + " 4: array([ 20, 22, 54, 61, 65, 89, 112, 119, 127, 131, 137,\n", + " 147, 154, 187, 208, 209, 229, 230, 284, 328, 337, 342,\n", + " 355, 391, 432, 450, 484, 488, 542, 562, 587, 590, 596,\n", + " 597, 661, 692, 701, 719, 770, 804, 806, 809, 821, 836,\n", + " 859, 882, 895, 931, 955, 967, 978, 993, 999, 1017, 1032,\n", + " 1068, 1094, 1106, 1115, 1123, 1125, 1127, 1129, 1190, 1223, 1246,\n", + " 1294, 1318, 1350, 1354, 1360, 1377, 1379, 1381, 1389]),\n", + " 5: array([ 180, 188, 196, 223, 279, 349, 576, 671, 724, 736, 794,\n", + " 827, 925, 1147, 1215]),\n", + " 6: array([702])},\n", + " 8: {0: array([ 0, 78, 205, 226, 275, 323, 413, 515, 601, 655, 737,\n", + " 744, 793, 810, 879, 907, 920, 1117, 1344, 1362]),\n", + " 1: array([ 1, 3, 5, 7, 9, 10, 11, 12, 13, 15, 16,\n", + " 17, 18, 23, 25, 26, 27, 28, 29, 30, 31, 32,\n", + " 35, 36, 37, 38, 39, 41, 42, 43, 44, 45, 46,\n", + " 48, 49, 51, 53, 56, 58, 60, 64, 66, 67, 70,\n", + " 74, 76, 80, 82, 86, 87, 90, 92, 94, 97, 99,\n", + " 102, 103, 106, 107, 108, 110, 111, 114, 118, 122, 124,\n", + " 128, 129, 130, 132, 135, 138, 139, 140, 141, 142, 143,\n", + " 144, 145, 146, 148, 149, 156, 158, 159, 162, 163, 164,\n", + " 165, 166, 167, 168, 169, 170, 172, 174, 175, 177, 178,\n", + " 179, 186, 189, 190, 192, 193, 195, 197, 200, 201, 202,\n", + " 206, 207, 210, 211, 212, 213, 214, 216, 218, 219, 220,\n", + " 221, 222, 227, 232, 233, 234, 235, 236, 238, 240, 241,\n", + " 242, 244, 245, 246, 247, 248, 249, 251, 252, 253, 255,\n", + " 257, 259, 262, 263, 264, 265, 267, 269, 271, 272, 273,\n", + " 274, 277, 278, 281, 283, 285, 286, 288, 289, 290, 291,\n", + " 294, 296, 298, 300, 301, 302, 304, 305, 306, 307, 310,\n", + " 311, 312, 314, 315, 318, 322, 324, 325, 327, 331, 332,\n", + " 334, 335, 340, 341, 343, 344, 346, 347, 350, 352, 353,\n", + " 354, 357, 361, 365, 366, 368, 369, 371, 372, 373, 375,\n", + " 376, 378, 381, 383, 384, 385, 386, 388, 390, 393, 394,\n", + " 396, 397, 398, 400, 401, 402, 403, 405, 407, 408, 409,\n", + " 411, 412, 414, 416, 418, 419, 420, 421, 422, 423, 424,\n", + " 425, 426, 427, 428, 429, 430, 431, 433, 434, 436, 437,\n", + " 439, 442, 444, 445, 446, 448, 449, 452, 453, 455, 456,\n", + " 459, 460, 461, 462, 463, 464, 465, 467, 468, 471, 472,\n", + " 473, 475, 476, 477, 478, 480, 482, 483, 485, 486, 487,\n", + " 489, 490, 491, 492, 493, 495, 496, 499, 500, 501, 502,\n", + " 503, 504, 505, 507, 508, 509, 510, 511, 512, 513, 517,\n", + " 518, 520, 522, 527, 528, 529, 530, 532, 533, 534, 535,\n", + " 536, 537, 539, 540, 543, 544, 545, 546, 547, 548, 550,\n", + " 551, 552, 554, 555, 556, 557, 558, 560, 564, 565, 567,\n", + " 569, 570, 572, 574, 575, 579, 580, 581, 582, 583, 589,\n", + " 591, 592, 594, 598, 602, 603, 604, 607, 608, 609, 611,\n", + " 612, 614, 615, 616, 617, 618, 620, 622, 623, 624, 627,\n", + " 629, 630, 633, 634, 635, 636, 637, 639, 643, 644, 645,\n", + " 647, 648, 650, 652, 654, 657, 658, 662, 664, 665, 666,\n", + " 667, 668, 669, 673, 674, 675, 676, 677, 682, 684, 687,\n", + " 688, 689, 690, 694, 695, 697, 698, 700, 703, 704, 705,\n", + " 707, 709, 710, 711, 712, 714, 715, 716, 718, 725, 727,\n", + " 728, 730, 731, 732, 739, 741, 742, 743, 746, 747, 750,\n", + " 756, 757, 758, 759, 760, 761, 762, 764, 765, 766, 767,\n", + " 768, 769, 772, 774, 775, 776, 777, 778, 779, 780, 782,\n", + " 785, 789, 790, 792, 796, 798, 799, 803, 811, 812, 814,\n", + " 817, 819, 820, 823, 824, 828, 830, 831, 832, 833, 834,\n", + " 835, 837, 839, 840, 841, 842, 843, 844, 845, 846, 848,\n", + " 849, 850, 851, 852, 853, 856, 857, 860, 861, 862, 863,\n", + " 865, 867, 869, 871, 872, 874, 876, 878, 883, 884, 886,\n", + " 887, 890, 891, 893, 896, 899, 903, 904, 905, 906, 908,\n", + " 909, 911, 912, 913, 914, 916, 917, 918, 919, 921, 922,\n", + " 923, 924, 926, 929, 932, 933, 936, 938, 939, 940, 941,\n", + " 942, 943, 944, 946, 947, 950, 951, 953, 954, 960, 961,\n", + " 962, 964, 965, 968, 969, 970, 971, 972, 974, 979, 982,\n", + " 984, 985, 986, 987, 989, 990, 991, 992, 994, 996, 997,\n", + " 1001, 1002, 1003, 1006, 1008, 1009, 1010, 1011, 1013, 1019, 1021,\n", + " 1023, 1025, 1026, 1027, 1028, 1029, 1031, 1034, 1036, 1037, 1038,\n", + " 1041, 1042, 1043, 1044, 1045, 1046, 1052, 1053, 1054, 1057, 1060,\n", + " 1062, 1063, 1064, 1065, 1069, 1070, 1071, 1075, 1076, 1079, 1080,\n", + " 1082, 1083, 1084, 1085, 1086, 1088, 1092, 1095, 1100, 1101, 1102,\n", + " 1103, 1105, 1108, 1109, 1111, 1113, 1119, 1121, 1126, 1130, 1131,\n", + " 1132, 1135, 1137, 1144, 1145, 1146, 1148, 1150, 1151, 1154, 1155,\n", + " 1157, 1158, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1168, 1169,\n", + " 1170, 1171, 1173, 1174, 1175, 1177, 1179, 1180, 1182, 1184, 1185,\n", + " 1186, 1192, 1193, 1195, 1196, 1198, 1199, 1200, 1201, 1202, 1203,\n", + " 1204, 1206, 1209, 1210, 1212, 1214, 1217, 1218, 1219, 1220, 1225,\n", + " 1226, 1227, 1228, 1230, 1231, 1232, 1234, 1236, 1237, 1239, 1243,\n", + " 1244, 1247, 1252, 1253, 1258, 1259, 1261, 1264, 1266, 1267, 1268,\n", + " 1269, 1272, 1273, 1274, 1277, 1278, 1279, 1280, 1282, 1289, 1291,\n", + " 1292, 1297, 1299, 1301, 1303, 1305, 1306, 1309, 1311, 1315, 1316,\n", + " 1317, 1320, 1321, 1322, 1323, 1324, 1326, 1328, 1329, 1330, 1332,\n", + " 1333, 1334, 1335, 1336, 1337, 1339, 1341, 1345, 1346, 1349, 1351,\n", + " 1355, 1359, 1363, 1364, 1367, 1368, 1369, 1370, 1371, 1372, 1373,\n", + " 1374, 1376, 1378, 1380, 1384, 1385, 1386, 1388, 1390, 1392, 1396,\n", + " 1398, 1399]),\n", + " 2: array([ 2, 101, 105, 123, 133, 182, 268, 309, 362, 380, 435,\n", + " 641, 733, 773, 864, 915, 952, 957, 1007, 1153, 1191, 1208,\n", + " 1284, 1298, 1308, 1327, 1348]),\n", + " 3: array([ 4, 14, 24, 33, 34, 40, 50, 57, 62, 69, 71,\n", + " 72, 73, 75, 79, 81, 83, 84, 85, 91, 95, 96,\n", + " 100, 113, 116, 117, 125, 126, 134, 136, 152, 157, 160,\n", + " 161, 171, 173, 176, 183, 185, 191, 198, 203, 204, 215,\n", + " 217, 224, 225, 237, 243, 250, 256, 260, 266, 270, 276,\n", + " 287, 293, 303, 308, 313, 317, 319, 320, 321, 326, 329,\n", + " 330, 333, 345, 356, 358, 359, 360, 364, 367, 370, 374,\n", + " 377, 379, 382, 387, 395, 406, 410, 415, 417, 438, 440,\n", + " 441, 443, 447, 451, 457, 458, 466, 469, 470, 479, 481,\n", + " 498, 506, 514, 516, 519, 523, 524, 525, 531, 538, 541,\n", + " 549, 566, 568, 571, 577, 584, 585, 586, 588, 593, 595,\n", + " 600, 610, 613, 621, 625, 626, 631, 632, 638, 642, 646,\n", + " 649, 651, 653, 656, 659, 660, 663, 679, 681, 685, 693,\n", + " 706, 713, 721, 722, 723, 729, 734, 735, 738, 745, 749,\n", + " 751, 752, 753, 754, 755, 771, 781, 784, 787, 788, 791,\n", + " 795, 800, 801, 805, 807, 808, 813, 822, 826, 829, 847,\n", + " 854, 855, 866, 870, 877, 881, 889, 892, 894, 897, 898,\n", + " 900, 902, 927, 930, 945, 948, 949, 956, 958, 959, 963,\n", + " 966, 976, 977, 981, 988, 995, 998, 1004, 1012, 1015, 1016,\n", + " 1018, 1024, 1035, 1049, 1051, 1055, 1058, 1059, 1061, 1066, 1067,\n", + " 1072, 1074, 1077, 1078, 1081, 1087, 1089, 1090, 1091, 1093, 1096,\n", + " 1097, 1098, 1099, 1104, 1107, 1112, 1114, 1116, 1120, 1128, 1133,\n", + " 1136, 1139, 1140, 1143, 1149, 1152, 1156, 1159, 1167, 1176, 1178,\n", + " 1181, 1187, 1188, 1189, 1194, 1197, 1205, 1207, 1213, 1216, 1221,\n", + " 1222, 1224, 1229, 1233, 1235, 1238, 1242, 1248, 1250, 1256, 1257,\n", + " 1260, 1263, 1270, 1271, 1276, 1281, 1285, 1287, 1288, 1290, 1293,\n", + " 1295, 1300, 1302, 1304, 1307, 1310, 1313, 1325, 1331, 1338, 1340,\n", + " 1343, 1347, 1352, 1356, 1357, 1365, 1366, 1375, 1382, 1391, 1393,\n", + " 1395, 1397]),\n", + " 4: array([ 6, 8, 19, 21, 47, 52, 55, 59, 63, 68, 77,\n", + " 88, 93, 98, 104, 109, 115, 120, 121, 150, 151, 153,\n", + " 155, 181, 184, 194, 199, 228, 231, 239, 254, 258, 261,\n", + " 280, 282, 292, 295, 297, 299, 316, 336, 338, 339, 348,\n", + " 351, 363, 389, 392, 399, 404, 454, 474, 494, 497, 521,\n", + " 526, 553, 559, 561, 563, 573, 578, 599, 605, 606, 619,\n", + " 628, 640, 670, 672, 678, 680, 683, 686, 691, 696, 699,\n", + " 708, 717, 720, 726, 740, 748, 763, 783, 786, 797, 802,\n", + " 815, 816, 818, 825, 838, 858, 868, 873, 875, 880, 885,\n", + " 888, 901, 910, 928, 934, 935, 937, 973, 975, 980, 983,\n", + " 1000, 1005, 1014, 1020, 1022, 1030, 1033, 1039, 1040, 1047, 1048,\n", + " 1050, 1056, 1073, 1110, 1118, 1122, 1124, 1134, 1138, 1141, 1142,\n", + " 1172, 1183, 1211, 1240, 1241, 1245, 1249, 1251, 1254, 1255, 1262,\n", + " 1265, 1275, 1283, 1286, 1296, 1312, 1314, 1319, 1342, 1353, 1358,\n", + " 1361, 1383, 1387, 1394]),\n", + " 5: array([ 20, 22, 54, 61, 65, 89, 112, 119, 127, 131, 137,\n", + " 147, 154, 187, 208, 209, 229, 230, 284, 328, 337, 342,\n", + " 355, 391, 432, 450, 484, 488, 542, 562, 587, 590, 596,\n", + " 597, 661, 692, 701, 719, 770, 804, 806, 809, 821, 836,\n", + " 859, 882, 895, 931, 955, 967, 978, 993, 999, 1017, 1032,\n", + " 1068, 1094, 1106, 1115, 1123, 1125, 1127, 1129, 1190, 1223, 1246,\n", + " 1294, 1318, 1350, 1354, 1360, 1377, 1379, 1381, 1389]),\n", + " 6: array([ 180, 188, 196, 223, 279, 349, 576, 671, 724, 736, 794,\n", + " 827, 925, 1147, 1215]),\n", + " 7: array([702])},\n", + " 9: {0: array([ 0, 78, 205, 226, 275, 323, 413, 515, 601, 655, 737,\n", + " 744, 793, 810, 879, 907, 920, 1117, 1344, 1362]),\n", + " 1: array([ 1, 3, 5, 7, 9, 10, 11, 12, 13, 15, 16,\n", + " 17, 18, 23, 25, 26, 27, 28, 29, 30, 31, 32,\n", + " 35, 36, 37, 38, 39, 41, 42, 43, 44, 45, 46,\n", + " 48, 49, 51, 53, 56, 58, 60, 64, 66, 67, 70,\n", + " 74, 76, 80, 82, 86, 87, 90, 92, 94, 97, 99,\n", + " 102, 103, 106, 107, 108, 110, 111, 114, 118, 122, 124,\n", + " 128, 129, 130, 132, 135, 138, 139, 140, 141, 142, 143,\n", + " 144, 145, 146, 148, 149, 156, 158, 159, 162, 163, 164,\n", + " 165, 166, 167, 168, 169, 170, 172, 174, 175, 177, 178,\n", + " 179, 186, 189, 190, 192, 193, 195, 197, 200, 201, 202,\n", + " 206, 207, 210, 211, 212, 213, 214, 216, 218, 219, 220,\n", + " 221, 222, 227, 232, 233, 234, 235, 236, 238, 240, 241,\n", + " 242, 244, 245, 246, 247, 248, 249, 251, 252, 253, 255,\n", + " 257, 259, 262, 263, 264, 265, 267, 269, 271, 272, 273,\n", + " 274, 277, 278, 281, 283, 285, 286, 288, 289, 290, 291,\n", + " 294, 296, 298, 300, 301, 302, 304, 305, 306, 307, 310,\n", + " 311, 312, 314, 315, 318, 322, 324, 325, 327, 331, 332,\n", + " 334, 335, 340, 341, 343, 344, 346, 347, 350, 352, 353,\n", + " 354, 357, 361, 365, 366, 368, 369, 371, 372, 373, 375,\n", + " 376, 378, 381, 383, 384, 385, 386, 388, 390, 393, 394,\n", + " 396, 397, 398, 400, 401, 402, 403, 405, 407, 408, 409,\n", + " 411, 412, 414, 416, 418, 419, 420, 421, 422, 423, 424,\n", + " 425, 426, 427, 428, 429, 430, 431, 433, 434, 436, 437,\n", + " 439, 442, 444, 445, 446, 448, 449, 452, 453, 455, 456,\n", + " 459, 460, 461, 462, 463, 464, 465, 467, 468, 471, 472,\n", + " 473, 475, 476, 477, 478, 480, 482, 483, 485, 486, 487,\n", + " 489, 490, 491, 492, 493, 495, 496, 499, 500, 501, 502,\n", + " 503, 504, 505, 507, 508, 509, 510, 511, 512, 513, 517,\n", + " 518, 520, 522, 527, 528, 529, 530, 532, 533, 534, 535,\n", + " 536, 537, 539, 540, 543, 544, 545, 546, 547, 548, 550,\n", + " 551, 552, 554, 555, 556, 557, 558, 560, 564, 565, 567,\n", + " 569, 570, 572, 574, 575, 579, 580, 581, 582, 583, 589,\n", + " 591, 592, 594, 598, 602, 603, 604, 607, 608, 609, 611,\n", + " 612, 614, 615, 616, 617, 618, 620, 622, 623, 624, 627,\n", + " 629, 630, 633, 634, 635, 636, 637, 639, 643, 644, 645,\n", + " 647, 648, 650, 652, 654, 657, 658, 662, 664, 665, 666,\n", + " 667, 668, 669, 673, 674, 675, 676, 677, 682, 684, 687,\n", + " 688, 689, 690, 694, 695, 697, 698, 700, 703, 704, 705,\n", + " 707, 709, 710, 711, 712, 714, 715, 716, 718, 725, 727,\n", + " 728, 730, 731, 732, 739, 741, 742, 743, 746, 747, 750,\n", + " 756, 757, 758, 759, 760, 761, 762, 764, 765, 766, 767,\n", + " 768, 769, 772, 774, 775, 776, 777, 778, 779, 780, 782,\n", + " 785, 789, 790, 792, 796, 798, 799, 803, 811, 812, 814,\n", + " 817, 819, 820, 823, 824, 828, 830, 831, 832, 833, 834,\n", + " 835, 837, 839, 840, 841, 842, 843, 844, 845, 846, 848,\n", + " 849, 850, 851, 852, 853, 856, 857, 860, 861, 862, 863,\n", + " 865, 867, 869, 871, 872, 874, 876, 878, 883, 884, 886,\n", + " 887, 890, 891, 893, 896, 899, 903, 904, 905, 906, 908,\n", + " 909, 911, 912, 913, 914, 916, 917, 918, 919, 921, 922,\n", + " 923, 924, 926, 929, 932, 933, 936, 938, 939, 940, 941,\n", + " 942, 943, 944, 946, 947, 950, 951, 953, 954, 960, 961,\n", + " 962, 964, 965, 968, 969, 970, 971, 972, 974, 979, 982,\n", + " 984, 985, 986, 987, 989, 990, 991, 992, 994, 996, 997,\n", + " 1001, 1002, 1003, 1006, 1008, 1009, 1010, 1011, 1013, 1019, 1021,\n", + " 1023, 1025, 1026, 1027, 1028, 1029, 1031, 1034, 1036, 1037, 1038,\n", + " 1041, 1042, 1043, 1044, 1045, 1046, 1052, 1053, 1054, 1057, 1060,\n", + " 1062, 1063, 1064, 1065, 1069, 1070, 1071, 1075, 1076, 1079, 1080,\n", + " 1082, 1083, 1084, 1085, 1086, 1088, 1092, 1095, 1100, 1101, 1102,\n", + " 1103, 1105, 1108, 1109, 1111, 1113, 1119, 1121, 1126, 1130, 1131,\n", + " 1132, 1135, 1137, 1144, 1145, 1146, 1148, 1150, 1151, 1154, 1155,\n", + " 1157, 1158, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1168, 1169,\n", + " 1170, 1171, 1173, 1174, 1175, 1177, 1179, 1180, 1182, 1184, 1185,\n", + " 1186, 1192, 1193, 1195, 1196, 1198, 1199, 1200, 1201, 1202, 1203,\n", + " 1204, 1206, 1209, 1210, 1212, 1214, 1217, 1218, 1219, 1220, 1225,\n", + " 1226, 1227, 1228, 1230, 1231, 1232, 1234, 1236, 1237, 1239, 1243,\n", + " 1244, 1247, 1252, 1253, 1258, 1259, 1261, 1264, 1266, 1267, 1268,\n", + " 1269, 1272, 1273, 1274, 1277, 1278, 1279, 1280, 1282, 1289, 1291,\n", + " 1292, 1297, 1299, 1301, 1303, 1305, 1306, 1309, 1311, 1315, 1316,\n", + " 1317, 1320, 1321, 1322, 1323, 1324, 1326, 1328, 1329, 1330, 1332,\n", + " 1333, 1334, 1335, 1336, 1337, 1339, 1341, 1345, 1346, 1349, 1351,\n", + " 1355, 1359, 1363, 1364, 1367, 1368, 1369, 1370, 1371, 1372, 1373,\n", + " 1374, 1376, 1378, 1380, 1384, 1385, 1386, 1388, 1390, 1392, 1396,\n", + " 1398, 1399]),\n", + " 2: array([ 2, 101, 105, 123, 133, 182, 268, 309, 362, 380, 435,\n", + " 641, 733, 773, 864, 915, 952, 957, 1007, 1153, 1191, 1208,\n", + " 1284, 1298, 1308, 1327, 1348]),\n", + " 3: array([ 4, 14, 24, 33, 34, 50, 57, 62, 69, 71, 72,\n", + " 73, 75, 79, 81, 83, 84, 85, 91, 95, 100, 117,\n", + " 125, 126, 134, 136, 152, 157, 160, 161, 173, 176, 183,\n", + " 185, 198, 203, 204, 217, 224, 225, 237, 243, 256, 260,\n", + " 266, 287, 293, 303, 308, 313, 317, 319, 320, 321, 326,\n", + " 330, 333, 345, 356, 358, 359, 367, 370, 377, 379, 382,\n", + " 387, 395, 406, 410, 415, 417, 440, 441, 443, 447, 451,\n", + " 457, 458, 466, 469, 470, 479, 481, 498, 506, 514, 516,\n", + " 519, 523, 524, 525, 531, 538, 541, 549, 566, 568, 571,\n", + " 577, 584, 585, 586, 593, 595, 600, 610, 613, 621, 625,\n", + " 626, 631, 632, 638, 642, 646, 649, 651, 653, 656, 659,\n", + " 660, 663, 679, 681, 693, 706, 713, 721, 722, 723, 729,\n", + " 734, 735, 738, 745, 749, 751, 752, 754, 755, 771, 781,\n", + " 784, 787, 791, 795, 800, 801, 805, 807, 808, 813, 822,\n", + " 826, 829, 854, 855, 866, 870, 877, 881, 889, 892, 894,\n", + " 897, 898, 900, 902, 927, 930, 945, 948, 949, 958, 959,\n", + " 976, 977, 981, 988, 995, 998, 1004, 1012, 1015, 1018, 1024,\n", + " 1035, 1049, 1051, 1055, 1058, 1059, 1066, 1067, 1072, 1074, 1077,\n", + " 1078, 1081, 1087, 1089, 1090, 1091, 1093, 1096, 1097, 1098, 1099,\n", + " 1104, 1107, 1112, 1114, 1116, 1120, 1128, 1133, 1139, 1140, 1143,\n", + " 1149, 1152, 1156, 1159, 1167, 1176, 1178, 1181, 1187, 1189, 1194,\n", + " 1205, 1207, 1213, 1216, 1221, 1222, 1229, 1233, 1238, 1242, 1248,\n", + " 1250, 1256, 1257, 1260, 1263, 1271, 1276, 1281, 1285, 1287, 1288,\n", + " 1290, 1293, 1295, 1300, 1302, 1304, 1307, 1310, 1313, 1325, 1331,\n", + " 1338, 1340, 1343, 1347, 1352, 1356, 1357, 1365, 1375, 1382, 1391,\n", + " 1393, 1395, 1397]),\n", + " 4: array([ 6, 8, 19, 21, 47, 52, 55, 59, 63, 68, 77,\n", + " 88, 93, 98, 104, 109, 115, 120, 121, 150, 151, 153,\n", + " 155, 181, 184, 194, 199, 228, 231, 239, 254, 258, 261,\n", + " 280, 282, 292, 295, 297, 299, 316, 336, 338, 339, 348,\n", + " 351, 363, 389, 392, 399, 404, 454, 474, 494, 497, 521,\n", + " 526, 553, 559, 561, 563, 573, 578, 599, 605, 606, 619,\n", + " 628, 640, 670, 672, 678, 680, 683, 686, 691, 696, 699,\n", + " 708, 717, 720, 726, 740, 748, 763, 783, 786, 797, 802,\n", + " 815, 816, 818, 825, 838, 858, 868, 873, 875, 880, 885,\n", + " 888, 901, 910, 928, 934, 935, 937, 973, 975, 980, 983,\n", + " 1000, 1005, 1014, 1020, 1022, 1030, 1033, 1039, 1040, 1047, 1048,\n", + " 1050, 1056, 1073, 1110, 1118, 1122, 1124, 1134, 1138, 1141, 1142,\n", + " 1172, 1183, 1211, 1240, 1241, 1245, 1249, 1251, 1254, 1255, 1262,\n", + " 1265, 1275, 1283, 1286, 1296, 1312, 1314, 1319, 1342, 1353, 1358,\n", + " 1361, 1383, 1387, 1394]),\n", + " 5: array([ 20, 22, 54, 61, 65, 89, 112, 119, 127, 131, 137,\n", + " 147, 154, 187, 208, 209, 229, 230, 284, 328, 337, 342,\n", + " 355, 391, 432, 450, 484, 488, 542, 562, 587, 590, 596,\n", + " 597, 661, 692, 701, 719, 770, 804, 806, 809, 821, 836,\n", + " 859, 882, 895, 931, 955, 967, 978, 993, 999, 1017, 1032,\n", + " 1068, 1094, 1106, 1115, 1123, 1125, 1127, 1129, 1190, 1223, 1246,\n", + " 1294, 1318, 1350, 1354, 1360, 1377, 1379, 1381, 1389]),\n", + " 6: array([ 40, 96, 113, 116, 171, 191, 215, 250, 270, 276, 329,\n", + " 360, 364, 374, 438, 588, 685, 753, 788, 847, 956, 963,\n", + " 966, 1016, 1061, 1136, 1188, 1197, 1224, 1235, 1270, 1366]),\n", + " 7: array([ 180, 188, 196, 223, 279, 349, 576, 671, 724, 736, 794,\n", + " 827, 925, 1147, 1215]),\n", + " 8: array([702])},\n", + " 10: {0: array([ 0, 78, 205, 226, 275, 323, 413, 515, 601, 655, 737,\n", + " 744, 793, 810, 879, 907, 920, 1117, 1344, 1362]),\n", + " 1: array([ 1, 3, 5, 7, 9, 10, 11, 12, 13, 15, 16,\n", + " 17, 18, 23, 25, 26, 27, 28, 29, 30, 31, 32,\n", + " 35, 36, 37, 38, 39, 41, 42, 43, 44, 45, 46,\n", + " 48, 49, 51, 53, 56, 58, 60, 64, 66, 67, 70,\n", + " 74, 76, 80, 82, 86, 87, 90, 92, 94, 97, 99,\n", + " 102, 103, 106, 107, 108, 110, 111, 114, 118, 122, 124,\n", + " 128, 129, 130, 132, 135, 138, 139, 140, 141, 142, 143,\n", + " 144, 145, 146, 148, 149, 156, 158, 159, 162, 163, 164,\n", + " 165, 166, 167, 168, 169, 170, 172, 174, 175, 177, 178,\n", + " 179, 186, 189, 190, 192, 193, 195, 197, 200, 201, 202,\n", + " 206, 207, 210, 211, 212, 213, 214, 216, 218, 219, 220,\n", + " 221, 222, 227, 232, 233, 234, 235, 236, 238, 240, 241,\n", + " 242, 244, 245, 246, 247, 248, 249, 251, 252, 253, 255,\n", + " 257, 259, 262, 263, 264, 265, 267, 269, 271, 272, 273,\n", + " 274, 277, 278, 281, 283, 285, 286, 288, 289, 290, 291,\n", + " 294, 296, 298, 300, 301, 302, 304, 305, 306, 307, 310,\n", + " 311, 312, 314, 315, 318, 322, 324, 325, 327, 331, 332,\n", + " 334, 335, 340, 341, 343, 344, 346, 347, 350, 352, 353,\n", + " 354, 357, 361, 365, 366, 368, 369, 371, 372, 373, 375,\n", + " 376, 378, 381, 383, 384, 385, 386, 388, 390, 393, 394,\n", + " 396, 397, 398, 400, 401, 402, 403, 405, 407, 408, 409,\n", + " 411, 412, 414, 416, 418, 419, 420, 421, 422, 423, 424,\n", + " 425, 426, 427, 428, 429, 430, 431, 433, 434, 436, 437,\n", + " 439, 442, 444, 445, 446, 448, 449, 452, 453, 455, 456,\n", + " 459, 460, 461, 462, 463, 464, 465, 467, 468, 471, 472,\n", + " 473, 475, 476, 477, 478, 480, 482, 483, 485, 486, 487,\n", + " 489, 490, 491, 492, 493, 495, 496, 499, 500, 501, 502,\n", + " 503, 504, 505, 507, 508, 509, 510, 511, 512, 513, 517,\n", + " 518, 520, 522, 527, 528, 529, 530, 532, 533, 534, 535,\n", + " 536, 537, 539, 540, 543, 544, 545, 546, 547, 548, 550,\n", + " 551, 552, 554, 555, 556, 557, 558, 560, 564, 565, 567,\n", + " 569, 570, 572, 574, 575, 579, 580, 581, 582, 583, 589,\n", + " 591, 592, 594, 598, 602, 603, 604, 607, 608, 609, 611,\n", + " 612, 614, 615, 616, 617, 618, 620, 622, 623, 624, 627,\n", + " 629, 630, 633, 634, 635, 636, 637, 639, 643, 644, 645,\n", + " 647, 648, 650, 652, 654, 657, 658, 662, 664, 665, 666,\n", + " 667, 668, 669, 673, 674, 675, 676, 677, 682, 684, 687,\n", + " 688, 689, 690, 694, 695, 697, 698, 700, 703, 704, 705,\n", + " 707, 709, 710, 711, 712, 714, 715, 716, 718, 725, 727,\n", + " 728, 730, 731, 732, 739, 741, 742, 743, 746, 747, 750,\n", + " 756, 757, 758, 759, 760, 761, 762, 764, 765, 766, 767,\n", + " 768, 769, 772, 774, 775, 776, 777, 778, 779, 780, 782,\n", + " 785, 789, 790, 792, 796, 798, 799, 803, 811, 812, 814,\n", + " 817, 819, 820, 823, 824, 828, 830, 831, 832, 833, 834,\n", + " 835, 837, 839, 840, 841, 842, 843, 844, 845, 846, 848,\n", + " 849, 850, 851, 852, 853, 856, 857, 860, 861, 862, 863,\n", + " 865, 867, 869, 871, 872, 874, 876, 878, 883, 884, 886,\n", + " 887, 890, 891, 893, 896, 899, 903, 904, 905, 906, 908,\n", + " 909, 911, 912, 913, 914, 916, 917, 918, 919, 921, 922,\n", + " 923, 924, 926, 929, 932, 933, 936, 938, 939, 940, 941,\n", + " 942, 943, 944, 946, 947, 950, 951, 953, 954, 960, 961,\n", + " 962, 964, 965, 968, 969, 970, 971, 972, 974, 979, 982,\n", + " 984, 985, 986, 987, 989, 990, 991, 992, 994, 996, 997,\n", + " 1001, 1002, 1003, 1006, 1008, 1009, 1010, 1011, 1013, 1019, 1021,\n", + " 1023, 1025, 1026, 1027, 1028, 1029, 1031, 1034, 1036, 1037, 1038,\n", + " 1041, 1042, 1043, 1044, 1045, 1046, 1052, 1053, 1054, 1057, 1060,\n", + " 1062, 1063, 1064, 1065, 1069, 1070, 1071, 1075, 1076, 1079, 1080,\n", + " 1082, 1083, 1084, 1085, 1086, 1088, 1092, 1095, 1100, 1101, 1102,\n", + " 1103, 1105, 1108, 1109, 1111, 1113, 1119, 1121, 1126, 1130, 1131,\n", + " 1132, 1135, 1137, 1144, 1145, 1146, 1148, 1150, 1151, 1154, 1155,\n", + " 1157, 1158, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1168, 1169,\n", + " 1170, 1171, 1173, 1174, 1175, 1177, 1179, 1180, 1182, 1184, 1185,\n", + " 1186, 1192, 1193, 1195, 1196, 1198, 1199, 1200, 1201, 1202, 1203,\n", + " 1204, 1206, 1209, 1210, 1212, 1214, 1217, 1218, 1219, 1220, 1225,\n", + " 1226, 1227, 1228, 1230, 1231, 1232, 1234, 1236, 1237, 1239, 1243,\n", + " 1244, 1247, 1252, 1253, 1258, 1259, 1261, 1264, 1266, 1267, 1268,\n", + " 1269, 1272, 1273, 1274, 1277, 1278, 1279, 1280, 1282, 1289, 1291,\n", + " 1292, 1297, 1299, 1301, 1303, 1305, 1306, 1309, 1311, 1315, 1316,\n", + " 1317, 1320, 1321, 1322, 1323, 1324, 1326, 1328, 1329, 1330, 1332,\n", + " 1333, 1334, 1335, 1336, 1337, 1339, 1341, 1345, 1346, 1349, 1351,\n", + " 1355, 1359, 1363, 1364, 1367, 1368, 1369, 1370, 1371, 1372, 1373,\n", + " 1374, 1376, 1378, 1380, 1384, 1385, 1386, 1388, 1390, 1392, 1396,\n", + " 1398, 1399]),\n", + " 2: array([ 2, 101, 105, 123, 133, 182, 268, 309, 362, 380, 435,\n", + " 641, 733, 773, 864, 915, 952, 957, 1007, 1153, 1191, 1208,\n", + " 1284, 1298, 1308, 1327, 1348]),\n", + " 3: array([ 4, 14, 24, 33, 34, 50, 57, 62, 69, 71, 72,\n", + " 73, 75, 79, 81, 83, 84, 85, 91, 95, 100, 117,\n", + " 125, 126, 134, 136, 152, 157, 160, 161, 173, 176, 183,\n", + " 185, 198, 203, 204, 217, 224, 225, 237, 243, 256, 260,\n", + " 266, 287, 293, 303, 308, 313, 317, 319, 320, 321, 326,\n", + " 330, 333, 345, 356, 358, 359, 367, 370, 377, 379, 382,\n", + " 387, 395, 406, 410, 415, 417, 440, 441, 443, 447, 451,\n", + " 457, 458, 466, 469, 470, 479, 481, 498, 506, 514, 516,\n", + " 519, 523, 524, 525, 531, 538, 541, 549, 566, 568, 571,\n", + " 577, 584, 585, 586, 593, 595, 600, 610, 613, 621, 625,\n", + " 626, 631, 632, 638, 642, 646, 649, 651, 653, 656, 659,\n", + " 660, 663, 679, 681, 693, 706, 713, 721, 722, 723, 729,\n", + " 734, 735, 738, 745, 749, 751, 752, 754, 755, 771, 781,\n", + " 784, 787, 791, 795, 800, 801, 805, 807, 808, 813, 822,\n", + " 826, 829, 854, 855, 866, 870, 877, 881, 889, 892, 894,\n", + " 897, 898, 900, 902, 927, 930, 945, 948, 949, 958, 959,\n", + " 976, 977, 981, 988, 995, 998, 1004, 1012, 1015, 1018, 1024,\n", + " 1035, 1049, 1051, 1055, 1058, 1059, 1066, 1067, 1072, 1074, 1077,\n", + " 1078, 1081, 1087, 1089, 1090, 1091, 1093, 1096, 1097, 1098, 1099,\n", + " 1104, 1107, 1112, 1114, 1116, 1120, 1128, 1133, 1139, 1140, 1143,\n", + " 1149, 1152, 1156, 1159, 1167, 1176, 1178, 1181, 1187, 1189, 1194,\n", + " 1205, 1207, 1213, 1216, 1221, 1222, 1229, 1233, 1238, 1242, 1248,\n", + " 1250, 1256, 1257, 1260, 1263, 1271, 1276, 1281, 1285, 1287, 1288,\n", + " 1290, 1293, 1295, 1300, 1302, 1304, 1307, 1310, 1313, 1325, 1331,\n", + " 1338, 1340, 1343, 1347, 1352, 1356, 1357, 1365, 1375, 1382, 1391,\n", + " 1393, 1395, 1397]),\n", + " 4: array([ 6, 292, 339, 599, 670, 678, 763, 783, 816, 888, 935,\n", + " 1022, 1033, 1211]),\n", + " 5: array([ 8, 19, 21, 47, 52, 55, 59, 63, 68, 77, 88,\n", + " 93, 98, 104, 109, 115, 120, 121, 150, 151, 153, 155,\n", + " 181, 184, 194, 199, 228, 231, 239, 254, 258, 261, 280,\n", + " 282, 295, 297, 299, 316, 336, 338, 348, 351, 363, 389,\n", + " 392, 399, 404, 454, 474, 494, 497, 521, 526, 553, 559,\n", + " 561, 563, 573, 578, 605, 606, 619, 628, 640, 672, 680,\n", + " 683, 686, 691, 696, 699, 708, 717, 720, 726, 740, 748,\n", + " 786, 797, 802, 815, 818, 825, 838, 858, 868, 873, 875,\n", + " 880, 885, 901, 910, 928, 934, 937, 973, 975, 980, 983,\n", + " 1000, 1005, 1014, 1020, 1030, 1039, 1040, 1047, 1048, 1050, 1056,\n", + " 1073, 1110, 1118, 1122, 1124, 1134, 1138, 1141, 1142, 1172, 1183,\n", + " 1240, 1241, 1245, 1249, 1251, 1254, 1255, 1262, 1265, 1275, 1283,\n", + " 1286, 1296, 1312, 1314, 1319, 1342, 1353, 1358, 1361, 1383, 1387,\n", + " 1394]),\n", + " 6: array([ 20, 22, 54, 61, 65, 89, 112, 119, 127, 131, 137,\n", + " 147, 154, 187, 208, 209, 229, 230, 284, 328, 337, 342,\n", + " 355, 391, 432, 450, 484, 488, 542, 562, 587, 590, 596,\n", + " 597, 661, 692, 701, 719, 770, 804, 806, 809, 821, 836,\n", + " 859, 882, 895, 931, 955, 967, 978, 993, 999, 1017, 1032,\n", + " 1068, 1094, 1106, 1115, 1123, 1125, 1127, 1129, 1190, 1223, 1246,\n", + " 1294, 1318, 1350, 1354, 1360, 1377, 1379, 1381, 1389]),\n", + " 7: array([ 40, 96, 113, 116, 171, 191, 215, 250, 270, 276, 329,\n", + " 360, 364, 374, 438, 588, 685, 753, 788, 847, 956, 963,\n", + " 966, 1016, 1061, 1136, 1188, 1197, 1224, 1235, 1270, 1366]),\n", + " 8: array([ 180, 188, 196, 223, 279, 349, 576, 671, 724, 736, 794,\n", + " 827, 925, 1147, 1215]),\n", + " 9: array([702])}},\n", + " 'nonl2_ranking': {2: {0: array([ 0, 2, 6, 8, 19, 20, 21, 22, 41, 47, 52,\n", + " 54, 55, 59, 61, 63, 65, 68, 77, 78, 88, 89,\n", + " 93, 96, 98, 101, 104, 105, 109, 112, 113, 115, 119,\n", + " 120, 121, 123, 126, 127, 131, 133, 137, 147, 150, 151,\n", + " 153, 154, 155, 181, 182, 184, 187, 194, 199, 205, 208,\n", + " 209, 226, 228, 229, 230, 231, 239, 254, 258, 260, 261,\n", + " 266, 268, 275, 276, 280, 282, 284, 292, 295, 297, 299,\n", + " 309, 316, 323, 328, 336, 337, 338, 339, 342, 348, 351,\n", + " 355, 362, 363, 370, 380, 389, 391, 392, 399, 404, 409,\n", + " 413, 432, 435, 450, 454, 474, 484, 488, 494, 497, 515,\n", + " 521, 526, 542, 553, 559, 561, 562, 563, 570, 573, 578,\n", + " 587, 590, 597, 599, 601, 605, 606, 619, 628, 631, 640,\n", + " 641, 655, 661, 665, 670, 672, 678, 680, 683, 686, 687,\n", + " 691, 692, 696, 699, 701, 702, 708, 717, 719, 720, 726,\n", + " 733, 737, 740, 744, 748, 753, 759, 763, 765, 770, 773,\n", + " 783, 786, 787, 793, 797, 802, 804, 806, 809, 810, 815,\n", + " 816, 818, 821, 825, 836, 838, 858, 859, 864, 868, 873,\n", + " 875, 879, 880, 882, 885, 888, 889, 895, 901, 904, 907,\n", + " 910, 915, 920, 928, 931, 932, 934, 935, 937, 952, 955,\n", + " 957, 967, 973, 975, 978, 980, 983, 993, 999, 1000, 1005,\n", + " 1007, 1010, 1014, 1017, 1020, 1022, 1030, 1032, 1033, 1039, 1040,\n", + " 1047, 1048, 1050, 1056, 1068, 1073, 1094, 1106, 1110, 1115, 1117,\n", + " 1118, 1122, 1123, 1124, 1125, 1127, 1129, 1134, 1138, 1141, 1142,\n", + " 1153, 1172, 1183, 1190, 1191, 1208, 1211, 1221, 1223, 1240, 1241,\n", + " 1245, 1246, 1249, 1251, 1254, 1255, 1262, 1265, 1275, 1283, 1284,\n", + " 1286, 1292, 1294, 1296, 1298, 1308, 1312, 1314, 1317, 1318, 1319,\n", + " 1327, 1342, 1344, 1348, 1350, 1352, 1353, 1354, 1358, 1360, 1361,\n", + " 1362, 1377, 1379, 1381, 1383, 1387, 1389, 1394]),\n", + " 1: array([ 1, 3, 4, ..., 1397, 1398, 1399])},\n", + " 3: {0: array([ 0, 2, 6, 8, 19, 20, 21, 22, 41, 47, 52,\n", + " 54, 55, 59, 61, 63, 65, 68, 77, 78, 88, 89,\n", + " 93, 96, 98, 101, 104, 105, 109, 112, 113, 115, 119,\n", + " 120, 121, 123, 126, 127, 131, 133, 137, 147, 150, 151,\n", + " 153, 154, 155, 181, 182, 184, 187, 194, 199, 205, 208,\n", + " 209, 226, 228, 229, 230, 231, 239, 254, 258, 260, 261,\n", + " 266, 268, 275, 276, 280, 282, 284, 292, 295, 297, 299,\n", + " 309, 316, 323, 328, 336, 337, 338, 339, 342, 348, 351,\n", + " 355, 362, 363, 370, 380, 389, 391, 392, 399, 404, 409,\n", + " 413, 432, 435, 450, 454, 474, 484, 488, 494, 497, 515,\n", + " 521, 526, 542, 553, 559, 561, 562, 563, 570, 573, 578,\n", + " 587, 590, 597, 599, 601, 605, 606, 619, 628, 631, 640,\n", + " 641, 655, 661, 665, 670, 672, 678, 680, 683, 686, 687,\n", + " 691, 692, 696, 699, 701, 702, 708, 717, 719, 720, 726,\n", + " 733, 737, 740, 744, 748, 753, 759, 763, 765, 770, 773,\n", + " 783, 786, 787, 793, 797, 802, 804, 806, 809, 810, 815,\n", + " 816, 818, 821, 825, 836, 838, 858, 859, 864, 868, 873,\n", + " 875, 879, 880, 882, 885, 888, 889, 895, 901, 904, 907,\n", + " 910, 915, 920, 928, 931, 932, 934, 935, 937, 952, 955,\n", + " 957, 967, 973, 975, 978, 980, 983, 993, 999, 1000, 1005,\n", + " 1007, 1010, 1014, 1017, 1020, 1022, 1030, 1032, 1033, 1039, 1040,\n", + " 1047, 1048, 1050, 1056, 1068, 1073, 1094, 1106, 1110, 1115, 1117,\n", + " 1118, 1122, 1123, 1124, 1125, 1127, 1129, 1134, 1138, 1141, 1142,\n", + " 1153, 1172, 1183, 1190, 1191, 1208, 1211, 1221, 1223, 1240, 1241,\n", + " 1245, 1246, 1249, 1251, 1254, 1255, 1262, 1265, 1275, 1283, 1284,\n", + " 1286, 1292, 1294, 1296, 1298, 1308, 1312, 1314, 1317, 1318, 1319,\n", + " 1327, 1342, 1344, 1348, 1350, 1352, 1353, 1354, 1358, 1360, 1361,\n", + " 1362, 1377, 1379, 1381, 1383, 1387, 1389, 1394]),\n", + " 1: array([ 1, 3, 4, 5, 7, 9, 12, 13, 14, 17, 18,\n", + " 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n", + " 36, 38, 42, 46, 48, 49, 50, 51, 56, 57, 58,\n", + " 60, 64, 66, 67, 70, 72, 73, 74, 80, 81, 82,\n", + " 83, 84, 85, 86, 87, 90, 91, 92, 94, 95, 97,\n", + " 99, 102, 108, 111, 114, 117, 118, 122, 124, 125, 128,\n", + " 130, 132, 134, 135, 136, 138, 139, 140, 141, 142, 143,\n", + " 144, 145, 146, 148, 149, 152, 157, 158, 159, 160, 161,\n", + " 162, 166, 167, 169, 170, 171, 172, 173, 174, 175, 177,\n", + " 178, 179, 180, 183, 185, 188, 189, 190, 192, 195, 196,\n", + " 197, 198, 200, 201, 202, 203, 204, 206, 207, 211, 212,\n", + " 213, 214, 216, 217, 218, 219, 221, 222, 223, 224, 225,\n", + " 227, 232, 235, 236, 237, 238, 240, 241, 243, 244, 245,\n", + " 246, 247, 249, 252, 253, 255, 256, 259, 262, 264, 265,\n", + " 267, 270, 271, 272, 273, 274, 277, 278, 279, 283, 287,\n", + " 288, 289, 290, 291, 293, 294, 296, 298, 300, 301, 302,\n", + " 303, 304, 305, 306, 307, 310, 312, 313, 314, 315, 320,\n", + " 321, 322, 324, 325, 327, 331, 332, 334, 335, 340, 341,\n", + " 344, 345, 349, 352, 353, 354, 356, 357, 358, 360, 361,\n", + " 364, 365, 366, 367, 368, 369, 371, 372, 373, 376, 378,\n", + " 379, 381, 382, 383, 384, 385, 386, 387, 388, 390, 394,\n", + " 395, 397, 398, 400, 401, 402, 406, 407, 408, 410, 411,\n", + " 412, 414, 416, 417, 418, 419, 420, 421, 422, 423, 425,\n", + " 426, 427, 428, 429, 430, 431, 433, 434, 436, 440, 442,\n", + " 444, 445, 446, 447, 448, 449, 452, 453, 455, 457, 458,\n", + " 459, 460, 462, 463, 464, 465, 466, 467, 468, 470, 471,\n", + " 472, 475, 476, 479, 480, 482, 483, 485, 486, 489, 490,\n", + " 491, 492, 495, 496, 499, 500, 501, 503, 505, 506, 508,\n", + " 509, 510, 512, 514, 518, 519, 520, 522, 523, 524, 525,\n", + " 527, 528, 529, 530, 533, 534, 535, 537, 538, 539, 540,\n", + " 541, 543, 545, 547, 548, 549, 550, 551, 552, 554, 558,\n", + " 560, 564, 567, 568, 569, 572, 574, 576, 577, 579, 580,\n", + " 581, 582, 588, 589, 591, 592, 593, 594, 596, 598, 600,\n", + " 602, 603, 604, 607, 611, 612, 613, 614, 615, 616, 617,\n", + " 618, 620, 621, 622, 624, 625, 626, 627, 629, 632, 633,\n", + " 634, 635, 637, 638, 639, 643, 644, 645, 646, 647, 648,\n", + " 650, 652, 653, 656, 657, 658, 660, 662, 663, 666, 668,\n", + " 671, 673, 674, 675, 676, 677, 681, 682, 684, 685, 688,\n", + " 689, 690, 693, 695, 697, 700, 703, 704, 706, 707, 709,\n", + " 711, 713, 714, 715, 716, 718, 721, 723, 724, 728, 730,\n", + " 731, 732, 734, 735, 736, 741, 742, 743, 745, 747, 749,\n", + " 750, 752, 755, 756, 757, 761, 762, 764, 766, 767, 769,\n", + " 771, 772, 774, 775, 777, 779, 780, 781, 782, 784, 785,\n", + " 789, 792, 794, 796, 798, 799, 801, 803, 805, 807, 811,\n", + " 812, 813, 814, 819, 822, 823, 824, 827, 828, 829, 830,\n", + " 831, 832, 835, 837, 839, 840, 841, 843, 844, 846, 848,\n", + " 849, 853, 854, 855, 856, 857, 861, 867, 869, 871, 872,\n", + " 874, 876, 881, 883, 884, 886, 887, 890, 891, 892, 894,\n", + " 896, 897, 899, 900, 905, 908, 909, 912, 913, 914, 916,\n", + " 917, 918, 919, 921, 923, 924, 925, 926, 927, 929, 930,\n", + " 933, 938, 939, 940, 941, 942, 943, 944, 945, 947, 948,\n", + " 950, 951, 953, 959, 960, 961, 962, 964, 965, 968, 971,\n", + " 974, 976, 977, 979, 984, 985, 986, 987, 988, 990, 991,\n", + " 992, 996, 997, 998, 1001, 1003, 1004, 1006, 1009, 1011, 1013,\n", + " 1021, 1023, 1025, 1026, 1028, 1029, 1031, 1035, 1037, 1038, 1041,\n", + " 1042, 1044, 1045, 1046, 1049, 1051, 1052, 1053, 1054, 1058, 1060,\n", + " 1062, 1063, 1064, 1065, 1066, 1067, 1069, 1071, 1072, 1074, 1075,\n", + " 1077, 1080, 1083, 1084, 1085, 1087, 1088, 1089, 1091, 1092, 1095,\n", + " 1096, 1097, 1098, 1099, 1100, 1102, 1103, 1104, 1107, 1108, 1109,\n", + " 1112, 1113, 1119, 1120, 1121, 1126, 1128, 1130, 1131, 1133, 1135,\n", + " 1137, 1139, 1143, 1146, 1147, 1148, 1149, 1152, 1154, 1155, 1158,\n", + " 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1169, 1170,\n", + " 1171, 1173, 1177, 1179, 1180, 1182, 1184, 1185, 1189, 1192, 1193,\n", + " 1194, 1195, 1196, 1198, 1200, 1201, 1203, 1204, 1205, 1206, 1209,\n", + " 1210, 1212, 1213, 1214, 1215, 1216, 1218, 1219, 1220, 1222, 1225,\n", + " 1226, 1227, 1229, 1230, 1231, 1232, 1234, 1235, 1236, 1237, 1238,\n", + " 1239, 1244, 1247, 1252, 1253, 1256, 1257, 1258, 1259, 1261, 1264,\n", + " 1266, 1267, 1269, 1272, 1273, 1274, 1276, 1278, 1279, 1281, 1282,\n", + " 1285, 1287, 1289, 1290, 1291, 1293, 1295, 1299, 1300, 1301, 1302,\n", + " 1303, 1304, 1305, 1306, 1311, 1315, 1316, 1320, 1321, 1322, 1324,\n", + " 1325, 1326, 1328, 1329, 1330, 1331, 1332, 1334, 1336, 1337, 1338,\n", + " 1339, 1341, 1345, 1346, 1347, 1351, 1355, 1356, 1357, 1363, 1364,\n", + " 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1376,\n", + " 1378, 1380, 1382, 1384, 1385, 1386, 1388, 1390, 1391, 1393, 1397,\n", + " 1398]),\n", + " 2: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 43, 44,\n", + " 45, 53, 62, 69, 71, 75, 76, 79, 100, 103, 106,\n", + " 107, 110, 116, 129, 156, 163, 164, 165, 168, 176, 186,\n", + " 191, 193, 210, 215, 220, 233, 234, 242, 248, 250, 251,\n", + " 257, 263, 269, 281, 285, 286, 308, 311, 317, 318, 319,\n", + " 326, 329, 330, 333, 343, 346, 347, 350, 359, 374, 375,\n", + " 377, 393, 396, 403, 405, 415, 424, 437, 438, 439, 441,\n", + " 443, 451, 456, 461, 469, 473, 477, 478, 481, 487, 493,\n", + " 498, 502, 504, 507, 511, 513, 516, 517, 531, 532, 536,\n", + " 544, 546, 555, 556, 557, 565, 566, 571, 575, 583, 584,\n", + " 585, 586, 595, 608, 609, 610, 623, 630, 636, 642, 649,\n", + " 651, 654, 659, 664, 667, 669, 679, 694, 698, 705, 710,\n", + " 712, 722, 725, 727, 729, 738, 739, 746, 751, 754, 758,\n", + " 760, 768, 776, 778, 788, 790, 791, 795, 800, 808, 817,\n", + " 820, 826, 833, 834, 842, 845, 847, 850, 851, 852, 860,\n", + " 862, 863, 865, 866, 870, 877, 878, 893, 898, 902, 903,\n", + " 906, 911, 922, 936, 946, 949, 954, 956, 958, 963, 966,\n", + " 969, 970, 972, 981, 982, 989, 994, 995, 1002, 1008, 1012,\n", + " 1015, 1016, 1018, 1019, 1024, 1027, 1034, 1036, 1043, 1055, 1057,\n", + " 1059, 1061, 1070, 1076, 1078, 1079, 1081, 1082, 1086, 1090, 1093,\n", + " 1101, 1105, 1111, 1114, 1116, 1132, 1136, 1140, 1144, 1145, 1150,\n", + " 1151, 1156, 1157, 1168, 1174, 1175, 1176, 1178, 1181, 1186, 1187,\n", + " 1188, 1197, 1199, 1202, 1207, 1217, 1224, 1228, 1233, 1242, 1243,\n", + " 1248, 1250, 1260, 1263, 1268, 1270, 1271, 1277, 1280, 1288, 1297,\n", + " 1307, 1309, 1310, 1313, 1323, 1333, 1335, 1340, 1343, 1349, 1359,\n", + " 1375, 1392, 1395, 1396, 1399])},\n", + " 4: {0: array([ 0, 2, 6, 8, 19, 20, 21, 22, 41, 47, 52,\n", + " 54, 55, 59, 61, 63, 65, 68, 77, 78, 88, 89,\n", + " 93, 96, 98, 101, 104, 105, 109, 112, 113, 115, 119,\n", + " 120, 121, 123, 126, 127, 131, 133, 137, 147, 150, 151,\n", + " 153, 154, 155, 181, 182, 184, 187, 194, 199, 205, 208,\n", + " 209, 226, 228, 229, 230, 231, 239, 254, 258, 260, 261,\n", + " 266, 268, 275, 276, 280, 282, 284, 292, 295, 297, 299,\n", + " 309, 316, 323, 328, 336, 337, 338, 339, 342, 348, 351,\n", + " 355, 362, 363, 370, 380, 389, 391, 392, 399, 404, 409,\n", + " 413, 432, 435, 450, 454, 474, 484, 488, 494, 497, 515,\n", + " 521, 526, 542, 553, 559, 561, 562, 563, 570, 573, 578,\n", + " 587, 590, 597, 599, 601, 605, 606, 619, 628, 631, 640,\n", + " 641, 655, 661, 665, 670, 672, 678, 680, 683, 686, 687,\n", + " 691, 692, 696, 699, 701, 702, 708, 717, 719, 720, 726,\n", + " 733, 737, 740, 744, 748, 753, 759, 763, 765, 770, 773,\n", + " 783, 786, 787, 793, 797, 802, 804, 806, 809, 810, 815,\n", + " 816, 818, 821, 825, 836, 838, 858, 859, 864, 868, 873,\n", + " 875, 879, 880, 882, 885, 888, 889, 895, 901, 904, 907,\n", + " 910, 915, 920, 928, 931, 932, 934, 935, 937, 952, 955,\n", + " 957, 967, 973, 975, 978, 980, 983, 993, 999, 1000, 1005,\n", + " 1007, 1010, 1014, 1017, 1020, 1022, 1030, 1032, 1033, 1039, 1040,\n", + " 1047, 1048, 1050, 1056, 1068, 1073, 1094, 1106, 1110, 1115, 1117,\n", + " 1118, 1122, 1123, 1124, 1125, 1127, 1129, 1134, 1138, 1141, 1142,\n", + " 1153, 1172, 1183, 1190, 1191, 1208, 1211, 1221, 1223, 1240, 1241,\n", + " 1245, 1246, 1249, 1251, 1254, 1255, 1262, 1265, 1275, 1283, 1284,\n", + " 1286, 1292, 1294, 1296, 1298, 1308, 1312, 1314, 1317, 1318, 1319,\n", + " 1327, 1342, 1344, 1348, 1350, 1352, 1353, 1354, 1358, 1360, 1361,\n", + " 1362, 1377, 1379, 1381, 1383, 1387, 1389, 1394]),\n", + " 1: array([ 1, 3, 5, 7, 9, 12, 13, 17, 18, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 36, 38, 42, 46, 48,\n", + " 49, 51, 56, 58, 60, 64, 66, 67, 70, 74, 80,\n", + " 82, 86, 87, 90, 92, 94, 97, 99, 102, 108, 111,\n", + " 114, 118, 122, 124, 128, 130, 132, 135, 138, 139, 140,\n", + " 141, 142, 143, 144, 145, 146, 148, 149, 158, 159, 162,\n", + " 166, 167, 169, 170, 172, 173, 174, 175, 177, 178, 179,\n", + " 189, 190, 192, 195, 197, 200, 201, 202, 206, 207, 211,\n", + " 212, 213, 214, 216, 218, 219, 221, 222, 227, 232, 235,\n", + " 236, 238, 240, 241, 244, 245, 246, 247, 249, 252, 253,\n", + " 255, 259, 262, 264, 265, 267, 271, 272, 273, 274, 277,\n", + " 278, 283, 288, 289, 290, 291, 294, 296, 298, 300, 301,\n", + " 302, 303, 304, 305, 306, 307, 310, 312, 314, 315, 322,\n", + " 324, 325, 327, 331, 332, 334, 335, 340, 341, 344, 352,\n", + " 353, 354, 357, 360, 361, 364, 365, 366, 368, 369, 371,\n", + " 372, 373, 376, 378, 381, 383, 384, 385, 386, 388, 390,\n", + " 394, 397, 398, 400, 401, 402, 407, 408, 411, 412, 414,\n", + " 416, 418, 419, 420, 421, 422, 423, 425, 426, 427, 428,\n", + " 429, 430, 431, 433, 434, 436, 442, 444, 445, 446, 448,\n", + " 449, 452, 453, 455, 459, 460, 462, 463, 464, 465, 467,\n", + " 468, 471, 472, 475, 476, 480, 482, 483, 485, 486, 489,\n", + " 490, 491, 492, 495, 496, 499, 500, 501, 503, 505, 508,\n", + " 509, 510, 512, 518, 520, 522, 527, 528, 529, 530, 533,\n", + " 534, 535, 537, 539, 540, 543, 545, 547, 548, 550, 551,\n", + " 552, 554, 558, 560, 564, 567, 569, 572, 574, 579, 580,\n", + " 581, 582, 589, 591, 592, 594, 596, 598, 602, 603, 604,\n", + " 607, 611, 612, 614, 615, 616, 617, 618, 620, 622, 624,\n", + " 627, 629, 633, 634, 635, 637, 639, 643, 644, 645, 647,\n", + " 648, 650, 652, 657, 658, 662, 666, 668, 673, 674, 675,\n", + " 676, 677, 681, 682, 684, 685, 688, 689, 690, 695, 697,\n", + " 700, 703, 704, 707, 709, 711, 714, 715, 716, 718, 728,\n", + " 730, 731, 732, 741, 742, 743, 747, 750, 756, 757, 761,\n", + " 762, 764, 766, 767, 769, 772, 774, 775, 777, 779, 780,\n", + " 782, 785, 789, 792, 796, 798, 799, 803, 811, 812, 814,\n", + " 819, 823, 824, 828, 830, 831, 832, 835, 837, 839, 840,\n", + " 841, 843, 844, 846, 848, 849, 853, 856, 857, 861, 867,\n", + " 869, 871, 872, 874, 876, 883, 884, 886, 887, 890, 891,\n", + " 896, 899, 905, 908, 909, 912, 913, 914, 916, 917, 918,\n", + " 919, 921, 923, 924, 926, 929, 933, 938, 939, 940, 941,\n", + " 942, 943, 944, 947, 950, 951, 953, 960, 961, 962, 964,\n", + " 965, 968, 971, 974, 979, 984, 985, 986, 987, 990, 991,\n", + " 992, 996, 997, 1001, 1003, 1006, 1009, 1011, 1013, 1021, 1023,\n", + " 1025, 1026, 1028, 1029, 1031, 1037, 1038, 1041, 1042, 1044, 1045,\n", + " 1046, 1052, 1053, 1054, 1060, 1062, 1063, 1064, 1065, 1069, 1071,\n", + " 1075, 1080, 1083, 1084, 1085, 1088, 1092, 1095, 1100, 1102, 1103,\n", + " 1108, 1109, 1113, 1119, 1121, 1126, 1130, 1131, 1135, 1137, 1146,\n", + " 1148, 1154, 1155, 1158, 1160, 1161, 1162, 1163, 1164, 1165, 1166,\n", + " 1169, 1170, 1171, 1173, 1177, 1179, 1180, 1182, 1184, 1185, 1192,\n", + " 1193, 1195, 1196, 1198, 1200, 1201, 1203, 1204, 1206, 1209, 1210,\n", + " 1212, 1214, 1218, 1219, 1220, 1225, 1226, 1227, 1230, 1231, 1232,\n", + " 1234, 1236, 1237, 1239, 1244, 1247, 1252, 1253, 1258, 1259, 1261,\n", + " 1264, 1266, 1267, 1269, 1272, 1273, 1274, 1278, 1279, 1282, 1289,\n", + " 1291, 1299, 1301, 1303, 1305, 1306, 1311, 1315, 1316, 1320, 1321,\n", + " 1322, 1324, 1326, 1328, 1329, 1330, 1332, 1334, 1336, 1337, 1339,\n", + " 1341, 1345, 1346, 1351, 1355, 1363, 1364, 1367, 1368, 1369, 1370,\n", + " 1371, 1372, 1373, 1374, 1376, 1378, 1380, 1384, 1385, 1386, 1388,\n", + " 1390, 1398]),\n", + " 2: array([ 4, 14, 24, 33, 34, 50, 57, 72, 73, 81, 83,\n", + " 84, 85, 91, 95, 117, 125, 134, 136, 152, 157, 160,\n", + " 161, 171, 180, 183, 185, 188, 196, 198, 203, 204, 217,\n", + " 223, 224, 225, 237, 243, 256, 270, 279, 287, 293, 313,\n", + " 320, 321, 345, 349, 356, 358, 367, 379, 382, 387, 395,\n", + " 406, 410, 417, 440, 447, 457, 458, 466, 470, 479, 506,\n", + " 514, 519, 523, 524, 525, 538, 541, 549, 568, 576, 577,\n", + " 588, 593, 600, 613, 621, 625, 626, 632, 638, 646, 653,\n", + " 656, 660, 663, 671, 693, 706, 713, 721, 723, 724, 734,\n", + " 735, 736, 745, 749, 752, 755, 771, 781, 784, 794, 801,\n", + " 805, 807, 813, 822, 827, 829, 854, 855, 881, 892, 894,\n", + " 897, 900, 925, 927, 930, 945, 948, 959, 976, 977, 988,\n", + " 998, 1004, 1035, 1049, 1051, 1058, 1066, 1067, 1072, 1074, 1077,\n", + " 1087, 1089, 1091, 1096, 1097, 1098, 1099, 1104, 1107, 1112, 1120,\n", + " 1128, 1133, 1139, 1143, 1147, 1149, 1152, 1159, 1167, 1189, 1194,\n", + " 1205, 1213, 1215, 1216, 1222, 1229, 1235, 1238, 1256, 1257, 1276,\n", + " 1281, 1285, 1287, 1290, 1293, 1295, 1300, 1302, 1304, 1325, 1331,\n", + " 1338, 1347, 1356, 1357, 1365, 1366, 1382, 1391, 1393, 1397]),\n", + " 3: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 43, 44,\n", + " 45, 53, 62, 69, 71, 75, 76, 79, 100, 103, 106,\n", + " 107, 110, 116, 129, 156, 163, 164, 165, 168, 176, 186,\n", + " 191, 193, 210, 215, 220, 233, 234, 242, 248, 250, 251,\n", + " 257, 263, 269, 281, 285, 286, 308, 311, 317, 318, 319,\n", + " 326, 329, 330, 333, 343, 346, 347, 350, 359, 374, 375,\n", + " 377, 393, 396, 403, 405, 415, 424, 437, 438, 439, 441,\n", + " 443, 451, 456, 461, 469, 473, 477, 478, 481, 487, 493,\n", + " 498, 502, 504, 507, 511, 513, 516, 517, 531, 532, 536,\n", + " 544, 546, 555, 556, 557, 565, 566, 571, 575, 583, 584,\n", + " 585, 586, 595, 608, 609, 610, 623, 630, 636, 642, 649,\n", + " 651, 654, 659, 664, 667, 669, 679, 694, 698, 705, 710,\n", + " 712, 722, 725, 727, 729, 738, 739, 746, 751, 754, 758,\n", + " 760, 768, 776, 778, 788, 790, 791, 795, 800, 808, 817,\n", + " 820, 826, 833, 834, 842, 845, 847, 850, 851, 852, 860,\n", + " 862, 863, 865, 866, 870, 877, 878, 893, 898, 902, 903,\n", + " 906, 911, 922, 936, 946, 949, 954, 956, 958, 963, 966,\n", + " 969, 970, 972, 981, 982, 989, 994, 995, 1002, 1008, 1012,\n", + " 1015, 1016, 1018, 1019, 1024, 1027, 1034, 1036, 1043, 1055, 1057,\n", + " 1059, 1061, 1070, 1076, 1078, 1079, 1081, 1082, 1086, 1090, 1093,\n", + " 1101, 1105, 1111, 1114, 1116, 1132, 1136, 1140, 1144, 1145, 1150,\n", + " 1151, 1156, 1157, 1168, 1174, 1175, 1176, 1178, 1181, 1186, 1187,\n", + " 1188, 1197, 1199, 1202, 1207, 1217, 1224, 1228, 1233, 1242, 1243,\n", + " 1248, 1250, 1260, 1263, 1268, 1270, 1271, 1277, 1280, 1288, 1297,\n", + " 1307, 1309, 1310, 1313, 1323, 1333, 1335, 1340, 1343, 1349, 1359,\n", + " 1375, 1392, 1395, 1396, 1399])},\n", + " 5: {0: array([ 0, 2, 20, 22, 54, 61, 65, 78, 89, 101, 105,\n", + " 112, 119, 123, 127, 133, 137, 147, 154, 182, 187, 205,\n", + " 208, 226, 230, 268, 275, 284, 309, 323, 328, 337, 342,\n", + " 355, 362, 380, 391, 413, 432, 435, 484, 488, 515, 542,\n", + " 562, 587, 590, 597, 601, 641, 655, 661, 692, 701, 719,\n", + " 733, 737, 744, 770, 773, 793, 806, 809, 810, 821, 836,\n", + " 859, 864, 879, 882, 895, 907, 915, 920, 931, 952, 955,\n", + " 957, 967, 978, 993, 999, 1007, 1017, 1032, 1068, 1094, 1115,\n", + " 1117, 1123, 1125, 1127, 1153, 1190, 1191, 1208, 1223, 1246, 1284,\n", + " 1294, 1298, 1308, 1318, 1327, 1344, 1348, 1350, 1354, 1360, 1362,\n", + " 1377, 1379, 1381, 1389]),\n", + " 1: array([ 1, 3, 5, 7, 9, 12, 13, 17, 18, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 36, 38, 42, 46, 48,\n", + " 49, 51, 56, 58, 60, 64, 66, 67, 70, 74, 80,\n", + " 82, 86, 87, 90, 92, 94, 97, 99, 102, 108, 111,\n", + " 114, 118, 122, 124, 128, 130, 132, 135, 138, 139, 140,\n", + " 141, 142, 143, 144, 145, 146, 148, 149, 158, 159, 162,\n", + " 166, 167, 169, 170, 172, 173, 174, 175, 177, 178, 179,\n", + " 189, 190, 192, 195, 197, 200, 201, 202, 206, 207, 211,\n", + " 212, 213, 214, 216, 218, 219, 221, 222, 227, 232, 235,\n", + " 236, 238, 240, 241, 244, 245, 246, 247, 249, 252, 253,\n", + " 255, 259, 262, 264, 265, 267, 271, 272, 273, 274, 277,\n", + " 278, 283, 288, 289, 290, 291, 294, 296, 298, 300, 301,\n", + " 302, 303, 304, 305, 306, 307, 310, 312, 314, 315, 322,\n", + " 324, 325, 327, 331, 332, 334, 335, 340, 341, 344, 352,\n", + " 353, 354, 357, 360, 361, 364, 365, 366, 368, 369, 371,\n", + " 372, 373, 376, 378, 381, 383, 384, 385, 386, 388, 390,\n", + " 394, 397, 398, 400, 401, 402, 407, 408, 411, 412, 414,\n", + " 416, 418, 419, 420, 421, 422, 423, 425, 426, 427, 428,\n", + " 429, 430, 431, 433, 434, 436, 442, 444, 445, 446, 448,\n", + " 449, 452, 453, 455, 459, 460, 462, 463, 464, 465, 467,\n", + " 468, 471, 472, 475, 476, 480, 482, 483, 485, 486, 489,\n", + " 490, 491, 492, 495, 496, 499, 500, 501, 503, 505, 508,\n", + " 509, 510, 512, 518, 520, 522, 527, 528, 529, 530, 533,\n", + " 534, 535, 537, 539, 540, 543, 545, 547, 548, 550, 551,\n", + " 552, 554, 558, 560, 564, 567, 569, 572, 574, 579, 580,\n", + " 581, 582, 589, 591, 592, 594, 596, 598, 602, 603, 604,\n", + " 607, 611, 612, 614, 615, 616, 617, 618, 620, 622, 624,\n", + " 627, 629, 633, 634, 635, 637, 639, 643, 644, 645, 647,\n", + " 648, 650, 652, 657, 658, 662, 666, 668, 673, 674, 675,\n", + " 676, 677, 681, 682, 684, 685, 688, 689, 690, 695, 697,\n", + " 700, 703, 704, 707, 709, 711, 714, 715, 716, 718, 728,\n", + " 730, 731, 732, 741, 742, 743, 747, 750, 756, 757, 761,\n", + " 762, 764, 766, 767, 769, 772, 774, 775, 777, 779, 780,\n", + " 782, 785, 789, 792, 796, 798, 799, 803, 811, 812, 814,\n", + " 819, 823, 824, 828, 830, 831, 832, 835, 837, 839, 840,\n", + " 841, 843, 844, 846, 848, 849, 853, 856, 857, 861, 867,\n", + " 869, 871, 872, 874, 876, 883, 884, 886, 887, 890, 891,\n", + " 896, 899, 905, 908, 909, 912, 913, 914, 916, 917, 918,\n", + " 919, 921, 923, 924, 926, 929, 933, 938, 939, 940, 941,\n", + " 942, 943, 944, 947, 950, 951, 953, 960, 961, 962, 964,\n", + " 965, 968, 971, 974, 979, 984, 985, 986, 987, 990, 991,\n", + " 992, 996, 997, 1001, 1003, 1006, 1009, 1011, 1013, 1021, 1023,\n", + " 1025, 1026, 1028, 1029, 1031, 1037, 1038, 1041, 1042, 1044, 1045,\n", + " 1046, 1052, 1053, 1054, 1060, 1062, 1063, 1064, 1065, 1069, 1071,\n", + " 1075, 1080, 1083, 1084, 1085, 1088, 1092, 1095, 1100, 1102, 1103,\n", + " 1108, 1109, 1113, 1119, 1121, 1126, 1130, 1131, 1135, 1137, 1146,\n", + " 1148, 1154, 1155, 1158, 1160, 1161, 1162, 1163, 1164, 1165, 1166,\n", + " 1169, 1170, 1171, 1173, 1177, 1179, 1180, 1182, 1184, 1185, 1192,\n", + " 1193, 1195, 1196, 1198, 1200, 1201, 1203, 1204, 1206, 1209, 1210,\n", + " 1212, 1214, 1218, 1219, 1220, 1225, 1226, 1227, 1230, 1231, 1232,\n", + " 1234, 1236, 1237, 1239, 1244, 1247, 1252, 1253, 1258, 1259, 1261,\n", + " 1264, 1266, 1267, 1269, 1272, 1273, 1274, 1278, 1279, 1282, 1289,\n", + " 1291, 1299, 1301, 1303, 1305, 1306, 1311, 1315, 1316, 1320, 1321,\n", + " 1322, 1324, 1326, 1328, 1329, 1330, 1332, 1334, 1336, 1337, 1339,\n", + " 1341, 1345, 1346, 1351, 1355, 1363, 1364, 1367, 1368, 1369, 1370,\n", + " 1371, 1372, 1373, 1374, 1376, 1378, 1380, 1384, 1385, 1386, 1388,\n", + " 1390, 1398]),\n", + " 2: array([ 4, 14, 24, 33, 34, 50, 57, 72, 73, 81, 83,\n", + " 84, 85, 91, 95, 117, 125, 134, 136, 152, 157, 160,\n", + " 161, 171, 180, 183, 185, 188, 196, 198, 203, 204, 217,\n", + " 223, 224, 225, 237, 243, 256, 270, 279, 287, 293, 313,\n", + " 320, 321, 345, 349, 356, 358, 367, 379, 382, 387, 395,\n", + " 406, 410, 417, 440, 447, 457, 458, 466, 470, 479, 506,\n", + " 514, 519, 523, 524, 525, 538, 541, 549, 568, 576, 577,\n", + " 588, 593, 600, 613, 621, 625, 626, 632, 638, 646, 653,\n", + " 656, 660, 663, 671, 693, 706, 713, 721, 723, 724, 734,\n", + " 735, 736, 745, 749, 752, 755, 771, 781, 784, 794, 801,\n", + " 805, 807, 813, 822, 827, 829, 854, 855, 881, 892, 894,\n", + " 897, 900, 925, 927, 930, 945, 948, 959, 976, 977, 988,\n", + " 998, 1004, 1035, 1049, 1051, 1058, 1066, 1067, 1072, 1074, 1077,\n", + " 1087, 1089, 1091, 1096, 1097, 1098, 1099, 1104, 1107, 1112, 1120,\n", + " 1128, 1133, 1139, 1143, 1147, 1149, 1152, 1159, 1167, 1189, 1194,\n", + " 1205, 1213, 1215, 1216, 1222, 1229, 1235, 1238, 1256, 1257, 1276,\n", + " 1281, 1285, 1287, 1290, 1293, 1295, 1300, 1302, 1304, 1325, 1331,\n", + " 1338, 1347, 1356, 1357, 1365, 1366, 1382, 1391, 1393, 1397]),\n", + " 3: array([ 6, 8, 19, 21, 41, 47, 52, 55, 59, 63, 68,\n", + " 77, 88, 93, 96, 98, 104, 109, 113, 115, 120, 121,\n", + " 126, 131, 150, 151, 153, 155, 181, 184, 194, 199, 209,\n", + " 228, 229, 231, 239, 254, 258, 260, 261, 266, 276, 280,\n", + " 282, 292, 295, 297, 299, 316, 336, 338, 339, 348, 351,\n", + " 363, 370, 389, 392, 399, 404, 409, 450, 454, 474, 494,\n", + " 497, 521, 526, 553, 559, 561, 563, 570, 573, 578, 599,\n", + " 605, 606, 619, 628, 631, 640, 665, 670, 672, 678, 680,\n", + " 683, 686, 687, 691, 696, 699, 702, 708, 717, 720, 726,\n", + " 740, 748, 753, 759, 763, 765, 783, 786, 787, 797, 802,\n", + " 804, 815, 816, 818, 825, 838, 858, 868, 873, 875, 880,\n", + " 885, 888, 889, 901, 904, 910, 928, 932, 934, 935, 937,\n", + " 973, 975, 980, 983, 1000, 1005, 1010, 1014, 1020, 1022, 1030,\n", + " 1033, 1039, 1040, 1047, 1048, 1050, 1056, 1073, 1106, 1110, 1118,\n", + " 1122, 1124, 1129, 1134, 1138, 1141, 1142, 1172, 1183, 1211, 1221,\n", + " 1240, 1241, 1245, 1249, 1251, 1254, 1255, 1262, 1265, 1275, 1283,\n", + " 1286, 1292, 1296, 1312, 1314, 1317, 1319, 1342, 1352, 1353, 1358,\n", + " 1361, 1383, 1387, 1394]),\n", + " 4: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 43, 44,\n", + " 45, 53, 62, 69, 71, 75, 76, 79, 100, 103, 106,\n", + " 107, 110, 116, 129, 156, 163, 164, 165, 168, 176, 186,\n", + " 191, 193, 210, 215, 220, 233, 234, 242, 248, 250, 251,\n", + " 257, 263, 269, 281, 285, 286, 308, 311, 317, 318, 319,\n", + " 326, 329, 330, 333, 343, 346, 347, 350, 359, 374, 375,\n", + " 377, 393, 396, 403, 405, 415, 424, 437, 438, 439, 441,\n", + " 443, 451, 456, 461, 469, 473, 477, 478, 481, 487, 493,\n", + " 498, 502, 504, 507, 511, 513, 516, 517, 531, 532, 536,\n", + " 544, 546, 555, 556, 557, 565, 566, 571, 575, 583, 584,\n", + " 585, 586, 595, 608, 609, 610, 623, 630, 636, 642, 649,\n", + " 651, 654, 659, 664, 667, 669, 679, 694, 698, 705, 710,\n", + " 712, 722, 725, 727, 729, 738, 739, 746, 751, 754, 758,\n", + " 760, 768, 776, 778, 788, 790, 791, 795, 800, 808, 817,\n", + " 820, 826, 833, 834, 842, 845, 847, 850, 851, 852, 860,\n", + " 862, 863, 865, 866, 870, 877, 878, 893, 898, 902, 903,\n", + " 906, 911, 922, 936, 946, 949, 954, 956, 958, 963, 966,\n", + " 969, 970, 972, 981, 982, 989, 994, 995, 1002, 1008, 1012,\n", + " 1015, 1016, 1018, 1019, 1024, 1027, 1034, 1036, 1043, 1055, 1057,\n", + " 1059, 1061, 1070, 1076, 1078, 1079, 1081, 1082, 1086, 1090, 1093,\n", + " 1101, 1105, 1111, 1114, 1116, 1132, 1136, 1140, 1144, 1145, 1150,\n", + " 1151, 1156, 1157, 1168, 1174, 1175, 1176, 1178, 1181, 1186, 1187,\n", + " 1188, 1197, 1199, 1202, 1207, 1217, 1224, 1228, 1233, 1242, 1243,\n", + " 1248, 1250, 1260, 1263, 1268, 1270, 1271, 1277, 1280, 1288, 1297,\n", + " 1307, 1309, 1310, 1313, 1323, 1333, 1335, 1340, 1343, 1349, 1359,\n", + " 1375, 1392, 1395, 1396, 1399])},\n", + " 6: {0: array([ 0, 2, 20, 22, 54, 61, 65, 78, 89, 101, 105,\n", + " 112, 119, 123, 127, 133, 137, 147, 154, 182, 187, 205,\n", + " 208, 226, 230, 268, 275, 284, 309, 323, 328, 337, 342,\n", + " 355, 362, 380, 391, 413, 432, 435, 484, 488, 515, 542,\n", + " 562, 587, 590, 597, 601, 641, 655, 661, 692, 701, 719,\n", + " 733, 737, 744, 770, 773, 793, 806, 809, 810, 821, 836,\n", + " 859, 864, 879, 882, 895, 907, 915, 920, 931, 952, 955,\n", + " 957, 967, 978, 993, 999, 1007, 1017, 1032, 1068, 1094, 1115,\n", + " 1117, 1123, 1125, 1127, 1153, 1190, 1191, 1208, 1223, 1246, 1284,\n", + " 1294, 1298, 1308, 1318, 1327, 1344, 1348, 1350, 1354, 1360, 1362,\n", + " 1377, 1379, 1381, 1389]),\n", + " 1: array([ 1, 3, 5, 7, 9, 12, 13, 17, 18, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 36, 38, 42, 46, 48,\n", + " 49, 51, 56, 58, 60, 64, 66, 67, 70, 74, 80,\n", + " 82, 86, 87, 90, 92, 94, 97, 99, 102, 108, 111,\n", + " 114, 118, 122, 124, 128, 130, 132, 135, 138, 139, 140,\n", + " 141, 142, 143, 144, 145, 146, 148, 149, 158, 159, 162,\n", + " 166, 167, 169, 170, 172, 173, 174, 175, 177, 178, 179,\n", + " 189, 190, 192, 195, 197, 200, 201, 202, 206, 207, 211,\n", + " 212, 213, 214, 216, 218, 219, 221, 222, 227, 232, 235,\n", + " 236, 238, 240, 241, 244, 245, 246, 247, 249, 252, 253,\n", + " 255, 259, 262, 264, 265, 267, 271, 272, 273, 274, 277,\n", + " 278, 283, 288, 289, 290, 291, 294, 296, 298, 300, 301,\n", + " 302, 303, 304, 305, 306, 307, 310, 312, 314, 315, 322,\n", + " 324, 325, 327, 331, 332, 334, 335, 340, 341, 344, 352,\n", + " 353, 354, 357, 360, 361, 364, 365, 366, 368, 369, 371,\n", + " 372, 373, 376, 378, 381, 383, 384, 385, 386, 388, 390,\n", + " 394, 397, 398, 400, 401, 402, 407, 408, 411, 412, 414,\n", + " 416, 418, 419, 420, 421, 422, 423, 425, 426, 427, 428,\n", + " 429, 430, 431, 433, 434, 436, 442, 444, 445, 446, 448,\n", + " 449, 452, 453, 455, 459, 460, 462, 463, 464, 465, 467,\n", + " 468, 471, 472, 475, 476, 480, 482, 483, 485, 486, 489,\n", + " 490, 491, 492, 495, 496, 499, 500, 501, 503, 505, 508,\n", + " 509, 510, 512, 518, 520, 522, 527, 528, 529, 530, 533,\n", + " 534, 535, 537, 539, 540, 543, 545, 547, 548, 550, 551,\n", + " 552, 554, 558, 560, 564, 567, 569, 572, 574, 579, 580,\n", + " 581, 582, 589, 591, 592, 594, 596, 598, 602, 603, 604,\n", + " 607, 611, 612, 614, 615, 616, 617, 618, 620, 622, 624,\n", + " 627, 629, 633, 634, 635, 637, 639, 643, 644, 645, 647,\n", + " 648, 650, 652, 657, 658, 662, 666, 668, 673, 674, 675,\n", + " 676, 677, 681, 682, 684, 685, 688, 689, 690, 695, 697,\n", + " 700, 703, 704, 707, 709, 711, 714, 715, 716, 718, 728,\n", + " 730, 731, 732, 741, 742, 743, 747, 750, 756, 757, 761,\n", + " 762, 764, 766, 767, 769, 772, 774, 775, 777, 779, 780,\n", + " 782, 785, 789, 792, 796, 798, 799, 803, 811, 812, 814,\n", + " 819, 823, 824, 828, 830, 831, 832, 835, 837, 839, 840,\n", + " 841, 843, 844, 846, 848, 849, 853, 856, 857, 861, 867,\n", + " 869, 871, 872, 874, 876, 883, 884, 886, 887, 890, 891,\n", + " 896, 899, 905, 908, 909, 912, 913, 914, 916, 917, 918,\n", + " 919, 921, 923, 924, 926, 929, 933, 938, 939, 940, 941,\n", + " 942, 943, 944, 947, 950, 951, 953, 960, 961, 962, 964,\n", + " 965, 968, 971, 974, 979, 984, 985, 986, 987, 990, 991,\n", + " 992, 996, 997, 1001, 1003, 1006, 1009, 1011, 1013, 1021, 1023,\n", + " 1025, 1026, 1028, 1029, 1031, 1037, 1038, 1041, 1042, 1044, 1045,\n", + " 1046, 1052, 1053, 1054, 1060, 1062, 1063, 1064, 1065, 1069, 1071,\n", + " 1075, 1080, 1083, 1084, 1085, 1088, 1092, 1095, 1100, 1102, 1103,\n", + " 1108, 1109, 1113, 1119, 1121, 1126, 1130, 1131, 1135, 1137, 1146,\n", + " 1148, 1154, 1155, 1158, 1160, 1161, 1162, 1163, 1164, 1165, 1166,\n", + " 1169, 1170, 1171, 1173, 1177, 1179, 1180, 1182, 1184, 1185, 1192,\n", + " 1193, 1195, 1196, 1198, 1200, 1201, 1203, 1204, 1206, 1209, 1210,\n", + " 1212, 1214, 1218, 1219, 1220, 1225, 1226, 1227, 1230, 1231, 1232,\n", + " 1234, 1236, 1237, 1239, 1244, 1247, 1252, 1253, 1258, 1259, 1261,\n", + " 1264, 1266, 1267, 1269, 1272, 1273, 1274, 1278, 1279, 1282, 1289,\n", + " 1291, 1299, 1301, 1303, 1305, 1306, 1311, 1315, 1316, 1320, 1321,\n", + " 1322, 1324, 1326, 1328, 1329, 1330, 1332, 1334, 1336, 1337, 1339,\n", + " 1341, 1345, 1346, 1351, 1355, 1363, 1364, 1367, 1368, 1369, 1370,\n", + " 1371, 1372, 1373, 1374, 1376, 1378, 1380, 1384, 1385, 1386, 1388,\n", + " 1390, 1398]),\n", + " 2: array([ 4, 14, 24, 33, 34, 50, 57, 72, 73, 81, 83,\n", + " 84, 85, 91, 95, 117, 125, 134, 136, 152, 157, 160,\n", + " 161, 171, 180, 183, 185, 188, 196, 198, 203, 204, 217,\n", + " 223, 224, 225, 237, 243, 256, 270, 279, 287, 293, 313,\n", + " 320, 321, 345, 349, 356, 358, 367, 379, 382, 387, 395,\n", + " 406, 410, 417, 440, 447, 457, 458, 466, 470, 479, 506,\n", + " 514, 519, 523, 524, 525, 538, 541, 549, 568, 576, 577,\n", + " 588, 593, 600, 613, 621, 625, 626, 632, 638, 646, 653,\n", + " 656, 660, 663, 671, 693, 706, 713, 721, 723, 724, 734,\n", + " 735, 736, 745, 749, 752, 755, 771, 781, 784, 794, 801,\n", + " 805, 807, 813, 822, 827, 829, 854, 855, 881, 892, 894,\n", + " 897, 900, 925, 927, 930, 945, 948, 959, 976, 977, 988,\n", + " 998, 1004, 1035, 1049, 1051, 1058, 1066, 1067, 1072, 1074, 1077,\n", + " 1087, 1089, 1091, 1096, 1097, 1098, 1099, 1104, 1107, 1112, 1120,\n", + " 1128, 1133, 1139, 1143, 1147, 1149, 1152, 1159, 1167, 1189, 1194,\n", + " 1205, 1213, 1215, 1216, 1222, 1229, 1235, 1238, 1256, 1257, 1276,\n", + " 1281, 1285, 1287, 1290, 1293, 1295, 1300, 1302, 1304, 1325, 1331,\n", + " 1338, 1347, 1356, 1357, 1365, 1366, 1382, 1391, 1393, 1397]),\n", + " 3: array([ 6, 8, 19, 21, 41, 47, 52, 55, 59, 63, 68,\n", + " 77, 88, 93, 96, 98, 104, 109, 113, 115, 120, 121,\n", + " 126, 131, 150, 151, 153, 155, 181, 184, 194, 199, 209,\n", + " 228, 229, 231, 239, 254, 258, 260, 261, 266, 276, 280,\n", + " 282, 292, 295, 297, 299, 316, 336, 338, 339, 348, 351,\n", + " 363, 370, 389, 392, 399, 404, 409, 450, 454, 474, 494,\n", + " 497, 521, 526, 553, 559, 561, 563, 570, 573, 578, 599,\n", + " 605, 606, 619, 628, 631, 640, 665, 670, 672, 678, 680,\n", + " 683, 686, 687, 691, 696, 699, 702, 708, 717, 720, 726,\n", + " 740, 748, 753, 759, 763, 765, 783, 786, 787, 797, 802,\n", + " 804, 815, 816, 818, 825, 838, 858, 868, 873, 875, 880,\n", + " 885, 888, 889, 901, 904, 910, 928, 932, 934, 935, 937,\n", + " 973, 975, 980, 983, 1000, 1005, 1010, 1014, 1020, 1022, 1030,\n", + " 1033, 1039, 1040, 1047, 1048, 1050, 1056, 1073, 1106, 1110, 1118,\n", + " 1122, 1124, 1129, 1134, 1138, 1141, 1142, 1172, 1183, 1211, 1221,\n", + " 1240, 1241, 1245, 1249, 1251, 1254, 1255, 1262, 1265, 1275, 1283,\n", + " 1286, 1292, 1296, 1312, 1314, 1317, 1319, 1342, 1352, 1353, 1358,\n", + " 1361, 1383, 1387, 1394]),\n", + " 4: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 43, 44,\n", + " 45, 53, 76, 79, 103, 106, 107, 110, 116, 129, 156,\n", + " 163, 164, 165, 168, 186, 191, 193, 210, 215, 220, 233,\n", + " 234, 242, 248, 250, 251, 257, 263, 269, 281, 285, 286,\n", + " 311, 318, 329, 343, 346, 347, 350, 374, 375, 393, 396,\n", + " 403, 405, 424, 437, 438, 439, 456, 461, 473, 477, 478,\n", + " 487, 493, 502, 504, 507, 511, 513, 517, 532, 536, 544,\n", + " 546, 555, 556, 557, 565, 575, 583, 608, 609, 623, 630,\n", + " 636, 654, 664, 667, 669, 694, 698, 705, 710, 712, 722,\n", + " 725, 727, 739, 746, 758, 760, 768, 776, 778, 788, 790,\n", + " 800, 817, 820, 833, 834, 842, 845, 847, 850, 851, 852,\n", + " 860, 862, 863, 865, 877, 878, 893, 903, 906, 911, 922,\n", + " 936, 946, 954, 956, 963, 966, 969, 970, 972, 981, 982,\n", + " 989, 994, 1002, 1008, 1015, 1016, 1019, 1027, 1034, 1036, 1043,\n", + " 1057, 1061, 1070, 1076, 1079, 1082, 1086, 1101, 1105, 1111, 1132,\n", + " 1136, 1144, 1145, 1150, 1151, 1157, 1168, 1174, 1175, 1186, 1188,\n", + " 1197, 1199, 1202, 1217, 1224, 1228, 1243, 1268, 1270, 1277, 1280,\n", + " 1297, 1309, 1323, 1333, 1335, 1349, 1359, 1375, 1392, 1396, 1399]),\n", + " 5: array([ 62, 69, 71, 75, 100, 176, 308, 317, 319, 326, 330,\n", + " 333, 359, 377, 415, 441, 443, 451, 469, 481, 498, 516,\n", + " 531, 566, 571, 584, 585, 586, 595, 610, 642, 649, 651,\n", + " 659, 679, 729, 738, 751, 754, 791, 795, 808, 826, 866,\n", + " 870, 898, 902, 949, 958, 995, 1012, 1018, 1024, 1055, 1059,\n", + " 1078, 1081, 1090, 1093, 1114, 1116, 1140, 1156, 1176, 1178, 1181,\n", + " 1187, 1207, 1233, 1242, 1248, 1250, 1260, 1263, 1271, 1288, 1307,\n", + " 1310, 1313, 1340, 1343, 1395])},\n", + " 7: {0: array([ 0, 2, 20, 22, 54, 61, 65, 78, 89, 101, 105,\n", + " 112, 119, 123, 127, 133, 137, 147, 154, 182, 187, 205,\n", + " 208, 226, 230, 268, 275, 284, 309, 323, 328, 337, 342,\n", + " 355, 362, 380, 391, 413, 432, 435, 484, 488, 515, 542,\n", + " 562, 587, 590, 597, 601, 641, 655, 661, 692, 701, 719,\n", + " 733, 737, 744, 770, 773, 793, 806, 809, 810, 821, 836,\n", + " 859, 864, 879, 882, 895, 907, 915, 920, 931, 952, 955,\n", + " 957, 967, 978, 993, 999, 1007, 1017, 1032, 1068, 1094, 1115,\n", + " 1117, 1123, 1125, 1127, 1153, 1190, 1191, 1208, 1223, 1246, 1284,\n", + " 1294, 1298, 1308, 1318, 1327, 1344, 1348, 1350, 1354, 1360, 1362,\n", + " 1377, 1379, 1381, 1389]),\n", + " 1: array([ 1, 3, 5, 7, 9, 12, 13, 17, 18, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 36, 38, 42, 46, 48,\n", + " 49, 51, 56, 58, 60, 64, 66, 67, 70, 74, 80,\n", + " 82, 86, 87, 90, 92, 94, 97, 99, 102, 108, 111,\n", + " 114, 118, 122, 124, 128, 130, 132, 135, 138, 139, 140,\n", + " 141, 142, 143, 144, 145, 146, 148, 149, 158, 159, 162,\n", + " 166, 167, 169, 170, 172, 173, 174, 175, 177, 178, 179,\n", + " 189, 190, 192, 195, 197, 200, 201, 202, 206, 207, 211,\n", + " 212, 213, 214, 216, 218, 219, 221, 222, 227, 232, 235,\n", + " 236, 238, 240, 241, 244, 245, 246, 247, 249, 252, 253,\n", + " 255, 259, 262, 264, 265, 267, 271, 272, 273, 274, 277,\n", + " 278, 283, 288, 289, 290, 291, 294, 296, 298, 300, 301,\n", + " 302, 303, 304, 305, 306, 307, 310, 312, 314, 315, 322,\n", + " 324, 325, 327, 331, 332, 334, 335, 340, 341, 344, 352,\n", + " 353, 354, 357, 360, 361, 364, 365, 366, 368, 369, 371,\n", + " 372, 373, 376, 378, 381, 383, 384, 385, 386, 388, 390,\n", + " 394, 397, 398, 400, 401, 402, 407, 408, 411, 412, 414,\n", + " 416, 418, 419, 420, 421, 422, 423, 425, 426, 427, 428,\n", + " 429, 430, 431, 433, 434, 436, 442, 444, 445, 446, 448,\n", + " 449, 452, 453, 455, 459, 460, 462, 463, 464, 465, 467,\n", + " 468, 471, 472, 475, 476, 480, 482, 483, 485, 486, 489,\n", + " 490, 491, 492, 495, 496, 499, 500, 501, 503, 505, 508,\n", + " 509, 510, 512, 518, 520, 522, 527, 528, 529, 530, 533,\n", + " 534, 535, 537, 539, 540, 543, 545, 547, 548, 550, 551,\n", + " 552, 554, 558, 560, 564, 567, 569, 572, 574, 579, 580,\n", + " 581, 582, 589, 591, 592, 594, 596, 598, 602, 603, 604,\n", + " 607, 611, 612, 614, 615, 616, 617, 618, 620, 622, 624,\n", + " 627, 629, 633, 634, 635, 637, 639, 643, 644, 645, 647,\n", + " 648, 650, 652, 657, 658, 662, 666, 668, 673, 674, 675,\n", + " 676, 677, 681, 682, 684, 685, 688, 689, 690, 695, 697,\n", + " 700, 703, 704, 707, 709, 711, 714, 715, 716, 718, 728,\n", + " 730, 731, 732, 741, 742, 743, 747, 750, 756, 757, 761,\n", + " 762, 764, 766, 767, 769, 772, 774, 775, 777, 779, 780,\n", + " 782, 785, 789, 792, 796, 798, 799, 803, 811, 812, 814,\n", + " 819, 823, 824, 828, 830, 831, 832, 835, 837, 839, 840,\n", + " 841, 843, 844, 846, 848, 849, 853, 856, 857, 861, 867,\n", + " 869, 871, 872, 874, 876, 883, 884, 886, 887, 890, 891,\n", + " 896, 899, 905, 908, 909, 912, 913, 914, 916, 917, 918,\n", + " 919, 921, 923, 924, 926, 929, 933, 938, 939, 940, 941,\n", + " 942, 943, 944, 947, 950, 951, 953, 960, 961, 962, 964,\n", + " 965, 968, 971, 974, 979, 984, 985, 986, 987, 990, 991,\n", + " 992, 996, 997, 1001, 1003, 1006, 1009, 1011, 1013, 1021, 1023,\n", + " 1025, 1026, 1028, 1029, 1031, 1037, 1038, 1041, 1042, 1044, 1045,\n", + " 1046, 1052, 1053, 1054, 1060, 1062, 1063, 1064, 1065, 1069, 1071,\n", + " 1075, 1080, 1083, 1084, 1085, 1088, 1092, 1095, 1100, 1102, 1103,\n", + " 1108, 1109, 1113, 1119, 1121, 1126, 1130, 1131, 1135, 1137, 1146,\n", + " 1148, 1154, 1155, 1158, 1160, 1161, 1162, 1163, 1164, 1165, 1166,\n", + " 1169, 1170, 1171, 1173, 1177, 1179, 1180, 1182, 1184, 1185, 1192,\n", + " 1193, 1195, 1196, 1198, 1200, 1201, 1203, 1204, 1206, 1209, 1210,\n", + " 1212, 1214, 1218, 1219, 1220, 1225, 1226, 1227, 1230, 1231, 1232,\n", + " 1234, 1236, 1237, 1239, 1244, 1247, 1252, 1253, 1258, 1259, 1261,\n", + " 1264, 1266, 1267, 1269, 1272, 1273, 1274, 1278, 1279, 1282, 1289,\n", + " 1291, 1299, 1301, 1303, 1305, 1306, 1311, 1315, 1316, 1320, 1321,\n", + " 1322, 1324, 1326, 1328, 1329, 1330, 1332, 1334, 1336, 1337, 1339,\n", + " 1341, 1345, 1346, 1351, 1355, 1363, 1364, 1367, 1368, 1369, 1370,\n", + " 1371, 1372, 1373, 1374, 1376, 1378, 1380, 1384, 1385, 1386, 1388,\n", + " 1390, 1398]),\n", + " 2: array([ 4, 14, 24, 33, 34, 50, 57, 72, 73, 81, 83,\n", + " 84, 85, 91, 95, 117, 125, 134, 136, 152, 157, 160,\n", + " 161, 171, 180, 183, 185, 188, 196, 198, 203, 204, 217,\n", + " 223, 224, 225, 237, 243, 256, 270, 279, 287, 293, 313,\n", + " 320, 321, 345, 349, 356, 358, 367, 379, 382, 387, 395,\n", + " 406, 410, 417, 440, 447, 457, 458, 466, 470, 479, 506,\n", + " 514, 519, 523, 524, 525, 538, 541, 549, 568, 576, 577,\n", + " 588, 593, 600, 613, 621, 625, 626, 632, 638, 646, 653,\n", + " 656, 660, 663, 671, 693, 706, 713, 721, 723, 724, 734,\n", + " 735, 736, 745, 749, 752, 755, 771, 781, 784, 794, 801,\n", + " 805, 807, 813, 822, 827, 829, 854, 855, 881, 892, 894,\n", + " 897, 900, 925, 927, 930, 945, 948, 959, 976, 977, 988,\n", + " 998, 1004, 1035, 1049, 1051, 1058, 1066, 1067, 1072, 1074, 1077,\n", + " 1087, 1089, 1091, 1096, 1097, 1098, 1099, 1104, 1107, 1112, 1120,\n", + " 1128, 1133, 1139, 1143, 1147, 1149, 1152, 1159, 1167, 1189, 1194,\n", + " 1205, 1213, 1215, 1216, 1222, 1229, 1235, 1238, 1256, 1257, 1276,\n", + " 1281, 1285, 1287, 1290, 1293, 1295, 1300, 1302, 1304, 1325, 1331,\n", + " 1338, 1347, 1356, 1357, 1365, 1366, 1382, 1391, 1393, 1397]),\n", + " 3: array([ 6, 41, 126, 131, 209, 229, 260, 266, 292, 339, 370,\n", + " 450, 570, 599, 631, 665, 670, 678, 687, 759, 763, 765,\n", + " 783, 787, 804, 816, 888, 889, 904, 932, 935, 1010, 1022,\n", + " 1033, 1129, 1211, 1221, 1292, 1317, 1352]),\n", + " 4: array([ 8, 19, 21, 47, 52, 55, 59, 63, 68, 77, 88,\n", + " 93, 96, 98, 104, 109, 113, 115, 120, 121, 150, 151,\n", + " 153, 155, 181, 184, 194, 199, 228, 231, 239, 254, 258,\n", + " 261, 276, 280, 282, 295, 297, 299, 316, 336, 338, 348,\n", + " 351, 363, 389, 392, 399, 404, 409, 454, 474, 494, 497,\n", + " 521, 526, 553, 559, 561, 563, 573, 578, 605, 606, 619,\n", + " 628, 640, 672, 680, 683, 686, 691, 696, 699, 702, 708,\n", + " 717, 720, 726, 740, 748, 753, 786, 797, 802, 815, 818,\n", + " 825, 838, 858, 868, 873, 875, 880, 885, 901, 910, 928,\n", + " 934, 937, 973, 975, 980, 983, 1000, 1005, 1014, 1020, 1030,\n", + " 1039, 1040, 1047, 1048, 1050, 1056, 1073, 1106, 1110, 1118, 1122,\n", + " 1124, 1134, 1138, 1141, 1142, 1172, 1183, 1240, 1241, 1245, 1249,\n", + " 1251, 1254, 1255, 1262, 1265, 1275, 1283, 1286, 1296, 1312, 1314,\n", + " 1319, 1342, 1353, 1358, 1361, 1383, 1387, 1394]),\n", + " 5: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 43, 44,\n", + " 45, 53, 76, 79, 103, 106, 107, 110, 116, 129, 156,\n", + " 163, 164, 165, 168, 186, 191, 193, 210, 215, 220, 233,\n", + " 234, 242, 248, 250, 251, 257, 263, 269, 281, 285, 286,\n", + " 311, 318, 329, 343, 346, 347, 350, 374, 375, 393, 396,\n", + " 403, 405, 424, 437, 438, 439, 456, 461, 473, 477, 478,\n", + " 487, 493, 502, 504, 507, 511, 513, 517, 532, 536, 544,\n", + " 546, 555, 556, 557, 565, 575, 583, 608, 609, 623, 630,\n", + " 636, 654, 664, 667, 669, 694, 698, 705, 710, 712, 722,\n", + " 725, 727, 739, 746, 758, 760, 768, 776, 778, 788, 790,\n", + " 800, 817, 820, 833, 834, 842, 845, 847, 850, 851, 852,\n", + " 860, 862, 863, 865, 877, 878, 893, 903, 906, 911, 922,\n", + " 936, 946, 954, 956, 963, 966, 969, 970, 972, 981, 982,\n", + " 989, 994, 1002, 1008, 1015, 1016, 1019, 1027, 1034, 1036, 1043,\n", + " 1057, 1061, 1070, 1076, 1079, 1082, 1086, 1101, 1105, 1111, 1132,\n", + " 1136, 1144, 1145, 1150, 1151, 1157, 1168, 1174, 1175, 1186, 1188,\n", + " 1197, 1199, 1202, 1217, 1224, 1228, 1243, 1268, 1270, 1277, 1280,\n", + " 1297, 1309, 1323, 1333, 1335, 1349, 1359, 1375, 1392, 1396, 1399]),\n", + " 6: array([ 62, 69, 71, 75, 100, 176, 308, 317, 319, 326, 330,\n", + " 333, 359, 377, 415, 441, 443, 451, 469, 481, 498, 516,\n", + " 531, 566, 571, 584, 585, 586, 595, 610, 642, 649, 651,\n", + " 659, 679, 729, 738, 751, 754, 791, 795, 808, 826, 866,\n", + " 870, 898, 902, 949, 958, 995, 1012, 1018, 1024, 1055, 1059,\n", + " 1078, 1081, 1090, 1093, 1114, 1116, 1140, 1156, 1176, 1178, 1181,\n", + " 1187, 1207, 1233, 1242, 1248, 1250, 1260, 1263, 1271, 1288, 1307,\n", + " 1310, 1313, 1340, 1343, 1395])},\n", + " 8: {0: array([ 0, 2, 20, 22, 54, 61, 65, 78, 89, 101, 105,\n", + " 112, 119, 123, 127, 133, 137, 147, 154, 182, 187, 205,\n", + " 208, 226, 230, 268, 275, 284, 309, 323, 328, 337, 342,\n", + " 355, 362, 380, 391, 413, 432, 435, 484, 488, 515, 542,\n", + " 562, 587, 590, 597, 601, 641, 655, 661, 692, 701, 719,\n", + " 733, 737, 744, 770, 773, 793, 806, 809, 810, 821, 836,\n", + " 859, 864, 879, 882, 895, 907, 915, 920, 931, 952, 955,\n", + " 957, 967, 978, 993, 999, 1007, 1017, 1032, 1068, 1094, 1115,\n", + " 1117, 1123, 1125, 1127, 1153, 1190, 1191, 1208, 1223, 1246, 1284,\n", + " 1294, 1298, 1308, 1318, 1327, 1344, 1348, 1350, 1354, 1360, 1362,\n", + " 1377, 1379, 1381, 1389]),\n", + " 1: array([ 1, 3, 5, 7, 9, 12, 13, 17, 18, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 36, 38, 42, 46, 48,\n", + " 49, 51, 56, 58, 60, 64, 66, 67, 70, 74, 80,\n", + " 82, 86, 87, 90, 92, 94, 97, 99, 102, 108, 111,\n", + " 114, 118, 122, 124, 128, 130, 132, 135, 138, 139, 140,\n", + " 141, 142, 143, 144, 145, 146, 148, 149, 158, 159, 162,\n", + " 166, 167, 169, 170, 172, 173, 174, 175, 177, 178, 179,\n", + " 189, 190, 192, 195, 197, 200, 201, 202, 206, 207, 211,\n", + " 212, 213, 214, 216, 218, 219, 221, 222, 227, 232, 235,\n", + " 236, 238, 240, 241, 244, 245, 246, 247, 249, 252, 253,\n", + " 255, 259, 262, 264, 265, 267, 271, 272, 273, 274, 277,\n", + " 278, 283, 288, 289, 290, 291, 294, 296, 298, 300, 301,\n", + " 302, 303, 304, 305, 306, 307, 310, 312, 314, 315, 322,\n", + " 324, 325, 327, 331, 332, 334, 335, 340, 341, 344, 352,\n", + " 353, 354, 357, 360, 361, 364, 365, 366, 368, 369, 371,\n", + " 372, 373, 376, 378, 381, 383, 384, 385, 386, 388, 390,\n", + " 394, 397, 398, 400, 401, 402, 407, 408, 411, 412, 414,\n", + " 416, 418, 419, 420, 421, 422, 423, 425, 426, 427, 428,\n", + " 429, 430, 431, 433, 434, 436, 442, 444, 445, 446, 448,\n", + " 449, 452, 453, 455, 459, 460, 462, 463, 464, 465, 467,\n", + " 468, 471, 472, 475, 476, 480, 482, 483, 485, 486, 489,\n", + " 490, 491, 492, 495, 496, 499, 500, 501, 503, 505, 508,\n", + " 509, 510, 512, 518, 520, 522, 527, 528, 529, 530, 533,\n", + " 534, 535, 537, 539, 540, 543, 545, 547, 548, 550, 551,\n", + " 552, 554, 558, 560, 564, 567, 569, 572, 574, 579, 580,\n", + " 581, 582, 589, 591, 592, 594, 596, 598, 602, 603, 604,\n", + " 607, 611, 612, 614, 615, 616, 617, 618, 620, 622, 624,\n", + " 627, 629, 633, 634, 635, 637, 639, 643, 644, 645, 647,\n", + " 648, 650, 652, 657, 658, 662, 666, 668, 673, 674, 675,\n", + " 676, 677, 681, 682, 684, 685, 688, 689, 690, 695, 697,\n", + " 700, 703, 704, 707, 709, 711, 714, 715, 716, 718, 728,\n", + " 730, 731, 732, 741, 742, 743, 747, 750, 756, 757, 761,\n", + " 762, 764, 766, 767, 769, 772, 774, 775, 777, 779, 780,\n", + " 782, 785, 789, 792, 796, 798, 799, 803, 811, 812, 814,\n", + " 819, 823, 824, 828, 830, 831, 832, 835, 837, 839, 840,\n", + " 841, 843, 844, 846, 848, 849, 853, 856, 857, 861, 867,\n", + " 869, 871, 872, 874, 876, 883, 884, 886, 887, 890, 891,\n", + " 896, 899, 905, 908, 909, 912, 913, 914, 916, 917, 918,\n", + " 919, 921, 923, 924, 926, 929, 933, 938, 939, 940, 941,\n", + " 942, 943, 944, 947, 950, 951, 953, 960, 961, 962, 964,\n", + " 965, 968, 971, 974, 979, 984, 985, 986, 987, 990, 991,\n", + " 992, 996, 997, 1001, 1003, 1006, 1009, 1011, 1013, 1021, 1023,\n", + " 1025, 1026, 1028, 1029, 1031, 1037, 1038, 1041, 1042, 1044, 1045,\n", + " 1046, 1052, 1053, 1054, 1060, 1062, 1063, 1064, 1065, 1069, 1071,\n", + " 1075, 1080, 1083, 1084, 1085, 1088, 1092, 1095, 1100, 1102, 1103,\n", + " 1108, 1109, 1113, 1119, 1121, 1126, 1130, 1131, 1135, 1137, 1146,\n", + " 1148, 1154, 1155, 1158, 1160, 1161, 1162, 1163, 1164, 1165, 1166,\n", + " 1169, 1170, 1171, 1173, 1177, 1179, 1180, 1182, 1184, 1185, 1192,\n", + " 1193, 1195, 1196, 1198, 1200, 1201, 1203, 1204, 1206, 1209, 1210,\n", + " 1212, 1214, 1218, 1219, 1220, 1225, 1226, 1227, 1230, 1231, 1232,\n", + " 1234, 1236, 1237, 1239, 1244, 1247, 1252, 1253, 1258, 1259, 1261,\n", + " 1264, 1266, 1267, 1269, 1272, 1273, 1274, 1278, 1279, 1282, 1289,\n", + " 1291, 1299, 1301, 1303, 1305, 1306, 1311, 1315, 1316, 1320, 1321,\n", + " 1322, 1324, 1326, 1328, 1329, 1330, 1332, 1334, 1336, 1337, 1339,\n", + " 1341, 1345, 1346, 1351, 1355, 1363, 1364, 1367, 1368, 1369, 1370,\n", + " 1371, 1372, 1373, 1374, 1376, 1378, 1380, 1384, 1385, 1386, 1388,\n", + " 1390, 1398]),\n", + " 2: array([ 4, 14, 24, 33, 34, 50, 57, 72, 73, 81, 83,\n", + " 84, 85, 91, 95, 117, 125, 134, 136, 152, 157, 160,\n", + " 161, 171, 183, 185, 198, 203, 204, 217, 224, 225, 237,\n", + " 243, 256, 270, 287, 293, 313, 320, 321, 345, 356, 358,\n", + " 367, 379, 382, 387, 395, 406, 410, 417, 440, 447, 457,\n", + " 458, 466, 470, 479, 506, 514, 519, 523, 524, 525, 538,\n", + " 541, 549, 568, 577, 588, 593, 600, 613, 621, 625, 626,\n", + " 632, 638, 646, 653, 656, 660, 663, 693, 706, 713, 721,\n", + " 723, 734, 735, 745, 749, 752, 755, 771, 781, 784, 801,\n", + " 805, 807, 813, 822, 827, 829, 854, 855, 881, 892, 894,\n", + " 897, 900, 927, 930, 945, 948, 959, 976, 977, 988, 998,\n", + " 1004, 1035, 1049, 1051, 1058, 1066, 1067, 1072, 1074, 1077, 1087,\n", + " 1089, 1091, 1096, 1097, 1098, 1099, 1104, 1107, 1112, 1120, 1128,\n", + " 1133, 1139, 1143, 1149, 1152, 1159, 1167, 1189, 1194, 1205, 1213,\n", + " 1216, 1222, 1229, 1235, 1238, 1256, 1257, 1276, 1281, 1285, 1287,\n", + " 1290, 1293, 1295, 1300, 1302, 1304, 1325, 1331, 1338, 1347, 1356,\n", + " 1357, 1365, 1366, 1382, 1391, 1393, 1397]),\n", + " 3: array([ 6, 41, 126, 131, 209, 229, 260, 266, 292, 339, 370,\n", + " 450, 570, 599, 631, 665, 670, 678, 687, 759, 763, 765,\n", + " 783, 787, 804, 816, 888, 889, 904, 932, 935, 1010, 1022,\n", + " 1033, 1129, 1211, 1221, 1292, 1317, 1352]),\n", + " 4: array([ 8, 19, 21, 47, 52, 55, 59, 63, 68, 77, 88,\n", + " 93, 96, 98, 104, 109, 113, 115, 120, 121, 150, 151,\n", + " 153, 155, 181, 184, 194, 199, 228, 231, 239, 254, 258,\n", + " 261, 276, 280, 282, 295, 297, 299, 316, 336, 338, 348,\n", + " 351, 363, 389, 392, 399, 404, 409, 454, 474, 494, 497,\n", + " 521, 526, 553, 559, 561, 563, 573, 578, 605, 606, 619,\n", + " 628, 640, 672, 680, 683, 686, 691, 696, 699, 702, 708,\n", + " 717, 720, 726, 740, 748, 753, 786, 797, 802, 815, 818,\n", + " 825, 838, 858, 868, 873, 875, 880, 885, 901, 910, 928,\n", + " 934, 937, 973, 975, 980, 983, 1000, 1005, 1014, 1020, 1030,\n", + " 1039, 1040, 1047, 1048, 1050, 1056, 1073, 1106, 1110, 1118, 1122,\n", + " 1124, 1134, 1138, 1141, 1142, 1172, 1183, 1240, 1241, 1245, 1249,\n", + " 1251, 1254, 1255, 1262, 1265, 1275, 1283, 1286, 1296, 1312, 1314,\n", + " 1319, 1342, 1353, 1358, 1361, 1383, 1387, 1394]),\n", + " 5: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 43, 44,\n", + " 45, 53, 76, 79, 103, 106, 107, 110, 116, 129, 156,\n", + " 163, 164, 165, 168, 186, 191, 193, 210, 215, 220, 233,\n", + " 234, 242, 248, 250, 251, 257, 263, 269, 281, 285, 286,\n", + " 311, 318, 329, 343, 346, 347, 350, 374, 375, 393, 396,\n", + " 403, 405, 424, 437, 438, 439, 456, 461, 473, 477, 478,\n", + " 487, 493, 502, 504, 507, 511, 513, 517, 532, 536, 544,\n", + " 546, 555, 556, 557, 565, 575, 583, 608, 609, 623, 630,\n", + " 636, 654, 664, 667, 669, 694, 698, 705, 710, 712, 722,\n", + " 725, 727, 739, 746, 758, 760, 768, 776, 778, 788, 790,\n", + " 800, 817, 820, 833, 834, 842, 845, 847, 850, 851, 852,\n", + " 860, 862, 863, 865, 877, 878, 893, 903, 906, 911, 922,\n", + " 936, 946, 954, 956, 963, 966, 969, 970, 972, 981, 982,\n", + " 989, 994, 1002, 1008, 1015, 1016, 1019, 1027, 1034, 1036, 1043,\n", + " 1057, 1061, 1070, 1076, 1079, 1082, 1086, 1101, 1105, 1111, 1132,\n", + " 1136, 1144, 1145, 1150, 1151, 1157, 1168, 1174, 1175, 1186, 1188,\n", + " 1197, 1199, 1202, 1217, 1224, 1228, 1243, 1268, 1270, 1277, 1280,\n", + " 1297, 1309, 1323, 1333, 1335, 1349, 1359, 1375, 1392, 1396, 1399]),\n", + " 6: array([ 62, 69, 71, 75, 100, 176, 308, 317, 319, 326, 330,\n", + " 333, 359, 377, 415, 441, 443, 451, 469, 481, 498, 516,\n", + " 531, 566, 571, 584, 585, 586, 595, 610, 642, 649, 651,\n", + " 659, 679, 729, 738, 751, 754, 791, 795, 808, 826, 866,\n", + " 870, 898, 902, 949, 958, 995, 1012, 1018, 1024, 1055, 1059,\n", + " 1078, 1081, 1090, 1093, 1114, 1116, 1140, 1156, 1176, 1178, 1181,\n", + " 1187, 1207, 1233, 1242, 1248, 1250, 1260, 1263, 1271, 1288, 1307,\n", + " 1310, 1313, 1340, 1343, 1395]),\n", + " 7: array([ 180, 188, 196, 223, 279, 349, 576, 671, 724, 736, 794,\n", + " 925, 1147, 1215])},\n", + " 9: {0: array([ 0, 2, 20, 22, 54, 61, 65, 78, 89, 101, 105,\n", + " 112, 119, 123, 127, 133, 137, 147, 154, 182, 187, 205,\n", + " 208, 226, 230, 268, 275, 284, 309, 323, 328, 337, 342,\n", + " 355, 362, 380, 391, 413, 432, 435, 484, 488, 515, 542,\n", + " 562, 587, 590, 597, 601, 641, 655, 661, 692, 701, 719,\n", + " 733, 737, 744, 770, 773, 793, 806, 809, 810, 821, 836,\n", + " 859, 864, 879, 882, 895, 907, 915, 920, 931, 952, 955,\n", + " 957, 967, 978, 993, 999, 1007, 1017, 1032, 1068, 1094, 1115,\n", + " 1117, 1123, 1125, 1127, 1153, 1190, 1191, 1208, 1223, 1246, 1284,\n", + " 1294, 1298, 1308, 1318, 1327, 1344, 1348, 1350, 1354, 1360, 1362,\n", + " 1377, 1379, 1381, 1389]),\n", + " 1: array([ 1, 3, 13, 25, 26, 27, 28, 29, 30, 31, 48,\n", + " 49, 58, 64, 74, 80, 82, 86, 90, 97, 99, 102,\n", + " 114, 118, 122, 124, 128, 132, 140, 142, 144, 145, 159,\n", + " 162, 166, 170, 173, 174, 177, 178, 190, 195, 201, 202,\n", + " 212, 213, 216, 218, 219, 227, 232, 253, 255, 262, 267,\n", + " 271, 272, 283, 289, 290, 291, 294, 296, 298, 303, 304,\n", + " 305, 306, 310, 315, 322, 331, 332, 334, 335, 341, 344,\n", + " 353, 357, 360, 364, 365, 366, 368, 371, 372, 373, 378,\n", + " 381, 383, 385, 388, 390, 398, 401, 402, 407, 420, 422,\n", + " 423, 426, 428, 430, 431, 433, 434, 436, 444, 445, 446,\n", + " 448, 455, 462, 463, 471, 472, 475, 476, 482, 483, 485,\n", + " 490, 491, 496, 500, 503, 508, 510, 518, 520, 527, 528,\n", + " 530, 540, 543, 545, 547, 551, 567, 569, 574, 579, 591,\n", + " 592, 594, 596, 604, 607, 611, 614, 615, 617, 622, 627,\n", + " 629, 637, 639, 644, 647, 648, 650, 652, 657, 658, 666,\n", + " 673, 676, 677, 681, 685, 689, 695, 714, 715, 716, 731,\n", + " 742, 747, 757, 761, 762, 767, 772, 775, 777, 779, 780,\n", + " 782, 785, 789, 792, 796, 799, 812, 823, 828, 840, 841,\n", + " 843, 844, 846, 848, 853, 876, 883, 886, 890, 891, 899,\n", + " 905, 909, 912, 913, 914, 916, 917, 918, 919, 921, 923,\n", + " 924, 926, 933, 940, 944, 951, 965, 968, 974, 979, 986,\n", + " 990, 1009, 1023, 1029, 1031, 1038, 1041, 1044, 1052, 1054, 1062,\n", + " 1064, 1065, 1069, 1071, 1075, 1085, 1088, 1092, 1095, 1102, 1108,\n", + " 1109, 1126, 1148, 1155, 1158, 1160, 1162, 1177, 1184, 1192, 1196,\n", + " 1203, 1204, 1206, 1212, 1214, 1226, 1230, 1232, 1234, 1236, 1239,\n", + " 1247, 1252, 1259, 1261, 1266, 1269, 1272, 1274, 1291, 1299, 1301,\n", + " 1303, 1305, 1311, 1320, 1322, 1324, 1326, 1328, 1329, 1330, 1332,\n", + " 1339, 1351, 1364, 1367, 1370, 1374, 1376, 1378, 1386, 1388, 1398]),\n", + " 2: array([ 4, 14, 24, 33, 34, 50, 57, 72, 73, 81, 83,\n", + " 84, 85, 91, 95, 117, 125, 134, 136, 152, 157, 160,\n", + " 161, 171, 183, 185, 198, 203, 204, 217, 224, 225, 237,\n", + " 243, 256, 270, 287, 293, 313, 320, 321, 345, 356, 358,\n", + " 367, 379, 382, 387, 395, 406, 410, 417, 440, 447, 457,\n", + " 458, 466, 470, 479, 506, 514, 519, 523, 524, 525, 538,\n", + " 541, 549, 568, 577, 588, 593, 600, 613, 621, 625, 626,\n", + " 632, 638, 646, 653, 656, 660, 663, 693, 706, 713, 721,\n", + " 723, 734, 735, 745, 749, 752, 755, 771, 781, 784, 801,\n", + " 805, 807, 813, 822, 827, 829, 854, 855, 881, 892, 894,\n", + " 897, 900, 927, 930, 945, 948, 959, 976, 977, 988, 998,\n", + " 1004, 1035, 1049, 1051, 1058, 1066, 1067, 1072, 1074, 1077, 1087,\n", + " 1089, 1091, 1096, 1097, 1098, 1099, 1104, 1107, 1112, 1120, 1128,\n", + " 1133, 1139, 1143, 1149, 1152, 1159, 1167, 1189, 1194, 1205, 1213,\n", + " 1216, 1222, 1229, 1235, 1238, 1256, 1257, 1276, 1281, 1285, 1287,\n", + " 1290, 1293, 1295, 1300, 1302, 1304, 1325, 1331, 1338, 1347, 1356,\n", + " 1357, 1365, 1366, 1382, 1391, 1393, 1397]),\n", + " 3: array([ 5, 7, 9, 12, 17, 18, 32, 36, 38, 42, 46,\n", + " 51, 56, 60, 66, 67, 70, 87, 92, 94, 108, 111,\n", + " 130, 135, 138, 139, 141, 143, 146, 148, 149, 158, 167,\n", + " 169, 172, 175, 179, 189, 192, 197, 200, 206, 207, 211,\n", + " 214, 221, 222, 235, 236, 238, 240, 241, 244, 245, 246,\n", + " 247, 249, 252, 259, 264, 265, 273, 274, 277, 278, 288,\n", + " 300, 301, 302, 307, 312, 314, 324, 325, 327, 340, 352,\n", + " 354, 361, 369, 376, 384, 386, 394, 397, 400, 408, 411,\n", + " 412, 414, 416, 418, 419, 421, 425, 427, 429, 442, 449,\n", + " 452, 453, 459, 460, 464, 465, 467, 468, 480, 486, 489,\n", + " 492, 495, 499, 501, 505, 509, 512, 522, 529, 533, 534,\n", + " 535, 537, 539, 548, 550, 552, 554, 558, 560, 564, 572,\n", + " 580, 581, 582, 589, 598, 602, 603, 612, 616, 618, 620,\n", + " 624, 633, 634, 635, 643, 645, 662, 668, 674, 675, 682,\n", + " 684, 688, 690, 697, 700, 703, 704, 707, 709, 711, 718,\n", + " 728, 730, 732, 741, 743, 750, 756, 764, 766, 769, 774,\n", + " 798, 803, 811, 814, 819, 824, 830, 831, 832, 835, 837,\n", + " 839, 849, 856, 857, 861, 867, 869, 871, 872, 874, 884,\n", + " 887, 896, 908, 929, 938, 939, 941, 942, 943, 947, 950,\n", + " 953, 960, 961, 962, 964, 971, 984, 985, 987, 991, 992,\n", + " 996, 997, 1001, 1003, 1006, 1011, 1013, 1021, 1025, 1026, 1028,\n", + " 1037, 1042, 1045, 1046, 1053, 1060, 1063, 1080, 1083, 1084, 1100,\n", + " 1103, 1113, 1119, 1121, 1130, 1131, 1135, 1137, 1146, 1154, 1161,\n", + " 1163, 1164, 1165, 1166, 1169, 1170, 1171, 1173, 1179, 1180, 1182,\n", + " 1185, 1193, 1195, 1198, 1200, 1201, 1209, 1210, 1218, 1219, 1220,\n", + " 1225, 1227, 1231, 1237, 1244, 1253, 1258, 1264, 1267, 1273, 1278,\n", + " 1279, 1282, 1289, 1306, 1315, 1316, 1321, 1334, 1336, 1337, 1341,\n", + " 1345, 1346, 1355, 1363, 1368, 1369, 1371, 1372, 1373, 1380, 1384,\n", + " 1385, 1390]),\n", + " 4: array([ 6, 41, 126, 131, 209, 229, 260, 266, 292, 339, 370,\n", + " 450, 570, 599, 631, 665, 670, 678, 687, 759, 763, 765,\n", + " 783, 787, 804, 816, 888, 889, 904, 932, 935, 1010, 1022,\n", + " 1033, 1129, 1211, 1221, 1292, 1317, 1352]),\n", + " 5: array([ 8, 19, 21, 47, 52, 55, 59, 63, 68, 77, 88,\n", + " 93, 96, 98, 104, 109, 113, 115, 120, 121, 150, 151,\n", + " 153, 155, 181, 184, 194, 199, 228, 231, 239, 254, 258,\n", + " 261, 276, 280, 282, 295, 297, 299, 316, 336, 338, 348,\n", + " 351, 363, 389, 392, 399, 404, 409, 454, 474, 494, 497,\n", + " 521, 526, 553, 559, 561, 563, 573, 578, 605, 606, 619,\n", + " 628, 640, 672, 680, 683, 686, 691, 696, 699, 702, 708,\n", + " 717, 720, 726, 740, 748, 753, 786, 797, 802, 815, 818,\n", + " 825, 838, 858, 868, 873, 875, 880, 885, 901, 910, 928,\n", + " 934, 937, 973, 975, 980, 983, 1000, 1005, 1014, 1020, 1030,\n", + " 1039, 1040, 1047, 1048, 1050, 1056, 1073, 1106, 1110, 1118, 1122,\n", + " 1124, 1134, 1138, 1141, 1142, 1172, 1183, 1240, 1241, 1245, 1249,\n", + " 1251, 1254, 1255, 1262, 1265, 1275, 1283, 1286, 1296, 1312, 1314,\n", + " 1319, 1342, 1353, 1358, 1361, 1383, 1387, 1394]),\n", + " 6: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 43, 44,\n", + " 45, 53, 76, 79, 103, 106, 107, 110, 116, 129, 156,\n", + " 163, 164, 165, 168, 186, 191, 193, 210, 215, 220, 233,\n", + " 234, 242, 248, 250, 251, 257, 263, 269, 281, 285, 286,\n", + " 311, 318, 329, 343, 346, 347, 350, 374, 375, 393, 396,\n", + " 403, 405, 424, 437, 438, 439, 456, 461, 473, 477, 478,\n", + " 487, 493, 502, 504, 507, 511, 513, 517, 532, 536, 544,\n", + " 546, 555, 556, 557, 565, 575, 583, 608, 609, 623, 630,\n", + " 636, 654, 664, 667, 669, 694, 698, 705, 710, 712, 722,\n", + " 725, 727, 739, 746, 758, 760, 768, 776, 778, 788, 790,\n", + " 800, 817, 820, 833, 834, 842, 845, 847, 850, 851, 852,\n", + " 860, 862, 863, 865, 877, 878, 893, 903, 906, 911, 922,\n", + " 936, 946, 954, 956, 963, 966, 969, 970, 972, 981, 982,\n", + " 989, 994, 1002, 1008, 1015, 1016, 1019, 1027, 1034, 1036, 1043,\n", + " 1057, 1061, 1070, 1076, 1079, 1082, 1086, 1101, 1105, 1111, 1132,\n", + " 1136, 1144, 1145, 1150, 1151, 1157, 1168, 1174, 1175, 1186, 1188,\n", + " 1197, 1199, 1202, 1217, 1224, 1228, 1243, 1268, 1270, 1277, 1280,\n", + " 1297, 1309, 1323, 1333, 1335, 1349, 1359, 1375, 1392, 1396, 1399]),\n", + " 7: array([ 62, 69, 71, 75, 100, 176, 308, 317, 319, 326, 330,\n", + " 333, 359, 377, 415, 441, 443, 451, 469, 481, 498, 516,\n", + " 531, 566, 571, 584, 585, 586, 595, 610, 642, 649, 651,\n", + " 659, 679, 729, 738, 751, 754, 791, 795, 808, 826, 866,\n", + " 870, 898, 902, 949, 958, 995, 1012, 1018, 1024, 1055, 1059,\n", + " 1078, 1081, 1090, 1093, 1114, 1116, 1140, 1156, 1176, 1178, 1181,\n", + " 1187, 1207, 1233, 1242, 1248, 1250, 1260, 1263, 1271, 1288, 1307,\n", + " 1310, 1313, 1340, 1343, 1395]),\n", + " 8: array([ 180, 188, 196, 223, 279, 349, 576, 671, 724, 736, 794,\n", + " 925, 1147, 1215])},\n", + " 10: {0: array([ 0, 20, 22, 54, 65, 112, 119, 137, 147, 154, 205,\n", + " 230, 309, 323, 328, 337, 355, 391, 432, 484, 515, 542,\n", + " 562, 587, 597, 601, 701, 744, 770, 793, 806, 809, 810,\n", + " 821, 836, 859, 879, 895, 907, 920, 952, 978, 999, 1032,\n", + " 1068, 1117, 1123, 1223, 1344, 1354, 1360, 1362, 1377, 1379, 1381]),\n", + " 1: array([ 1, 3, 13, 25, 26, 27, 28, 29, 30, 31, 48,\n", + " 49, 58, 64, 74, 80, 82, 86, 90, 97, 99, 102,\n", + " 114, 118, 122, 124, 128, 132, 140, 142, 144, 145, 159,\n", + " 162, 166, 170, 173, 174, 177, 178, 190, 195, 201, 202,\n", + " 212, 213, 216, 218, 219, 227, 232, 253, 255, 262, 267,\n", + " 271, 272, 283, 289, 290, 291, 294, 296, 298, 303, 304,\n", + " 305, 306, 310, 315, 322, 331, 332, 334, 335, 341, 344,\n", + " 353, 357, 360, 364, 365, 366, 368, 371, 372, 373, 378,\n", + " 381, 383, 385, 388, 390, 398, 401, 402, 407, 420, 422,\n", + " 423, 426, 428, 430, 431, 433, 434, 436, 444, 445, 446,\n", + " 448, 455, 462, 463, 471, 472, 475, 476, 482, 483, 485,\n", + " 490, 491, 496, 500, 503, 508, 510, 518, 520, 527, 528,\n", + " 530, 540, 543, 545, 547, 551, 567, 569, 574, 579, 591,\n", + " 592, 594, 596, 604, 607, 611, 614, 615, 617, 622, 627,\n", + " 629, 637, 639, 644, 647, 648, 650, 652, 657, 658, 666,\n", + " 673, 676, 677, 681, 685, 689, 695, 714, 715, 716, 731,\n", + " 742, 747, 757, 761, 762, 767, 772, 775, 777, 779, 780,\n", + " 782, 785, 789, 792, 796, 799, 812, 823, 828, 840, 841,\n", + " 843, 844, 846, 848, 853, 876, 883, 886, 890, 891, 899,\n", + " 905, 909, 912, 913, 914, 916, 917, 918, 919, 921, 923,\n", + " 924, 926, 933, 940, 944, 951, 965, 968, 974, 979, 986,\n", + " 990, 1009, 1023, 1029, 1031, 1038, 1041, 1044, 1052, 1054, 1062,\n", + " 1064, 1065, 1069, 1071, 1075, 1085, 1088, 1092, 1095, 1102, 1108,\n", + " 1109, 1126, 1148, 1155, 1158, 1160, 1162, 1177, 1184, 1192, 1196,\n", + " 1203, 1204, 1206, 1212, 1214, 1226, 1230, 1232, 1234, 1236, 1239,\n", + " 1247, 1252, 1259, 1261, 1266, 1269, 1272, 1274, 1291, 1299, 1301,\n", + " 1303, 1305, 1311, 1320, 1322, 1324, 1326, 1328, 1329, 1330, 1332,\n", + " 1339, 1351, 1364, 1367, 1370, 1374, 1376, 1378, 1386, 1388, 1398]),\n", + " 2: array([ 2, 61, 78, 89, 101, 105, 123, 127, 133, 182, 187,\n", + " 208, 226, 268, 275, 284, 342, 362, 380, 413, 435, 488,\n", + " 590, 641, 655, 661, 692, 719, 733, 737, 773, 864, 882,\n", + " 915, 931, 955, 957, 967, 993, 1007, 1017, 1094, 1115, 1125,\n", + " 1127, 1153, 1190, 1191, 1208, 1246, 1284, 1294, 1298, 1308, 1318,\n", + " 1327, 1348, 1350, 1389]),\n", + " 3: array([ 4, 14, 24, 33, 34, 50, 57, 72, 73, 81, 83,\n", + " 84, 85, 91, 95, 117, 125, 134, 136, 152, 157, 160,\n", + " 161, 171, 183, 185, 198, 203, 204, 217, 224, 225, 237,\n", + " 243, 256, 270, 287, 293, 313, 320, 321, 345, 356, 358,\n", + " 367, 379, 382, 387, 395, 406, 410, 417, 440, 447, 457,\n", + " 458, 466, 470, 479, 506, 514, 519, 523, 524, 525, 538,\n", + " 541, 549, 568, 577, 588, 593, 600, 613, 621, 625, 626,\n", + " 632, 638, 646, 653, 656, 660, 663, 693, 706, 713, 721,\n", + " 723, 734, 735, 745, 749, 752, 755, 771, 781, 784, 801,\n", + " 805, 807, 813, 822, 827, 829, 854, 855, 881, 892, 894,\n", + " 897, 900, 927, 930, 945, 948, 959, 976, 977, 988, 998,\n", + " 1004, 1035, 1049, 1051, 1058, 1066, 1067, 1072, 1074, 1077, 1087,\n", + " 1089, 1091, 1096, 1097, 1098, 1099, 1104, 1107, 1112, 1120, 1128,\n", + " 1133, 1139, 1143, 1149, 1152, 1159, 1167, 1189, 1194, 1205, 1213,\n", + " 1216, 1222, 1229, 1235, 1238, 1256, 1257, 1276, 1281, 1285, 1287,\n", + " 1290, 1293, 1295, 1300, 1302, 1304, 1325, 1331, 1338, 1347, 1356,\n", + " 1357, 1365, 1366, 1382, 1391, 1393, 1397]),\n", + " 4: array([ 5, 7, 9, 12, 17, 18, 32, 36, 38, 42, 46,\n", + " 51, 56, 60, 66, 67, 70, 87, 92, 94, 108, 111,\n", + " 130, 135, 138, 139, 141, 143, 146, 148, 149, 158, 167,\n", + " 169, 172, 175, 179, 189, 192, 197, 200, 206, 207, 211,\n", + " 214, 221, 222, 235, 236, 238, 240, 241, 244, 245, 246,\n", + " 247, 249, 252, 259, 264, 265, 273, 274, 277, 278, 288,\n", + " 300, 301, 302, 307, 312, 314, 324, 325, 327, 340, 352,\n", + " 354, 361, 369, 376, 384, 386, 394, 397, 400, 408, 411,\n", + " 412, 414, 416, 418, 419, 421, 425, 427, 429, 442, 449,\n", + " 452, 453, 459, 460, 464, 465, 467, 468, 480, 486, 489,\n", + " 492, 495, 499, 501, 505, 509, 512, 522, 529, 533, 534,\n", + " 535, 537, 539, 548, 550, 552, 554, 558, 560, 564, 572,\n", + " 580, 581, 582, 589, 598, 602, 603, 612, 616, 618, 620,\n", + " 624, 633, 634, 635, 643, 645, 662, 668, 674, 675, 682,\n", + " 684, 688, 690, 697, 700, 703, 704, 707, 709, 711, 718,\n", + " 728, 730, 732, 741, 743, 750, 756, 764, 766, 769, 774,\n", + " 798, 803, 811, 814, 819, 824, 830, 831, 832, 835, 837,\n", + " 839, 849, 856, 857, 861, 867, 869, 871, 872, 874, 884,\n", + " 887, 896, 908, 929, 938, 939, 941, 942, 943, 947, 950,\n", + " 953, 960, 961, 962, 964, 971, 984, 985, 987, 991, 992,\n", + " 996, 997, 1001, 1003, 1006, 1011, 1013, 1021, 1025, 1026, 1028,\n", + " 1037, 1042, 1045, 1046, 1053, 1060, 1063, 1080, 1083, 1084, 1100,\n", + " 1103, 1113, 1119, 1121, 1130, 1131, 1135, 1137, 1146, 1154, 1161,\n", + " 1163, 1164, 1165, 1166, 1169, 1170, 1171, 1173, 1179, 1180, 1182,\n", + " 1185, 1193, 1195, 1198, 1200, 1201, 1209, 1210, 1218, 1219, 1220,\n", + " 1225, 1227, 1231, 1237, 1244, 1253, 1258, 1264, 1267, 1273, 1278,\n", + " 1279, 1282, 1289, 1306, 1315, 1316, 1321, 1334, 1336, 1337, 1341,\n", + " 1345, 1346, 1355, 1363, 1368, 1369, 1371, 1372, 1373, 1380, 1384,\n", + " 1385, 1390]),\n", + " 5: array([ 6, 41, 126, 131, 209, 229, 260, 266, 292, 339, 370,\n", + " 450, 570, 599, 631, 665, 670, 678, 687, 759, 763, 765,\n", + " 783, 787, 804, 816, 888, 889, 904, 932, 935, 1010, 1022,\n", + " 1033, 1129, 1211, 1221, 1292, 1317, 1352]),\n", + " 6: array([ 8, 19, 21, 47, 52, 55, 59, 63, 68, 77, 88,\n", + " 93, 96, 98, 104, 109, 113, 115, 120, 121, 150, 151,\n", + " 153, 155, 181, 184, 194, 199, 228, 231, 239, 254, 258,\n", + " 261, 276, 280, 282, 295, 297, 299, 316, 336, 338, 348,\n", + " 351, 363, 389, 392, 399, 404, 409, 454, 474, 494, 497,\n", + " 521, 526, 553, 559, 561, 563, 573, 578, 605, 606, 619,\n", + " 628, 640, 672, 680, 683, 686, 691, 696, 699, 702, 708,\n", + " 717, 720, 726, 740, 748, 753, 786, 797, 802, 815, 818,\n", + " 825, 838, 858, 868, 873, 875, 880, 885, 901, 910, 928,\n", + " 934, 937, 973, 975, 980, 983, 1000, 1005, 1014, 1020, 1030,\n", + " 1039, 1040, 1047, 1048, 1050, 1056, 1073, 1106, 1110, 1118, 1122,\n", + " 1124, 1134, 1138, 1141, 1142, 1172, 1183, 1240, 1241, 1245, 1249,\n", + " 1251, 1254, 1255, 1262, 1265, 1275, 1283, 1286, 1296, 1312, 1314,\n", + " 1319, 1342, 1353, 1358, 1361, 1383, 1387, 1394]),\n", + " 7: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 43, 44,\n", + " 45, 53, 76, 79, 103, 106, 107, 110, 116, 129, 156,\n", + " 163, 164, 165, 168, 186, 191, 193, 210, 215, 220, 233,\n", + " 234, 242, 248, 250, 251, 257, 263, 269, 281, 285, 286,\n", + " 311, 318, 329, 343, 346, 347, 350, 374, 375, 393, 396,\n", + " 403, 405, 424, 437, 438, 439, 456, 461, 473, 477, 478,\n", + " 487, 493, 502, 504, 507, 511, 513, 517, 532, 536, 544,\n", + " 546, 555, 556, 557, 565, 575, 583, 608, 609, 623, 630,\n", + " 636, 654, 664, 667, 669, 694, 698, 705, 710, 712, 722,\n", + " 725, 727, 739, 746, 758, 760, 768, 776, 778, 788, 790,\n", + " 800, 817, 820, 833, 834, 842, 845, 847, 850, 851, 852,\n", + " 860, 862, 863, 865, 877, 878, 893, 903, 906, 911, 922,\n", + " 936, 946, 954, 956, 963, 966, 969, 970, 972, 981, 982,\n", + " 989, 994, 1002, 1008, 1015, 1016, 1019, 1027, 1034, 1036, 1043,\n", + " 1057, 1061, 1070, 1076, 1079, 1082, 1086, 1101, 1105, 1111, 1132,\n", + " 1136, 1144, 1145, 1150, 1151, 1157, 1168, 1174, 1175, 1186, 1188,\n", + " 1197, 1199, 1202, 1217, 1224, 1228, 1243, 1268, 1270, 1277, 1280,\n", + " 1297, 1309, 1323, 1333, 1335, 1349, 1359, 1375, 1392, 1396, 1399]),\n", + " 8: array([ 62, 69, 71, 75, 100, 176, 308, 317, 319, 326, 330,\n", + " 333, 359, 377, 415, 441, 443, 451, 469, 481, 498, 516,\n", + " 531, 566, 571, 584, 585, 586, 595, 610, 642, 649, 651,\n", + " 659, 679, 729, 738, 751, 754, 791, 795, 808, 826, 866,\n", + " 870, 898, 902, 949, 958, 995, 1012, 1018, 1024, 1055, 1059,\n", + " 1078, 1081, 1090, 1093, 1114, 1116, 1140, 1156, 1176, 1178, 1181,\n", + " 1187, 1207, 1233, 1242, 1248, 1250, 1260, 1263, 1271, 1288, 1307,\n", + " 1310, 1313, 1340, 1343, 1395]),\n", + " 9: array([ 180, 188, 196, 223, 279, 349, 576, 671, 724, 736, 794,\n", + " 925, 1147, 1215])}},\n", + " 'l2_ranking_nonloo': {2: {0: array([ 0, 2, 6, 8, 19, 20, 21, 22, 47, 52, 54,\n", + " 55, 59, 61, 63, 65, 68, 77, 78, 88, 89, 93,\n", + " 98, 101, 104, 105, 109, 112, 115, 119, 120, 121, 123,\n", + " 127, 131, 133, 137, 147, 150, 151, 153, 154, 155, 181,\n", + " 182, 184, 187, 194, 199, 205, 208, 209, 226, 228, 229,\n", + " 230, 231, 239, 254, 258, 261, 268, 275, 280, 282, 284,\n", + " 292, 295, 297, 299, 309, 316, 323, 328, 336, 337, 338,\n", + " 339, 342, 348, 351, 355, 362, 363, 380, 389, 391, 392,\n", + " 399, 404, 413, 432, 435, 450, 454, 474, 484, 488, 494,\n", + " 497, 515, 521, 526, 542, 553, 559, 561, 562, 563, 573,\n", + " 578, 587, 590, 596, 597, 599, 601, 605, 606, 619, 628,\n", + " 640, 641, 655, 661, 670, 672, 678, 680, 683, 686, 691,\n", + " 692, 696, 699, 701, 702, 708, 717, 719, 720, 726, 733,\n", + " 737, 740, 744, 748, 763, 770, 773, 783, 786, 793, 797,\n", + " 802, 804, 806, 809, 810, 815, 816, 818, 821, 825, 836,\n", + " 838, 858, 859, 864, 868, 873, 875, 879, 880, 882, 885,\n", + " 888, 895, 901, 907, 910, 915, 920, 928, 931, 934, 935,\n", + " 937, 952, 955, 957, 967, 973, 975, 978, 980, 983, 993,\n", + " 999, 1000, 1005, 1007, 1014, 1017, 1020, 1022, 1030, 1032, 1033,\n", + " 1039, 1040, 1047, 1048, 1050, 1056, 1068, 1073, 1094, 1106, 1110,\n", + " 1115, 1117, 1118, 1122, 1123, 1124, 1125, 1127, 1129, 1134, 1138,\n", + " 1141, 1142, 1153, 1172, 1183, 1190, 1191, 1208, 1211, 1223, 1240,\n", + " 1241, 1245, 1246, 1249, 1251, 1254, 1255, 1262, 1265, 1275, 1283,\n", + " 1284, 1286, 1294, 1296, 1298, 1308, 1312, 1314, 1318, 1319, 1327,\n", + " 1342, 1344, 1348, 1350, 1353, 1354, 1358, 1360, 1361, 1362, 1377,\n", + " 1379, 1381, 1383, 1387, 1389, 1394]),\n", + " 1: array([ 1, 3, 4, ..., 1397, 1398, 1399])},\n", + " 3: {0: array([ 0, 2, 20, 22, 54, 61, 65, 78, 89, 101, 105,\n", + " 112, 119, 123, 127, 131, 133, 137, 147, 154, 182, 187,\n", + " 205, 208, 209, 226, 229, 230, 268, 275, 284, 309, 323,\n", + " 328, 337, 342, 355, 362, 380, 391, 413, 432, 435, 450,\n", + " 484, 488, 515, 542, 562, 587, 590, 596, 597, 601, 641,\n", + " 655, 661, 692, 701, 702, 719, 733, 737, 744, 770, 773,\n", + " 793, 804, 806, 809, 810, 821, 836, 859, 864, 879, 882,\n", + " 895, 907, 915, 920, 931, 952, 955, 957, 967, 978, 993,\n", + " 999, 1007, 1017, 1032, 1068, 1094, 1106, 1115, 1117, 1123, 1125,\n", + " 1127, 1129, 1153, 1190, 1191, 1208, 1223, 1246, 1284, 1294, 1298,\n", + " 1308, 1318, 1327, 1344, 1348, 1350, 1354, 1360, 1362, 1377, 1379,\n", + " 1381, 1389]),\n", + " 1: array([ 1, 3, 4, ..., 1397, 1398, 1399]),\n", + " 2: array([ 6, 8, 19, 21, 47, 52, 55, 59, 63, 68, 77,\n", + " 88, 93, 98, 104, 109, 115, 120, 121, 150, 151, 153,\n", + " 155, 181, 184, 194, 199, 228, 231, 239, 254, 258, 261,\n", + " 280, 282, 292, 295, 297, 299, 316, 336, 338, 339, 348,\n", + " 351, 363, 389, 392, 399, 404, 454, 474, 494, 497, 521,\n", + " 526, 553, 559, 561, 563, 573, 578, 599, 605, 606, 619,\n", + " 628, 640, 670, 672, 678, 680, 683, 686, 691, 696, 699,\n", + " 708, 717, 720, 726, 740, 748, 763, 783, 786, 797, 802,\n", + " 815, 816, 818, 825, 838, 858, 868, 873, 875, 880, 885,\n", + " 888, 901, 910, 928, 934, 935, 937, 973, 975, 980, 983,\n", + " 1000, 1005, 1014, 1020, 1022, 1030, 1033, 1039, 1040, 1047, 1048,\n", + " 1050, 1056, 1073, 1110, 1118, 1122, 1124, 1134, 1138, 1141, 1142,\n", + " 1172, 1183, 1211, 1240, 1241, 1245, 1249, 1251, 1254, 1255, 1262,\n", + " 1265, 1275, 1283, 1286, 1296, 1312, 1314, 1319, 1342, 1353, 1358,\n", + " 1361, 1383, 1387, 1394])},\n", + " 4: {0: array([ 0, 2, 20, 22, 54, 61, 65, 78, 89, 101, 105,\n", + " 112, 119, 123, 127, 131, 133, 137, 147, 154, 182, 187,\n", + " 205, 208, 209, 226, 229, 230, 268, 275, 284, 309, 323,\n", + " 328, 337, 342, 355, 362, 380, 391, 413, 432, 435, 450,\n", + " 484, 488, 515, 542, 562, 587, 590, 596, 597, 601, 641,\n", + " 655, 661, 692, 701, 702, 719, 733, 737, 744, 770, 773,\n", + " 793, 804, 806, 809, 810, 821, 836, 859, 864, 879, 882,\n", + " 895, 907, 915, 920, 931, 952, 955, 957, 967, 978, 993,\n", + " 999, 1007, 1017, 1032, 1068, 1094, 1106, 1115, 1117, 1123, 1125,\n", + " 1127, 1129, 1153, 1190, 1191, 1208, 1223, 1246, 1284, 1294, 1298,\n", + " 1308, 1318, 1327, 1344, 1348, 1350, 1354, 1360, 1362, 1377, 1379,\n", + " 1381, 1389]),\n", + " 1: array([ 1, 3, 5, 7, 9, 10, 11, 12, 13, 15, 16,\n", + " 17, 18, 23, 25, 26, 27, 28, 29, 30, 31, 32,\n", + " 35, 36, 37, 38, 39, 41, 42, 43, 44, 45, 46,\n", + " 48, 49, 51, 53, 56, 58, 60, 64, 66, 67, 70,\n", + " 74, 76, 80, 82, 86, 87, 90, 92, 94, 97, 99,\n", + " 102, 103, 106, 107, 108, 110, 111, 114, 118, 122, 124,\n", + " 128, 129, 130, 132, 135, 138, 139, 140, 141, 142, 143,\n", + " 144, 145, 146, 148, 149, 156, 158, 159, 162, 163, 164,\n", + " 165, 166, 167, 168, 169, 170, 172, 174, 175, 177, 178,\n", + " 179, 186, 189, 190, 192, 193, 195, 197, 200, 201, 202,\n", + " 206, 207, 210, 211, 212, 213, 214, 216, 218, 219, 220,\n", + " 221, 222, 227, 232, 233, 234, 235, 236, 238, 240, 241,\n", + " 242, 244, 245, 246, 247, 248, 249, 251, 252, 253, 255,\n", + " 257, 259, 262, 263, 264, 265, 267, 269, 271, 272, 273,\n", + " 274, 277, 278, 281, 283, 285, 286, 288, 289, 290, 291,\n", + " 294, 296, 298, 300, 301, 302, 304, 305, 306, 307, 310,\n", + " 311, 312, 314, 315, 318, 322, 324, 325, 327, 331, 332,\n", + " 334, 335, 340, 341, 343, 344, 346, 347, 350, 352, 353,\n", + " 354, 357, 361, 365, 366, 368, 369, 371, 372, 373, 375,\n", + " 376, 378, 381, 383, 384, 385, 386, 388, 390, 393, 394,\n", + " 396, 397, 398, 400, 401, 402, 403, 405, 407, 408, 409,\n", + " 411, 412, 414, 416, 418, 419, 420, 421, 422, 423, 424,\n", + " 425, 426, 427, 428, 429, 430, 431, 433, 434, 436, 437,\n", + " 439, 442, 444, 445, 446, 448, 449, 452, 453, 455, 456,\n", + " 459, 460, 461, 462, 463, 464, 465, 467, 468, 471, 472,\n", + " 473, 475, 476, 477, 478, 480, 482, 483, 485, 486, 487,\n", + " 489, 490, 491, 492, 493, 495, 496, 499, 500, 501, 502,\n", + " 503, 504, 505, 507, 508, 509, 510, 511, 512, 513, 517,\n", + " 518, 520, 522, 527, 528, 529, 530, 532, 533, 534, 535,\n", + " 536, 537, 539, 540, 543, 544, 545, 546, 547, 548, 550,\n", + " 551, 552, 554, 555, 556, 557, 558, 560, 564, 565, 567,\n", + " 569, 570, 572, 574, 575, 579, 580, 581, 582, 583, 589,\n", + " 591, 592, 594, 598, 602, 603, 604, 607, 608, 609, 611,\n", + " 612, 614, 615, 616, 617, 618, 620, 622, 623, 624, 627,\n", + " 629, 630, 633, 634, 635, 636, 637, 639, 643, 644, 645,\n", + " 647, 648, 650, 652, 654, 657, 658, 662, 664, 665, 666,\n", + " 667, 668, 669, 673, 674, 675, 676, 677, 682, 684, 687,\n", + " 688, 689, 690, 694, 695, 697, 698, 700, 703, 704, 705,\n", + " 707, 709, 710, 711, 712, 714, 715, 716, 718, 725, 727,\n", + " 728, 730, 731, 732, 739, 741, 742, 743, 746, 747, 750,\n", + " 756, 757, 758, 759, 760, 761, 762, 764, 765, 766, 767,\n", + " 768, 769, 772, 774, 775, 776, 777, 778, 779, 780, 782,\n", + " 785, 789, 790, 792, 796, 798, 799, 803, 811, 812, 814,\n", + " 817, 819, 820, 823, 824, 828, 830, 831, 832, 833, 834,\n", + " 835, 837, 839, 840, 841, 842, 843, 844, 845, 846, 848,\n", + " 849, 850, 851, 852, 853, 856, 857, 860, 861, 862, 863,\n", + " 865, 867, 869, 871, 872, 874, 876, 878, 883, 884, 886,\n", + " 887, 890, 891, 893, 896, 899, 903, 904, 905, 906, 908,\n", + " 909, 911, 912, 913, 914, 916, 917, 918, 919, 921, 922,\n", + " 923, 924, 926, 929, 932, 933, 936, 938, 939, 940, 941,\n", + " 942, 943, 944, 946, 947, 950, 951, 953, 954, 960, 961,\n", + " 962, 964, 965, 968, 969, 970, 971, 972, 974, 979, 982,\n", + " 984, 985, 986, 987, 989, 990, 991, 992, 994, 996, 997,\n", + " 1001, 1002, 1003, 1006, 1008, 1009, 1010, 1011, 1013, 1019, 1021,\n", + " 1023, 1025, 1026, 1027, 1028, 1029, 1031, 1034, 1036, 1037, 1038,\n", + " 1041, 1042, 1043, 1044, 1045, 1046, 1052, 1053, 1054, 1057, 1060,\n", + " 1062, 1063, 1064, 1065, 1069, 1070, 1071, 1075, 1076, 1079, 1080,\n", + " 1082, 1083, 1084, 1085, 1086, 1088, 1092, 1095, 1100, 1101, 1102,\n", + " 1103, 1105, 1108, 1109, 1111, 1113, 1119, 1121, 1126, 1130, 1131,\n", + " 1132, 1135, 1137, 1144, 1145, 1146, 1148, 1150, 1151, 1154, 1155,\n", + " 1157, 1158, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1168, 1169,\n", + " 1170, 1171, 1173, 1174, 1175, 1177, 1179, 1180, 1182, 1184, 1185,\n", + " 1186, 1192, 1193, 1195, 1196, 1198, 1199, 1200, 1201, 1202, 1203,\n", + " 1204, 1206, 1209, 1210, 1212, 1214, 1217, 1218, 1219, 1220, 1225,\n", + " 1226, 1227, 1228, 1230, 1231, 1232, 1234, 1236, 1237, 1239, 1243,\n", + " 1244, 1247, 1252, 1253, 1258, 1259, 1261, 1264, 1266, 1267, 1268,\n", + " 1269, 1272, 1273, 1274, 1277, 1278, 1279, 1280, 1282, 1289, 1291,\n", + " 1292, 1297, 1299, 1301, 1303, 1305, 1306, 1309, 1311, 1315, 1316,\n", + " 1317, 1320, 1321, 1322, 1323, 1324, 1326, 1328, 1329, 1330, 1332,\n", + " 1333, 1334, 1335, 1336, 1337, 1339, 1341, 1345, 1346, 1349, 1351,\n", + " 1355, 1359, 1363, 1364, 1367, 1368, 1369, 1370, 1371, 1372, 1373,\n", + " 1374, 1376, 1378, 1380, 1384, 1385, 1386, 1388, 1390, 1392, 1396,\n", + " 1398, 1399]),\n", + " 2: array([ 4, 14, 24, 33, 34, 40, 50, 57, 62, 69, 71,\n", + " 72, 73, 75, 79, 81, 83, 84, 85, 91, 95, 96,\n", + " 100, 113, 116, 117, 125, 126, 134, 136, 152, 157, 160,\n", + " 161, 171, 173, 176, 180, 183, 185, 188, 191, 196, 198,\n", + " 203, 204, 215, 217, 223, 224, 225, 237, 243, 250, 256,\n", + " 260, 266, 270, 276, 279, 287, 293, 303, 308, 313, 317,\n", + " 319, 320, 321, 326, 329, 330, 333, 345, 349, 356, 358,\n", + " 359, 360, 364, 367, 370, 374, 377, 379, 382, 387, 395,\n", + " 406, 410, 415, 417, 438, 440, 441, 443, 447, 451, 457,\n", + " 458, 466, 469, 470, 479, 481, 498, 506, 514, 516, 519,\n", + " 523, 524, 525, 531, 538, 541, 549, 566, 568, 571, 576,\n", + " 577, 584, 585, 586, 588, 593, 595, 600, 610, 613, 621,\n", + " 625, 626, 631, 632, 638, 642, 646, 649, 651, 653, 656,\n", + " 659, 660, 663, 671, 679, 681, 685, 693, 706, 713, 721,\n", + " 722, 723, 724, 729, 734, 735, 736, 738, 745, 749, 751,\n", + " 752, 753, 754, 755, 771, 781, 784, 787, 788, 791, 794,\n", + " 795, 800, 801, 805, 807, 808, 813, 822, 826, 827, 829,\n", + " 847, 854, 855, 866, 870, 877, 881, 889, 892, 894, 897,\n", + " 898, 900, 902, 925, 927, 930, 945, 948, 949, 956, 958,\n", + " 959, 963, 966, 976, 977, 981, 988, 995, 998, 1004, 1012,\n", + " 1015, 1016, 1018, 1024, 1035, 1049, 1051, 1055, 1058, 1059, 1061,\n", + " 1066, 1067, 1072, 1074, 1077, 1078, 1081, 1087, 1089, 1090, 1091,\n", + " 1093, 1096, 1097, 1098, 1099, 1104, 1107, 1112, 1114, 1116, 1120,\n", + " 1128, 1133, 1136, 1139, 1140, 1143, 1147, 1149, 1152, 1156, 1159,\n", + " 1167, 1176, 1178, 1181, 1187, 1188, 1189, 1194, 1197, 1205, 1207,\n", + " 1213, 1215, 1216, 1221, 1222, 1224, 1229, 1233, 1235, 1238, 1242,\n", + " 1248, 1250, 1256, 1257, 1260, 1263, 1270, 1271, 1276, 1281, 1285,\n", + " 1287, 1288, 1290, 1293, 1295, 1300, 1302, 1304, 1307, 1310, 1313,\n", + " 1325, 1331, 1338, 1340, 1343, 1347, 1352, 1356, 1357, 1365, 1366,\n", + " 1375, 1382, 1391, 1393, 1395, 1397]),\n", + " 3: array([ 6, 8, 19, 21, 47, 52, 55, 59, 63, 68, 77,\n", + " 88, 93, 98, 104, 109, 115, 120, 121, 150, 151, 153,\n", + " 155, 181, 184, 194, 199, 228, 231, 239, 254, 258, 261,\n", + " 280, 282, 292, 295, 297, 299, 316, 336, 338, 339, 348,\n", + " 351, 363, 389, 392, 399, 404, 454, 474, 494, 497, 521,\n", + " 526, 553, 559, 561, 563, 573, 578, 599, 605, 606, 619,\n", + " 628, 640, 670, 672, 678, 680, 683, 686, 691, 696, 699,\n", + " 708, 717, 720, 726, 740, 748, 763, 783, 786, 797, 802,\n", + " 815, 816, 818, 825, 838, 858, 868, 873, 875, 880, 885,\n", + " 888, 901, 910, 928, 934, 935, 937, 973, 975, 980, 983,\n", + " 1000, 1005, 1014, 1020, 1022, 1030, 1033, 1039, 1040, 1047, 1048,\n", + " 1050, 1056, 1073, 1110, 1118, 1122, 1124, 1134, 1138, 1141, 1142,\n", + " 1172, 1183, 1211, 1240, 1241, 1245, 1249, 1251, 1254, 1255, 1262,\n", + " 1265, 1275, 1283, 1286, 1296, 1312, 1314, 1319, 1342, 1353, 1358,\n", + " 1361, 1383, 1387, 1394])},\n", + " 5: {0: array([ 0, 2, 20, 22, 54, 61, 65, 78, 89, 101, 105,\n", + " 112, 119, 123, 127, 131, 133, 137, 147, 154, 182, 187,\n", + " 205, 208, 209, 226, 229, 230, 268, 275, 284, 309, 323,\n", + " 328, 337, 342, 355, 362, 380, 391, 413, 432, 435, 450,\n", + " 484, 488, 515, 542, 562, 587, 590, 596, 597, 601, 641,\n", + " 655, 661, 692, 701, 719, 733, 737, 744, 770, 773, 793,\n", + " 804, 806, 809, 810, 821, 836, 859, 864, 879, 882, 895,\n", + " 907, 915, 920, 931, 952, 955, 957, 967, 978, 993, 999,\n", + " 1007, 1017, 1032, 1068, 1094, 1106, 1115, 1117, 1123, 1125, 1127,\n", + " 1129, 1153, 1190, 1191, 1208, 1223, 1246, 1284, 1294, 1298, 1308,\n", + " 1318, 1327, 1344, 1348, 1350, 1354, 1360, 1362, 1377, 1379, 1381,\n", + " 1389]),\n", + " 1: array([ 1, 3, 5, 7, 9, 10, 11, 12, 13, 15, 16,\n", + " 17, 18, 23, 25, 26, 27, 28, 29, 30, 31, 32,\n", + " 35, 36, 37, 38, 39, 41, 42, 43, 44, 45, 46,\n", + " 48, 49, 51, 53, 56, 58, 60, 64, 66, 67, 70,\n", + " 74, 76, 80, 82, 86, 87, 90, 92, 94, 97, 99,\n", + " 102, 103, 106, 107, 108, 110, 111, 114, 118, 122, 124,\n", + " 128, 129, 130, 132, 135, 138, 139, 140, 141, 142, 143,\n", + " 144, 145, 146, 148, 149, 156, 158, 159, 162, 163, 164,\n", + " 165, 166, 167, 168, 169, 170, 172, 174, 175, 177, 178,\n", + " 179, 186, 189, 190, 192, 193, 195, 197, 200, 201, 202,\n", + " 206, 207, 210, 211, 212, 213, 214, 216, 218, 219, 220,\n", + " 221, 222, 227, 232, 233, 234, 235, 236, 238, 240, 241,\n", + " 242, 244, 245, 246, 247, 248, 249, 251, 252, 253, 255,\n", + " 257, 259, 262, 263, 264, 265, 267, 269, 271, 272, 273,\n", + " 274, 277, 278, 281, 283, 285, 286, 288, 289, 290, 291,\n", + " 294, 296, 298, 300, 301, 302, 304, 305, 306, 307, 310,\n", + " 311, 312, 314, 315, 318, 322, 324, 325, 327, 331, 332,\n", + " 334, 335, 340, 341, 343, 344, 346, 347, 350, 352, 353,\n", + " 354, 357, 361, 365, 366, 368, 369, 371, 372, 373, 375,\n", + " 376, 378, 381, 383, 384, 385, 386, 388, 390, 393, 394,\n", + " 396, 397, 398, 400, 401, 402, 403, 405, 407, 408, 409,\n", + " 411, 412, 414, 416, 418, 419, 420, 421, 422, 423, 424,\n", + " 425, 426, 427, 428, 429, 430, 431, 433, 434, 436, 437,\n", + " 439, 442, 444, 445, 446, 448, 449, 452, 453, 455, 456,\n", + " 459, 460, 461, 462, 463, 464, 465, 467, 468, 471, 472,\n", + " 473, 475, 476, 477, 478, 480, 482, 483, 485, 486, 487,\n", + " 489, 490, 491, 492, 493, 495, 496, 499, 500, 501, 502,\n", + " 503, 504, 505, 507, 508, 509, 510, 511, 512, 513, 517,\n", + " 518, 520, 522, 527, 528, 529, 530, 532, 533, 534, 535,\n", + " 536, 537, 539, 540, 543, 544, 545, 546, 547, 548, 550,\n", + " 551, 552, 554, 555, 556, 557, 558, 560, 564, 565, 567,\n", + " 569, 570, 572, 574, 575, 579, 580, 581, 582, 583, 589,\n", + " 591, 592, 594, 598, 602, 603, 604, 607, 608, 609, 611,\n", + " 612, 614, 615, 616, 617, 618, 620, 622, 623, 624, 627,\n", + " 629, 630, 633, 634, 635, 636, 637, 639, 643, 644, 645,\n", + " 647, 648, 650, 652, 654, 657, 658, 662, 664, 665, 666,\n", + " 667, 668, 669, 673, 674, 675, 676, 677, 682, 684, 687,\n", + " 688, 689, 690, 694, 695, 697, 698, 700, 703, 704, 705,\n", + " 707, 709, 710, 711, 712, 714, 715, 716, 718, 725, 727,\n", + " 728, 730, 731, 732, 739, 741, 742, 743, 746, 747, 750,\n", + " 756, 757, 758, 759, 760, 761, 762, 764, 765, 766, 767,\n", + " 768, 769, 772, 774, 775, 776, 777, 778, 779, 780, 782,\n", + " 785, 789, 790, 792, 796, 798, 799, 803, 811, 812, 814,\n", + " 817, 819, 820, 823, 824, 828, 830, 831, 832, 833, 834,\n", + " 835, 837, 839, 840, 841, 842, 843, 844, 845, 846, 848,\n", + " 849, 850, 851, 852, 853, 856, 857, 860, 861, 862, 863,\n", + " 865, 867, 869, 871, 872, 874, 876, 878, 883, 884, 886,\n", + " 887, 890, 891, 893, 896, 899, 903, 904, 905, 906, 908,\n", + " 909, 911, 912, 913, 914, 916, 917, 918, 919, 921, 922,\n", + " 923, 924, 926, 929, 932, 933, 936, 938, 939, 940, 941,\n", + " 942, 943, 944, 946, 947, 950, 951, 953, 954, 960, 961,\n", + " 962, 964, 965, 968, 969, 970, 971, 972, 974, 979, 982,\n", + " 984, 985, 986, 987, 989, 990, 991, 992, 994, 996, 997,\n", + " 1001, 1002, 1003, 1006, 1008, 1009, 1010, 1011, 1013, 1019, 1021,\n", + " 1023, 1025, 1026, 1027, 1028, 1029, 1031, 1034, 1036, 1037, 1038,\n", + " 1041, 1042, 1043, 1044, 1045, 1046, 1052, 1053, 1054, 1057, 1060,\n", + " 1062, 1063, 1064, 1065, 1069, 1070, 1071, 1075, 1076, 1079, 1080,\n", + " 1082, 1083, 1084, 1085, 1086, 1088, 1092, 1095, 1100, 1101, 1102,\n", + " 1103, 1105, 1108, 1109, 1111, 1113, 1119, 1121, 1126, 1130, 1131,\n", + " 1132, 1135, 1137, 1144, 1145, 1146, 1148, 1150, 1151, 1154, 1155,\n", + " 1157, 1158, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1168, 1169,\n", + " 1170, 1171, 1173, 1174, 1175, 1177, 1179, 1180, 1182, 1184, 1185,\n", + " 1186, 1192, 1193, 1195, 1196, 1198, 1199, 1200, 1201, 1202, 1203,\n", + " 1204, 1206, 1209, 1210, 1212, 1214, 1217, 1218, 1219, 1220, 1225,\n", + " 1226, 1227, 1228, 1230, 1231, 1232, 1234, 1236, 1237, 1239, 1243,\n", + " 1244, 1247, 1252, 1253, 1258, 1259, 1261, 1264, 1266, 1267, 1268,\n", + " 1269, 1272, 1273, 1274, 1277, 1278, 1279, 1280, 1282, 1289, 1291,\n", + " 1292, 1297, 1299, 1301, 1303, 1305, 1306, 1309, 1311, 1315, 1316,\n", + " 1317, 1320, 1321, 1322, 1323, 1324, 1326, 1328, 1329, 1330, 1332,\n", + " 1333, 1334, 1335, 1336, 1337, 1339, 1341, 1345, 1346, 1349, 1351,\n", + " 1355, 1359, 1363, 1364, 1367, 1368, 1369, 1370, 1371, 1372, 1373,\n", + " 1374, 1376, 1378, 1380, 1384, 1385, 1386, 1388, 1390, 1392, 1396,\n", + " 1398, 1399]),\n", + " 2: array([ 4, 14, 24, 33, 34, 40, 50, 57, 62, 69, 71,\n", + " 72, 73, 75, 79, 81, 83, 84, 85, 91, 95, 96,\n", + " 100, 113, 116, 117, 125, 126, 134, 136, 152, 157, 160,\n", + " 161, 171, 173, 176, 180, 183, 185, 188, 191, 196, 198,\n", + " 203, 204, 215, 217, 223, 224, 225, 237, 243, 250, 256,\n", + " 260, 266, 270, 276, 279, 287, 293, 303, 308, 313, 317,\n", + " 319, 320, 321, 326, 329, 330, 333, 345, 349, 356, 358,\n", + " 359, 360, 364, 367, 370, 374, 377, 379, 382, 387, 395,\n", + " 406, 410, 415, 417, 438, 440, 441, 443, 447, 451, 457,\n", + " 458, 466, 469, 470, 479, 481, 498, 506, 514, 516, 519,\n", + " 523, 524, 525, 531, 538, 541, 549, 566, 568, 571, 576,\n", + " 577, 584, 585, 586, 588, 593, 595, 600, 610, 613, 621,\n", + " 625, 626, 631, 632, 638, 642, 646, 649, 651, 653, 656,\n", + " 659, 660, 663, 671, 679, 681, 685, 693, 706, 713, 721,\n", + " 722, 723, 724, 729, 734, 735, 736, 738, 745, 749, 751,\n", + " 752, 753, 754, 755, 771, 781, 784, 787, 788, 791, 794,\n", + " 795, 800, 801, 805, 807, 808, 813, 822, 826, 827, 829,\n", + " 847, 854, 855, 866, 870, 877, 881, 889, 892, 894, 897,\n", + " 898, 900, 902, 925, 927, 930, 945, 948, 949, 956, 958,\n", + " 959, 963, 966, 976, 977, 981, 988, 995, 998, 1004, 1012,\n", + " 1015, 1016, 1018, 1024, 1035, 1049, 1051, 1055, 1058, 1059, 1061,\n", + " 1066, 1067, 1072, 1074, 1077, 1078, 1081, 1087, 1089, 1090, 1091,\n", + " 1093, 1096, 1097, 1098, 1099, 1104, 1107, 1112, 1114, 1116, 1120,\n", + " 1128, 1133, 1136, 1139, 1140, 1143, 1147, 1149, 1152, 1156, 1159,\n", + " 1167, 1176, 1178, 1181, 1187, 1188, 1189, 1194, 1197, 1205, 1207,\n", + " 1213, 1215, 1216, 1221, 1222, 1224, 1229, 1233, 1235, 1238, 1242,\n", + " 1248, 1250, 1256, 1257, 1260, 1263, 1270, 1271, 1276, 1281, 1285,\n", + " 1287, 1288, 1290, 1293, 1295, 1300, 1302, 1304, 1307, 1310, 1313,\n", + " 1325, 1331, 1338, 1340, 1343, 1347, 1352, 1356, 1357, 1365, 1366,\n", + " 1375, 1382, 1391, 1393, 1395, 1397]),\n", + " 3: array([ 6, 8, 19, 21, 47, 52, 55, 59, 63, 68, 77,\n", + " 88, 93, 98, 104, 109, 115, 120, 121, 150, 151, 153,\n", + " 155, 181, 184, 194, 199, 228, 231, 239, 254, 258, 261,\n", + " 280, 282, 292, 295, 297, 299, 316, 336, 338, 339, 348,\n", + " 351, 363, 389, 392, 399, 404, 454, 474, 494, 497, 521,\n", + " 526, 553, 559, 561, 563, 573, 578, 599, 605, 606, 619,\n", + " 628, 640, 670, 672, 678, 680, 683, 686, 691, 696, 699,\n", + " 708, 717, 720, 726, 740, 748, 763, 783, 786, 797, 802,\n", + " 815, 816, 818, 825, 838, 858, 868, 873, 875, 880, 885,\n", + " 888, 901, 910, 928, 934, 935, 937, 973, 975, 980, 983,\n", + " 1000, 1005, 1014, 1020, 1022, 1030, 1033, 1039, 1040, 1047, 1048,\n", + " 1050, 1056, 1073, 1110, 1118, 1122, 1124, 1134, 1138, 1141, 1142,\n", + " 1172, 1183, 1211, 1240, 1241, 1245, 1249, 1251, 1254, 1255, 1262,\n", + " 1265, 1275, 1283, 1286, 1296, 1312, 1314, 1319, 1342, 1353, 1358,\n", + " 1361, 1383, 1387, 1394]),\n", + " 4: array([702])},\n", + " 6: {0: array([ 0, 2, 20, 22, 54, 61, 65, 78, 89, 101, 105,\n", + " 112, 119, 123, 127, 131, 133, 137, 147, 154, 182, 187,\n", + " 205, 208, 209, 226, 229, 230, 268, 275, 284, 309, 323,\n", + " 328, 337, 342, 355, 362, 380, 391, 413, 432, 435, 450,\n", + " 484, 488, 515, 542, 562, 587, 590, 596, 597, 601, 641,\n", + " 655, 661, 692, 701, 719, 733, 737, 744, 770, 773, 793,\n", + " 804, 806, 809, 810, 821, 836, 859, 864, 879, 882, 895,\n", + " 907, 915, 920, 931, 952, 955, 957, 967, 978, 993, 999,\n", + " 1007, 1017, 1032, 1068, 1094, 1106, 1115, 1117, 1123, 1125, 1127,\n", + " 1129, 1153, 1190, 1191, 1208, 1223, 1246, 1284, 1294, 1298, 1308,\n", + " 1318, 1327, 1344, 1348, 1350, 1354, 1360, 1362, 1377, 1379, 1381,\n", + " 1389]),\n", + " 1: array([ 1, 3, 5, 7, 9, 10, 11, 12, 13, 15, 16,\n", + " 17, 18, 23, 25, 26, 27, 28, 29, 30, 31, 32,\n", + " 35, 36, 37, 38, 39, 41, 42, 43, 44, 45, 46,\n", + " 48, 49, 51, 53, 56, 58, 60, 64, 66, 67, 70,\n", + " 74, 76, 80, 82, 86, 87, 90, 92, 94, 97, 99,\n", + " 102, 103, 106, 107, 108, 110, 111, 114, 118, 122, 124,\n", + " 128, 129, 130, 132, 135, 138, 139, 140, 141, 142, 143,\n", + " 144, 145, 146, 148, 149, 156, 158, 159, 162, 163, 164,\n", + " 165, 166, 167, 168, 169, 170, 172, 174, 175, 177, 178,\n", + " 179, 186, 189, 190, 192, 193, 195, 197, 200, 201, 202,\n", + " 206, 207, 210, 211, 212, 213, 214, 216, 218, 219, 220,\n", + " 221, 222, 227, 232, 233, 234, 235, 236, 238, 240, 241,\n", + " 242, 244, 245, 246, 247, 248, 249, 251, 252, 253, 255,\n", + " 257, 259, 262, 263, 264, 265, 267, 269, 271, 272, 273,\n", + " 274, 277, 278, 281, 283, 285, 286, 288, 289, 290, 291,\n", + " 294, 296, 298, 300, 301, 302, 304, 305, 306, 307, 310,\n", + " 311, 312, 314, 315, 318, 322, 324, 325, 327, 331, 332,\n", + " 334, 335, 340, 341, 343, 344, 346, 347, 350, 352, 353,\n", + " 354, 357, 361, 365, 366, 368, 369, 371, 372, 373, 375,\n", + " 376, 378, 381, 383, 384, 385, 386, 388, 390, 393, 394,\n", + " 396, 397, 398, 400, 401, 402, 403, 405, 407, 408, 409,\n", + " 411, 412, 414, 416, 418, 419, 420, 421, 422, 423, 424,\n", + " 425, 426, 427, 428, 429, 430, 431, 433, 434, 436, 437,\n", + " 439, 442, 444, 445, 446, 448, 449, 452, 453, 455, 456,\n", + " 459, 460, 461, 462, 463, 464, 465, 467, 468, 471, 472,\n", + " 473, 475, 476, 477, 478, 480, 482, 483, 485, 486, 487,\n", + " 489, 490, 491, 492, 493, 495, 496, 499, 500, 501, 502,\n", + " 503, 504, 505, 507, 508, 509, 510, 511, 512, 513, 517,\n", + " 518, 520, 522, 527, 528, 529, 530, 532, 533, 534, 535,\n", + " 536, 537, 539, 540, 543, 544, 545, 546, 547, 548, 550,\n", + " 551, 552, 554, 555, 556, 557, 558, 560, 564, 565, 567,\n", + " 569, 570, 572, 574, 575, 579, 580, 581, 582, 583, 589,\n", + " 591, 592, 594, 598, 602, 603, 604, 607, 608, 609, 611,\n", + " 612, 614, 615, 616, 617, 618, 620, 622, 623, 624, 627,\n", + " 629, 630, 633, 634, 635, 636, 637, 639, 643, 644, 645,\n", + " 647, 648, 650, 652, 654, 657, 658, 662, 664, 665, 666,\n", + " 667, 668, 669, 673, 674, 675, 676, 677, 682, 684, 687,\n", + " 688, 689, 690, 694, 695, 697, 698, 700, 703, 704, 705,\n", + " 707, 709, 710, 711, 712, 714, 715, 716, 718, 725, 727,\n", + " 728, 730, 731, 732, 739, 741, 742, 743, 746, 747, 750,\n", + " 756, 757, 758, 759, 760, 761, 762, 764, 765, 766, 767,\n", + " 768, 769, 772, 774, 775, 776, 777, 778, 779, 780, 782,\n", + " 785, 789, 790, 792, 796, 798, 799, 803, 811, 812, 814,\n", + " 817, 819, 820, 823, 824, 828, 830, 831, 832, 833, 834,\n", + " 835, 837, 839, 840, 841, 842, 843, 844, 845, 846, 848,\n", + " 849, 850, 851, 852, 853, 856, 857, 860, 861, 862, 863,\n", + " 865, 867, 869, 871, 872, 874, 876, 878, 883, 884, 886,\n", + " 887, 890, 891, 893, 896, 899, 903, 904, 905, 906, 908,\n", + " 909, 911, 912, 913, 914, 916, 917, 918, 919, 921, 922,\n", + " 923, 924, 926, 929, 932, 933, 936, 938, 939, 940, 941,\n", + " 942, 943, 944, 946, 947, 950, 951, 953, 954, 960, 961,\n", + " 962, 964, 965, 968, 969, 970, 971, 972, 974, 979, 982,\n", + " 984, 985, 986, 987, 989, 990, 991, 992, 994, 996, 997,\n", + " 1001, 1002, 1003, 1006, 1008, 1009, 1010, 1011, 1013, 1019, 1021,\n", + " 1023, 1025, 1026, 1027, 1028, 1029, 1031, 1034, 1036, 1037, 1038,\n", + " 1041, 1042, 1043, 1044, 1045, 1046, 1052, 1053, 1054, 1057, 1060,\n", + " 1062, 1063, 1064, 1065, 1069, 1070, 1071, 1075, 1076, 1079, 1080,\n", + " 1082, 1083, 1084, 1085, 1086, 1088, 1092, 1095, 1100, 1101, 1102,\n", + " 1103, 1105, 1108, 1109, 1111, 1113, 1119, 1121, 1126, 1130, 1131,\n", + " 1132, 1135, 1137, 1144, 1145, 1146, 1148, 1150, 1151, 1154, 1155,\n", + " 1157, 1158, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1168, 1169,\n", + " 1170, 1171, 1173, 1174, 1175, 1177, 1179, 1180, 1182, 1184, 1185,\n", + " 1186, 1192, 1193, 1195, 1196, 1198, 1199, 1200, 1201, 1202, 1203,\n", + " 1204, 1206, 1209, 1210, 1212, 1214, 1217, 1218, 1219, 1220, 1225,\n", + " 1226, 1227, 1228, 1230, 1231, 1232, 1234, 1236, 1237, 1239, 1243,\n", + " 1244, 1247, 1252, 1253, 1258, 1259, 1261, 1264, 1266, 1267, 1268,\n", + " 1269, 1272, 1273, 1274, 1277, 1278, 1279, 1280, 1282, 1289, 1291,\n", + " 1292, 1297, 1299, 1301, 1303, 1305, 1306, 1309, 1311, 1315, 1316,\n", + " 1317, 1320, 1321, 1322, 1323, 1324, 1326, 1328, 1329, 1330, 1332,\n", + " 1333, 1334, 1335, 1336, 1337, 1339, 1341, 1345, 1346, 1349, 1351,\n", + " 1355, 1359, 1363, 1364, 1367, 1368, 1369, 1370, 1371, 1372, 1373,\n", + " 1374, 1376, 1378, 1380, 1384, 1385, 1386, 1388, 1390, 1392, 1396,\n", + " 1398, 1399]),\n", + " 2: array([ 4, 14, 24, 33, 34, 40, 50, 57, 62, 69, 71,\n", + " 72, 73, 75, 79, 81, 83, 84, 85, 91, 95, 96,\n", + " 100, 113, 116, 117, 125, 126, 134, 136, 152, 157, 160,\n", + " 161, 171, 173, 176, 183, 185, 191, 198, 203, 204, 215,\n", + " 217, 224, 225, 237, 243, 250, 256, 260, 266, 270, 276,\n", + " 287, 293, 303, 308, 313, 317, 319, 320, 321, 326, 329,\n", + " 330, 333, 345, 356, 358, 359, 360, 364, 367, 370, 374,\n", + " 377, 379, 382, 387, 395, 406, 410, 415, 417, 438, 440,\n", + " 441, 443, 447, 451, 457, 458, 466, 469, 470, 479, 481,\n", + " 498, 506, 514, 516, 519, 523, 524, 525, 531, 538, 541,\n", + " 549, 566, 568, 571, 577, 584, 585, 586, 588, 593, 595,\n", + " 600, 610, 613, 621, 625, 626, 631, 632, 638, 642, 646,\n", + " 649, 651, 653, 656, 659, 660, 663, 679, 681, 685, 693,\n", + " 706, 713, 721, 722, 723, 729, 734, 735, 738, 745, 749,\n", + " 751, 752, 753, 754, 755, 771, 781, 784, 787, 788, 791,\n", + " 795, 800, 801, 805, 807, 808, 813, 822, 826, 829, 847,\n", + " 854, 855, 866, 870, 877, 881, 889, 892, 894, 897, 898,\n", + " 900, 902, 927, 930, 945, 948, 949, 956, 958, 959, 963,\n", + " 966, 976, 977, 981, 988, 995, 998, 1004, 1012, 1015, 1016,\n", + " 1018, 1024, 1035, 1049, 1051, 1055, 1058, 1059, 1061, 1066, 1067,\n", + " 1072, 1074, 1077, 1078, 1081, 1087, 1089, 1090, 1091, 1093, 1096,\n", + " 1097, 1098, 1099, 1104, 1107, 1112, 1114, 1116, 1120, 1128, 1133,\n", + " 1136, 1139, 1140, 1143, 1149, 1152, 1156, 1159, 1167, 1176, 1178,\n", + " 1181, 1187, 1188, 1189, 1194, 1197, 1205, 1207, 1213, 1216, 1221,\n", + " 1222, 1224, 1229, 1233, 1235, 1238, 1242, 1248, 1250, 1256, 1257,\n", + " 1260, 1263, 1270, 1271, 1276, 1281, 1285, 1287, 1288, 1290, 1293,\n", + " 1295, 1300, 1302, 1304, 1307, 1310, 1313, 1325, 1331, 1338, 1340,\n", + " 1343, 1347, 1352, 1356, 1357, 1365, 1366, 1375, 1382, 1391, 1393,\n", + " 1395, 1397]),\n", + " 3: array([ 6, 8, 19, 21, 47, 52, 55, 59, 63, 68, 77,\n", + " 88, 93, 98, 104, 109, 115, 120, 121, 150, 151, 153,\n", + " 155, 181, 184, 194, 199, 228, 231, 239, 254, 258, 261,\n", + " 280, 282, 292, 295, 297, 299, 316, 336, 338, 339, 348,\n", + " 351, 363, 389, 392, 399, 404, 454, 474, 494, 497, 521,\n", + " 526, 553, 559, 561, 563, 573, 578, 599, 605, 606, 619,\n", + " 628, 640, 670, 672, 678, 680, 683, 686, 691, 696, 699,\n", + " 708, 717, 720, 726, 740, 748, 763, 783, 786, 797, 802,\n", + " 815, 816, 818, 825, 838, 858, 868, 873, 875, 880, 885,\n", + " 888, 901, 910, 928, 934, 935, 937, 973, 975, 980, 983,\n", + " 1000, 1005, 1014, 1020, 1022, 1030, 1033, 1039, 1040, 1047, 1048,\n", + " 1050, 1056, 1073, 1110, 1118, 1122, 1124, 1134, 1138, 1141, 1142,\n", + " 1172, 1183, 1211, 1240, 1241, 1245, 1249, 1251, 1254, 1255, 1262,\n", + " 1265, 1275, 1283, 1286, 1296, 1312, 1314, 1319, 1342, 1353, 1358,\n", + " 1361, 1383, 1387, 1394]),\n", + " 4: array([ 180, 188, 196, 223, 279, 349, 576, 671, 724, 736, 794,\n", + " 827, 925, 1147, 1215]),\n", + " 5: array([702])},\n", + " 7: {0: array([ 0, 2, 78, 101, 105, 123, 133, 182, 205, 226, 268,\n", + " 275, 309, 323, 362, 380, 413, 435, 515, 601, 641, 655,\n", + " 733, 737, 744, 773, 793, 810, 864, 879, 907, 915, 920,\n", + " 952, 957, 1007, 1117, 1153, 1191, 1208, 1284, 1298, 1308, 1327,\n", + " 1344, 1348, 1362]),\n", + " 1: array([ 1, 3, 5, 7, 9, 10, 11, 12, 13, 15, 16,\n", + " 17, 18, 23, 25, 26, 27, 28, 29, 30, 31, 32,\n", + " 35, 36, 37, 38, 39, 41, 42, 43, 44, 45, 46,\n", + " 48, 49, 51, 53, 56, 58, 60, 64, 66, 67, 70,\n", + " 74, 76, 80, 82, 86, 87, 90, 92, 94, 97, 99,\n", + " 102, 103, 106, 107, 108, 110, 111, 114, 118, 122, 124,\n", + " 128, 129, 130, 132, 135, 138, 139, 140, 141, 142, 143,\n", + " 144, 145, 146, 148, 149, 156, 158, 159, 162, 163, 164,\n", + " 165, 166, 167, 168, 169, 170, 172, 174, 175, 177, 178,\n", + " 179, 186, 189, 190, 192, 193, 195, 197, 200, 201, 202,\n", + " 206, 207, 210, 211, 212, 213, 214, 216, 218, 219, 220,\n", + " 221, 222, 227, 232, 233, 234, 235, 236, 238, 240, 241,\n", + " 242, 244, 245, 246, 247, 248, 249, 251, 252, 253, 255,\n", + " 257, 259, 262, 263, 264, 265, 267, 269, 271, 272, 273,\n", + " 274, 277, 278, 281, 283, 285, 286, 288, 289, 290, 291,\n", + " 294, 296, 298, 300, 301, 302, 304, 305, 306, 307, 310,\n", + " 311, 312, 314, 315, 318, 322, 324, 325, 327, 331, 332,\n", + " 334, 335, 340, 341, 343, 344, 346, 347, 350, 352, 353,\n", + " 354, 357, 361, 365, 366, 368, 369, 371, 372, 373, 375,\n", + " 376, 378, 381, 383, 384, 385, 386, 388, 390, 393, 394,\n", + " 396, 397, 398, 400, 401, 402, 403, 405, 407, 408, 409,\n", + " 411, 412, 414, 416, 418, 419, 420, 421, 422, 423, 424,\n", + " 425, 426, 427, 428, 429, 430, 431, 433, 434, 436, 437,\n", + " 439, 442, 444, 445, 446, 448, 449, 452, 453, 455, 456,\n", + " 459, 460, 461, 462, 463, 464, 465, 467, 468, 471, 472,\n", + " 473, 475, 476, 477, 478, 480, 482, 483, 485, 486, 487,\n", + " 489, 490, 491, 492, 493, 495, 496, 499, 500, 501, 502,\n", + " 503, 504, 505, 507, 508, 509, 510, 511, 512, 513, 517,\n", + " 518, 520, 522, 527, 528, 529, 530, 532, 533, 534, 535,\n", + " 536, 537, 539, 540, 543, 544, 545, 546, 547, 548, 550,\n", + " 551, 552, 554, 555, 556, 557, 558, 560, 564, 565, 567,\n", + " 569, 570, 572, 574, 575, 579, 580, 581, 582, 583, 589,\n", + " 591, 592, 594, 598, 602, 603, 604, 607, 608, 609, 611,\n", + " 612, 614, 615, 616, 617, 618, 620, 622, 623, 624, 627,\n", + " 629, 630, 633, 634, 635, 636, 637, 639, 643, 644, 645,\n", + " 647, 648, 650, 652, 654, 657, 658, 662, 664, 665, 666,\n", + " 667, 668, 669, 673, 674, 675, 676, 677, 682, 684, 687,\n", + " 688, 689, 690, 694, 695, 697, 698, 700, 703, 704, 705,\n", + " 707, 709, 710, 711, 712, 714, 715, 716, 718, 725, 727,\n", + " 728, 730, 731, 732, 739, 741, 742, 743, 746, 747, 750,\n", + " 756, 757, 758, 759, 760, 761, 762, 764, 765, 766, 767,\n", + " 768, 769, 772, 774, 775, 776, 777, 778, 779, 780, 782,\n", + " 785, 789, 790, 792, 796, 798, 799, 803, 811, 812, 814,\n", + " 817, 819, 820, 823, 824, 828, 830, 831, 832, 833, 834,\n", + " 835, 837, 839, 840, 841, 842, 843, 844, 845, 846, 848,\n", + " 849, 850, 851, 852, 853, 856, 857, 860, 861, 862, 863,\n", + " 865, 867, 869, 871, 872, 874, 876, 878, 883, 884, 886,\n", + " 887, 890, 891, 893, 896, 899, 903, 904, 905, 906, 908,\n", + " 909, 911, 912, 913, 914, 916, 917, 918, 919, 921, 922,\n", + " 923, 924, 926, 929, 932, 933, 936, 938, 939, 940, 941,\n", + " 942, 943, 944, 946, 947, 950, 951, 953, 954, 960, 961,\n", + " 962, 964, 965, 968, 969, 970, 971, 972, 974, 979, 982,\n", + " 984, 985, 986, 987, 989, 990, 991, 992, 994, 996, 997,\n", + " 1001, 1002, 1003, 1006, 1008, 1009, 1010, 1011, 1013, 1019, 1021,\n", + " 1023, 1025, 1026, 1027, 1028, 1029, 1031, 1034, 1036, 1037, 1038,\n", + " 1041, 1042, 1043, 1044, 1045, 1046, 1052, 1053, 1054, 1057, 1060,\n", + " 1062, 1063, 1064, 1065, 1069, 1070, 1071, 1075, 1076, 1079, 1080,\n", + " 1082, 1083, 1084, 1085, 1086, 1088, 1092, 1095, 1100, 1101, 1102,\n", + " 1103, 1105, 1108, 1109, 1111, 1113, 1119, 1121, 1126, 1130, 1131,\n", + " 1132, 1135, 1137, 1144, 1145, 1146, 1148, 1150, 1151, 1154, 1155,\n", + " 1157, 1158, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1168, 1169,\n", + " 1170, 1171, 1173, 1174, 1175, 1177, 1179, 1180, 1182, 1184, 1185,\n", + " 1186, 1192, 1193, 1195, 1196, 1198, 1199, 1200, 1201, 1202, 1203,\n", + " 1204, 1206, 1209, 1210, 1212, 1214, 1217, 1218, 1219, 1220, 1225,\n", + " 1226, 1227, 1228, 1230, 1231, 1232, 1234, 1236, 1237, 1239, 1243,\n", + " 1244, 1247, 1252, 1253, 1258, 1259, 1261, 1264, 1266, 1267, 1268,\n", + " 1269, 1272, 1273, 1274, 1277, 1278, 1279, 1280, 1282, 1289, 1291,\n", + " 1292, 1297, 1299, 1301, 1303, 1305, 1306, 1309, 1311, 1315, 1316,\n", + " 1317, 1320, 1321, 1322, 1323, 1324, 1326, 1328, 1329, 1330, 1332,\n", + " 1333, 1334, 1335, 1336, 1337, 1339, 1341, 1345, 1346, 1349, 1351,\n", + " 1355, 1359, 1363, 1364, 1367, 1368, 1369, 1370, 1371, 1372, 1373,\n", + " 1374, 1376, 1378, 1380, 1384, 1385, 1386, 1388, 1390, 1392, 1396,\n", + " 1398, 1399]),\n", + " 2: array([ 4, 14, 24, 33, 34, 40, 50, 57, 62, 69, 71,\n", + " 72, 73, 75, 79, 81, 83, 84, 85, 91, 95, 96,\n", + " 100, 113, 116, 117, 125, 126, 134, 136, 152, 157, 160,\n", + " 161, 171, 173, 176, 183, 185, 191, 198, 203, 204, 215,\n", + " 217, 224, 225, 237, 243, 250, 256, 260, 266, 270, 276,\n", + " 287, 293, 303, 308, 313, 317, 319, 320, 321, 326, 329,\n", + " 330, 333, 345, 356, 358, 359, 360, 364, 367, 370, 374,\n", + " 377, 379, 382, 387, 395, 406, 410, 415, 417, 438, 440,\n", + " 441, 443, 447, 451, 457, 458, 466, 469, 470, 479, 481,\n", + " 498, 506, 514, 516, 519, 523, 524, 525, 531, 538, 541,\n", + " 549, 566, 568, 571, 577, 584, 585, 586, 588, 593, 595,\n", + " 600, 610, 613, 621, 625, 626, 631, 632, 638, 642, 646,\n", + " 649, 651, 653, 656, 659, 660, 663, 679, 681, 685, 693,\n", + " 706, 713, 721, 722, 723, 729, 734, 735, 738, 745, 749,\n", + " 751, 752, 753, 754, 755, 771, 781, 784, 787, 788, 791,\n", + " 795, 800, 801, 805, 807, 808, 813, 822, 826, 829, 847,\n", + " 854, 855, 866, 870, 877, 881, 889, 892, 894, 897, 898,\n", + " 900, 902, 927, 930, 945, 948, 949, 956, 958, 959, 963,\n", + " 966, 976, 977, 981, 988, 995, 998, 1004, 1012, 1015, 1016,\n", + " 1018, 1024, 1035, 1049, 1051, 1055, 1058, 1059, 1061, 1066, 1067,\n", + " 1072, 1074, 1077, 1078, 1081, 1087, 1089, 1090, 1091, 1093, 1096,\n", + " 1097, 1098, 1099, 1104, 1107, 1112, 1114, 1116, 1120, 1128, 1133,\n", + " 1136, 1139, 1140, 1143, 1149, 1152, 1156, 1159, 1167, 1176, 1178,\n", + " 1181, 1187, 1188, 1189, 1194, 1197, 1205, 1207, 1213, 1216, 1221,\n", + " 1222, 1224, 1229, 1233, 1235, 1238, 1242, 1248, 1250, 1256, 1257,\n", + " 1260, 1263, 1270, 1271, 1276, 1281, 1285, 1287, 1288, 1290, 1293,\n", + " 1295, 1300, 1302, 1304, 1307, 1310, 1313, 1325, 1331, 1338, 1340,\n", + " 1343, 1347, 1352, 1356, 1357, 1365, 1366, 1375, 1382, 1391, 1393,\n", + " 1395, 1397]),\n", + " 3: array([ 6, 8, 19, 21, 47, 52, 55, 59, 63, 68, 77,\n", + " 88, 93, 98, 104, 109, 115, 120, 121, 150, 151, 153,\n", + " 155, 181, 184, 194, 199, 228, 231, 239, 254, 258, 261,\n", + " 280, 282, 292, 295, 297, 299, 316, 336, 338, 339, 348,\n", + " 351, 363, 389, 392, 399, 404, 454, 474, 494, 497, 521,\n", + " 526, 553, 559, 561, 563, 573, 578, 599, 605, 606, 619,\n", + " 628, 640, 670, 672, 678, 680, 683, 686, 691, 696, 699,\n", + " 708, 717, 720, 726, 740, 748, 763, 783, 786, 797, 802,\n", + " 815, 816, 818, 825, 838, 858, 868, 873, 875, 880, 885,\n", + " 888, 901, 910, 928, 934, 935, 937, 973, 975, 980, 983,\n", + " 1000, 1005, 1014, 1020, 1022, 1030, 1033, 1039, 1040, 1047, 1048,\n", + " 1050, 1056, 1073, 1110, 1118, 1122, 1124, 1134, 1138, 1141, 1142,\n", + " 1172, 1183, 1211, 1240, 1241, 1245, 1249, 1251, 1254, 1255, 1262,\n", + " 1265, 1275, 1283, 1286, 1296, 1312, 1314, 1319, 1342, 1353, 1358,\n", + " 1361, 1383, 1387, 1394]),\n", + " 4: array([ 20, 22, 54, 61, 65, 89, 112, 119, 127, 131, 137,\n", + " 147, 154, 187, 208, 209, 229, 230, 284, 328, 337, 342,\n", + " 355, 391, 432, 450, 484, 488, 542, 562, 587, 590, 596,\n", + " 597, 661, 692, 701, 719, 770, 804, 806, 809, 821, 836,\n", + " 859, 882, 895, 931, 955, 967, 978, 993, 999, 1017, 1032,\n", + " 1068, 1094, 1106, 1115, 1123, 1125, 1127, 1129, 1190, 1223, 1246,\n", + " 1294, 1318, 1350, 1354, 1360, 1377, 1379, 1381, 1389]),\n", + " 5: array([ 180, 188, 196, 223, 279, 349, 576, 671, 724, 736, 794,\n", + " 827, 925, 1147, 1215]),\n", + " 6: array([702])},\n", + " 8: {0: array([ 0, 78, 205, 226, 275, 323, 413, 515, 601, 655, 737,\n", + " 744, 793, 810, 879, 907, 920, 1117, 1344, 1362]),\n", + " 1: array([ 1, 3, 5, 7, 9, 10, 11, 12, 13, 15, 16,\n", + " 17, 18, 23, 25, 26, 27, 28, 29, 30, 31, 32,\n", + " 35, 36, 37, 38, 39, 41, 42, 43, 44, 45, 46,\n", + " 48, 49, 51, 53, 56, 58, 60, 64, 66, 67, 70,\n", + " 74, 76, 80, 82, 86, 87, 90, 92, 94, 97, 99,\n", + " 102, 103, 106, 107, 108, 110, 111, 114, 118, 122, 124,\n", + " 128, 129, 130, 132, 135, 138, 139, 140, 141, 142, 143,\n", + " 144, 145, 146, 148, 149, 156, 158, 159, 162, 163, 164,\n", + " 165, 166, 167, 168, 169, 170, 172, 174, 175, 177, 178,\n", + " 179, 186, 189, 190, 192, 193, 195, 197, 200, 201, 202,\n", + " 206, 207, 210, 211, 212, 213, 214, 216, 218, 219, 220,\n", + " 221, 222, 227, 232, 233, 234, 235, 236, 238, 240, 241,\n", + " 242, 244, 245, 246, 247, 248, 249, 251, 252, 253, 255,\n", + " 257, 259, 262, 263, 264, 265, 267, 269, 271, 272, 273,\n", + " 274, 277, 278, 281, 283, 285, 286, 288, 289, 290, 291,\n", + " 294, 296, 298, 300, 301, 302, 304, 305, 306, 307, 310,\n", + " 311, 312, 314, 315, 318, 322, 324, 325, 327, 331, 332,\n", + " 334, 335, 340, 341, 343, 344, 346, 347, 350, 352, 353,\n", + " 354, 357, 361, 365, 366, 368, 369, 371, 372, 373, 375,\n", + " 376, 378, 381, 383, 384, 385, 386, 388, 390, 393, 394,\n", + " 396, 397, 398, 400, 401, 402, 403, 405, 407, 408, 409,\n", + " 411, 412, 414, 416, 418, 419, 420, 421, 422, 423, 424,\n", + " 425, 426, 427, 428, 429, 430, 431, 433, 434, 436, 437,\n", + " 439, 442, 444, 445, 446, 448, 449, 452, 453, 455, 456,\n", + " 459, 460, 461, 462, 463, 464, 465, 467, 468, 471, 472,\n", + " 473, 475, 476, 477, 478, 480, 482, 483, 485, 486, 487,\n", + " 489, 490, 491, 492, 493, 495, 496, 499, 500, 501, 502,\n", + " 503, 504, 505, 507, 508, 509, 510, 511, 512, 513, 517,\n", + " 518, 520, 522, 527, 528, 529, 530, 532, 533, 534, 535,\n", + " 536, 537, 539, 540, 543, 544, 545, 546, 547, 548, 550,\n", + " 551, 552, 554, 555, 556, 557, 558, 560, 564, 565, 567,\n", + " 569, 570, 572, 574, 575, 579, 580, 581, 582, 583, 589,\n", + " 591, 592, 594, 598, 602, 603, 604, 607, 608, 609, 611,\n", + " 612, 614, 615, 616, 617, 618, 620, 622, 623, 624, 627,\n", + " 629, 630, 633, 634, 635, 636, 637, 639, 643, 644, 645,\n", + " 647, 648, 650, 652, 654, 657, 658, 662, 664, 665, 666,\n", + " 667, 668, 669, 673, 674, 675, 676, 677, 682, 684, 687,\n", + " 688, 689, 690, 694, 695, 697, 698, 700, 703, 704, 705,\n", + " 707, 709, 710, 711, 712, 714, 715, 716, 718, 725, 727,\n", + " 728, 730, 731, 732, 739, 741, 742, 743, 746, 747, 750,\n", + " 756, 757, 758, 759, 760, 761, 762, 764, 765, 766, 767,\n", + " 768, 769, 772, 774, 775, 776, 777, 778, 779, 780, 782,\n", + " 785, 789, 790, 792, 796, 798, 799, 803, 811, 812, 814,\n", + " 817, 819, 820, 823, 824, 828, 830, 831, 832, 833, 834,\n", + " 835, 837, 839, 840, 841, 842, 843, 844, 845, 846, 848,\n", + " 849, 850, 851, 852, 853, 856, 857, 860, 861, 862, 863,\n", + " 865, 867, 869, 871, 872, 874, 876, 878, 883, 884, 886,\n", + " 887, 890, 891, 893, 896, 899, 903, 904, 905, 906, 908,\n", + " 909, 911, 912, 913, 914, 916, 917, 918, 919, 921, 922,\n", + " 923, 924, 926, 929, 932, 933, 936, 938, 939, 940, 941,\n", + " 942, 943, 944, 946, 947, 950, 951, 953, 954, 960, 961,\n", + " 962, 964, 965, 968, 969, 970, 971, 972, 974, 979, 982,\n", + " 984, 985, 986, 987, 989, 990, 991, 992, 994, 996, 997,\n", + " 1001, 1002, 1003, 1006, 1008, 1009, 1010, 1011, 1013, 1019, 1021,\n", + " 1023, 1025, 1026, 1027, 1028, 1029, 1031, 1034, 1036, 1037, 1038,\n", + " 1041, 1042, 1043, 1044, 1045, 1046, 1052, 1053, 1054, 1057, 1060,\n", + " 1062, 1063, 1064, 1065, 1069, 1070, 1071, 1075, 1076, 1079, 1080,\n", + " 1082, 1083, 1084, 1085, 1086, 1088, 1092, 1095, 1100, 1101, 1102,\n", + " 1103, 1105, 1108, 1109, 1111, 1113, 1119, 1121, 1126, 1130, 1131,\n", + " 1132, 1135, 1137, 1144, 1145, 1146, 1148, 1150, 1151, 1154, 1155,\n", + " 1157, 1158, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1168, 1169,\n", + " 1170, 1171, 1173, 1174, 1175, 1177, 1179, 1180, 1182, 1184, 1185,\n", + " 1186, 1192, 1193, 1195, 1196, 1198, 1199, 1200, 1201, 1202, 1203,\n", + " 1204, 1206, 1209, 1210, 1212, 1214, 1217, 1218, 1219, 1220, 1225,\n", + " 1226, 1227, 1228, 1230, 1231, 1232, 1234, 1236, 1237, 1239, 1243,\n", + " 1244, 1247, 1252, 1253, 1258, 1259, 1261, 1264, 1266, 1267, 1268,\n", + " 1269, 1272, 1273, 1274, 1277, 1278, 1279, 1280, 1282, 1289, 1291,\n", + " 1292, 1297, 1299, 1301, 1303, 1305, 1306, 1309, 1311, 1315, 1316,\n", + " 1317, 1320, 1321, 1322, 1323, 1324, 1326, 1328, 1329, 1330, 1332,\n", + " 1333, 1334, 1335, 1336, 1337, 1339, 1341, 1345, 1346, 1349, 1351,\n", + " 1355, 1359, 1363, 1364, 1367, 1368, 1369, 1370, 1371, 1372, 1373,\n", + " 1374, 1376, 1378, 1380, 1384, 1385, 1386, 1388, 1390, 1392, 1396,\n", + " 1398, 1399]),\n", + " 2: array([ 2, 101, 105, 123, 133, 182, 268, 309, 362, 380, 435,\n", + " 641, 733, 773, 864, 915, 952, 957, 1007, 1153, 1191, 1208,\n", + " 1284, 1298, 1308, 1327, 1348]),\n", + " 3: array([ 4, 14, 24, 33, 34, 40, 50, 57, 62, 69, 71,\n", + " 72, 73, 75, 79, 81, 83, 84, 85, 91, 95, 96,\n", + " 100, 113, 116, 117, 125, 126, 134, 136, 152, 157, 160,\n", + " 161, 171, 173, 176, 183, 185, 191, 198, 203, 204, 215,\n", + " 217, 224, 225, 237, 243, 250, 256, 260, 266, 270, 276,\n", + " 287, 293, 303, 308, 313, 317, 319, 320, 321, 326, 329,\n", + " 330, 333, 345, 356, 358, 359, 360, 364, 367, 370, 374,\n", + " 377, 379, 382, 387, 395, 406, 410, 415, 417, 438, 440,\n", + " 441, 443, 447, 451, 457, 458, 466, 469, 470, 479, 481,\n", + " 498, 506, 514, 516, 519, 523, 524, 525, 531, 538, 541,\n", + " 549, 566, 568, 571, 577, 584, 585, 586, 588, 593, 595,\n", + " 600, 610, 613, 621, 625, 626, 631, 632, 638, 642, 646,\n", + " 649, 651, 653, 656, 659, 660, 663, 679, 681, 685, 693,\n", + " 706, 713, 721, 722, 723, 729, 734, 735, 738, 745, 749,\n", + " 751, 752, 753, 754, 755, 771, 781, 784, 787, 788, 791,\n", + " 795, 800, 801, 805, 807, 808, 813, 822, 826, 829, 847,\n", + " 854, 855, 866, 870, 877, 881, 889, 892, 894, 897, 898,\n", + " 900, 902, 927, 930, 945, 948, 949, 956, 958, 959, 963,\n", + " 966, 976, 977, 981, 988, 995, 998, 1004, 1012, 1015, 1016,\n", + " 1018, 1024, 1035, 1049, 1051, 1055, 1058, 1059, 1061, 1066, 1067,\n", + " 1072, 1074, 1077, 1078, 1081, 1087, 1089, 1090, 1091, 1093, 1096,\n", + " 1097, 1098, 1099, 1104, 1107, 1112, 1114, 1116, 1120, 1128, 1133,\n", + " 1136, 1139, 1140, 1143, 1149, 1152, 1156, 1159, 1167, 1176, 1178,\n", + " 1181, 1187, 1188, 1189, 1194, 1197, 1205, 1207, 1213, 1216, 1221,\n", + " 1222, 1224, 1229, 1233, 1235, 1238, 1242, 1248, 1250, 1256, 1257,\n", + " 1260, 1263, 1270, 1271, 1276, 1281, 1285, 1287, 1288, 1290, 1293,\n", + " 1295, 1300, 1302, 1304, 1307, 1310, 1313, 1325, 1331, 1338, 1340,\n", + " 1343, 1347, 1352, 1356, 1357, 1365, 1366, 1375, 1382, 1391, 1393,\n", + " 1395, 1397]),\n", + " 4: array([ 6, 8, 19, 21, 47, 52, 55, 59, 63, 68, 77,\n", + " 88, 93, 98, 104, 109, 115, 120, 121, 150, 151, 153,\n", + " 155, 181, 184, 194, 199, 228, 231, 239, 254, 258, 261,\n", + " 280, 282, 292, 295, 297, 299, 316, 336, 338, 339, 348,\n", + " 351, 363, 389, 392, 399, 404, 454, 474, 494, 497, 521,\n", + " 526, 553, 559, 561, 563, 573, 578, 599, 605, 606, 619,\n", + " 628, 640, 670, 672, 678, 680, 683, 686, 691, 696, 699,\n", + " 708, 717, 720, 726, 740, 748, 763, 783, 786, 797, 802,\n", + " 815, 816, 818, 825, 838, 858, 868, 873, 875, 880, 885,\n", + " 888, 901, 910, 928, 934, 935, 937, 973, 975, 980, 983,\n", + " 1000, 1005, 1014, 1020, 1022, 1030, 1033, 1039, 1040, 1047, 1048,\n", + " 1050, 1056, 1073, 1110, 1118, 1122, 1124, 1134, 1138, 1141, 1142,\n", + " 1172, 1183, 1211, 1240, 1241, 1245, 1249, 1251, 1254, 1255, 1262,\n", + " 1265, 1275, 1283, 1286, 1296, 1312, 1314, 1319, 1342, 1353, 1358,\n", + " 1361, 1383, 1387, 1394]),\n", + " 5: array([ 20, 22, 54, 61, 65, 89, 112, 119, 127, 131, 137,\n", + " 147, 154, 187, 208, 209, 229, 230, 284, 328, 337, 342,\n", + " 355, 391, 432, 450, 484, 488, 542, 562, 587, 590, 596,\n", + " 597, 661, 692, 701, 719, 770, 804, 806, 809, 821, 836,\n", + " 859, 882, 895, 931, 955, 967, 978, 993, 999, 1017, 1032,\n", + " 1068, 1094, 1106, 1115, 1123, 1125, 1127, 1129, 1190, 1223, 1246,\n", + " 1294, 1318, 1350, 1354, 1360, 1377, 1379, 1381, 1389]),\n", + " 6: array([ 180, 188, 196, 223, 279, 349, 576, 671, 724, 736, 794,\n", + " 827, 925, 1147, 1215]),\n", + " 7: array([702])},\n", + " 9: {0: array([ 0, 78, 205, 226, 275, 323, 413, 515, 601, 655, 737,\n", + " 744, 793, 810, 879, 907, 920, 1117, 1344, 1362]),\n", + " 1: array([ 1, 3, 5, 7, 9, 10, 11, 12, 13, 15, 16,\n", + " 17, 18, 23, 25, 26, 27, 28, 29, 30, 31, 32,\n", + " 35, 36, 37, 38, 39, 41, 42, 43, 44, 45, 46,\n", + " 48, 49, 51, 53, 56, 58, 60, 64, 66, 67, 70,\n", + " 74, 76, 80, 82, 86, 87, 90, 92, 94, 97, 99,\n", + " 102, 103, 106, 107, 108, 110, 111, 114, 118, 122, 124,\n", + " 128, 129, 130, 132, 135, 138, 139, 140, 141, 142, 143,\n", + " 144, 145, 146, 148, 149, 156, 158, 159, 162, 163, 164,\n", + " 165, 166, 167, 168, 169, 170, 172, 174, 175, 177, 178,\n", + " 179, 186, 189, 190, 192, 193, 195, 197, 200, 201, 202,\n", + " 206, 207, 210, 211, 212, 213, 214, 216, 218, 219, 220,\n", + " 221, 222, 227, 232, 233, 234, 235, 236, 238, 240, 241,\n", + " 242, 244, 245, 246, 247, 248, 249, 251, 252, 253, 255,\n", + " 257, 259, 262, 263, 264, 265, 267, 269, 271, 272, 273,\n", + " 274, 277, 278, 281, 283, 285, 286, 288, 289, 290, 291,\n", + " 294, 296, 298, 300, 301, 302, 304, 305, 306, 307, 310,\n", + " 311, 312, 314, 315, 318, 322, 324, 325, 327, 331, 332,\n", + " 334, 335, 340, 341, 343, 344, 346, 347, 350, 352, 353,\n", + " 354, 357, 361, 365, 366, 368, 369, 371, 372, 373, 375,\n", + " 376, 378, 381, 383, 384, 385, 386, 388, 390, 393, 394,\n", + " 396, 397, 398, 400, 401, 402, 403, 405, 407, 408, 409,\n", + " 411, 412, 414, 416, 418, 419, 420, 421, 422, 423, 424,\n", + " 425, 426, 427, 428, 429, 430, 431, 433, 434, 436, 437,\n", + " 439, 442, 444, 445, 446, 448, 449, 452, 453, 455, 456,\n", + " 459, 460, 461, 462, 463, 464, 465, 467, 468, 471, 472,\n", + " 473, 475, 476, 477, 478, 480, 482, 483, 485, 486, 487,\n", + " 489, 490, 491, 492, 493, 495, 496, 499, 500, 501, 502,\n", + " 503, 504, 505, 507, 508, 509, 510, 511, 512, 513, 517,\n", + " 518, 520, 522, 527, 528, 529, 530, 532, 533, 534, 535,\n", + " 536, 537, 539, 540, 543, 544, 545, 546, 547, 548, 550,\n", + " 551, 552, 554, 555, 556, 557, 558, 560, 564, 565, 567,\n", + " 569, 570, 572, 574, 575, 579, 580, 581, 582, 583, 589,\n", + " 591, 592, 594, 598, 602, 603, 604, 607, 608, 609, 611,\n", + " 612, 614, 615, 616, 617, 618, 620, 622, 623, 624, 627,\n", + " 629, 630, 633, 634, 635, 636, 637, 639, 643, 644, 645,\n", + " 647, 648, 650, 652, 654, 657, 658, 662, 664, 665, 666,\n", + " 667, 668, 669, 673, 674, 675, 676, 677, 682, 684, 687,\n", + " 688, 689, 690, 694, 695, 697, 698, 700, 703, 704, 705,\n", + " 707, 709, 710, 711, 712, 714, 715, 716, 718, 725, 727,\n", + " 728, 730, 731, 732, 739, 741, 742, 743, 746, 747, 750,\n", + " 756, 757, 758, 759, 760, 761, 762, 764, 765, 766, 767,\n", + " 768, 769, 772, 774, 775, 776, 777, 778, 779, 780, 782,\n", + " 785, 789, 790, 792, 796, 798, 799, 803, 811, 812, 814,\n", + " 817, 819, 820, 823, 824, 828, 830, 831, 832, 833, 834,\n", + " 835, 837, 839, 840, 841, 842, 843, 844, 845, 846, 848,\n", + " 849, 850, 851, 852, 853, 856, 857, 860, 861, 862, 863,\n", + " 865, 867, 869, 871, 872, 874, 876, 878, 883, 884, 886,\n", + " 887, 890, 891, 893, 896, 899, 903, 904, 905, 906, 908,\n", + " 909, 911, 912, 913, 914, 916, 917, 918, 919, 921, 922,\n", + " 923, 924, 926, 929, 932, 933, 936, 938, 939, 940, 941,\n", + " 942, 943, 944, 946, 947, 950, 951, 953, 954, 960, 961,\n", + " 962, 964, 965, 968, 969, 970, 971, 972, 974, 979, 982,\n", + " 984, 985, 986, 987, 989, 990, 991, 992, 994, 996, 997,\n", + " 1001, 1002, 1003, 1006, 1008, 1009, 1010, 1011, 1013, 1019, 1021,\n", + " 1023, 1025, 1026, 1027, 1028, 1029, 1031, 1034, 1036, 1037, 1038,\n", + " 1041, 1042, 1043, 1044, 1045, 1046, 1052, 1053, 1054, 1057, 1060,\n", + " 1062, 1063, 1064, 1065, 1069, 1070, 1071, 1075, 1076, 1079, 1080,\n", + " 1082, 1083, 1084, 1085, 1086, 1088, 1092, 1095, 1100, 1101, 1102,\n", + " 1103, 1105, 1108, 1109, 1111, 1113, 1119, 1121, 1126, 1130, 1131,\n", + " 1132, 1135, 1137, 1144, 1145, 1146, 1148, 1150, 1151, 1154, 1155,\n", + " 1157, 1158, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1168, 1169,\n", + " 1170, 1171, 1173, 1174, 1175, 1177, 1179, 1180, 1182, 1184, 1185,\n", + " 1186, 1192, 1193, 1195, 1196, 1198, 1199, 1200, 1201, 1202, 1203,\n", + " 1204, 1206, 1209, 1210, 1212, 1214, 1217, 1218, 1219, 1220, 1225,\n", + " 1226, 1227, 1228, 1230, 1231, 1232, 1234, 1236, 1237, 1239, 1243,\n", + " 1244, 1247, 1252, 1253, 1258, 1259, 1261, 1264, 1266, 1267, 1268,\n", + " 1269, 1272, 1273, 1274, 1277, 1278, 1279, 1280, 1282, 1289, 1291,\n", + " 1292, 1297, 1299, 1301, 1303, 1305, 1306, 1309, 1311, 1315, 1316,\n", + " 1317, 1320, 1321, 1322, 1323, 1324, 1326, 1328, 1329, 1330, 1332,\n", + " 1333, 1334, 1335, 1336, 1337, 1339, 1341, 1345, 1346, 1349, 1351,\n", + " 1355, 1359, 1363, 1364, 1367, 1368, 1369, 1370, 1371, 1372, 1373,\n", + " 1374, 1376, 1378, 1380, 1384, 1385, 1386, 1388, 1390, 1392, 1396,\n", + " 1398, 1399]),\n", + " 2: array([ 2, 101, 105, 123, 133, 182, 268, 309, 362, 380, 435,\n", + " 641, 733, 773, 864, 915, 952, 957, 1007, 1153, 1191, 1208,\n", + " 1284, 1298, 1308, 1327, 1348]),\n", + " 3: array([ 4, 14, 24, 33, 34, 50, 57, 62, 69, 71, 72,\n", + " 73, 75, 79, 81, 83, 84, 85, 91, 95, 100, 117,\n", + " 125, 126, 134, 136, 152, 157, 160, 161, 173, 176, 183,\n", + " 185, 198, 203, 204, 217, 224, 225, 237, 243, 256, 260,\n", + " 266, 287, 293, 303, 308, 313, 317, 319, 320, 321, 326,\n", + " 330, 333, 345, 356, 358, 359, 367, 370, 377, 379, 382,\n", + " 387, 395, 406, 410, 415, 417, 440, 441, 443, 447, 451,\n", + " 457, 458, 466, 469, 470, 479, 481, 498, 506, 514, 516,\n", + " 519, 523, 524, 525, 531, 538, 541, 549, 566, 568, 571,\n", + " 577, 584, 585, 586, 593, 595, 600, 610, 613, 621, 625,\n", + " 626, 631, 632, 638, 642, 646, 649, 651, 653, 656, 659,\n", + " 660, 663, 679, 681, 693, 706, 713, 721, 722, 723, 729,\n", + " 734, 735, 738, 745, 749, 751, 752, 754, 755, 771, 781,\n", + " 784, 787, 791, 795, 800, 801, 805, 807, 808, 813, 822,\n", + " 826, 829, 854, 855, 866, 870, 877, 881, 889, 892, 894,\n", + " 897, 898, 900, 902, 927, 930, 945, 948, 949, 958, 959,\n", + " 976, 977, 981, 988, 995, 998, 1004, 1012, 1015, 1018, 1024,\n", + " 1035, 1049, 1051, 1055, 1058, 1059, 1066, 1067, 1072, 1074, 1077,\n", + " 1078, 1081, 1087, 1089, 1090, 1091, 1093, 1096, 1097, 1098, 1099,\n", + " 1104, 1107, 1112, 1114, 1116, 1120, 1128, 1133, 1139, 1140, 1143,\n", + " 1149, 1152, 1156, 1159, 1167, 1176, 1178, 1181, 1187, 1189, 1194,\n", + " 1205, 1207, 1213, 1216, 1221, 1222, 1229, 1233, 1238, 1242, 1248,\n", + " 1250, 1256, 1257, 1260, 1263, 1271, 1276, 1281, 1285, 1287, 1288,\n", + " 1290, 1293, 1295, 1300, 1302, 1304, 1307, 1310, 1313, 1325, 1331,\n", + " 1338, 1340, 1343, 1347, 1352, 1356, 1357, 1365, 1375, 1382, 1391,\n", + " 1393, 1395, 1397]),\n", + " 4: array([ 6, 8, 19, 21, 47, 52, 55, 59, 63, 68, 77,\n", + " 88, 93, 98, 104, 109, 115, 120, 121, 150, 151, 153,\n", + " 155, 181, 184, 194, 199, 228, 231, 239, 254, 258, 261,\n", + " 280, 282, 292, 295, 297, 299, 316, 336, 338, 339, 348,\n", + " 351, 363, 389, 392, 399, 404, 454, 474, 494, 497, 521,\n", + " 526, 553, 559, 561, 563, 573, 578, 599, 605, 606, 619,\n", + " 628, 640, 670, 672, 678, 680, 683, 686, 691, 696, 699,\n", + " 708, 717, 720, 726, 740, 748, 763, 783, 786, 797, 802,\n", + " 815, 816, 818, 825, 838, 858, 868, 873, 875, 880, 885,\n", + " 888, 901, 910, 928, 934, 935, 937, 973, 975, 980, 983,\n", + " 1000, 1005, 1014, 1020, 1022, 1030, 1033, 1039, 1040, 1047, 1048,\n", + " 1050, 1056, 1073, 1110, 1118, 1122, 1124, 1134, 1138, 1141, 1142,\n", + " 1172, 1183, 1211, 1240, 1241, 1245, 1249, 1251, 1254, 1255, 1262,\n", + " 1265, 1275, 1283, 1286, 1296, 1312, 1314, 1319, 1342, 1353, 1358,\n", + " 1361, 1383, 1387, 1394]),\n", + " 5: array([ 20, 22, 54, 61, 65, 89, 112, 119, 127, 131, 137,\n", + " 147, 154, 187, 208, 209, 229, 230, 284, 328, 337, 342,\n", + " 355, 391, 432, 450, 484, 488, 542, 562, 587, 590, 596,\n", + " 597, 661, 692, 701, 719, 770, 804, 806, 809, 821, 836,\n", + " 859, 882, 895, 931, 955, 967, 978, 993, 999, 1017, 1032,\n", + " 1068, 1094, 1106, 1115, 1123, 1125, 1127, 1129, 1190, 1223, 1246,\n", + " 1294, 1318, 1350, 1354, 1360, 1377, 1379, 1381, 1389]),\n", + " 6: array([ 40, 96, 113, 116, 171, 191, 215, 250, 270, 276, 329,\n", + " 360, 364, 374, 438, 588, 685, 753, 788, 847, 956, 963,\n", + " 966, 1016, 1061, 1136, 1188, 1197, 1224, 1235, 1270, 1366]),\n", + " 7: array([ 180, 188, 196, 223, 279, 349, 576, 671, 724, 736, 794,\n", + " 827, 925, 1147, 1215]),\n", + " 8: array([702])},\n", + " 10: {0: array([ 0, 78, 205, 226, 275, 323, 413, 515, 601, 655, 737,\n", + " 744, 793, 810, 879, 907, 920, 1117, 1344, 1362]),\n", + " 1: array([ 1, 3, 5, 7, 9, 10, 11, 12, 13, 15, 16,\n", + " 17, 18, 23, 25, 26, 27, 28, 29, 30, 31, 32,\n", + " 35, 36, 37, 38, 39, 41, 42, 43, 44, 45, 46,\n", + " 48, 49, 51, 53, 56, 58, 60, 64, 66, 67, 70,\n", + " 74, 76, 80, 82, 86, 87, 90, 92, 94, 97, 99,\n", + " 102, 103, 106, 107, 108, 110, 111, 114, 118, 122, 124,\n", + " 128, 129, 130, 132, 135, 138, 139, 140, 141, 142, 143,\n", + " 144, 145, 146, 148, 149, 156, 158, 159, 162, 163, 164,\n", + " 165, 166, 167, 168, 169, 170, 172, 174, 175, 177, 178,\n", + " 179, 186, 189, 190, 192, 193, 195, 197, 200, 201, 202,\n", + " 206, 207, 210, 211, 212, 213, 214, 216, 218, 219, 220,\n", + " 221, 222, 227, 232, 233, 234, 235, 236, 238, 240, 241,\n", + " 242, 244, 245, 246, 247, 248, 249, 251, 252, 253, 255,\n", + " 257, 259, 262, 263, 264, 265, 267, 269, 271, 272, 273,\n", + " 274, 277, 278, 281, 283, 285, 286, 288, 289, 290, 291,\n", + " 294, 296, 298, 300, 301, 302, 304, 305, 306, 307, 310,\n", + " 311, 312, 314, 315, 318, 322, 324, 325, 327, 331, 332,\n", + " 334, 335, 340, 341, 343, 344, 346, 347, 350, 352, 353,\n", + " 354, 357, 361, 365, 366, 368, 369, 371, 372, 373, 375,\n", + " 376, 378, 381, 383, 384, 385, 386, 388, 390, 393, 394,\n", + " 396, 397, 398, 400, 401, 402, 403, 405, 407, 408, 409,\n", + " 411, 412, 414, 416, 418, 419, 420, 421, 422, 423, 424,\n", + " 425, 426, 427, 428, 429, 430, 431, 433, 434, 436, 437,\n", + " 439, 442, 444, 445, 446, 448, 449, 452, 453, 455, 456,\n", + " 459, 460, 461, 462, 463, 464, 465, 467, 468, 471, 472,\n", + " 473, 475, 476, 477, 478, 480, 482, 483, 485, 486, 487,\n", + " 489, 490, 491, 492, 493, 495, 496, 499, 500, 501, 502,\n", + " 503, 504, 505, 507, 508, 509, 510, 511, 512, 513, 517,\n", + " 518, 520, 522, 527, 528, 529, 530, 532, 533, 534, 535,\n", + " 536, 537, 539, 540, 543, 544, 545, 546, 547, 548, 550,\n", + " 551, 552, 554, 555, 556, 557, 558, 560, 564, 565, 567,\n", + " 569, 570, 572, 574, 575, 579, 580, 581, 582, 583, 589,\n", + " 591, 592, 594, 598, 602, 603, 604, 607, 608, 609, 611,\n", + " 612, 614, 615, 616, 617, 618, 620, 622, 623, 624, 627,\n", + " 629, 630, 633, 634, 635, 636, 637, 639, 643, 644, 645,\n", + " 647, 648, 650, 652, 654, 657, 658, 662, 664, 665, 666,\n", + " 667, 668, 669, 673, 674, 675, 676, 677, 682, 684, 687,\n", + " 688, 689, 690, 694, 695, 697, 698, 700, 703, 704, 705,\n", + " 707, 709, 710, 711, 712, 714, 715, 716, 718, 725, 727,\n", + " 728, 730, 731, 732, 739, 741, 742, 743, 746, 747, 750,\n", + " 756, 757, 758, 759, 760, 761, 762, 764, 765, 766, 767,\n", + " 768, 769, 772, 774, 775, 776, 777, 778, 779, 780, 782,\n", + " 785, 789, 790, 792, 796, 798, 799, 803, 811, 812, 814,\n", + " 817, 819, 820, 823, 824, 828, 830, 831, 832, 833, 834,\n", + " 835, 837, 839, 840, 841, 842, 843, 844, 845, 846, 848,\n", + " 849, 850, 851, 852, 853, 856, 857, 860, 861, 862, 863,\n", + " 865, 867, 869, 871, 872, 874, 876, 878, 883, 884, 886,\n", + " 887, 890, 891, 893, 896, 899, 903, 904, 905, 906, 908,\n", + " 909, 911, 912, 913, 914, 916, 917, 918, 919, 921, 922,\n", + " 923, 924, 926, 929, 932, 933, 936, 938, 939, 940, 941,\n", + " 942, 943, 944, 946, 947, 950, 951, 953, 954, 960, 961,\n", + " 962, 964, 965, 968, 969, 970, 971, 972, 974, 979, 982,\n", + " 984, 985, 986, 987, 989, 990, 991, 992, 994, 996, 997,\n", + " 1001, 1002, 1003, 1006, 1008, 1009, 1010, 1011, 1013, 1019, 1021,\n", + " 1023, 1025, 1026, 1027, 1028, 1029, 1031, 1034, 1036, 1037, 1038,\n", + " 1041, 1042, 1043, 1044, 1045, 1046, 1052, 1053, 1054, 1057, 1060,\n", + " 1062, 1063, 1064, 1065, 1069, 1070, 1071, 1075, 1076, 1079, 1080,\n", + " 1082, 1083, 1084, 1085, 1086, 1088, 1092, 1095, 1100, 1101, 1102,\n", + " 1103, 1105, 1108, 1109, 1111, 1113, 1119, 1121, 1126, 1130, 1131,\n", + " 1132, 1135, 1137, 1144, 1145, 1146, 1148, 1150, 1151, 1154, 1155,\n", + " 1157, 1158, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1168, 1169,\n", + " 1170, 1171, 1173, 1174, 1175, 1177, 1179, 1180, 1182, 1184, 1185,\n", + " 1186, 1192, 1193, 1195, 1196, 1198, 1199, 1200, 1201, 1202, 1203,\n", + " 1204, 1206, 1209, 1210, 1212, 1214, 1217, 1218, 1219, 1220, 1225,\n", + " 1226, 1227, 1228, 1230, 1231, 1232, 1234, 1236, 1237, 1239, 1243,\n", + " 1244, 1247, 1252, 1253, 1258, 1259, 1261, 1264, 1266, 1267, 1268,\n", + " 1269, 1272, 1273, 1274, 1277, 1278, 1279, 1280, 1282, 1289, 1291,\n", + " 1292, 1297, 1299, 1301, 1303, 1305, 1306, 1309, 1311, 1315, 1316,\n", + " 1317, 1320, 1321, 1322, 1323, 1324, 1326, 1328, 1329, 1330, 1332,\n", + " 1333, 1334, 1335, 1336, 1337, 1339, 1341, 1345, 1346, 1349, 1351,\n", + " 1355, 1359, 1363, 1364, 1367, 1368, 1369, 1370, 1371, 1372, 1373,\n", + " 1374, 1376, 1378, 1380, 1384, 1385, 1386, 1388, 1390, 1392, 1396,\n", + " 1398, 1399]),\n", + " 2: array([ 2, 101, 105, 123, 133, 182, 268, 309, 362, 380, 435,\n", + " 641, 733, 773, 864, 915, 952, 957, 1007, 1153, 1191, 1208,\n", + " 1284, 1298, 1308, 1327, 1348]),\n", + " 3: array([ 4, 14, 24, 33, 34, 50, 57, 62, 69, 71, 72,\n", + " 73, 75, 79, 81, 83, 84, 85, 91, 95, 100, 117,\n", + " 125, 126, 134, 136, 152, 157, 160, 161, 173, 176, 183,\n", + " 185, 198, 203, 204, 217, 224, 225, 237, 243, 256, 260,\n", + " 266, 287, 293, 303, 308, 313, 317, 319, 320, 321, 326,\n", + " 330, 333, 345, 356, 358, 359, 367, 370, 377, 379, 382,\n", + " 387, 395, 406, 410, 415, 417, 440, 441, 443, 447, 451,\n", + " 457, 458, 466, 469, 470, 479, 481, 498, 506, 514, 516,\n", + " 519, 523, 524, 525, 531, 538, 541, 549, 566, 568, 571,\n", + " 577, 584, 585, 586, 593, 595, 600, 610, 613, 621, 625,\n", + " 626, 631, 632, 638, 642, 646, 649, 651, 653, 656, 659,\n", + " 660, 663, 679, 681, 693, 706, 713, 721, 722, 723, 729,\n", + " 734, 735, 738, 745, 749, 751, 752, 754, 755, 771, 781,\n", + " 784, 787, 791, 795, 800, 801, 805, 807, 808, 813, 822,\n", + " 826, 829, 854, 855, 866, 870, 877, 881, 889, 892, 894,\n", + " 897, 898, 900, 902, 927, 930, 945, 948, 949, 958, 959,\n", + " 976, 977, 981, 988, 995, 998, 1004, 1012, 1015, 1018, 1024,\n", + " 1035, 1049, 1051, 1055, 1058, 1059, 1066, 1067, 1072, 1074, 1077,\n", + " 1078, 1081, 1087, 1089, 1090, 1091, 1093, 1096, 1097, 1098, 1099,\n", + " 1104, 1107, 1112, 1114, 1116, 1120, 1128, 1133, 1139, 1140, 1143,\n", + " 1149, 1152, 1156, 1159, 1167, 1176, 1178, 1181, 1187, 1189, 1194,\n", + " 1205, 1207, 1213, 1216, 1221, 1222, 1229, 1233, 1238, 1242, 1248,\n", + " 1250, 1256, 1257, 1260, 1263, 1271, 1276, 1281, 1285, 1287, 1288,\n", + " 1290, 1293, 1295, 1300, 1302, 1304, 1307, 1310, 1313, 1325, 1331,\n", + " 1338, 1340, 1343, 1347, 1352, 1356, 1357, 1365, 1375, 1382, 1391,\n", + " 1393, 1395, 1397]),\n", + " 4: array([ 6, 292, 339, 599, 670, 678, 763, 783, 816, 888, 935,\n", + " 1022, 1033, 1211]),\n", + " 5: array([ 8, 19, 21, 47, 52, 55, 59, 63, 68, 77, 88,\n", + " 93, 98, 104, 109, 115, 120, 121, 150, 151, 153, 155,\n", + " 181, 184, 194, 199, 228, 231, 239, 254, 258, 261, 280,\n", + " 282, 295, 297, 299, 316, 336, 338, 348, 351, 363, 389,\n", + " 392, 399, 404, 454, 474, 494, 497, 521, 526, 553, 559,\n", + " 561, 563, 573, 578, 605, 606, 619, 628, 640, 672, 680,\n", + " 683, 686, 691, 696, 699, 708, 717, 720, 726, 740, 748,\n", + " 786, 797, 802, 815, 818, 825, 838, 858, 868, 873, 875,\n", + " 880, 885, 901, 910, 928, 934, 937, 973, 975, 980, 983,\n", + " 1000, 1005, 1014, 1020, 1030, 1039, 1040, 1047, 1048, 1050, 1056,\n", + " 1073, 1110, 1118, 1122, 1124, 1134, 1138, 1141, 1142, 1172, 1183,\n", + " 1240, 1241, 1245, 1249, 1251, 1254, 1255, 1262, 1265, 1275, 1283,\n", + " 1286, 1296, 1312, 1314, 1319, 1342, 1353, 1358, 1361, 1383, 1387,\n", + " 1394]),\n", + " 6: array([ 20, 22, 54, 61, 65, 89, 112, 119, 127, 131, 137,\n", + " 147, 154, 187, 208, 209, 229, 230, 284, 328, 337, 342,\n", + " 355, 391, 432, 450, 484, 488, 542, 562, 587, 590, 596,\n", + " 597, 661, 692, 701, 719, 770, 804, 806, 809, 821, 836,\n", + " 859, 882, 895, 931, 955, 967, 978, 993, 999, 1017, 1032,\n", + " 1068, 1094, 1106, 1115, 1123, 1125, 1127, 1129, 1190, 1223, 1246,\n", + " 1294, 1318, 1350, 1354, 1360, 1377, 1379, 1381, 1389]),\n", + " 7: array([ 40, 96, 113, 116, 171, 191, 215, 250, 270, 276, 329,\n", + " 360, 364, 374, 438, 588, 685, 753, 788, 847, 956, 963,\n", + " 966, 1016, 1061, 1136, 1188, 1197, 1224, 1235, 1270, 1366]),\n", + " 8: array([ 180, 188, 196, 223, 279, 349, 576, 671, 724, 736, 794,\n", + " 827, 925, 1147, 1215]),\n", + " 9: array([702])}},\n", + " 'nonl2_ranking_nonloo': {2: {0: array([ 0, 2, 6, 8, 19, 20, 21, 22, 41, 47, 52,\n", + " 54, 55, 59, 61, 63, 65, 68, 77, 78, 88, 89,\n", + " 93, 96, 98, 101, 104, 105, 109, 112, 113, 115, 119,\n", + " 120, 121, 123, 126, 127, 131, 133, 137, 147, 150, 151,\n", + " 153, 154, 155, 181, 182, 184, 187, 194, 199, 205, 208,\n", + " 209, 226, 228, 229, 230, 231, 239, 254, 258, 260, 261,\n", + " 266, 268, 275, 276, 280, 282, 284, 292, 295, 297, 299,\n", + " 309, 316, 323, 328, 336, 337, 338, 339, 342, 348, 351,\n", + " 355, 362, 363, 370, 380, 389, 391, 392, 399, 404, 409,\n", + " 413, 432, 435, 450, 454, 474, 484, 488, 494, 497, 515,\n", + " 521, 526, 542, 553, 559, 561, 562, 563, 570, 573, 578,\n", + " 587, 590, 597, 599, 601, 605, 606, 619, 628, 631, 640,\n", + " 641, 655, 661, 665, 670, 672, 678, 680, 683, 686, 687,\n", + " 691, 692, 696, 699, 701, 702, 708, 717, 719, 720, 726,\n", + " 733, 737, 740, 744, 748, 753, 759, 763, 765, 770, 773,\n", + " 783, 786, 787, 793, 797, 802, 804, 806, 809, 810, 815,\n", + " 816, 818, 821, 825, 836, 838, 858, 859, 864, 868, 873,\n", + " 875, 879, 880, 882, 885, 888, 889, 895, 901, 904, 907,\n", + " 910, 915, 920, 928, 931, 932, 934, 935, 937, 952, 955,\n", + " 957, 967, 973, 975, 978, 980, 983, 993, 999, 1000, 1005,\n", + " 1007, 1010, 1014, 1017, 1020, 1022, 1030, 1032, 1033, 1039, 1040,\n", + " 1047, 1048, 1050, 1056, 1068, 1073, 1094, 1106, 1110, 1115, 1117,\n", + " 1118, 1122, 1123, 1124, 1125, 1127, 1129, 1134, 1138, 1141, 1142,\n", + " 1153, 1172, 1183, 1190, 1191, 1208, 1211, 1221, 1223, 1240, 1241,\n", + " 1245, 1246, 1249, 1251, 1254, 1255, 1262, 1265, 1275, 1283, 1284,\n", + " 1286, 1292, 1294, 1296, 1298, 1308, 1312, 1314, 1317, 1318, 1319,\n", + " 1327, 1342, 1344, 1348, 1350, 1352, 1353, 1354, 1358, 1360, 1361,\n", + " 1362, 1377, 1379, 1381, 1383, 1387, 1389, 1394]),\n", + " 1: array([ 1, 3, 4, ..., 1397, 1398, 1399])},\n", + " 3: {0: array([ 0, 2, 6, 8, 19, 20, 21, 22, 41, 47, 52,\n", + " 54, 55, 59, 61, 63, 65, 68, 77, 78, 88, 89,\n", + " 93, 96, 98, 101, 104, 105, 109, 112, 113, 115, 119,\n", + " 120, 121, 123, 126, 127, 131, 133, 137, 147, 150, 151,\n", + " 153, 154, 155, 181, 182, 184, 187, 194, 199, 205, 208,\n", + " 209, 226, 228, 229, 230, 231, 239, 254, 258, 260, 261,\n", + " 266, 268, 275, 276, 280, 282, 284, 292, 295, 297, 299,\n", + " 309, 316, 323, 328, 336, 337, 338, 339, 342, 348, 351,\n", + " 355, 362, 363, 370, 380, 389, 391, 392, 399, 404, 409,\n", + " 413, 432, 435, 450, 454, 474, 484, 488, 494, 497, 515,\n", + " 521, 526, 542, 553, 559, 561, 562, 563, 570, 573, 578,\n", + " 587, 590, 597, 599, 601, 605, 606, 619, 628, 631, 640,\n", + " 641, 655, 661, 665, 670, 672, 678, 680, 683, 686, 687,\n", + " 691, 692, 696, 699, 701, 702, 708, 717, 719, 720, 726,\n", + " 733, 737, 740, 744, 748, 753, 759, 763, 765, 770, 773,\n", + " 783, 786, 787, 793, 797, 802, 804, 806, 809, 810, 815,\n", + " 816, 818, 821, 825, 836, 838, 858, 859, 864, 868, 873,\n", + " 875, 879, 880, 882, 885, 888, 889, 895, 901, 904, 907,\n", + " 910, 915, 920, 928, 931, 932, 934, 935, 937, 952, 955,\n", + " 957, 967, 973, 975, 978, 980, 983, 993, 999, 1000, 1005,\n", + " 1007, 1010, 1014, 1017, 1020, 1022, 1030, 1032, 1033, 1039, 1040,\n", + " 1047, 1048, 1050, 1056, 1068, 1073, 1094, 1106, 1110, 1115, 1117,\n", + " 1118, 1122, 1123, 1124, 1125, 1127, 1129, 1134, 1138, 1141, 1142,\n", + " 1153, 1172, 1183, 1190, 1191, 1208, 1211, 1221, 1223, 1240, 1241,\n", + " 1245, 1246, 1249, 1251, 1254, 1255, 1262, 1265, 1275, 1283, 1284,\n", + " 1286, 1292, 1294, 1296, 1298, 1308, 1312, 1314, 1317, 1318, 1319,\n", + " 1327, 1342, 1344, 1348, 1350, 1352, 1353, 1354, 1358, 1360, 1361,\n", + " 1362, 1377, 1379, 1381, 1383, 1387, 1389, 1394]),\n", + " 1: array([ 1, 3, 4, 5, 7, 9, 12, 13, 14, 17, 18,\n", + " 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n", + " 36, 38, 42, 46, 48, 49, 50, 51, 56, 57, 58,\n", + " 60, 64, 66, 67, 69, 70, 72, 73, 74, 80, 81,\n", + " 82, 83, 84, 85, 86, 87, 90, 91, 92, 94, 95,\n", + " 97, 99, 102, 108, 111, 114, 117, 118, 122, 124, 125,\n", + " 128, 130, 132, 134, 135, 136, 138, 139, 140, 141, 142,\n", + " 143, 144, 145, 146, 148, 149, 152, 157, 158, 159, 160,\n", + " 161, 162, 166, 167, 169, 170, 171, 172, 173, 174, 175,\n", + " 177, 178, 179, 180, 183, 185, 188, 189, 190, 192, 195,\n", + " 196, 197, 198, 200, 201, 202, 203, 204, 206, 207, 211,\n", + " 212, 213, 214, 216, 217, 218, 219, 221, 222, 223, 224,\n", + " 225, 227, 232, 235, 236, 237, 238, 240, 241, 243, 244,\n", + " 245, 246, 247, 249, 252, 253, 255, 256, 259, 262, 264,\n", + " 265, 267, 270, 271, 272, 273, 274, 277, 278, 279, 283,\n", + " 287, 288, 289, 290, 291, 293, 294, 296, 298, 300, 301,\n", + " 302, 303, 304, 305, 306, 307, 310, 312, 313, 314, 315,\n", + " 319, 320, 321, 322, 324, 325, 326, 327, 331, 332, 334,\n", + " 335, 340, 341, 344, 345, 349, 352, 353, 354, 356, 357,\n", + " 358, 360, 361, 364, 365, 366, 367, 368, 369, 371, 372,\n", + " 373, 376, 377, 378, 379, 381, 382, 383, 384, 385, 386,\n", + " 387, 388, 390, 394, 395, 397, 398, 400, 401, 402, 406,\n", + " 407, 408, 410, 411, 412, 414, 415, 416, 417, 418, 419,\n", + " 420, 421, 422, 423, 425, 426, 427, 428, 429, 430, 431,\n", + " 433, 434, 436, 440, 442, 444, 445, 446, 447, 448, 449,\n", + " 452, 453, 455, 457, 458, 459, 460, 462, 463, 464, 465,\n", + " 466, 467, 468, 469, 470, 471, 472, 475, 476, 479, 480,\n", + " 482, 483, 485, 486, 489, 490, 491, 492, 495, 496, 499,\n", + " 500, 501, 503, 505, 506, 508, 509, 510, 512, 514, 516,\n", + " 518, 519, 520, 522, 523, 524, 525, 527, 528, 529, 530,\n", + " 531, 533, 534, 535, 537, 538, 539, 540, 541, 543, 545,\n", + " 547, 548, 549, 550, 551, 552, 554, 558, 560, 564, 566,\n", + " 567, 568, 569, 572, 574, 576, 577, 579, 580, 581, 582,\n", + " 588, 589, 591, 592, 593, 594, 596, 598, 600, 602, 603,\n", + " 604, 607, 611, 612, 613, 614, 615, 616, 617, 618, 620,\n", + " 621, 622, 624, 625, 626, 627, 629, 632, 633, 634, 635,\n", + " 637, 638, 639, 642, 643, 644, 645, 646, 647, 648, 649,\n", + " 650, 651, 652, 653, 656, 657, 658, 659, 660, 662, 663,\n", + " 666, 668, 671, 673, 674, 675, 676, 677, 679, 681, 682,\n", + " 684, 685, 688, 689, 690, 693, 695, 697, 700, 703, 704,\n", + " 706, 707, 709, 711, 713, 714, 715, 716, 718, 721, 723,\n", + " 724, 728, 730, 731, 732, 734, 735, 736, 741, 742, 743,\n", + " 745, 747, 749, 750, 751, 752, 755, 756, 757, 761, 762,\n", + " 764, 766, 767, 769, 771, 772, 774, 775, 777, 779, 780,\n", + " 781, 782, 784, 785, 789, 792, 794, 796, 798, 799, 801,\n", + " 803, 805, 807, 811, 812, 813, 814, 819, 822, 823, 824,\n", + " 827, 828, 829, 830, 831, 832, 835, 837, 839, 840, 841,\n", + " 843, 844, 846, 848, 849, 853, 854, 855, 856, 857, 861,\n", + " 867, 869, 871, 872, 874, 876, 881, 883, 884, 886, 887,\n", + " 890, 891, 892, 894, 896, 897, 898, 899, 900, 905, 908,\n", + " 909, 912, 913, 914, 916, 917, 918, 919, 921, 923, 924,\n", + " 925, 926, 927, 929, 930, 933, 938, 939, 940, 941, 942,\n", + " 943, 944, 945, 947, 948, 950, 951, 953, 959, 960, 961,\n", + " 962, 964, 965, 968, 971, 974, 976, 977, 979, 984, 985,\n", + " 986, 987, 988, 990, 991, 992, 996, 997, 998, 1001, 1003,\n", + " 1004, 1006, 1009, 1011, 1013, 1021, 1023, 1024, 1025, 1026, 1028,\n", + " 1029, 1031, 1035, 1037, 1038, 1041, 1042, 1044, 1045, 1046, 1049,\n", + " 1051, 1052, 1053, 1054, 1055, 1058, 1060, 1062, 1063, 1064, 1065,\n", + " 1066, 1067, 1069, 1071, 1072, 1074, 1075, 1077, 1080, 1083, 1084,\n", + " 1085, 1087, 1088, 1089, 1091, 1092, 1095, 1096, 1097, 1098, 1099,\n", + " 1100, 1102, 1103, 1104, 1107, 1108, 1109, 1112, 1113, 1119, 1120,\n", + " 1121, 1126, 1128, 1130, 1131, 1133, 1135, 1137, 1139, 1143, 1146,\n", + " 1147, 1148, 1149, 1152, 1154, 1155, 1158, 1159, 1160, 1161, 1162,\n", + " 1163, 1164, 1165, 1166, 1167, 1169, 1170, 1171, 1173, 1177, 1178,\n", + " 1179, 1180, 1182, 1184, 1185, 1189, 1192, 1193, 1194, 1195, 1196,\n", + " 1198, 1200, 1201, 1203, 1204, 1205, 1206, 1209, 1210, 1212, 1213,\n", + " 1214, 1215, 1216, 1218, 1219, 1220, 1222, 1225, 1226, 1227, 1229,\n", + " 1230, 1231, 1232, 1234, 1235, 1236, 1237, 1238, 1239, 1244, 1247,\n", + " 1252, 1253, 1256, 1257, 1258, 1259, 1260, 1261, 1264, 1266, 1267,\n", + " 1269, 1271, 1272, 1273, 1274, 1276, 1278, 1279, 1281, 1282, 1285,\n", + " 1287, 1289, 1290, 1291, 1293, 1295, 1299, 1300, 1301, 1302, 1303,\n", + " 1304, 1305, 1306, 1311, 1313, 1315, 1316, 1320, 1321, 1322, 1324,\n", + " 1325, 1326, 1328, 1329, 1330, 1331, 1332, 1334, 1336, 1337, 1338,\n", + " 1339, 1341, 1343, 1345, 1346, 1347, 1351, 1355, 1356, 1357, 1363,\n", + " 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374,\n", + " 1376, 1378, 1380, 1382, 1384, 1385, 1386, 1388, 1390, 1391, 1393,\n", + " 1397, 1398]),\n", + " 2: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 43, 44,\n", + " 45, 53, 62, 71, 75, 76, 79, 100, 103, 106, 107,\n", + " 110, 116, 129, 156, 163, 164, 165, 168, 176, 186, 191,\n", + " 193, 210, 215, 220, 233, 234, 242, 248, 250, 251, 257,\n", + " 263, 269, 281, 285, 286, 308, 311, 317, 318, 329, 330,\n", + " 333, 343, 346, 347, 350, 359, 374, 375, 393, 396, 403,\n", + " 405, 424, 437, 438, 439, 441, 443, 451, 456, 461, 473,\n", + " 477, 478, 481, 487, 493, 498, 502, 504, 507, 511, 513,\n", + " 517, 532, 536, 544, 546, 555, 556, 557, 565, 571, 575,\n", + " 583, 584, 585, 586, 595, 608, 609, 610, 623, 630, 636,\n", + " 654, 664, 667, 669, 694, 698, 705, 710, 712, 722, 725,\n", + " 727, 729, 738, 739, 746, 754, 758, 760, 768, 776, 778,\n", + " 788, 790, 791, 795, 800, 808, 817, 820, 826, 833, 834,\n", + " 842, 845, 847, 850, 851, 852, 860, 862, 863, 865, 866,\n", + " 870, 877, 878, 893, 902, 903, 906, 911, 922, 936, 946,\n", + " 949, 954, 956, 958, 963, 966, 969, 970, 972, 981, 982,\n", + " 989, 994, 995, 1002, 1008, 1012, 1015, 1016, 1018, 1019, 1027,\n", + " 1034, 1036, 1043, 1057, 1059, 1061, 1070, 1076, 1078, 1079, 1081,\n", + " 1082, 1086, 1090, 1093, 1101, 1105, 1111, 1114, 1116, 1132, 1136,\n", + " 1140, 1144, 1145, 1150, 1151, 1156, 1157, 1168, 1174, 1175, 1176,\n", + " 1181, 1186, 1187, 1188, 1197, 1199, 1202, 1207, 1217, 1224, 1228,\n", + " 1233, 1242, 1243, 1248, 1250, 1263, 1268, 1270, 1277, 1280, 1288,\n", + " 1297, 1307, 1309, 1310, 1323, 1333, 1335, 1340, 1349, 1359, 1375,\n", + " 1392, 1395, 1396, 1399])},\n", + " 4: {0: array([ 0, 2, 6, 8, 19, 20, 21, 22, 41, 47, 52,\n", + " 54, 55, 59, 61, 63, 65, 68, 77, 78, 88, 89,\n", + " 93, 96, 98, 101, 104, 105, 109, 112, 113, 115, 119,\n", + " 120, 121, 123, 126, 127, 131, 133, 137, 147, 150, 151,\n", + " 153, 154, 155, 181, 182, 184, 187, 194, 199, 205, 208,\n", + " 209, 226, 228, 229, 230, 231, 239, 254, 258, 260, 261,\n", + " 266, 268, 275, 276, 280, 282, 284, 292, 295, 297, 299,\n", + " 309, 316, 323, 328, 336, 337, 338, 339, 342, 348, 351,\n", + " 355, 362, 363, 370, 380, 389, 391, 392, 399, 404, 409,\n", + " 413, 432, 435, 450, 454, 474, 484, 488, 494, 497, 515,\n", + " 521, 526, 542, 553, 559, 561, 562, 563, 570, 573, 578,\n", + " 587, 590, 597, 599, 601, 605, 606, 619, 628, 631, 640,\n", + " 641, 655, 661, 665, 670, 672, 678, 680, 683, 686, 687,\n", + " 691, 692, 696, 699, 701, 702, 708, 717, 719, 720, 726,\n", + " 733, 737, 740, 744, 748, 753, 759, 763, 765, 770, 773,\n", + " 783, 786, 787, 793, 797, 802, 804, 806, 809, 810, 815,\n", + " 816, 818, 821, 825, 836, 838, 858, 859, 864, 868, 873,\n", + " 875, 879, 880, 882, 885, 888, 889, 895, 901, 904, 907,\n", + " 910, 915, 920, 928, 931, 932, 934, 935, 937, 952, 955,\n", + " 957, 967, 973, 975, 978, 980, 983, 993, 999, 1000, 1005,\n", + " 1007, 1010, 1014, 1017, 1020, 1022, 1030, 1032, 1033, 1039, 1040,\n", + " 1047, 1048, 1050, 1056, 1068, 1073, 1094, 1106, 1110, 1115, 1117,\n", + " 1118, 1122, 1123, 1124, 1125, 1127, 1129, 1134, 1138, 1141, 1142,\n", + " 1153, 1172, 1183, 1190, 1191, 1208, 1211, 1221, 1223, 1240, 1241,\n", + " 1245, 1246, 1249, 1251, 1254, 1255, 1262, 1265, 1275, 1283, 1284,\n", + " 1286, 1292, 1294, 1296, 1298, 1308, 1312, 1314, 1317, 1318, 1319,\n", + " 1327, 1342, 1344, 1348, 1350, 1352, 1353, 1354, 1358, 1360, 1361,\n", + " 1362, 1377, 1379, 1381, 1383, 1387, 1389, 1394]),\n", + " 1: array([ 1, 3, 5, 7, 9, 12, 13, 17, 18, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 36, 38, 42, 46, 48,\n", + " 49, 51, 56, 58, 60, 64, 66, 67, 70, 74, 80,\n", + " 82, 86, 87, 90, 92, 94, 97, 99, 102, 108, 111,\n", + " 114, 118, 122, 124, 128, 130, 132, 135, 138, 139, 140,\n", + " 141, 142, 143, 144, 145, 146, 148, 149, 158, 159, 162,\n", + " 166, 167, 169, 170, 172, 173, 174, 175, 177, 178, 179,\n", + " 189, 190, 192, 195, 197, 200, 201, 202, 206, 207, 211,\n", + " 212, 213, 214, 216, 218, 219, 221, 222, 227, 232, 235,\n", + " 236, 238, 240, 241, 244, 245, 246, 247, 249, 252, 253,\n", + " 255, 259, 262, 264, 265, 267, 271, 272, 273, 274, 277,\n", + " 278, 283, 288, 289, 290, 291, 294, 296, 298, 300, 301,\n", + " 302, 303, 304, 305, 306, 307, 310, 312, 314, 315, 322,\n", + " 324, 325, 327, 331, 332, 334, 335, 340, 341, 344, 352,\n", + " 353, 354, 357, 360, 361, 364, 365, 366, 368, 369, 371,\n", + " 372, 373, 376, 378, 381, 383, 384, 385, 386, 388, 390,\n", + " 394, 397, 398, 400, 401, 402, 407, 408, 411, 412, 414,\n", + " 416, 418, 419, 420, 421, 422, 423, 425, 426, 427, 428,\n", + " 429, 430, 431, 433, 434, 436, 442, 444, 445, 446, 448,\n", + " 449, 452, 453, 455, 459, 460, 462, 463, 464, 465, 467,\n", + " 468, 471, 472, 475, 476, 480, 482, 483, 485, 486, 489,\n", + " 490, 491, 492, 495, 496, 499, 500, 501, 503, 505, 508,\n", + " 509, 510, 512, 518, 520, 522, 527, 528, 529, 530, 533,\n", + " 534, 535, 537, 539, 540, 543, 545, 547, 548, 550, 551,\n", + " 552, 554, 558, 560, 564, 567, 569, 572, 574, 579, 580,\n", + " 581, 582, 589, 591, 592, 594, 596, 598, 602, 603, 604,\n", + " 607, 611, 612, 614, 615, 616, 617, 618, 620, 622, 624,\n", + " 627, 629, 633, 634, 635, 637, 639, 643, 644, 645, 647,\n", + " 648, 650, 652, 657, 658, 662, 666, 668, 673, 674, 675,\n", + " 676, 677, 681, 682, 684, 685, 688, 689, 690, 695, 697,\n", + " 700, 703, 704, 707, 709, 711, 714, 715, 716, 718, 728,\n", + " 730, 731, 732, 741, 742, 743, 747, 750, 756, 757, 761,\n", + " 762, 764, 766, 767, 769, 772, 774, 775, 777, 779, 780,\n", + " 782, 785, 789, 792, 796, 798, 799, 803, 811, 812, 814,\n", + " 819, 823, 824, 828, 830, 831, 832, 835, 837, 839, 840,\n", + " 841, 843, 844, 846, 848, 849, 853, 856, 857, 861, 867,\n", + " 869, 871, 872, 874, 876, 883, 884, 886, 887, 890, 891,\n", + " 896, 899, 905, 908, 909, 912, 913, 914, 916, 917, 918,\n", + " 919, 921, 923, 924, 926, 929, 933, 938, 939, 940, 941,\n", + " 942, 943, 944, 947, 950, 951, 953, 960, 961, 962, 964,\n", + " 965, 968, 971, 974, 979, 984, 985, 986, 987, 990, 991,\n", + " 992, 996, 997, 1001, 1003, 1006, 1009, 1011, 1013, 1021, 1023,\n", + " 1025, 1026, 1028, 1029, 1031, 1037, 1038, 1041, 1042, 1044, 1045,\n", + " 1046, 1052, 1053, 1054, 1060, 1062, 1063, 1064, 1065, 1069, 1071,\n", + " 1075, 1080, 1083, 1084, 1085, 1088, 1092, 1095, 1100, 1102, 1103,\n", + " 1108, 1109, 1113, 1119, 1121, 1126, 1130, 1131, 1135, 1137, 1146,\n", + " 1148, 1154, 1155, 1158, 1160, 1161, 1162, 1163, 1164, 1165, 1166,\n", + " 1169, 1170, 1171, 1173, 1177, 1179, 1180, 1182, 1184, 1185, 1192,\n", + " 1193, 1195, 1196, 1198, 1200, 1201, 1203, 1204, 1206, 1209, 1210,\n", + " 1212, 1214, 1218, 1219, 1220, 1225, 1226, 1227, 1230, 1231, 1232,\n", + " 1234, 1236, 1237, 1239, 1244, 1247, 1252, 1253, 1258, 1259, 1261,\n", + " 1264, 1266, 1267, 1269, 1272, 1273, 1274, 1278, 1279, 1282, 1289,\n", + " 1291, 1299, 1301, 1303, 1305, 1306, 1311, 1315, 1316, 1320, 1321,\n", + " 1322, 1324, 1326, 1328, 1329, 1330, 1332, 1334, 1336, 1337, 1339,\n", + " 1341, 1345, 1346, 1351, 1355, 1363, 1364, 1367, 1368, 1369, 1370,\n", + " 1371, 1372, 1373, 1374, 1376, 1378, 1380, 1384, 1385, 1386, 1388,\n", + " 1390, 1398]),\n", + " 2: array([ 4, 14, 24, 33, 34, 50, 57, 69, 72, 73, 81,\n", + " 83, 84, 85, 91, 95, 117, 125, 134, 136, 152, 157,\n", + " 160, 161, 171, 180, 183, 185, 188, 196, 198, 203, 204,\n", + " 217, 223, 224, 225, 237, 243, 256, 270, 279, 287, 293,\n", + " 313, 319, 320, 321, 326, 345, 349, 356, 358, 367, 377,\n", + " 379, 382, 387, 395, 406, 410, 415, 417, 440, 447, 457,\n", + " 458, 466, 469, 470, 479, 506, 514, 516, 519, 523, 524,\n", + " 525, 531, 538, 541, 549, 566, 568, 576, 577, 588, 593,\n", + " 600, 613, 621, 625, 626, 632, 638, 642, 646, 649, 651,\n", + " 653, 656, 659, 660, 663, 671, 679, 693, 706, 713, 721,\n", + " 723, 724, 734, 735, 736, 745, 749, 751, 752, 755, 771,\n", + " 781, 784, 794, 801, 805, 807, 813, 822, 827, 829, 854,\n", + " 855, 881, 892, 894, 897, 898, 900, 925, 927, 930, 945,\n", + " 948, 959, 976, 977, 988, 998, 1004, 1024, 1035, 1049, 1051,\n", + " 1055, 1058, 1066, 1067, 1072, 1074, 1077, 1087, 1089, 1091, 1096,\n", + " 1097, 1098, 1099, 1104, 1107, 1112, 1120, 1128, 1133, 1139, 1143,\n", + " 1147, 1149, 1152, 1159, 1167, 1178, 1189, 1194, 1205, 1213, 1215,\n", + " 1216, 1222, 1229, 1235, 1238, 1256, 1257, 1260, 1271, 1276, 1281,\n", + " 1285, 1287, 1290, 1293, 1295, 1300, 1302, 1304, 1313, 1325, 1331,\n", + " 1338, 1343, 1347, 1356, 1357, 1365, 1366, 1382, 1391, 1393, 1397]),\n", + " 3: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 43, 44,\n", + " 45, 53, 62, 71, 75, 76, 79, 100, 103, 106, 107,\n", + " 110, 116, 129, 156, 163, 164, 165, 168, 176, 186, 191,\n", + " 193, 210, 215, 220, 233, 234, 242, 248, 250, 251, 257,\n", + " 263, 269, 281, 285, 286, 308, 311, 317, 318, 329, 330,\n", + " 333, 343, 346, 347, 350, 359, 374, 375, 393, 396, 403,\n", + " 405, 424, 437, 438, 439, 441, 443, 451, 456, 461, 473,\n", + " 477, 478, 481, 487, 493, 498, 502, 504, 507, 511, 513,\n", + " 517, 532, 536, 544, 546, 555, 556, 557, 565, 571, 575,\n", + " 583, 584, 585, 586, 595, 608, 609, 610, 623, 630, 636,\n", + " 654, 664, 667, 669, 694, 698, 705, 710, 712, 722, 725,\n", + " 727, 729, 738, 739, 746, 754, 758, 760, 768, 776, 778,\n", + " 788, 790, 791, 795, 800, 808, 817, 820, 826, 833, 834,\n", + " 842, 845, 847, 850, 851, 852, 860, 862, 863, 865, 866,\n", + " 870, 877, 878, 893, 902, 903, 906, 911, 922, 936, 946,\n", + " 949, 954, 956, 958, 963, 966, 969, 970, 972, 981, 982,\n", + " 989, 994, 995, 1002, 1008, 1012, 1015, 1016, 1018, 1019, 1027,\n", + " 1034, 1036, 1043, 1057, 1059, 1061, 1070, 1076, 1078, 1079, 1081,\n", + " 1082, 1086, 1090, 1093, 1101, 1105, 1111, 1114, 1116, 1132, 1136,\n", + " 1140, 1144, 1145, 1150, 1151, 1156, 1157, 1168, 1174, 1175, 1176,\n", + " 1181, 1186, 1187, 1188, 1197, 1199, 1202, 1207, 1217, 1224, 1228,\n", + " 1233, 1242, 1243, 1248, 1250, 1263, 1268, 1270, 1277, 1280, 1288,\n", + " 1297, 1307, 1309, 1310, 1323, 1333, 1335, 1340, 1349, 1359, 1375,\n", + " 1392, 1395, 1396, 1399])},\n", + " 5: {0: array([ 0, 2, 20, 22, 54, 61, 65, 78, 89, 101, 105,\n", + " 112, 119, 123, 127, 133, 137, 147, 154, 182, 187, 205,\n", + " 208, 226, 230, 268, 275, 284, 309, 323, 328, 337, 342,\n", + " 355, 362, 380, 391, 413, 432, 435, 484, 488, 515, 542,\n", + " 562, 587, 590, 597, 601, 641, 655, 661, 692, 701, 719,\n", + " 733, 737, 744, 770, 773, 793, 806, 809, 810, 821, 836,\n", + " 859, 864, 879, 882, 895, 907, 915, 920, 931, 952, 955,\n", + " 957, 967, 978, 993, 999, 1007, 1017, 1032, 1068, 1094, 1115,\n", + " 1117, 1123, 1125, 1127, 1153, 1190, 1191, 1208, 1223, 1246, 1284,\n", + " 1294, 1298, 1308, 1318, 1327, 1344, 1348, 1350, 1354, 1360, 1362,\n", + " 1377, 1379, 1381, 1389, 1394]),\n", + " 1: array([ 1, 3, 5, 7, 9, 12, 13, 17, 18, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 36, 38, 42, 46, 48,\n", + " 49, 51, 56, 58, 60, 64, 66, 67, 70, 74, 80,\n", + " 82, 86, 87, 90, 92, 94, 97, 99, 102, 108, 111,\n", + " 114, 118, 122, 124, 128, 130, 132, 135, 138, 139, 140,\n", + " 141, 142, 143, 144, 145, 146, 148, 149, 158, 159, 162,\n", + " 166, 167, 169, 170, 172, 173, 174, 175, 177, 178, 179,\n", + " 189, 190, 192, 195, 197, 200, 201, 202, 206, 207, 211,\n", + " 212, 213, 214, 216, 218, 219, 221, 222, 227, 232, 235,\n", + " 236, 238, 240, 241, 244, 245, 246, 247, 249, 252, 253,\n", + " 255, 259, 262, 264, 265, 267, 271, 272, 273, 274, 277,\n", + " 278, 283, 288, 289, 290, 291, 294, 296, 298, 300, 301,\n", + " 302, 303, 304, 305, 306, 307, 310, 312, 314, 315, 322,\n", + " 324, 325, 327, 331, 332, 334, 335, 340, 341, 344, 352,\n", + " 353, 354, 357, 360, 361, 364, 365, 366, 368, 369, 371,\n", + " 372, 373, 376, 378, 381, 383, 384, 385, 386, 388, 390,\n", + " 394, 397, 398, 400, 401, 402, 407, 408, 411, 412, 414,\n", + " 416, 418, 419, 420, 421, 422, 423, 425, 426, 427, 428,\n", + " 429, 430, 431, 433, 434, 436, 442, 444, 445, 446, 448,\n", + " 449, 452, 453, 455, 459, 460, 462, 463, 464, 465, 467,\n", + " 468, 471, 472, 475, 476, 480, 482, 483, 485, 486, 489,\n", + " 490, 491, 492, 495, 496, 499, 500, 501, 503, 505, 508,\n", + " 509, 510, 512, 518, 520, 522, 527, 528, 529, 530, 533,\n", + " 534, 535, 537, 539, 540, 543, 545, 547, 548, 550, 551,\n", + " 552, 554, 558, 560, 564, 567, 569, 572, 574, 579, 580,\n", + " 581, 582, 589, 591, 592, 594, 596, 598, 602, 603, 604,\n", + " 607, 611, 612, 614, 615, 616, 617, 618, 620, 622, 624,\n", + " 627, 629, 633, 634, 635, 637, 639, 643, 644, 645, 647,\n", + " 648, 650, 652, 657, 658, 662, 666, 668, 673, 674, 675,\n", + " 676, 677, 681, 682, 684, 685, 688, 689, 690, 695, 697,\n", + " 700, 703, 704, 707, 709, 711, 714, 715, 716, 718, 728,\n", + " 730, 731, 732, 741, 742, 743, 747, 750, 756, 757, 761,\n", + " 762, 764, 766, 767, 769, 772, 774, 775, 777, 779, 780,\n", + " 782, 785, 789, 792, 796, 798, 799, 803, 811, 812, 814,\n", + " 819, 823, 824, 828, 830, 831, 832, 835, 837, 839, 840,\n", + " 841, 843, 844, 846, 848, 849, 853, 856, 857, 861, 867,\n", + " 869, 871, 872, 874, 876, 883, 884, 886, 887, 890, 891,\n", + " 896, 899, 905, 908, 909, 912, 913, 914, 916, 917, 918,\n", + " 919, 921, 923, 924, 926, 929, 933, 938, 939, 940, 941,\n", + " 942, 943, 944, 947, 950, 951, 953, 960, 961, 962, 964,\n", + " 965, 968, 971, 974, 979, 984, 985, 986, 987, 990, 991,\n", + " 992, 996, 997, 1001, 1003, 1006, 1009, 1011, 1013, 1021, 1023,\n", + " 1025, 1026, 1028, 1029, 1031, 1037, 1038, 1041, 1042, 1044, 1045,\n", + " 1046, 1052, 1053, 1054, 1060, 1062, 1063, 1064, 1065, 1069, 1071,\n", + " 1075, 1080, 1083, 1084, 1085, 1088, 1092, 1095, 1100, 1102, 1103,\n", + " 1108, 1109, 1113, 1119, 1121, 1126, 1130, 1131, 1135, 1137, 1146,\n", + " 1148, 1154, 1155, 1158, 1160, 1161, 1162, 1163, 1164, 1165, 1166,\n", + " 1169, 1170, 1171, 1173, 1177, 1179, 1180, 1182, 1184, 1185, 1192,\n", + " 1193, 1195, 1196, 1198, 1200, 1201, 1203, 1204, 1206, 1209, 1210,\n", + " 1212, 1214, 1218, 1219, 1220, 1225, 1226, 1227, 1230, 1231, 1232,\n", + " 1234, 1236, 1237, 1239, 1244, 1247, 1252, 1253, 1258, 1259, 1261,\n", + " 1264, 1266, 1267, 1269, 1272, 1273, 1274, 1278, 1279, 1282, 1289,\n", + " 1291, 1299, 1301, 1303, 1305, 1306, 1311, 1315, 1316, 1320, 1321,\n", + " 1322, 1324, 1326, 1328, 1329, 1330, 1332, 1334, 1336, 1337, 1339,\n", + " 1341, 1345, 1346, 1351, 1355, 1363, 1364, 1367, 1368, 1369, 1370,\n", + " 1371, 1372, 1373, 1374, 1376, 1378, 1380, 1384, 1385, 1386, 1388,\n", + " 1390, 1398]),\n", + " 2: array([ 4, 14, 24, 33, 34, 50, 57, 69, 72, 73, 81,\n", + " 83, 84, 85, 91, 95, 117, 125, 134, 136, 152, 157,\n", + " 160, 161, 171, 180, 183, 185, 188, 196, 198, 203, 204,\n", + " 217, 223, 224, 225, 237, 243, 256, 270, 279, 287, 293,\n", + " 313, 319, 320, 321, 326, 345, 349, 356, 358, 367, 377,\n", + " 379, 382, 387, 395, 406, 410, 415, 417, 440, 447, 457,\n", + " 458, 466, 469, 470, 479, 506, 514, 516, 519, 523, 524,\n", + " 525, 531, 538, 541, 549, 566, 568, 576, 577, 588, 593,\n", + " 600, 613, 621, 625, 626, 632, 638, 642, 646, 649, 651,\n", + " 653, 656, 659, 660, 663, 671, 679, 693, 706, 713, 721,\n", + " 723, 724, 734, 735, 736, 745, 749, 751, 752, 755, 771,\n", + " 781, 784, 794, 801, 805, 807, 813, 822, 827, 829, 854,\n", + " 855, 881, 892, 894, 897, 898, 900, 925, 927, 930, 945,\n", + " 948, 959, 976, 977, 988, 998, 1004, 1024, 1035, 1049, 1051,\n", + " 1055, 1058, 1066, 1067, 1072, 1074, 1077, 1087, 1089, 1091, 1096,\n", + " 1097, 1098, 1099, 1104, 1107, 1112, 1120, 1128, 1133, 1139, 1143,\n", + " 1147, 1149, 1152, 1159, 1167, 1178, 1189, 1194, 1205, 1213, 1215,\n", + " 1216, 1222, 1229, 1235, 1238, 1256, 1257, 1260, 1271, 1276, 1281,\n", + " 1285, 1287, 1290, 1293, 1295, 1300, 1302, 1304, 1313, 1325, 1331,\n", + " 1338, 1343, 1347, 1356, 1357, 1365, 1366, 1382, 1391, 1393, 1397]),\n", + " 3: array([ 6, 8, 19, 21, 41, 47, 52, 55, 59, 63, 68,\n", + " 77, 88, 93, 96, 98, 104, 109, 113, 115, 120, 121,\n", + " 126, 131, 150, 151, 153, 155, 181, 184, 194, 199, 209,\n", + " 228, 229, 231, 239, 254, 258, 260, 261, 266, 276, 280,\n", + " 282, 292, 295, 297, 299, 316, 336, 338, 339, 348, 351,\n", + " 363, 370, 389, 392, 399, 404, 409, 450, 454, 474, 494,\n", + " 497, 521, 526, 553, 559, 561, 563, 570, 573, 578, 599,\n", + " 605, 606, 619, 628, 631, 640, 665, 670, 672, 678, 680,\n", + " 683, 686, 687, 691, 696, 699, 702, 708, 717, 720, 726,\n", + " 740, 748, 753, 759, 763, 765, 783, 786, 787, 797, 802,\n", + " 804, 815, 816, 818, 825, 838, 858, 868, 873, 875, 880,\n", + " 885, 888, 889, 901, 904, 910, 928, 932, 934, 935, 937,\n", + " 973, 975, 980, 983, 1000, 1005, 1010, 1014, 1020, 1022, 1030,\n", + " 1033, 1039, 1040, 1047, 1048, 1050, 1056, 1073, 1106, 1110, 1118,\n", + " 1122, 1124, 1129, 1134, 1138, 1141, 1142, 1172, 1183, 1211, 1221,\n", + " 1240, 1241, 1245, 1249, 1251, 1254, 1255, 1262, 1265, 1275, 1283,\n", + " 1286, 1292, 1296, 1312, 1314, 1317, 1319, 1342, 1352, 1353, 1358,\n", + " 1361, 1383, 1387]),\n", + " 4: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 43, 44,\n", + " 45, 53, 62, 71, 75, 76, 79, 100, 103, 106, 107,\n", + " 110, 116, 129, 156, 163, 164, 165, 168, 176, 186, 191,\n", + " 193, 210, 215, 220, 233, 234, 242, 248, 250, 251, 257,\n", + " 263, 269, 281, 285, 286, 308, 311, 317, 318, 329, 330,\n", + " 333, 343, 346, 347, 350, 359, 374, 375, 393, 396, 403,\n", + " 405, 424, 437, 438, 439, 441, 443, 451, 456, 461, 473,\n", + " 477, 478, 481, 487, 493, 498, 502, 504, 507, 511, 513,\n", + " 517, 532, 536, 544, 546, 555, 556, 557, 565, 571, 575,\n", + " 583, 584, 585, 586, 595, 608, 609, 610, 623, 630, 636,\n", + " 654, 664, 667, 669, 694, 698, 705, 710, 712, 722, 725,\n", + " 727, 729, 738, 739, 746, 754, 758, 760, 768, 776, 778,\n", + " 788, 790, 791, 795, 800, 808, 817, 820, 826, 833, 834,\n", + " 842, 845, 847, 850, 851, 852, 860, 862, 863, 865, 866,\n", + " 870, 877, 878, 893, 902, 903, 906, 911, 922, 936, 946,\n", + " 949, 954, 956, 958, 963, 966, 969, 970, 972, 981, 982,\n", + " 989, 994, 995, 1002, 1008, 1012, 1015, 1016, 1018, 1019, 1027,\n", + " 1034, 1036, 1043, 1057, 1059, 1061, 1070, 1076, 1078, 1079, 1081,\n", + " 1082, 1086, 1090, 1093, 1101, 1105, 1111, 1114, 1116, 1132, 1136,\n", + " 1140, 1144, 1145, 1150, 1151, 1156, 1157, 1168, 1174, 1175, 1176,\n", + " 1181, 1186, 1187, 1188, 1197, 1199, 1202, 1207, 1217, 1224, 1228,\n", + " 1233, 1242, 1243, 1248, 1250, 1263, 1268, 1270, 1277, 1280, 1288,\n", + " 1297, 1307, 1309, 1310, 1323, 1333, 1335, 1340, 1349, 1359, 1375,\n", + " 1392, 1395, 1396, 1399])},\n", + " 6: {0: array([ 0, 2, 20, 22, 54, 61, 65, 78, 89, 101, 105,\n", + " 112, 119, 123, 127, 133, 137, 147, 154, 182, 187, 205,\n", + " 208, 226, 230, 268, 275, 284, 309, 323, 328, 337, 342,\n", + " 355, 362, 380, 391, 413, 432, 435, 484, 488, 515, 542,\n", + " 562, 587, 590, 597, 601, 641, 655, 661, 692, 701, 719,\n", + " 733, 737, 744, 770, 773, 793, 806, 809, 810, 821, 836,\n", + " 859, 864, 879, 882, 895, 907, 915, 920, 931, 952, 955,\n", + " 957, 967, 978, 993, 999, 1007, 1017, 1032, 1068, 1094, 1115,\n", + " 1117, 1123, 1125, 1127, 1153, 1190, 1191, 1208, 1223, 1246, 1284,\n", + " 1294, 1298, 1308, 1318, 1327, 1344, 1348, 1350, 1354, 1360, 1362,\n", + " 1377, 1379, 1381, 1389, 1394]),\n", + " 1: array([ 1, 3, 5, 7, 9, 12, 13, 17, 18, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 36, 38, 42, 46, 48,\n", + " 49, 51, 56, 58, 60, 64, 66, 67, 70, 74, 80,\n", + " 82, 86, 87, 90, 92, 94, 97, 99, 102, 108, 111,\n", + " 114, 118, 122, 124, 128, 130, 132, 135, 138, 139, 140,\n", + " 141, 142, 143, 144, 145, 146, 148, 149, 158, 159, 162,\n", + " 166, 167, 169, 170, 172, 173, 174, 175, 177, 178, 179,\n", + " 189, 190, 192, 195, 197, 200, 201, 202, 206, 207, 211,\n", + " 212, 213, 214, 216, 218, 219, 221, 222, 227, 232, 235,\n", + " 236, 238, 240, 241, 244, 245, 246, 247, 249, 252, 253,\n", + " 255, 259, 262, 264, 265, 267, 271, 272, 273, 274, 277,\n", + " 278, 283, 288, 289, 290, 291, 294, 296, 298, 300, 301,\n", + " 302, 303, 304, 305, 306, 307, 310, 312, 314, 315, 322,\n", + " 324, 325, 327, 331, 332, 334, 335, 340, 341, 344, 352,\n", + " 353, 354, 357, 360, 361, 364, 365, 366, 368, 369, 371,\n", + " 372, 373, 376, 378, 381, 383, 384, 385, 386, 388, 390,\n", + " 394, 397, 398, 400, 401, 402, 407, 408, 411, 412, 414,\n", + " 416, 418, 419, 420, 421, 422, 423, 425, 426, 427, 428,\n", + " 429, 430, 431, 433, 434, 436, 442, 444, 445, 446, 448,\n", + " 449, 452, 453, 455, 459, 460, 462, 463, 464, 465, 467,\n", + " 468, 471, 472, 475, 476, 480, 482, 483, 485, 486, 489,\n", + " 490, 491, 492, 495, 496, 499, 500, 501, 503, 505, 508,\n", + " 509, 510, 512, 518, 520, 522, 527, 528, 529, 530, 533,\n", + " 534, 535, 537, 539, 540, 543, 545, 547, 548, 550, 551,\n", + " 552, 554, 558, 560, 564, 567, 569, 572, 574, 579, 580,\n", + " 581, 582, 589, 591, 592, 594, 596, 598, 602, 603, 604,\n", + " 607, 611, 612, 614, 615, 616, 617, 618, 620, 622, 624,\n", + " 627, 629, 633, 634, 635, 637, 639, 643, 644, 645, 647,\n", + " 648, 650, 652, 657, 658, 662, 666, 668, 673, 674, 675,\n", + " 676, 677, 681, 682, 684, 685, 688, 689, 690, 695, 697,\n", + " 700, 703, 704, 707, 709, 711, 714, 715, 716, 718, 728,\n", + " 730, 731, 732, 741, 742, 743, 747, 750, 756, 757, 761,\n", + " 762, 764, 766, 767, 769, 772, 774, 775, 777, 779, 780,\n", + " 782, 785, 789, 792, 796, 798, 799, 803, 811, 812, 814,\n", + " 819, 823, 824, 828, 830, 831, 832, 835, 837, 839, 840,\n", + " 841, 843, 844, 846, 848, 849, 853, 856, 857, 861, 867,\n", + " 869, 871, 872, 874, 876, 883, 884, 886, 887, 890, 891,\n", + " 896, 899, 905, 908, 909, 912, 913, 914, 916, 917, 918,\n", + " 919, 921, 923, 924, 926, 929, 933, 938, 939, 940, 941,\n", + " 942, 943, 944, 947, 950, 951, 953, 960, 961, 962, 964,\n", + " 965, 968, 971, 974, 979, 984, 985, 986, 987, 990, 991,\n", + " 992, 996, 997, 1001, 1003, 1006, 1009, 1011, 1013, 1021, 1023,\n", + " 1025, 1026, 1028, 1029, 1031, 1037, 1038, 1041, 1042, 1044, 1045,\n", + " 1046, 1052, 1053, 1054, 1060, 1062, 1063, 1064, 1065, 1069, 1071,\n", + " 1075, 1080, 1083, 1084, 1085, 1088, 1092, 1095, 1100, 1102, 1103,\n", + " 1108, 1109, 1113, 1119, 1121, 1126, 1130, 1131, 1135, 1137, 1146,\n", + " 1148, 1154, 1155, 1158, 1160, 1161, 1162, 1163, 1164, 1165, 1166,\n", + " 1169, 1170, 1171, 1173, 1177, 1179, 1180, 1182, 1184, 1185, 1192,\n", + " 1193, 1195, 1196, 1198, 1200, 1201, 1203, 1204, 1206, 1209, 1210,\n", + " 1212, 1214, 1218, 1219, 1220, 1225, 1226, 1227, 1230, 1231, 1232,\n", + " 1234, 1236, 1237, 1239, 1244, 1247, 1252, 1253, 1258, 1259, 1261,\n", + " 1264, 1266, 1267, 1269, 1272, 1273, 1274, 1278, 1279, 1282, 1289,\n", + " 1291, 1299, 1301, 1303, 1305, 1306, 1311, 1315, 1316, 1320, 1321,\n", + " 1322, 1324, 1326, 1328, 1329, 1330, 1332, 1334, 1336, 1337, 1339,\n", + " 1341, 1345, 1346, 1351, 1355, 1363, 1364, 1367, 1368, 1369, 1370,\n", + " 1371, 1372, 1373, 1374, 1376, 1378, 1380, 1384, 1385, 1386, 1388,\n", + " 1390, 1398]),\n", + " 2: array([ 4, 14, 24, 33, 34, 50, 57, 69, 72, 73, 81,\n", + " 83, 84, 85, 91, 95, 117, 125, 134, 136, 152, 157,\n", + " 160, 161, 171, 180, 183, 185, 188, 196, 198, 203, 204,\n", + " 217, 223, 224, 225, 237, 243, 256, 270, 279, 287, 293,\n", + " 313, 319, 320, 321, 326, 345, 349, 356, 358, 367, 377,\n", + " 379, 382, 387, 395, 406, 410, 415, 417, 440, 447, 457,\n", + " 458, 466, 469, 470, 479, 506, 514, 516, 519, 523, 524,\n", + " 525, 531, 538, 541, 549, 566, 568, 576, 577, 588, 593,\n", + " 600, 613, 621, 625, 626, 632, 638, 642, 646, 649, 651,\n", + " 653, 656, 659, 660, 663, 671, 679, 693, 706, 713, 721,\n", + " 723, 724, 734, 735, 736, 745, 749, 751, 752, 755, 771,\n", + " 781, 784, 794, 801, 805, 807, 813, 822, 827, 829, 854,\n", + " 855, 881, 892, 894, 897, 898, 900, 925, 927, 930, 945,\n", + " 948, 959, 976, 977, 988, 998, 1004, 1024, 1035, 1049, 1051,\n", + " 1055, 1058, 1066, 1067, 1072, 1074, 1077, 1087, 1089, 1091, 1096,\n", + " 1097, 1098, 1099, 1104, 1107, 1112, 1120, 1128, 1133, 1139, 1143,\n", + " 1147, 1149, 1152, 1159, 1167, 1178, 1189, 1194, 1205, 1213, 1215,\n", + " 1216, 1222, 1229, 1235, 1238, 1256, 1257, 1260, 1271, 1276, 1281,\n", + " 1285, 1287, 1290, 1293, 1295, 1300, 1302, 1304, 1313, 1325, 1331,\n", + " 1338, 1343, 1347, 1356, 1357, 1365, 1366, 1382, 1391, 1393, 1397]),\n", + " 3: array([ 6, 41, 126, 131, 209, 229, 260, 266, 292, 339, 370,\n", + " 450, 570, 599, 631, 665, 670, 678, 687, 759, 763, 765,\n", + " 783, 787, 804, 816, 888, 889, 904, 932, 935, 1010, 1022,\n", + " 1033, 1129, 1211, 1221, 1292, 1317, 1352]),\n", + " 4: array([ 8, 19, 21, 47, 52, 55, 59, 63, 68, 77, 88,\n", + " 93, 96, 98, 104, 109, 113, 115, 120, 121, 150, 151,\n", + " 153, 155, 181, 184, 194, 199, 228, 231, 239, 254, 258,\n", + " 261, 276, 280, 282, 295, 297, 299, 316, 336, 338, 348,\n", + " 351, 363, 389, 392, 399, 404, 409, 454, 474, 494, 497,\n", + " 521, 526, 553, 559, 561, 563, 573, 578, 605, 606, 619,\n", + " 628, 640, 672, 680, 683, 686, 691, 696, 699, 702, 708,\n", + " 717, 720, 726, 740, 748, 753, 786, 797, 802, 815, 818,\n", + " 825, 838, 858, 868, 873, 875, 880, 885, 901, 910, 928,\n", + " 934, 937, 973, 975, 980, 983, 1000, 1005, 1014, 1020, 1030,\n", + " 1039, 1040, 1047, 1048, 1050, 1056, 1073, 1106, 1110, 1118, 1122,\n", + " 1124, 1134, 1138, 1141, 1142, 1172, 1183, 1240, 1241, 1245, 1249,\n", + " 1251, 1254, 1255, 1262, 1265, 1275, 1283, 1286, 1296, 1312, 1314,\n", + " 1319, 1342, 1353, 1358, 1361, 1383, 1387]),\n", + " 5: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 43, 44,\n", + " 45, 53, 62, 71, 75, 76, 79, 100, 103, 106, 107,\n", + " 110, 116, 129, 156, 163, 164, 165, 168, 176, 186, 191,\n", + " 193, 210, 215, 220, 233, 234, 242, 248, 250, 251, 257,\n", + " 263, 269, 281, 285, 286, 308, 311, 317, 318, 329, 330,\n", + " 333, 343, 346, 347, 350, 359, 374, 375, 393, 396, 403,\n", + " 405, 424, 437, 438, 439, 441, 443, 451, 456, 461, 473,\n", + " 477, 478, 481, 487, 493, 498, 502, 504, 507, 511, 513,\n", + " 517, 532, 536, 544, 546, 555, 556, 557, 565, 571, 575,\n", + " 583, 584, 585, 586, 595, 608, 609, 610, 623, 630, 636,\n", + " 654, 664, 667, 669, 694, 698, 705, 710, 712, 722, 725,\n", + " 727, 729, 738, 739, 746, 754, 758, 760, 768, 776, 778,\n", + " 788, 790, 791, 795, 800, 808, 817, 820, 826, 833, 834,\n", + " 842, 845, 847, 850, 851, 852, 860, 862, 863, 865, 866,\n", + " 870, 877, 878, 893, 902, 903, 906, 911, 922, 936, 946,\n", + " 949, 954, 956, 958, 963, 966, 969, 970, 972, 981, 982,\n", + " 989, 994, 995, 1002, 1008, 1012, 1015, 1016, 1018, 1019, 1027,\n", + " 1034, 1036, 1043, 1057, 1059, 1061, 1070, 1076, 1078, 1079, 1081,\n", + " 1082, 1086, 1090, 1093, 1101, 1105, 1111, 1114, 1116, 1132, 1136,\n", + " 1140, 1144, 1145, 1150, 1151, 1156, 1157, 1168, 1174, 1175, 1176,\n", + " 1181, 1186, 1187, 1188, 1197, 1199, 1202, 1207, 1217, 1224, 1228,\n", + " 1233, 1242, 1243, 1248, 1250, 1263, 1268, 1270, 1277, 1280, 1288,\n", + " 1297, 1307, 1309, 1310, 1323, 1333, 1335, 1340, 1349, 1359, 1375,\n", + " 1392, 1395, 1396, 1399])},\n", + " 7: {0: array([ 0, 2, 20, 22, 54, 61, 65, 78, 89, 101, 105,\n", + " 112, 119, 123, 127, 133, 137, 147, 154, 182, 187, 205,\n", + " 208, 226, 230, 268, 275, 284, 309, 323, 328, 337, 342,\n", + " 355, 362, 380, 391, 413, 432, 435, 484, 488, 515, 542,\n", + " 562, 587, 590, 597, 601, 641, 655, 661, 692, 701, 719,\n", + " 733, 737, 744, 770, 773, 793, 806, 809, 810, 821, 836,\n", + " 859, 864, 879, 882, 895, 907, 915, 920, 931, 952, 955,\n", + " 957, 967, 978, 993, 999, 1007, 1017, 1032, 1068, 1094, 1115,\n", + " 1117, 1123, 1125, 1127, 1153, 1190, 1191, 1208, 1223, 1246, 1284,\n", + " 1294, 1298, 1308, 1318, 1327, 1344, 1348, 1350, 1354, 1360, 1362,\n", + " 1377, 1379, 1381, 1389, 1394]),\n", + " 1: array([ 1, 3, 5, 7, 9, 12, 13, 17, 18, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 36, 38, 42, 46, 48,\n", + " 49, 51, 56, 58, 60, 64, 66, 67, 70, 74, 80,\n", + " 82, 86, 87, 90, 92, 94, 97, 99, 102, 108, 111,\n", + " 114, 118, 122, 124, 128, 130, 132, 135, 138, 139, 140,\n", + " 141, 142, 143, 144, 145, 146, 148, 149, 158, 159, 162,\n", + " 166, 167, 169, 170, 172, 173, 174, 175, 177, 178, 179,\n", + " 189, 190, 192, 195, 197, 200, 201, 202, 206, 207, 211,\n", + " 212, 213, 214, 216, 218, 219, 221, 222, 227, 232, 235,\n", + " 236, 238, 240, 241, 244, 245, 246, 247, 249, 252, 253,\n", + " 255, 259, 262, 264, 265, 267, 271, 272, 273, 274, 277,\n", + " 278, 283, 288, 289, 290, 291, 294, 296, 298, 300, 301,\n", + " 302, 303, 304, 305, 306, 307, 310, 312, 314, 315, 322,\n", + " 324, 325, 327, 331, 332, 334, 335, 340, 341, 344, 352,\n", + " 353, 354, 357, 360, 361, 364, 365, 366, 368, 369, 371,\n", + " 372, 373, 376, 378, 381, 383, 384, 385, 386, 388, 390,\n", + " 394, 397, 398, 400, 401, 402, 407, 408, 411, 412, 414,\n", + " 416, 418, 419, 420, 421, 422, 423, 425, 426, 427, 428,\n", + " 429, 430, 431, 433, 434, 436, 442, 444, 445, 446, 448,\n", + " 449, 452, 453, 455, 459, 460, 462, 463, 464, 465, 467,\n", + " 468, 471, 472, 475, 476, 480, 482, 483, 485, 486, 489,\n", + " 490, 491, 492, 495, 496, 499, 500, 501, 503, 505, 508,\n", + " 509, 510, 512, 518, 520, 522, 527, 528, 529, 530, 533,\n", + " 534, 535, 537, 539, 540, 543, 545, 547, 548, 550, 551,\n", + " 552, 554, 558, 560, 564, 567, 569, 572, 574, 579, 580,\n", + " 581, 582, 589, 591, 592, 594, 596, 598, 602, 603, 604,\n", + " 607, 611, 612, 614, 615, 616, 617, 618, 620, 622, 624,\n", + " 627, 629, 633, 634, 635, 637, 639, 643, 644, 645, 647,\n", + " 648, 650, 652, 657, 658, 662, 666, 668, 673, 674, 675,\n", + " 676, 677, 681, 682, 684, 685, 688, 689, 690, 695, 697,\n", + " 700, 703, 704, 707, 709, 711, 714, 715, 716, 718, 728,\n", + " 730, 731, 732, 741, 742, 743, 747, 750, 756, 757, 761,\n", + " 762, 764, 766, 767, 769, 772, 774, 775, 777, 779, 780,\n", + " 782, 785, 789, 792, 796, 798, 799, 803, 811, 812, 814,\n", + " 819, 823, 824, 828, 830, 831, 832, 835, 837, 839, 840,\n", + " 841, 843, 844, 846, 848, 849, 853, 856, 857, 861, 867,\n", + " 869, 871, 872, 874, 876, 883, 884, 886, 887, 890, 891,\n", + " 896, 899, 905, 908, 909, 912, 913, 914, 916, 917, 918,\n", + " 919, 921, 923, 924, 926, 929, 933, 938, 939, 940, 941,\n", + " 942, 943, 944, 947, 950, 951, 953, 960, 961, 962, 964,\n", + " 965, 968, 971, 974, 979, 984, 985, 986, 987, 990, 991,\n", + " 992, 996, 997, 1001, 1003, 1006, 1009, 1011, 1013, 1021, 1023,\n", + " 1025, 1026, 1028, 1029, 1031, 1037, 1038, 1041, 1042, 1044, 1045,\n", + " 1046, 1052, 1053, 1054, 1060, 1062, 1063, 1064, 1065, 1069, 1071,\n", + " 1075, 1080, 1083, 1084, 1085, 1088, 1092, 1095, 1100, 1102, 1103,\n", + " 1108, 1109, 1113, 1119, 1121, 1126, 1130, 1131, 1135, 1137, 1146,\n", + " 1148, 1154, 1155, 1158, 1160, 1161, 1162, 1163, 1164, 1165, 1166,\n", + " 1169, 1170, 1171, 1173, 1177, 1179, 1180, 1182, 1184, 1185, 1192,\n", + " 1193, 1195, 1196, 1198, 1200, 1201, 1203, 1204, 1206, 1209, 1210,\n", + " 1212, 1214, 1218, 1219, 1220, 1225, 1226, 1227, 1230, 1231, 1232,\n", + " 1234, 1236, 1237, 1239, 1244, 1247, 1252, 1253, 1258, 1259, 1261,\n", + " 1264, 1266, 1267, 1269, 1272, 1273, 1274, 1278, 1279, 1282, 1289,\n", + " 1291, 1299, 1301, 1303, 1305, 1306, 1311, 1315, 1316, 1320, 1321,\n", + " 1322, 1324, 1326, 1328, 1329, 1330, 1332, 1334, 1336, 1337, 1339,\n", + " 1341, 1345, 1346, 1351, 1355, 1363, 1364, 1367, 1368, 1369, 1370,\n", + " 1371, 1372, 1373, 1374, 1376, 1378, 1380, 1384, 1385, 1386, 1388,\n", + " 1390, 1398]),\n", + " 2: array([ 4, 14, 24, 33, 34, 50, 57, 69, 72, 73, 81,\n", + " 83, 84, 85, 91, 95, 117, 125, 134, 136, 152, 157,\n", + " 160, 161, 171, 180, 183, 185, 188, 196, 198, 203, 204,\n", + " 217, 223, 224, 225, 237, 243, 256, 270, 279, 287, 293,\n", + " 313, 319, 320, 321, 326, 345, 349, 356, 358, 367, 377,\n", + " 379, 382, 387, 395, 406, 410, 415, 417, 440, 447, 457,\n", + " 458, 466, 469, 470, 479, 506, 514, 516, 519, 523, 524,\n", + " 525, 531, 538, 541, 549, 566, 568, 576, 577, 588, 593,\n", + " 600, 613, 621, 625, 626, 632, 638, 642, 646, 649, 651,\n", + " 653, 656, 659, 660, 663, 671, 679, 693, 706, 713, 721,\n", + " 723, 724, 734, 735, 736, 745, 749, 751, 752, 755, 771,\n", + " 781, 784, 794, 801, 805, 807, 813, 822, 827, 829, 854,\n", + " 855, 881, 892, 894, 897, 898, 900, 925, 927, 930, 945,\n", + " 948, 959, 976, 977, 988, 998, 1004, 1024, 1035, 1049, 1051,\n", + " 1055, 1058, 1066, 1067, 1072, 1074, 1077, 1087, 1089, 1091, 1096,\n", + " 1097, 1098, 1099, 1104, 1107, 1112, 1120, 1128, 1133, 1139, 1143,\n", + " 1147, 1149, 1152, 1159, 1167, 1178, 1189, 1194, 1205, 1213, 1215,\n", + " 1216, 1222, 1229, 1235, 1238, 1256, 1257, 1260, 1271, 1276, 1281,\n", + " 1285, 1287, 1290, 1293, 1295, 1300, 1302, 1304, 1313, 1325, 1331,\n", + " 1338, 1343, 1347, 1356, 1357, 1365, 1366, 1382, 1391, 1393, 1397]),\n", + " 3: array([ 6, 41, 126, 131, 209, 229, 260, 266, 292, 339, 370,\n", + " 450, 570, 599, 631, 665, 670, 678, 687, 759, 763, 765,\n", + " 783, 787, 804, 816, 888, 889, 904, 932, 935, 1010, 1022,\n", + " 1033, 1129, 1211, 1221, 1292, 1317, 1352]),\n", + " 4: array([ 8, 19, 21, 47, 52, 55, 59, 63, 68, 77, 88,\n", + " 93, 96, 98, 104, 109, 113, 115, 120, 121, 150, 151,\n", + " 153, 155, 181, 184, 194, 199, 228, 231, 239, 254, 258,\n", + " 261, 276, 280, 282, 295, 297, 299, 316, 336, 338, 348,\n", + " 351, 363, 389, 392, 399, 404, 409, 454, 474, 494, 497,\n", + " 521, 526, 553, 559, 561, 563, 573, 578, 605, 606, 619,\n", + " 628, 640, 672, 680, 683, 686, 691, 696, 699, 702, 708,\n", + " 717, 720, 726, 740, 748, 753, 786, 797, 802, 815, 818,\n", + " 825, 838, 858, 868, 873, 875, 880, 885, 901, 910, 928,\n", + " 934, 937, 973, 975, 980, 983, 1000, 1005, 1014, 1020, 1030,\n", + " 1039, 1040, 1047, 1048, 1050, 1056, 1073, 1106, 1110, 1118, 1122,\n", + " 1124, 1134, 1138, 1141, 1142, 1172, 1183, 1240, 1241, 1245, 1249,\n", + " 1251, 1254, 1255, 1262, 1265, 1275, 1283, 1286, 1296, 1312, 1314,\n", + " 1319, 1342, 1353, 1358, 1361, 1383, 1387]),\n", + " 5: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 43, 44,\n", + " 45, 53, 76, 79, 103, 106, 107, 110, 116, 129, 156,\n", + " 163, 164, 165, 168, 186, 191, 193, 210, 215, 220, 233,\n", + " 234, 242, 248, 250, 251, 257, 263, 269, 281, 285, 286,\n", + " 311, 318, 329, 343, 346, 347, 350, 374, 375, 393, 396,\n", + " 403, 405, 424, 437, 438, 439, 456, 461, 473, 477, 478,\n", + " 487, 493, 502, 504, 507, 511, 513, 517, 532, 536, 544,\n", + " 546, 555, 556, 557, 565, 575, 583, 608, 609, 623, 630,\n", + " 636, 654, 664, 667, 669, 694, 698, 705, 710, 712, 722,\n", + " 725, 727, 739, 746, 758, 760, 768, 776, 778, 788, 790,\n", + " 800, 817, 820, 833, 834, 842, 845, 847, 850, 851, 852,\n", + " 860, 862, 863, 865, 877, 878, 893, 903, 906, 911, 922,\n", + " 936, 946, 954, 956, 963, 966, 969, 970, 972, 981, 982,\n", + " 989, 994, 1002, 1008, 1015, 1016, 1019, 1027, 1034, 1036, 1043,\n", + " 1057, 1061, 1070, 1076, 1079, 1082, 1086, 1101, 1105, 1111, 1132,\n", + " 1136, 1144, 1145, 1150, 1151, 1157, 1168, 1174, 1175, 1186, 1188,\n", + " 1197, 1199, 1202, 1217, 1224, 1228, 1243, 1268, 1270, 1277, 1280,\n", + " 1297, 1309, 1323, 1333, 1335, 1349, 1359, 1375, 1392, 1396, 1399]),\n", + " 6: array([ 62, 71, 75, 100, 176, 308, 317, 330, 333, 359, 441,\n", + " 443, 451, 481, 498, 571, 584, 585, 586, 595, 610, 729,\n", + " 738, 754, 791, 795, 808, 826, 866, 870, 902, 949, 958,\n", + " 995, 1012, 1018, 1059, 1078, 1081, 1090, 1093, 1114, 1116, 1140,\n", + " 1156, 1176, 1181, 1187, 1207, 1233, 1242, 1248, 1250, 1263, 1288,\n", + " 1307, 1310, 1340, 1395])},\n", + " 8: {0: array([ 0, 2, 20, 22, 54, 61, 65, 78, 89, 101, 105,\n", + " 112, 119, 123, 127, 133, 137, 147, 154, 182, 187, 205,\n", + " 208, 226, 230, 268, 275, 284, 309, 323, 328, 337, 342,\n", + " 355, 362, 380, 391, 413, 432, 435, 484, 488, 515, 542,\n", + " 562, 587, 590, 597, 601, 641, 655, 661, 692, 701, 719,\n", + " 733, 737, 744, 770, 773, 793, 806, 809, 810, 821, 836,\n", + " 859, 864, 879, 882, 895, 907, 915, 920, 931, 952, 955,\n", + " 957, 967, 978, 993, 999, 1007, 1017, 1032, 1068, 1094, 1115,\n", + " 1117, 1123, 1125, 1127, 1153, 1190, 1191, 1208, 1223, 1246, 1284,\n", + " 1294, 1298, 1308, 1318, 1327, 1344, 1348, 1350, 1354, 1360, 1362,\n", + " 1377, 1379, 1381, 1389, 1394]),\n", + " 1: array([ 1, 3, 5, 7, 9, 12, 13, 17, 18, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 36, 38, 42, 46, 48,\n", + " 49, 51, 56, 58, 60, 64, 66, 67, 70, 74, 80,\n", + " 82, 86, 87, 90, 92, 94, 97, 99, 102, 108, 111,\n", + " 114, 118, 122, 124, 128, 130, 132, 135, 138, 139, 140,\n", + " 141, 142, 143, 144, 145, 146, 148, 149, 158, 159, 162,\n", + " 166, 167, 169, 170, 172, 173, 174, 175, 177, 178, 179,\n", + " 189, 190, 192, 195, 197, 200, 201, 202, 206, 207, 211,\n", + " 212, 213, 214, 216, 218, 219, 221, 222, 227, 232, 235,\n", + " 236, 238, 240, 241, 244, 245, 246, 247, 249, 252, 253,\n", + " 255, 259, 262, 264, 265, 267, 271, 272, 273, 274, 277,\n", + " 278, 283, 288, 289, 290, 291, 294, 296, 298, 300, 301,\n", + " 302, 303, 304, 305, 306, 307, 310, 312, 314, 315, 322,\n", + " 324, 325, 327, 331, 332, 334, 335, 340, 341, 344, 352,\n", + " 353, 354, 357, 360, 361, 364, 365, 366, 368, 369, 371,\n", + " 372, 373, 376, 378, 381, 383, 384, 385, 386, 388, 390,\n", + " 394, 397, 398, 400, 401, 402, 407, 408, 411, 412, 414,\n", + " 416, 418, 419, 420, 421, 422, 423, 425, 426, 427, 428,\n", + " 429, 430, 431, 433, 434, 436, 442, 444, 445, 446, 448,\n", + " 449, 452, 453, 455, 459, 460, 462, 463, 464, 465, 467,\n", + " 468, 471, 472, 475, 476, 480, 482, 483, 485, 486, 489,\n", + " 490, 491, 492, 495, 496, 499, 500, 501, 503, 505, 508,\n", + " 509, 510, 512, 518, 520, 522, 527, 528, 529, 530, 533,\n", + " 534, 535, 537, 539, 540, 543, 545, 547, 548, 550, 551,\n", + " 552, 554, 558, 560, 564, 567, 569, 572, 574, 579, 580,\n", + " 581, 582, 589, 591, 592, 594, 596, 598, 602, 603, 604,\n", + " 607, 611, 612, 614, 615, 616, 617, 618, 620, 622, 624,\n", + " 627, 629, 633, 634, 635, 637, 639, 643, 644, 645, 647,\n", + " 648, 650, 652, 657, 658, 662, 666, 668, 673, 674, 675,\n", + " 676, 677, 681, 682, 684, 685, 688, 689, 690, 695, 697,\n", + " 700, 703, 704, 707, 709, 711, 714, 715, 716, 718, 728,\n", + " 730, 731, 732, 741, 742, 743, 747, 750, 756, 757, 761,\n", + " 762, 764, 766, 767, 769, 772, 774, 775, 777, 779, 780,\n", + " 782, 785, 789, 792, 796, 798, 799, 803, 811, 812, 814,\n", + " 819, 823, 824, 828, 830, 831, 832, 835, 837, 839, 840,\n", + " 841, 843, 844, 846, 848, 849, 853, 856, 857, 861, 867,\n", + " 869, 871, 872, 874, 876, 883, 884, 886, 887, 890, 891,\n", + " 896, 899, 905, 908, 909, 912, 913, 914, 916, 917, 918,\n", + " 919, 921, 923, 924, 926, 929, 933, 938, 939, 940, 941,\n", + " 942, 943, 944, 947, 950, 951, 953, 960, 961, 962, 964,\n", + " 965, 968, 971, 974, 979, 984, 985, 986, 987, 990, 991,\n", + " 992, 996, 997, 1001, 1003, 1006, 1009, 1011, 1013, 1021, 1023,\n", + " 1025, 1026, 1028, 1029, 1031, 1037, 1038, 1041, 1042, 1044, 1045,\n", + " 1046, 1052, 1053, 1054, 1060, 1062, 1063, 1064, 1065, 1069, 1071,\n", + " 1075, 1080, 1083, 1084, 1085, 1088, 1092, 1095, 1100, 1102, 1103,\n", + " 1108, 1109, 1113, 1119, 1121, 1126, 1130, 1131, 1135, 1137, 1146,\n", + " 1148, 1154, 1155, 1158, 1160, 1161, 1162, 1163, 1164, 1165, 1166,\n", + " 1169, 1170, 1171, 1173, 1177, 1179, 1180, 1182, 1184, 1185, 1192,\n", + " 1193, 1195, 1196, 1198, 1200, 1201, 1203, 1204, 1206, 1209, 1210,\n", + " 1212, 1214, 1218, 1219, 1220, 1225, 1226, 1227, 1230, 1231, 1232,\n", + " 1234, 1236, 1237, 1239, 1244, 1247, 1252, 1253, 1258, 1259, 1261,\n", + " 1264, 1266, 1267, 1269, 1272, 1273, 1274, 1278, 1279, 1282, 1289,\n", + " 1291, 1299, 1301, 1303, 1305, 1306, 1311, 1315, 1316, 1320, 1321,\n", + " 1322, 1324, 1326, 1328, 1329, 1330, 1332, 1334, 1336, 1337, 1339,\n", + " 1341, 1345, 1346, 1351, 1355, 1363, 1364, 1367, 1368, 1369, 1370,\n", + " 1371, 1372, 1373, 1374, 1376, 1378, 1380, 1384, 1385, 1386, 1388,\n", + " 1390, 1398]),\n", + " 2: array([ 4, 14, 24, 33, 34, 50, 57, 69, 72, 73, 81,\n", + " 83, 84, 85, 91, 95, 117, 125, 134, 136, 152, 157,\n", + " 160, 161, 171, 183, 185, 198, 203, 204, 217, 224, 225,\n", + " 237, 243, 256, 270, 287, 293, 313, 319, 320, 321, 326,\n", + " 345, 356, 358, 367, 377, 379, 382, 387, 395, 406, 410,\n", + " 415, 417, 440, 447, 457, 458, 466, 469, 470, 479, 506,\n", + " 514, 516, 519, 523, 524, 525, 531, 538, 541, 549, 566,\n", + " 568, 577, 588, 593, 600, 613, 621, 625, 626, 632, 638,\n", + " 642, 646, 649, 651, 653, 656, 659, 660, 663, 679, 693,\n", + " 706, 713, 721, 723, 734, 735, 745, 749, 751, 752, 755,\n", + " 771, 781, 784, 801, 805, 807, 813, 822, 827, 829, 854,\n", + " 855, 881, 892, 894, 897, 898, 900, 927, 930, 945, 948,\n", + " 959, 976, 977, 988, 998, 1004, 1024, 1035, 1049, 1051, 1055,\n", + " 1058, 1066, 1067, 1072, 1074, 1077, 1087, 1089, 1091, 1096, 1097,\n", + " 1098, 1099, 1104, 1107, 1112, 1120, 1128, 1133, 1139, 1143, 1149,\n", + " 1152, 1159, 1167, 1178, 1189, 1194, 1205, 1213, 1216, 1222, 1229,\n", + " 1235, 1238, 1256, 1257, 1260, 1271, 1276, 1281, 1285, 1287, 1290,\n", + " 1293, 1295, 1300, 1302, 1304, 1313, 1325, 1331, 1338, 1343, 1347,\n", + " 1356, 1357, 1365, 1366, 1382, 1391, 1393, 1397]),\n", + " 3: array([ 6, 41, 126, 131, 209, 229, 260, 266, 292, 339, 370,\n", + " 450, 570, 599, 631, 665, 670, 678, 687, 759, 763, 765,\n", + " 783, 787, 804, 816, 888, 889, 904, 932, 935, 1010, 1022,\n", + " 1033, 1129, 1211, 1221, 1292, 1317, 1352]),\n", + " 4: array([ 8, 19, 21, 47, 52, 55, 59, 63, 68, 77, 88,\n", + " 93, 96, 98, 104, 109, 113, 115, 120, 121, 150, 151,\n", + " 153, 155, 181, 184, 194, 199, 228, 231, 239, 254, 258,\n", + " 261, 276, 280, 282, 295, 297, 299, 316, 336, 338, 348,\n", + " 351, 363, 389, 392, 399, 404, 409, 454, 474, 494, 497,\n", + " 521, 526, 553, 559, 561, 563, 573, 578, 605, 606, 619,\n", + " 628, 640, 672, 680, 683, 686, 691, 696, 699, 702, 708,\n", + " 717, 720, 726, 740, 748, 753, 786, 797, 802, 815, 818,\n", + " 825, 838, 858, 868, 873, 875, 880, 885, 901, 910, 928,\n", + " 934, 937, 973, 975, 980, 983, 1000, 1005, 1014, 1020, 1030,\n", + " 1039, 1040, 1047, 1048, 1050, 1056, 1073, 1106, 1110, 1118, 1122,\n", + " 1124, 1134, 1138, 1141, 1142, 1172, 1183, 1240, 1241, 1245, 1249,\n", + " 1251, 1254, 1255, 1262, 1265, 1275, 1283, 1286, 1296, 1312, 1314,\n", + " 1319, 1342, 1353, 1358, 1361, 1383, 1387]),\n", + " 5: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 43, 44,\n", + " 45, 53, 76, 79, 103, 106, 107, 110, 116, 129, 156,\n", + " 163, 164, 165, 168, 186, 191, 193, 210, 215, 220, 233,\n", + " 234, 242, 248, 250, 251, 257, 263, 269, 281, 285, 286,\n", + " 311, 318, 329, 343, 346, 347, 350, 374, 375, 393, 396,\n", + " 403, 405, 424, 437, 438, 439, 456, 461, 473, 477, 478,\n", + " 487, 493, 502, 504, 507, 511, 513, 517, 532, 536, 544,\n", + " 546, 555, 556, 557, 565, 575, 583, 608, 609, 623, 630,\n", + " 636, 654, 664, 667, 669, 694, 698, 705, 710, 712, 722,\n", + " 725, 727, 739, 746, 758, 760, 768, 776, 778, 788, 790,\n", + " 800, 817, 820, 833, 834, 842, 845, 847, 850, 851, 852,\n", + " 860, 862, 863, 865, 877, 878, 893, 903, 906, 911, 922,\n", + " 936, 946, 954, 956, 963, 966, 969, 970, 972, 981, 982,\n", + " 989, 994, 1002, 1008, 1015, 1016, 1019, 1027, 1034, 1036, 1043,\n", + " 1057, 1061, 1070, 1076, 1079, 1082, 1086, 1101, 1105, 1111, 1132,\n", + " 1136, 1144, 1145, 1150, 1151, 1157, 1168, 1174, 1175, 1186, 1188,\n", + " 1197, 1199, 1202, 1217, 1224, 1228, 1243, 1268, 1270, 1277, 1280,\n", + " 1297, 1309, 1323, 1333, 1335, 1349, 1359, 1375, 1392, 1396, 1399]),\n", + " 6: array([ 62, 71, 75, 100, 176, 308, 317, 330, 333, 359, 441,\n", + " 443, 451, 481, 498, 571, 584, 585, 586, 595, 610, 729,\n", + " 738, 754, 791, 795, 808, 826, 866, 870, 902, 949, 958,\n", + " 995, 1012, 1018, 1059, 1078, 1081, 1090, 1093, 1114, 1116, 1140,\n", + " 1156, 1176, 1181, 1187, 1207, 1233, 1242, 1248, 1250, 1263, 1288,\n", + " 1307, 1310, 1340, 1395]),\n", + " 7: array([ 180, 188, 196, 223, 279, 349, 576, 671, 724, 736, 794,\n", + " 925, 1147, 1215])},\n", + " 9: {0: array([ 0, 2, 20, 22, 54, 61, 65, 78, 89, 101, 105,\n", + " 112, 119, 123, 127, 133, 137, 147, 154, 182, 187, 205,\n", + " 208, 226, 230, 268, 275, 284, 309, 323, 328, 337, 342,\n", + " 355, 362, 380, 391, 413, 432, 435, 484, 488, 515, 542,\n", + " 562, 587, 590, 597, 601, 641, 655, 661, 692, 701, 719,\n", + " 733, 737, 744, 770, 773, 793, 806, 809, 810, 821, 836,\n", + " 859, 864, 879, 882, 895, 907, 915, 920, 931, 952, 955,\n", + " 957, 967, 978, 993, 999, 1007, 1017, 1032, 1068, 1094, 1115,\n", + " 1117, 1123, 1125, 1127, 1153, 1190, 1191, 1208, 1223, 1246, 1284,\n", + " 1294, 1298, 1308, 1318, 1327, 1344, 1348, 1350, 1354, 1360, 1362,\n", + " 1377, 1379, 1381, 1389, 1394]),\n", + " 1: array([ 1, 3, 13, 26, 27, 28, 29, 30, 31, 48, 49,\n", + " 58, 64, 74, 80, 82, 90, 97, 99, 102, 114, 118,\n", + " 122, 124, 128, 132, 140, 142, 144, 145, 159, 162, 166,\n", + " 170, 173, 174, 177, 178, 190, 195, 201, 202, 212, 213,\n", + " 216, 218, 219, 227, 232, 253, 255, 262, 267, 271, 272,\n", + " 283, 289, 290, 291, 294, 296, 298, 303, 304, 305, 306,\n", + " 310, 315, 322, 331, 332, 334, 335, 341, 344, 353, 357,\n", + " 360, 364, 365, 366, 368, 371, 372, 373, 378, 381, 383,\n", + " 385, 388, 390, 398, 401, 402, 407, 420, 422, 423, 426,\n", + " 428, 430, 431, 433, 434, 436, 444, 445, 446, 448, 455,\n", + " 462, 463, 471, 472, 475, 476, 482, 483, 485, 490, 491,\n", + " 496, 500, 503, 508, 510, 518, 520, 527, 528, 530, 540,\n", + " 543, 545, 547, 551, 567, 569, 574, 579, 591, 592, 594,\n", + " 596, 604, 607, 611, 614, 615, 617, 622, 627, 629, 637,\n", + " 639, 644, 647, 648, 650, 652, 657, 658, 666, 673, 676,\n", + " 677, 681, 685, 689, 695, 714, 715, 716, 731, 742, 747,\n", + " 757, 761, 762, 767, 772, 775, 777, 780, 782, 785, 789,\n", + " 792, 796, 799, 812, 823, 828, 841, 843, 844, 846, 848,\n", + " 853, 876, 883, 886, 890, 891, 899, 905, 909, 912, 913,\n", + " 914, 916, 917, 918, 919, 921, 923, 924, 926, 933, 940,\n", + " 944, 951, 965, 968, 974, 979, 986, 990, 1009, 1023, 1031,\n", + " 1038, 1041, 1044, 1052, 1054, 1062, 1064, 1065, 1069, 1071, 1075,\n", + " 1085, 1088, 1092, 1095, 1102, 1108, 1109, 1126, 1148, 1155, 1158,\n", + " 1160, 1162, 1177, 1184, 1192, 1196, 1203, 1204, 1206, 1214, 1226,\n", + " 1230, 1232, 1234, 1236, 1247, 1252, 1259, 1261, 1266, 1269, 1272,\n", + " 1274, 1291, 1299, 1301, 1303, 1305, 1311, 1320, 1322, 1324, 1326,\n", + " 1328, 1329, 1330, 1332, 1339, 1351, 1364, 1367, 1370, 1374, 1376,\n", + " 1378, 1386, 1388, 1398]),\n", + " 2: array([ 4, 14, 24, 33, 34, 50, 57, 69, 72, 73, 81,\n", + " 83, 84, 85, 91, 95, 117, 125, 134, 136, 152, 157,\n", + " 160, 161, 171, 183, 185, 198, 203, 204, 217, 224, 225,\n", + " 237, 243, 256, 270, 287, 293, 313, 319, 320, 321, 326,\n", + " 345, 356, 358, 367, 377, 379, 382, 387, 395, 406, 410,\n", + " 415, 417, 440, 447, 457, 458, 466, 469, 470, 479, 506,\n", + " 514, 516, 519, 523, 524, 525, 531, 538, 541, 549, 566,\n", + " 568, 577, 588, 593, 600, 613, 621, 625, 626, 632, 638,\n", + " 642, 646, 649, 651, 653, 656, 659, 660, 663, 679, 693,\n", + " 706, 713, 721, 723, 734, 735, 745, 749, 751, 752, 755,\n", + " 771, 781, 784, 801, 805, 807, 813, 822, 827, 829, 854,\n", + " 855, 881, 892, 894, 897, 898, 900, 927, 930, 945, 948,\n", + " 959, 976, 977, 988, 998, 1004, 1024, 1035, 1049, 1051, 1055,\n", + " 1058, 1066, 1067, 1072, 1074, 1077, 1087, 1089, 1091, 1096, 1097,\n", + " 1098, 1099, 1104, 1107, 1112, 1120, 1128, 1133, 1139, 1143, 1149,\n", + " 1152, 1159, 1167, 1178, 1189, 1194, 1205, 1213, 1216, 1222, 1229,\n", + " 1235, 1238, 1256, 1257, 1260, 1271, 1276, 1281, 1285, 1287, 1290,\n", + " 1293, 1295, 1300, 1302, 1304, 1313, 1325, 1331, 1338, 1343, 1347,\n", + " 1356, 1357, 1365, 1366, 1382, 1391, 1393, 1397]),\n", + " 3: array([ 5, 7, 9, 12, 17, 18, 25, 32, 36, 38, 42,\n", + " 46, 51, 56, 60, 66, 67, 70, 86, 87, 92, 94,\n", + " 108, 111, 130, 135, 138, 139, 141, 143, 146, 148, 149,\n", + " 158, 167, 169, 172, 175, 179, 189, 192, 197, 200, 206,\n", + " 207, 211, 214, 221, 222, 235, 236, 238, 240, 241, 244,\n", + " 245, 246, 247, 249, 252, 259, 264, 265, 273, 274, 277,\n", + " 278, 288, 300, 301, 302, 307, 312, 314, 324, 325, 327,\n", + " 340, 352, 354, 361, 369, 376, 384, 386, 394, 397, 400,\n", + " 408, 411, 412, 414, 416, 418, 419, 421, 425, 427, 429,\n", + " 442, 449, 452, 453, 459, 460, 464, 465, 467, 468, 480,\n", + " 486, 489, 492, 495, 499, 501, 505, 509, 512, 522, 529,\n", + " 533, 534, 535, 537, 539, 548, 550, 552, 554, 558, 560,\n", + " 564, 572, 580, 581, 582, 589, 598, 602, 603, 612, 616,\n", + " 618, 620, 624, 633, 634, 635, 643, 645, 662, 668, 674,\n", + " 675, 682, 684, 688, 690, 697, 700, 703, 704, 707, 709,\n", + " 711, 718, 728, 730, 732, 741, 743, 750, 756, 764, 766,\n", + " 769, 774, 779, 798, 803, 811, 814, 819, 824, 830, 831,\n", + " 832, 835, 837, 839, 840, 849, 856, 857, 861, 867, 869,\n", + " 871, 872, 874, 884, 887, 896, 908, 929, 938, 939, 941,\n", + " 942, 943, 947, 950, 953, 960, 961, 962, 964, 971, 984,\n", + " 985, 987, 991, 992, 996, 997, 1001, 1003, 1006, 1011, 1013,\n", + " 1021, 1025, 1026, 1028, 1029, 1037, 1042, 1045, 1046, 1053, 1060,\n", + " 1063, 1080, 1083, 1084, 1100, 1103, 1113, 1119, 1121, 1130, 1131,\n", + " 1135, 1137, 1146, 1154, 1161, 1163, 1164, 1165, 1166, 1169, 1170,\n", + " 1171, 1173, 1179, 1180, 1182, 1185, 1193, 1195, 1198, 1200, 1201,\n", + " 1209, 1210, 1212, 1218, 1219, 1220, 1225, 1227, 1231, 1237, 1239,\n", + " 1244, 1253, 1258, 1264, 1267, 1273, 1278, 1279, 1282, 1289, 1306,\n", + " 1315, 1316, 1321, 1334, 1336, 1337, 1341, 1345, 1346, 1355, 1363,\n", + " 1368, 1369, 1371, 1372, 1373, 1380, 1384, 1385, 1390]),\n", + " 4: array([ 6, 41, 126, 131, 209, 229, 260, 266, 292, 339, 370,\n", + " 450, 570, 599, 631, 665, 670, 678, 687, 759, 763, 765,\n", + " 783, 787, 804, 816, 888, 889, 904, 932, 935, 1010, 1022,\n", + " 1033, 1129, 1211, 1221, 1292, 1317, 1352]),\n", + " 5: array([ 8, 19, 21, 47, 52, 55, 59, 63, 68, 77, 88,\n", + " 93, 96, 98, 104, 109, 113, 115, 120, 121, 150, 151,\n", + " 153, 155, 181, 184, 194, 199, 228, 231, 239, 254, 258,\n", + " 261, 276, 280, 282, 295, 297, 299, 316, 336, 338, 348,\n", + " 351, 363, 389, 392, 399, 404, 409, 454, 474, 494, 497,\n", + " 521, 526, 553, 559, 561, 563, 573, 578, 605, 606, 619,\n", + " 628, 640, 672, 680, 683, 686, 691, 696, 699, 702, 708,\n", + " 717, 720, 726, 740, 748, 753, 786, 797, 802, 815, 818,\n", + " 825, 838, 858, 868, 873, 875, 880, 885, 901, 910, 928,\n", + " 934, 937, 973, 975, 980, 983, 1000, 1005, 1014, 1020, 1030,\n", + " 1039, 1040, 1047, 1048, 1050, 1056, 1073, 1106, 1110, 1118, 1122,\n", + " 1124, 1134, 1138, 1141, 1142, 1172, 1183, 1240, 1241, 1245, 1249,\n", + " 1251, 1254, 1255, 1262, 1265, 1275, 1283, 1286, 1296, 1312, 1314,\n", + " 1319, 1342, 1353, 1358, 1361, 1383, 1387]),\n", + " 6: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 43, 44,\n", + " 45, 53, 76, 79, 103, 106, 107, 110, 116, 129, 156,\n", + " 163, 164, 165, 168, 186, 191, 193, 210, 215, 220, 233,\n", + " 234, 242, 248, 250, 251, 257, 263, 269, 281, 285, 286,\n", + " 311, 318, 329, 343, 346, 347, 350, 374, 375, 393, 396,\n", + " 403, 405, 424, 437, 438, 439, 456, 461, 473, 477, 478,\n", + " 487, 493, 502, 504, 507, 511, 513, 517, 532, 536, 544,\n", + " 546, 555, 556, 557, 565, 575, 583, 608, 609, 623, 630,\n", + " 636, 654, 664, 667, 669, 694, 698, 705, 710, 712, 722,\n", + " 725, 727, 739, 746, 758, 760, 768, 776, 778, 788, 790,\n", + " 800, 817, 820, 833, 834, 842, 845, 847, 850, 851, 852,\n", + " 860, 862, 863, 865, 877, 878, 893, 903, 906, 911, 922,\n", + " 936, 946, 954, 956, 963, 966, 969, 970, 972, 981, 982,\n", + " 989, 994, 1002, 1008, 1015, 1016, 1019, 1027, 1034, 1036, 1043,\n", + " 1057, 1061, 1070, 1076, 1079, 1082, 1086, 1101, 1105, 1111, 1132,\n", + " 1136, 1144, 1145, 1150, 1151, 1157, 1168, 1174, 1175, 1186, 1188,\n", + " 1197, 1199, 1202, 1217, 1224, 1228, 1243, 1268, 1270, 1277, 1280,\n", + " 1297, 1309, 1323, 1333, 1335, 1349, 1359, 1375, 1392, 1396, 1399]),\n", + " 7: array([ 62, 71, 75, 100, 176, 308, 317, 330, 333, 359, 441,\n", + " 443, 451, 481, 498, 571, 584, 585, 586, 595, 610, 729,\n", + " 738, 754, 791, 795, 808, 826, 866, 870, 902, 949, 958,\n", + " 995, 1012, 1018, 1059, 1078, 1081, 1090, 1093, 1114, 1116, 1140,\n", + " 1156, 1176, 1181, 1187, 1207, 1233, 1242, 1248, 1250, 1263, 1288,\n", + " 1307, 1310, 1340, 1395]),\n", + " 8: array([ 180, 188, 196, 223, 279, 349, 576, 671, 724, 736, 794,\n", + " 925, 1147, 1215])},\n", + " 10: {0: array([ 0, 20, 22, 54, 65, 112, 119, 137, 147, 154, 205,\n", + " 230, 309, 323, 328, 337, 355, 391, 432, 484, 515, 542,\n", + " 562, 587, 597, 601, 701, 744, 770, 793, 806, 809, 810,\n", + " 821, 836, 859, 879, 895, 907, 920, 952, 978, 999, 1032,\n", + " 1068, 1117, 1123, 1223, 1344, 1354, 1360, 1362, 1377, 1379, 1381]),\n", + " 1: array([ 1, 3, 13, 26, 27, 28, 29, 30, 31, 48, 49,\n", + " 58, 64, 74, 80, 82, 90, 97, 99, 102, 114, 118,\n", + " 122, 124, 128, 132, 140, 142, 144, 145, 159, 162, 166,\n", + " 170, 173, 174, 177, 178, 190, 195, 201, 202, 212, 213,\n", + " 216, 218, 219, 227, 232, 253, 255, 262, 267, 271, 272,\n", + " 283, 289, 290, 291, 294, 296, 298, 303, 304, 305, 306,\n", + " 310, 315, 322, 331, 332, 334, 335, 341, 344, 353, 357,\n", + " 360, 364, 365, 366, 368, 371, 372, 373, 378, 381, 383,\n", + " 385, 388, 390, 398, 401, 402, 407, 420, 422, 423, 426,\n", + " 428, 430, 431, 433, 434, 436, 444, 445, 446, 448, 455,\n", + " 462, 463, 471, 472, 475, 476, 482, 483, 485, 490, 491,\n", + " 496, 500, 503, 508, 510, 518, 520, 527, 528, 530, 540,\n", + " 543, 545, 547, 551, 567, 569, 574, 579, 591, 592, 594,\n", + " 596, 604, 607, 611, 614, 615, 617, 622, 627, 629, 637,\n", + " 639, 644, 647, 648, 650, 652, 657, 658, 666, 673, 676,\n", + " 677, 681, 685, 689, 695, 714, 715, 716, 731, 742, 747,\n", + " 757, 761, 762, 767, 772, 775, 777, 780, 782, 785, 789,\n", + " 792, 796, 799, 812, 823, 828, 841, 843, 844, 846, 848,\n", + " 853, 876, 883, 886, 890, 891, 899, 905, 909, 912, 913,\n", + " 914, 916, 917, 918, 919, 921, 923, 924, 926, 933, 940,\n", + " 944, 951, 965, 968, 974, 979, 986, 990, 1009, 1023, 1031,\n", + " 1038, 1041, 1044, 1052, 1054, 1062, 1064, 1065, 1069, 1071, 1075,\n", + " 1085, 1088, 1092, 1095, 1102, 1108, 1109, 1126, 1148, 1155, 1158,\n", + " 1160, 1162, 1177, 1184, 1192, 1196, 1203, 1204, 1206, 1214, 1226,\n", + " 1230, 1232, 1234, 1236, 1247, 1252, 1259, 1261, 1266, 1269, 1272,\n", + " 1274, 1291, 1299, 1301, 1303, 1305, 1311, 1320, 1322, 1324, 1326,\n", + " 1328, 1329, 1330, 1332, 1339, 1351, 1364, 1367, 1370, 1374, 1376,\n", + " 1378, 1386, 1388, 1398]),\n", + " 2: array([ 2, 61, 78, 89, 101, 105, 123, 127, 133, 182, 187,\n", + " 208, 226, 268, 275, 284, 342, 362, 380, 413, 435, 488,\n", + " 590, 641, 655, 661, 692, 719, 733, 737, 773, 864, 882,\n", + " 915, 931, 955, 957, 967, 993, 1007, 1017, 1094, 1115, 1125,\n", + " 1127, 1153, 1190, 1191, 1208, 1246, 1284, 1294, 1298, 1308, 1318,\n", + " 1327, 1348, 1350, 1389, 1394]),\n", + " 3: array([ 4, 14, 24, 33, 34, 50, 57, 69, 72, 73, 81,\n", + " 83, 84, 85, 91, 95, 117, 125, 134, 136, 152, 157,\n", + " 160, 161, 171, 183, 185, 198, 203, 204, 217, 224, 225,\n", + " 237, 243, 256, 270, 287, 293, 313, 319, 320, 321, 326,\n", + " 345, 356, 358, 367, 377, 379, 382, 387, 395, 406, 410,\n", + " 415, 417, 440, 447, 457, 458, 466, 469, 470, 479, 506,\n", + " 514, 516, 519, 523, 524, 525, 531, 538, 541, 549, 566,\n", + " 568, 577, 588, 593, 600, 613, 621, 625, 626, 632, 638,\n", + " 642, 646, 649, 651, 653, 656, 659, 660, 663, 679, 693,\n", + " 706, 713, 721, 723, 734, 735, 745, 749, 751, 752, 755,\n", + " 771, 781, 784, 801, 805, 807, 813, 822, 827, 829, 854,\n", + " 855, 881, 892, 894, 897, 898, 900, 927, 930, 945, 948,\n", + " 959, 976, 977, 988, 998, 1004, 1024, 1035, 1049, 1051, 1055,\n", + " 1058, 1066, 1067, 1072, 1074, 1077, 1087, 1089, 1091, 1096, 1097,\n", + " 1098, 1099, 1104, 1107, 1112, 1120, 1128, 1133, 1139, 1143, 1149,\n", + " 1152, 1159, 1167, 1178, 1189, 1194, 1205, 1213, 1216, 1222, 1229,\n", + " 1235, 1238, 1256, 1257, 1260, 1271, 1276, 1281, 1285, 1287, 1290,\n", + " 1293, 1295, 1300, 1302, 1304, 1313, 1325, 1331, 1338, 1343, 1347,\n", + " 1356, 1357, 1365, 1366, 1382, 1391, 1393, 1397]),\n", + " 4: array([ 5, 7, 9, 12, 17, 18, 25, 32, 36, 38, 42,\n", + " 46, 51, 56, 60, 66, 67, 70, 86, 87, 92, 94,\n", + " 108, 111, 130, 135, 138, 139, 141, 143, 146, 148, 149,\n", + " 158, 167, 169, 172, 175, 179, 189, 192, 197, 200, 206,\n", + " 207, 211, 214, 221, 222, 235, 236, 238, 240, 241, 244,\n", + " 245, 246, 247, 249, 252, 259, 264, 265, 273, 274, 277,\n", + " 278, 288, 300, 301, 302, 307, 312, 314, 324, 325, 327,\n", + " 340, 352, 354, 361, 369, 376, 384, 386, 394, 397, 400,\n", + " 408, 411, 412, 414, 416, 418, 419, 421, 425, 427, 429,\n", + " 442, 449, 452, 453, 459, 460, 464, 465, 467, 468, 480,\n", + " 486, 489, 492, 495, 499, 501, 505, 509, 512, 522, 529,\n", + " 533, 534, 535, 537, 539, 548, 550, 552, 554, 558, 560,\n", + " 564, 572, 580, 581, 582, 589, 598, 602, 603, 612, 616,\n", + " 618, 620, 624, 633, 634, 635, 643, 645, 662, 668, 674,\n", + " 675, 682, 684, 688, 690, 697, 700, 703, 704, 707, 709,\n", + " 711, 718, 728, 730, 732, 741, 743, 750, 756, 764, 766,\n", + " 769, 774, 779, 798, 803, 811, 814, 819, 824, 830, 831,\n", + " 832, 835, 837, 839, 840, 849, 856, 857, 861, 867, 869,\n", + " 871, 872, 874, 884, 887, 896, 908, 929, 938, 939, 941,\n", + " 942, 943, 947, 950, 953, 960, 961, 962, 964, 971, 984,\n", + " 985, 987, 991, 992, 996, 997, 1001, 1003, 1006, 1011, 1013,\n", + " 1021, 1025, 1026, 1028, 1029, 1037, 1042, 1045, 1046, 1053, 1060,\n", + " 1063, 1080, 1083, 1084, 1100, 1103, 1113, 1119, 1121, 1130, 1131,\n", + " 1135, 1137, 1146, 1154, 1161, 1163, 1164, 1165, 1166, 1169, 1170,\n", + " 1171, 1173, 1179, 1180, 1182, 1185, 1193, 1195, 1198, 1200, 1201,\n", + " 1209, 1210, 1212, 1218, 1219, 1220, 1225, 1227, 1231, 1237, 1239,\n", + " 1244, 1253, 1258, 1264, 1267, 1273, 1278, 1279, 1282, 1289, 1306,\n", + " 1315, 1316, 1321, 1334, 1336, 1337, 1341, 1345, 1346, 1355, 1363,\n", + " 1368, 1369, 1371, 1372, 1373, 1380, 1384, 1385, 1390]),\n", + " 5: array([ 6, 41, 126, 131, 209, 229, 260, 266, 292, 339, 370,\n", + " 450, 570, 599, 631, 665, 670, 678, 687, 759, 763, 765,\n", + " 783, 787, 804, 816, 888, 889, 904, 932, 935, 1010, 1022,\n", + " 1033, 1129, 1211, 1221, 1292, 1317, 1352]),\n", + " 6: array([ 8, 19, 21, 47, 52, 55, 59, 63, 68, 77, 88,\n", + " 93, 96, 98, 104, 109, 113, 115, 120, 121, 150, 151,\n", + " 153, 155, 181, 184, 194, 199, 228, 231, 239, 254, 258,\n", + " 261, 276, 280, 282, 295, 297, 299, 316, 336, 338, 348,\n", + " 351, 363, 389, 392, 399, 404, 409, 454, 474, 494, 497,\n", + " 521, 526, 553, 559, 561, 563, 573, 578, 605, 606, 619,\n", + " 628, 640, 672, 680, 683, 686, 691, 696, 699, 702, 708,\n", + " 717, 720, 726, 740, 748, 753, 786, 797, 802, 815, 818,\n", + " 825, 838, 858, 868, 873, 875, 880, 885, 901, 910, 928,\n", + " 934, 937, 973, 975, 980, 983, 1000, 1005, 1014, 1020, 1030,\n", + " 1039, 1040, 1047, 1048, 1050, 1056, 1073, 1106, 1110, 1118, 1122,\n", + " 1124, 1134, 1138, 1141, 1142, 1172, 1183, 1240, 1241, 1245, 1249,\n", + " 1251, 1254, 1255, 1262, 1265, 1275, 1283, 1286, 1296, 1312, 1314,\n", + " 1319, 1342, 1353, 1358, 1361, 1383, 1387]),\n", + " 7: array([ 10, 11, 15, 16, 23, 35, 37, 39, 40, 43, 44,\n", + " 45, 53, 76, 79, 103, 106, 107, 110, 116, 129, 156,\n", + " 163, 164, 165, 168, 186, 191, 193, 210, 215, 220, 233,\n", + " 234, 242, 248, 250, 251, 257, 263, 269, 281, 285, 286,\n", + " 311, 318, 329, 343, 346, 347, 350, 374, 375, 393, 396,\n", + " 403, 405, 424, 437, 438, 439, 456, 461, 473, 477, 478,\n", + " 487, 493, 502, 504, 507, 511, 513, 517, 532, 536, 544,\n", + " 546, 555, 556, 557, 565, 575, 583, 608, 609, 623, 630,\n", + " 636, 654, 664, 667, 669, 694, 698, 705, 710, 712, 722,\n", + " 725, 727, 739, 746, 758, 760, 768, 776, 778, 788, 790,\n", + " 800, 817, 820, 833, 834, 842, 845, 847, 850, 851, 852,\n", + " 860, 862, 863, 865, 877, 878, 893, 903, 906, 911, 922,\n", + " 936, 946, 954, 956, 963, 966, 969, 970, 972, 981, 982,\n", + " 989, 994, 1002, 1008, 1015, 1016, 1019, 1027, 1034, 1036, 1043,\n", + " 1057, 1061, 1070, 1076, 1079, 1082, 1086, 1101, 1105, 1111, 1132,\n", + " 1136, 1144, 1145, 1150, 1151, 1157, 1168, 1174, 1175, 1186, 1188,\n", + " 1197, 1199, 1202, 1217, 1224, 1228, 1243, 1268, 1270, 1277, 1280,\n", + " 1297, 1309, 1323, 1333, 1335, 1349, 1359, 1375, 1392, 1396, 1399]),\n", + " 8: array([ 62, 71, 75, 100, 176, 308, 317, 330, 333, 359, 441,\n", + " 443, 451, 481, 498, 571, 584, 585, 586, 595, 610, 729,\n", + " 738, 754, 791, 795, 808, 826, 866, 870, 902, 949, 958,\n", + " 995, 1012, 1018, 1059, 1078, 1081, 1090, 1093, 1114, 1116, 1140,\n", + " 1156, 1176, 1181, 1187, 1207, 1233, 1242, 1248, 1250, 1263, 1288,\n", + " 1307, 1310, 1340, 1395]),\n", + " 9: array([ 180, 188, 196, 223, 279, 349, 576, 671, 724, 736, 794,\n", + " 925, 1147, 1215])}},\n", + " 'baseline': {2: {0: array([ 0, 1, 2, ..., 1396, 1398, 1399]),\n", + " 1: array([ 4, 14, 24, 33, 34, 50, 57, 62, 69, 71, 72,\n", + " 73, 75, 81, 83, 84, 85, 91, 95, 100, 117, 125,\n", + " 134, 136, 152, 157, 160, 161, 171, 173, 176, 180, 183,\n", + " 185, 188, 196, 198, 203, 204, 217, 223, 224, 225, 237,\n", + " 243, 256, 270, 279, 287, 293, 308, 313, 317, 319, 320,\n", + " 321, 326, 330, 333, 345, 349, 356, 358, 359, 364, 367,\n", + " 377, 379, 382, 387, 395, 406, 410, 415, 417, 440, 441,\n", + " 443, 447, 451, 457, 458, 466, 469, 470, 479, 481, 498,\n", + " 506, 514, 516, 519, 523, 524, 525, 531, 538, 541, 549,\n", + " 566, 568, 571, 576, 577, 584, 585, 586, 588, 593, 595,\n", + " 596, 600, 610, 613, 621, 625, 626, 632, 638, 642, 646,\n", + " 649, 651, 653, 656, 659, 660, 663, 671, 679, 681, 693,\n", + " 706, 713, 721, 723, 724, 729, 734, 735, 736, 738, 745,\n", + " 749, 751, 752, 754, 755, 771, 781, 784, 791, 794, 795,\n", + " 800, 801, 805, 807, 808, 813, 822, 826, 827, 829, 854,\n", + " 855, 866, 870, 881, 892, 894, 897, 898, 900, 902, 925,\n", + " 927, 930, 945, 948, 949, 958, 959, 963, 976, 977, 988,\n", + " 995, 998, 1004, 1012, 1018, 1024, 1035, 1049, 1051, 1055, 1058,\n", + " 1059, 1066, 1067, 1072, 1074, 1077, 1078, 1081, 1087, 1089, 1090,\n", + " 1091, 1093, 1096, 1097, 1098, 1099, 1104, 1107, 1112, 1114, 1116,\n", + " 1120, 1128, 1133, 1136, 1139, 1140, 1143, 1147, 1149, 1152, 1156,\n", + " 1159, 1167, 1176, 1178, 1181, 1187, 1189, 1194, 1205, 1207, 1213,\n", + " 1215, 1216, 1222, 1229, 1233, 1235, 1238, 1242, 1248, 1250, 1256,\n", + " 1257, 1260, 1263, 1271, 1276, 1281, 1285, 1287, 1288, 1290, 1293,\n", + " 1295, 1300, 1302, 1304, 1307, 1310, 1313, 1325, 1331, 1338, 1340,\n", + " 1343, 1347, 1356, 1357, 1365, 1366, 1382, 1391, 1393, 1395, 1397])},\n", + " 3: {0: array([ 0, 1, 2, ..., 1396, 1398, 1399]),\n", + " 1: array([ 4, 14, 24, 33, 34, 50, 57, 69, 72, 73, 81,\n", + " 83, 84, 85, 91, 95, 117, 125, 134, 136, 152, 157,\n", + " 160, 161, 171, 173, 180, 183, 185, 188, 196, 198, 203,\n", + " 204, 217, 223, 224, 225, 237, 243, 256, 270, 279, 287,\n", + " 293, 313, 317, 319, 320, 321, 330, 345, 349, 356, 358,\n", + " 364, 367, 377, 379, 382, 387, 395, 406, 410, 415, 417,\n", + " 440, 447, 451, 457, 458, 466, 469, 470, 479, 506, 514,\n", + " 516, 519, 523, 524, 525, 531, 538, 541, 549, 568, 571,\n", + " 576, 577, 584, 588, 593, 596, 600, 613, 621, 625, 626,\n", + " 632, 638, 642, 646, 649, 651, 653, 656, 659, 660, 663,\n", + " 671, 679, 681, 693, 706, 713, 721, 723, 724, 729, 734,\n", + " 735, 736, 745, 749, 751, 752, 755, 771, 781, 784, 791,\n", + " 794, 795, 800, 801, 805, 807, 808, 813, 822, 826, 827,\n", + " 829, 854, 855, 881, 892, 894, 897, 898, 900, 902, 925,\n", + " 927, 930, 945, 948, 958, 959, 976, 977, 988, 998, 1004,\n", + " 1024, 1035, 1049, 1051, 1055, 1058, 1066, 1067, 1072, 1074, 1077,\n", + " 1087, 1089, 1090, 1091, 1093, 1096, 1097, 1098, 1099, 1104, 1107,\n", + " 1112, 1114, 1116, 1120, 1128, 1133, 1139, 1143, 1147, 1149, 1152,\n", + " 1159, 1167, 1178, 1189, 1194, 1205, 1213, 1215, 1216, 1222, 1229,\n", + " 1233, 1235, 1238, 1256, 1257, 1260, 1271, 1276, 1281, 1285, 1287,\n", + " 1290, 1293, 1295, 1300, 1302, 1304, 1313, 1325, 1331, 1338, 1343,\n", + " 1347, 1356, 1357, 1365, 1366, 1382, 1391, 1393, 1397]),\n", + " 2: array([ 62, 71, 75, 100, 176, 308, 326, 333, 359, 441, 443,\n", + " 481, 498, 566, 585, 586, 595, 610, 738, 754, 866, 870,\n", + " 949, 963, 995, 1012, 1018, 1059, 1078, 1081, 1136, 1140, 1156,\n", + " 1176, 1181, 1187, 1207, 1242, 1248, 1250, 1263, 1288, 1307, 1310,\n", + " 1340, 1395])},\n", + " 4: {0: array([ 0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11,\n", + " 12, 16, 17, 18, 19, 21, 23, 25, 26, 32, 35,\n", + " 36, 37, 38, 39, 42, 43, 45, 46, 47, 51, 52,\n", + " 53, 54, 56, 58, 59, 60, 66, 67, 68, 70, 74,\n", + " 77, 78, 79, 86, 87, 94, 102, 103, 105, 106, 108,\n", + " 111, 115, 120, 121, 122, 126, 130, 131, 133, 135, 138,\n", + " 139, 140, 141, 143, 148, 149, 150, 153, 154, 156, 158,\n", + " 163, 165, 166, 167, 168, 169, 172, 175, 179, 182, 186,\n", + " 187, 189, 190, 192, 194, 197, 199, 200, 205, 206, 207,\n", + " 209, 211, 214, 216, 220, 221, 222, 228, 229, 231, 233,\n", + " 235, 236, 240, 241, 244, 245, 246, 247, 248, 249, 252,\n", + " 253, 254, 257, 258, 259, 260, 261, 264, 265, 266, 267,\n", + " 269, 271, 273, 277, 278, 280, 281, 282, 284, 285, 288,\n", + " 289, 292, 299, 300, 301, 302, 305, 306, 307, 311, 312,\n", + " 314, 316, 323, 324, 325, 335, 338, 339, 340, 342, 344,\n", + " 346, 347, 350, 352, 354, 361, 363, 366, 369, 370, 371,\n", + " 380, 384, 386, 388, 389, 391, 392, 393, 394, 397, 398,\n", + " 399, 400, 401, 404, 405, 408, 409, 411, 412, 416, 418,\n", + " 419, 421, 424, 425, 427, 429, 430, 431, 435, 439, 442,\n", + " 448, 449, 450, 452, 453, 454, 456, 459, 460, 464, 465,\n", + " 467, 468, 473, 478, 480, 484, 486, 489, 490, 491, 492,\n", + " 494, 495, 496, 499, 501, 505, 507, 509, 512, 513, 515,\n", + " 517, 521, 522, 529, 532, 533, 534, 535, 537, 543, 544,\n", + " 546, 548, 550, 552, 554, 556, 558, 560, 561, 564, 572,\n", + " 575, 581, 582, 583, 589, 592, 598, 599, 601, 602, 603,\n", + " 604, 606, 607, 609, 612, 616, 618, 620, 624, 628, 629,\n", + " 631, 633, 634, 635, 636, 640, 643, 645, 654, 655, 657,\n", + " 662, 667, 668, 669, 670, 673, 674, 675, 678, 680, 682,\n", + " 683, 684, 686, 688, 690, 691, 694, 696, 697, 698, 700,\n", + " 702, 703, 704, 705, 707, 709, 710, 711, 717, 718, 719,\n", + " 722, 728, 730, 732, 733, 739, 740, 741, 743, 744, 747,\n", + " 748, 750, 756, 758, 759, 760, 761, 763, 764, 766, 768,\n", + " 769, 772, 774, 777, 778, 779, 780, 783, 787, 790, 793,\n", + " 797, 798, 802, 803, 804, 810, 811, 814, 815, 816, 817,\n", + " 818, 819, 823, 824, 825, 828, 830, 831, 832, 833, 835,\n", + " 837, 839, 840, 842, 844, 849, 850, 852, 856, 857, 858,\n", + " 860, 861, 862, 863, 864, 865, 867, 868, 869, 871, 872,\n", + " 873, 874, 876, 877, 878, 879, 883, 884, 885, 887, 888,\n", + " 889, 896, 899, 901, 903, 904, 905, 907, 908, 910, 911,\n", + " 914, 916, 919, 920, 928, 933, 934, 935, 936, 937, 938,\n", + " 939, 941, 942, 943, 946, 947, 950, 952, 953, 954, 957,\n", + " 960, 961, 962, 964, 968, 970, 971, 972, 975, 980, 981,\n", + " 983, 984, 985, 990, 991, 992, 993, 996, 997, 999, 1001,\n", + " 1003, 1005, 1006, 1008, 1011, 1013, 1015, 1019, 1021, 1022, 1025,\n", + " 1026, 1027, 1028, 1029, 1030, 1033, 1034, 1037, 1039, 1041, 1042,\n", + " 1043, 1045, 1046, 1047, 1048, 1052, 1053, 1057, 1060, 1062, 1063,\n", + " 1068, 1070, 1071, 1073, 1080, 1083, 1084, 1086, 1094, 1100, 1101,\n", + " 1103, 1106, 1110, 1111, 1113, 1117, 1119, 1121, 1122, 1123, 1124,\n", + " 1129, 1131, 1132, 1135, 1137, 1138, 1144, 1145, 1146, 1148, 1150,\n", + " 1154, 1157, 1158, 1161, 1163, 1164, 1165, 1166, 1168, 1169, 1170,\n", + " 1171, 1173, 1174, 1175, 1179, 1180, 1182, 1183, 1185, 1186, 1193,\n", + " 1195, 1196, 1198, 1200, 1201, 1202, 1204, 1208, 1209, 1210, 1211,\n", + " 1212, 1218, 1219, 1220, 1221, 1225, 1227, 1228, 1230, 1231, 1234,\n", + " 1237, 1239, 1240, 1241, 1243, 1244, 1253, 1255, 1258, 1264, 1268,\n", + " 1273, 1277, 1278, 1279, 1282, 1283, 1286, 1289, 1292, 1294, 1296,\n", + " 1301, 1305, 1306, 1308, 1309, 1314, 1316, 1319, 1321, 1322, 1323,\n", + " 1327, 1333, 1334, 1335, 1336, 1337, 1341, 1342, 1344, 1345, 1349,\n", + " 1351, 1352, 1355, 1358, 1359, 1361, 1362, 1363, 1368, 1369, 1370,\n", + " 1371, 1372, 1373, 1376, 1380, 1384, 1387, 1388, 1390, 1392, 1394,\n", + " 1399]),\n", + " 1: array([ 4, 14, 24, 33, 34, 50, 57, 69, 72, 73, 81,\n", + " 83, 84, 85, 91, 95, 117, 125, 134, 136, 152, 157,\n", + " 160, 161, 171, 173, 180, 183, 185, 188, 196, 198, 203,\n", + " 204, 217, 223, 224, 225, 237, 243, 256, 270, 279, 287,\n", + " 293, 313, 317, 319, 320, 321, 330, 345, 349, 356, 358,\n", + " 364, 367, 377, 379, 382, 387, 395, 406, 410, 415, 417,\n", + " 440, 447, 451, 457, 458, 466, 469, 470, 479, 506, 514,\n", + " 516, 519, 523, 524, 525, 531, 538, 541, 549, 568, 571,\n", + " 576, 577, 584, 588, 593, 596, 600, 613, 621, 625, 626,\n", + " 632, 638, 642, 646, 649, 651, 653, 656, 659, 660, 663,\n", + " 671, 679, 681, 693, 706, 713, 721, 723, 724, 729, 734,\n", + " 735, 736, 745, 749, 751, 752, 755, 771, 781, 784, 791,\n", + " 794, 795, 800, 801, 805, 807, 808, 813, 822, 826, 827,\n", + " 829, 854, 855, 881, 892, 894, 897, 898, 900, 902, 925,\n", + " 927, 930, 945, 948, 958, 959, 976, 977, 988, 998, 1004,\n", + " 1024, 1035, 1049, 1051, 1055, 1058, 1066, 1067, 1072, 1074, 1077,\n", + " 1087, 1089, 1090, 1091, 1093, 1096, 1097, 1098, 1099, 1104, 1107,\n", + " 1112, 1114, 1116, 1120, 1128, 1133, 1139, 1143, 1147, 1149, 1152,\n", + " 1159, 1167, 1178, 1189, 1194, 1205, 1213, 1215, 1216, 1222, 1229,\n", + " 1233, 1235, 1238, 1256, 1257, 1260, 1271, 1276, 1281, 1285, 1287,\n", + " 1290, 1293, 1295, 1300, 1302, 1304, 1313, 1325, 1331, 1338, 1343,\n", + " 1347, 1356, 1357, 1365, 1366, 1382, 1391, 1393, 1397]),\n", + " 2: array([ 13, 15, 20, 22, 27, 28, 29, 30, 31, 40, 41,\n", + " 44, 48, 49, 55, 61, 63, 64, 65, 76, 80, 82,\n", + " 88, 89, 90, 92, 93, 96, 97, 98, 99, 101, 104,\n", + " 107, 109, 110, 112, 113, 114, 116, 118, 119, 123, 124,\n", + " 127, 128, 129, 132, 137, 142, 144, 145, 146, 147, 151,\n", + " 155, 159, 162, 164, 170, 174, 177, 178, 181, 184, 191,\n", + " 193, 195, 201, 202, 208, 210, 212, 213, 215, 218, 219,\n", + " 226, 227, 230, 232, 234, 238, 239, 242, 250, 251, 255,\n", + " 262, 263, 268, 272, 274, 275, 276, 283, 286, 290, 291,\n", + " 294, 295, 296, 297, 298, 303, 304, 309, 310, 315, 318,\n", + " 322, 327, 328, 329, 331, 332, 334, 336, 337, 341, 343,\n", + " 348, 351, 353, 355, 357, 360, 362, 365, 368, 372, 373,\n", + " 374, 375, 376, 378, 381, 383, 385, 390, 396, 402, 403,\n", + " 407, 413, 414, 420, 422, 423, 426, 428, 432, 433, 434,\n", + " 436, 437, 438, 444, 445, 446, 455, 461, 462, 463, 471,\n", + " 472, 474, 475, 476, 477, 482, 483, 485, 487, 488, 493,\n", + " 497, 500, 502, 503, 504, 508, 510, 511, 518, 520, 526,\n", + " 527, 528, 530, 536, 539, 540, 542, 545, 547, 551, 553,\n", + " 555, 557, 559, 562, 563, 565, 567, 569, 570, 573, 574,\n", + " 578, 579, 580, 587, 590, 591, 594, 597, 605, 608, 611,\n", + " 614, 615, 617, 619, 622, 623, 627, 630, 637, 639, 641,\n", + " 644, 647, 648, 650, 652, 658, 661, 664, 665, 666, 672,\n", + " 676, 677, 685, 687, 689, 692, 695, 699, 701, 708, 712,\n", + " 714, 715, 716, 720, 725, 726, 727, 731, 737, 742, 746,\n", + " 753, 757, 762, 765, 767, 770, 773, 775, 776, 782, 785,\n", + " 786, 788, 789, 792, 796, 799, 806, 809, 812, 820, 821,\n", + " 834, 836, 838, 841, 843, 845, 846, 847, 848, 851, 853,\n", + " 859, 875, 880, 882, 886, 890, 891, 893, 895, 906, 909,\n", + " 912, 913, 915, 917, 918, 921, 922, 923, 924, 926, 929,\n", + " 931, 932, 940, 944, 951, 955, 956, 965, 966, 967, 969,\n", + " 973, 974, 978, 979, 982, 986, 987, 989, 994, 1000, 1002,\n", + " 1007, 1009, 1010, 1014, 1016, 1017, 1020, 1023, 1031, 1032, 1036,\n", + " 1038, 1040, 1044, 1050, 1054, 1056, 1061, 1064, 1065, 1069, 1075,\n", + " 1076, 1079, 1082, 1085, 1088, 1092, 1095, 1102, 1105, 1108, 1109,\n", + " 1115, 1118, 1125, 1126, 1127, 1130, 1134, 1141, 1142, 1151, 1153,\n", + " 1155, 1160, 1162, 1172, 1177, 1184, 1188, 1190, 1191, 1192, 1197,\n", + " 1199, 1203, 1206, 1214, 1217, 1223, 1224, 1226, 1232, 1236, 1245,\n", + " 1246, 1247, 1249, 1251, 1252, 1254, 1259, 1261, 1262, 1265, 1266,\n", + " 1267, 1269, 1270, 1272, 1274, 1275, 1280, 1284, 1291, 1297, 1298,\n", + " 1299, 1303, 1311, 1312, 1315, 1317, 1318, 1320, 1324, 1326, 1328,\n", + " 1329, 1330, 1332, 1339, 1346, 1348, 1350, 1353, 1354, 1360, 1364,\n", + " 1367, 1374, 1375, 1377, 1378, 1379, 1381, 1383, 1385, 1386, 1389,\n", + " 1396, 1398]),\n", + " 3: array([ 62, 71, 75, 100, 176, 308, 326, 333, 359, 441, 443,\n", + " 481, 498, 566, 585, 586, 595, 610, 738, 754, 866, 870,\n", + " 949, 963, 995, 1012, 1018, 1059, 1078, 1081, 1136, 1140, 1156,\n", + " 1176, 1181, 1187, 1207, 1242, 1248, 1250, 1263, 1288, 1307, 1310,\n", + " 1340, 1395])},\n", + " 5: {0: array([ 0, 1, 2, 3, 5, 7, 8, 9, 10, 11, 12,\n", + " 16, 17, 18, 19, 21, 23, 25, 26, 32, 35, 36,\n", + " 37, 38, 39, 42, 43, 45, 46, 47, 51, 52, 53,\n", + " 54, 56, 58, 59, 60, 66, 67, 68, 70, 74, 77,\n", + " 78, 79, 86, 87, 94, 102, 103, 105, 106, 108, 111,\n", + " 115, 120, 121, 122, 126, 130, 131, 133, 135, 138, 139,\n", + " 140, 141, 143, 148, 149, 150, 153, 154, 156, 158, 163,\n", + " 165, 166, 167, 168, 169, 172, 175, 179, 182, 186, 187,\n", + " 189, 190, 192, 194, 197, 199, 200, 205, 206, 207, 209,\n", + " 211, 214, 216, 220, 221, 222, 228, 229, 231, 233, 235,\n", + " 236, 240, 241, 244, 245, 246, 247, 248, 249, 252, 253,\n", + " 254, 257, 258, 259, 260, 261, 264, 265, 266, 267, 269,\n", + " 271, 273, 277, 278, 280, 281, 282, 284, 285, 288, 289,\n", + " 299, 300, 301, 302, 305, 306, 307, 311, 312, 314, 316,\n", + " 323, 324, 325, 335, 338, 340, 342, 344, 346, 347, 350,\n", + " 352, 354, 361, 363, 366, 369, 370, 371, 380, 384, 386,\n", + " 388, 389, 391, 392, 393, 394, 397, 398, 399, 400, 401,\n", + " 404, 405, 408, 409, 411, 412, 416, 418, 419, 421, 424,\n", + " 425, 427, 429, 430, 431, 435, 439, 442, 448, 449, 450,\n", + " 452, 453, 454, 456, 459, 460, 464, 465, 467, 468, 473,\n", + " 478, 480, 484, 486, 489, 490, 491, 492, 494, 495, 496,\n", + " 499, 501, 505, 507, 509, 512, 513, 515, 517, 521, 522,\n", + " 529, 532, 533, 534, 535, 537, 543, 544, 546, 548, 550,\n", + " 552, 554, 556, 558, 560, 561, 564, 572, 575, 581, 582,\n", + " 583, 589, 592, 598, 601, 602, 603, 604, 606, 607, 609,\n", + " 612, 616, 618, 620, 624, 628, 629, 631, 633, 634, 635,\n", + " 636, 640, 643, 645, 654, 655, 657, 662, 667, 668, 669,\n", + " 673, 674, 675, 680, 682, 683, 684, 686, 688, 690, 691,\n", + " 694, 696, 697, 698, 700, 702, 703, 704, 705, 707, 709,\n", + " 710, 711, 717, 718, 722, 728, 730, 732, 733, 739, 740,\n", + " 741, 743, 744, 747, 748, 750, 756, 758, 759, 760, 761,\n", + " 764, 766, 768, 769, 772, 774, 777, 778, 779, 780, 787,\n", + " 790, 793, 797, 798, 802, 803, 804, 810, 811, 814, 815,\n", + " 817, 818, 819, 823, 824, 825, 828, 830, 831, 832, 833,\n", + " 835, 837, 839, 840, 842, 844, 849, 850, 852, 856, 857,\n", + " 858, 860, 861, 862, 863, 864, 865, 867, 868, 869, 871,\n", + " 872, 873, 874, 876, 878, 879, 883, 884, 885, 887, 889,\n", + " 896, 899, 901, 903, 904, 905, 907, 908, 910, 911, 914,\n", + " 916, 919, 920, 928, 933, 934, 936, 937, 938, 939, 941,\n", + " 942, 943, 946, 947, 950, 952, 953, 954, 957, 960, 961,\n", + " 962, 964, 968, 970, 971, 972, 975, 980, 981, 983, 984,\n", + " 985, 990, 991, 992, 996, 997, 1001, 1003, 1005, 1006, 1008,\n", + " 1011, 1013, 1019, 1021, 1025, 1026, 1027, 1028, 1029, 1030, 1034,\n", + " 1037, 1039, 1041, 1042, 1043, 1045, 1046, 1047, 1048, 1052, 1053,\n", + " 1057, 1060, 1062, 1063, 1068, 1070, 1071, 1073, 1080, 1083, 1084,\n", + " 1086, 1100, 1101, 1103, 1106, 1110, 1111, 1113, 1117, 1119, 1121,\n", + " 1122, 1124, 1129, 1131, 1132, 1135, 1137, 1138, 1144, 1145, 1146,\n", + " 1148, 1150, 1154, 1157, 1158, 1161, 1163, 1164, 1165, 1166, 1168,\n", + " 1169, 1170, 1171, 1173, 1174, 1175, 1179, 1180, 1182, 1183, 1185,\n", + " 1186, 1193, 1195, 1196, 1198, 1200, 1201, 1202, 1204, 1208, 1209,\n", + " 1210, 1212, 1218, 1219, 1220, 1221, 1225, 1227, 1228, 1230, 1231,\n", + " 1234, 1237, 1239, 1240, 1241, 1243, 1244, 1253, 1255, 1258, 1264,\n", + " 1268, 1273, 1277, 1278, 1279, 1282, 1283, 1286, 1289, 1292, 1294,\n", + " 1296, 1301, 1305, 1306, 1308, 1309, 1314, 1316, 1319, 1321, 1322,\n", + " 1323, 1327, 1333, 1334, 1335, 1336, 1337, 1341, 1342, 1344, 1345,\n", + " 1349, 1351, 1352, 1355, 1358, 1359, 1361, 1362, 1363, 1368, 1369,\n", + " 1370, 1371, 1372, 1373, 1376, 1380, 1384, 1387, 1388, 1390, 1392,\n", + " 1394, 1399]),\n", + " 1: array([ 4, 14, 24, 33, 34, 50, 57, 69, 72, 73, 81,\n", + " 83, 84, 85, 91, 95, 117, 125, 134, 136, 152, 157,\n", + " 160, 161, 171, 173, 180, 183, 185, 188, 196, 198, 203,\n", + " 204, 217, 223, 224, 225, 237, 243, 256, 270, 279, 287,\n", + " 293, 313, 317, 319, 320, 321, 330, 345, 349, 356, 358,\n", + " 364, 367, 377, 379, 382, 387, 395, 406, 410, 415, 417,\n", + " 440, 447, 451, 457, 458, 466, 469, 470, 479, 506, 514,\n", + " 516, 519, 523, 524, 525, 531, 538, 541, 549, 568, 571,\n", + " 576, 577, 584, 588, 593, 596, 600, 613, 621, 625, 626,\n", + " 632, 638, 642, 646, 649, 651, 653, 656, 659, 660, 663,\n", + " 671, 679, 681, 693, 706, 713, 721, 723, 724, 729, 734,\n", + " 735, 736, 745, 749, 751, 752, 755, 771, 781, 784, 791,\n", + " 794, 795, 800, 801, 805, 807, 808, 813, 822, 826, 827,\n", + " 829, 854, 855, 881, 892, 894, 897, 898, 900, 902, 925,\n", + " 927, 930, 945, 948, 958, 959, 976, 977, 988, 998, 1004,\n", + " 1024, 1035, 1049, 1051, 1055, 1058, 1066, 1067, 1072, 1074, 1077,\n", + " 1087, 1089, 1090, 1091, 1093, 1096, 1097, 1098, 1099, 1104, 1107,\n", + " 1112, 1114, 1116, 1120, 1128, 1133, 1139, 1143, 1147, 1149, 1152,\n", + " 1159, 1167, 1178, 1189, 1194, 1205, 1213, 1215, 1216, 1222, 1229,\n", + " 1233, 1235, 1238, 1256, 1257, 1260, 1271, 1276, 1281, 1285, 1287,\n", + " 1290, 1293, 1295, 1300, 1302, 1304, 1313, 1325, 1331, 1338, 1343,\n", + " 1347, 1356, 1357, 1365, 1366, 1382, 1391, 1393, 1397]),\n", + " 2: array([ 6, 292, 339, 599, 670, 678, 719, 763, 783, 816, 877,\n", + " 888, 935, 993, 999, 1015, 1022, 1033, 1094, 1123, 1211]),\n", + " 3: array([ 13, 15, 20, 22, 27, 28, 29, 30, 31, 40, 41,\n", + " 44, 48, 49, 55, 61, 63, 64, 65, 76, 80, 82,\n", + " 88, 89, 90, 92, 93, 96, 97, 98, 99, 101, 104,\n", + " 107, 109, 110, 112, 113, 114, 116, 118, 119, 123, 124,\n", + " 127, 128, 129, 132, 137, 142, 144, 145, 146, 147, 151,\n", + " 155, 159, 162, 164, 170, 174, 177, 178, 181, 184, 191,\n", + " 193, 195, 201, 202, 208, 210, 212, 213, 215, 218, 219,\n", + " 226, 227, 230, 232, 234, 238, 239, 242, 250, 251, 255,\n", + " 262, 263, 268, 272, 274, 275, 276, 283, 286, 290, 291,\n", + " 294, 295, 296, 297, 298, 303, 304, 309, 310, 315, 318,\n", + " 322, 327, 328, 329, 331, 332, 334, 336, 337, 341, 343,\n", + " 348, 351, 353, 355, 357, 360, 362, 365, 368, 372, 373,\n", + " 374, 375, 376, 378, 381, 383, 385, 390, 396, 402, 403,\n", + " 407, 413, 414, 420, 422, 423, 426, 428, 432, 433, 434,\n", + " 436, 437, 438, 444, 445, 446, 455, 461, 462, 463, 471,\n", + " 472, 474, 475, 476, 477, 482, 483, 485, 487, 488, 493,\n", + " 497, 500, 502, 503, 504, 508, 510, 511, 518, 520, 526,\n", + " 527, 528, 530, 536, 539, 540, 542, 545, 547, 551, 553,\n", + " 555, 557, 559, 562, 563, 565, 567, 569, 570, 573, 574,\n", + " 578, 579, 580, 587, 590, 591, 594, 597, 605, 608, 611,\n", + " 614, 615, 617, 619, 622, 623, 627, 630, 637, 639, 641,\n", + " 644, 647, 648, 650, 652, 658, 661, 664, 665, 666, 672,\n", + " 676, 677, 685, 687, 689, 692, 695, 699, 701, 708, 712,\n", + " 714, 715, 716, 720, 725, 726, 727, 731, 737, 742, 746,\n", + " 753, 757, 762, 765, 767, 770, 773, 775, 776, 782, 785,\n", + " 786, 788, 789, 792, 796, 799, 806, 809, 812, 820, 821,\n", + " 834, 836, 838, 841, 843, 845, 846, 847, 848, 851, 853,\n", + " 859, 875, 880, 882, 886, 890, 891, 893, 895, 906, 909,\n", + " 912, 913, 915, 917, 918, 921, 922, 923, 924, 926, 929,\n", + " 931, 932, 940, 944, 951, 955, 956, 965, 966, 967, 969,\n", + " 973, 974, 978, 979, 982, 986, 987, 989, 994, 1000, 1002,\n", + " 1007, 1009, 1010, 1014, 1016, 1017, 1020, 1023, 1031, 1032, 1036,\n", + " 1038, 1040, 1044, 1050, 1054, 1056, 1061, 1064, 1065, 1069, 1075,\n", + " 1076, 1079, 1082, 1085, 1088, 1092, 1095, 1102, 1105, 1108, 1109,\n", + " 1115, 1118, 1125, 1126, 1127, 1130, 1134, 1141, 1142, 1151, 1153,\n", + " 1155, 1160, 1162, 1172, 1177, 1184, 1188, 1190, 1191, 1192, 1197,\n", + " 1199, 1203, 1206, 1214, 1217, 1223, 1224, 1226, 1232, 1236, 1245,\n", + " 1246, 1247, 1249, 1251, 1252, 1254, 1259, 1261, 1262, 1265, 1266,\n", + " 1267, 1269, 1270, 1272, 1274, 1275, 1280, 1284, 1291, 1297, 1298,\n", + " 1299, 1303, 1311, 1312, 1315, 1317, 1318, 1320, 1324, 1326, 1328,\n", + " 1329, 1330, 1332, 1339, 1346, 1348, 1350, 1353, 1354, 1360, 1364,\n", + " 1367, 1374, 1375, 1377, 1378, 1379, 1381, 1383, 1385, 1386, 1389,\n", + " 1396, 1398]),\n", + " 4: array([ 62, 71, 75, 100, 176, 308, 326, 333, 359, 441, 443,\n", + " 481, 498, 566, 585, 586, 595, 610, 738, 754, 866, 870,\n", + " 949, 963, 995, 1012, 1018, 1059, 1078, 1081, 1136, 1140, 1156,\n", + " 1176, 1181, 1187, 1207, 1242, 1248, 1250, 1263, 1288, 1307, 1310,\n", + " 1340, 1395])},\n", + " 6: {0: array([ 0, 1, 2, 3, 5, 7, 8, 9, 10, 11, 12,\n", + " 16, 17, 18, 19, 21, 23, 25, 26, 32, 35, 36,\n", + " 37, 38, 39, 42, 43, 45, 46, 47, 51, 52, 53,\n", + " 54, 56, 58, 59, 60, 66, 67, 68, 70, 74, 77,\n", + " 78, 79, 86, 87, 94, 102, 103, 105, 106, 108, 111,\n", + " 115, 120, 121, 122, 126, 130, 131, 133, 135, 138, 139,\n", + " 140, 141, 143, 148, 149, 150, 153, 154, 156, 158, 163,\n", + " 165, 166, 167, 168, 169, 172, 175, 179, 182, 186, 187,\n", + " 189, 190, 192, 194, 197, 199, 200, 205, 206, 207, 209,\n", + " 211, 214, 216, 220, 221, 222, 228, 229, 231, 233, 235,\n", + " 236, 240, 241, 244, 245, 246, 247, 248, 249, 252, 253,\n", + " 254, 257, 258, 259, 260, 261, 264, 265, 266, 267, 269,\n", + " 271, 273, 277, 278, 280, 281, 282, 284, 285, 288, 289,\n", + " 299, 300, 301, 302, 305, 306, 307, 311, 312, 314, 316,\n", + " 323, 324, 325, 335, 338, 340, 342, 344, 346, 347, 350,\n", + " 352, 354, 361, 363, 366, 369, 370, 371, 380, 384, 386,\n", + " 388, 389, 391, 392, 393, 394, 397, 398, 399, 400, 401,\n", + " 404, 405, 408, 409, 411, 412, 416, 418, 419, 421, 424,\n", + " 425, 427, 429, 430, 431, 435, 439, 442, 448, 449, 450,\n", + " 452, 453, 454, 456, 459, 460, 464, 465, 467, 468, 473,\n", + " 478, 480, 484, 486, 489, 490, 491, 492, 494, 495, 496,\n", + " 499, 501, 505, 507, 509, 512, 513, 515, 517, 521, 522,\n", + " 529, 532, 533, 534, 535, 537, 543, 544, 546, 548, 550,\n", + " 552, 554, 556, 558, 560, 561, 564, 572, 575, 581, 582,\n", + " 583, 589, 592, 598, 601, 602, 603, 604, 606, 607, 609,\n", + " 612, 616, 618, 620, 624, 628, 629, 631, 633, 634, 635,\n", + " 636, 640, 643, 645, 654, 655, 657, 662, 667, 668, 669,\n", + " 673, 674, 675, 680, 682, 683, 684, 686, 688, 690, 691,\n", + " 694, 696, 697, 698, 700, 702, 703, 704, 705, 707, 709,\n", + " 710, 711, 717, 718, 722, 728, 730, 732, 733, 739, 740,\n", + " 741, 743, 744, 747, 748, 750, 756, 758, 759, 760, 761,\n", + " 764, 766, 768, 769, 772, 774, 777, 778, 779, 780, 787,\n", + " 790, 793, 797, 798, 802, 803, 804, 810, 811, 814, 815,\n", + " 817, 818, 819, 823, 824, 825, 828, 830, 831, 832, 833,\n", + " 835, 837, 839, 840, 842, 844, 849, 850, 852, 856, 857,\n", + " 858, 860, 861, 862, 863, 864, 865, 867, 868, 869, 871,\n", + " 872, 873, 874, 876, 878, 879, 883, 884, 885, 887, 889,\n", + " 896, 899, 901, 903, 904, 905, 907, 908, 910, 911, 914,\n", + " 916, 919, 920, 928, 933, 934, 936, 937, 938, 939, 941,\n", + " 942, 943, 946, 947, 950, 952, 953, 954, 957, 960, 961,\n", + " 962, 964, 968, 970, 971, 972, 975, 980, 981, 983, 984,\n", + " 985, 990, 991, 992, 996, 997, 1001, 1003, 1005, 1006, 1008,\n", + " 1011, 1013, 1019, 1021, 1025, 1026, 1027, 1028, 1029, 1030, 1034,\n", + " 1037, 1039, 1041, 1042, 1043, 1045, 1046, 1047, 1048, 1052, 1053,\n", + " 1057, 1060, 1062, 1063, 1068, 1070, 1071, 1073, 1080, 1083, 1084,\n", + " 1086, 1100, 1101, 1103, 1106, 1110, 1111, 1113, 1117, 1119, 1121,\n", + " 1122, 1124, 1129, 1131, 1132, 1135, 1137, 1138, 1144, 1145, 1146,\n", + " 1148, 1150, 1154, 1157, 1158, 1161, 1163, 1164, 1165, 1166, 1168,\n", + " 1169, 1170, 1171, 1173, 1174, 1175, 1179, 1180, 1182, 1183, 1185,\n", + " 1186, 1193, 1195, 1196, 1198, 1200, 1201, 1202, 1204, 1208, 1209,\n", + " 1210, 1212, 1218, 1219, 1220, 1221, 1225, 1227, 1228, 1230, 1231,\n", + " 1234, 1237, 1239, 1240, 1241, 1243, 1244, 1253, 1255, 1258, 1264,\n", + " 1268, 1273, 1277, 1278, 1279, 1282, 1283, 1286, 1289, 1292, 1294,\n", + " 1296, 1301, 1305, 1306, 1308, 1309, 1314, 1316, 1319, 1321, 1322,\n", + " 1323, 1327, 1333, 1334, 1335, 1336, 1337, 1341, 1342, 1344, 1345,\n", + " 1349, 1351, 1352, 1355, 1358, 1359, 1361, 1362, 1363, 1368, 1369,\n", + " 1370, 1371, 1372, 1373, 1376, 1380, 1384, 1387, 1388, 1390, 1392,\n", + " 1394, 1399]),\n", + " 1: array([ 4, 14, 24, 33, 34, 50, 57, 69, 72, 73, 81,\n", + " 83, 84, 85, 91, 95, 117, 125, 134, 136, 152, 157,\n", + " 160, 161, 171, 173, 180, 183, 185, 188, 196, 198, 203,\n", + " 204, 217, 223, 224, 225, 237, 243, 256, 270, 279, 287,\n", + " 293, 313, 317, 319, 320, 321, 330, 345, 349, 356, 358,\n", + " 364, 367, 377, 379, 382, 387, 395, 406, 410, 415, 417,\n", + " 440, 447, 451, 457, 458, 466, 469, 470, 479, 506, 514,\n", + " 516, 519, 523, 524, 525, 531, 538, 541, 549, 568, 571,\n", + " 576, 577, 584, 588, 593, 596, 600, 613, 621, 625, 626,\n", + " 632, 638, 642, 646, 649, 651, 653, 656, 659, 660, 663,\n", + " 671, 679, 681, 693, 706, 713, 721, 723, 724, 729, 734,\n", + " 735, 736, 745, 749, 751, 752, 755, 771, 781, 784, 791,\n", + " 794, 795, 800, 801, 805, 807, 808, 813, 822, 826, 827,\n", + " 829, 854, 855, 881, 892, 894, 897, 898, 900, 902, 925,\n", + " 927, 930, 945, 948, 958, 959, 976, 977, 988, 998, 1004,\n", + " 1024, 1035, 1049, 1051, 1055, 1058, 1066, 1067, 1072, 1074, 1077,\n", + " 1087, 1089, 1090, 1091, 1093, 1096, 1097, 1098, 1099, 1104, 1107,\n", + " 1112, 1114, 1116, 1120, 1128, 1133, 1139, 1143, 1147, 1149, 1152,\n", + " 1159, 1167, 1178, 1189, 1194, 1205, 1213, 1215, 1216, 1222, 1229,\n", + " 1233, 1235, 1238, 1256, 1257, 1260, 1271, 1276, 1281, 1285, 1287,\n", + " 1290, 1293, 1295, 1300, 1302, 1304, 1313, 1325, 1331, 1338, 1343,\n", + " 1347, 1356, 1357, 1365, 1366, 1382, 1391, 1393, 1397]),\n", + " 2: array([ 6, 292, 339, 599, 670, 678, 719, 763, 783, 816, 877,\n", + " 888, 935, 993, 999, 1015, 1022, 1033, 1094, 1123, 1211]),\n", + " 3: array([ 13, 27, 28, 29, 30, 31, 41, 44, 48, 49, 55,\n", + " 61, 63, 64, 80, 82, 88, 89, 90, 92, 93, 96,\n", + " 97, 98, 99, 104, 107, 109, 110, 113, 114, 118, 124,\n", + " 127, 128, 132, 142, 144, 145, 151, 155, 159, 162, 170,\n", + " 174, 177, 178, 181, 184, 193, 195, 201, 202, 208, 210,\n", + " 212, 213, 218, 219, 227, 232, 234, 238, 239, 255, 262,\n", + " 268, 272, 274, 276, 283, 286, 290, 291, 294, 295, 296,\n", + " 297, 298, 303, 304, 310, 315, 322, 327, 331, 332, 334,\n", + " 336, 341, 343, 348, 351, 353, 357, 360, 365, 368, 372,\n", + " 373, 375, 376, 378, 381, 383, 385, 390, 396, 402, 407,\n", + " 414, 420, 422, 423, 426, 428, 433, 434, 436, 437, 444,\n", + " 445, 446, 455, 462, 463, 471, 472, 474, 475, 476, 482,\n", + " 483, 485, 487, 488, 497, 500, 502, 503, 504, 508, 510,\n", + " 511, 518, 520, 526, 527, 528, 530, 536, 539, 540, 545,\n", + " 547, 551, 553, 555, 559, 563, 565, 567, 569, 570, 573,\n", + " 574, 578, 579, 580, 590, 591, 594, 605, 608, 611, 614,\n", + " 615, 617, 619, 622, 623, 627, 637, 639, 644, 647, 648,\n", + " 650, 652, 658, 661, 665, 666, 672, 676, 677, 685, 687,\n", + " 689, 692, 695, 699, 708, 712, 714, 715, 716, 720, 726,\n", + " 727, 731, 742, 753, 757, 762, 765, 767, 775, 776, 782,\n", + " 785, 786, 789, 792, 796, 799, 812, 834, 838, 841, 843,\n", + " 845, 846, 848, 853, 875, 880, 882, 886, 890, 891, 906,\n", + " 909, 912, 913, 917, 918, 921, 922, 923, 924, 926, 929,\n", + " 931, 932, 940, 944, 951, 955, 965, 973, 974, 979, 986,\n", + " 987, 989, 994, 1000, 1002, 1009, 1010, 1014, 1017, 1020, 1023,\n", + " 1031, 1036, 1038, 1040, 1044, 1050, 1054, 1056, 1064, 1065, 1069,\n", + " 1075, 1079, 1082, 1085, 1088, 1092, 1095, 1102, 1108, 1109, 1115,\n", + " 1118, 1125, 1126, 1127, 1130, 1134, 1141, 1142, 1155, 1160, 1162,\n", + " 1172, 1177, 1184, 1190, 1192, 1203, 1206, 1214, 1217, 1226, 1232,\n", + " 1236, 1245, 1246, 1247, 1249, 1251, 1252, 1254, 1259, 1261, 1262,\n", + " 1265, 1266, 1267, 1269, 1272, 1274, 1275, 1280, 1291, 1298, 1299,\n", + " 1303, 1311, 1312, 1315, 1317, 1318, 1320, 1324, 1326, 1328, 1329,\n", + " 1330, 1332, 1339, 1346, 1350, 1353, 1364, 1367, 1374, 1375, 1378,\n", + " 1383, 1385, 1386, 1389, 1398]),\n", + " 4: array([ 15, 20, 22, 40, 65, 76, 101, 112, 116, 119, 123,\n", + " 129, 137, 146, 147, 164, 191, 215, 226, 230, 242, 250,\n", + " 251, 263, 275, 309, 318, 328, 329, 337, 355, 362, 374,\n", + " 403, 413, 432, 438, 461, 477, 493, 542, 557, 562, 587,\n", + " 597, 630, 641, 664, 701, 725, 737, 746, 770, 773, 788,\n", + " 806, 809, 820, 821, 836, 847, 851, 859, 893, 895, 915,\n", + " 956, 966, 967, 969, 978, 982, 1007, 1016, 1032, 1061, 1076,\n", + " 1105, 1151, 1153, 1188, 1191, 1197, 1199, 1223, 1224, 1270, 1284,\n", + " 1297, 1348, 1354, 1360, 1377, 1379, 1381, 1396]),\n", + " 5: array([ 62, 71, 75, 100, 176, 308, 326, 333, 359, 441, 443,\n", + " 481, 498, 566, 585, 586, 595, 610, 738, 754, 866, 870,\n", + " 949, 963, 995, 1012, 1018, 1059, 1078, 1081, 1136, 1140, 1156,\n", + " 1176, 1181, 1187, 1207, 1242, 1248, 1250, 1263, 1288, 1307, 1310,\n", + " 1340, 1395])},\n", + " 7: {0: array([ 0, 1, 2, 3, 8, 10, 11, 12, 17, 18, 19,\n", + " 21, 23, 25, 26, 35, 36, 42, 43, 47, 51, 52,\n", + " 53, 54, 56, 58, 59, 60, 67, 68, 70, 74, 77,\n", + " 78, 79, 86, 94, 102, 105, 106, 111, 115, 120, 121,\n", + " 122, 126, 131, 133, 138, 140, 150, 153, 154, 156, 165,\n", + " 166, 168, 172, 182, 186, 187, 190, 192, 194, 199, 200,\n", + " 205, 206, 207, 209, 216, 222, 228, 229, 231, 233, 235,\n", + " 236, 240, 245, 246, 247, 248, 249, 253, 254, 257, 258,\n", + " 259, 260, 261, 265, 266, 267, 269, 271, 278, 280, 282,\n", + " 284, 289, 299, 300, 301, 302, 305, 306, 307, 311, 312,\n", + " 314, 316, 323, 324, 325, 335, 338, 342, 344, 346, 347,\n", + " 350, 352, 361, 363, 366, 369, 370, 371, 380, 384, 388,\n", + " 389, 391, 392, 393, 394, 397, 398, 399, 401, 404, 405,\n", + " 408, 409, 416, 418, 419, 421, 424, 425, 430, 431, 435,\n", + " 439, 442, 448, 450, 453, 454, 460, 465, 473, 478, 484,\n", + " 486, 490, 491, 492, 494, 496, 499, 505, 507, 513, 515,\n", + " 517, 521, 522, 529, 532, 533, 534, 537, 544, 546, 550,\n", + " 556, 558, 560, 561, 581, 582, 583, 592, 598, 601, 602,\n", + " 603, 604, 606, 607, 612, 616, 618, 628, 629, 631, 633,\n", + " 635, 640, 654, 655, 657, 667, 668, 669, 673, 674, 680,\n", + " 682, 683, 684, 686, 691, 694, 696, 697, 698, 700, 702,\n", + " 703, 704, 710, 711, 717, 728, 732, 733, 739, 740, 741,\n", + " 744, 747, 748, 750, 756, 758, 759, 760, 761, 768, 772,\n", + " 777, 779, 780, 787, 790, 793, 797, 802, 804, 810, 811,\n", + " 814, 815, 818, 823, 824, 825, 828, 830, 831, 832, 833,\n", + " 835, 837, 840, 842, 844, 849, 852, 858, 862, 863, 864,\n", + " 865, 868, 869, 871, 872, 873, 876, 879, 883, 884, 885,\n", + " 889, 899, 901, 904, 905, 907, 908, 910, 911, 914, 916,\n", + " 919, 920, 928, 933, 934, 936, 937, 941, 946, 950, 952,\n", + " 953, 954, 957, 968, 970, 972, 975, 980, 983, 984, 985,\n", + " 990, 991, 992, 1001, 1003, 1005, 1006, 1013, 1019, 1029, 1030,\n", + " 1034, 1037, 1039, 1041, 1042, 1043, 1047, 1048, 1052, 1053, 1057,\n", + " 1060, 1062, 1063, 1068, 1070, 1071, 1073, 1080, 1083, 1101, 1106,\n", + " 1110, 1117, 1121, 1122, 1124, 1129, 1131, 1132, 1138, 1144, 1146,\n", + " 1148, 1150, 1154, 1157, 1158, 1161, 1168, 1169, 1170, 1171, 1173,\n", + " 1174, 1175, 1180, 1182, 1183, 1185, 1186, 1193, 1195, 1196, 1198,\n", + " 1200, 1202, 1204, 1208, 1212, 1219, 1220, 1221, 1225, 1227, 1228,\n", + " 1230, 1231, 1234, 1237, 1239, 1240, 1241, 1255, 1258, 1268, 1277,\n", + " 1278, 1282, 1283, 1286, 1292, 1294, 1296, 1301, 1305, 1306, 1308,\n", + " 1309, 1314, 1316, 1319, 1322, 1327, 1333, 1335, 1336, 1341, 1342,\n", + " 1344, 1345, 1349, 1351, 1352, 1358, 1361, 1362, 1363, 1368, 1369,\n", + " 1370, 1371, 1373, 1376, 1380, 1384, 1387, 1388, 1390, 1392, 1394,\n", + " 1399]),\n", + " 1: array([ 4, 14, 24, 33, 34, 50, 57, 69, 72, 73, 81,\n", + " 83, 84, 85, 91, 95, 117, 125, 134, 136, 152, 157,\n", + " 160, 161, 171, 173, 180, 183, 185, 188, 196, 198, 203,\n", + " 204, 217, 223, 224, 225, 237, 243, 256, 270, 279, 287,\n", + " 293, 313, 317, 319, 320, 321, 330, 345, 349, 356, 358,\n", + " 364, 367, 377, 379, 382, 387, 395, 406, 410, 415, 417,\n", + " 440, 447, 451, 457, 458, 466, 469, 470, 479, 506, 514,\n", + " 516, 519, 523, 524, 525, 531, 538, 541, 549, 568, 571,\n", + " 576, 577, 584, 588, 593, 596, 600, 613, 621, 625, 626,\n", + " 632, 638, 642, 646, 649, 651, 653, 656, 659, 660, 663,\n", + " 671, 679, 681, 693, 706, 713, 721, 723, 724, 729, 734,\n", + " 735, 736, 745, 749, 751, 752, 755, 771, 781, 784, 791,\n", + " 794, 795, 800, 801, 805, 807, 808, 813, 822, 826, 827,\n", + " 829, 854, 855, 881, 892, 894, 897, 898, 900, 902, 925,\n", + " 927, 930, 945, 948, 958, 959, 976, 977, 988, 998, 1004,\n", + " 1024, 1035, 1049, 1051, 1055, 1058, 1066, 1067, 1072, 1074, 1077,\n", + " 1087, 1089, 1090, 1091, 1093, 1096, 1097, 1098, 1099, 1104, 1107,\n", + " 1112, 1114, 1116, 1120, 1128, 1133, 1139, 1143, 1147, 1149, 1152,\n", + " 1159, 1167, 1178, 1189, 1194, 1205, 1213, 1215, 1216, 1222, 1229,\n", + " 1233, 1235, 1238, 1256, 1257, 1260, 1271, 1276, 1281, 1285, 1287,\n", + " 1290, 1293, 1295, 1300, 1302, 1304, 1313, 1325, 1331, 1338, 1343,\n", + " 1347, 1356, 1357, 1365, 1366, 1382, 1391, 1393, 1397]),\n", + " 2: array([ 5, 7, 9, 16, 32, 37, 38, 39, 45, 46, 66,\n", + " 87, 103, 108, 130, 135, 139, 141, 143, 148, 149, 158,\n", + " 163, 167, 169, 175, 179, 189, 197, 211, 214, 220, 221,\n", + " 241, 244, 252, 264, 273, 277, 281, 285, 288, 340, 354,\n", + " 386, 400, 411, 412, 427, 429, 449, 452, 456, 459, 464,\n", + " 467, 468, 480, 489, 495, 501, 509, 512, 535, 543, 548,\n", + " 552, 554, 564, 572, 575, 589, 609, 620, 624, 634, 636,\n", + " 643, 645, 662, 675, 688, 690, 705, 707, 709, 718, 722,\n", + " 730, 743, 764, 766, 769, 774, 778, 798, 803, 817, 819,\n", + " 839, 850, 856, 857, 860, 861, 867, 874, 878, 887, 896,\n", + " 903, 938, 939, 942, 943, 947, 960, 961, 962, 964, 971,\n", + " 981, 996, 997, 1008, 1011, 1021, 1025, 1026, 1027, 1028, 1045,\n", + " 1046, 1084, 1086, 1100, 1103, 1111, 1113, 1119, 1135, 1137, 1145,\n", + " 1163, 1164, 1165, 1166, 1179, 1201, 1209, 1210, 1218, 1243, 1244,\n", + " 1253, 1264, 1273, 1279, 1289, 1321, 1323, 1334, 1337, 1355, 1359,\n", + " 1372]),\n", + " 3: array([ 6, 292, 339, 599, 670, 678, 719, 763, 783, 816, 877,\n", + " 888, 935, 993, 999, 1015, 1022, 1033, 1094, 1123, 1211]),\n", + " 4: array([ 13, 27, 28, 29, 30, 31, 41, 44, 48, 49, 55,\n", + " 61, 63, 64, 80, 82, 88, 89, 90, 92, 93, 96,\n", + " 97, 98, 99, 104, 107, 109, 110, 113, 114, 118, 124,\n", + " 127, 128, 132, 142, 144, 145, 151, 155, 159, 162, 170,\n", + " 174, 177, 178, 181, 184, 193, 195, 201, 202, 208, 210,\n", + " 212, 213, 218, 219, 227, 232, 234, 238, 239, 255, 262,\n", + " 268, 272, 274, 276, 283, 286, 290, 291, 294, 295, 296,\n", + " 297, 298, 303, 304, 310, 315, 322, 327, 331, 332, 334,\n", + " 336, 341, 343, 348, 351, 353, 357, 360, 365, 368, 372,\n", + " 373, 375, 376, 378, 381, 383, 385, 390, 396, 402, 407,\n", + " 414, 420, 422, 423, 426, 428, 433, 434, 436, 437, 444,\n", + " 445, 446, 455, 462, 463, 471, 472, 474, 475, 476, 482,\n", + " 483, 485, 487, 488, 497, 500, 502, 503, 504, 508, 510,\n", + " 511, 518, 520, 526, 527, 528, 530, 536, 539, 540, 545,\n", + " 547, 551, 553, 555, 559, 563, 565, 567, 569, 570, 573,\n", + " 574, 578, 579, 580, 590, 591, 594, 605, 608, 611, 614,\n", + " 615, 617, 619, 622, 623, 627, 637, 639, 644, 647, 648,\n", + " 650, 652, 658, 661, 665, 666, 672, 676, 677, 685, 687,\n", + " 689, 692, 695, 699, 708, 712, 714, 715, 716, 720, 726,\n", + " 727, 731, 742, 753, 757, 762, 765, 767, 775, 776, 782,\n", + " 785, 786, 789, 792, 796, 799, 812, 834, 838, 841, 843,\n", + " 845, 846, 848, 853, 875, 880, 882, 886, 890, 891, 906,\n", + " 909, 912, 913, 917, 918, 921, 922, 923, 924, 926, 929,\n", + " 931, 932, 940, 944, 951, 955, 965, 973, 974, 979, 986,\n", + " 987, 989, 994, 1000, 1002, 1009, 1010, 1014, 1017, 1020, 1023,\n", + " 1031, 1036, 1038, 1040, 1044, 1050, 1054, 1056, 1064, 1065, 1069,\n", + " 1075, 1079, 1082, 1085, 1088, 1092, 1095, 1102, 1108, 1109, 1115,\n", + " 1118, 1125, 1126, 1127, 1130, 1134, 1141, 1142, 1155, 1160, 1162,\n", + " 1172, 1177, 1184, 1190, 1192, 1203, 1206, 1214, 1217, 1226, 1232,\n", + " 1236, 1245, 1246, 1247, 1249, 1251, 1252, 1254, 1259, 1261, 1262,\n", + " 1265, 1266, 1267, 1269, 1272, 1274, 1275, 1280, 1291, 1298, 1299,\n", + " 1303, 1311, 1312, 1315, 1317, 1318, 1320, 1324, 1326, 1328, 1329,\n", + " 1330, 1332, 1339, 1346, 1350, 1353, 1364, 1367, 1374, 1375, 1378,\n", + " 1383, 1385, 1386, 1389, 1398]),\n", + " 5: array([ 15, 20, 22, 40, 65, 76, 101, 112, 116, 119, 123,\n", + " 129, 137, 146, 147, 164, 191, 215, 226, 230, 242, 250,\n", + " 251, 263, 275, 309, 318, 328, 329, 337, 355, 362, 374,\n", + " 403, 413, 432, 438, 461, 477, 493, 542, 557, 562, 587,\n", + " 597, 630, 641, 664, 701, 725, 737, 746, 770, 773, 788,\n", + " 806, 809, 820, 821, 836, 847, 851, 859, 893, 895, 915,\n", + " 956, 966, 967, 969, 978, 982, 1007, 1016, 1032, 1061, 1076,\n", + " 1105, 1151, 1153, 1188, 1191, 1197, 1199, 1223, 1224, 1270, 1284,\n", + " 1297, 1348, 1354, 1360, 1377, 1379, 1381, 1396]),\n", + " 6: array([ 62, 71, 75, 100, 176, 308, 326, 333, 359, 441, 443,\n", + " 481, 498, 566, 585, 586, 595, 610, 738, 754, 866, 870,\n", + " 949, 963, 995, 1012, 1018, 1059, 1078, 1081, 1136, 1140, 1156,\n", + " 1176, 1181, 1187, 1207, 1242, 1248, 1250, 1263, 1288, 1307, 1310,\n", + " 1340, 1395])},\n", + " 8: {0: array([ 0, 1, 2, 3, 8, 10, 11, 12, 17, 18, 19,\n", + " 21, 23, 25, 26, 35, 36, 42, 43, 47, 51, 52,\n", + " 53, 54, 56, 58, 59, 60, 67, 68, 70, 74, 77,\n", + " 78, 79, 86, 94, 102, 105, 106, 111, 115, 120, 121,\n", + " 122, 126, 131, 133, 138, 140, 150, 153, 154, 156, 165,\n", + " 166, 168, 172, 182, 186, 187, 190, 192, 194, 199, 200,\n", + " 205, 206, 207, 209, 216, 222, 228, 229, 231, 233, 235,\n", + " 236, 240, 245, 246, 247, 248, 249, 253, 254, 257, 258,\n", + " 259, 260, 261, 265, 266, 267, 269, 271, 278, 280, 282,\n", + " 284, 289, 299, 300, 301, 302, 305, 306, 307, 311, 312,\n", + " 314, 316, 323, 324, 325, 335, 338, 342, 344, 346, 347,\n", + " 350, 352, 361, 363, 366, 369, 370, 371, 380, 384, 388,\n", + " 389, 391, 392, 393, 394, 397, 398, 399, 401, 404, 405,\n", + " 408, 409, 416, 418, 419, 421, 424, 425, 430, 431, 435,\n", + " 439, 442, 448, 450, 453, 454, 460, 465, 473, 478, 484,\n", + " 486, 490, 491, 492, 494, 496, 499, 505, 507, 513, 515,\n", + " 517, 521, 522, 529, 532, 533, 534, 537, 544, 546, 550,\n", + " 556, 558, 560, 561, 581, 582, 583, 592, 598, 601, 602,\n", + " 603, 604, 606, 607, 612, 616, 618, 628, 629, 631, 633,\n", + " 635, 640, 654, 655, 657, 667, 668, 669, 673, 674, 680,\n", + " 682, 683, 684, 686, 691, 694, 696, 697, 698, 700, 702,\n", + " 703, 704, 710, 711, 717, 728, 732, 733, 739, 740, 741,\n", + " 744, 747, 748, 750, 756, 758, 759, 760, 761, 768, 772,\n", + " 777, 779, 780, 787, 790, 793, 797, 802, 804, 810, 811,\n", + " 814, 815, 818, 823, 824, 825, 828, 830, 831, 832, 833,\n", + " 835, 837, 840, 842, 844, 849, 852, 858, 862, 863, 864,\n", + " 865, 868, 869, 871, 872, 873, 876, 879, 883, 884, 885,\n", + " 889, 899, 901, 904, 905, 907, 908, 910, 911, 914, 916,\n", + " 919, 920, 928, 933, 934, 936, 937, 941, 946, 950, 952,\n", + " 953, 954, 957, 968, 970, 972, 975, 980, 983, 984, 985,\n", + " 990, 991, 992, 1001, 1003, 1005, 1006, 1013, 1019, 1029, 1030,\n", + " 1034, 1037, 1039, 1041, 1042, 1043, 1047, 1048, 1052, 1053, 1057,\n", + " 1060, 1062, 1063, 1068, 1070, 1071, 1073, 1080, 1083, 1101, 1106,\n", + " 1110, 1117, 1121, 1122, 1124, 1129, 1131, 1132, 1138, 1144, 1146,\n", + " 1148, 1150, 1154, 1157, 1158, 1161, 1168, 1169, 1170, 1171, 1173,\n", + " 1174, 1175, 1180, 1182, 1183, 1185, 1186, 1193, 1195, 1196, 1198,\n", + " 1200, 1202, 1204, 1208, 1212, 1219, 1220, 1221, 1225, 1227, 1228,\n", + " 1230, 1231, 1234, 1237, 1239, 1240, 1241, 1255, 1258, 1268, 1277,\n", + " 1278, 1282, 1283, 1286, 1292, 1294, 1296, 1301, 1305, 1306, 1308,\n", + " 1309, 1314, 1316, 1319, 1322, 1327, 1333, 1335, 1336, 1341, 1342,\n", + " 1344, 1345, 1349, 1351, 1352, 1358, 1361, 1362, 1363, 1368, 1369,\n", + " 1370, 1371, 1373, 1376, 1380, 1384, 1387, 1388, 1390, 1392, 1394,\n", + " 1399]),\n", + " 1: array([ 4, 14, 24, 33, 34, 50, 57, 69, 72, 73, 81,\n", + " 83, 84, 85, 91, 95, 117, 125, 134, 136, 152, 157,\n", + " 160, 161, 171, 173, 180, 183, 185, 188, 196, 198, 203,\n", + " 204, 217, 223, 224, 225, 237, 243, 256, 270, 279, 287,\n", + " 293, 313, 317, 319, 320, 321, 330, 345, 349, 356, 358,\n", + " 364, 367, 377, 379, 382, 387, 395, 406, 410, 415, 417,\n", + " 440, 447, 451, 457, 458, 466, 469, 470, 479, 506, 514,\n", + " 516, 519, 523, 524, 525, 531, 538, 541, 549, 568, 571,\n", + " 576, 577, 584, 588, 593, 596, 600, 613, 621, 625, 626,\n", + " 632, 638, 642, 646, 649, 651, 653, 656, 659, 660, 663,\n", + " 671, 679, 681, 693, 706, 713, 721, 723, 724, 729, 734,\n", + " 735, 736, 745, 749, 751, 752, 755, 771, 781, 784, 791,\n", + " 794, 795, 800, 801, 805, 807, 808, 813, 822, 826, 827,\n", + " 829, 854, 855, 881, 892, 894, 897, 898, 900, 902, 925,\n", + " 927, 930, 945, 948, 958, 959, 976, 977, 988, 998, 1004,\n", + " 1024, 1035, 1049, 1051, 1055, 1058, 1066, 1067, 1072, 1074, 1077,\n", + " 1087, 1089, 1090, 1091, 1093, 1096, 1097, 1098, 1099, 1104, 1107,\n", + " 1112, 1114, 1116, 1120, 1128, 1133, 1139, 1143, 1147, 1149, 1152,\n", + " 1159, 1167, 1178, 1189, 1194, 1205, 1213, 1215, 1216, 1222, 1229,\n", + " 1233, 1235, 1238, 1256, 1257, 1260, 1271, 1276, 1281, 1285, 1287,\n", + " 1290, 1293, 1295, 1300, 1302, 1304, 1313, 1325, 1331, 1338, 1343,\n", + " 1347, 1356, 1357, 1365, 1366, 1382, 1391, 1393, 1397]),\n", + " 2: array([ 5, 7, 9, 16, 32, 37, 38, 39, 45, 46, 66,\n", + " 87, 103, 108, 130, 135, 139, 141, 143, 148, 149, 158,\n", + " 163, 167, 169, 175, 179, 189, 197, 211, 214, 220, 221,\n", + " 241, 244, 252, 264, 273, 277, 281, 285, 288, 340, 354,\n", + " 386, 400, 411, 412, 427, 429, 449, 452, 456, 459, 464,\n", + " 467, 468, 480, 489, 495, 501, 509, 512, 535, 543, 548,\n", + " 552, 554, 564, 572, 575, 589, 609, 620, 624, 634, 636,\n", + " 643, 645, 662, 675, 688, 690, 705, 707, 709, 718, 722,\n", + " 730, 743, 764, 766, 769, 774, 778, 798, 803, 817, 819,\n", + " 839, 850, 856, 857, 860, 861, 867, 874, 878, 887, 896,\n", + " 903, 938, 939, 942, 943, 947, 960, 961, 962, 964, 971,\n", + " 981, 996, 997, 1008, 1011, 1021, 1025, 1026, 1027, 1028, 1045,\n", + " 1046, 1084, 1086, 1100, 1103, 1111, 1113, 1119, 1135, 1137, 1145,\n", + " 1163, 1164, 1165, 1166, 1179, 1201, 1209, 1210, 1218, 1243, 1244,\n", + " 1253, 1264, 1273, 1279, 1289, 1321, 1323, 1334, 1337, 1355, 1359,\n", + " 1372]),\n", + " 3: array([ 6, 292, 339, 599, 670, 678, 719, 763, 783, 816, 877,\n", + " 888, 935, 993, 999, 1015, 1022, 1033, 1094, 1123, 1211]),\n", + " 4: array([ 13, 27, 28, 29, 30, 31, 41, 44, 48, 49, 55,\n", + " 61, 63, 64, 80, 82, 88, 89, 90, 92, 93, 96,\n", + " 97, 98, 99, 104, 107, 109, 110, 113, 114, 118, 124,\n", + " 127, 128, 132, 142, 145, 151, 155, 159, 162, 170, 174,\n", + " 177, 178, 181, 184, 193, 195, 201, 208, 210, 212, 213,\n", + " 218, 219, 227, 232, 234, 238, 239, 255, 262, 268, 272,\n", + " 274, 276, 283, 286, 290, 291, 294, 295, 296, 297, 298,\n", + " 303, 304, 310, 315, 322, 327, 331, 332, 334, 336, 341,\n", + " 343, 348, 351, 353, 357, 360, 365, 368, 372, 373, 375,\n", + " 376, 378, 381, 383, 385, 390, 396, 402, 407, 414, 420,\n", + " 422, 423, 426, 428, 433, 434, 436, 437, 444, 445, 455,\n", + " 462, 463, 471, 472, 474, 475, 476, 482, 487, 488, 497,\n", + " 500, 502, 503, 504, 508, 510, 511, 518, 520, 526, 527,\n", + " 528, 530, 536, 539, 545, 551, 553, 555, 559, 563, 565,\n", + " 567, 569, 570, 573, 574, 578, 579, 580, 590, 591, 594,\n", + " 605, 608, 611, 614, 615, 617, 619, 622, 623, 627, 637,\n", + " 639, 644, 647, 648, 650, 652, 658, 661, 665, 666, 672,\n", + " 676, 677, 685, 687, 689, 692, 695, 699, 708, 712, 714,\n", + " 715, 716, 720, 726, 727, 742, 753, 757, 762, 765, 767,\n", + " 775, 776, 782, 785, 786, 789, 792, 796, 799, 812, 834,\n", + " 838, 841, 843, 845, 846, 848, 853, 875, 880, 882, 886,\n", + " 891, 906, 909, 912, 913, 917, 918, 921, 922, 923, 924,\n", + " 926, 929, 931, 932, 940, 944, 951, 955, 965, 973, 974,\n", + " 979, 986, 987, 989, 994, 1000, 1002, 1009, 1010, 1014, 1017,\n", + " 1020, 1023, 1031, 1036, 1038, 1040, 1050, 1054, 1056, 1064, 1065,\n", + " 1069, 1075, 1079, 1082, 1085, 1088, 1092, 1095, 1102, 1108, 1109,\n", + " 1115, 1118, 1125, 1126, 1127, 1130, 1134, 1141, 1142, 1155, 1160,\n", + " 1162, 1172, 1177, 1184, 1190, 1192, 1203, 1206, 1214, 1217, 1226,\n", + " 1232, 1236, 1245, 1246, 1247, 1249, 1251, 1252, 1254, 1259, 1261,\n", + " 1262, 1265, 1266, 1267, 1269, 1272, 1274, 1275, 1280, 1291, 1298,\n", + " 1299, 1303, 1311, 1312, 1315, 1317, 1318, 1320, 1324, 1326, 1328,\n", + " 1329, 1330, 1332, 1339, 1346, 1350, 1353, 1364, 1367, 1374, 1378,\n", + " 1383, 1385, 1386, 1389, 1398]),\n", + " 5: array([ 15, 20, 22, 40, 65, 76, 101, 112, 116, 119, 123,\n", + " 129, 137, 146, 147, 164, 191, 215, 226, 230, 242, 250,\n", + " 251, 263, 275, 309, 318, 328, 329, 337, 355, 362, 374,\n", + " 403, 413, 432, 438, 461, 477, 493, 542, 557, 562, 587,\n", + " 597, 630, 641, 664, 701, 725, 737, 746, 770, 773, 788,\n", + " 806, 809, 820, 821, 836, 847, 851, 859, 893, 895, 915,\n", + " 956, 966, 967, 969, 978, 982, 1007, 1016, 1032, 1061, 1076,\n", + " 1105, 1151, 1153, 1188, 1191, 1197, 1199, 1223, 1224, 1270, 1284,\n", + " 1297, 1348, 1354, 1360, 1377, 1379, 1381, 1396]),\n", + " 6: array([ 62, 71, 75, 100, 176, 308, 326, 333, 359, 441, 443,\n", + " 481, 498, 566, 585, 586, 595, 610, 738, 754, 866, 870,\n", + " 949, 963, 995, 1012, 1018, 1059, 1078, 1081, 1136, 1140, 1156,\n", + " 1176, 1181, 1187, 1207, 1242, 1248, 1250, 1263, 1288, 1307, 1310,\n", + " 1340, 1395]),\n", + " 7: array([ 144, 202, 446, 483, 485, 540, 547, 731, 890, 1044, 1375])},\n", + " 9: {0: array([ 0, 1, 2, 3, 8, 10, 11, 12, 17, 18, 19,\n", + " 21, 23, 25, 26, 35, 36, 42, 43, 47, 51, 52,\n", + " 53, 54, 56, 58, 59, 60, 67, 68, 70, 74, 77,\n", + " 78, 79, 86, 94, 102, 105, 106, 111, 115, 120, 121,\n", + " 122, 126, 131, 133, 138, 140, 150, 153, 154, 156, 165,\n", + " 166, 168, 172, 182, 186, 187, 190, 192, 194, 199, 200,\n", + " 205, 206, 207, 209, 216, 222, 228, 229, 231, 233, 235,\n", + " 236, 240, 245, 246, 247, 248, 249, 253, 254, 257, 258,\n", + " 259, 260, 261, 265, 266, 267, 269, 271, 278, 280, 282,\n", + " 284, 289, 299, 300, 301, 302, 305, 306, 307, 311, 312,\n", + " 314, 316, 323, 324, 325, 335, 338, 342, 344, 346, 347,\n", + " 350, 352, 361, 363, 366, 369, 370, 371, 380, 384, 388,\n", + " 389, 391, 392, 393, 394, 397, 398, 399, 401, 404, 405,\n", + " 408, 409, 416, 418, 419, 421, 424, 425, 430, 431, 435,\n", + " 439, 442, 448, 450, 453, 454, 460, 465, 473, 478, 484,\n", + " 486, 490, 491, 492, 494, 496, 499, 505, 507, 513, 515,\n", + " 517, 521, 522, 529, 532, 533, 534, 537, 544, 546, 550,\n", + " 556, 558, 560, 561, 581, 582, 583, 592, 598, 601, 602,\n", + " 603, 604, 606, 607, 612, 616, 618, 628, 629, 631, 633,\n", + " 635, 640, 654, 655, 657, 667, 668, 669, 673, 674, 680,\n", + " 682, 683, 684, 686, 691, 694, 696, 697, 698, 700, 702,\n", + " 703, 704, 710, 711, 717, 728, 732, 733, 739, 740, 741,\n", + " 744, 747, 748, 750, 756, 758, 759, 760, 761, 768, 772,\n", + " 777, 779, 780, 787, 790, 793, 797, 802, 804, 810, 811,\n", + " 814, 815, 818, 823, 824, 825, 828, 830, 831, 832, 833,\n", + " 835, 837, 840, 842, 844, 849, 852, 858, 862, 863, 864,\n", + " 865, 868, 869, 871, 872, 873, 876, 879, 883, 884, 885,\n", + " 889, 899, 901, 904, 905, 907, 908, 910, 911, 914, 916,\n", + " 919, 920, 928, 933, 934, 936, 937, 941, 946, 950, 952,\n", + " 953, 954, 957, 968, 970, 972, 975, 980, 983, 984, 985,\n", + " 990, 991, 992, 1001, 1003, 1005, 1006, 1013, 1019, 1029, 1030,\n", + " 1034, 1037, 1039, 1041, 1042, 1043, 1047, 1048, 1052, 1053, 1057,\n", + " 1060, 1062, 1063, 1068, 1070, 1071, 1073, 1080, 1083, 1101, 1106,\n", + " 1110, 1117, 1121, 1122, 1124, 1129, 1131, 1132, 1138, 1144, 1146,\n", + " 1148, 1150, 1154, 1157, 1158, 1161, 1168, 1169, 1170, 1171, 1173,\n", + " 1174, 1175, 1180, 1182, 1183, 1185, 1186, 1193, 1195, 1196, 1198,\n", + " 1200, 1202, 1204, 1208, 1212, 1219, 1220, 1221, 1225, 1227, 1228,\n", + " 1230, 1231, 1234, 1237, 1239, 1240, 1241, 1255, 1258, 1268, 1277,\n", + " 1278, 1282, 1283, 1286, 1292, 1294, 1296, 1301, 1305, 1306, 1308,\n", + " 1309, 1314, 1316, 1319, 1322, 1327, 1333, 1335, 1336, 1341, 1342,\n", + " 1344, 1345, 1349, 1351, 1352, 1358, 1361, 1362, 1363, 1368, 1369,\n", + " 1370, 1371, 1373, 1376, 1380, 1384, 1387, 1388, 1390, 1392, 1394,\n", + " 1399]),\n", + " 1: array([ 4, 14, 24, 33, 34, 50, 57, 72, 73, 81, 83,\n", + " 84, 85, 91, 95, 117, 125, 134, 136, 152, 157, 160,\n", + " 161, 171, 180, 183, 185, 188, 196, 198, 203, 204, 217,\n", + " 223, 224, 225, 237, 243, 256, 270, 279, 287, 293, 313,\n", + " 320, 321, 345, 349, 356, 358, 367, 379, 382, 387, 406,\n", + " 410, 417, 440, 447, 457, 458, 466, 470, 479, 506, 514,\n", + " 519, 523, 524, 525, 538, 541, 549, 568, 576, 577, 588,\n", + " 593, 600, 613, 621, 625, 626, 632, 638, 646, 653, 656,\n", + " 660, 663, 671, 693, 706, 713, 721, 723, 724, 734, 735,\n", + " 736, 745, 749, 752, 755, 771, 781, 784, 794, 801, 805,\n", + " 807, 813, 822, 829, 854, 855, 881, 892, 894, 897, 900,\n", + " 925, 927, 930, 945, 948, 959, 976, 977, 988, 998, 1004,\n", + " 1035, 1049, 1051, 1058, 1066, 1067, 1072, 1074, 1077, 1087, 1089,\n", + " 1091, 1096, 1097, 1098, 1099, 1104, 1107, 1112, 1120, 1128, 1133,\n", + " 1139, 1143, 1147, 1149, 1152, 1159, 1167, 1189, 1194, 1205, 1213,\n", + " 1215, 1216, 1222, 1229, 1235, 1238, 1256, 1257, 1276, 1281, 1285,\n", + " 1287, 1290, 1293, 1295, 1300, 1302, 1304, 1325, 1331, 1338, 1347,\n", + " 1356, 1357, 1365, 1366, 1382, 1391, 1393, 1397]),\n", + " 2: array([ 5, 7, 9, 16, 32, 37, 38, 39, 45, 46, 66,\n", + " 87, 103, 108, 130, 135, 139, 141, 143, 148, 149, 158,\n", + " 163, 167, 169, 175, 179, 189, 197, 211, 214, 220, 221,\n", + " 241, 244, 252, 264, 273, 277, 281, 285, 288, 340, 354,\n", + " 386, 400, 411, 412, 427, 429, 449, 452, 456, 459, 464,\n", + " 467, 468, 480, 489, 495, 501, 509, 512, 535, 543, 548,\n", + " 552, 554, 564, 572, 575, 589, 609, 620, 624, 634, 636,\n", + " 643, 645, 662, 675, 688, 690, 705, 707, 709, 718, 722,\n", + " 730, 743, 764, 766, 769, 774, 778, 798, 803, 817, 819,\n", + " 839, 850, 856, 857, 860, 861, 867, 874, 878, 887, 896,\n", + " 903, 938, 939, 942, 943, 947, 960, 961, 962, 964, 971,\n", + " 981, 996, 997, 1008, 1011, 1021, 1025, 1026, 1027, 1028, 1045,\n", + " 1046, 1084, 1086, 1100, 1103, 1111, 1113, 1119, 1135, 1137, 1145,\n", + " 1163, 1164, 1165, 1166, 1179, 1201, 1209, 1210, 1218, 1243, 1244,\n", + " 1253, 1264, 1273, 1279, 1289, 1321, 1323, 1334, 1337, 1355, 1359,\n", + " 1372]),\n", + " 3: array([ 6, 292, 339, 599, 670, 678, 719, 763, 783, 816, 877,\n", + " 888, 935, 993, 999, 1015, 1022, 1033, 1094, 1123, 1211]),\n", + " 4: array([ 13, 27, 28, 29, 30, 31, 41, 44, 48, 49, 55,\n", + " 61, 63, 64, 80, 82, 88, 89, 90, 92, 93, 96,\n", + " 97, 98, 99, 104, 107, 109, 110, 113, 114, 118, 124,\n", + " 127, 128, 132, 142, 145, 151, 155, 159, 162, 170, 174,\n", + " 177, 178, 181, 184, 193, 195, 201, 208, 210, 212, 213,\n", + " 218, 219, 227, 232, 234, 238, 239, 255, 262, 268, 272,\n", + " 274, 276, 283, 286, 290, 291, 294, 295, 296, 297, 298,\n", + " 303, 304, 310, 315, 322, 327, 331, 332, 334, 336, 341,\n", + " 343, 348, 351, 353, 357, 360, 365, 368, 372, 373, 375,\n", + " 376, 378, 381, 383, 385, 390, 396, 402, 407, 414, 420,\n", + " 422, 423, 426, 428, 433, 434, 436, 437, 444, 445, 455,\n", + " 462, 463, 471, 472, 474, 475, 476, 482, 487, 488, 497,\n", + " 500, 502, 503, 504, 508, 510, 511, 518, 520, 526, 527,\n", + " 528, 530, 536, 539, 545, 551, 553, 555, 559, 563, 565,\n", + " 567, 569, 570, 573, 574, 578, 579, 580, 590, 591, 594,\n", + " 605, 608, 611, 614, 615, 617, 619, 622, 623, 627, 637,\n", + " 639, 644, 647, 648, 650, 652, 658, 661, 665, 666, 672,\n", + " 676, 677, 685, 687, 689, 692, 695, 699, 708, 712, 714,\n", + " 715, 716, 720, 726, 727, 742, 753, 757, 762, 765, 767,\n", + " 775, 776, 782, 785, 786, 789, 792, 796, 799, 812, 834,\n", + " 838, 841, 843, 845, 846, 848, 853, 875, 880, 882, 886,\n", + " 891, 906, 909, 912, 913, 917, 918, 921, 922, 923, 924,\n", + " 926, 929, 931, 932, 940, 944, 951, 955, 965, 973, 974,\n", + " 979, 986, 987, 989, 994, 1000, 1002, 1009, 1010, 1014, 1017,\n", + " 1020, 1023, 1031, 1036, 1038, 1040, 1050, 1054, 1056, 1064, 1065,\n", + " 1069, 1075, 1079, 1082, 1085, 1088, 1092, 1095, 1102, 1108, 1109,\n", + " 1115, 1118, 1125, 1126, 1127, 1130, 1134, 1141, 1142, 1155, 1160,\n", + " 1162, 1172, 1177, 1184, 1190, 1192, 1203, 1206, 1214, 1217, 1226,\n", + " 1232, 1236, 1245, 1246, 1247, 1249, 1251, 1252, 1254, 1259, 1261,\n", + " 1262, 1265, 1266, 1267, 1269, 1272, 1274, 1275, 1280, 1291, 1298,\n", + " 1299, 1303, 1311, 1312, 1315, 1317, 1318, 1320, 1324, 1326, 1328,\n", + " 1329, 1330, 1332, 1339, 1346, 1350, 1353, 1364, 1367, 1374, 1378,\n", + " 1383, 1385, 1386, 1389, 1398]),\n", + " 5: array([ 15, 20, 22, 40, 65, 76, 101, 112, 116, 119, 123,\n", + " 129, 137, 146, 147, 164, 191, 215, 226, 230, 242, 250,\n", + " 251, 263, 275, 309, 318, 328, 329, 337, 355, 362, 374,\n", + " 403, 413, 432, 438, 461, 477, 493, 542, 557, 562, 587,\n", + " 597, 630, 641, 664, 701, 725, 737, 746, 770, 773, 788,\n", + " 806, 809, 820, 821, 836, 847, 851, 859, 893, 895, 915,\n", + " 956, 966, 967, 969, 978, 982, 1007, 1016, 1032, 1061, 1076,\n", + " 1105, 1151, 1153, 1188, 1191, 1197, 1199, 1223, 1224, 1270, 1284,\n", + " 1297, 1348, 1354, 1360, 1377, 1379, 1381, 1396]),\n", + " 6: array([ 62, 71, 75, 100, 176, 308, 326, 333, 359, 441, 443,\n", + " 481, 498, 566, 585, 586, 595, 610, 738, 754, 866, 870,\n", + " 949, 963, 995, 1012, 1018, 1059, 1078, 1081, 1136, 1140, 1156,\n", + " 1176, 1181, 1187, 1207, 1242, 1248, 1250, 1263, 1288, 1307, 1310,\n", + " 1340, 1395]),\n", + " 7: array([ 69, 173, 317, 319, 330, 364, 377, 395, 415, 451, 469,\n", + " 516, 531, 571, 584, 596, 642, 649, 651, 659, 679, 681,\n", + " 729, 751, 791, 795, 800, 808, 826, 827, 898, 902, 958,\n", + " 1024, 1055, 1090, 1093, 1114, 1116, 1178, 1233, 1260, 1271, 1313,\n", + " 1343]),\n", + " 8: array([ 144, 202, 446, 483, 485, 540, 547, 731, 890, 1044, 1375])},\n", + " 10: {0: array([ 0, 2, 8, 10, 11, 12, 17, 18, 21, 23, 25,\n", + " 35, 36, 42, 43, 47, 51, 52, 53, 54, 56, 59,\n", + " 60, 67, 68, 70, 77, 79, 86, 94, 105, 106, 111,\n", + " 115, 120, 121, 131, 133, 138, 153, 154, 156, 165, 166,\n", + " 168, 172, 182, 186, 187, 192, 194, 200, 205, 206, 209,\n", + " 222, 229, 231, 233, 235, 236, 240, 245, 246, 247, 248,\n", + " 249, 254, 257, 258, 259, 261, 265, 269, 278, 280, 282,\n", + " 284, 299, 300, 301, 307, 311, 312, 314, 316, 323, 324,\n", + " 325, 338, 342, 346, 347, 350, 352, 361, 363, 369, 380,\n", + " 384, 389, 391, 392, 393, 394, 397, 399, 404, 405, 408,\n", + " 409, 416, 418, 419, 421, 424, 425, 430, 435, 439, 442,\n", + " 450, 453, 454, 465, 473, 478, 484, 486, 490, 492, 494,\n", + " 499, 505, 507, 513, 515, 517, 522, 529, 532, 533, 534,\n", + " 537, 544, 546, 550, 556, 558, 560, 561, 581, 582, 583,\n", + " 598, 601, 602, 603, 606, 612, 616, 618, 628, 633, 635,\n", + " 640, 654, 667, 668, 669, 673, 674, 680, 682, 683, 684,\n", + " 686, 691, 694, 696, 697, 698, 700, 703, 704, 710, 717,\n", + " 728, 732, 733, 739, 740, 741, 744, 748, 750, 756, 758,\n", + " 760, 768, 779, 790, 793, 797, 804, 810, 811, 814, 815,\n", + " 818, 824, 825, 830, 831, 832, 833, 835, 837, 840, 842,\n", + " 849, 852, 862, 863, 864, 865, 868, 869, 871, 872, 873,\n", + " 879, 884, 885, 901, 907, 908, 910, 911, 920, 928, 934,\n", + " 936, 937, 941, 946, 950, 952, 953, 954, 957, 970, 972,\n", + " 975, 980, 983, 984, 985, 990, 991, 992, 1001, 1003, 1005,\n", + " 1006, 1013, 1019, 1029, 1030, 1034, 1037, 1039, 1042, 1043, 1047,\n", + " 1048, 1053, 1057, 1060, 1063, 1068, 1070, 1073, 1080, 1083, 1101,\n", + " 1106, 1110, 1117, 1122, 1124, 1129, 1131, 1132, 1138, 1144, 1146,\n", + " 1150, 1154, 1157, 1158, 1161, 1168, 1169, 1170, 1171, 1173, 1174,\n", + " 1175, 1180, 1183, 1185, 1186, 1193, 1195, 1198, 1200, 1202, 1208,\n", + " 1212, 1219, 1220, 1225, 1227, 1228, 1231, 1237, 1239, 1240, 1241,\n", + " 1255, 1258, 1268, 1277, 1278, 1282, 1283, 1286, 1292, 1294, 1296,\n", + " 1306, 1308, 1309, 1314, 1316, 1319, 1322, 1327, 1333, 1335, 1336,\n", + " 1341, 1342, 1344, 1345, 1349, 1358, 1361, 1362, 1363, 1368, 1369,\n", + " 1371, 1373, 1380, 1384, 1387, 1390, 1392, 1394, 1399]),\n", + " 1: array([ 1, 3, 19, 26, 58, 74, 78, 102, 122, 126, 140,\n", + " 150, 190, 199, 207, 216, 228, 253, 260, 266, 267, 271,\n", + " 289, 302, 305, 306, 335, 344, 366, 370, 371, 388, 398,\n", + " 401, 431, 448, 460, 491, 496, 521, 592, 604, 607, 629,\n", + " 631, 655, 657, 702, 711, 747, 759, 761, 772, 777, 780,\n", + " 787, 802, 823, 828, 844, 858, 876, 883, 889, 899, 904,\n", + " 905, 914, 916, 919, 933, 968, 1041, 1052, 1062, 1071, 1121,\n", + " 1148, 1182, 1196, 1204, 1221, 1230, 1234, 1301, 1305, 1351, 1352,\n", + " 1370, 1376, 1388]),\n", + " 2: array([ 4, 14, 24, 33, 34, 50, 57, 72, 73, 81, 83,\n", + " 84, 85, 91, 95, 117, 125, 134, 136, 152, 157, 160,\n", + " 161, 171, 180, 183, 185, 188, 196, 198, 203, 204, 217,\n", + " 223, 224, 225, 237, 243, 256, 270, 279, 287, 293, 313,\n", + " 320, 321, 345, 349, 356, 358, 367, 379, 382, 387, 406,\n", + " 410, 417, 440, 447, 457, 458, 466, 470, 479, 506, 514,\n", + " 519, 523, 524, 525, 538, 541, 549, 568, 576, 577, 588,\n", + " 593, 600, 613, 621, 625, 626, 632, 638, 646, 653, 656,\n", + " 660, 663, 671, 693, 706, 713, 721, 723, 724, 734, 735,\n", + " 736, 745, 749, 752, 755, 771, 781, 784, 794, 801, 805,\n", + " 807, 813, 822, 829, 854, 855, 881, 892, 894, 897, 900,\n", + " 925, 927, 930, 945, 948, 959, 976, 977, 988, 998, 1004,\n", + " 1035, 1049, 1051, 1058, 1066, 1067, 1072, 1074, 1077, 1087, 1089,\n", + " 1091, 1096, 1097, 1098, 1099, 1104, 1107, 1112, 1120, 1128, 1133,\n", + " 1139, 1143, 1147, 1149, 1152, 1159, 1167, 1189, 1194, 1205, 1213,\n", + " 1215, 1216, 1222, 1229, 1235, 1238, 1256, 1257, 1276, 1281, 1285,\n", + " 1287, 1290, 1293, 1295, 1300, 1302, 1304, 1325, 1331, 1338, 1347,\n", + " 1356, 1357, 1365, 1366, 1382, 1391, 1393, 1397]),\n", + " 3: array([ 5, 7, 9, 16, 32, 37, 38, 39, 45, 46, 66,\n", + " 87, 103, 108, 130, 135, 139, 141, 143, 148, 149, 158,\n", + " 163, 167, 169, 175, 179, 189, 197, 211, 214, 220, 221,\n", + " 241, 244, 252, 264, 273, 277, 281, 285, 288, 340, 354,\n", + " 386, 400, 411, 412, 427, 429, 449, 452, 456, 459, 464,\n", + " 467, 468, 480, 489, 495, 501, 509, 512, 535, 543, 548,\n", + " 552, 554, 564, 572, 575, 589, 609, 620, 624, 634, 636,\n", + " 643, 645, 662, 675, 688, 690, 705, 707, 709, 718, 722,\n", + " 730, 743, 764, 766, 769, 774, 778, 798, 803, 817, 819,\n", + " 839, 850, 856, 857, 860, 861, 867, 874, 878, 887, 896,\n", + " 903, 938, 939, 942, 943, 947, 960, 961, 962, 964, 971,\n", + " 981, 996, 997, 1008, 1011, 1021, 1025, 1026, 1027, 1028, 1045,\n", + " 1046, 1084, 1086, 1100, 1103, 1111, 1113, 1119, 1135, 1137, 1145,\n", + " 1163, 1164, 1165, 1166, 1179, 1201, 1209, 1210, 1218, 1243, 1244,\n", + " 1253, 1264, 1273, 1279, 1289, 1321, 1323, 1334, 1337, 1355, 1359,\n", + " 1372]),\n", + " 4: array([ 6, 292, 339, 599, 670, 678, 719, 763, 783, 816, 877,\n", + " 888, 935, 993, 999, 1015, 1022, 1033, 1094, 1123, 1211]),\n", + " 5: array([ 13, 27, 28, 29, 30, 31, 41, 44, 48, 49, 55,\n", + " 61, 63, 64, 80, 82, 88, 89, 90, 92, 93, 96,\n", + " 97, 98, 99, 104, 107, 109, 110, 113, 114, 118, 124,\n", + " 127, 128, 132, 142, 145, 151, 155, 159, 162, 170, 174,\n", + " 177, 178, 181, 184, 193, 195, 201, 208, 210, 212, 213,\n", + " 218, 219, 227, 232, 234, 238, 239, 255, 262, 268, 272,\n", + " 274, 276, 283, 286, 290, 291, 294, 295, 296, 297, 298,\n", + " 303, 304, 310, 315, 322, 327, 331, 332, 334, 336, 341,\n", + " 343, 348, 351, 353, 357, 360, 365, 368, 372, 373, 375,\n", + " 376, 378, 381, 383, 385, 390, 396, 402, 407, 414, 420,\n", + " 422, 423, 426, 428, 433, 434, 436, 437, 444, 445, 455,\n", + " 462, 463, 471, 472, 474, 475, 476, 482, 487, 488, 497,\n", + " 500, 502, 503, 504, 508, 510, 511, 518, 520, 526, 527,\n", + " 528, 530, 536, 539, 545, 551, 553, 555, 559, 563, 565,\n", + " 567, 569, 570, 573, 574, 578, 579, 580, 590, 591, 594,\n", + " 605, 608, 611, 614, 615, 617, 619, 622, 623, 627, 637,\n", + " 639, 644, 647, 648, 650, 652, 658, 661, 665, 666, 672,\n", + " 676, 677, 685, 687, 689, 692, 695, 699, 708, 712, 714,\n", + " 715, 716, 720, 726, 727, 742, 753, 757, 762, 765, 767,\n", + " 775, 776, 782, 785, 786, 789, 792, 796, 799, 812, 834,\n", + " 838, 841, 843, 845, 846, 848, 853, 875, 880, 882, 886,\n", + " 891, 906, 909, 912, 913, 917, 918, 921, 922, 923, 924,\n", + " 926, 929, 931, 932, 940, 944, 951, 955, 965, 973, 974,\n", + " 979, 986, 987, 989, 994, 1000, 1002, 1009, 1010, 1014, 1017,\n", + " 1020, 1023, 1031, 1036, 1038, 1040, 1050, 1054, 1056, 1064, 1065,\n", + " 1069, 1075, 1079, 1082, 1085, 1088, 1092, 1095, 1102, 1108, 1109,\n", + " 1115, 1118, 1125, 1126, 1127, 1130, 1134, 1141, 1142, 1155, 1160,\n", + " 1162, 1172, 1177, 1184, 1190, 1192, 1203, 1206, 1214, 1217, 1226,\n", + " 1232, 1236, 1245, 1246, 1247, 1249, 1251, 1252, 1254, 1259, 1261,\n", + " 1262, 1265, 1266, 1267, 1269, 1272, 1274, 1275, 1280, 1291, 1298,\n", + " 1299, 1303, 1311, 1312, 1315, 1317, 1318, 1320, 1324, 1326, 1328,\n", + " 1329, 1330, 1332, 1339, 1346, 1350, 1353, 1364, 1367, 1374, 1378,\n", + " 1383, 1385, 1386, 1389, 1398]),\n", + " 6: array([ 15, 20, 22, 40, 65, 76, 101, 112, 116, 119, 123,\n", + " 129, 137, 146, 147, 164, 191, 215, 226, 230, 242, 250,\n", + " 251, 263, 275, 309, 318, 328, 329, 337, 355, 362, 374,\n", + " 403, 413, 432, 438, 461, 477, 493, 542, 557, 562, 587,\n", + " 597, 630, 641, 664, 701, 725, 737, 746, 770, 773, 788,\n", + " 806, 809, 820, 821, 836, 847, 851, 859, 893, 895, 915,\n", + " 956, 966, 967, 969, 978, 982, 1007, 1016, 1032, 1061, 1076,\n", + " 1105, 1151, 1153, 1188, 1191, 1197, 1199, 1223, 1224, 1270, 1284,\n", + " 1297, 1348, 1354, 1360, 1377, 1379, 1381, 1396]),\n", + " 7: array([ 62, 71, 75, 100, 176, 308, 326, 333, 359, 441, 443,\n", + " 481, 498, 566, 585, 586, 595, 610, 738, 754, 866, 870,\n", + " 949, 963, 995, 1012, 1018, 1059, 1078, 1081, 1136, 1140, 1156,\n", + " 1176, 1181, 1187, 1207, 1242, 1248, 1250, 1263, 1288, 1307, 1310,\n", + " 1340, 1395]),\n", + " 8: array([ 69, 173, 317, 319, 330, 364, 377, 395, 415, 451, 469,\n", + " 516, 531, 571, 584, 596, 642, 649, 651, 659, 679, 681,\n", + " 729, 751, 791, 795, 800, 808, 826, 827, 898, 902, 958,\n", + " 1024, 1055, 1090, 1093, 1114, 1116, 1178, 1233, 1260, 1271, 1313,\n", + " 1343]),\n", + " 9: array([ 144, 202, 446, 483, 485, 540, 547, 731, 890, 1044, 1375])}}}" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mnotebook controller is DISPOSED. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mnotebook controller is DISPOSED. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "train_clusters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'shap': {2: {0: array([ 0, 1, 2, 3, 4, 5, 6, 9, 10, 11, 12, 13, 14,\n", + " 15, 16, 17, 18, 19, 20, 21, 22, 25, 26, 27, 28, 29,\n", + " 30, 31, 32, 33, 34, 35, 36, 37, 39, 43, 45, 46, 49,\n", + " 51, 52, 53, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65,\n", + " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", + " 79, 81, 82, 84, 86, 87, 88, 89, 92, 93, 95, 96, 97,\n", + " 98, 99, 100, 101, 102, 103, 104, 106, 107, 108, 109, 110, 111,\n", + " 112, 113, 114, 115, 116, 118, 119, 120, 122, 123, 124, 127, 128,\n", + " 129, 132, 134, 135, 136, 137, 139, 140, 141, 142, 146, 147, 148,\n", + " 149, 151, 152, 153, 154, 155, 157, 158, 161, 162, 163, 164, 167,\n", + " 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 179, 180, 181,\n", + " 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194,\n", + " 196, 197, 198, 199, 200, 202, 203, 207, 208, 210, 211, 212, 214,\n", + " 215, 216, 217, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228,\n", + " 230, 231, 232, 234, 235, 236, 237, 239, 240, 241, 242, 243, 244,\n", + " 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257,\n", + " 259, 261, 262, 264, 265, 266, 267, 269, 270, 271, 272, 273, 274,\n", + " 275, 276, 277, 278, 279, 282, 283, 284, 286, 287, 289, 290, 291,\n", + " 292, 293, 294, 295, 296, 299, 300, 303, 304, 307, 308, 309, 310,\n", + " 311, 312, 313, 314, 315, 316, 318, 320, 321, 322, 323, 324, 327,\n", + " 328, 329, 330, 331, 334, 336, 338, 340, 341, 342, 343, 344, 345,\n", + " 346, 347, 348, 350, 351, 352, 353, 354, 356, 358, 359, 360, 361,\n", + " 362, 363, 364, 365, 367, 368, 369, 370, 371, 374, 375, 378, 379,\n", + " 380, 382, 383, 384, 385, 387, 388, 389, 390, 391, 392, 393, 394,\n", + " 395, 396, 398, 399, 400, 401, 402, 403, 405, 407, 408, 409, 410,\n", + " 411, 412, 413, 415, 416, 417, 418, 419, 420, 421, 422, 424, 425,\n", + " 426, 428, 429, 430, 432, 433, 434, 435, 436, 437, 439, 440, 441,\n", + " 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 453, 454, 455,\n", + " 456, 457, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469,\n", + " 470, 471, 472, 473, 474, 475, 476, 477, 480, 481, 482, 483, 484,\n", + " 485, 486, 487, 488, 489, 491, 493, 494, 496, 497, 498, 499, 501,\n", + " 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 515,\n", + " 516, 517, 518, 519, 521, 522, 523, 525, 526, 527, 528, 529, 534,\n", + " 535, 537, 538, 539, 540, 542, 543, 544, 545, 546, 547, 549, 550,\n", + " 552, 553, 555, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567,\n", + " 568, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 581, 582,\n", + " 583, 584, 585, 586, 587, 588, 589, 591, 592, 593, 594, 595, 597,\n", + " 598, 599]),\n", + " 1: array([ 7, 8, 23, 24, 38, 40, 41, 42, 44, 47, 48, 50, 55,\n", + " 60, 80, 83, 85, 90, 91, 94, 105, 117, 121, 125, 126, 130,\n", + " 131, 133, 138, 143, 144, 145, 150, 156, 159, 160, 165, 166, 178,\n", + " 195, 201, 204, 205, 206, 209, 213, 218, 229, 233, 238, 258, 260,\n", + " 263, 268, 280, 281, 285, 288, 297, 298, 301, 302, 305, 306, 317,\n", + " 319, 325, 326, 332, 333, 335, 337, 339, 349, 355, 357, 366, 372,\n", + " 373, 376, 377, 381, 386, 397, 404, 406, 414, 423, 427, 431, 438,\n", + " 452, 458, 478, 479, 490, 492, 495, 500, 514, 520, 524, 530, 531,\n", + " 532, 533, 536, 541, 548, 551, 554, 556, 557, 569, 580, 590, 596])},\n", + " 3: {0: array([ 1, 12, 14, 16, 19, 21, 22, 27, 29, 36, 45, 49, 53,\n", + " 56, 58, 64, 68, 72, 78, 81, 84, 86, 92, 95, 96, 98,\n", + " 102, 108, 113, 115, 124, 128, 132, 134, 137, 139, 140, 142, 161,\n", + " 163, 167, 168, 170, 177, 184, 186, 187, 189, 192, 197, 214, 216,\n", + " 217, 219, 220, 225, 234, 237, 240, 241, 246, 248, 249, 250, 253,\n", + " 254, 256, 283, 293, 294, 300, 308, 311, 312, 331, 334, 336, 343,\n", + " 344, 345, 346, 353, 360, 361, 369, 370, 379, 382, 390, 391, 392,\n", + " 393, 394, 398, 402, 411, 419, 420, 421, 428, 432, 433, 444, 451,\n", + " 453, 463, 467, 469, 474, 475, 477, 483, 484, 485, 496, 498, 501,\n", + " 502, 505, 509, 511, 512, 515, 516, 526, 527, 529, 535, 546, 550,\n", + " 553, 555, 559, 561, 566, 567, 579, 581, 587, 589, 594, 598]),\n", + " 1: array([ 7, 8, 23, 24, 38, 40, 41, 42, 44, 47, 48, 50, 55,\n", + " 60, 80, 83, 85, 90, 91, 94, 105, 117, 121, 125, 126, 130,\n", + " 131, 133, 138, 143, 144, 145, 150, 156, 159, 160, 165, 166, 178,\n", + " 195, 201, 204, 205, 206, 209, 213, 218, 229, 233, 238, 258, 260,\n", + " 263, 268, 280, 281, 285, 288, 297, 298, 301, 302, 305, 306, 317,\n", + " 319, 325, 326, 332, 333, 335, 337, 339, 349, 355, 357, 366, 372,\n", + " 373, 376, 377, 381, 386, 397, 404, 406, 414, 423, 427, 431, 438,\n", + " 452, 458, 478, 479, 490, 492, 495, 500, 514, 520, 524, 530, 531,\n", + " 532, 533, 536, 541, 548, 551, 554, 556, 557, 569, 580, 590, 596]),\n", + " 2: array([ 0, 2, 3, 4, 5, 6, 9, 10, 11, 13, 15, 17, 18,\n", + " 20, 25, 26, 28, 30, 31, 32, 33, 34, 35, 37, 39, 43,\n", + " 46, 51, 52, 54, 57, 59, 61, 62, 63, 65, 66, 67, 69,\n", + " 70, 71, 73, 74, 75, 76, 77, 79, 82, 87, 88, 89, 93,\n", + " 97, 99, 100, 101, 103, 104, 106, 107, 109, 110, 111, 112, 114,\n", + " 116, 118, 119, 120, 122, 123, 127, 129, 135, 136, 141, 146, 147,\n", + " 148, 149, 151, 152, 153, 154, 155, 157, 158, 162, 164, 169, 171,\n", + " 172, 173, 174, 175, 176, 179, 180, 181, 182, 183, 185, 188, 190,\n", + " 191, 193, 194, 196, 198, 199, 200, 202, 203, 207, 208, 210, 211,\n", + " 212, 215, 221, 222, 223, 224, 226, 227, 228, 230, 231, 232, 235,\n", + " 236, 239, 242, 243, 244, 245, 247, 251, 252, 255, 257, 259, 261,\n", + " 262, 264, 265, 266, 267, 269, 270, 271, 272, 273, 274, 275, 276,\n", + " 277, 278, 279, 282, 284, 286, 287, 289, 290, 291, 292, 295, 296,\n", + " 299, 303, 304, 307, 309, 310, 313, 314, 315, 316, 318, 320, 321,\n", + " 322, 323, 324, 327, 328, 329, 330, 338, 340, 341, 342, 347, 348,\n", + " 350, 351, 352, 354, 356, 358, 359, 362, 363, 364, 365, 367, 368,\n", + " 371, 374, 375, 378, 380, 383, 384, 385, 387, 388, 389, 395, 396,\n", + " 399, 400, 401, 403, 405, 407, 408, 409, 410, 412, 413, 415, 416,\n", + " 417, 418, 422, 424, 425, 426, 429, 430, 434, 435, 436, 437, 439,\n", + " 440, 441, 442, 443, 445, 446, 447, 448, 449, 450, 454, 455, 456,\n", + " 457, 459, 460, 461, 462, 464, 465, 466, 468, 470, 471, 472, 473,\n", + " 476, 480, 481, 482, 486, 487, 488, 489, 491, 493, 494, 497, 499,\n", + " 503, 504, 506, 507, 508, 510, 513, 517, 518, 519, 521, 522, 523,\n", + " 525, 528, 534, 537, 538, 539, 540, 542, 543, 544, 545, 547, 549,\n", + " 552, 558, 560, 562, 563, 564, 565, 568, 570, 571, 572, 573, 574,\n", + " 575, 576, 577, 578, 582, 583, 584, 585, 586, 588, 591, 592, 593,\n", + " 595, 597, 599])},\n", + " 4: {0: array([ 1, 12, 14, 16, 19, 21, 22, 27, 29, 36, 45, 49, 53,\n", + " 58, 64, 68, 72, 78, 81, 84, 86, 92, 95, 96, 98, 102,\n", + " 108, 113, 115, 124, 128, 132, 134, 139, 140, 142, 161, 163, 167,\n", + " 168, 170, 177, 184, 186, 187, 189, 197, 214, 216, 217, 219, 220,\n", + " 225, 234, 237, 240, 241, 246, 248, 249, 250, 254, 256, 275, 283,\n", + " 293, 294, 300, 308, 311, 312, 331, 334, 343, 344, 345, 346, 353,\n", + " 360, 361, 369, 370, 379, 382, 390, 391, 392, 393, 394, 398, 402,\n", + " 411, 419, 420, 421, 428, 432, 433, 444, 451, 453, 463, 467, 469,\n", + " 474, 475, 477, 483, 484, 485, 496, 498, 501, 502, 505, 509, 511,\n", + " 512, 515, 516, 526, 527, 529, 546, 550, 553, 555, 559, 561, 566,\n", + " 567, 579, 581, 587, 589, 594, 598]),\n", + " 1: array([ 7, 8, 23, 24, 38, 40, 41, 42, 44, 47, 48, 50, 55,\n", + " 60, 80, 83, 85, 90, 91, 94, 105, 117, 121, 125, 126, 130,\n", + " 131, 133, 138, 143, 144, 145, 150, 156, 159, 160, 165, 166, 178,\n", + " 195, 201, 204, 205, 206, 209, 213, 218, 229, 233, 238, 258, 260,\n", + " 263, 268, 280, 281, 285, 288, 297, 298, 301, 302, 305, 306, 317,\n", + " 319, 325, 326, 332, 333, 335, 337, 339, 349, 355, 357, 366, 372,\n", + " 373, 376, 377, 381, 386, 397, 404, 406, 414, 423, 427, 431, 438,\n", + " 452, 458, 478, 479, 490, 492, 495, 500, 514, 520, 524, 530, 531,\n", + " 532, 533, 536, 541, 548, 551, 554, 556, 557, 569, 580, 590, 596]),\n", + " 2: array([ 0, 2, 3, 4, 5, 6, 9, 10, 11, 13, 15, 17, 18,\n", + " 20, 25, 26, 28, 30, 31, 32, 33, 34, 35, 37, 39, 43,\n", + " 46, 51, 52, 54, 56, 59, 61, 62, 63, 65, 66, 67, 69,\n", + " 70, 71, 73, 74, 75, 76, 77, 79, 82, 88, 89, 93, 97,\n", + " 99, 100, 104, 106, 107, 109, 111, 112, 114, 116, 118, 119, 120,\n", + " 122, 127, 129, 135, 136, 137, 141, 146, 147, 148, 149, 151, 153,\n", + " 154, 155, 157, 158, 162, 164, 169, 171, 172, 173, 174, 175, 176,\n", + " 179, 180, 181, 182, 183, 185, 190, 191, 192, 193, 194, 196, 198,\n", + " 199, 200, 202, 203, 207, 208, 210, 211, 212, 215, 221, 222, 223,\n", + " 224, 226, 227, 228, 230, 231, 232, 235, 236, 242, 243, 244, 245,\n", + " 247, 251, 252, 253, 255, 259, 261, 262, 264, 265, 266, 267, 269,\n", + " 270, 271, 272, 273, 274, 276, 277, 278, 279, 282, 284, 286, 287,\n", + " 289, 290, 291, 292, 295, 296, 299, 303, 304, 307, 309, 310, 314,\n", + " 315, 316, 318, 320, 322, 323, 327, 328, 329, 330, 336, 338, 340,\n", + " 341, 342, 347, 348, 350, 351, 352, 354, 356, 358, 362, 363, 364,\n", + " 365, 367, 368, 371, 374, 375, 378, 380, 383, 384, 385, 387, 388,\n", + " 395, 396, 399, 400, 401, 403, 405, 407, 409, 410, 412, 413, 415,\n", + " 416, 417, 418, 422, 424, 425, 426, 429, 430, 434, 435, 436, 437,\n", + " 439, 440, 441, 442, 443, 445, 447, 448, 449, 450, 454, 455, 456,\n", + " 457, 459, 460, 461, 462, 464, 465, 466, 468, 470, 471, 472, 473,\n", + " 476, 480, 481, 482, 486, 487, 488, 489, 491, 493, 494, 497, 499,\n", + " 503, 504, 506, 507, 508, 510, 513, 517, 518, 519, 521, 522, 523,\n", + " 525, 528, 534, 535, 537, 538, 539, 540, 542, 543, 544, 547, 549,\n", + " 552, 558, 560, 562, 563, 564, 565, 570, 571, 572, 573, 574, 575,\n", + " 576, 577, 578, 582, 583, 584, 585, 586, 588, 591, 592, 593, 595,\n", + " 597, 599]),\n", + " 3: array([ 57, 87, 101, 103, 110, 123, 152, 188, 239, 257, 313, 321, 324,\n", + " 359, 389, 408, 446, 545, 568])},\n", + " 5: {0: array([ 1, 14, 16, 19, 21, 22, 27, 29, 36, 45, 49, 53, 58,\n", + " 64, 68, 72, 78, 81, 84, 86, 92, 95, 96, 98, 102, 108,\n", + " 113, 115, 124, 128, 132, 134, 139, 140, 142, 161, 163, 167, 168,\n", + " 177, 184, 186, 187, 189, 197, 214, 216, 217, 219, 220, 225, 234,\n", + " 240, 241, 246, 248, 249, 250, 254, 256, 275, 283, 293, 294, 300,\n", + " 308, 311, 312, 331, 334, 343, 344, 345, 346, 353, 360, 361, 369,\n", + " 370, 379, 382, 390, 391, 392, 393, 394, 398, 402, 411, 419, 420,\n", + " 421, 428, 432, 433, 451, 453, 467, 469, 474, 475, 477, 483, 484,\n", + " 485, 496, 498, 501, 502, 505, 509, 511, 512, 515, 516, 526, 527,\n", + " 529, 546, 550, 553, 555, 559, 561, 566, 567, 579, 581, 587, 589,\n", + " 594, 598]),\n", + " 1: array([ 7, 8, 40, 41, 44, 47, 48, 50, 55, 60, 80, 83, 85,\n", + " 90, 91, 105, 117, 121, 125, 126, 130, 131, 133, 138, 143, 144,\n", + " 145, 150, 156, 159, 160, 165, 166, 178, 195, 201, 204, 206, 209,\n", + " 213, 218, 229, 233, 238, 258, 260, 263, 268, 280, 281, 285, 288,\n", + " 297, 301, 302, 305, 306, 319, 325, 332, 333, 335, 337, 339, 349,\n", + " 357, 366, 372, 373, 376, 377, 381, 386, 397, 404, 406, 414, 423,\n", + " 427, 438, 452, 458, 478, 479, 490, 492, 500, 514, 520, 524, 532,\n", + " 533, 536, 541, 548, 554, 556, 569, 580, 590, 596]),\n", + " 2: array([ 0, 2, 3, 4, 5, 6, 9, 10, 11, 13, 15, 17, 18,\n", + " 20, 25, 26, 28, 30, 31, 32, 33, 34, 35, 37, 39, 43,\n", + " 46, 51, 52, 54, 56, 59, 61, 62, 63, 65, 66, 67, 69,\n", + " 70, 71, 73, 74, 75, 76, 77, 79, 82, 88, 89, 93, 97,\n", + " 99, 100, 104, 106, 107, 109, 111, 112, 114, 116, 118, 119, 120,\n", + " 122, 127, 129, 135, 136, 137, 141, 146, 147, 148, 149, 151, 153,\n", + " 154, 155, 157, 158, 162, 164, 169, 171, 172, 173, 174, 175, 176,\n", + " 179, 180, 181, 182, 183, 185, 190, 191, 192, 193, 194, 196, 198,\n", + " 199, 200, 202, 203, 207, 208, 210, 211, 212, 215, 221, 222, 223,\n", + " 224, 226, 227, 228, 230, 231, 232, 235, 236, 242, 243, 244, 245,\n", + " 247, 251, 252, 253, 255, 259, 261, 262, 264, 265, 266, 267, 269,\n", + " 270, 271, 272, 273, 274, 276, 277, 278, 279, 282, 284, 286, 287,\n", + " 289, 290, 291, 292, 295, 296, 299, 303, 304, 307, 309, 310, 314,\n", + " 315, 316, 318, 320, 322, 323, 327, 328, 329, 330, 336, 338, 340,\n", + " 341, 342, 347, 348, 350, 351, 352, 354, 356, 358, 362, 363, 364,\n", + " 365, 367, 368, 371, 374, 375, 378, 380, 383, 384, 385, 387, 388,\n", + " 395, 396, 399, 400, 401, 403, 405, 407, 409, 410, 412, 413, 415,\n", + " 416, 417, 418, 422, 424, 425, 426, 429, 430, 434, 435, 436, 437,\n", + " 439, 440, 441, 442, 443, 445, 447, 448, 449, 450, 454, 455, 456,\n", + " 457, 459, 460, 461, 462, 464, 465, 466, 468, 470, 471, 472, 473,\n", + " 476, 480, 481, 482, 486, 487, 488, 489, 491, 493, 494, 497, 499,\n", + " 503, 504, 506, 507, 508, 510, 513, 517, 518, 519, 521, 522, 523,\n", + " 525, 528, 534, 535, 537, 538, 539, 540, 542, 543, 544, 547, 549,\n", + " 552, 558, 560, 562, 563, 564, 565, 570, 571, 572, 573, 574, 575,\n", + " 576, 577, 578, 582, 583, 584, 585, 586, 588, 591, 592, 593, 595,\n", + " 597, 599]),\n", + " 3: array([ 57, 87, 101, 103, 110, 123, 152, 188, 239, 257, 313, 321, 324,\n", + " 359, 389, 408, 446, 545, 568]),\n", + " 4: array([ 12, 23, 24, 38, 42, 94, 170, 205, 237, 298, 317, 326, 355,\n", + " 431, 444, 463, 495, 530, 531, 551, 557])},\n", + " 6: {0: array([ 1, 14, 16, 21, 22, 27, 29, 36, 45, 49, 53, 68, 72,\n", + " 78, 81, 86, 95, 96, 98, 102, 108, 113, 115, 124, 132, 139,\n", + " 140, 161, 167, 168, 177, 184, 186, 187, 189, 197, 214, 216, 217,\n", + " 219, 220, 225, 234, 246, 248, 250, 253, 254, 256, 283, 293, 294,\n", + " 300, 308, 311, 331, 334, 343, 344, 345, 346, 353, 360, 369, 370,\n", + " 379, 382, 390, 391, 392, 393, 394, 398, 411, 420, 421, 428, 433,\n", + " 451, 453, 469, 475, 477, 483, 484, 485, 496, 498, 501, 505, 509,\n", + " 511, 512, 515, 516, 526, 527, 529, 546, 550, 553, 555, 559, 561,\n", + " 566, 567, 579, 581, 587, 589, 594]),\n", + " 1: array([ 7, 8, 40, 41, 44, 47, 48, 50, 55, 60, 80, 83, 85,\n", + " 90, 91, 105, 117, 121, 125, 126, 130, 131, 133, 138, 143, 144,\n", + " 145, 150, 156, 159, 160, 165, 166, 178, 195, 201, 204, 206, 209,\n", + " 213, 218, 229, 233, 238, 258, 260, 263, 268, 280, 281, 285, 288,\n", + " 297, 301, 302, 305, 306, 319, 325, 332, 333, 335, 337, 339, 349,\n", + " 357, 366, 372, 373, 376, 377, 381, 386, 397, 404, 406, 414, 423,\n", + " 427, 438, 452, 458, 478, 479, 490, 492, 500, 514, 520, 524, 532,\n", + " 533, 536, 541, 548, 554, 556, 569, 580, 590, 596]),\n", + " 2: array([ 0, 3, 4, 6, 11, 15, 17, 18, 19, 20, 28, 30, 34,\n", + " 37, 39, 43, 46, 51, 56, 58, 61, 62, 63, 64, 66, 71,\n", + " 73, 74, 76, 77, 79, 84, 88, 89, 92, 93, 104, 106, 107,\n", + " 109, 112, 116, 118, 119, 120, 128, 129, 134, 135, 136, 137, 142,\n", + " 147, 148, 157, 163, 164, 171, 172, 173, 174, 176, 180, 182, 183,\n", + " 185, 190, 192, 193, 198, 202, 203, 208, 212, 215, 221, 224, 226,\n", + " 228, 230, 231, 232, 235, 240, 241, 242, 243, 244, 247, 249, 255,\n", + " 259, 261, 265, 266, 269, 271, 272, 273, 274, 275, 276, 277, 279,\n", + " 282, 284, 286, 287, 290, 291, 309, 312, 315, 318, 320, 322, 327,\n", + " 328, 336, 338, 340, 341, 342, 350, 352, 354, 356, 358, 361, 362,\n", + " 363, 365, 368, 375, 378, 385, 396, 399, 400, 402, 403, 407, 409,\n", + " 410, 412, 416, 418, 419, 424, 432, 434, 435, 436, 437, 440, 441,\n", + " 445, 448, 449, 455, 457, 460, 461, 462, 465, 466, 467, 471, 472,\n", + " 473, 474, 480, 486, 488, 489, 497, 499, 502, 503, 504, 508, 510,\n", + " 517, 521, 522, 528, 535, 537, 538, 539, 540, 542, 543, 544, 547,\n", + " 549, 552, 560, 563, 564, 570, 571, 574, 575, 576, 578, 584, 585,\n", + " 591, 592, 593, 597, 598]),\n", + " 3: array([ 2, 5, 9, 10, 13, 25, 26, 31, 32, 33, 35, 52, 54,\n", + " 59, 65, 67, 69, 70, 75, 82, 97, 99, 100, 111, 114, 122,\n", + " 127, 141, 146, 149, 151, 153, 154, 155, 158, 162, 169, 175, 179,\n", + " 181, 191, 194, 196, 199, 200, 207, 210, 211, 222, 223, 227, 236,\n", + " 245, 251, 252, 262, 264, 267, 270, 278, 289, 292, 295, 296, 299,\n", + " 303, 304, 307, 310, 314, 316, 323, 329, 330, 347, 348, 351, 364,\n", + " 367, 371, 374, 380, 383, 384, 387, 388, 395, 401, 405, 413, 415,\n", + " 417, 422, 425, 426, 429, 430, 439, 442, 443, 447, 450, 454, 456,\n", + " 459, 464, 468, 470, 476, 481, 482, 487, 491, 493, 494, 506, 507,\n", + " 513, 518, 519, 523, 525, 534, 558, 562, 565, 572, 573, 577, 582,\n", + " 583, 586, 588, 595, 599]),\n", + " 4: array([ 57, 87, 101, 103, 110, 123, 152, 188, 239, 257, 313, 321, 324,\n", + " 359, 389, 408, 446, 545, 568]),\n", + " 5: array([ 12, 23, 24, 38, 42, 94, 170, 205, 237, 298, 317, 326, 355,\n", + " 431, 444, 463, 495, 530, 531, 551, 557])},\n", + " 7: {0: array([ 1, 14, 16, 21, 22, 27, 29, 36, 49, 53, 64, 72, 78,\n", + " 81, 86, 95, 96, 98, 102, 108, 113, 115, 124, 132, 134, 139,\n", + " 140, 142, 161, 167, 168, 177, 184, 186, 187, 189, 197, 214, 216,\n", + " 217, 219, 220, 225, 234, 241, 246, 248, 249, 250, 253, 254, 256,\n", + " 266, 283, 293, 294, 300, 308, 311, 312, 331, 334, 343, 344, 345,\n", + " 346, 353, 360, 369, 370, 379, 382, 390, 391, 392, 393, 394, 398,\n", + " 411, 420, 421, 428, 433, 451, 453, 467, 469, 474, 475, 477, 483,\n", + " 484, 485, 496, 498, 501, 505, 511, 512, 515, 516, 526, 527, 529,\n", + " 546, 550, 553, 555, 561, 566, 567, 579, 581, 587, 589, 594, 598]),\n", + " 1: array([ 7, 8, 40, 41, 44, 47, 48, 50, 55, 60, 80, 83, 85,\n", + " 90, 91, 105, 117, 121, 125, 126, 130, 131, 133, 138, 143, 144,\n", + " 145, 150, 156, 159, 160, 165, 166, 178, 195, 201, 204, 206, 209,\n", + " 213, 218, 229, 233, 238, 258, 260, 263, 268, 280, 281, 285, 288,\n", + " 297, 301, 302, 305, 306, 319, 325, 332, 333, 335, 337, 339, 349,\n", + " 357, 366, 372, 373, 376, 377, 381, 386, 397, 404, 406, 414, 423,\n", + " 427, 438, 452, 458, 478, 479, 490, 492, 500, 514, 520, 524, 532,\n", + " 533, 536, 541, 548, 554, 556, 569, 580, 590, 596]),\n", + " 2: array([ 0, 3, 4, 6, 11, 15, 17, 18, 19, 20, 28, 30, 34,\n", + " 37, 39, 43, 46, 51, 56, 58, 61, 62, 63, 66, 71, 73,\n", + " 74, 76, 77, 79, 84, 88, 89, 92, 93, 104, 106, 107, 109,\n", + " 112, 116, 118, 119, 120, 128, 129, 135, 136, 137, 147, 148, 157,\n", + " 163, 164, 171, 172, 173, 174, 176, 180, 182, 183, 185, 190, 192,\n", + " 193, 198, 202, 203, 208, 212, 215, 221, 224, 226, 228, 230, 231,\n", + " 232, 235, 240, 242, 243, 244, 247, 255, 259, 261, 265, 269, 271,\n", + " 272, 273, 274, 275, 276, 277, 279, 282, 284, 286, 287, 290, 291,\n", + " 309, 315, 318, 320, 322, 327, 328, 336, 338, 340, 341, 342, 350,\n", + " 352, 354, 356, 358, 361, 362, 363, 365, 368, 375, 378, 385, 396,\n", + " 399, 402, 403, 407, 409, 410, 412, 416, 418, 419, 424, 432, 434,\n", + " 435, 436, 437, 440, 441, 445, 448, 449, 455, 457, 460, 461, 462,\n", + " 465, 466, 471, 472, 473, 480, 486, 488, 489, 497, 499, 502, 503,\n", + " 504, 508, 510, 517, 521, 522, 528, 535, 537, 538, 539, 540, 542,\n", + " 543, 544, 547, 549, 552, 560, 563, 564, 570, 571, 574, 575, 576,\n", + " 578, 584, 585, 591, 592, 593, 597]),\n", + " 3: array([ 45, 68, 400, 509, 559]),\n", + " 4: array([ 2, 5, 9, 10, 13, 25, 26, 31, 32, 33, 35, 52, 54,\n", + " 59, 65, 67, 69, 70, 75, 82, 97, 99, 100, 111, 114, 122,\n", + " 127, 141, 146, 149, 151, 153, 154, 155, 158, 162, 169, 175, 179,\n", + " 181, 191, 194, 196, 199, 200, 207, 210, 211, 222, 223, 227, 236,\n", + " 245, 251, 252, 262, 264, 267, 270, 278, 289, 292, 295, 296, 299,\n", + " 303, 304, 307, 310, 314, 316, 323, 329, 330, 347, 348, 351, 364,\n", + " 367, 371, 374, 380, 383, 384, 387, 388, 395, 401, 405, 413, 415,\n", + " 417, 422, 425, 426, 429, 430, 439, 442, 443, 447, 450, 454, 456,\n", + " 459, 464, 468, 470, 476, 481, 482, 487, 491, 493, 494, 506, 507,\n", + " 513, 518, 519, 523, 525, 534, 558, 562, 565, 572, 573, 577, 582,\n", + " 583, 586, 588, 595, 599]),\n", + " 5: array([ 57, 87, 101, 103, 110, 123, 152, 188, 239, 257, 313, 321, 324,\n", + " 359, 389, 408, 446, 545, 568]),\n", + " 6: array([ 12, 23, 24, 38, 42, 94, 170, 205, 237, 298, 317, 326, 355,\n", + " 431, 444, 463, 495, 530, 531, 551, 557])},\n", + " 8: {0: array([ 1, 14, 16, 21, 22, 27, 29, 36, 49, 53, 72, 78, 81,\n", + " 86, 95, 96, 98, 102, 108, 113, 115, 132, 139, 140, 161, 167,\n", + " 168, 184, 186, 187, 189, 197, 216, 217, 219, 225, 234, 246, 248,\n", + " 250, 253, 254, 256, 283, 293, 294, 300, 311, 331, 334, 343, 344,\n", + " 345, 346, 353, 360, 369, 370, 379, 382, 390, 391, 392, 393, 394,\n", + " 398, 411, 420, 421, 428, 433, 451, 453, 469, 475, 477, 483, 484,\n", + " 485, 496, 498, 501, 505, 511, 512, 515, 516, 526, 527, 546, 550,\n", + " 553, 555, 561, 566, 567, 587, 589, 594]),\n", + " 1: array([ 7, 8, 40, 41, 44, 47, 48, 50, 55, 60, 80, 83, 85,\n", + " 90, 91, 105, 117, 121, 125, 126, 130, 131, 133, 138, 143, 144,\n", + " 145, 150, 156, 159, 160, 165, 166, 178, 195, 201, 204, 206, 209,\n", + " 213, 218, 229, 233, 238, 258, 260, 263, 268, 280, 281, 285, 288,\n", + " 297, 301, 302, 305, 306, 319, 325, 332, 333, 335, 337, 339, 349,\n", + " 357, 366, 372, 373, 376, 377, 381, 386, 397, 404, 406, 414, 423,\n", + " 427, 438, 452, 458, 478, 479, 490, 492, 500, 514, 520, 524, 532,\n", + " 533, 536, 541, 548, 554, 556, 569, 580, 590, 596]),\n", + " 2: array([ 4, 6, 11, 15, 17, 18, 19, 28, 37, 39, 43, 46, 56,\n", + " 58, 64, 77, 84, 89, 92, 93, 107, 109, 116, 118, 119, 124,\n", + " 128, 129, 134, 135, 136, 137, 142, 147, 157, 163, 164, 172, 174,\n", + " 176, 177, 180, 183, 185, 192, 198, 212, 214, 220, 226, 228, 230,\n", + " 231, 232, 240, 241, 242, 244, 247, 249, 259, 266, 272, 275, 277,\n", + " 279, 282, 284, 286, 290, 308, 312, 322, 336, 341, 342, 352, 354,\n", + " 356, 361, 362, 368, 375, 396, 399, 402, 403, 407, 409, 410, 412,\n", + " 416, 419, 432, 434, 435, 440, 449, 457, 460, 462, 467, 473, 474,\n", + " 482, 486, 488, 497, 499, 502, 504, 508, 510, 521, 522, 529, 535,\n", + " 539, 543, 547, 552, 560, 563, 570, 571, 574, 575, 576, 579, 581,\n", + " 584, 585, 591, 592, 593, 597, 598]),\n", + " 3: array([ 45, 68, 400, 509, 559]),\n", + " 4: array([ 0, 3, 13, 20, 30, 33, 34, 51, 54, 61, 62, 63, 66,\n", + " 71, 73, 74, 76, 79, 88, 104, 106, 112, 120, 148, 171, 173,\n", + " 182, 190, 191, 193, 202, 203, 208, 211, 215, 221, 224, 243, 255,\n", + " 261, 262, 265, 269, 271, 273, 274, 276, 287, 309, 315, 318, 320,\n", + " 327, 328, 338, 340, 350, 358, 363, 365, 378, 385, 405, 415, 418,\n", + " 424, 436, 437, 441, 445, 448, 455, 461, 465, 466, 468, 471, 472,\n", + " 480, 489, 503, 507, 517, 528, 537, 538, 540, 542, 544, 549, 564,\n", + " 578]),\n", + " 5: array([ 2, 5, 9, 10, 25, 26, 31, 32, 35, 52, 59, 65, 67,\n", + " 69, 70, 75, 82, 97, 99, 100, 111, 114, 122, 127, 141, 146,\n", + " 149, 151, 153, 154, 155, 158, 162, 169, 175, 179, 181, 194, 196,\n", + " 199, 200, 207, 210, 222, 223, 227, 235, 236, 245, 251, 252, 264,\n", + " 267, 270, 278, 289, 291, 292, 295, 296, 299, 303, 304, 307, 310,\n", + " 314, 316, 323, 329, 330, 347, 348, 351, 364, 367, 371, 374, 380,\n", + " 383, 384, 387, 388, 395, 401, 413, 417, 422, 425, 426, 429, 430,\n", + " 439, 442, 443, 447, 450, 454, 456, 459, 464, 470, 476, 481, 487,\n", + " 491, 493, 494, 506, 513, 518, 519, 523, 525, 534, 558, 562, 565,\n", + " 572, 573, 577, 582, 583, 586, 588, 595, 599]),\n", + " 6: array([ 57, 87, 101, 103, 110, 123, 152, 188, 239, 257, 313, 321, 324,\n", + " 359, 389, 408, 446, 545, 568]),\n", + " 7: array([ 12, 23, 24, 38, 42, 94, 170, 205, 237, 298, 317, 326, 355,\n", + " 431, 444, 463, 495, 530, 531, 551, 557])},\n", + " 9: {0: array([ 1, 14, 16, 21, 22, 27, 29, 36, 49, 53, 72, 78, 81,\n", + " 86, 95, 96, 98, 102, 108, 113, 115, 132, 139, 140, 161, 167,\n", + " 168, 184, 186, 187, 189, 197, 216, 217, 219, 225, 234, 246, 248,\n", + " 250, 253, 254, 256, 283, 293, 294, 300, 311, 331, 334, 343, 344,\n", + " 345, 346, 353, 360, 369, 370, 379, 382, 390, 391, 392, 393, 394,\n", + " 398, 411, 420, 421, 428, 433, 451, 453, 469, 475, 477, 483, 484,\n", + " 485, 496, 498, 501, 505, 511, 512, 515, 516, 526, 527, 546, 550,\n", + " 553, 555, 561, 566, 567, 587, 589, 594]),\n", + " 1: array([ 7, 8, 40, 41, 44, 47, 48, 50, 55, 60, 80, 83, 85,\n", + " 90, 91, 105, 117, 121, 125, 126, 130, 131, 133, 138, 143, 144,\n", + " 145, 150, 156, 159, 160, 165, 166, 178, 195, 201, 204, 206, 209,\n", + " 213, 218, 229, 233, 238, 258, 260, 263, 268, 280, 281, 285, 288,\n", + " 297, 301, 302, 305, 306, 319, 325, 332, 333, 335, 337, 339, 349,\n", + " 357, 366, 372, 373, 376, 377, 381, 386, 397, 404, 406, 414, 423,\n", + " 427, 438, 452, 458, 478, 479, 490, 492, 500, 514, 520, 524, 532,\n", + " 533, 536, 541, 548, 554, 556, 569, 580, 590, 596]),\n", + " 2: array([ 4, 6, 11, 15, 17, 18, 19, 28, 37, 39, 43, 46, 56,\n", + " 58, 64, 77, 84, 89, 92, 93, 107, 109, 116, 118, 119, 124,\n", + " 128, 129, 134, 135, 136, 137, 142, 147, 157, 163, 164, 172, 174,\n", + " 176, 177, 180, 183, 185, 192, 198, 212, 214, 220, 226, 228, 230,\n", + " 231, 232, 240, 241, 242, 244, 247, 249, 259, 266, 272, 275, 277,\n", + " 279, 282, 284, 286, 290, 308, 312, 322, 336, 341, 342, 352, 354,\n", + " 356, 361, 362, 368, 375, 396, 399, 402, 403, 407, 409, 410, 412,\n", + " 416, 419, 432, 434, 435, 440, 449, 457, 460, 462, 467, 473, 474,\n", + " 482, 486, 488, 497, 499, 502, 504, 508, 510, 521, 522, 529, 535,\n", + " 539, 543, 547, 552, 560, 563, 570, 571, 574, 575, 576, 579, 581,\n", + " 584, 585, 591, 592, 593, 597, 598]),\n", + " 3: array([ 45, 68, 400, 509, 559]),\n", + " 4: array([ 0, 3, 13, 20, 30, 33, 34, 51, 54, 61, 62, 63, 66,\n", + " 71, 73, 74, 76, 79, 88, 104, 106, 112, 120, 148, 171, 173,\n", + " 182, 190, 191, 193, 202, 203, 208, 211, 215, 221, 224, 243, 255,\n", + " 261, 262, 265, 269, 271, 273, 274, 276, 287, 309, 315, 318, 320,\n", + " 327, 328, 338, 340, 350, 358, 363, 365, 378, 385, 405, 415, 418,\n", + " 424, 436, 437, 441, 445, 448, 455, 461, 465, 466, 468, 471, 472,\n", + " 480, 489, 503, 507, 517, 528, 537, 538, 540, 542, 544, 549, 564,\n", + " 578]),\n", + " 5: array([ 2, 5, 9, 10, 25, 26, 31, 32, 35, 52, 59, 65, 67,\n", + " 69, 70, 75, 82, 97, 99, 100, 111, 114, 122, 127, 141, 146,\n", + " 149, 151, 153, 154, 155, 158, 162, 169, 175, 179, 181, 194, 196,\n", + " 199, 200, 207, 210, 222, 223, 227, 235, 236, 245, 251, 252, 264,\n", + " 267, 270, 278, 289, 291, 292, 295, 296, 299, 303, 304, 307, 310,\n", + " 314, 316, 323, 329, 330, 347, 348, 351, 364, 367, 371, 374, 380,\n", + " 383, 384, 387, 388, 395, 401, 413, 417, 422, 425, 426, 429, 430,\n", + " 439, 442, 443, 447, 450, 454, 456, 459, 464, 470, 476, 481, 487,\n", + " 491, 493, 494, 506, 513, 518, 519, 523, 525, 534, 558, 562, 565,\n", + " 572, 573, 577, 582, 583, 586, 588, 595, 599]),\n", + " 6: array([ 87, 103, 110, 152, 188, 257, 313, 321, 359, 408, 568]),\n", + " 7: array([ 12, 23, 24, 38, 42, 94, 170, 205, 237, 298, 317, 326, 355,\n", + " 431, 444, 463, 495, 530, 531, 551, 557]),\n", + " 8: array([ 57, 101, 123, 239, 324, 389, 446, 545])},\n", + " 10: {0: array([ 1, 14, 16, 21, 22, 27, 29, 36, 49, 53, 72, 78, 81,\n", + " 86, 95, 96, 98, 102, 108, 113, 132, 139, 140, 161, 167, 168,\n", + " 184, 186, 187, 189, 197, 216, 217, 225, 234, 246, 248, 250, 253,\n", + " 254, 256, 283, 293, 294, 300, 311, 331, 334, 343, 344, 345, 353,\n", + " 360, 369, 370, 379, 382, 390, 391, 392, 393, 394, 398, 411, 420,\n", + " 428, 433, 451, 453, 469, 475, 477, 483, 484, 485, 496, 498, 501,\n", + " 511, 512, 515, 516, 527, 546, 550, 553, 555, 561, 566, 567, 579,\n", + " 589, 594]),\n", + " 1: array([ 7, 8, 40, 41, 44, 47, 48, 50, 55, 60, 80, 83, 85,\n", + " 90, 91, 105, 117, 121, 125, 126, 130, 131, 133, 138, 143, 144,\n", + " 145, 150, 156, 159, 160, 165, 166, 178, 195, 201, 204, 206, 209,\n", + " 213, 218, 229, 233, 238, 258, 260, 263, 268, 280, 281, 285, 288,\n", + " 297, 301, 302, 305, 306, 319, 325, 332, 333, 335, 337, 339, 349,\n", + " 357, 366, 372, 373, 376, 377, 381, 386, 397, 404, 406, 414, 423,\n", + " 427, 438, 452, 458, 478, 479, 490, 492, 500, 514, 520, 524, 532,\n", + " 533, 536, 541, 548, 554, 556, 569, 580, 590, 596]),\n", + " 2: array([ 6, 37, 77, 109, 134, 135, 176, 180, 183, 198, 228, 231, 232,\n", + " 244, 247, 284, 290, 322, 354, 362, 368, 375, 399, 403, 407, 409,\n", + " 435, 462, 473, 482, 488, 504, 508, 510, 552, 563, 570, 571, 575,\n", + " 584, 591, 592]),\n", + " 3: array([ 45, 68, 400, 509, 559]),\n", + " 4: array([ 4, 11, 15, 17, 18, 19, 28, 39, 43, 46, 56, 58, 64,\n", + " 84, 89, 92, 93, 107, 115, 116, 118, 119, 124, 128, 129, 136,\n", + " 137, 142, 147, 157, 163, 164, 172, 174, 177, 185, 192, 212, 214,\n", + " 219, 220, 226, 230, 240, 241, 242, 249, 259, 266, 272, 275, 277,\n", + " 279, 282, 286, 308, 312, 336, 341, 342, 346, 352, 356, 361, 396,\n", + " 402, 410, 412, 416, 419, 421, 432, 434, 440, 449, 457, 460, 467,\n", + " 472, 474, 486, 497, 499, 502, 505, 521, 522, 526, 529, 535, 539,\n", + " 543, 547, 560, 574, 576, 578, 581, 585, 587, 593, 597, 598]),\n", + " 5: array([ 0, 3, 13, 20, 30, 33, 34, 51, 54, 61, 62, 63, 66,\n", + " 71, 73, 74, 76, 79, 88, 104, 106, 112, 120, 148, 171, 173,\n", + " 182, 190, 191, 193, 202, 203, 208, 211, 215, 221, 224, 243, 255,\n", + " 261, 262, 265, 269, 271, 273, 274, 276, 287, 309, 315, 318, 320,\n", + " 327, 328, 338, 340, 350, 358, 363, 365, 378, 385, 405, 415, 418,\n", + " 424, 436, 437, 441, 445, 448, 455, 461, 465, 466, 468, 471, 480,\n", + " 489, 503, 507, 517, 528, 537, 538, 540, 542, 544, 549, 564]),\n", + " 6: array([ 2, 5, 9, 10, 25, 26, 31, 32, 35, 52, 59, 65, 67,\n", + " 69, 70, 75, 82, 97, 99, 100, 111, 114, 122, 127, 141, 146,\n", + " 149, 151, 153, 154, 155, 158, 162, 169, 175, 179, 181, 194, 196,\n", + " 199, 200, 207, 210, 222, 223, 227, 235, 236, 245, 251, 252, 264,\n", + " 267, 270, 278, 289, 291, 292, 295, 296, 299, 303, 304, 307, 310,\n", + " 314, 316, 323, 329, 330, 347, 348, 351, 364, 367, 371, 374, 380,\n", + " 383, 384, 387, 388, 395, 401, 413, 417, 422, 425, 426, 429, 430,\n", + " 439, 442, 443, 447, 450, 454, 456, 459, 464, 470, 476, 481, 487,\n", + " 491, 493, 494, 506, 513, 518, 519, 523, 525, 534, 558, 562, 565,\n", + " 572, 573, 577, 582, 583, 586, 588, 595, 599]),\n", + " 7: array([ 87, 103, 110, 152, 188, 257, 313, 321, 359, 408, 568]),\n", + " 8: array([ 12, 23, 24, 38, 42, 94, 170, 205, 237, 298, 317, 326, 355,\n", + " 431, 444, 463, 495, 530, 531, 551, 557]),\n", + " 9: array([ 57, 101, 123, 239, 324, 389, 446, 545])}},\n", + " 'signed_nonnormalized_l2_avg': {2: {0: array([ 3, 13, 14, 18, 29, 33, 43, 45, 46, 51, 54, 57, 63,\n", + " 71, 81, 88, 89, 93, 98, 104, 110, 123, 148, 152, 161, 163,\n", + " 171, 174, 188, 191, 193, 197, 202, 203, 208, 212, 221, 239, 243,\n", + " 246, 256, 272, 286, 287, 294, 309, 313, 315, 321, 340, 341, 343,\n", + " 350, 358, 359, 361, 363, 369, 398, 400, 402, 405, 408, 410, 415,\n", + " 416, 418, 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 472,\n", + " 489, 497, 507, 509, 511, 512, 516, 517, 521, 526, 527, 537, 538,\n", + " 540, 544, 545, 550, 553, 559, 560, 564, 568, 576, 585, 587]),\n", + " 1: array([ 0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15,\n", + " 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 30,\n", + " 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 42, 44, 47,\n", + " 48, 49, 50, 52, 53, 55, 56, 58, 59, 60, 61, 62, 64,\n", + " 65, 66, 67, 68, 69, 70, 72, 73, 74, 75, 76, 77, 78,\n", + " 79, 80, 82, 83, 84, 85, 86, 87, 90, 91, 92, 94, 95,\n", + " 96, 97, 99, 100, 101, 102, 103, 105, 106, 107, 108, 109, 111,\n", + " 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125,\n", + " 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,\n", + " 139, 140, 141, 142, 143, 144, 145, 146, 147, 149, 150, 151, 153,\n", + " 154, 155, 156, 157, 158, 159, 160, 162, 164, 165, 166, 167, 168,\n", + " 169, 170, 172, 173, 175, 176, 177, 178, 179, 180, 181, 182, 183,\n", + " 184, 185, 186, 187, 189, 190, 192, 194, 195, 196, 198, 199, 200,\n", + " 201, 204, 205, 206, 207, 209, 210, 211, 213, 214, 215, 216, 217,\n", + " 218, 219, 220, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231,\n", + " 232, 233, 234, 235, 236, 237, 238, 240, 241, 242, 244, 245, 247,\n", + " 248, 249, 250, 251, 252, 253, 254, 255, 257, 258, 259, 260, 261,\n", + " 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 273, 274, 275,\n", + " 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 288, 289, 290,\n", + " 291, 292, 293, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304,\n", + " 305, 306, 307, 308, 310, 311, 312, 314, 316, 317, 318, 319, 320,\n", + " 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334,\n", + " 335, 336, 337, 338, 339, 342, 344, 345, 346, 347, 348, 349, 351,\n", + " 352, 353, 354, 355, 356, 357, 360, 362, 364, 365, 366, 367, 368,\n", + " 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382,\n", + " 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395,\n", + " 396, 397, 399, 401, 403, 404, 406, 407, 409, 411, 412, 413, 414,\n", + " 417, 419, 420, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431,\n", + " 432, 434, 435, 436, 437, 438, 439, 440, 442, 443, 444, 446, 447,\n", + " 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 462, 463,\n", + " 464, 467, 469, 470, 471, 473, 474, 475, 476, 477, 478, 479, 480,\n", + " 481, 482, 483, 484, 485, 486, 487, 488, 490, 491, 492, 493, 494,\n", + " 495, 496, 498, 499, 500, 501, 502, 503, 504, 505, 506, 508, 510,\n", + " 513, 514, 515, 518, 519, 520, 522, 523, 524, 525, 528, 529, 530,\n", + " 531, 532, 533, 534, 535, 536, 539, 541, 542, 543, 546, 547, 548,\n", + " 549, 551, 552, 554, 555, 556, 557, 558, 561, 562, 563, 565, 566,\n", + " 567, 569, 570, 571, 572, 573, 574, 575, 577, 578, 579, 580, 581,\n", + " 582, 583, 584, 586, 588, 589, 590, 591, 592, 593, 594, 595, 596,\n", + " 597, 598, 599])},\n", + " 3: {0: array([ 57, 71, 81, 98, 110, 123, 152, 161, 171, 188, 197, 203, 208,\n", + " 239, 246, 256, 313, 321, 343, 359, 402, 512, 516, 538, 550, 553,\n", + " 568]),\n", + " 1: array([ 0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15,\n", + " 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 30,\n", + " 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 42, 44, 47,\n", + " 48, 49, 50, 52, 53, 55, 56, 58, 59, 60, 61, 62, 64,\n", + " 65, 66, 67, 69, 70, 72, 73, 74, 75, 76, 77, 78, 79,\n", + " 80, 82, 83, 84, 85, 86, 87, 90, 91, 92, 94, 95, 96,\n", + " 97, 99, 100, 101, 102, 103, 105, 106, 107, 108, 109, 111, 112,\n", + " 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125, 126,\n", + " 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n", + " 140, 141, 142, 143, 144, 145, 146, 147, 149, 150, 151, 153, 154,\n", + " 155, 156, 157, 158, 159, 160, 162, 164, 165, 166, 167, 168, 169,\n", + " 170, 172, 173, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184,\n", + " 185, 186, 187, 189, 190, 192, 194, 195, 196, 198, 199, 200, 201,\n", + " 204, 205, 206, 207, 209, 210, 211, 213, 214, 215, 216, 217, 218,\n", + " 219, 220, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232,\n", + " 233, 234, 235, 236, 237, 238, 240, 241, 242, 244, 245, 247, 248,\n", + " 249, 250, 251, 252, 253, 254, 255, 257, 258, 259, 260, 261, 262,\n", + " 263, 264, 265, 266, 267, 268, 269, 270, 271, 273, 274, 275, 276,\n", + " 277, 278, 279, 280, 281, 282, 283, 284, 285, 288, 289, 290, 291,\n", + " 292, 293, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305,\n", + " 306, 307, 308, 310, 311, 312, 314, 316, 317, 318, 319, 322, 323,\n", + " 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336,\n", + " 337, 338, 339, 342, 344, 345, 346, 347, 348, 349, 351, 352, 353,\n", + " 354, 355, 356, 357, 360, 362, 364, 365, 366, 367, 368, 370, 371,\n", + " 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384,\n", + " 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397,\n", + " 399, 401, 403, 404, 406, 407, 409, 411, 412, 413, 414, 417, 419,\n", + " 420, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 434,\n", + " 435, 436, 437, 438, 439, 440, 442, 443, 444, 446, 447, 449, 450,\n", + " 451, 452, 453, 454, 455, 456, 457, 458, 459, 462, 463, 464, 467,\n", + " 469, 470, 471, 473, 474, 475, 476, 478, 479, 480, 481, 482, 484,\n", + " 485, 486, 487, 488, 490, 491, 492, 493, 494, 495, 496, 498, 499,\n", + " 500, 501, 502, 503, 504, 505, 506, 508, 510, 513, 514, 518, 519,\n", + " 520, 522, 523, 524, 525, 528, 529, 530, 531, 532, 533, 534, 535,\n", + " 536, 539, 541, 542, 543, 546, 547, 548, 549, 551, 552, 554, 555,\n", + " 556, 557, 558, 561, 562, 563, 565, 566, 567, 569, 570, 571, 572,\n", + " 573, 574, 575, 577, 578, 579, 580, 581, 582, 583, 584, 586, 588,\n", + " 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599]),\n", + " 2: array([ 3, 13, 14, 18, 29, 33, 43, 45, 46, 51, 54, 63, 68,\n", + " 88, 89, 93, 104, 148, 163, 174, 191, 193, 202, 212, 221, 243,\n", + " 272, 286, 287, 294, 309, 315, 320, 340, 341, 350, 358, 361, 363,\n", + " 369, 398, 400, 405, 408, 410, 415, 416, 418, 421, 433, 441, 445,\n", + " 448, 460, 461, 465, 466, 468, 472, 477, 483, 489, 497, 507, 509,\n", + " 511, 515, 517, 521, 526, 527, 537, 540, 544, 545, 559, 560, 564,\n", + " 576, 585, 587])},\n", + " 4: {0: array([ 57, 71, 81, 98, 110, 123, 152, 161, 171, 188, 197, 203, 208,\n", + " 239, 246, 256, 313, 321, 343, 359, 402, 512, 516, 538, 550, 553,\n", + " 568]),\n", + " 1: array([ 0, 1, 2, 4, 5, 6, 9, 10, 11, 15, 16, 17, 19,\n", + " 20, 21, 22, 25, 26, 27, 28, 30, 31, 32, 34, 35, 36,\n", + " 37, 39, 49, 52, 53, 56, 58, 59, 61, 62, 64, 65, 66,\n", + " 67, 69, 70, 72, 73, 74, 75, 76, 77, 78, 79, 82, 84,\n", + " 86, 87, 92, 95, 96, 97, 99, 100, 101, 102, 103, 106, 107,\n", + " 108, 109, 111, 112, 113, 114, 115, 116, 118, 119, 120, 122, 124,\n", + " 127, 128, 129, 132, 134, 135, 136, 137, 139, 140, 141, 142, 146,\n", + " 147, 149, 151, 153, 154, 155, 157, 158, 162, 164, 167, 168, 169,\n", + " 172, 173, 175, 176, 177, 179, 180, 181, 182, 183, 184, 185, 186,\n", + " 187, 189, 190, 192, 194, 196, 198, 199, 200, 207, 210, 211, 214,\n", + " 215, 216, 217, 219, 220, 222, 223, 224, 225, 226, 227, 228, 230,\n", + " 231, 232, 234, 235, 236, 240, 241, 242, 244, 245, 247, 248, 249,\n", + " 250, 251, 252, 253, 254, 255, 257, 259, 261, 262, 264, 265, 266,\n", + " 267, 269, 270, 271, 273, 274, 275, 276, 277, 278, 279, 282, 283,\n", + " 284, 289, 290, 291, 292, 293, 295, 296, 299, 300, 303, 304, 307,\n", + " 308, 310, 311, 312, 314, 316, 318, 322, 323, 324, 327, 328, 329,\n", + " 330, 331, 334, 336, 338, 342, 344, 345, 346, 347, 348, 351, 352,\n", + " 353, 354, 356, 360, 362, 364, 365, 367, 368, 370, 371, 374, 375,\n", + " 378, 379, 380, 383, 384, 385, 387, 388, 389, 390, 391, 392, 393,\n", + " 394, 395, 396, 399, 401, 403, 407, 409, 411, 412, 413, 417, 419,\n", + " 420, 422, 424, 425, 426, 428, 429, 430, 431, 432, 434, 435, 436,\n", + " 437, 439, 440, 442, 443, 446, 447, 449, 450, 451, 453, 454, 455,\n", + " 456, 457, 459, 462, 464, 467, 469, 470, 471, 473, 474, 475, 476,\n", + " 480, 481, 482, 484, 485, 486, 487, 488, 491, 493, 494, 496, 498,\n", + " 499, 501, 502, 503, 504, 505, 506, 508, 510, 513, 518, 519, 522,\n", + " 523, 525, 528, 529, 534, 535, 539, 542, 543, 546, 547, 549, 552,\n", + " 555, 558, 561, 562, 563, 565, 566, 567, 570, 571, 572, 573, 574,\n", + " 575, 577, 578, 579, 581, 582, 583, 584, 586, 588, 589, 591, 592,\n", + " 593, 594, 595, 597, 598, 599]),\n", + " 2: array([ 7, 8, 12, 23, 24, 38, 40, 41, 42, 44, 47, 48, 50,\n", + " 55, 60, 80, 83, 85, 90, 91, 94, 105, 117, 121, 125, 126,\n", + " 130, 131, 133, 138, 143, 144, 145, 150, 156, 159, 160, 165, 166,\n", + " 170, 178, 195, 201, 204, 205, 206, 209, 213, 218, 229, 233, 237,\n", + " 238, 258, 260, 263, 268, 280, 281, 285, 288, 297, 298, 301, 302,\n", + " 305, 306, 317, 319, 325, 326, 332, 333, 335, 337, 339, 349, 355,\n", + " 357, 366, 372, 373, 376, 377, 381, 382, 386, 397, 404, 406, 414,\n", + " 423, 427, 438, 444, 452, 458, 463, 478, 479, 490, 492, 495, 500,\n", + " 514, 520, 524, 530, 531, 532, 533, 536, 541, 548, 551, 554, 556,\n", + " 557, 569, 580, 590, 596]),\n", + " 3: array([ 3, 13, 14, 18, 29, 33, 43, 45, 46, 51, 54, 63, 68,\n", + " 88, 89, 93, 104, 148, 163, 174, 191, 193, 202, 212, 221, 243,\n", + " 272, 286, 287, 294, 309, 315, 320, 340, 341, 350, 358, 361, 363,\n", + " 369, 398, 400, 405, 408, 410, 415, 416, 418, 421, 433, 441, 445,\n", + " 448, 460, 461, 465, 466, 468, 472, 477, 483, 489, 497, 507, 509,\n", + " 511, 515, 517, 521, 526, 527, 537, 540, 544, 545, 559, 560, 564,\n", + " 576, 585, 587])},\n", + " 5: {0: array([ 57, 71, 81, 98, 110, 123, 152, 161, 171, 188, 197, 203, 208,\n", + " 239, 246, 256, 313, 321, 343, 359, 402, 512, 516, 538, 550, 553,\n", + " 568]),\n", + " 1: array([ 1, 2, 5, 6, 9, 10, 15, 16, 17, 21, 22, 25, 26,\n", + " 27, 28, 31, 32, 35, 36, 39, 49, 52, 53, 56, 58, 59,\n", + " 65, 67, 69, 70, 72, 74, 75, 77, 78, 82, 86, 92, 96,\n", + " 97, 99, 100, 102, 107, 108, 109, 111, 113, 114, 115, 116, 118,\n", + " 119, 122, 124, 127, 129, 132, 134, 135, 136, 137, 139, 140, 141,\n", + " 142, 146, 149, 151, 153, 154, 155, 157, 158, 162, 167, 168, 169,\n", + " 175, 176, 177, 179, 180, 181, 183, 184, 185, 187, 189, 192, 194,\n", + " 196, 198, 199, 200, 207, 210, 211, 214, 216, 217, 219, 220, 222,\n", + " 223, 225, 226, 227, 228, 230, 231, 232, 234, 235, 236, 240, 241,\n", + " 242, 244, 245, 247, 248, 249, 250, 251, 252, 253, 254, 259, 262,\n", + " 264, 266, 267, 270, 277, 278, 279, 282, 283, 284, 289, 290, 291,\n", + " 292, 293, 295, 296, 299, 300, 303, 304, 307, 308, 310, 311, 312,\n", + " 314, 316, 322, 323, 329, 330, 331, 334, 336, 342, 344, 345, 346,\n", + " 347, 348, 351, 353, 354, 356, 360, 362, 364, 367, 368, 370, 371,\n", + " 374, 375, 379, 380, 382, 383, 384, 387, 388, 390, 391, 392, 393,\n", + " 394, 395, 396, 399, 401, 403, 407, 409, 411, 412, 413, 417, 419,\n", + " 420, 422, 425, 426, 428, 429, 430, 435, 439, 440, 442, 443, 447,\n", + " 450, 451, 453, 454, 456, 457, 459, 462, 464, 467, 469, 470, 473,\n", + " 474, 475, 476, 481, 482, 484, 485, 486, 487, 488, 491, 493, 494,\n", + " 496, 498, 499, 501, 502, 504, 505, 506, 508, 510, 513, 518, 519,\n", + " 522, 523, 525, 529, 534, 535, 539, 546, 547, 552, 555, 558, 561,\n", + " 562, 563, 565, 566, 567, 570, 571, 572, 573, 574, 575, 577, 581,\n", + " 582, 583, 584, 586, 588, 589, 591, 592, 593, 594, 595, 598, 599]),\n", + " 2: array([ 7, 8, 12, 23, 38, 40, 42, 44, 47, 48, 50, 55, 60,\n", + " 80, 83, 85, 90, 91, 94, 105, 117, 121, 125, 126, 130, 131,\n", + " 133, 138, 143, 144, 145, 150, 156, 159, 160, 165, 166, 170, 178,\n", + " 195, 201, 204, 205, 206, 209, 213, 218, 229, 233, 237, 238, 258,\n", + " 260, 263, 268, 280, 281, 285, 288, 297, 298, 301, 302, 306, 319,\n", + " 325, 332, 333, 335, 337, 339, 349, 357, 366, 372, 373, 376, 377,\n", + " 381, 386, 397, 404, 406, 414, 423, 427, 438, 444, 452, 458, 463,\n", + " 478, 479, 490, 492, 500, 514, 520, 524, 530, 532, 533, 536, 541,\n", + " 548, 551, 554, 556, 557, 569, 580, 590, 596]),\n", + " 3: array([ 3, 13, 14, 18, 29, 33, 43, 45, 46, 51, 54, 63, 68,\n", + " 88, 89, 93, 104, 148, 163, 174, 191, 193, 202, 212, 221, 243,\n", + " 272, 286, 287, 294, 309, 315, 320, 340, 341, 350, 358, 361, 363,\n", + " 369, 398, 400, 405, 410, 415, 416, 418, 421, 433, 441, 445, 448,\n", + " 460, 461, 465, 466, 468, 472, 477, 483, 489, 497, 507, 509, 511,\n", + " 515, 517, 521, 526, 527, 537, 540, 544, 545, 559, 560, 564, 576,\n", + " 585, 587]),\n", + " 4: array([ 0, 4, 11, 19, 20, 24, 30, 34, 37, 41, 61, 62, 64,\n", + " 66, 73, 76, 79, 84, 87, 95, 101, 103, 106, 112, 120, 128,\n", + " 147, 164, 172, 173, 182, 186, 190, 215, 224, 255, 257, 261, 265,\n", + " 269, 271, 273, 274, 275, 276, 305, 317, 318, 324, 326, 327, 328,\n", + " 338, 352, 355, 365, 378, 385, 389, 408, 424, 431, 432, 434, 436,\n", + " 437, 446, 449, 455, 471, 480, 495, 503, 528, 531, 542, 543, 549,\n", + " 578, 579, 597])},\n", + " 6: {0: array([ 57, 71, 81, 98, 110, 123, 152, 161, 171, 188, 197, 203, 208,\n", + " 239, 246, 256, 313, 321, 343, 359, 402, 512, 516, 538, 550, 553,\n", + " 568]),\n", + " 1: array([ 1, 2, 5, 6, 9, 10, 15, 16, 17, 21, 22, 25, 26,\n", + " 27, 28, 31, 32, 35, 36, 39, 49, 52, 53, 56, 58, 59,\n", + " 65, 67, 69, 70, 72, 74, 75, 77, 78, 82, 86, 92, 96,\n", + " 97, 99, 100, 102, 107, 108, 109, 111, 113, 114, 115, 116, 118,\n", + " 119, 122, 124, 127, 129, 132, 134, 135, 136, 137, 139, 140, 141,\n", + " 142, 146, 149, 151, 153, 154, 155, 157, 158, 162, 167, 168, 169,\n", + " 175, 176, 177, 179, 180, 181, 183, 184, 185, 187, 189, 192, 194,\n", + " 196, 198, 199, 200, 207, 210, 211, 214, 216, 217, 219, 220, 222,\n", + " 223, 225, 226, 227, 228, 230, 231, 232, 234, 235, 236, 240, 241,\n", + " 242, 244, 245, 247, 248, 249, 250, 251, 252, 253, 254, 259, 262,\n", + " 264, 266, 267, 270, 277, 278, 279, 282, 283, 284, 289, 290, 291,\n", + " 292, 293, 295, 296, 299, 300, 303, 304, 307, 308, 310, 311, 312,\n", + " 314, 316, 322, 323, 329, 330, 331, 334, 336, 342, 344, 345, 346,\n", + " 347, 348, 351, 353, 354, 356, 360, 362, 364, 367, 368, 370, 371,\n", + " 374, 375, 379, 380, 382, 383, 384, 387, 388, 390, 391, 392, 393,\n", + " 394, 395, 396, 399, 401, 403, 407, 409, 411, 412, 413, 417, 419,\n", + " 420, 422, 425, 426, 428, 429, 430, 435, 439, 440, 442, 443, 447,\n", + " 450, 451, 453, 454, 456, 457, 459, 462, 464, 467, 469, 470, 473,\n", + " 474, 475, 476, 481, 482, 484, 485, 486, 487, 488, 491, 493, 494,\n", + " 496, 498, 499, 501, 502, 504, 505, 506, 508, 510, 513, 518, 519,\n", + " 522, 523, 525, 529, 534, 535, 539, 546, 547, 552, 555, 558, 561,\n", + " 562, 563, 565, 566, 567, 570, 571, 572, 573, 574, 575, 577, 581,\n", + " 582, 583, 584, 586, 588, 589, 591, 592, 593, 594, 595, 598, 599]),\n", + " 2: array([ 7, 8, 12, 23, 38, 40, 42, 44, 48, 50, 55, 60, 80,\n", + " 83, 85, 90, 91, 94, 105, 117, 121, 125, 126, 130, 131, 133,\n", + " 138, 143, 144, 145, 150, 156, 159, 160, 165, 166, 170, 178, 195,\n", + " 204, 205, 206, 209, 213, 218, 229, 233, 237, 238, 258, 260, 263,\n", + " 268, 280, 281, 285, 288, 297, 298, 301, 302, 305, 306, 319, 325,\n", + " 333, 335, 337, 339, 349, 357, 366, 372, 373, 377, 386, 397, 404,\n", + " 406, 414, 423, 427, 438, 444, 452, 458, 463, 478, 479, 490, 492,\n", + " 500, 514, 520, 524, 530, 532, 533, 536, 541, 548, 551, 554, 556,\n", + " 557, 569, 580, 590, 596]),\n", + " 3: array([ 3, 13, 14, 18, 29, 33, 43, 45, 46, 51, 54, 63, 68,\n", + " 88, 89, 93, 104, 148, 163, 174, 191, 193, 202, 212, 221, 243,\n", + " 272, 286, 287, 294, 309, 315, 320, 340, 341, 350, 358, 361, 363,\n", + " 369, 398, 400, 405, 410, 415, 416, 418, 421, 433, 441, 445, 448,\n", + " 460, 461, 465, 466, 468, 472, 477, 483, 489, 497, 507, 509, 511,\n", + " 515, 517, 521, 526, 527, 537, 540, 544, 545, 559, 560, 564, 576,\n", + " 585, 587]),\n", + " 4: array([ 0, 4, 11, 19, 20, 24, 30, 34, 37, 41, 61, 62, 64,\n", + " 66, 73, 76, 79, 84, 87, 95, 101, 103, 106, 112, 120, 128,\n", + " 147, 164, 172, 173, 182, 186, 190, 215, 224, 255, 257, 261, 265,\n", + " 269, 271, 273, 274, 275, 276, 317, 318, 324, 326, 327, 328, 338,\n", + " 352, 355, 365, 378, 385, 389, 408, 424, 431, 432, 434, 436, 437,\n", + " 446, 449, 455, 471, 480, 495, 503, 528, 531, 542, 543, 549, 578,\n", + " 579, 597]),\n", + " 5: array([ 47, 201, 332, 376, 381])},\n", + " 7: {0: array([ 57, 71, 81, 98, 110, 123, 152, 161, 171, 188, 197, 203, 208,\n", + " 239, 246, 256, 313, 321, 343, 359, 402, 512, 516, 538, 550, 553,\n", + " 568]),\n", + " 1: array([ 1, 2, 5, 6, 9, 10, 15, 16, 17, 21, 22, 25, 26,\n", + " 27, 28, 31, 32, 35, 36, 39, 49, 52, 53, 56, 58, 59,\n", + " 65, 67, 69, 70, 72, 74, 75, 77, 78, 82, 86, 92, 96,\n", + " 97, 99, 100, 102, 107, 108, 109, 111, 113, 114, 115, 116, 118,\n", + " 119, 122, 124, 127, 129, 132, 134, 135, 136, 137, 139, 140, 141,\n", + " 142, 146, 149, 151, 153, 154, 155, 157, 158, 162, 167, 168, 169,\n", + " 175, 176, 177, 179, 180, 181, 183, 184, 185, 187, 189, 192, 194,\n", + " 196, 198, 199, 200, 207, 210, 211, 214, 216, 217, 219, 220, 222,\n", + " 223, 225, 226, 227, 228, 230, 231, 232, 234, 235, 236, 240, 241,\n", + " 242, 244, 245, 247, 248, 249, 250, 251, 252, 253, 254, 259, 262,\n", + " 264, 266, 267, 270, 277, 278, 279, 282, 283, 284, 289, 290, 291,\n", + " 292, 293, 295, 296, 299, 300, 303, 304, 307, 308, 310, 311, 312,\n", + " 314, 316, 322, 323, 329, 330, 331, 334, 336, 342, 344, 345, 346,\n", + " 347, 348, 351, 353, 354, 356, 360, 362, 364, 367, 368, 370, 371,\n", + " 374, 375, 379, 380, 383, 384, 387, 388, 390, 391, 392, 393, 394,\n", + " 395, 396, 399, 401, 403, 407, 409, 411, 412, 413, 417, 419, 420,\n", + " 422, 425, 426, 428, 429, 430, 435, 439, 440, 442, 443, 447, 450,\n", + " 451, 453, 454, 456, 457, 459, 462, 464, 467, 469, 470, 473, 474,\n", + " 475, 476, 481, 482, 484, 485, 486, 487, 488, 491, 493, 494, 496,\n", + " 498, 499, 501, 502, 504, 505, 506, 508, 510, 513, 518, 519, 522,\n", + " 523, 525, 529, 534, 535, 539, 546, 547, 552, 555, 558, 561, 562,\n", + " 563, 565, 566, 567, 570, 571, 572, 573, 574, 575, 577, 581, 582,\n", + " 583, 584, 586, 588, 589, 591, 592, 593, 594, 595, 598, 599]),\n", + " 2: array([ 7, 8, 40, 48, 50, 55, 80, 83, 85, 90, 91, 105, 117,\n", + " 121, 125, 126, 133, 138, 145, 150, 156, 159, 160, 166, 178, 195,\n", + " 204, 205, 209, 213, 218, 229, 233, 238, 263, 268, 280, 288, 297,\n", + " 301, 306, 319, 333, 335, 337, 339, 349, 357, 366, 372, 373, 377,\n", + " 382, 397, 404, 406, 414, 423, 427, 438, 452, 458, 478, 500, 520,\n", + " 524, 536, 556, 569, 590, 596]),\n", + " 3: array([ 3, 13, 14, 18, 29, 33, 43, 45, 46, 51, 54, 63, 68,\n", + " 88, 89, 93, 104, 148, 163, 174, 191, 193, 202, 212, 221, 243,\n", + " 272, 286, 287, 294, 309, 315, 320, 340, 341, 350, 358, 361, 363,\n", + " 369, 398, 400, 405, 410, 415, 416, 418, 421, 433, 441, 445, 448,\n", + " 460, 461, 465, 466, 468, 472, 477, 483, 489, 497, 507, 509, 511,\n", + " 515, 517, 521, 526, 527, 537, 540, 544, 545, 559, 560, 564, 576,\n", + " 585, 587]),\n", + " 4: array([ 0, 4, 11, 19, 20, 30, 34, 37, 61, 62, 64, 66, 73,\n", + " 76, 79, 84, 87, 95, 101, 103, 106, 112, 120, 128, 147, 164,\n", + " 172, 173, 182, 186, 190, 215, 224, 255, 257, 261, 265, 269, 271,\n", + " 273, 274, 275, 276, 318, 324, 327, 328, 338, 352, 365, 378, 385,\n", + " 389, 408, 424, 432, 434, 436, 437, 446, 449, 455, 471, 480, 503,\n", + " 528, 542, 543, 549, 578, 579, 597]),\n", + " 5: array([ 12, 23, 24, 38, 41, 42, 44, 60, 94, 130, 131, 143, 144,\n", + " 165, 170, 206, 237, 258, 260, 281, 285, 298, 302, 305, 317, 325,\n", + " 326, 355, 386, 431, 444, 463, 479, 490, 492, 495, 514, 530, 531,\n", + " 532, 533, 541, 548, 551, 554, 557, 580]),\n", + " 6: array([ 47, 201, 332, 376, 381])},\n", + " 8: {0: array([ 57, 123, 161, 239, 343, 516, 550]),\n", + " 1: array([ 1, 2, 5, 6, 9, 10, 15, 16, 17, 21, 22, 25, 26,\n", + " 27, 28, 31, 32, 35, 36, 39, 49, 52, 53, 56, 58, 59,\n", + " 65, 67, 69, 70, 72, 74, 75, 77, 78, 82, 86, 92, 96,\n", + " 97, 99, 100, 102, 107, 108, 109, 111, 113, 114, 115, 116, 118,\n", + " 119, 122, 124, 127, 129, 132, 134, 135, 136, 137, 139, 140, 141,\n", + " 142, 146, 149, 151, 153, 154, 155, 157, 158, 162, 167, 168, 169,\n", + " 175, 176, 177, 179, 180, 181, 183, 184, 185, 187, 189, 192, 194,\n", + " 196, 198, 199, 200, 207, 210, 211, 214, 216, 217, 219, 220, 222,\n", + " 223, 225, 226, 227, 228, 230, 231, 232, 234, 235, 236, 240, 241,\n", + " 242, 244, 245, 247, 248, 249, 250, 251, 252, 253, 254, 259, 262,\n", + " 264, 266, 267, 270, 277, 278, 279, 282, 283, 284, 289, 290, 291,\n", + " 292, 293, 295, 296, 299, 300, 303, 304, 307, 308, 310, 311, 312,\n", + " 314, 316, 322, 323, 329, 330, 331, 334, 336, 342, 344, 345, 346,\n", + " 347, 348, 351, 353, 354, 356, 360, 362, 364, 367, 368, 370, 371,\n", + " 374, 375, 379, 380, 383, 384, 387, 388, 390, 391, 392, 393, 394,\n", + " 395, 396, 399, 401, 403, 407, 409, 411, 412, 413, 417, 419, 420,\n", + " 422, 425, 426, 428, 429, 430, 435, 439, 440, 442, 443, 447, 450,\n", + " 451, 453, 454, 456, 457, 459, 462, 464, 467, 469, 470, 473, 474,\n", + " 475, 476, 481, 482, 484, 485, 486, 487, 488, 491, 493, 494, 496,\n", + " 498, 499, 501, 502, 504, 505, 506, 508, 510, 513, 518, 519, 522,\n", + " 523, 525, 529, 534, 535, 539, 546, 547, 552, 555, 558, 561, 562,\n", + " 563, 565, 566, 567, 570, 571, 572, 573, 574, 575, 577, 581, 582,\n", + " 583, 584, 586, 588, 589, 591, 592, 593, 594, 595, 598, 599]),\n", + " 2: array([ 71, 81, 98, 110, 152, 171, 188, 197, 203, 208, 246, 256, 313,\n", + " 321, 359, 400, 402, 472, 512, 538, 553, 568]),\n", + " 3: array([ 7, 8, 40, 48, 50, 55, 80, 83, 85, 90, 91, 105, 117,\n", + " 121, 125, 126, 133, 138, 145, 150, 156, 159, 160, 166, 178, 195,\n", + " 204, 205, 209, 213, 218, 229, 233, 238, 263, 268, 280, 288, 297,\n", + " 301, 306, 319, 333, 335, 337, 339, 349, 357, 366, 372, 373, 377,\n", + " 382, 397, 404, 406, 414, 423, 427, 438, 452, 458, 478, 500, 520,\n", + " 524, 536, 556, 569, 590, 596]),\n", + " 4: array([ 3, 13, 14, 18, 29, 33, 43, 45, 46, 51, 54, 63, 68,\n", + " 88, 89, 93, 104, 148, 163, 174, 191, 193, 202, 212, 221, 243,\n", + " 272, 286, 287, 294, 309, 315, 320, 340, 341, 350, 358, 361, 363,\n", + " 369, 398, 405, 410, 415, 416, 418, 421, 433, 441, 445, 448, 460,\n", + " 461, 465, 466, 468, 477, 483, 489, 497, 507, 509, 511, 515, 517,\n", + " 521, 526, 527, 537, 540, 544, 545, 559, 560, 564, 576, 585, 587]),\n", + " 5: array([ 0, 4, 11, 19, 20, 30, 34, 37, 61, 62, 64, 66, 73,\n", + " 76, 79, 84, 87, 95, 101, 103, 106, 112, 120, 128, 147, 164,\n", + " 172, 173, 182, 186, 190, 215, 224, 255, 257, 261, 265, 269, 271,\n", + " 273, 274, 275, 276, 318, 324, 327, 328, 338, 352, 365, 378, 385,\n", + " 389, 408, 424, 432, 434, 436, 437, 446, 449, 455, 471, 480, 503,\n", + " 528, 542, 543, 549, 578, 579, 597]),\n", + " 6: array([ 12, 23, 24, 38, 41, 42, 44, 60, 94, 130, 131, 143, 144,\n", + " 165, 170, 206, 237, 258, 260, 281, 285, 298, 302, 305, 317, 325,\n", + " 326, 355, 386, 431, 444, 463, 479, 490, 492, 495, 514, 530, 531,\n", + " 532, 533, 541, 548, 551, 554, 557, 580]),\n", + " 7: array([ 47, 201, 332, 376, 381])},\n", + " 9: {0: array([ 57, 123, 161, 239, 343, 516, 550]),\n", + " 1: array([ 1, 2, 5, 6, 9, 10, 15, 16, 17, 21, 22, 25, 26,\n", + " 27, 28, 31, 32, 35, 36, 39, 49, 52, 53, 56, 58, 59,\n", + " 65, 67, 69, 70, 72, 74, 75, 77, 78, 82, 86, 92, 96,\n", + " 97, 99, 100, 102, 107, 108, 109, 111, 113, 114, 115, 116, 118,\n", + " 119, 122, 124, 127, 129, 132, 134, 135, 136, 137, 139, 140, 141,\n", + " 142, 146, 149, 151, 153, 154, 155, 157, 158, 162, 167, 168, 169,\n", + " 175, 176, 177, 179, 180, 181, 183, 184, 185, 187, 189, 192, 194,\n", + " 196, 198, 199, 200, 207, 210, 211, 214, 216, 217, 219, 220, 222,\n", + " 223, 225, 226, 227, 228, 230, 231, 232, 234, 235, 236, 240, 241,\n", + " 242, 244, 245, 247, 248, 249, 250, 251, 252, 253, 254, 259, 262,\n", + " 264, 266, 267, 270, 277, 278, 279, 282, 283, 284, 289, 290, 291,\n", + " 292, 293, 295, 296, 299, 300, 303, 304, 307, 308, 310, 311, 312,\n", + " 314, 316, 322, 323, 329, 330, 331, 334, 336, 342, 344, 345, 346,\n", + " 347, 348, 351, 353, 354, 356, 360, 362, 364, 367, 368, 370, 371,\n", + " 374, 375, 379, 380, 383, 384, 387, 388, 390, 391, 392, 393, 394,\n", + " 395, 396, 399, 401, 403, 407, 409, 411, 412, 413, 417, 419, 420,\n", + " 422, 425, 426, 428, 429, 430, 435, 439, 440, 442, 443, 447, 450,\n", + " 451, 453, 454, 456, 457, 459, 462, 464, 467, 469, 470, 473, 474,\n", + " 475, 476, 481, 482, 484, 485, 486, 487, 488, 491, 493, 494, 496,\n", + " 498, 499, 501, 502, 504, 505, 506, 508, 510, 513, 518, 519, 522,\n", + " 523, 525, 529, 534, 535, 539, 546, 547, 552, 555, 558, 561, 562,\n", + " 563, 565, 566, 567, 570, 571, 572, 573, 574, 575, 577, 581, 582,\n", + " 583, 584, 586, 588, 589, 591, 592, 593, 594, 595, 598, 599]),\n", + " 2: array([ 51, 71, 81, 98, 104, 110, 152, 171, 188, 197, 203, 208, 246,\n", + " 256, 313, 321, 359, 402, 512, 538, 540, 553, 564, 568]),\n", + " 3: array([ 7, 8, 40, 48, 50, 55, 80, 83, 85, 90, 91, 105, 117,\n", + " 121, 125, 126, 133, 138, 145, 150, 156, 159, 160, 166, 178, 195,\n", + " 204, 205, 209, 213, 218, 229, 233, 238, 263, 268, 280, 288, 297,\n", + " 301, 306, 319, 333, 335, 337, 339, 349, 357, 366, 372, 373, 377,\n", + " 382, 397, 404, 406, 414, 423, 427, 438, 452, 458, 478, 500, 520,\n", + " 524, 536, 556, 569, 590, 596]),\n", + " 4: array([ 29, 45, 68, 309, 400, 418, 472, 477, 483, 509, 515, 545, 559]),\n", + " 5: array([ 3, 13, 14, 18, 33, 43, 46, 54, 63, 88, 89, 93, 148,\n", + " 163, 174, 191, 193, 202, 212, 221, 243, 272, 286, 287, 294, 315,\n", + " 340, 341, 350, 358, 361, 363, 369, 398, 405, 410, 415, 416, 421,\n", + " 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497, 507, 511,\n", + " 517, 521, 526, 527, 537, 544, 560, 576, 585, 587]),\n", + " 6: array([ 0, 4, 11, 19, 20, 30, 34, 37, 61, 62, 64, 66, 73,\n", + " 76, 79, 84, 87, 95, 101, 103, 106, 112, 120, 128, 147, 164,\n", + " 172, 173, 182, 186, 190, 215, 224, 255, 257, 261, 265, 269, 271,\n", + " 273, 274, 275, 276, 318, 320, 324, 327, 328, 338, 352, 365, 378,\n", + " 385, 389, 408, 424, 432, 434, 436, 437, 446, 449, 455, 471, 480,\n", + " 503, 528, 542, 543, 549, 578, 579, 597]),\n", + " 7: array([ 12, 23, 24, 38, 41, 42, 44, 60, 94, 130, 131, 143, 144,\n", + " 165, 170, 206, 237, 258, 260, 281, 285, 298, 302, 305, 317, 325,\n", + " 326, 355, 386, 431, 444, 463, 479, 490, 492, 495, 514, 530, 531,\n", + " 532, 533, 541, 548, 551, 554, 557, 580]),\n", + " 8: array([ 47, 201, 332, 376, 381])},\n", + " 10: {0: array([ 57, 123, 161, 239, 343, 516, 550]),\n", + " 1: array([ 1, 2, 5, 6, 9, 10, 15, 16, 17, 21, 22, 25, 26,\n", + " 27, 28, 31, 32, 35, 36, 39, 49, 52, 53, 56, 58, 59,\n", + " 65, 67, 69, 70, 72, 74, 75, 77, 78, 82, 86, 92, 96,\n", + " 97, 99, 100, 102, 107, 108, 109, 111, 113, 114, 115, 116, 118,\n", + " 119, 122, 124, 127, 129, 132, 134, 135, 136, 137, 139, 140, 141,\n", + " 142, 146, 149, 151, 153, 154, 155, 157, 158, 162, 167, 168, 169,\n", + " 175, 176, 177, 179, 180, 181, 183, 184, 185, 187, 189, 192, 194,\n", + " 196, 198, 199, 200, 207, 210, 211, 214, 216, 217, 219, 220, 222,\n", + " 223, 225, 226, 227, 228, 230, 231, 232, 234, 235, 236, 240, 241,\n", + " 242, 244, 245, 247, 248, 249, 250, 251, 252, 253, 254, 259, 262,\n", + " 264, 266, 267, 270, 277, 278, 279, 282, 283, 284, 289, 290, 291,\n", + " 292, 293, 295, 296, 299, 300, 303, 304, 307, 308, 310, 311, 312,\n", + " 314, 316, 322, 323, 329, 330, 331, 334, 336, 342, 344, 345, 346,\n", + " 347, 348, 351, 353, 354, 356, 360, 362, 364, 367, 368, 370, 371,\n", + " 374, 375, 379, 380, 383, 384, 387, 388, 390, 391, 392, 393, 395,\n", + " 396, 399, 401, 403, 407, 409, 411, 412, 413, 417, 419, 420, 422,\n", + " 425, 426, 428, 429, 430, 435, 439, 440, 442, 443, 447, 450, 451,\n", + " 453, 454, 456, 457, 459, 462, 464, 467, 469, 470, 473, 474, 475,\n", + " 476, 481, 482, 484, 485, 486, 487, 488, 491, 493, 494, 496, 498,\n", + " 499, 501, 502, 504, 505, 506, 508, 510, 513, 518, 519, 522, 523,\n", + " 525, 529, 534, 535, 539, 546, 547, 552, 555, 558, 561, 562, 563,\n", + " 565, 566, 567, 570, 571, 572, 573, 574, 575, 577, 581, 582, 583,\n", + " 584, 586, 588, 589, 591, 592, 593, 594, 595, 598, 599]),\n", + " 2: array([ 51, 71, 81, 98, 104, 110, 152, 171, 188, 197, 203, 208, 246,\n", + " 256, 313, 321, 359, 402, 512, 538, 540, 553, 564, 568]),\n", + " 3: array([ 7, 8, 40, 48, 50, 55, 80, 83, 85, 90, 91, 105, 117,\n", + " 121, 125, 126, 133, 138, 145, 150, 156, 159, 160, 166, 178, 195,\n", + " 204, 205, 209, 213, 218, 229, 233, 238, 263, 268, 280, 288, 297,\n", + " 301, 306, 319, 333, 335, 337, 339, 349, 357, 366, 372, 373, 377,\n", + " 382, 397, 404, 406, 414, 423, 427, 438, 452, 458, 478, 500, 520,\n", + " 524, 536, 556, 569, 590, 596]),\n", + " 4: array([ 29, 45, 68, 309, 400, 418, 472, 477, 483, 509, 515, 545, 559]),\n", + " 5: array([ 3, 13, 14, 18, 33, 43, 46, 54, 63, 88, 89, 93, 148,\n", + " 163, 174, 191, 193, 202, 212, 221, 243, 272, 286, 287, 294, 315,\n", + " 340, 341, 350, 358, 361, 363, 369, 398, 405, 410, 415, 416, 421,\n", + " 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497, 507, 511,\n", + " 517, 521, 526, 527, 537, 544, 560, 576, 585, 587]),\n", + " 6: array([ 0, 4, 11, 19, 20, 30, 34, 37, 61, 62, 64, 66, 73,\n", + " 76, 79, 84, 95, 101, 106, 112, 120, 128, 147, 164, 172, 182,\n", + " 186, 190, 215, 224, 255, 269, 273, 275, 276, 318, 320, 324, 327,\n", + " 328, 338, 352, 365, 385, 389, 394, 424, 432, 434, 436, 437, 446,\n", + " 449, 455, 471, 480, 503, 528, 542, 543, 549, 578, 579, 597]),\n", + " 7: array([ 87, 103, 173, 257, 261, 265, 271, 274, 378, 408, 431]),\n", + " 8: array([ 12, 23, 24, 38, 41, 42, 44, 60, 94, 130, 131, 143, 144,\n", + " 165, 170, 206, 237, 258, 260, 281, 285, 298, 302, 305, 317, 325,\n", + " 326, 355, 386, 444, 463, 479, 490, 492, 495, 514, 530, 531, 532,\n", + " 533, 541, 548, 551, 554, 557, 580]),\n", + " 9: array([ 47, 201, 332, 376, 381])}},\n", + " 'signed_nonnormalized_l2_noavg': {2: {0: array([ 3, 13, 14, 18, 33, 43, 46, 51, 54, 57, 63, 71, 81,\n", + " 88, 89, 93, 98, 104, 110, 123, 148, 152, 161, 163, 171, 174,\n", + " 188, 191, 193, 197, 202, 203, 208, 212, 221, 239, 243, 246, 256,\n", + " 272, 286, 287, 294, 309, 313, 315, 321, 340, 341, 343, 358, 359,\n", + " 361, 363, 369, 398, 400, 402, 405, 410, 415, 416, 418, 421, 433,\n", + " 441, 445, 448, 460, 461, 465, 466, 468, 472, 489, 497, 507, 509,\n", + " 511, 512, 516, 517, 521, 526, 527, 537, 538, 540, 544, 550, 553,\n", + " 559, 560, 564, 568, 576, 585, 587]),\n", + " 1: array([ 0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15,\n", + " 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,\n", + " 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 42, 44,\n", + " 45, 47, 48, 49, 50, 52, 53, 55, 56, 58, 59, 60, 61,\n", + " 62, 64, 65, 66, 67, 68, 69, 70, 72, 73, 74, 75, 76,\n", + " 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 90, 91, 92,\n", + " 94, 95, 96, 97, 99, 100, 101, 102, 103, 105, 106, 107, 108,\n", + " 109, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122,\n", + " 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136,\n", + " 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 149, 150,\n", + " 151, 153, 154, 155, 156, 157, 158, 159, 160, 162, 164, 165, 166,\n", + " 167, 168, 169, 170, 172, 173, 175, 176, 177, 178, 179, 180, 181,\n", + " 182, 183, 184, 185, 186, 187, 189, 190, 192, 194, 195, 196, 198,\n", + " 199, 200, 201, 204, 205, 206, 207, 209, 210, 211, 213, 214, 215,\n", + " 216, 217, 218, 219, 220, 222, 223, 224, 225, 226, 227, 228, 229,\n", + " 230, 231, 232, 233, 234, 235, 236, 237, 238, 240, 241, 242, 244,\n", + " 245, 247, 248, 249, 250, 251, 252, 253, 254, 255, 257, 258, 259,\n", + " 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 273,\n", + " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 288,\n", + " 289, 290, 291, 292, 293, 295, 296, 297, 298, 299, 300, 301, 302,\n", + " 303, 304, 305, 306, 307, 308, 310, 311, 312, 314, 316, 317, 318,\n", + " 319, 320, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332,\n", + " 333, 334, 335, 336, 337, 338, 339, 342, 344, 345, 346, 347, 348,\n", + " 349, 350, 351, 352, 353, 354, 355, 356, 357, 360, 362, 364, 365,\n", + " 366, 367, 368, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379,\n", + " 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392,\n", + " 393, 394, 395, 396, 397, 399, 401, 403, 404, 406, 407, 408, 409,\n", + " 411, 412, 413, 414, 417, 419, 420, 422, 423, 424, 425, 426, 427,\n", + " 428, 429, 430, 431, 432, 434, 435, 436, 437, 438, 439, 440, 442,\n", + " 443, 444, 446, 447, 449, 450, 451, 452, 453, 454, 455, 456, 457,\n", + " 458, 459, 462, 463, 464, 467, 469, 470, 471, 473, 474, 475, 476,\n", + " 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 490,\n", + " 491, 492, 493, 494, 495, 496, 498, 499, 500, 501, 502, 503, 504,\n", + " 505, 506, 508, 510, 513, 514, 515, 518, 519, 520, 522, 523, 524,\n", + " 525, 528, 529, 530, 531, 532, 533, 534, 535, 536, 539, 541, 542,\n", + " 543, 545, 546, 547, 548, 549, 551, 552, 554, 555, 556, 557, 558,\n", + " 561, 562, 563, 565, 566, 567, 569, 570, 571, 572, 573, 574, 575,\n", + " 577, 578, 579, 580, 581, 582, 583, 584, 586, 588, 589, 590, 591,\n", + " 592, 593, 594, 595, 596, 597, 598, 599])},\n", + " 3: {0: array([ 57, 71, 81, 98, 110, 123, 152, 161, 171, 188, 197, 203, 208,\n", + " 239, 246, 256, 309, 313, 321, 343, 359, 400, 402, 418, 472, 512,\n", + " 516, 538, 550, 553, 559, 568]),\n", + " 1: array([ 0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15,\n", + " 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,\n", + " 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 42, 44,\n", + " 45, 47, 48, 49, 50, 52, 53, 55, 56, 58, 59, 60, 61,\n", + " 62, 64, 65, 66, 67, 68, 69, 70, 72, 73, 74, 75, 76,\n", + " 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 90, 91, 92,\n", + " 94, 95, 96, 97, 99, 100, 101, 102, 103, 105, 106, 107, 108,\n", + " 109, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122,\n", + " 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136,\n", + " 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 149, 150,\n", + " 151, 153, 154, 155, 156, 157, 158, 159, 160, 162, 164, 165, 166,\n", + " 167, 168, 169, 170, 172, 173, 175, 176, 177, 178, 179, 180, 181,\n", + " 182, 183, 184, 185, 186, 187, 189, 190, 192, 194, 195, 196, 198,\n", + " 199, 200, 201, 204, 205, 206, 207, 209, 210, 211, 213, 214, 215,\n", + " 216, 217, 218, 219, 220, 222, 223, 224, 225, 226, 227, 228, 229,\n", + " 230, 231, 232, 233, 234, 235, 236, 237, 238, 240, 241, 242, 244,\n", + " 245, 247, 248, 249, 250, 251, 252, 253, 254, 255, 257, 258, 259,\n", + " 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 273,\n", + " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 288,\n", + " 289, 290, 291, 292, 293, 295, 296, 297, 298, 299, 300, 301, 302,\n", + " 303, 304, 305, 306, 307, 308, 310, 311, 312, 314, 316, 317, 318,\n", + " 319, 320, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332,\n", + " 333, 334, 335, 336, 337, 338, 339, 342, 344, 345, 346, 347, 348,\n", + " 349, 351, 352, 353, 354, 355, 356, 357, 360, 362, 364, 365, 366,\n", + " 367, 368, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380,\n", + " 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393,\n", + " 394, 395, 396, 397, 399, 401, 403, 404, 406, 407, 408, 409, 411,\n", + " 412, 413, 414, 417, 419, 420, 422, 423, 424, 425, 426, 427, 428,\n", + " 429, 430, 431, 432, 434, 435, 436, 437, 438, 439, 440, 442, 443,\n", + " 444, 446, 447, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458,\n", + " 459, 462, 463, 464, 467, 469, 470, 471, 473, 474, 475, 476, 477,\n", + " 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 490, 491,\n", + " 492, 493, 494, 495, 496, 498, 499, 500, 501, 502, 503, 504, 505,\n", + " 506, 508, 510, 513, 514, 515, 518, 519, 520, 522, 523, 524, 525,\n", + " 528, 529, 530, 531, 532, 533, 534, 535, 536, 539, 541, 542, 543,\n", + " 545, 546, 547, 548, 549, 551, 552, 554, 555, 556, 557, 558, 561,\n", + " 562, 563, 565, 566, 567, 569, 570, 571, 572, 573, 574, 575, 577,\n", + " 578, 579, 580, 581, 582, 583, 584, 586, 588, 589, 590, 591, 592,\n", + " 593, 594, 595, 596, 597, 598, 599]),\n", + " 2: array([ 3, 13, 14, 18, 33, 43, 46, 51, 54, 63, 88, 89, 93,\n", + " 104, 148, 163, 174, 191, 193, 202, 212, 221, 243, 272, 286, 287,\n", + " 294, 315, 340, 341, 350, 358, 361, 363, 369, 398, 405, 410, 415,\n", + " 416, 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497,\n", + " 507, 509, 511, 517, 521, 526, 527, 537, 540, 544, 560, 564, 576,\n", + " 585, 587])},\n", + " 4: {0: array([ 57, 71, 81, 98, 110, 123, 152, 161, 171, 188, 197, 203, 208,\n", + " 239, 246, 256, 309, 313, 321, 343, 359, 400, 402, 418, 472, 512,\n", + " 516, 538, 550, 553, 559, 568]),\n", + " 1: array([ 0, 1, 2, 4, 5, 6, 9, 10, 11, 15, 16, 17, 19,\n", + " 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32,\n", + " 34, 35, 36, 37, 38, 39, 41, 45, 49, 52, 53, 56, 58,\n", + " 59, 61, 62, 64, 65, 66, 67, 68, 69, 70, 72, 73, 74,\n", + " 75, 76, 77, 78, 79, 82, 84, 86, 87, 92, 95, 96, 97,\n", + " 99, 100, 101, 102, 103, 106, 107, 108, 109, 111, 112, 113, 114,\n", + " 115, 116, 118, 119, 120, 122, 124, 127, 128, 129, 132, 134, 135,\n", + " 136, 137, 139, 140, 141, 142, 144, 146, 147, 149, 151, 153, 154,\n", + " 155, 157, 158, 162, 164, 165, 167, 168, 169, 172, 173, 175, 176,\n", + " 177, 179, 180, 181, 182, 183, 184, 185, 186, 187, 189, 190, 192,\n", + " 194, 196, 198, 199, 200, 207, 210, 211, 214, 215, 216, 217, 219,\n", + " 220, 222, 223, 224, 225, 226, 227, 228, 230, 231, 232, 234, 235,\n", + " 236, 240, 241, 242, 244, 245, 247, 248, 249, 250, 251, 252, 253,\n", + " 254, 255, 257, 259, 261, 262, 264, 265, 266, 267, 269, 270, 271,\n", + " 273, 274, 275, 276, 277, 278, 279, 281, 282, 283, 284, 289, 290,\n", + " 291, 292, 293, 295, 296, 299, 300, 303, 304, 305, 307, 308, 310,\n", + " 311, 312, 314, 316, 317, 318, 320, 322, 323, 324, 326, 327, 328,\n", + " 329, 330, 331, 334, 336, 338, 342, 344, 345, 346, 347, 348, 351,\n", + " 352, 353, 354, 355, 356, 360, 362, 364, 365, 367, 368, 370, 371,\n", + " 374, 375, 378, 379, 380, 383, 384, 385, 387, 388, 389, 390, 391,\n", + " 392, 393, 394, 395, 396, 399, 401, 403, 407, 408, 409, 411, 412,\n", + " 413, 417, 419, 420, 422, 424, 425, 426, 428, 429, 430, 431, 432,\n", + " 434, 435, 436, 437, 439, 440, 442, 443, 446, 447, 449, 450, 451,\n", + " 453, 454, 455, 456, 457, 459, 462, 464, 467, 469, 470, 471, 473,\n", + " 474, 475, 476, 477, 480, 481, 482, 483, 484, 485, 486, 487, 488,\n", + " 490, 491, 493, 494, 495, 496, 498, 499, 501, 502, 503, 504, 505,\n", + " 506, 508, 510, 513, 515, 518, 519, 522, 523, 525, 528, 529, 530,\n", + " 531, 534, 535, 539, 542, 543, 545, 546, 547, 549, 552, 555, 557,\n", + " 558, 561, 562, 563, 565, 566, 567, 570, 571, 572, 573, 574, 575,\n", + " 577, 578, 579, 581, 582, 583, 584, 586, 588, 589, 591, 592, 593,\n", + " 594, 595, 597, 598, 599]),\n", + " 2: array([ 7, 8, 12, 40, 42, 44, 47, 48, 50, 55, 60, 80, 83,\n", + " 85, 90, 91, 94, 105, 117, 121, 125, 126, 130, 131, 133, 138,\n", + " 143, 145, 150, 156, 159, 160, 166, 170, 178, 195, 201, 204, 205,\n", + " 206, 209, 213, 218, 229, 233, 237, 238, 258, 260, 263, 268, 280,\n", + " 285, 288, 297, 298, 301, 302, 306, 319, 325, 332, 333, 335, 337,\n", + " 339, 349, 357, 366, 372, 373, 376, 377, 381, 382, 386, 397, 404,\n", + " 406, 414, 423, 427, 438, 444, 452, 458, 463, 478, 479, 492, 500,\n", + " 514, 520, 524, 532, 533, 536, 541, 548, 551, 554, 556, 569, 580,\n", + " 590, 596]),\n", + " 3: array([ 3, 13, 14, 18, 33, 43, 46, 51, 54, 63, 88, 89, 93,\n", + " 104, 148, 163, 174, 191, 193, 202, 212, 221, 243, 272, 286, 287,\n", + " 294, 315, 340, 341, 350, 358, 361, 363, 369, 398, 405, 410, 415,\n", + " 416, 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497,\n", + " 507, 509, 511, 517, 521, 526, 527, 537, 540, 544, 560, 564, 576,\n", + " 585, 587])},\n", + " 5: {0: array([ 81, 110, 161, 171, 188, 197, 203, 246, 309, 313, 321, 343, 359,\n", + " 402, 418, 472, 509, 516, 538, 559]),\n", + " 1: array([ 0, 1, 2, 4, 5, 6, 9, 10, 11, 15, 16, 17, 19,\n", + " 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32,\n", + " 34, 35, 36, 37, 38, 39, 41, 45, 49, 52, 53, 56, 58,\n", + " 59, 61, 62, 64, 65, 66, 67, 68, 69, 70, 72, 73, 74,\n", + " 75, 76, 77, 78, 79, 82, 84, 86, 87, 92, 95, 96, 97,\n", + " 99, 100, 101, 102, 103, 106, 107, 108, 109, 111, 112, 113, 114,\n", + " 115, 116, 118, 119, 120, 122, 124, 127, 128, 129, 132, 134, 135,\n", + " 136, 137, 139, 140, 141, 142, 144, 146, 147, 149, 151, 153, 154,\n", + " 155, 157, 158, 162, 164, 165, 167, 168, 169, 172, 173, 175, 176,\n", + " 177, 179, 180, 181, 182, 183, 184, 185, 186, 187, 189, 190, 192,\n", + " 194, 196, 198, 199, 200, 207, 210, 211, 214, 215, 216, 217, 219,\n", + " 220, 222, 223, 224, 225, 226, 227, 228, 230, 231, 232, 234, 235,\n", + " 236, 240, 241, 242, 244, 245, 247, 248, 249, 250, 251, 252, 253,\n", + " 254, 255, 257, 259, 261, 262, 264, 265, 266, 267, 269, 270, 271,\n", + " 273, 274, 275, 276, 277, 278, 279, 281, 282, 283, 284, 289, 290,\n", + " 291, 292, 293, 295, 296, 299, 300, 303, 304, 305, 307, 308, 310,\n", + " 311, 312, 314, 316, 317, 318, 320, 322, 323, 324, 326, 327, 328,\n", + " 329, 330, 331, 334, 336, 338, 342, 344, 345, 346, 347, 348, 351,\n", + " 352, 353, 354, 355, 356, 360, 362, 364, 365, 367, 368, 370, 371,\n", + " 374, 375, 378, 379, 380, 383, 384, 385, 387, 388, 389, 390, 391,\n", + " 392, 393, 394, 395, 396, 399, 401, 403, 407, 408, 409, 411, 412,\n", + " 413, 417, 419, 420, 422, 424, 425, 426, 428, 429, 430, 431, 432,\n", + " 434, 435, 436, 437, 439, 440, 442, 443, 446, 447, 449, 450, 451,\n", + " 453, 454, 455, 456, 457, 459, 462, 464, 467, 469, 470, 471, 473,\n", + " 474, 475, 476, 477, 480, 481, 482, 483, 484, 485, 486, 487, 488,\n", + " 490, 491, 493, 494, 495, 496, 498, 499, 501, 502, 503, 504, 505,\n", + " 506, 508, 510, 513, 515, 518, 519, 522, 523, 525, 528, 529, 530,\n", + " 531, 534, 535, 539, 542, 543, 545, 546, 547, 549, 552, 555, 557,\n", + " 558, 561, 562, 563, 565, 566, 567, 570, 571, 572, 573, 574, 575,\n", + " 577, 578, 579, 581, 582, 583, 584, 586, 588, 589, 591, 592, 593,\n", + " 594, 595, 597, 598, 599]),\n", + " 2: array([ 57, 71, 98, 123, 152, 208, 239, 256, 400, 512, 544, 550, 553,\n", + " 568]),\n", + " 3: array([ 7, 8, 12, 40, 42, 44, 47, 48, 50, 55, 60, 80, 83,\n", + " 85, 90, 91, 94, 105, 117, 121, 125, 126, 130, 131, 133, 138,\n", + " 143, 145, 150, 156, 159, 160, 166, 170, 178, 195, 201, 204, 205,\n", + " 206, 209, 213, 218, 229, 233, 237, 238, 258, 260, 263, 268, 280,\n", + " 285, 288, 297, 298, 301, 302, 306, 319, 325, 332, 333, 335, 337,\n", + " 339, 349, 357, 366, 372, 373, 376, 377, 381, 382, 386, 397, 404,\n", + " 406, 414, 423, 427, 438, 444, 452, 458, 463, 478, 479, 492, 500,\n", + " 514, 520, 524, 532, 533, 536, 541, 548, 551, 554, 556, 569, 580,\n", + " 590, 596]),\n", + " 4: array([ 3, 13, 14, 18, 33, 43, 46, 51, 54, 63, 88, 89, 93,\n", + " 104, 148, 163, 174, 191, 193, 202, 212, 221, 243, 272, 286, 287,\n", + " 294, 315, 340, 341, 350, 358, 361, 363, 369, 398, 405, 410, 415,\n", + " 416, 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497,\n", + " 507, 511, 517, 521, 526, 527, 537, 540, 560, 564, 576, 585, 587])},\n", + " 6: {0: array([ 81, 110, 161, 171, 188, 197, 203, 246, 309, 313, 321, 343, 359,\n", + " 402, 418, 472, 509, 516, 538, 559]),\n", + " 1: array([ 0, 1, 2, 4, 5, 6, 9, 10, 11, 15, 16, 17, 19,\n", + " 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32,\n", + " 34, 35, 36, 37, 38, 39, 41, 45, 49, 52, 53, 56, 58,\n", + " 59, 61, 62, 64, 65, 66, 67, 68, 69, 70, 72, 73, 74,\n", + " 75, 76, 77, 78, 79, 82, 84, 86, 87, 92, 95, 96, 97,\n", + " 99, 100, 101, 102, 103, 106, 107, 108, 109, 111, 112, 113, 114,\n", + " 115, 116, 118, 119, 120, 122, 124, 127, 128, 129, 132, 134, 135,\n", + " 136, 137, 139, 140, 141, 142, 144, 146, 147, 149, 151, 153, 154,\n", + " 155, 157, 158, 162, 164, 165, 167, 168, 169, 172, 173, 175, 176,\n", + " 177, 179, 180, 181, 182, 183, 184, 185, 186, 187, 189, 190, 192,\n", + " 194, 196, 198, 199, 200, 207, 210, 211, 214, 215, 216, 217, 219,\n", + " 220, 222, 223, 224, 225, 226, 227, 228, 230, 231, 232, 234, 235,\n", + " 236, 240, 241, 242, 244, 245, 247, 248, 249, 250, 251, 252, 253,\n", + " 254, 255, 257, 259, 261, 262, 264, 265, 266, 267, 269, 270, 271,\n", + " 273, 274, 275, 276, 277, 278, 279, 281, 282, 283, 284, 289, 290,\n", + " 291, 292, 293, 295, 296, 299, 300, 303, 304, 305, 307, 308, 310,\n", + " 311, 312, 314, 316, 317, 318, 320, 322, 323, 324, 326, 327, 328,\n", + " 329, 330, 331, 334, 336, 338, 342, 344, 345, 346, 347, 348, 351,\n", + " 352, 353, 354, 355, 356, 360, 362, 364, 365, 367, 368, 370, 371,\n", + " 374, 375, 378, 379, 380, 383, 384, 385, 387, 388, 389, 390, 391,\n", + " 392, 393, 394, 395, 396, 399, 401, 403, 407, 408, 409, 411, 412,\n", + " 413, 417, 419, 420, 422, 424, 425, 426, 428, 429, 430, 431, 432,\n", + " 434, 435, 436, 437, 439, 440, 442, 443, 446, 447, 449, 450, 451,\n", + " 453, 454, 455, 456, 457, 459, 462, 464, 467, 469, 470, 471, 473,\n", + " 474, 475, 476, 477, 480, 481, 482, 483, 484, 485, 486, 487, 488,\n", + " 490, 491, 493, 494, 495, 496, 498, 499, 501, 502, 503, 504, 505,\n", + " 506, 508, 510, 513, 515, 518, 519, 522, 523, 525, 528, 529, 530,\n", + " 531, 534, 535, 539, 542, 543, 545, 546, 547, 549, 552, 555, 557,\n", + " 558, 561, 562, 563, 565, 566, 567, 570, 571, 572, 573, 574, 575,\n", + " 577, 578, 579, 581, 582, 583, 584, 586, 588, 589, 591, 592, 593,\n", + " 594, 595, 597, 598, 599]),\n", + " 2: array([ 57, 71, 98, 123, 152, 208, 239, 256, 400, 512, 544, 550, 553,\n", + " 568]),\n", + " 3: array([ 7, 8, 12, 40, 42, 44, 47, 48, 50, 55, 60, 80, 83,\n", + " 85, 90, 91, 94, 105, 117, 121, 125, 126, 130, 131, 133, 138,\n", + " 143, 145, 150, 156, 159, 160, 166, 170, 178, 195, 201, 204, 205,\n", + " 206, 209, 213, 218, 229, 233, 237, 238, 258, 260, 263, 268, 280,\n", + " 285, 288, 297, 298, 301, 302, 306, 319, 325, 332, 333, 335, 337,\n", + " 339, 349, 357, 366, 372, 373, 376, 377, 381, 382, 386, 397, 404,\n", + " 406, 414, 423, 427, 438, 444, 452, 458, 463, 478, 479, 492, 500,\n", + " 514, 520, 524, 532, 533, 536, 541, 548, 551, 554, 556, 569, 580,\n", + " 590, 596]),\n", + " 4: array([ 3, 13, 14, 18, 33, 43, 46, 51, 54, 63, 88, 89, 93,\n", + " 104, 148, 163, 174, 191, 193, 202, 212, 221, 243, 272, 286, 287,\n", + " 294, 315, 340, 341, 350, 358, 361, 363, 369, 398, 405, 410, 415,\n", + " 416, 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497,\n", + " 507, 511, 517, 521, 526, 527, 537, 540, 560, 564, 576, 585, 587]),\n", + " 5: array([], dtype=int64)},\n", + " 7: {0: array([ 81, 110, 161, 171, 188, 197, 203, 246, 309, 313, 321, 343, 359,\n", + " 402, 418, 472, 509, 516, 538, 559]),\n", + " 1: array([ 1, 2, 5, 6, 9, 10, 15, 16, 17, 21, 22, 25, 26,\n", + " 27, 28, 29, 31, 32, 35, 36, 39, 45, 49, 52, 53, 56,\n", + " 58, 59, 65, 67, 68, 69, 70, 72, 74, 75, 77, 78, 82,\n", + " 86, 92, 96, 97, 99, 100, 102, 107, 108, 109, 111, 113, 114,\n", + " 115, 116, 118, 119, 122, 124, 127, 129, 132, 134, 135, 136, 137,\n", + " 139, 140, 141, 142, 146, 149, 151, 153, 154, 155, 157, 158, 162,\n", + " 167, 168, 169, 175, 176, 177, 179, 180, 181, 183, 184, 185, 187,\n", + " 189, 192, 194, 196, 198, 199, 200, 207, 210, 211, 214, 216, 217,\n", + " 219, 220, 222, 223, 225, 226, 227, 228, 230, 231, 232, 234, 235,\n", + " 236, 240, 241, 242, 244, 245, 247, 248, 249, 250, 251, 252, 253,\n", + " 254, 259, 262, 264, 266, 267, 270, 277, 278, 279, 282, 283, 284,\n", + " 289, 290, 291, 292, 293, 295, 296, 299, 300, 303, 304, 307, 308,\n", + " 310, 311, 312, 314, 316, 320, 322, 323, 329, 330, 331, 334, 336,\n", + " 342, 344, 345, 346, 347, 348, 351, 353, 354, 356, 360, 362, 364,\n", + " 367, 368, 370, 371, 374, 375, 379, 380, 383, 384, 387, 388, 390,\n", + " 391, 392, 393, 394, 395, 396, 399, 401, 403, 407, 409, 411, 412,\n", + " 413, 417, 419, 420, 422, 425, 426, 428, 429, 430, 435, 437, 439,\n", + " 440, 442, 443, 447, 450, 451, 453, 454, 456, 457, 459, 462, 464,\n", + " 467, 469, 470, 473, 474, 475, 476, 477, 481, 482, 483, 484, 485,\n", + " 486, 487, 488, 491, 493, 494, 496, 498, 499, 501, 502, 504, 505,\n", + " 506, 508, 510, 513, 515, 518, 519, 522, 523, 525, 529, 534, 535,\n", + " 539, 546, 547, 552, 555, 558, 561, 562, 563, 565, 566, 567, 570,\n", + " 571, 572, 573, 574, 575, 577, 581, 582, 583, 584, 586, 588, 589,\n", + " 591, 592, 593, 594, 595, 598, 599]),\n", + " 2: array([ 57, 71, 98, 123, 152, 208, 239, 256, 400, 512, 544, 550, 553,\n", + " 568]),\n", + " 3: array([ 7, 8, 12, 40, 42, 44, 47, 48, 50, 55, 60, 80, 83,\n", + " 85, 90, 91, 105, 117, 121, 125, 126, 130, 131, 133, 138, 143,\n", + " 145, 150, 156, 159, 160, 166, 170, 178, 195, 201, 204, 205, 206,\n", + " 209, 213, 218, 229, 233, 237, 238, 258, 260, 263, 268, 280, 285,\n", + " 288, 297, 301, 302, 306, 319, 325, 332, 333, 335, 337, 339, 349,\n", + " 357, 366, 372, 373, 376, 377, 381, 382, 386, 397, 404, 406, 414,\n", + " 423, 427, 438, 444, 452, 458, 463, 478, 479, 492, 500, 514, 520,\n", + " 524, 532, 533, 536, 541, 548, 551, 554, 556, 569, 580, 590, 596]),\n", + " 4: array([ 3, 13, 14, 18, 33, 43, 46, 51, 54, 63, 88, 89, 93,\n", + " 104, 148, 163, 174, 191, 193, 202, 212, 221, 243, 272, 286, 287,\n", + " 294, 315, 340, 341, 350, 358, 361, 363, 369, 398, 405, 410, 415,\n", + " 416, 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497,\n", + " 507, 511, 517, 521, 526, 527, 537, 540, 560, 564, 576, 585, 587]),\n", + " 5: array([ 0, 4, 11, 19, 20, 23, 24, 30, 34, 37, 38, 41, 61,\n", + " 62, 64, 66, 73, 76, 79, 84, 87, 94, 95, 101, 103, 106,\n", + " 112, 120, 128, 144, 147, 164, 165, 172, 173, 182, 186, 190, 215,\n", + " 224, 255, 257, 261, 265, 269, 271, 273, 274, 275, 276, 281, 298,\n", + " 305, 317, 318, 324, 326, 327, 328, 338, 352, 355, 365, 378, 385,\n", + " 389, 408, 424, 431, 432, 434, 436, 446, 449, 455, 471, 480, 490,\n", + " 495, 503, 528, 530, 531, 542, 543, 545, 549, 557, 578, 579, 597]),\n", + " 6: array([], dtype=int64)},\n", + " 8: {0: array([ 81, 110, 161, 171, 188, 197, 203, 246, 309, 313, 321, 343, 359,\n", + " 402, 418, 472, 509, 516, 538, 559]),\n", + " 1: array([ 1, 2, 5, 6, 9, 10, 15, 16, 17, 21, 22, 25, 26,\n", + " 27, 28, 29, 31, 32, 35, 36, 39, 45, 49, 52, 53, 56,\n", + " 58, 59, 65, 67, 68, 69, 70, 72, 74, 75, 77, 78, 82,\n", + " 86, 92, 96, 97, 99, 100, 102, 107, 108, 109, 111, 113, 114,\n", + " 115, 116, 118, 119, 122, 124, 127, 129, 132, 134, 135, 136, 137,\n", + " 139, 140, 141, 142, 146, 149, 151, 153, 154, 155, 157, 158, 162,\n", + " 167, 168, 169, 175, 176, 177, 179, 180, 181, 183, 184, 185, 187,\n", + " 189, 192, 194, 196, 198, 199, 200, 207, 210, 211, 214, 216, 217,\n", + " 219, 220, 222, 223, 225, 226, 227, 228, 230, 231, 232, 234, 235,\n", + " 236, 240, 241, 242, 244, 245, 247, 248, 249, 250, 251, 252, 253,\n", + " 254, 259, 262, 264, 266, 267, 270, 277, 278, 279, 282, 283, 284,\n", + " 289, 290, 291, 292, 293, 295, 296, 299, 300, 303, 304, 307, 308,\n", + " 310, 311, 312, 314, 316, 320, 322, 323, 329, 330, 331, 334, 336,\n", + " 342, 344, 345, 346, 347, 348, 351, 353, 354, 356, 360, 362, 364,\n", + " 367, 368, 370, 371, 374, 375, 379, 380, 383, 384, 387, 388, 390,\n", + " 391, 392, 393, 394, 395, 396, 399, 401, 403, 407, 409, 411, 412,\n", + " 413, 417, 419, 420, 422, 425, 426, 428, 429, 430, 435, 437, 439,\n", + " 440, 442, 443, 447, 450, 451, 453, 454, 456, 457, 459, 462, 464,\n", + " 467, 469, 470, 473, 474, 475, 476, 477, 481, 482, 483, 484, 485,\n", + " 486, 487, 488, 491, 493, 494, 496, 498, 499, 501, 502, 504, 505,\n", + " 506, 508, 510, 513, 515, 518, 519, 522, 523, 525, 529, 534, 535,\n", + " 539, 546, 547, 552, 555, 558, 561, 562, 563, 565, 566, 567, 570,\n", + " 571, 572, 573, 574, 575, 577, 581, 582, 583, 584, 586, 588, 589,\n", + " 591, 592, 593, 594, 595, 598, 599]),\n", + " 2: array([ 57, 71, 98, 123, 152, 208, 239, 256, 400, 512, 544, 550, 553,\n", + " 568]),\n", + " 3: array([ 7, 8, 12, 40, 42, 44, 48, 50, 55, 60, 80, 83, 85,\n", + " 90, 91, 94, 105, 117, 121, 125, 126, 130, 131, 133, 138, 143,\n", + " 145, 150, 156, 159, 160, 166, 170, 178, 195, 204, 205, 206, 209,\n", + " 213, 218, 229, 233, 238, 258, 260, 263, 268, 280, 285, 288, 297,\n", + " 301, 302, 306, 319, 325, 333, 335, 337, 339, 349, 357, 366, 372,\n", + " 373, 377, 382, 386, 397, 404, 406, 414, 423, 427, 438, 444, 452,\n", + " 458, 463, 478, 479, 492, 500, 514, 520, 524, 532, 536, 541, 548,\n", + " 551, 554, 556, 569, 580, 590, 596]),\n", + " 4: array([ 3, 13, 14, 18, 33, 43, 46, 51, 54, 63, 88, 89, 93,\n", + " 104, 148, 163, 174, 191, 193, 202, 212, 221, 243, 272, 286, 287,\n", + " 294, 315, 340, 341, 350, 358, 361, 363, 369, 398, 405, 410, 415,\n", + " 416, 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497,\n", + " 507, 511, 517, 521, 526, 527, 537, 540, 560, 564, 576, 585, 587]),\n", + " 5: array([ 0, 4, 11, 19, 20, 23, 24, 30, 34, 37, 38, 41, 61,\n", + " 62, 64, 66, 73, 76, 79, 84, 87, 95, 101, 103, 106, 112,\n", + " 120, 128, 144, 147, 164, 165, 172, 173, 182, 186, 190, 215, 224,\n", + " 255, 257, 261, 265, 269, 271, 273, 274, 275, 276, 281, 298, 305,\n", + " 317, 318, 324, 326, 327, 328, 338, 352, 355, 365, 378, 385, 389,\n", + " 408, 424, 431, 432, 434, 436, 446, 449, 455, 471, 480, 490, 495,\n", + " 503, 528, 530, 531, 542, 543, 545, 549, 557, 578, 579, 597]),\n", + " 6: array([ 47, 201, 237, 332, 376, 381, 533]),\n", + " 7: array([], dtype=int64)},\n", + " 9: {0: array([ 81, 161, 246, 309, 343, 516]),\n", + " 1: array([ 1, 2, 5, 6, 9, 10, 15, 16, 17, 21, 22, 25, 26,\n", + " 27, 28, 29, 31, 32, 35, 36, 39, 45, 49, 52, 53, 56,\n", + " 58, 59, 65, 67, 68, 69, 70, 72, 74, 75, 77, 78, 82,\n", + " 86, 92, 96, 97, 99, 100, 102, 107, 108, 109, 111, 113, 114,\n", + " 115, 116, 118, 119, 122, 124, 127, 129, 132, 134, 135, 136, 137,\n", + " 139, 140, 141, 142, 146, 149, 151, 153, 154, 155, 157, 158, 162,\n", + " 167, 168, 169, 175, 176, 177, 179, 180, 181, 183, 184, 185, 187,\n", + " 189, 192, 194, 196, 198, 199, 200, 207, 210, 211, 214, 216, 217,\n", + " 219, 220, 222, 223, 225, 226, 227, 228, 230, 231, 232, 234, 235,\n", + " 236, 240, 241, 242, 244, 245, 247, 248, 249, 250, 251, 252, 253,\n", + " 254, 259, 262, 264, 266, 267, 270, 277, 278, 279, 282, 283, 284,\n", + " 289, 290, 291, 292, 293, 295, 296, 299, 300, 303, 304, 307, 308,\n", + " 310, 311, 312, 314, 316, 320, 322, 323, 329, 330, 331, 334, 336,\n", + " 342, 344, 345, 346, 347, 348, 351, 353, 354, 356, 360, 362, 364,\n", + " 367, 368, 370, 371, 374, 375, 379, 380, 383, 384, 387, 388, 390,\n", + " 391, 392, 393, 394, 395, 396, 399, 401, 403, 407, 409, 411, 412,\n", + " 413, 417, 419, 420, 422, 425, 426, 428, 429, 430, 435, 437, 439,\n", + " 440, 442, 443, 447, 450, 451, 453, 454, 456, 457, 459, 462, 464,\n", + " 467, 469, 470, 473, 474, 475, 476, 477, 481, 482, 483, 484, 485,\n", + " 486, 487, 488, 491, 493, 494, 496, 498, 499, 501, 502, 504, 505,\n", + " 506, 508, 510, 513, 515, 518, 519, 522, 523, 525, 529, 534, 535,\n", + " 539, 546, 547, 552, 555, 558, 561, 562, 563, 565, 566, 567, 570,\n", + " 571, 572, 573, 574, 575, 577, 581, 582, 583, 584, 586, 588, 589,\n", + " 591, 592, 593, 594, 595, 598, 599]),\n", + " 2: array([ 57, 71, 98, 123, 152, 208, 239, 256, 400, 512, 544, 550, 553,\n", + " 568]),\n", + " 3: array([ 7, 8, 12, 40, 42, 44, 48, 50, 55, 60, 80, 83, 85,\n", + " 90, 91, 94, 105, 117, 121, 125, 126, 130, 131, 133, 138, 143,\n", + " 145, 150, 156, 159, 160, 166, 170, 178, 195, 204, 205, 206, 209,\n", + " 213, 218, 229, 233, 238, 258, 260, 263, 268, 280, 285, 288, 297,\n", + " 301, 302, 306, 319, 325, 333, 335, 337, 339, 349, 357, 366, 372,\n", + " 373, 377, 382, 386, 397, 404, 406, 414, 423, 427, 438, 444, 452,\n", + " 458, 463, 478, 479, 492, 500, 514, 520, 524, 532, 536, 541, 548,\n", + " 551, 554, 556, 569, 580, 590, 596]),\n", + " 4: array([110, 171, 188, 197, 203, 313, 321, 359, 402, 418, 472, 509, 538,\n", + " 559]),\n", + " 5: array([ 3, 13, 14, 18, 33, 43, 46, 51, 54, 63, 88, 89, 93,\n", + " 104, 148, 163, 174, 191, 193, 202, 212, 221, 243, 272, 286, 287,\n", + " 294, 315, 340, 341, 350, 358, 361, 363, 369, 398, 405, 410, 415,\n", + " 416, 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497,\n", + " 507, 511, 517, 521, 526, 527, 537, 540, 560, 564, 576, 585, 587]),\n", + " 6: array([ 0, 4, 11, 19, 20, 23, 24, 30, 34, 37, 38, 41, 61,\n", + " 62, 64, 66, 73, 76, 79, 84, 87, 95, 101, 103, 106, 112,\n", + " 120, 128, 144, 147, 164, 165, 172, 173, 182, 186, 190, 215, 224,\n", + " 255, 257, 261, 265, 269, 271, 273, 274, 275, 276, 281, 298, 305,\n", + " 317, 318, 324, 326, 327, 328, 338, 352, 355, 365, 378, 385, 389,\n", + " 408, 424, 431, 432, 434, 436, 446, 449, 455, 471, 480, 490, 495,\n", + " 503, 528, 530, 531, 542, 543, 545, 549, 557, 578, 579, 597]),\n", + " 7: array([ 47, 201, 237, 332, 376, 381, 533]),\n", + " 8: array([], dtype=int64)},\n", + " 10: {0: array([ 81, 161, 246, 309, 343, 516]),\n", + " 1: array([ 1, 2, 5, 6, 9, 10, 15, 16, 17, 21, 22, 25, 26,\n", + " 27, 28, 29, 31, 32, 35, 36, 39, 45, 49, 52, 53, 56,\n", + " 58, 59, 65, 67, 68, 69, 70, 72, 74, 75, 77, 78, 82,\n", + " 86, 92, 96, 97, 99, 100, 102, 107, 108, 109, 111, 113, 114,\n", + " 115, 116, 118, 119, 122, 124, 127, 129, 132, 134, 135, 136, 137,\n", + " 139, 140, 141, 142, 146, 149, 151, 153, 154, 155, 157, 158, 162,\n", + " 167, 168, 169, 175, 176, 177, 179, 180, 181, 183, 184, 185, 187,\n", + " 189, 192, 194, 196, 198, 199, 200, 207, 210, 211, 214, 216, 217,\n", + " 219, 220, 222, 223, 225, 226, 227, 228, 230, 231, 232, 234, 235,\n", + " 236, 240, 241, 242, 244, 245, 247, 248, 249, 250, 251, 252, 253,\n", + " 254, 259, 262, 264, 266, 267, 270, 277, 278, 279, 282, 283, 284,\n", + " 289, 290, 291, 292, 293, 295, 296, 299, 300, 303, 304, 307, 308,\n", + " 310, 311, 312, 314, 316, 320, 322, 323, 329, 330, 331, 334, 336,\n", + " 342, 344, 345, 346, 347, 348, 351, 353, 354, 356, 360, 362, 364,\n", + " 367, 368, 370, 371, 374, 375, 379, 380, 383, 384, 387, 388, 390,\n", + " 391, 392, 393, 394, 395, 396, 399, 401, 403, 407, 409, 411, 412,\n", + " 413, 417, 419, 420, 422, 425, 426, 428, 429, 430, 435, 437, 439,\n", + " 440, 442, 443, 447, 450, 451, 453, 454, 456, 457, 459, 462, 464,\n", + " 467, 469, 470, 473, 474, 475, 476, 477, 481, 482, 483, 484, 485,\n", + " 486, 487, 488, 491, 493, 494, 496, 498, 499, 501, 502, 504, 505,\n", + " 506, 508, 510, 513, 515, 518, 519, 522, 523, 525, 529, 534, 535,\n", + " 539, 546, 547, 552, 555, 558, 561, 562, 563, 565, 566, 567, 570,\n", + " 571, 572, 573, 574, 575, 577, 581, 582, 583, 584, 586, 588, 589,\n", + " 591, 592, 593, 594, 595, 598, 599]),\n", + " 2: array([ 57, 71, 98, 123, 152, 208, 239, 256, 400, 512, 544, 550, 553,\n", + " 568]),\n", + " 3: array([ 7, 8, 12, 40, 42, 44, 48, 50, 55, 60, 80, 83, 85,\n", + " 90, 91, 94, 105, 117, 121, 125, 126, 130, 131, 133, 138, 143,\n", + " 145, 150, 156, 159, 160, 166, 170, 178, 195, 204, 205, 206, 209,\n", + " 213, 218, 229, 233, 238, 258, 260, 263, 268, 280, 285, 288, 297,\n", + " 301, 302, 306, 319, 325, 333, 335, 337, 339, 349, 357, 366, 372,\n", + " 373, 377, 382, 386, 397, 404, 406, 414, 423, 427, 438, 444, 452,\n", + " 458, 463, 478, 479, 492, 500, 514, 520, 524, 532, 536, 541, 548,\n", + " 551, 554, 556, 569, 580, 590, 596]),\n", + " 4: array([110, 171, 188, 197, 203, 313, 321, 359, 402, 418, 472, 509, 538,\n", + " 559]),\n", + " 5: array([ 3, 13, 14, 18, 33, 43, 46, 51, 54, 63, 88, 89, 93,\n", + " 104, 148, 163, 174, 191, 193, 202, 212, 221, 243, 272, 286, 287,\n", + " 294, 315, 340, 341, 350, 358, 361, 363, 369, 398, 405, 410, 415,\n", + " 416, 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497,\n", + " 507, 511, 517, 521, 526, 527, 537, 540, 560, 564, 576, 585, 587]),\n", + " 6: array([ 0, 4, 11, 19, 20, 23, 24, 30, 34, 37, 38, 41, 61,\n", + " 62, 64, 66, 73, 76, 79, 84, 87, 95, 101, 103, 106, 112,\n", + " 120, 128, 144, 147, 164, 165, 172, 173, 182, 186, 190, 215, 224,\n", + " 255, 257, 261, 265, 269, 271, 273, 274, 275, 276, 281, 298, 305,\n", + " 317, 318, 324, 326, 327, 328, 338, 352, 355, 365, 378, 385, 389,\n", + " 408, 424, 431, 432, 434, 436, 446, 449, 455, 471, 480, 490, 495,\n", + " 503, 528, 530, 531, 542, 543, 545, 549, 557, 578, 579, 597]),\n", + " 7: array([ 47, 201, 237, 332, 376, 381, 533]),\n", + " 8: array([], dtype=int64),\n", + " 9: array([], dtype=int64)}},\n", + " 'nonl2_avg': {2: {0: array([ 3, 13, 14, 18, 29, 33, 43, 45, 46, 51, 54, 57, 63,\n", + " 68, 71, 81, 87, 88, 89, 93, 98, 103, 104, 110, 123, 148,\n", + " 152, 161, 163, 171, 174, 188, 191, 193, 197, 202, 203, 208, 211,\n", + " 212, 221, 239, 243, 246, 256, 262, 272, 286, 287, 294, 309, 313,\n", + " 315, 320, 321, 340, 341, 343, 350, 358, 359, 361, 363, 369, 398,\n", + " 400, 402, 405, 408, 410, 415, 416, 418, 421, 433, 441, 445, 448,\n", + " 460, 461, 465, 466, 468, 472, 477, 489, 497, 507, 509, 511, 512,\n", + " 515, 516, 517, 521, 526, 527, 537, 538, 540, 544, 545, 550, 553,\n", + " 559, 560, 564, 568, 576, 585, 587]),\n", + " 1: array([ 0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15,\n", + " 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 30,\n", + " 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 42, 44, 47,\n", + " 48, 49, 50, 52, 53, 55, 56, 58, 59, 60, 61, 62, 64,\n", + " 65, 66, 67, 69, 70, 72, 73, 74, 75, 76, 77, 78, 79,\n", + " 80, 82, 83, 84, 85, 86, 90, 91, 92, 94, 95, 96, 97,\n", + " 99, 100, 101, 102, 105, 106, 107, 108, 109, 111, 112, 113, 114,\n", + " 115, 116, 117, 118, 119, 120, 121, 122, 124, 125, 126, 127, 128,\n", + " 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141,\n", + " 142, 143, 144, 145, 146, 147, 149, 150, 151, 153, 154, 155, 156,\n", + " 157, 158, 159, 160, 162, 164, 165, 166, 167, 168, 169, 170, 172,\n", + " 173, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186,\n", + " 187, 189, 190, 192, 194, 195, 196, 198, 199, 200, 201, 204, 205,\n", + " 206, 207, 209, 210, 213, 214, 215, 216, 217, 218, 219, 220, 222,\n", + " 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235,\n", + " 236, 237, 238, 240, 241, 242, 244, 245, 247, 248, 249, 250, 251,\n", + " 252, 253, 254, 255, 257, 258, 259, 260, 261, 263, 264, 265, 266,\n", + " 267, 268, 269, 270, 271, 273, 274, 275, 276, 277, 278, 279, 280,\n", + " 281, 282, 283, 284, 285, 288, 289, 290, 291, 292, 293, 295, 296,\n", + " 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 310,\n", + " 311, 312, 314, 316, 317, 318, 319, 322, 323, 324, 325, 326, 327,\n", + " 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 342,\n", + " 344, 345, 346, 347, 348, 349, 351, 352, 353, 354, 355, 356, 357,\n", + " 360, 362, 364, 365, 366, 367, 368, 370, 371, 372, 373, 374, 375,\n", + " 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388,\n", + " 389, 390, 391, 392, 393, 394, 395, 396, 397, 399, 401, 403, 404,\n", + " 406, 407, 409, 411, 412, 413, 414, 417, 419, 420, 422, 423, 424,\n", + " 425, 426, 427, 428, 429, 430, 431, 432, 434, 435, 436, 437, 438,\n", + " 439, 440, 442, 443, 444, 446, 447, 449, 450, 451, 452, 453, 454,\n", + " 455, 456, 457, 458, 459, 462, 463, 464, 467, 469, 470, 471, 473,\n", + " 474, 475, 476, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487,\n", + " 488, 490, 491, 492, 493, 494, 495, 496, 498, 499, 500, 501, 502,\n", + " 503, 504, 505, 506, 508, 510, 513, 514, 518, 519, 520, 522, 523,\n", + " 524, 525, 528, 529, 530, 531, 532, 533, 534, 535, 536, 539, 541,\n", + " 542, 543, 546, 547, 548, 549, 551, 552, 554, 555, 556, 557, 558,\n", + " 561, 562, 563, 565, 566, 567, 569, 570, 571, 572, 573, 574, 575,\n", + " 577, 578, 579, 580, 581, 582, 583, 584, 586, 588, 589, 590, 591,\n", + " 592, 593, 594, 595, 596, 597, 598, 599])},\n", + " 3: {0: array([ 3, 13, 14, 18, 29, 33, 43, 45, 46, 51, 54, 57, 63,\n", + " 68, 71, 81, 88, 89, 93, 98, 104, 110, 123, 148, 152, 161,\n", + " 163, 171, 174, 188, 191, 193, 197, 202, 203, 208, 211, 212, 221,\n", + " 239, 243, 246, 256, 262, 272, 286, 287, 294, 309, 313, 315, 320,\n", + " 321, 340, 341, 343, 350, 358, 359, 361, 363, 369, 398, 400, 402,\n", + " 405, 410, 415, 416, 418, 421, 433, 441, 445, 448, 460, 461, 465,\n", + " 466, 468, 472, 477, 489, 497, 507, 509, 511, 512, 515, 516, 517,\n", + " 521, 526, 527, 537, 538, 540, 544, 550, 553, 559, 560, 564, 568,\n", + " 576, 585, 587]),\n", + " 1: array([ 1, 2, 5, 6, 7, 8, 9, 10, 15, 16, 17, 21, 22,\n", + " 25, 26, 27, 28, 31, 32, 35, 36, 39, 40, 42, 47, 48,\n", + " 49, 50, 52, 53, 55, 56, 58, 59, 65, 67, 69, 70, 72,\n", + " 74, 75, 77, 78, 80, 82, 83, 85, 86, 90, 91, 92, 96,\n", + " 97, 99, 100, 102, 105, 107, 108, 109, 111, 113, 114, 115, 116,\n", + " 117, 118, 119, 121, 122, 124, 125, 126, 127, 129, 132, 133, 134,\n", + " 135, 136, 137, 138, 139, 140, 141, 142, 145, 146, 149, 150, 151,\n", + " 153, 154, 155, 156, 157, 158, 159, 160, 162, 166, 167, 168, 169,\n", + " 175, 176, 177, 178, 179, 180, 181, 183, 184, 185, 187, 189, 192,\n", + " 194, 195, 196, 198, 199, 200, 201, 204, 205, 207, 209, 210, 213,\n", + " 214, 216, 217, 218, 219, 220, 222, 223, 225, 226, 227, 228, 229,\n", + " 230, 231, 232, 233, 234, 235, 236, 237, 238, 240, 241, 242, 244,\n", + " 245, 247, 248, 249, 250, 251, 252, 253, 254, 258, 259, 260, 263,\n", + " 264, 266, 267, 268, 270, 277, 278, 279, 280, 282, 283, 284, 285,\n", + " 288, 289, 290, 291, 292, 293, 295, 296, 297, 299, 300, 301, 303,\n", + " 304, 306, 307, 308, 310, 311, 312, 314, 316, 319, 322, 323, 325,\n", + " 329, 330, 331, 332, 333, 334, 335, 336, 337, 339, 342, 344, 345,\n", + " 346, 347, 348, 349, 351, 353, 354, 356, 357, 360, 362, 364, 366,\n", + " 367, 368, 370, 371, 372, 373, 374, 375, 376, 377, 379, 380, 381,\n", + " 382, 383, 384, 387, 388, 390, 391, 392, 393, 394, 395, 396, 397,\n", + " 399, 401, 403, 404, 406, 407, 409, 411, 412, 413, 414, 417, 419,\n", + " 420, 422, 423, 425, 426, 427, 428, 429, 430, 435, 438, 439, 440,\n", + " 442, 443, 447, 450, 451, 452, 453, 454, 456, 457, 458, 459, 462,\n", + " 464, 467, 469, 470, 473, 474, 475, 476, 478, 481, 482, 483, 484,\n", + " 485, 486, 487, 488, 491, 493, 494, 496, 498, 499, 500, 501, 502,\n", + " 504, 505, 506, 508, 510, 513, 518, 519, 520, 522, 523, 524, 525,\n", + " 529, 532, 534, 535, 536, 539, 546, 547, 551, 552, 554, 555, 556,\n", + " 558, 561, 562, 563, 565, 566, 567, 569, 570, 571, 572, 573, 574,\n", + " 575, 577, 581, 582, 583, 584, 586, 588, 589, 590, 591, 592, 593,\n", + " 594, 595, 596, 598, 599]),\n", + " 2: array([ 0, 4, 11, 12, 19, 20, 23, 24, 30, 34, 37, 38, 41,\n", + " 44, 60, 61, 62, 64, 66, 73, 76, 79, 84, 87, 94, 95,\n", + " 101, 103, 106, 112, 120, 128, 130, 131, 143, 144, 147, 164, 165,\n", + " 170, 172, 173, 182, 186, 190, 206, 215, 224, 255, 257, 261, 265,\n", + " 269, 271, 273, 274, 275, 276, 281, 298, 302, 305, 317, 318, 324,\n", + " 326, 327, 328, 338, 352, 355, 365, 378, 385, 386, 389, 408, 424,\n", + " 431, 432, 434, 436, 437, 444, 446, 449, 455, 463, 471, 479, 480,\n", + " 490, 492, 495, 503, 514, 528, 530, 531, 533, 541, 542, 543, 545,\n", + " 548, 549, 557, 578, 579, 580, 597])},\n", + " 4: {0: array([ 3, 13, 14, 18, 29, 33, 43, 45, 46, 51, 54, 57, 63,\n", + " 68, 71, 81, 88, 89, 93, 98, 104, 110, 123, 148, 152, 161,\n", + " 163, 171, 174, 188, 191, 193, 197, 202, 203, 208, 212, 221, 239,\n", + " 243, 246, 256, 272, 286, 287, 294, 309, 313, 315, 320, 321, 340,\n", + " 341, 343, 350, 358, 359, 361, 363, 369, 398, 400, 402, 405, 410,\n", + " 415, 416, 418, 421, 433, 441, 445, 448, 460, 461, 465, 466, 468,\n", + " 472, 477, 489, 497, 507, 509, 511, 512, 515, 516, 517, 521, 526,\n", + " 527, 537, 538, 540, 544, 550, 553, 559, 560, 564, 568, 576, 585,\n", + " 587]),\n", + " 1: array([ 1, 2, 5, 6, 9, 10, 15, 16, 17, 21, 22, 25, 26,\n", + " 27, 28, 31, 32, 35, 36, 39, 49, 52, 53, 56, 58, 59,\n", + " 65, 67, 69, 70, 72, 74, 75, 77, 78, 82, 86, 92, 96,\n", + " 97, 99, 100, 102, 107, 108, 109, 111, 113, 114, 115, 116, 118,\n", + " 119, 122, 124, 127, 129, 132, 134, 135, 136, 137, 139, 140, 141,\n", + " 142, 146, 149, 151, 153, 154, 155, 157, 158, 162, 167, 168, 169,\n", + " 175, 176, 177, 179, 180, 181, 183, 184, 185, 187, 189, 192, 194,\n", + " 196, 198, 199, 200, 207, 210, 211, 214, 216, 217, 219, 220, 222,\n", + " 223, 225, 226, 227, 228, 230, 231, 232, 234, 235, 236, 240, 241,\n", + " 242, 244, 245, 247, 248, 249, 250, 251, 252, 253, 254, 259, 262,\n", + " 264, 266, 267, 270, 277, 278, 279, 282, 283, 284, 289, 290, 291,\n", + " 292, 293, 295, 296, 299, 300, 303, 304, 307, 308, 310, 311, 312,\n", + " 314, 316, 322, 323, 329, 330, 331, 334, 336, 342, 344, 345, 346,\n", + " 347, 348, 351, 353, 354, 356, 360, 362, 364, 367, 368, 370, 371,\n", + " 374, 375, 379, 380, 383, 384, 387, 388, 390, 391, 392, 393, 394,\n", + " 395, 396, 399, 401, 403, 407, 409, 411, 412, 413, 417, 419, 420,\n", + " 422, 425, 426, 428, 429, 430, 435, 439, 440, 442, 443, 447, 450,\n", + " 451, 453, 454, 456, 457, 459, 462, 464, 467, 469, 470, 473, 474,\n", + " 475, 476, 481, 482, 483, 484, 485, 486, 487, 488, 491, 493, 494,\n", + " 496, 498, 499, 501, 502, 504, 505, 506, 508, 510, 513, 518, 519,\n", + " 522, 523, 525, 529, 534, 535, 539, 546, 547, 552, 555, 558, 561,\n", + " 562, 563, 565, 566, 567, 570, 571, 572, 573, 574, 575, 577, 581,\n", + " 582, 583, 584, 586, 588, 589, 591, 592, 593, 594, 595, 598, 599]),\n", + " 2: array([ 7, 8, 40, 42, 44, 47, 48, 50, 55, 60, 80, 83, 85,\n", + " 90, 91, 105, 117, 121, 125, 126, 130, 133, 138, 143, 145, 150,\n", + " 156, 159, 160, 166, 178, 195, 201, 204, 205, 206, 209, 213, 218,\n", + " 229, 233, 238, 258, 260, 263, 268, 280, 285, 288, 297, 301, 306,\n", + " 319, 325, 332, 333, 335, 337, 339, 349, 357, 366, 372, 373, 376,\n", + " 377, 381, 382, 386, 397, 404, 406, 414, 423, 427, 438, 452, 458,\n", + " 478, 479, 500, 520, 524, 532, 536, 551, 554, 556, 569, 580, 590,\n", + " 596]),\n", + " 3: array([ 0, 4, 11, 12, 19, 20, 23, 24, 30, 34, 37, 38, 41,\n", + " 61, 62, 64, 66, 73, 76, 79, 84, 87, 94, 95, 101, 103,\n", + " 106, 112, 120, 128, 131, 144, 147, 164, 165, 170, 172, 173, 182,\n", + " 186, 190, 215, 224, 237, 255, 257, 261, 265, 269, 271, 273, 274,\n", + " 275, 276, 281, 298, 302, 305, 317, 318, 324, 326, 327, 328, 338,\n", + " 352, 355, 365, 378, 385, 389, 408, 424, 431, 432, 434, 436, 437,\n", + " 444, 446, 449, 455, 463, 471, 480, 490, 492, 495, 503, 514, 528,\n", + " 530, 531, 533, 541, 542, 543, 545, 548, 549, 557, 578, 579, 597])},\n", + " 5: {0: array([ 51, 57, 71, 81, 98, 104, 110, 123, 152, 161, 171, 188, 197,\n", + " 203, 208, 239, 246, 256, 313, 321, 343, 359, 400, 402, 408, 472,\n", + " 512, 516, 538, 540, 550, 553, 559, 564, 568]),\n", + " 1: array([ 1, 2, 5, 6, 9, 10, 15, 16, 17, 21, 22, 25, 26,\n", + " 27, 28, 31, 32, 35, 36, 39, 49, 52, 53, 56, 58, 59,\n", + " 65, 67, 69, 70, 72, 74, 75, 77, 78, 82, 86, 92, 96,\n", + " 97, 99, 100, 102, 107, 108, 109, 111, 113, 114, 115, 116, 118,\n", + " 119, 122, 124, 127, 129, 132, 134, 135, 136, 137, 139, 140, 141,\n", + " 142, 146, 149, 151, 153, 154, 155, 157, 158, 162, 167, 168, 169,\n", + " 175, 176, 177, 179, 180, 181, 183, 184, 185, 187, 189, 192, 194,\n", + " 196, 198, 199, 200, 207, 210, 214, 216, 217, 219, 220, 222, 223,\n", + " 225, 226, 227, 228, 230, 231, 232, 234, 235, 236, 240, 241, 242,\n", + " 244, 245, 247, 248, 249, 250, 251, 252, 253, 254, 259, 264, 266,\n", + " 267, 270, 277, 278, 279, 282, 283, 284, 289, 290, 291, 292, 293,\n", + " 295, 296, 299, 300, 303, 304, 307, 308, 310, 311, 312, 314, 316,\n", + " 322, 323, 329, 330, 331, 334, 336, 342, 344, 345, 346, 347, 348,\n", + " 351, 353, 354, 356, 360, 362, 364, 367, 368, 370, 371, 374, 375,\n", + " 379, 380, 383, 384, 387, 388, 390, 391, 392, 393, 394, 395, 396,\n", + " 399, 401, 403, 407, 409, 411, 412, 413, 417, 419, 420, 422, 425,\n", + " 426, 428, 429, 430, 435, 439, 440, 442, 443, 447, 450, 451, 453,\n", + " 454, 456, 457, 459, 462, 464, 467, 469, 470, 473, 474, 475, 476,\n", + " 481, 482, 484, 485, 486, 487, 488, 491, 493, 494, 496, 498, 499,\n", + " 501, 502, 504, 505, 506, 508, 510, 513, 518, 519, 522, 523, 525,\n", + " 529, 534, 535, 539, 546, 547, 552, 555, 558, 561, 562, 563, 565,\n", + " 566, 567, 570, 571, 572, 573, 574, 575, 577, 581, 582, 583, 584,\n", + " 586, 588, 589, 591, 592, 593, 594, 595, 598, 599]),\n", + " 2: array([ 7, 8, 40, 42, 44, 47, 48, 50, 55, 60, 80, 83, 85,\n", + " 90, 91, 105, 117, 121, 125, 126, 130, 133, 138, 143, 145, 150,\n", + " 156, 159, 160, 166, 178, 195, 201, 204, 205, 206, 209, 213, 218,\n", + " 229, 233, 238, 258, 260, 263, 268, 280, 285, 288, 297, 301, 306,\n", + " 319, 325, 332, 333, 335, 337, 339, 349, 357, 366, 372, 373, 376,\n", + " 377, 381, 382, 386, 397, 404, 406, 414, 423, 427, 438, 452, 458,\n", + " 478, 479, 500, 520, 524, 532, 536, 551, 554, 556, 569, 580, 590,\n", + " 596]),\n", + " 3: array([ 3, 13, 14, 18, 29, 33, 43, 45, 46, 54, 63, 68, 88,\n", + " 89, 93, 148, 163, 174, 191, 193, 202, 211, 212, 221, 243, 262,\n", + " 272, 286, 287, 294, 309, 315, 320, 340, 341, 350, 358, 361, 363,\n", + " 369, 398, 405, 410, 415, 416, 418, 421, 433, 441, 445, 448, 460,\n", + " 461, 465, 466, 468, 477, 483, 489, 497, 507, 509, 511, 515, 517,\n", + " 521, 526, 527, 537, 544, 560, 576, 585, 587]),\n", + " 4: array([ 0, 4, 11, 12, 19, 20, 23, 24, 30, 34, 37, 38, 41,\n", + " 61, 62, 64, 66, 73, 76, 79, 84, 87, 94, 95, 101, 103,\n", + " 106, 112, 120, 128, 131, 144, 147, 164, 165, 170, 172, 173, 182,\n", + " 186, 190, 215, 224, 237, 255, 257, 261, 265, 269, 271, 273, 274,\n", + " 275, 276, 281, 298, 302, 305, 317, 318, 324, 326, 327, 328, 338,\n", + " 352, 355, 365, 378, 385, 389, 424, 431, 432, 434, 436, 437, 444,\n", + " 446, 449, 455, 463, 471, 480, 490, 492, 495, 503, 514, 528, 530,\n", + " 531, 533, 541, 542, 543, 545, 548, 549, 557, 578, 579, 597])},\n", + " 6: {0: array([ 51, 57, 71, 81, 98, 104, 110, 123, 152, 161, 171, 188, 197,\n", + " 203, 208, 239, 246, 256, 313, 321, 343, 359, 400, 402, 472, 512,\n", + " 516, 538, 540, 550, 553, 559, 564, 568]),\n", + " 1: array([ 1, 2, 5, 6, 9, 10, 15, 16, 17, 21, 22, 25, 26,\n", + " 27, 28, 31, 32, 35, 36, 39, 49, 52, 53, 56, 58, 59,\n", + " 65, 67, 69, 70, 72, 74, 75, 77, 78, 82, 86, 92, 96,\n", + " 97, 99, 100, 102, 107, 108, 109, 111, 113, 114, 115, 116, 118,\n", + " 119, 122, 124, 127, 129, 132, 134, 135, 136, 137, 139, 140, 141,\n", + " 142, 146, 149, 151, 153, 154, 155, 157, 158, 162, 167, 168, 169,\n", + " 175, 176, 177, 179, 180, 181, 183, 184, 185, 187, 189, 192, 194,\n", + " 196, 198, 199, 200, 207, 210, 214, 216, 217, 219, 220, 222, 223,\n", + " 225, 226, 227, 228, 230, 231, 232, 234, 235, 236, 240, 241, 242,\n", + " 244, 245, 247, 248, 249, 250, 251, 252, 253, 254, 259, 264, 266,\n", + " 267, 270, 277, 278, 279, 282, 283, 284, 289, 290, 291, 292, 293,\n", + " 295, 296, 299, 300, 303, 304, 307, 308, 310, 311, 312, 314, 316,\n", + " 322, 323, 329, 330, 331, 334, 336, 342, 344, 345, 346, 347, 348,\n", + " 351, 353, 354, 356, 360, 362, 364, 367, 368, 370, 371, 374, 375,\n", + " 379, 380, 383, 384, 387, 388, 390, 391, 392, 393, 394, 395, 396,\n", + " 399, 401, 403, 407, 409, 411, 412, 413, 417, 419, 420, 422, 425,\n", + " 426, 428, 429, 430, 435, 439, 440, 442, 443, 447, 450, 451, 453,\n", + " 454, 456, 457, 459, 462, 464, 467, 469, 470, 473, 474, 475, 476,\n", + " 481, 482, 484, 485, 486, 487, 488, 491, 493, 494, 496, 498, 499,\n", + " 501, 502, 504, 505, 506, 508, 510, 513, 518, 519, 522, 523, 525,\n", + " 529, 534, 535, 539, 546, 547, 552, 555, 558, 561, 562, 563, 565,\n", + " 566, 567, 570, 571, 572, 573, 574, 575, 577, 581, 582, 583, 584,\n", + " 586, 588, 589, 591, 592, 593, 594, 595, 598, 599]),\n", + " 2: array([ 7, 8, 40, 47, 48, 50, 55, 80, 83, 85, 90, 91, 105,\n", + " 117, 121, 125, 126, 133, 138, 145, 150, 156, 159, 160, 166, 178,\n", + " 195, 201, 204, 205, 209, 213, 218, 229, 233, 238, 260, 263, 268,\n", + " 280, 288, 297, 301, 306, 319, 332, 333, 335, 337, 339, 349, 357,\n", + " 366, 372, 373, 376, 377, 381, 382, 397, 404, 406, 414, 423, 427,\n", + " 438, 452, 458, 478, 500, 520, 524, 532, 536, 556, 569, 590, 596]),\n", + " 3: array([ 3, 13, 14, 18, 29, 33, 43, 45, 46, 54, 63, 68, 88,\n", + " 89, 93, 148, 163, 174, 191, 193, 202, 211, 212, 221, 243, 262,\n", + " 272, 286, 287, 294, 309, 315, 320, 340, 341, 350, 358, 361, 363,\n", + " 369, 398, 405, 410, 415, 416, 418, 421, 433, 441, 445, 448, 460,\n", + " 461, 465, 466, 468, 477, 483, 489, 497, 507, 509, 511, 515, 517,\n", + " 521, 526, 527, 537, 544, 560, 576, 585, 587]),\n", + " 4: array([ 0, 4, 11, 19, 20, 30, 34, 37, 61, 62, 64, 66, 73,\n", + " 76, 79, 84, 87, 95, 101, 103, 106, 112, 120, 128, 147, 164,\n", + " 172, 173, 182, 186, 190, 215, 224, 255, 257, 261, 265, 269, 271,\n", + " 273, 274, 275, 276, 318, 324, 327, 328, 338, 352, 365, 378, 385,\n", + " 389, 408, 424, 431, 432, 434, 436, 437, 446, 449, 455, 471, 480,\n", + " 503, 528, 542, 543, 545, 549, 578, 579, 597]),\n", + " 5: array([ 12, 23, 24, 38, 41, 42, 44, 60, 94, 130, 131, 143, 144,\n", + " 165, 170, 206, 237, 258, 281, 285, 298, 302, 305, 317, 325, 326,\n", + " 355, 386, 444, 463, 479, 490, 492, 495, 514, 530, 531, 533, 541,\n", + " 548, 551, 554, 557, 580])},\n", + " 7: {0: array([ 51, 57, 71, 81, 98, 104, 110, 123, 152, 161, 171, 188, 197,\n", + " 203, 208, 239, 246, 256, 313, 321, 343, 359, 400, 402, 472, 512,\n", + " 516, 538, 540, 550, 553, 564, 568]),\n", + " 1: array([ 1, 2, 5, 6, 9, 10, 15, 16, 17, 21, 22, 25, 26,\n", + " 27, 28, 31, 32, 35, 36, 39, 49, 52, 53, 56, 58, 59,\n", + " 65, 67, 69, 70, 72, 74, 75, 77, 78, 82, 86, 92, 96,\n", + " 97, 99, 100, 102, 107, 108, 109, 111, 113, 114, 115, 116, 118,\n", + " 119, 122, 124, 127, 129, 132, 134, 135, 136, 137, 139, 140, 141,\n", + " 142, 146, 149, 151, 153, 154, 155, 157, 158, 162, 167, 168, 169,\n", + " 175, 176, 177, 179, 180, 181, 183, 184, 185, 187, 189, 192, 194,\n", + " 196, 198, 199, 200, 207, 210, 214, 216, 217, 219, 220, 222, 223,\n", + " 225, 226, 227, 228, 230, 231, 232, 234, 235, 236, 240, 241, 242,\n", + " 244, 245, 247, 248, 249, 250, 251, 252, 253, 254, 259, 264, 266,\n", + " 267, 270, 277, 278, 279, 282, 283, 284, 289, 290, 291, 292, 293,\n", + " 295, 296, 299, 300, 303, 304, 307, 308, 310, 311, 312, 314, 316,\n", + " 322, 323, 329, 330, 331, 334, 336, 342, 344, 345, 346, 347, 348,\n", + " 351, 353, 354, 356, 360, 362, 364, 367, 368, 370, 371, 374, 375,\n", + " 379, 380, 383, 384, 387, 388, 390, 391, 392, 393, 394, 395, 396,\n", + " 399, 401, 403, 407, 409, 411, 412, 413, 417, 419, 420, 422, 425,\n", + " 426, 428, 429, 430, 435, 439, 440, 442, 443, 447, 450, 451, 453,\n", + " 454, 456, 457, 459, 462, 464, 467, 469, 470, 473, 474, 475, 476,\n", + " 481, 482, 484, 485, 486, 487, 488, 491, 493, 494, 496, 498, 499,\n", + " 501, 502, 504, 505, 506, 508, 510, 513, 518, 519, 522, 523, 525,\n", + " 529, 534, 535, 539, 546, 547, 552, 555, 558, 561, 562, 563, 565,\n", + " 566, 567, 570, 571, 572, 573, 574, 575, 577, 581, 582, 583, 584,\n", + " 586, 588, 589, 591, 592, 593, 594, 595, 598, 599]),\n", + " 2: array([ 7, 8, 40, 47, 48, 50, 55, 80, 83, 85, 90, 91, 105,\n", + " 117, 121, 125, 126, 133, 138, 145, 150, 156, 159, 160, 166, 178,\n", + " 195, 201, 204, 205, 209, 213, 218, 229, 233, 238, 260, 263, 268,\n", + " 280, 288, 297, 301, 306, 319, 332, 333, 335, 337, 339, 349, 357,\n", + " 366, 372, 373, 376, 377, 381, 382, 397, 404, 406, 414, 423, 427,\n", + " 438, 452, 458, 478, 500, 520, 524, 532, 536, 556, 569, 590, 596]),\n", + " 3: array([ 29, 45, 68, 211, 262, 309, 320, 477, 483, 509, 515, 559]),\n", + " 4: array([ 3, 13, 14, 18, 33, 43, 46, 54, 63, 88, 89, 93, 148,\n", + " 163, 174, 191, 193, 202, 212, 221, 243, 272, 286, 287, 294, 315,\n", + " 340, 341, 350, 358, 361, 363, 369, 398, 405, 410, 415, 416, 418,\n", + " 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497, 507,\n", + " 511, 517, 521, 526, 527, 537, 544, 560, 576, 585, 587]),\n", + " 5: array([ 0, 4, 11, 19, 20, 30, 34, 37, 61, 62, 64, 66, 73,\n", + " 76, 79, 84, 87, 95, 101, 103, 106, 112, 120, 128, 147, 164,\n", + " 172, 173, 182, 186, 190, 215, 224, 255, 257, 261, 265, 269, 271,\n", + " 273, 274, 275, 276, 318, 324, 327, 328, 338, 352, 365, 378, 385,\n", + " 389, 408, 424, 431, 432, 434, 436, 437, 446, 449, 455, 471, 480,\n", + " 503, 528, 542, 543, 545, 549, 578, 579, 597]),\n", + " 6: array([ 12, 23, 24, 38, 41, 42, 44, 60, 94, 130, 131, 143, 144,\n", + " 165, 170, 206, 237, 258, 281, 285, 298, 302, 305, 317, 325, 326,\n", + " 355, 386, 444, 463, 479, 490, 492, 495, 514, 530, 531, 533, 541,\n", + " 548, 551, 554, 557, 580])},\n", + " 8: {0: array([ 51, 57, 71, 81, 98, 104, 110, 123, 152, 161, 171, 188, 197,\n", + " 203, 208, 239, 246, 256, 313, 321, 343, 359, 400, 402, 472, 512,\n", + " 516, 538, 540, 550, 553, 564, 568]),\n", + " 1: array([ 2, 5, 9, 10, 25, 26, 31, 32, 35, 52, 59, 65, 67,\n", + " 69, 70, 75, 82, 86, 97, 99, 102, 108, 111, 114, 127, 141,\n", + " 146, 149, 151, 153, 154, 155, 158, 162, 169, 175, 179, 181, 194,\n", + " 199, 200, 207, 210, 216, 222, 223, 225, 227, 235, 236, 245, 250,\n", + " 251, 253, 254, 264, 270, 278, 283, 291, 292, 295, 296, 299, 303,\n", + " 304, 307, 310, 314, 316, 323, 329, 330, 334, 347, 348, 351, 360,\n", + " 364, 367, 371, 374, 380, 383, 384, 387, 388, 392, 395, 401, 411,\n", + " 413, 417, 422, 425, 426, 428, 429, 430, 439, 442, 443, 447, 450,\n", + " 454, 456, 459, 464, 470, 475, 476, 481, 487, 491, 493, 494, 506,\n", + " 513, 518, 519, 523, 525, 534, 546, 558, 562, 566, 572, 573, 577,\n", + " 582, 583, 586, 588, 595, 599]),\n", + " 2: array([ 7, 8, 40, 47, 48, 50, 55, 80, 83, 85, 90, 91, 105,\n", + " 117, 121, 125, 126, 133, 138, 145, 150, 156, 159, 160, 166, 178,\n", + " 195, 201, 204, 205, 209, 213, 218, 229, 233, 238, 260, 263, 268,\n", + " 280, 288, 297, 301, 306, 319, 332, 333, 335, 337, 339, 349, 357,\n", + " 366, 372, 373, 376, 377, 381, 382, 397, 404, 406, 414, 423, 427,\n", + " 438, 452, 458, 478, 500, 520, 524, 532, 536, 556, 569, 590, 596]),\n", + " 3: array([ 1, 6, 15, 16, 17, 21, 22, 27, 28, 36, 39, 49, 53,\n", + " 56, 58, 72, 74, 77, 78, 92, 96, 100, 107, 109, 113, 115,\n", + " 116, 118, 119, 122, 124, 129, 132, 134, 135, 136, 137, 139, 140,\n", + " 142, 157, 167, 168, 176, 177, 180, 183, 184, 185, 186, 187, 189,\n", + " 192, 196, 198, 214, 217, 219, 220, 226, 228, 230, 231, 232, 234,\n", + " 240, 241, 242, 244, 247, 248, 249, 252, 259, 266, 267, 277, 279,\n", + " 282, 284, 289, 290, 293, 300, 308, 311, 312, 322, 331, 336, 342,\n", + " 344, 345, 346, 353, 354, 356, 362, 368, 370, 375, 379, 390, 391,\n", + " 393, 394, 396, 399, 403, 407, 409, 412, 419, 420, 435, 440, 451,\n", + " 453, 457, 462, 467, 469, 473, 474, 482, 484, 485, 486, 488, 496,\n", + " 498, 499, 501, 502, 504, 505, 508, 510, 522, 529, 535, 539, 547,\n", + " 552, 555, 561, 563, 565, 567, 570, 571, 574, 575, 579, 581, 584,\n", + " 589, 591, 592, 593, 594, 598]),\n", + " 4: array([ 29, 45, 68, 211, 262, 309, 320, 477, 483, 509, 515, 559]),\n", + " 5: array([ 3, 13, 14, 18, 33, 43, 46, 54, 63, 88, 89, 93, 148,\n", + " 163, 174, 191, 193, 202, 212, 221, 243, 272, 286, 287, 294, 315,\n", + " 340, 341, 350, 358, 361, 363, 369, 398, 405, 410, 415, 416, 418,\n", + " 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497, 507,\n", + " 511, 517, 521, 526, 527, 537, 544, 560, 576, 585, 587]),\n", + " 6: array([ 0, 4, 11, 19, 20, 30, 34, 37, 61, 62, 64, 66, 73,\n", + " 76, 79, 84, 87, 95, 101, 103, 106, 112, 120, 128, 147, 164,\n", + " 172, 173, 182, 190, 215, 224, 255, 257, 261, 265, 269, 271, 273,\n", + " 274, 275, 276, 318, 324, 327, 328, 338, 352, 365, 378, 385, 389,\n", + " 408, 424, 431, 432, 434, 436, 437, 446, 449, 455, 471, 480, 503,\n", + " 528, 542, 543, 545, 549, 578, 597]),\n", + " 7: array([ 12, 23, 24, 38, 41, 42, 44, 60, 94, 130, 131, 143, 144,\n", + " 165, 170, 206, 237, 258, 281, 285, 298, 302, 305, 317, 325, 326,\n", + " 355, 386, 444, 463, 479, 490, 492, 495, 514, 530, 531, 533, 541,\n", + " 548, 551, 554, 557, 580])},\n", + " 9: {0: array([ 51, 57, 71, 81, 98, 104, 110, 123, 152, 161, 171, 188, 197,\n", + " 203, 208, 239, 246, 256, 313, 321, 343, 359, 400, 402, 472, 512,\n", + " 516, 538, 540, 550, 553, 564, 568]),\n", + " 1: array([ 2, 5, 9, 10, 25, 26, 31, 32, 35, 52, 59, 65, 67,\n", + " 69, 70, 75, 82, 86, 97, 99, 102, 108, 111, 114, 127, 141,\n", + " 146, 149, 151, 153, 154, 155, 158, 162, 169, 175, 179, 181, 194,\n", + " 199, 200, 207, 210, 216, 222, 223, 225, 227, 235, 236, 245, 250,\n", + " 251, 253, 254, 264, 270, 278, 283, 291, 292, 295, 296, 299, 303,\n", + " 304, 307, 310, 314, 316, 323, 329, 330, 334, 347, 348, 351, 360,\n", + " 364, 367, 371, 374, 380, 383, 384, 387, 388, 392, 395, 401, 411,\n", + " 413, 417, 422, 425, 426, 428, 429, 430, 439, 442, 443, 447, 450,\n", + " 454, 456, 459, 464, 470, 475, 476, 481, 487, 491, 493, 494, 506,\n", + " 513, 518, 519, 523, 525, 534, 546, 558, 562, 566, 572, 573, 577,\n", + " 582, 583, 586, 588, 595, 599]),\n", + " 2: array([ 7, 8, 40, 48, 50, 55, 80, 83, 85, 90, 91, 105, 117,\n", + " 121, 125, 126, 133, 138, 145, 150, 156, 159, 160, 166, 178, 195,\n", + " 204, 205, 209, 213, 218, 229, 233, 238, 260, 263, 268, 280, 288,\n", + " 297, 301, 306, 319, 333, 335, 337, 339, 349, 357, 366, 372, 373,\n", + " 377, 382, 397, 404, 406, 414, 423, 427, 438, 452, 458, 478, 500,\n", + " 520, 524, 532, 536, 556, 569, 590, 596]),\n", + " 3: array([ 1, 6, 15, 16, 17, 21, 22, 27, 28, 36, 39, 49, 53,\n", + " 56, 58, 72, 74, 77, 78, 92, 96, 100, 107, 109, 113, 115,\n", + " 116, 118, 119, 122, 124, 129, 132, 134, 135, 136, 137, 139, 140,\n", + " 142, 157, 167, 168, 176, 177, 180, 183, 184, 185, 186, 187, 189,\n", + " 192, 196, 198, 214, 217, 219, 220, 226, 228, 230, 231, 232, 234,\n", + " 240, 241, 242, 244, 247, 248, 249, 252, 259, 266, 267, 277, 279,\n", + " 282, 284, 289, 290, 293, 300, 308, 311, 312, 322, 331, 336, 342,\n", + " 344, 345, 346, 353, 354, 356, 362, 368, 370, 375, 379, 390, 391,\n", + " 393, 394, 396, 399, 403, 407, 409, 412, 419, 420, 435, 440, 451,\n", + " 453, 457, 462, 467, 469, 473, 474, 482, 484, 485, 486, 488, 496,\n", + " 498, 499, 501, 502, 504, 505, 508, 510, 522, 529, 535, 539, 547,\n", + " 552, 555, 561, 563, 565, 567, 570, 571, 574, 575, 579, 581, 584,\n", + " 589, 591, 592, 593, 594, 598]),\n", + " 4: array([ 29, 45, 68, 211, 262, 309, 320, 477, 483, 509, 515, 559]),\n", + " 5: array([ 3, 13, 14, 18, 33, 43, 46, 54, 63, 88, 89, 93, 148,\n", + " 163, 174, 191, 193, 202, 212, 221, 243, 272, 286, 287, 294, 315,\n", + " 340, 341, 350, 358, 361, 363, 369, 398, 405, 410, 415, 416, 418,\n", + " 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497, 507,\n", + " 511, 517, 521, 526, 527, 537, 544, 560, 576, 585, 587]),\n", + " 6: array([ 0, 4, 11, 19, 20, 30, 34, 37, 61, 62, 64, 66, 73,\n", + " 76, 79, 84, 87, 95, 101, 103, 106, 112, 120, 128, 147, 164,\n", + " 172, 173, 182, 190, 215, 224, 255, 257, 261, 265, 269, 271, 273,\n", + " 274, 275, 276, 318, 324, 327, 328, 338, 352, 365, 378, 385, 389,\n", + " 408, 424, 431, 432, 434, 436, 437, 446, 449, 455, 471, 480, 503,\n", + " 528, 542, 543, 545, 549, 578, 597]),\n", + " 7: array([ 12, 23, 24, 38, 41, 42, 44, 60, 94, 130, 131, 143, 144,\n", + " 165, 170, 206, 237, 258, 281, 285, 298, 302, 305, 317, 325, 326,\n", + " 355, 386, 444, 463, 479, 490, 492, 495, 514, 530, 531, 533, 541,\n", + " 548, 551, 554, 557, 580]),\n", + " 8: array([ 47, 201, 332, 376, 381])},\n", + " 10: {0: array([ 51, 57, 71, 81, 98, 104, 110, 123, 152, 161, 171, 188, 197,\n", + " 203, 208, 239, 246, 256, 313, 321, 343, 359, 400, 402, 472, 512,\n", + " 516, 538, 540, 550, 553, 564, 568]),\n", + " 1: array([ 2, 5, 9, 10, 25, 26, 31, 32, 35, 52, 59, 65, 67,\n", + " 69, 70, 75, 82, 86, 97, 99, 102, 108, 111, 114, 127, 141,\n", + " 146, 149, 151, 153, 154, 155, 158, 162, 169, 175, 179, 181, 194,\n", + " 199, 200, 207, 210, 216, 222, 223, 225, 227, 235, 236, 245, 250,\n", + " 251, 253, 254, 264, 270, 278, 283, 291, 292, 295, 296, 299, 303,\n", + " 304, 307, 310, 314, 316, 323, 329, 330, 334, 347, 348, 351, 360,\n", + " 364, 367, 371, 374, 380, 383, 384, 387, 388, 392, 395, 401, 411,\n", + " 413, 417, 422, 425, 426, 428, 429, 430, 439, 442, 443, 447, 450,\n", + " 454, 456, 459, 464, 470, 475, 476, 481, 487, 491, 493, 494, 506,\n", + " 513, 518, 519, 523, 525, 534, 546, 558, 562, 566, 572, 573, 577,\n", + " 582, 583, 586, 588, 595, 599]),\n", + " 2: array([ 7, 8, 40, 48, 50, 55, 80, 83, 85, 90, 91, 105, 117,\n", + " 121, 125, 126, 133, 138, 145, 150, 156, 159, 160, 166, 178, 195,\n", + " 204, 205, 209, 213, 218, 229, 233, 238, 260, 263, 268, 280, 288,\n", + " 297, 301, 306, 319, 333, 335, 337, 339, 349, 357, 366, 372, 373,\n", + " 377, 382, 397, 404, 406, 414, 423, 427, 438, 452, 478, 500, 520,\n", + " 524, 532, 536, 556, 569, 590, 596]),\n", + " 3: array([ 1, 6, 15, 16, 17, 21, 22, 27, 28, 36, 39, 49, 53,\n", + " 56, 58, 72, 74, 77, 78, 92, 96, 100, 107, 109, 113, 115,\n", + " 116, 118, 119, 122, 124, 129, 132, 134, 135, 136, 137, 139, 140,\n", + " 142, 157, 167, 168, 176, 177, 180, 183, 184, 185, 186, 187, 189,\n", + " 192, 196, 198, 214, 217, 219, 220, 226, 228, 230, 231, 232, 234,\n", + " 240, 241, 242, 244, 247, 248, 249, 252, 259, 266, 267, 277, 279,\n", + " 282, 284, 289, 290, 293, 300, 308, 311, 312, 322, 331, 336, 342,\n", + " 344, 345, 346, 353, 354, 356, 362, 368, 370, 375, 379, 390, 391,\n", + " 393, 394, 396, 399, 403, 407, 409, 412, 419, 420, 435, 440, 451,\n", + " 453, 457, 462, 467, 469, 473, 474, 482, 484, 485, 486, 488, 496,\n", + " 498, 499, 501, 502, 504, 505, 508, 510, 522, 529, 535, 539, 547,\n", + " 552, 555, 561, 563, 565, 567, 570, 571, 574, 575, 579, 581, 584,\n", + " 589, 591, 592, 593, 594, 598]),\n", + " 4: array([ 29, 45, 68, 211, 262, 309, 320, 477, 483, 509, 515, 559]),\n", + " 5: array([ 3, 13, 14, 18, 33, 43, 46, 54, 63, 88, 89, 93, 148,\n", + " 163, 174, 191, 193, 202, 212, 221, 243, 272, 286, 287, 294, 315,\n", + " 340, 341, 350, 358, 361, 363, 369, 398, 405, 410, 415, 416, 418,\n", + " 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497, 507,\n", + " 511, 517, 521, 526, 527, 537, 544, 560, 576, 585, 587]),\n", + " 6: array([ 0, 4, 11, 19, 20, 30, 34, 37, 61, 62, 64, 66, 73,\n", + " 76, 79, 84, 87, 95, 101, 103, 106, 112, 120, 128, 147, 164,\n", + " 172, 173, 182, 190, 215, 224, 255, 257, 261, 265, 269, 271, 273,\n", + " 274, 275, 276, 318, 324, 327, 328, 338, 352, 365, 378, 385, 389,\n", + " 408, 424, 431, 432, 434, 436, 437, 446, 449, 455, 471, 480, 503,\n", + " 528, 542, 543, 545, 549, 578, 597]),\n", + " 7: array([ 12, 170, 444, 463]),\n", + " 8: array([ 23, 24, 38, 41, 42, 44, 60, 94, 130, 131, 143, 144, 165,\n", + " 206, 237, 258, 281, 285, 298, 302, 305, 317, 325, 326, 355, 386,\n", + " 458, 479, 490, 492, 495, 514, 530, 531, 533, 541, 548, 551, 554,\n", + " 557, 580]),\n", + " 9: array([ 47, 201, 332, 376, 381])}},\n", + " 'nonl2_noavg': {2: {0: array([ 3, 13, 14, 18, 29, 33, 43, 45, 46, 51, 54, 57, 63,\n", + " 68, 71, 81, 88, 89, 93, 98, 104, 110, 123, 148, 152, 161,\n", + " 163, 171, 174, 188, 191, 193, 197, 202, 203, 208, 212, 221, 239,\n", + " 243, 246, 256, 272, 286, 287, 294, 309, 313, 315, 321, 340, 341,\n", + " 343, 350, 358, 359, 361, 363, 369, 398, 400, 402, 405, 410, 415,\n", + " 416, 418, 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 472,\n", + " 477, 483, 489, 497, 507, 509, 511, 512, 515, 516, 517, 521, 526,\n", + " 527, 537, 538, 540, 544, 550, 553, 559, 560, 564, 568, 576, 585,\n", + " 587]),\n", + " 1: array([ 0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15,\n", + " 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 30,\n", + " 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 42, 44, 47,\n", + " 48, 49, 50, 52, 53, 55, 56, 58, 59, 60, 61, 62, 64,\n", + " 65, 66, 67, 69, 70, 72, 73, 74, 75, 76, 77, 78, 79,\n", + " 80, 82, 83, 84, 85, 86, 87, 90, 91, 92, 94, 95, 96,\n", + " 97, 99, 100, 101, 102, 103, 105, 106, 107, 108, 109, 111, 112,\n", + " 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125, 126,\n", + " 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n", + " 140, 141, 142, 143, 144, 145, 146, 147, 149, 150, 151, 153, 154,\n", + " 155, 156, 157, 158, 159, 160, 162, 164, 165, 166, 167, 168, 169,\n", + " 170, 172, 173, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184,\n", + " 185, 186, 187, 189, 190, 192, 194, 195, 196, 198, 199, 200, 201,\n", + " 204, 205, 206, 207, 209, 210, 211, 213, 214, 215, 216, 217, 218,\n", + " 219, 220, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232,\n", + " 233, 234, 235, 236, 237, 238, 240, 241, 242, 244, 245, 247, 248,\n", + " 249, 250, 251, 252, 253, 254, 255, 257, 258, 259, 260, 261, 262,\n", + " 263, 264, 265, 266, 267, 268, 269, 270, 271, 273, 274, 275, 276,\n", + " 277, 278, 279, 280, 281, 282, 283, 284, 285, 288, 289, 290, 291,\n", + " 292, 293, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305,\n", + " 306, 307, 308, 310, 311, 312, 314, 316, 317, 318, 319, 320, 322,\n", + " 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335,\n", + " 336, 337, 338, 339, 342, 344, 345, 346, 347, 348, 349, 351, 352,\n", + " 353, 354, 355, 356, 357, 360, 362, 364, 365, 366, 367, 368, 370,\n", + " 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383,\n", + " 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396,\n", + " 397, 399, 401, 403, 404, 406, 407, 408, 409, 411, 412, 413, 414,\n", + " 417, 419, 420, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431,\n", + " 432, 434, 435, 436, 437, 438, 439, 440, 442, 443, 444, 446, 447,\n", + " 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 462, 463,\n", + " 464, 467, 469, 470, 471, 473, 474, 475, 476, 478, 479, 480, 481,\n", + " 482, 484, 485, 486, 487, 488, 490, 491, 492, 493, 494, 495, 496,\n", + " 498, 499, 500, 501, 502, 503, 504, 505, 506, 508, 510, 513, 514,\n", + " 518, 519, 520, 522, 523, 524, 525, 528, 529, 530, 531, 532, 533,\n", + " 534, 535, 536, 539, 541, 542, 543, 545, 546, 547, 548, 549, 551,\n", + " 552, 554, 555, 556, 557, 558, 561, 562, 563, 565, 566, 567, 569,\n", + " 570, 571, 572, 573, 574, 575, 577, 578, 579, 580, 581, 582, 583,\n", + " 584, 586, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598,\n", + " 599])},\n", + " 3: {0: array([ 3, 13, 14, 18, 29, 33, 43, 45, 46, 51, 54, 57, 63,\n", + " 68, 71, 81, 88, 89, 93, 98, 104, 110, 123, 148, 152, 161,\n", + " 163, 171, 174, 188, 191, 193, 197, 202, 203, 208, 212, 221, 239,\n", + " 243, 246, 256, 272, 286, 287, 294, 309, 313, 315, 321, 340, 341,\n", + " 343, 350, 358, 359, 361, 363, 369, 398, 400, 402, 405, 410, 415,\n", + " 416, 418, 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 472,\n", + " 489, 497, 507, 509, 511, 512, 516, 517, 521, 526, 527, 537, 538,\n", + " 540, 544, 550, 553, 559, 560, 564, 568, 576, 585, 587]),\n", + " 1: array([ 1, 2, 5, 6, 7, 8, 9, 10, 15, 16, 17, 21, 22,\n", + " 25, 26, 27, 28, 31, 32, 35, 36, 39, 40, 42, 47, 48,\n", + " 49, 50, 52, 53, 55, 56, 58, 59, 65, 67, 69, 70, 72,\n", + " 74, 75, 77, 78, 80, 82, 83, 85, 86, 90, 91, 92, 96,\n", + " 97, 99, 100, 102, 105, 107, 108, 109, 111, 113, 114, 115, 116,\n", + " 117, 118, 119, 121, 122, 124, 125, 126, 127, 129, 132, 133, 134,\n", + " 135, 136, 137, 138, 139, 140, 141, 142, 145, 146, 149, 150, 151,\n", + " 153, 154, 155, 156, 157, 158, 159, 160, 162, 166, 167, 168, 169,\n", + " 175, 176, 177, 178, 179, 180, 181, 183, 184, 185, 187, 189, 192,\n", + " 194, 195, 196, 198, 199, 200, 201, 204, 205, 207, 209, 210, 211,\n", + " 213, 214, 216, 217, 218, 219, 220, 222, 223, 225, 226, 227, 228,\n", + " 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 240, 241, 242,\n", + " 244, 245, 247, 248, 249, 250, 251, 252, 253, 254, 259, 260, 262,\n", + " 263, 264, 266, 267, 268, 270, 277, 278, 279, 280, 282, 283, 284,\n", + " 288, 289, 290, 291, 292, 293, 295, 296, 297, 299, 300, 301, 303,\n", + " 304, 306, 307, 308, 310, 311, 312, 314, 316, 319, 320, 322, 323,\n", + " 325, 329, 330, 331, 332, 333, 334, 335, 336, 337, 339, 342, 344,\n", + " 345, 346, 347, 348, 349, 351, 353, 354, 356, 357, 360, 362, 364,\n", + " 366, 367, 368, 370, 371, 372, 373, 374, 375, 376, 377, 379, 380,\n", + " 381, 382, 383, 384, 387, 388, 390, 391, 392, 393, 394, 395, 396,\n", + " 397, 399, 401, 403, 404, 406, 407, 409, 411, 412, 413, 414, 417,\n", + " 419, 420, 422, 423, 425, 426, 427, 428, 429, 430, 435, 438, 439,\n", + " 440, 442, 443, 447, 450, 451, 452, 453, 454, 456, 457, 458, 459,\n", + " 462, 464, 467, 469, 470, 473, 474, 475, 476, 477, 478, 481, 482,\n", + " 483, 484, 485, 486, 487, 488, 491, 493, 494, 496, 498, 499, 500,\n", + " 501, 502, 504, 505, 506, 508, 510, 513, 515, 518, 519, 520, 522,\n", + " 523, 524, 525, 529, 534, 535, 536, 539, 546, 547, 551, 552, 555,\n", + " 556, 558, 561, 562, 563, 565, 566, 567, 569, 570, 571, 572, 573,\n", + " 574, 575, 577, 581, 582, 583, 584, 586, 588, 589, 590, 591, 592,\n", + " 593, 594, 595, 596, 598, 599]),\n", + " 2: array([ 0, 4, 11, 12, 19, 20, 23, 24, 30, 34, 37, 38, 41,\n", + " 44, 60, 61, 62, 64, 66, 73, 76, 79, 84, 87, 94, 95,\n", + " 101, 103, 106, 112, 120, 128, 130, 131, 143, 144, 147, 164, 165,\n", + " 170, 172, 173, 182, 186, 190, 206, 215, 224, 255, 257, 258, 261,\n", + " 265, 269, 271, 273, 274, 275, 276, 281, 285, 298, 302, 305, 317,\n", + " 318, 324, 326, 327, 328, 338, 352, 355, 365, 378, 385, 386, 389,\n", + " 408, 424, 431, 432, 434, 436, 437, 444, 446, 449, 455, 463, 471,\n", + " 479, 480, 490, 492, 495, 503, 514, 528, 530, 531, 532, 533, 541,\n", + " 542, 543, 545, 548, 549, 554, 557, 578, 579, 580, 597])},\n", + " 4: {0: array([ 3, 13, 14, 18, 29, 33, 43, 45, 46, 51, 54, 57, 63,\n", + " 68, 71, 81, 88, 89, 93, 98, 104, 110, 123, 148, 152, 161,\n", + " 163, 171, 174, 188, 191, 193, 197, 202, 203, 208, 212, 221, 239,\n", + " 243, 246, 256, 272, 286, 287, 294, 309, 313, 315, 321, 340, 341,\n", + " 343, 350, 358, 359, 361, 363, 369, 398, 400, 402, 405, 410, 415,\n", + " 416, 418, 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 472,\n", + " 477, 483, 489, 497, 507, 509, 511, 512, 515, 516, 517, 521, 526,\n", + " 527, 537, 538, 540, 544, 550, 553, 559, 560, 564, 568, 576, 585,\n", + " 587]),\n", + " 1: array([ 1, 2, 5, 6, 9, 10, 15, 16, 17, 21, 22, 25, 26,\n", + " 27, 28, 31, 32, 35, 36, 39, 42, 49, 52, 53, 56, 58,\n", + " 59, 65, 67, 69, 70, 72, 74, 75, 77, 78, 82, 86, 92,\n", + " 96, 97, 99, 100, 102, 107, 108, 109, 111, 113, 114, 115, 116,\n", + " 118, 119, 122, 124, 127, 129, 132, 134, 135, 136, 137, 139, 140,\n", + " 141, 142, 146, 149, 151, 153, 154, 155, 157, 158, 162, 167, 168,\n", + " 169, 175, 176, 177, 179, 180, 181, 183, 184, 185, 187, 189, 192,\n", + " 194, 196, 198, 199, 200, 207, 210, 211, 214, 216, 217, 219, 220,\n", + " 222, 223, 225, 226, 227, 228, 230, 231, 232, 234, 235, 236, 240,\n", + " 241, 242, 244, 245, 247, 248, 249, 250, 251, 252, 253, 254, 259,\n", + " 262, 264, 266, 267, 270, 277, 278, 279, 282, 283, 284, 289, 290,\n", + " 291, 292, 293, 295, 296, 299, 300, 303, 304, 307, 308, 310, 311,\n", + " 312, 314, 316, 320, 322, 323, 329, 330, 331, 334, 336, 342, 344,\n", + " 345, 346, 347, 348, 351, 353, 354, 356, 360, 362, 364, 367, 368,\n", + " 370, 371, 374, 375, 379, 380, 383, 384, 387, 388, 390, 391, 392,\n", + " 393, 394, 395, 396, 399, 401, 403, 407, 409, 411, 412, 413, 417,\n", + " 419, 420, 422, 425, 426, 428, 429, 430, 435, 439, 440, 442, 443,\n", + " 447, 450, 451, 453, 454, 456, 457, 459, 462, 464, 467, 469, 470,\n", + " 473, 474, 475, 476, 481, 482, 484, 485, 486, 487, 488, 491, 493,\n", + " 494, 496, 498, 499, 501, 502, 504, 505, 506, 508, 510, 513, 518,\n", + " 519, 522, 523, 525, 529, 534, 535, 539, 546, 547, 551, 552, 555,\n", + " 558, 561, 562, 563, 565, 566, 567, 570, 571, 572, 573, 574, 575,\n", + " 577, 581, 582, 583, 584, 586, 588, 589, 591, 592, 593, 594, 595,\n", + " 598, 599]),\n", + " 2: array([ 7, 8, 40, 47, 48, 50, 55, 80, 83, 85, 90, 91, 105,\n", + " 117, 121, 125, 126, 130, 131, 133, 138, 145, 150, 156, 159, 160,\n", + " 166, 178, 195, 201, 204, 205, 209, 213, 218, 229, 233, 237, 238,\n", + " 260, 263, 268, 280, 285, 288, 297, 301, 306, 319, 325, 332, 333,\n", + " 335, 337, 339, 349, 357, 366, 372, 373, 376, 377, 381, 382, 397,\n", + " 404, 406, 414, 423, 427, 438, 452, 458, 478, 500, 520, 524, 532,\n", + " 536, 548, 556, 569, 590, 596]),\n", + " 3: array([ 0, 4, 11, 12, 19, 20, 23, 24, 30, 34, 37, 38, 41,\n", + " 44, 60, 61, 62, 64, 66, 73, 76, 79, 84, 87, 94, 95,\n", + " 101, 103, 106, 112, 120, 128, 143, 144, 147, 164, 165, 170, 172,\n", + " 173, 182, 186, 190, 206, 215, 224, 255, 257, 258, 261, 265, 269,\n", + " 271, 273, 274, 275, 276, 281, 298, 302, 305, 317, 318, 324, 326,\n", + " 327, 328, 338, 352, 355, 365, 378, 385, 386, 389, 408, 424, 431,\n", + " 432, 434, 436, 437, 444, 446, 449, 455, 463, 471, 479, 480, 490,\n", + " 492, 495, 503, 514, 528, 530, 531, 533, 541, 542, 543, 545, 549,\n", + " 554, 557, 578, 579, 580, 597])},\n", + " 5: {0: array([ 51, 57, 71, 81, 98, 104, 110, 123, 152, 161, 171, 188, 197,\n", + " 203, 208, 239, 246, 256, 313, 321, 343, 359, 400, 402, 472, 509,\n", + " 512, 516, 538, 540, 550, 553, 559, 564, 568]),\n", + " 1: array([ 1, 2, 5, 6, 9, 10, 15, 16, 17, 21, 22, 25, 26,\n", + " 27, 28, 31, 32, 35, 36, 39, 42, 49, 52, 53, 56, 58,\n", + " 59, 65, 67, 69, 70, 72, 74, 75, 77, 78, 82, 86, 92,\n", + " 96, 97, 99, 100, 102, 107, 108, 109, 111, 113, 114, 115, 116,\n", + " 118, 119, 122, 124, 127, 129, 132, 134, 135, 136, 137, 139, 140,\n", + " 141, 142, 146, 149, 151, 153, 154, 155, 157, 158, 162, 167, 168,\n", + " 169, 175, 176, 177, 179, 180, 181, 183, 184, 185, 187, 189, 192,\n", + " 194, 196, 198, 199, 200, 207, 210, 211, 214, 216, 217, 219, 220,\n", + " 222, 223, 225, 226, 227, 228, 230, 231, 232, 234, 235, 236, 240,\n", + " 241, 242, 244, 245, 247, 248, 249, 250, 251, 252, 253, 254, 259,\n", + " 262, 264, 266, 267, 270, 277, 278, 279, 282, 283, 284, 289, 290,\n", + " 291, 292, 293, 295, 296, 299, 300, 303, 304, 307, 308, 310, 311,\n", + " 312, 314, 316, 320, 322, 323, 329, 330, 331, 334, 336, 342, 344,\n", + " 345, 346, 347, 348, 351, 353, 354, 356, 360, 362, 364, 367, 368,\n", + " 370, 371, 374, 375, 379, 380, 383, 384, 387, 388, 390, 391, 392,\n", + " 393, 394, 395, 396, 399, 401, 403, 407, 409, 411, 412, 413, 417,\n", + " 419, 420, 422, 425, 426, 428, 429, 430, 435, 439, 440, 442, 443,\n", + " 447, 450, 451, 453, 454, 456, 457, 459, 462, 464, 467, 469, 470,\n", + " 473, 474, 475, 476, 481, 482, 484, 485, 486, 487, 488, 491, 493,\n", + " 494, 496, 498, 499, 501, 502, 504, 505, 506, 508, 510, 513, 518,\n", + " 519, 522, 523, 525, 529, 534, 535, 539, 546, 547, 551, 552, 555,\n", + " 558, 561, 562, 563, 565, 566, 567, 570, 571, 572, 573, 574, 575,\n", + " 577, 581, 582, 583, 584, 586, 588, 589, 591, 592, 593, 594, 595,\n", + " 598, 599]),\n", + " 2: array([ 7, 8, 40, 47, 48, 50, 55, 80, 83, 85, 90, 91, 105,\n", + " 117, 121, 125, 126, 130, 131, 133, 138, 145, 150, 156, 159, 160,\n", + " 166, 178, 195, 201, 204, 205, 209, 213, 218, 229, 233, 237, 238,\n", + " 260, 263, 268, 280, 285, 288, 297, 301, 306, 319, 325, 332, 333,\n", + " 335, 337, 339, 349, 357, 366, 372, 373, 376, 377, 381, 382, 397,\n", + " 404, 406, 414, 423, 427, 438, 452, 458, 478, 500, 520, 524, 532,\n", + " 536, 548, 556, 569, 590, 596]),\n", + " 3: array([ 3, 13, 14, 18, 29, 33, 43, 45, 46, 54, 63, 68, 88,\n", + " 89, 93, 148, 163, 174, 191, 193, 202, 212, 221, 243, 272, 286,\n", + " 287, 294, 309, 315, 340, 341, 350, 358, 361, 363, 369, 398, 405,\n", + " 410, 415, 416, 418, 421, 433, 441, 445, 448, 460, 461, 465, 466,\n", + " 468, 477, 483, 489, 497, 507, 511, 515, 517, 521, 526, 527, 537,\n", + " 544, 560, 576, 585, 587]),\n", + " 4: array([ 0, 4, 11, 12, 19, 20, 23, 24, 30, 34, 37, 38, 41,\n", + " 44, 60, 61, 62, 64, 66, 73, 76, 79, 84, 87, 94, 95,\n", + " 101, 103, 106, 112, 120, 128, 143, 144, 147, 164, 165, 170, 172,\n", + " 173, 182, 186, 190, 206, 215, 224, 255, 257, 258, 261, 265, 269,\n", + " 271, 273, 274, 275, 276, 281, 298, 302, 305, 317, 318, 324, 326,\n", + " 327, 328, 338, 352, 355, 365, 378, 385, 386, 389, 408, 424, 431,\n", + " 432, 434, 436, 437, 444, 446, 449, 455, 463, 471, 479, 480, 490,\n", + " 492, 495, 503, 514, 528, 530, 531, 533, 541, 542, 543, 545, 549,\n", + " 554, 557, 578, 579, 580, 597])},\n", + " 6: {0: array([ 51, 57, 71, 81, 98, 104, 110, 123, 152, 161, 171, 188, 197,\n", + " 203, 208, 239, 246, 256, 313, 321, 343, 359, 400, 402, 472, 509,\n", + " 512, 516, 538, 540, 550, 553, 559, 564, 568]),\n", + " 1: array([ 1, 2, 5, 6, 9, 10, 15, 16, 17, 21, 22, 25, 26,\n", + " 27, 28, 31, 32, 35, 36, 39, 42, 49, 52, 53, 56, 58,\n", + " 59, 65, 67, 69, 70, 72, 74, 75, 77, 78, 82, 86, 92,\n", + " 96, 97, 99, 100, 102, 107, 108, 109, 111, 113, 114, 115, 116,\n", + " 118, 119, 122, 124, 127, 129, 132, 134, 135, 136, 137, 139, 140,\n", + " 141, 142, 146, 149, 151, 153, 154, 155, 157, 158, 162, 167, 168,\n", + " 169, 175, 176, 177, 179, 180, 181, 183, 184, 185, 187, 189, 192,\n", + " 194, 196, 198, 199, 200, 207, 210, 211, 214, 216, 217, 219, 220,\n", + " 222, 223, 225, 226, 227, 228, 230, 231, 232, 234, 235, 236, 240,\n", + " 241, 242, 244, 245, 247, 248, 249, 250, 251, 252, 253, 254, 259,\n", + " 262, 264, 266, 267, 270, 277, 278, 279, 282, 283, 284, 289, 290,\n", + " 291, 292, 293, 295, 296, 299, 300, 303, 304, 307, 308, 310, 311,\n", + " 312, 314, 316, 320, 322, 323, 329, 330, 331, 334, 336, 342, 344,\n", + " 345, 346, 347, 348, 351, 353, 354, 356, 360, 362, 364, 367, 368,\n", + " 370, 371, 374, 375, 379, 380, 383, 384, 387, 388, 390, 391, 392,\n", + " 393, 394, 395, 396, 399, 401, 403, 407, 409, 411, 412, 413, 417,\n", + " 419, 420, 422, 425, 426, 428, 429, 430, 435, 439, 440, 442, 443,\n", + " 447, 450, 451, 453, 454, 456, 457, 459, 462, 464, 467, 469, 470,\n", + " 473, 474, 475, 476, 481, 482, 484, 485, 486, 487, 488, 491, 493,\n", + " 494, 496, 498, 499, 501, 502, 504, 505, 506, 508, 510, 513, 518,\n", + " 519, 522, 523, 525, 529, 534, 535, 539, 546, 547, 551, 552, 555,\n", + " 558, 561, 562, 563, 565, 566, 567, 570, 571, 572, 573, 574, 575,\n", + " 577, 581, 582, 583, 584, 586, 588, 589, 591, 592, 593, 594, 595,\n", + " 598, 599]),\n", + " 2: array([ 7, 8, 40, 47, 48, 50, 55, 80, 83, 85, 90, 91, 105,\n", + " 117, 121, 125, 126, 133, 138, 145, 150, 156, 159, 160, 166, 178,\n", + " 195, 201, 204, 205, 209, 213, 218, 229, 233, 237, 238, 260, 263,\n", + " 268, 280, 288, 297, 301, 306, 319, 332, 333, 335, 337, 339, 349,\n", + " 357, 366, 372, 373, 376, 377, 381, 382, 397, 404, 406, 414, 423,\n", + " 427, 438, 452, 458, 478, 500, 520, 524, 536, 556, 569, 590, 596]),\n", + " 3: array([ 3, 13, 14, 18, 29, 33, 43, 45, 46, 54, 63, 68, 88,\n", + " 89, 93, 148, 163, 174, 191, 193, 202, 212, 221, 243, 272, 286,\n", + " 287, 294, 309, 315, 340, 341, 350, 358, 361, 363, 369, 398, 405,\n", + " 410, 415, 416, 418, 421, 433, 441, 445, 448, 460, 461, 465, 466,\n", + " 468, 477, 483, 489, 497, 507, 511, 515, 517, 521, 526, 527, 537,\n", + " 544, 560, 576, 585, 587]),\n", + " 4: array([ 0, 4, 11, 19, 20, 30, 34, 37, 61, 62, 64, 66, 73,\n", + " 76, 79, 84, 87, 95, 101, 103, 106, 112, 120, 128, 147, 164,\n", + " 172, 173, 182, 186, 190, 215, 224, 255, 257, 261, 265, 269, 271,\n", + " 273, 274, 275, 276, 318, 324, 327, 328, 338, 352, 365, 378, 385,\n", + " 389, 408, 424, 431, 432, 434, 436, 437, 446, 449, 455, 471, 480,\n", + " 503, 528, 542, 543, 545, 549, 578, 579, 597]),\n", + " 5: array([ 12, 23, 24, 38, 41, 44, 60, 94, 130, 131, 143, 144, 165,\n", + " 170, 206, 258, 281, 285, 298, 302, 305, 317, 325, 326, 355, 386,\n", + " 444, 463, 479, 490, 492, 495, 514, 530, 531, 532, 533, 541, 548,\n", + " 554, 557, 580])},\n", + " 7: {0: array([ 51, 57, 71, 81, 98, 104, 110, 123, 152, 161, 171, 188, 197,\n", + " 203, 208, 239, 246, 256, 313, 321, 343, 359, 400, 402, 472, 512,\n", + " 516, 538, 540, 550, 553, 559, 564, 568]),\n", + " 1: array([ 1, 2, 5, 6, 9, 10, 15, 16, 17, 21, 22, 25, 26,\n", + " 27, 28, 31, 32, 35, 36, 39, 42, 49, 52, 53, 56, 58,\n", + " 59, 65, 67, 69, 70, 72, 74, 75, 77, 78, 82, 86, 92,\n", + " 96, 97, 99, 100, 102, 107, 108, 109, 111, 113, 114, 115, 116,\n", + " 118, 119, 122, 124, 127, 129, 132, 134, 135, 136, 137, 139, 140,\n", + " 141, 142, 146, 149, 151, 153, 154, 155, 157, 158, 162, 167, 168,\n", + " 169, 175, 176, 177, 179, 180, 181, 183, 184, 185, 187, 189, 192,\n", + " 194, 196, 198, 199, 200, 207, 210, 211, 214, 216, 217, 219, 220,\n", + " 222, 223, 225, 226, 227, 228, 230, 231, 232, 234, 235, 236, 240,\n", + " 241, 242, 244, 245, 247, 248, 249, 250, 251, 252, 253, 254, 259,\n", + " 262, 264, 266, 267, 270, 277, 278, 279, 282, 283, 284, 289, 290,\n", + " 291, 292, 293, 295, 296, 299, 300, 303, 304, 307, 308, 310, 311,\n", + " 312, 314, 316, 320, 322, 323, 329, 330, 331, 334, 336, 342, 344,\n", + " 345, 346, 347, 348, 351, 353, 354, 356, 360, 362, 364, 367, 368,\n", + " 370, 371, 374, 375, 379, 380, 383, 384, 387, 388, 390, 391, 392,\n", + " 393, 394, 395, 396, 399, 401, 403, 407, 409, 411, 412, 413, 417,\n", + " 419, 420, 422, 425, 426, 428, 429, 430, 435, 439, 440, 442, 443,\n", + " 447, 450, 451, 453, 454, 456, 457, 459, 462, 464, 467, 469, 470,\n", + " 473, 474, 475, 476, 481, 482, 484, 485, 486, 487, 488, 491, 493,\n", + " 494, 496, 498, 499, 501, 502, 504, 505, 506, 508, 510, 513, 518,\n", + " 519, 522, 523, 525, 529, 534, 535, 539, 546, 547, 551, 552, 555,\n", + " 558, 561, 562, 563, 565, 566, 567, 570, 571, 572, 573, 574, 575,\n", + " 577, 581, 582, 583, 584, 586, 588, 589, 591, 592, 593, 594, 595,\n", + " 598, 599]),\n", + " 2: array([ 7, 8, 40, 47, 48, 50, 55, 80, 83, 85, 90, 91, 105,\n", + " 117, 121, 125, 126, 133, 138, 145, 150, 156, 159, 160, 166, 178,\n", + " 195, 201, 204, 205, 209, 213, 218, 229, 233, 237, 238, 260, 263,\n", + " 268, 280, 288, 297, 301, 306, 319, 332, 333, 335, 337, 339, 349,\n", + " 357, 366, 372, 373, 376, 377, 381, 382, 397, 404, 406, 414, 423,\n", + " 427, 438, 452, 458, 478, 500, 520, 524, 536, 556, 569, 590, 596]),\n", + " 3: array([ 29, 45, 68, 477, 483, 509, 515]),\n", + " 4: array([ 3, 13, 14, 18, 33, 43, 46, 54, 63, 88, 89, 93, 148,\n", + " 163, 174, 191, 193, 202, 212, 221, 243, 272, 286, 287, 294, 309,\n", + " 315, 340, 341, 350, 358, 361, 363, 369, 398, 405, 410, 415, 416,\n", + " 418, 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497,\n", + " 507, 511, 517, 521, 526, 527, 537, 544, 560, 576, 585, 587]),\n", + " 5: array([ 0, 4, 11, 19, 20, 30, 34, 37, 61, 62, 64, 66, 73,\n", + " 76, 79, 84, 87, 95, 101, 103, 106, 112, 120, 128, 147, 164,\n", + " 172, 173, 182, 186, 190, 215, 224, 255, 257, 261, 265, 269, 271,\n", + " 273, 274, 275, 276, 318, 324, 327, 328, 338, 352, 365, 378, 385,\n", + " 389, 408, 424, 431, 432, 434, 436, 437, 446, 449, 455, 471, 480,\n", + " 503, 528, 542, 543, 545, 549, 578, 579, 597]),\n", + " 6: array([ 12, 23, 24, 38, 41, 44, 60, 94, 130, 131, 143, 144, 165,\n", + " 170, 206, 258, 281, 285, 298, 302, 305, 317, 325, 326, 355, 386,\n", + " 444, 463, 479, 490, 492, 495, 514, 530, 531, 532, 533, 541, 548,\n", + " 554, 557, 580])},\n", + " 8: {0: array([ 51, 57, 71, 81, 98, 104, 110, 123, 152, 161, 171, 188, 197,\n", + " 203, 208, 239, 246, 256, 313, 321, 343, 359, 400, 402, 472, 512,\n", + " 516, 538, 540, 550, 553, 559, 564, 568]),\n", + " 1: array([ 1, 2, 5, 6, 9, 10, 15, 16, 17, 21, 22, 25, 26,\n", + " 27, 28, 31, 32, 35, 36, 39, 42, 49, 52, 53, 56, 58,\n", + " 59, 65, 67, 69, 70, 72, 74, 75, 77, 78, 82, 86, 92,\n", + " 96, 97, 99, 100, 102, 107, 108, 109, 111, 113, 114, 115, 116,\n", + " 118, 119, 122, 124, 127, 129, 132, 134, 135, 136, 137, 139, 140,\n", + " 141, 142, 146, 149, 151, 153, 154, 155, 157, 158, 162, 167, 168,\n", + " 169, 175, 176, 177, 179, 180, 181, 183, 184, 185, 187, 189, 192,\n", + " 194, 196, 198, 199, 200, 207, 210, 211, 214, 216, 217, 219, 220,\n", + " 222, 223, 225, 226, 227, 228, 230, 231, 232, 234, 235, 236, 240,\n", + " 241, 242, 244, 245, 247, 248, 249, 250, 251, 252, 253, 254, 259,\n", + " 262, 264, 266, 267, 270, 277, 278, 279, 282, 283, 284, 289, 290,\n", + " 291, 292, 293, 295, 296, 299, 300, 303, 304, 307, 308, 310, 311,\n", + " 312, 314, 316, 320, 322, 323, 329, 330, 331, 334, 336, 342, 344,\n", + " 345, 346, 347, 348, 351, 353, 354, 356, 360, 362, 364, 367, 368,\n", + " 370, 371, 374, 375, 379, 380, 383, 384, 387, 388, 390, 391, 392,\n", + " 393, 394, 395, 396, 399, 401, 403, 407, 409, 411, 412, 413, 417,\n", + " 419, 420, 422, 425, 426, 428, 429, 430, 435, 439, 440, 442, 443,\n", + " 447, 450, 451, 453, 454, 456, 457, 459, 462, 464, 467, 469, 470,\n", + " 473, 474, 475, 476, 481, 482, 484, 485, 486, 487, 488, 491, 493,\n", + " 494, 496, 498, 499, 501, 502, 504, 505, 506, 508, 510, 513, 518,\n", + " 519, 522, 523, 525, 529, 534, 535, 539, 546, 547, 551, 552, 555,\n", + " 558, 561, 562, 563, 565, 566, 567, 570, 571, 572, 573, 574, 575,\n", + " 577, 581, 582, 583, 584, 586, 588, 589, 591, 592, 593, 594, 595,\n", + " 598, 599]),\n", + " 2: array([ 7, 8, 40, 48, 50, 55, 80, 83, 85, 90, 91, 105, 117,\n", + " 121, 125, 126, 133, 138, 145, 150, 156, 159, 160, 166, 178, 195,\n", + " 204, 205, 209, 213, 218, 229, 233, 238, 260, 263, 268, 280, 288,\n", + " 297, 301, 306, 319, 333, 335, 337, 339, 349, 357, 366, 372, 373,\n", + " 377, 382, 397, 404, 406, 414, 423, 427, 438, 452, 458, 478, 500,\n", + " 520, 524, 536, 556, 569, 590, 596]),\n", + " 3: array([ 29, 45, 68, 477, 483, 509, 515]),\n", + " 4: array([ 3, 13, 14, 18, 33, 43, 46, 54, 63, 88, 89, 93, 148,\n", + " 163, 174, 191, 193, 202, 212, 221, 243, 272, 286, 287, 294, 309,\n", + " 315, 340, 341, 350, 358, 361, 363, 369, 398, 405, 410, 415, 416,\n", + " 418, 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497,\n", + " 507, 511, 517, 521, 526, 527, 537, 544, 560, 576, 585, 587]),\n", + " 5: array([ 0, 4, 11, 19, 20, 30, 34, 37, 61, 62, 64, 66, 73,\n", + " 76, 79, 84, 87, 95, 101, 103, 106, 112, 120, 128, 147, 164,\n", + " 172, 173, 182, 186, 190, 215, 224, 255, 257, 261, 265, 269, 271,\n", + " 273, 274, 275, 276, 318, 324, 327, 328, 338, 352, 365, 378, 385,\n", + " 389, 408, 424, 431, 432, 434, 436, 437, 446, 449, 455, 471, 480,\n", + " 503, 528, 542, 543, 545, 549, 578, 579, 597]),\n", + " 6: array([ 12, 23, 24, 38, 41, 44, 60, 94, 130, 131, 143, 144, 165,\n", + " 170, 206, 258, 281, 285, 298, 302, 305, 317, 325, 326, 355, 386,\n", + " 444, 463, 479, 490, 492, 495, 514, 530, 531, 532, 541, 548, 554,\n", + " 557, 580]),\n", + " 7: array([ 47, 201, 237, 332, 376, 381, 533])},\n", + " 9: {0: array([ 51, 57, 71, 81, 98, 104, 110, 123, 152, 161, 171, 188, 197,\n", + " 203, 208, 239, 246, 256, 313, 321, 343, 359, 400, 402, 472, 512,\n", + " 516, 538, 540, 550, 553, 559, 564, 568]),\n", + " 1: array([ 2, 5, 9, 10, 25, 26, 31, 32, 35, 52, 59, 65, 67,\n", + " 69, 70, 74, 75, 82, 86, 97, 99, 102, 108, 111, 114, 127,\n", + " 141, 146, 149, 151, 153, 154, 155, 158, 162, 169, 175, 179, 181,\n", + " 194, 199, 200, 207, 210, 211, 216, 222, 223, 225, 227, 235, 236,\n", + " 245, 250, 251, 253, 254, 262, 264, 270, 278, 283, 291, 292, 295,\n", + " 296, 299, 303, 304, 307, 310, 314, 316, 320, 323, 329, 330, 334,\n", + " 347, 348, 351, 360, 364, 367, 371, 374, 375, 380, 383, 384, 387,\n", + " 388, 392, 395, 401, 411, 413, 417, 422, 425, 426, 428, 429, 430,\n", + " 439, 442, 443, 447, 450, 454, 456, 457, 459, 464, 470, 475, 476,\n", + " 481, 487, 491, 493, 494, 506, 513, 518, 519, 523, 525, 534, 546,\n", + " 547, 558, 562, 566, 572, 573, 577, 582, 583, 586, 588, 595, 599]),\n", + " 2: array([ 7, 8, 40, 48, 50, 55, 80, 83, 85, 90, 91, 105, 117,\n", + " 121, 125, 126, 133, 138, 145, 150, 156, 159, 160, 166, 178, 195,\n", + " 204, 205, 209, 213, 218, 229, 233, 238, 260, 263, 268, 280, 288,\n", + " 297, 301, 306, 319, 333, 335, 337, 339, 349, 357, 366, 372, 373,\n", + " 377, 382, 397, 404, 406, 414, 423, 427, 438, 452, 458, 478, 500,\n", + " 520, 524, 536, 556, 569, 590, 596]),\n", + " 3: array([ 1, 6, 15, 16, 17, 21, 22, 27, 28, 36, 39, 42, 49,\n", + " 53, 56, 58, 72, 77, 78, 92, 96, 100, 107, 109, 113, 115,\n", + " 116, 118, 119, 122, 124, 129, 132, 134, 135, 136, 137, 139, 140,\n", + " 142, 157, 167, 168, 176, 177, 180, 183, 184, 185, 187, 189, 192,\n", + " 196, 198, 214, 217, 219, 220, 226, 228, 230, 231, 232, 234, 240,\n", + " 241, 242, 244, 247, 248, 249, 252, 259, 266, 267, 277, 279, 282,\n", + " 284, 289, 290, 293, 300, 308, 311, 312, 322, 331, 336, 342, 344,\n", + " 345, 346, 353, 354, 356, 362, 368, 370, 379, 390, 391, 393, 394,\n", + " 396, 399, 403, 407, 409, 412, 419, 420, 435, 440, 451, 453, 462,\n", + " 467, 469, 473, 474, 482, 484, 485, 486, 488, 496, 498, 499, 501,\n", + " 502, 504, 505, 508, 510, 522, 529, 535, 539, 551, 552, 555, 561,\n", + " 563, 565, 567, 570, 571, 574, 575, 581, 584, 589, 591, 592, 593,\n", + " 594, 598]),\n", + " 4: array([ 29, 45, 68, 477, 483, 509, 515]),\n", + " 5: array([ 3, 13, 14, 18, 33, 43, 46, 54, 63, 88, 89, 93, 148,\n", + " 163, 174, 191, 193, 202, 212, 221, 243, 272, 286, 287, 294, 309,\n", + " 315, 340, 341, 350, 358, 361, 363, 369, 398, 405, 410, 415, 416,\n", + " 418, 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497,\n", + " 507, 511, 517, 521, 526, 527, 537, 544, 560, 576, 585, 587]),\n", + " 6: array([ 0, 4, 11, 19, 20, 30, 34, 37, 61, 62, 64, 66, 73,\n", + " 76, 79, 84, 87, 95, 101, 103, 106, 112, 120, 128, 147, 164,\n", + " 172, 173, 182, 186, 190, 215, 224, 255, 257, 261, 265, 269, 271,\n", + " 273, 274, 275, 276, 318, 324, 327, 328, 338, 352, 365, 378, 385,\n", + " 389, 408, 424, 431, 432, 434, 436, 437, 446, 449, 455, 471, 480,\n", + " 503, 528, 542, 543, 545, 549, 578, 579, 597]),\n", + " 7: array([ 12, 23, 24, 38, 41, 44, 60, 94, 130, 131, 143, 144, 165,\n", + " 170, 206, 258, 281, 285, 298, 302, 305, 317, 325, 326, 355, 386,\n", + " 444, 463, 479, 490, 492, 495, 514, 530, 531, 532, 541, 548, 554,\n", + " 557, 580]),\n", + " 8: array([ 47, 201, 237, 332, 376, 381, 533])},\n", + " 10: {0: array([ 81, 110, 152, 161, 188, 197, 313, 321, 343, 359, 402, 516, 553,\n", + " 568]),\n", + " 1: array([ 2, 5, 9, 10, 25, 26, 31, 32, 35, 52, 59, 65, 67,\n", + " 69, 70, 74, 75, 82, 86, 97, 99, 102, 108, 111, 114, 127,\n", + " 141, 146, 149, 151, 153, 154, 155, 158, 162, 169, 175, 179, 181,\n", + " 194, 199, 200, 207, 210, 211, 216, 222, 223, 225, 227, 235, 236,\n", + " 245, 250, 251, 253, 254, 262, 264, 270, 278, 283, 291, 292, 295,\n", + " 296, 299, 303, 304, 307, 310, 314, 316, 320, 323, 329, 330, 334,\n", + " 347, 348, 351, 360, 364, 367, 371, 374, 375, 380, 383, 384, 387,\n", + " 388, 392, 395, 401, 411, 413, 417, 422, 425, 426, 428, 429, 430,\n", + " 439, 442, 443, 447, 450, 454, 456, 457, 459, 464, 470, 475, 476,\n", + " 481, 487, 491, 493, 494, 506, 513, 518, 519, 523, 525, 534, 546,\n", + " 547, 558, 562, 566, 572, 573, 577, 582, 583, 586, 588, 595, 599]),\n", + " 2: array([ 51, 57, 71, 98, 104, 123, 171, 203, 208, 239, 246, 256, 400,\n", + " 472, 512, 538, 540, 550, 559, 564]),\n", + " 3: array([ 7, 8, 40, 48, 50, 55, 80, 83, 85, 90, 91, 105, 117,\n", + " 121, 125, 126, 133, 138, 145, 150, 156, 159, 160, 166, 178, 195,\n", + " 204, 205, 209, 213, 218, 229, 233, 238, 260, 263, 268, 280, 288,\n", + " 297, 301, 306, 319, 333, 335, 337, 339, 349, 357, 366, 372, 373,\n", + " 377, 382, 397, 404, 406, 414, 423, 427, 438, 452, 458, 478, 500,\n", + " 520, 524, 536, 556, 569, 590, 596]),\n", + " 4: array([ 1, 6, 15, 16, 17, 21, 22, 27, 28, 36, 39, 42, 49,\n", + " 53, 56, 58, 72, 77, 78, 92, 96, 100, 107, 109, 113, 115,\n", + " 116, 118, 119, 122, 124, 129, 132, 134, 135, 136, 137, 139, 140,\n", + " 142, 157, 167, 168, 176, 177, 180, 183, 184, 185, 187, 189, 192,\n", + " 196, 198, 214, 217, 219, 220, 226, 228, 230, 231, 232, 234, 240,\n", + " 241, 242, 244, 247, 248, 249, 252, 259, 266, 267, 277, 279, 282,\n", + " 284, 289, 290, 293, 300, 308, 311, 312, 322, 331, 336, 342, 344,\n", + " 345, 346, 353, 354, 356, 362, 368, 370, 379, 390, 391, 393, 394,\n", + " 396, 399, 403, 407, 409, 412, 419, 420, 435, 440, 451, 453, 462,\n", + " 467, 469, 473, 474, 482, 484, 485, 486, 488, 496, 498, 499, 501,\n", + " 502, 504, 505, 508, 510, 522, 529, 535, 539, 551, 552, 555, 561,\n", + " 563, 565, 567, 570, 571, 574, 575, 581, 584, 589, 591, 592, 593,\n", + " 594, 598]),\n", + " 5: array([ 29, 45, 68, 477, 483, 509, 515]),\n", + " 6: array([ 3, 13, 14, 18, 33, 43, 46, 54, 63, 88, 89, 93, 148,\n", + " 163, 174, 191, 193, 202, 212, 221, 243, 272, 286, 287, 294, 309,\n", + " 315, 340, 341, 350, 358, 361, 363, 369, 398, 405, 410, 415, 416,\n", + " 418, 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497,\n", + " 507, 511, 517, 521, 526, 527, 537, 544, 560, 576, 585, 587]),\n", + " 7: array([ 0, 4, 11, 19, 20, 30, 34, 37, 61, 62, 64, 66, 73,\n", + " 76, 79, 84, 87, 95, 101, 103, 106, 112, 120, 128, 147, 164,\n", + " 172, 173, 182, 186, 190, 215, 224, 255, 257, 261, 265, 269, 271,\n", + " 273, 274, 275, 276, 318, 324, 327, 328, 338, 352, 365, 378, 385,\n", + " 389, 408, 424, 431, 432, 434, 436, 437, 446, 449, 455, 471, 480,\n", + " 503, 528, 542, 543, 545, 549, 578, 579, 597]),\n", + " 8: array([ 12, 23, 24, 38, 41, 44, 60, 94, 130, 131, 143, 144, 165,\n", + " 170, 206, 258, 281, 285, 298, 302, 305, 317, 325, 326, 355, 386,\n", + " 444, 463, 479, 490, 492, 495, 514, 530, 531, 532, 541, 548, 554,\n", + " 557, 580]),\n", + " 9: array([ 47, 201, 237, 332, 376, 381, 533])}},\n", + " 'l2_ranking': {2: {0: array([ 3, 13, 14, 18, 33, 43, 46, 51, 54, 57, 63, 71, 81,\n", + " 88, 89, 93, 98, 104, 110, 123, 148, 152, 161, 163, 171, 174,\n", + " 188, 191, 193, 197, 202, 203, 208, 212, 221, 239, 243, 246, 256,\n", + " 272, 286, 287, 294, 309, 313, 315, 321, 340, 341, 343, 358, 359,\n", + " 361, 363, 369, 398, 400, 402, 405, 410, 415, 416, 418, 421, 433,\n", + " 441, 445, 448, 460, 461, 465, 466, 468, 472, 489, 497, 507, 509,\n", + " 511, 512, 516, 517, 521, 526, 527, 537, 538, 540, 544, 550, 553,\n", + " 559, 560, 564, 568, 576, 585, 587]),\n", + " 1: array([ 0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15,\n", + " 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,\n", + " 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 42, 44,\n", + " 45, 47, 48, 49, 50, 52, 53, 55, 56, 58, 59, 60, 61,\n", + " 62, 64, 65, 66, 67, 68, 69, 70, 72, 73, 74, 75, 76,\n", + " 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 90, 91, 92,\n", + " 94, 95, 96, 97, 99, 100, 101, 102, 103, 105, 106, 107, 108,\n", + " 109, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122,\n", + " 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136,\n", + " 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 149, 150,\n", + " 151, 153, 154, 155, 156, 157, 158, 159, 160, 162, 164, 165, 166,\n", + " 167, 168, 169, 170, 172, 173, 175, 176, 177, 178, 179, 180, 181,\n", + " 182, 183, 184, 185, 186, 187, 189, 190, 192, 194, 195, 196, 198,\n", + " 199, 200, 201, 204, 205, 206, 207, 209, 210, 211, 213, 214, 215,\n", + " 216, 217, 218, 219, 220, 222, 223, 224, 225, 226, 227, 228, 229,\n", + " 230, 231, 232, 233, 234, 235, 236, 237, 238, 240, 241, 242, 244,\n", + " 245, 247, 248, 249, 250, 251, 252, 253, 254, 255, 257, 258, 259,\n", + " 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 273,\n", + " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 288,\n", + " 289, 290, 291, 292, 293, 295, 296, 297, 298, 299, 300, 301, 302,\n", + " 303, 304, 305, 306, 307, 308, 310, 311, 312, 314, 316, 317, 318,\n", + " 319, 320, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332,\n", + " 333, 334, 335, 336, 337, 338, 339, 342, 344, 345, 346, 347, 348,\n", + " 349, 350, 351, 352, 353, 354, 355, 356, 357, 360, 362, 364, 365,\n", + " 366, 367, 368, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379,\n", + " 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392,\n", + " 393, 394, 395, 396, 397, 399, 401, 403, 404, 406, 407, 408, 409,\n", + " 411, 412, 413, 414, 417, 419, 420, 422, 423, 424, 425, 426, 427,\n", + " 428, 429, 430, 431, 432, 434, 435, 436, 437, 438, 439, 440, 442,\n", + " 443, 444, 446, 447, 449, 450, 451, 452, 453, 454, 455, 456, 457,\n", + " 458, 459, 462, 463, 464, 467, 469, 470, 471, 473, 474, 475, 476,\n", + " 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 490,\n", + " 491, 492, 493, 494, 495, 496, 498, 499, 500, 501, 502, 503, 504,\n", + " 505, 506, 508, 510, 513, 514, 515, 518, 519, 520, 522, 523, 524,\n", + " 525, 528, 529, 530, 531, 532, 533, 534, 535, 536, 539, 541, 542,\n", + " 543, 545, 546, 547, 548, 549, 551, 552, 554, 555, 556, 557, 558,\n", + " 561, 562, 563, 565, 566, 567, 569, 570, 571, 572, 573, 574, 575,\n", + " 577, 578, 579, 580, 581, 582, 583, 584, 586, 588, 589, 590, 591,\n", + " 592, 593, 594, 595, 596, 597, 598, 599])},\n", + " 3: {0: array([ 57, 71, 81, 98, 123, 152, 161, 171, 188, 197, 203, 208, 239,\n", + " 246, 256, 309, 313, 321, 343, 400, 402, 418, 472, 512, 516, 538,\n", + " 550, 553, 559, 568]),\n", + " 1: array([ 0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15,\n", + " 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,\n", + " 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 42, 44,\n", + " 45, 47, 48, 49, 50, 52, 53, 55, 56, 58, 59, 60, 61,\n", + " 62, 64, 65, 66, 67, 68, 69, 70, 72, 73, 74, 75, 76,\n", + " 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 90, 91, 92,\n", + " 94, 95, 96, 97, 99, 100, 101, 102, 103, 105, 106, 107, 108,\n", + " 109, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122,\n", + " 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136,\n", + " 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 149, 150,\n", + " 151, 153, 154, 155, 156, 157, 158, 159, 160, 162, 164, 165, 166,\n", + " 167, 168, 169, 170, 172, 173, 175, 176, 177, 178, 179, 180, 181,\n", + " 182, 183, 184, 185, 186, 187, 189, 190, 192, 194, 195, 196, 198,\n", + " 199, 200, 201, 204, 205, 206, 207, 209, 210, 211, 213, 214, 215,\n", + " 216, 217, 218, 219, 220, 222, 223, 224, 225, 226, 227, 228, 229,\n", + " 230, 231, 232, 233, 234, 235, 236, 238, 240, 241, 242, 244, 245,\n", + " 247, 248, 249, 250, 251, 252, 253, 254, 255, 257, 258, 259, 260,\n", + " 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 273, 274,\n", + " 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 288, 289,\n", + " 290, 291, 292, 293, 295, 296, 297, 298, 299, 300, 301, 302, 303,\n", + " 304, 305, 306, 307, 308, 310, 311, 312, 314, 316, 317, 318, 319,\n", + " 320, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333,\n", + " 334, 335, 336, 337, 338, 339, 342, 344, 345, 346, 347, 348, 349,\n", + " 351, 352, 353, 354, 355, 356, 357, 360, 362, 364, 365, 366, 367,\n", + " 368, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381,\n", + " 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394,\n", + " 395, 396, 397, 399, 401, 403, 404, 406, 407, 408, 409, 411, 412,\n", + " 413, 414, 417, 419, 420, 422, 423, 424, 425, 426, 427, 428, 429,\n", + " 430, 431, 432, 434, 435, 436, 437, 438, 439, 440, 442, 443, 444,\n", + " 446, 447, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459,\n", + " 462, 463, 464, 467, 469, 470, 471, 473, 474, 475, 476, 477, 478,\n", + " 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 490, 491, 492,\n", + " 493, 494, 495, 496, 498, 499, 500, 501, 502, 503, 504, 505, 506,\n", + " 508, 510, 513, 514, 515, 518, 519, 520, 522, 523, 524, 525, 528,\n", + " 529, 530, 531, 532, 533, 534, 535, 536, 539, 541, 542, 543, 545,\n", + " 546, 547, 548, 549, 551, 552, 554, 555, 556, 557, 558, 561, 562,\n", + " 563, 565, 566, 567, 569, 570, 571, 572, 573, 574, 575, 577, 578,\n", + " 579, 580, 581, 582, 583, 584, 586, 588, 589, 590, 591, 592, 593,\n", + " 594, 595, 596, 597, 598, 599]),\n", + " 2: array([ 3, 13, 14, 18, 33, 43, 46, 51, 54, 63, 88, 89, 93,\n", + " 104, 110, 148, 163, 174, 191, 193, 202, 212, 221, 237, 243, 272,\n", + " 286, 287, 294, 315, 340, 341, 350, 358, 359, 361, 363, 369, 398,\n", + " 405, 410, 415, 416, 421, 433, 441, 445, 448, 460, 461, 465, 466,\n", + " 468, 489, 497, 507, 509, 511, 517, 521, 526, 527, 537, 540, 544,\n", + " 560, 564, 576, 585, 587])},\n", + " 4: {0: array([ 57, 71, 81, 98, 123, 152, 161, 171, 188, 197, 203, 208, 239,\n", + " 246, 256, 309, 313, 321, 343, 400, 402, 418, 472, 512, 516, 538,\n", + " 550, 553, 559, 568]),\n", + " 1: array([ 0, 1, 2, 4, 5, 6, 9, 10, 11, 15, 16, 17, 19,\n", + " 20, 21, 22, 25, 26, 27, 28, 30, 31, 32, 34, 35, 36,\n", + " 37, 39, 45, 49, 52, 53, 56, 58, 59, 61, 62, 65, 66,\n", + " 67, 69, 70, 72, 73, 74, 75, 76, 77, 78, 79, 82, 84,\n", + " 86, 87, 92, 95, 96, 97, 99, 100, 101, 102, 103, 106, 107,\n", + " 108, 109, 111, 112, 113, 114, 115, 116, 118, 119, 120, 122, 124,\n", + " 127, 129, 132, 134, 135, 136, 137, 139, 140, 141, 142, 146, 147,\n", + " 149, 151, 153, 154, 155, 157, 158, 162, 164, 167, 168, 169, 172,\n", + " 175, 176, 177, 179, 180, 181, 182, 183, 184, 185, 187, 189, 190,\n", + " 192, 194, 196, 198, 199, 200, 207, 210, 211, 214, 215, 216, 217,\n", + " 219, 220, 222, 223, 224, 225, 226, 227, 228, 230, 231, 232, 234,\n", + " 235, 236, 240, 241, 242, 244, 245, 247, 248, 249, 250, 251, 252,\n", + " 253, 254, 255, 259, 262, 264, 266, 267, 269, 270, 273, 275, 276,\n", + " 277, 278, 279, 282, 283, 284, 289, 290, 291, 292, 293, 295, 296,\n", + " 299, 300, 303, 304, 307, 308, 310, 311, 312, 314, 316, 318, 320,\n", + " 322, 323, 324, 327, 328, 329, 330, 331, 334, 336, 338, 342, 344,\n", + " 345, 346, 347, 348, 351, 352, 353, 354, 356, 360, 362, 364, 365,\n", + " 367, 368, 370, 371, 374, 375, 379, 380, 383, 384, 385, 387, 388,\n", + " 389, 390, 391, 392, 393, 394, 395, 396, 399, 401, 403, 407, 409,\n", + " 411, 412, 413, 417, 419, 420, 422, 424, 425, 426, 428, 429, 430,\n", + " 432, 434, 435, 436, 437, 439, 440, 442, 443, 446, 447, 449, 450,\n", + " 451, 453, 454, 455, 456, 457, 459, 462, 464, 467, 469, 470, 471,\n", + " 473, 474, 475, 476, 480, 481, 482, 484, 485, 486, 487, 488, 491,\n", + " 493, 494, 496, 498, 499, 501, 502, 503, 504, 505, 506, 508, 510,\n", + " 513, 518, 519, 522, 523, 525, 528, 529, 534, 535, 539, 542, 543,\n", + " 545, 546, 547, 549, 552, 555, 558, 561, 562, 563, 565, 566, 567,\n", + " 570, 571, 572, 573, 574, 575, 577, 578, 581, 582, 583, 584, 586,\n", + " 588, 589, 591, 592, 593, 594, 595, 597, 598, 599]),\n", + " 2: array([ 7, 8, 12, 23, 24, 29, 38, 40, 41, 42, 44, 47, 48,\n", + " 50, 55, 60, 64, 68, 80, 83, 85, 90, 91, 94, 105, 110,\n", + " 117, 121, 125, 126, 128, 130, 131, 133, 138, 143, 144, 145, 150,\n", + " 156, 159, 160, 165, 166, 170, 173, 178, 186, 195, 201, 204, 205,\n", + " 206, 209, 213, 218, 229, 233, 237, 238, 257, 258, 260, 261, 263,\n", + " 265, 268, 271, 274, 280, 281, 285, 288, 297, 298, 301, 302, 305,\n", + " 306, 317, 319, 325, 326, 332, 333, 335, 337, 339, 349, 350, 355,\n", + " 357, 366, 372, 373, 376, 377, 378, 381, 382, 386, 397, 404, 406,\n", + " 408, 414, 423, 427, 431, 438, 444, 452, 458, 463, 477, 478, 479,\n", + " 483, 490, 492, 495, 500, 514, 515, 520, 524, 530, 531, 532, 533,\n", + " 536, 541, 548, 551, 554, 556, 557, 569, 579, 580, 590, 596]),\n", + " 3: array([ 3, 13, 14, 18, 33, 43, 46, 51, 54, 63, 88, 89, 93,\n", + " 104, 148, 163, 174, 191, 193, 202, 212, 221, 243, 272, 286, 287,\n", + " 294, 315, 340, 341, 358, 359, 361, 363, 369, 398, 405, 410, 415,\n", + " 416, 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497,\n", + " 507, 509, 511, 517, 521, 526, 527, 537, 540, 544, 560, 564, 576,\n", + " 585, 587])},\n", + " 5: {0: array([ 57, 71, 81, 98, 123, 152, 161, 171, 188, 197, 203, 208, 239,\n", + " 246, 256, 309, 313, 321, 343, 400, 402, 418, 472, 512, 516, 538,\n", + " 550, 553, 559, 568]),\n", + " 1: array([ 0, 1, 2, 4, 5, 6, 9, 10, 11, 15, 16, 17, 19,\n", + " 20, 21, 22, 25, 26, 27, 28, 30, 31, 32, 34, 35, 36,\n", + " 37, 39, 45, 49, 52, 53, 56, 58, 59, 61, 62, 65, 66,\n", + " 67, 69, 70, 72, 73, 74, 75, 76, 77, 78, 79, 82, 84,\n", + " 86, 87, 92, 95, 96, 97, 99, 100, 101, 102, 103, 106, 107,\n", + " 108, 109, 111, 112, 113, 114, 115, 116, 118, 119, 120, 122, 124,\n", + " 127, 129, 132, 134, 135, 136, 137, 139, 140, 141, 142, 146, 147,\n", + " 149, 151, 153, 154, 155, 157, 158, 162, 164, 167, 168, 169, 172,\n", + " 175, 176, 177, 179, 180, 181, 182, 183, 184, 185, 187, 189, 190,\n", + " 192, 194, 196, 198, 199, 200, 207, 210, 211, 214, 215, 216, 217,\n", + " 219, 220, 222, 223, 224, 225, 226, 227, 228, 230, 231, 232, 234,\n", + " 235, 236, 240, 241, 242, 244, 245, 247, 248, 249, 250, 251, 252,\n", + " 253, 254, 255, 259, 262, 264, 266, 267, 269, 270, 273, 275, 276,\n", + " 277, 278, 279, 282, 283, 284, 289, 290, 291, 292, 293, 295, 296,\n", + " 299, 300, 303, 304, 307, 308, 310, 311, 312, 314, 316, 318, 320,\n", + " 322, 323, 324, 327, 328, 329, 330, 331, 334, 336, 338, 342, 344,\n", + " 345, 346, 347, 348, 351, 352, 353, 354, 356, 360, 362, 364, 365,\n", + " 367, 368, 370, 371, 374, 375, 379, 380, 383, 384, 385, 387, 388,\n", + " 389, 390, 391, 392, 393, 394, 395, 396, 399, 401, 403, 407, 409,\n", + " 411, 412, 413, 417, 419, 420, 422, 424, 425, 426, 428, 429, 430,\n", + " 432, 434, 435, 436, 437, 439, 440, 442, 443, 446, 447, 449, 450,\n", + " 451, 453, 454, 455, 456, 457, 459, 462, 464, 467, 469, 470, 471,\n", + " 473, 474, 475, 476, 480, 481, 482, 484, 485, 486, 487, 488, 491,\n", + " 493, 494, 496, 498, 499, 501, 502, 503, 504, 505, 506, 508, 510,\n", + " 513, 518, 519, 522, 523, 525, 528, 529, 534, 535, 539, 542, 543,\n", + " 545, 546, 547, 549, 552, 555, 558, 561, 562, 563, 565, 566, 567,\n", + " 570, 571, 572, 573, 574, 575, 577, 578, 581, 582, 583, 584, 586,\n", + " 588, 589, 591, 592, 593, 594, 595, 597, 598, 599]),\n", + " 2: array([ 7, 8, 12, 23, 24, 29, 38, 40, 41, 42, 44, 47, 48,\n", + " 50, 55, 60, 64, 68, 80, 83, 85, 90, 91, 94, 105, 110,\n", + " 117, 121, 125, 126, 128, 130, 131, 133, 138, 143, 144, 145, 150,\n", + " 156, 159, 160, 165, 166, 170, 173, 178, 186, 195, 201, 204, 205,\n", + " 206, 209, 213, 218, 229, 233, 237, 238, 257, 258, 260, 261, 263,\n", + " 265, 268, 271, 274, 280, 281, 285, 288, 297, 298, 301, 302, 305,\n", + " 306, 317, 319, 325, 326, 332, 333, 335, 337, 339, 349, 350, 355,\n", + " 357, 366, 372, 373, 376, 377, 378, 381, 382, 386, 397, 404, 406,\n", + " 408, 414, 423, 427, 431, 438, 444, 452, 458, 463, 477, 478, 479,\n", + " 483, 490, 492, 495, 500, 514, 515, 520, 524, 530, 531, 532, 533,\n", + " 536, 541, 548, 551, 554, 556, 557, 569, 579, 580, 590, 596]),\n", + " 3: array([ 3, 13, 14, 18, 33, 43, 46, 51, 54, 63, 88, 89, 93,\n", + " 104, 148, 163, 174, 191, 193, 202, 212, 221, 243, 272, 286, 287,\n", + " 294, 315, 340, 341, 358, 359, 361, 363, 369, 398, 405, 410, 415,\n", + " 416, 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497,\n", + " 507, 509, 511, 517, 521, 526, 527, 537, 540, 544, 560, 564, 576,\n", + " 585, 587]),\n", + " 4: array([], dtype=int64)},\n", + " 6: {0: array([ 57, 71, 81, 98, 123, 152, 161, 171, 188, 197, 203, 208, 239,\n", + " 246, 256, 309, 313, 321, 343, 400, 402, 418, 472, 512, 516, 538,\n", + " 550, 553, 559, 568]),\n", + " 1: array([ 0, 1, 2, 4, 5, 6, 9, 10, 11, 15, 16, 17, 19,\n", + " 20, 21, 22, 25, 26, 27, 28, 30, 31, 32, 34, 35, 36,\n", + " 37, 39, 45, 49, 52, 53, 56, 58, 59, 61, 62, 65, 66,\n", + " 67, 69, 70, 72, 73, 74, 75, 76, 77, 78, 79, 82, 84,\n", + " 86, 87, 92, 95, 96, 97, 99, 100, 101, 102, 103, 106, 107,\n", + " 108, 109, 111, 112, 113, 114, 115, 116, 118, 119, 120, 122, 124,\n", + " 127, 129, 132, 134, 135, 136, 137, 139, 140, 141, 142, 146, 147,\n", + " 149, 151, 153, 154, 155, 157, 158, 162, 164, 167, 168, 169, 172,\n", + " 175, 176, 177, 179, 180, 181, 182, 183, 184, 185, 187, 189, 190,\n", + " 192, 194, 196, 198, 199, 200, 207, 210, 211, 214, 215, 216, 217,\n", + " 219, 220, 222, 223, 224, 225, 226, 227, 228, 230, 231, 232, 234,\n", + " 235, 236, 240, 241, 242, 244, 245, 247, 248, 249, 250, 251, 252,\n", + " 253, 254, 255, 259, 262, 264, 266, 267, 269, 270, 273, 275, 276,\n", + " 277, 278, 279, 282, 283, 284, 289, 290, 291, 292, 293, 295, 296,\n", + " 299, 300, 303, 304, 307, 308, 310, 311, 312, 314, 316, 318, 320,\n", + " 322, 323, 324, 327, 328, 329, 330, 331, 334, 336, 338, 342, 344,\n", + " 345, 346, 347, 348, 351, 352, 353, 354, 356, 360, 362, 364, 365,\n", + " 367, 368, 370, 371, 374, 375, 379, 380, 383, 384, 385, 387, 388,\n", + " 389, 390, 391, 392, 393, 394, 395, 396, 399, 401, 403, 407, 409,\n", + " 411, 412, 413, 417, 419, 420, 422, 424, 425, 426, 428, 429, 430,\n", + " 432, 434, 435, 436, 437, 439, 440, 442, 443, 446, 447, 449, 450,\n", + " 451, 453, 454, 455, 456, 457, 459, 462, 464, 467, 469, 470, 471,\n", + " 473, 474, 475, 476, 480, 481, 482, 484, 485, 486, 487, 488, 491,\n", + " 493, 494, 496, 498, 499, 501, 502, 503, 504, 505, 506, 508, 510,\n", + " 513, 518, 519, 522, 523, 525, 528, 529, 534, 535, 539, 542, 543,\n", + " 545, 546, 547, 549, 552, 555, 558, 561, 562, 563, 565, 566, 567,\n", + " 570, 571, 572, 573, 574, 575, 577, 578, 581, 582, 583, 584, 586,\n", + " 588, 589, 591, 592, 593, 594, 595, 597, 598, 599]),\n", + " 2: array([ 7, 8, 12, 23, 24, 29, 38, 40, 41, 42, 44, 48, 50,\n", + " 55, 60, 64, 68, 80, 83, 85, 90, 91, 94, 105, 110, 117,\n", + " 121, 125, 126, 128, 130, 131, 133, 138, 143, 144, 145, 150, 156,\n", + " 159, 160, 165, 166, 170, 173, 178, 186, 195, 204, 205, 206, 209,\n", + " 213, 218, 229, 233, 238, 257, 258, 260, 261, 263, 265, 268, 271,\n", + " 274, 280, 281, 285, 288, 297, 298, 301, 302, 305, 306, 317, 319,\n", + " 325, 326, 333, 335, 337, 339, 349, 350, 355, 357, 366, 372, 373,\n", + " 377, 378, 382, 386, 397, 404, 406, 408, 414, 423, 427, 431, 438,\n", + " 444, 452, 458, 463, 477, 478, 479, 483, 490, 492, 495, 500, 514,\n", + " 515, 520, 524, 530, 531, 532, 536, 541, 548, 551, 554, 556, 557,\n", + " 569, 579, 580, 590, 596]),\n", + " 3: array([ 3, 13, 14, 18, 33, 43, 46, 51, 54, 63, 88, 89, 93,\n", + " 104, 148, 163, 174, 191, 193, 202, 212, 221, 243, 272, 286, 287,\n", + " 294, 315, 340, 341, 358, 359, 361, 363, 369, 398, 405, 410, 415,\n", + " 416, 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497,\n", + " 507, 509, 511, 517, 521, 526, 527, 537, 540, 544, 560, 564, 576,\n", + " 585, 587]),\n", + " 4: array([ 47, 201, 237, 332, 376, 381, 533]),\n", + " 5: array([], dtype=int64)},\n", + " 7: {0: array([ 57, 81, 98, 123, 161, 197, 239, 246, 256, 309, 343, 400, 512,\n", + " 516, 550, 553]),\n", + " 1: array([ 0, 1, 2, 4, 5, 6, 9, 10, 11, 15, 16, 17, 19,\n", + " 20, 21, 22, 25, 26, 27, 28, 30, 31, 32, 34, 35, 36,\n", + " 37, 39, 45, 49, 52, 53, 56, 58, 59, 61, 62, 65, 66,\n", + " 67, 69, 70, 72, 73, 74, 75, 76, 77, 78, 79, 82, 84,\n", + " 86, 87, 92, 95, 96, 97, 99, 100, 101, 102, 103, 106, 107,\n", + " 108, 109, 111, 112, 113, 114, 115, 116, 118, 119, 120, 122, 124,\n", + " 127, 129, 132, 134, 135, 136, 137, 139, 140, 141, 142, 146, 147,\n", + " 149, 151, 153, 154, 155, 157, 158, 162, 164, 167, 168, 169, 172,\n", + " 175, 176, 177, 179, 180, 181, 182, 183, 184, 185, 187, 189, 190,\n", + " 192, 194, 196, 198, 199, 200, 207, 210, 211, 214, 215, 216, 217,\n", + " 219, 220, 222, 223, 224, 225, 226, 227, 228, 230, 231, 232, 234,\n", + " 235, 236, 240, 241, 242, 244, 245, 247, 248, 249, 250, 251, 252,\n", + " 253, 254, 255, 259, 262, 264, 266, 267, 269, 270, 273, 275, 276,\n", + " 277, 278, 279, 282, 283, 284, 289, 290, 291, 292, 293, 295, 296,\n", + " 299, 300, 303, 304, 307, 308, 310, 311, 312, 314, 316, 318, 320,\n", + " 322, 323, 324, 327, 328, 329, 330, 331, 334, 336, 338, 342, 344,\n", + " 345, 346, 347, 348, 351, 352, 353, 354, 356, 360, 362, 364, 365,\n", + " 367, 368, 370, 371, 374, 375, 379, 380, 383, 384, 385, 387, 388,\n", + " 389, 390, 391, 392, 393, 394, 395, 396, 399, 401, 403, 407, 409,\n", + " 411, 412, 413, 417, 419, 420, 422, 424, 425, 426, 428, 429, 430,\n", + " 432, 434, 435, 436, 437, 439, 440, 442, 443, 446, 447, 449, 450,\n", + " 451, 453, 454, 455, 456, 457, 459, 462, 464, 467, 469, 470, 471,\n", + " 473, 474, 475, 476, 480, 481, 482, 484, 485, 486, 487, 488, 491,\n", + " 493, 494, 496, 498, 499, 501, 502, 503, 504, 505, 506, 508, 510,\n", + " 513, 518, 519, 522, 523, 525, 528, 529, 534, 535, 539, 542, 543,\n", + " 545, 546, 547, 549, 552, 555, 558, 561, 562, 563, 565, 566, 567,\n", + " 570, 571, 572, 573, 574, 575, 577, 578, 581, 582, 583, 584, 586,\n", + " 588, 589, 591, 592, 593, 594, 595, 597, 598, 599]),\n", + " 2: array([ 7, 8, 12, 23, 24, 29, 38, 40, 41, 42, 44, 48, 50,\n", + " 55, 60, 64, 68, 80, 83, 85, 90, 91, 94, 105, 117, 121,\n", + " 125, 126, 128, 130, 131, 133, 138, 143, 144, 145, 150, 156, 159,\n", + " 160, 165, 166, 170, 173, 178, 186, 195, 204, 205, 206, 209, 213,\n", + " 218, 229, 233, 238, 257, 258, 260, 261, 263, 265, 268, 271, 274,\n", + " 280, 281, 285, 288, 297, 298, 301, 302, 305, 306, 317, 319, 325,\n", + " 326, 333, 335, 337, 339, 349, 350, 355, 357, 366, 372, 373, 377,\n", + " 378, 382, 386, 397, 404, 406, 408, 414, 423, 427, 431, 438, 444,\n", + " 452, 458, 463, 477, 478, 479, 483, 490, 492, 495, 500, 514, 515,\n", + " 520, 524, 530, 531, 532, 536, 541, 548, 551, 554, 556, 557, 569,\n", + " 579, 580, 590, 596]),\n", + " 3: array([ 3, 13, 14, 18, 33, 43, 46, 51, 54, 63, 88, 89, 93,\n", + " 104, 148, 163, 174, 191, 193, 202, 212, 221, 243, 272, 286, 287,\n", + " 294, 315, 340, 341, 358, 361, 363, 369, 398, 405, 410, 415, 416,\n", + " 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497, 507,\n", + " 511, 517, 521, 526, 527, 537, 540, 544, 560, 564, 576, 585, 587]),\n", + " 4: array([ 71, 110, 152, 171, 188, 203, 208, 313, 321, 359, 402, 418, 472,\n", + " 509, 538, 559, 568]),\n", + " 5: array([ 47, 201, 237, 332, 376, 381, 533]),\n", + " 6: array([], dtype=int64)},\n", + " 8: {0: array([ 81, 161, 197, 246, 309, 343, 516]),\n", + " 1: array([ 0, 1, 2, 4, 5, 6, 9, 10, 11, 15, 16, 17, 19,\n", + " 20, 21, 22, 25, 26, 27, 28, 30, 31, 32, 34, 35, 36,\n", + " 37, 39, 45, 49, 52, 53, 56, 58, 59, 61, 62, 65, 66,\n", + " 67, 69, 70, 72, 73, 74, 75, 76, 77, 78, 79, 82, 84,\n", + " 86, 87, 92, 95, 96, 97, 99, 100, 101, 102, 103, 106, 107,\n", + " 108, 109, 111, 112, 113, 114, 115, 116, 118, 119, 120, 122, 124,\n", + " 127, 129, 132, 134, 135, 136, 137, 139, 140, 141, 142, 146, 147,\n", + " 149, 151, 153, 154, 155, 157, 158, 162, 164, 167, 168, 169, 172,\n", + " 175, 176, 177, 179, 180, 181, 182, 183, 184, 185, 187, 189, 190,\n", + " 192, 194, 196, 198, 199, 200, 207, 210, 211, 214, 215, 216, 217,\n", + " 219, 220, 222, 223, 224, 225, 226, 227, 228, 230, 231, 232, 234,\n", + " 235, 236, 240, 241, 242, 244, 245, 247, 248, 249, 250, 251, 252,\n", + " 253, 254, 255, 259, 262, 264, 266, 267, 269, 270, 273, 275, 276,\n", + " 277, 278, 279, 282, 283, 284, 289, 290, 291, 292, 293, 295, 296,\n", + " 299, 300, 303, 304, 307, 308, 310, 311, 312, 314, 316, 318, 320,\n", + " 322, 323, 324, 327, 328, 329, 330, 331, 334, 336, 338, 342, 344,\n", + " 345, 346, 347, 348, 351, 352, 353, 354, 356, 360, 362, 364, 365,\n", + " 367, 368, 370, 371, 374, 375, 379, 380, 383, 384, 385, 387, 388,\n", + " 389, 390, 391, 392, 393, 394, 395, 396, 399, 401, 403, 407, 409,\n", + " 411, 412, 413, 417, 419, 420, 422, 424, 425, 426, 428, 429, 430,\n", + " 432, 434, 435, 436, 437, 439, 440, 442, 443, 446, 447, 449, 450,\n", + " 451, 453, 454, 455, 456, 457, 459, 462, 464, 467, 469, 470, 471,\n", + " 473, 474, 475, 476, 480, 481, 482, 484, 485, 486, 487, 488, 491,\n", + " 493, 494, 496, 498, 499, 501, 502, 503, 504, 505, 506, 508, 510,\n", + " 513, 518, 519, 522, 523, 525, 528, 529, 534, 535, 539, 542, 543,\n", + " 545, 546, 547, 549, 552, 555, 558, 561, 562, 563, 565, 566, 567,\n", + " 570, 571, 572, 573, 574, 575, 577, 578, 581, 582, 583, 584, 586,\n", + " 588, 589, 591, 592, 593, 594, 595, 597, 598, 599]),\n", + " 2: array([ 57, 98, 123, 239, 256, 400, 512, 544, 550, 553]),\n", + " 3: array([ 7, 8, 12, 23, 24, 29, 38, 40, 41, 42, 44, 48, 50,\n", + " 55, 60, 64, 68, 80, 83, 85, 90, 91, 94, 105, 117, 121,\n", + " 125, 126, 128, 130, 131, 133, 138, 143, 144, 145, 150, 156, 159,\n", + " 160, 165, 166, 170, 173, 178, 186, 195, 204, 205, 206, 209, 213,\n", + " 218, 229, 233, 238, 257, 258, 260, 261, 263, 265, 268, 271, 274,\n", + " 280, 281, 285, 288, 297, 298, 301, 302, 305, 306, 317, 319, 325,\n", + " 326, 333, 335, 337, 339, 349, 350, 355, 357, 366, 372, 373, 377,\n", + " 378, 382, 386, 397, 404, 406, 408, 414, 423, 427, 431, 438, 444,\n", + " 452, 458, 463, 477, 478, 479, 483, 490, 492, 495, 500, 514, 515,\n", + " 520, 524, 530, 531, 532, 536, 541, 548, 551, 554, 556, 557, 569,\n", + " 579, 580, 590, 596]),\n", + " 4: array([ 3, 13, 14, 18, 33, 43, 46, 51, 54, 63, 88, 89, 93,\n", + " 104, 148, 163, 174, 191, 193, 202, 212, 221, 243, 272, 286, 287,\n", + " 294, 315, 340, 341, 358, 361, 363, 369, 398, 405, 410, 415, 416,\n", + " 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497, 507,\n", + " 511, 517, 521, 526, 527, 537, 540, 560, 564, 576, 585, 587]),\n", + " 5: array([ 71, 110, 152, 171, 188, 203, 208, 313, 321, 359, 402, 418, 472,\n", + " 509, 538, 559, 568]),\n", + " 6: array([ 47, 201, 237, 332, 376, 381, 533]),\n", + " 7: array([], dtype=int64)},\n", + " 9: {0: array([ 81, 161, 197, 246, 309, 343, 516]),\n", + " 1: array([ 0, 1, 2, 4, 5, 6, 9, 10, 11, 15, 16, 17, 19,\n", + " 20, 21, 22, 25, 26, 27, 28, 30, 31, 32, 34, 35, 36,\n", + " 37, 39, 45, 49, 52, 53, 56, 58, 59, 61, 62, 65, 66,\n", + " 67, 68, 69, 70, 72, 73, 74, 75, 76, 77, 78, 79, 82,\n", + " 84, 86, 87, 92, 95, 96, 97, 99, 100, 101, 102, 103, 106,\n", + " 107, 108, 109, 111, 112, 113, 114, 115, 116, 118, 119, 120, 122,\n", + " 124, 127, 128, 129, 132, 134, 135, 136, 137, 139, 140, 141, 142,\n", + " 146, 147, 149, 151, 153, 154, 155, 157, 158, 162, 164, 167, 168,\n", + " 169, 172, 175, 176, 177, 179, 180, 181, 182, 183, 184, 185, 187,\n", + " 189, 190, 192, 194, 196, 198, 199, 200, 207, 210, 211, 214, 215,\n", + " 216, 217, 219, 220, 222, 223, 224, 225, 226, 227, 228, 230, 231,\n", + " 232, 234, 235, 236, 240, 241, 242, 244, 245, 247, 248, 249, 250,\n", + " 251, 252, 253, 254, 255, 257, 259, 262, 264, 266, 267, 269, 270,\n", + " 273, 275, 276, 277, 278, 279, 282, 283, 284, 289, 290, 291, 292,\n", + " 293, 295, 296, 299, 300, 303, 304, 307, 308, 310, 311, 312, 314,\n", + " 316, 318, 320, 322, 323, 324, 327, 328, 329, 330, 331, 334, 336,\n", + " 338, 342, 344, 345, 346, 347, 348, 351, 352, 353, 354, 356, 360,\n", + " 362, 364, 365, 367, 368, 370, 371, 374, 375, 379, 380, 383, 384,\n", + " 385, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 399, 401,\n", + " 403, 407, 409, 411, 412, 413, 417, 419, 420, 422, 424, 425, 426,\n", + " 428, 429, 430, 432, 434, 435, 436, 437, 439, 440, 442, 443, 446,\n", + " 447, 449, 450, 451, 453, 454, 455, 456, 457, 459, 462, 464, 467,\n", + " 469, 470, 471, 473, 474, 475, 476, 480, 481, 482, 484, 485, 486,\n", + " 487, 488, 491, 493, 494, 496, 498, 499, 501, 502, 503, 504, 505,\n", + " 506, 508, 510, 513, 518, 519, 522, 523, 525, 528, 529, 534, 535,\n", + " 539, 542, 543, 545, 546, 547, 549, 552, 555, 558, 561, 562, 563,\n", + " 565, 566, 567, 570, 571, 572, 573, 574, 575, 577, 578, 581, 582,\n", + " 583, 584, 586, 588, 589, 591, 592, 593, 594, 595, 597, 598, 599]),\n", + " 2: array([ 57, 98, 123, 239, 256, 400, 512, 544, 550, 553]),\n", + " 3: array([ 7, 8, 12, 23, 29, 38, 40, 42, 44, 48, 50, 55, 60,\n", + " 80, 83, 85, 90, 91, 94, 105, 117, 121, 125, 126, 130, 131,\n", + " 133, 138, 143, 144, 145, 150, 156, 159, 160, 165, 166, 170, 186,\n", + " 204, 205, 206, 209, 213, 218, 229, 233, 238, 258, 260, 263, 268,\n", + " 281, 285, 288, 297, 298, 301, 302, 305, 306, 319, 325, 326, 333,\n", + " 335, 337, 339, 349, 355, 357, 366, 372, 373, 377, 382, 386, 397,\n", + " 404, 406, 414, 423, 427, 438, 444, 452, 458, 463, 477, 478, 479,\n", + " 483, 490, 492, 495, 500, 514, 515, 520, 524, 530, 532, 536, 541,\n", + " 548, 551, 554, 556, 557, 569, 580, 590, 596]),\n", + " 4: array([ 3, 13, 14, 18, 33, 43, 46, 51, 54, 63, 88, 89, 93,\n", + " 104, 148, 163, 174, 191, 193, 212, 221, 243, 272, 286, 287, 294,\n", + " 315, 340, 341, 358, 361, 363, 369, 398, 405, 410, 415, 416, 421,\n", + " 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497, 507, 511,\n", + " 517, 521, 526, 527, 537, 540, 560, 564, 576, 585, 587]),\n", + " 5: array([ 71, 110, 152, 171, 188, 203, 208, 313, 321, 359, 402, 418, 472,\n", + " 509, 538, 559, 568]),\n", + " 6: array([ 24, 41, 64, 173, 178, 195, 202, 261, 265, 271, 274, 280, 317,\n", + " 350, 378, 408, 431, 531, 579]),\n", + " 7: array([ 47, 201, 237, 332, 376, 381, 533]),\n", + " 8: array([], dtype=int64)},\n", + " 10: {0: array([ 81, 161, 197, 246, 309, 343, 516]),\n", + " 1: array([ 0, 1, 2, 4, 5, 6, 9, 10, 11, 15, 16, 17, 19,\n", + " 20, 21, 22, 25, 26, 27, 28, 30, 31, 32, 34, 35, 36,\n", + " 37, 39, 49, 52, 53, 56, 58, 59, 61, 62, 65, 66, 67,\n", + " 69, 70, 72, 73, 74, 75, 76, 77, 78, 79, 82, 84, 86,\n", + " 87, 92, 95, 96, 97, 99, 100, 101, 102, 103, 106, 107, 108,\n", + " 109, 111, 112, 113, 114, 115, 116, 118, 119, 120, 122, 124, 127,\n", + " 128, 129, 132, 134, 135, 136, 137, 139, 140, 141, 142, 146, 147,\n", + " 149, 151, 153, 154, 155, 157, 158, 162, 164, 167, 168, 169, 172,\n", + " 175, 176, 177, 179, 180, 181, 182, 183, 184, 185, 187, 189, 190,\n", + " 192, 194, 196, 198, 199, 200, 207, 210, 211, 214, 215, 216, 217,\n", + " 219, 220, 222, 223, 224, 225, 226, 227, 228, 230, 231, 232, 234,\n", + " 235, 236, 240, 241, 242, 244, 245, 247, 248, 249, 250, 251, 252,\n", + " 253, 254, 255, 257, 259, 262, 264, 266, 267, 269, 270, 273, 275,\n", + " 276, 277, 278, 279, 282, 283, 284, 289, 290, 291, 292, 293, 295,\n", + " 296, 299, 300, 303, 304, 307, 308, 310, 311, 312, 314, 316, 318,\n", + " 320, 322, 323, 324, 327, 328, 329, 330, 331, 334, 336, 338, 342,\n", + " 344, 345, 346, 347, 348, 351, 352, 353, 354, 356, 360, 362, 364,\n", + " 365, 367, 368, 370, 371, 374, 375, 379, 380, 383, 384, 385, 387,\n", + " 388, 389, 390, 391, 392, 393, 394, 395, 396, 399, 401, 403, 407,\n", + " 409, 411, 412, 413, 417, 419, 420, 422, 424, 425, 426, 428, 429,\n", + " 430, 432, 434, 435, 436, 437, 439, 440, 442, 443, 446, 447, 449,\n", + " 450, 451, 453, 454, 455, 456, 457, 459, 462, 464, 467, 469, 470,\n", + " 471, 473, 474, 475, 476, 480, 481, 482, 484, 485, 486, 487, 488,\n", + " 491, 493, 494, 496, 498, 499, 501, 502, 503, 504, 505, 506, 508,\n", + " 510, 513, 518, 519, 522, 523, 525, 528, 529, 534, 535, 539, 542,\n", + " 543, 545, 546, 547, 549, 552, 555, 558, 561, 562, 563, 565, 566,\n", + " 567, 570, 571, 572, 573, 574, 575, 577, 578, 581, 582, 583, 584,\n", + " 586, 588, 589, 591, 592, 593, 594, 595, 597, 598, 599]),\n", + " 2: array([ 57, 98, 123, 239, 256, 400, 512, 544, 550, 553]),\n", + " 3: array([ 7, 8, 12, 23, 29, 38, 40, 42, 44, 48, 50, 55, 60,\n", + " 80, 83, 85, 90, 91, 94, 105, 117, 121, 125, 126, 130, 131,\n", + " 133, 138, 143, 144, 145, 150, 156, 159, 160, 165, 166, 170, 186,\n", + " 204, 205, 206, 209, 213, 218, 229, 233, 238, 258, 260, 263, 268,\n", + " 281, 285, 288, 297, 298, 301, 302, 305, 306, 319, 325, 326, 333,\n", + " 335, 337, 339, 349, 355, 357, 366, 372, 373, 377, 382, 386, 397,\n", + " 404, 406, 414, 423, 427, 438, 444, 452, 458, 463, 477, 478, 479,\n", + " 483, 490, 492, 495, 500, 514, 515, 520, 524, 530, 532, 536, 541,\n", + " 548, 551, 554, 556, 557, 569, 580, 590, 596]),\n", + " 4: array([ 45, 68, 509]),\n", + " 5: array([ 3, 13, 14, 18, 33, 43, 46, 51, 54, 63, 88, 89, 93,\n", + " 104, 148, 163, 174, 191, 193, 212, 221, 243, 272, 286, 287, 294,\n", + " 315, 340, 341, 358, 361, 363, 369, 398, 405, 410, 415, 416, 421,\n", + " 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497, 507, 511,\n", + " 517, 521, 526, 527, 537, 540, 560, 564, 576, 585, 587]),\n", + " 6: array([ 71, 110, 152, 171, 188, 203, 208, 313, 321, 359, 402, 418, 472,\n", + " 538, 559, 568]),\n", + " 7: array([ 24, 41, 64, 173, 178, 195, 202, 261, 265, 271, 274, 280, 317,\n", + " 350, 378, 408, 431, 531, 579]),\n", + " 8: array([ 47, 201, 237, 332, 376, 381, 533]),\n", + " 9: array([], dtype=int64)}},\n", + " 'nonl2_ranking': {2: {0: array([ 3, 13, 14, 18, 29, 33, 43, 45, 46, 51, 54, 57, 63,\n", + " 68, 71, 81, 88, 89, 93, 98, 104, 110, 123, 148, 152, 161,\n", + " 163, 171, 174, 188, 191, 193, 197, 202, 203, 208, 212, 221, 239,\n", + " 243, 246, 256, 272, 286, 287, 294, 309, 313, 315, 321, 340, 341,\n", + " 343, 350, 358, 359, 361, 363, 369, 398, 400, 402, 405, 410, 415,\n", + " 416, 418, 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 472,\n", + " 477, 483, 489, 497, 507, 509, 511, 512, 515, 516, 517, 521, 526,\n", + " 527, 537, 538, 540, 544, 550, 553, 559, 560, 564, 568, 576, 585,\n", + " 587]),\n", + " 1: array([ 0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15,\n", + " 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 30,\n", + " 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 42, 44, 47,\n", + " 48, 49, 50, 52, 53, 55, 56, 58, 59, 60, 61, 62, 64,\n", + " 65, 66, 67, 69, 70, 72, 73, 74, 75, 76, 77, 78, 79,\n", + " 80, 82, 83, 84, 85, 86, 87, 90, 91, 92, 94, 95, 96,\n", + " 97, 99, 100, 101, 102, 103, 105, 106, 107, 108, 109, 111, 112,\n", + " 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125, 126,\n", + " 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n", + " 140, 141, 142, 143, 144, 145, 146, 147, 149, 150, 151, 153, 154,\n", + " 155, 156, 157, 158, 159, 160, 162, 164, 165, 166, 167, 168, 169,\n", + " 170, 172, 173, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184,\n", + " 185, 186, 187, 189, 190, 192, 194, 195, 196, 198, 199, 200, 201,\n", + " 204, 205, 206, 207, 209, 210, 211, 213, 214, 215, 216, 217, 218,\n", + " 219, 220, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232,\n", + " 233, 234, 235, 236, 237, 238, 240, 241, 242, 244, 245, 247, 248,\n", + " 249, 250, 251, 252, 253, 254, 255, 257, 258, 259, 260, 261, 262,\n", + " 263, 264, 265, 266, 267, 268, 269, 270, 271, 273, 274, 275, 276,\n", + " 277, 278, 279, 280, 281, 282, 283, 284, 285, 288, 289, 290, 291,\n", + " 292, 293, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305,\n", + " 306, 307, 308, 310, 311, 312, 314, 316, 317, 318, 319, 320, 322,\n", + " 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335,\n", + " 336, 337, 338, 339, 342, 344, 345, 346, 347, 348, 349, 351, 352,\n", + " 353, 354, 355, 356, 357, 360, 362, 364, 365, 366, 367, 368, 370,\n", + " 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383,\n", + " 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396,\n", + " 397, 399, 401, 403, 404, 406, 407, 408, 409, 411, 412, 413, 414,\n", + " 417, 419, 420, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431,\n", + " 432, 434, 435, 436, 437, 438, 439, 440, 442, 443, 444, 446, 447,\n", + " 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 462, 463,\n", + " 464, 467, 469, 470, 471, 473, 474, 475, 476, 478, 479, 480, 481,\n", + " 482, 484, 485, 486, 487, 488, 490, 491, 492, 493, 494, 495, 496,\n", + " 498, 499, 500, 501, 502, 503, 504, 505, 506, 508, 510, 513, 514,\n", + " 518, 519, 520, 522, 523, 524, 525, 528, 529, 530, 531, 532, 533,\n", + " 534, 535, 536, 539, 541, 542, 543, 545, 546, 547, 548, 549, 551,\n", + " 552, 554, 555, 556, 557, 558, 561, 562, 563, 565, 566, 567, 569,\n", + " 570, 571, 572, 573, 574, 575, 577, 578, 579, 580, 581, 582, 583,\n", + " 584, 586, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598,\n", + " 599])},\n", + " 3: {0: array([ 3, 13, 14, 18, 29, 33, 43, 45, 46, 51, 54, 57, 63,\n", + " 68, 71, 81, 88, 89, 93, 98, 104, 110, 123, 148, 152, 161,\n", + " 163, 171, 174, 188, 191, 193, 197, 202, 203, 208, 212, 221, 239,\n", + " 243, 246, 256, 272, 286, 287, 294, 309, 313, 315, 321, 340, 341,\n", + " 343, 350, 358, 359, 361, 363, 369, 398, 400, 402, 405, 410, 415,\n", + " 416, 418, 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 472,\n", + " 489, 497, 507, 509, 511, 512, 516, 517, 521, 526, 527, 537, 538,\n", + " 540, 544, 550, 553, 559, 560, 564, 568, 576, 585, 587]),\n", + " 1: array([ 1, 2, 5, 6, 7, 8, 9, 10, 15, 16, 17, 21, 22,\n", + " 25, 26, 27, 28, 31, 32, 35, 36, 39, 40, 42, 47, 48,\n", + " 49, 50, 52, 53, 55, 56, 58, 59, 65, 67, 69, 70, 72,\n", + " 74, 75, 77, 78, 80, 82, 83, 85, 86, 90, 91, 92, 96,\n", + " 97, 99, 100, 102, 105, 107, 108, 109, 111, 113, 114, 115, 116,\n", + " 117, 118, 119, 121, 122, 124, 125, 126, 127, 129, 132, 133, 134,\n", + " 135, 136, 137, 138, 139, 140, 141, 142, 145, 146, 149, 150, 151,\n", + " 153, 154, 155, 156, 157, 158, 159, 160, 162, 166, 167, 168, 169,\n", + " 175, 176, 177, 178, 179, 180, 181, 183, 184, 185, 187, 189, 192,\n", + " 194, 195, 196, 198, 199, 200, 201, 204, 205, 207, 209, 210, 211,\n", + " 213, 214, 216, 217, 218, 219, 220, 222, 223, 225, 226, 227, 228,\n", + " 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 240, 241, 242,\n", + " 244, 245, 247, 248, 249, 250, 251, 252, 253, 254, 259, 260, 262,\n", + " 263, 264, 266, 267, 268, 270, 277, 278, 279, 280, 282, 283, 284,\n", + " 288, 289, 290, 291, 292, 293, 295, 296, 297, 299, 300, 301, 303,\n", + " 304, 306, 307, 308, 310, 311, 312, 314, 316, 319, 320, 322, 323,\n", + " 325, 329, 330, 331, 332, 333, 334, 335, 336, 337, 339, 342, 344,\n", + " 345, 346, 347, 348, 349, 351, 353, 354, 356, 357, 360, 362, 364,\n", + " 366, 367, 368, 370, 371, 372, 373, 374, 375, 376, 377, 379, 380,\n", + " 381, 382, 383, 384, 387, 388, 390, 391, 392, 393, 394, 395, 396,\n", + " 397, 399, 401, 403, 404, 406, 407, 409, 411, 412, 413, 414, 417,\n", + " 419, 420, 422, 423, 425, 426, 427, 428, 429, 430, 435, 438, 439,\n", + " 440, 442, 443, 447, 450, 451, 452, 453, 454, 456, 457, 458, 459,\n", + " 462, 464, 467, 469, 470, 473, 474, 475, 476, 477, 478, 481, 482,\n", + " 483, 484, 485, 486, 487, 488, 491, 493, 494, 496, 498, 499, 500,\n", + " 501, 502, 504, 505, 506, 508, 510, 513, 515, 518, 519, 520, 522,\n", + " 523, 524, 525, 529, 534, 535, 536, 539, 546, 547, 551, 552, 555,\n", + " 556, 558, 561, 562, 563, 565, 566, 567, 569, 570, 571, 572, 573,\n", + " 574, 575, 577, 581, 582, 583, 584, 586, 588, 589, 590, 591, 592,\n", + " 593, 594, 595, 596, 598, 599]),\n", + " 2: array([ 0, 4, 11, 12, 19, 20, 23, 24, 30, 34, 37, 38, 41,\n", + " 44, 60, 61, 62, 64, 66, 73, 76, 79, 84, 87, 94, 95,\n", + " 101, 103, 106, 112, 120, 128, 130, 131, 143, 144, 147, 164, 165,\n", + " 170, 172, 173, 182, 186, 190, 206, 215, 224, 255, 257, 258, 261,\n", + " 265, 269, 271, 273, 274, 275, 276, 281, 285, 298, 302, 305, 317,\n", + " 318, 324, 326, 327, 328, 338, 352, 355, 365, 378, 385, 386, 389,\n", + " 408, 424, 431, 432, 434, 436, 437, 444, 446, 449, 455, 463, 471,\n", + " 479, 480, 490, 492, 495, 503, 514, 528, 530, 531, 532, 533, 541,\n", + " 542, 543, 545, 548, 549, 554, 557, 578, 579, 580, 597])},\n", + " 4: {0: array([ 3, 13, 14, 18, 29, 33, 43, 45, 46, 51, 54, 57, 63,\n", + " 68, 71, 81, 88, 89, 93, 98, 104, 110, 123, 148, 152, 161,\n", + " 163, 171, 174, 188, 191, 193, 197, 202, 203, 208, 212, 221, 239,\n", + " 243, 246, 256, 272, 286, 287, 294, 309, 313, 315, 321, 340, 341,\n", + " 343, 350, 358, 359, 361, 363, 369, 398, 400, 402, 405, 410, 415,\n", + " 416, 418, 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 472,\n", + " 477, 483, 489, 497, 507, 509, 511, 512, 515, 516, 517, 521, 526,\n", + " 527, 537, 538, 540, 544, 550, 553, 559, 560, 564, 568, 576, 585,\n", + " 587]),\n", + " 1: array([ 1, 2, 5, 6, 9, 10, 15, 16, 17, 21, 22, 25, 26,\n", + " 27, 28, 31, 32, 35, 36, 39, 42, 49, 52, 53, 56, 58,\n", + " 59, 65, 67, 69, 70, 72, 74, 75, 77, 78, 82, 86, 92,\n", + " 96, 97, 99, 100, 102, 107, 108, 109, 111, 113, 114, 115, 116,\n", + " 118, 119, 122, 124, 127, 129, 132, 134, 135, 136, 137, 139, 140,\n", + " 141, 142, 146, 149, 151, 153, 154, 155, 157, 158, 162, 167, 168,\n", + " 169, 175, 176, 177, 179, 180, 181, 183, 184, 185, 187, 189, 192,\n", + " 194, 196, 198, 199, 200, 207, 210, 211, 214, 216, 217, 219, 220,\n", + " 222, 223, 225, 226, 227, 228, 230, 231, 232, 234, 235, 236, 240,\n", + " 241, 242, 244, 245, 247, 248, 249, 250, 251, 252, 253, 254, 259,\n", + " 262, 264, 266, 267, 270, 277, 278, 279, 282, 283, 284, 289, 290,\n", + " 291, 292, 293, 295, 296, 299, 300, 303, 304, 307, 308, 310, 311,\n", + " 312, 314, 316, 320, 322, 323, 329, 330, 331, 334, 336, 342, 344,\n", + " 345, 346, 347, 348, 351, 353, 354, 356, 360, 362, 364, 367, 368,\n", + " 370, 371, 374, 375, 379, 380, 383, 384, 387, 388, 390, 391, 392,\n", + " 393, 394, 395, 396, 399, 401, 403, 407, 409, 411, 412, 413, 417,\n", + " 419, 420, 422, 425, 426, 428, 429, 430, 435, 439, 440, 442, 443,\n", + " 447, 450, 451, 453, 454, 456, 457, 459, 462, 464, 467, 469, 470,\n", + " 473, 474, 475, 476, 481, 482, 484, 485, 486, 487, 488, 491, 493,\n", + " 494, 496, 498, 499, 501, 502, 504, 505, 506, 508, 510, 513, 518,\n", + " 519, 522, 523, 525, 529, 534, 535, 539, 546, 547, 551, 552, 555,\n", + " 558, 561, 562, 563, 565, 566, 567, 570, 571, 572, 573, 574, 575,\n", + " 577, 581, 582, 583, 584, 586, 588, 589, 591, 592, 593, 594, 595,\n", + " 598, 599]),\n", + " 2: array([ 7, 8, 40, 47, 48, 50, 55, 80, 83, 85, 90, 91, 105,\n", + " 117, 121, 125, 126, 130, 131, 133, 138, 145, 150, 156, 159, 160,\n", + " 166, 178, 195, 201, 204, 205, 209, 213, 218, 229, 233, 237, 238,\n", + " 260, 263, 268, 280, 285, 288, 297, 301, 306, 319, 325, 332, 333,\n", + " 335, 337, 339, 349, 357, 366, 372, 373, 376, 377, 381, 382, 397,\n", + " 404, 406, 414, 423, 427, 438, 452, 458, 478, 500, 520, 524, 532,\n", + " 536, 548, 556, 569, 590, 596]),\n", + " 3: array([ 0, 4, 11, 12, 19, 20, 23, 24, 30, 34, 37, 38, 41,\n", + " 44, 60, 61, 62, 64, 66, 73, 76, 79, 84, 87, 94, 95,\n", + " 101, 103, 106, 112, 120, 128, 143, 144, 147, 164, 165, 170, 172,\n", + " 173, 182, 186, 190, 206, 215, 224, 255, 257, 258, 261, 265, 269,\n", + " 271, 273, 274, 275, 276, 281, 298, 302, 305, 317, 318, 324, 326,\n", + " 327, 328, 338, 352, 355, 365, 378, 385, 386, 389, 408, 424, 431,\n", + " 432, 434, 436, 437, 444, 446, 449, 455, 463, 471, 479, 480, 490,\n", + " 492, 495, 503, 514, 528, 530, 531, 533, 541, 542, 543, 545, 549,\n", + " 554, 557, 578, 579, 580, 597])},\n", + " 5: {0: array([ 51, 57, 71, 81, 98, 104, 110, 123, 152, 161, 171, 188, 197,\n", + " 203, 208, 239, 246, 256, 313, 321, 343, 359, 400, 402, 472, 509,\n", + " 512, 516, 538, 540, 550, 553, 559, 564, 568]),\n", + " 1: array([ 1, 2, 5, 6, 9, 10, 15, 16, 17, 21, 22, 25, 26,\n", + " 27, 28, 31, 32, 35, 36, 39, 42, 49, 52, 53, 56, 58,\n", + " 59, 65, 67, 69, 70, 72, 74, 75, 77, 78, 82, 86, 92,\n", + " 96, 97, 99, 100, 102, 107, 108, 109, 111, 113, 114, 115, 116,\n", + " 118, 119, 122, 124, 127, 129, 132, 134, 135, 136, 137, 139, 140,\n", + " 141, 142, 146, 149, 151, 153, 154, 155, 157, 158, 162, 167, 168,\n", + " 169, 175, 176, 177, 179, 180, 181, 183, 184, 185, 187, 189, 192,\n", + " 194, 196, 198, 199, 200, 207, 210, 211, 214, 216, 217, 219, 220,\n", + " 222, 223, 225, 226, 227, 228, 230, 231, 232, 234, 235, 236, 240,\n", + " 241, 242, 244, 245, 247, 248, 249, 250, 251, 252, 253, 254, 259,\n", + " 262, 264, 266, 267, 270, 277, 278, 279, 282, 283, 284, 289, 290,\n", + " 291, 292, 293, 295, 296, 299, 300, 303, 304, 307, 308, 310, 311,\n", + " 312, 314, 316, 320, 322, 323, 329, 330, 331, 334, 336, 342, 344,\n", + " 345, 346, 347, 348, 351, 353, 354, 356, 360, 362, 364, 367, 368,\n", + " 370, 371, 374, 375, 379, 380, 383, 384, 387, 388, 390, 391, 392,\n", + " 393, 394, 395, 396, 399, 401, 403, 407, 409, 411, 412, 413, 417,\n", + " 419, 420, 422, 425, 426, 428, 429, 430, 435, 439, 440, 442, 443,\n", + " 447, 450, 451, 453, 454, 456, 457, 459, 462, 464, 467, 469, 470,\n", + " 473, 474, 475, 476, 481, 482, 484, 485, 486, 487, 488, 491, 493,\n", + " 494, 496, 498, 499, 501, 502, 504, 505, 506, 508, 510, 513, 518,\n", + " 519, 522, 523, 525, 529, 534, 535, 539, 546, 547, 551, 552, 555,\n", + " 558, 561, 562, 563, 565, 566, 567, 570, 571, 572, 573, 574, 575,\n", + " 577, 581, 582, 583, 584, 586, 588, 589, 591, 592, 593, 594, 595,\n", + " 598, 599]),\n", + " 2: array([ 7, 8, 40, 47, 48, 50, 55, 80, 83, 85, 90, 91, 105,\n", + " 117, 121, 125, 126, 130, 131, 133, 138, 145, 150, 156, 159, 160,\n", + " 166, 178, 195, 201, 204, 205, 209, 213, 218, 229, 233, 237, 238,\n", + " 260, 263, 268, 280, 285, 288, 297, 301, 306, 319, 325, 332, 333,\n", + " 335, 337, 339, 349, 357, 366, 372, 373, 376, 377, 381, 382, 397,\n", + " 404, 406, 414, 423, 427, 438, 452, 458, 478, 500, 520, 524, 532,\n", + " 536, 548, 556, 569, 590, 596]),\n", + " 3: array([ 3, 13, 14, 18, 29, 33, 43, 45, 46, 54, 63, 68, 88,\n", + " 89, 93, 148, 163, 174, 191, 193, 202, 212, 221, 243, 272, 286,\n", + " 287, 294, 309, 315, 340, 341, 350, 358, 361, 363, 369, 398, 405,\n", + " 410, 415, 416, 418, 421, 433, 441, 445, 448, 460, 461, 465, 466,\n", + " 468, 477, 483, 489, 497, 507, 511, 515, 517, 521, 526, 527, 537,\n", + " 544, 560, 576, 585, 587]),\n", + " 4: array([ 0, 4, 11, 12, 19, 20, 23, 24, 30, 34, 37, 38, 41,\n", + " 44, 60, 61, 62, 64, 66, 73, 76, 79, 84, 87, 94, 95,\n", + " 101, 103, 106, 112, 120, 128, 143, 144, 147, 164, 165, 170, 172,\n", + " 173, 182, 186, 190, 206, 215, 224, 255, 257, 258, 261, 265, 269,\n", + " 271, 273, 274, 275, 276, 281, 298, 302, 305, 317, 318, 324, 326,\n", + " 327, 328, 338, 352, 355, 365, 378, 385, 386, 389, 408, 424, 431,\n", + " 432, 434, 436, 437, 444, 446, 449, 455, 463, 471, 479, 480, 490,\n", + " 492, 495, 503, 514, 528, 530, 531, 533, 541, 542, 543, 545, 549,\n", + " 554, 557, 578, 579, 580, 597])},\n", + " 6: {0: array([ 51, 57, 71, 81, 98, 104, 110, 123, 152, 161, 171, 188, 197,\n", + " 203, 208, 239, 246, 256, 313, 321, 343, 359, 400, 402, 472, 509,\n", + " 512, 516, 538, 540, 550, 553, 559, 564, 568]),\n", + " 1: array([ 1, 2, 5, 6, 9, 10, 15, 16, 17, 21, 22, 25, 26,\n", + " 27, 28, 31, 32, 35, 36, 39, 42, 49, 52, 53, 56, 58,\n", + " 59, 65, 67, 69, 70, 72, 74, 75, 77, 78, 82, 86, 92,\n", + " 96, 97, 99, 100, 102, 107, 108, 109, 111, 113, 114, 115, 116,\n", + " 118, 119, 122, 124, 127, 129, 132, 134, 135, 136, 137, 139, 140,\n", + " 141, 142, 146, 149, 151, 153, 154, 155, 157, 158, 162, 167, 168,\n", + " 169, 175, 176, 177, 179, 180, 181, 183, 184, 185, 187, 189, 192,\n", + " 194, 196, 198, 199, 200, 207, 210, 211, 214, 216, 217, 219, 220,\n", + " 222, 223, 225, 226, 227, 228, 230, 231, 232, 234, 235, 236, 240,\n", + " 241, 242, 244, 245, 247, 248, 249, 250, 251, 252, 253, 254, 259,\n", + " 262, 264, 266, 267, 270, 277, 278, 279, 282, 283, 284, 289, 290,\n", + " 291, 292, 293, 295, 296, 299, 300, 303, 304, 307, 308, 310, 311,\n", + " 312, 314, 316, 320, 322, 323, 329, 330, 331, 334, 336, 342, 344,\n", + " 345, 346, 347, 348, 351, 353, 354, 356, 360, 362, 364, 367, 368,\n", + " 370, 371, 374, 375, 379, 380, 383, 384, 387, 388, 390, 391, 392,\n", + " 393, 394, 395, 396, 399, 401, 403, 407, 409, 411, 412, 413, 417,\n", + " 419, 420, 422, 425, 426, 428, 429, 430, 435, 439, 440, 442, 443,\n", + " 447, 450, 451, 453, 454, 456, 457, 459, 462, 464, 467, 469, 470,\n", + " 473, 474, 475, 476, 481, 482, 484, 485, 486, 487, 488, 491, 493,\n", + " 494, 496, 498, 499, 501, 502, 504, 505, 506, 508, 510, 513, 518,\n", + " 519, 522, 523, 525, 529, 534, 535, 539, 546, 547, 551, 552, 555,\n", + " 558, 561, 562, 563, 565, 566, 567, 570, 571, 572, 573, 574, 575,\n", + " 577, 581, 582, 583, 584, 586, 588, 589, 591, 592, 593, 594, 595,\n", + " 598, 599]),\n", + " 2: array([ 7, 8, 40, 47, 48, 50, 55, 80, 83, 85, 90, 91, 105,\n", + " 117, 121, 125, 126, 133, 138, 145, 150, 156, 159, 160, 166, 178,\n", + " 195, 201, 204, 205, 209, 213, 218, 229, 233, 237, 238, 260, 263,\n", + " 268, 280, 288, 297, 301, 306, 319, 332, 333, 335, 337, 339, 349,\n", + " 357, 366, 372, 373, 376, 377, 381, 382, 397, 404, 406, 414, 423,\n", + " 427, 438, 452, 458, 478, 500, 520, 524, 536, 556, 569, 590, 596]),\n", + " 3: array([ 3, 13, 14, 18, 29, 33, 43, 45, 46, 54, 63, 68, 88,\n", + " 89, 93, 148, 163, 174, 191, 193, 202, 212, 221, 243, 272, 286,\n", + " 287, 294, 309, 315, 340, 341, 350, 358, 361, 363, 369, 398, 405,\n", + " 410, 415, 416, 418, 421, 433, 441, 445, 448, 460, 461, 465, 466,\n", + " 468, 477, 483, 489, 497, 507, 511, 515, 517, 521, 526, 527, 537,\n", + " 544, 560, 576, 585, 587]),\n", + " 4: array([ 0, 4, 11, 19, 20, 30, 34, 37, 61, 62, 64, 66, 73,\n", + " 76, 79, 84, 87, 95, 101, 103, 106, 112, 120, 128, 147, 164,\n", + " 172, 173, 182, 186, 190, 215, 224, 255, 257, 261, 265, 269, 271,\n", + " 273, 274, 275, 276, 318, 324, 327, 328, 338, 352, 365, 378, 385,\n", + " 389, 408, 424, 431, 432, 434, 436, 437, 446, 449, 455, 471, 480,\n", + " 503, 528, 542, 543, 545, 549, 578, 579, 597]),\n", + " 5: array([ 12, 23, 24, 38, 41, 44, 60, 94, 130, 131, 143, 144, 165,\n", + " 170, 206, 258, 281, 285, 298, 302, 305, 317, 325, 326, 355, 386,\n", + " 444, 463, 479, 490, 492, 495, 514, 530, 531, 532, 533, 541, 548,\n", + " 554, 557, 580])},\n", + " 7: {0: array([ 51, 57, 71, 81, 98, 104, 110, 123, 152, 161, 171, 188, 197,\n", + " 203, 208, 239, 246, 256, 313, 321, 343, 359, 400, 402, 472, 512,\n", + " 516, 538, 540, 550, 553, 559, 564, 568]),\n", + " 1: array([ 1, 2, 5, 6, 9, 10, 15, 16, 17, 21, 22, 25, 26,\n", + " 27, 28, 31, 32, 35, 36, 39, 42, 49, 52, 53, 56, 58,\n", + " 59, 65, 67, 69, 70, 72, 74, 75, 77, 78, 82, 86, 92,\n", + " 96, 97, 99, 100, 102, 107, 108, 109, 111, 113, 114, 115, 116,\n", + " 118, 119, 122, 124, 127, 129, 132, 134, 135, 136, 137, 139, 140,\n", + " 141, 142, 146, 149, 151, 153, 154, 155, 157, 158, 162, 167, 168,\n", + " 169, 175, 176, 177, 179, 180, 181, 183, 184, 185, 187, 189, 192,\n", + " 194, 196, 198, 199, 200, 207, 210, 211, 214, 216, 217, 219, 220,\n", + " 222, 223, 225, 226, 227, 228, 230, 231, 232, 234, 235, 236, 240,\n", + " 241, 242, 244, 245, 247, 248, 249, 250, 251, 252, 253, 254, 259,\n", + " 262, 264, 266, 267, 270, 277, 278, 279, 282, 283, 284, 289, 290,\n", + " 291, 292, 293, 295, 296, 299, 300, 303, 304, 307, 308, 310, 311,\n", + " 312, 314, 316, 320, 322, 323, 329, 330, 331, 334, 336, 342, 344,\n", + " 345, 346, 347, 348, 351, 353, 354, 356, 360, 362, 364, 367, 368,\n", + " 370, 371, 374, 375, 379, 380, 383, 384, 387, 388, 390, 391, 392,\n", + " 393, 394, 395, 396, 399, 401, 403, 407, 409, 411, 412, 413, 417,\n", + " 419, 420, 422, 425, 426, 428, 429, 430, 435, 439, 440, 442, 443,\n", + " 447, 450, 451, 453, 454, 456, 457, 459, 462, 464, 467, 469, 470,\n", + " 473, 474, 475, 476, 481, 482, 484, 485, 486, 487, 488, 491, 493,\n", + " 494, 496, 498, 499, 501, 502, 504, 505, 506, 508, 510, 513, 518,\n", + " 519, 522, 523, 525, 529, 534, 535, 539, 546, 547, 551, 552, 555,\n", + " 558, 561, 562, 563, 565, 566, 567, 570, 571, 572, 573, 574, 575,\n", + " 577, 581, 582, 583, 584, 586, 588, 589, 591, 592, 593, 594, 595,\n", + " 598, 599]),\n", + " 2: array([ 7, 8, 40, 47, 48, 50, 55, 80, 83, 85, 90, 91, 105,\n", + " 117, 121, 125, 126, 133, 138, 145, 150, 156, 159, 160, 166, 178,\n", + " 195, 201, 204, 205, 209, 213, 218, 229, 233, 237, 238, 260, 263,\n", + " 268, 280, 288, 297, 301, 306, 319, 332, 333, 335, 337, 339, 349,\n", + " 357, 366, 372, 373, 376, 377, 381, 382, 397, 404, 406, 414, 423,\n", + " 427, 438, 452, 458, 478, 500, 520, 524, 536, 556, 569, 590, 596]),\n", + " 3: array([ 29, 45, 68, 477, 483, 509, 515]),\n", + " 4: array([ 3, 13, 14, 18, 33, 43, 46, 54, 63, 88, 89, 93, 148,\n", + " 163, 174, 191, 193, 202, 212, 221, 243, 272, 286, 287, 294, 309,\n", + " 315, 340, 341, 350, 358, 361, 363, 369, 398, 405, 410, 415, 416,\n", + " 418, 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497,\n", + " 507, 511, 517, 521, 526, 527, 537, 544, 560, 576, 585, 587]),\n", + " 5: array([ 0, 4, 11, 19, 20, 30, 34, 37, 61, 62, 64, 66, 73,\n", + " 76, 79, 84, 87, 95, 101, 103, 106, 112, 120, 128, 147, 164,\n", + " 172, 173, 182, 186, 190, 215, 224, 255, 257, 261, 265, 269, 271,\n", + " 273, 274, 275, 276, 318, 324, 327, 328, 338, 352, 365, 378, 385,\n", + " 389, 408, 424, 431, 432, 434, 436, 437, 446, 449, 455, 471, 480,\n", + " 503, 528, 542, 543, 545, 549, 578, 579, 597]),\n", + " 6: array([ 12, 23, 24, 38, 41, 44, 60, 94, 130, 131, 143, 144, 165,\n", + " 170, 206, 258, 281, 285, 298, 302, 305, 317, 325, 326, 355, 386,\n", + " 444, 463, 479, 490, 492, 495, 514, 530, 531, 532, 533, 541, 548,\n", + " 554, 557, 580])},\n", + " 8: {0: array([ 51, 57, 71, 81, 98, 104, 110, 123, 152, 161, 171, 188, 197,\n", + " 203, 208, 239, 246, 256, 313, 321, 343, 359, 400, 402, 472, 512,\n", + " 516, 538, 540, 550, 553, 559, 564, 568]),\n", + " 1: array([ 1, 2, 5, 6, 9, 10, 15, 16, 17, 21, 22, 25, 26,\n", + " 27, 28, 31, 32, 35, 36, 39, 42, 49, 52, 53, 56, 58,\n", + " 59, 65, 67, 69, 70, 72, 74, 75, 77, 78, 82, 86, 92,\n", + " 96, 97, 99, 100, 102, 107, 108, 109, 111, 113, 114, 115, 116,\n", + " 118, 119, 122, 124, 127, 129, 132, 134, 135, 136, 137, 139, 140,\n", + " 141, 142, 146, 149, 151, 153, 154, 155, 157, 158, 162, 167, 168,\n", + " 169, 175, 176, 177, 179, 180, 181, 183, 184, 185, 187, 189, 192,\n", + " 194, 196, 198, 199, 200, 207, 210, 211, 214, 216, 217, 219, 220,\n", + " 222, 223, 225, 226, 227, 228, 230, 231, 232, 234, 235, 236, 240,\n", + " 241, 242, 244, 245, 247, 248, 249, 250, 251, 252, 253, 254, 259,\n", + " 262, 264, 266, 267, 270, 277, 278, 279, 282, 283, 284, 289, 290,\n", + " 291, 292, 293, 295, 296, 299, 300, 303, 304, 307, 308, 310, 311,\n", + " 312, 314, 316, 320, 322, 323, 329, 330, 331, 334, 336, 342, 344,\n", + " 345, 346, 347, 348, 351, 353, 354, 356, 360, 362, 364, 367, 368,\n", + " 370, 371, 374, 375, 379, 380, 383, 384, 387, 388, 390, 391, 392,\n", + " 393, 394, 395, 396, 399, 401, 403, 407, 409, 411, 412, 413, 417,\n", + " 419, 420, 422, 425, 426, 428, 429, 430, 435, 439, 440, 442, 443,\n", + " 447, 450, 451, 453, 454, 456, 457, 459, 462, 464, 467, 469, 470,\n", + " 473, 474, 475, 476, 481, 482, 484, 485, 486, 487, 488, 491, 493,\n", + " 494, 496, 498, 499, 501, 502, 504, 505, 506, 508, 510, 513, 518,\n", + " 519, 522, 523, 525, 529, 534, 535, 539, 546, 547, 551, 552, 555,\n", + " 558, 561, 562, 563, 565, 566, 567, 570, 571, 572, 573, 574, 575,\n", + " 577, 581, 582, 583, 584, 586, 588, 589, 591, 592, 593, 594, 595,\n", + " 598, 599]),\n", + " 2: array([ 7, 8, 40, 48, 50, 55, 80, 83, 85, 90, 91, 105, 117,\n", + " 121, 125, 126, 133, 138, 145, 150, 156, 159, 160, 166, 178, 195,\n", + " 204, 205, 209, 213, 218, 229, 233, 238, 260, 263, 268, 280, 288,\n", + " 297, 301, 306, 319, 333, 335, 337, 339, 349, 357, 366, 372, 373,\n", + " 377, 382, 397, 404, 406, 414, 423, 427, 438, 452, 458, 478, 500,\n", + " 520, 524, 536, 556, 569, 590, 596]),\n", + " 3: array([ 29, 45, 68, 477, 483, 509, 515]),\n", + " 4: array([ 3, 13, 14, 18, 33, 43, 46, 54, 63, 88, 89, 93, 148,\n", + " 163, 174, 191, 193, 202, 212, 221, 243, 272, 286, 287, 294, 309,\n", + " 315, 340, 341, 350, 358, 361, 363, 369, 398, 405, 410, 415, 416,\n", + " 418, 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497,\n", + " 507, 511, 517, 521, 526, 527, 537, 544, 560, 576, 585, 587]),\n", + " 5: array([ 0, 4, 11, 19, 20, 30, 34, 37, 61, 62, 64, 66, 73,\n", + " 76, 79, 84, 87, 95, 101, 103, 106, 112, 120, 128, 147, 164,\n", + " 172, 173, 182, 186, 190, 215, 224, 255, 257, 261, 265, 269, 271,\n", + " 273, 274, 275, 276, 318, 324, 327, 328, 338, 352, 365, 378, 385,\n", + " 389, 408, 424, 431, 432, 434, 436, 437, 446, 449, 455, 471, 480,\n", + " 503, 528, 542, 543, 545, 549, 578, 579, 597]),\n", + " 6: array([ 12, 23, 24, 38, 41, 44, 60, 94, 130, 131, 143, 144, 165,\n", + " 170, 206, 258, 281, 285, 298, 302, 305, 317, 325, 326, 355, 386,\n", + " 444, 463, 479, 490, 492, 495, 514, 530, 531, 532, 541, 548, 554,\n", + " 557, 580]),\n", + " 7: array([ 47, 201, 237, 332, 376, 381, 533])},\n", + " 9: {0: array([ 51, 57, 71, 81, 98, 104, 110, 123, 152, 161, 171, 188, 197,\n", + " 203, 208, 239, 246, 256, 313, 321, 343, 359, 400, 402, 472, 512,\n", + " 516, 538, 540, 550, 553, 559, 564, 568]),\n", + " 1: array([ 2, 5, 9, 10, 25, 26, 31, 32, 35, 52, 59, 65, 67,\n", + " 69, 70, 74, 75, 82, 86, 97, 99, 102, 108, 111, 114, 127,\n", + " 141, 146, 149, 151, 153, 154, 155, 158, 162, 169, 175, 179, 181,\n", + " 194, 199, 200, 207, 210, 211, 216, 222, 223, 225, 227, 235, 236,\n", + " 245, 250, 251, 253, 254, 262, 264, 270, 278, 283, 291, 292, 295,\n", + " 296, 299, 303, 304, 307, 310, 314, 316, 320, 323, 329, 330, 334,\n", + " 347, 348, 351, 360, 364, 367, 371, 374, 375, 380, 383, 384, 387,\n", + " 388, 392, 395, 401, 411, 413, 417, 422, 425, 426, 428, 429, 430,\n", + " 439, 442, 443, 447, 450, 454, 456, 457, 459, 464, 470, 475, 476,\n", + " 481, 487, 491, 493, 494, 506, 513, 518, 519, 523, 525, 534, 546,\n", + " 547, 558, 562, 566, 572, 573, 577, 582, 583, 586, 588, 595, 599]),\n", + " 2: array([ 7, 8, 40, 48, 50, 55, 80, 83, 85, 90, 91, 105, 117,\n", + " 121, 125, 126, 133, 138, 145, 150, 156, 159, 160, 166, 178, 195,\n", + " 204, 205, 209, 213, 218, 229, 233, 238, 260, 263, 268, 280, 288,\n", + " 297, 301, 306, 319, 333, 335, 337, 339, 349, 357, 366, 372, 373,\n", + " 377, 382, 397, 404, 406, 414, 423, 427, 438, 452, 458, 478, 500,\n", + " 520, 524, 536, 556, 569, 590, 596]),\n", + " 3: array([ 1, 6, 15, 16, 17, 21, 22, 27, 28, 36, 39, 42, 49,\n", + " 53, 56, 58, 72, 77, 78, 92, 96, 100, 107, 109, 113, 115,\n", + " 116, 118, 119, 122, 124, 129, 132, 134, 135, 136, 137, 139, 140,\n", + " 142, 157, 167, 168, 176, 177, 180, 183, 184, 185, 187, 189, 192,\n", + " 196, 198, 214, 217, 219, 220, 226, 228, 230, 231, 232, 234, 240,\n", + " 241, 242, 244, 247, 248, 249, 252, 259, 266, 267, 277, 279, 282,\n", + " 284, 289, 290, 293, 300, 308, 311, 312, 322, 331, 336, 342, 344,\n", + " 345, 346, 353, 354, 356, 362, 368, 370, 379, 390, 391, 393, 394,\n", + " 396, 399, 403, 407, 409, 412, 419, 420, 435, 440, 451, 453, 462,\n", + " 467, 469, 473, 474, 482, 484, 485, 486, 488, 496, 498, 499, 501,\n", + " 502, 504, 505, 508, 510, 522, 529, 535, 539, 551, 552, 555, 561,\n", + " 563, 565, 567, 570, 571, 574, 575, 581, 584, 589, 591, 592, 593,\n", + " 594, 598]),\n", + " 4: array([ 29, 45, 68, 477, 483, 509, 515]),\n", + " 5: array([ 3, 13, 14, 18, 33, 43, 46, 54, 63, 88, 89, 93, 148,\n", + " 163, 174, 191, 193, 202, 212, 221, 243, 272, 286, 287, 294, 309,\n", + " 315, 340, 341, 350, 358, 361, 363, 369, 398, 405, 410, 415, 416,\n", + " 418, 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497,\n", + " 507, 511, 517, 521, 526, 527, 537, 544, 560, 576, 585, 587]),\n", + " 6: array([ 0, 4, 11, 19, 20, 30, 34, 37, 61, 62, 64, 66, 73,\n", + " 76, 79, 84, 87, 95, 101, 103, 106, 112, 120, 128, 147, 164,\n", + " 172, 173, 182, 186, 190, 215, 224, 255, 257, 261, 265, 269, 271,\n", + " 273, 274, 275, 276, 318, 324, 327, 328, 338, 352, 365, 378, 385,\n", + " 389, 408, 424, 431, 432, 434, 436, 437, 446, 449, 455, 471, 480,\n", + " 503, 528, 542, 543, 545, 549, 578, 579, 597]),\n", + " 7: array([ 12, 23, 24, 38, 41, 44, 60, 94, 130, 131, 143, 144, 165,\n", + " 170, 206, 258, 281, 285, 298, 302, 305, 317, 325, 326, 355, 386,\n", + " 444, 463, 479, 490, 492, 495, 514, 530, 531, 532, 541, 548, 554,\n", + " 557, 580]),\n", + " 8: array([ 47, 201, 237, 332, 376, 381, 533])},\n", + " 10: {0: array([ 81, 110, 152, 161, 188, 197, 313, 321, 343, 359, 402, 516, 553,\n", + " 568]),\n", + " 1: array([ 2, 5, 9, 10, 25, 26, 31, 32, 35, 52, 59, 65, 67,\n", + " 69, 70, 74, 75, 82, 86, 97, 99, 102, 108, 111, 114, 127,\n", + " 141, 146, 149, 151, 153, 154, 155, 158, 162, 169, 175, 179, 181,\n", + " 194, 199, 200, 207, 210, 211, 216, 222, 223, 225, 227, 235, 236,\n", + " 245, 250, 251, 253, 254, 262, 264, 270, 278, 283, 291, 292, 295,\n", + " 296, 299, 303, 304, 307, 310, 314, 316, 320, 323, 329, 330, 334,\n", + " 347, 348, 351, 360, 364, 367, 371, 374, 375, 380, 383, 384, 387,\n", + " 388, 392, 395, 401, 411, 413, 417, 422, 425, 426, 428, 429, 430,\n", + " 439, 442, 443, 447, 450, 454, 456, 457, 459, 464, 470, 475, 476,\n", + " 481, 487, 491, 493, 494, 506, 513, 518, 519, 523, 525, 534, 546,\n", + " 547, 558, 562, 566, 572, 573, 577, 582, 583, 586, 588, 595, 599]),\n", + " 2: array([ 51, 57, 71, 98, 104, 123, 171, 203, 208, 239, 246, 256, 400,\n", + " 472, 512, 538, 540, 550, 559, 564]),\n", + " 3: array([ 7, 8, 40, 48, 50, 55, 80, 83, 85, 90, 91, 105, 117,\n", + " 121, 125, 126, 133, 138, 145, 150, 156, 159, 160, 166, 178, 195,\n", + " 204, 205, 209, 213, 218, 229, 233, 238, 260, 263, 268, 280, 288,\n", + " 297, 301, 306, 319, 333, 335, 337, 339, 349, 357, 366, 372, 373,\n", + " 377, 382, 397, 404, 406, 414, 423, 427, 438, 452, 458, 478, 500,\n", + " 520, 524, 536, 556, 569, 590, 596]),\n", + " 4: array([ 1, 6, 15, 16, 17, 21, 22, 27, 28, 36, 39, 42, 49,\n", + " 53, 56, 58, 72, 77, 78, 92, 96, 100, 107, 109, 113, 115,\n", + " 116, 118, 119, 122, 124, 129, 132, 134, 135, 136, 137, 139, 140,\n", + " 142, 157, 167, 168, 176, 177, 180, 183, 184, 185, 187, 189, 192,\n", + " 196, 198, 214, 217, 219, 220, 226, 228, 230, 231, 232, 234, 240,\n", + " 241, 242, 244, 247, 248, 249, 252, 259, 266, 267, 277, 279, 282,\n", + " 284, 289, 290, 293, 300, 308, 311, 312, 322, 331, 336, 342, 344,\n", + " 345, 346, 353, 354, 356, 362, 368, 370, 379, 390, 391, 393, 394,\n", + " 396, 399, 403, 407, 409, 412, 419, 420, 435, 440, 451, 453, 462,\n", + " 467, 469, 473, 474, 482, 484, 485, 486, 488, 496, 498, 499, 501,\n", + " 502, 504, 505, 508, 510, 522, 529, 535, 539, 551, 552, 555, 561,\n", + " 563, 565, 567, 570, 571, 574, 575, 581, 584, 589, 591, 592, 593,\n", + " 594, 598]),\n", + " 5: array([ 29, 45, 68, 477, 483, 509, 515]),\n", + " 6: array([ 3, 13, 14, 18, 33, 43, 46, 54, 63, 88, 89, 93, 148,\n", + " 163, 174, 191, 193, 202, 212, 221, 243, 272, 286, 287, 294, 309,\n", + " 315, 340, 341, 350, 358, 361, 363, 369, 398, 405, 410, 415, 416,\n", + " 418, 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497,\n", + " 507, 511, 517, 521, 526, 527, 537, 544, 560, 576, 585, 587]),\n", + " 7: array([ 0, 4, 11, 19, 20, 30, 34, 37, 61, 62, 64, 66, 73,\n", + " 76, 79, 84, 87, 95, 101, 103, 106, 112, 120, 128, 147, 164,\n", + " 172, 173, 182, 186, 190, 215, 224, 255, 257, 261, 265, 269, 271,\n", + " 273, 274, 275, 276, 318, 324, 327, 328, 338, 352, 365, 378, 385,\n", + " 389, 408, 424, 431, 432, 434, 436, 437, 446, 449, 455, 471, 480,\n", + " 503, 528, 542, 543, 545, 549, 578, 579, 597]),\n", + " 8: array([ 12, 23, 24, 38, 41, 44, 60, 94, 130, 131, 143, 144, 165,\n", + " 170, 206, 258, 281, 285, 298, 302, 305, 317, 325, 326, 355, 386,\n", + " 444, 463, 479, 490, 492, 495, 514, 530, 531, 532, 541, 548, 554,\n", + " 557, 580]),\n", + " 9: array([ 47, 201, 237, 332, 376, 381, 533])}},\n", + " 'l2_ranking_nonloo': {2: {0: array([ 3, 13, 14, 18, 33, 43, 46, 51, 54, 57, 63, 71, 81,\n", + " 88, 89, 93, 98, 104, 110, 123, 148, 152, 161, 163, 171, 174,\n", + " 188, 191, 193, 197, 202, 203, 208, 212, 221, 239, 243, 246, 256,\n", + " 272, 286, 287, 294, 309, 313, 315, 321, 340, 341, 343, 358, 359,\n", + " 361, 363, 369, 398, 400, 402, 405, 410, 415, 416, 418, 421, 433,\n", + " 441, 445, 448, 460, 461, 465, 466, 468, 472, 489, 497, 507, 509,\n", + " 511, 512, 516, 517, 521, 526, 527, 537, 538, 540, 544, 550, 553,\n", + " 559, 560, 564, 568, 576, 585, 587]),\n", + " 1: array([ 0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15,\n", + " 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,\n", + " 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 42, 44,\n", + " 45, 47, 48, 49, 50, 52, 53, 55, 56, 58, 59, 60, 61,\n", + " 62, 64, 65, 66, 67, 68, 69, 70, 72, 73, 74, 75, 76,\n", + " 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 90, 91, 92,\n", + " 94, 95, 96, 97, 99, 100, 101, 102, 103, 105, 106, 107, 108,\n", + " 109, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122,\n", + " 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136,\n", + " 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 149, 150,\n", + " 151, 153, 154, 155, 156, 157, 158, 159, 160, 162, 164, 165, 166,\n", + " 167, 168, 169, 170, 172, 173, 175, 176, 177, 178, 179, 180, 181,\n", + " 182, 183, 184, 185, 186, 187, 189, 190, 192, 194, 195, 196, 198,\n", + " 199, 200, 201, 204, 205, 206, 207, 209, 210, 211, 213, 214, 215,\n", + " 216, 217, 218, 219, 220, 222, 223, 224, 225, 226, 227, 228, 229,\n", + " 230, 231, 232, 233, 234, 235, 236, 237, 238, 240, 241, 242, 244,\n", + " 245, 247, 248, 249, 250, 251, 252, 253, 254, 255, 257, 258, 259,\n", + " 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 273,\n", + " 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 288,\n", + " 289, 290, 291, 292, 293, 295, 296, 297, 298, 299, 300, 301, 302,\n", + " 303, 304, 305, 306, 307, 308, 310, 311, 312, 314, 316, 317, 318,\n", + " 319, 320, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332,\n", + " 333, 334, 335, 336, 337, 338, 339, 342, 344, 345, 346, 347, 348,\n", + " 349, 350, 351, 352, 353, 354, 355, 356, 357, 360, 362, 364, 365,\n", + " 366, 367, 368, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379,\n", + " 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392,\n", + " 393, 394, 395, 396, 397, 399, 401, 403, 404, 406, 407, 408, 409,\n", + " 411, 412, 413, 414, 417, 419, 420, 422, 423, 424, 425, 426, 427,\n", + " 428, 429, 430, 431, 432, 434, 435, 436, 437, 438, 439, 440, 442,\n", + " 443, 444, 446, 447, 449, 450, 451, 452, 453, 454, 455, 456, 457,\n", + " 458, 459, 462, 463, 464, 467, 469, 470, 471, 473, 474, 475, 476,\n", + " 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 490,\n", + " 491, 492, 493, 494, 495, 496, 498, 499, 500, 501, 502, 503, 504,\n", + " 505, 506, 508, 510, 513, 514, 515, 518, 519, 520, 522, 523, 524,\n", + " 525, 528, 529, 530, 531, 532, 533, 534, 535, 536, 539, 541, 542,\n", + " 543, 545, 546, 547, 548, 549, 551, 552, 554, 555, 556, 557, 558,\n", + " 561, 562, 563, 565, 566, 567, 569, 570, 571, 572, 573, 574, 575,\n", + " 577, 578, 579, 580, 581, 582, 583, 584, 586, 588, 589, 590, 591,\n", + " 592, 593, 594, 595, 596, 597, 598, 599])},\n", + " 3: {0: array([ 57, 71, 81, 98, 123, 152, 161, 171, 188, 197, 203, 208, 239,\n", + " 246, 256, 309, 313, 321, 343, 400, 402, 418, 472, 512, 516, 538,\n", + " 550, 553, 559, 568]),\n", + " 1: array([ 0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15,\n", + " 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,\n", + " 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 42, 44,\n", + " 45, 47, 48, 49, 50, 52, 53, 55, 56, 58, 59, 60, 61,\n", + " 62, 64, 65, 66, 67, 68, 69, 70, 72, 73, 74, 75, 76,\n", + " 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 90, 91, 92,\n", + " 94, 95, 96, 97, 99, 100, 101, 102, 103, 105, 106, 107, 108,\n", + " 109, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122,\n", + " 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136,\n", + " 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 149, 150,\n", + " 151, 153, 154, 155, 156, 157, 158, 159, 160, 162, 164, 165, 166,\n", + " 167, 168, 169, 170, 172, 173, 175, 176, 177, 178, 179, 180, 181,\n", + " 182, 183, 184, 185, 186, 187, 189, 190, 192, 194, 195, 196, 198,\n", + " 199, 200, 201, 204, 205, 206, 207, 209, 210, 211, 213, 214, 215,\n", + " 216, 217, 218, 219, 220, 222, 223, 224, 225, 226, 227, 228, 229,\n", + " 230, 231, 232, 233, 234, 235, 236, 238, 240, 241, 242, 244, 245,\n", + " 247, 248, 249, 250, 251, 252, 253, 254, 255, 257, 258, 259, 260,\n", + " 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 273, 274,\n", + " 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 288, 289,\n", + " 290, 291, 292, 293, 295, 296, 297, 298, 299, 300, 301, 302, 303,\n", + " 304, 305, 306, 307, 308, 310, 311, 312, 314, 316, 317, 318, 319,\n", + " 320, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333,\n", + " 334, 335, 336, 337, 338, 339, 342, 344, 345, 346, 347, 348, 349,\n", + " 351, 352, 353, 354, 355, 356, 357, 360, 362, 364, 365, 366, 367,\n", + " 368, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381,\n", + " 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394,\n", + " 395, 396, 397, 399, 401, 403, 404, 406, 407, 408, 409, 411, 412,\n", + " 413, 414, 417, 419, 420, 422, 423, 424, 425, 426, 427, 428, 429,\n", + " 430, 431, 432, 434, 435, 436, 437, 438, 439, 440, 442, 443, 444,\n", + " 446, 447, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459,\n", + " 462, 463, 464, 467, 469, 470, 471, 473, 474, 475, 476, 477, 478,\n", + " 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 490, 491, 492,\n", + " 493, 494, 495, 496, 498, 499, 500, 501, 502, 503, 504, 505, 506,\n", + " 508, 510, 513, 514, 515, 518, 519, 520, 522, 523, 524, 525, 528,\n", + " 529, 530, 531, 532, 533, 534, 535, 536, 539, 541, 542, 543, 545,\n", + " 546, 547, 548, 549, 551, 552, 554, 555, 556, 557, 558, 561, 562,\n", + " 563, 565, 566, 567, 569, 570, 571, 572, 573, 574, 575, 577, 578,\n", + " 579, 580, 581, 582, 583, 584, 586, 588, 589, 590, 591, 592, 593,\n", + " 594, 595, 596, 597, 598, 599]),\n", + " 2: array([ 3, 13, 14, 18, 33, 43, 46, 51, 54, 63, 88, 89, 93,\n", + " 104, 110, 148, 163, 174, 191, 193, 202, 212, 221, 237, 243, 272,\n", + " 286, 287, 294, 315, 340, 341, 350, 358, 359, 361, 363, 369, 398,\n", + " 405, 410, 415, 416, 421, 433, 441, 445, 448, 460, 461, 465, 466,\n", + " 468, 489, 497, 507, 509, 511, 517, 521, 526, 527, 537, 540, 544,\n", + " 560, 564, 576, 585, 587])},\n", + " 4: {0: array([ 57, 71, 81, 98, 123, 152, 161, 171, 188, 197, 203, 208, 239,\n", + " 246, 256, 309, 313, 321, 343, 400, 402, 418, 472, 512, 516, 538,\n", + " 550, 553, 559, 568]),\n", + " 1: array([ 0, 1, 2, 4, 5, 6, 9, 10, 11, 15, 16, 17, 19,\n", + " 20, 21, 22, 25, 26, 27, 28, 30, 31, 32, 34, 35, 36,\n", + " 37, 39, 45, 49, 52, 53, 56, 58, 59, 61, 62, 65, 66,\n", + " 67, 69, 70, 72, 73, 74, 75, 76, 77, 78, 79, 82, 84,\n", + " 86, 87, 92, 95, 96, 97, 99, 100, 101, 102, 103, 106, 107,\n", + " 108, 109, 111, 112, 113, 114, 115, 116, 118, 119, 120, 122, 124,\n", + " 127, 129, 132, 134, 135, 136, 137, 139, 140, 141, 142, 146, 147,\n", + " 149, 151, 153, 154, 155, 157, 158, 162, 164, 167, 168, 169, 172,\n", + " 175, 176, 177, 179, 180, 181, 182, 183, 184, 185, 187, 189, 190,\n", + " 192, 194, 196, 198, 199, 200, 207, 210, 211, 214, 215, 216, 217,\n", + " 219, 220, 222, 223, 224, 225, 226, 227, 228, 230, 231, 232, 234,\n", + " 235, 236, 240, 241, 242, 244, 245, 247, 248, 249, 250, 251, 252,\n", + " 253, 254, 255, 259, 262, 264, 266, 267, 269, 270, 273, 275, 276,\n", + " 277, 278, 279, 282, 283, 284, 289, 290, 291, 292, 293, 295, 296,\n", + " 299, 300, 303, 304, 307, 308, 310, 311, 312, 314, 316, 318, 320,\n", + " 322, 323, 324, 327, 328, 329, 330, 331, 334, 336, 338, 342, 344,\n", + " 345, 346, 347, 348, 351, 352, 353, 354, 356, 360, 362, 364, 365,\n", + " 367, 368, 370, 371, 374, 375, 379, 380, 383, 384, 385, 387, 388,\n", + " 389, 390, 391, 392, 393, 394, 395, 396, 399, 401, 403, 407, 409,\n", + " 411, 412, 413, 417, 419, 420, 422, 424, 425, 426, 428, 429, 430,\n", + " 432, 434, 435, 436, 437, 439, 440, 442, 443, 446, 447, 449, 450,\n", + " 451, 453, 454, 455, 456, 457, 459, 462, 464, 467, 469, 470, 471,\n", + " 473, 474, 475, 476, 480, 481, 482, 484, 485, 486, 487, 488, 491,\n", + " 493, 494, 496, 498, 499, 501, 502, 503, 504, 505, 506, 508, 510,\n", + " 513, 518, 519, 522, 523, 525, 528, 529, 534, 535, 539, 542, 543,\n", + " 545, 546, 547, 549, 552, 555, 558, 561, 562, 563, 565, 566, 567,\n", + " 570, 571, 572, 573, 574, 575, 577, 578, 581, 582, 583, 584, 586,\n", + " 588, 589, 591, 592, 593, 594, 595, 597, 598, 599]),\n", + " 2: array([ 7, 8, 12, 23, 24, 29, 38, 40, 41, 42, 44, 47, 48,\n", + " 50, 55, 60, 64, 68, 80, 83, 85, 90, 91, 94, 105, 110,\n", + " 117, 121, 125, 126, 128, 130, 131, 133, 138, 143, 144, 145, 150,\n", + " 156, 159, 160, 165, 166, 170, 173, 178, 186, 195, 201, 204, 205,\n", + " 206, 209, 213, 218, 229, 233, 237, 238, 257, 258, 260, 261, 263,\n", + " 265, 268, 271, 274, 280, 281, 285, 288, 297, 298, 301, 302, 305,\n", + " 306, 317, 319, 325, 326, 332, 333, 335, 337, 339, 349, 350, 355,\n", + " 357, 366, 372, 373, 376, 377, 378, 381, 382, 386, 397, 404, 406,\n", + " 408, 414, 423, 427, 431, 438, 444, 452, 458, 463, 477, 478, 479,\n", + " 483, 490, 492, 495, 500, 514, 515, 520, 524, 530, 531, 532, 533,\n", + " 536, 541, 548, 551, 554, 556, 557, 569, 579, 580, 590, 596]),\n", + " 3: array([ 3, 13, 14, 18, 33, 43, 46, 51, 54, 63, 88, 89, 93,\n", + " 104, 148, 163, 174, 191, 193, 202, 212, 221, 243, 272, 286, 287,\n", + " 294, 315, 340, 341, 358, 359, 361, 363, 369, 398, 405, 410, 415,\n", + " 416, 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497,\n", + " 507, 509, 511, 517, 521, 526, 527, 537, 540, 544, 560, 564, 576,\n", + " 585, 587])},\n", + " 5: {0: array([ 57, 71, 81, 98, 123, 152, 161, 171, 188, 197, 203, 208, 239,\n", + " 246, 256, 309, 313, 321, 343, 400, 402, 418, 472, 512, 516, 538,\n", + " 550, 553, 559, 568]),\n", + " 1: array([ 0, 1, 2, 4, 5, 6, 9, 10, 11, 15, 16, 17, 19,\n", + " 20, 21, 22, 25, 26, 27, 28, 30, 31, 32, 34, 35, 36,\n", + " 37, 39, 45, 49, 52, 53, 56, 58, 59, 61, 62, 65, 66,\n", + " 67, 69, 70, 72, 73, 74, 75, 76, 77, 78, 79, 82, 84,\n", + " 86, 87, 92, 95, 96, 97, 99, 100, 101, 102, 103, 106, 107,\n", + " 108, 109, 111, 112, 113, 114, 115, 116, 118, 119, 120, 122, 124,\n", + " 127, 129, 132, 134, 135, 136, 137, 139, 140, 141, 142, 146, 147,\n", + " 149, 151, 153, 154, 155, 157, 158, 162, 164, 167, 168, 169, 172,\n", + " 175, 176, 177, 179, 180, 181, 182, 183, 184, 185, 187, 189, 190,\n", + " 192, 194, 196, 198, 199, 200, 207, 210, 211, 214, 215, 216, 217,\n", + " 219, 220, 222, 223, 224, 225, 226, 227, 228, 230, 231, 232, 234,\n", + " 235, 236, 240, 241, 242, 244, 245, 247, 248, 249, 250, 251, 252,\n", + " 253, 254, 255, 259, 262, 264, 266, 267, 269, 270, 273, 275, 276,\n", + " 277, 278, 279, 282, 283, 284, 289, 290, 291, 292, 293, 295, 296,\n", + " 299, 300, 303, 304, 307, 308, 310, 311, 312, 314, 316, 318, 320,\n", + " 322, 323, 324, 327, 328, 329, 330, 331, 334, 336, 338, 342, 344,\n", + " 345, 346, 347, 348, 351, 352, 353, 354, 356, 360, 362, 364, 365,\n", + " 367, 368, 370, 371, 374, 375, 379, 380, 383, 384, 385, 387, 388,\n", + " 389, 390, 391, 392, 393, 394, 395, 396, 399, 401, 403, 407, 409,\n", + " 411, 412, 413, 417, 419, 420, 422, 424, 425, 426, 428, 429, 430,\n", + " 432, 434, 435, 436, 437, 439, 440, 442, 443, 446, 447, 449, 450,\n", + " 451, 453, 454, 455, 456, 457, 459, 462, 464, 467, 469, 470, 471,\n", + " 473, 474, 475, 476, 480, 481, 482, 484, 485, 486, 487, 488, 491,\n", + " 493, 494, 496, 498, 499, 501, 502, 503, 504, 505, 506, 508, 510,\n", + " 513, 518, 519, 522, 523, 525, 528, 529, 534, 535, 539, 542, 543,\n", + " 545, 546, 547, 549, 552, 555, 558, 561, 562, 563, 565, 566, 567,\n", + " 570, 571, 572, 573, 574, 575, 577, 578, 581, 582, 583, 584, 586,\n", + " 588, 589, 591, 592, 593, 594, 595, 597, 598, 599]),\n", + " 2: array([ 7, 8, 12, 23, 24, 29, 38, 40, 41, 42, 44, 47, 48,\n", + " 50, 55, 60, 64, 68, 80, 83, 85, 90, 91, 94, 105, 110,\n", + " 117, 121, 125, 126, 128, 130, 131, 133, 138, 143, 144, 145, 150,\n", + " 156, 159, 160, 165, 166, 170, 173, 178, 186, 195, 201, 204, 205,\n", + " 206, 209, 213, 218, 229, 233, 237, 238, 257, 258, 260, 261, 263,\n", + " 265, 268, 271, 274, 280, 281, 285, 288, 297, 298, 301, 302, 305,\n", + " 306, 317, 319, 325, 326, 332, 333, 335, 337, 339, 349, 350, 355,\n", + " 357, 366, 372, 373, 376, 377, 378, 381, 382, 386, 397, 404, 406,\n", + " 408, 414, 423, 427, 431, 438, 444, 452, 458, 463, 477, 478, 479,\n", + " 483, 490, 492, 495, 500, 514, 515, 520, 524, 530, 531, 532, 533,\n", + " 536, 541, 548, 551, 554, 556, 557, 569, 579, 580, 590, 596]),\n", + " 3: array([ 3, 13, 14, 18, 33, 43, 46, 51, 54, 63, 88, 89, 93,\n", + " 104, 148, 163, 174, 191, 193, 202, 212, 221, 243, 272, 286, 287,\n", + " 294, 315, 340, 341, 358, 359, 361, 363, 369, 398, 405, 410, 415,\n", + " 416, 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497,\n", + " 507, 509, 511, 517, 521, 526, 527, 537, 540, 544, 560, 564, 576,\n", + " 585, 587]),\n", + " 4: array([], dtype=int64)},\n", + " 6: {0: array([ 57, 71, 81, 98, 123, 152, 161, 171, 188, 197, 203, 208, 239,\n", + " 246, 256, 309, 313, 321, 343, 400, 402, 418, 472, 512, 516, 538,\n", + " 550, 553, 559, 568]),\n", + " 1: array([ 0, 1, 2, 4, 5, 6, 9, 10, 11, 15, 16, 17, 19,\n", + " 20, 21, 22, 25, 26, 27, 28, 30, 31, 32, 34, 35, 36,\n", + " 37, 39, 45, 49, 52, 53, 56, 58, 59, 61, 62, 65, 66,\n", + " 67, 69, 70, 72, 73, 74, 75, 76, 77, 78, 79, 82, 84,\n", + " 86, 87, 92, 95, 96, 97, 99, 100, 101, 102, 103, 106, 107,\n", + " 108, 109, 111, 112, 113, 114, 115, 116, 118, 119, 120, 122, 124,\n", + " 127, 129, 132, 134, 135, 136, 137, 139, 140, 141, 142, 146, 147,\n", + " 149, 151, 153, 154, 155, 157, 158, 162, 164, 167, 168, 169, 172,\n", + " 175, 176, 177, 179, 180, 181, 182, 183, 184, 185, 187, 189, 190,\n", + " 192, 194, 196, 198, 199, 200, 207, 210, 211, 214, 215, 216, 217,\n", + " 219, 220, 222, 223, 224, 225, 226, 227, 228, 230, 231, 232, 234,\n", + " 235, 236, 240, 241, 242, 244, 245, 247, 248, 249, 250, 251, 252,\n", + " 253, 254, 255, 259, 262, 264, 266, 267, 269, 270, 273, 275, 276,\n", + " 277, 278, 279, 282, 283, 284, 289, 290, 291, 292, 293, 295, 296,\n", + " 299, 300, 303, 304, 307, 308, 310, 311, 312, 314, 316, 318, 320,\n", + " 322, 323, 324, 327, 328, 329, 330, 331, 334, 336, 338, 342, 344,\n", + " 345, 346, 347, 348, 351, 352, 353, 354, 356, 360, 362, 364, 365,\n", + " 367, 368, 370, 371, 374, 375, 379, 380, 383, 384, 385, 387, 388,\n", + " 389, 390, 391, 392, 393, 394, 395, 396, 399, 401, 403, 407, 409,\n", + " 411, 412, 413, 417, 419, 420, 422, 424, 425, 426, 428, 429, 430,\n", + " 432, 434, 435, 436, 437, 439, 440, 442, 443, 446, 447, 449, 450,\n", + " 451, 453, 454, 455, 456, 457, 459, 462, 464, 467, 469, 470, 471,\n", + " 473, 474, 475, 476, 480, 481, 482, 484, 485, 486, 487, 488, 491,\n", + " 493, 494, 496, 498, 499, 501, 502, 503, 504, 505, 506, 508, 510,\n", + " 513, 518, 519, 522, 523, 525, 528, 529, 534, 535, 539, 542, 543,\n", + " 545, 546, 547, 549, 552, 555, 558, 561, 562, 563, 565, 566, 567,\n", + " 570, 571, 572, 573, 574, 575, 577, 578, 581, 582, 583, 584, 586,\n", + " 588, 589, 591, 592, 593, 594, 595, 597, 598, 599]),\n", + " 2: array([ 7, 8, 12, 23, 24, 29, 38, 40, 41, 42, 44, 48, 50,\n", + " 55, 60, 64, 68, 80, 83, 85, 90, 91, 94, 105, 110, 117,\n", + " 121, 125, 126, 128, 130, 131, 133, 138, 143, 144, 145, 150, 156,\n", + " 159, 160, 165, 166, 170, 173, 178, 186, 195, 204, 205, 206, 209,\n", + " 213, 218, 229, 233, 238, 257, 258, 260, 261, 263, 265, 268, 271,\n", + " 274, 280, 281, 285, 288, 297, 298, 301, 302, 305, 306, 317, 319,\n", + " 325, 326, 333, 335, 337, 339, 349, 350, 355, 357, 366, 372, 373,\n", + " 377, 378, 382, 386, 397, 404, 406, 408, 414, 423, 427, 431, 438,\n", + " 444, 452, 458, 463, 477, 478, 479, 483, 490, 492, 495, 500, 514,\n", + " 515, 520, 524, 530, 531, 532, 536, 541, 548, 551, 554, 556, 557,\n", + " 569, 579, 580, 590, 596]),\n", + " 3: array([ 3, 13, 14, 18, 33, 43, 46, 51, 54, 63, 88, 89, 93,\n", + " 104, 148, 163, 174, 191, 193, 202, 212, 221, 243, 272, 286, 287,\n", + " 294, 315, 340, 341, 358, 359, 361, 363, 369, 398, 405, 410, 415,\n", + " 416, 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497,\n", + " 507, 509, 511, 517, 521, 526, 527, 537, 540, 544, 560, 564, 576,\n", + " 585, 587]),\n", + " 4: array([ 47, 201, 237, 332, 376, 381, 533]),\n", + " 5: array([], dtype=int64)},\n", + " 7: {0: array([ 57, 81, 98, 123, 161, 197, 239, 246, 256, 309, 343, 400, 512,\n", + " 516, 550, 553]),\n", + " 1: array([ 0, 1, 2, 4, 5, 6, 9, 10, 11, 15, 16, 17, 19,\n", + " 20, 21, 22, 25, 26, 27, 28, 30, 31, 32, 34, 35, 36,\n", + " 37, 39, 45, 49, 52, 53, 56, 58, 59, 61, 62, 65, 66,\n", + " 67, 69, 70, 72, 73, 74, 75, 76, 77, 78, 79, 82, 84,\n", + " 86, 87, 92, 95, 96, 97, 99, 100, 101, 102, 103, 106, 107,\n", + " 108, 109, 111, 112, 113, 114, 115, 116, 118, 119, 120, 122, 124,\n", + " 127, 129, 132, 134, 135, 136, 137, 139, 140, 141, 142, 146, 147,\n", + " 149, 151, 153, 154, 155, 157, 158, 162, 164, 167, 168, 169, 172,\n", + " 175, 176, 177, 179, 180, 181, 182, 183, 184, 185, 187, 189, 190,\n", + " 192, 194, 196, 198, 199, 200, 207, 210, 211, 214, 215, 216, 217,\n", + " 219, 220, 222, 223, 224, 225, 226, 227, 228, 230, 231, 232, 234,\n", + " 235, 236, 240, 241, 242, 244, 245, 247, 248, 249, 250, 251, 252,\n", + " 253, 254, 255, 259, 262, 264, 266, 267, 269, 270, 273, 275, 276,\n", + " 277, 278, 279, 282, 283, 284, 289, 290, 291, 292, 293, 295, 296,\n", + " 299, 300, 303, 304, 307, 308, 310, 311, 312, 314, 316, 318, 320,\n", + " 322, 323, 324, 327, 328, 329, 330, 331, 334, 336, 338, 342, 344,\n", + " 345, 346, 347, 348, 351, 352, 353, 354, 356, 360, 362, 364, 365,\n", + " 367, 368, 370, 371, 374, 375, 379, 380, 383, 384, 385, 387, 388,\n", + " 389, 390, 391, 392, 393, 394, 395, 396, 399, 401, 403, 407, 409,\n", + " 411, 412, 413, 417, 419, 420, 422, 424, 425, 426, 428, 429, 430,\n", + " 432, 434, 435, 436, 437, 439, 440, 442, 443, 446, 447, 449, 450,\n", + " 451, 453, 454, 455, 456, 457, 459, 462, 464, 467, 469, 470, 471,\n", + " 473, 474, 475, 476, 480, 481, 482, 484, 485, 486, 487, 488, 491,\n", + " 493, 494, 496, 498, 499, 501, 502, 503, 504, 505, 506, 508, 510,\n", + " 513, 518, 519, 522, 523, 525, 528, 529, 534, 535, 539, 542, 543,\n", + " 545, 546, 547, 549, 552, 555, 558, 561, 562, 563, 565, 566, 567,\n", + " 570, 571, 572, 573, 574, 575, 577, 578, 581, 582, 583, 584, 586,\n", + " 588, 589, 591, 592, 593, 594, 595, 597, 598, 599]),\n", + " 2: array([ 7, 8, 12, 23, 24, 29, 38, 40, 41, 42, 44, 48, 50,\n", + " 55, 60, 64, 68, 80, 83, 85, 90, 91, 94, 105, 117, 121,\n", + " 125, 126, 128, 130, 131, 133, 138, 143, 144, 145, 150, 156, 159,\n", + " 160, 165, 166, 170, 173, 178, 186, 195, 204, 205, 206, 209, 213,\n", + " 218, 229, 233, 238, 257, 258, 260, 261, 263, 265, 268, 271, 274,\n", + " 280, 281, 285, 288, 297, 298, 301, 302, 305, 306, 317, 319, 325,\n", + " 326, 333, 335, 337, 339, 349, 350, 355, 357, 366, 372, 373, 377,\n", + " 378, 382, 386, 397, 404, 406, 408, 414, 423, 427, 431, 438, 444,\n", + " 452, 458, 463, 477, 478, 479, 483, 490, 492, 495, 500, 514, 515,\n", + " 520, 524, 530, 531, 532, 536, 541, 548, 551, 554, 556, 557, 569,\n", + " 579, 580, 590, 596]),\n", + " 3: array([ 3, 13, 14, 18, 33, 43, 46, 51, 54, 63, 88, 89, 93,\n", + " 104, 148, 163, 174, 191, 193, 202, 212, 221, 243, 272, 286, 287,\n", + " 294, 315, 340, 341, 358, 361, 363, 369, 398, 405, 410, 415, 416,\n", + " 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497, 507,\n", + " 511, 517, 521, 526, 527, 537, 540, 544, 560, 564, 576, 585, 587]),\n", + " 4: array([ 71, 110, 152, 171, 188, 203, 208, 313, 321, 359, 402, 418, 472,\n", + " 509, 538, 559, 568]),\n", + " 5: array([ 47, 201, 237, 332, 376, 381, 533]),\n", + " 6: array([], dtype=int64)},\n", + " 8: {0: array([ 81, 161, 197, 246, 309, 343, 516]),\n", + " 1: array([ 0, 1, 2, 4, 5, 6, 9, 10, 11, 15, 16, 17, 19,\n", + " 20, 21, 22, 25, 26, 27, 28, 30, 31, 32, 34, 35, 36,\n", + " 37, 39, 45, 49, 52, 53, 56, 58, 59, 61, 62, 65, 66,\n", + " 67, 69, 70, 72, 73, 74, 75, 76, 77, 78, 79, 82, 84,\n", + " 86, 87, 92, 95, 96, 97, 99, 100, 101, 102, 103, 106, 107,\n", + " 108, 109, 111, 112, 113, 114, 115, 116, 118, 119, 120, 122, 124,\n", + " 127, 129, 132, 134, 135, 136, 137, 139, 140, 141, 142, 146, 147,\n", + " 149, 151, 153, 154, 155, 157, 158, 162, 164, 167, 168, 169, 172,\n", + " 175, 176, 177, 179, 180, 181, 182, 183, 184, 185, 187, 189, 190,\n", + " 192, 194, 196, 198, 199, 200, 207, 210, 211, 214, 215, 216, 217,\n", + " 219, 220, 222, 223, 224, 225, 226, 227, 228, 230, 231, 232, 234,\n", + " 235, 236, 240, 241, 242, 244, 245, 247, 248, 249, 250, 251, 252,\n", + " 253, 254, 255, 259, 262, 264, 266, 267, 269, 270, 273, 275, 276,\n", + " 277, 278, 279, 282, 283, 284, 289, 290, 291, 292, 293, 295, 296,\n", + " 299, 300, 303, 304, 307, 308, 310, 311, 312, 314, 316, 318, 320,\n", + " 322, 323, 324, 327, 328, 329, 330, 331, 334, 336, 338, 342, 344,\n", + " 345, 346, 347, 348, 351, 352, 353, 354, 356, 360, 362, 364, 365,\n", + " 367, 368, 370, 371, 374, 375, 379, 380, 383, 384, 385, 387, 388,\n", + " 389, 390, 391, 392, 393, 394, 395, 396, 399, 401, 403, 407, 409,\n", + " 411, 412, 413, 417, 419, 420, 422, 424, 425, 426, 428, 429, 430,\n", + " 432, 434, 435, 436, 437, 439, 440, 442, 443, 446, 447, 449, 450,\n", + " 451, 453, 454, 455, 456, 457, 459, 462, 464, 467, 469, 470, 471,\n", + " 473, 474, 475, 476, 480, 481, 482, 484, 485, 486, 487, 488, 491,\n", + " 493, 494, 496, 498, 499, 501, 502, 503, 504, 505, 506, 508, 510,\n", + " 513, 518, 519, 522, 523, 525, 528, 529, 534, 535, 539, 542, 543,\n", + " 545, 546, 547, 549, 552, 555, 558, 561, 562, 563, 565, 566, 567,\n", + " 570, 571, 572, 573, 574, 575, 577, 578, 581, 582, 583, 584, 586,\n", + " 588, 589, 591, 592, 593, 594, 595, 597, 598, 599]),\n", + " 2: array([ 57, 98, 123, 239, 256, 400, 512, 544, 550, 553]),\n", + " 3: array([ 7, 8, 12, 23, 24, 29, 38, 40, 41, 42, 44, 48, 50,\n", + " 55, 60, 64, 68, 80, 83, 85, 90, 91, 94, 105, 117, 121,\n", + " 125, 126, 128, 130, 131, 133, 138, 143, 144, 145, 150, 156, 159,\n", + " 160, 165, 166, 170, 173, 178, 186, 195, 204, 205, 206, 209, 213,\n", + " 218, 229, 233, 238, 257, 258, 260, 261, 263, 265, 268, 271, 274,\n", + " 280, 281, 285, 288, 297, 298, 301, 302, 305, 306, 317, 319, 325,\n", + " 326, 333, 335, 337, 339, 349, 350, 355, 357, 366, 372, 373, 377,\n", + " 378, 382, 386, 397, 404, 406, 408, 414, 423, 427, 431, 438, 444,\n", + " 452, 458, 463, 477, 478, 479, 483, 490, 492, 495, 500, 514, 515,\n", + " 520, 524, 530, 531, 532, 536, 541, 548, 551, 554, 556, 557, 569,\n", + " 579, 580, 590, 596]),\n", + " 4: array([ 3, 13, 14, 18, 33, 43, 46, 51, 54, 63, 88, 89, 93,\n", + " 104, 148, 163, 174, 191, 193, 202, 212, 221, 243, 272, 286, 287,\n", + " 294, 315, 340, 341, 358, 361, 363, 369, 398, 405, 410, 415, 416,\n", + " 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497, 507,\n", + " 511, 517, 521, 526, 527, 537, 540, 560, 564, 576, 585, 587]),\n", + " 5: array([ 71, 110, 152, 171, 188, 203, 208, 313, 321, 359, 402, 418, 472,\n", + " 509, 538, 559, 568]),\n", + " 6: array([ 47, 201, 237, 332, 376, 381, 533]),\n", + " 7: array([], dtype=int64)},\n", + " 9: {0: array([ 81, 161, 197, 246, 309, 343, 516]),\n", + " 1: array([ 0, 1, 2, 4, 5, 6, 9, 10, 11, 15, 16, 17, 19,\n", + " 20, 21, 22, 25, 26, 27, 28, 30, 31, 32, 34, 35, 36,\n", + " 37, 39, 45, 49, 52, 53, 56, 58, 59, 61, 62, 65, 66,\n", + " 67, 68, 69, 70, 72, 73, 74, 75, 76, 77, 78, 79, 82,\n", + " 84, 86, 87, 92, 95, 96, 97, 99, 100, 101, 102, 103, 106,\n", + " 107, 108, 109, 111, 112, 113, 114, 115, 116, 118, 119, 120, 122,\n", + " 124, 127, 128, 129, 132, 134, 135, 136, 137, 139, 140, 141, 142,\n", + " 146, 147, 149, 151, 153, 154, 155, 157, 158, 162, 164, 167, 168,\n", + " 169, 172, 175, 176, 177, 179, 180, 181, 182, 183, 184, 185, 187,\n", + " 189, 190, 192, 194, 196, 198, 199, 200, 207, 210, 211, 214, 215,\n", + " 216, 217, 219, 220, 222, 223, 224, 225, 226, 227, 228, 230, 231,\n", + " 232, 234, 235, 236, 240, 241, 242, 244, 245, 247, 248, 249, 250,\n", + " 251, 252, 253, 254, 255, 257, 259, 262, 264, 266, 267, 269, 270,\n", + " 273, 275, 276, 277, 278, 279, 282, 283, 284, 289, 290, 291, 292,\n", + " 293, 295, 296, 299, 300, 303, 304, 307, 308, 310, 311, 312, 314,\n", + " 316, 318, 320, 322, 323, 324, 327, 328, 329, 330, 331, 334, 336,\n", + " 338, 342, 344, 345, 346, 347, 348, 351, 352, 353, 354, 356, 360,\n", + " 362, 364, 365, 367, 368, 370, 371, 374, 375, 379, 380, 383, 384,\n", + " 385, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 399, 401,\n", + " 403, 407, 409, 411, 412, 413, 417, 419, 420, 422, 424, 425, 426,\n", + " 428, 429, 430, 432, 434, 435, 436, 437, 439, 440, 442, 443, 446,\n", + " 447, 449, 450, 451, 453, 454, 455, 456, 457, 459, 462, 464, 467,\n", + " 469, 470, 471, 473, 474, 475, 476, 480, 481, 482, 484, 485, 486,\n", + " 487, 488, 491, 493, 494, 496, 498, 499, 501, 502, 503, 504, 505,\n", + " 506, 508, 510, 513, 518, 519, 522, 523, 525, 528, 529, 534, 535,\n", + " 539, 542, 543, 545, 546, 547, 549, 552, 555, 558, 561, 562, 563,\n", + " 565, 566, 567, 570, 571, 572, 573, 574, 575, 577, 578, 581, 582,\n", + " 583, 584, 586, 588, 589, 591, 592, 593, 594, 595, 597, 598, 599]),\n", + " 2: array([ 57, 98, 123, 239, 256, 400, 512, 544, 550, 553]),\n", + " 3: array([ 7, 8, 12, 23, 29, 38, 40, 42, 44, 48, 50, 55, 60,\n", + " 80, 83, 85, 90, 91, 94, 105, 117, 121, 125, 126, 130, 131,\n", + " 133, 138, 143, 144, 145, 150, 156, 159, 160, 165, 166, 170, 186,\n", + " 204, 205, 206, 209, 213, 218, 229, 233, 238, 258, 260, 263, 268,\n", + " 281, 285, 288, 297, 298, 301, 302, 305, 306, 319, 325, 326, 333,\n", + " 335, 337, 339, 349, 355, 357, 366, 372, 373, 377, 382, 386, 397,\n", + " 404, 406, 414, 423, 427, 438, 444, 452, 458, 463, 477, 478, 479,\n", + " 483, 490, 492, 495, 500, 514, 515, 520, 524, 530, 532, 536, 541,\n", + " 548, 551, 554, 556, 557, 569, 580, 590, 596]),\n", + " 4: array([ 3, 13, 14, 18, 33, 43, 46, 51, 54, 63, 88, 89, 93,\n", + " 104, 148, 163, 174, 191, 193, 212, 221, 243, 272, 286, 287, 294,\n", + " 315, 340, 341, 358, 361, 363, 369, 398, 405, 410, 415, 416, 421,\n", + " 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497, 507, 511,\n", + " 517, 521, 526, 527, 537, 540, 560, 564, 576, 585, 587]),\n", + " 5: array([ 71, 110, 152, 171, 188, 203, 208, 313, 321, 359, 402, 418, 472,\n", + " 509, 538, 559, 568]),\n", + " 6: array([ 24, 41, 64, 173, 178, 195, 202, 261, 265, 271, 274, 280, 317,\n", + " 350, 378, 408, 431, 531, 579]),\n", + " 7: array([ 47, 201, 237, 332, 376, 381, 533]),\n", + " 8: array([], dtype=int64)},\n", + " 10: {0: array([ 81, 161, 197, 246, 309, 343, 516]),\n", + " 1: array([ 0, 1, 2, 4, 5, 6, 9, 10, 11, 15, 16, 17, 19,\n", + " 20, 21, 22, 25, 26, 27, 28, 30, 31, 32, 34, 35, 36,\n", + " 37, 39, 49, 52, 53, 56, 58, 59, 61, 62, 65, 66, 67,\n", + " 69, 70, 72, 73, 74, 75, 76, 77, 78, 79, 82, 84, 86,\n", + " 87, 92, 95, 96, 97, 99, 100, 101, 102, 103, 106, 107, 108,\n", + " 109, 111, 112, 113, 114, 115, 116, 118, 119, 120, 122, 124, 127,\n", + " 128, 129, 132, 134, 135, 136, 137, 139, 140, 141, 142, 146, 147,\n", + " 149, 151, 153, 154, 155, 157, 158, 162, 164, 167, 168, 169, 172,\n", + " 175, 176, 177, 179, 180, 181, 182, 183, 184, 185, 187, 189, 190,\n", + " 192, 194, 196, 198, 199, 200, 207, 210, 211, 214, 215, 216, 217,\n", + " 219, 220, 222, 223, 224, 225, 226, 227, 228, 230, 231, 232, 234,\n", + " 235, 236, 240, 241, 242, 244, 245, 247, 248, 249, 250, 251, 252,\n", + " 253, 254, 255, 257, 259, 262, 264, 266, 267, 269, 270, 273, 275,\n", + " 276, 277, 278, 279, 282, 283, 284, 289, 290, 291, 292, 293, 295,\n", + " 296, 299, 300, 303, 304, 307, 308, 310, 311, 312, 314, 316, 318,\n", + " 320, 322, 323, 324, 327, 328, 329, 330, 331, 334, 336, 338, 342,\n", + " 344, 345, 346, 347, 348, 351, 352, 353, 354, 356, 360, 362, 364,\n", + " 365, 367, 368, 370, 371, 374, 375, 379, 380, 383, 384, 385, 387,\n", + " 388, 389, 390, 391, 392, 393, 394, 395, 396, 399, 401, 403, 407,\n", + " 409, 411, 412, 413, 417, 419, 420, 422, 424, 425, 426, 428, 429,\n", + " 430, 432, 434, 435, 436, 437, 439, 440, 442, 443, 446, 447, 449,\n", + " 450, 451, 453, 454, 455, 456, 457, 459, 462, 464, 467, 469, 470,\n", + " 471, 473, 474, 475, 476, 480, 481, 482, 484, 485, 486, 487, 488,\n", + " 491, 493, 494, 496, 498, 499, 501, 502, 503, 504, 505, 506, 508,\n", + " 510, 513, 518, 519, 522, 523, 525, 528, 529, 534, 535, 539, 542,\n", + " 543, 545, 546, 547, 549, 552, 555, 558, 561, 562, 563, 565, 566,\n", + " 567, 570, 571, 572, 573, 574, 575, 577, 578, 581, 582, 583, 584,\n", + " 586, 588, 589, 591, 592, 593, 594, 595, 597, 598, 599]),\n", + " 2: array([ 57, 98, 123, 239, 256, 400, 512, 544, 550, 553]),\n", + " 3: array([ 7, 8, 12, 23, 29, 38, 40, 42, 44, 48, 50, 55, 60,\n", + " 80, 83, 85, 90, 91, 94, 105, 117, 121, 125, 126, 130, 131,\n", + " 133, 138, 143, 144, 145, 150, 156, 159, 160, 165, 166, 170, 186,\n", + " 204, 205, 206, 209, 213, 218, 229, 233, 238, 258, 260, 263, 268,\n", + " 281, 285, 288, 297, 298, 301, 302, 305, 306, 319, 325, 326, 333,\n", + " 335, 337, 339, 349, 355, 357, 366, 372, 373, 377, 382, 386, 397,\n", + " 404, 406, 414, 423, 427, 438, 444, 452, 458, 463, 477, 478, 479,\n", + " 483, 490, 492, 495, 500, 514, 515, 520, 524, 530, 532, 536, 541,\n", + " 548, 551, 554, 556, 557, 569, 580, 590, 596]),\n", + " 4: array([ 45, 68, 509]),\n", + " 5: array([ 3, 13, 14, 18, 33, 43, 46, 51, 54, 63, 88, 89, 93,\n", + " 104, 148, 163, 174, 191, 193, 212, 221, 243, 272, 286, 287, 294,\n", + " 315, 340, 341, 358, 361, 363, 369, 398, 405, 410, 415, 416, 421,\n", + " 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497, 507, 511,\n", + " 517, 521, 526, 527, 537, 540, 560, 564, 576, 585, 587]),\n", + " 6: array([ 71, 110, 152, 171, 188, 203, 208, 313, 321, 359, 402, 418, 472,\n", + " 538, 559, 568]),\n", + " 7: array([ 24, 41, 64, 173, 178, 195, 202, 261, 265, 271, 274, 280, 317,\n", + " 350, 378, 408, 431, 531, 579]),\n", + " 8: array([ 47, 201, 237, 332, 376, 381, 533]),\n", + " 9: array([], dtype=int64)}},\n", + " 'nonl2_ranking_nonloo': {2: {0: array([ 3, 13, 14, 18, 29, 33, 43, 45, 46, 51, 54, 57, 63,\n", + " 68, 71, 81, 88, 89, 93, 98, 104, 110, 123, 148, 152, 161,\n", + " 163, 171, 174, 188, 191, 193, 197, 202, 203, 208, 212, 221, 239,\n", + " 243, 246, 256, 272, 286, 287, 294, 309, 313, 315, 321, 340, 341,\n", + " 343, 350, 358, 359, 361, 363, 369, 398, 400, 402, 405, 410, 415,\n", + " 416, 418, 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 472,\n", + " 477, 483, 489, 497, 507, 509, 511, 512, 515, 516, 517, 521, 526,\n", + " 527, 537, 538, 540, 544, 550, 553, 559, 560, 564, 568, 576, 585,\n", + " 587]),\n", + " 1: array([ 0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15,\n", + " 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 30,\n", + " 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 42, 44, 47,\n", + " 48, 49, 50, 52, 53, 55, 56, 58, 59, 60, 61, 62, 64,\n", + " 65, 66, 67, 69, 70, 72, 73, 74, 75, 76, 77, 78, 79,\n", + " 80, 82, 83, 84, 85, 86, 87, 90, 91, 92, 94, 95, 96,\n", + " 97, 99, 100, 101, 102, 103, 105, 106, 107, 108, 109, 111, 112,\n", + " 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 124, 125, 126,\n", + " 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n", + " 140, 141, 142, 143, 144, 145, 146, 147, 149, 150, 151, 153, 154,\n", + " 155, 156, 157, 158, 159, 160, 162, 164, 165, 166, 167, 168, 169,\n", + " 170, 172, 173, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184,\n", + " 185, 186, 187, 189, 190, 192, 194, 195, 196, 198, 199, 200, 201,\n", + " 204, 205, 206, 207, 209, 210, 211, 213, 214, 215, 216, 217, 218,\n", + " 219, 220, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232,\n", + " 233, 234, 235, 236, 237, 238, 240, 241, 242, 244, 245, 247, 248,\n", + " 249, 250, 251, 252, 253, 254, 255, 257, 258, 259, 260, 261, 262,\n", + " 263, 264, 265, 266, 267, 268, 269, 270, 271, 273, 274, 275, 276,\n", + " 277, 278, 279, 280, 281, 282, 283, 284, 285, 288, 289, 290, 291,\n", + " 292, 293, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305,\n", + " 306, 307, 308, 310, 311, 312, 314, 316, 317, 318, 319, 320, 322,\n", + " 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335,\n", + " 336, 337, 338, 339, 342, 344, 345, 346, 347, 348, 349, 351, 352,\n", + " 353, 354, 355, 356, 357, 360, 362, 364, 365, 366, 367, 368, 370,\n", + " 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383,\n", + " 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396,\n", + " 397, 399, 401, 403, 404, 406, 407, 408, 409, 411, 412, 413, 414,\n", + " 417, 419, 420, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431,\n", + " 432, 434, 435, 436, 437, 438, 439, 440, 442, 443, 444, 446, 447,\n", + " 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 462, 463,\n", + " 464, 467, 469, 470, 471, 473, 474, 475, 476, 478, 479, 480, 481,\n", + " 482, 484, 485, 486, 487, 488, 490, 491, 492, 493, 494, 495, 496,\n", + " 498, 499, 500, 501, 502, 503, 504, 505, 506, 508, 510, 513, 514,\n", + " 518, 519, 520, 522, 523, 524, 525, 528, 529, 530, 531, 532, 533,\n", + " 534, 535, 536, 539, 541, 542, 543, 545, 546, 547, 548, 549, 551,\n", + " 552, 554, 555, 556, 557, 558, 561, 562, 563, 565, 566, 567, 569,\n", + " 570, 571, 572, 573, 574, 575, 577, 578, 579, 580, 581, 582, 583,\n", + " 584, 586, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598,\n", + " 599])},\n", + " 3: {0: array([ 3, 13, 14, 18, 29, 33, 43, 45, 46, 51, 54, 57, 63,\n", + " 68, 71, 81, 88, 89, 93, 98, 104, 110, 123, 148, 152, 161,\n", + " 163, 171, 174, 188, 191, 193, 197, 202, 203, 208, 212, 221, 239,\n", + " 243, 246, 256, 272, 286, 287, 294, 309, 313, 315, 321, 340, 341,\n", + " 343, 350, 358, 359, 361, 363, 369, 398, 400, 402, 405, 410, 415,\n", + " 416, 418, 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 472,\n", + " 489, 497, 507, 509, 511, 512, 516, 517, 521, 526, 527, 537, 538,\n", + " 540, 544, 550, 553, 559, 560, 564, 568, 576, 585, 587]),\n", + " 1: array([ 1, 2, 5, 6, 7, 8, 9, 10, 15, 16, 17, 21, 22,\n", + " 25, 26, 27, 28, 31, 32, 35, 36, 39, 40, 42, 47, 48,\n", + " 49, 50, 52, 53, 55, 56, 58, 59, 65, 67, 69, 70, 72,\n", + " 74, 75, 77, 78, 80, 82, 83, 85, 86, 90, 91, 92, 96,\n", + " 97, 99, 100, 102, 105, 107, 108, 109, 111, 113, 114, 115, 116,\n", + " 117, 118, 119, 121, 122, 124, 125, 126, 127, 129, 132, 133, 134,\n", + " 135, 136, 137, 138, 139, 140, 141, 142, 143, 145, 146, 149, 150,\n", + " 151, 153, 154, 155, 156, 157, 158, 159, 160, 162, 166, 167, 168,\n", + " 169, 175, 176, 177, 178, 179, 180, 181, 183, 184, 185, 187, 189,\n", + " 192, 194, 195, 196, 198, 199, 200, 201, 204, 205, 207, 209, 210,\n", + " 211, 213, 214, 216, 217, 218, 219, 220, 222, 223, 225, 226, 227,\n", + " 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 240, 241,\n", + " 242, 244, 245, 247, 248, 249, 250, 251, 252, 253, 254, 259, 260,\n", + " 262, 263, 264, 266, 267, 268, 270, 277, 278, 279, 280, 282, 283,\n", + " 284, 285, 288, 289, 290, 291, 292, 293, 295, 296, 297, 299, 300,\n", + " 301, 303, 304, 306, 307, 308, 310, 311, 312, 314, 316, 319, 320,\n", + " 322, 323, 325, 329, 330, 331, 332, 333, 334, 335, 336, 337, 339,\n", + " 342, 344, 345, 346, 347, 348, 349, 351, 353, 354, 356, 357, 360,\n", + " 362, 364, 366, 367, 368, 370, 371, 372, 373, 374, 375, 376, 377,\n", + " 379, 380, 381, 382, 383, 384, 387, 388, 390, 391, 392, 393, 394,\n", + " 395, 396, 397, 399, 401, 403, 404, 406, 407, 409, 411, 412, 413,\n", + " 414, 417, 419, 420, 422, 423, 425, 426, 427, 428, 429, 430, 435,\n", + " 438, 439, 440, 442, 443, 447, 450, 451, 452, 453, 454, 456, 457,\n", + " 458, 459, 462, 464, 467, 469, 470, 473, 474, 475, 476, 477, 478,\n", + " 481, 482, 483, 484, 485, 486, 487, 488, 491, 493, 494, 496, 498,\n", + " 499, 500, 501, 502, 504, 505, 506, 508, 510, 513, 515, 518, 519,\n", + " 520, 522, 523, 524, 525, 529, 534, 535, 536, 539, 546, 547, 551,\n", + " 552, 555, 556, 558, 561, 562, 563, 565, 566, 567, 569, 570, 571,\n", + " 572, 573, 574, 575, 577, 581, 582, 583, 584, 586, 588, 589, 590,\n", + " 591, 592, 593, 594, 595, 596, 598, 599]),\n", + " 2: array([ 0, 4, 11, 12, 19, 20, 23, 24, 30, 34, 37, 38, 41,\n", + " 44, 60, 61, 62, 64, 66, 73, 76, 79, 84, 87, 94, 95,\n", + " 101, 103, 106, 112, 120, 128, 130, 131, 144, 147, 164, 165, 170,\n", + " 172, 173, 182, 186, 190, 206, 215, 224, 255, 257, 258, 261, 265,\n", + " 269, 271, 273, 274, 275, 276, 281, 298, 302, 305, 317, 318, 324,\n", + " 326, 327, 328, 338, 352, 355, 365, 378, 385, 386, 389, 408, 424,\n", + " 431, 432, 434, 436, 437, 444, 446, 449, 455, 463, 471, 479, 480,\n", + " 490, 492, 495, 503, 514, 528, 530, 531, 532, 533, 541, 542, 543,\n", + " 545, 548, 549, 554, 557, 578, 579, 580, 597])},\n", + " 4: {0: array([ 3, 13, 14, 18, 29, 33, 43, 45, 46, 51, 54, 57, 63,\n", + " 68, 71, 81, 88, 89, 93, 98, 104, 110, 123, 148, 152, 161,\n", + " 163, 171, 174, 188, 191, 193, 197, 202, 203, 208, 212, 221, 239,\n", + " 243, 246, 256, 272, 286, 287, 294, 309, 313, 315, 321, 340, 341,\n", + " 343, 350, 358, 359, 361, 363, 369, 398, 400, 402, 405, 410, 415,\n", + " 416, 418, 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 472,\n", + " 477, 483, 489, 497, 507, 509, 511, 512, 515, 516, 517, 521, 526,\n", + " 527, 537, 538, 540, 544, 550, 553, 559, 560, 564, 568, 576, 585,\n", + " 587]),\n", + " 1: array([ 1, 2, 5, 6, 9, 10, 15, 16, 17, 21, 22, 25, 26,\n", + " 27, 28, 31, 32, 35, 36, 39, 42, 49, 52, 53, 56, 58,\n", + " 59, 65, 67, 69, 70, 72, 74, 75, 77, 78, 82, 86, 92,\n", + " 96, 97, 99, 100, 102, 107, 108, 109, 111, 113, 114, 115, 116,\n", + " 118, 119, 122, 124, 127, 129, 132, 134, 135, 136, 137, 139, 140,\n", + " 141, 142, 146, 149, 151, 153, 154, 155, 157, 158, 162, 167, 168,\n", + " 169, 175, 176, 177, 179, 180, 181, 183, 184, 185, 187, 189, 192,\n", + " 194, 196, 198, 199, 200, 207, 210, 211, 214, 216, 217, 219, 220,\n", + " 222, 223, 225, 226, 227, 228, 230, 231, 232, 234, 235, 236, 240,\n", + " 241, 242, 244, 245, 247, 248, 249, 250, 251, 252, 253, 254, 259,\n", + " 262, 264, 266, 267, 270, 277, 278, 279, 282, 283, 284, 289, 290,\n", + " 291, 292, 293, 295, 296, 299, 300, 303, 304, 307, 308, 310, 311,\n", + " 312, 314, 316, 320, 322, 323, 329, 330, 331, 334, 336, 342, 344,\n", + " 345, 346, 347, 348, 351, 353, 354, 356, 360, 362, 364, 367, 368,\n", + " 370, 371, 374, 375, 379, 380, 383, 384, 387, 388, 390, 391, 392,\n", + " 393, 394, 395, 396, 399, 401, 403, 407, 409, 411, 412, 413, 417,\n", + " 419, 420, 422, 425, 426, 428, 429, 430, 435, 439, 440, 442, 443,\n", + " 447, 450, 451, 453, 454, 456, 457, 459, 462, 464, 467, 469, 470,\n", + " 473, 474, 475, 476, 481, 482, 484, 485, 486, 487, 488, 491, 493,\n", + " 494, 496, 498, 499, 501, 502, 504, 505, 506, 508, 510, 513, 518,\n", + " 519, 522, 523, 525, 529, 534, 535, 539, 546, 547, 551, 552, 555,\n", + " 558, 561, 562, 563, 565, 566, 567, 570, 571, 572, 573, 574, 575,\n", + " 577, 581, 582, 583, 584, 586, 588, 589, 591, 592, 593, 594, 595,\n", + " 598, 599]),\n", + " 2: array([ 7, 8, 40, 47, 48, 50, 55, 60, 80, 83, 85, 90, 91,\n", + " 105, 117, 121, 125, 126, 130, 131, 133, 138, 143, 145, 150, 156,\n", + " 159, 160, 166, 178, 195, 201, 204, 205, 206, 209, 213, 218, 229,\n", + " 233, 237, 238, 258, 260, 263, 268, 280, 285, 288, 297, 301, 306,\n", + " 319, 325, 332, 333, 335, 337, 339, 349, 357, 366, 372, 373, 376,\n", + " 377, 381, 382, 397, 404, 406, 414, 423, 427, 438, 452, 458, 478,\n", + " 479, 500, 520, 524, 532, 536, 548, 556, 569, 580, 590, 596]),\n", + " 3: array([ 0, 4, 11, 12, 19, 20, 23, 24, 30, 34, 37, 38, 41,\n", + " 44, 61, 62, 64, 66, 73, 76, 79, 84, 87, 94, 95, 101,\n", + " 103, 106, 112, 120, 128, 144, 147, 164, 165, 170, 172, 173, 182,\n", + " 186, 190, 215, 224, 255, 257, 261, 265, 269, 271, 273, 274, 275,\n", + " 276, 281, 298, 302, 305, 317, 318, 324, 326, 327, 328, 338, 352,\n", + " 355, 365, 378, 385, 386, 389, 408, 424, 431, 432, 434, 436, 437,\n", + " 444, 446, 449, 455, 463, 471, 480, 490, 492, 495, 503, 514, 528,\n", + " 530, 531, 533, 541, 542, 543, 545, 549, 554, 557, 578, 579, 597])},\n", + " 5: {0: array([ 51, 57, 71, 81, 98, 104, 110, 123, 152, 161, 171, 188, 197,\n", + " 203, 208, 239, 246, 256, 313, 321, 343, 359, 400, 402, 472, 509,\n", + " 512, 516, 538, 540, 550, 553, 559, 564, 568]),\n", + " 1: array([ 1, 2, 5, 6, 9, 10, 15, 16, 17, 21, 22, 25, 26,\n", + " 27, 28, 31, 32, 35, 36, 39, 42, 49, 52, 53, 56, 58,\n", + " 59, 65, 67, 69, 70, 72, 74, 75, 77, 78, 82, 86, 92,\n", + " 96, 97, 99, 100, 102, 107, 108, 109, 111, 113, 114, 115, 116,\n", + " 118, 119, 122, 124, 127, 129, 132, 134, 135, 136, 137, 139, 140,\n", + " 141, 142, 146, 149, 151, 153, 154, 155, 157, 158, 162, 167, 168,\n", + " 169, 175, 176, 177, 179, 180, 181, 183, 184, 185, 187, 189, 192,\n", + " 194, 196, 198, 199, 200, 207, 210, 211, 214, 216, 217, 219, 220,\n", + " 222, 223, 225, 226, 227, 228, 230, 231, 232, 234, 235, 236, 240,\n", + " 241, 242, 244, 245, 247, 248, 249, 250, 251, 252, 253, 254, 259,\n", + " 262, 264, 266, 267, 270, 277, 278, 279, 282, 283, 284, 289, 290,\n", + " 291, 292, 293, 295, 296, 299, 300, 303, 304, 307, 308, 310, 311,\n", + " 312, 314, 316, 320, 322, 323, 329, 330, 331, 334, 336, 342, 344,\n", + " 345, 346, 347, 348, 351, 353, 354, 356, 360, 362, 364, 367, 368,\n", + " 370, 371, 374, 375, 379, 380, 383, 384, 387, 388, 390, 391, 392,\n", + " 393, 394, 395, 396, 399, 401, 403, 407, 409, 411, 412, 413, 417,\n", + " 419, 420, 422, 425, 426, 428, 429, 430, 435, 439, 440, 442, 443,\n", + " 447, 450, 451, 453, 454, 456, 457, 459, 462, 464, 467, 469, 470,\n", + " 473, 474, 475, 476, 481, 482, 484, 485, 486, 487, 488, 491, 493,\n", + " 494, 496, 498, 499, 501, 502, 504, 505, 506, 508, 510, 513, 518,\n", + " 519, 522, 523, 525, 529, 534, 535, 539, 546, 547, 551, 552, 555,\n", + " 558, 561, 562, 563, 565, 566, 567, 570, 571, 572, 573, 574, 575,\n", + " 577, 581, 582, 583, 584, 586, 588, 589, 591, 592, 593, 594, 595,\n", + " 598, 599]),\n", + " 2: array([ 7, 8, 40, 47, 48, 50, 55, 60, 80, 83, 85, 90, 91,\n", + " 105, 117, 121, 125, 126, 130, 131, 133, 138, 143, 145, 150, 156,\n", + " 159, 160, 166, 178, 195, 201, 204, 205, 206, 209, 213, 218, 229,\n", + " 233, 237, 238, 258, 260, 263, 268, 280, 285, 288, 297, 301, 306,\n", + " 319, 325, 332, 333, 335, 337, 339, 349, 357, 366, 372, 373, 376,\n", + " 377, 381, 382, 397, 404, 406, 414, 423, 427, 438, 452, 458, 478,\n", + " 479, 500, 520, 524, 532, 536, 548, 556, 569, 580, 590, 596]),\n", + " 3: array([ 3, 13, 14, 18, 29, 33, 43, 45, 46, 54, 63, 68, 88,\n", + " 89, 93, 148, 163, 174, 191, 193, 202, 212, 221, 243, 272, 286,\n", + " 287, 294, 309, 315, 340, 341, 350, 358, 361, 363, 369, 398, 405,\n", + " 410, 415, 416, 418, 421, 433, 441, 445, 448, 460, 461, 465, 466,\n", + " 468, 477, 483, 489, 497, 507, 511, 515, 517, 521, 526, 527, 537,\n", + " 544, 560, 576, 585, 587]),\n", + " 4: array([ 0, 4, 11, 12, 19, 20, 23, 24, 30, 34, 37, 38, 41,\n", + " 44, 61, 62, 64, 66, 73, 76, 79, 84, 87, 94, 95, 101,\n", + " 103, 106, 112, 120, 128, 144, 147, 164, 165, 170, 172, 173, 182,\n", + " 186, 190, 215, 224, 255, 257, 261, 265, 269, 271, 273, 274, 275,\n", + " 276, 281, 298, 302, 305, 317, 318, 324, 326, 327, 328, 338, 352,\n", + " 355, 365, 378, 385, 386, 389, 408, 424, 431, 432, 434, 436, 437,\n", + " 444, 446, 449, 455, 463, 471, 480, 490, 492, 495, 503, 514, 528,\n", + " 530, 531, 533, 541, 542, 543, 545, 549, 554, 557, 578, 579, 597])},\n", + " 6: {0: array([ 51, 57, 71, 81, 98, 104, 110, 123, 152, 161, 171, 188, 197,\n", + " 203, 208, 239, 246, 256, 313, 321, 343, 359, 400, 402, 472, 512,\n", + " 516, 538, 540, 550, 553, 559, 564, 568]),\n", + " 1: array([ 1, 2, 5, 6, 9, 10, 15, 16, 17, 21, 22, 25, 26,\n", + " 27, 28, 31, 32, 35, 36, 39, 42, 49, 52, 53, 56, 58,\n", + " 59, 65, 67, 69, 70, 72, 74, 75, 77, 78, 82, 86, 92,\n", + " 96, 97, 99, 100, 102, 107, 108, 109, 111, 113, 114, 115, 116,\n", + " 118, 119, 122, 124, 127, 129, 132, 134, 135, 136, 137, 139, 140,\n", + " 141, 142, 146, 149, 151, 153, 154, 155, 157, 158, 162, 167, 168,\n", + " 169, 175, 176, 177, 179, 180, 181, 183, 184, 185, 187, 189, 192,\n", + " 194, 196, 198, 199, 200, 207, 210, 211, 214, 216, 217, 219, 220,\n", + " 222, 223, 225, 226, 227, 228, 230, 231, 232, 234, 235, 236, 240,\n", + " 241, 242, 244, 245, 247, 248, 249, 250, 251, 252, 253, 254, 259,\n", + " 262, 264, 266, 267, 270, 277, 278, 279, 282, 283, 284, 289, 290,\n", + " 291, 292, 293, 295, 296, 299, 300, 303, 304, 307, 308, 310, 311,\n", + " 312, 314, 316, 320, 322, 323, 329, 330, 331, 334, 336, 342, 344,\n", + " 345, 346, 347, 348, 351, 353, 354, 356, 360, 362, 364, 367, 368,\n", + " 370, 371, 374, 375, 379, 380, 383, 384, 387, 388, 390, 391, 392,\n", + " 393, 394, 395, 396, 399, 401, 403, 407, 409, 411, 412, 413, 417,\n", + " 419, 420, 422, 425, 426, 428, 429, 430, 435, 439, 440, 442, 443,\n", + " 447, 450, 451, 453, 454, 456, 457, 459, 462, 464, 467, 469, 470,\n", + " 473, 474, 475, 476, 481, 482, 484, 485, 486, 487, 488, 491, 493,\n", + " 494, 496, 498, 499, 501, 502, 504, 505, 506, 508, 510, 513, 518,\n", + " 519, 522, 523, 525, 529, 534, 535, 539, 546, 547, 551, 552, 555,\n", + " 558, 561, 562, 563, 565, 566, 567, 570, 571, 572, 573, 574, 575,\n", + " 577, 581, 582, 583, 584, 586, 588, 589, 591, 592, 593, 594, 595,\n", + " 598, 599]),\n", + " 2: array([ 7, 8, 40, 47, 48, 50, 55, 60, 80, 83, 85, 90, 91,\n", + " 105, 117, 121, 125, 126, 130, 131, 133, 138, 143, 145, 150, 156,\n", + " 159, 160, 166, 178, 195, 201, 204, 205, 206, 209, 213, 218, 229,\n", + " 233, 237, 238, 258, 260, 263, 268, 280, 285, 288, 297, 301, 306,\n", + " 319, 325, 332, 333, 335, 337, 339, 349, 357, 366, 372, 373, 376,\n", + " 377, 381, 382, 397, 404, 406, 414, 423, 427, 438, 452, 458, 478,\n", + " 479, 500, 520, 524, 532, 536, 548, 556, 569, 580, 590, 596]),\n", + " 3: array([ 29, 45, 68, 477, 483, 509, 515]),\n", + " 4: array([ 3, 13, 14, 18, 33, 43, 46, 54, 63, 88, 89, 93, 148,\n", + " 163, 174, 191, 193, 202, 212, 221, 243, 272, 286, 287, 294, 309,\n", + " 315, 340, 341, 350, 358, 361, 363, 369, 398, 405, 410, 415, 416,\n", + " 418, 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497,\n", + " 507, 511, 517, 521, 526, 527, 537, 544, 560, 576, 585, 587]),\n", + " 5: array([ 0, 4, 11, 12, 19, 20, 23, 24, 30, 34, 37, 38, 41,\n", + " 44, 61, 62, 64, 66, 73, 76, 79, 84, 87, 94, 95, 101,\n", + " 103, 106, 112, 120, 128, 144, 147, 164, 165, 170, 172, 173, 182,\n", + " 186, 190, 215, 224, 255, 257, 261, 265, 269, 271, 273, 274, 275,\n", + " 276, 281, 298, 302, 305, 317, 318, 324, 326, 327, 328, 338, 352,\n", + " 355, 365, 378, 385, 386, 389, 408, 424, 431, 432, 434, 436, 437,\n", + " 444, 446, 449, 455, 463, 471, 480, 490, 492, 495, 503, 514, 528,\n", + " 530, 531, 533, 541, 542, 543, 545, 549, 554, 557, 578, 579, 597])},\n", + " 7: {0: array([ 51, 57, 71, 81, 98, 104, 110, 123, 152, 161, 171, 188, 197,\n", + " 203, 208, 239, 246, 256, 313, 321, 343, 359, 400, 402, 472, 512,\n", + " 516, 538, 540, 550, 553, 559, 564, 568]),\n", + " 1: array([ 1, 2, 5, 6, 9, 10, 15, 16, 17, 21, 22, 25, 26,\n", + " 27, 28, 31, 32, 35, 36, 39, 42, 49, 52, 53, 56, 58,\n", + " 59, 65, 67, 69, 70, 72, 74, 75, 77, 78, 82, 86, 92,\n", + " 96, 97, 99, 100, 102, 107, 108, 109, 111, 113, 114, 115, 116,\n", + " 118, 119, 122, 124, 127, 129, 132, 134, 135, 136, 137, 139, 140,\n", + " 141, 142, 146, 149, 151, 153, 154, 155, 157, 158, 162, 167, 168,\n", + " 169, 175, 176, 177, 179, 180, 181, 183, 184, 185, 187, 189, 192,\n", + " 194, 196, 198, 199, 200, 207, 210, 211, 214, 216, 217, 219, 220,\n", + " 222, 223, 225, 226, 227, 228, 230, 231, 232, 234, 235, 236, 240,\n", + " 241, 242, 244, 245, 247, 248, 249, 250, 251, 252, 253, 254, 259,\n", + " 262, 264, 266, 267, 270, 277, 278, 279, 282, 283, 284, 289, 290,\n", + " 291, 292, 293, 295, 296, 299, 300, 303, 304, 307, 308, 310, 311,\n", + " 312, 314, 316, 320, 322, 323, 329, 330, 331, 334, 336, 342, 344,\n", + " 345, 346, 347, 348, 351, 353, 354, 356, 360, 362, 364, 367, 368,\n", + " 370, 371, 374, 375, 379, 380, 383, 384, 387, 388, 390, 391, 392,\n", + " 393, 394, 395, 396, 399, 401, 403, 407, 409, 411, 412, 413, 417,\n", + " 419, 420, 422, 425, 426, 428, 429, 430, 435, 439, 440, 442, 443,\n", + " 447, 450, 451, 453, 454, 456, 457, 459, 462, 464, 467, 469, 470,\n", + " 473, 474, 475, 476, 481, 482, 484, 485, 486, 487, 488, 491, 493,\n", + " 494, 496, 498, 499, 501, 502, 504, 505, 506, 508, 510, 513, 518,\n", + " 519, 522, 523, 525, 529, 534, 535, 539, 546, 547, 551, 552, 555,\n", + " 558, 561, 562, 563, 565, 566, 567, 570, 571, 572, 573, 574, 575,\n", + " 577, 581, 582, 583, 584, 586, 588, 589, 591, 592, 593, 594, 595,\n", + " 598, 599]),\n", + " 2: array([ 7, 8, 40, 47, 48, 50, 55, 80, 83, 85, 90, 91, 105,\n", + " 117, 121, 125, 126, 130, 133, 138, 145, 150, 156, 159, 160, 166,\n", + " 178, 195, 201, 204, 205, 209, 213, 218, 229, 233, 238, 260, 263,\n", + " 268, 280, 288, 297, 301, 306, 319, 325, 332, 333, 335, 337, 339,\n", + " 349, 357, 366, 372, 373, 376, 377, 381, 382, 397, 404, 406, 414,\n", + " 423, 427, 438, 452, 458, 478, 500, 520, 524, 532, 536, 556, 569,\n", + " 590, 596]),\n", + " 3: array([ 29, 45, 68, 477, 483, 509, 515]),\n", + " 4: array([ 3, 13, 14, 18, 33, 43, 46, 54, 63, 88, 89, 93, 148,\n", + " 163, 174, 191, 193, 202, 212, 221, 243, 272, 286, 287, 294, 309,\n", + " 315, 340, 341, 350, 358, 361, 363, 369, 398, 405, 410, 415, 416,\n", + " 418, 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497,\n", + " 507, 511, 517, 521, 526, 527, 537, 544, 560, 576, 585, 587]),\n", + " 5: array([ 0, 4, 11, 19, 20, 30, 34, 37, 61, 62, 64, 66, 73,\n", + " 76, 79, 84, 87, 95, 101, 103, 106, 112, 120, 128, 147, 164,\n", + " 172, 173, 182, 186, 190, 215, 224, 255, 257, 261, 265, 269, 271,\n", + " 273, 274, 275, 276, 318, 324, 327, 328, 338, 352, 365, 378, 385,\n", + " 389, 408, 424, 431, 432, 434, 436, 437, 446, 449, 455, 471, 480,\n", + " 503, 528, 542, 543, 545, 549, 578, 579, 597]),\n", + " 6: array([ 12, 23, 24, 38, 41, 44, 60, 94, 131, 143, 144, 165, 170,\n", + " 206, 237, 258, 281, 285, 298, 302, 305, 317, 326, 355, 386, 444,\n", + " 463, 479, 490, 492, 495, 514, 530, 531, 533, 541, 548, 554, 557,\n", + " 580])},\n", + " 8: {0: array([ 51, 57, 71, 81, 98, 104, 110, 123, 152, 161, 171, 188, 197,\n", + " 203, 208, 239, 246, 256, 313, 321, 343, 359, 400, 402, 472, 512,\n", + " 516, 538, 540, 550, 553, 559, 564, 568]),\n", + " 1: array([ 1, 2, 5, 6, 9, 10, 15, 16, 17, 21, 22, 25, 26,\n", + " 27, 28, 31, 32, 35, 36, 39, 42, 49, 52, 53, 56, 58,\n", + " 59, 65, 67, 69, 70, 72, 74, 75, 77, 78, 82, 86, 92,\n", + " 96, 97, 99, 100, 102, 107, 108, 109, 111, 113, 114, 115, 116,\n", + " 118, 119, 122, 124, 127, 129, 132, 134, 135, 136, 137, 139, 140,\n", + " 141, 142, 146, 149, 151, 153, 154, 155, 157, 158, 162, 167, 168,\n", + " 169, 175, 176, 177, 179, 180, 181, 183, 184, 185, 187, 189, 192,\n", + " 194, 196, 198, 199, 200, 207, 210, 211, 214, 216, 217, 219, 220,\n", + " 222, 223, 225, 226, 227, 228, 230, 231, 232, 234, 235, 236, 240,\n", + " 241, 242, 244, 245, 247, 248, 249, 250, 251, 252, 253, 254, 259,\n", + " 262, 264, 266, 267, 270, 277, 278, 279, 282, 283, 284, 289, 290,\n", + " 291, 292, 293, 295, 296, 299, 300, 303, 304, 307, 308, 310, 311,\n", + " 312, 314, 316, 320, 322, 323, 329, 330, 331, 334, 336, 342, 344,\n", + " 345, 346, 347, 348, 351, 353, 354, 356, 360, 362, 364, 367, 368,\n", + " 370, 371, 374, 375, 379, 380, 383, 384, 387, 388, 390, 391, 392,\n", + " 393, 394, 395, 396, 399, 401, 403, 407, 409, 411, 412, 413, 417,\n", + " 419, 420, 422, 425, 426, 428, 429, 430, 435, 439, 440, 442, 443,\n", + " 447, 450, 451, 453, 454, 456, 457, 459, 462, 464, 467, 469, 470,\n", + " 473, 474, 475, 476, 481, 482, 484, 485, 486, 487, 488, 491, 493,\n", + " 494, 496, 498, 499, 501, 502, 504, 505, 506, 508, 510, 513, 518,\n", + " 519, 522, 523, 525, 529, 534, 535, 539, 546, 547, 551, 552, 555,\n", + " 558, 561, 562, 563, 565, 566, 567, 570, 571, 572, 573, 574, 575,\n", + " 577, 581, 582, 583, 584, 586, 588, 589, 591, 592, 593, 594, 595,\n", + " 598, 599]),\n", + " 2: array([ 7, 8, 40, 48, 50, 55, 80, 83, 85, 90, 91, 105, 117,\n", + " 121, 125, 126, 130, 133, 138, 145, 150, 156, 159, 160, 166, 178,\n", + " 195, 204, 205, 209, 213, 218, 229, 233, 238, 260, 263, 268, 280,\n", + " 288, 297, 301, 306, 319, 325, 333, 335, 337, 339, 349, 357, 366,\n", + " 372, 373, 377, 382, 397, 404, 406, 414, 423, 427, 438, 452, 458,\n", + " 478, 500, 520, 524, 532, 536, 556, 569, 590, 596]),\n", + " 3: array([ 29, 45, 68, 477, 483, 509, 515]),\n", + " 4: array([ 3, 13, 14, 18, 33, 43, 46, 54, 63, 88, 89, 93, 148,\n", + " 163, 174, 191, 193, 202, 212, 221, 243, 272, 286, 287, 294, 309,\n", + " 315, 340, 341, 350, 358, 361, 363, 369, 398, 405, 410, 415, 416,\n", + " 418, 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497,\n", + " 507, 511, 517, 521, 526, 527, 537, 544, 560, 576, 585, 587]),\n", + " 5: array([ 0, 4, 11, 19, 20, 30, 34, 37, 61, 62, 64, 66, 73,\n", + " 76, 79, 84, 87, 95, 101, 103, 106, 112, 120, 128, 147, 164,\n", + " 172, 173, 182, 186, 190, 215, 224, 255, 257, 261, 265, 269, 271,\n", + " 273, 274, 275, 276, 318, 324, 327, 328, 338, 352, 365, 378, 385,\n", + " 389, 408, 424, 431, 432, 434, 436, 437, 446, 449, 455, 471, 480,\n", + " 503, 528, 542, 543, 545, 549, 578, 579, 597]),\n", + " 6: array([ 12, 23, 24, 38, 41, 44, 60, 94, 131, 143, 144, 165, 170,\n", + " 206, 258, 281, 285, 298, 302, 305, 317, 326, 355, 386, 444, 463,\n", + " 479, 490, 492, 495, 514, 530, 531, 541, 548, 554, 557, 580]),\n", + " 7: array([ 47, 201, 237, 332, 376, 381, 533])},\n", + " 9: {0: array([ 51, 57, 71, 81, 98, 104, 110, 123, 152, 161, 171, 188, 197,\n", + " 203, 208, 239, 246, 256, 313, 321, 343, 359, 400, 402, 472, 512,\n", + " 516, 538, 540, 550, 553, 559, 564, 568]),\n", + " 1: array([ 2, 5, 9, 10, 25, 26, 32, 35, 52, 59, 65, 67, 69,\n", + " 70, 74, 75, 82, 86, 97, 99, 102, 108, 111, 114, 127, 141,\n", + " 146, 149, 151, 153, 154, 155, 158, 162, 169, 175, 179, 181, 194,\n", + " 199, 200, 207, 210, 211, 216, 222, 223, 225, 227, 235, 236, 245,\n", + " 250, 251, 253, 254, 262, 264, 270, 278, 283, 291, 292, 295, 296,\n", + " 299, 303, 304, 307, 310, 314, 316, 320, 323, 329, 330, 334, 347,\n", + " 348, 351, 360, 364, 367, 371, 374, 375, 380, 383, 384, 387, 388,\n", + " 392, 395, 401, 411, 413, 417, 422, 425, 426, 428, 429, 430, 439,\n", + " 442, 443, 447, 450, 454, 456, 457, 459, 464, 470, 475, 476, 481,\n", + " 487, 491, 493, 494, 506, 513, 518, 519, 523, 525, 534, 546, 547,\n", + " 558, 562, 566, 572, 573, 577, 582, 583, 586, 588, 595, 599]),\n", + " 2: array([ 7, 8, 40, 48, 50, 55, 80, 83, 85, 90, 91, 105, 117,\n", + " 121, 125, 126, 130, 133, 138, 145, 150, 156, 159, 160, 166, 178,\n", + " 195, 204, 205, 209, 213, 218, 229, 233, 238, 260, 263, 268, 280,\n", + " 288, 297, 301, 306, 319, 325, 333, 335, 337, 339, 349, 357, 366,\n", + " 372, 373, 377, 382, 397, 404, 406, 414, 423, 427, 438, 452, 458,\n", + " 478, 500, 520, 524, 532, 536, 556, 569, 590, 596]),\n", + " 3: array([ 1, 6, 15, 16, 17, 21, 22, 27, 28, 31, 36, 39, 42,\n", + " 49, 53, 56, 58, 72, 77, 78, 92, 96, 100, 107, 109, 113,\n", + " 115, 116, 118, 119, 122, 124, 129, 132, 134, 135, 136, 137, 139,\n", + " 140, 142, 157, 167, 168, 176, 177, 180, 183, 184, 185, 187, 189,\n", + " 192, 196, 198, 214, 217, 219, 220, 226, 228, 230, 231, 232, 234,\n", + " 240, 241, 242, 244, 247, 248, 249, 252, 259, 266, 267, 277, 279,\n", + " 282, 284, 289, 290, 293, 300, 308, 311, 312, 322, 331, 336, 342,\n", + " 344, 345, 346, 353, 354, 356, 362, 368, 370, 379, 390, 391, 393,\n", + " 394, 396, 399, 403, 407, 409, 412, 419, 420, 435, 440, 451, 453,\n", + " 462, 467, 469, 473, 474, 482, 484, 485, 486, 488, 496, 498, 499,\n", + " 501, 502, 504, 505, 508, 510, 522, 529, 535, 539, 551, 552, 555,\n", + " 561, 563, 565, 567, 570, 571, 574, 575, 581, 584, 589, 591, 592,\n", + " 593, 594, 598]),\n", + " 4: array([ 29, 45, 68, 477, 483, 509, 515]),\n", + " 5: array([ 3, 13, 14, 18, 33, 43, 46, 54, 63, 88, 89, 93, 148,\n", + " 163, 174, 191, 193, 202, 212, 221, 243, 272, 286, 287, 294, 309,\n", + " 315, 340, 341, 350, 358, 361, 363, 369, 398, 405, 410, 415, 416,\n", + " 418, 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497,\n", + " 507, 511, 517, 521, 526, 527, 537, 544, 560, 576, 585, 587]),\n", + " 6: array([ 0, 4, 11, 19, 20, 30, 34, 37, 61, 62, 64, 66, 73,\n", + " 76, 79, 84, 87, 95, 101, 103, 106, 112, 120, 128, 147, 164,\n", + " 172, 173, 182, 186, 190, 215, 224, 255, 257, 261, 265, 269, 271,\n", + " 273, 274, 275, 276, 318, 324, 327, 328, 338, 352, 365, 378, 385,\n", + " 389, 408, 424, 431, 432, 434, 436, 437, 446, 449, 455, 471, 480,\n", + " 503, 528, 542, 543, 545, 549, 578, 579, 597]),\n", + " 7: array([ 12, 23, 24, 38, 41, 44, 60, 94, 131, 143, 144, 165, 170,\n", + " 206, 258, 281, 285, 298, 302, 305, 317, 326, 355, 386, 444, 463,\n", + " 479, 490, 492, 495, 514, 530, 531, 541, 548, 554, 557, 580]),\n", + " 8: array([ 47, 201, 237, 332, 376, 381, 533])},\n", + " 10: {0: array([ 81, 110, 152, 161, 188, 197, 313, 321, 343, 359, 516, 553, 568]),\n", + " 1: array([ 2, 5, 9, 10, 25, 26, 32, 35, 52, 59, 65, 67, 69,\n", + " 70, 74, 75, 82, 86, 97, 99, 102, 108, 111, 114, 127, 141,\n", + " 146, 149, 151, 153, 154, 155, 158, 162, 169, 175, 179, 181, 194,\n", + " 199, 200, 207, 210, 211, 216, 222, 223, 225, 227, 235, 236, 245,\n", + " 250, 251, 253, 254, 262, 264, 270, 278, 283, 291, 292, 295, 296,\n", + " 299, 303, 304, 307, 310, 314, 316, 320, 323, 329, 330, 334, 347,\n", + " 348, 351, 360, 364, 367, 371, 374, 375, 380, 383, 384, 387, 388,\n", + " 392, 395, 401, 411, 413, 417, 422, 425, 426, 428, 429, 430, 439,\n", + " 442, 443, 447, 450, 454, 456, 457, 459, 464, 470, 475, 476, 481,\n", + " 487, 491, 493, 494, 506, 513, 518, 519, 523, 525, 534, 546, 547,\n", + " 558, 562, 566, 572, 573, 577, 582, 583, 586, 588, 595, 599]),\n", + " 2: array([ 51, 57, 71, 98, 104, 123, 171, 203, 208, 239, 246, 256, 400,\n", + " 402, 472, 512, 538, 540, 550, 559, 564]),\n", + " 3: array([ 7, 8, 40, 48, 50, 55, 80, 83, 85, 90, 91, 105, 117,\n", + " 121, 125, 126, 130, 133, 138, 145, 150, 156, 159, 160, 166, 178,\n", + " 195, 204, 205, 209, 213, 218, 229, 233, 238, 260, 263, 268, 280,\n", + " 288, 297, 301, 306, 319, 325, 333, 335, 337, 339, 349, 357, 366,\n", + " 372, 373, 377, 382, 397, 404, 406, 414, 423, 427, 438, 452, 458,\n", + " 478, 500, 520, 524, 532, 536, 556, 569, 590, 596]),\n", + " 4: array([ 1, 6, 15, 16, 17, 21, 22, 27, 28, 31, 36, 39, 42,\n", + " 49, 53, 56, 58, 72, 77, 78, 92, 96, 100, 107, 109, 113,\n", + " 115, 116, 118, 119, 122, 124, 129, 132, 134, 135, 136, 137, 139,\n", + " 140, 142, 157, 167, 168, 176, 177, 180, 183, 184, 185, 187, 189,\n", + " 192, 196, 198, 214, 217, 219, 220, 226, 228, 230, 231, 232, 234,\n", + " 240, 241, 242, 244, 247, 248, 249, 252, 259, 266, 267, 277, 279,\n", + " 282, 284, 289, 290, 293, 300, 308, 311, 312, 322, 331, 336, 342,\n", + " 344, 345, 346, 353, 354, 356, 362, 368, 370, 379, 390, 391, 393,\n", + " 394, 396, 399, 403, 407, 409, 412, 419, 420, 435, 440, 451, 453,\n", + " 462, 467, 469, 473, 474, 482, 484, 485, 486, 488, 496, 498, 499,\n", + " 501, 502, 504, 505, 508, 510, 522, 529, 535, 539, 551, 552, 555,\n", + " 561, 563, 565, 567, 570, 571, 574, 575, 581, 584, 589, 591, 592,\n", + " 593, 594, 598]),\n", + " 5: array([ 29, 45, 68, 477, 483, 509, 515]),\n", + " 6: array([ 3, 13, 14, 18, 33, 43, 46, 54, 63, 88, 89, 93, 148,\n", + " 163, 174, 191, 193, 202, 212, 221, 243, 272, 286, 287, 294, 309,\n", + " 315, 340, 341, 350, 358, 361, 363, 369, 398, 405, 410, 415, 416,\n", + " 418, 421, 433, 441, 445, 448, 460, 461, 465, 466, 468, 489, 497,\n", + " 507, 511, 517, 521, 526, 527, 537, 544, 560, 576, 585, 587]),\n", + " 7: array([ 0, 4, 11, 19, 20, 30, 34, 37, 61, 62, 64, 66, 73,\n", + " 76, 79, 84, 87, 95, 101, 103, 106, 112, 120, 128, 147, 164,\n", + " 172, 173, 182, 186, 190, 215, 224, 255, 257, 261, 265, 269, 271,\n", + " 273, 274, 275, 276, 318, 324, 327, 328, 338, 352, 365, 378, 385,\n", + " 389, 408, 424, 431, 432, 434, 436, 437, 446, 449, 455, 471, 480,\n", + " 503, 528, 542, 543, 545, 549, 578, 579, 597]),\n", + " 8: array([ 12, 23, 24, 38, 41, 44, 60, 94, 131, 143, 144, 165, 170,\n", + " 206, 258, 281, 285, 298, 302, 305, 317, 326, 355, 386, 444, 463,\n", + " 479, 490, 492, 495, 514, 530, 531, 541, 548, 554, 557, 580]),\n", + " 9: array([ 47, 201, 237, 332, 376, 381, 533])}},\n", + " 'baseline': {2: {0: array([ 0, 1, 2, 3, 4, 5, 6, 9, 10, 11, 13, 14, 15,\n", + " 16, 17, 18, 19, 20, 21, 22, 25, 26, 27, 28, 29, 30,\n", + " 31, 32, 33, 34, 35, 36, 37, 39, 43, 45, 46, 49, 51,\n", + " 52, 53, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65, 66,\n", + " 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79,\n", + " 81, 82, 84, 86, 87, 88, 89, 92, 93, 95, 96, 97, 98,\n", + " 99, 100, 101, 102, 103, 104, 106, 107, 108, 109, 110, 111, 112,\n", + " 113, 114, 115, 116, 118, 119, 120, 122, 123, 124, 127, 128, 129,\n", + " 132, 134, 135, 136, 137, 139, 140, 141, 142, 146, 147, 148, 149,\n", + " 151, 152, 153, 154, 155, 157, 158, 161, 162, 163, 164, 167, 168,\n", + " 169, 171, 172, 173, 174, 175, 176, 177, 179, 180, 181, 182, 183,\n", + " 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 196, 197,\n", + " 198, 199, 200, 202, 203, 207, 208, 210, 211, 212, 214, 215, 216,\n", + " 217, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 230, 231,\n", + " 232, 234, 235, 236, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n", + " 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 259, 261, 262,\n", + " 264, 265, 266, 267, 269, 270, 271, 272, 273, 274, 275, 276, 277,\n", + " 278, 279, 282, 283, 284, 286, 287, 289, 290, 291, 292, 293, 294,\n", + " 295, 296, 299, 300, 303, 304, 307, 308, 309, 310, 311, 312, 313,\n", + " 314, 315, 316, 318, 320, 321, 322, 323, 324, 327, 328, 329, 330,\n", + " 331, 334, 336, 338, 340, 341, 342, 343, 344, 345, 346, 347, 348,\n", + " 350, 351, 352, 353, 354, 356, 358, 359, 360, 361, 362, 363, 364,\n", + " 365, 367, 368, 369, 370, 371, 374, 375, 378, 379, 380, 382, 383,\n", + " 384, 385, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 398,\n", + " 399, 400, 401, 402, 403, 405, 407, 408, 409, 410, 411, 412, 413,\n", + " 415, 416, 417, 418, 419, 420, 421, 422, 424, 425, 426, 428, 429,\n", + " 430, 432, 433, 434, 435, 436, 437, 439, 440, 441, 442, 443, 445,\n", + " 446, 447, 448, 449, 450, 451, 453, 454, 455, 456, 457, 459, 460,\n", + " 461, 462, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474,\n", + " 475, 476, 477, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489,\n", + " 491, 493, 494, 496, 497, 498, 499, 501, 502, 503, 504, 505, 506,\n", + " 507, 508, 509, 510, 511, 512, 513, 515, 516, 517, 518, 519, 521,\n", + " 522, 523, 525, 526, 527, 528, 529, 534, 535, 537, 538, 539, 540,\n", + " 542, 543, 544, 545, 546, 547, 549, 550, 552, 553, 555, 558, 559,\n", + " 560, 561, 562, 563, 564, 565, 566, 567, 568, 570, 571, 572, 573,\n", + " 574, 575, 576, 577, 578, 579, 581, 582, 583, 584, 585, 586, 587,\n", + " 588, 589, 591, 592, 593, 594, 595, 597, 598, 599]),\n", + " 1: array([ 7, 8, 12, 23, 24, 38, 40, 41, 42, 44, 47, 48, 50,\n", + " 55, 60, 80, 83, 85, 90, 91, 94, 105, 117, 121, 125, 126,\n", + " 130, 131, 133, 138, 143, 144, 145, 150, 156, 159, 160, 165, 166,\n", + " 170, 178, 195, 201, 204, 205, 206, 209, 213, 218, 229, 233, 237,\n", + " 238, 258, 260, 263, 268, 280, 281, 285, 288, 297, 298, 301, 302,\n", + " 305, 306, 317, 319, 325, 326, 332, 333, 335, 337, 339, 349, 355,\n", + " 357, 366, 372, 373, 376, 377, 381, 386, 397, 404, 406, 414, 423,\n", + " 427, 431, 438, 444, 452, 458, 463, 478, 479, 490, 492, 495, 500,\n", + " 514, 520, 524, 530, 531, 532, 533, 536, 541, 548, 551, 554, 556,\n", + " 557, 569, 580, 590, 596])},\n", + " 3: {0: array([ 0, 1, 2, 3, 4, 5, 6, 9, 10, 11, 13, 14, 15,\n", + " 16, 17, 18, 19, 20, 21, 22, 25, 26, 27, 28, 29, 30,\n", + " 31, 32, 33, 34, 35, 36, 37, 39, 43, 45, 46, 49, 51,\n", + " 52, 53, 54, 56, 57, 58, 59, 61, 62, 63, 64, 65, 66,\n", + " 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79,\n", + " 81, 82, 84, 86, 87, 88, 89, 92, 93, 95, 96, 97, 98,\n", + " 99, 100, 101, 102, 103, 104, 106, 107, 108, 109, 110, 111, 112,\n", + " 113, 114, 115, 116, 118, 119, 120, 122, 123, 124, 127, 128, 129,\n", + " 132, 134, 135, 136, 137, 139, 140, 141, 142, 146, 147, 148, 149,\n", + " 151, 152, 153, 154, 155, 157, 158, 161, 162, 163, 164, 167, 168,\n", + " 169, 171, 172, 173, 174, 175, 176, 177, 179, 180, 181, 182, 183,\n", + " 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 196, 197,\n", + " 198, 199, 200, 202, 203, 207, 208, 210, 211, 212, 214, 215, 216,\n", + " 217, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 230, 231,\n", + " 232, 234, 235, 236, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n", + " 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 259, 261, 262,\n", + " 264, 265, 266, 267, 269, 270, 271, 272, 273, 274, 275, 276, 277,\n", + " 278, 279, 282, 283, 284, 286, 287, 289, 290, 291, 292, 293, 294,\n", + " 295, 296, 299, 300, 303, 304, 307, 308, 309, 310, 311, 312, 313,\n", + " 314, 315, 316, 318, 320, 321, 322, 323, 324, 327, 328, 329, 330,\n", + " 331, 334, 336, 338, 340, 341, 342, 343, 344, 345, 346, 347, 348,\n", + " 350, 351, 352, 353, 354, 356, 358, 359, 360, 361, 362, 363, 364,\n", + " 365, 367, 368, 369, 370, 371, 374, 375, 378, 379, 380, 382, 383,\n", + " 384, 385, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 398,\n", + " 399, 400, 401, 402, 403, 405, 407, 408, 409, 410, 411, 412, 413,\n", + " 415, 416, 417, 418, 419, 420, 421, 422, 424, 425, 426, 428, 429,\n", + " 430, 432, 433, 434, 435, 436, 437, 439, 440, 441, 442, 443, 445,\n", + " 446, 447, 448, 449, 450, 451, 453, 454, 455, 456, 457, 459, 460,\n", + " 461, 462, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474,\n", + " 475, 476, 477, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489,\n", + " 491, 493, 494, 496, 497, 498, 499, 501, 502, 503, 504, 505, 506,\n", + " 507, 508, 509, 510, 511, 512, 513, 515, 516, 517, 518, 519, 521,\n", + " 522, 523, 525, 526, 527, 528, 529, 534, 535, 537, 538, 539, 540,\n", + " 542, 543, 544, 545, 546, 547, 549, 550, 552, 553, 555, 558, 559,\n", + " 560, 561, 562, 563, 564, 565, 566, 567, 568, 570, 571, 572, 573,\n", + " 574, 575, 576, 577, 578, 579, 581, 582, 583, 584, 585, 586, 587,\n", + " 588, 589, 591, 592, 593, 594, 595, 597, 598, 599]),\n", + " 1: array([ 7, 8, 40, 41, 44, 47, 48, 50, 55, 60, 80, 83, 85,\n", + " 90, 91, 105, 117, 121, 125, 126, 130, 131, 133, 138, 143, 144,\n", + " 145, 150, 156, 159, 160, 165, 166, 178, 195, 201, 204, 206, 209,\n", + " 213, 218, 229, 233, 238, 258, 260, 263, 268, 280, 281, 285, 288,\n", + " 297, 301, 302, 305, 306, 319, 332, 333, 335, 337, 339, 349, 357,\n", + " 366, 372, 373, 376, 377, 381, 386, 397, 404, 406, 414, 423, 427,\n", + " 438, 452, 458, 478, 479, 490, 492, 500, 514, 520, 524, 532, 533,\n", + " 536, 541, 548, 554, 556, 569, 580, 590, 596]),\n", + " 2: array([ 12, 23, 24, 38, 42, 94, 170, 205, 237, 298, 317, 325, 326,\n", + " 355, 431, 444, 463, 495, 530, 531, 551, 557])},\n", + " 4: {0: array([ 0, 1, 4, 6, 11, 14, 15, 16, 17, 18, 19, 21, 22,\n", + " 27, 28, 29, 30, 34, 36, 37, 39, 43, 45, 46, 49, 53,\n", + " 56, 58, 64, 68, 72, 73, 77, 78, 79, 81, 84, 86, 89,\n", + " 92, 93, 95, 96, 98, 102, 106, 107, 108, 109, 112, 113, 115,\n", + " 116, 118, 119, 120, 124, 128, 129, 132, 134, 135, 136, 137, 139,\n", + " 140, 142, 147, 157, 161, 163, 164, 167, 168, 172, 174, 176, 177,\n", + " 180, 182, 183, 184, 185, 186, 187, 189, 190, 192, 197, 212, 214,\n", + " 215, 216, 217, 219, 220, 221, 224, 225, 226, 228, 230, 231, 232,\n", + " 234, 235, 240, 241, 242, 244, 246, 247, 248, 249, 250, 253, 254,\n", + " 256, 259, 266, 272, 275, 276, 277, 279, 282, 283, 284, 286, 290,\n", + " 291, 293, 294, 300, 308, 309, 311, 312, 318, 322, 331, 334, 336,\n", + " 341, 342, 343, 344, 345, 346, 352, 353, 354, 356, 360, 361, 362,\n", + " 363, 368, 369, 370, 375, 379, 382, 390, 391, 392, 393, 394, 396,\n", + " 398, 402, 407, 409, 410, 411, 412, 416, 418, 419, 420, 421, 424,\n", + " 425, 428, 432, 433, 434, 435, 436, 440, 449, 451, 453, 457, 460,\n", + " 462, 467, 469, 471, 472, 473, 474, 475, 477, 480, 483, 484, 485,\n", + " 486, 488, 496, 497, 498, 499, 501, 502, 503, 504, 505, 508, 509,\n", + " 510, 511, 512, 515, 516, 521, 522, 526, 527, 529, 535, 539, 542,\n", + " 543, 546, 547, 549, 550, 552, 553, 555, 559, 560, 561, 563, 566,\n", + " 567, 570, 571, 574, 575, 576, 578, 579, 581, 584, 585, 587, 589,\n", + " 591, 592, 593, 594, 597, 598]),\n", + " 1: array([ 7, 8, 40, 41, 44, 47, 48, 50, 55, 60, 80, 83, 85,\n", + " 90, 91, 105, 117, 121, 125, 126, 130, 131, 133, 138, 143, 144,\n", + " 145, 150, 156, 159, 160, 165, 166, 178, 195, 201, 204, 206, 209,\n", + " 213, 218, 229, 233, 238, 258, 260, 263, 268, 280, 281, 285, 288,\n", + " 297, 301, 302, 305, 306, 319, 332, 333, 335, 337, 339, 349, 357,\n", + " 366, 372, 373, 376, 377, 381, 386, 397, 404, 406, 414, 423, 427,\n", + " 438, 452, 458, 478, 479, 490, 492, 500, 514, 520, 524, 532, 533,\n", + " 536, 541, 548, 554, 556, 569, 580, 590, 596]),\n", + " 2: array([ 2, 3, 5, 9, 10, 13, 20, 25, 26, 31, 32, 33, 35,\n", + " 51, 52, 54, 57, 59, 61, 62, 63, 65, 66, 67, 69, 70,\n", + " 71, 74, 75, 76, 82, 87, 88, 97, 99, 100, 101, 103, 104,\n", + " 110, 111, 114, 122, 123, 127, 141, 146, 148, 149, 151, 152, 153,\n", + " 154, 155, 158, 162, 169, 171, 173, 175, 179, 181, 188, 191, 193,\n", + " 194, 196, 198, 199, 200, 202, 203, 207, 208, 210, 211, 222, 223,\n", + " 227, 236, 239, 243, 245, 251, 252, 255, 257, 261, 262, 264, 265,\n", + " 267, 269, 270, 271, 273, 274, 278, 287, 289, 292, 295, 296, 299,\n", + " 303, 304, 307, 310, 313, 314, 315, 316, 320, 321, 323, 324, 327,\n", + " 328, 329, 330, 338, 340, 347, 348, 350, 351, 358, 359, 364, 365,\n", + " 367, 371, 374, 378, 380, 383, 384, 385, 387, 388, 389, 395, 399,\n", + " 400, 401, 403, 405, 408, 413, 415, 417, 422, 426, 429, 430, 437,\n", + " 439, 441, 442, 443, 445, 446, 447, 448, 450, 454, 455, 456, 459,\n", + " 461, 464, 465, 466, 468, 470, 476, 481, 482, 487, 489, 491, 493,\n", + " 494, 506, 507, 513, 517, 518, 519, 523, 525, 528, 534, 537, 538,\n", + " 540, 544, 545, 558, 562, 564, 565, 568, 572, 573, 577, 582, 583,\n", + " 586, 588, 595, 599]),\n", + " 3: array([ 12, 23, 24, 38, 42, 94, 170, 205, 237, 298, 317, 325, 326,\n", + " 355, 431, 444, 463, 495, 530, 531, 551, 557])},\n", + " 5: {0: array([ 0, 1, 4, 6, 11, 14, 15, 16, 17, 18, 19, 21, 22,\n", + " 27, 28, 29, 30, 34, 36, 37, 39, 43, 46, 49, 53, 56,\n", + " 58, 64, 72, 73, 77, 78, 79, 81, 84, 86, 89, 92, 93,\n", + " 95, 96, 98, 102, 106, 107, 108, 109, 112, 113, 115, 116, 118,\n", + " 119, 120, 124, 128, 129, 132, 134, 135, 136, 137, 139, 140, 142,\n", + " 147, 157, 161, 163, 164, 167, 168, 172, 174, 176, 177, 180, 182,\n", + " 183, 184, 185, 186, 187, 189, 190, 192, 197, 212, 214, 215, 216,\n", + " 217, 219, 220, 221, 224, 225, 226, 228, 230, 231, 232, 234, 235,\n", + " 240, 241, 242, 244, 246, 247, 248, 249, 250, 253, 254, 256, 259,\n", + " 266, 272, 275, 276, 277, 279, 282, 283, 284, 286, 290, 291, 293,\n", + " 294, 300, 308, 309, 311, 312, 318, 322, 331, 334, 336, 341, 342,\n", + " 343, 344, 345, 346, 352, 353, 354, 356, 360, 361, 362, 363, 368,\n", + " 369, 370, 375, 379, 382, 390, 391, 392, 393, 394, 396, 398, 402,\n", + " 407, 409, 410, 411, 412, 416, 418, 419, 420, 421, 424, 425, 428,\n", + " 432, 433, 434, 435, 436, 440, 449, 451, 453, 457, 460, 462, 467,\n", + " 469, 471, 472, 473, 474, 475, 477, 480, 483, 484, 485, 486, 488,\n", + " 496, 497, 498, 499, 501, 502, 503, 504, 505, 508, 510, 511, 512,\n", + " 515, 516, 521, 522, 526, 527, 529, 535, 539, 542, 543, 546, 547,\n", + " 549, 550, 552, 553, 555, 560, 561, 563, 566, 567, 570, 571, 574,\n", + " 575, 576, 578, 579, 581, 584, 585, 587, 589, 591, 592, 593, 594,\n", + " 597, 598]),\n", + " 1: array([ 7, 8, 40, 41, 44, 47, 48, 50, 55, 60, 80, 83, 85,\n", + " 90, 91, 105, 117, 121, 125, 126, 130, 131, 133, 138, 143, 144,\n", + " 145, 150, 156, 159, 160, 165, 166, 178, 195, 201, 204, 206, 209,\n", + " 213, 218, 229, 233, 238, 258, 260, 263, 268, 280, 281, 285, 288,\n", + " 297, 301, 302, 305, 306, 319, 332, 333, 335, 337, 339, 349, 357,\n", + " 366, 372, 373, 376, 377, 381, 386, 397, 404, 406, 414, 423, 427,\n", + " 438, 452, 458, 478, 479, 490, 492, 500, 514, 520, 524, 532, 533,\n", + " 536, 541, 548, 554, 556, 569, 580, 590, 596]),\n", + " 2: array([ 45, 68, 400, 509, 559]),\n", + " 3: array([ 2, 3, 5, 9, 10, 13, 20, 25, 26, 31, 32, 33, 35,\n", + " 51, 52, 54, 57, 59, 61, 62, 63, 65, 66, 67, 69, 70,\n", + " 71, 74, 75, 76, 82, 87, 88, 97, 99, 100, 101, 103, 104,\n", + " 110, 111, 114, 122, 123, 127, 141, 146, 148, 149, 151, 152, 153,\n", + " 154, 155, 158, 162, 169, 171, 173, 175, 179, 181, 188, 191, 193,\n", + " 194, 196, 198, 199, 200, 202, 203, 207, 208, 210, 211, 222, 223,\n", + " 227, 236, 239, 243, 245, 251, 252, 255, 257, 261, 262, 264, 265,\n", + " 267, 269, 270, 271, 273, 274, 278, 287, 289, 292, 295, 296, 299,\n", + " 303, 304, 307, 310, 313, 314, 315, 316, 320, 321, 323, 324, 327,\n", + " 328, 329, 330, 338, 340, 347, 348, 350, 351, 358, 359, 364, 365,\n", + " 367, 371, 374, 378, 380, 383, 384, 385, 387, 388, 389, 395, 399,\n", + " 401, 403, 405, 408, 413, 415, 417, 422, 426, 429, 430, 437, 439,\n", + " 441, 442, 443, 445, 446, 447, 448, 450, 454, 455, 456, 459, 461,\n", + " 464, 465, 466, 468, 470, 476, 481, 482, 487, 489, 491, 493, 494,\n", + " 506, 507, 513, 517, 518, 519, 523, 525, 528, 534, 537, 538, 540,\n", + " 544, 545, 558, 562, 564, 565, 568, 572, 573, 577, 582, 583, 586,\n", + " 588, 595, 599]),\n", + " 4: array([ 12, 23, 24, 38, 42, 94, 170, 205, 237, 298, 317, 325, 326,\n", + " 355, 431, 444, 463, 495, 530, 531, 551, 557])},\n", + " 6: {0: array([ 0, 1, 4, 6, 11, 14, 15, 16, 17, 18, 19, 21, 22,\n", + " 27, 28, 29, 30, 34, 36, 37, 39, 43, 46, 49, 53, 56,\n", + " 58, 64, 72, 73, 77, 78, 79, 81, 84, 86, 89, 92, 93,\n", + " 95, 96, 98, 102, 106, 107, 108, 109, 112, 113, 115, 116, 118,\n", + " 119, 120, 124, 128, 129, 132, 134, 135, 136, 137, 139, 140, 142,\n", + " 147, 157, 161, 163, 164, 167, 168, 172, 174, 176, 177, 180, 182,\n", + " 183, 184, 185, 186, 187, 189, 190, 192, 197, 212, 214, 215, 216,\n", + " 217, 219, 220, 221, 224, 225, 226, 228, 230, 231, 232, 234, 240,\n", + " 241, 242, 244, 246, 247, 248, 249, 250, 253, 254, 256, 259, 265,\n", + " 266, 272, 275, 276, 277, 279, 282, 283, 284, 286, 290, 291, 293,\n", + " 294, 300, 308, 309, 311, 312, 318, 322, 331, 334, 336, 341, 342,\n", + " 343, 344, 345, 346, 352, 353, 354, 356, 360, 361, 362, 363, 368,\n", + " 369, 370, 375, 379, 382, 390, 391, 392, 393, 394, 396, 398, 399,\n", + " 402, 407, 409, 410, 411, 412, 416, 418, 419, 420, 421, 424, 425,\n", + " 428, 432, 433, 434, 435, 436, 440, 449, 451, 453, 457, 460, 462,\n", + " 467, 469, 471, 472, 473, 474, 475, 477, 480, 483, 484, 485, 486,\n", + " 488, 496, 497, 498, 499, 501, 502, 503, 504, 505, 508, 510, 511,\n", + " 512, 515, 516, 521, 522, 526, 527, 529, 535, 539, 542, 543, 546,\n", + " 547, 549, 550, 552, 553, 555, 560, 561, 563, 566, 567, 570, 571,\n", + " 574, 575, 576, 578, 579, 581, 584, 585, 587, 589, 591, 592, 593,\n", + " 594, 597, 598]),\n", + " 1: array([ 7, 8, 40, 41, 44, 47, 48, 50, 55, 60, 80, 83, 85,\n", + " 90, 91, 105, 117, 121, 125, 126, 130, 131, 133, 138, 143, 144,\n", + " 145, 150, 156, 159, 160, 165, 166, 178, 195, 201, 204, 206, 209,\n", + " 213, 218, 229, 233, 238, 258, 260, 263, 268, 280, 281, 285, 288,\n", + " 297, 301, 302, 305, 306, 319, 332, 333, 335, 337, 339, 349, 357,\n", + " 366, 372, 373, 376, 377, 381, 386, 397, 404, 406, 414, 423, 427,\n", + " 438, 452, 458, 478, 479, 490, 492, 500, 514, 520, 524, 532, 533,\n", + " 536, 541, 548, 554, 556, 569, 580, 590, 596]),\n", + " 2: array([ 45, 68, 400, 509, 559]),\n", + " 3: array([ 2, 3, 5, 9, 10, 13, 20, 25, 26, 31, 32, 33, 35,\n", + " 51, 52, 54, 59, 61, 62, 63, 65, 66, 67, 69, 70, 71,\n", + " 74, 75, 76, 82, 88, 97, 99, 100, 104, 111, 114, 122, 127,\n", + " 141, 146, 148, 149, 151, 153, 154, 155, 158, 162, 169, 171, 175,\n", + " 179, 181, 191, 193, 194, 196, 198, 199, 200, 202, 203, 207, 208,\n", + " 210, 211, 222, 223, 227, 235, 236, 243, 245, 251, 252, 255, 262,\n", + " 264, 267, 269, 270, 273, 278, 287, 289, 292, 295, 296, 299, 303,\n", + " 304, 307, 310, 314, 315, 316, 320, 323, 327, 328, 329, 330, 338,\n", + " 340, 347, 348, 350, 351, 358, 364, 365, 367, 371, 374, 380, 383,\n", + " 384, 385, 387, 388, 395, 401, 403, 405, 413, 415, 417, 422, 426,\n", + " 429, 430, 437, 439, 441, 442, 443, 445, 447, 448, 450, 454, 455,\n", + " 456, 459, 461, 464, 465, 466, 468, 470, 476, 481, 482, 487, 489,\n", + " 491, 493, 494, 506, 507, 513, 517, 518, 519, 523, 525, 528, 534,\n", + " 537, 538, 540, 544, 558, 562, 564, 565, 572, 573, 577, 582, 583,\n", + " 586, 588, 595, 599]),\n", + " 4: array([ 57, 87, 101, 103, 110, 123, 152, 173, 188, 239, 257, 261, 271,\n", + " 274, 313, 321, 324, 359, 378, 389, 408, 446, 545, 568]),\n", + " 5: array([ 12, 23, 24, 38, 42, 94, 170, 205, 237, 298, 317, 325, 326,\n", + " 355, 431, 444, 463, 495, 530, 531, 551, 557])},\n", + " 7: {0: array([ 0, 1, 4, 11, 14, 16, 18, 19, 22, 27, 28, 29, 30,\n", + " 36, 39, 43, 46, 49, 53, 56, 64, 78, 81, 84, 86, 89,\n", + " 92, 93, 95, 96, 98, 102, 106, 107, 108, 113, 115, 116, 118,\n", + " 119, 124, 128, 129, 132, 139, 140, 147, 161, 163, 164, 167, 168,\n", + " 172, 174, 184, 187, 189, 190, 192, 197, 212, 214, 216, 217, 219,\n", + " 220, 221, 225, 226, 234, 241, 246, 248, 249, 250, 253, 254, 256,\n", + " 266, 272, 275, 276, 277, 279, 283, 286, 291, 293, 294, 300, 308,\n", + " 309, 311, 312, 331, 334, 336, 341, 342, 343, 344, 345, 346, 352,\n", + " 353, 356, 360, 361, 363, 369, 370, 382, 390, 391, 392, 393, 394,\n", + " 398, 402, 410, 411, 416, 418, 419, 420, 421, 425, 428, 432, 433,\n", + " 434, 436, 440, 449, 451, 453, 457, 460, 467, 469, 471, 472, 475,\n", + " 477, 483, 484, 485, 486, 496, 497, 498, 501, 502, 505, 511, 512,\n", + " 515, 516, 521, 526, 527, 529, 535, 539, 543, 546, 547, 550, 553,\n", + " 555, 560, 561, 566, 567, 576, 578, 581, 585, 587, 589, 594, 597,\n", + " 598]),\n", + " 1: array([ 7, 8, 40, 41, 44, 47, 48, 50, 55, 60, 80, 83, 85,\n", + " 90, 91, 105, 117, 121, 125, 126, 130, 131, 133, 138, 143, 144,\n", + " 145, 150, 156, 159, 160, 165, 166, 178, 195, 201, 204, 206, 209,\n", + " 213, 218, 229, 233, 238, 258, 260, 263, 268, 280, 281, 285, 288,\n", + " 297, 301, 302, 305, 306, 319, 332, 333, 335, 337, 339, 349, 357,\n", + " 366, 372, 373, 376, 377, 381, 386, 397, 404, 406, 414, 423, 427,\n", + " 438, 452, 458, 478, 479, 490, 492, 500, 514, 520, 524, 532, 533,\n", + " 536, 541, 548, 554, 556, 569, 580, 590, 596]),\n", + " 2: array([ 6, 15, 17, 21, 34, 37, 58, 72, 73, 77, 79, 109, 112,\n", + " 120, 134, 135, 136, 137, 142, 157, 176, 177, 180, 182, 183, 185,\n", + " 186, 198, 215, 224, 228, 230, 231, 232, 240, 242, 244, 247, 259,\n", + " 282, 284, 290, 318, 322, 354, 362, 368, 375, 379, 396, 399, 403,\n", + " 407, 409, 412, 424, 435, 462, 473, 474, 480, 488, 499, 503, 504,\n", + " 508, 510, 522, 542, 549, 552, 563, 570, 571, 574, 575, 579, 584,\n", + " 591, 592, 593]),\n", + " 3: array([ 45, 68, 400, 509, 559]),\n", + " 4: array([ 2, 3, 5, 9, 10, 13, 20, 25, 26, 31, 32, 33, 35,\n", + " 51, 52, 54, 59, 61, 62, 63, 65, 66, 67, 69, 70, 71,\n", + " 74, 75, 76, 82, 88, 97, 99, 100, 104, 111, 114, 122, 127,\n", + " 141, 146, 148, 149, 151, 153, 154, 155, 158, 162, 169, 171, 175,\n", + " 179, 181, 191, 193, 194, 196, 199, 200, 202, 203, 207, 208, 210,\n", + " 211, 222, 223, 227, 235, 236, 243, 245, 251, 252, 255, 262, 264,\n", + " 267, 269, 270, 273, 278, 287, 289, 292, 295, 296, 299, 303, 304,\n", + " 307, 310, 314, 315, 316, 320, 323, 327, 328, 329, 330, 338, 340,\n", + " 347, 348, 350, 351, 358, 364, 365, 367, 371, 374, 380, 383, 384,\n", + " 385, 387, 388, 395, 401, 405, 413, 415, 417, 422, 426, 429, 430,\n", + " 437, 439, 441, 442, 443, 445, 447, 448, 450, 454, 455, 456, 459,\n", + " 461, 464, 465, 466, 468, 470, 476, 481, 482, 487, 489, 491, 493,\n", + " 494, 506, 507, 513, 517, 518, 519, 523, 525, 528, 534, 537, 538,\n", + " 540, 544, 558, 562, 564, 565, 572, 573, 577, 582, 583, 586, 588,\n", + " 595, 599]),\n", + " 5: array([ 57, 87, 101, 103, 110, 123, 152, 173, 188, 239, 257, 261, 265,\n", + " 271, 274, 313, 321, 324, 359, 378, 389, 408, 446, 545, 568]),\n", + " 6: array([ 12, 23, 24, 38, 42, 94, 170, 205, 237, 298, 317, 325, 326,\n", + " 355, 431, 444, 463, 495, 530, 531, 551, 557])},\n", + " 8: {0: array([ 0, 1, 4, 11, 14, 16, 18, 19, 22, 27, 28, 29, 30,\n", + " 36, 39, 43, 46, 49, 53, 56, 64, 78, 81, 84, 86, 89,\n", + " 92, 93, 95, 96, 98, 102, 106, 107, 108, 113, 115, 116, 118,\n", + " 119, 124, 128, 129, 132, 139, 140, 147, 161, 163, 164, 167, 168,\n", + " 172, 174, 184, 187, 189, 190, 192, 197, 212, 214, 216, 217, 219,\n", + " 220, 221, 225, 226, 234, 241, 246, 248, 249, 250, 253, 254, 256,\n", + " 266, 272, 275, 276, 277, 279, 283, 286, 291, 293, 294, 300, 308,\n", + " 309, 311, 312, 331, 334, 336, 341, 342, 343, 344, 345, 346, 352,\n", + " 353, 356, 360, 361, 363, 369, 370, 382, 390, 391, 392, 393, 394,\n", + " 398, 402, 410, 411, 416, 418, 419, 420, 421, 425, 428, 432, 433,\n", + " 434, 436, 440, 449, 451, 453, 457, 460, 467, 469, 471, 472, 475,\n", + " 477, 483, 484, 485, 486, 496, 497, 498, 501, 502, 505, 511, 512,\n", + " 515, 516, 521, 526, 527, 529, 535, 539, 543, 546, 547, 550, 553,\n", + " 555, 560, 561, 566, 567, 576, 578, 581, 585, 587, 589, 594, 597,\n", + " 598]),\n", + " 1: array([ 7, 8, 40, 41, 44, 47, 48, 50, 55, 60, 80, 83, 85,\n", + " 90, 91, 105, 117, 121, 125, 126, 130, 131, 133, 138, 143, 144,\n", + " 145, 150, 156, 159, 160, 165, 166, 178, 195, 201, 204, 206, 209,\n", + " 213, 218, 229, 233, 238, 258, 260, 263, 268, 280, 281, 285, 288,\n", + " 297, 301, 302, 305, 306, 319, 332, 333, 335, 337, 339, 349, 357,\n", + " 366, 372, 373, 376, 377, 381, 386, 397, 404, 406, 414, 423, 427,\n", + " 438, 452, 458, 478, 479, 490, 492, 500, 514, 520, 524, 532, 533,\n", + " 536, 541, 548, 554, 556, 569, 580, 590, 596]),\n", + " 2: array([ 6, 15, 17, 21, 34, 37, 58, 72, 73, 77, 79, 109, 112,\n", + " 120, 134, 135, 136, 137, 142, 157, 176, 177, 180, 182, 183, 185,\n", + " 186, 198, 215, 224, 228, 230, 231, 232, 240, 242, 244, 247, 259,\n", + " 282, 284, 290, 318, 322, 354, 362, 368, 375, 379, 396, 399, 403,\n", + " 407, 409, 412, 424, 435, 462, 473, 474, 480, 488, 499, 503, 504,\n", + " 508, 510, 522, 542, 549, 552, 563, 570, 571, 574, 575, 579, 584,\n", + " 591, 592, 593]),\n", + " 3: array([ 45, 68, 400, 509, 559]),\n", + " 4: array([ 2, 3, 5, 9, 13, 20, 25, 26, 31, 32, 33, 35, 51,\n", + " 52, 54, 59, 61, 62, 63, 65, 66, 67, 69, 70, 71, 74,\n", + " 75, 76, 82, 88, 97, 99, 100, 104, 111, 114, 122, 127, 141,\n", + " 146, 148, 149, 151, 154, 155, 158, 169, 171, 175, 179, 181, 191,\n", + " 193, 196, 199, 200, 202, 203, 207, 208, 210, 211, 222, 223, 227,\n", + " 235, 236, 243, 245, 251, 252, 255, 262, 264, 267, 269, 270, 273,\n", + " 278, 287, 289, 292, 295, 299, 303, 304, 307, 310, 314, 315, 316,\n", + " 320, 323, 327, 328, 329, 330, 338, 340, 347, 348, 350, 351, 358,\n", + " 364, 365, 367, 371, 374, 380, 383, 384, 385, 387, 388, 395, 401,\n", + " 405, 413, 415, 422, 426, 429, 430, 437, 439, 441, 442, 443, 445,\n", + " 447, 448, 450, 454, 455, 456, 459, 461, 464, 465, 466, 468, 470,\n", + " 476, 481, 487, 489, 491, 493, 494, 506, 507, 513, 517, 518, 519,\n", + " 523, 525, 528, 534, 537, 538, 540, 544, 562, 564, 565, 572, 573,\n", + " 577, 582, 583, 586, 588, 595, 599]),\n", + " 5: array([ 57, 87, 101, 103, 110, 123, 152, 173, 188, 239, 257, 261, 265,\n", + " 271, 274, 313, 321, 324, 359, 378, 389, 408, 446, 545, 568]),\n", + " 6: array([ 12, 23, 24, 38, 42, 94, 170, 205, 237, 298, 317, 325, 326,\n", + " 355, 431, 444, 463, 495, 530, 531, 551, 557]),\n", + " 7: array([ 10, 153, 162, 194, 296, 417, 482, 558])},\n", + " 9: {0: array([ 0, 1, 4, 11, 14, 16, 18, 19, 22, 27, 28, 29, 30,\n", + " 36, 39, 43, 46, 49, 53, 56, 64, 78, 81, 84, 86, 89,\n", + " 92, 93, 95, 96, 98, 102, 106, 107, 108, 113, 115, 116, 118,\n", + " 119, 124, 128, 129, 132, 139, 140, 147, 161, 163, 164, 167, 168,\n", + " 172, 174, 184, 187, 189, 190, 192, 197, 212, 214, 216, 217, 219,\n", + " 220, 221, 225, 226, 234, 241, 246, 248, 249, 250, 253, 254, 256,\n", + " 266, 272, 275, 276, 277, 279, 283, 286, 291, 293, 294, 300, 308,\n", + " 309, 311, 312, 331, 334, 336, 341, 342, 343, 344, 345, 346, 352,\n", + " 353, 356, 360, 361, 363, 369, 370, 382, 390, 391, 392, 393, 394,\n", + " 398, 402, 410, 411, 416, 418, 419, 420, 421, 425, 428, 432, 433,\n", + " 434, 436, 440, 449, 451, 453, 457, 460, 467, 469, 471, 472, 475,\n", + " 477, 483, 484, 485, 486, 496, 497, 498, 501, 502, 505, 511, 512,\n", + " 515, 516, 521, 526, 527, 529, 535, 539, 543, 546, 547, 550, 553,\n", + " 555, 560, 561, 566, 567, 576, 578, 581, 585, 587, 589, 594, 597,\n", + " 598]),\n", + " 1: array([ 7, 8, 40, 47, 48, 50, 55, 80, 83, 85, 90, 91, 105,\n", + " 117, 121, 125, 126, 133, 138, 145, 150, 156, 159, 160, 166, 178,\n", + " 195, 201, 204, 209, 213, 218, 229, 233, 238, 263, 268, 280, 288,\n", + " 297, 301, 306, 319, 332, 333, 335, 337, 339, 349, 357, 366, 372,\n", + " 373, 376, 377, 381, 397, 404, 406, 414, 423, 427, 438, 452, 478,\n", + " 500, 520, 524, 536, 556, 569, 590, 596]),\n", + " 2: array([ 6, 15, 17, 21, 34, 37, 58, 72, 73, 77, 79, 109, 112,\n", + " 120, 134, 135, 136, 137, 142, 157, 176, 177, 180, 182, 183, 185,\n", + " 186, 198, 215, 224, 228, 230, 231, 232, 240, 242, 244, 247, 259,\n", + " 282, 284, 290, 318, 322, 354, 362, 368, 375, 379, 396, 399, 403,\n", + " 407, 409, 412, 424, 435, 462, 473, 474, 480, 488, 499, 503, 504,\n", + " 508, 510, 522, 542, 549, 552, 563, 570, 571, 574, 575, 579, 584,\n", + " 591, 592, 593]),\n", + " 3: array([ 45, 68, 400, 509, 559]),\n", + " 4: array([ 2, 3, 5, 9, 13, 20, 25, 26, 31, 32, 33, 35, 51,\n", + " 52, 54, 59, 61, 62, 63, 65, 66, 67, 69, 70, 71, 74,\n", + " 75, 76, 82, 88, 97, 99, 100, 104, 111, 114, 122, 127, 141,\n", + " 146, 148, 149, 151, 154, 155, 158, 169, 171, 175, 179, 181, 191,\n", + " 193, 196, 199, 200, 202, 203, 207, 208, 210, 211, 222, 223, 227,\n", + " 235, 236, 243, 245, 251, 252, 255, 262, 264, 267, 269, 270, 273,\n", + " 278, 287, 289, 292, 295, 299, 303, 304, 307, 310, 314, 315, 316,\n", + " 320, 323, 327, 328, 329, 330, 338, 340, 347, 348, 350, 351, 358,\n", + " 364, 365, 367, 371, 374, 380, 383, 384, 385, 387, 388, 395, 401,\n", + " 405, 413, 415, 422, 426, 429, 430, 437, 439, 441, 442, 443, 445,\n", + " 447, 448, 450, 454, 455, 456, 459, 461, 464, 465, 466, 468, 470,\n", + " 476, 481, 487, 489, 491, 493, 494, 506, 507, 513, 517, 518, 519,\n", + " 523, 525, 528, 534, 537, 538, 540, 544, 562, 564, 565, 572, 573,\n", + " 577, 582, 583, 586, 588, 595, 599]),\n", + " 5: array([ 57, 87, 101, 103, 110, 123, 152, 173, 188, 239, 257, 261, 265,\n", + " 271, 274, 313, 321, 324, 359, 378, 389, 408, 446, 545, 568]),\n", + " 6: array([ 12, 23, 24, 38, 42, 94, 170, 205, 237, 298, 317, 325, 326,\n", + " 355, 431, 444, 463, 495, 530, 531, 551, 557]),\n", + " 7: array([ 41, 44, 60, 130, 131, 143, 144, 165, 206, 258, 260, 281, 285,\n", + " 302, 305, 386, 458, 479, 490, 492, 514, 532, 533, 541, 548, 554,\n", + " 580]),\n", + " 8: array([ 10, 153, 162, 194, 296, 417, 482, 558])},\n", + " 10: {0: array([ 0, 1, 4, 11, 14, 16, 18, 19, 22, 27, 28, 39, 43,\n", + " 46, 53, 56, 64, 78, 81, 84, 89, 92, 93, 95, 96, 98,\n", + " 106, 107, 113, 115, 118, 119, 124, 128, 129, 132, 139, 140, 147,\n", + " 161, 163, 164, 167, 168, 172, 174, 184, 187, 189, 190, 192, 197,\n", + " 212, 214, 217, 219, 220, 221, 226, 234, 241, 246, 248, 249, 256,\n", + " 266, 272, 275, 276, 277, 279, 286, 294, 300, 308, 312, 331, 336,\n", + " 341, 342, 343, 344, 345, 346, 352, 356, 361, 363, 369, 370, 390,\n", + " 391, 393, 394, 398, 402, 410, 416, 418, 419, 420, 421, 432, 433,\n", + " 434, 436, 440, 449, 451, 453, 457, 460, 467, 469, 471, 472, 484,\n", + " 485, 486, 496, 497, 501, 502, 505, 511, 512, 516, 521, 526, 527,\n", + " 529, 535, 539, 543, 547, 550, 553, 555, 560, 561, 567, 576, 578,\n", + " 581, 585, 587, 589, 594, 597, 598]),\n", + " 1: array([ 29, 35, 36, 49, 52, 86, 102, 108, 114, 127, 200, 216, 225,\n", + " 235, 250, 253, 254, 283, 291, 293, 309, 311, 320, 334, 353, 358,\n", + " 360, 380, 382, 392, 411, 425, 428, 475, 477, 483, 498, 515, 544,\n", + " 546, 566, 577]),\n", + " 2: array([ 7, 8, 40, 47, 48, 50, 55, 80, 83, 85, 90, 91, 105,\n", + " 117, 121, 125, 126, 133, 138, 145, 150, 156, 159, 160, 166, 178,\n", + " 195, 201, 204, 209, 213, 218, 229, 233, 238, 263, 268, 280, 288,\n", + " 297, 301, 306, 319, 332, 333, 335, 337, 339, 349, 357, 366, 372,\n", + " 373, 376, 377, 381, 397, 404, 406, 414, 423, 427, 438, 452, 478,\n", + " 500, 520, 524, 536, 556, 569, 590, 596]),\n", + " 3: array([ 6, 15, 17, 21, 34, 37, 58, 72, 73, 77, 79, 109, 112,\n", + " 120, 134, 135, 136, 137, 142, 157, 176, 177, 180, 182, 183, 185,\n", + " 186, 198, 215, 224, 228, 230, 231, 232, 240, 242, 244, 247, 259,\n", + " 282, 284, 290, 318, 322, 354, 362, 368, 375, 379, 396, 399, 403,\n", + " 407, 409, 412, 424, 435, 462, 473, 474, 480, 488, 499, 503, 504,\n", + " 508, 510, 522, 542, 549, 552, 563, 570, 571, 574, 575, 579, 584,\n", + " 591, 592, 593]),\n", + " 4: array([ 45, 68, 400, 509, 559]),\n", + " 5: array([ 2, 3, 5, 9, 13, 20, 25, 26, 30, 31, 32, 33, 51,\n", + " 54, 59, 61, 62, 63, 65, 66, 67, 69, 70, 71, 74, 75,\n", + " 76, 82, 88, 97, 99, 100, 104, 111, 116, 122, 141, 146, 148,\n", + " 149, 151, 154, 155, 158, 169, 171, 175, 179, 181, 191, 193, 196,\n", + " 199, 202, 203, 207, 208, 210, 211, 222, 223, 227, 236, 243, 245,\n", + " 251, 252, 255, 262, 264, 267, 269, 270, 273, 278, 287, 289, 292,\n", + " 295, 299, 303, 304, 307, 310, 314, 315, 316, 323, 327, 328, 329,\n", + " 330, 338, 340, 347, 348, 350, 351, 364, 365, 367, 371, 374, 383,\n", + " 384, 385, 387, 388, 395, 401, 405, 413, 415, 422, 426, 429, 430,\n", + " 437, 439, 441, 442, 443, 445, 447, 448, 450, 454, 455, 456, 459,\n", + " 461, 464, 465, 466, 468, 470, 476, 481, 487, 489, 491, 493, 494,\n", + " 506, 507, 513, 517, 518, 519, 523, 525, 528, 534, 537, 538, 540,\n", + " 562, 564, 565, 572, 573, 582, 583, 586, 588, 595, 599]),\n", + " 6: array([ 57, 87, 101, 103, 110, 123, 152, 173, 188, 239, 257, 261, 265,\n", + " 271, 274, 313, 321, 324, 359, 378, 389, 408, 446, 545, 568]),\n", + " 7: array([ 12, 23, 24, 38, 42, 94, 170, 205, 237, 298, 317, 325, 326,\n", + " 355, 431, 444, 463, 495, 530, 531, 551, 557]),\n", + " 8: array([ 41, 44, 60, 130, 131, 143, 144, 165, 206, 258, 260, 281, 285,\n", + " 302, 305, 386, 458, 479, 490, 492, 514, 532, 533, 541, 548, 554,\n", + " 580]),\n", + " 9: array([ 10, 153, 162, 194, 296, 417, 482, 558])}}}" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mnotebook controller is DISPOSED. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mnotebook controller is DISPOSED. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "test_clusters" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE for shap w/ Cluster 1/2 is 0.0011445284932732054.\n", + "RMSE for shap w/ Cluster 2/2 is 0.0009922498157503208.\n", + "RMSE for shap w/ Cluster 1/3 is 0.027281400903993308.\n", + "RMSE for shap w/ Cluster 2/3 is 0.009942837383779714.\n", + "RMSE for shap w/ Cluster 3/3 is 0.000994189767380801.\n", + "RMSE for shap w/ Cluster 1/4 is 0.0009196394707618619.\n", + "RMSE for shap w/ Cluster 2/4 is 5.560614551822814e-05.\n", + "RMSE for shap w/ Cluster 3/4 is 0.011793098028449795.\n", + "RMSE for shap w/ Cluster 4/4 is 0.03515880072413344.\n", + "RMSE for shap w/ Cluster 1/5 is 6.491663718862225e-05.\n", + "RMSE for shap w/ Cluster 2/5 is 5.458092742669756e-05.\n", + "RMSE for shap w/ Cluster 3/5 is 0.004552416992647418.\n", + "RMSE for shap w/ Cluster 4/5 is 0.02673274375572193.\n", + "RMSE for shap w/ Cluster 5/5 is 0.03579808908925724.\n", + "RMSE for shap w/ Cluster 1/6 is 6.491663718862225e-05.\n", + "RMSE for shap w/ Cluster 2/6 is 5.458092742669756e-05.\n", + "RMSE for shap w/ Cluster 3/6 is 0.007584884108137434.\n", + "RMSE for shap w/ Cluster 4/6 is 0.0004192880871736979.\n", + "RMSE for shap w/ Cluster 5/6 is 0.000271032554636866.\n", + "RMSE for shap w/ Cluster 6/6 is 0.0018926067265016622.\n", + "RMSE for shap w/ Cluster 1/7 is 0.013196855974599965.\n", + "RMSE for shap w/ Cluster 2/7 is 5.405365124062153e-05.\n", + "RMSE for shap w/ Cluster 3/7 is 0.007584884108137434.\n", + "RMSE for shap w/ Cluster 4/7 is 0.010803120199327214.\n", + "RMSE for shap w/ Cluster 5/7 is 0.000271032554636866.\n", + "RMSE for shap w/ Cluster 6/7 is 0.6931064326231492.\n", + "RMSE for shap w/ Cluster 7/7 is 0.0018926067265016622.\n", + "RMSE for shap w/ Cluster 1/8 is 0.009992011768501058.\n", + "RMSE for shap w/ Cluster 2/8 is 2.7292411725787875e-05.\n", + "RMSE for shap w/ Cluster 3/8 is 0.007584884108137434.\n", + "RMSE for shap w/ Cluster 4/8 is 0.0003893275009280155.\n", + "RMSE for shap w/ Cluster 5/8 is 0.00029798005058094334.\n", + "RMSE for shap w/ Cluster 6/8 is 0.7132006352758887.\n", + "RMSE for shap w/ Cluster 7/8 is 0.00019794696679613765.\n", + "RMSE for shap w/ Cluster 8/8 is 0.0018926067265016622.\n", + "RMSE for shap w/ Cluster 1/9 is 0.009992011768501058.\n", + "RMSE for shap w/ Cluster 2/9 is 2.762305113296116e-05.\n", + "RMSE for shap w/ Cluster 3/9 is 0.0004572782569068577.\n", + "RMSE for shap w/ Cluster 4/9 is 0.00037972976749151314.\n", + "RMSE for shap w/ Cluster 5/9 is 1.9039629593835442e-05.\n", + "RMSE for shap w/ Cluster 6/9 is 0.00402141964482971.\n", + "RMSE for shap w/ Cluster 7/9 is 0.7132006352758887.\n", + "RMSE for shap w/ Cluster 8/9 is 9.745438060051576e-05.\n", + "RMSE for shap w/ Cluster 9/9 is 0.001529599776681095.\n", + "RMSE for shap w/ Cluster 1/10 is 0.009992011768501058.\n", + "RMSE for shap w/ Cluster 2/10 is 2.762305113296116e-05.\n", + "RMSE for shap w/ Cluster 3/10 is 0.0004604755319457458.\n", + "RMSE for shap w/ Cluster 4/10 is 4.567674400878377e-06.\n", + "RMSE for shap w/ Cluster 5/10 is 0.022866989781089482.\n", + "RMSE for shap w/ Cluster 6/10 is 1.8465360902119215e-05.\n", + "RMSE for shap w/ Cluster 7/10 is 0.0047911677892702766.\n", + "RMSE for shap w/ Cluster 8/10 is 0.5194856600935099.\n", + "RMSE for shap w/ Cluster 9/10 is 5.0846623978486496e-05.\n", + "RMSE for shap w/ Cluster 10/10 is 0.001529599776681095.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 1/2 is 4.0272473508681505e-06.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 2/2 is 0.0021083280680036223.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 1/3 is 4.0272473508681505e-06.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 2/3 is 0.00034013378468516613.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 3/3 is 0.0009328395030102154.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 1/4 is 4.0272473508681505e-06.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 2/4 is 0.00034013378468516613.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 3/4 is 0.0004784391608492909.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 4/4 is 1.0599297826175415e-05.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 1/5 is 4.0272473508681505e-06.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 2/5 is 0.0001534348282118829.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 3/5 is 0.0004784391608492909.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 4/5 is 0.000310706784984491.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 5/5 is 1.0599297826175415e-05.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 1/6 is 4.0272473508681505e-06.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 2/6 is 0.0001534348282118829.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 3/6 is 1.0401675694821044e-05.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 4/6 is 0.000310706784984491.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 5/6 is 1.0599297826175415e-05.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 6/6 is 3.454406872485271e-05.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 1/7 is 4.0272473508681505e-06.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 2/7 is 0.0001534348282118829.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 3/7 is 1.0401675694821044e-05.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 4/7 is 0.000310706784984491.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 5/7 is 1.0599297826175415e-05.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 6/7 is 9.58951826078474e-06.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 7/7 is 6.638014909900322e-06.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 1/8 is 4.0272473508681505e-06.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 2/8 is 6.285788771653481e-05.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 3/8 is 0.0001467694117063379.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 4/8 is 1.0401675694821044e-05.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 5/8 is 0.00029996166864558086.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 6/8 is 1.0599297826175415e-05.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 7/8 is 9.58951826078474e-06.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 8/8 is 6.638014909900322e-06.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 1/9 is 4.247783842214531e-06.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 2/9 is 3.5960275851830527e-06.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 3/9 is 6.285788771653481e-05.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 4/9 is 0.0001467694117063379.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 5/9 is 1.0401675694821044e-05.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 6/9 is 0.00029996166864558086.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 7/9 is 1.0599297826175415e-05.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 8/9 is 9.58951826078474e-06.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 9/9 is 6.638014909900322e-06.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 1/10 is 4.247783842214531e-06.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 2/10 is 3.5960275851830527e-06.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 3/10 is 6.285788771653481e-05.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 4/10 is 0.0001439286694984269.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 5/10 is 1.0401675694821044e-05.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 6/10 is 0.00013183123529799388.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 7/10 is 1.0599297826175415e-05.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 8/10 is 9.58951826078474e-06.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 9/10 is 6.638014909900322e-06.\n", + "RMSE for signed_nonnormalized_l2_avg w/ Cluster 10/10 is 0.00023958467676516958.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 1/2 is 4.0272473508681505e-06.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 2/2 is 0.0021083280680036223.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 1/3 is 4.0272473508681505e-06.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 2/3 is 0.00034013378468516613.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 3/3 is 0.0009328395030102154.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 1/4 is 4.0272473508681505e-06.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 2/4 is 0.00034013378468516613.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 3/4 is 0.0004784391608492909.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 4/4 is 1.0599297826175415e-05.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 1/5 is 4.0272473508681505e-06.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 2/5 is 0.00014867466967818333.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 3/5 is 0.0004784391608492909.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 4/5 is 0.00030034762834589416.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 5/5 is 1.0599297826175415e-05.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 1/6 is 4.0272473508681505e-06.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 2/6 is 0.00014867466967818333.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 3/6 is 1.0401675694821044e-05.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 4/6 is 0.00030034762834589416.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 5/6 is 1.0599297826175415e-05.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 6/6 is 3.454406872485271e-05.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 1/7 is 4.0272473508681505e-06.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 2/7 is 0.00014867466967818333.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 3/7 is 1.0401675694821044e-05.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 4/7 is 0.00030034762834589416.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 5/7 is 1.0599297826175415e-05.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 6/7 is 9.58951826078474e-06.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 7/7 is 6.638014909900322e-06.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 1/8 is 4.0272473508681505e-06.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 2/8 is 4.6424875038649024e-05.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 3/8 is 0.0001598846422910644.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 4/8 is 1.0401675694821044e-05.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 5/8 is 0.00029190874255684296.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 6/8 is 1.0599297826175415e-05.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 7/8 is 9.58951826078474e-06.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 8/8 is 6.638014909900322e-06.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 1/9 is 4.106444896188493e-06.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 2/9 is 4.132309481914539e-06.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 3/9 is 4.6424875038649024e-05.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 4/9 is 0.0001598846422910644.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 5/9 is 1.0401675694821044e-05.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 6/9 is 0.00029190874255684296.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 7/9 is 1.0599297826175415e-05.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 8/9 is 9.58951826078474e-06.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 9/9 is 6.638014909900322e-06.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 1/10 is 4.106444896188493e-06.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 2/10 is 4.132309481914539e-06.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 3/10 is 4.6424875038649024e-05.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 4/10 is 0.0001467319067471272.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 5/10 is 1.0401675694821044e-05.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 6/10 is 0.0001362238269706991.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 7/10 is 1.0599297826175415e-05.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 8/10 is 9.58951826078474e-06.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 9/10 is 6.638014909900322e-06.\n", + "RMSE for signed_nonnormalized_l2_noavg w/ Cluster 10/10 is 0.00023691258203983068.\n", + "RMSE for nonl2_avg w/ Cluster 1/2 is 0.00011245127608437777.\n", + "RMSE for nonl2_avg w/ Cluster 2/2 is 0.0007485736928907467.\n", + "RMSE for nonl2_avg w/ Cluster 1/3 is 5.8447001462932495e-05.\n", + "RMSE for nonl2_avg w/ Cluster 2/3 is 0.0007819074261852497.\n", + "RMSE for nonl2_avg w/ Cluster 3/3 is 0.008051735643996027.\n", + "RMSE for nonl2_avg w/ Cluster 1/4 is 5.8447001462932495e-05.\n", + "RMSE for nonl2_avg w/ Cluster 2/4 is 0.00034013378468516613.\n", + "RMSE for nonl2_avg w/ Cluster 3/4 is 1.9702018243371422e-05.\n", + "RMSE for nonl2_avg w/ Cluster 4/4 is 7.122637492858282e-06.\n", + "RMSE for nonl2_avg w/ Cluster 1/5 is 4.0272473508681505e-06.\n", + "RMSE for nonl2_avg w/ Cluster 2/5 is 0.00034013378468516613.\n", + "RMSE for nonl2_avg w/ Cluster 3/5 is 1.9702018243371422e-05.\n", + "RMSE for nonl2_avg w/ Cluster 4/5 is 1.0599297826175415e-05.\n", + "RMSE for nonl2_avg w/ Cluster 5/5 is 7.122637492858282e-06.\n", + "RMSE for nonl2_avg w/ Cluster 1/6 is 4.0272473508681505e-06.\n", + "RMSE for nonl2_avg w/ Cluster 2/6 is 0.00034013378468516613.\n", + "RMSE for nonl2_avg w/ Cluster 3/6 is 1.9702018243371422e-05.\n", + "RMSE for nonl2_avg w/ Cluster 4/6 is 1.0599297826175415e-05.\n", + "RMSE for nonl2_avg w/ Cluster 5/6 is 6.633737576869345e-06.\n", + "RMSE for nonl2_avg w/ Cluster 6/6 is 6.638014909900322e-06.\n", + "RMSE for nonl2_avg w/ Cluster 1/7 is 4.0272473508681505e-06.\n", + "RMSE for nonl2_avg w/ Cluster 2/7 is 0.00034013378468516613.\n", + "RMSE for nonl2_avg w/ Cluster 3/7 is 1.0401675694821044e-05.\n", + "RMSE for nonl2_avg w/ Cluster 4/7 is 1.0599297826175415e-05.\n", + "RMSE for nonl2_avg w/ Cluster 5/7 is 6.633737576869345e-06.\n", + "RMSE for nonl2_avg w/ Cluster 6/7 is 3.333575692901328e-06.\n", + "RMSE for nonl2_avg w/ Cluster 7/7 is 6.638014909900322e-06.\n", + "RMSE for nonl2_avg w/ Cluster 1/8 is 4.0272473508681505e-06.\n", + "RMSE for nonl2_avg w/ Cluster 2/8 is 0.0001694455349540867.\n", + "RMSE for nonl2_avg w/ Cluster 3/8 is 1.0401675694821044e-05.\n", + "RMSE for nonl2_avg w/ Cluster 4/8 is 0.0003602992203605742.\n", + "RMSE for nonl2_avg w/ Cluster 5/8 is 1.0599297826175415e-05.\n", + "RMSE for nonl2_avg w/ Cluster 6/8 is 6.633737576869345e-06.\n", + "RMSE for nonl2_avg w/ Cluster 7/8 is 3.333575692901328e-06.\n", + "RMSE for nonl2_avg w/ Cluster 8/8 is 6.638014909900322e-06.\n", + "RMSE for nonl2_avg w/ Cluster 1/9 is 4.0272473508681505e-06.\n", + "RMSE for nonl2_avg w/ Cluster 2/9 is 6.120019226705923e-05.\n", + "RMSE for nonl2_avg w/ Cluster 3/9 is 0.00021694086955642482.\n", + "RMSE for nonl2_avg w/ Cluster 4/9 is 1.0401675694821044e-05.\n", + "RMSE for nonl2_avg w/ Cluster 5/9 is 0.0003623004803568542.\n", + "RMSE for nonl2_avg w/ Cluster 6/9 is 1.0599297826175415e-05.\n", + "RMSE for nonl2_avg w/ Cluster 7/9 is 6.633737576869345e-06.\n", + "RMSE for nonl2_avg w/ Cluster 8/9 is 3.333575692901328e-06.\n", + "RMSE for nonl2_avg w/ Cluster 9/9 is 6.638014909900322e-06.\n", + "RMSE for nonl2_avg w/ Cluster 1/10 is 4.0272473508681505e-06.\n", + "RMSE for nonl2_avg w/ Cluster 2/10 is 6.120019226705923e-05.\n", + "RMSE for nonl2_avg w/ Cluster 3/10 is 0.0002082840928568997.\n", + "RMSE for nonl2_avg w/ Cluster 4/10 is 1.0401675694821044e-05.\n", + "RMSE for nonl2_avg w/ Cluster 5/10 is 0.00030585258190878994.\n", + "RMSE for nonl2_avg w/ Cluster 6/10 is 1.0599297826175415e-05.\n", + "RMSE for nonl2_avg w/ Cluster 7/10 is 6.633737576869345e-06.\n", + "RMSE for nonl2_avg w/ Cluster 8/10 is 3.333575692901328e-06.\n", + "RMSE for nonl2_avg w/ Cluster 9/10 is 6.638014909900322e-06.\n", + "RMSE for nonl2_avg w/ Cluster 10/10 is 0.00016668190024042882.\n", + "RMSE for nonl2_noavg w/ Cluster 1/2 is 0.00011245127608437777.\n", + "RMSE for nonl2_noavg w/ Cluster 2/2 is 0.0007485736928907467.\n", + "RMSE for nonl2_noavg w/ Cluster 1/3 is 5.8447001462932495e-05.\n", + "RMSE for nonl2_noavg w/ Cluster 2/3 is 0.0007794246760518096.\n", + "RMSE for nonl2_noavg w/ Cluster 3/3 is 0.007941093384437737.\n", + "RMSE for nonl2_noavg w/ Cluster 1/4 is 5.8447001462932495e-05.\n", + "RMSE for nonl2_noavg w/ Cluster 2/4 is 0.00034013378468516613.\n", + "RMSE for nonl2_noavg w/ Cluster 3/4 is 1.9702018243371422e-05.\n", + "RMSE for nonl2_noavg w/ Cluster 4/4 is 7.122637492858282e-06.\n", + "RMSE for nonl2_noavg w/ Cluster 1/5 is 4.0272473508681505e-06.\n", + "RMSE for nonl2_noavg w/ Cluster 2/5 is 0.00034013378468516613.\n", + "RMSE for nonl2_noavg w/ Cluster 3/5 is 1.9702018243371422e-05.\n", + "RMSE for nonl2_noavg w/ Cluster 4/5 is 1.0599297826175415e-05.\n", + "RMSE for nonl2_noavg w/ Cluster 5/5 is 7.122637492858282e-06.\n", + "RMSE for nonl2_noavg w/ Cluster 1/6 is 4.0272473508681505e-06.\n", + "RMSE for nonl2_noavg w/ Cluster 2/6 is 0.00034013378468516613.\n", + "RMSE for nonl2_noavg w/ Cluster 3/6 is 1.9702018243371422e-05.\n", + "RMSE for nonl2_noavg w/ Cluster 4/6 is 1.0599297826175415e-05.\n", + "RMSE for nonl2_noavg w/ Cluster 5/6 is 6.633737576869345e-06.\n", + "RMSE for nonl2_noavg w/ Cluster 6/6 is 6.638014909900322e-06.\n", + "RMSE for nonl2_noavg w/ Cluster 1/7 is 4.0272473508681505e-06.\n", + "RMSE for nonl2_noavg w/ Cluster 2/7 is 0.00034013378468516613.\n", + "RMSE for nonl2_noavg w/ Cluster 3/7 is 1.0401675694821044e-05.\n", + "RMSE for nonl2_noavg w/ Cluster 4/7 is 1.0599297826175415e-05.\n", + "RMSE for nonl2_noavg w/ Cluster 5/7 is 6.633737576869345e-06.\n", + "RMSE for nonl2_noavg w/ Cluster 6/7 is 3.333575692901328e-06.\n", + "RMSE for nonl2_noavg w/ Cluster 7/7 is 6.638014909900322e-06.\n", + "RMSE for nonl2_noavg w/ Cluster 1/8 is 4.0272473508681505e-06.\n", + "RMSE for nonl2_noavg w/ Cluster 2/8 is 0.0001413425124172811.\n", + "RMSE for nonl2_noavg w/ Cluster 3/8 is 1.0401675694821044e-05.\n", + "RMSE for nonl2_noavg w/ Cluster 4/8 is 0.0003563232255262309.\n", + "RMSE for nonl2_noavg w/ Cluster 5/8 is 1.0599297826175415e-05.\n", + "RMSE for nonl2_noavg w/ Cluster 6/8 is 6.633737576869345e-06.\n", + "RMSE for nonl2_noavg w/ Cluster 7/8 is 3.333575692901328e-06.\n", + "RMSE for nonl2_noavg w/ Cluster 8/8 is 6.638014909900322e-06.\n", + "RMSE for nonl2_noavg w/ Cluster 1/9 is 4.0272473508681505e-06.\n", + "RMSE for nonl2_noavg w/ Cluster 2/9 is 2.7750197543182316e-05.\n", + "RMSE for nonl2_noavg w/ Cluster 3/9 is 0.00014834393954572307.\n", + "RMSE for nonl2_noavg w/ Cluster 4/9 is 1.0401675694821044e-05.\n", + "RMSE for nonl2_noavg w/ Cluster 5/9 is 0.0003675334479221558.\n", + "RMSE for nonl2_noavg w/ Cluster 6/9 is 1.0599297826175415e-05.\n", + "RMSE for nonl2_noavg w/ Cluster 7/9 is 6.633737576869345e-06.\n", + "RMSE for nonl2_noavg w/ Cluster 8/9 is 3.333575692901328e-06.\n", + "RMSE for nonl2_noavg w/ Cluster 9/9 is 6.638014909900322e-06.\n", + "RMSE for nonl2_noavg w/ Cluster 1/10 is 4.0272473508681505e-06.\n", + "RMSE for nonl2_noavg w/ Cluster 2/10 is 2.7750197543182316e-05.\n", + "RMSE for nonl2_noavg w/ Cluster 3/10 is 0.00013122090999124653.\n", + "RMSE for nonl2_noavg w/ Cluster 4/10 is 1.0401675694821044e-05.\n", + "RMSE for nonl2_noavg w/ Cluster 5/10 is 0.00017981478799808802.\n", + "RMSE for nonl2_noavg w/ Cluster 6/10 is 1.0599297826175415e-05.\n", + "RMSE for nonl2_noavg w/ Cluster 7/10 is 6.633737576869345e-06.\n", + "RMSE for nonl2_noavg w/ Cluster 8/10 is 3.333575692901328e-06.\n", + "RMSE for nonl2_noavg w/ Cluster 9/10 is 6.638014909900322e-06.\n", + "RMSE for nonl2_noavg w/ Cluster 10/10 is 0.00030198737154599214.\n", + "RMSE for l2_ranking w/ Cluster 1/2 is 4.0272473508681505e-06.\n", + "RMSE for l2_ranking w/ Cluster 2/2 is 0.0021083280680036223.\n", + "RMSE for l2_ranking w/ Cluster 1/3 is 4.0272473508681505e-06.\n", + "RMSE for l2_ranking w/ Cluster 2/3 is 0.0008496362118029093.\n", + "RMSE for l2_ranking w/ Cluster 3/3 is 0.0004784391608492909.\n", + "RMSE for l2_ranking w/ Cluster 1/4 is 4.0272473508681505e-06.\n", + "RMSE for l2_ranking w/ Cluster 2/4 is 0.00048137886828486287.\n", + "RMSE for l2_ranking w/ Cluster 3/4 is 0.0004784391608492909.\n", + "RMSE for l2_ranking w/ Cluster 4/4 is 0.0004963236648634372.\n", + "RMSE for l2_ranking w/ Cluster 1/5 is 4.0272473508681505e-06.\n", + "RMSE for l2_ranking w/ Cluster 2/5 is 0.00048137886828486287.\n", + "RMSE for l2_ranking w/ Cluster 3/5 is 2.049589679677874e-05.\n", + "RMSE for l2_ranking w/ Cluster 4/5 is 0.0004963236648634372.\n", + "RMSE for l2_ranking w/ Cluster 5/5 is 9.58951826078474e-06.\n", + "RMSE for l2_ranking w/ Cluster 1/6 is 4.0272473508681505e-06.\n", + "RMSE for l2_ranking w/ Cluster 2/6 is 0.0004779436598764783.\n", + "RMSE for l2_ranking w/ Cluster 3/6 is 2.049589679677874e-05.\n", + "RMSE for l2_ranking w/ Cluster 4/6 is 0.00014399391985005496.\n", + "RMSE for l2_ranking w/ Cluster 5/6 is 0.0005018156227725879.\n", + "RMSE for l2_ranking w/ Cluster 6/6 is 9.58951826078474e-06.\n", + "RMSE for l2_ranking w/ Cluster 1/7 is 4.0272473508681505e-06.\n", + "RMSE for l2_ranking w/ Cluster 2/7 is 3.8753239547945234e-05.\n", + "RMSE for l2_ranking w/ Cluster 3/7 is 0.000411370004353001.\n", + "RMSE for l2_ranking w/ Cluster 4/7 is 2.049589679677874e-05.\n", + "RMSE for l2_ranking w/ Cluster 5/7 is 0.00014399391985005496.\n", + "RMSE for l2_ranking w/ Cluster 6/7 is 0.00043617223314583426.\n", + "RMSE for l2_ranking w/ Cluster 7/7 is 9.58951826078474e-06.\n", + "RMSE for l2_ranking w/ Cluster 1/8 is 3.793330837175096e-06.\n", + "RMSE for l2_ranking w/ Cluster 2/8 is 3.8753239547945234e-05.\n", + "RMSE for l2_ranking w/ Cluster 3/8 is 0.000411370004353001.\n", + "RMSE for l2_ranking w/ Cluster 4/8 is 2.049589679677874e-05.\n", + "RMSE for l2_ranking w/ Cluster 5/8 is 0.00014399391985005496.\n", + "RMSE for l2_ranking w/ Cluster 6/8 is 0.00043617223314583426.\n", + "RMSE for l2_ranking w/ Cluster 7/8 is 9.58951826078474e-06.\n", + "RMSE for l2_ranking w/ Cluster 8/8 is 3.595485731056101e-06.\n", + "RMSE for l2_ranking w/ Cluster 1/9 is 3.889402890461791e-06.\n", + "RMSE for l2_ranking w/ Cluster 2/9 is 4.387484905639293e-06.\n", + "RMSE for l2_ranking w/ Cluster 3/9 is 3.8753239547945234e-05.\n", + "RMSE for l2_ranking w/ Cluster 4/9 is 0.000411370004353001.\n", + "RMSE for l2_ranking w/ Cluster 5/9 is 2.049589679677874e-05.\n", + "RMSE for l2_ranking w/ Cluster 6/9 is 0.00014399391985005496.\n", + "RMSE for l2_ranking w/ Cluster 7/9 is 0.00043617223314583426.\n", + "RMSE for l2_ranking w/ Cluster 8/9 is 9.58951826078474e-06.\n", + "RMSE for l2_ranking w/ Cluster 9/9 is 3.972311591580289e-06.\n", + "RMSE for l2_ranking w/ Cluster 1/10 is 3.889402890461791e-06.\n", + "RMSE for l2_ranking w/ Cluster 2/10 is 4.387484905639293e-06.\n", + "RMSE for l2_ranking w/ Cluster 3/10 is 3.255359024562427e-05.\n", + "RMSE for l2_ranking w/ Cluster 4/10 is 0.00032170282974821224.\n", + "RMSE for l2_ranking w/ Cluster 5/10 is 2.049589679677874e-05.\n", + "RMSE for l2_ranking w/ Cluster 6/10 is 0.00014399391985005496.\n", + "RMSE for l2_ranking w/ Cluster 7/10 is 0.0004613937901166488.\n", + "RMSE for l2_ranking w/ Cluster 8/10 is 9.58951826078474e-06.\n", + "RMSE for l2_ranking w/ Cluster 9/10 is 8.471881304984892e-05.\n", + "RMSE for l2_ranking w/ Cluster 10/10 is 3.972311591580289e-06.\n", + "RMSE for nonl2_ranking w/ Cluster 1/2 is 0.00011245127608437777.\n", + "RMSE for nonl2_ranking w/ Cluster 2/2 is 0.0007485736928907467.\n", + "RMSE for nonl2_ranking w/ Cluster 1/3 is 5.8447001462932495e-05.\n", + "RMSE for nonl2_ranking w/ Cluster 2/3 is 0.0007794246760518096.\n", + "RMSE for nonl2_ranking w/ Cluster 3/3 is 0.007941093384437737.\n", + "RMSE for nonl2_ranking w/ Cluster 1/4 is 5.8447001462932495e-05.\n", + "RMSE for nonl2_ranking w/ Cluster 2/4 is 0.00034013378468516613.\n", + "RMSE for nonl2_ranking w/ Cluster 3/4 is 1.9702018243371422e-05.\n", + "RMSE for nonl2_ranking w/ Cluster 4/4 is 7.122637492858282e-06.\n", + "RMSE for nonl2_ranking w/ Cluster 1/5 is 4.0272473508681505e-06.\n", + "RMSE for nonl2_ranking w/ Cluster 2/5 is 0.00034013378468516613.\n", + "RMSE for nonl2_ranking w/ Cluster 3/5 is 1.9702018243371422e-05.\n", + "RMSE for nonl2_ranking w/ Cluster 4/5 is 1.0599297826175415e-05.\n", + "RMSE for nonl2_ranking w/ Cluster 5/5 is 7.122637492858282e-06.\n", + "RMSE for nonl2_ranking w/ Cluster 1/6 is 4.0272473508681505e-06.\n", + "RMSE for nonl2_ranking w/ Cluster 2/6 is 0.00034013378468516613.\n", + "RMSE for nonl2_ranking w/ Cluster 3/6 is 1.9702018243371422e-05.\n", + "RMSE for nonl2_ranking w/ Cluster 4/6 is 1.0599297826175415e-05.\n", + "RMSE for nonl2_ranking w/ Cluster 5/6 is 6.633737576869345e-06.\n", + "RMSE for nonl2_ranking w/ Cluster 6/6 is 6.638014909900322e-06.\n", + "RMSE for nonl2_ranking w/ Cluster 1/7 is 4.0272473508681505e-06.\n", + "RMSE for nonl2_ranking w/ Cluster 2/7 is 0.00034013378468516613.\n", + "RMSE for nonl2_ranking w/ Cluster 3/7 is 1.0401675694821044e-05.\n", + "RMSE for nonl2_ranking w/ Cluster 4/7 is 1.0599297826175415e-05.\n", + "RMSE for nonl2_ranking w/ Cluster 5/7 is 6.633737576869345e-06.\n", + "RMSE for nonl2_ranking w/ Cluster 6/7 is 3.333575692901328e-06.\n", + "RMSE for nonl2_ranking w/ Cluster 7/7 is 6.638014909900322e-06.\n", + "RMSE for nonl2_ranking w/ Cluster 1/8 is 4.0272473508681505e-06.\n", + "RMSE for nonl2_ranking w/ Cluster 2/8 is 0.0001413425124172811.\n", + "RMSE for nonl2_ranking w/ Cluster 3/8 is 1.0401675694821044e-05.\n", + "RMSE for nonl2_ranking w/ Cluster 4/8 is 0.0003563232255262309.\n", + "RMSE for nonl2_ranking w/ Cluster 5/8 is 1.0599297826175415e-05.\n", + "RMSE for nonl2_ranking w/ Cluster 6/8 is 6.633737576869345e-06.\n", + "RMSE for nonl2_ranking w/ Cluster 7/8 is 3.333575692901328e-06.\n", + "RMSE for nonl2_ranking w/ Cluster 8/8 is 6.638014909900322e-06.\n", + "RMSE for nonl2_ranking w/ Cluster 1/9 is 4.0272473508681505e-06.\n", + "RMSE for nonl2_ranking w/ Cluster 2/9 is 2.7750197543182316e-05.\n", + "RMSE for nonl2_ranking w/ Cluster 3/9 is 0.00014834393954572307.\n", + "RMSE for nonl2_ranking w/ Cluster 4/9 is 1.0401675694821044e-05.\n", + "RMSE for nonl2_ranking w/ Cluster 5/9 is 0.0003675334479221558.\n", + "RMSE for nonl2_ranking w/ Cluster 6/9 is 1.0599297826175415e-05.\n", + "RMSE for nonl2_ranking w/ Cluster 7/9 is 6.633737576869345e-06.\n", + "RMSE for nonl2_ranking w/ Cluster 8/9 is 3.333575692901328e-06.\n", + "RMSE for nonl2_ranking w/ Cluster 9/9 is 6.638014909900322e-06.\n", + "RMSE for nonl2_ranking w/ Cluster 1/10 is 4.0272473508681505e-06.\n", + "RMSE for nonl2_ranking w/ Cluster 2/10 is 2.7750197543182316e-05.\n", + "RMSE for nonl2_ranking w/ Cluster 3/10 is 0.00013122090999124653.\n", + "RMSE for nonl2_ranking w/ Cluster 4/10 is 1.0401675694821044e-05.\n", + "RMSE for nonl2_ranking w/ Cluster 5/10 is 0.00017981478799808802.\n", + "RMSE for nonl2_ranking w/ Cluster 6/10 is 1.0599297826175415e-05.\n", + "RMSE for nonl2_ranking w/ Cluster 7/10 is 6.633737576869345e-06.\n", + "RMSE for nonl2_ranking w/ Cluster 8/10 is 3.333575692901328e-06.\n", + "RMSE for nonl2_ranking w/ Cluster 9/10 is 6.638014909900322e-06.\n", + "RMSE for nonl2_ranking w/ Cluster 10/10 is 0.00030198737154599214.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 1/2 is 4.0272473508681505e-06.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 2/2 is 0.0021083280680036223.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 1/3 is 4.0272473508681505e-06.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 2/3 is 0.0008496362118029093.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 3/3 is 0.0004784391608492909.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 1/4 is 4.0272473508681505e-06.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 2/4 is 0.00047201529622710095.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 3/4 is 0.0004784391608492909.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 4/4 is 0.00047969193197213653.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 1/5 is 4.0272473508681505e-06.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 2/5 is 0.00047201529622710095.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 3/5 is 2.049589679677874e-05.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 4/5 is 0.00047969193197213653.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 5/5 is 9.58951826078474e-06.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 1/6 is 4.0272473508681505e-06.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 2/6 is 3.8582131030099725e-05.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 3/6 is 0.00043452119073327775.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 4/6 is 2.049589679677874e-05.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 5/6 is 0.0005129633744596466.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 6/6 is 9.58951826078474e-06.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 1/7 is 4.0272473508681505e-06.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 2/7 is 3.8582131030099725e-05.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 3/7 is 0.00045047433325007106.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 4/7 is 2.049589679677874e-05.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 5/7 is 0.00014399391985005496.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 6/7 is 0.0004124128592745916.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 7/7 is 9.58951826078474e-06.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 1/8 is 3.8173593372536324e-06.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 2/8 is 3.8582131030099725e-05.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 3/8 is 0.00045047433325007106.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 4/8 is 2.049589679677874e-05.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 5/8 is 0.00014399391985005496.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 6/8 is 0.0004124128592745916.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 7/8 is 9.58951826078474e-06.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 8/8 is 4.169684664493694e-06.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 1/9 is 3.8173593372536324e-06.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 2/9 is 3.378469725629816e-05.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 3/9 is 0.0003156010398635287.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 4/9 is 2.049589679677874e-05.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 5/9 is 0.00014399391985005496.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 6/9 is 0.0004421278898400869.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 7/9 is 9.58951826078474e-06.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 8/9 is 4.169684664493694e-06.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 9/9 is 7.957905687424783e-05.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 1/10 is 3.832802834659623e-06.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 3/10 is 3.378469725629816e-05.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 4/10 is 0.0003156010398635287.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 5/10 is 2.049589679677874e-05.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 6/10 is 0.00014399391985005496.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 7/10 is 0.0004421278898400869.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 8/10 is 9.58951826078474e-06.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 9/10 is 4.4379485219741265e-06.\n", + "RMSE for l2_ranking_nonloo w/ Cluster 10/10 is 7.957905687424783e-05.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 1/2 is 0.00011245127608437777.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 2/2 is 0.0007485736928907467.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 1/3 is 5.8447001462932495e-05.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 2/3 is 0.0007813771161145901.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 3/3 is 0.008052268916745577.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 1/4 is 5.8447001462932495e-05.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 2/4 is 0.00034013378468516613.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 3/4 is 1.9702018243371422e-05.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 4/4 is 7.122637492858282e-06.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 1/5 is 4.0272473508681505e-06.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 2/5 is 0.00034013378468516613.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 3/5 is 1.9702018243371422e-05.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 4/5 is 1.0599297826175415e-05.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 5/5 is 7.122637492858282e-06.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 1/6 is 4.0272473508681505e-06.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 2/6 is 0.00034013378468516613.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 3/6 is 1.9702018243371422e-05.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 4/6 is 1.0599297826175415e-05.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 5/6 is 6.633737576869345e-06.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 6/6 is 6.638014909900322e-06.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 1/7 is 4.0272473508681505e-06.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 2/7 is 0.00034013378468516613.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 3/7 is 1.0401675694821044e-05.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 4/7 is 1.0599297826175415e-05.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 5/7 is 6.633737576869345e-06.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 6/7 is 3.333575692901328e-06.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 7/7 is 6.638014909900322e-06.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 1/8 is 4.0272473508681505e-06.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 2/8 is 0.00016576340185693722.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 3/8 is 1.0401675694821044e-05.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 4/8 is 0.0003562386059832098.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 5/8 is 1.0599297826175415e-05.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 6/8 is 6.633737576869345e-06.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 7/8 is 3.333575692901328e-06.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 8/8 is 6.638014909900322e-06.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 1/9 is 4.0272473508681505e-06.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 2/9 is 3.626414555280291e-05.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 3/9 is 0.0001923426083441784.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 4/9 is 1.0401675694821044e-05.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 5/9 is 0.00037626983332048844.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 6/9 is 1.0599297826175415e-05.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 7/9 is 6.633737576869345e-06.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 8/9 is 3.333575692901328e-06.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 9/9 is 6.638014909900322e-06.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 1/10 is 4.0272473508681505e-06.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 2/10 is 3.626414555280291e-05.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 3/10 is 0.0001973451594436984.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 4/10 is 1.0401675694821044e-05.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 5/10 is 0.00016844949325362196.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 6/10 is 1.0599297826175415e-05.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 7/10 is 6.633737576869345e-06.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 8/10 is 3.333575692901328e-06.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 9/10 is 6.638014909900322e-06.\n", + "RMSE for nonl2_ranking_nonloo w/ Cluster 10/10 is 0.00036102176266555777.\n", + "RMSE for baseline w/ Cluster 1/2 is 0.00134620254220644.\n", + "RMSE for baseline w/ Cluster 2/2 is 0.0014320625813770067.\n", + "RMSE for baseline w/ Cluster 1/3 is 0.0032562441042964757.\n", + "RMSE for baseline w/ Cluster 2/3 is 0.0013758126610831864.\n", + "RMSE for baseline w/ Cluster 3/3 is 0.001324991643748449.\n", + "RMSE for baseline w/ Cluster 1/4 is 0.08668454451804129.\n", + "RMSE for baseline w/ Cluster 2/4 is 0.0009491277512018928.\n", + "RMSE for baseline w/ Cluster 3/4 is 0.0013736704656912328.\n", + "RMSE for baseline w/ Cluster 4/4 is 0.0012803521277198009.\n", + "RMSE for baseline w/ Cluster 1/5 is 0.09223430720366693.\n", + "RMSE for baseline w/ Cluster 2/5 is 0.000907598193932148.\n", + "RMSE for baseline w/ Cluster 3/5 is 0.0013682665563982434.\n", + "RMSE for baseline w/ Cluster 4/5 is 0.00476680807189198.\n", + "RMSE for baseline w/ Cluster 5/5 is 0.00013707481313801286.\n", + "RMSE for baseline w/ Cluster 1/6 is 0.07574120001301929.\n", + "RMSE for baseline w/ Cluster 2/6 is 0.000907598193932148.\n", + "RMSE for baseline w/ Cluster 3/6 is 0.001683398013698217.\n", + "RMSE for baseline w/ Cluster 4/6 is 0.0006931622721329584.\n", + "RMSE for baseline w/ Cluster 5/6 is 0.005106011418929797.\n", + "RMSE for baseline w/ Cluster 6/6 is 0.00013707481313801286.\n", + "RMSE for baseline w/ Cluster 1/7 is 0.015217309823435008.\n", + "RMSE for baseline w/ Cluster 2/7 is 0.000907598193932148.\n", + "RMSE for baseline w/ Cluster 3/7 is 0.0015425921020318948.\n", + "RMSE for baseline w/ Cluster 4/7 is 0.000701754568077621.\n", + "RMSE for baseline w/ Cluster 5/7 is 0.005005072189952829.\n", + "RMSE for baseline w/ Cluster 6/7 is 0.00013925826398738.\n", + "RMSE for baseline w/ Cluster 7/7 is 0.03410942069921635.\n", + "RMSE for baseline w/ Cluster 1/8 is 0.000910068311768959.\n", + "RMSE for baseline w/ Cluster 2/8 is 0.0009164653901417426.\n", + "RMSE for baseline w/ Cluster 3/8 is 0.0015404419337185422.\n", + "RMSE for baseline w/ Cluster 4/8 is 0.000701754568077621.\n", + "RMSE for baseline w/ Cluster 5/8 is 0.008535625839144781.\n", + "RMSE for baseline w/ Cluster 6/8 is 0.017170603300720493.\n", + "RMSE for baseline w/ Cluster 7/8 is 0.00013925826398738.\n", + "RMSE for baseline w/ Cluster 8/8 is 0.037709018375984324.\n", + "RMSE for baseline w/ Cluster 1/9 is 0.0008796389973912452.\n", + "RMSE for baseline w/ Cluster 2/9 is 0.0009164653901417426.\n", + "RMSE for baseline w/ Cluster 3/9 is 0.0016352073690197309.\n", + "RMSE for baseline w/ Cluster 4/9 is 4.2969581085139826e-05.\n", + "RMSE for baseline w/ Cluster 5/9 is 0.008535625839144781.\n", + "RMSE for baseline w/ Cluster 6/9 is 0.017170603300720493.\n", + "RMSE for baseline w/ Cluster 7/9 is 0.00045385284186173335.\n", + "RMSE for baseline w/ Cluster 8/9 is 0.00013925826398738.\n", + "RMSE for baseline w/ Cluster 9/9 is 0.037709018375984324.\n", + "RMSE for baseline w/ Cluster 1/10 is 0.0008590616984245176.\n", + "RMSE for baseline w/ Cluster 2/10 is 4.725989629942399e-05.\n", + "RMSE for baseline w/ Cluster 3/10 is 0.0016352073690197309.\n", + "RMSE for baseline w/ Cluster 4/10 is 4.2969581085139826e-05.\n", + "RMSE for baseline w/ Cluster 5/10 is 0.008753666328541637.\n", + "RMSE for baseline w/ Cluster 6/10 is 0.008133518736948347.\n", + "RMSE for baseline w/ Cluster 7/10 is 0.014757592061761388.\n", + "RMSE for baseline w/ Cluster 8/10 is 0.00045385284186173335.\n", + "RMSE for baseline w/ Cluster 9/10 is 0.00013732663135267352.\n", + "RMSE for baseline w/ Cluster 10/10 is 0.037709018375984324.\n" + ] + } + ], + "source": [ + "# for each method, for each number of clusters, train a linear model on the training set for each cluster and use it to predict the testing set for each cluster\n", + "train_test_accuracies = {}\n", + "train_test_r2 = {}\n", + "for method in train_clusters.keys():\n", + " method_accuracies = {}\n", + " method_r2s = {}\n", + " for num_clusters in range(2, 11):\n", + " r2 = []\n", + " accuracy = []\n", + " num_samples = []\n", + " for cluster_idx in range(num_clusters):\n", + " X_cluster_train = X_train[train_clusters[method][num_clusters][cluster_idx]]\n", + " y_cluster_train = y_train[train_clusters[method][num_clusters][cluster_idx]]\n", + " X_cluster_test = X_test[test_clusters[method][num_clusters][cluster_idx]]\n", + " y_cluster_test = y_test[test_clusters[method][num_clusters][cluster_idx]]\n", + " if X_cluster_test.shape[0] == 0:\n", + " continue\n", + " if task == \"classification\":\n", + " model = LogisticRegression()\n", + " else:\n", + " model = LinearRegression()\n", + " model.fit(X_cluster_train, y_cluster_train)\n", + " cluster_predictions = model.predict(X_cluster_test)\n", + " # get model accuracy\n", + " if task == \"classification\":\n", + " accuracy.append(accuracy_score(y_cluster_test, cluster_predictions))\n", + " else:\n", + " rmse = root_mean_squared_error(y_cluster_test, cluster_predictions)\n", + " print(f\"RMSE for {method} w/ Cluster {cluster_idx+1}/{num_clusters} is {rmse}.\")\n", + " accuracy.append(root_mean_squared_error(y_cluster_test, cluster_predictions))\n", + " r2.append(r2_score(y_cluster_test, cluster_predictions))\n", + " num_samples.append(X_cluster_test.shape[0])\n", + " method_accuracies[num_clusters] = weighted_metric(np.array(accuracy), np.array(num_samples))\n", + " method_r2s[num_clusters] = weighted_metric(np.array(r2), np.array(num_samples))\n", + " # average accuracy across clusters\n", + " train_test_accuracies[method] = method_accuracies\n", + " train_test_r2[method] = method_r2s" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'shap': {2: 86.61862554046574,\n", + " 3: 76.26400990618936,\n", + " 4: 81.50552937094741,\n", + " 5: 1045.9281421999347,\n", + " 6: 1783.4559847177343,\n", + " 7: 1779.2898374341335,\n", + " 8: 1138.1924065568594,\n", + " 9: 1143.5602298235876,\n", + " 10: 2372.6308743301242},\n", + " 'signed_nonnormalized_l2_avg': {2: 24.953502111873004,\n", + " 3: 362.23399172452343,\n", + " 4: 381.0593692158767,\n", + " 5: 486.2769066190062,\n", + " 6: 522.023542752005,\n", + " 7: 1803.3453095303553,\n", + " 8: 1491.8768329672278,\n", + " 9: 1538.0817941453593,\n", + " 10: 1690.110392265115},\n", + " 'signed_nonnormalized_l2_noavg': {2: 25.54774620816979,\n", + " 3: 270.55857754353457,\n", + " 4: 363.85662723771935,\n", + " 5: 496.31707728979035,\n", + " 6: 366.8079229401296,\n", + " 7: 376.44421691895076,\n", + " 8: 319.0252403141974,\n", + " 9: 1201.1033485099247,\n", + " 10: 1217.637018890846},\n", + " 'nonl2_avg': {2: 115.03388507029837,\n", + " 3: 374.35230063401576,\n", + " 4: 535.3206119738915,\n", + " 5: 446.8612771761584,\n", + " 6: 1213.6365571393274,\n", + " 7: 1223.372364938818,\n", + " 8: 1300.6500788958385,\n", + " 9: 1315.6993109788154,\n", + " 10: 888.7873827286251},\n", + " 'nonl2_noavg': {2: 22.73022666845054,\n", + " 3: 286.3316430877477,\n", + " 4: 426.55715657485376,\n", + " 5: 505.80619376390183,\n", + " 6: 1096.656246531987,\n", + " 7: 1162.318522086654,\n", + " 8: 1297.7917188379051,\n", + " 9: 1419.4953386229888,\n", + " 10: 1669.074972184242},\n", + " 'l2_ranking': {2: 35.203457440229705,\n", + " 3: 115.68378374170696,\n", + " 4: 113.58271810922517,\n", + " 5: 111.03838656061663,\n", + " 6: 148.9801349803648,\n", + " 7: 196.73667986291105,\n", + " 8: 843.7203910549725,\n", + " 9: 905.2541607356178,\n", + " 10: 965.0704152014281},\n", + " 'nonl2_ranking': {2: 22.73022666845054,\n", + " 3: 286.3316430877477,\n", + " 4: 426.55715657485376,\n", + " 5: 505.80619376390183,\n", + " 6: 1096.656246531987,\n", + " 7: 1162.318522086654,\n", + " 8: 1297.7917188379051,\n", + " 9: 1419.4953386229888,\n", + " 10: 1669.074972184242},\n", + " 'l2_ranking_nonloo': {2: 35.203457440229705,\n", + " 3: 115.68378374170696,\n", + " 4: 113.58271810922517,\n", + " 5: 111.03838656061663,\n", + " 6: 148.9801349803648,\n", + " 7: 196.73667986291105,\n", + " 8: 843.7203910549725,\n", + " 9: 905.2541607356178,\n", + " 10: 965.0704152014281},\n", + " 'nonl2_ranking_nonloo': {2: 22.73022666845054,\n", + " 3: 798.1002424894695,\n", + " 4: 1374.1822106662705,\n", + " 5: 1439.4469535721444,\n", + " 6: 1509.7086123230054,\n", + " 7: 1294.1398232020665,\n", + " 8: 696.5432104218788,\n", + " 9: 882.1119755358897,\n", + " 10: 1056.6905654202292},\n", + " 'baseline': {2: 37.01867173455836,\n", + " 3: 778.4406791092389,\n", + " 4: 782.3324061969315,\n", + " 5: 798.520188736378,\n", + " 6: 813.9426802835328,\n", + " 7: 861.130943842992,\n", + " 8: 876.0333767889099,\n", + " 9: 1240.9503257658737,\n", + " 10: 1444.9194135606535}}" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mnotebook controller is DISPOSED. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mnotebook controller is DISPOSED. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "train_test_accuracies" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/IPython/core/pylabtools.py:77: DeprecationWarning: backend2gui is deprecated since IPython 8.24, backends are managed in matplotlib and can be externally registered.\n", + " warnings.warn(\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/IPython/core/pylabtools.py:77: DeprecationWarning: backend2gui is deprecated since IPython 8.24, backends are managed in matplotlib and can be externally registered.\n", + " warnings.warn(\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/IPython/core/pylabtools.py:77: DeprecationWarning: backend2gui is deprecated since IPython 8.24, backends are managed in matplotlib and can be externally registered.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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0rVWSprVKEpuQwpUH4dx/EcvjFzE8D4snKUVFSGQiIa/GOL3JwlT2OnlysKCYvSXODhYUsTMXczl95ESiJAiC8JFIKYDjk7LLysKEhtVL8EXj1FmdFQoV0fEpvAhP4GVEAi/C43kZkcDL8ATCo5NISFamrk33PEanHKlEgpOtmbblqdgbyZSVuWiF+hiIREnIFYa87EirVo346qtv6N3bP7+rUmi9ePGcr776nOnT59C4cTMA/PzaUr++N8OHj87z8y9Z8gPHjx/jl1/2Zmr/gQP7YmFhwbx5iwE4e/YMv//+K9evXyMyMgJn5+K0bt2Wjh07I5MVnlvPFYqCPT4pJyQSCbZyU2zlpniUttN5LkWhIjgyUS+BehGRQHJK6nPBkYlcfqsVytJMRjEHC5ztLV/9NzWBcrIVrVCFiUiUhFzRo8d3JCYm5nc1BAMya9Z8rKys87sambJnz/+RnJxE797+FC1ajOvXr7F2bRD//feQSZOm5nf1co22683Apwb40EyMjShVRE6pInKd7RqNhqi4FF6+Sp5et0YlEBGTRHySUjt4/E1GUgmOtubaxOnNMVFizqeCRyRKQq4oUaJkfldByEBychKmpmYf/LwVKrh/8HNmV0DAWGxtbbWPa9b0RKNRs3r1CgYPHopcbpN/lcslKpUa9auFaD+GrrfcIJFIsLMyxc7KFI8y9jrPJStUBEckaFuf3kyk0p4LjkiAe7plys2NtV13byZSohXKcIlESci0Bw/u8+OPS7hx4zrJyUkUKVKUzz5rR5cu3dPtert8+RKLF8/j0aP/KFXKhYEDh7J8+VLc3CpolymZNm0yN29eZ9iwUQQGLuTJk8eULevKiBFjcXf30Jal0WjYvn0Lv/22m+DgFzg6FsHPryNff91Fp47Hjx9jxYpAXr58Qbly5bPc7ZPWhTRx4jSuX7/KgQP7MTU1oXlzX/r1G4hM9vojc+nSBVauXMadO7cxNzejQQMfBg4cirW1TZbKWrs2iB07trBy5XoWLJjNnTu3KF68BAMHDqNOnXra86nVajZtWsfvv+8hPDwMZ+fidOzYmS++6KDdJ62sJUtWsGTJD9y9e5vvvvseW1tbZs2aypo1mwgKWs6VK5coUqQow4ePplYtL9asWcnevb8C0KbN5/Tt2x+pNPWi/ejRf6xbF8TVq1eIjo7C2bk4bdq04+uvOwMZX9jf7HpL+1ukZ9y4ybRu3RaAa9eusGrVj9y4cQ0jIyPq1fNmyJAR2Nm9/pIKCwtl/vxZnDv3P6ysrPnqq05ZeIXT92aSlMbNrSIajYawsLBMJUqJiYmsWLGUs2fPEBISjJ2dPXXq1OP77wcjl6e2VGTURX3y5HFGjx7Gtm2/4OJSBoVCwfLli/nrr31oNGqaNGlO9eq1mDZtAj///BvOzsWzHGPa+CSZkRSpVIyrySlTYyNcilrhUtRKZ7tGoyEyNlkncUprkQqPSSYuUcG9Z9Hcexatc5yRVIKTrbnOgPK0Lj25ufGHDE14i0iU8oFGowFlSib2k6J5dXHLVTKTbA1AHD16OPb29owZMxG5XM7Tp08IDQ1Jd9+wsDACAgZRoYI706bNJi4ujgUL5hAfH4ebWwWdfSMiwlmyZAFduvRALpcTFLSMceMC2LlzjzaZWLJkAXv3/kq3br2oVKkK165dYcWKQExNTfniCz8A7t69zYQJo6lTpz6DBg3j+fPnTJo0lpQURZZjXbXqRz79tCHTp8/m6tUrrFu3ipIlS2rPdevWTYYNG0CNGrWYPn0OkZHhrFy5jIcPH7By5TqMjIwyXRaAUqlk2rQJ+Pl1okeP79i6dSMTJozil1/2YmNjC8Dy5Uv45ZcddOvWi08+qcapU8dZsGA2KpWSDh2+1palUCiYOnUCHTt2xt9/ANbWNty5cwuAGTMm065dBzp1+pYtWzYwfvwofH3bEB8fz4QJU7lxI7XLydW1PC1atAIgNDQEF5cyNG/ui4WFBffu3WHt2iASExPo27dfpv6eDg6OrFy5Xmfb/v2/89tvuylZshSQmiQNGuRP3boNmDp1NklJiaxevYIxY0YQFPT62DFjRhAaGkxAwFjkcjlbtmwkJCRY52+eG65cuYSJiQnFi5fI1P5JSUmo1Wr69u2Pra0dISHBbNq0jrFjRxAYGARAs2Yt2bfvdx48uIera3ntsYcO/UWFCu64uJQBYOXKQPbs+T969/bHza0ix44dZuXKwBzFo51o0kDWeCusJBIJ9tZm2FubUentVqgUFcGRbyRQb4yJSlGotdveJjc31iZQzg6W2q48R1szZO/4sSLkDpEofWAajYaE32aiDr73/p3ziFFRN8w/H5elZCkqKooXL54xZMgIvL19gNTuiYzs3LkVIyMj5s9fjIVF6uy4zs4lGDDgO719Y2JiCAxchatrOQDMzMwYPLgf169fo1q16jx79pRdu3YSEDCWdu3aA+DlVYekpCTWr1/N55+3RyqVsmXLBooUKcbs2Qu0X5qmpqbMmTM903GmqVSpCkOHjnx1rrpcuHCOo0ePaJObTZvWYW/vwLx5i7XJXJEixRg+fCCnT5/U/o0yUxakJjf9+g2kXj1vAFxcSvPVV5/z77+naNmyNVFRUeza9RPffNNVOyi9du26REVFsX79Gr74wk8bs1KppG/f/jRt2kJbflqi1KHD13z5Zep5nZyc6NatE7du3dQmInXq1OPEiX84evSQNlHy9KyNp2dtIPX9W7VqdZKSkti1a2emEyUTExOqVPlE+/jq1cv88cdv9O7tT9Wq1QFYuXIZ7u4ezJo1X/vedHUtT7duX3P69Anq1fPm339PcevWDZYsWUGtWl4A1KjhSfv2bbC2zr3xUE+ePOaXX3bQrl0HLCwsUGbiB4udnR0BAWO1j5VKJc7Oxenf/zseP36Ei0tpatXywtbWjoMH/8LfPzVRSkpK4sSJf+jZsw8AMTHR7N69i+7de/Pttz2A1NdlyJD+hIQEZzsmMT4p/5mapN8KpdZoiIpN5kVaN154Ai8iUhOoiFetUHefRnP3qX4rVFF7C7719aBKadsPGMnHRSRK+UBCwWv2trGxoVgxZ4KClhEbG0OtWl4UKVI0w/1v3rxBjRqe2iQJoFq16tpuqTc5OjppkySAsmVdAQgNTf1SOHv2DACNGjVBqVRq9/P0rM3WramtCcWKOXPjxnUaNPDRaVlo3LhpthKl2rXr6jwuU8aVCxfOah9fuXKRZs1a6nTF1a5dF7nciitXLukkSu8rC0AqleLpWUf72Nm5OKampoSEpLbY3bhxDaVSqb2jLE3Tps05dOgvnjx5TJkyZbXb0xKut3l5vT5HqVKlAbQJx+vtLjx58lj7ODk5mS1bNnDgwD6Cg1/qvAYJCQmYmGRt/FNISDDjx4+iQYNP6d69N5CaLFy9epkBA4agUql06lKkSFFu3rxBvXre3LhxDblcrlNnuVyOp2dtbTKYU/HxcYwfPxJn5+L07ds/S8fu3/8HP/20ladPn+jc3PDkyWNcXEojk8lo3LgZR44cxN9/AJDa7ZaUlEizZqmJ7f3790hJSaZBAx+dsj/91Ifz5/+X7bg+5jveDJ30jVaoyum0Qr2MeJU4heuOiUpRqnkeFs/CbReY/l1titpZ5FMEhZtIlD4wiUSC+efjMtX1JpNJM/VLNsuy0fUmkUhYuHAZq1b9yMKFc0lMTKRiRQ8GDRpG9eo19fYPDw+jZEkXve12dnZ629LGb2irJ0vtj09JSf0bRUdHodFoaNOmmd6xAMHBqYlSeHiYXvmWlnJMTLK+SObbdTI2NtbWByA2NlZn3Ewae3t7YmN174B5X1mQ2vJlbGyczn7Jr84Xoy3/TXZ2DkBqK0QaMzMzLCzSv2DK5a9/yaad781tb58XYMWKQPbu3U3Pnn2oWNEDKysrjh//m40b15KSkpylRCk5OYmxYwOwtbVl/PjXd5PFxsagUqlYunQhS5cu1DsurSUlPDwMW1v999Dbf5fsUigUjBs3ktjYWFauXIe5uXmmj/3776PMmDGZzz//kr59+2NtbUt4eBjjxgXo/D2bNWvJ7t0/c+PGNSpVqsKhQ39RrVoN7Q+P8PAwQP+zkt77LbM0Gs1HNYdSYWJqYkTpYlaULqbfChUZk8zmA7e5cj+c1XtvMPbbmhhJxeub20SilA8kEgkYv//LWyKTIpHkQaKUTS4upZkxYy5KpZKrVy+zatVyRo8exu7d+/T2dXBwJCoqUm97ZKT+tvextrZBIpHw449r9JKJtHqlnfPt8uPj43S+pHKLlZV1urFERETkyS3xad1KkZERODkV0W6PjAx/9fzrlrrcngDv6NFDtGvXXtsNBHDq1IlslTV79nSeP3/GmjWbdJIQudwKiURC16498fFppHdc2jitjN5XERER2arPm9RqNVOnTuD27ZssX76GokWLZen4o0cP4eZWgVGjxmu3Xbx4Xm+/qlWrUaRIUQ4fPoCLSxnOnDnF4MHDtc87ODgCqZ8VR0cn7fbIyOzHqFRpUsdGSkSiVFhIJRIcbMzo1caD8av+5cHzGA6cfYJvndL5XbVCR3xihCyTyWTUqFGLLl16EB8fT1hYqN4+Hh6VuHDhLAkJ8dptly9f1Gn5yKy0bpbo6Gjc3Svp/Uvr3vPwqMzJk8d1um6OHj2c5fNlRtWq1Tl+/JhON9TZs/8SFxerHXOTmzw8qiCTyfTiOXLkEHZ29pQqpd96l1uSk5O1rXwAKpWKw4cPZLmczZs3cPToIaZOnaU3nYS5uTlVqnzCo0cP032N0+7y8vCoTFxcHOfPv+66jIuL49y57HdJpVm4cC6nTh1n9uwfKFeu/PsPeMvbfyeAAwf26+0nkUho1qwFR44c4tixw6hUKho1et1a6upaDhMTU06c+FvnuOPH/367qExTvBqfZGxkJGaSLmTsrc34rl0VAHb/85AX4fHvOULIKoNrUbp//z4zZszg4sWLWFpa0q5dO4YOHYqJybsn6dJoNKxevZpt27YRERGBh4cHY8eOpXr16nr7Hjt2jJUrV3Lr1i2MjY1xd3dn/vz5FCuWtV+QH5N79+6ybNkimjZtQYkSJYmLi2Pz5vU4OxdPdw6ljh27sHv3L4wcOZRvvulKXFws69evxtbWNssXaheX0rRv/xUzZkzim2+6UqlSFZRKJU+ePObixXPMnv0DAN9+250+fbozdmwAX37px/Pnz9ixY0u2ut7ep1u3Xnz/fS9GjRqGn9/XRESk3vXm4VGZevUa5Pr5bG1t6dDha7Zt24SJiQmVK3/C6dMnOXhwP8OGjcz1O77e5OVVh717f6VsWVdsbGzZvfvnLN9JeOXKJVav/pGmTVtgYWHJtWtXtc+VKFESOzs7+vcfwpAh3zNp0liaNm2BlZUVoaEhnD17htat21Kzpid169Z/dSflBPr1G4SVlRWbN2/A0tLyHWd/v02b1vHrr7vo3LkrxsYmOvVzcyuHqen7x354edVh4cK5bNiwhsqVP+Hff09mOKaoWbOWbNu2mTVrVlK7dl2d6QlsbGz58ssObNq0DhMTE9zcKnL06CHtuDGJJOu/b9O63UzEHW+FUlMvF46ee8KV++Gs++MmY7+tJaaAyEUGlShFR0fTvXt3ypQpQ2BgIMHBwcyZM4ekpCQmTZr0zmNXr17N0qVLCQgIoGLFimzdupVevXqxZ88eSpUqpd1vz549jB8/nl69ejF06FDi4+M5d+4cycm53z1TmDg4OODg4MDmzesJCwvF0lJOtWrVmTRperpf0o6OjixYsJTFi+czceJoSpQoyZAhASxaNE9vzE5mDB06EheX0uzZ839s2LAGc3MLXFxK07hxU+0+qV+gc1i5MpDx40dStmw5pkyZxYgRA3MUe3rc3T1YuHAZQUHLmTBhFGZm5nh7p86jlFdJy4ABQ7CysmLv3l/ZuHEtxYoVJyBgrM48Snlh2LCRzJ8/m0WL5mNmZoav72f4+DRm7twZmS7j6dMnqNVqDh7cz8GDuq0safMoffJJNX78cQ1r1wYxe/ZUFAoFTk5F8fT00k4hIJFImDPnBxYsmM38+bOxsrJ6lahGcPz4sWzH+L///QvAtm2b2bZts85zy5evolo1/XF4b2vXrj3Pnz/jl19+Ytu2zdSuXZfJk2fi799Db9/UqQBK8/jxI77/fpDe8/36DUKpVLJ58wY0GjU+Po3p0qV7tj8/CjE+qVCTSCT0bO3BuFWnuf+qC65VnbxrZf7YSDQajSa/K5EmKCiIlStXcvToUe0vrJ9++ompU6dy9OhRihZN/y6r5ORk6tevT5cuXRg+PLWvPyUlhVatWuHj48OUKVOA1FvcmzZtyogRI+jcuXOu1VulUhMRod/cqVCkEB7+AgcHZ4yNsz5tfZ4N5s4nT548pksXP8aOnYSv72dA4YvxbSK+gs9QYpw+fSJXrlzm559/y/Kxz0LjUCjVFLEzx8JMt3swo/hyev0yBDKZFDu71EVxDeE1zAtvxnjk/FM27LuFsUzKlJ5eODvkrKXVEOTla2hvb4lRJmZDN6ifF//88w/16tXTaYb29fVFrVZz8uTJDI+7cOECcXFx+Pr6areZmJjQvHlz/vnnH+22ffv2oVar8fPzS68YIZetXLmMgwf3c/Hief744zdGjhyCg4MjDRs2ye+qCYJBu3jxPJs2rePMmdOcPn2SH36Yy4ED+7M1C7lGo0GhSmtREnMoFWafVnWmcll7FEo16/+8pV2yRsgZg+p6e/DgAR066HYjWFtb4+TkxIMHD955HICrq6vO9nLlyrFx40aSkpIwMzPj8uXLlC1bll9//ZUVK1YQHByMm5sbw4cPp2HDhjmquyydJm21Ovt9xGnDeCQSMJw2v6xRKBSsWBFIZGQEpqam1KhRi/79h2hvXf/QMWo0Gp2B3m+TSqXaZTtyQ2F4Dd/FUONTqVS8q6H8zbmv3uf1cDoNSuWHe++Ym1tw6tQJtm7dSHJyMs7OxRk0aBgdO2a9JVypUoMmtXvm7a63zLyGRkaSdK9vBUFaa0FmWg0Kqrdj7P1ZJcYFnebes2iOXHxKqwJ+F5whvIYGlSjFxMSkO7uujY0N0dEZ3y0VExODiYkJpqa6g3atra3RaDRER0djZmZGaGgoDx8+ZMmSJYwcORInJye2bt1K//79+fXXX3Fzc8tWvaVSCXZ2+k2cSUlGhIVJc3ShKcgf8GHDRjBs2Ij37vehYjx//hwDBvTN8PnWrdvmyUrxBfk1zAxDi8/Pry0vX77I8Pl//72Q5TL37/+DGTOmZPh879596dMnc7OUZ0aVKpVZs2ZDrpSVmJJ6Z6apsVGG16H0XkO1WoJUKsXGxgIzsw+/qHJusrbO/HxYBVVajHZ2lnzXrgrLfr7ML0fv07CWC8Wdsj6uzdDk52toUIlSXtNoNCQkJLBgwQKaNk0dBFy7dm1atmzJ6tWrmTdvXrbKVas1xMTor8+TkpKMWq1GpdJkuW9VIkm9eKlUaoP6tZ6bPnSMbm4VWbNmU4bP29jY5mofeGF/DQ01vrlzF6FQZDyha1Ze47QY69f3fud7x9HRyWDHwCQlp7aEpTcW6V2voUqlQa1WEx2dQGJixq1phszISIq1tTkxMYmoVIb5+uRUejF6VXCkcll7rj+MYMGWc4zv5llg74LLy9fQ2to8Uz/0DCpRsra2JjY2Vm97dHQ0NjYZr95tbW1NSkoKycnJOq1KMTExSCQS7bFprVV1675eUsLY2BgvLy/u3r2bo7qnd5FUqbL/7ZF20TKkL6Dc9qFjtLCwxN290oc5GYX/NTTU+LIzB1JG0mKztrbF2to218r9kFIUaWu86X8hZOY1zM4PPUOjUqkLfAzv83aM3VtVZOLa/3H3aTR/nXlMc69S7zja8OXna2hQbeaurq56Y5FiY2MJDQ3VG3/09nEADx8+1Nn+4MEDihcvrm02Ll8+4wuomB5AEITCSEwN8HFytDHn68ap33m7/r5PcKR+r4eQOQb1yfHx8eHUqVPExLxeK2v//v1IpVIaNMh4Er+aNWsil8vZt+/1UhoKhYIDBw7g4/N6YcnGjRsDcPr0ae22lJQUzp49S+XKlXMzFEEQhHynVmtSB3MjFsP9GDWsXhyP0nakKNWs/+MmakNr/i0gDKrrrVOnTmzevJkBAwbg7+9PcHAw8+bNo1OnTjpzKHXv3p3nz59z8OBBIHVBUX9/fwIDA7G3t6dChQps376dqKgoevfurT2ucuXKtGzZkokTJxIVFYWTkxPbtm0jLCxMZz9BEITCIK01SSqVGNygeyHvSSQSevq6M3Ht/7jzNJoj55/SzLNgd8HlB4NKlGxsbNi4cSPTp09nwIABWFpa4ufnx7Bhw3T2Sx0grTu4sE+fPmg0GtatW6ddwmTt2rU6s3IDzJkzh4ULF/LDDz8QFxdH5cqVWb9+PRUrVszz+ARBED6klFdTGpgYi/mTPlaOtuZ0bFyOzQfu8Mvf96lazoEidu9fkkd4zaBm5i6oxMzc2VfYYxTxFXwFOcbwmCRi41OwsjTBwTr9W/zFzNwFW2ZiVGs0LNh+kVuPo6hYypaRnWsgLSCLI4uZuQVBEIQ8o1CI8UkCSF+tBWdqbMTtJ1EcvfAsv6tUoIhPj5ArZs6cQteuHfO7Gulq1aoRa9cG5Xc1CrUXL57j7e3J0aOHtNv8/NqycOHcD3L+JUt+wM+vbab3HziwL6NGDdU+Pnv2DJMnj8XPry1Nmzbg22+/Ytu2TSiVyjyo7Yej7XoTS5d89JxszfFrVA6AX47dJyQqMZ9rVHAY1BgloeDq0eM7EhPFB094bdas+VhZ6c+0b4j27Pk/kpOT6N3bn6JFi3H9+jXWrg3iv/8e5sls7R+CSqXWrvUlpgYQABrXLMG5WyHcfhLFhj9vEvBNwemCy08iURJyRYkSJfO7CkIGkpOTMDX98EtQVKjg/sHPmV0BAWN1FuOuWdMTjUbN6tUrGDx4KHJ5xhPeGqqUV+M5ZEbSAjsrs5C7Urvg3Jm07n/cehzF3xef0bimuHa/j/iZIWTagwf3CQgYTOvWTWnatAHffNOerVs3Aul3vV2+fImePTvTpEl9unfvxNmz/9KjR2dmzpyi3WfatMl07dqRCxfO0bNnZ5o186ZPn27cunVTpyyNRsO2bZvp1Kk9jRvX46uv2vHTT1v16nj8+DE6d+5Akyb16dOnGzdvXs9SjGldSH/99ScLF86lVavGtGvXkmXLFut1w1y6dIF+/XrRpEkD2rRpyqxZU4mJidYpq27dmu8ta+3aIJo3/5T79+/x/fe9adq0AV27duTMmdM651Or1WzYsAY/v7Y0blyPzp078Ouvu3T2SSvrxo1r+Pv3pEmT+uza9TN//rkXb29Pbt26wbBhA7Sv39mzZ1Cr1axa9SNt27agbdsWrFy5DLX69aDJR4/+Y/LksbRv30bbLbV9+xadfdLzZtdb2t81vX9//rlXe8y1a1cYPLgfzZp507JlQ6ZMGU9kZIROuWFhoYwePYymTRvwxRe+2vdgTryZJKVxc6uIRqMhLCwsU2VcuHAOb29Pzp79lylTxtO8uQ8dOnyWbv3+/vsIPXqkfjbatWtFYOBCnUlvExMTWbhwLt98056mTRvg59eW+fNnERcXp90no+7ukyeP4+3t+cYEvCoWL56Pr28TWrVqxLx5MzlwYD/e3p68ePE8U7EJhUcROwv8GqZ2we08ep8w0QX3XqJFKR9oNBpS1Ir37qdCgjIHy6BkxERqjCQbza2jRw/H3t6eMWMmIpfLefr0CaGhIenuGxYWRkDAICpUcGfatNnExcWxYMEc4uPjcHOroLNvREQ4S5YsoEuXHsjlcoKCljFuXAA7d+7RrvS+ZMkC9u79lW7delGpUhWuXbvCihWBmJqa8sUXfgDcvXubCRNGU6dOfQYNGsbz58+ZNGksKSnv/1u/bdWqH/n004ZMnz6bq1evsG7dKkqWLKk9161bNxk2bAA1atRi+vQ5REaGs3LlMh4+fMDKleswMjLKdFkASqWSadMm4OfXiR49vmPr1o1MmDCKX37Zi42NLQDLly/hl1920K1bLz75pBqnTh1nwYLZqFRKOnT4WluWQqFg6tQJdOzYGX//AVhb23Dnzi0AZsyYTLt2HejU6Vu2bNnA+PGj8PVtQ3x8PBMmTOXGjdQuJ1fX8rRo0QqA0NAQXFzK0Ly5LxYWFty7d4e1a4NITEygb9/MLQTr4ODIypXrdbbt3/87v/22m5IlU6fwuHbtCoMG+VO3bgOmTp1NUlIiq1evYMyYEQQFvT52zJgRhIYGExAwFrlczpYtGwkJCdb5m+eGK1cuYWJiQvHiJbJ03Pz5s2nZsjWzZs3n+PFjrFgRSLlybtStWx+AEyf+ZsKE0TRt2oJ+/Qby+PF/BAUtJzj4JTNmpK43mZSUhFqtpm/f/tja2hESEsymTesYO3YEgYGp4+2aNWvJvn2/8+DBPVxdX686cOjQX1So4E4R55LEJSjYvnkVf/7+f/Tu7Y+bW0WOHTvMypWBufI3EgqmJrVKcu5WCHeeRrN+3y0COlXP1nfCx0IkSh+YRqNh4YUfeRD9KN/q4GpThuE1v8/SByMqKooXL54xZMgIvL1TZzuvWdMzw/137tyKkZER8+cvxsLCEgBn5xIMGPCd3r4xMTEEBq7C1TX1V46ZmRmDB/fj+vVrVKtWnWfPnrJr104CAsbSrl17ALy86pCUlMT69av5/PP2SKVStmzZQJEixZg9e4H2S9PU1JQ5c6ZnOs40lSpVYejQka/OVZcLF85x9OgRbXKzadM67O0dmDdvsTaZK1KkGMOHD+T06ZPav1FmyoLU5KZfv4HUq+cNgItLab766nP+/fcULVu2Jioqil27fuKbb7rSu7c/ALVr1yUqKor169fwxRd+2piVSiV9+/anadMW2vLTEqUOHb7myy9Tz+vk5ES3bp24deumNhGpU6ceJ078w9Gjh7SJkqdnbTw9awOp79+qVauTlJTErl07M50omZiYUKXKJ9rHV69e5o8/fqN3b3+qVq0OwMqVy3B392DWrPna96ara3m6dfua06dPUK+eN//+e4pbt26wZMkKatXyAqBGDU/at2+jXcsxNzx58phfftlBu3YdsLCwyNJtyY0aNdG+Rp6etTl9+iTHjh3WJkrr1q2icuVPmDJlJgB169bH1NSM+fNncf/+PcqVK4+dnR0BAWO1ZSqVSpydi9O//3c8fvwIF5fS1Krlha2tHQcP/oW/f2qilJSUxIkT/9CzZx8UCjVxsTHs+2M33bv35ttvewCpr/GQIf0JCQnOjT+VUABJJRJ6tvFg8tr/cfNRJH9fek6jGln7QfAxEV1v+aLgZe42NjYUK+ZMUNAy9u37/b0X2Zs3b1Cjhqc2SQKoVq061tb6Yz0cHZ20SRJA2bKpa/eFhqae4+zZM0DqF5BSqdT+8/SsTXh4uLYuN25cp0GDT3VaFho3bpqteGvXrqvzuEwZV219AK5cucinnzbUJklpx8jlVly5cilLZQFIpVI8PetoHzs7F8fU1JSQkJBXsV1DqVTSuHEzneOaNm1OVFQkT5481tmelnC9zcvr9TlKlSoNoE04Xm930Xl9k5OTWbs2iK+//oLGjevRqFFdVq36kfDwMBISsr5+VEhIMOPHj6JBg0/p3j11RvykpCSuXr1M48bNUKlU2te4VCkXihQpys2bN7R/B7lcrlNnuVyuTeRyQ3x8HOPHj8TZuTh9+/bP8vFeXq9fb4lEQunSZbSvY0JCAnfv3qFRoyY6x6QltW++d/bv/4OePTvTvPmnNGpUl/79U39kpL3WMpmMxo2bceTIQe0xJ08eJykpkaZNm5OiVPPk8UNSUlJo0OB14g7w6ae6j4WPT1E7Czq86oL76eg9wqJFF1xGRIvSByaRSBhe8/tMdb3JjAyn600ikbBw4TJWrfqRhQvnkpiYSMWKHgwaNIzq1Wvq7R8eHkbJki562+3s7PS2yeVynccymTGQug4fQHR0FBqNhjZtmukdCxAcHEyxYs6Eh4fplW9pKcfExDRzQb6jTsbGxtr6QOpizXZ29nrH2dvbExsbo7PtfWVBasuXsbFxOvslvzpfjLb8N9nZOQDojI0yMzPDwiL9mXflciud8t/e9vZ5AVasCGTv3t307NmHihU9sLKy4vjxv9m4cS0pKcmYmGR+oHhychJjxwZga2vL+PGv7yaLjY1BpVKxdOlCli5dqHdcWuIWHh6Gra3+e+jtv0t2KRQKxo0bSWxsLCtXrsPc3DzLZVhZ6f89Y2NjAYiLi0Wj0WBv76Czj1wux8TERPs6/v33UWbMmMznn39J3779sba2JTw8jHHjAnRem2bNWrJ798/cuHGNSpWqcOjQX1SrVgN7hyI8C40jKioc0P/cpffeFT4+TT1Lcu52CHefRrNh3y1GfC264NIjEqV8IJFIMDV6/0y3MpkUIwxnNlkXl9LMmDEXpVLJ1auXWbVqOaNHD2P37n16+zo4OBIVFam3PTJSf9v7WFvbIJFI+PHHNXrJRFq90s75dvnx8XE6Xyy5xcrKOt1YIiIi8uSW+LRupcjICJycimi3R0aGv3r+dUtdbl/ojh49RLt27bVdNwCnTp3IVlmzZ0/n+fNnrFmzSScJkcutkEgkdO3aEx+fRnrHpY3Tyuh9FRERobctq9RqNVOnTuD27ZssX76GokWL5bjMt6XF+fYA9bi4OFJSUrSv49Gjh3Bzq8CoUeO1+1y8eF6vvKpVq1GkSFEOHz6Ai0sZzpw5xeDBw1G8mj/J0cEJSP3cOTo6aY97+/zCx0kqkdCrtQeT1v2PG/9F8s/l5zSsLrrg3ia63oQsk8lk1KhRiy5dehAfH09YWKjePh4elbhw4SwJCa+Xdrl8+aJOy0dmpXWzREdH4+5eSe9fWveeh0dlTp48rrMO4NGjh7N8vsyoWrU6x48f07l77ezZf4mLi9WOuclNHh5VkMlkevEcOXIIOzt7SpXSb73LLcnJydpWPgCVSsXhwweyXM7mzRs4evQQU6fO0ptOwtzcnCpVPuHRo4fpvsbOzsWB1Nc4Li6O8+fPao+Ni4vj3Ln/ZTO61xYunMupU8eZPfsHypUr//4DssHCwgI3twocO/b265jafZb23nn7bw5w4MB+vfIkEgnNmrXgyJFDHDt2GJVKRaNGzbRTA7iWK4eJiSknTvytc9zx43/rlSV8nIraW9DBJ3W4w09H7hEenZTPNTI8okVJyJR79+6ybNkimjZtQYkSJYmLi2Pz5vU4OxdPdw6ljh27sHv3L4wcOZRvvulKXFws69evxtbWNsstHi4upWnf/itmzJjEN990pVKlKiiVSp48eczFi+eYPfsHAL79tjt9+nRn7NgAvvzSj+fPn7Fjx5Zsdb29T7duvfj++16MGjUMP7+viYhIvevNw6My9eo1yPXz2dra0qHD12zbtgkTExMqV/6E06dPcvDgfoYNG5nrd3y9ycurDnv3/krZsq7Y2Niye/fPWb6T8MqVS6xe/SNNm7bAwsKSa9euap8rUaIkdnZ29O8/hCFDvmfSpLE0bdoCKysrQkNDOHv2DK1bt6VmTU/q1q3/6k7KCfTrNwgrKys2b96ApaXlO87+fps2rePXX3fRuXNXjI1NdOrn5lYOU9PcW0S0V6++jB0bwLRpE2nRwpfHjx+xatVyGjVqok3QvLzqsHDhXDZsWEPlyp/w778nOX8+/WSwWbOWbNu2mTVrVlK7dl1sbW0JfXXLt4O9HV9+2YFNm9ZhYmKCm1tFjh49pB3nJJGI38oCNPMsxbnbodx7Fs2GfTcZLrrgdIhEScgUBwcHHBwc2Lx5PWFhoVhayqlWrTqTJk1P90va0dGRBQuWsnjxfCZOHE2JEiUZMiSARYvm6Y3ZyYyhQ0fi4lKaPXv+jw0b1mBuboGLS2mdwdqpX6BzWLkykPHjR1K2bDmmTJnFiBEDcxR7etzdPVi4cBlBQcuZMGEUZmbmeHv7MHDg0DxLWgYMGIKVlRV79/7Kxo1rKVasOAEBY/niiw55cr40w4aNZP782SxaNB8zMzN8fT/Dx6cxc+fOyHQZT58+Qa1Wc/Dgfg4e1G0ZGTduMq1bt+WTT6rx449rWLs2iNmzp6JQKHByKoqnp5d2CgGJRMKcOT+wYMFs5s+fjZWV1atENYLjx49lO8b//e9fALZt28y2bZt1nlu+fBXVqumPw8sub++GTJ8+h/Xr1zB27Aisra35/PMv8fd//T5t1649z58/45dffmLbts3Url2XyZNn4u/fQ6+8ChXccXEpzePHj/j++0EApCjSli6R0q/fIJRKJZs3b0CjUePj05guXbpn+7MoFD5SaepElFPWn+X6f5Ecv/ICn2rF87taBkOi0Whyf7TwR0alUhMREa+3PaerbxfkVcvT8+TJY7p08WPs2En4+n4GFL4Y3ybiK/gKWowajYZHwbGggRJO8nSXL5k+fSJXrlzm559/yzC+nF6/DEFerjxvKHIzxv1nHrPz6D3MTIyY3rsODjYffkb/t+Xla2hvb4mR0ftbVUWLkpBnVq5cRrly5XF0dOL582ds3rweBwdHGjZs8v6DBUHIFoVSDZrU1jeZkYSLF89z9eplKlb0QK1Wc+rUCQ4c2M+gQcPyu6qCgWnhVYrzt0O4/zyGjftvMaxjNdEFh0iUhDykUChYsSKQyMgITE1NqVGjFv37D8nw1vW8ptFodAZ6v00qlSKVijEbBZ1KpeJdDeVvzn2VWQXpvZM2kNvEWIpEIsHc3IJTp06wdetGkpOTcXYuzqBBw+jYsXM+11QwNFKphF5tPJi87izXHkZw4soLPhVdcKLrLTeIrrfs+5AxXrhwjsGDM55J2tf3M8aPn5Kr5yzsr6Ehxufn15aXL19k+PyJE+eyVJ5MJuW33/Ywa9bUDPfp2bOPdjbu/BYZm0R0XApyC2Mcbd4/D5ToeivY8iLGfWce8fPR+5ibpnbB2VvnXxec6HoThA/I3d2DNWs2Zfh82lw9QsE2d+4iFIqU9++YBQ0afPrO986bcxTltxTFqxYlWd7dCSkUbi29XDh/O5QHz2PYuP82Q7+q+lF3wYlESfhoWFhY4u5eKb+rIeSxvJgDycbGtsAk0opXv7rTG8QtCJkhlaZORDll/VmuPgjn5NWXeFd1zu9q5RvxSRIEQSgk1GoNSlVai5K4vAvZV9zRki8+LQvA9sN3iYzN/RUOCgrxSRIEQSgk0lqTpFJJpsZeCMK7tKxdirLOViQmK9m4/9Y7b5IozMQnSRAEoZBIebXGm4mxGJ8k5JyRVEqv1h7IjCRcuR/OqWsv87tK+UIkSoIgCIVEihifJOSyEk5y2nm/6oI79HF2wYlPkyAIQiGhUIjxSULua1XHhdLFrEhIVrLpI+yCE58mQRCEQkLb9SamBhBykZFUSu82HhhJJVy+H86/14Pzu0oflEiUBIPl7e2ps0Dpr7/uYtiwAXz+eUtatGhI3749crQQqiAUJiqVGrU69Ze+6HoTcltJJzmfv+qC23boDlFxH08XnPg0CQXGpk3rKFbMmREjxjBz5lzKlXNj7NgA9u37Pb+rJgj5Lm18ksxIilT68U4OKOQd3zoulC5qRXySkk37b380XXBiwkmhwFi3biu2trbax15edXn58jnbt2/G1/ez/KuYIBgA7USTxuL3r5A3ZEZSerXxYNqGs1y6F8aZG8HUrVwsv6uV50SilA80Gg2alPcvsaBWSVHnwfpEEhOTbE1HP3PmFG7dusGwYaMIDFzIkyePKVvWlREjxuLu7gFAcnIyq1Yt59ChA8TGxuDiUoaePfvQsGHjdMtZtmwRjx8/0isnPW8mSWnc3Cpy+/atTMfw6NF/rFsXxNWrV4iOjsLZuTht2rTj6687axc1/eqrz6lXrwHDh4/WOXbZssUcPnyAXbt+RyqVEhISzIIFszl//iw2NrZ8/XVnXr58yfHjx/jll72ZrpMg5IbX45NEoiTknVJF5LRtUIZfjz9k68E7eJS2w0Zumt/VylMiUfrANBoNT+bMJOn+vXyrg1l5N0qNHpetZCkiIpwlSxbQpUsP5HI5QUHLGDcugJ079yCTyZg2bQJnzpymb9/+uLiUYf/+P5gwYRSzZy/A27uhXjnduvXE3NxSr5zMunr1EqVLl8n0/qGhIbi4lKF5c18sLCy4d+8Oa9cGkZiYQK9efQFo2rQFf/65lyFDAjAySh0Uq9FoOHLkIE2aNEcqlaLRaBgzZgSRkRGMHDkOuVzOtm2befnyhcGsIi98XBRijTfhA2ldtzQX7oTyODiOTX/dZmD7Twr1WnAiUcoPBfgNFRMTQ2DgKlxdywFgZmbG4MH9uH79GpaWlvz991ECAsbyxRcdAKhbtz4vX75g3brVOolSWjkVKrihVKp1yqlWrXqm6nLgwH6uXr3CrFkLMl1/T8/aeHrWBlKTn6pVq5OUlMSuXTu1iVKzZi3ZsmUDFy6cxcurLgCXL18kJCSY5s1bAvDvvye5c+cWy5evplq1GgDUrOlF+/atkcutMl0fQcgNGo1GzKEkfDAyIym921Ri2oazXLwbxv9uhlCnUtH8rlaeEYnSByaRSCg1elymut5kMilKA+p6g9RV0tOSJICyZV0BCA0N5t69aACaNGmmc0yTJs0JDFxIYmIi5ubm7y0nM+7du8uCBbNp3botPj6NMl3/5ORktmzZwIED+wgOfolSqdQ+l5CQgIWFBeXLu1GmjCuHDh3QJkqHDh2gZEkX7aK6N2/eQC630iZJABYWFtSq5ZWlrkBByA1KlSZ1YK1EJErCh1GqiJy29cvw64nULjj30nbYWJrkd7XyhPhE5QOJRILU1DTf/uWkiVQul+s8lsmMAUhJSSE2NgaZTIa1tY3OPvb29mg0GuLiYjNVzvu8fPmCgIDBeHhUZtSo8Vmq/4oVgWzbtom2bb9g/vwlrFmzie7de+udu1mzFvz991EUCgVKpZJjxw5rW5MAwsPDsLW10yvfzs4+S/URhNygeDU+ydjIqFB3gQiGpXW90rgUkROXqGDLX4X3LjiRKAm5xtraBqVSSUxMjM72iIgIJBJJrnRJRUVFMXz4QOzs7Jg1a16WxjMBHD16iHbt2vPttz3w8qqDu3sl7TikNzVr1pK4uFjOnDnF+fNniYqKpFmz14mSg4MjUVGResdFRkZkPShByKG0bjcTcceb8AGl3QVnJJVw/k4oZ2+F5HeV8oT4VAm5pmrV6kBqMvKmo0cP4eZWUdvtll0JCQkEBAxGoVAyf/5SLC3l7z/oLcnJydrWKwCVSsXhwwf09itZshQeHpU4ePAvDh36Cze3CjqDxj08KhEXF8ulSxd06nf+/Nks10kQckohxicJ+cSlqBVt6pUGYMuBO8TEv79XoKARY5SEXFO+vBsNGzZm2bJFJCcn4+JSmgMH9nHt2hVmz/4hx+WPHz+Su3dvM3bsJF6+fMHLly+0z1Wp8kmmyvDyqsPevb9StqwrNja27N79MykpinT3bdasJWvWrMTIyIhu3XrpPFe3bgMqVHBn6tQJ+PsPQC63Ytu2TVhYWCKRiC8r4cNKUYipAYT881n9Mly4E8bT0Di2HLxD/y+q5HeVcpVIlIRcNWnSdIKClrN16wZiYlLnUZoxYy7e3j45Lvvs2TMAzJgxWe+5EyfOZaqMYcNGMn/+bBYtmo+ZmRm+vp/h49OYuXNn6O3btGkLli9fgkajoWnTFjrPSSQS5sz5gfnzZzFv3iysrKzw8+vEkyePuHv3djaiE4Ts0Wg0KFRpLUpiagDhw0u9C86D6RvPce5WCGdvheDlXiS/q5VrJBoDG311//59ZsyYwcWLF7G0tKRdu3YMHToUE5N3j6bXaDSsXr2abdu2ERERgYeHB2PHjqV69erafc6cOUO3bt30jm3dujWLFi3Kdp1VKjUREfF62xWKFMLDX+Dg4IyxcdbvBsiru94MSWGKUaFQ8O23X1GtWg3GjUtN5gpTfOkp7PGB4ceYolDxPCweiUSCS1F5lgdzZxRfTq9fhkAmk2JnZ0lkZLxBv4Y5YUgx7v7nAXtP/YeVhTHTv6uDtUXO3zd5GZ+9vSVGRu9vhTWoFqXo6Gi6d+9OmTJlCAwMJDg4mDlz5pCUlMSkSZPeeezq1atZunQpAQEBVKxYka1bt9KrVy/27NlDqVKldPadPXs2rq6u2sd2dvp3LwnC++zZ839oNGpcXMoQGxvD7t2/8PLlC6ZOnZXfVRM+Im8O5BZ3vAn5qW2DMly8G8rT0Hi2HbxDv3aFowvOoBKlHTt2EB8fz7Jly7TLVahUKqZOnYq/vz9Fi6Y/oVVycjJBQUH06tWLHj16AFCrVi1atWrF2rVrmTJlis7+bm5ufPJJ5sa0CAWHWq1Grc74F4dRLt86bWJiwtatG3nx4jkA5ctXYN68xdq5lgThQ9BODSDGJwn5LO0uuBkbz/O/myF4VgzBsxB0wRlUovTPP/9Qr149nTW9fH19mTx5MidPnqR9+/bpHnfhwgXi4uLw9fXVbjMxMaF58+YcPHgwr6stGIjZs6exb9/vGT6/dOlKatb0zLXz+fp+JhbjFfJdili6RDAgZYpZ07qeC7+fesSWA7ep6GKLVS50weUng0qUHjx4QIcOHXS2WVtb4+TkxIMHD955HKDTnQZQrlw5Nm7cSFJSEmZmZtrtffv2JSoqCicnJ9q0acOQIUN0ns8OWTq/5tTq7LdepDV8SCRgWKPIck9ux9irV186dOiY4fMuLqVzfpIsKOyvYWGPDwpGjIoczKGUmfiMjCTpXt8KgrTxJ5kZh1JQGWKMX/qU4+LdMJ6FxrP98F36f5n9HhxDiM+gEqWYmBisra31ttvY2BAdHf3O40xMTDA11V3B2NraGo1GQ3R0NGZmZlhZWfHdd9/h5eWFqakp//77L+vWrePBgwcEBQVlu95SqQQ7O0u97UlJRoSFSXN0oTGkN39eya0YS5UqSalSJXOlrNxU2F/Dwh4fGG6MarUG5as73sxNjTEyyt6Ps/TiU6slSKVSbGwscvxDMr9ZW+dsDreCwNBiHNGlFgFLj/Pv9WCaeLlQ75PiOSovP+MzqEQpr1WqVIlKlV6PH6lXrx5FihRh2rRpXLlyhapVq2arXLVaQ0xMgt72lJRk1Go1KpUmy6P1JZLUi5dKpTbYX7I5VdhjFPEVfIYeY/Kr+ZOMpBI0Gg1KZdYq+a74VCoNarWa6OgEEhNVuVXlD8rISIq1tTkxMYmoVIXzrjdDjdFRbkKbuqXZe+o/lv98mRL25tnqgsvL+KytzQveXW/W1tbExsbqbY+OjsbGxiadI14fl5KSQnJysk6rUkxMDBKJ5J3H+vr6Mm3aNK5du5btRAlINxFSqbJ/ZU27aBnixTm3FPYYRXwFn6HHmDbRpLFx9sYnZSa+7PzQMzQqlbrAx/A+hhjjZ/XLcP5OKM/D4tm8/zZ9P6+c7bLyMz6Dak92dXXVG4sUGxtLaGio3vijt48DePjwoc72Bw8eULx48QLfbCwIgpCeFLF0iWDAjGVSerX2QCKBf28Ec+FOaH5XKVsM6tPl4+PDqVOndBZV3b9/P1KplAYNGmR4XM2aNZHL5ezbt0+7TaFQcODAAXx83j0j9B9//AEgpgsQBKHAUWjveDOoS7kgaLkWt6ZVHRcANv11m7jE9JeMMmQG1fXWqVMnNm/ezIABA/D39yc4OJh58+bRqVMnnTmUunfvzvPnz7W3/puamuLv709gYCD29vZUqFCB7du3ExUVRe/evbXHBQQEULp0aSpVqqQdzL1hwwaaNWsmEiVBEAqcFGXaGm9iagDBcH3hXZZLd8N4EZ7A9kN36NM2+11w+cGgEiUbGxs2btzI9OnTGTBgAJaWlvj5+TFs2DCd/VIHSOsOLuzTpw8ajYZ169ZplzBZu3atzqzcbm5u7N27l3Xr1qFQKChRogT9+vWjb9++HyQ+QRCE3KJSqVGrUwcXia43wZAZy4zo1dqDWVvOc/p6MJ7uRajh5pTf1co0g1vrrSASa71l37ti9Pb2pH//IXTu3BWAX3/dxd9/H+H+/XskJSVRpkxZunbtwaefNvqANc6awv4aFvb4wHBjTExWEhyRgMxISski8myXI9Z6K9gKUow7j95j/5nH2FiaMP27OsjNjd97jCGs9SZ+hggFxqZN6yhWzJkRI8Ywc+ZcypVzY+zYgHfOxi0IhVXaRJPG2ZhoUhDywxfeZSlmb0F0fAo7Dt/N7+pkmkF1vQnCu6xbt1VneRsvr7q8fPmc7ds3i6VEhI/O6/FJIlESCgYTYyN6tfFg9ubznLr2Ek/3IlQv75jf1Xov8QnLBxqNBkWKKt/+Zbe3debMKXTt2pELF87Rs2dnmjXzpk+fbty6dVO7T3JyMoGBC2nXrhVNmtSnR4/O/P330QzL6dbtm3TLSc+bSVIaN7eKhIWFZTqGtWuDaN78U+7fv8f33/emadMGdO3akTNnTuvsp1ar2bBhDX5+bWncuB6dO3fg11936ezz6NF/TJ48lvbt29C0aQO+/fYrtm/forMw75dffsbChXP16rFs2WK+/LK1dt+QkGBGjRpK06YNaN++DT/9tJUlS37Az69tpmMTPi4KscabUACVL2FDi9qpY4c37b9FfJLh3wUnWpQ+MI1Gw69bLvHyWcz7d84jxUpa80WX6kgkWV/uICIinCVLFtClSw/kcjlBQcsYNy6AnTv3IJPJmDZtAmfOnKZv3/64uJRh//4/mDBhFLNnL8Dbu6FeOd269cTc3FKvnMy6evUSpUuXyVIMSqWSadMm4OfXiR49vmPr1o1MmDCKX37Zi42NLQDLly/hl1920K1bLz75pBqnTh1nwYLZqFRKOnT4GoDQ0BBcXMrQvLkvFhYW3Lt3h7Vrg0hMTKBXr9QbBJo3b8nvv//GkCEBGBmlfqFpNBqOHDlIkybNkUqlaDQaxowZQWRkBCNHjkMul7Nt22ZevnyBVCp+ywj6NBqNmENJKLC+/NSVS/fCCY5IYMfhu/RuU+n9B+UjkSjlh+yvlZvvYmJiCAxchatrOQDMzMwYPLgf169fw9LSkr//PkpAwFi++CJ1ceO6devz8uUL1q1brZMopZVToYIbSqVap5xq1apnqi4HDuzn6tUrzJq1IEsxKBQK+vUbSL163kDqYrlfffU5//57ipYtWxMVFcWuXT/xzTdd6d3bH4DatesSFRXF+vVr+OILP4yMjPD0rI2nZ20g9YuratXqJCUlsWvXTp1EadOm9Vy4cBYvr7oAXL58kZCQYJo3bwnAv/+e5M6dWyxfvppq1WoAULOmF+3bt0Yut8pSbMLHQanSpLYMS0SiJBQ8JsZG9GrtzpwtFzh59SVe7kWoWs5wu+BEovSBSSQSvuhSHaXi/aP38+puG5mxNFutSQCOjk7aJAmgbNnUWdFDQ4O5dy914eImTZrpHNOkSXMCAxeSmJiIubn5e8vJjHv37rJgwWxat26Lj0+jLMUglUrx9KyjfezsXBxTU1NCQkIAuHHjGkqlksaNdeNo2rQ5hw79xZMnjylTpizJycls2bKBAwf2ERz8EqVSqd03ISEBCwsL3NwqUKaMK4cOHdAmSocOHaBkSRfc3VN/Rd28eQO53EqbJAFYWFhQq5YXt2/fylJswsdB8Wp8krGRUbY/y4KQn9xK2tLcqxQHzj5h4/7bTO9tg4XZ+++Cyw/ip0g+kEgkGJsY5du/nFxY5XLd25BlstQ3dkpKCrGxMchkMqytddfWs7e3R6PREBcXm6ly3uflyxcEBAzGw6Myo0aNz3IMpqamGBvrfiCNjY1JSUkGIDY2RlvvN9nZOQAQE5OaEK5YEci2bZto2/YL5s9fwpo1m+jevbdeHM2ateDvv4+iUChQKpUcO3ZY25oEEB4ehq2tnV497ezs9bYJArxeusRE3PEmFGBf+rhSxM6cyNhkdhy5l9/VyZD4lAm5xtraBqVSqbMEDUBERAQSiSRXupGioqIYPnwgdnZ2zJo1L0vjmTLL2toagMjICJ3tkZHhr55PTQSPHj1Eu3bt+fbbHnh51cHdvZJ2HNKbmjVrSVxcLGfOnOL8+bNERUXSrNnrRMnBwZGoqEi9494+vyCkUYjxSUIhYGqcOhGlBDhx5QVXH4Tnd5XSJT5lQq6pWrU6kJpAvOno0UO4uVXUdrtlV0JCAgEBg1EolMyfvxRLy+xPsvcuHh5VkMlkHD16WGf7kSOHsLOzp1Sp1HWLkpOTtS1hACqVisOHD+iVV7JkKTw8KnHw4F8cOvQXbm4VdAage3hUIi4ulkuXLmi3JSQkcP782VyOTCgsUhRiagChcKhQypamniUB2LDvFglJyvcc8eGJMUpCrilf3o2GDRuzbNkikpOTcXEpzYED+7h27QqzZ/+Q4/LHjx/J3bu3GTt2Ei9fvuDlyxfa56pUyb21+mxtbenQ4Wu2bduEiYkJlSt/wunTJzl4cD/Dho3Uthp5edVh795fKVvWFRsbW3bv/pmUlPRvdW3WrCVr1qzEyMiIbt166TxXt24DKlRwZ+rUCfj7D0Aut2Lbtk1YWFgikYgvQkGXRqNBoUprURJTAwgFXwefcly5F05IVCI7j96lh69HfldJh0iUhFw1adJ0goKWs3XrBmJiYnBxKcOMGXPx9vbJcdlnz54BYMaMyXrPnThxLsflv2nAgCFYWVmxd++vbNy4lmLFiuvczQcwbNhI5s+fzaJF8zEzM8PX9zN8fBozd+4MvfKaNm3B8uVL0Gg0NG3aQuc5iUTCnDk/MH/+LObNm4WVlRV+fp148uQRd+/eztW4hIJPoVSDJvV9IzMSA7mFgs/UxIierd2Zu+0i/1x+gad7EaqUdcjvammJtd5ygVjrLfsKe4zZjU+hUPDtt19RrVoNxo3TTwwNRWF//cDwYoxLVBAWlYipiRHODpY5Lk+s9VawFaYYtx68w+HzT7G3NmV67zqYm8oMYq030aIkCAZgz57/Q6NR4+JShtjYGHbv/oWXL18wdeqs/K6aYGC0UwOI8UlCIePXsBxX7ocRGpXEzqP36N7KPb+rBIhESShE1Gq1zvIhbzMy4DlnTExM2Lp1Iy9ePAegfPkKzJu3WDvXkiCkSRFLlwiFlKmJET19PZi3/SJ/X3qOp3sRqhnAWnAiURIKjdmzp7Fv3+8ZPr906Upq1vT8gDXKPF/fz8TCvkKmiKkBhMLMvbQdTWqW4MiFZ2z48yaz/OuhP8vchyUSJaHQ6NWrLx06dMzweReX0h+wNoKQ+9RqDUpVWouSSJSEwsmvUTmu3A8nLDqJnw7fZViX/P2BKxIlodBwdi6Os3Px/K6GIOSZtNYkqVSSqUGoglAQmZnI6Onrzvwdlzhy4RlNvErj4mSRb/URnzRBEIQCIuXVQG4TYzE+SSjcPMrY07hGCQACf75Eft6gLxIlQRCEAiJFjE8SPiJ+jcpR3NGSZIVKO8lqfhBdb4IgCAWEQiHGJwkfD3NTGdO+q42trSUJcUn5Nk+U+LQJgiAUENquNzE1gPCRMJEZYZrPXc0iURIEQSgAVCo1anXqOA3R9SYIH47oehMybebMKdy6dYPNm3dy8+Z1du/+hcuXLxIWFoqTUxEaNWpK9+69MTc3z++q6hg4sC8WFhbMm7c4w338/NpSv743w4eP/nAVE4QsSBufJDOSIpUa5sSpglAYiURJyJbDhw/y9OkTOnfuRqlSLjx8+IC1a4O4ceMaS5euzO/qZdmsWfOxsrLO72oIQoa0E00ai9YkQfiQRKIkZEuXLt2xs3s9X2rNmp5YWVkzbdoEbt26ibu7R7bKTU5OwtTULLeqmWkVKhjGmkKCkJHX45NEoiQIH5L4xAnZ8maSlKZChYoAhIWFZqqMFy+eU7duTf78cy9z586gdeum9OnTHYBTp04wdGh/PvusOS1aNKRPn+78++8pneP//HMv3t6e3LlzixEjBtOsmTedOn35zmVMIDUZGzlyCF991Y5nz54CqV1vCxfO1e4zc+YUunbtyIUL5+jZszPNmnnTp083bt26qVNWXFwc06ZNpHlzHz77rDlBQcvZvn0L3t6GuVSKUHApxBpvgpAvRItSPtBoNKiUKe/fUS3VLleQm4xkJnmyOOyVK5cAKF26TJaOCwpaRr163kyZMlO7qO2LF89o0MCHb77pilQq4d9/TzFy5BCWLFmht17btGkTadv2Czp16sxvv/3KrFlT8fCoTJkyZfXOlZCQwOjRwwgPD+PHH1fj5FQkw3pFRISzZMkCunTpgVwuJyhoGePGBbBz5x5kstSPzqxZU7lw4Sz9+w+mWLFi/Pbbr9y+fTPDMgUhOzQajZhDSRDyiUiUPjCNRsPhnxYT/vxhvtXBsbgrTb4ekqvJUlRUFOvWreLTTxtSqpRLlo4tX74iY8ZM1NnWocPX2v9Xq9XUqOHJw4cP+O233XqJUvv2HWnf/isAqlSpxunTJzh27DA9enyns19MTAwBAYNJSUlh+fLV2NnZv7NeMTExBAauwtW1HABmZmYMHtyP69evUa1adR4+fMA//xxlwoSptGrVBoA6derTubNfluIXhPdRqjSpMxNLRKIkCB+aSJTygYTCdceKUqlk8uRxAIwYMTbLx9ev30BvW0hIMKtW/ci5c/8jPDxMO319xYr6Y59q166r/X9zc3OKFXMmNDREZ5+oqCgGD+6HiYkJS5euxNr6/QO3HR2dtEkSQNmyrgCEhgYDcOvWDQC8vRtq95FKpTRo8Ck//bT1veULQmYpXo1PMjYyypPWYEEQMiYSpQ9MIpHQ5Oshmep6kxkZftebRqNh9uyp3Lx5neXLV+Po6JjlMuzsHHQeq9VqxowZTlxcHN9950+JEqUwNzdnzZqVBAe/1DteLrfSeSyTGZOSovv3ffLkMbGxMQwePCJTSVJquXK9cgFt2WFhYchkMr390hu/JQg5kdbtZiLueBOED04kSvlAIpEgMzZ9734ymRTyacr2zFq2bDFHjhxi/vwluLlVyFYZb+dsT58+4c6d28yevYBPP22k3Z6cnJzten7ySVU8PWuzbNkibGxsaNmydbbLSuPo6IhSqSQuLk4nWYqMjMxx2YLwJoUYnyQI+UZ86oRs27x5Azt3bmPcuMl4etbOtXLTEqK0FhyAly9fcPXq5RyV27FjZ/r0+Z5Zs6Zy9OihHJUFr7sBjx8/pt2mVqs5efJ4jssWhDelKMTUAIKQX0SLkpAtBw7sJyhoGS1b+uLsXIJr165qnytRomSOup9Kly5DkSJFWblyGWq1msTEBNauDXrnHWqZ1bVrT5KTk5k6dQImJqY0aPBptstydS2Hj09jlixZQHJyEkWLOvPbb7tJSUkW40iEXKPRaLQrpxuLqQEE4YMTiZKQLWfP/gvAX3/t46+/9uk8N27cZFq3bpvtsk1MTJg5cx4LF85l4sQxFClSlO7de3HhwjntAOqc+O67fiQnJzNx4mjmzl2El1edbJc1duwkFi2ax/LlSzAxMaFVq89wdS3Hrl07c1xPQYBX3W6aV132RiIBF4QPTaJJu51IyDaVSk1ERLzedoUihfDwFzg4OGNsbJLlcmUyKUoDH6OUU4UxxgED+iCVSgkMDCqU8b2psMcH+R9jXKKCsKhETE2McHawzPXyM4ovp9cvQyCTSbGzsyQyMr7Qvk8Le4x5GZ+9vSVGRu/vzhYtSoKQA8eOHSY4+CWuruVJTk7i4MH9XL58kVmzFuR31YRCQjs1gBifJAj5QiRKQp7QaDSoVKoMn5dKpUilBf/Cb25uwV9//cmTJ09QKhW4uJRh0qTp+Pg0yu+qCYVEili6RBDylcElSvfv32fGjBlcvHgRS0tL2rVrx9ChQzExeXfTr0ajYfXq1Wzbto2IiAg8PDwYO3Ys1atXT3d/tVqNn58f169fZ8mSJbRq1SoPovl4Xbx4nsGD+2X4vK/vZ4wfP+XDVSiP1KlTjzp16uV3NYRCTEwNIAj5y6ASpejoaLp3706ZMmUIDAwkODiYOXPmkJSUxKRJk9557OrVq1m6dCkBAQFUrFiRrVu30qtXL/bs2UOpUqX09t+xYwfBwcF5FcpHz93dgzVrNmX4vI2N7YerjCAUUGq1RjvprJgaQBDyh0ElSjt27CA+Pp5ly5Zha2sLgEqlYurUqfj7+1O0aNF0j0tOTiYoKIhevXrRo0cPAGrVqkWrVq1Yu3YtU6ZM0dk/IiKCJUuWMGrUKMaNG5eHEX28LCwscXevlN/VEIQCLa01SSqVZGrQqSAIuc+gPnn//PMP9erV0yZJAL6+vq8m8TuZ4XEXLlwgLi4OX19f7TYTExOaN2/OP//8o7f/woULqVOnDnXqZP+2cEEQhLyW8mogt4mxGJ8kCPnFoFqUHjx4QIcOHXS2WVtb4+TkxIMHD955HICrq6vO9nLlyrFx40aSkpIwMzMD4MqVK/z+++/8/vvvuVp3WTrN4mp19uc8SZuvUCKBwjqBQ2GPUcRX8OV3jGktSnnV7ZaZ+IyMJOle3wqCtFa4wtwaV9hjNIT4DCpRiomJSXfBUhsbG6Kjo995nImJCaamuuunWVtbo9FoiI6OxszMDLVazdSpU+nZsyclS5bk6dOnuVJvqVSCnZ3+/CZJSUaEhUlzdKEprG/+NxX2GEV8BV9+xZg2I7eZqSxPk5X04lOrJUilUmxsLLQ/NAsqa2vz/K5CnivsMeZnfAaVKOW1n3/+mbCwMPr27Zur5arVGmJiEvS2p6Qko1arUak0WZ4oSyJJvXipVOpC/Wu9MMco4iv48jvGlJTUrjcjqSRPJhN8V3wqlQa1Wk10dAKJiRlP9WHIjIykWFubExOTiEpV+CZjhMIfY17GZ21tXvAmnLS2tiY2NlZve3R0NDY2Nu88LiUlheTkZJ1WpZiYGCQSCTY2NsTHx7Nw4UKGDRuGQqFAoVAQFxcHQFJSkt4K8FmV3kVMpcr+lTXtolVYv4Cg8Mco4iv48jNGlUqNSp16YuM8atHKTHzZ+aFnaFQqdYGP4X0Ke4z5GZ9BtZm7urrqjUWKjY0lNDRUb/zR28cBPHz4UGf7gwcPKF68OGZmZkRGRhIVFcXkyZPx8vLCy8uLdu3aATB69GhatmyZy9EIgiBkX8qrLwWZkRSpVKzxJgj5xaASJR8fH06dOkVMTIx22/79+5FKpTRo0CDD42rWrIlcLmffvteLsyoUCg4cOICPjw8ATk5ObNq0SeffwoULARg0aBCBgYF5FJWQXd7enmzbtln7+NdfdzFs2AA+/7wlLVo0pG/fHhw/fiy/qpehFy+e4+3tydGjhzLc58KFc3h7e+bKIr9C4aSdaNLYoC7TgvDRMaiut06dOrF582YGDBiAv78/wcHBzJs3j06dOunModS9e3eeP3/OwYMHATA1NcXf35/AwEDs7e2pUKEC27dvJyoqit69e2v3eXs6gLTB3OXLl6dmzZofKEohuzZtWkedOvX44gs/LCzMOXLkMGPHBjB+/BR8fT/L7+plScWK7qxcuZ7Spcvmd1UEA6WdGqCA3nEmCIWFQSVKNjY2bNy4kenTpzNgwAAsLS3x8/Nj2LBhOvulDpDWHVzYp08fNBoN69at0y5hsnbt2nRn5RYKpnXrturMseXlVZeXL5+zffvmHCVKyclJmJp+2Lt6LC3lVKnyyQc9p1CwKMQab4JgEAwqUYLUuY82bNjwzn02b96st00ikeDv74+/v3+mz1WyZElu376d1Sp+tGbOnMKtWzcYNmwUgYELefLkMWXLujJixFjc3T2A1FnSV61azqFDB4iNjcHFpQw9e/ahYcPG6ZazbNkiHj9+pFdOet5MktK4uVXk9u1bmY5h7dogduzYwpIlK1iy5Afu3r3Nd999T+fOXVmxIpDTp0/w4sVzLC3lVKtWg0GDhuPo6Kg9fuDAvlhYWODr+xmrVv1IWFgoHh6VGT16AiVKlMzwvLdv32LEiIHUr/8pY8ZM5NKlCwwe3I81azZpZzD39vbk++8HkZSUxK+/7kKtVtGggQ/Dho3C3Pz1rbGXL19i8eJ5PHr0H6VKuTBw4FCWL1+Km1uFQrF+npC6dmWKWONNEAyC+ATmA41Gg0ahyr9/ObiFJyIinCVLFvDNN92YNm0OKSkpjBsXgFKpBGDatAns2fN/dOnSjVmzFlCmTFkmTBjFiRN/p1tOly7pl5NZV69eonTpMlk6RqFQMHXqBFq08GXBgqXUrl0XgMjICLp27cm8eYsZMmQEL1++YODAvnp1unv3Dtu2baZfv0GMGzeZp0+fMG3axAzPd+XKJYYM6UezZi0ZO3YSUmnGH7tdu3by9OkTxo+fQo8efTh4cD8bNqzRPh8WFkZAwCAsLCyZNm0233zTlQUL5hAWFpKlv4Fg2JQqTernVCISJUHIbwbXolTYaTQa4vbdQxWqP+/Sh2JUxAJ5q/JIJFm/kyYmJobAwFW4upYDwMzMjMGD+3H9+jUsLS35+++jBASM5YsvUmdYr1u3Pi9fvmDdutV4ezfUK6dCBTeUSrVOOdWqVc9UXQ4c2M/Vq1eYNWtBlmJQKpX07dufpk1b6GwfN26y9v9VKhVVqlTlyy9bc+HCOW0yBRAXF8u6dVuxs7MDIDExkVmzphISEkyRIrrrEf7vf2cYPXo4fn6d8Pcf8N66OTg4MnnyDCD1b3fnzi2OHTvM998PAmDnzq0YGRkxf/5iLCxSJzl1di7BgAHfZelvIBg2xavxScZG0mx9TgVByD0iUcoPBfi65+jopE2SAMqWTZ2aITQ0mHv3UmdPb9Kkmc4xTZo0JzBwIYmJidoupHeVkxn37t1lwYLZtG7dFh+fRlmOo149b71tp0+fZOPGtTx8eJ/4+Hjt9idPHukkSuXLV9AmSQBlyqQOyA4JCdFJlE6dOsHhwwfo2bMPXbv2zFS9vLx0bzgoU6Yshw8f0D6+efMGNWp4apMkgGrVqmNtnfE8Y0LBk9btJtZ4E4T8JxKlD0wikSBvVR4yMXGWTCbNmwm2ZNn/lfr2pJwymTEAKSkpxMbGIJPJ9L607e3tU1vS4mK1idK7ynmfly9fEBAwGA+PyowaNT7LMZiZmWFhYaGz7ebN64wZM5xPP23It992x9bW/tW4tx4kJ+vWycrKSuexsXFa3ZN1tp88eRwzMzO9lqt3kcv1y37zbxIeHkbJki56x72ZuAkFn0KMTxIEgyESpXwgkUggE78UJTlIaPKDtbUNSqVSb82+iIiI1ATxrSQgO6Kiohg+fCB2dnbMmjUPmSzrb+H0/qb//HMMuVzOtGlztGOIXr58kaO6Dho0jL17f2XIkP4sX75Kr1suOxwcHImKitTbHhmpv00ouFIUYmoAQTAU4lMo5JqqVasD6E20ePToIdzcKurcuZUdCQkJBAQMRqFQMn/+Uiwts7/kzNuSk5OQyWQ6SdSBA/veccT7mZmZsWjRUmxsbBgy5HsiIsJzWk08PCpx4cJZEhJedw1evnyRmJiMF40WChaNRqNdDNdYTA0gCPkuy4lSnz59OHPmjPZxcnIyq1ev5sUL/V/fhw4domnTpjmroVBglC/vRsOGjVm2bBE7d27n339PMW3aRK5du0KvXjlfiHj8+JHcvXub3r378vLlC65du6r9l1NeXnUIDw9n0aJ5nDv3PzZsWMO+fb/nuFxLSzkLFy7D1NSMoUP7Ex0dlaPyOnbsglqtZuTIoZw48Q/79//BrFlTsbW1LVCtj0LGFEo1aFJbPmVG4jUVhPyW5UTp+PHjhIS8vhU5ISGBhQsX8t9//+ntm5CQwPPnz3NUQaFgmTRpOm3bfsHWrRsYO3YE9+/fY8aMuXh7++S47LNnz6BSqZgxYzL9+vXU+ZdT9ep58/33gzhx4h/GjBnO5csXmTdvcY7LhdRFmxctWo5arWbYsIHaxZizw9HRkQULlpKQEM/EiaPZsmUDQ4YEYG5ukaNFnQXD8Xogd8HqeheEwkqiyeKkOu7u7syfP5+2bdsCqWMj6tWrx/r166lXr57Ovr/99hujR4/m5s2buVdjA6RSqYmIiNfbrlCkEB7+AgcHZ4yNTbJcbp4N5jYghT3GDxHfkyeP6dLFj7FjJ33wpVwK++sHHz7GyNgkouNSkFsY42iTs+7qzMgovpxevwyBTCbFzs6SyMj4Qvs+Lewx5mV89vaWGBm9v71IDOYWhAJm5cpllCtXHkdHJ54/f8bmzetxcHCkYcMm+V01IRekiKVLBMGgiERJKDTUajVqdca/OIyMjApFV4ZCoWDFikAiIyMwNTWlRo1a9O8/RG/KA6FgElMDCIJhyVailN6XTWH4AhIKttmzp71zAPbSpSupWdPzA9YobwwaNIxBg4a9f0ehwFGrNShVaS1KIlESBEOQrURp3bp1/P576hdS2jpYixcv1lu09M1B34KQ13r16kuHDh0zfN7FpfQHrI0gZF1aa5JUKsnU2AlBEPJelhOl4sWLExUVRVRUlM62kJCQdBMjZ2fnHFVQEDLL2bk4zs7F87sagpBtKa/WeBNLlwiC4chyonTkyJG8qIcgCMJHL0WMTxIEgyM+jYIgCAZCoRDjkwTB0OTqXW/3799n//79hIaGUrZsWTp06CAmwRMEQcgkbdebmBpAEAxGlhOlLVu2sHnzZrZv3469vb12+5EjRxgyZAgKhUJn359++klnP0EQBEGfSqVGrU6d/1d0vQmC4cjyp/HIkSOUKlVKJ/lRKpVMmDABIyMjZs+ezd69exkxYgTPnz9n5cqVuVphQRCEwihtfJLMSIpUKqZbEQRDkeVE6d69e1SvXl1n25kzZ4iIiKB79+58+eWXuLm50adPH1q1asXff/+dW3UVBEEotLQTTRqL1iRBMCRZ/kRGRUVRrFgxnW2nT59GIpHQvHlzne01a9bkxYsXOauhYDBmzpxC166p8xTdvHmdWbOm8vXXX9C0aQM6dfqSlSuXkZiYmM+11DdwYF9GjRr6zn38/NqycOHcD1MhA+Dt7cm2bZvzuxrCG16PTxKJkiAYkiyPUXJ0dCQsLExn27lz5zAzM8Pd3V1nu4mJCcbGxjmroWCQDh8+yNOnT+jcuRulSrnw8OED1q4N4saNayxdWvC6W2fNmo+VlXV+V0P4iCnEGm+CYJCynChVqVKF3bt38+233yKXy7l79y5Xr16ladOmyGS6xT148ECv9UkoHLp06Y6dnZ32cc2anlhZWTNt2gRu3bqJu7tHtspNTk7C1NQst6qZaRUquL9/J0HIIxqNRsyhJAgGKsufyAEDBvD8+XNatmxJ9+7d+eabb5BIJPTt21dv34MHD1KjRo1cqahgWN5MktJUqFARgLCw0EyV8eLFc+rWrcmff+5l7twZtG7dlD59ugNw6tQJhg7tz2efNadFi4b06dOdf/89pXP8n3/uxdvbkzt3bjFixGCaNfOmU6cv37neG6QmYyNHDuGrr9rx7NlTQL/rLa2b8cKFc/Ts2Zlmzbzp06cbt27d1CkrLi6OadMm0ry5D5991pygoOVs374Fb+/MrymX2XMlJycTGLiQdu1a0aRJfXr06Mzffx/NVlnp+fXXXXzzTXsaN66Hn19bNmxYo7fI8P379xg+fCDNmnnTsmVDxo4dycuXLzMdq5A+pUqDRqMBiUiUBMHQZPkTWbFiRTZu3EjlypUJCQmhWrVqrFq1iipVqujsd+bMGczNzWnVqlWuVbaw0Gg0KBSKfPun0WjyJK4rVy4BULp0mSwdFxS0DI1Gw5QpM+nffwgAL148o0EDHyZOnMbMmXOpWrUaI0cO4cKFc3rHT5s2kdq16zB79gLc3Coya9ZU/vvvYbrnSkhIICBgCM+ePeXHH1dTokTJDOsVERHOkiUL+OabbkybNoeUlBTGjQvQrm8IMGvWVE6dOk7//oMZP34y//33kJ9/3p6l+DN7rmnTJrBnz//RpUs3Zs1aQJkyZZkwYRQnTvyd5bLe9ssvO1iwYDZ16tRj7txF+Pp+xvr1q/nxx6XafYKDXzJgQB+io6OZOHE6AQFjuX37FoMG9SUhIT7LMQuvKV6NTzI2kooFxgXBwGRrwsmaNWuyatWqd+5Tp04d9u7dm61KFWYajYb9+38jNDQ43+rg5FSUVq0+z9ULclRUFOvWreLTTxtSqpRLlo4tX74iY8ZM1NnWocPX2v9Xq9XUqOHJw4cP+O233dSsqdta0759R9q3/wqAKlWqcfr0CY4dO0yPHt/p7BcTE0NAwGBSUlJYvnw1dnbvnt8rJiaGwMBVuLqWA8DMzIzBg/tx/fo1qlWrzsOHD/jnn6NMmDCVVq3aAFCnTn06d/bLUvyZOde9e3f5+++jBASM5YsvOgBQt259Xr58wbp1q/H2bpjpst6mUqnYsGENTZu2YOjQkQDUrl0XpVLJjh1b6Nq1BzY2tuzcuQ2VSsmiRcuwtrYBwMPDg2++8ePPP/fi59cpy3ELqdK63cQab4JgeEQbr5BjSqWSyZPHATBixNgsH1+/fgO9bSEhwcyYMZkvvvClYcM6NGpUl//971+ePHmst2/t2nW1/29ubk6xYs6Ehuou0BwVFcXgwf0AWLp05XuTJABHRydtsgFQtqwrgDbJvXXrBoBOkiKVSmnQ4NP3lp3Vc12+fBGAJk2a6RzXpElz7t69rXO34fvKetujR/8RFRWVbtkKhYIbN66/qsMlatb01CZJAGXKlKV8eTeuXLmctYAFHQoxPkkQDFaWW5QOHDiQ5ZO0aNEiy8cUVhKJhFatPn9nN0gamUyKUql+735ZJZPJcq01SaPRMHv2VG7evM7y5atxdHTMchl2dg46j9VqNWPGDCcuLo7vvvOnRIlSmJubs2bNSoKD9cfDyOVWOo9lMmNSUlJ0tj158pjY2BgGDx6BtXXm7m57e/kdmSz1Ds60ssPCwpDJZHr7pTd+K6fnio2NQSaT6SQpAPb29mg0GuLiYjE3N89UWW+LjY19VW/d5DFtUtnY2Bjtf93cKugdb2fnQExM9HsiFN4lRSGmBhAEQ5XlRGnw4MHaL9nMjHWRSCTcvPn+gaQfE4lEkqlpE2QyKRJJ7idKuWnZssUcOXKI+fOXpPslmhlv52xPnz7hzp3bzJ69gE8/baTdnpycnO16fvJJVTw9a7Ns2SJsbGxo2bJ1tstK4+joiFKpJC4uTic5iYyMzHHZb7O2tkGpVBITE6OT6EVERCCRSPSSxayVnVre2/WOiIgA0E6bYG1tTWRkhN7xkZHhlCpVOtvn/9hpNBoUqrQWJdH1JgiGJltjlExNTWnYsCG+vr5iHbeP2ObNG9i5cxuTJk3H07N2rpWblhCltYQAvHz5gqtXL2d5/NObOnbsTHJyMrNmTcXExITGjZu9/6B3qFgxdQqE48eP4ev7GZDaGnby5PEclZueqlWrA3D06CHatWuv3X706CHc3CpqW5Oyw8WlNLa2dhw9eoiGDRtrtx85chBjY2MqVaqsrcNvv+3WSdYePfqP+/fv0abN59k+/8dOoVSDJvUHlMxIDOQWBEOT5URp3bp17N27l4MHD3L48GHq1atH27ZtadasGRYWFnlRR8EAHTiwn6CgZbRs6YuzcwmuXbuqfa5EiZLZ6n5KU7p0GYoUKcrKlctQq9UkJiawdm0QTk5Fclzvrl17kpyczNSpEzAxMc3WeKI0rq7l8PFpzJIlC0hOTqJoUWd++203KSnJuX7nUvnybjRs2JhlyxaRnJyMi0tpDhzYx7VrV5g9+4cclW1kZESPHr1ZvHgBdnb21KvXgOvXr7Jt2ya++uobbGxsgdRE848/9jJ8+EC6detFSkoya9asoGjRYrRu3TYXovw4vR7ILe54EwRDlOVEqX79+tSvX5+pU6dy+PBh/vjjD8aPH8/kyZNp1KgRbdu2xcfHR2/ySaFwOXv2XwD++msff/21T+e5ceMm5+iL08TEhJkz57Fw4VwmThxDkSJF6d69FxcunNMOoM6J777rR3JyMhMnjmbu3EV4edXJdlljx05i0aJ5LF++BBMTE1q1+gxX13Ls2rUzx/V826RJ0wkKWs7WrRuIiYnBxaUMM2bMxdvbJ8dl+/l1QiaTsWPHNnbv/hkHB0d69uxDt269tPsULVqMZctWsXz5YqZNm4BUakTt2nUYOHAYFhaWOa7Dx0o7NYAYnyQIBkmiyYVJdWJjY9m/fz979+7l/PnzyOVyJk+eTOvWOR8HUhCoVGoiIvTnkVEoUggPf4GDgzPGxiZZLjevBnMbksIY44ABfZBKpQQGBhXK+N5U2OODvI8xOCKBxGQl9tZmWFtm/TqRUxnFl9PrlyGQyaTY2VkSGRlfaN+nhT3GvIzP3t4SI6P3/0DJlWYfKysrvvzyS+zt7VGr1Zw7d44HDx7kRtGCYNCOHTtMcPBLXF3Lk5ycxMGD+7l8+SKzZi3I76oJBYSYGkAQDFuOE6UzZ87w+++/c+DAAeLi4vDy8mLGjBliRu6PnEajQaVSZfi8VCpFKi34Xwzm5hb89defPHnyBKVSgYtLGSZNmo6PTyMgdTJHpTLjv4Poov64qdUalKq0xXAL/udBEAqjbF2lr169yh9//MGff/5JSEgIVapU4fvvv6dNmzY4OTnldh2FAujixfPaCR7T4+v7GePHT/lwFcojderUo06dehk+P3BgPy5ePJ/h8z///BvOzsXzompCAZDWmiSVSjLVBSAIwoeX5USpZcuWPH78mLJly/L111/Ttm1bXFyyf8u2UDi5u3uwZs2mDJ9Pu5OqsBszZjyxsXEZPu/oKH5YfMxSXrU2iqVLBMFwZTlRevToEWZmZhgZGbF//37279//zv0lEgm//fZbpsu/f/8+M2bM4OLFi1haWtKuXTuGDh2Kicm7BxNqNBpWr17Ntm3biIiIwMPDg7Fjx1K9enXtPleuXGHRokXcuXOH6OhoHB0dqV+/PkOGDKFo0aKZrqPwfhYWlri7V8rvauS70qXLFMoBlkLuSBHjkwTB4GU5UfLy8sqLegAQHR1N9+7dKVOmDIGBgQQHBzNnzhySkpKYNGnSO49dvXo1S5cuJSAggIoVK7J161Z69erFnj17KFWqFJC6WKirqytfffUVDg4OPHnyhB9//JGrV6+ya9eu9yZjgiAIuUmhEOOTBMHQZTlR2rx5c5b2z8rsAzt27CA+Pp5ly5Zha2sLpA6GnTp1Kv7+/hm2+iQnJxMUFESvXr3o0aMHALVq1aJVq1asXbuWKVOmAODt7Y23t7f2uDp16uDs7EyvXr24du0aNWvWzFJsgiAIOaHtehNLlwiCwcqznzEpKSn89NNPWbr77Z9//qFevXraJAnA19f31bIQJzM87sKFC8TFxeHr66vdZmJiQvPmzfnnn3/eec60cykUikzXUxAEIadUKjVqdeoPSdH1JgiGK1t3vaWkpHDkyBEeP36MjY0NjRo10rb2JCYmsmXLFjZu3EhYWFiWBno/ePCADh066GyztrbGycnpnfMypT3n6uqqs71cuXJs3LiRpKQkzMzMtNtVKhUqlYonT54wf/58KleuTK1atTJdz/TI0rnQqdXZX44gbSUDiQRyPiWoYSrsMYr4Cr68jDFtIVyZkRSpNH+WLslMfEZGknSvbwVB2p2EhfmOwsIeoyHEl+VEKTg4mG7duvH48WNtt5qZmRkrVqzA2NiYESNGEBwcTNWqVZk4cSItWrTIdNlvr4yexsbGhujo6HceZ2Jigqmpqc52a2trNBoN0dHROonSt99+y4ULFwCoUqUKq1atytF8NlKpBDs7/SUckpKMCAuT5uhCU1jf/G8q7DGK+Aq+vIhRmZh6/TQ1Mcr3RCS9+NRqCVKpFBsbC53rZ0FkbZ39RaMLisIeY37Gl+XsYPHixTx9+pTvvvsOT09Pnj59yvLly5k4cSKRkZG4ubkxf/58atfOvdXkc9vMmTOJjY3l0aNHrF69mp49e7J9+3bkcnm2ylOrNcTEJOhtT0lJRq1Wo1Jpsnznk0SSevFSqdSF+td6YY5RxFfw5WWMSSmp45NkRvm3DMy74lOpNKjVaqKjE0hMzHjSVENmZCTF2tqcmJhEVKrCefdpYY8xL+OztjbPmyVMTp48Sfv27RkxYoR2m6OjI0OGDKFRo0b8+OOP2Z5x2dramtjYWL3t0dHR2NjYvPO4lJQUkpOTdVqVYmJikEgkesemddFVq1aN+vXr07hxY3766Sd69+6drXoD6V7oVKrsX1nTLlqF9QsI3h+jt7cn/fsPoXPnrgD8+usu/v77CPfv3yMpKYkyZcrStWsPPv200YepcCa9ePGcr776nOnT59C8eYt047tw4RyDB/djzZpNBXYahay8R//8cy+zZk3l998P6YxBNHR5+TlUKNIGcudfa1Jm4svODz1Do1KpC3wM71PYY8zP+LL8CQ0PD6datWo629LmKurQoUOOlqVwdXXVG4sUGxtLaGio3vijt48DePjwoc72Bw8eULx48Xc2Gzs6OlKsWDEePXqU7XoLH8amTesoVsyZESPGMHPmXMqVc2Ps2AD27fs9v6uWZRUrurNy5XpKly6b31UR8oFGoxFzKAlCAZHlFiWVSqU3Fiht/qHsdl2l8fHxYeXKlTpjlfbv349UKqVBgwYZHlezZk3kcjn79u3D3d0dSL2L7cCBA/j4+LzznC9evOD58+fauZYEw7Vu3Vad1ggvr7q8fPmc7ds34+v7WbbLTU5OwtT0w47BsLSUU6XKJx/0nILhUKo0qWM8JSJREgRDl60RzM+ePeP69evax2ndZY8ePUp3MHblypUzVW6nTp3YvHkzAwYMwN/fn+DgYObNm0enTp105lDq3r07z58/5+DBgwCYmpri7+9PYGAg9vb2VKhQge3btxMVFaXTnTZp0iTs7Oz45JNPkMvlPHz4kPXr1+Pg4ICfn192/hQflZkzp3Dr1g2GDRtFYOBCnjx5TNmyrowYMRZ3dw8gdU6rVauWc+jQAWJjY3BxKUPPnn1o2LBxuuUsW7aIx48f6ZWTnvS6bNzcKnL79q1Mx7B2bRA7dmxhyZIVLFnyA3fv3ua7776nc+eurFgRyOnTJ3jx4jmWlnKqVavBoEHDcXR01B4/cGBfLCws8PX9jFWrfiQsLBQPj8qMHj2BEiVKZnje27dvMWLEQOrX/5QxYyZy6dIFva43b29Pvv9+EElJSfz66y7UahUNGvgwbNgozM1fD2S8fPkSixfP49Gj/yhVyoWBA4eyfPlS3NwqZHr9vMye6/79eyxfvpgrVy5hZGSEl1cdBg4cTrFixbJc1ttiYqJZtmwxJ0/+Q2JiEhUqVKRfv4FUr647n9mvv+7ip5+28vLlCxwcHPnss3Z069arQC+qrHg1f5KxkRSJJH/ueBMEIXOylSgtWbKEJUuW6G2fOnWqzmONRoNEIuHmzZuZKtfGxoaNGzcyffp0BgwYgKWlJX5+fgwbNkxnv9QB0rqDC/v06YNGo2HdunXaJUzWrl2r01JUtWpVdu7cybZt20hJScHZ2RkfHx/69euHnZ1dZsPPMY1Gg0b9/nmb1BIp6jwYnCeRGmf74hwREc6SJQvo0qUHcrmcoKBljBsXwM6de5DJZEybNoEzZ07Tt29/XFzKsH//H0yYMIrZsxfg7d1Qr5xu3Xpibm6pV05mXb16idKly2QpBoVCwdSpE+jYsTP+/gOwtk4dwxYZGUHXrj1xdHQiKiqSHTu2MnBgX7Zs2alTp7t37xAZuZl+/QahVqsIDFzEtGkTCQpan+75rly5xKhRQ2nVqg1DhgS882+/a9dOqlWrwfjxU3jy5DE//rgEOzt7vv9+EABhYWEEBAyiQgV3pk2bTVxcHAsWzCE+Pg43twpZ+ju871zBwS8ZMKAPJUqUZOLE6aSkJLNq1QoGDerLxo3bsbCwzHRZb1OpVIwYMZgXL57x/feDsLNz4JdfdjBs2ABWrFinTZh/+WUHixcvwM/va+rX/5SrVy+zfv1q4uLiGDhwaJbiNSRp3W5ijTdBMHxZTpRmz56dF/XQKleuHBs2bHjnPunNDi6RSPD398ff3z/D4/z8/PK95Uij0RB8dz0p8U/zrQ6mlqUo4tYjW8lSTEwMgYGrcHUtB6RODTF4cD+uX7+GpaUlf/99lICAsXzxRep8WHXr1uflyxesW7daJ1FKK6dCBTeUSrVOOdWqVc9UXQ4c2M/Vq1eYNWtBlmJQKpX07dufpk11p64YN26y9v9VKhVVqlTlyy9bc+HCOWrXrqt9Li4ulnXrtmqT68TERGbNmkpISDBFiujOHv+//51h9Ojh+Pl1wt9/wHvr5uDgyOTJM4DUv92dO7c4duywNuHYuXMrRkZGzJ+/WJuoODuXYMCA77L0N8jcubahUilZtGiZNpmsUMGdb7/9ij//3IufX6dMl/W206dPcPPmdX74IZA6deoBUKdOPb7++gs2b17HzJnzUalUbNiwhqZNWzB06EgAateui1KpZMeOLXTt2qPALq6sEOOTBKHAyHKi9OWXX+ZFPT4qEgpuU7ujo5M2SQIoWzZ1IH1oaDD37qXOddWkSTOdY5o0aU5g4EISExO1XTHvKicz7t27y4IFs2ndui0+Po2yHEe9et56206fPsnGjWt5+PA+8fHx2u1PnjzSSZTKl6+g0wJZpkzqgOyQkBCdROnUqRMcPnyAnj370LVrz0zVy8urjs7jMmXKcvjwAe3jmzdvUKOGp05rTrVq1bWJTFa871yXL1+iZk1PnbJLly5D+fJuXLlyWSdRel9Zb7t8+RKWlpbaJAlAJpPRsGFjDh78C4BHj/4jKioq3ffT5s3ruXHjOvXqZTx20ZClGMAdb4IgZE72Z1kUskUikVDErUemut5ksryZXyUnXW9vD9iXyYyB1NnaY2NjkMlkel/a9vb2aDQa4uJitYnSu8p5n5cvXxAQMBgPj8qMGjU+yzGYmZlhYWGhs+3mzeuMGTOcTz9tyLffdsfW1v5VK2UPkpN162RlZaXz2Ng4re7JOttPnjyOmZmZXsvVu8jl+mW/+TcJDw+jZEn92e6z03X8vnPFxsak251nZ+dATIzuBLDvK+ttsbEx2NnZv7PstLGPb+9nb2+vLaMg0mg02lm5jcUab4Jg8ESilA8kEgkSI5P37ic1kiLVFJx5MaytbVAqlXozrEdERCCRSPS+TLMjKiqK4cMHYmdnx6xZ87I1o3p6SeI//xxDLpczbdoc7SDhly9f5KiugwYNY+/eXxkypD/Ll6/S65bLDgcHR6KiIvW2R0bqb8spa2trIiMj0jlXOKVKlc6zstMS7bT30NuxRUSkHmdlpX/jSEGgUKpBk/o+lBkV3NZlQfhYiHZfIddUrVodgKNHD+lsP3r0EG5uFd95B1RmJCQkEBAwGIVCyfz5S7G0zNl0FG9KTk5CJpPpJFEHDuzLUZlmZmYsWrQUGxsbhgz5noiI8JxWEw+PSly4cJaEhNddg5cvX9Rr4ckNVatW5/z5s8TEvG65efz4P+7fv0fVqtXecWTmyo6Pj+d///tXu02pVPLPP8e0Zbu4lMbW1k7v/XTkyEGMjY2pVClzd9MamtcDucUdb4JQEIgWJSHXlC/vRsOGjVm2bBHJycm4uJTmwIF9XLt2hdmzf8hx+ePHj+Tu3duMHTuJly9f6LT45HROIi+vOuzcuZ1Fi+bh49OYa9eu8Ndff+a0ylhaylm4cBmDB/dj6ND+BAYG5WgAcseOXdi9+xdGjhzKN990JS4ulvXrV2Nra5vrX7odO3bmjz/2Mnz4QLp160VKSjKrV6+gaNFitG7dNkdl16vnjYdHZaZNm0i/fgOxt3fgl19+Ijw8jK5dewFgZGREjx69Wbx4AXZ29tSr14Dr16+ybdsmvvrqmwI8kPvV1ABifJIgFAgiURJy1aRJ0wkKWs7WrRuIiUmdR2nGjLl4e7974s/MOHv2DAAzZkzWe+7EiXM5KrtePW++/34Qu3bt5M8/9/LJJ9WYN28x33zTPkflQmoX0qJFyxk0qC/Dhg1k6dKV2S7L0dGRBQuWsnjxfCZOHE2JEiUZMiSARYvm5XjC17cVLVqMZctWsXz5YqZNm4BUaoSXV20GDRquM5g8O4yMjFiwYAnLly/hxx+XkpSUSIUK7ixcuExnLi0/v07IZDJ27NjG7t0/4+DgSM+efejWrVdOw8s3KYpXLUpifJIgFAgSjaYwryb2YahUaiIi4vW2KxQphIe/wMHBGWPj949JelteDeY2JIU9xg8R35Mnj+nSxY+xYyflaIby7Cjsrx/kfoxPQ+JQqtQUtbfA3DT/f6tmFF9Or1+GQCaTYmdnSWRkfKF9nxb2GPMyPnt7y7xZFFcQhPy1cuUyypUrj6OjE8+fP2Pz5vU4ODjSsGGT/K6a8B5qtQalKq1FSXS9CUJBIBIlodBQq9Wo1Rn/4jAyMioUg2cVCgUrVgQSGRmBqakpNWrUon//IdopD5RKZYbHSiQSjIxEl09+SZtoUiqVZOqXrCAI+U8kSkKhMXv2NPbt+z3D55cuXUnNmp4fsEZ5Y9CgYQwaNCzd5168eM5XX32e4bHVq9dk2bJVeVU14T1SXg3kFkuXCELBIRIlodDo1asvHTp0zPB5F5eczf1TEDg6OrFmzaYMn397ok3hw0oRS5cIQoEjEiWh0HB2Lo6zc/H8rka+MjY2xt29Un5XQ8iAQiHGJwlCQSM+rYIgCB+ItutNTA0gCAWGSJQEQRA+AJVKjVqdOhuL6HoThIJDfFoFQRA+gLTxSTIjKVJpwb/7UhA+FiJREgRB+ADSpgYwNhaXXUEoSMQnVhAE4QN4PT5JXHYFoSARn1hBEIQPQCHWeBOEAkkkSkKmzZw5ha5dM56n6EPz9vZk27bN2scDB/Zl1Kih+VchQciARqMRcygJQgEl5lESCo0RI8aIZSEEg6RUadBoNCARiZIgFDQiURIKjbJlXfO7CoKQLsWr8UnGRtJCsd6gIHxMRKKUDzQaDYpX86m8i1qCdqXx3GQsleToYn369El+/HEJz549pUwZV4YPH02VKp8AsG/f7/z2227+++8hGo2G8uXd6N9/MJUqVdEeHxISTGDgIi5dukB8fBwODo58+mlDBg8eod3nv/8esnJlIBcvnkelUlGjRi2GDh1JiRIlM6zXwIF9sbCwYN68xQCsXRvEjh1bWLlyPQsWzObOnVsUL16CgQOHUadOPZ1j//xzLz/9tJUnTx5jbW2Dr+9nfPddP7GArJAr0rrdxBpvglDwiETpA9NoNATdesrjuKR8q0NpuRl93UtmK1kKDw9n4cK59OrVFysrK7Zs2ciIEQPZsWM3dnb2vHz5glat2lCiREkUCgWHDv3FwIF92bBhu3attRkzJhMWFsrQoQE4Ojry/Plzbt++qT3Hs2dP6devF66u5Rg3bgpSqYRNm9YxZMj3bNu2CxMTk0zXV6lUMm3aBPz8OtGjx3ds3bqRCRNG8csve7GxsQVgx44trFgRSMeOnRk4cCj//fcfq1b9iFqt5vvvB2X5byQIb1OI8UmCUGCJRCkfFOSG95iYaKZPn0OtWl4AVK9ei/bt2/DTT9vo128gPXv20e6rVqvx8qrDzZvX2bfvd/z9BwBw8+Z1/P0H0LRpC2QyKdWqqfH1/Ux73Pr1q7G2tmbRouWYmpoCUKVKNTp2bMfvv++hffuvMl1fhUJBv34DqVfPG0hdGPerrz7n339P0bJlaxIS4lm7dhWdO3fT1s/Lqy7GxjICAxfRuXNXbUIlCNmVohBTAwhCQSUSpQ9MIpHQ171kprreZDIpSqVhdb3J5XJtkpT22NOzNjduXANSu8yCgpZz7doVIiMjtPs9efJI+/8VKrizffsWjIxk1K1bF2dn3e60s2f/pWnTFhgZGaFUKgGwsrKiQoWK3Lp1I0v1lUqleHrW0T52di6OqakpISEhAFy9eoXExAQaN26qPReAp2cdkpOTefDgPjVq1MrSOQXhTRqNBoUqrUVJdL0JQkEjEqV8IJFIMDF6f6IiM5IifX8+9UHZ2trpbbO3t+fRo4ckJMQzfPhAbG1tGTRoGEWLOmNqasKcOTNISUnR7j916mxWrVrOqlU/8sMPc3BxKY2//wAaNmwCQFRUFDt3bmfnzu1655LJjLNUX1NTU4yNdY8xNjYmJSUZgOjoKAB69fo23eNDQoKzdD5BeJtCqQZN6udelonPvSAIhkUkSkKWREVF6m2LiIjAwcGRa9euEhISzNy5i3Bzq6B9Pj4+Diiifezo6Mi4cZNRq9Xcu3ebdetWM2nSWLZt20WJEiWxtrahXr0G6XaxWVhY5Go8VlbWAMycOZ+iRYvqPe/sXDxXzyd8fF4P5BZ3vAlCQSQSJSFL4uLiOH/+rLb7LS4ujnPn/kf79l+RnJw6QP3NFpyrVy/z4sXzdG/dl0qlVKpUmT59+nPixD88e/aUEiVK4ulZm4cP7+PmVjHP7zqrUqUqZmZmhIYG07Bh4zw9l/Bx0k4NIMYnCUKBJBIlIUusrW2YM2e6zl1vGo2Gjh2/AcDc3IKFC+fy7bc9CA0NYe3aIJycXrcmxcXFMXz4QFq2bI2LS2k0GhU//bQDudyKChXcAejd25/vvuvG8OGD+Pz/27vz8KjKs/Hj33NmTWbJRoAQCEuQsK8qIIqK5VWqgqIidcNXimJxQ/lZta1oa1+sfdtacQXxFRUVLCgugNqqqKC2CoKyCQQCBLIvk0lmP+f3R5IhC4EASWYyuT/XlStz1rmfWe95zrNMvpLk5GRKSorZvHkTw4YNZ+LES1qsPA6Hg5kzZ/PsswspKChgxIhRGAwGDh8+xBdffM4f//gEVqu1xe5PdDx+mbpEiHZNEiVxUlJSUrj99rvC4yj17t2Hv/51IcnJKQD84Q+P88wzT/LAA/fRo0cG/+//PcSyZUvDx5vNZjIz+7Jy5XLy8/OwWKz07z+Av/3taRITEwHo3r0HixcvZfHi5/jrXx/H4/GQktKJYcNGkJl5RouX6Re/uIHU1FSWL1/GypXLMRqNpKd355xzzsNolLeIOD0yNIAQ7Zui63qUNRduf0IhjZKSykbrAwE/xcVHSElJw2Rq/tg/tVqr11s0ifUySvnav9Mpo6bpHMivAKBHZ3tUTrHTVPlO9/MrGhiNKklJNkpLK2P2dRrrZWzN8iUn25r1noy+d60QQsSI2tokVVWiMkkSQpyYvHOFEKKV+GsacsvUJUK0X5IoCSFEK/FL+yQh2j159wohRCsJhHu8yUetEO2VvHvbgLSXF6JjCl96a4dDA8jnlhDVJFFqRbWDJdZOlyGE6DhCIQ2tZk7H9njprfZzy2CQITJExxZ174C9e/fy2GOPsXnzZmw2G1OmTOGee+7BbD5+91Rd11m8eDGvv/46JSUlDBgwgAcffJDhw4eH99m4cSNvvfUWW7Zsobi4mPT0dKZOncqMGTMazQfWElTVQFycHbe7etoPs9lyUlMYaJpCKBTbv+pivYxSvvbvVMvo9QdBD2JQFUKhAKFQKwTXAhqWT9d1/H4fbncpcXF2VLX9JXlCtKSoSpTKy8uZMWMGvXr1YuHCheTn5/P444/j9Xp5+OGHj3vs4sWLeeqpp5g3bx5ZWVksW7aMW265hdWrV9OjRw8A3nzzTbxeL3fddRdpaWls2bKFhQsXsnfvXhYsWNAqZXI6kwHCydLJUFUVTYu9cTHqivUySvnav1Mto8cXotIbwGw0UIyrFSJrGU2VLy7OHv78EqIji6oBJ1944QWef/55Pv300/AozcuXL+fRRx/l008/PeakpQA+n49zzjmH66+/nnvvvRcAv9/PJZdcwvjx43nkkUeA6slbk5Prv/Gff/55nnzySTZu3NhoW3M1NeBkXZqmEQoFm31Og0EhISGe8vKqmP3FHutllPK1f6dTxrc/38u3uwq5YEQ3Jp6Z0UoRnp6mymcwGGOiJinWB2OE2C9jNAw4GVU1Sp9//jljx44NJ0kAkyZNYv78+WzYsIGpU6ce87hNmzbhdruZNGlSeJ3ZbGbixIl8/PHH4XXHSoQGDBiArusUFhaecqLUHKqqoqrNH93WaFSxWq14PKGYfPFD7JdRytf+nU4Zd+dWUuQK0inREbUjW3eE51CI0xVVPxmys7Pp06f+LPNOp5PU1FSys7OPexzQ6NjMzEwOHz6M1+tt8thNmzZhNpvp3r37aUQuhBBHabrOoaLqWub0TrYIRyOEOB1RVaPkcrlwOp2N1ickJFBeXn7c48xmMxaLpd56p9OJruuUl5cfcwb4/fv388orrzB9+nRsttP7MDO2cK+W2urAWJ72INbLKOVr/061jIVlHnz+EAZVIb2zHWOUPkax/hzGevkg9ssYDeWLqkSpLbndbu688066d+/O3LlzT+tcqqqQlNQ6vxqdzrhWOW80ifUySvnav5Mt4+7DNRPhdnGQ2snRGiG1qFh/DmO9fBD7ZYxk+aIqUXI6nVRUVDRaX15eTkJCwnGP8/v9+Hy+erVKLpcLRVEaHev3+5kzZw7l5eUsX76c+Pj404pb03RcrqrTOkdDBoOK0xmHy+UhFIrNtgOxXkYpX/t3qmXcua8IgLTkeEpLj9/RI5Ji/TmM9fJB7JexNcvndMa1v8bcffr0adQWqaKigsLCwkbtjxoeB7Bv3z769+8fXp+dnU23bt3qXXbTNI158+axbds2li1bRlpaWovE3loNIUMhLeYbWcZ6GWOpfL6QRoHHT4HHR6EvgGoy0NdmpZfNinoSY4S1Nyf7HB7IdwPQrVN8u3juY+k1eiyxXj6I/TJGsnxRlSiNHz+e559/vl5bpXXr1qGqKuPGjWvyuJEjR2K321m7dm04UQoEAnz00UeMHz++3r61Qw0sWbKErKys1iuMEO1Y3YQo3+OnwOunwOOnzN94iIvPAKfJwLAUJyNSHHSNtzTap6PJLaxOlNJT7RGORAhxuqIqUZo+fTqvvvoqc+bM4bbbbiM/P58nnniC6dOn1xtDacaMGRw+fDjc9d9isXDbbbexcOFCkpOT6devH2+88QZlZWXMnDkzfNzzzz/Pm2++ycyZMzGbzXz//ffhbX379sVulw810bHUTYgKvP7qpKiJhKiWw2Qg1WomzWbBYDLyn8MluAIhvsgr5Yu8UtLiLYxIcTAsxYHDFFUfMW0iGNI4Ulx9Kb679HgTot2Lqk+xhIQEli5dyh/+8AfmzJmDzWbj6quvbtTYunrwxvrzAcyaNQtd13nppZfCU5gsWbIkPCo3wIYNGwBYsmQJS5YsqXf8K6+8wujRo1upZEJE1ukkRF3izHSOs9A5rvp2fM0Er7UDwV3SLYltxW42F7nYVV7JkSofR6p8rDtYRN+EeEakOBmYZMMUAwMYNkd+SRUhTcdqNpCS0Li3rRCifYmqkbnbq+aMzH2yYn20VYj9MkaifK2REDXlWOWrCobYWlLB5qIKDlYeHb/MoqoMTrYzIsVBL0dcu2nPdCrP4Tfb83nh3W1kpjv5zY1ntnKEp0feg+1frJdRRuYWQpyScELkrdOO6AQJkd1oCCdBnWuSouYkRCcj3mhgTOdExnROpMjrZ3NxBd8XuSj1B/muyMV3RS4SzUaGpzgYnuKkc1x0jlh9OnKLatondZJL+ULEAkmUhIhi0ZoQNUcnq5mJ6Slc1C2ZHLeXzUUufih1U+YP8tmRUj47Ukp3m4URKU6GJjuwmdo2vtZyqKC6drl7qrRPEiIWSKIkRBRozwnRiaiKQm9HHL0dcVzeM5UdZZVsLnKxu7yKQ5U+DlUW8sHBQrISbIxIcdA/0YaxHbdnCtcoSY83IWKCJEpCtCFfSKPQ4ye/JiEq8FS3I4qFhKg5TKrK0GQHQ5MduANBthRX8H1xBblVPnaUVbKjrBKrQWVosp0RKU4y7FaUdtKeCcDrD1JYVt02K11qlISICZIoCdFKSn0Bth8qJrvQRX6Vr0MlRM1hNxkZ1zWJcV2TyPf4+L6oOmkqDwT5d6GLfxe6SLaYGJHiYHiKgxRr9LdnOlxUPSyA02bGGR/98QohTkwSJSFawUG3l8U7DxE8RqfS2oToaFIU2wlRc3SJs3BxDwsTu6eQXeHh+yIXP5a6KfEF+NfhEv51uISedivDU5wMTbYTF6WP1aGagSalfZIQsUMSJSFamDcUYnl2HkFdJ91upafdSqrFJAlRM6iKQl9nPH2d8UwOaWwvc7O5qII9ripy3F5y3F7eP1DIgMTq9kxnJNgwqtFzaS63sLoht/R4EyJ2SKIkRAvSdZ139hdQ4guQZDFy/9h++Ny+mBzfpLWZDSrDU5wMT3Hi8le3Z9pc7CLP4+fHUjc/lrqJNxoYmmxnZIqTdJsl4u2ZpEZJiNgjiZIQLWhTcQVbS9yowHVndCPeZMSHL9JhtXtOs5Hz0pI4Ly2JI1U+Nhe52FJSQUUgxNcF5XxdUE4nq4mRKU6GpzhItJgiEmduUU2NkvR4EyJmSKIkRAsp9Ph5N6cAgJ+lp9DTERfhiGJTWryFtIxULu7Rib2uKjYXVbC9zE2RN8BHucV8lFtMb0ccI1IcDE62YzW0zaVOV5UfV6UfgHSZ402ImCGJkhAtIKhpvJmdR0DT6eOIY3xaUqRDinkGRaFfgo1+CTa8oRDbStxsLq4gu8LDvpq/d3MKGZhkY0SKk74J8Rha8dJcbfuk1EQrFrO0QxMiVkiiJEQLWHeomCNVPuKNBqb16dpu5jKLFVaDgVGpCYxKTaDMF+D7mvZMhd4AW0vcbC1xYzcaGJbiYEQnJ2lx5hZvz3S0fZJcdhMilkiiJMRp2lnmZmN+GQBX9+6C0yxvq0hKtJi4oFsy56clkRtuz+TGHQyxIb+MDflldIkzM6KmPVNLPV+5hbUjcstlNyFiiXyiC3Eayv1B/rEvH4BxXRLpnyhfktFCURS626x0t1n5eY9UfnJVsrmogh1lleR7/Kw7VMSHh4rIdMYzIsXBoCQ75mbMJN6U2ktvUqMkRGyRREmIU6TpOiuy86gKanSLt3Bx95RIhySaYFAVBiTaGZBoxxMM8UOJm83FLnLcXva4qtjjqmJ1TgGDkqqnTunjjDupy6earnOotsebNOQWIqZIoiTEKVp/pJR9FR7MqsL0zK7teiLXjiTOaODszgmc3TmBEm+AzcUuNhdXUOILsLm4gs3FFThNRoanODizSwJJSSdOfErKvfj8IQyqQpfk+DYohRCirUiiJMQpyKnw8K/cYgAm9+xMp3YwD5loLNlq4qL0FCZ0S+ZgpZdNRRVsLanAFQjyeV4pn+eVMjyvlMu6dyL+OInwoZrLbmkp8RhP4/KdECL6SKIkxEnyBKunKNGA4ckORqQ4Ih2SOE2KopBhjyPDHsdlGZ3YWVbF5mIXu8or+T6/nF1FFVyakcqIFMcxe8vlFkmPNyFilSRKQpwEXddZtb+AMn+QZIuJyb1SIz5thmhZRlVlcLKdwcl2Cn1+Vu0vJMdVxT/25bO1pIIrenZuNPJ3bY2S9HgTIvZIHbEQJ+E/hS62lbpRFZjep2ubjfosIiPNZuXBc7KYlNEJo6LwU3kVf//xAN8UlKPpeni/o0MDSI2SELFGEiUhminf4+P9A4UAXJzeie52a4QjEm3BoCpcmJ7CHYMyyLBZ8Wkaq3MKeGlXLsVeP8GQxpHiKgC6S483IWKOJEpCNENA03hjbx5BXecMZzzjuiZGOiTRxjrHmbl1QHcu7dEJk6qQXeHhqW0H+Gh/ISFNx2I2kJIgybMQsUbaKAnRDGsOFFHg8WM3Gri6TxeZoqSDUhWFcV2T6J9oY9X+AvZVePiyxEXyqM4kFPulvZoQMUhqlIQ4gR9L3HxTWA7ANX264DDJ74uOLsVqZmZWOlN6dkbVwZxowdvHzvojJYTqtF0SQrR/kigJcRxlvgCr9ldPUTK+axJnJEgbFFFNVRRGd04gJdeLr8iDrih8eKiY57cfJK/KF+nwhBAtRBIlIZoQ0nWWZ+fhDWl0t1mYmC5TlIjG8vIrKN1SxDkOO1aDSm6Vj2e2H+BfucUENaldEqK9k0RJiCZ8eriEHLcXi6oyvU8aBlXan4j6vP4ghWVeAMZnpHDP4J4MSLQR0uFfh0t4dvsBciu9EY5SCHE6pLGFEMeQ7ari08MlAFzRqzPJVtMJjoh9uq7jqgpQVO6huNxLSYWPpIQ4zurXqcM2bj9cVD0sgNNmxhlfPY3NDX3T+KHEzbsHCsnz+Hlu+0HO7ZrERenJmGQ+QCHaHUmUhGigKhhiRXY+OjCqk5NhHWSKEl3XqagKUFTuDSdDReG/6mV/UGt03PqMRO6YOpR4a8f7ODlUWDt1ydG2a4qiMDTFQR9nHO8fKGRriZvP80rZXubmql5d6OmIi1S4QohT0PE+2YQ4Dl3XWbkvH1cgSCericsyUiMdUovRdZ0KT6BOAuShqNxLcbmXwrKmE6G6FCDRYaFTgpVOCXFs2VvEzgNlPPHGJu6dNhynrWNNDpxbO3VJp8YjcttNRqZnpjE02c3qnAKKvAEW7TzE2C6J/Fd6CmaZPFeIdkESJSHq+LqgnB1llRgUhemZaVja0ZeZruu4PYFw8tMwGSoq9+ILhI57jtpEKCXBWpMMVSdEtbeTnVaMNY+J0ahSUhng4UVfcSDfzYLXvuO+a4fTKbHj1Jgcq0apoYFJdno74vjgYCGbiirYmF/GzrJKruzVmUxnfFuFKoQ4RZIoCVHjSJWPtQeLAJjUoxPd4i0Rjqg+Xdep9Aark58yb50EyEORqyYR8h8/EQJItJvDyU84IUqsXk52WDEZm58cZnZP5LczzuRPr20iv9TDgmWbuPfa4aR3kKk8cotqJ8M9/hxvcUYDV/fuytBkB2/vL6DEF2DJrlzOTk3gkh4pMmegEFFMEiUhAH9I4829RwjqOv0TbIztnNDmMei6TpUvWJMEecLtg4rr1Ax5m5EIJdjN9WqCUhKspNbcTnZaMBlb9ku5a3I8D904ir8s/57DRZU8/tp33DNtGJnd2v4xbEuuKj+uSj9AsxPDfgk27h6cwYcHi/mmsJx/F5azq7y6dqmfjNElRFSSREkI4P0DhRR6AzhNBq7q3aXVpqKo8lZfGiss81JcJxkqKvdS7PLg8TUjEbKZ69QGHb0s1ikxjpRWSISaI8lh4YHrR/LkW1vIPuzif9/4njumDmFQ7+Q2j6Wt1LZPSk20YjE3/zG3GgxM6dWZwcn2cO3Syz8dZmQnB5f2SCUuAs+fEKJpkiiJDm9rcQXfFrlQgGv6dMVmOrUvKq8/SJnbT7nbR5nbj6vKT6UvxKH8CgrLqpMijy94wvM4beZw8tMwGUpxWjGfYnytzR5nYt704Tyz6ge27S/lybe2cOvkQZzVv3OkQ2sVR9snHf+yW1MynfHcNSiDj3OL2ZhfxqaiCnaXVzGlZ2cGJp3aOYUQLU8SJdGhlfgCvJ1TAMAFacmNGtfquo7HF6K80kdZhY+ySj/lbj9lbh9lbt/R25X+ZrUPAnDGm0ipWxOUYCUlIY7UxOrG0pYoTYSaw2o2ctfVw1j8/na+3VnA8+/8SOUlWVwwPD3SobW43JpEKf04DblPxGxQuTQjlcFJdlbuz6fIG+C1PUcYkmzn8oxU7DKvoBARJ+9C0SHpuo7LE+DV3YfxhTSSFBX/oQpe31kcrhWqTYJO1GW+LovZQKLNTKLdQpLDQlpnBw6rgWSHpTo5cp7cZZr2yGRUmT15EK9ZjXz2/WFeWbeLSk+An4/p2WqXNCOh9tLbqdYo1dXTEcedgzL4JLeEL/JK+aHEzV6Xh8szUhmabI+px02I9ibqEqW9e/fy2GOPsXnzZmw2G1OmTOGee+7BbD7++Cy6rrN48WJef/11SkpKGDBgAA8++CDDhw8P71NSUsKzzz7Lli1b2LFjByaTic2bN7dyiURbqu0if7TWx3+05qeybi2QH2svB/ZeTrSAxq5/H2aHt+kaoTiLkUS7mQSbmUSHhUSbhQR7dUKUaDeTYLeQYDMTZzn6ljIaVZKSbJSWVhI8iWQrFqiqwo0XZ2GLM/HBVzmsXJ9NRVWAaRP6xsQo3pquc6i2x1sL9fAzqSoX9+jE4GQ7K/flk+fxszw7j60lNqb07IzTHHUf10J0CFH1zisvL2fGjBn06tWLhQsXkp+fz+OPP47X6+Xhhx8+7rGLFy/mqaeeYt68eWRlZbFs2TJuueUWVq9eTY8ePQDIz89nzZo1DB06lMGDB7Nr1662KJZoAVrNqNHl9ZKf6kteZRU+yiuPtg0KNWMiUnOSBVvP6hG3jbmV9O/qJMFmIdFhPmYS1J4vh0WKoihcdX4m9jgTyz/Zw0f/OUilJ8DNP++PoZ1P5VFSMxSDQVXoktyyYyGl26z8amAG64+U8NmREnaUVbKvIodLM1IZmeKQ2iUh2lhUJUpvvvkmlZWVPP300yQmJgIQCoV49NFHue222+jSpcsxj/P5fLzwwgvccsst3HzzzQCMGjWKSy65hCVLlvDII48AkJWVxcaNGwFYuHChJEpRIKTplFX4KCr31GsIXS8hqqzuht2cBKiWPc4UTnISa5KehJpLYpY4E+8Wl1AZ0jg71ckVZ53RiiUUF5+dgT3OxP+t2cmGH/Oo8gWZPWVQRHrntZRDNZfd0lLiwwNwtiSjqnBRegqDkqprl3KrfKzcl8/W4gqu7NWZRIvMPShEW4mqROnzzz9n7Nix4SQJYNKkScyfP58NGzYwderUYx63adMm3G43kyZNCq8zm81MnDiRjz/+OLxObee/YmPJ19vyWPV5NiUuL83NfxTAEW+qTnrsDWp9GtQGNfXlpek6r+w+TGVIo3OcmZ/3iJ0pSqLZuCFpxFuNPPfONjbvLuKvy7dw19VD612qbE9yi06vx1tzdY23MHtgDzbklfLP3BJ2u6p48sccJvXoxFmpCTFxGVOIaBdVn1LZ2dlcddVV9dY5nU5SU1PJzs4+7nEAffr0qbc+MzOTpUuX4vV6sVqtLR+wOGmaprNy/V7WfnMgvE5RqrvEJ9osjWuBwsmQBUe86bR/vW/ML+On8iqMisL0Pl1lvq02NOKMVO67dhh//8dWdh0s44nXNzN32rB2OT9cbY3S6fR4ay6DojA+LZkBidU94w64vazOqZ5sd2qvzqRY29/jJ0R7ElWJksvlwul0NlqfkJBAeXn5cY8zm81YLPWnnHA6nei6Tnl5easnSsaTmPahOQw1X+CGGPoir/IGee6dH9iypxiAKef2ZupF/VA1DV1v/mW1U3XI7eXDQ9VTlFzeqzPdna07J1ksPod1nUr5BvVJ4aEbR/HnNzaTk1/BgmWb+PV1I6J2frimyni4piF3RldHi7/3m5LmsPKrwRlszCtj7YFC9lV4eGrbAS7J6MS4rkmnVLskr9H2L9bLGA3li6pEqb1SVYWkpNb5Zels5S/ztnK40M1jr3zLoQI3ZpOBu68dzvgR3dvs/r3BEG9u2U9Ih5FdEpnUv1ubNYqNleewKSdbvqQkG3++y8HvXthIfkkVf3z1O35/61gyujb+kRQt6pYxGNI4UlydKA3q25mkpLad2PbyZDtjeqWy9IccdhW7eW9/IdvLqpgxtCdp9lP7QSiv0fYv1ssYyfJFVaLkdDqpqKhotL68vJyEhKbnjXI6nfj9fnw+X71aJZfLhaIoxz22JWiajstV1aLnNBhUnM44XC4PoVD77lr+Y3YxT6/6gSpvkGSHhbunDaN3mhOXy9NmZXxz9xEKqnwkmo1M7tGJsrKWfb6OJZaew2M5nfLFGxV+c+Mo/vz6ZnKLKrn/6S+ZN304menRNT/cscp4qNBNMKRjNRswoVFaWtnmcRmBW87oxjcJ5XyQU8jeskp+/8UOJvZIYXy3ZAzN/BEgr9H2L9bL2JrlczrjmlVTFVWJUp8+fRq1RaqoqKCwsLBR+6OGxwHs27eP/v37h9dnZ2fTrVu3Nmmf1Frj5IRCWrsdg0fXdf757SHe/GQ3ug6Z6U7uuHIICXZLvTK1dhk3F7nYVDNFybQ+XTErSps+pu35OWyOUy2fM97Mr68fyd9WbGHfERePv7aJO64awqBe0Tc/XN0y5hyp/jGX3slGKKQDrX/ZuClnpjjpa4/jnZwCfiqvYu2BIrYWVTC1dxfS4i0nPkENeY22f7FexkiWL6ouao4fP56NGzficrnC69atW4eqqowbN67J40aOHIndbmft2rXhdYFAgI8++ojx48e3aszi2AJBjf9bs5M3/lWdJJ07JI37fzGSBHvzP7xbQpHXz+qaKUouSk+mlyO2q6fbG3ucif/3i+EM6pWELxDiyRVb+HZnQaTDOq7aHm/prdzjrbkSLSZmnNGNq3t3wWpQya3y8cz2A/wzt5jgSQypIYQ4tqiqUZo+fTqvvvoqc+bM4bbbbiM/P58nnniC6dOn1xtDacaMGRw+fDjc9d9isXDbbbexcOFCkpOT6devH2+88QZlZWXMnDmz3n2sW7cOgD179hAKhcLLQ4YMIT099uajioRyt4+n3/6BvbkuFAWmTziDn53Zvc0HygtqOsv35uHXdHo74rggLfpqKkTj+eGee+dHbroki/OjdH64QwVt1+OtuRRFYWQnJ32d8bybU8D2sko+OVzC9lI3U3t3obtNev0KcaqiKlFKSEhg6dKl/OEPf2DOnDnYbDauvvpq5s6dW28/TdMIhepPNzFr1ix0Xeell14KT2GyZMmS8Kjcte6+++5jLi9YsKDJcZpE8+XkVfDUyq2UVviItxiZfcUgBvdOiUgsHx0qIrfKR5xBZVqfLjLmTBSrnR/uVauR9d8fZum6XbijdH64thpD6VQ4zUau75vGD6Vu3s0pJM/j57ntBzmvaxIXpSdjkrHkhDhpit4W/bJjXCikUVLSsg062+M8Yf/ekc9LH+zAH9RIS4nnrquGHnd6h9Ys466ySpbuPgzAjX3TGJDU9l9q7fE5PBmtUT5d11n1eTYffJUDwMVn92DahX0jliw1LKPXH+RXf/0cgCfvOhdnfPSOYeQOBHn/QPV4SwCdrCau6tWFnnUuP8trtP2L9TK2ZvmSk23NaswtPy/EadP06kEkn1+9DX9QY2hmCr+58cwWnwOruVz+IP/Ylw/A2M4JEUmSxKmpnR9u2oV9Afjw3wd5ac0OQlp0fAEcLqruLem0maM6SQKwm4xMz0zjhr5pOEwGirwBFu08xPsHCvHHYO8oIVpLVF16E+2Pxxdk8Xvb+X5P9UCOk0ZncNX5mahqZGoANF3nH/vyqAyGSIszc0mPThGJQ5yeS0ZXzw/38tqdbPghjypvdMwPd6iw9rJb9LRPOpGBSXZ6O+JYc7CI74pcbMwvY0eZm6m9upCVLD8ihDgRqVESp6ygtIr/efU7vt9ThNGgMuuygVxzYd+IJUkAX+SVssflwaQqXJuZJm0y2rFzh6Yx58rBGA0qm3cX8bcVW/D4ghGNKbd26pJO7SvBiDMauKp3F27u140Es5FSX5Alu3JZmZ1HZSCyj6kQ0U5qlMQp2bG/hGff+ZFKb5BEu5k7rxpK77TIjqx8wO3h40PV06NcnpFK57jovjQiTmxEv1TunTaMp1ZuZeeByM8P1x5rlOrql2Dj7sEZfHiwmG8Ky/kmv5xvPt6KWVWwmQzYjDV/JgM2o7HObUO922ZVibpG9kK0FkmUxEnRdZ1PNuXyxj93o+k6vdOc3DF1CEmOth0fqSFvMMTyvXlowJBkO6M6Re90GOLk9O+ZxK+vG8lfV3wfnh/uvmuH0Smh7cfEyi2qHRqgfdUo1WU1GJjSqzNDku2szimk0OvHr+n4fUFKm1ljZ1SOJlb2YyRSDW9bDKokVqLdkkRJNFswpPHaRz/x+Zbq3mRjB3Xl5klZEW83ous6b+cUUOoPkmQxcmXPzvKhHGN6dnXw4A2j+Mubm8kvqWLBa5u479rhdOvUdjU7rio/rko/UD0qd3vXxxnPvOG9sDqsHCqswOUNUBkMVf8FQk3eDuo6QV2n3B+k3N+8xMqgKNiMajiBijdW11iFk6wGCZbVoMpwHjEmpOn4NQ1fSKtOzEMaPk3Dr2n4Q3W3VS/7NA1/SCOg62SmOBgXwR+/kiiJZnFV+nnm7R/YfagcRYFrLujLxWf3iIqE5LsiFz+UuFEVmN4nDWuEEzfROromx1cnS8u/50hxFY8v28Q91wyjT7e2+QCtbZ+UmmjFYo6N15iiKMSbjKTGmUkynfjrQNd1/JpeJ3kKHjepqgyG8Gs6IV3HFQjhCoTAc+K4VKhOpo5VQ3WMdfFGgyRWLUSvSYSPlbw0lczUW669XbO+NjEKncZIRDtKKzkryR6xRtWSKIkTOpBfwcKVWyl2+YizGLlt8iCGZkZmEMmGCjx+3jtQCMDE9BR6nOLs6aJ9SHZaeeD6kTz51hb2Hangz29sbrP54Y62T2q/l91Ol6IoWAwKFoNKMqZmHeMPac2qqaq97QtpaIA7GMIdDJ3w/AAK1Q3WGyZSDrORTmWVeD1+NE1HUar3VVDq3K4uV22aVb3+2NuVOtuP7ttw+8nsWxu/glqznSbOcXRfUOvEa0TH4AtQ7PVT5Q8drampSVAaJiwNtx9d1muSnOrHv7UYFAWzWv0aMqsqZoOCWVXrLVtUFXPNcpxJZUBaEma99eZUPRFJlMRxfbuzgBc/2I4/oNElOZ67rhpCWkp0XHYIaBpv7j1CQNPp64zjvK5JkQ5JtAFHvJl500fwzNs/sH1/KX9/awu3Xj6IM/t3btX7zS2sneMtOl7/7YXZUP2ll2RpXmIV1LRmJ1WVgRCekIYOVAVDVAVDFHobnDC3xYvUYZhUpX7yEk5g6iY6DZeP3rY02G5SVYwn2SvaaFRJSqwecDJSJFESx6TpOu9+uY93N+wHYHDvZG6bMgibtXkfdm1h7cEi8jx+bEYDV/fuKlXvHUicxcjdVw9j8Xvb+HZXIc+t/pGbvK07P1ztpbeOXKPUFoyqSoJZJcHcvM+akKYfTZwaJFKeUIiQquLzB9E0HR0dXQedmj9dp/aCUPV6vWZ9431qrxw1dQ69zjmod446++u1ZwDtBOdrKqZjUSCckBytnVHCtTJ1l+smLw2XG9byyGdqNUmURCNef5AX39/Bpp+qL2ldfHYPrr4gE0MUjUm0o9TN1wXlAFzduwtOs7yUOxqTUWX2lMG88uEuPt9SPT9cpTfIz8f0bPH70nSdQ7U93mKgIXcsMagKTrPxmJ8BsTi9R8PEzGhU6JRsp6ysKmbKGG3k20XUU1Tm4amVWzlUWInRoDDjkv6MG5IW6bDqKfcHwlOUnNslkaxE+eLqqFRVYcYlWdjjTKz5Ood/fLYXd1WAay7MbNGOBsXlXnz+EAZVidjUPEJA/fZUKNU1cNHQqSaWSaIkwnYdKOWZt3/E7QmQYDNzx9QhZKYnRDqsejRdZ0V2Pp6QRnq8hf/qLlOUdHSKonD1BZnY40ys+HQP6/59ALcnwIxJWS1WC3qooLp9UlpKPMZmTKIphIgdkigJAD7dnMvrH/9ESNPp2dXBnVOHkOyMvh5knx4uYV+FB7OqMD2z60k3DBSx65LRGdjijLy8didf/nCESm+gxeaHkx5vQnRc8tOogwuGNF79cBevfriLkKYzZmAXHrx+ZFQmSfsrPHxyuASAKT07k2KVKUpEfecN7cacK4e0+PxwBwukx5sQHZUkSh1YRZWfvy7/nk8356IAV1+QyazLB2I2Rd9gelXBEMuz89CBESkORsgUJaIJI/ulMnfaMKxmQ/X8cG9sxlXlP61zhifDlRolITocSZQ6qIMFbv6w9Ft2HijDajZw59VD+fmYnlHZKFDXdVbty6fcHyTFYmJyz9YdL0e0fwN6JnH/dSOwx5nIyatgwWubKC5vOMBO8wRDGodrerx1lx5vQnQ4kih1QN/tKuR/Xv2OonIvnRPj+M1NZzK8b/Q2iv53YTnbyyoxKDA9sysWaUwrmqFXVycP3jCSFKeF/JIq/ue178IJz8nILXQT0nQsZgMpCdF3SVoI0brkG6cD0XWddzfs45m3f8AXCDGwVxK/nXFmVI8Lk1fl44MDRQBc3L0T6Tb5ohLNl5Zi48EbRpGWEk9phY/Hl21i3xHXSZ0jp2b/7p1sUVnjKoRoXZIodRA+f4jnVm/jnS/2AfCzM7szd9ow7HHRM9J2Q/6Qxpt78wjqOv0S4jmnS2KkQxLtUO38cL3THLg9AZ54YzPb95c0+/icvApA2icJ0VFJotQBFJV7WPDad3y7swCDqnDzpP5c97N+UTXS9rF8cLCQAq8fh8nA1b27yHD64pTVzg83oGcSPn+IJ9/awne7Cpp1bG2NkvR4E6Jjiu5vSnHafjpYxh+WfsuBAjfOeBP3XzeC8cO6RTqsE/qhpIL/FLpQgGv6dMVukiG/xOmJsxi555phjMpKJRjSefadH/l8y+ETHpeTV3PpTWqUhOiQJFGKYZ9vOcyf39hMRVWAjC52fjfjLM7onhjpsE6o1Bfg7f3Vv/bHd02ir1OmjBAtw2RUuX3KYMYPS0PX4eW1O1n7dU6T+3v9QfKKqwCpURKio5Kf6TEoGNJY/ske/vXdIQDO6t+ZWy4dgCUKx0dqKKTrLM/OwxvS6GGz8rP0lEiHJGJM9fxw/bHFmVj79QHe+mwvFZ4A11zQeH642vGTEmxmnPEywKkQHZEkSjHG7Qnw3Ds/siOnFIArx/fhsrHROT7SsXySW8IBtxeLQeXazK4YZIoS0QoUReGaC/riiDNXzw/3zQEqPQFuuqT+/HC1c7x17yyX3YToqCRRiiG5hW6eWrmVwjIvFrOBWy8byIh+qZEOq9n2uqr47Eh1b6Qre3Ym2RK9PfJEbKg7P9wXW49Q6Q1y2+SB4fnhjs7xJpfdhOiopI1SjPh+dxGPvfodhWVeOiVY+c0No9pVklQZCPFWzRQlZ3ZyMjTFEemQRAdx3tBu/OqKIRgNCpt+KuTJt7aG54c7WFAzIrfUKAnRYUmNUjun6zprvs5h1fpsdKB/RiK3XzEYR5S2p/AV5VKevZnKsiPoBg9GbNhSMnjP0g9XIESq1cRlGe0nwROxYVRWKnOnDeeplVvZkVPKn9/YzD3ThpEbrlGSREmIjkoSpXbMFwjxf2t28O8d1T3EJoxMZ/pFZ2CMkik+glUuXHs348rbj6a4UO2VqBY/mMAQzoXK+Dpk4ye3H4Me4tz8f1Pu2U5i1lmYEiRhEm1nQM8k7v/FCP62Ygv78yr4n1e+o7yyejJdSZSE6LgkUWqnSlxeFq78gZz8CgyqwvUT+3HBiPSIxaMFA1Tm/EhZzi78wVIM8ZUo8VUoCqid6lzj1UH1OtErEwh6bZTZdb6yDQMFxho20yVlLx7Ak/0tocp49Eo7cfY0kvufiSUlcuUTHUPvtOr54f6y/HsKyjwAdE2Jx2I2EAxqEY5OCBEJkii1Q3tyy3l61Q+4Kv3Y40zMuXIwWRlJbXb/mqbhLcihbM9WPO4CVGslqr0SxRCCJKjbBFvxW1Gqkgi4nQRDyVg69SShXzccne2EFHj3xwNoVT56+gP0K4xDsWWg2EvQrG4MtiqwVRGggPwDW9B2xRFy27FYUknuN4L4tMw2K7PoONJSbDx0wyj+svx7jhRX0Sc9IdIhCSEiSBKlduaLrYd59cNdBEM63VPt3HXVEDolxrXqfQYqSindtQl38SEwVmCwu1HMfrCBqW5noJARtSqRYGUCQV8CRkd3Evv1xpnuQD3GdCmr9+WTX+UjwWzkxhF9iDcOBKCiwE3xtmyC5TmY40pRHSVoVhdqnAc1zoNGIUV529FyLIQqHBiVFJLPGIytx4Bj3o8QJ6t2frgvth5h4phekQ5HCBFBkihFqZVvPUV5mR9F1TAYwGo1UxUwsr/YSJyawhmZffnlZYOwmlv2KdQCPsp2b8V1aA8hyjDYKlHjqtBV0FMNBDFShYmAFk/Ql4DPm4A3YCdoTsLQqQvGtDiCOvg1DV9Iw++vwr+nEp+m4Q9pNet1/JqGN6ShANP7phFvPDoYpqOzHUfnocBQALwuHwU/7MNftBejpQSjvQwtvgzV4kO1+IAiSkt2UZJnJljhQNWTSOo1AGefoaiG6B9kU0QnR7yZyef2JinJRmlpZaTDEUJEiCRKUaq8LAAGOzoQ1MBdPYsCGU7IoBhc+Sxfth4IoKgaJhNYrRacDiedOnWnS/cs4pxdCOjgD2n4ND2cqNQu+4IhKsuLcZUW49cCBI06ASMEMRJI6kMAY/VfyERAN0LDQStN1L/O5vXAYU+zy6gAU7O6kZkQf9z2H1anhYxx/YH+AAR8QQp+PIDn0G4MxiJMjjK0+FIUsx9TSjFQjMu9h/JNawm5nBBMIKHbGSQNGIVqlLGZhBBCNJ8kSlHqvImXsX7bVtx+Db+mgmpGM5jQVDOawYiuGtCa+NODRtjvBZqew+ooBcydmrVbLbOqYFZVzAYVi0HFrCo1/1XMhuptlprt9bfVX3ZajXTvnHDSv9ZNFiPpo/rAqD4AhIIaxT/lUZG9HUUpwGQvR7eVoBiDGJNLgBIqg/twf/8vtAonms+JLaUXqUPPwmCReeSEEEI0TRKlKPVPdxz7koee3kl0HYMexEQQsxLArFTfNimB6v8EMepBCOngA82nE/JphNQgDoeZ1OROdE3tQYqjM1aTAbOqYlIV1BaaDsVobJn2RAajSueB3eg8sBtQ3di8/EAppTt/QA/mY7KVgb0ExRjAkFiKgVL85JD74xeE3A40jxOrvTupI87GbJOGu0IIIY6SRClKOb0avhIvekjHbjEyqGcSdrOxuhZHVWpqZ2r/wHdoP768HIxaKZY4F5a4MgyGYKOrZT6fCVeVkbIqIy6PCbfXhK4f3Umh+kXhKYYD+/M4oG9F1wLoBEAJoRpCmEwqcfFWEhOS6No1g65d+uJwdsGgRsfLSVVVknqlkNTrgvC6ivwKin74gZDnEKb4clRHCbrJh8FZjsFZToiD5O36Gq3SQajSidnaldShZ2FNlrGchBCiI4uObzbRSPaPhZQeKufCEen84oL6g0hW5B6iePtWQr5C1PgKDLYKrMYApDQ4SdCM7k4gWOUAtROOHv1IH9ILg1ElGAxSVHCQw4d2U1J8hIoKF15fgGAIdN0AihFUEygqisGCgqX6nDoE/NV/rjIPB3J2gbYdXa+TTBk1TGaV+DgriYkppHXNoHNqbxzOrhFLphxdHDi6nBNe9rg8FH6/DX95Dsa4UgyOUnSzB9XuQrW70DlEwf5v0bfbCboTMBhTSR00Elta94jEL4QQIjIUXdf1SAdR1969e3nsscfYvHkzNpuNKVOmcM8992A2H39KDl3XWbx4Ma+//jolJSUMGDCABx98kOHDh9fbLz8/n8cee4wvv/wSk8nExIkTefDBB7HbT33k3VBIo6SkZXvFuKr8eEOQpHjJ27QZX8VhVEs5RnsFWI7RYFpToSqBYKUDLZREfGpvUob0xxJ36lOZ+H1eCvMPciR3N8Ul+VS63fj8AYIhpU4y1czERwui6wE0pbZmSsNsVjGZjICCqqioqopqMGBUVFTViNFoxGio+TOaa/6MGI1xmM1mTKY4TCYrZpMVsyUekzkOo9GEajBgMBhQ1eb3eAv6guR/vx1P4V4M5hKMjjJ0a+PnVPfYCFY4UUgh+YwhJPY5o8lzGo1quMdULA5WGOvlg9gvo5Sv/Yv1MrZm+ZKTbRiaMZNFVNUolZeXM2PGDHr16sXChQvJz8/n8ccfx+v18vDDDx/32MWLF/PUU08xb948srKyWLZsGbfccgurV6+mR48eAAQCAX75y18C8Je//AWv18uf/vQn7rvvPl544YVWL9/JKPpiDQbbfrzxFeDQMdedI1YHvHZClU5CfidmRzqpQ4YQl+Rs0RjMFivpGWeQntF0MuD1uCk4cpC8I9mUlOTjrqwk4A8S1EDXaxIpxQCqEQUjBmrGfNIg4K3+OzYdCNT8nSJdrzmPXv2v9nbt+cPrQKm7HgAjccYkkmw6ifYQdrsPq9WPEleJKa4SOIKr/EeKvjZR6bbgchuo8BkJKEZU1YiqqBiMRkwmE7qmQ20iqBowGKr/Vy/XrjOg1PyvTfIMBgOqwVjz34Ch5rbBYMJgNKKqBowmU/V6oxGDwYhqMKIoCkoLtSMTQoiOLqoSpTfffJPKykqefvppEhMTAQiFQjz66KPcdtttdOnS5ZjH+Xw+XnjhBW655RZuvvlmAEaNGsUll1zCkiVLeOSRRwD48MMP2b17N2vWrKFPn+oeU06nk5kzZ7J161aGDj3NxtMtyGA9iGJzVS8ELGhuJyGPE4M1leR+Q3D2SItsgDWscXYy+gwgo8+AY27XNI0qdzkFeYfIz9tHWWkRlVWV+ANBQlrNl7muhNMV6v5X6i7Xua3UWXe8hECpc9wJ8oZjVatW6VDlhlw3gA2jQSMh3k+CzU+iLYAjLoDZUv2XVHPZ0xdQKa80U1ZpprzShLfCQOM625NLYk5Y5Vt3h9rkUD+aFOr1lvXaNTRMHPXwsnJ0P4X6xyg1+ys1+yv1n7Wjt5Xw0xS+DeGOAGrNc6NAOLFTGi4rCgoqigKKooaXVQVQDKhKdU0kqoKqGFBqlhW1epuiqOGaSkU1oCiGmvMcjas21trXUd2Xk6IoqKpCfJwFj7c2Ya9JQuvEWLuudr16jHV191MV9ei68Hlq19U8XjXlRVFQ1OryKtScu87jUff4uvGoqlJ92bzu/aCgqPX3081GAn4TgYA/JmsjdF1tVL6GPyLa+48KRVEJBYOEQkG0UO37M7y1Zp+Gy+27zG0tqhKlzz//nLFjx4aTJIBJkyYxf/58NmzYwNSpU4953KZNm3C73UyaNCm8zmw2M3HiRD7++ON658/KygonSQDjxo0jMTGR9evXR1WilNDtYty5+7Cl9caR2QejMaqeqmZTVRW7Mwm7M4k+/YbU29YSVarBUAC/r6r6L+DF76siEPDg9/sIBr34A34CAR8hLUAwGCAQDBIKBQiFggS1EFooRFDT0PQQoVD1f03X0TQdXdPR0NH06ku7fh0qPTq5VaAXKFh1M93McSQ7FOwOP9Z4DxaTRudEL50Tm6wqa/ei62J9GwiC6URvv7o550lqeOgpP7yneGDdQUSOnkIJLzQ8rX6MleEfOnrDdXWWjxGfXvuN3tRxesPzKCeIof5C/R9gTex/nMetOfsc6/wn/x5RTvG42vuLbOJzUj/mToGn0sR5k+/hZH9ktpSo+vbNzs7mqquuqrfO6XSSmppKdnb2cY8D6iVAAJmZmSxduhSv14vVaiU7O7vRPoqi0Lt37+Oevzlaqqt7rS6DB3LGOaNwuTyEQrH3Sw8IXxtuzjXiphiNFqwWC9B2c901xVNSzpFN/yFQdRhDfDmqvQwMoUiH1eLkx2jrOuWHt0Wfl1P5ZutoGbRoK5rTS+nhg6R07xWR+4+qRMnlcuF0Nm5nk5CQQHl5+XGPM5vNWCyWeuudTie6rlNeXo7VasXlcuFwOBodf6Lzn4iqKiQl2U684ylwOlt3HrdoECtlTEqy0S1zSng54PXhq6gKL+t6g6RJ0xosNv6iadjXQtcbJM1aw+0NlhvcR6O+G43O1zCmhgE1ON8xYo4WIV1DC4UI6UG0YBBdDxHUAmghDS0URNNDBPUgeqh2vwAhHXQtQCgUQqupaWzsWGVu+LifXNpQ92Lo0ZV6/f919qt3r7oOTdQoaHWf75on8+gl1trXQ01NkK6jaHVOVftU67WXZWsujeq1x1b/Ha3jqblkq1OdTev60eoyRUdHQdH1mr1qLgHVOVf4Mm6jB65O9ZBSZ5V+NGlvMkfUm9hYex/H2NZwVcPHHBr8WNDrBtX4BE2F0DiYujEo9bcoep1tDY9rrV8uTZ2/7d/zqtHOWZcMavP7rRVViVJ7pWk6LlfViXc8CQaDitMZF/M1SrFcRoNBxZma1Kbla/iRdrofocfrNxjrzx/EfhmlfO1frJexNcvndMa1v15vTqeTioqKRuvLy8tJSEg47nF+vx+fz1evVsnlcqEoSvhYp9OJ2+0+5vnT0k6vcXRrNYQMhbSYbGRZV6yXUcrX/sV6GaV87V+slzGS5WvZhjWnqU+fPo3aClVUVFBYWNiobVHD4wD27dtXb312djbdunXDarU2eX5d19m3b99xzy+EEEKIjimqEqXx48ezceNGXC5XeN26detQVZVx48Y1edzIkSOx2+2sXbs2vC4QCPDRRx8xfvz4euffuXMn+/fvD6/76quvKCsr4/zzz2/ZwgghhBCi3YuqRGn69OnYbDbmzJnDl19+ycqVK3niiSeYPn16vTGUZsyYwcSJE8PLFouF2267jZdeeomlS5fy1Vdfcd9991FWVsbMmTPD+1188cWcccYZ3HnnnXz66aesWbOGhx56iAsuuCCqhgYQQgghRHSIqjZKCQkJLF26lD/84Q/MmTMHm83G1Vdfzdy5c+vtp2kaoVD93iizZs1C13Veeuml8BQmS5YsCY/KDWAymXjxxRd57LHHuPfeezEajUycOJGHHnqoTconhBBCiPYl6uZ6a49aY663WJ+/B2K/jFK+9i/Wyyjla/9ivYzRMNdbVF16E0IIIYSIJpIoCSGEEEI0QRIlIYQQQogmSKIkhBBCCNEESZSEEEIIIZogiZIQQgghRBMkURJCCCGEaIKMo9QCdF1H01r+YTQY1JicDbquWC+jlK/9i/UySvnav1gvY2uVT1UVFEU54X6SKAkhhBBCNEEuvQkhhBBCNEESJSGEEEKIJkiiJIQQQgjRBEmUhBBCCCGaIImSEEIIIUQTJFESQgghhGiCJEpCCCGEEE2QREkIIYQQogmSKAkhhBBCNEESJSGEEEKIJkiiJIQQQgjRBEmUhBBCCCGaIImSEEIIIUQTJFGKImvXruX2229n/PjxDB8+nClTpvCPf/wDXdcjHVqLWb9+PTfccANjxoxh8ODBXHTRRSxYsICKiopIh9YqKisrGT9+PFlZWfzwww+RDqdFrFq1iqysrEZ///u//xvp0FrU22+/zRVXXMGQIUMYPXo0v/zlL/F6vZEO67TdeOONx3z+srKy+OCDDyIdXov517/+xTXXXMOIESM499xzufvuuzl48GCkw2oxn376KVdeeSWDBw/m/PPP56mnniIUCkU6rFOSk5PDww8/zJQpUxg4cCCXXXbZMfd76623uPjiixkyZAiTJ0/m008/bZP4jG1yL6JZXn75ZdLT03nggQdISkpi48aN/O53vyMvL4877rgj0uG1iLKyMoYOHcqNN95IYmIiu3fvZuHChezevZuXXnop0uG1uGeffbbdfnidyIsvvojD4Qgvd+nSJYLRtKznnnuOxYsXM3v2bIYPH05paSlfffVVTDyX8+fPx+1211u3dOlSPvroI8aOHRuhqFrWN998wx133MEVV1zB3LlzKSsr4+9//zu33HIL7733HlarNdIhnpbvv/+eX/3qV1x66aXce++97NmzhyeffBKPx8Ovf/3rSId30nbv3s369esZNmwYmqYds3Lggw8+4He/+x2zZ89mzJgxrFmzhjvuuINly5YxfPjw1g1QF1GjuLi40brf/va3+siRI/VQKBSBiNrG8uXL9X79+ul5eXmRDqVF7dmzRx8+fLj+xhtv6P369dO3bt0a6ZBaxMqVK/V+/fod8/UaC/bu3asPHDhQ/+yzzyIdSpuZMGGCPmvWrEiH0WJ+97vf6RMmTNA1TQuv++qrr/R+/frp//nPfyIYWcu45ZZb9CuvvLLeuiVLluiDBg3SCwsLIxTVqav7/fbrX/9av/TSSxvt81//9V/6vffeW2/dtddeq//yl79s9fjk0lsUSU5ObrRuwIABuN1uqqqqIhBR20hMTAQgEAhENpAW9thjjzF9+nR69+4d6VDESVi1ahXdu3fn/PPPj3QobWLTpk0cOnSIyy+/PNKhtJhgMIjNZkNRlPC62tpPPQaaMuzYsYNx48bVW3fuuecSCAT48ssvIxTVqVPV46ciBw8eZP/+/UyaNKne+p///Od89dVX+P3+1gxP2ihFu++++44uXbpgt9sjHUqLCoVC+Hw+tm3bxjPPPMOECRPo3r17pMNqMevWreOnn35izpw5kQ6l1Vx22WUMGDCAiy66iBdeeCEmLksBbNmyhX79+vHss88yduxYBg8ezPTp09myZUukQ2sV77//PvHx8Vx00UWRDqXFTJ06lb1797Js2TIqKio4ePAgf/3rXxk4cCAjR46MdHinzefzYTab662rXd67d28kQmpV2dnZAI1+dGZmZhIIBFq97Zm0UYpi3377LWvWrGmX15xP5MILLyQ/Px+A8847j7/85S8RjqjleDweHn/8cebOnRtzCS5Aamoqd955J8OGDUNRFD755BOefPJJ8vPzefjhhyMd3mkrLCzkxx9/5KeffmL+/PnExcXx/PPPc8stt/DRRx+RkpIS6RBbTDAYZO3atUyYMIH4+PhIh9NizjzzTJ5++mnuu+8+fv/73wPVtfMvvvgiBoMhwtGdvp49e7J169Z6677//nsAysvLIxBR66otk9PprLe+drm1yyyJUpTKy8tj7ty5jB49mptuuinS4bS4RYsW4fF42LNnD8899xyzZ8/m//7v/2LiQ+y5554jJSWFq666KtKhtIrzzjuP8847L7x87rnnYrFYWLp0KbNnz6Zz584RjO706bpOVVUVf//73+nfvz8Aw4YNY8KECbz22mvcfffdEY6w5WzYsIGSkpImexm1V5s2beL+++9n2rRpXHDBBZSVlfHss89y66238vrrr7f7xtzXXXcdv/nNb1i6dClTpkwJN+aOhc/PaCSX3qKQy+Vi1qxZJCYmsnDhwhNev22P+vfvz4gRI7jmmmt49tln+eabb/j4448jHdZpy83N5aWXXuKuu+6ioqICl8sVbl9WVVVFZWVlhCNsHZMmTSIUCrFjx45Ih3LanE4niYmJ4SQJqtvRDRw4kD179kQwspb3/vvvk5iYyLnnnhvpUFrUY489xpgxY3jggQcYM2YMl1xyCYsWLWL79u2sXr060uGdtqlTpzJjxgyeeOIJRo8ezc0338z06dNJSEho9z9UjiUhIQGg0TAyLper3vbWIjVKUcbr9XLbbbdRUVHB8uXL63W/jlVZWVmYTCYOHDgQ6VBO26FDhwgEAtx6662Ntt10000MGzaMFStWRCAy0Vx9+/Zt8rXo8/naOJrW4/V6+ec//8nkyZMxmUyRDqdF7d27t1Gbq65du5KUlBQTnzOqqvLQQw9x5513kpubS7du3QgGg/ztb39j2LBhkQ6vxfXp0weobqtUe7t22WQy0aNHj1a9f0mUokgwGOSee+4hOzubZcuWxdS4NMezZcsWAoFATDTmHjBgAK+88kq9dTt27GDBggU8+uijDBkyJEKRta41a9ZgMBgYOHBgpEM5bRdeeCGrVq1ix44dDBgwAIDS0lK2bdvGzTffHNngWtAnn3xCVVVVTPV2q9WtWze2b99eb11ubi6lpaWkp6dHKKqW53A4wjWff//73+nevTvnnHNOhKNqeT169KBXr16sW7eOn/3sZ+H1a9asYezYsY0atrc0SZSiyKOPPsqnn37KAw88gNvtDjfOAxg4cGCrvxjawh133MHgwYPJysrCarWyc+dOlixZQlZWVr03QHvldDoZPXr0MbcNGjSIQYMGtXFELW/mzJmMHj2arKwsoHoE5BUrVnDTTTeRmpoa4ehO389+9jOGDBnCXXfdxdy5c7FYLCxatAiz2cx1110X6fBazHvvvUe3bt0YNWpUpENpcdOnT+d//ud/eOyxx5gwYQJlZWXhtoMNu5i3R1u3buXf//43AwYMwOv18sknn7B69WoWL17cLtspeTwe1q9fD1QntG63m3Xr1gFw9tlnk5yczJ133sm8efPIyMhg9OjRrFmzhq1bt/Laa6+1enySKEWRDRs2APD444832vavf/0rJmpchg4dypo1a1i0aBG6rpOens4111zDzJkzYyIR7Ah69+7NypUrycvLQ9M0evXqxUMPPcSNN94Y6dBahKqqLFq0iAULFvDwww8TCAQ488wzWbZsWUwkglDdS+iLL75gxowZ9cYaihU33XQTZrOZN954g5UrV2Kz2Rg+fDhPPvkkSUlJkQ7vtJlMJj766COeeeYZoLqzwauvvsqIESMiHNmpKS4ubtRJonb5lVdeYfTo0Vx22WV4PB4WL17MokWL6N27N08//XSblFnRY2H0LSGEEEKIVhB73amEEEIIIVqIJEpCCCGEEE2QREkIIYQQogmSKAkhhBBCNEESJSGEEEKIJkiiJIQQQgjRBEmUhBBCCCGaIImSEEIIIUQTJFESQkTMN998Q1ZWVni6gmhXVFTEXXfdFZ7C5eWXX26R8y5cuDA8JYwQIrrIFCZCxLhVq1bx4IMPYjab+ec//9losuUbb7yR0tJS3n///QhF2H4sWLCAL774gjvuuINOnToxePDg4+7v8/l44403+OCDD8jOzsbv99OtWzfGjRvHjTfeSO/evdsk7vfee4/i4uKYmtRXiLYiiZIQHYTf72fRokX87ne/i3Qo7dbXX3/NRRddxMyZM0+4b0lJCb/85S/Ztm0bF154IZdddhnx8fHs27ePNWvWsGLFCn788cc2iBref/99du/eLYmSEKdAEiUhOogBAwawYsUKbr311ka1SrGuqqqK+Pj40z5PcXExTqezWfs++OCD7Nixg6eeeoqLL7643rZ77rmHv/3tb6cdTyRpmkYgEMBisUQ6FCFalbRREqKDuO2229A0jcWLFx93v0OHDpGVlcWqVasabcvKymLhwoXh5dq2Nfv27WPevHmMGjWKMWPG8OSTT6LrOkeOHOH2229n5MiRjBs3jpdeeumY96lpGn/9618ZN24cw4cPZ/bs2Rw5cqTRflu2bGHmzJmMGjWKYcOGccMNN/Ddd9/V26c2pj179nDfffdx1llncd111x23zAcPHuSuu+7i7LPPZtiwYUybNo3PPvssvH3VqlVkZWWh6zrLli0jKyvruG2KtmzZwmeffcbVV1/dKEkCMJvN/PrXv27y+JN5DtxuN3/84x+ZMGECgwcPZuzYsfz3f/8327ZtA6ovrX722Wfk5uaG454wYUL4eL/fz1NPPcXEiRMZPHgw559/Pk888QR+v7/R/f7+97/n3Xff5dJLL2XIkCF88cUXAHzwwQdMnTqVESNGMHLkSC6//HKWLl3aZPmEaE+kRkmIDqJ79+5MmTKFFStWMGvWrBatVZo7dy6ZmZncd999rF+/nueee47ExETefPNNxowZw7x583jvvff405/+xJAhQzjrrLPqHf/cc8+hKAqzZs2iuLiYpUuXcvPNN7N69WqsVisAX331FbNmzWLw4MHccccdKIrCqlWrmDFjBq+//jpDhw6td867776bnj17MnfuXHRdbzL2oqIipk+fjsfj4cYbbyQpKYm3336b22+/PZxAnHXWWTzxxBPcf//9jBs3jilTphz38fjkk08ATrhfS5g/fz4ffvghN9xwA5mZmZSVlfHdd9+xd+9eBg0axOzZs6moqCAvL48HH3wQAJvNBlQnqLfffjvfffcd06ZNIzMzk59++omlS5eyf/9+nn322Xr39fXXX7N27Vquv/56kpKSSE9PZ8OGDdx7772MHTuWefPmAZCdnc2mTZuYMWNGq5dfiNYmiZIQHcjtt9/O6tWrWbx4Mb/97W9b7LxDhw7l97//PQDXXnstEyZM4PHHH+fee+/l1ltvBeCyyy7jvPPOY+XKlY0SpfLyctasWYPdbgdg4MCB3HPPPaxYsYKbbroJXdd55JFHGD16NC+++CKKogAwffp0Lr30Up588slGtVX9+/fnL3/5ywljX7RoEUVFRSxbtowzzzwTgGuuuYbJkyezYMECLrroInr06EGPHj24//776dWr1wkToL179wLQr1+/E97/6Vq/fj3Tpk3jgQceCK+bNWtW+Pa4ceN45ZVXcLlcjeJ+77332LhxI6+++mq47ABnnHEG8+fPZ9OmTYwcOTK8ft++fbz33nv07ds3vO6Pf/wjdrudJUuWYDAYWqOIQkSUXHoTogPp0aMHkydPZsWKFRQUFLTYea+++urwbYPBwODBg9F1vd56p9NJ7969OXjwYKPjr7jiinCSBHDJJZeQmprK+vXrAdixYwf79+/n8ssvp7S0lJKSEkpKSqiqqmLs2LH85z//QdO0euecPn16s2Jfv349Q4cOrZco2Gw2rr32WnJzc9mzZ0/zHoQ63G53+Dytzel0smXLFvLz80/62HXr1pGZmUmfPn3Cj2lJSQljxowBqodvqOuss86qlyTV3r/H42HDhg2nXgghopjUKAnRwfzqV7/i3XffZdGiRS1Wq9StW7d6yw6HA4vFQnJycqP1ZWVljY7v2bNnvWVFUejZsye5ubkA7N+/H+C47XoqKipISEgIL3fv3r1ZsR8+fJhhw4Y1Wt+nT5/w9pOtGapN+iorK5vd+PtUzZs3jwceeIALLriAQYMGcf7553PFFVfQo0ePEx6bk5PD3r17GTt27DG3FxcX11s+1mN63XXXsXbt2vDl3HHjxjFp0iTGjx9/agUSIspIoiREB1O3Vqn2slhdtZe1GgqFQk2eU1UbV043dRnmeO2FmlJ7zP3338+AAQOOuU/DXm2R7I1Vm2T99NNP9WqqmutknoOf//znnHnmmXz88cds2LCBJUuWsHjxYhYuXMj5559/3PvRNI1+/fqF2y411LVr13rLte3F6kpJSeGdd97hyy+/5PPPP+fzzz9n1apVXHHFFfzpT3867v0L0R5IoiREB3T77bfz7rvvHrMHXG2tjMvlqrf+8OHDrRZPTk5OvWVd18nJyQn3LKutHbHb7Zxzzjktet/dunVj3759jdZnZ2eHt5+sCy+8kBdeeIF33333lBKlk30OOnfuzPXXX8/1119PcXExV155Jc8//3w4UWoq8crIyGDnzp2MHTu2yX2aw2w2M2HCBCZMmICmaTzyyCMsX76cX/3qV41qC4Vob6SNkhAdUEZGBpMnT2b58uUUFhbW22a320lKSuLbb7+tt/71119vtXjeeeedcLseqG47U1hYGL58M3jwYDIyMnjppZeorKxsdHxJSckp3/f555/P1q1b2bx5c3hdVVUVK1asID09vVGbnOYYMWIE5513Hm+99Rb//Oc/G233+/3HrW1p7nMQCoWoqKioty4lJYXOnTvX694fFxfXaD+ASZMmkZ+fz4oVKxpt83q9VFVVNRljrdLS0nrLqqqGE9yGQwwI0R5JjZIQHdTs2bNZvXo1+/bt44wzzqi37ZprrmHRokX85je/YfDgwXz77bfHrHVpKQkJCVx33XVMnTo1PDxAz549mTZtGlD95fvYY48xa9YsLrvsMqZOnUqXLl3Iz8/nm2++wW638/zzz5/Sfd9666188MEHzJo1ixtvvJGEhATeeecdDh06xMKFC495WbE5nnjiCW655RbuuOMOLrzwQsaOHUtcXBw5OTmsWbOGgoKC47a5as5zUFlZyfnnn8/FF19M//79iY+PZ+PGjfzwww/1esENGjSINWvWsGDBAoYMGUJ8fDwTJkxgypQprF27lvnz5/PNN98wcuRIQqEQ2dnZrFu3jhdffJEhQ4Yct5y//e1vKS8vZ8yYMXTp0oXDhw/z2muvMWDAADIzM0/psRMimkiiJEQH1bNnTyZPnszbb7/daNucOXMoKSnhww8/ZO3atYwfP54XX3yxyUa/p2v27Nns2rWLRYsWUVlZydixY5k/fz5xcXHhfUaPHs3y5ct59tlnee2116iqqiI1NZWhQ4dy7bXXnvJ9d+rUiTfffJM///nPvPbaa/h8PrKysnj++ee54IILTvm8ycnJvPnmm7z++uusWbOGv/3tbwQCAdLT05kwYQI33XTTcY9vznNgtVr5xS9+wYYNG/joo4/QdZ2MjAzmz59fb5DN6667jh07drBq1SpefvnlcAyqqvLMM8/w8ssvs3r1aj7++GPi4uLo3r17s+eiq23v9vrrr+NyuUhNTWXSpEnceeedp5xkChFNFP1UWlYKIYQQQnQAku4LIYQQQjRBEiUhhBBCiCZIoiSEEEII0QRJlIQQQgghmiCJkhBCCCFEEyRREkIIIYRogiRKQgghhBBNkERJCCGEEKIJkigJIYQQQjRBEiUhhBBCiCZIoiSEEEII0QRJlIQQQgghmvD/AR/7mZe/GpqYAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot the results such that each line has a unique color\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "sns.set()\n", + "fig, ax = plt.subplots()\n", + "for method, predictions in train_test_accuracies.items():\n", + " ax.plot(list(predictions.keys()), list(predictions.values()), label = method)\n", + "ax.set_xlabel(\"Number of Clusters\")\n", + "ax.set_ylabel(\"RMSE\")\n", + "ax.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mnotebook controller is DISPOSED. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mnotebook controller is DISPOSED. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "# plot the results such that each line has a unique color\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "sns.set()\n", + "fig, ax = plt.subplots()\n", + "for method, predictions in train_test_r2.items():\n", + " ax.plot(list(predictions.keys()), list(predictions.values()), label = method)\n", + "ax.set_xlabel(\"Number of Clusters\")\n", + "ax.set_ylabel(\"Mean R2\")\n", + "ax.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mnotebook controller is DISPOSED. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mnotebook controller is DISPOSED. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mnotebook controller is DISPOSED. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mnotebook controller is DISPOSED. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mnotebook controller is DISPOSED. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mnotebook controller is DISPOSED. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], + "source": [ + "# import matplotlib.pyplot as plt\n", + "# for method, predictions in train_test_predictions.items():\n", + "# if method == \"l2_ranking\":\n", + "# continue\n", + "# plt.plot(list(predictions.keys()), list(predictions.values()), label = method)\n", + "# plt.xlabel(\"Number of Clusters\")\n", + "# plt.ylabel(\"Average Accuracy\")\n", + "# plt.legend()\n", + "# plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mdi", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/feature_importance/subgroup/current/subgroup-incase.py b/feature_importance/subgroup/current/subgroup-incase.py new file mode 100644 index 0000000..6cafaf7 --- /dev/null +++ b/feature_importance/subgroup/current/subgroup-incase.py @@ -0,0 +1,362 @@ +# imports +import numpy as np +import pandas as pd +from subgroup_detection import * +from subgroup_experiment import * +from sklearn.model_selection import train_test_split +from sklearn.linear_model import LogisticRegression, LinearRegression +from imodels.tree.rf_plus.rf_plus.rf_plus_models import \ + RandomForestPlusRegressor, RandomForestPlusClassifier +from imodels.tree.rf_plus.feature_importance.rfplus_explainer import \ + RFPlusMDI, AloRFPlusMDI +from scipy import cluster +from scipy.cluster.hierarchy import fcluster, cut_tree +from sklearn.metrics import roc_auc_score, average_precision_score, f1_score, \ + accuracy_score, r2_score, f1_score, log_loss, root_mean_squared_error +from imodels import get_clean_dataset +import openml +from ucimlrepo import fetch_ucirepo +import argparse +import os +from os.path import join as oj + +def get_openml_data(id, num_samples=2000): + + # check that the dataset_id is in the set of tested datasets + known_ids = {361247, 361243, 361242, 361251, 361253, 361260, 361259, 361256, + 361254, 361622} + if id not in known_ids: + raise ValueError(f"Data ID {id} is not in the set of known datasets.") + + # get the dataset from openml + task = openml.tasks.get_task(id) + dataset = task.get_dataset() + X, y, _, _ = dataset.get_data(target=dataset.default_target_attribute) + + # subsample the data if necessary + if num_samples is not None and num_samples < X.shape[0]: + X = X.sample(num_samples) + y = y.loc[X.index] + + # reset the index of X and y + X = X.reset_index(drop=True) + y = y.reset_index(drop=True) + + # convert X and y to numpy arrays + X = X.to_numpy() + y = y.to_numpy() + + # perform transformations if needed + log_transform = {361260, 361622} + if id in log_transform: + y = np.log(y) + + return X, y + +def fit_models(X_train, y_train, task): + # fit models + if task == "classification": + rf = RandomForestClassifier(n_estimators=100, min_samples_leaf=3, + max_features='sqrt', random_state=42) + rf.fit(X_train, y_train) + rf_plus_baseline = RandomForestPlusClassifier(rf_model=rf, + include_raw=False, fit_on="inbag", + prediction_model=LogisticRegression()) + rf_plus_baseline.fit(X_train, y_train) + rf_plus = RandomForestPlusClassifier(rf_model=rf) + rf_plus.fit(X_train, y_train) + elif task == "regression": + rf = RandomForestRegressor(n_estimators=100, min_samples_leaf=5, + max_features=0.33, random_state=42) + rf.fit(X_train, y_train) + rf_plus_baseline = RandomForestPlusRegressor(rf_model=rf, + include_raw=False, fit_on="inbag", + prediction_model=LinearRegression()) + rf_plus_baseline.fit(X_train, y_train) + rf_plus = RandomForestPlusRegressor(rf_model=rf) + rf_plus.fit(X_train, y_train) + else: + raise ValueError("Task must be 'classification' or 'regression'.") + return rf, rf_plus_baseline, rf_plus + +def get_shap(X, shap_explainer, task): + if task == "classification": + # the shap values are an array of shape + # (# of samples, # of features, # of classes), and in this binary + # classification case, we want the shap values for the positive class. + # check_additivity=False is used to speed up computation. + shap_values = \ + shap_explainer.shap_values(X, check_additivity = False)[:, :, 1] + elif task == "regression": + # check_additivity=False is used to speed up computation. + shap_values = shap_explainer.shap_values(X, check_additivity = False) + else: + raise ValueError("Task must be 'classification' or 'regression'.") + # get the rankings of the shap values. negative absolute value is taken + # because np.argsort sorts from smallest to largest. + shap_rankings = np.argsort(-np.abs(shap_values), axis = 1) + return shap_values, shap_rankings + +def get_lmdi_explainers(rf_plus, lmdi_variants, rf_plus_baseline = None): + # create the lmdi explainer objects + lmdi_explainers = {} + if rf_plus_baseline is not None: + lmdi_explainers["baseline"] = RFPlusMDI(rf_plus_baseline, + mode = "only_k", + evaluate_on = "inbag") + for variant_name in lmdi_variants.keys(): + if lmdi_variants[variant_name]["loo"]: + lmdi_explainers[variant_name] = AloRFPlusMDI(rf_plus, + mode = "only_k") + else: + lmdi_explainers[variant_name] = RFPlusMDI(rf_plus, mode = "only_k") + return lmdi_explainers + + +def get_lmdi(X, y, lmdi_variants, lmdi_explainers): + + # initialize storage mappings + lmdi_values = {} + lmdi_rankings = {} + + # check if the explainer mapping has a "baseline" + if len(lmdi_explainers) == len(lmdi_variants) + 1 and \ + "baseline" in lmdi_explainers: + lmdi_values["baseline"] = lmdi_explainers["baseline"].explain_linear_partial(X, y, + l2norm=False, sign=False, + normalize=False,leaf_average=False) + lmdi_rankings["baseline"] = lmdi_explainers["baseline"].get_rankings(np.abs(lmdi_values["baseline"])) + + for name, explainer in lmdi_explainers.items(): + if name == "baseline": + continue + variant_args = lmdi_variants[name] + lmdi_values[name] = explainer.explain_linear_partial(X, y, + l2norm=variant_args["l2norm"], + sign=variant_args["sign"], + normalize=variant_args["normalize"], + leaf_average=variant_args["leaf_average"]) + lmdi_rankings[name] = explainer.get_rankings(np.abs(lmdi_values[name])) + + return lmdi_values, lmdi_rankings + +def get_train_clusters(lfi_train_values, method): + # make sure method is valid + if method not in ["kmeans", "hierarchical"]: + raise ValueError("Method must be 'kmeans' or 'hierarchical'.") + if method == "hierarchical": + train_linkage = {} + for method, values in lfi_train_values.items(): + train_linkage[method] = cluster.hierarchy.ward(values) + train_clusters = {} + for method, link in train_linkage.items(): + num_cluster_map = {} + for num_clusters in range(2, 11): + num_cluster_map[num_clusters] = cut_tree(link, n_clusters=num_clusters).flatten() + train_clusters[method] = num_cluster_map + elif method == "kmeans": + train_clusters = {} + for method, values in lfi_train_values.items(): + num_cluster_map = {} + for num_clusters in range(2, 11): + centroids, _ = cluster.vq.kmeans(obs=values, k_or_guess=num_clusters) + kmeans, _ = cluster.vq.vq(values, centroids) + num_cluster_map[num_clusters] = kmeans + train_clusters[method] = num_cluster_map + train_clusters_final = {} + for method, clusters in train_clusters.items(): + num_cluster_map = {} + for num_clusters, cluster_labels in clusters.items(): + cluster_map = {} + for cluster_num in range(num_clusters): + cluster_indices = np.where(cluster_labels == cluster_num)[0] + cluster_map[cluster_num] = cluster_indices + num_cluster_map[num_clusters] = cluster_map + train_clusters_final[method] = num_cluster_map + return train_clusters, train_clusters_final + +def get_cluster_centroids(lfi_train_values, train_clusters): + # for each method, for each number of clusters, get the clusters and compute their centroids + cluster_centroids = {} + for method, clusters in train_clusters.items(): + num_cluster_centroids = {} + for num_clusters, cluster_labels in clusters.items(): + centroids = np.zeros((num_clusters, X.shape[1])) + for cluster_num in range(num_clusters): + cluster_indices = np.where(cluster_labels == cluster_num)[0] + cluster_values = lfi_train_values[method][cluster_indices] + centroids[cluster_num] = np.mean(cluster_values, axis = 0) + num_cluster_centroids[num_clusters] = centroids + cluster_centroids[method] = num_cluster_centroids + return cluster_centroids + +def get_test_clusters(lfi_test_values, cluster_centroids): + # for each method, for its test values, assign the test values to the closest centroid + test_value_clusters = {} + for method, centroids in cluster_centroids.items(): + num_cluster_map = {} + for num_clusters, centroid_values in centroids.items(): + cluster_membership = np.zeros(len(lfi_test_values[method])) + for i, test_value in enumerate(lfi_test_values[method]): + distances = np.linalg.norm(centroid_values - test_value, axis=1) + cluster_membership[i] = np.argmin(distances) + num_cluster_map[num_clusters] = cluster_membership + test_value_clusters[method] = num_cluster_map + test_clusters = {} + for method, clusters in test_value_clusters.items(): + num_cluster_map = {} + for num_clusters, cluster_labels in clusters.items(): + cluster_map = {} + for cluster_num in range(num_clusters): + cluster_indices = np.where(cluster_labels == cluster_num)[0] + cluster_map[cluster_num] = cluster_indices + num_cluster_map[num_clusters] = cluster_map + test_clusters[method] = num_cluster_map + return test_clusters + + +if __name__ == '__main__': + + # store command-line arguments + parser = argparse.ArgumentParser() + parser.add_argument('--seed', type=int, default=None) + parser.add_argument('--dataid', type=int, default=None) + parser.add_argument('--clustertype', type=str, default=None) + parser.add_argument('--njobs', type=int, default=1) + args = parser.parse_args() + + # convert namespace to a dictionary + args_dict = vars(args) + + # assign the arguments to variables + seed = args_dict['seed'] + dataid = args_dict['dataid'] + clustertype = args_dict['clustertype'] + njobs = args_dict['njobs'] + + # get data + X, y = get_openml_data(dataid) + + # split data + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, + random_state=seed) + + # check if task is regression or classification + if len(np.unique(y)) == 2: + task = 'classification' + else: + task = 'regression' + + # fit the prediction models + rf, rf_plus_baseline, rf_plus = fit_models(X_train, y_train, task) + + # obtain shap feature importances + shap_explainer = shap.TreeExplainer(rf) + shap_train_values, shap_train_rankings = get_shap(X_train, shap_explainer, + task) + shap_test_values, shap_test_rankings = get_shap(X_test, shap_explainer, + task) + + # create list of lmdi variants + loo = {True: "aloo", False: "nonloo"} + l2norm = {True: "l2", False: "nonl2"} + sign = {True: "signed", False: "unsigned"} + normalize = {True: "normed", False: "nonnormed"} + leaf_average = {True: "leafavg", False: "noleafavg"} + ranking = {True: "rank", False: "norank"} + lmdi_variants = {} + for l in loo: + for n in l2norm: + for s in sign: + for nn in normalize: + # sign and normalize are only relevant if l2norm is True + if (not n) and (s or nn): + continue + for la in leaf_average: + for r in ranking: + variant_name = f"{loo[l]}_{l2norm[n]}_{sign[s]}_{normalize[nn]}_{leaf_average[la]}_{ranking[r]}" + arg_map = {"loo": l, "l2norm": n, "sign": s, + "normalize": nn, "leaf_average": la, + "ranking": r} + lmdi_variants[variant_name] = arg_map + + # obtain lmdi feature importances + lmdi_explainers = get_lmdi_explainers(rf_plus, lmdi_variants, + rf_plus_baseline = rf_plus_baseline) + lfi_train_values, lfi_train_rankings = get_lmdi(X_train, y_train, + lmdi_variants, + lmdi_explainers) + lfi_test_values, lfi_test_rankings = get_lmdi(X_test, None, + lmdi_variants, + lmdi_explainers) + # add shap to the dictionaries + lfi_train_values["shap"] = shap_train_values + lfi_train_rankings["shap"] = shap_train_rankings + lfi_test_values["shap"] = shap_test_values + lfi_test_rankings["shap"] = shap_test_rankings + + # get the clusterings + train_clusters_for_centroids, train_clusters = get_train_clusters(lfi_train_values, clustertype) + cluster_centroids = get_cluster_centroids(lfi_train_values, train_clusters_for_centroids) + test_clusters = get_test_clusters(lfi_test_values, cluster_centroids) + + # create a mapping of metrics to measure + if task == "classification": + metrics = {"accuracy": accuracy_score, "roc_auc": roc_auc_score, + "average_precision": average_precision_score, + "f1": f1_score, "log_loss": log_loss} + else: + metrics = {"r2": r2_score, "rmse": root_mean_squared_error} + + # for each method, for each number of clusters, + # train a linear model on the training set for each cluster and + # use it to predict the testing set for each cluster. save the results. + metrics_to_methods = {} + for metric_name, metric_func in metrics.items(): + metrics_to_methods[metric_name] = {} + for method in train_clusters.keys(): + methods_to_scores = {} + for num_clusters in range(2, 11): + cluster_scores = [] + cluster_sizes = [] + for cluster_idx in range(num_clusters): + X_cluster_train = X_train[train_clusters[method][num_clusters][cluster_idx]] + y_cluster_train = y_train[train_clusters[method][num_clusters][cluster_idx]] + X_cluster_test = X_test[test_clusters[method][num_clusters][cluster_idx]] + y_cluster_test = y_test[test_clusters[method][num_clusters][cluster_idx]] + if X_cluster_test.shape[0] == 0: + continue + if task == "classification": + model = LogisticRegression() + else: + model = LinearRegression() + model.fit(X_cluster_train, y_cluster_train) + # print("Method:", method, "; # Clusters:", num_clusters, "; Cluster:", cluster_idx) + # print(X_cluster_test.shape) + # print(X_cluster_train.shape) + y_cluster_pred = model.predict(X_cluster_test) + cluster_scores.append(metric_func(y_cluster_test, y_cluster_pred)) + cluster_sizes.append(X_cluster_test.shape[0]) + methods_to_scores[num_clusters] = \ + weighted_metric(np.array(cluster_scores), np.array(cluster_sizes)) + # average accuracy across clusters + metrics_to_methods[metric_name][method] = methods_to_scores + + # save the results + result_dir = oj(os.path.dirname(os.path.realpath(__file__)), 'results/') + # print(result_dir) + # print(metrics_to_methods) + for metric_name in metrics_to_methods.keys(): + # write the results to a csv file + print(f"Saving {metric_name} results...") + for method in metrics_to_methods[metric_name].keys(): + print("Method:", method) + # print(metrics_to_methods[metric_name]) + df = pd.DataFrame(list(metrics_to_methods[metric_name][method].items()), columns=["nclust", f"{metric_name}"]) + # print(df) + if not os.path.exists(oj(result_dir, f"dataid{dataid}/seed{seed}/metric{metric_name}/{clustertype}")): + os.makedirs(oj(result_dir, f"dataid{dataid}/seed{seed}/metric{metric_name}/{clustertype}")) + df.to_csv(oj(result_dir, + f"dataid{dataid}/seed{seed}/metric{metric_name}/{clustertype}", f"{method}.csv")) + + print("Results saved!") \ No newline at end of file diff --git a/feature_importance/subgroup/current/subgroup-runner.sh b/feature_importance/subgroup/current/subgroup-runner.sh new file mode 100644 index 0000000..9ef5ba9 --- /dev/null +++ b/feature_importance/subgroup/current/subgroup-runner.sh @@ -0,0 +1,9 @@ +#!/bin/bash + +slurm_script="subgroup.sh" + +ids=(361247 361243 361242 361251 361253 361260 361259 361256 361254 361622) + +for id in "${ids[@]}"; do + sbatch $slurm_script $id # Submit SLURM job using the specified script +done diff --git a/feature_importance/subgroup/current/subgroup.ipynb b/feature_importance/subgroup/current/subgroup.ipynb new file mode 100644 index 0000000..1bd2a36 --- /dev/null +++ b/feature_importance/subgroup/current/subgroup.ipynb @@ -0,0 +1,400 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import os\n", + "from os.path import join as oj" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# set the path we want to look at\n", + "# dataids = [\"361247\", \"361243\", \"361242\", \"361251\", \"361253\", \"361260\", \"361259\", \"361256\", \"361254\", \"361622\"]\n", + "dataids = [\"361247\", \"361243\", \"361242\", \"361251\", \"361253\", \"361260\", \"361259\", \"361254\", \"361622\"]\n", + "seed = \"1\"\n", + "metric = \"rmse\"\n", + "clustertype = \"kmeans\"\n", + "paths = []\n", + "for dataid in dataids:\n", + " paths.append(oj(\"results\", f\"dataid{dataid}\", f\"seed{seed}\", f\"metric{metric}\", str(clustertype)))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "data_results = []\n", + "for path in paths:\n", + " files = os.listdir(path)\n", + " method_results = []\n", + " for file in files:\n", + " method_result = pd.read_csv(oj(path, file))\n", + " method_result = method_result.rename(columns={\"rmse\": file[:-4]})\n", + " method_results.append(method_result)\n", + " data_result = pd.concat(method_results, axis=1)\n", + " data_result = data_result.loc[:, ~data_result.columns.str.contains('^Unnamed')]\n", + " data_result = data_result.loc[:, ~data_result.columns.duplicated()]\n", + " data_results.append(data_result)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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tSE1NPW0go/mlJY/HgxtuuAFHjhzBhx9+eNpgxGq1wmq1NrtuNpsj8pcoUvfVEtHnKPr8APHnyPnpn+hz5Pw6ds+20HQgowQxhYWF+Oijj5CSkqL2kIiIiEhDVA1kHA4HDhw4EPz+0KFD2LNnD5KTk5GVlYVf/OIX2L17N9599134fD4UFxcDAJKTk2GxWNQaNhEREWmEqoHMzp07cdlllwW/nzt3LgBg+vTpyM/Px7p16wAAw4YNC3ndRx99hDFjxnTVMImIiKgFP1XWodIFqFluq2ogM2bMmFNOXiN1yERERB1SWeyExyHuaUDPfngAa/aYUJN8CL8be6YqYxD3p0tERKQij9uHtU/uQem2aHg9frWHExGFpbUAgD5pMaqNgYEMERFRBFQW1cJT74PslVBd4lR7OGHn98s4eMIBAOiXHqvaOBjIEBERRUDF8drgnyuLxQtkfqqsQ53HD5Mko1eyehvOMpAhIiKKgKaBTMVx8QKZ/SU1AIAMG2A0SKqNg4EMERFRBFQUNc3I1J7imfq0vzQQyGRGq9uYw0CGiIgoAkKWlorEy8gUlgTqYzJtDGSIiIiE4q73oqaiPvi9vbweHrdPxRGFn7K0lKXyecwMZIiIiMJMWVayxZlhMPsBGagSqODX55dxoJQZGSIiIiEpy0pJWdEwxwX2kCk/7lBzSGF1tMIJl9cPq8mAlCh1x8JAhoiIKMyUjExSVgxMsYFApmnNjN4py0p9UmOgYsMSAAYyREREYVepZGQyo2FWApkicQKZwlL1N8JTMJAhIiIKMyVoSc5uXFoSMSPTL129owkUDGSIiIjCyFXnhaPSBQBIyoyBKTbQrVRTXg93vVfNoYXN/hJmZIiIiIRU2ZCNiUmwwBptgtES6F4CxDiqwNfkjKW+GQxkiIiIhKIsISVnNy67JGfFNDym/86lI+W1cHv9iDIbkJNoU3s4DGSIiIjCKRjIZDVmK5Iado0ToU5GWVbqmx4Lg9otS2AgQ0REFFYVRYEP+qYZmWAgI0DnUmFDoW//9DiVRxLAQIaIiCiMWlpaEiojo7ReZzCQISIiEkp9rQe11W4Agc3wFEmZgT87Kl1w1+m7cymYkdFAoS/AQIaIiChslKWj2CQrrDZT8Lo12oSYBEvIc/TI6/PjhxOB8fdnRoaIiEgsjYW+zTeKS+4RG/IcPTpc7oTb54fNbEQPDXQsAQxkiIiIwiZ4xlJ2C4FMsAVbv4GMsqzUL0MbHUsAAxkiIqKwOWVGpiG4Ubqa9KhxR19tLCsBDGSIiIjCRsnIpGQ3L4QVISOzv1Rbhb4AAxkiIqKwqHd4UGdXOpaimz2uBDK11W7U13q6dGzh0tixxIwMERGRUJQlo7jkKFiiTM0et9hMiE22NjxXf1kZj8+PQ2WBcfdjRoaIiEgsLW2EdzLl2AI9Li8dLquFxycjxqKdjiWAgQwREVFYnKrQV9FY8Ku/QCZ4xlJGHCRJGx1LAAMZIiKisFCCk1NnZPRb8Ls/eMaSdpaVAAYyREREYdGmQEbHGZnCUu0V+gIMZIiIiDqtrsaNuppAJ5JyrlJLkjID3Ux1djfqHO4uGVu4BPeQ0VChL8BAhoiIqNOUpaL41CiYrcZWn2eJMiE+NSrkNXrg9vpxuExbZywpGMgQERF1UnBZ6RSFvgo91skcKquF1y8jzmpCVkKU2sMJwUCGiIiok8rb0Hqt0GOdjFLo2zcjVlMdSwADGSIiok6rOB6oHxE1IxPc0VdDZywpGMgQERF1gizLTTqWTl8Iqzyn4ngtZFmO6NjCRauFvgADGSIiok5x2t1w1XoBqbEr6VQSM6MBCaiv9QQ7nbRuv0ZbrwEGMkRERJ2iZGMSUm0wWVrvWFKYLUYkpAa2+FeWpLTM5fXhSLkTAAMZIiIi4bTljKWT6ang94cTtfD5ZcRFmZARb1V7OM0wkCEiIuqE9rReK/RU8Bs8mkBjZywpGMgQERF1QqXgGZnChkLf/hos9AUYyBAREXVYaMdSBwIZHXQuKRmZfhpsvQYYyBAREXWYs9oNl9MLSQISM07fsaRIzIiGZJDgcnrhrNb2mUuFpUpGhoEMERGRUJQal4T0aJjMp+9YUpjMRiSkKZ1L2l1eqvf4cKRcOWOJS0tERERC6Uihr0IPdTIHTzjgl4EEmxlpcdrrWAIYyBAREXVY8GiCdtTHKBo7l7S7l8yB0sZCXy12LAEqBzKffPIJJk2ahOzsbEiShLVr14Y8LssyHnroIWRlZcFms2Hs2LEoLCxUZ7BEREQnCR4WKWhGJljoq9H6GEDlQKa2thZDhw7FihUrWnz88ccfx7PPPosXXngBn3/+OWJiYnDllVeivr6+i0dKREQUSpZlVHagY0nRdC8ZrXYuKWcs9U/XZn0MAJjUfPMJEyZgwoQJLT4myzKefvpp/PGPf8Q111wDAPj73/+OjIwMrF27Fr/85S+7cqhEREQhHJUuuOt9kAwSEtPb3rGkSMyIhsEgwV3vg6PShbjkqAiMsnMKS7R7xpJC1UDmVA4dOoTi4mKMHTs2eC0hIQHnn38+tm3b1mog43K54HK5gt/b7XYAgMfjgccTvsO5lHuF855aI/ocRZ8fIP4cOT/90/McTxytBgAkpEfBDx/8Hl+z55xufgnpNlQWO3HiaDWi4tre9dQV6j0+HKkInLGUlxLV4hwi+ftr6z0lWSP5LEmSsGbNGkyePBkAsHXrVowePRrHjx9HVlZW8Hk33HADJEnCm2++2eJ98vPzsWjRombXV69ejejo9kfMRERELak5ZEb13ijYMj1IGd6xkofyL6JQV2xGwln1iMvTVjD3Uy3wxFcmxJhkPDrSh66u9XU6nZg6dSqqq6sRHx/f6vM0m5HpqPnz52Pu3LnB7+12O3JycjB+/PhT/iDay+PxoKCgAOPGjYPZbA7bfbVE9DmKPj9A/Dlyfvqn5zlu/ud+VKMEA4afgZE/z23xOaeb3y4cwa73f0RGYi7G/Lx/pIfcLu/sOQ589Q3O7pmMiRPPbfE5kfz9KSsqp6PZQCYzMxMAUFJSEpKRKSkpwbBhw1p9ndVqhdXavNfdbDZH5F+SSN1XS0Sfo+jzA8SfI+enf3qcY1VxHQAgrWf8acfe2vxSe8YH76W1+R8sD8zvzMy4Ds+vM9p6P83uI5OXl4fMzExs2rQpeM1ut+Pzzz/HqFGjVBwZERF1dyEdSx1ovVY0bcGW/Zqo9AjSQ6EvoHJGxuFw4MCBA8HvDx06hD179iA5ORm9evXC7Nmz8cgjj6Bfv37Iy8vDggULkJ2dHayjISIiUkNNRT08Lh8MRgkJGbYO3ych3QaDSYLX5UNNRT3iUzt+r3BTWq+1elikQtVAZufOnbjsssuC3yu1LdOnT8crr7yCP/zhD6itrcWvf/1rVFVV4aKLLsL69esRFaW9FjUiIuo+lPOREjOiYTR2fHHDaDQgKSMa5cdqUVFUq5lAps7tw9HKQMeSVs9YUqgayIwZM+aUmwBJkoTFixdj8eLFXTgqIiKiU+vMGUsnS86KCQQyx2vRe3Bqp+8XDgdKHZBlICXGgpRYbZ6xpNBsjQwREZFWVR7v+I6+J9PiUQWNRxNoOxsDMJAhIiJqt/BmZALBgrJcpQX7S/VR6AswkCEiImoX2S83BjJhzMhUaqhzqVAp9GUgQ0REJBZ7eT28bj8MJgkJaZ0vzo1Ps8FoNsDr8cPesHeL2pSlJS0fFqlgIENERNQOSjYmKSMahk50LCkMBglJmYEjdLSwvFTr8uKnykBAxaUlIiIiwVQcDyy7JGeHL1uh1NpooeD3QGlgfqmxViTFWFQezekxkCEiImqHcBb6KoKdSxrIyASXlXTQsQQwkCEiImqXijC2XiuUoKhcA4FMYUNGRg/LSgADGSIiojbz+2VUFgd2vA1vRiaQ/agqdsKvcueSnvaQARjIEBERtZm9rA4+jx9GswHxYehYUsSnRMFkMcDn9cN+Qt3OJaX1mhkZIiIiwSjLSkmZ0TAYpLDdVzJISMpUv07G4fLiWFUgkOqng9ZrgIEMERFRm0Wi0FfReFSBI+z3bqvChmWltDgrEqO137EEMJAhIiJqs0gU+iqCLdgqZmQal5X0kY0BGMgQERG1WVdkZNTsXAoW+qbroz4GYCBDRETUJn6/jCqlYykSGZmGe1aVOOHz+cN+/7bYr7PWa4CBDBERUZvYT9TB5/XDZDYgPiV8HUuKuOQomK1G+H0yqkvV6Vwq1NlmeAADGSIiojYpbziaICkrBlIYO5YUkiQhScU6GXu9B0XV9QD0ceq1goEMERFRGwQLfSNQH6No7Fzq+kBGKfTNiLciwWbu8vfvKAYyREREbRAs9I1AfYyisXOp61uwG5eV9JONARjIEBERtUkkW68VKSoeHrm/ISOjp44lgIEMERHRafl8flSVhP+MpZMpQVJ1aaCwuCsVluqv0BdgIENERHRa1aV18PtkmKxGxCVHRex9YhKtsEQZA63eDYFTV2k8LJIZGSIiIqEEl5UyoyPSsaSQJEmVgt/qOg9K7C4A+jn1WsFAhoiI6DS6otBXocZRBUqhb1ZCFOKj9NOxBDCQISIiOq3G1uvIZyuSs2ND3rMrBAt9dbasBDCQISIiOq0uzciosLSk1Mf0T9fXshLAQIaIiOiUfF4/qksid8bSyRo7l5zwenwRfz+gaccSMzJERERCqSp1wu+XYY4yIjbJGvH3i463wBptgiyjyzqXGpeWmJEhIiISStOjCSQpch1LipDOpS6ok6lyunGiRulYYkaGiIhIKMH6mAhuhHeyruxcUrIxPRJtiLWaIv5+4cZAhoiI6BS64miCkynvVd4lgYyyEZ7+lpUABjJERESnpE4g09CC3QWdS3o9LFLBQIaIiKgVPo8f1SfqAHTNHjIKZWnJXlYHjzuynUuNh0UyI0NERCSUyhInZL8Mi82EmERLl71vdLwFUbFmQAaqiiPbuaTn1muAgQwREVGrKooC2Yqu6lhqqrHg1xGx96iodaPM4QYA9GVGhoiISCxq1McouqLgVyn07ZlkQ4wOO5YABjJEREStarqHTFdL6YKjCvRe6AswkCEiImpVV56xdLKu2BRPzzv6KhjIEBERtcDr8cGudCypEcg0dEnVlNfDXe+NyHs0HhbJjAwREZFQKoudkGXAGm1CdHzXdSwpomLNsDW8b2WEOpcOlAYyMlxaIiIiEkzTQt+u7lhSRPKognKHC+W1bkiSfjuWAAYyRERELVLjjKWTNdbJhL8FW6mPyUmKhs1iDPv9uwoDGSIiohao2XqtiGTnUuNGePrNxgAMZIiIiFqkiYxMBJeWGg+L1G99DMBAhoiIqBmP2wd7mdKxpF7GQskGOSpdcNeFt3NJWVpiRoaIiEgwlUW1gBzoHFKjY0lhjTYjJiHw/uFcXpJlObgZXj8dt14DGg9kfD4fFixYgLy8PNhsNpxxxhl4+OGHIcuy2kMjIiKBaWFZSRGJjfHKHG5UOj0w6LxjCQA0fbDCsmXLsHLlSrz66qsYOHAgdu7cidtuuw0JCQm4++671R4eEREJSguFvorkrFgc/b4yrIGMko3plRyNKLN+O5YAjQcyW7duxTXXXIOJEycCAHr37o3XX38d27dvV3lkREQkMk1lZHoonUvha8EWpdAX0PjS0oUXXohNmzZh//79AIAvv/wSW7ZswYQJE1QeGRERiUxbGZnwLy3tLxWj0BfQeEZm3rx5sNvtOOuss2A0GuHz+fDoo49i2rRprb7G5XLB5XIFv7fb7QAAj8cDj8cTtrEp9wrnPbVG9DmKPj9A/DlyfvqnxTl6XD7UlNcDAOLTrJ0aWzjmF5caKPatrXbDUV0Ha3TnP7r3Fwc+G/ukRKs+v9Pd+3QkWcOVs2+88Qbuu+8+PPHEExg4cCD27NmD2bNnY/ny5Zg+fXqLr8nPz8eiRYuaXV+9ejWio6MjPWQiItI5d5UBpdtiYLD4kX1F5E6ebo+ij2Lgqzcg7QInrEm+Tt1LloEHdhjh9En4wxAveqifdGqR0+nE1KlTUV1djfj4+Fafp+lAJicnB/PmzcPMmTOD1x555BH84x//wN69e1t8TUsZmZycHJSVlZ3yB9FeHo8HBQUFGDduHMxmc9juqyWiz1H0+QHiz5Hz0z8tznHff0vw8T/3I7tfAq66e0in7hWu+b3//Dc4+n0lLrqxL86+KKtTYyqtcWH04x/DIAFfLbgC1k4U+0by92e325GamnraQEbTS0tOpxMGQ2gZj9FohN/vb/U1VqsVVqu12XWz2RyRf0kidV8tEX2Oos8PEH+OnJ/+aWmO1aWBZaWUHnFhG1Nn55fSMw5Hv69EdWl9p8d0qLwaANA7JQax0VGdupciEr+/tt5P04HMpEmT8Oijj6JXr14YOHAgvvjiCyxfvhwzZsxQe2hERCQoLRX6KsJZ8NvYsaT/Ql9A44HMc889hwULFuCuu+5CaWkpsrOz8Zvf/AYPPfSQ2kMjIiJBKW3OWmi9ViSH8fDIxsMi9d96DWg8kImLi8PTTz+Np59+Wu2hEBFRN+Cu88JREaiz1FJGJikz0KxSZ3ejzuGGLbbjxyYoZyyJsIcMoPF9ZIiIiLqSkvGITrAgKkYbNTsAYIkyIS4lUM/SmeUlWZaDS0si7CEDMJAhIiIK0tKOvicLx5lLJXYXauq9MBok5KVqb44dwUCGiIiogRYLfRUpYaiTUbIxvVOiYTXp+4wlBQMZIiKiBprOyIShc6lxWUmM+hiAgQwREVFQY0ZGe/Ujypg6k5EpFKzQF2AgQ0REBABwOT2orWroWMrS3pE2iZnRgATUOzxw2t0dusf+UrEKfQEGMkRERACAiiInACAm0QprtHY6lhRmixHxqTYAQMVxR7tfL8syDpQop14zI0NERCQUJTjQYqGvIlgn04HlpaLqetS4vDAZJPRO0e4c24uBDBEREbRd6KtI6UQLtlLom5caA4tJnI9/cWZCRETUCVpuvVZ05qiCQgGXlQAGMkRERAD0kZFpuimeLMvteq1oh0UqGMgQEVG3V1/rgbM60Amk5UAmMSMakgS4nN52dy7tL21ovU5nRoaIiEgoSjYmNtkKi0275ymbzEYkpAdawyuOtX15KdCxJF7rNcBAhoiIqLE+Jkv7H/IdqZM5VlWHWrcPZqOE3oKcsaRgIENERN2eHgp9FY1HFbR9Lxml0DcvNQZmo1gf/WLNhoiIqAMqihr2kNFwfYyiIxmZxkJfsepjAAYyREREOs3ItL1zab/Sei1YoS/AQIaIiLq5OocbdTUeAEBSpvbOWDpZYkY0DAYJ7nofHJWuNr2mUMAzlhQMZIiIqFtTsjFxKVGwRGm3Y0lhNBmQkN5w5lIblpf8flnIU68VDGSIiKhb09OykiI5O5BZactRBceq6lDn8cFiNKB3ivYzTu3FQIaIiLo1Pezoe7L2FPwqhb590mJgEqxjCWAgQ0RE3ZwuMzJZbT88cr/Ay0oAAxkiIurm9JyRqSyqhew/dedSobKjb7p4hb4AAxkiIurGnHY36h0eQAKSdBTIJKTbYDBK8Lh8qKmoP+Vz95eKu4cMwECGiIi6MSUbE58SBbPFqPJo2s5oNARbxU9VJ+P3yzjQcFikiK3XAAMZIiLqxhrrY/T3Id+WOpmjlU7Ue/ywmAzITdFPxqk9GMgQEVG3FayP0VGhr6ItnUtKoe8ZabEwGqQuGVdXYyBDRETdlnLwop4KfRXKSd2nysgordeiLisBDGSIiKibkmVZl63XirZ0LgU7lgQt9AUYyBARUTfltLvhcnohSfo4Y+lk8Wk2GE0GeD1+2MvrWnxOcA8ZQVuvAQYyRETUTSnZmPg0G0xm/XQsKQwGCUlZDZ1LLSwv+fwyDp5QOpaYkSEiIhJKcFlJh/UximDnUgsFvz9WOOHy+mE1GZCTrL+MU1sxkCEiom5Jzx1LimDnUgsZGaXQt2+6uB1LAAMZIiLqpvRc6Ks4VUamcSM8cZeVAAYyRETUDcmy3OSMJf0WwjZ2LjnhP6lzScnI9BO49RpgIENERN1QbZUb7jovJIOEpAz91o/Ep9hgMhvg8/phPxHauaR0LPVPZ0aGiIhIKBVFgQ/5hDQbjGb9fhRKBil42GXTOpnu0rEEMJAhIqJuSIT6GEXjUQWO4LUj5bVwe/2wmY3omWRTa2hdol2BTGlp6Skf93q92L59e6cGREREFGkidCwpWjo8UllW6pseC4PAHUtAOwOZrKyskGBm8ODBOHr0aPD78vJyjBo1KnyjIyIiigAR9pBRtHR4ZGE3KfQF2hnIyHJoRfThw4fh8XhO+RwiIiItCelYEigjU1nshM/nBwDs7yat10AEamQkSewUFhER6Zuj0gVPvQ8Gg4TEdP12LCnikqNgthrh98moLg10LhV2g1OvFSz2JSKibkVZVkrIiIbRpP+PwZM7l7w+P344EZhjP8FbrwHA1J4nS5KEmpoaREVFQZZlSJIEh8MBu90OAMF/EhERaZVI9TGK5OwYlB62B5bMcqLh9vkRbTGiR6LYHUtAOwMZWZbRv3//kO+HDx8e8j2XloiISMuUNmUR6mMUTTuXykuiAAD9ukHHEtDOQOajjz6K1DiIiIi6hKgZGQCoOO7AiRIrAKBfNyj0BdoZyFx66aWRGkerjh07hvvvvx/vv/8+nE4n+vbti1WrVmHkyJFdPhYiItI32S+jotgJQMyMTHVpHfYVB8o8ukOhL9DOQMbr9cLn88FqtQavlZSU4IUXXkBtbS2uvvpqXHTRRWEbXGVlJUaPHo3LLrsM77//PtLS0lBYWIikpKSwvQcREXUfNRX18Lp8MBglJKSLUz8Sm2SFJcoId70P+44HAhlmZFrwP//zP7BYLHjxxRcBADU1NTj33HNRX1+PrKwsPPXUU3jnnXfw85//PCyDW7ZsGXJycrBq1argtby8vLDcm4iIuh9l/5jEjGgYjfrvWFJIkoTk7Bgc+6EaRyrrAXSPPWSAdrZff/bZZ7j++uuD3//973+Hz+dDYWEhvvzyS8ydOxdPPPFE2Aa3bt06jBw5ElOmTEF6ejqGDx+Ol19+OWz3JyKi7kWkM5ZOlpwVg0qDDK8sI9ZqQnZClNpD6hLtysgcO3YM/fr1C36/adMmXH/99UhISAAATJ8+PSR70lk//PADVq5ciblz5+KBBx7Ajh07cPfdd8NisWD69OktvsblcsHlcgW/V1rCPR5Ps12IO0O5VzjvqTWiz1H0+QHiz5Hz07+unmPZscBGcYkZti55z66cX0KGDeXGwO76Z6TFwOv1Rvw9Izm/tt5TkttxpkBKSgo+/fRTnH322QCA7OxsPPHEE5g2bRqAQOAxaNAgOJ3ODgy5OYvFgpEjR2Lr1q3Ba3fffTd27NiBbdu2tfia/Px8LFq0qNn11atXIzpa/zs4EhFRx5V8Fg2P3YiU4XWwZUb+g74r1ZcZ8c7XZmyN8uL8ND+m9vWrPaROcTqdmDp1KqqrqxEfH9/q89qVkRk2bBhee+01LFmyBJ9++ilKSkpw+eWXBx8/ePAgsrOzOz7qk2RlZQWDJsWAAQPw73//u9XXzJ8/H3Pnzg1+b7fbkZOTg/Hjx5/yB9FeHo8HBQUFGDduHMxmc9juqyWiz1H0+QHiz5Hz07+unKPsl7Fq01YAflxx1UVdcjxBV86vttqFv377KQBgzLAz8fNLIl9TGsn5tXWT3XYFMg899BAmTJiAt956C0VFRbj11luRlZUVfHzNmjUYPXp0+0Z6CqNHj8a+fftCru3fvx+5ubmtvsZqtYZ0VSnMZnNE/hJF6r5aIvocRZ8fIP4cOT/964o5Vp+og9fth9FkQEpWfJduFtcV80tIMaGi4S16Rlm79O9MJObX1vu1ex+ZXbt2YePGjcjMzMSUKVNCHh82bBjOO++89tzylObMmYMLL7wQjz32GG644QZs374dL730El566aWwvQcREXUPFccDO/omZkYLueOtxyejQgosJyV7xJtfa9oVyACBpZ0BAwa0+Nivf/3rTg+oqXPPPRdr1qzB/PnzsXjxYuTl5eHpp58O1uQQERG1ldJ6LdKOvk0dKquFH4BFBgyVbrWH02XaFch88sknbXreJZdc0qHBtOSqq67CVVddFbb7ERFR9yRy6zUA7C8JdGSl+gyoKApP040etCuQGTNmTPBQyNaanSRJgs/n6/zIiIiIwkj0jExhMJCRgnPtDtoVyCQlJSEuLg633norbrnlFqSmpkZqXERERGHj98uoFPCMpab2lwRqgFL8BtjL6uBx+2C2GFUeVeS1a2ffoqIiLFu2DNu2bcPgwYNx++23Y+vWrYiPj0dCQkLwi4iISEvsJ+rg8/hhNBsQnyrOGUtN7S8NZGSyLWZABqqKu8fyUrsCGYvFghtvvBEbNmzA3r17MWTIEMyaNQs5OTl48MEHu2QXQSIiovZSllqSBO1Ycnl9OFIeCFz6pQVOvVa6tETX4ROzevXqhYceeggffPAB+vfvj6VLl7Z58xoiIqKuJHqh7w8nauHzy4iPMqF3z8Bhkd2lTqZDgYzL5cLq1asxduxYDBo0CKmpqXjvvfeQnJwc7vERERF1muiFvkrHUv+MOKRkBzIy5ce7RyDTrmLf7du3Y9WqVXjjjTfQu3dv3HbbbXjrrbcYwBARkaY1ZmRiVR5JZBQ2FPr2y4gLZp0qGMg0d8EFF6BXr164++67MWLECADAli1bmj3v6quvDs/oiIiIOsnv86OyJPChniLo0lJjRiY2GMjUlNfDXe+FJarde9/qSrtn9+OPP+Lhhx9u9XHuI0NERFpSfaIOfq8Mk8WAuOQotYcTEYWlgYxM/4w42GItsMVbUGd3o7LYiYze4TswWYvaVSPj9/tP+1VTUxOpsRIREbVb0/oYScCOpXqPD0fKA3PslxFYOlNqgbrD8lKHu5ZO5nK5sHz5cvTp0ydctyQiIuq0YH2MoIW+B0844JeBxGgz0mKtANCkTkb8Fux2BTIulwvz58/HyJEjceGFF2Lt2rUAgL/97W/Iy8vDU089hTlz5kRinERERB2iBDJJgtbHKIW+/dPjgscIBTMy3aAFu101Mg899BBefPFFjB07Flu3bsWUKVNw22234b///S+WL1+OKVOmwGgUfztkIiLSj+7Seq0sKwHoVp1L7Qpk/vWvf+Hvf/87rr76anzzzTcYMmQIvF4vvvzyy2AUSEREpBU+nx9VJd3jjKV+6U0CmYagzVHpgrvOC4tN3M6ldi0t/fTTT8G260GDBsFqtWLOnDkMYoiISJOqS+rg98kwW40Cdyw1boaniIoxIybBAkD85aV2BTI+nw8WiyX4vclkQmysmJsLERGR/gXPWMqKEfJ/uuvcPvxY0XDGUpNABmiyvCR4INOuXJMsy7j11lthtQaqouvr63HnnXciJiY0Xff222+Hb4REREQdpHTtiLqsdPCEA7IMJEWbkRprCXksOSsWR7+vRMUxBjJB06dPD/n+5ptvDutgiIiIwqn7FPrGNcs4NWZkxG7Bblcgs2rVqkiNg4iIKOxEP/VaKfTtn9G8zKO7dC6FbUM8IiIiLfF5/agurQMgbkamsKR5oa9CmXNttRsup6dLx9WVGMgQEZGQqkqc8PtlWKKMiE2yqj2ciNjf0LHUL715IGOxmYLzFjkrw0CGiIiEFKyPyRazY8np9uJoRSDj1NLSEtA9OpcYyBARkZBEP2PpQMOJ1ykxFqTEtpxxUuZezowMERGRvjRmZMTc7yy4o28r2RigexT8MpAhIiIhiZ6ROVWhr0IJ4ri0REREpCNejw/VpaKfsdS4h0xrkjKjAQB1djfqHWJ2LjGQISIi4VSVOCHLgDXahOgEy+lfoEOFDTUy/dNbX1qyRJkQlxI4Y0rUjfEYyBARkXCaLiuJ2LFU6/Lip0qlY6n1jAzQmJEqF/SoAgYyREQkHCWQSRJ0WUnpWEqNtSIp5tQZJ6VGSNQ6GQYyREQknO5yxlJr+8c0JXrnEgMZIiISjuhnLAXrY06zrAQAKYJ3LjGQISIioXjdPlSXiX3GUmPH0ukzMomZ0YAE1Ds8cNrdkR5al2MgQ0REQqksdgIyYI0xITpe0I6lkrZnZMwWI+JTbQDEzMowkCEiIqEoH9Yp2bFCdiw5XF4cq2roWGrhsMiWBAt+j4vXgs1AhoiIhNJddvRNj7MiIdrcpteIXPDLQIaIiITS9NRrEbVnWUmRIvAp2AxkiIhIKMryiagZmfYU+iqaZmRkWY7IuNTCQIaIiIThcflgL68HIG5GZn87Wq8ViRnRkCTA5fQK17nEQIaIiIRRWVwLyIAtzgxbnKgdS23fDE9hMhuRkB44QLJCsKMKGMgQEZEwRC/0tdd7UFQdyDj1bWPHkkLUowoYyBARkTBED2SUQt/M+Cgk2NrWsaRorJMRqwWbgQwREQlD/I6l9hf6KpIF7VxiIENERMIQ/Yyl/R1ovVY0boonVucSAxkiIhKCu96LmoqGjqWs9mcs9KCwtP2FvorEjGgYDBLc9T7UVrnCPTTVMJAhIiIhVBY5AQC2eAuiYttXP6IXjXvItD8jYzQZkJAeOHOpXKAdfhnIEBGRECqKxN4Ir7rOgxJ7IJPSL71jGScRjyrQVSCzdOlSSJKE2bNnqz0UIiLSGOXDOUXQ+hil0Dc7IQpxUR3LOInYgq2bQGbHjh148cUXMWTIELWHQkREGiR6x5JS6NuRZSVFcnYgk8OMTBdzOByYNm0aXn75ZSQlJak9HCIi0iDR95DZ34EdfU+mBHmVRbWQ/WJ0LukikJk5cyYmTpyIsWPHqj0UIiLSIHedF47KQP2IqBkZpWOpMxmZhHQbDEYJHpcv2OGldya1B3A6b7zxBnbv3o0dO3a06fkulwsuV2Nbmd1uBwB4PB54PJ6wjUu5VzjvqTWiz1H0+QHiz5Hz079wzbH0aOC/9dEJFhjM2vmZhfN3uL84EMj0SbF16n4J6TZUFjlx4qgdtoTOhQGR/Dva1ntKsoZ3xTl69ChGjhyJgoKCYG3MmDFjMGzYMDz99NMtviY/Px+LFi1qdn316tWIjo6O5HCJiEgltUfNqPwmCtYUL9LOq1N7OGFX6wEe2BkIOh4/zwurseP3Kt8ThboiMxLOdCGuj3ZPwnY6nZg6dSqqq6sRHx/f6vM0HcisXbsW1157LYzGxt+Yz+eDJEkwGAxwuVwhjwEtZ2RycnJQVlZ2yh9Ee3k8HhQUFGDcuHEwm8Xcr0D0OYo+P0D8OXJ++heuOW7990F8s/k4Bo3JxoXXnxHGEXZOuOa343Alpv51B3okRmHzvZd0aky71/+Ine8dQb/z0nHZLWd26l6R/Dtqt9uRmpp62kBG00tLV1xxBb7++uuQa7fddhvOOuss3H///c2CGACwWq2wWq3NrpvN5oj8hyBS99US0eco+vwA8efI+elfZ+dYXRLIwqT1jNfkz6qz8/uhPDC//hlxnZ5fWs9AUFBVXBe2n1Uk/o629X6aDmTi4uIwaNCgkGsxMTFISUlpdp2IiLov0c9YKgx2LHW80FdxcueSZJA6fU816aJriYiIqDUupwe11YFajyRhW687v4eMIj7NBqPJAK/HD3u5/uuJNJ2RacnmzZvVHgIREWmIko2JTbLCatPdx1qbdOawyJMZDBISM6NR/pMDFcdrkZCm70YYZmSIiEjXgjv6CpqNqah1o8wRyDj17eAZSycT6agCBjJERKRrotfHKDv65iTbEG0JT8YppYc4h0cykCEiIl0T/YylYKFveufrYxTMyBAREWlE4xlL4Vl20ZpwFvoqGjuXnPDr/MwlBjJERKRb9bUeOO1Kx5K+i1ZbE47DIk8Wn2KDyWyAz+uH/YS+O5cYyBARkW4p2Zi45ChYokTtWApkZMKxh4xCMkjBVnW918kwkCEiIt0SvT6mzOFCRa0bkgSckRbepbPGOhlHWO/b1RjIEBGRbjXWx4gZyCjLSr2So2GzdOKkyBYowR8zMkRERCqpOB7IJoiakSlUCn3D2LGkCAYyOu9cYiBDRES6JfrSUiQKfRVKFquy2Amfzx/2+3cVBjJERKRLdTVu1NV4AABJmWIGMkpGJpyFvoq45CiYrEb4fTKqS/XbucRAhoiIdEmp7YhPjYLZGt76ES2QZRn7G85Y6heBjIxkkJCcGWhZ13OdDAMZIiLSJdHPWDrhcKHK6YEhAh1LiuQegfvquU6GgQwREemS6GcsKctKuSkxiDJHJuOULMBeMgxkiIhIlxoLfUU9miCwrBSuE69bIkLnEgMZIiLSHVmWu8EeMkqhbwQDmYafXXWJEz6vPjuXGMgQEZHu1NV4UF/rgSQBSZlinrEUPPU6Ah1LitgkKyxRRvj9MqpKnBF7n0hiIENERLqjbIQXn2qDKcw73mqBLMvBpaVIbIankKQmZy7pdHmJgQwREemO6Bvhlda4YK/3wiABfdIiO8cUnR9VwECGiIh0R/z6mEA2pncEO5YUSrE0MzJERERdRPSMTPCMpQgW+ir03oLNQIaIiHQlpGNJ1ECmNPKFvgrlZ1hd6oTX44v4+4UbAxkiItIVZ7UbLqcXkgQkZojZsbQ/mJGJfCATnWCBNdoEWYYuO5cYyBARka4o2ZiE9GiYIlw/ooamHUuR3ENGIUmSrpeXGMgQEZGuiH7GUondhZp6L4wGCXmpXTPHZB13LjGQISIiXVH2kBG1PqaxYykaVlPXZJz0fFQBAxkiItIV0TMy+7tgR9+TKT/LcmZkiIiIIqdbdCx1YaGvQtlLxl5WB49bX51LDGSIiEg3aqtccNf7YDBI4nYslXZdoa/CFmdGVIwZkIGqYn11LjGQISIi3WjsWLLBaBLvI0yWZRwInnrddRkZSZKaFPw6uux9w0G8vwVERCQs0Xf0LaquR43LC5NBQu+Urp2jXgt+GcgQEZFudJczlvJSY2Dp4oyTXgt+GcgQEZFuNGZkuq5+pCsVqrCspNDrXjIMZIiISBdkWe42rdddcVjkyZRApqa8Hu56b5e/f0cxkCEiIl1wVLrgqffBYJSQkGFTezgRsb9UvYyMLdYCW5wZAFCpo84lBjJERKQL5ccCH/KJGdEwGsX7+Ap0LHV963VTypKdnpaXxPubQEREQhJ9WelYVR1q3T6YjRJyu7hjSaHHziUGMkREpAuV3WRH3z6psTCrlHFqPAVbP3vJMJAhIiJdED0jo2ahr0KPnUsMZIiISPNkvyz8Znj7VWy9VihBoqPSBXedPjqXGMgQEZHm1VTUw+v2w2CSkJAmZsdSoQpnLJ0sKsaM6AQLAP3UyTCQISIizVOWOpIyYmAQsGPJ75dVOfW6JSk6K/gV728DEREJR/RlpWNVdajz+GAxGpCbrO6p3slZ+mrBZiBDRESa113OWOqTFgOTyhknvZ2CzUCGiIg0T/SMjBYKfRV661xiIENERJom+2VUCt56Xajyjr5NJTX8jGur3XA5PSqP5vQYyBARkabZy+vg9fhhNBsQL2jH0v5SZQ8Z9TMyVpsJsUlWAPrIymg6kFmyZAnOPfdcxMXFIT09HZMnT8a+ffvUHhYREXWhYMdSZjQMBknl0YSf3y/jgIqHRbZET0cVaDqQ+fjjjzFz5kz897//RUFBATweD8aPH4/aWu3/YImIKDxE39H3aKUT9R4/rCYDeqncsaRQftblOsjImNQewKmsX78+5PtXXnkF6enp2LVrFy655BKVRkVERF2p/Fj3KPQ9Iy0WRo1knPRU8KvpQOZk1dXVAIDk5ORWn+NyueByuYLf2+12AIDH44HHE76iJeVe4byn1og+R9HnB4g/R85P/9oyR6UNOD49Snc/i7bMb+/xKgBA37QYzcwvPi0KQOBnf6oxRfLvaFvvKcmyLIf93SPA7/fj6quvRlVVFbZs2dLq8/Lz87Fo0aJm11evXo3oaG2k7IiIqG1kGTi2MRbwS8i8xAFTjC4+strl74UG7Coz4KpePozroY35+b3A8YJAvU7WFTUwWrp+DE6nE1OnTkV1dTXi4+NbfZ5uApnf/va3eP/997Flyxb07Nmz1ee1lJHJyclBWVnZKX8Q7eXxeFBQUIBx48bBbDaH7b5aIvocRZ8fIP4cOT/9O90cq0qdeOvhXTCaDZjxpwshaWTppa3a8ju8esU2fF9cgxemDsMVA9K7eISte33hdtRUuDDpniHI6pvQ4nMi+XfUbrcjNTX1tIGMLpaWZs2ahXfffReffPLJKYMYALBarbBarc2um83miPyHIFL31RLR5yj6/ADx58j56V9rc6wpdQMIFJ9arCqkBcKktfn5/DJ+KAvUoQzokaip33Nyj1jUVLhgL61HrwGpp3xuJP6OtvV+mu5akmUZs2bNwpo1a/Dhhx8iLy9P7SEREVEXqigK1MeIWuj7Y4UTLq8fUWYDcpK0Vf6gl84lTWdkZs6cidWrV+Odd95BXFwciouLAQAJCQmw2cTcFImIiBp1lzOW+qbHam6PHL10Lmk6I7Ny5UpUV1djzJgxyMrKCn69+eabag+NiIi6gOhnLAWPJkjXxkZ4TSnBo9Y3xdN0RkYndchERBQBfp8flSVOACJnZAJLZ1o4muBkSVkxgATUOzxw2t2IjtdmjZKmMzJERNR9VZ+og98rw2Q1Ii45Su3hRMR+DR0WeTKzxYj41EAZh5azMgxkiIhIk4L1MZnRumu7bguvz48fTgTmqJUzlk4WXF5q2JRQixjIEBGRJoleH3Okwgm3zw+b2YgeidpsYNFDwS8DGSIi0qTGjiXtLbuEg1Lo2y9Dex1LCj0U/DKQISIiTVL2LxE1IxMs9NVgx5KiaUZGqw04DGSIiEhzfF4/qpWOJWEDGe0W+iqSMqMhSYDL6YXT7lZ7OC1iIENERJpTVeqE3y/DHGVEbFLzY2dEUNiQkdFqoS8AmMxGJKQHdhzWap0MAxkiItKcpjv6SpI260c6w+Pz44cyZQ8Z7WZkgKadSwxkiIiI2kT4jqXyWnh8MmIs2u1YUjTWyWizBZuBDBERaU6l8GcsBYKCvhlxms84ab1ziYEMERFpjugZmWChb7q2l5UA7XcuMZAhIiJN8Xn8qCqtAyDyHjLaL/RVJGZEw2CQ4K73obbKpfZwmmEgQ0REmlJV6oTsl2GxmRCTqM2DCjtrf5PN8LTOaDIgIb3hzCUNFvwykCEiIk3pDh1Lh8q0fcbSyZTlpXIGMkRERKcmen3M4bJaeP0yYq0mZCXo41RvLRf8MpAhIiJNqeguHUvpsbrJOCVnB5bAuLRERER0GqJnZPRwNMHJlKCyskh7nUsMZIiISDO8Hh+qS8U+Y6mwVAlk9FEfAwAJGTYYjBI8Lh9qKurVHk4IBjJERKQZlcVOyDJgjTYhOl7UjiXlaAL9BDJGowGJGdo8c4mBDBERaUawPiZbzI4lt9ePw8GOJf0sLQGhG+NpCQMZIiLSjGB9jKCFvocaOpbirCZkxuujY0mh1c4lBjJERKQZjRkZfWUr2qrpRnh6yzgxI0NERHQaoncsFZbor9BXkdIQXFYW10L2a6dziYEMERFpgsftg71MOWNJzEBGj4W+ivg0G4wmA7xuP+zl2ulcYiBDRESaUFXsBGQgKtYsbsdSqf72kFEYDBISM5XOJYfKo2nEQIaIiDRB+XAUNRvj8vpwpDywR44el5YAbRb8MpAhIiJNEL0+5ocTtfD5ZcRHmZAeZ1V7OB2ixYJfBjJERKQJ4p+x1Fjoq7eOJQUzMkRERK0QPSNTqONCX0VKD+XMJSf8GulcYiBDRESq87h8sJcFOmFEDWT0eFjkyeJTbDCZDfB5/bCfqFN7OAAYyBARkQZUlQSKYG1xZthixexYKiwNZGT0WugLAJJBQlKWtupkGMgQEZHqKovEPvHa5fHhSHngg7+fjjMyQNM6GW20YDOQISIi1TWesaTvD/nWHCyrhV8GEqPNSIvVZ8eSQmudSwxkiIhIdaJnZApLG068Ttdvx5JCa51LDGSIiEh1ogcyB0qVjiX9Z5yU31FliRN+n1/l0TCQISIilfm9gKPSBUDcPWREKPRVxCVHwWQ1wu+VUX1C/TOXGMgQEZGqPI7AR1F0ggVRMWaVRxMZhQJlZCSDhOSGM5cqNbC8xECGiIhU5a0JfBSJmo1x+4CjlYE9V0TIyABNlpcalgTVxECGiIhU5XEYAYhbH1NSB8gykBxjQarOO5YUSndZZTEDGSIi6uaUpSVRMzLFdYEupX7p+l9WUiT3YEaGiIgIAOBVAplscT7omypyBgIZUZaVgMags6q0DrLKjUsMZIiISDXuOi989UpGJlrl0URGccORRHo+Y+lksUlWWKKMkP0yvLXqhhIMZIiISDVKjUVMogXWaDE7loobMjJ6PvX6ZJLUeOaSsjSoFgYyRESkGqXGIilTzGyM0+1FuUu8pSWgsTjbU8NAhoiIuillm/skQQt9D54IzC8lxoLkGLFO9VbqZLwqZ2RMqr47ERF1Tz4P4KpB5dEKAEBSqgHweQGjWB9LwY3w0sUL1FIairPVXlrSxd+YFStW4IknnkBxcTGGDh2K5557Duedd57awyIi6l78PsDtAFwOwFUT+HLXNP45eN3e8Lym10+65g1sbV9Z+lcAyUjbdBPwaSFgMAPmaMBsAyzRjX8225r8uem1mBauNXleS/cwWoAuOrhROSxSpNZrhbK05K01wOvxw6xSiZPmA5k333wTc+fOxQsvvIDzzz8fTz/9NK688krs27cP6enpag+PiEjbZBnwOJsEFU2+3E0CDyUIaemact3tCOvQ6v0xcPqTAQDJpqOBi34P4KoOfEWKZGhfMNQsqGr4p8kGGM2BwMhgAkzWwD8NJsBgAPx+FB4tBgD0tdUAJd8F3luSAEiBf0oN2Yzgn6Umj7f0XOmka2197smPhwZysiwH/tnwBQA+rxc+jxt+dx18nnrIHhd8Hhf8Hlfgz+56pFq/gt/tQtF3yegzcnjkfmenIMnK6DXq/PPPx7nnnos///nPAAC/34+cnBz87ne/w7x58077ervdjoSEBFRXVyM+Pj5s43pi0Wz44IO+D2M/PRkQeo6izw8Qf46iz09x0sdOC9e0QkJrHyoyGj5Im10HAGUzkqaPS02uo4XHQ6+178Os5fu0+pNtdvNTv6dyvUa2QAaQZqhFjOSBEX4Y4IMBgCT5YZD9MMIPE/yQ4IcBfhgkf8Pz/DDJgetGNF4zwA8jZBjgC15r9k+56XP9LT638Xm+k+7tCz6vrf4PY/Dz/Hfa/Py2aOvnt6YzMm63G7t27cL8+fOD1wwGA8aOHYtt27a1+BqXywWXyxX83m63AwA8Hg88Hk/YxtbHfBDXuz8J2/2IiEhASkzUNNWhc27ZCC9M8MIID4zwwoh9MVkYF8bPWABt/szWdCBTVlYGn8+HjIyMkOsZGRnYu3dvi69ZsmQJFi1a1Oz6xo0bER0dvvY+Hwyol8Xc84DEImv0/9uJugs/pIYP/dAPfx+M8MjGkMd8TR5X/ulteI4XpsZrTb48MMIrm+CFodnzPCe9j/JY8/cxwScbgn9u+X1M8LfS7DzM8iP6/N//hfXn5nS27fgDTQcyHTF//nzMnTs3+L3dbkdOTg7Gjx8f1qUlz7hx2FBQgHHjxsGsVoVThHk8HhQIPEfR5weIP0fOT/9En6PH48EHDfOLFnR+gd/f9LD//pQVldPRdCCTmpoKo9GIkpKSkOslJSXIzMxs8TVWqxVWa/PTRc1mc0T+JYnUfbVE9DmKPj9A/Dlyfvon+hw5v47dsy00vSGexWLBiBEjsGnTpuA1v9+PTZs2YdSoUSqOjIiIiLRA0xkZAJg7dy6mT5+OkSNH4rzzzsPTTz+N2tpa3HbbbWoPjYiIiFSm+UDmxhtvxIkTJ/DQQw+huLgYw4YNw/r165sVABMREVH3o/lABgBmzZqFWbNmqT0MIiIi0hhN18gQERERnQoDGSIiItItBjJERESkWwxkiIiISLcYyBAREZFuMZAhIiIi3WIgQ0RERLrFQIaIiIh0i4EMERER6ZYudvbtDFmWAbT9OPC28ng8cDqdsNvtwp5oKvocRZ8fIP4cOT/9E32OnF/HKZ/byud4a4QPZGpqagAAOTk5Ko+EiIiI2qumpgYJCQmtPi7Jpwt1dM7v9+P48eOIi4uDJElhu6/dbkdOTg6OHj2K+Pj4sN1XS0Sfo+jzA8SfI+enf6LPkfPrOFmWUVNTg+zsbBgMrVfCCJ+RMRgM6NmzZ8TuHx8fL+RfzqZEn6Po8wPEnyPnp3+iz5Hz65hTZWIULPYlIiIi3WIgQ0RERLrFQKaDrFYrFi5cCKvVqvZQIkb0OYo+P0D8OXJ++if6HDm/yBO+2JeIiIjExYwMERER6RYDGSIiItItBjJERESkWwxkiIiISLcYyLTTkiVLcO655yIuLg7p6emYPHky9u3bp/awwmblypUYMmRIcHOjUaNG4f3331d7WBGzdOlSSJKE2bNnqz2UsMnPz4ckSSFfZ511ltrDCqtjx47h5ptvRkpKCmw2GwYPHoydO3eqPayw6d27d7PfoSRJmDlzptpDCwufz4cFCxYgLy8PNpsNZ5xxBh5++OHTnqmjJzU1NZg9ezZyc3Nhs9lw4YUXYseOHWoPq8M++eQTTJo0CdnZ2ZAkCWvXrg15XJZlPPTQQ8jKyoLNZsPYsWNRWFjYJWNjINNOH3/8MWbOnIn//ve/KCgogMfjwfjx41FbW6v20MKiZ8+eWLp0KXbt2oWdO3fi8ssvxzXXXINvv/1W7aGF3Y4dO/Diiy9iyJAhag8l7AYOHIiioqLg15YtW9QeUthUVlZi9OjRMJvNeP/99/Hdd9/hySefRFJSktpDC5sdO3aE/P4KCgoAAFOmTFF5ZOGxbNkyrFy5En/+85/x/fffY9myZXj88cfx3HPPqT20sLnjjjtQUFCA1157DV9//TXGjx+PsWPH4tixY2oPrUNqa2sxdOhQrFixosXHH3/8cTz77LN44YUX8PnnnyMmJgZXXnkl6uvrIz84mTqltLRUBiB//PHHag8lYpKSkuS//OUvag8jrGpqauR+/frJBQUF8qWXXirfc889ag8pbBYuXCgPHTpU7WFEzP333y9fdNFFag+jS91zzz3yGWecIfv9frWHEhYTJ06UZ8yYEXLtuuuuk6dNm6bSiMLL6XTKRqNRfvfdd0Oun3POOfKDDz6o0qjCB4C8Zs2a4Pd+v1/OzMyUn3jiieC1qqoq2Wq1yq+//nrEx8OMTCdVV1cDAJKTk1UeSfj5fD688cYbqK2txahRo9QeTljNnDkTEydOxNixY9UeSkQUFhYiOzsbffr0wbRp0/Djjz+qPaSwWbduHUaOHIkpU6YgPT0dw4cPx8svv6z2sCLG7XbjH//4B2bMmBHWg2/VdOGFF2LTpk3Yv38/AODLL7/Eli1bMGHCBJVHFh5erxc+nw9RUVEh1202m1DZUcWhQ4dQXFwc8t/ThIQEnH/++di2bVvE31/4QyMjye/3Y/bs2Rg9ejQGDRqk9nDC5uuvv8aoUaNQX1+P2NhYrFmzBmeffbbawwqbN954A7t379b1evWpnH/++XjllVdw5plnoqioCIsWLcLFF1+Mb775BnFxcWoPr9N++OEHrFy5EnPnzsUDDzyAHTt24O6774bFYsH06dPVHl7YrV27FlVVVbj11lvVHkrYzJs3D3a7HWeddRaMRiN8Ph8effRRTJs2Te2hhUVcXBxGjRqFhx9+GAMGDEBGRgZef/11bNu2DX379lV7eGFXXFwMAMjIyAi5npGREXwskhjIdMLMmTPxzTffCBdhn3nmmdizZw+qq6vxv//7v5g+fTo+/vhjIYKZo0eP4p577kFBQUGz/1sSRdP/qx0yZAjOP/985Obm4q233sLtt9+u4sjCw+/3Y+TIkXjssccAAMOHD8c333yDF154QchA5q9//SsmTJiA7OxstYcSNm+99Rb++c9/YvXq1Rg4cCD27NmD2bNnIzs7W5jf4WuvvYYZM2agR48eMBqNOOecc3DTTTdh165dag9NOFxa6qBZs2bh3XffxUcffYSePXuqPZywslgs6Nu3L0aMGIElS5Zg6NCheOaZZ9QeVljs2rULpaWlOOecc2AymWAymfDxxx/j2Wefhclkgs/nU3uIYZeYmIj+/fvjwIEDag8lLLKyspoF1QMGDBBq+Uxx5MgRfPDBB7jjjjvUHkpY3XfffZg3bx5++ctfYvDgwbjlllswZ84cLFmyRO2hhc0ZZ5yBjz/+GA6HA0ePHsX27dvh8XjQp08ftYcWdpmZmQCAkpKSkOslJSXBxyKJgUw7ybKMWbNmYc2aNfjwww+Rl5en9pAizu/3w+VyqT2MsLjiiivw9ddfY8+ePcGvkSNHYtq0adizZw+MRqPaQww7h8OBgwcPIisrS+2hhMXo0aObbXmwf/9+5ObmqjSiyFm1ahXS09MxceJEtYcSVk6nEwZD6MeP0WiE3+9XaUSRExMTg6ysLFRWVmLDhg245ppr1B5S2OXl5SEzMxObNm0KXrPb7fj888+7pL6SS0vtNHPmTKxevRrvvPMO4uLigut/CQkJsNlsKo+u8+bPn48JEyagV69eqKmpwerVq7F582Zs2LBB7aGFRVxcXLN6ppiYGKSkpAhT5/T73/8ekyZNQm5uLo4fP46FCxfCaDTipptuUntoYTFnzhxceOGFeOyxx3DDDTdg+/bteOmll/DSSy+pPbSw8vv9WLVqFaZPnw6TSaz/VE+aNAmPPvooevXqhYEDB+KLL77A8uXLMWPGDLWHFjYbNmyALMs488wzceDAAdx3330466yzcNttt6k9tA5xOBwhWd1Dhw5hz549SE5ORq9evTB79mw88sgj6NevH/Ly8rBgwQJkZ2dj8uTJkR9cxPuiBAOgxa9Vq1apPbSwmDFjhpybmytbLBY5LS1NvuKKK+SNGzeqPayIEq39+sYbb5SzsrJki8Ui9+jRQ77xxhvlAwcOqD2ssPrPf/4jDxo0SLZarfJZZ50lv/TSS2oPKew2bNggA5D37dun9lDCzm63y/fcc4/cq1cvOSoqSu7Tp4/84IMPyi6XS+2hhc2bb74p9+nTR7ZYLHJmZqY8c+ZMuaqqSu1hddhHH33U4mff9OnTZVkOtGAvWLBAzsjIkK1Wq3zFFVd02d9dSZYF2kqRiIiIuhXWyBAREZFuMZAhIiIi3WIgQ0RERLrFQIaIiIh0i4EMERER6RYDGSIiItItBjJERESkWwxkiKhDDh8+DEmSsGfPHrWHErR3715ccMEFiIqKwrBhwzp1L0mSsHbt2rCMi4gih4EMkU7deuutkCQJS5cuDbm+du1aSJKk0qjUtXDhQsTExGDfvn0h576crLi4GL/73e/Qp08fWK1W5OTkYNKkSad8TWds3rwZkiShqqoqIvcn6s4YyBDpWFRUFJYtW4bKykq1hxI2bre7w689ePAgLrroIuTm5iIlJaXF5xw+fBgjRozAhx9+iCeeeAJff/011q9fj8suuwwzZ87s8Ht3BVmW4fV61R4GkaYwkCHSsbFjxyIzMxNLlixp9Tn5+fnNllmefvpp9O7dO/j9rbfeismTJ+Oxxx5DRkYGEhMTsXjxYni9Xtx3331ITk5Gz549sWrVqmb337t3Ly688EJERUVh0KBB+Pjjj0Me/+abbzBhwgTExsYiIyMDt9xyC8rKyoKPjxkzBrNmzcLs2bORmpqKK6+8ssV5+P1+LF68GD179oTVasWwYcOwfv364OOSJGHXrl1YvHgxJElCfn5+i/e56667IEkStm/fjuuvvx79+/fHwIEDMXfuXPz3v/9t8TUtZVT27NkDSZJw+PBhAMCRI0cwadIkJCUlISYmBgMHDsT//d//4fDhw7jssssAAElJSZAkCbfeemtwTkuWLEFeXh5sNhuGDh2K//3f/232vu+//z5GjBgBq9WKLVu24Msvv8Rll12GuLg4xMfHY8SIEdi5c2eLYycSHQMZIh0zGo147LHH8Nxzz+Gnn37q1L0+/PBDHD9+HJ988gmWL1+OhQsX4qqrrkJSUhI+//xz3HnnnfjNb37T7H3uu+8+3Hvvvfjiiy8watQoTJo0CeXl5QCAqqoqXH755Rg+fDh27tyJ9evXo6SkBDfccEPIPV599VVYLBZ89tlneOGFF1oc3zPPPIMnn3wSf/rTn/DVV1/hyiuvxNVXX43CwkIAQFFREQYOHIh7770XRUVF+P3vf9/sHhUVFVi/fj1mzpyJmJiYZo8nJiZ25EcHAJg5cyZcLhc++eQTfP3111i2bBliY2ORk5ODf//73wCAffv2oaioCM888wwAYMmSJfj73/+OF154Ad9++y3mzJmDm2++uVkwOG/ePCxduhTff/89hgwZgmnTpqFnz57YsWMHdu3ahXnz5sFsNnd47ES61iVHUxJR2E2fPl2+5pprZFmW5QsuuECeMWOGLMuyvGbNGrnpv9oLFy6Uhw4dGvLap556Ss7NzQ25V25uruzz+YLXzjzzTPniiy8Ofu/1euWYmBj59ddfl2VZlg8dOiQDkJcuXRp8jsfjkXv27CkvW7ZMlmVZfvjhh+Xx48eHvPfRo0dDTnW+9NJL5eHDh592vtnZ2fKjjz4acu3cc8+V77rrruD3Q4cOlRcuXNjqPT7//HMZgPz222+f9v0AyGvWrJFlufHk38rKyuDjX3zxhQxAPnTokCzLsjx48GA5Pz+/xXu19Pr6+no5Ojpa3rp1a8hzb7/9dvmmm24Ked3atWtDnhMXFye/8sorp50DUXdgUi2CIqKwWbZsGS6//PIWsxBtNXDgQBgMjUnajIwMDBo0KPi90WhESkoKSktLQ143atSo4J9NJhNGjhyJ77//HgDw5Zdf4qOPPkJsbGyz9zt48CD69+8PABgxYsQpx2a323H8+HGMHj065Pro0aPx5ZdftnGGgRqTSLn77rvx29/+Fhs3bsTYsWNx/fXXY8iQIa0+/8CBA3A6nRg3blzIdbfbjeHDh4dcGzlyZMj3c+fOxR133IHXXnsNY8eOxZQpU3DGGWeEbzJEOsKlJSIBXHLJJbjyyisxf/78Zo8ZDIZmH+Aej6fZ805empAkqcVrfr+/zeNyOByYNGkS9uzZE/JVWFiISy65JPi8lpZ5IqFfv36QJAl79+5t1+uUAK/pz/Hkn+Edd9yBH374Abfccgu+/vprjBw5Es8991yr93Q4HACA9957L+Rn891334XUyQDNfz75+fn49ttvMXHiRHz44Yc4++yzsWbNmnbNiUgUDGSIBLF06VL85z//wbZt20Kup6Wlobi4OORDOJx7vzQtkPV6vdi1axcGDBgAADjnnHPw7bffonfv3ujbt2/IV3uCl/j4eGRnZ+Ozzz4Luf7ZZ5/h7LPPbvN9kpOTceWVV2LFihWora1t9nhr7dFpaWkAAnU4ipZ+hjk5Objzzjvx9ttv495778XLL78MALBYLAAAn88XfO7ZZ58Nq9WKH3/8sdnPJicn57Rz6d+/P+bMmYONGzfiuuuua7EQm6g7YCBDJIjBgwdj2rRpePbZZ0OujxkzBidOnMDjjz+OgwcPYsWKFXj//ffD9r4rVqzAmjVrsHfvXsycOROVlZWYMWMGgEABbEVFBW666Sbs2LEDBw8exIYNG3DbbbeFfKi3xX333Ydly5bhzTffxL59+zBv3jzs2bMH99xzT7vH6/P5cN555+Hf//43CgsL8f333+PZZ58NWSZrSgku8vPzUVhYiPfeew9PPvlkyHNmz56NDRs24NChQ9i9ezc++uijYECXm5sLSZLw7rvv4sSJE3A4HIiLi8Pvf/97zJkzB6+++ioOHjyI3bt347nnnsOrr77a6vjr6uowa9YsbN68GUeOHMFnn32GHTt2BN+LqLthIEMkkMWLFzdb+hkwYACef/55rFixAkOHDsX27ds7VUtzsqVLl2Lp0qUYOnQotmzZgnXr1iE1NRUAglkUn8+H8ePHY/DgwZg9ezYSExND6nHa4u6778bcuXNx7733YvDgwVi/fj3WrVuHfv36tes+ffr0we7du3HZZZfh3nvvxaBBgzBu3Dhs2rQJK1eubPE1ZrMZr7/+Ovbu3YshQ4Zg2bJleOSRR0Ke4/P5MHPmTAwYMAA/+9nP0L9/fzz//PMAgB49emDRokWYN28eMjIyMGvWLADAww8/jAULFmDJkiXB17333nvIy8trdfxGoxHl5eX41a9+hf79++OGG27AhAkTsGjRonb9HIhEIcmRrH4jIiIiiiBmZIiIiEi3GMgQERGRbjGQISIiIt1iIENERES6xUCGiIiIdIuBDBEREekWAxkiIiLSLQYyREREpFsMZIiIiEi3GMgQERGRbjGQISIiIt1iIENERES69f949b9hGPbPhQAAAABJRU5ErkJggg==", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot the data, with nclust on x-axis and the rest being lines\n", + "i = 0\n", + "for data_result in data_results:\n", + " data_result = data_result.set_index(\"nclust\")\n", + " data_result.plot(grid=True)\n", + " # y axis label is RMSE\n", + " plt.ylabel(\"RMSE\")\n", + " # x axis label is number of clusters\n", + " plt.xlabel(\"Number of Clusters\")\n", + " # title is the dataid\n", + " plt.title(\"DataID: \" + dataids[i])\n", + " # move legend to side\n", + " # plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n", + " # remove legend\n", + " plt.legend().remove()\n", + " plt.show()\n", + " i+=1\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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", 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du3bl+v7gwYPp2bMnjzzyCAD16tUjKSmJxx57jNdeey3XX4ze3t54e3vn2O7p6Znrl24YBkeOHCE2NjbP9RuGQVhYGDExMW65hk5+t89utxMVFYWXl5fLz51XF/vz4S7UvqLP3duo9hV9rm5jXs5VqMJNYmIiu3fvvugcmuTk5BwBxuFwAOSph+VSzgWbMmXK4Ofnl6df4k6nk8TERAICAizvgcgP+dm+c4srxsTEULFiRbcMhyIiUjAsDTcvvPACnTp1IjIyksOHDzNkyBAcDgddu3YFoFevXpQvX57hw4cD0KlTJ0aNGkWjRo2yhqUGDx5Mp06dskLOtcjMzMwKNqVKlcrz8U6nk7S0NHx8fNw23ORn+0JDQzl8+DAZGRlu/y8aERHJP5aGm4MHD9K1a1dOnjxJaGgo1113HcuWLSM0NBSA/fv3Z/sl+vrrr2Oz2Xj99dc5dOgQoaGhdOrUiXfeeccl9Zybia2JxtY4NxyVmZmpcCMiIlfN0nAzZcqUS76/YMGCbK89PDwYMmQIQ4YMyceqrmyBIHE9fe8iIuIK7jd2IiIiIsWawo2IiIi4FYUbN3L8+HGeeOIJKlasiLe3N2FhYXTs2JHFixcD5u0q/vOf/+Q4bujQoTRs2DDH9oMHD+Ll5UXdunVz/TybzZb1CA4Opk2bNvz555+ubJKIiEieKdy4kfvuu4+1a9cyceJEduzYwYwZM7jhhhs4efLkVZ1vwoQJdOnShfj4+Ivev2v8+PHExMSwePFiSpcuzZ133smePXuupRkiIu7LMHA4Uy+/n1yTQrXOjVy92NhY/vnnHxYsWEC7du0AiIyMpHnz5ld1PsMwGD9+PJ9//jkVKlRg7NixNGvWLMd+ISEhhIWFERYWxpgxYyhfvjzz58/n8ccfv6b2iIi4I/vC4dy5fhTO0+OhaV+o1Qk8fawuy+0o3FyGYRicSc+8on2dTidn0jLxSMtwyTowvp6OK76CKCAggICAAKZPn07Lli1zXZU5L/766y+Sk5Np37495cuXp3Xr1nz44YeXrtfXF4C0tLRr+mwREbeUdAL78s8BsO9fAvuXgG9JaNgNGveG0OoWF+g+FG4u40x6JrXfmGvJZ295syN+Xlf2I/Lw8GDChAk8+uijfPHFFzRu3Jh27drx0EMPUb9+/az9Xn75ZV5//fVsx6alpVG7du1s28aOHctDDz2Ew+Ggbt26VK5cmalTp3Lvvffm+vnJycm8/vrrOByOrJ4jERG5wMqx2DJSiPWtSGDTh3Cs/y/EH4Kln5mPyDbQ5GH15riA5ty4kfvuu4/Dhw8zY8YMbr31VhYsWEDjxo2ZMGFC1j4vvvgi69aty/bo379/tvPExsbyyy+/0KNHj6xtPXr0YPz48Tk+s2vXrgQEBBAYGMjPP//M2LFjs4UpEREB0lNgxVcA7Cp7J87rX4JnNkK3H6HG7WCzw77F8MsjMKomzPk/OL7D4qKLLvXcXIavp4Mtb3a8on2dTicJ8QkEBgW6bFgqr3x8fOjQoQMdOnRg8ODBPPLIIwwZMoQ+ffoAULp0aapWrZrtmJIlS2Z7PXnyZFJSUmjRokXWNsMwcDqd7Nq1i8aNG2dt/+ijj2jfvj3BwcFZK0uLiMi/bJgCyScwgipwOKQZDQDsDqje0XzEHYK138GabyH+ICwbbT4i20CTPlDrLvXm5IHCzWXYbLYrHhpyOp1keDnw8/IoNPeWql27NtOnT8/TMWPHjuX555/PCkTnPPnkk3z33XfZwk1YWFiOsCQiIhdwOmHpaPNp88cxTubyD9fg8nDDy3D9C7Drd1g9AXbMMXtz9i0G35egQTdo0htCaxRs/UVQ4fgNLNfs5MmT3HTTTXz33Xds2LCBvXv3MnXqVN577z3uvvvuKz7PunXrWLNmDY888gh169bN9njwwQeZMmUKGRkZ+dgSERE3s3MenNgB3kE4G/a49L7nenO6fg/PbIIbX4OgCnDmtNmTM7o5jLsNNvxoDnVJrhRu3ERAQAAtWrTgo48+4vrrr6du3boMHjyYRx99lM8+++yKzzN27Fhq165NzZo1c7zXuXNnjh8/zqxZs1xZuoiIe1t69u/gJn3AO/DKjwsuD+1egmc2QLep5+fm7F8Cvzx6dm7Oq3B8e76UXZRpWMpNeHt7M3z4cIYPH37RfaKjo3PdPnToUIYOHQrAp59+etHjw8LCOHnyJEFBQYA5D0dERC7h8FqI/gfsHtCi/+X3z43dAdVvMR/xh8/PzYk7AMs+Nx8VW5vhqfbdmpuDem5ERETyz5KzvTZ17zN7Yq5VULjZmzNo/dnenDvA5jB7c6Y9Bh/WMHtzjm279s8qwtRzIyIikh9iD8DmaebzVgNde+4r6s1pZa6bU/su8PR17ecXcuq5ERERyQ/LvwAjE6LaQbl8XP/rwt6c7j9BzTvP9uYsPdubUxNmv1KsenPUcyMiIuJqKXGweqL5vPVTBfOZdgdU62A+4g/D2v/Cmolmb87yMeajYqsL5ua4b2+Oem5ERERcbfVESEuA0JpQtX3Bf35QOLR78SK9OY+7fW+Oem5ERERcKTPdHJICc67NFd4AOV9k682JgXXfwepvIW7/+d6ciJbQ9GG36s1Rz42IiIgrbZ5m3hDTvwzU72J1NecFlYPrX4RB66D7z+d7cw4sO9ubUwNmvwzHtlpd6TVTz42IiIirGAYs+cR83uIx8PC2tp7c2B1Qrb35yNGb84X5iGhpzs2pc0+R7M1Rz42IiIir7P0bjmwETz9o2s/qai7vUr050/sX2d4c9dyIiIi4ypKzq7w37A5+Ja2tJS8u7M1JOHJ23ZyJEHthb04Lc92cItCbo54bN9GnTx/uueeeXN+rVKkSNpuNKVOm5HivTp062Gw2JkyYkGN/m82Gr68vlSpVokuXLvz555/Zjo2OjsZms7Fu3ToXtkREpIg6thV2zQds0OpJq6u5eoFh5t3Jn14PPX6GWp3O9uYsP9+bM+slOLrF6kovSuGmmIiIiGD8+PHZti1btowjR47g7++fY/8333yTmJgYtm/fzrfffktISAi33HILH3zwwRV/5oIFC6hUqdK1li4iUjScu0FmrU5QsrK1tbiC3W5exv7gd/DcFrhpMIRUNNfwWfEljGkFY2+BdZMhLdnqarNRuCkmunfvzsKFCzlw4EDWtnHjxtG9e3c8PHKOTgYGBhIWFkbFihW5/vrr+eqrr3j99dcZPnw427frDrQiItkkHIUNP5rPC2rRvoKUrTfnFzPA2T3O9uY8Yd6hvBD15ijcXI5hQFrSlT/Sk/O2/6UeLrzrdtmyZenYsSMTJ5orZiYnJ/PDDz/Qt2/fKz7H008/jWEYzJgxw2V1iYi4hRVfQWaaOS8lornV1eQfux2q3mz25jy7GW5+A0Iis/XmOCbcRsTJf8CZaVmZmlB8OenJ8G74Fe1qB0Jc+dn/dxi8cg4ZXa2+ffvy/PPP89prr/HTTz9RpUoVGjZseMXHlyxZktDQUKKjo11Wk4hIkZeWBCu/MZ+7+gaZhVlgGLR9Hto8C3v+gtUTYPss7IdWUtXnMNjetaw0hZti5I477uDxxx/n77//Zty4cXnqtTnHMAxsl1htMyAgIOt5ZmYmqamp2bb16NGDL774Is+fKyJSaK2bDCmxUCIKat5hdTUF71xvTtWbIeEomau/Zefe49S3cGVmhZvL8fQze1CugNPpJD4hgaDAQOx2F4z4efpd+zku4OHhQc+ePRkyZAjLly9n2rRpeTr+5MmTnDhxgqioqIvuc+GVU8uXL+fll19mwYIFWduCgoLyWraISOHlzDw/kbjVAPOS6uIssCzONs9wMG4W+Xgf9MtSuLkcm+3Kh4acTvDMNPd3RbjJB3379uWDDz7gwQcfpESJEnk69pNPPsFut3P33XdfdJ+qVatmPT948CAeHh7ZtomIuJVtM+F0NPiWgIbdrK5GzlK4cSNxcXE51pwpVapUtte1atXixIkT+PldulcoISGBI0eOkJ6ezt69e/nuu+/45ptveOONNxRWRETOObdoX9N+Lp0jKddG4caNLFiwgEaNGmXb1q9fzuW//x14cvPGG2/wxhtv4OXlRVhYGC1btmT+/Pk0adLEZfWKiBRp+5fDwRXg8ILmj1ldjVxA4cZNTJgwIdsqw3kRGxub7fXFroZyOp3Ex8dnva5UqRLGJS5Xv+GGG3RllYi4r6Vne23qd4HAstbWItkUzokhIiIihdmpPbD1N/N5cbr8u4hQuBEREcmrpZ8DBlTtAGVqWV2N/IvCjYiISF4kn4J1/zWfu+OtFtyAwo2IiEherBprrl4fVg+irre6GsmFwk0uLjVJVvKPvncRKfQyUmH5V+bz1k+ba6FJoaNwcwFPT0/AvKmkFLy0tDQAHI5ivsKniBReG36EpGMQVB7qdLa6GrkIXQp+AYfDQUhICMeOHQPAz8/vkvdR+jen00laWhopKSmuuf1CIZOf7XM6nRw/fhw/Pz88PPTHUkQKIcM4f6uFFv3B4WltPXJR+i3yL2FhYQBZAScvDMPgzJkz+Pr65ikUFRX53T673U7FihXd8rsTETew63c4vg28AqFJb6urkUtQuPkXm81GuXLlKFOmDOnp6Xk6Nj09nb///pvrr78+a4jLneR3+7y8vNyyx0tE3MS5Wy006Q0+wdbWIpekcHMRDocjz3M/HA4HGRkZ+Pj4uGW4cff2iYhcVMwG2LsQbA5zSEoKNf0zWURE5HLOzbWp0xlCIqytRS5L4UZERORS4g7Bpp/N5611q4WiQOFGRETkUpZ/Ac4MqNQWwhtZXY1cAYUbERGRi0mJh9UTzOe6QWaRoXAjIiJyMWsnQWo8lK4O1W6xuhq5Qgo3IiIiucnMgGVjzOetBoCWqigy9JMSERHJzZbpEHcA/EOh/kNWVyN5oHAjIiLyb4ZxftG+Zo+Cp4+19UieKNyIiIj8277FELMOPHyg2SNWVyN5pHAjIiLyb+d6bRp2A/9S1tYieaZwIyIicqHjO2DHHMAGLQdYXY1cBYUbERGRC5271ULNO6B0VWtrkauicCMiInJO4jFYP8V8rkX7iiyFGxERkXNWfgOZqVC+KVRsaXU1cpUUbkRERADSkmHF1+bz1gPBZrO2HrlqCjciIiIA67+HM6cgJBJqdrK6GrkGCjciIiJOJywdbT5v+SQ4PKytR66Jwo2IiMiO2XBqN/gEQ6MeVlcj10jhRkRE5NyifU37gneAtbXINVO4ERGR4u3gKti/FOye0Pxxq6sRF1C4ERGR4u1cr029ByConLW1iEso3IiISPF1Ohq2zjCft9aife5C4UZERIqvZWPAcEKVm6BsHaurERexNNwMHToUm82W7VGzZs1LHhMbG8uAAQMoV64c3t7eVK9enVmzZhVQxSIi4jbOnIY1k8znrZ+ythZxKcsv5K9Tpw6///571msPj4uXlJaWRocOHShTpgw//fQT5cuXZ9++fYSEhBRApSIi4lZWjYf0JChbFyrfaHU14kKWhxsPDw/CwsKuaN9x48Zx6tQplixZgqenJwCVKlXKx+pERMQtZaTB8i/N5610qwV3Y3m42blzJ+Hh4fj4+NCqVSuGDx9OxYoVc913xowZtGrVigEDBvDrr78SGhpKt27dePnll3E4HLkek5qaSmpqatbr+Ph4ANLT00lPT3dpW86dz9XnLSzcvX3g/m1U+4o+d29jQbXPtuEHPBKPYASEkVHzLiig79Pdf36Qf23My/lshmEYLv30PJg9ezaJiYnUqFGDmJgYhg0bxqFDh9i0aROBgYE59q9ZsybR0dF0796dJ598kl27dvHkk0/y9NNPM2TIkFw/Y+jQoQwbNizH9smTJ+Pn5+fyNomISCFnGNyw7XWCUw6wObwLu8reaXVFcgWSk5Pp1q0bcXFxBAUFXXJfS8PNv8XGxhIZGcmoUaPo169fjverV69OSkoKe/fuzeqpGTVqFO+//z4xMTG5njO3npuIiAhOnDhx2S8nr9LT05k/fz4dOnTIGjZzJ+7ePnD/Nqp9RZ+7t7Eg2mfb8xce3z+A4elPxlPrwTckXz4nN+7+84P8a2N8fDylS5e+onBj+bDUhUJCQqhevTq7du3K9f1y5crh6emZbQiqVq1aHDlyhLS0NLy8vHIc4+3tjbe3d47tnp6e+fYHKz/PXRi4e/vA/duo9hV97t7GfG3fijEA2Br3wjMoNH8+4zLc/ecHrm9jXs5VqNa5SUxMZPfu3ZQrl/sKkW3atGHXrl04nc6sbTt27KBcuXK5BhsREZFsjmyC3X+CzQ4t+1tdjeQTS8PNCy+8wMKFC4mOjmbJkiV07twZh8NB165dAejVqxevvvpq1v5PPPEEp06dYtCgQezYsYOZM2fy7rvvMmDAAKuaICIiRcnS0eZ/a98NJSpZWorkH0uHpQ4ePEjXrl05efIkoaGhXHfddSxbtozQULObcP/+/djt5/NXREQEc+fO5dlnn6V+/fqUL1+eQYMG8fLLL1vVBBERKSriY2DjVPN5Ky3a584sDTdTpky55PsLFizIsa1Vq1YsW7YsnyoSERG3teJLcKZDxdZQoYnV1Ug+KlRzbkRERPJFaiKsGmc+1w0y3Z7CjYiIuL+130FKHJSsAtVvs7oayWcKNyIi4t4yM2DZ2YnErQaAXb/63J1+wiIi4t62/Q9i94NfKWjQ1epqpAAo3IiIiPsyDFjyqfm82SPgpdvuFAcKNyIi4r72L4NDq8HhDc0etboaKSAKNyIi4r7O9do0eAgCrLnVghQ8hRsREXFPJ3bB9lnm81a6/Ls4UbgRERH3tGw0YJiXfodWt7oaKUAKNyIi4n6STsC6yeZzLdpX7CjciIiI+1k5FjJSILwRRLaxuhopYAo3IiLiXtLPwIqvzOetBoLNZm09UuAUbkRExL1s+AGST0BwBNS+x+pqxAIKNyIi4j6cTljymfm85RPg8LC2HrGEwo2IiLiPnfPg5E7wDobGvayuRiyicCMiIu7j3KJ9TXqDd6C1tYhlFG5ERMQ9HFoD+xaB3QNa9Le6GrGQwo2IiLiHpWfn2tS9D4LLW1uLWErhRkREir7Y/bB5uvlct1oo9hRuRESk6Fv2BRiZENUOytW3uhqxmMKNiIgUbWdiYc1E83nrpy0tRQoHhRsRESna1kyEtEQIrQVVb7a6GikEFG5ERKToykgzh6TAvEGmbrUgKNyIiEhRtnkaJByGgLJQ7wGrq5FCQuFGRESKJsOApWcX7Wv+GHh4W1uPFBoKNyIiUjTtXQhHNoKnHzTta3U1Uogo3IiISNF07gaZjXqAX0lra5FCReFGRESKnmNbYdd8wGbe/VvkAgo3IiJS9Jy71UKtTlCysrW1SKGjcCMiIkVLwlHY8KP5vPVT1tYihZLCjYiIFC0rvoLMNIhoARHNra5GCiGFGxERKTrSkmDlN+Zz3SBTLkLhRkREio51kyElFkpEQc07rK5GCimFGxERKRqcmecnErcaAHaHtfVIoaVwIyIiRcO2mXA6GnxLQMNuVlcjhZjCjYiIFA1Lzt5qoWk/8PK3thYp1BRuRESk8Nu/HA6uAIeXeR8pkUtQuBERKSqcTjiyEb/U4+ZNI4uTczfIrN8FAstaW4sUeh5WFyAiIlcg+RRMfwLPHXPoABj734eKLaFiK6jYAsrWA4eb/pV+cjds/c18rsu/5Qq46f8JIiJu5MBK+OlhiDuAYffEcDqxJx6BLdPNB4CnP1RoejbwtIQKzcA70MqqXWfZGMCAardAmVpWVyNFgMKNiEhhZRjmpc+/DwVnBpSsTEbnscxdtYtb65XB4/BKcy7KgRWQGgd7F5oPAJsdytY1g05EC7OHJ7i8pc25KsmnYO135nP12sgVUrgRESmMkk/B9Cdhx2zzdZ17odPH4PAl034AI7INVL3BfM/phONbYf8y83FgGcTuhyMbzMeKr8z9giMuCDstoUztwr9WzKqxkHEGwupD1PVWVyNFhMKNiEhhc8EwFA5vuHU4NO0LNhukp+fc326HsnXMR7N+5rb4w2eDznLYvxSObDTPt/EAbJxq7uMdZA5fnZu3U75J4brEOj0Flp8NZq2fMtsvcgUUbkRECgvDgGWfw/w3zGGoElHQZSKUa5D3cwWFQ917zQdAagIcXHU+7BxcBanxsPsP8wFg9zB7SM6FnYiW1l6ZtHEqJB2DoPJQp7N1dUiRo3AjIlIYnDltDkNtn2W+rn0P3PUp+AS55vzegVDlRvMBkJkBxzafH8ravwwSDsPhNeZj2WhzvxJR5ycpR7SE0tXNnqL8dm6+EUCL/uDwzP/PFLehcCMiYrWDq2FqH4jbby5Sd+twcxXe/ByGcXiYPULlGkCLx80wEXfggnk7y+HoZji913ys/948zreEOWfn3CTl8Ebg6ePy8my7/4Dj28ArEJr0dvn5xb0p3IiIWMUwzMuc578BznQoUQkemAjhDQu+FpsNQiqaj/pdzG1nYs3hq/1LzbBzcJXZw7RjjvkAM4yFNzofdiJagH+pay7Hvvxsz1GT3uATfM3nk+JF4UZExApnTsOvA2Hb2cXpat99dhiqEP0i9w2Bau3NB0BmOsRsMK/G2r/UvAw96ZgZfA4shyWfmPuVrn7+iqyKraBk5Tz1QgUnR2OP/gdsDnNISiSPFG5ERArawdXwUx/zcm2HF3R8F5o9UvivBnJ4QoUm5qPVALPn6dSes5OUzw5nndgOJ3aYj7WTzOP8Q8+HnYiW5lCYh9dFP6bKsbO9QnU6Q0hEATRM3I3CjYhIQTEMWP4FzBtsDkOFRJpXQ4U3srqyq2OzQakq5qNhN3Nb8qnzYefAcji0GpKOmz1U53qpPHzMy87PhZ2IZuZcHoD4Q5Q/vdx83lqL9snVUbgRESkIZ2Lh1wHnf8HXusschvINsbIq1/MrCTVuMx8AGalweN35eTv7l8GZU7BvsfkAwGbeViGiBY6Eo9jJxBnZBntRDX1iOYUbF4qJSyEt0+oqRKTQObQapj4MsfvA7mkOQzV/tPAPQ7mCh7e5Zk7FFuZrw4ATO8/O2zn7OLUbjm2BY1s4d5G5s8UACuCCc3FTCjcusvlwHH3GrSDM004np4FWZBARDMO89cHc184PQz0wAco3troy69hsEFrdfDTuZW5LPJbVq+Pcv5yDZ7woV7W9tXVKkaZw4yJJqZnEpWRwPNHOmzO38k7n+tiKw7/KRCR3KXHm1VBbZ5iva94Jd492v2EoVwgoA7U6Qa1OZKans3bWLMrZ1G8jV09/elykeVRJPry/HjYMJq84yOcLdltdkohY5fBa+PJ6M9jYPeHWkfDgdwo2IgVE4caFbq1TlnsrOQF4f+52pq46YHFFIlKgDMO80ePYW+B0tLkgXr+50LJ/8ZhfI1JIKNy42PXlDB5rWwmAV37ZyF/bj1lbkIgUjJQ4mNobZr8ImWnmMNTjf5uXPItIgVK4yQcvdKjGvY3Kk+k0GPDfNaw/EGt1SSKSnw6vM4ehtvx6dhhqxNlhqBJWVyZSLCnc5AObzcaI++rTtlppktMy6TthJftOJlldloi4mmHAiq9hbAdzGCq4IvSdCy2f0DCUiIUUbvKJl4edMT2aUCc8iJNJafQat4ITialWlyUirpISZ97Je9YL5jBUjTug/9/mrQlExFIKN/kowNuD8Q83I6KkL/tOJtN3wkqSUjOsLktErlXMeviyHWyZDnYPc1G+h/6rYSiRQkLhJp+VCfRh4sPNKeHnyYaDcQyYvIb0TKfVZYnI1TAMWPkNfNMeTu+F4AhzGKrVAA1DiRQiCjcFoHJoAOP6NMPH086C7cf5v182YhiG1WWJSF6kxMNPfWHm82eHoW43r4aq0NTqykTkXxRuCkijiiUY3a0xdhtMXX2QUfN3WF2SiFypmA3wVTvY/Is5DHXLO/DQZPMmkSJS6CjcFKCba5Xl3c71APj0z118t2yfxRWJyCUZBqwaZw5DndoDQRXg4dnQeqCGoUQKMd1bqoA91LwiR+JT+M/vO3nj102EBnrTsU6Y1WWJyL+lJsD/BsGmn83X1W+Fe8aot0akCLC052bo0KHYbLZsj5o1a17RsVOmTMFms3HPPffkb5H5YNDN1ejaPAKnAU9/v5bV+05ZXZKIXOjIRvNqqE0/nx2Gehu6TlGwESkiLO+5qVOnDr///nvWaw+Py5cUHR3NCy+8QNu2bfOztHxjs9l46+66HItP5Y9tx+g3cRU/9W9N1TIBVpcmUrwZBqyeALNfhsxUcxjqgfEQ0dzqykQkDyyfc+Ph4UFYWFjWo3Tp0pfcPzMzk+7duzNs2DAqV65cQFW6nofDzqfdGtEwIoTY5HR6j1vB0fgUq8sSKb5SE+DnR+C3Z8xgU60j9P9HwUakCLI83OzcuZPw8HAqV65M9+7d2b9//yX3f/PNNylTpgz9+vUroAqvkGFgn/caofGbrvgQPy8PxvZuSlRpfw7FnqHP+JXEp6TnY5Eikqsjm+CrG2DTT2BzQIc3NQwlUoRZOizVokULJkyYQI0aNYiJiWHYsGG0bduWTZs2ERgYmGP/RYsWMXbsWNatW3fFn5Gamkpq6vnbHsTHxwOQnp5OerrrgoRt7bd4rPySFjZP0rfVg5q3XdFxQd52vunZiAe/XsHWmHge/3YV3/RsjJeH5bkzh3Pflyu/t8LG3duo9v2LYWBb9x2Oea9iy0jBCAwn895vMCo0h8xM81HI6GdYtLl7+yD/2piX89mMQrSaXGxsLJGRkYwaNSpHz0xCQgL169fn888/57bbzODQp08fYmNjmT59+kXPOXToUIYNG5Zj++TJk/Hz83NZ7XZnOk2jP6dc3GqcOFhV6QliSlx5d/aBRPh0s4NUp43GpZz0rObEritNRfKNIzOFBgcmEHF6CQBHghqwNvIx0jxy/sNKRKyXnJxMt27diIuLIygo6JL7FqpwA9CsWTPat2/P8OHDs21ft24djRo1wuFwZG1zOs3bGNjtdrZv306VKlVynC+3npuIiAhOnDhx2S8nr9JTkjk1rgsVTi/DsNnJvPNTjPoPXvHx/+w6wWOT1pLhNOjXJpJXbq3h0vquVXp6OvPnz6dDhw54enpaXU6+cPc2qn1nHduCxy99sZ3chWFz4LzxNZwtB4Kt8PWY/pt+hkWbu7cP8q+N8fHxlC5d+orCjeVXS10oMTGR3bt307Nnzxzv1axZk40bN2bb9vrrr5OQkMDHH39MREREruf09vbG29s7x3ZPT898+IPlx+rI/oRXrIJ9/X/x+N8AMNKgad8rOvqmWuV47/5MnvtxPWMX7yO8hD/9rotycY3XLn++u8LF3dtYbNtnGLB2Esx6ETJSIDAc2/3jcES2wpFz70Kt2P4M3YS7tw9c38a8nMvScPPCCy/QqVMnIiMjOXz4MEOGDMHhcNC1a1cAevXqRfny5Rk+fDg+Pj7UrVs32/EhISEAObZbymYn8/aPsHsHwIov4bdnIf2MeWO9K3Bv4wocjU9l5JxtvPXbFsoEetOpQXg+Fy1SDKQmwsznYMMP5uuq7aHzV+Bfytq6RMTlLA03Bw8epGvXrpw8eZLQ0FCuu+46li1bRmhoKAD79+/Hbi/83cQ52Oxw20jw9IXF/4G5/wdpyXD9C1e0ZHv/dpU5EneGiUv38fyP6ykV4EXrKpe+RF5ELuHoFpjaG07sMK+Guul1aPMMFMW/X0TksiwNN1OmTLnk+wsWLLjk+xMmTHBdMa5ms0H7oeAVAH+9bT7Sk+DmIZcNODabjTc61eFYQiqzNx3h8W9XM/WJVtQMc+0cIRG3Zxiw9ruzw1BnIDAc7h8Lka2trkxE8pH+2ZKfbDZo96J5B2GARR/BnFfg7EToS3HYbXz0YEOaVypJQmoGvcet4FDsmXwuWMSNpCXB9CdgxkAz2FS52VyUT8FGxO0p3BSE1gPhjlHm8+VfwP+eBufl18/w8XTwda+mVCsTwNH4VHqPW0Fsclo+FyviBo5tha9uhPXfm8PEN78B3X8Cfw3vihQHeQo3x44du+T7GRkZrFix4poKclvN+sE9X5h/0a6dBNMeh8zLL0gU7OfJxL7NCQvyYdexRB79dhUp6YVvYTGRwiLi5D94jL8FTmyHwHLQ+zdo+7zm14gUI3n6v71cuXLZAk69evU4cOBA1uuTJ0/SqlUr11Xnbhp2hfvHmXcZ3jgVpvaBjNTLHhYe4suEvs0I9PFgZfRpnpmyjkxnoVqeSMR6CUdxTHuExvu/xpZxBqrcBI//A5XaWF2ZiBSwPIWbf6/3Fx0dnWM55EK2JmDhU6czPPhfcHjDtt9gSjfzSqrLqBkWxFc9m+LlsDNn8xGG/W+zvmsRMOewrRoPo5th3zIdAxuZ7f4Puv8MAaFWVyciFnB5P63tCi51LvZq3ArdfgBPP9j1O0zuYt6R+DJaVSnFqAcbYLPBt0v3MWbh7gIoVqQQO7oZxt9q3sk7JQ5nWAMW1hiK87rnNAwlUozp/36rVLkRevwCXoEQ/Q9M6gxnYi972J31wxl8R20A3puznZ9XH8znQkUKobRk+H0ofHk9HFhuLrlw60gyH55HnF/hW9VbRApWnsKNzWYjISGB+Ph44uLisNlsJCYmEh8fn/WQPIhsBb1/BZ8QOLgSJnaCpJOXPazvdVE8fn1lAF7+eQMLdxzP50JFCpGd8+HzlubSCs4MqHknDFgBLfuDvajdREFE8kOe59xUr16dEiVKULJkSRITE2nUqBElSpSgRIkS1KhRuG70WCSUbwJ9ZoJ/KBzZABNuh4Qjlz3s5Vtrck/DcDKcBk98t5qNB+MKoFgRCyUcMSfh//d+iN0HQRXgoe/hof9CcHmrqxORQiRPKxT/9ddf+VVH8RZWF/rMgm/vhuPbYPxt0GsGhOR+M1AAu93Ge/c34HhiKot3neThCSv45Yk2VCzlV4CFixQApxNWj4Pfh0FqvLmcQssn4YZXwTvA6upEpBDKU7hp165dftUhodWh72xzaOrUnrMB51coVeWih3h52PmiRxMe/HIZW2Li6TVuOT8/0ZpSATnvgi5SJB3ZZE4WPrjSfB3eGDr9B8o1sLIqESnk8jQslZGRQWpq9nVZjh49yrBhw3jppZdYtGiRS4srdkpUgofnQKmqEHcAxt8Ox7Zd8pBAH08mPNyM8iG+RJ9Mpu/EVSSnZRRMvSL5JS0J5g02JwwfXGlOvL/tfXjkdwUbEbmsPIWbRx99lKeffjrrdUJCAs2aNWP06NHMnTuXG2+8kVmzZrm8yGIluDw8PBvK1IbEI+YcnJgNlzykTJAP3/ZrToifJ+sPxDJw8loyMi9//yqRQmnHXBjdEpZ8AkYm1L4bBq6AFo9pwrCIXJE8hZvFixdz3333Zb3+9ttvyczMZOfOnaxfv57nnnuO999/3+VFFjsBZcxJxuGNIPkkTLwTDq665CFVQgMY27sZ3h52/tx2jNembdIif1K0xMfAj73MdZ/i9kNwBHT9Abp8C0HhVlcnIkVInsLNoUOHqFatWtbrP/74g/vuu4/g4GAAevfuzebNm11bYXHlV9KccxPRElLizMnG0Zce9msSWYLPujXGboMfVh3go993FlCxItfAmQkrvobPmsGWX8HmgNZPw4Dl5oKXIiJ5lKdw4+Pjw5kzZ7JeL1u2jBYtWmR7PzEx0XXVFXc+wdDzF4hqB2mJ8N395orGl9ChdlneuqcuAJ/8sZPJy/cXRKUiVydmA3zTHma9AGkJUL4pPL4QbnkLvPytrk5Eiqg8hZuGDRsyadIkAP755x+OHj3KTTfdlPX+7t27CQ9X97FLeflDtx+hWkfIOAPfd4VtMy95SPcWkTx9U1UAXp++kflbjhZEpSJXLjUR5r4GX90Ah9eAdxDc/gH0mwdh9ayuTkSKuDyFmzfeeIOPP/6YKlWq0LFjR/r06UO5cuWy3p82bRpt2ugOvC7n6QMPfmdOrMxMgx96wsafLnnIsx2q06VpBZwGPPX9GlbvO11AxYpcxvbZMLoFLP3MnDBcpzMMXAnNH9WEYRFxiTyvc7N69WrmzZtHWFgYDzzwQLb3GzZsSPPmzV1aoJzl4QX3jQOPAbBhCvz8CKSfgcY9c93dZrPxTud6HE9I5a/tx3lk4kp+eqI1VUK16JlYJO4QzH4Jtv1mvg6pCHeMgmodrK1LRNxOnsINQK1atahVq1au7z322GPXXJBcgsMD7hkDnr6wejzMGGgGnBa5f++eDjujuzem61fLWH8wjt7jVvDLE60pE+RTwIVLsXZuwvCfb5lzx+we0GogtHsZvLSitoi4Xp7Czd9//31F+11//fVXVYxcAbsd7vwIPP1g2WiY/SKkJ8N1z+S6u5+XB+P6NOO+MUuIPplMn/Er+eHxlgT6eBZs3VI8HV5nrjB8eK35ukJzc4XhsnUsLEpE3F2ews0NN9yAzWYDuOgaKjabjczMzGuvTC7OZoOO75iTjf9+D34fYgacG1413/uXUgHeTOzbnPvGLGFLTDxPfLeGcX2a4eWRpylXIlcuNQH+eheWfwGGE7yDocNQaNzHDOgiIvkoT3/LlChRgoiICAYPHszOnTs5ffp0jsepU6fyq1a5kM0GN70GNw8xXy8cCfNeh4uEzshS/ozr0ww/LweLdp3gpZ/W43RqkT/JB9tmmhOGl31uBpu695sThpv2VbARkQKRp79pYmJiGDlyJEuXLqVevXr069ePJUuWEBQURHBwcNZDClDb5+DWkebzpZ/BzOfNuyjnon6FED7v3hgPu43p6w4zcu6l71slkidxB+H7bjClG8QfMu+V1uNnuH8sBJa1ujoRKUbyFG68vLx48MEHmTt3Ltu2baN+/foMHDiQiIgIXnvtNTIydMNGS7TsD3d9Cthg1Vj4dQBk5v6zuKFGGUbcVx+ALxfuYfzivQVYqLilzAxY+jl81hy2zzQnDLd9Hp5cBlXbW12diBRDV91HXLFiRd544w1+//13qlevzogRI4iPj3dlbZIXjXvBvV+bS9evnwy/PAKZ6bnuen+TCrzYsQYAb/62hZkbYgqyUnEnh9bA1zfC3FchPcm8XUj/RXDzG+ZVfSIiFriqcJOamsrkyZNp3749devWpXTp0sycOZOSJUu6uj7Ji/oPQJeJYPeEzdPMxf7SU3Ld9ckbqtCzZSSGAc/+sI5le04WcLFSpKXEw+yX4Zub4cgG81YhnT4+e0f73JeKEBEpKHkKNytWrOCJJ54gLCyM999/n7vuuosDBw7w448/cuutusFdoVCrE3SdAh4+sGM2fP8gpCXl2M1mszH0rjp0rFOWtEwnj367iu1HEiwoWIoUw4AtM2B08/NXQtXrAgNXQZM+mjAsIoVCni4Fb9myJRUrVuTpp5+mSZMmACxalPNO1XfddZdrqpOrU609dP8JJj8IexbAd/eZ96fyCcq2m8Nu4+OHGtHjm+Ws2nfaXOTvydaEh2g4QXIRux9mvWSGZoASUXDnKKhy06WPExEpYHleoXj//v289dZbF31f69wUElFtodevZrDZvxS+vdu8csUv+9Chj6eDb3o35f4vlrLrWCJ9xq9g6uOtCfbTIn9yVmYGLB9jrluTnmwOe173jDlpWPNqRKQQylMfstPpvOwjIUFDG4VGRDPoPQN8S5p3Xp7YCRKP59gtxM+LiX2bUzbImx1HE3l00ipS0hVQBTi4Gr6+wVxDKT0ZKraGJxbDTa8r2IhIoeWyAfLU1FRGjRpF5cqVXXVKcYXwhvDwLAgoC0c3wfjbIP5wjt3Kh/gy4eHmBHp7sGLvKZ77cR2ZWuSv+EqJg5kvnJ0wvBF8S8Bdn0GfmRBaw+rqREQuKU/hJjU1lVdffZWmTZvSunVrpk+fDsC4ceOIiorio48+4tlnn82POuValKllXsUSVAFO7oRxt8Lp6By71SoXxJe9muDlsDNr4xHe+m3LRW+zIW7KMGDzdHPNmpVfAwY06GpOGG7cUxOGRaRIyNPfVG+88QZjxoyhUqVKREdH88ADD/DYY4/xn//8h1GjRhEdHc3LL7+cX7XKtShVBfrONieBxu6D8bfDiV05dmtdpTQfdmkAwIQl0Xz5956CrlSscnofTO4CU3tD4hEoWQV6zYDOX4B/aaurExG5YnmaUDx16lS+/fZb7rrrLjZt2kT9+vXJyMhg/fr1WTfUlEIspKLZg/Pt3XBiuzlE1etXKFs7226dGoRzND6Ft2duZcTsbZQN8qZzowoWFS35LjPdvA/UX8Mh4ww4vOC6Z+G658DTx+rqRETyLE89NwcPHsy6BLxu3bp4e3vz7LPPKtgUJUHlzDk4YfUg6RhMuB0Or82x2yNtK/No2ygAXpy6gX925pyILG7gwEr46gaY/4YZbCKvg/6L4cb/U7ARkSIrT+EmMzMTLy+vrNceHh4EBAS4vCjJZ/6loff/oHxTOHMaJt4F+5fl2O3V22pxV4NwMpwG/SetZvNh3V7DbaTEwW/PwdgO5kRz35Jw9+fQ5zcIrW51dSIi1yRPw1KGYdCnTx+8vb0BSElJoX///vj7+2fb75dffnFdhZI/fEtAr+nmQn/7FsOkztD1e6h8Q9YudruN9x+oz4nEVJbsPskjk9bwZDXLKhZXMAzCTy/D44sXzJ47gIbdocNb4F/K2tpERFwkT+Gmd+/e2V736NHDpcVIAfMONFcy/qE77P4T/tsFHpwE1Tue38XDwRc9m9Dli6VsO5LAF1sddEpOJzRYi/wVGYYBx7fB7j9xbJlBswNne+lKVYM7PzIXfBQRcSN5Cjfjx4/PrzrEKl5+5r2opj4M22fClO5w/1iofXfWLkE+nkzs25zOoxdzOC6FQT+u59u+LfBw6LLgQivxmHnrjd1/wZ6/IMG887sdyLR5QNvncVz/PHh4W1qmiEh+0G8nMX/BdZkIde8DZzpM7QPrp2TbpWyQD1/2aISX3WDJ7lMMn73Nmlold+kpZpCZNxjGXAcfVINfHoX1k81g4+EDVW4m8+Zh/FlrJM62LyrYiIjbyvO9pcRNOTzh3q/NJfXXfgfT+kP6GWj6cNYuNcMC6V7VyfgdDsYu2kutckHc30SXiFvCMODoZnM4cc9fsG8JZKRk3yesPlS50byxZURL8PTBmZ5O8qxZ1tQsIlJAFG7kPLsDOn0Knn6w4iv47Rkz4LR6MmuXhqUMBt5Qmc8W7OH/ftlIlVB/GlUsYV3NxUnCkfPDTLv/Oj8h+JzAcmaQqXITRLWDgFBr6hQRsZjCjWRnt8Nt75k9OIs/hrmvQnoSXP9i1i5P3ViFHceSmLflKI9PWs3/nrqOskFaE8Xl0pJh/xIzyOz+E45tyf6+px9Uug4qn+2dCa0BWnNKREThRnJhs0H7YeAVAH+9A3++bf6ivf5VwLxEfNSDDbn388XsOJrIY5NW88NjLfHxdFhceBHndMLRjWaQ2f0X7F8KmWkX7GCDcg3O985ENNe8GRGRXCjcSO5sNmj3ktmDM+91WDQKe2oiGG0ACPD24OteTbnrs8WsPxDLa9M28cED9bVadV7FHTo/zLRnASSfyP5+UIWz82ZuhKgbtBaNiMgVULiRS2v9lBlwZj6PY+VXNAnZgG1TMpSqTGRIRUZ3bUjvCav4ec1BaocH0e+6KKsrLtzSkiB68dnemT/Ne3xdyCvAHGo61ztTqqqGmkRE8kjhRi6v2SPg6Yfx6wAqxC6DX8/fquE6hxdrgsuyITGYQ3NDiT7eiEpVapk36QypCAFh5jye4sqZCTHrz17VtMC8zYUz/fz7NjuENzKDTOUboUIz8PC66OlEROTyFG7kyjTsRqZfGQ7P+Q8VAp3Y4w6YQyqZaQSfOUBbxwFzv/V/wfoLjnN4QXCF82EnpCKERLp3+Ik9cHao6WygOXM6+/shFc+Hmajrwa+kJWWKiLgrhRu5YkZUO9ZGJlHu9tuxe3pCZgYkHIbY/aSdjObnP5bgiD9INe9TNAiMxx530JwQe2qP+ciN3RNCIop2+ElNgOhF5ycCn9yZ/X3vIKjU9vyaMyUra6hJRCQfKdzI1XN4ZIUQr0rXcXO1++n02SKOxqfSIbIsXw5sgD3xCMTu/9djn/nfuIPmEM3lwk+2np/I7EEoMMxcn6cgOTPh8NrzYebgCnBmnH/fZjfvuF7lJjPQlG9iLpIoIiIFQuFGXKZMkA9f9mxKly+XMn/LUf7z5x6eu6WG2TNDm5wHZGaYtwa4XPg5vdd85Kagws/p6PPrzexdCClx2d8vEXW+Z6ZSW/ANufbPFBGRq6JwIy7VMCKE4Z3r8fzU9Xzy5y5qlgvi9nrlct/Z4XF2SOoqw0/8ofwLPynxsGvp+dsb/LtnyTsYKl9/fu5MSV0lJiJSWCjciMvd16QCW2LiGbtoL8//uJ6o0v7UKheU9xNdTfiJu/D5lfT8eGQLP3bvYK7bMQ+PdXvAyDy/n81hLpp3bjXg8EZmfSIiUujob2fJF6/eVpMdRxP4Z+cJHv12FTMGXkdJfxdf4ny58OPMzKXnZ9+/wk+GOeR0Oto8JZC1TF6pqufDTKXrwOcqApqIiBQ4hRvJFx4OO592bcTdoxez72QyT/53NZP6tcDTUYBXPtkdZq9McAWIbJ3z/VzCT2Z8DBuPGdS56yk8Q6sUXK0iIuIyhfwaWynKQvy8+LpXU/y9HCzbc4q3f9ty+YMK0rnwE9kaGjwE7V7Ceet77Ct9ozlMJSIiRZLCjeSr6mUD+c9DjQCYuHQfU1bst7giERFxdwo3ku861C7L8x2qAzD4102sij5lcUUiIuLOFG6kQAy8qSq31wsjPdOg/3drOBx7xuqSRETETSncSIGw2Wx88EADaoYFciIxlccnrSYlPfPyB4qIiOSRwo0UGD8vD77u1ZQSfp5sPBTHyz9vwDAMq8sSERE3o3AjBSqipB+fd2+Ch93Gr+sO89XfF7mnlIiIyFVSuJEC16pKKYZ0qg3AiDnb+Gv7MYsrEhERd6JwI5bo0TKSrs0jMAx4+vu17D6eaHVJIiLiJhRuxBI2m41hd9WlaWQJElIyePTbVcSnpFtdloiIuAGFG7GMl4edMT2aUC7Yhz3Hkxj0/VoynZpgLCIi18bScDN06FBsNlu2R82aNS+6/9dff03btm0pUaIEJUqUoH379qxYsaIAKxZXCw305queTfH2sPPX9uN8MG+71SWJiEgRZ3nPTZ06dYiJicl6LFq06KL7LliwgK5du/LXX3+xdOlSIiIiuOWWWzh06FABViyuVq9CMO/dXx+AMQt28+s6/TxFROTqWX5XcA8PD8LCwq5o3//+97/ZXn/zzTf8/PPP/PHHH/Tq1Ss/ypMCcnfD8myNSeCLhbt5+ecNVAkNoG75YKvLEhGRIsjycLNz507Cw8Px8fGhVatWDB8+nIoVr+yOzMnJyaSnp1OyZMmL7pOamkpqamrW6/j4eADS09NJT3ftBNZz53P1eQuL/G7fMzdVZuvhOBbuPMEjE1cy7YmWlA7wzpfPuhj9DIs2d28fuH8b1b6iL7/amJfz2QwLl4idPXs2iYmJ1KhRg5iYGIYNG8ahQ4fYtGkTgYGBlz3+ySefZO7cuWzevBkfH59c9xk6dCjDhg3LsX3y5Mn4+fldcxvEtZIz4KONDo6l2KgcaDCgdiYelg+eioiI1ZKTk+nWrRtxcXEEBQVdcl9Lw82/xcbGEhkZyahRo+jXr98l9x0xYgTvvfceCxYsoH79+hfdL7eem4iICE6cOHHZLyev0tPTmT9/Ph06dMDT09Ol5y4MCqp9e44ncd+Xy0lMzeDBphV4665a2Gy2fPu8C+lnWLS5e/vA/duo9hV9+dXG+Ph4SpcufUXhxvJhqQuFhIRQvXp1du3adcn9PvjgA0aMGMHvv/9+yWAD4O3tjbd3zqENT0/PfPuDlZ/nLgzyu301wkP4tGsj+k5cyQ+rDlK3Qgg9W0bm2+flRj/Dos3d2wfu30a1r+hzdRvzcq5C1eGfmJjI7t27KVeu3EX3ee+993jrrbeYM2cOTZs2LcDqpCDdWLMML3U0lwUYNmMzy/actLgiEREpKiwNNy+88AILFy4kOjqaJUuW0LlzZxwOB127dgWgV69evPrqq1n7jxw5ksGDBzNu3DgqVarEkSNHOHLkCImJWrrfHfVvV5m7GoST4TR48r9rOHAq2eqSRESkCLA03Bw8eJCuXbtSo0YNunTpQqlSpVi2bBmhoaEA7N+/n5iYmKz9x4wZQ1paGvfffz/lypXLenzwwQdWNUHykc1mY+R99albPohTSWk8Nmk1yWkZVpclIiKFnKVzbqZMmXLJ9xcsWJDtdXR0dP4VI4WSr5eDL3s25e7PFrE1Jp4Xp27gs26NCmyCsYiIFD2Fas6NSG7Kh/gypkcTPB02Zm6MYfRfl55wLiIixZvCjRQJzSqV5M276wLwwbwdzN9y1OKKRESksFK4kSKja/OKWZeEP/vDOnYeTbC4IhERKYwUbqRIeaNTbVpElSQxNYNHv11FXLL7LmEuIiJXR+FGihRPh53PuzemfIgv0SeTGfj9GjIynVaXJSIihYjCjRQ5pQK8+apXE3w9Hfyz8wQj52yzuiQRESlEFG6kSKoTHswHDzQA4Ot/9vLLmoMWVyQiIoWFwo0UWXfUL8dTN1UF4JVfNrLuQKy1BYmISKGgcCNF2rPtq9O+VhnSMpw8PmkVx+JTrC5JREQspnAjRZrdbuOjBxtSrUwAR+NTefy71aRmZFpdloiIWEjhRoq8QB9Pvu7VlCAfD9buj+X1aZswDMPqskRExCIKN+IWKpX257NujbHbYOrqg0xYEm11SSIiYhGFG3Eb11cP5f9urwXA2zO3snjXCYsrEhERKyjciFvpd10U9zYqT6bTYMDkNew/mWx1SSIiUsAUbsSt2Gw23r23Hg0qBBObnM4j364kMTXD6rJERKQAKdyI2/HxdPBlz6aEBnqz42giz/+4DqdTE4xFRIoLhRtxS2HBPnzZswleDjtzNx/l4z92Wl2SiIgUEIUbcVuNK5bg7c51Afj4j53M2RRjcUUiIlIQFG7ErXVpGsHDbSoB8NyP69l2JN7agkREJN8p3Ijbe+32WrSpWorktEwe/XYVp5LSrC5JRETykcKNuD0Ph53PujamYkk/Dpw6w4D/riE902l1WSIikk8UbqRYKOHvxde9muLn5WDpnpO8M3Or1SWJiEg+UbiRYqNGWCCjujQEYMKSaH5cecDagkREJF8o3EixcmvdMJ5tXx2A16ZvZPW+UxZXJCIirqZwI8XOUzdV5dY6YaRnGjw+aQ0xcWesLklERFxI4UaKHbvdxoddGlAzLJATiak8Pmk1KemZVpclIiIuonAjxZK/twdf92pKiJ8nGw7G8eovGzEM3aJBRMQdKNxIsRVR0o/PuzXGYbcxbe0hvvlnr9UliYiICyjcSLHWumppBt9RC4Dhs7fyz84TFlckIiLXSuFGir3erSvxYNMInAYM+nEDK4/bOHj6jIapRESKKA+rCxCxms1m48176rDzWAJr9sfy3S4H3436h7JB3jSNLEmTyBI0iSxB7fAgPB3694CISGGncCMCeHs4GNu7GaP/2sn8dXs5lGznaHwqMzfGMHOjeTdxX08HDSKCaRJZgqaRJWlcsQTBfp4WVy4iIv+mcCNyVgl/L17uWJ16mbu4sX17thxNYvW+01mPuDPpLNtzimV7TgG7AaheNuBsz05JmkaWILKUHzabzdqGiIgUcwo3Irnw9XLQsnIpWlYuBYDTabD7eCKr951m1dmws/dEEjuOJrLjaCLfrzBv5VA6wIvGFUvQtJI5lFW3fDDeHg4rmyIiUuwo3IhcAbvdRrWygVQrG8hDzSsCcCIxlTVng86qfafZeDCOE4lpzNtylHlbjgLg5WGnfvlgmlQqQZOKZuApFeBtZVPcRkamk4Onz7D3RBJ7TyQRfTKJPccTOXrMTr1WyVQuE2x1iSJiEYUbkatUOsCbW+qEcUudMABSMzLZdCiOVdFm2Fmz7zQnk9JYdTb8nFO5tD+NI0vQNNLs4alcOgC7XUNZuXE6DQ7HmQEm+kQSe08ks/dEItEnkzlwKpkMZ25XtNnpO3ENPz/RWkFSpJhSuBFxEW8PB00iS9IksiSPA4ZhEH0ymVXRp1iz/zSrok+z81gie04ksedEEj+tPghAiJ8njc/26jSNLEH9CiH4ehWfoSzDMDiWkMqe42bvS/TZ7yf6RBL7TiWTluG86LE+nnYqlfInqrQ/lUr7Uz7Ym1FzNhN9Mpm+E1Yy+dGW+HvrrzmR4kb/14vkE5vNRlRp8xfvA00jAIhNTmPt/lhW7TvFqujTrD8YS2xyOn9uO8af244B4GG3Uad8ME0jzweeMkE+VjblmhmGwcmktLO9L2aI2Xu2J2bfySSS0y5+by9Ph42KJf2IKh1AVGk/KpX2J6qUP1Gh/pQN9MnW65Wenk7yvg2M2eHL+oNxPPHfNYzt3VSX8IsUMwo3IgUoxM+LG2uW4caaZQBIz3Sy5XD82UnKZuA5lpDK+gOxrD8Qy9hF5i0hIkr6mpefnw071csG4iiEQ1lxyensPZmUFWIuDDIJKRkXPc5ht1GhhK/ZA3O2J+bcIzzEN09tLesLX/VoRK/xq/l7x3Fe/mkDH3ZpoKvYRIoRhRsRC3k67DSICKFBRAj9rovCMAwOnj6Tdfn5qn2n2XYkngOnznDg1CGmrT0EQKC3Bw0rhtA0siRNK5WgQUQIAQU0/JKUmpEVWi4cQoo+mcyppLSLHmezQXjw2QBT2o9KpfypHGqGmQol/PDycF3vSsOIEEZ3b8Sj367ml7WHCA3y5tXbarns/CJSuCnciBQiNpuNiJJ+RJT0455G5QFISEln7f7YrMCzdv9pElIz+Gfniax7YdltUKtckDmUVclcVbl8iO9V15GSnsm+k8nne19OJGX1yBxLSL3ksWUCvbN6XSqd7YmpHOpPxZJ++HgW3Fyim2qWZcS99Xjxpw18uXAPZQJ96HddVIF9vohYR+FGpJAL9PHk+uqhXF89FDAvgd5+NMHs2Yk2A8+h2DNsPhzP5sPxTFy6D4BywT5Zt45oGlmSWuUCs503LcPJgdPJOYaQok8kczjuDJe6tVZJf68LhpDM+TDnemMK0wTeB5pGcCwhlffnbuet37YQGujNXQ3CrS5LRPJZ4flbSESuiIfDTp3wYOqEB9OrVSUAYuLOZAs7W2LiiYlL4bcNMfy24YLbR1QIIu60nQ+3/8Oh2BQyc72U2hTo40Hlf/W+VCplvg72LTq3nXjyhiocT0hlwpJonv9xHaX8vWhTtbTVZYlIPlK4EXED5YJ9ubO+L3fWN3slktMyWHcgltXRp1m93ww8CSkZLNt7GrADZwDw83JccCn1BVcklfKnpL+XW0zCtdlsvHFnbY4npjJzQwyPT1rNlMdaUre8FvkTcVcKNyJuyM/Lg9ZVStO6itlD4XQa7DyWyKq9J9iwcSOdbmhBtbBgygR6u0WAuRy73caoLg04lZjG0j0n6TN+Jb880ZqKpfysLk1E8oEWfxApBux2GzXCAunStAKtyxq0iCpJ2SCfYhFszvH2cPBlrybUKhfEicRUeo1bzonES0+OFpGiSeFGRIqNIB9PJj7cjAolfLNWMU5Kvfj6OyJSNCnciEixUibIh2/7NqekvxcbDsbR/7vVl7zFg4gUPQo3IlLsVA4NYGzvpvh6Ovhn5wle/nkDzktcOSYiRYvCjYgUS40qluDzHo1x2G1MW3uIkXO2WV2SiLiIwo2IFFs31ijDyPvqA/Dl33v45p89FlckIq6gcCMixdr9TSrw8q01AXh75lZ+XXfI4opE5Fop3IhIsde/XWX6tK4EwAtT1/PPzuPWFiQi10ThRkSKvXOrGN9RvxzpmQb9J61m06E4q8sSkaukcCMiwvlVjFtXKUVSWiZ9xq9g38kkq8sSkaugcCMicpa3h4Mve55bxTiNXuNWaBVjkSJI4UZE5AKBF6xivO9kMg+PX0miVjEWKVIUbkRE/uXCVYw3HorjCa1iLFKkKNyIiOSicmgA4/o0y1rF+KWf1msVY5EiQuFGROQiGkaEMKZHYzzsNqavO8wIrWIsUiQo3IiIXMINF6xi/JVWMRYpEhRuREQu474mFXjlNq1iLFJUKNyIiFyBx6+vTN82UYBWMRa5lAU7jhOXZm0NCjciIlfAZrPx+h216NQgPGsV440HtYqxyDmZToMP523n0UlrmbDDQXqmdVcYKtyIiFwhu93GBw/Up01VcxXjhydoFWMRgNNJaTw8YSWf/rkLgAr+BoaFFxcq3IiI5IG3h4MvejShTri5inHPsSs4nqBVjKX42ngwjjs/XcTfO47j42nng/vqcl+UEy8P6yKGwo2ISB4F+ngy/uFmRJT0Zf+pZB6esEKrGEux9OPKA9z3xRIOxZ4hspQf055sw90Nw60uy9pwM3ToUGw2W7ZHzZo1L3nM1KlTqVmzJj4+PtSrV49Zs2YVULUiIueVCfTh274tKOXvxaZD8fSfpFWMpfhISc/k1V828NLPG0jLcNK+VhlmDLyOWuWCrC4NKAQ9N3Xq1CEmJibrsWjRoovuu2TJErp27Uq/fv1Yu3Yt99xzD/fccw+bNm0qwIpFRExRpf0Z16cZfl4OFu06wYtaxViKgUOxZ+jy5VK+X3EAmw1euKU6X/VsSrCvp9WlZbE83Hh4eBAWFpb1KF269EX3/fjjj7n11lt58cUXqVWrFm+99RaNGzfms88+K8CKRUTOaxARwpgeTfCw2/h13WHenbXV6pJE8s2inSe485N/2HAwjhA/TyY83JyBN1XDbrdZXVo2HlYXsHPnTsLDw/Hx8aFVq1YMHz6cihUr5rrv0qVLee6557Jt69ixI9OnT7/o+VNTU0lNPT/ZLz4+HoD09HTS09OvvQEXOHc+V5+3sHD39oH7t1Htyx+to0IY3rkOL/68iW8W7aV0gCf92lTKl8/Sz7BoK6rtczoNvvpnLx/9sQunAXXDg/j0oQZUKOGboy351ca8nM9mGNZdrDV79mwSExOpUaMGMTExDBs2jEOHDrFp0yYCAwNz7O/l5cXEiRPp2rVr1rbPP/+cYcOGcfTo0Vw/Y+jQoQwbNizH9smTJ+Pn5+e6xohIsffHIRsz9jsA6FE1k2ahGqKSou9MBvx3l52Np83BnpZlnNwf5cSzgMd+kpOT6datG3FxcQQFXXpuj6U9N7fddlvW8/r169OiRQsiIyP58ccf6devn0s+49VXX83W2xMfH09ERAS33HLLZb+cvEpPT2f+/Pl06NABT8/CM/boKu7ePnD/Nqp9+es2w6DUnB2MX7KPKXs8uKlNI9pWvfhQ+9Wwuo35Te0rXHYcTeDJyevZdzoZT4eNoXfWokvTCpc8Jr/aeG7k5UpYPix1oZCQEKpXr86uXbtyfT8sLCxHD83Ro0cJCwu76Dm9vb3x9vbOsd3T0zPf/mDl57kLA3dvH7h/G9W+/DP4zjqcTEpnxvrDDPx+PVMea0n9CiEu/xz9DIu2otC+X9cd4pWfN3ImPZPyIb583r0xDSJCrvh4V7cxL+eyfELxhRITE9m9ezflypXL9f1WrVrxxx9/ZNs2f/58WrVqVRDliYhclrmKcQOuq1qa5LRMHh6/kugTWsVYio70TCfD/reZQVPWcSY9k7bVSvO/p67LU7CxmqXh5oUXXmDhwoVER0ezZMkSOnfujMPhyJpT06tXL1599dWs/QcNGsScOXP48MMP2bZtG0OHDmXVqlUMHDjQqiaIiOTg5WFnTI/G1AkP4mRSGr3GreBYQorVZYlc1rH4FLp+tYzxi6MBGHBjFSY83JyS/l7WFpZHloabgwcP0rVrV2rUqEGXLl0oVaoUy5YtIzQ0FID9+/cTExOTtX/r1q2ZPHkyX331FQ0aNOCnn35i+vTp1K1b16omiIjkKtDHvEy2Ykk/cxXj8Su1irEUaiv2nuKOTxexat9pAr09+KpnE17sWBNHIbvM+0pYOudmypQpl3x/wYIFObY98MADPPDAA/lUkYiI64QGevNt3+bcN2YJmw+bqxiP69PM0nvuiPybYRiMWxzNu7O2kuk0qFE2kC96NiGqtL/VpV01/R8mIpKPKpX2Z/zD51cxfmGqVjGWwiMpNYOnp6zjrd+2kOk0uKtBONMGtC7SwQYUbkRE8l39CiF8cXYV4xnrD/POrK1YuMSYCAB7jifS+fPF/G/9YTzsNoZ0qs3HDzXEz6tQXUh9VRRuREQKwPXVQ/nggQYAjF20l6//2WNxRVKczd18hLs+W8yOo4mEBnrz/WMtebhNFDZb0Ztfk5uiH89ERIqIexqV53hCKu/M2sq7s7ZROsCbextfekE0EVfKdBp8MG87YxbsBqB5pZJ81q0RZYJ8LK7MtRRuREQK0KPXV+ZofArfLNrLSz9toFSAN+2qh1pdlhQDJxNTGTRlHYt2nQCg33VRvHJbTTwd7jeI434tEhEp5P7v9lrc3TCcDKfBE9+tZv2BWKtLEje37kAsnT5dxKJdJ/DzcvBp10YMvrO2WwYbULgRESlwdruN9+9vQNtqZ1cxnrCSvVrFWPKBYRhMXr6fLl8s5XBcCpVL+zN9QBs6NQi3urR8pXAjImIBcxXjJtQtH8SppDR6jVuuVYzFpVLSM3nppw3837SNpGU6uaV2WaYPbEP1soFWl5bvFG5ERCwS4O3B+D7NiSzlx4FTZ+gzbiUJKelWlyVu4MCpZO7/YglTVx/EboOXb63Jlz2bEORTuG/W6SoKNyIiFjq3inHpAC+2xMTT/7vVpGZkWl2WFGELth+j02eL2HQonpL+Xkzq14InbqjiNpd5XwmFGxERi0WW8md8n+b4ezlYvOskz/+oVYwl75xOg0/+2MnDE1YSm5xOg4gQfnvqOtpULW11aQVO4UZEpBCoVyGYL3qaqxj/tiGGt2Zu0SrGcsXiktN55NtVjJq/A8OAbi0q8uPjLQkP8bW6NEso3IiIFBJtq51fxXj84mi+/FurGMvlbTkcT6fPFvHntmN4edh57/76vNu5Ht4eDqtLs4wW8RMRKUTuaVSeE4mpvD1zKyNmbyM0wJv7mmgVY8ndtLUHefWXjaSkO6lQwpcvejShbvlgq8uynMKNiEgh80hbcxXjr//Zy8s/b6BUgBc31ChjdVlSiKRlOHl75ha+XboPgHbVQ/n4oYaE+HlZXFnhoHAjIlIIvXpbLY4npDJ93WGe+G4N3z/WkoYRIVaXlWdOp0FiWgbxZ9KJP5NB3Jl04lPSzdcpZ7enmO/Fp6QTl5zGmTg7McHRtKoSSp3wIDzcdBXdq3UkLoUn/ruatftjARh0czWevrkaDnvxuRrqchRuREQKIbvdxnv3N+BkUhr/7DxB3wkr+al/KyJCvAu0DsMwSErLNEPJmYuHkvgz6RcEl/PbElIzyPu8aDsb5+wAduDv5aBxZAlaRJWkeVQp6lcIxsez+M4lWbr7JE99v4YTiWkE+Xjwn4caclPNslaXVego3IiIFFLnVjHu+tUyNh6Ko9e4Ffz4aPM8ncMwDJLTMnOEjqzXuYWSf+3riqvSvT3sBPt6EuTrSZCPx9n/ehLk63H2v54E+3ri52Hjz+VrSfQNY9W+WOLOpPPPzhP8s/NE1nfSMCKElmfDTuPIEPy83P9XmWEYfP3PHkbO2U6m06BWuSC+6NGYyFL+VpdWKLn/nwgRkSIswNuD8Q83474xS9h3Mpm+367h+hAbGetjSEp3XrIn5dz2DBekE0+H7YJwcvGQkluACfTxuOLelvT0dDhgcPvtjXA4PNhxLIEVe0+xfO8plu85xYnEVFbsPcWKvaeAXXjYbdQtH3y2Z6ckTSNLEuznXqvwJqZm8NJP65m18QgA9zYqzzud6+HrVXx7sC5H4UZEpJArHWCuYnzfmCVsO5LAtiMO2LYxT+fwsNuyhY7gXHpOsgLJ2feCL3jP28Ne4Cvc2u02aoYFUTMsiF6tKmEYBtEnk1mx92RW2DkUe4Z1B2JZdyCWL//eg80GNcOCssJOs0olCQ0s2KE8V9p1LIHHJ61m9/EkPB023uhUhx4tKhar1YavhsKNiEgREFnKnwkPN+et3zZz6NgpIsqWIsTPK2dA+dcwz7n3fT0dRf4Xos1mI6q0P1Gl/XmwWUUADp5OZmX0qazenT3Hk9gaE8/WmHgmLIkGoHKof1bYaR5VivJFZGG7WRtjeHHqepLSMgkL8uHzHo1pXLGE1WUVCQo3IiJFRN3ywXzXtxmzZs3i9tub4unpXsMvV6NCCT8qlPCjcyNzLaDjCanZws62I/HsOZ7EnuNJfL/iAADlQ3wvCDsliSrtX6iCX0amk/fmbuers4s4tqxcks+6NaZ0QNHtgSpoCjciIuI2QgO9ub1eOW6vVw4wb0uwat/5sLPxUByHYs/wy9pD/LL2UNYxzaNKZgWe6mUCsVt0WfXxhFSe+n4Ny/acAuDx6yvzYscauhw+jxRuRETEbQX7eXJzrbLcXMu8XDopNYO1+2Oz5u2sPRDL8YRUZm6IYeaGGPMYX0+aVTofdgpqrZ01+0/z5HdrOBKfgr+Xgw8eaMBtZ0Oa5I3CjYiIFBv+3h5cV60011Uz75SdmpHJhoNxrNh7imV7TrJ632nizqTz+9aj/L71qHlMPq+1YxgG3y3bx5u/bSE906BKqD9f9mxK1TIBLvuM4kbhRkREii1vDwfNKplXVQ24sSoZmU42H47PGsZaGX3qomvtnOvZaVyxBP7eV/fr9ExaJq9N25g1RHZ7vTDeu78BAVd5PjHp2xMRETnLw2GnQUQIDSJCePT6yjidRra1dlbsPcXxhAvX2gHHhWvtnA1KV7LWzr6TSfT/bg1bY+Jx2G28cmtNHmkbVagmNxdVCjciIiIXcbm1dlbsPcXB02dYfyCW9Qdi+ersWjs1ygbSsnKpi66189f247zw00biUzIoHeDFp10b06pKKYta6X4UbkRERK5QbmvtHIo9w8pzqyjvPcme40lnF1tMyLHWTpOIYObttzNv6VoAGlcM4fPuTQgL9rGqSW5J4UZEROQalA/xpXyj8tzTqDxwJWvtmFde9WoVyet31MbLQ5d5u5rCjYiIiAtdaq2dZXtOcvhELC/dUY8HmkVaXKn7UrgRERHJRxeutZOenm6uMN0w3Oqy3Jr6wkRERMStKNyIiIiIW1G4EREREbeicCMiIiJuReFGRERE3IrCjYiIiLgVhRsRERFxKwo3IiIi4lYUbkRERMStKNyIiIiIW1G4EREREbeicCMiIiJuReFGRERE3IrCjYiIiLgVD6sLKGiGYQAQHx/v8nOnp6eTnJxMfHw8np6eLj+/1dy9feD+bVT7ij53b6PaV/TlVxvP/d4+93v8UopduElISAAgIiLC4kpEREQkrxISEggODr7kPjbjSiKQG3E6nRw+fJjAwEBsNptLzx0fH09ERAQHDhwgKCjIpecuDNy9feD+bVT7ij53b6PaV/TlVxsNwyAhIYHw8HDs9kvPqil2PTd2u50KFSrk62cEBQW57R9acP/2gfu3Ue0r+ty9jWpf0Zcfbbxcj805mlAsIiIibkXhRkRERNyKwo0LeXt7M2TIELy9va0uJV+4e/vA/duo9hV97t5Gta/oKwxtLHYTikVERMS9qedGRERE3IrCjYiIiLgVhRsRERFxKwo3IiIi4lYUbq7R8OHDadasGYGBgZQpU4Z77rmH7du3W12WS40ZM4b69etnLcjUqlUrZs+ebXVZ+WbEiBHYbDaeeeYZq0txmaFDh2Kz2bI9atasaXVZLnXo0CF69OhBqVKl8PX1pV69eqxatcrqslyiUqVKOX5+NpuNAQMGWF2ay2RmZjJ48GCioqLw9fWlSpUqvPXWW1d0H6GiIiEhgWeeeYbIyEh8fX1p3bo1K1eutLqsq/L333/TqVMnwsPDsdlsTJ8+Pdv7hmHwxhtvUK5cOXx9fWnfvj07d+4ssPoUbq7RwoULGTBgAMuWLWP+/Pmkp6dzyy23kJSUZHVpLlOhQgVGjBjB6tWrWbVqFTfddBN33303mzdvtro0l1u5ciVffvkl9evXt7oUl6tTpw4xMTFZj0WLFlldksucPn2aNm3a4OnpyezZs9myZQsffvghJUqUsLo0l1i5cmW2n938+fMBeOCBByyuzHVGjhzJmDFj+Oyzz9i6dSsjR47kvffe49NPP7W6NJd55JFHmD9/PpMmTWLjxo3ccssttG/fnkOHDlldWp4lJSXRoEEDRo8enev77733Hp988glffPEFy5cvx9/fn44dO5KSklIwBRriUseOHTMAY+HChVaXkq9KlChhfPPNN1aX4VIJCQlGtWrVjPnz5xvt2rUzBg0aZHVJLjNkyBCjQYMGVpeRb15++WXjuuuus7qMAjNo0CCjSpUqhtPptLoUl7njjjuMvn37Ztt27733Gt27d7eoItdKTk42HA6H8dtvv2Xb3rhxY+O1116zqCrXAIxp06ZlvXY6nUZYWJjx/vvvZ22LjY01vL29je+//75AalLPjYvFxcUBULJkSYsryR+ZmZlMmTKFpKQkWrVqZXU5LjVgwADuuOMO2rdvb3Up+WLnzp2Eh4dTuXJlunfvzv79+60uyWVmzJhB06ZNeeCBByhTpgyNGjXi66+/trqsfJGWlsZ3331H3759XX7zXyu1bt2aP/74gx07dgCwfv16Fi1axG233WZxZa6RkZFBZmYmPj4+2bb7+vq6VS8qwN69ezly5Ei2v0uDg4Np0aIFS5cuLZAait2NM/OT0+nkmWeeoU2bNtStW9fqclxq48aNtGrVipSUFAICApg2bRq1a9e2uiyXmTJlCmvWrCmy49+X06JFCyZMmECNGjWIiYlh2LBhtG3blk2bNhEYGGh1eddsz549jBkzhueee47/+7//Y+XKlTz99NN4eXnRu3dvq8tzqenTpxMbG0ufPn2sLsWlXnnlFeLj46lZsyYOh4PMzEzeeecdunfvbnVpLhEYGEirVq146623qFWrFmXLluX7779n6dKlVK1a1eryXOrIkSMAlC1bNtv2smXLZr2X3xRuXGjAgAFs2rTJ7VI4QI0aNVi3bh1xcXH89NNP9O7dm4ULF7pFwDlw4ACDBg1i/vz5Of5V5S4u/Ndv/fr1adGiBZGRkfz444/069fPwspcw+l00rRpU959910AGjVqxKZNm/jiiy/cLtyMHTuW2267jfDwcKtLcakff/yR//73v0yePJk6deqwbt06nnnmGcLDw93mZzhp0iT69u1L+fLlcTgcNG7cmK5du7J69WqrS3M7GpZykYEDB/Lbb7/x119/UaFCBavLcTkvLy+qVq1KkyZNGD58OA0aNODjjz+2uiyXWL16NceOHaNx48Z4eHjg4eHBwoUL+eSTT/Dw8CAzM9PqEl0uJCSE6tWrs2vXLqtLcYly5crlCNq1atVyq6E3gH379vH777/zyCOPWF2Ky7344ou88sorPPTQQ9SrV4+ePXvy7LPPMnz4cKtLc5kqVaqwcOFCEhMTOXDgACtWrCA9PZ3KlStbXZpLhYWFAXD06NFs248ePZr1Xn5TuLlGhmEwcOBApk2bxp9//klUVJTVJRUIp9NJamqq1WW4xM0338zGjRtZt25d1qNp06Z0796ddevW4XA4rC7R5RITE9m9ezflypWzuhSXaNOmTY4lGHbs2EFkZKRFFeWP8ePHU6ZMGe644w6rS3G55ORk7Pbsv5IcDgdOp9OiivKPv78/5cqV4/Tp08ydO5e7777b6pJcKioqirCwMP7444+sbfHx8SxfvrzA5mpqWOoaDRgwgMmTJ/Prr78SGBiYNZ4YHByMr6+vxdW5xquvvsptt91GxYoVSUhIYPLkySxYsIC5c+daXZpLBAYG5pgj5e/vT6lSpdxm7tQLL7xAp06diIyM5PDhwwwZMgSHw0HXrl2tLs0lnn32WVq3bs27775Lly5dWLFiBV999RVfffWV1aW5jNPpZPz48fTu3RsPD/f7q7tTp0688847VKxYkTp16rB27VpGjRpF3759rS7NZebOnYthGNSoUYNdu3bx4osvUrNmTR5++GGrS8uzxMTEbD2/e/fuZd26dZQsWZKKFSvyzDPP8Pbbb1OtWjWioqIYPHgw4eHh3HPPPQVTYIFck+XGgFwf48ePt7o0l+nbt68RGRlpeHl5GaGhocbNN99szJs3z+qy8pW7XQr+4IMPGuXKlTO8vLyM8uXLGw8++KCxa9cuq8tyqf/9739G3bp1DW9vb6NmzZrGV199ZXVJLjV37lwDMLZv3251KfkiPj7eGDRokFGxYkXDx8fHqFy5svHaa68ZqampVpfmMj/88INRuXJlw8vLywgLCzMGDBhgxMbGWl3WVfnrr79y/d3Xu3dvwzDMy8EHDx5slC1b1vD29jZuvvnmAv2zazMMN1r+UURERIo9zbkRERERt6JwIyIiIm5F4UZERETcisKNiIiIuBWFGxEREXErCjciIiLiVhRuRERExK0o3IiIS0RHR2Oz2Vi3bp3VpWTZtm0bLVu2xMfHh4YNG17TuWw2G9OnT3dJXSKSvxRuRNxEnz59sNlsjBgxItv26dOnY7PZLKrKWkOGDMHf35/t27dnu8/Nvx05coSnnnqKypUr4+3tTUREBJ06dbrkMddiwYIF2Gw2YmNj8+X8IsWdwo2IG/Hx8WHkyJGcPn3a6lJcJi0t7aqP3b17N9dddx2RkZGUKlUq132io6Np0qQJf/75J++//z4bN25kzpw53HjjjQwYMOCqP7sgGIZBRkaG1WWIFDoKNyJupH379oSFhTF8+PCL7jN06NAcQzT/+c9/qFSpUtbrPn36cM899/Duu+9StmxZQkJCePPNN8nIyODFF1+kZMmSVKhQgfHjx+c4/7Zt22jdujU+Pj7UrVuXhQsXZnt/06ZN3HbbbQQEBFC2bFl69uzJiRMnst6/4YYbGDhwIM888wylS5emY8eOubbD6XTy5ptvUqFCBby9vWnYsCFz5szJet9ms7F69WrefPNNbDYbQ4cOzfU8Tz75JDabjRUrVnDfffdRvXp16tSpw3PPPceyZctyPSa3npd169Zhs9mIjo4GYN++fXTq1IkSJUrg7+9PnTp1mDVrFtHR0dx4440AlChRApvNRp8+fbLaNHz4cKKiovD19aVBgwb89NNPOT539uzZNGnSBG9vbxYtWsT69eu58cYbCQwMJCgoiCZNmrBq1apcaxcpDhRuRNyIw+Hg3Xff5dNPP+XgwYPXdK4///yTw4cP8/fffzNq1CiGDBnCnXfeSYkSJVi+fDn9+/fn8ccfz/E5L774Is8//zxr166lVatWdOrUiZMnTwIQGxvLTTfdRKNGjVi1ahVz5szh6NGjdOnSJds5Jk6ciJeXF4sXL+aLL77Itb6PP/6YDz/8kA8++IANGzbQsWNH7rrrLnbu3AlATEwMderU4fnnnycmJoYXXnghxzlOnTrFnDlzGDBgAP7+/jneDwkJuZqvDoABAwaQmprK33//zcaNGxk5ciQBAQFERETw888/A7B9+3ZiYmL4+OOPARg+fDjffvstX3zxBZs3b+bZZ5+lR48eOQLiK6+8wogRI9i6dSv169ene/fuVKhQgZUrV7J69WpeeeUVPD09r7p2kSKvwG7RKSL5qnfv3sbdd99tGIZhtGzZ0ujbt69hGIYxbdo048L/1YcMGWI0aNAg27EfffSRERkZme1ckZGRRmZmZta2GjVqGG3bts16nZGRYfj7+xvff/+9YRiGsXfvXgMwRowYkbVPenq6UaFCBWPkyJGGYRjGW2+9Zdxyyy3ZPvvAgQPZ7nbdrl07o1GjRpdtb3h4uPHOO+9k29asWTPjySefzHrdoEEDY8iQIRc9x/Llyw3A+OWXXy77eYAxbdo0wzDO3xH59OnTWe+vXbvWAIy9e/cahmEY9erVM4YOHZrruXI7PiUlxfDz8zOWLFmSbd9+/foZXbt2zXbc9OnTs+0TGBhoTJgw4bJtECkuPCxLVSKSb0aOHMlNN92Ua2/FlapTpw52+/nO3bJly1K3bt2s1w6Hg1KlSnHs2LFsx7Vq1SrruYeHB02bNmXr1q0ArF+/nr/++ouAgIAcn7d7926qV68OQJMmTS5ZW3x8PIcPH6ZNmzbZtrdp04b169dfYQvNOSv55emnn+aJJ55g3rx5tG/fnvvuu4/69etfdP9du3aRnJxMhw4dsm1PS0ujUaNG2bY1bdo02+vnnnuORx55hEmTJtG+fXseeOABqlSp4rrGiBQxGpYScUPXX389HTt25NVXX83xnt1uz/FLPT09Pcd+/x7WsNlsuW5zOp1XXFdiYiKdOnVi3bp12R47d+7k+uuvz9ovtyGi/FCtWjVsNhvbtm3L03HnQt+F3+O/v8NHHnmEPXv20LNnTzZu3EjTpk359NNPL3rOxMREAGbOnJntu9myZUu2eTeQ8/sZOnQomzdv5o477uDPP/+kdu3aTJs2LU9tEnEnCjcibmrEiBH873//Y+nSpdm2h4aGcuTIkWy/mF25Ns2Fk3AzMjJYvXo1tWrVAqBx48Zs3ryZSpUqUbVq1WyPvASaoKAgwsPDWbx4cbbtixcvpnbt2ld8npIlS9KxY0dGjx5NUlJSjvcvdql2aGgoYM7rOSe37zAiIoL+/fvzyy+/8Pzzz/P1118D4OXlBUBmZmbWvrVr18bb25v9+/fn+G4iIiIu25bq1avz7LPPMm/ePO69995cJ3uLFBcKNyJuql69enTv3p1PPvkk2/YbbriB48eP895777F7925Gjx7N7NmzXfa5o0ePZtq0aWzbto0BAwZw+vRp+vbtC5iTbE+dOkXXrl1ZuXIlu3fvZu7cuTz88MPZftFfiRdffJGRI0fyww8/sH37dl555RXWrVvHoEGD8lxvZmYmzZs35+eff2bnzp1s3bqVTz75JNsQ24XOBY6hQ4eyc+dOZs6cyYcffphtn2eeeYa5c+eyd+9e1qxZw19//ZUV8iIjI7HZbPz2228cP36cxMREAgMDeeGFF3j22WeZOHEiu3fvZs2aNXz66adMnDjxovWfOXOGgQMHsmDBAvbt28fixYtZuXJl1meJFEcKNyJu7M0338wxbFSrVi0+//xzRo8eTYMGDVixYsU1zc35txEjRjBixAgaNGjAokWLmDFjBqVLlwbI6m3JzMzklltuoV69ejzzzDOEhIRkm99zJZ5++mmee+45nn/+eerVq8ecOXOYMWMG1apVy9N5KleuzJo1a7jxxht5/vnnqVu3Lh06dOCPP/5gzJgxuR7j6enJ999/z7Zt26hfvz4jR47k7bffzrZPZmYmAwYMoFatWtx6661Ur16dzz//HIDy5cszbNgwXnnlFcqWLcvAgQMBeOuttxg8eDDDhw/POm7mzJlERUVdtH6Hw8HJkyfp1asX1atXp0uXLtx2220MGzYsT9+DiDuxGfk5o05ERESkgKnnRkRERNyKwo2IiIi4FYUbERERcSsKNyIiIuJWFG5ERETErSjciIiIiFtRuBERERG3onAjIiIibkXhRkRERNyKwo2IiIi4FYUbERERcSsKNyIiIuJW/h906NQdpnHrgQAAAABJRU5ErkJggg==", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot the data, only shap and aloo_l2_unsigned_nonnormed_leafavg_rank\n", + "i=0\n", + "for data_result in data_results:\n", + " data_result = data_result.set_index(\"nclust\")\n", + " data_result[[\"shap\", \"nonloo_l2_signed_nonnormed_leafavg_rank\"]].plot(grid=True)\n", + " # move legend to side\n", + " plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n", + " # call aloo_l2_unsigned_nonnormed_leafavg_rank lmdi+\n", + " plt.legend([\"SHAP\", \"LMDI+\"])\n", + " # y axis label is RMSE\n", + " plt.ylabel(\"RMSE\")\n", + " # x axis label is number of clusters\n", + " plt.xlabel(\"Number of Clusters\")\n", + " # title is the dataid\n", + " plt.title(\"DataID: \" + dataids[i])\n", + " plt.show()\n", + " i+=1" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "auroc_ranks = []\n", + "for data_result in data_results:\n", + " auroc = data_result.drop(\"nclust\", axis=1).apply(np.trapz, x=data_result[\"nclust\"])\n", + " # convert to ranking, i.e. 1 is lowest, 2 is second lowest, etc.\n", + " auroc_rank = auroc.rank()\n", + " auroc_ranks.append(auroc_rank)\n", + "# merge series in auroc_ranks by averaging the ranks corresponding to the same names\n", + "auroc_ranks = pd.concat(auroc_ranks, axis=1)\n", + "auroc_ranks = auroc_ranks.mean(axis=1)\n", + "auroc_ranks = auroc_ranks.sort_values()\n", + "auroc_ranks.plot(kind=\"bar\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "nonloo_l2_signed_nonnormed_leafavg_rank 11.666667\n", + "aloo_l2_unsigned_nonnormed_leafavg_rank 13.000000\n", + "aloo_l2_unsigned_nonnormed_leafavg_norank 13.888889\n", + "nonloo_l2_signed_nonnormed_leafavg_norank 14.333333\n", + "aloo_l2_signed_nonnormed_leafavg_norank 15.444444\n", + "nonloo_l2_unsigned_nonnormed_leafavg_norank 16.444444\n", + "nonloo_l2_unsigned_nonnormed_leafavg_rank 17.000000\n", + "aloo_l2_signed_nonnormed_leafavg_rank 17.555556\n", + "aloo_l2_signed_nonnormed_noleafavg_norank 18.222222\n", + "aloo_nonl2_unsigned_nonnormed_noleafavg_rank 19.000000\n", + "nonloo_l2_signed_nonnormed_noleafavg_rank 19.333333\n", + "nonloo_l2_signed_nonnormed_noleafavg_norank 19.444444\n", + "aloo_l2_signed_normed_leafavg_rank 19.666667\n", + "aloo_l2_signed_normed_leafavg_norank 19.777778\n", + "shap 19.888889\n", + "aloo_l2_signed_nonnormed_noleafavg_rank 20.000000\n", + "nonloo_l2_signed_normed_leafavg_rank 20.333333\n", + "nonloo_nonl2_unsigned_nonnormed_noleafavg_norank 21.000000\n", + "nonloo_l2_signed_normed_leafavg_norank 21.000000\n", + "nonloo_nonl2_unsigned_nonnormed_leafavg_rank 21.222222\n", + "aloo_nonl2_unsigned_nonnormed_leafavg_rank 21.333333\n", + "nonloo_nonl2_unsigned_nonnormed_noleafavg_rank 21.555556\n", + "aloo_nonl2_unsigned_nonnormed_noleafavg_norank 21.555556\n", + "aloo_nonl2_unsigned_nonnormed_leafavg_norank 21.666667\n", + "nonloo_l2_unsigned_nonnormed_noleafavg_rank 22.444444\n", + "nonloo_nonl2_unsigned_nonnormed_leafavg_norank 22.555556\n", + "nonloo_l2_unsigned_nonnormed_noleafavg_norank 22.666667\n", + "aloo_l2_unsigned_nonnormed_noleafavg_rank 23.555556\n", + "aloo_l2_unsigned_nonnormed_noleafavg_norank 23.888889\n", + "aloo_l2_unsigned_normed_leafavg_rank 24.555556\n", + "aloo_l2_unsigned_normed_leafavg_norank 24.777778\n", + "aloo_l2_signed_normed_noleafavg_norank 25.111111\n", + "nonloo_l2_unsigned_normed_leafavg_rank 25.222222\n", + "nonloo_l2_unsigned_normed_leafavg_norank 25.444444\n", + "aloo_l2_signed_normed_noleafavg_rank 26.000000\n", + "nonloo_l2_signed_normed_noleafavg_rank 26.444444\n", + "nonloo_l2_signed_normed_noleafavg_norank 26.555556\n", + "aloo_l2_unsigned_normed_noleafavg_norank 26.666667\n", + "nonloo_l2_unsigned_normed_noleafavg_rank 27.555556\n", + "baseline 27.666667\n", + "aloo_l2_unsigned_normed_noleafavg_rank 28.000000\n", + "nonloo_l2_unsigned_normed_noleafavg_norank 29.555556\n", + "dtype: float64" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "auroc_ranks" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mdi", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/feature_importance/subgroup/current/subgroup.py b/feature_importance/subgroup/current/subgroup.py new file mode 100644 index 0000000..e69de29 diff --git a/feature_importance/subgroup/current/subgroup.sh b/feature_importance/subgroup/current/subgroup.sh new file mode 100644 index 0000000..b188b7e --- /dev/null +++ b/feature_importance/subgroup/current/subgroup.sh @@ -0,0 +1,14 @@ +#!/bin/bash +#SBATCH --cpus-per-task=32 + +seed=1 +# dataid=361247 +clustertype="kmeans" +njobs=32 + +source activate mdi +command="subgroup-incase.py --seed $seed --dataid ${1} --clustertype $clustertype --njobs $njobs" +# command="subgroup-incase.py --seed $seed --dataid $dataid --clustertype $clustertype --njobs $njobs" + +# Execute the command +python $command \ No newline at end of file diff --git a/feature_importance/subgroup/current/subgroup_detection.py b/feature_importance/subgroup/current/subgroup_detection.py new file mode 100644 index 0000000..12e1456 --- /dev/null +++ b/feature_importance/subgroup/current/subgroup_detection.py @@ -0,0 +1,421 @@ +import numpy as np +import pandas as pd +import rbo +from sklearn.cluster import AgglomerativeClustering +from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor +from scipy.cluster.hierarchy import linkage, dendrogram, fcluster +from scipy.spatial.distance import pdist, squareform +import matplotlib.pyplot as plt +import seaborn as sns + +def weighted_metric(metrics, sample_sizes): + """ + Calculate the weighted average of a set of metrics. + + Args: + sample_sizes (np.ndarray): the number of samples in each subgroup + metrics (np.ndarray): the metrics of each subgroup + + Returns: + float: the weighted average of the metrics + """ + + # calculate the total number of samples + total_samples = np.sum(sample_sizes) + + # calculate the weighted average + weighted_metric = np.sum(sample_sizes * metrics) / total_samples + + return weighted_metric + +def compute_rbo_matrix(rankings, form, p=0.9, k=None, ext=False): + """ + Compute the distance matrix based on Rank-based Overlap (RBO). + Inputs: + * rankings: numpy array of shape (n, p) where the (i,j)th entry denotes that + the jth feature is ranked (j-1) in terms of importance for the ith instance. + * p: float between 0 and 1 + * k: int (optional) evaluation depth for extrapolation + * ext: bool (optional) whether to use extrapolation + Outputs: + * numpy array of shape (n, n) where the (i,j)th entry is the RBO between the + ith and jth rankings. + """ + # ensure form is either "distance" or "similarity" + if form not in ['distance', 'similarity']: + raise ValueError('form must be either "distance" or "similarity"') + n = rankings.shape[0] + rbo_matrix = np.zeros((n, n)) + for i in range(n): + for j in range(i, n): + rbo_matrix[i, j] = rbo.RankingSimilarity(rankings[i,:], + rankings[j,:]).rbo(p=p,k=k, + ext=ext) + rbo_matrix[j, i] = rbo_matrix[i, j] + + if form == "distance": + # since rbo is a similarity metric, 1 means the rankings are identical + return rbo_matrix.max()-rbo_matrix + else: + return rbo_matrix + +def assign_training_clusters(rbo_distance_matrix, num_clusters, + linkage_method='ward'): + + # convert to condensed distance matrix for scipy compatibility + condensed_distance_matrix = squareform(rbo_distance_matrix) + + # perform hierarchical clustering + linkage_matrix = linkage(condensed_distance_matrix, method=linkage_method) + + # Determine cluster memberships using fcluster + clusters = fcluster(linkage_matrix, num_clusters, criterion='maxclust') + + return clusters + +def plot_training_clusters(lfi, rbo_distance_matrix, + clusters, linkage_method='ward'): + + # convert to condensed distance matrix for scipy compatibility + condensed_distance_matrix = squareform(rbo_distance_matrix) + + # perform hierarchical clustering + linkage_matrix = linkage(condensed_distance_matrix, method=linkage_method) + clustergrid = sns.clustermap(lfi, row_linkage=linkage_matrix, + col_cluster=False, cmap='viridis', + cbar_pos = (1, 0.2, 0.05, 0.5)) + + # Get the reordered row indices + reordered_indices = clustergrid.dendrogram_row.reordered_ind + + # determine number of clusters + num_clusters = np.unique(clusters).shape[0] + + # Reorder clusters to match the heatmap + ordered_clusters = clusters[reordered_indices] + + # Create a new DataFrame for annotations + annotations = pd.DataFrame(data=np.zeros_like(lfi, dtype=object), + index=lfi.index, columns=lfi.columns) + + # Add cluster numbers as annotations + for i, cluster in enumerate(ordered_clusters): + annotations.iloc[i, :] = cluster + + # Find the boundaries where clusters change + boundaries = np.where(np.diff(ordered_clusters))[0] + 1 + + # Plot the horizontal dashed red lines + for boundary in boundaries: + clustergrid.ax_heatmap.hlines(boundary, + *clustergrid.ax_heatmap.get_xlim(), + colors='red', linestyles='dashed') + + ax = clustergrid.ax_heatmap + total_obs = 0 + for cluster in range(1, num_clusters + 1): + num_obs = np.sum(clusters == cluster) + x_position = lfi.shape[1]//2 # for alignment + y_position = total_obs + num_obs//2 # for alignment + ax.text(x_position, y_position, "Cluster #" + str(cluster), color='red', + ha='center', va='center', fontsize=10, fontweight='bold') + total_obs += num_obs + + + plt.suptitle('Heatmap with Hierarchical Clustering', fontsize=24) + return + +def find_geometric_median(distance_matrix): + + # calculate the sum of distances for each point + distance_sums = np.sum(distance_matrix, axis=1) + + # find the index of the point with the minimum sum of distances + geometric_median_index = np.argmin(distance_sums) + + return geometric_median_index + +def assign_testing_centroid_approx(rbo_distance_matrix: np.ndarray, + lfi_train_ranking: np.ndarray, + lfi_test_ranking: np.ndarray, + clusters: np.ndarray) -> np.ndarray: + """ + Assigns testing points to clusters based on similarity to median ranking. + + Args: + rbo_distance_matrix (np.ndarray): distance matrix based on the rbo + lfi_train_ranking (np.ndarray): the local feature importance rankings of + the training points + lfi_test_ranking (np.ndarray): the local feature importance rankings of + the testing points + clusters (np.ndarray): the cluster labels for the training points + + Returns: + np.ndarray: cluster lables for the testing points + """ + + # initialize array to store cluster assignments for testing points + test_clust = np.zeros(lfi_test_ranking.shape[0]) + + # iterate over testing points and + # assign to cluster with most similar median ranking + for i in range(lfi_test_ranking.shape[0]): + king_of_hill = float('-inf') # start at negative infinity + # loop through clusters, calculate median ranking, and compare to point + for j in range(np.unique(clusters).shape[0]): + # get distance matrix for just this cluster + rbo_distance_clust = \ + rbo_distance_matrix[clusters == j+1, :][:, clusters == j+1] + median_index = find_geometric_median(rbo_distance_clust) + median_ranking = lfi_train_ranking[clusters==j+1,:][median_index,:] + similarity = rbo.RankingSimilarity(median_ranking, + lfi_test_ranking[i, :]).rbo(p=0.9, k=None) + if similarity > king_of_hill: + king_of_hill = similarity + test_clust[i] = j+1 + return test_clust + +def assign_testing_centroid_exact(rbo_distance_matrix: np.ndarray, + lfi_train_ranking: np.ndarray, + lfi_test_ranking: np.ndarray, + clusters: np.ndarray) -> np.ndarray: + """ + Assigns testing points to clusters based on similarity to average ranking. + + Args: + rbo_distance_matrix (np.ndarray): distance matrix based on the rbo + lfi_train_ranking (np.ndarray): the local feature importance rankings of + the training points + lfi_test_ranking (np.ndarray): the local feature importance rankings of + the testing points + clusters (np.ndarray): the cluster labels for the training points + + Returns: + np.ndarray: cluster lables for the testing points + """ + + # initialize array to store cluster assignments for testing points + test_clust = np.zeros(lfi_test_ranking.shape[0]) + + # iterate over testing points and calculate rbo similarity to each cluster + # assign to cluster with most similar points (on average) + for i in range(lfi_test_ranking.shape[0]): + king_of_hill = float('-inf') # start at negative infinity + # loop through clusters + for j in range(np.unique(clusters).shape[0]): + # get distance matrix for just this cluster + curr_clust = lfi_train_ranking[clusters == j+1, :] + similarity = np.zeros(curr_clust.shape[0]) + # compute similarity to each point in cluster + for k in range(curr_clust.shape[0]): + similarity[k] = rbo.RankingSimilarity(curr_clust[k, :], + lfi_test_ranking[i, :]).rbo(p=0.9, k=None) + # assign to cluster with most similar rankings on average + if similarity.mean() > king_of_hill: + king_of_hill = similarity.mean() + test_clust[i] = j+1 + + return test_clust + +def within_cluster_variance(rbo_distance_matrix: np.ndarray): + """ + Calculates the variance of a set of points given a distance matrix. + + Args: + rbo_distance_matrix (numpy.ndarray): a square matrix representing the + pairwise distances between points. + + Returns: + float: The variance of the set of points. + """ + n = rbo_distance_matrix.shape[0] + + # calculate the mean distance + mean_distance = np.mean(rbo_distance_matrix) + + # calculate the squared differences + squared_diffs = (rbo_distance_matrix - mean_distance)**2 + + # sum squared differences, divide by the total number of pairwise distances + variance = np.sum(squared_diffs) / (n * (n - 1)) + + return variance + +def rbo_distance_offset(num_features): + return rbo.RankingSimilarity(np.arange(1, num_features+1), + np.arange(1, num_features+1)).rbo(p=0.9,k=None) + +def assign_testing_variance_exact(rbo_distance_matrix: np.ndarray, + lfi_train_ranking: np.ndarray, + lfi_test_ranking: np.ndarray, + clusters: np.ndarray) -> np.ndarray: + """ + Assigns testing points to clusters based on smallest variance increase. + + Args: + rbo_distance_matrix (np.ndarray): distance matrix based on the rbo + lfi_train_ranking (np.ndarray): the local feature importance rankings of + the training points + lfi_test_ranking (np.ndarray): the local feature importance rankings of + the testing points + clusters (np.ndarray): the cluster labels for the training points + + Returns: + np.ndarray: cluster lables for the testing points + """ + + # initialize array to store cluster assignments for testing points + test_clust = np.zeros(lfi_test_ranking.shape[0]) + + # iterate over testing points and + # assign to cluster with smallest variance increase + for i in range(lfi_test_ranking.shape[0]): + king_of_hill = float('inf') # start at negative infinity + # loop through clusters, calculate median ranking, and compare to point + for j in range(np.unique(clusters).shape[0]): + # get distance matrix for just this cluster + rbo_distance_clust = \ + rbo_distance_matrix[clusters == j+1, :][:, clusters == j+1] + current_variance = within_cluster_variance(rbo_distance_clust) + distance_to_point = np.zeros(rbo_distance_clust.shape[0]) + for k in range(rbo_distance_clust.shape[0]): + distance_to_point[k] = \ + rbo_distance_offset(lfi_test_ranking.shape[1]) - \ + rbo.RankingSimilarity( + lfi_train_ranking[clusters == j+1, :][k, :], + lfi_test_ranking[i,:]).rbo(p=0.9, k=None) + # add distance_to_point as new row and column, diagonal elem is zero + extended_rbo_distance_clust = np.zeros((rbo_distance_clust.shape[0]+1, + rbo_distance_clust.shape[1]+1)) + extended_rbo_distance_clust[:-1, :-1] = rbo_distance_clust + # add distance_to_point as last row and column + extended_rbo_distance_clust[-1, :-1] = distance_to_point + extended_rbo_distance_clust[:-1, -1] = distance_to_point + + new_variance = within_cluster_variance(extended_rbo_distance_clust) + variance_increase = new_variance - current_variance + if variance_increase < king_of_hill: + king_of_hill = variance_increase + test_clust[i] = j+1 + return test_clust + +def assign_testing_clusters(method: str, median_approx: bool, + rbo_distance_matrix: np.ndarray, + lfi_train_ranking: np.ndarray, + lfi_test_ranking: np.ndarray, + clusters: np.ndarray) -> np.ndarray: + """ + Assigns testing points to clusters based on the method specified. + + Args: + method (str): the method to use for assigning testing points to clusters + median_approx (bool): whether to use the median approximation + rbo_distance_matrix (np.ndarray): distance matrix based on the rbo + lfi_train_ranking (np.ndarray): the local feature importance rankings of + the training points + lfi_test_ranking (np.ndarray): the local feature importance rankings of + the testing points + clusters (np.ndarray): the cluster labels for the training points + + Raises: + ValueError: if the method is not centroid or variance + + Returns: + np.ndarray: cluster lables for the testing points + """ + + # ensure the method is either centroid or variance + if method not in ['centroid', 'variance']: + raise ValueError('method must be either centroid or variance') + + # compute the testing point cluster assignments as specified by arguments + if method == "centroid": + if median_approx: + return assign_testing_centroid_approx(rbo_distance_matrix, + lfi_train_ranking, + lfi_test_ranking, clusters) + else: + return assign_testing_centroid_exact(rbo_distance_matrix, + lfi_train_ranking, + lfi_test_ranking, clusters) + else: + if median_approx: + return assign_testing_variance_exact(rbo_distance_matrix, + lfi_train_ranking, + lfi_test_ranking, clusters) + else: + return assign_testing_variance_exact(rbo_distance_matrix, + lfi_train_ranking, + lfi_test_ranking, clusters) + +def match_subgroups(cluster1, cluster2): + """ + Match subgroups between two clusterings. + + Args: + cluster1 (np.ndarray): cluster assignments for the first clustering + cluster2 (np.ndarray): cluster assignments for the second clustering + + Returns: + np.ndarray: an array of shape (num_clusters1,) where the ith entry is + the cluster number in the second clustering that best matches the ith + cluster in the first clustering + """ + + # create storage dictionaries + dict1to2 = {} + dict2to1 = {} + + # find the number of clusters in each clustering + num_clusters1 = np.unique(cluster1).shape[0] + num_clusters2 = np.unique(cluster2).shape[0] + + # check that each cluster has at least one point + if num_clusters1 != num_clusters2: + print("Warning: Number of clusters in clusterings do not match. Returning None.") + return None + + for clust1 in range(1, num_clusters1 + 1): + # get the points in cluster 1 + points1 = np.where(cluster1 == clust1)[0] + # for each cluster in the second clustering, find the percentage of its points in points1 + best_percentage = 0 + best_match = None + for clust2 in range(1, num_clusters2 + 1): + # get the points in cluster 2 + points2 = np.where(cluster2 == clust2)[0] + # calculate the percentage of points in points2 that are in points1 + percentage = len(np.intersect1d(points1, points2)) / len(points2) + if percentage > best_percentage: + best_percentage = percentage + best_match = clust2 + dict1to2[clust1] = best_match + for clust2 in range(1, num_clusters2 + 1): + # get the points in cluster 2 + points2 = np.where(cluster2 == clust2)[0] + # for each cluster in the first clustering, find the percentage of its points in points2 + best_percentage = 0 + best_match = None + for clust1 in range(1, num_clusters1 + 1): + # get the points in cluster 1 + points1 = np.where(cluster1 == clust1)[0] + # calculate the percentage of points in points1 that are in points2 + percentage = len(np.intersect1d(points1, points2)) / len(points1) + if percentage > best_percentage: + best_percentage = percentage + best_match = clust1 + dict2to1[clust2] = best_match + print("dict1to2") + print(dict1to2) + print("dict2to1") + print(dict2to1) + # check that the dictionaries agree with each other + for clust1 in range(1, num_clusters1 + 1): + if dict2to1[dict1to2[clust1]] != clust1: + print("Warning: Dictionaries do not agree with each other. Returning None.") + return None + + converted_membership = [dict2to1[cls] for cls in cluster2] + return converted_membership + + \ No newline at end of file diff --git a/feature_importance/subgroup/current/subgroup_experiment.py b/feature_importance/subgroup/current/subgroup_experiment.py new file mode 100644 index 0000000..c19a025 --- /dev/null +++ b/feature_importance/subgroup/current/subgroup_experiment.py @@ -0,0 +1,135 @@ +# import required packages +from imodels import get_clean_dataset +import numpy as np +import pandas as pd +from sklearn.model_selection import train_test_split +from sklearn.metrics import roc_auc_score, average_precision_score, f1_score +from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier +from imodels.tree.rf_plus.rf_plus.rf_plus_models import RandomForestPlusRegressor, RandomForestPlusClassifier +from imodels.tree.rf_plus.feature_importance.rfplus_explainer import AloRFPlusMDI, RFPlusMDI +import shap +from subgroup_detection import * +import warnings +import matplotlib.pyplot as plt +from sklearn.linear_model import RidgeCV, LogisticRegression + +global_task = None + +def split_data(X, y, seed = 1): + # split data into train, validation, and test sets + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, + random_state=seed) + X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, + test_size=0.25, + random_state=seed) + return X_train, X_valid, X_test, y_train, y_valid, y_test + +def fit_models(X_train, y_train, task): + # fit models + if task == 'classification': + global_task = 'classification' + rf = RandomForestClassifier(n_estimators=100) + rf.fit(X_train, y_train) + rf_plus = RandomForestPlusClassifier(rf_model=rf, + prediction_model=LogisticRegression()) + rf_plus.fit(X_train, y_train) + elif task == 'regression': + global_task = 'regression' + rf = RandomForestRegressor(n_estimators=100) + rf.fit(X_train, y_train) + rf_plus = RandomForestPlusRegressor(rf_model=rf, + prediction_model=RidgeCV()) + rf_plus.fit(X_train, y_train) + return rf, rf_plus + +def get_shap(X, shap_explainer): + if global_task == 'classification': + shap_values = shap_explainer.shap_values(X, check_additivity=False)[:,:,1] + else: + shap_values = shap_explainer.shap_values(X, check_additivity=False) + shap_rankings = np.argsort(-np.abs(shap_values), axis = 1) + return shap_values, shap_rankings + +def get_lmdi(X, y, lmdi_explainer): + # get feature importances + lmdi = np.abs(lmdi_explainer.explain_linear_partial(np.asarray(X), y, + l2norm=True)) + mdi_rankings = lmdi_explainer.get_rankings(lmdi) + return lmdi, mdi_rankings + +def get_num_clusters(X_train, y_train, X_valid, y_valid, shap_explainer, + shap_rbo_train, shap_train_rankings): + if global_task == 'classification': + shap_valid_values = np.abs(shap_explainer.shap_values(X_valid, + check_additivity=False))[:,:,1] + else: + shap_valid_values = np.abs(shap_explainer.shap_values(X_valid, + check_additivity=False)) + shap_valid_rankings = np.argsort(-shap_valid_values, axis = 1) + + + lowest_error = np.inf + opt_num_clusters = -1 + error_lst = [] + time_since_king = 0 + opt_clusters = None + for num_clusters in np.arange(2, 21): + shap_train_clusters = assign_training_clusters(shap_rbo_train, num_clusters) + valid_clusters = assign_testing_clusters(method="centroid", + median_approx=True, + rbo_distance_matrix=shap_rbo_train, + lfi_train_ranking=shap_train_rankings, + lfi_test_ranking=shap_valid_rankings, + clusters = shap_train_clusters) + total_error = 0 + for cluster in np.arange(1, num_clusters + 1): + if global_task == 'classification': + local_rf = RandomForestClassifier(n_estimators=100, random_state=0) + else: + local_rf = RandomForestRegressor(n_estimators=100, random_state=0) + local_rf.fit(X_train[shap_train_clusters == cluster], y_train[shap_train_clusters == cluster]) + local_preds = local_rf.predict(X_valid[valid_clusters == cluster]) + if global_task == 'classification': + local_error = np.sum(local_preds != y_valid[valid_clusters == cluster]) + else: + local_error = np.sum((local_preds - y_valid[valid_clusters == cluster])**2) + total_error += local_error + if total_error < lowest_error: + lowest_error = total_error + opt_num_clusters = num_clusters + time_since_king = 0 + opt_clusters = shap_train_clusters + else: + time_since_king += 1 + if time_since_king > 2: + break + return opt_num_clusters, opt_clusters + +def run_experiment(X, y, task, seed = 1): + # split data + X_train, X_valid, X_test, y_train, y_valid, y_test = split_data(X, y, seed) + # fit models + rf, rf_plus = fit_models(X_train, y_train, task) + # get shap values + shap_explainer = shap.TreeExplainer(rf) + shap_values, shap_rankings = get_shap(X_train, shap_explainer) + # get lmdi values + lmdi_explainer = RFPlusMDI(rf_plus) + lmdi_train, lmdi_train_rankings = get_lmdi(X_train, y_train, lmdi_explainer) + # get shap rbo + shap_rbo_train = compute_rbo_matrix(shap_rankings) + # get optimal number of clusters + opt_num_clusters, opt_clusters = get_num_clusters(X_train, y_train, X_valid, + y_valid, shap_explainer, + shap_rbo_train, shap_rankings) + lmdi_rbo_train = compute_rbo_matrix(lmdi_train_rankings) + lmdi_train_clusters = assign_training_clusters(lmdi_rbo_train, opt_num_clusters) + lmdi_test, lmdi_test_rankings = get_lmdi(X_test, y_test, lmdi_explainer) + lmdi_test_clusters = assign_testing_clusters(method="centroid", + median_approx=False, + rbo_distance_matrix=lmdi_rbo_train, + lfi_train_ranking=lmdi_train_rankings, + lfi_test_ranking=lmdi_test_rankings, + clusters=lmdi_train_clusters) + + return opt_num_clusters, opt_clusters \ No newline at end of file diff --git a/feature_importance/subgroup/insurance.ipynb b/feature_importance/subgroup/insurance.ipynb deleted file mode 100644 index be915a3..0000000 --- a/feature_importance/subgroup/insurance.ipynb +++ /dev/null @@ -1,774 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# import required packages\n", - "from imodels import get_clean_dataset\n", - "import numpy as np\n", - "import pandas as pd\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import r2_score\n", - "from sklearn.ensemble import RandomForestRegressor\n", - "from imodels.tree.rf_plus.rf_plus.rf_plus_models import RandomForestPlusRegressor\n", - "from imodels.tree.rf_plus.feature_importance.rfplus_explainer import AloRFPlusMDI\n", - "from subgroup_detection import detect_subgroups, compute_rbo_matrix\n", - "import warnings\n", - "warnings.filterwarnings('ignore', category=DeprecationWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# get pre-cleaned compas dataset from imodels\n", - "X, _, feature_names = get_clean_dataset(43463, data_source='openml')\n", - "X = pd.DataFrame(X, columns=feature_names)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## About the Dataset\n", - "Throughout this report, we will be looking at the `insurance` dataset, which can be found on OpenML [here](https://www.openml.org/search?type=data&status=active&id=43463). Each row in this dataset represents an individual on some (unknown) health insurance plan. The task is to predict someone's medical expenses using demographic information such as age, sex, smoker status, and more. We take a peek at the data below:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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\n", - "
" - ], - "text/plain": [ - " age sex bmi children smoker region charges\n", - "0 19.0 0.0 27.900 0.0 1.0 3.0 16884.92400\n", - "1 18.0 1.0 33.770 1.0 0.0 2.0 1725.55230\n", - "2 28.0 1.0 33.000 3.0 0.0 2.0 4449.46200\n", - "3 33.0 1.0 22.705 0.0 0.0 1.0 21984.47061\n", - "4 32.0 1.0 28.880 0.0 0.0 1.0 3866.85520" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X.head()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dimensions of the Insurance Dataset (Covariates): (1338, 6)\n", - "Dimensions of the Response: (1338,)\n" - ] - } - ], - "source": [ - "y = X.pop('charges')\n", - "print(\"Dimensions of the Insurance Dataset (Covariates):\", X.shape)\n", - "print(\"Dimensions of the Response:\", y.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Before we begin analyzing our data, we first perform some simple exploratory data analysis." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "---------------------------------------------\n", - "The Pearson Correlation Coefficient Between Age and Charges is: 0.3\n", - "---------------------------------------------\n", - "The Average Charge for Men is: 13956.75\n", - "The Average Charge for Women is: 12569.58\n", - "---------------------------------------------\n", - "The Pearson Correlation Coefficient Between BMI and Charges is: 0.2\n", - "---------------------------------------------\n", - "The Pearson Correlation Coefficient Between # of Children and Charges is: 0.07\n", - "---------------------------------------------\n", - "The Average Charge for Smokers is: 32050.23\n", - "The Average Charge for Non-Smokers is: 8434.27\n", - "---------------------------------------------\n", - "The Average Charge for Region #1 is: 13406.38\n", - "The Average Charge for Region #2 is: 12417.58\n", - "The Average Charge for Region #3 is: 14735.41\n", - "The Average Charge for Region #4 is: 12346.94\n", - "---------------------------------------------\n" - ] - } - ], - "source": [ - "print(\"---------------------------------------------\")\n", - "# get correlation between age and charges\n", - "print(\"The Pearson Correlation Coefficient Between Age and Charges is:\",\n", - " round(X['age'].corr(y), 2))\n", - "print(\"---------------------------------------------\")\n", - "# get the average charge by sex\n", - "print(\"The Average Charge for Men is:\", round(y[X['sex']==1].mean(), 2))\n", - "print(\"The Average Charge for Women is:\", round(y[X['sex']==0].mean(), 2))\n", - "print(\"---------------------------------------------\")\n", - "# get correlation between bmi and charges\n", - "print(\"The Pearson Correlation Coefficient Between BMI and Charges is:\",\n", - " round(X['bmi'].corr(y), 2))\n", - "print(\"---------------------------------------------\")\n", - "# get the correlation between children and charges\n", - "print(\"The Pearson Correlation Coefficient Between # of \\\n", - "Children and Charges is:\", round(X['children'].corr(y), 2))\n", - "print(\"---------------------------------------------\")\n", - "# get the average charge by smoker status\n", - "print(\"The Average Charge for Smokers is:\", round(y[X['smoker']==1].mean(), 2))\n", - "print(\"The Average Charge for Non-Smokers is:\",\n", - " round(y[X['smoker']==0].mean(), 2))\n", - "print(\"---------------------------------------------\")\n", - "# get the average charge by region\n", - "print(\"The Average Charge for Region #1 is:\",\n", - " round(y[X['region']==0].mean(), 2))\n", - "print(\"The Average Charge for Region #2 is:\",\n", - " round(y[X['region']==1].mean(), 2))\n", - "print(\"The Average Charge for Region #3 is:\",\n", - " round(y[X['region']==2].mean(), 2))\n", - "print(\"The Average Charge for Region #4 is:\",\n", - " round(y[X['region']==3].mean(), 2))\n", - "print(\"---------------------------------------------\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It is apparent that while multiple variables might impact the medical expenses of an individual, the most eye-popping difference is the average expense difference between smokers and non-smokers.\n", - "\n", - "## Modeling\n", - "\n", - "Now, we split the data into training and testing datasets using a 70/30 split. We check for covariate balance in our train/test split below. We do not include `region` in this covariate balance check for the sake of brevity." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# split data into training and testing sets\n", - "# we won't actually use the test set here though, since 'discovery' would be\n", - "# a post-hoc analysis in real life\n", - "# proportion of training data is small so rf+ can fit without taking hours\n", - "y = np.asarray(y)\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,\n", - " random_state=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "---------------------------------------------\n", - "Number of Observations in Training Data: 936\n", - "Number of Observations in Testing Data: 402\n", - "---------------------------------------------\n", - "Average of Age in Training Data: 39.47115384615385\n", - "Average of Age in Testing Data: 38.592039800995025\n", - "---------------------------------------------\n", - "Proportion of Men in Training Data: 0.48824786324786323\n", - "Proportion of Men in Testing Data: 0.5447761194029851\n", - "---------------------------------------------\n", - "Proportion of Women in Training Data: 0.5117521367521367\n", - "Proportion of Women in Testing Data: 0.4552238805970149\n", - "---------------------------------------------\n", - "Average of # of Children in Training Data: 1.0811965811965811\n", - "Average of # of Children in Testing Data: 1.126865671641791\n", - "---------------------------------------------\n", - "Proportion of Smokers in Training Data: 0.202991452991453\n", - "Proportion of Smokers in Testing Data: 0.208955223880597\n", - "---------------------------------------------\n", - "Average Charge in Training Data: 13232.916467179486\n", - "Average Charge in Testing Data: 13357.749197708954\n", - "---------------------------------------------\n" - ] - } - ], - "source": [ - "print(\"---------------------------------------------\")\n", - "print(\"Number of Observations in Training Data:\", X_train.shape[0])\n", - "print(\"Number of Observations in Testing Data:\", X_test.shape[0])\n", - "print(\"---------------------------------------------\")\n", - "print(\"Average of Age in Training Data:\", X_train['age'].mean())\n", - "print(\"Average of Age in Testing Data:\", X_test['age'].mean())\n", - "print(\"---------------------------------------------\")\n", - "print(\"Proportion of Men in Training Data:\", X_train['sex'].mean())\n", - "print(\"Proportion of Men in Testing Data:\", X_test['sex'].mean())\n", - "print(\"---------------------------------------------\")\n", - "print(\"Proportion of Women in Training Data:\", 1-X_train['sex'].mean())\n", - "print(\"Proportion of Women in Testing Data:\", 1-X_test['sex'].mean())\n", - "print(\"---------------------------------------------\")\n", - "print(\"Average of # of Children in Training Data:\", X_train['children'].mean())\n", - "print(\"Average of # of Children in Testing Data:\", X_test['children'].mean())\n", - "print(\"---------------------------------------------\")\n", - "print(\"Proportion of Smokers in Training Data:\", X_train['smoker'].mean())\n", - "print(\"Proportion of Smokers in Testing Data:\", X_test['smoker'].mean())\n", - "print(\"---------------------------------------------\")\n", - "print(\"Average Charge in Training Data:\", y_train.mean())\n", - "print(\"Average Charge in Testing Data:\", y_test.mean())\n", - "print(\"---------------------------------------------\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The train and test datasets seem reasonable, so we continue with our analysis. We begin by fitting a RF+ to the training data. The R^2 on the test data is reported below. The total squared error (TSE) is also reported, as we will use this to compare the 'global' model fit on all of the data to the 'local' models fit on each cluster." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "%%capture\n", - "# fit RF+ model\n", - "rf = RandomForestRegressor(n_estimators=100, random_state=0)\n", - "rf_plus = RandomForestPlusRegressor(rf)\n", - "rf_plus.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RF+ Test Set R^2: 0.8669625283228367\n", - "RF+ Test Set TSE: 8528596608.051979\n" - ] - } - ], - "source": [ - "# compute r^2 on the test set\n", - "y_pred = rf_plus.predict(X_test)\n", - "r2 = r2_score(y_test, y_pred)\n", - "tse = np.sum((y_test - y_pred)**2)\n", - "print(f'RF+ Test Set R^2: {r2}')\n", - "print(f'RF+ Test Set TSE: {tse}')" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# get Local MDI+ feature importances\n", - "mdi_explainer = AloRFPlusMDI(rf_plus,evaluate_on='oob')\n", - "mdi, partial_preds = mdi_explainer.explain(np.asarray(X_train), y_train)\n", - "mdi_rankings = mdi_explainer.get_rankings(mdi)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Detecting Subgroups\n", - "\n", - "At this point, we are ready to cluster our data. We compute the ranking-based overlap (RBO) between each pair of points, and then use this as the distance matrix for hierarchical clustering with Ward linkage. Seven clusters are chosen due to the appearance of the heatmap below. It may be worth trying this with a range of cluster amounts and checking how it changes model performance - we could perhaps make a train/validate/test split, where we choose the number of clusters that results in the lowest total squared error in the 'local' models (or perhaps make an elbow plot, since the performance should only improve as # of clusters increases)." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "num_obs: 97\n", - "num_obs: 83\n", - "num_obs: 74\n", - "num_obs: 158\n", - "num_obs: 260\n", - "num_obs: 140\n", - "num_obs: 124\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "mdi_copy = pd.DataFrame(mdi, columns=feature_names[:-1]).copy()\n", - "num_clusters = 7\n", - "clusters = detect_subgroups(mdi_copy, mdi_rankings, num_clusters)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It is easy to see that these subgroups differ in feature importance, as shown above. However, just because they differ in feature importance does not necessarily imply that they differ in the values of those features themselves. We now check some useful summary statistics for each cluster to better understand how we have grouped the data." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "---------------------------------------------\n", - "Proportion of Data in Cluster #1: 0.10363247863247864\n", - "Proportion of Data in Cluster #2: 0.08867521367521368\n", - "Proportion of Data in Cluster #3: 0.07905982905982906\n", - "Proportion of Data in Cluster #4: 0.16880341880341881\n", - "Proportion of Data in Cluster #5: 0.2777777777777778\n", - "Proportion of Data in Cluster #6: 0.14957264957264957\n", - "Proportion of Data in Cluster #7: 0.13247863247863248\n", - "---------------------------------------------\n", - "Average Age in Cluster #1: 49.24742268041237\n", - "Average Age in Cluster #2: 49.89156626506024\n", - "Average Age in Cluster #3: 42.270270270270274\n", - "Average Age in Cluster #4: 29.018987341772153\n", - "Average Age in Cluster #5: 34.36923076923077\n", - "Average Age in Cluster #6: 40.27857142857143\n", - "Average Age in Cluster #7: 46.28225806451613\n", - "---------------------------------------------\n", - "Proportion of Women in Cluster #1: 0.5463917525773196\n", - "Proportion of Women in Cluster #2: 0.6144578313253012\n", - "Proportion of Women in Cluster #3: 0.43243243243243246\n", - "Proportion of Women in Cluster #4: 0.5063291139240507\n", - "Proportion of Women in Cluster #5: 0.5384615384615384\n", - "Proportion of Women in Cluster #6: 0.41428571428571426\n", - "Proportion of Women in Cluster #7: 0.5241935483870968\n", - "---------------------------------------------\n", - "Proportion of Men in Cluster #1: 0.4536082474226804\n", - "Proportion of Men in Cluster #2: 0.3855421686746988\n", - "Proportion of Men in Cluster #3: 0.5675675675675675\n", - "Proportion of Men in Cluster #4: 0.4936708860759494\n", - "Proportion of Men in Cluster #5: 0.46153846153846156\n", - "Proportion of Men in Cluster #6: 0.5857142857142857\n", - "Proportion of Men in Cluster #7: 0.47580645161290325\n", - "---------------------------------------------\n", - "Average BMI in Cluster #1: 30.418453608247425\n", - "Average BMI in Cluster #2: 30.555180722891567\n", - "Average BMI in Cluster #3: 30.453986486486485\n", - "Average BMI in Cluster #4: 29.297436708860758\n", - "Average BMI in Cluster #5: 29.931326923076924\n", - "Average BMI in Cluster #6: 33.29010714285714\n", - "Average BMI in Cluster #7: 32.033830645161295\n", - "---------------------------------------------\n", - "Average # of Children in Cluster #1: 0.9587628865979382\n", - "Average # of Children in Cluster #2: 1.108433734939759\n", - "Average # of Children in Cluster #3: 1.1216216216216217\n", - "Average # of Children in Cluster #4: 0.7341772151898734\n", - "Average # of Children in Cluster #5: 1.2615384615384615\n", - "Average # of Children in Cluster #6: 1.1642857142857144\n", - "Average # of Children in Cluster #7: 1.1048387096774193\n", - "---------------------------------------------\n", - "Proportion of Smokers in Cluster #1: 0.16494845360824742\n", - "Proportion of Smokers in Cluster #2: 0.21686746987951808\n", - "Proportion of Smokers in Cluster #3: 0.2702702702702703\n", - "Proportion of Smokers in Cluster #4: 0.10126582278481013\n", - "Proportion of Smokers in Cluster #5: 0.09615384615384616\n", - "Proportion of Smokers in Cluster #6: 0.6571428571428571\n", - "Proportion of Smokers in Cluster #7: 0.024193548387096774\n", - "---------------------------------------------\n", - "Average Medical Expenses in Cluster #1: 13847.465920721652\n", - "Average Medical Expenses in Cluster #2: 14060.11317433735\n", - "Average Medical Expenses in Cluster #3: 16707.996409189192\n", - "Average Medical Expenses in Cluster #4: 8996.781937974685\n", - "Average Medical Expenses in Cluster #5: 7141.413066115385\n", - "Average Medical Expenses in Cluster #6: 30156.70440964286\n", - "Average Medical Expenses in Cluster #7: 9187.313955483869\n", - "---------------------------------------------\n" - ] - } - ], - "source": [ - "# add clusters as column to X_train\n", - "X_train['cluster'] = clusters\n", - "print(\"---------------------------------------------\")\n", - "# calculate average charge for each cluster\n", - "for i in range(num_clusters):\n", - " print(f\"Proportion of Data in Cluster #{i+1}:\", np.sum(X_train['cluster']==i+1)/X_train.shape[0])\n", - "print(\"---------------------------------------------\")\n", - "# get average age in each cluster\n", - "for i in range(num_clusters):\n", - " print(f\"Average Age in Cluster #{i+1}:\", X_train[X_train['cluster']==i+1]['age'].mean())\n", - "print(\"---------------------------------------------\")\n", - "# get percentage women in each cluster\n", - "for i in range(num_clusters):\n", - " print(f\"Proportion of Women in Cluster #{i+1}:\", 1-X_train[X_train['cluster']==i+1]['sex'].mean())\n", - "print(\"---------------------------------------------\")\n", - "# get percentage men in each cluster\n", - "for i in range(num_clusters):\n", - " print(f\"Proportion of Men in Cluster #{i+1}:\", X_train[X_train['cluster']==i+1]['sex'].mean())\n", - "print(\"---------------------------------------------\")\n", - "# get average bmi in each cluster\n", - "for i in range(num_clusters):\n", - " print(f\"Average BMI in Cluster #{i+1}:\", X_train[X_train['cluster']==i+1]['bmi'].mean())\n", - "print(\"---------------------------------------------\")\n", - "# get average number of children in each cluster\n", - "for i in range(num_clusters):\n", - " print(f\"Average # of Children in Cluster #{i+1}:\", X_train[X_train['cluster']==i+1]['children'].mean())\n", - "print(\"---------------------------------------------\")\n", - "# get proportion of smokers in each cluster\n", - "for i in range(num_clusters):\n", - " print(f\"Proportion of Smokers in Cluster #{i+1}:\", X_train[X_train['cluster']==i+1]['smoker'].mean())\n", - "print(\"---------------------------------------------\")\n", - "# get proportion of smokers in each cluster\n", - "for i in range(num_clusters):\n", - " print(f\"Average Medical Expenses in Cluster #{i+1}:\", y_train[X_train['cluster']==i+1].mean())\n", - "print(\"---------------------------------------------\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Evaluating Cluster Performance\n", - "Now that we have a good idea of what our clustering has done, we can check if this helps improve our predictions. We will take the data in each cluster and create a train and test set, fitting a RF+ on the training data and using it to predict the test data for that cluster. We can then compute the R^2 and total squared error for each cluster's model. By summing the TSE across cluster models and comparing this to the original TSE reported above, we can get a good idea of how well these clusters improve model accuracy." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# split each cluster into its own set\n", - "cluster1X = X_train[X_train['cluster']==1]\n", - "cluster2X = X_train[X_train['cluster']==2]\n", - "cluster3X = X_train[X_train['cluster']==3]\n", - "cluster4X = X_train[X_train['cluster']==4]\n", - "cluster5X = X_train[X_train['cluster']==5]\n", - "cluster6X = X_train[X_train['cluster']==6]\n", - "cluster7X = X_train[X_train['cluster']==7]\n", - "cluster1y = y_train[X_train['cluster']==1]\n", - "cluster2y = y_train[X_train['cluster']==2]\n", - "cluster3y = y_train[X_train['cluster']==3]\n", - "cluster4y = y_train[X_train['cluster']==4]\n", - "cluster5y = y_train[X_train['cluster']==5]\n", - "cluster6y = y_train[X_train['cluster']==6]\n", - "cluster7y = y_train[X_train['cluster']==7]\n", - "\n", - "# remove cluster column from cluster X's\n", - "cluster1X = cluster1X.drop(columns='cluster')\n", - "cluster2X = cluster2X.drop(columns='cluster')\n", - "cluster3X = cluster3X.drop(columns='cluster')\n", - "cluster4X = cluster4X.drop(columns='cluster')\n", - "cluster5X = cluster5X.drop(columns='cluster')\n", - "cluster6X = cluster6X.drop(columns='cluster')\n", - "cluster7X = cluster7X.drop(columns='cluster')\n", - "\n", - "# split each cluster into train/test\n", - "cluster1_trainX, cluster1_testX, cluster1_trainy, cluster1_testy = \\\n", - " train_test_split(cluster1X, cluster1y, test_size=0.3, random_state=0)\n", - "cluster2_trainX, cluster2_testX, cluster2_trainy, cluster2_testy = \\\n", - " train_test_split(cluster2X, cluster2y, test_size=0.3, random_state=0)\n", - "cluster3_trainX, cluster3_testX, cluster3_trainy, cluster3_testy = \\\n", - " train_test_split(cluster3X, cluster3y, test_size=0.3, random_state=0)\n", - "cluster4_trainX, cluster4_testX, cluster4_trainy, cluster4_testy = \\\n", - " train_test_split(cluster4X, cluster4y, test_size=0.3, random_state=0)\n", - "cluster5_trainX, cluster5_testX, cluster5_trainy, cluster5_testy = \\\n", - " train_test_split(cluster5X, cluster5y, test_size=0.3, random_state=0)\n", - "cluster6_trainX, cluster6_testX, cluster6_trainy, cluster6_testy = \\\n", - " train_test_split(cluster6X, cluster6y, test_size=0.3, random_state=0)\n", - "cluster7_trainX, cluster7_testX, cluster7_trainy, cluster7_testy = \\\n", - " train_test_split(cluster7X, cluster7y, test_size=0.3, random_state=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "%%capture\n", - "# fit RF+ on each training set, predict test\n", - "rf1 = RandomForestRegressor(n_estimators=100, random_state=0)\n", - "rf_plus1 = RandomForestPlusRegressor(rf1)\n", - "rf_plus1.fit(cluster1_trainX, cluster1_trainy)\n", - "\n", - "rf2 = RandomForestRegressor(n_estimators=100, random_state=0)\n", - "rf_plus2 = RandomForestPlusRegressor(rf2)\n", - "rf_plus2.fit(cluster2_trainX, cluster2_trainy)\n", - "\n", - "rf3 = RandomForestRegressor(n_estimators=100, random_state=0)\n", - "rf_plus3 = RandomForestPlusRegressor(rf3)\n", - "rf_plus3.fit(cluster3_trainX, cluster3_trainy)\n", - "\n", - "rf4 = RandomForestRegressor(n_estimators=100, random_state=0)\n", - "rf_plus4 = RandomForestPlusRegressor(rf4)\n", - "rf_plus4.fit(cluster4_trainX, cluster4_trainy)\n", - "\n", - "rf5 = RandomForestRegressor(n_estimators=100, random_state=0)\n", - "rf_plus5 = RandomForestPlusRegressor(rf5)\n", - "rf_plus5.fit(cluster5_trainX, cluster5_trainy)\n", - "\n", - "rf6 = RandomForestRegressor(n_estimators=100, random_state=0)\n", - "rf_plus6 = RandomForestPlusRegressor(rf6)\n", - "rf_plus6.fit(cluster6_trainX, cluster6_trainy)\n", - "\n", - "rf7 = RandomForestRegressor(n_estimators=100, random_state=0)\n", - "rf_plus7 = RandomForestPlusRegressor(rf7)\n", - "rf_plus7.fit(cluster7_trainX, cluster7_trainy)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "---------------------------------------------\n", - "RF+ Cluster #1 Test Set R^2: 0.5167922711186593\n", - "RF+ Cluster #1 Test Set TSE: 242907052.0269257\n", - "---------------------------------------------\n", - "RF+ Cluster #2 Test Set R^2: 0.8952890795407058\n", - "RF+ Cluster #2 Test Set TSE: 17546046.36145893\n", - "---------------------------------------------\n", - "RF+ Cluster #3 Test Set R^2: 0.5833946595708773\n", - "RF+ Cluster #3 Test Set TSE: 208336698.9759806\n", - "---------------------------------------------\n", - "RF+ Cluster #4 Test Set R^2: 0.9590512375368682\n", - "RF+ Cluster #4 Test Set TSE: 222032244.56791243\n", - "---------------------------------------------\n", - "RF+ Cluster #5 Test Set R^2: 0.897084283656301\n", - "RF+ Cluster #5 Test Set TSE: 355765586.3226504\n", - "---------------------------------------------\n", - "RF+ Cluster #6 Test Set R^2: 0.9914856554873769\n", - "RF+ Cluster #6 Test Set TSE: 87653925.97047529\n", - "---------------------------------------------\n", - "RF+ Cluster #7 Test Set R^2: 0.12692299915862415\n", - "RF+ Cluster #7 Test Set TSE: 561678899.0255064\n", - "---------------------------------------------\n" - ] - } - ], - "source": [ - "# compute r^2 on the test set\n", - "print(\"---------------------------------------------\")\n", - "y_pred = rf_plus1.predict(cluster1_testX)\n", - "r2 = r2_score(cluster1_testy, y_pred)\n", - "tse1 = np.sum((cluster1_testy - y_pred)**2)\n", - "print(f'RF+ Cluster #1 Test Set R^2: {r2}')\n", - "print(f'RF+ Cluster #1 Test Set TSE: {tse1}')\n", - "print(\"---------------------------------------------\")\n", - "y_pred = rf_plus2.predict(cluster2_testX)\n", - "r2 = r2_score(cluster2_testy, y_pred)\n", - "tse2 = np.sum((cluster2_testy - y_pred)**2)\n", - "print(f'RF+ Cluster #2 Test Set R^2: {r2}')\n", - "print(f'RF+ Cluster #2 Test Set TSE: {tse2}')\n", - "print(\"---------------------------------------------\")\n", - "y_pred = rf_plus3.predict(cluster3_testX)\n", - "r2 = r2_score(cluster3_testy, y_pred)\n", - "tse3 = np.sum((cluster3_testy - y_pred)**2)\n", - "print(f'RF+ Cluster #3 Test Set R^2: {r2}')\n", - "print(f'RF+ Cluster #3 Test Set TSE: {tse3}')\n", - "print(\"---------------------------------------------\")\n", - "y_pred = rf_plus4.predict(cluster4_testX)\n", - "r2 = r2_score(cluster4_testy, y_pred)\n", - "tse4 = np.sum((cluster4_testy - y_pred)**2)\n", - "print(f'RF+ Cluster #4 Test Set R^2: {r2}')\n", - "print(f'RF+ Cluster #4 Test Set TSE: {tse4}')\n", - "print(\"---------------------------------------------\")\n", - "y_pred = rf_plus5.predict(cluster5_testX)\n", - "r2 = r2_score(cluster5_testy, y_pred)\n", - "tse5 = np.sum((cluster5_testy - y_pred)**2)\n", - "print(f'RF+ Cluster #5 Test Set R^2: {r2}')\n", - "print(f'RF+ Cluster #5 Test Set TSE: {tse5}')\n", - "print(\"---------------------------------------------\")\n", - "y_pred = rf_plus6.predict(cluster6_testX)\n", - "r2 = r2_score(cluster6_testy, y_pred)\n", - "tse6 = np.sum((cluster6_testy - y_pred)**2)\n", - "print(f'RF+ Cluster #6 Test Set R^2: {r2}')\n", - "print(f'RF+ Cluster #6 Test Set TSE: {tse6}')\n", - "print(\"---------------------------------------------\")\n", - "y_pred = rf_plus7.predict(cluster7_testX)\n", - "r2 = r2_score(cluster7_testy, y_pred)\n", - "tse7 = np.sum((cluster7_testy - y_pred)**2)\n", - "print(f'RF+ Cluster #7 Test Set R^2: {r2}')\n", - "print(f'RF+ Cluster #7 Test Set TSE: {tse7}')\n", - "print(\"---------------------------------------------\")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "---------------------------------------------\n", - "Difference in TSE (Global - Sum of Clusters): 6832676154.8\n", - "Percent Improvement Over Global Model: 80.11%\n", - "---------------------------------------------\n" - ] - } - ], - "source": [ - "print(\"---------------------------------------------\")\n", - "print(\"Difference in TSE (Global - Sum of Clusters):\", round(tse - (tse1 + tse2 + tse3 + tse4 + tse5 + tse6 + tse7), 2))\n", - "print(f\"Percent Improvement Over Global Model: {round(100*(tse - (tse1 + tse2 + tse3 + tse4 + tse5 + tse6 + tse7))/tse, 2)}%\")\n", - "print(\"---------------------------------------------\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is a huge improvement!" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "mdi", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": 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diff --git a/feature_importance/subgroup/legacy/abalone-eda-ablate.ipynb b/feature_importance/subgroup/legacy/abalone-eda-ablate.ipynb new file mode 100644 index 0000000..2739764 --- /dev/null +++ b/feature_importance/subgroup/legacy/abalone-eda-ablate.ipynb @@ -0,0 +1,899 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# import required packages\n", + "from imodels import get_clean_dataset\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n", + "from imodels.tree.rf_plus.rf_plus.rf_plus_models import RandomForestPlusRegressor, RandomForestPlusClassifier\n", + "from imodels.tree.rf_plus.feature_importance.rfplus_explainer import RFPlusMDI\n", + "from sklearn.linear_model import RidgeCV, LogisticRegression\n", + "import shap\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "from joypy import joyplot" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fetching diabetes from sklearn\n" + ] + } + ], + "source": [ + "# get pre-cleaned compas dataset from imodels\n", + "X, y, feature_names = get_clean_dataset('diabetes_regr', data_source='imodels')\n", + "X = pd.DataFrame(X, columns=feature_names)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " age sex bmi bp s1 s2 s3 \\\n", + "age 1.000000 0.173737 0.185085 0.335428 0.260061 0.219243 -0.075181 \n", + "sex 0.173737 1.000000 0.088161 0.241010 0.035277 0.142637 -0.379090 \n", + "bmi 0.185085 0.088161 1.000000 0.395411 0.249777 0.261170 -0.366811 \n", + "bp 0.335428 0.241010 0.395411 1.000000 0.242464 0.185548 -0.178762 \n", + "s1 0.260061 0.035277 0.249777 0.242464 1.000000 0.896663 0.051519 \n", + "s2 0.219243 0.142637 0.261170 0.185548 0.896663 1.000000 -0.196455 \n", + "s3 -0.075181 -0.379090 -0.366811 -0.178762 0.051519 -0.196455 1.000000 \n", + "s4 0.203841 0.332115 0.413807 0.257650 0.542207 0.659817 -0.738493 \n", + "s5 0.270774 0.149916 0.446157 0.393480 0.515503 0.318357 -0.398577 \n", + "s6 0.301731 0.208133 0.388680 0.390430 0.325717 0.290600 -0.273697 \n", + "\n", + " s4 s5 s6 \n", + "age 0.203841 0.270774 0.301731 \n", + "sex 0.332115 0.149916 0.208133 \n", + "bmi 0.413807 0.446157 0.388680 \n", + "bp 0.257650 0.393480 0.390430 \n", + "s1 0.542207 0.515503 0.325717 \n", + "s2 0.659817 0.318357 0.290600 \n", + "s3 -0.738493 -0.398577 -0.273697 \n", + "s4 1.000000 0.617859 0.417212 \n", + "s5 0.617859 1.000000 0.464669 \n", + "s6 0.417212 0.464669 1.000000 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# get correlation matrix of X's\n", + "corr = X.corr()\n", + "corr" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# set seed\n", + "np.random.seed(0)\n", + "# get pre-cleaned compas dataset from imodels\n", + "X, y, feature_names = get_clean_dataset(183, data_source='openml')\n", + "X = pd.DataFrame(X, columns=feature_names)\n", + "# convert y to float\n", + "y = y.astype(float)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Proportion of data with Gender 0: 0.31290399808474983\n", + "Proportion of data with Gender 1: 0.32128321762030165\n", + "Proportion of data with Gender 2: 0.3658127842949485\n" + ] + } + ], + "source": [ + "# get proportion of X that has gender 0/1/2\n", + "gender = X[\"Sex\"]\n", + "print(\"Proportion of data with Gender 0: \", sum(gender == 0)/len(gender))\n", + "print(\"Proportion of data with Gender 1: \", sum(gender == 1)/len(gender))\n", + "print(\"Proportion of data with Gender 2: \", sum(gender == 2)/len(gender))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot demonstrating subgroup impact on outcome:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/IPython/core/pylabtools.py:77: DeprecationWarning: backend2gui is deprecated since IPython 8.24, backends are managed in matplotlib and can be externally registered.\n", + " warnings.warn(\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/IPython/core/pylabtools.py:77: DeprecationWarning: backend2gui is deprecated since IPython 8.24, backends are managed in matplotlib and can be externally registered.\n", + " warnings.warn(\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/IPython/core/pylabtools.py:77: DeprecationWarning: backend2gui is deprecated since IPython 8.24, backends are managed in matplotlib and can be externally registered.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": [ + "
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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"Plot demonstrating subgroup impact on outcome:\")\n", + "# Create the joyplot\n", + "plt.figure(figsize=(10, 6))\n", + "joyplot(\n", + " data=X.assign(y=y),\n", + " by='Sex',\n", + " column='y',\n", + " fade=True,\n", + " grid=True,\n", + " linecolor='k',\n", + " linewidth=0.5\n", + ")\n", + "\n", + "plt.title('Distribution of Outcome by Gender')\n", + "plt.xlabel('Outcome')\n", + "plt.ylabel('Density')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# split data into train, validation, and test sets\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,\n", + " random_state=2)\n", + "X_train = X_train.reset_index(drop=True)\n", + "X_test = X_test.reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 16 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 18 tasks | elapsed: 26.7s\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 1.3min finished\n" + ] + } + ], + "source": [ + "# fit RF model\n", + "rf = RandomForestRegressor(n_estimators=100, random_state=0)\n", + "rf.fit(X_train, y_train)\n", + "\n", + "# fit RF+ model\n", + "rf_plus = RandomForestPlusRegressor(rf_model = rf, prediction_model = RidgeCV())\n", + "rf_plus.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# get TreeSHAP importances and rankings\n", + "explainer = shap.TreeExplainer(rf)\n", + "shap_values = np.abs(explainer.shap_values(X_train, check_additivity=False))\n", + "shap_rankings = np.argsort(-shap_values, axis = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# get LMDI+ importances and rankings\n", + "rfplus_explainer = RFPlusMDI(rf_plus)\n", + "lmdi_values = np.abs(rfplus_explainer.explain_linear_partial(np.asarray(X_train), y_train, l2norm=True, njobs = 1))\n", + "lmdi_rankings = rfplus_explainer.get_rankings(lmdi_values)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# sort based highest y to lowest y\n", + "sorted_indices = np.argsort(-y_train)\n", + "sorted_lmdi_values = lmdi_values[sorted_indices]\n", + "sorted_lmdi_rankings = lmdi_rankings[sorted_indices]\n", + "sorted_shap_values = shap_values[sorted_indices]\n", + "sorted_shap_rankings = shap_rankings[sorted_indices]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot heatmap of LMDI+ importances based on true y\n", + "plt.figure(figsize=(10, 6))\n", + "sns.heatmap(sorted_shap_values, cmap='viridis')\n", + "plt.title('TreeSHAP Importances')\n", + "plt.xlabel('Features')\n", + "plt.ylabel('Samples')\n", + "plt.xticks(ticks = np.arange(X_train.shape[1]) + 0.5, labels = X_train.columns, rotation = 90)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot heatmap of LMDI+ importances based on true y\n", + "plt.figure(figsize=(10, 6))\n", + "sns.heatmap(sorted_lmdi_values, cmap='viridis')\n", + "plt.title('LMDI+ Importances')\n", + "plt.xlabel('Features')\n", + "plt.ylabel('Samples')\n", + "plt.xticks(ticks = np.arange(X_train.shape[1]) + 0.5, labels = X_train.columns, rotation = 90)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [], + "source": [ + "lmdi_sub_imp = pd.concat([X_train[\"Sex\"].reset_index(drop=True), pd.DataFrame(lmdi_rankings)], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [], + "source": [ + "shap_sub_imp = pd.concat([X_train[\"Sex\"].reset_index(drop=True), pd.DataFrame(shap_rankings)], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Subgroup feature rankings for LMDI+:\n" + ] + }, + { + "data": { + "text/html": [ + "
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SexLengthDiameterHeightWhole_weightShucked_weightViscera_weightShell_weight
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18.07.06.04.03.05.02.01.0
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" + ], + "text/plain": [ + " Sex Length Diameter Height Whole_weight Shucked_weight \\\n", + "0 8.0 7.0 6.0 5.0 4.0 3.0 \n", + "1 8.0 7.0 6.0 4.0 3.0 5.0 \n", + "2 8.0 7.0 6.0 5.0 4.0 3.0 \n", + "\n", + " Viscera_weight Shell_weight \n", + "0 2.0 1.0 \n", + "1 2.0 1.0 \n", + "2 2.0 1.0 " + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lmdi_mean_rankings = lmdi_sub_imp.groupby('Sex', observed=False).mean().reset_index()\n", + "# rank features for each row\n", + "lmdi_ranked_mean_rankings = lmdi_mean_rankings.drop(columns=[\"Sex\"]).rank(axis=1)\n", + "lmdi_ranked_mean_rankings.columns = X.columns\n", + "print(\"Subgroup feature rankings for LMDI+:\")\n", + "lmdi_ranked_mean_rankings" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Subgroup feature rankings for TreeSHAP:\n" + ] + }, + { + "data": { + "text/html": [ + "
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SexLengthDiameterHeightWhole_weightShucked_weightViscera_weightShell_weight
08.07.06.05.04.03.01.02.0
18.07.03.06.04.02.01.05.0
28.07.06.05.04.02.01.03.0
\n", + "
" + ], + "text/plain": [ + " Sex Length Diameter Height Whole_weight Shucked_weight \\\n", + "0 8.0 7.0 6.0 5.0 4.0 3.0 \n", + "1 8.0 7.0 3.0 6.0 4.0 2.0 \n", + "2 8.0 7.0 6.0 5.0 4.0 2.0 \n", + "\n", + " Viscera_weight Shell_weight \n", + "0 1.0 2.0 \n", + "1 1.0 5.0 \n", + "2 1.0 3.0 " + ] + }, + "execution_count": 110, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "shap_mean_rankings = shap_sub_imp.groupby('Sex', observed=False).mean().reset_index()\n", + "# rank features for each row\n", + "shap_ranked_mean_rankings = shap_mean_rankings.drop(columns=[\"Sex\"]).rank(axis=1)\n", + "shap_ranked_mean_rankings.columns = X.columns\n", + "print(\"Subgroup feature rankings for TreeSHAP:\")\n", + "shap_ranked_mean_rankings" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [], + "source": [ + "def mask_feature(row, i, ranked_mean_rankings):\n", + " return ranked_mean_rankings.iloc[int(row['Sex'])].sort_values().index[i]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [], + "source": [ + "X_train_copy = X_train.copy()\n", + "mse_list = []\n", + "mse_list.append(np.mean((rf.predict(X_train_copy) - y_train)**2))\n", + "for i in range(X_train.shape[1]):\n", + " # if the gender is 0, mask the feature with importance == i in the 0th row of shap_ranked_mean_rankings\n", + " for j in range(X_train.shape[0]):\n", + " feature_to_mask = mask_feature(X_train.iloc[j,], i, shap_ranked_mean_rankings)\n", + " X_train_copy.loc[j, feature_to_mask] = X_train[feature_to_mask].mean()\n", + " mse = np.mean((rf.predict(X_train_copy) - y_train)**2)\n", + " mse_list.append(mse)\n", + "mse_arr_shap = np.array(mse_list)\n", + "# get difference between elements of mse_list\n", + "diff_shap = np.abs(np.diff(mse_arr_shap))\n", + "# get cumulative sum of differences\n", + "cumulative_diff_shap = np.cumsum(diff_shap)" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [], + "source": [ + "X_train_copy = X_train.copy()\n", + "mse_list = []\n", + "mse_list.append(np.mean((rf_plus.predict(X_train_copy) - y_train)**2))\n", + "for i in range(X_train.shape[1]):\n", + " # if the gender is 0, mask the feature with importance == i in the 0th row of shap_ranked_mean_rankings\n", + " for j in range(X_train.shape[0]):\n", + " feature_to_mask = mask_feature(X_train.iloc[j,], i, lmdi_ranked_mean_rankings)\n", + " X_train_copy.loc[j, feature_to_mask] = X_train[feature_to_mask].mean()\n", + " mse = np.mean((rf_plus.predict(X_train_copy) - y_train)**2)\n", + " mse_list.append(mse)\n", + "mse_arr_lmdi = np.array(mse_list)\n", + "# get difference between elements of mse_list\n", + "diff_lmdi = np.abs(np.diff(mse_arr_lmdi))\n", + "# get cumulative sum of differences\n", + "cumulative_diff_lmdi = np.cumsum(diff_lmdi)" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot cumulative differences in mse vs number of features ablated\n", + "plt.plot(cumulative_diff_shap, label = 'SHAP')\n", + "plt.plot(cumulative_diff_lmdi, label = 'LMDI+')\n", + "plt.xlabel('# of Features Ablated')\n", + "plt.ylabel('Cumulative Difference in MSE')\n", + "# x ticks should be labeled 1-X_train.shape[1]\n", + "plt.xticks(np.arange(0, X_train.shape[1], 1), np.arange(1, X_train.shape[1]+1, 1))\n", + "plt.title('Training Data: Abalone Dataset w/ Gender Subgroups')\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [], + "source": [ + "X_test_copy = X_test.copy()\n", + "mse_list = []\n", + "mse_list.append(np.mean((rf.predict(X_test_copy) - y_test)**2))\n", + "for i in range(X_test.shape[1]):\n", + " # if the gender is 0, mask the feature with importance == i in the 0th row of shap_ranked_mean_rankings\n", + " for j in range(X_test.shape[0]):\n", + " feature_to_mask = mask_feature(X_test.iloc[j,], i, shap_ranked_mean_rankings)\n", + " X_test_copy.loc[j, feature_to_mask] = X_test[feature_to_mask].mean()\n", + " mse = np.mean((rf.predict(X_test_copy) - y_test)**2)\n", + " mse_list.append(mse)\n", + "mse_arr_shap = np.array(mse_list)\n", + "# get difference between elements of mse_list\n", + "diff_shap = np.abs(np.diff(mse_arr_shap))\n", + "# get cumulative sum of differences\n", + "cumulative_diff_shap_test = np.cumsum(diff_shap)" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [], + "source": [ + "X_test_copy = X_test.copy()\n", + "mse_list = []\n", + "mse_list.append(np.mean((rf_plus.predict(X_test_copy) - y_test)**2))\n", + "for i in range(X_test.shape[1]):\n", + " # if the gender is 0, mask the feature with importance == i in the 0th row of shap_ranked_mean_rankings\n", + " for j in range(X_test.shape[0]):\n", + " feature_to_mask = mask_feature(X_test.iloc[j,], i, lmdi_ranked_mean_rankings)\n", + " X_test_copy.loc[j, feature_to_mask] = X_test[feature_to_mask].mean()\n", + " mse = np.mean((rf_plus.predict(X_test_copy) - y_test)**2)\n", + " mse_list.append(mse)\n", + "mse_arr_lmdi = np.array(mse_list)\n", + "# get difference between elements of mse_list\n", + "diff_lmdi = np.abs(np.diff(mse_arr_lmdi))\n", + "# get cumulative sum of differences\n", + "cumulative_diff_lmdi_test = np.cumsum(diff_lmdi)" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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63ftn9+7dSEtLw4gRI3S2y65B9MaNG9GoUSOUKFFC57Vu2bIlVCpVllM23bp1054+z41MJsOOHTswZcoUlChRAuvXr0dISAh8fHzQq1evN2ojExwcjKCgIO28t7c3OnXqhB07dkClUuXr87JJkyYICAjQzufn7zqvPD090aVLF+28g4MD3n//fZw9exZRUVEANN81ly9fRlhYWL4eQ0osZCQSFhaGuLg4uLq66nzYuLi4IDExETExMQA0b/Zu3bph0qRJcHZ2RqdOnbBixQqkpqa+0eN7e3tnWVaiRAlt+5yYmBg8e/YM/v7+WbbLbll+JCYmAgDs7e21yy5fvowuXbpAqVTCwcEBLi4u2sbCr7ZnyMmqVatQtWpV7XleFxcX/PXXX3m+f3bWrl0LW1tblC1bFjdu3MCNGzdgZWUFX19frFu3Lsv2np6esLW11VlWvnx5ANA5f52frA8fPkRycjIqVKiQZV2lSpWgVqt12nUAWX/fJUqUAADt7zvzw6t58+ZZ3o87d+7Uvh/fRGJios7v+tmzZ5gwYYK2nYSzszNcXFwQGxub599VXt4vZcqUwejRo7F06VI4OzujTZs2mD9/vs5jhIWFQQiBcuXKZXn+//33X76fv1wuR3BwMA4dOgRAU8g0atQIDRs2hEqlwvHjx3HlyhU8efIkSyHz119/oX379rnuP/P1zPxbetmiRYuwa9curF27Vmf5jRs3IITA+PHjszzXiRMnAkCW5/u6909mQVOuXDmd7VxcXLTbZgoLC8M///yT5bFbtmyZ7WOXKVMm19fgZQqFAl9//TX+++8/PHjwAOvXr0e9evWwYcOGN+oL5tXnBWj+npOTk/Hw4cN8fV6++rzy83edV/7+/lna1Lz6eTR58mTExsaifPnyqFKlCj7//HOTuWyfVy1JRK1Ww9XVNdsvQQDa/0BkMhk2bdqE48eP448//sCOHTswcOBA/PDDDzh+/Hi2/4nlhVwuz3a5ECJf+8uPS5cuAXjxhx4bG4smTZrAwcEBkydPhp+fH6ysrHDmzBl8+eWXeep3Zu3atejfvz86d+6Mzz//HK6urpDL5Zg+fbq2MaC+hBBYv349kpKSdP6DyhQTE4PExES9fxcFkTUnr/t9Z762a9asgbu7e5bt3vTKt/T0dFy/fl2no8ARI0ZgxYoVGDVqFIKDg6FUKiGTydC7d+88/a71eb/88MMP6N+/P7Zt24adO3di5MiRmD59Oo4fP47SpUtDrVZrG9tm91rl9+8MABo2bIipU6ciJSUFhw4dwtdffw1HR0cEBgbi0KFDcHNzAwCdQiYiIgJXr17FggULct23UqmEh4eH9m/pZXXr1gWALP3vZL4un332Gdq0aZPtfl/98jXk54VarUarVq3wxRdfZLs+8ws2U24NnXPj4eGB3r17o1u3bqhcuTI2bNiAlStXwtzcPMcGva82Ei9I+X1eAAokf+PGjREeHq79G1m6dClmz56NhQsX4oMPPsj3fgsDCxmJ+Pn5Yffu3WjQoEGe3tD16tVDvXr1MHXqVISGhqJv37745Zdf8MEHHxRIz72urq6wsrLCjRs3sqzLbll+rFmzBjKZDK1atQKgafH/+PFjbN68GY0bN9ZuFxERkeW+OT3nTZs2oWzZsti8ebPONpn/aebHgQMHcO/ePUyePDlLvz9Pnz7Fhx9+iK1bt+pcZv7gwQMkJSXpHJXJvDor81RBfrO6uLjAxsYG165dy7Lu6tWrMDMzy3KFyutknmZ0dXXV/mdsSJs2bcKzZ890vjg3bdqEfv364YcfftAuS0lJyXIKIKfftT7vFwCoUqUKqlSpgnHjxuHo0aNo0KABFi5ciClTpsDPzw9CCJQpUybLF+mr9P17a9SoEdLS0rB+/Xrcv39fW7A0btxYW8iUL19eW9AAmqMxSqUSDRs2fO3+27dvj6VLl+Lff/9FnTp1Xrt95mkLCwsLg/2uM/unCQsL0zkt8vDhwyxXYfr5+SExMbFA3mfZsbCwQNWqVREWFqY9XViiRIlsTzVlHll6VXanW65fvw4bGxvtP51v+nmpz9915lGu2NhYbfOD3PJnHoV7+b376ucRADg5OWHAgAEYMGAAEhMT0bhxY3zzzTdGX8jw1JJEevbsCZVKhW+//TbLuoyMDO0f2dOnT7P811O9enUA0J5esrGxAQCD9pMgl8vRsmVLbN26FQ8ePNAuv3HjRpZLRPNjxowZ2LlzJ3r16qU9bJv5X9/LzzctLQ0///xzlvvb2tpme/ohu32cOHECx44dy7JtXi+/zjyt9Pnnn6N79+46t8GDB6NcuXJZjqxlZGRg0aJFOs9j0aJFcHFx0Z5r1yfrq8+xdevW2LZtm85/29HR0QgNDUXDhg317mSwTZs2cHBwwLRp07J9Pd6kZ97z589j1KhRKFGiBEJCQnSex6vv7R9//DHLf5WZxeCr7++8vl/i4+ORkZGhs6xKlSowMzPT/g117doVcrkckyZNypJJCIHHjx/r5NHnNGXdunVhYWGBmTNnwsnJCZUrVwagKXCOHz+OAwcOZNs+pnXr1nk6EvbFF1/AxsYGAwcORHR0dJb1rz4fV1dXNG3aFIsWLUJkZGSW7fPzu27ZsiUsLCzw448/6jxedj0g9+zZE8eOHcu2bVlsbGyW31VehYWF4c6dO9nu89ixYyhRooS26PDz80NcXJzOqZPIyEhs2bIl230fO3ZMp93W3bt3sW3bNrRu3Rpyudwgn5f6/F1n/uPxcnuipKQkrFq1Ktt9P3jwQOe5xcfHY/Xq1ahevbr2COzL73FAcxTS39//jZsxFAYekZFIkyZNMGTIEEyfPh3nzp1D69atYWFhgbCwMGzcuBFz585F9+7dsWrVKvz888/o0qUL/Pz8kJCQgCVLlsDBwQFvvfUWAM0hyoCAAPz6668oX748nJycEBgY+Mbj/XzzzTfYuXMnGjRogKFDh0KlUuGnn35CYGAgzp07l6d9ZGRkaM/Rp6Sk4Pbt2/j9999x4cIFNGvWDIsXL9ZuW79+fZQoUQL9+vXDyJEjIZPJsGbNmmwPXwcFBeHXX3/F6NGjUbt2bdjZ2aFjx47o0KEDNm/ejC5duqB9+/aIiIjAwoULERAQkKUdwZgxY7Bq1SpERETk2KAyNTUVv/32G1q1apVjZ1Vvv/025s6di5iYGLi6ugLQtJGZOXMmbt26hfLly+PXX3/FuXPnsHjxYlhYWACAXllfNWXKFOzatQsNGzbEsGHDYG5ujkWLFiE1NRXfffddrvfNjoODAxYsWID33nsPNWvWRO/eveHi4oI7d+7gr7/+QoMGDXT6gMnJoUOHkJKSApVKhcePH+PIkSP4/fffoVQqsWXLFp3TVh06dMCaNWugVCoREBCAY8eOYffu3Tr9fQCawl0ul2PmzJmIi4uDQqFA8+bN8/x+2bt3L4YPH44ePXqgfPnyyMjIwJo1ayCXy9GtWzcAmi+GKVOmYMyYMbh16xY6d+4Me3t7REREYMuWLfjwww/x2WefAcj5vZcTGxsbBAUF4fjx49o+ZADNEZmkpCQkJSXpFDLPnj3Dvn37svQ7lJNy5cohNDQUffr0QYUKFbQ9+wohEBERgdDQUJiZmaF06dLa+8yfPx8NGzZElSpVMHjwYJQtWxbR0dE4duwY7t27h/Pnz+fpsTO5uLjgs88+w/Tp09GhQwe89dZbOHv2LLZv3w5nZ2edbT///HP8/vvv6NChA/r374+goCAkJSXh4sWL2LRpE27dupXlPnlx/vx5vPPOO2jXrh0aNWoEJycn3L9/H6tWrcKDBw8wZ84cbfHbu3dvfPnll+jSpQtGjhyJ5ORkLFiwAOXLl8+2oXlgYCDatGmDkSNHQqFQaIvlzB7VAcN8Xub177p169bw9vbGoEGD8Pnnn0Mul2P58uXav9lXlS9fHoMGDcLJkyfh5uaG5cuXIzo6GitWrNBuExAQgKZNmyIoKAhOTk44deoUNm3aZBrjTBXiFVLFWnb9yAghxOLFi0VQUJCwtrYW9vb2okqVKuKLL74QDx48EEJo+ibo06eP8Pb2FgqFQri6uooOHTroXAoohKYPhaCgIGFpaalzqW5OlxOGhIRkyeLj46NzmaQQmktAa9SoISwtLYWfn59YunSp+PTTT4WVldVrn3Pm5c2ZNxsbG+Hr6yu6desmNm3aJFQqVZb7HDlyRNSrV09YW1sLT09P8cUXX4gdO3Zkufw2MTFRvPPOO8LR0VEA0F5KqVarxbRp04SPj49QKBSiRo0a4s8//8z2csu8XH7922+/Zen/5VX79+8XAMTcuXOFEJrLrytXrixOnTolgoODhZWVlfDx8cnSH4g+WV/+nWY6c+aMaNOmjbCzsxM2NjaiWbNm4ujRozrbZF6O+erl+5mXS796SfO+fftEmzZthFKpFFZWVsLPz0/0798/y/vtVZn7y7xZWFgIFxcX0bhxYzF16tRsL599+vSpGDBggHB2dhZ2dnaiTZs24urVq9m+D5csWSLKli0r5HK5Tu68vF9u3rwpBg4cKPz8/ISVlZVwcnISzZo1E7t3786S6bfffhMNGzYUtra2wtbWVlSsWFGEhISIa9euabfJ6b2Xm88//1wAEDNnztRZ7u/vLwDoXG77559/CplMJqKjo1+735fduHFDDB06VPj7+wsrKythbW0tKlasKD766CNx7ty5LNuHh4eL999/X7i7uwsLCwtRqlQp0aFDB7Fp0ybtNvq8f1QqlZg0aZLw8PAQ1tbWomnTpuLSpUvZ/j4TEhLEmDFjhL+/v7C0tBTOzs6ifv364n//+5+2e4LMy6+///77PD3/6OhoMWPGDNGkSRPh4eEhzM3NRYkSJUTz5s11nlOmnTt3isDAQGFpaSkqVKgg1q5dm+vn5dq1a0W5cuW0f6vZdQeQ18/LnD6Dhcjb37UQQpw+fVrUrVtXWFpaCm9vbzFr1qwcL79u37692LFjh6hatapQKBSiYsWKWbojmDJliqhTp45wdHTUvnemTp2q/X0YM5kQhdi6k4qEzp07m+xlekTGbtiwYTh16hT+/fdfqaOQAUj9eenr64vAwED8+eefkjx+YWAbGcrVs2fPdObDwsLw999/ZxmckogMo3r16jqnLMh08PNSGjwiQ7ny8PBA//79UbZsWdy+fRsLFixAamoqzp49m23fCkRExZUxfl4WhyMybOxLuWrbti3Wr1+PqKgoKBQKBAcHY9q0aSxiiIhewc9LafCIDBEREZkstpEhIiIik8VChoiIiExWkW8jo1ar8eDBA9jb2xdIV/5ERERkeEIIJCQkwNPTE2ZmOR93KfKFzIMHD/Qed4aIiIiMw927d3V6pn5VkS9kMoe5v3v3rt7jzxAREZE04uPj4eXlpf0ez0mRL2QyTyc5ODiwkCEiIjIxr2sWwsa+REREZLJYyBAREZHJYiFDREREJqvIt5HJK5VKhfT0dKljFBsWFhaQy+VSxyAiIhNX7AsZIQSioqIQGxsrdZRix9HREe7u7uzfh4iI8q3YFzKZRYyrqytsbGz4pVoIhBBITk5GTEwMAM2IsURERPlRrAsZlUqlLWJKliwpdZxixdraGgAQExMDV1dXnmYiIqJ8KdaNfTPbxNjY2EicpHjKfN3ZNomIiPKrWBcymXg6SRp83YmI6E2xkCEiIiKTxUKGiIiITBYLGRP18OFDDB06FN7e3lAoFHB3d0ebNm1w5MgRAICvry/mzJmT5X7ffPMNqlevnmX5vXv3YGlpicDAwGwfTyaTaW9KpRINGjTA3r17DfmUiIiI9MZCxkR169YNZ8+exapVq3D9+nX8/vvvaNq0KR4/fpyv/a1cuRI9e/ZEfHw8Tpw4ke02K1asQGRkJI4cOQJnZ2d06NABN2/efJOnQUREUhMCUKsBVQaQkQakpwBpyUBqIpASDzyLBZKfAEmPgcSHQEI0EB8JxN0HYu8CT28DqQmSxS/Wl1+bqtjYWBw6dAj79+9HkyZNAAA+Pj6oU6dOvvYnhMCKFSvw888/o3Tp0li2bBnq1q2bZbvMDuzc3d2xYMEClCpVCrt27cKQIUPe6PkQkZETAkh7/qWWGv/SzzjNcrUKgNBs9/J9tMty+onXbPN8vUH299I+3nh/2SwXat0bMpdls07n9rr1b7qPHPK9mvVNdZwLBPV/8/3kAwuZlwgh8CxdJcljW1vI83wVj52dHezs7LB161bUq1cPCoXijR573759SE5ORsuWLVGqVCnUr18fs2fPhq2tbc55n/cDk5aW9kaPTUQFTK3WFBs6BchLhUi2y1/+Gaf5b1uopX4mZDRkgMws600iLGRe8ixdhYAJOyR57CuT28DGMm+/DnNzc6xcuRKDBw/GwoULUbNmTTRp0gS9e/dG1apVtdt9+eWXGDdunM5909LSEBAQoLNs2bJl6N27N+RyOQIDA1G2bFls3LgR/fv3z/bxk5OTMW7cOMjlcu0RISIqAGo1kJaQc4GRawHy0k9D/McNADI5YOUAKBye/1QCCjvA7Plnl0wGzZdc5j9lsleW5fJT5/55uW8et8l2/3n5+er+c3k87Rf5y1/wOXzZ69zyus3rtnuDx8pXZuPqOoOFjInq1q0b2rdvj0OHDuH48ePYvn07vvvuOyxdulRbgHz++edZipF58+bh4MGD2vnY2Fhs3rwZhw8f1i579913sWzZsiz37dOnD+RyOZ49ewYXFxcsW7ZMp3Aiopeo1ZoiIt9HQuKftzswUBFiZv5SAeIAWClfmc/up1J33sLG6L7EiFjIvMTaQo4rk9tI9tj6srKyQqtWrdCqVSuMHz8eH3zwASZOnKgtQJydneHv769zHycnJ5350NBQpKSk6LSJEUJArVbj+vXrKF++vHb57Nmz0bJlSyiVSri4uOidl6jIubQZuPpn9oVImgEbP5pZZFNovK4QeWW9hTWLECqSWMi8RCaT5fn0jjEKCAjA1q1b9brPsmXL8Omnn2Y5+jJs2DAsX74cM2bM0C5zd3fPUhgRFVtx94HNHwLq1wyxIVe8/miHwj73IyHmVixCiHJgut/axdjjx4/Ro0cPDBw4EFWrVoW9vT1OnTqF7777Dp06dcrzfs6dO4czZ85g3bp1qFixos66Pn36YPLkyZgyZQrMzfk2Icri+M+aIsajGlDnw5yPhpi/WWN8Isodv6FMkJ2dHerWrYvZs2cjPDwc6enp8PLywuDBgzF27Ng872fZsmUICAjIUsQAQJcuXTB8+HD8/fffePvttw0Zn8j0PXsKnF6pmW72NVBemlPSRATIhBAGaklmnOLj46FUKhEXFwcHBweddSkpKYiIiECZMmVgZWUlUcLii68/mayD/wP2fgu4BgBDj/K0D1EByO37+2Xs2ZeISB/pz4ATCzXTDT5mEUMkMRYyRET6OBcKJD0ElF5AYDep0xAVeyxkiIjySq0Cjv6omQ4OAeQW0uYhIhYyRER59t/vwNMIwLoEUPN9qdMQEVjIEBHljRDA4Tma6TofApY5j0VGRIWHhQwRUV5EHAAizwHm1kAdjvhOZCwkLWQWLFiAqlWrwsHBAQ4ODggODsb27du165s2bQqZTKZz++ijjyRMTETFVubRmJrvAbYlJY1CRC9I2iFe6dKlMWPGDJQrVw5CCKxatQqdOnXC2bNnUblyZQDA4MGDMXnyZO19bGxspIpLRMXVg3PAzX2a0Z+Dh0udhoheImkh07FjR535qVOnYsGCBTh+/Li2kLGxsYG7u7sU8YiINI7M1fwM7AqU8JE2CxHpMJo2MiqVCr/88guSkpIQHBysXb5u3To4OzsjMDAQY8aMQXJycq77SU1NRXx8vM6NiCjfntwErmzVTDf4WNIoRJSV5IXMxYsXYWdnB4VCgY8++ghbtmxBQEAAAOCdd97B2rVrsW/fPowZMwZr1qzBu+++m+v+pk+fDqVSqb15eXkVxtModP3790fnzp2zXefr6wuZTIZffvkly7rKlStDJpNh5cqVWbaXyWSwtraGr68vevbsib179+rc99atW5DJZDh37pwBnwmRkTv6EyDUgH9LwL2K1GmI6BWSFzIVKlTAuXPncOLECQwdOhT9+vXDlStXAAAffvgh2rRpgypVqqBv375YvXo1tmzZgvDw8Bz3N2bMGMTFxWlvd+/eLaynYlS8vLywYsUKnWXHjx9HVFQUbG2zXjY6efJkREZG4tq1a1i9ejUcHR3RsmVLTJ06Nc+PuX//fvj6+r5pdCLjkfgQOLdOM91glKRRiCh7khcylpaW8Pf3R1BQEKZPn45q1aph7ty52W5bt25dAMCNGzdy3J9CodBeBZV5K4769u2LAwcO6BRyy5cvR9++fWFunrVplL29Pdzd3eHt7Y3GjRtj8eLFGD9+PCZMmIBr164VZnQi43FiIZCRApQKAnwbSp2GiLIheSHzKrVajdTU1GzXZZ7S8PDwKJgHFwJIS5LmZuBByN3c3NCmTRusWrUKAJCcnIxff/0VAwcOzPM+Pv74YwghsG3bNoNmIzIJqQnAySWa6QajODgkkZGS9KqlMWPGoF27dvD29kZCQgJCQ0Oxf/9+7NixA+Hh4QgNDcVbb72FkiVL4sKFC/jkk0/QuHFjVK1atWACpScD0zwLZt+vM/aBwXsKHThwID799FN8/fXX2LRpE/z8/FC9evU839/JyQmurq64deuWQXMRmYTTq4CUOKCkP1CxvdRpiCgHkhYyMTExeP/99xEZGQmlUomqVatix44daNWqFe7evYvdu3djzpw5SEpKgpeXF7p164Zx48ZJGdmktG/fHkOGDMHBgwexfPlyvY7GZBJCQJbLf6J2dnbaaZVKhdTUVJ1l7777LhYuXKj34xJJKiMNODZfM11/JGAmlzYPEeVI0kJm2bJlOa7z8vLCgQMHCjENAAsbzZERKVgYvqM/c3NzvPfee5g4cSJOnDiBLVu26HX/x48f4+HDhyhTpkyO27x8BdOJEyfw5ZdfYv/+/dplxbWNEpm4ixuBhAeAnRtQtZfUaYgoF5IWMkZHJityA8ENHDgQ//vf/9CrVy+UKFFCr/vOnTsXZmZmOV7mDQD+/v7a6Xv37sHc3FxnGZHJUatfdIBXbyhgYSVtHiLKFQsZExYXF5elT5eSJXXHgKlUqRIePXr02qEdEhISEBUVhfT0dERERGDt2rVYunQppk+fzsKEipfr/wCPrgEKB6CW/qdjiahwsZAxYfv370eNGjV0lg0aNCjLdq8WN9mZMGECJkyYAEtLS7i7u6NevXrYs2cPmjVrZrC8RCbhyBzNz1oDACulpFGI6PVkQhj4ul8jEx8fD6VSibi4uCztNVJSUhAREYEyZcrAyoqHjwsbX38yOrePASvaAnJLYNRFwJ7jvBFJJbfv75cZXT8yRESSyTwaU603ixgiE8FChogIAKKvaNrHQAbU5+CQRKaChQwREQAcnaf5WakD4MwG7kSmgoUMEVHsXU3fMQDQ4BNpsxCRXljIQNN7LRU+vu5kNI4vANQZgG8joHSQ1GmISA/FupCxsLAAoBlQkQpf5uue+XsgkkTyE+D0Ss10g1FSJiGifCjW/cjI5XI4OjoiJiYGAGBjY5PruEJkGEIIJCcnIyYmBo6OjpDLOY4NSejkMiA9CXCrAvi3kDoNEempWBcyAODurrnEMrOYocLj6Oioff2JJJH+DDjxfFDTBh9rhikhIpNS7AsZmUwGDw8PuLq6Ij09Xeo4xYaFhQWPxJD0zq4Fkh8Bjt5A5S5SpyGifCj2hUwmuVzOL1ai4kSVARz9UTMdPAKQ8+OQyBQV68a+RFSMXdkKxN4GbEoCNd6VOg0R5RMLGSIqfoR4MRxBnSGAZe6jwxOR8WIhQ0TFT/heIOoiYGED1BksdRoiegMsZIio+Mk8GlOzH2DjJGkUInozLGSIqHi5fwaIOAiYmQPBIVKnIaI3xEKGiIqXzKMxgd0BRy9JoxDRm2MhQ0TFx+Nw4MrvmukGH0ubhYgMgoUMERUfR+cBEEC5NoBbgNRpiMgAWMgQUfGQEA2cW6+Z5tEYoiKDhQwRFQ8nFgCqVKB0bcCnvtRpiMhAWMgQUdGXEg+cXK6ZbjCKg0MSFSEsZIio6Du9AkiNA5zLAxXekjoNERkQCxkiKtoyUoFjP2um648EzPixR1SU8C+aiIq2C78CiVGAvQdQtafUaYjIwFjIEFHRpVYDR+ZppusNA8wV0uYhIoNjIUNERde1v4DHYYBCCQT1lzoNERUAFjJEVDQJARyeo5muPQiwcpA0DhEVDBYyRFQ03T4K3D8FyBVAvaFSpyGiAsJChoiKpszBIau/A9i5ShqFiAoOCxkiKnqiLwNhOwGZGVB/hNRpiKgAsZAhoqLnyFzNz0pvAyX9pM1CRAVK0kJmwYIFqFq1KhwcHODg4IDg4GBs375duz4lJQUhISEoWbIk7Ozs0K1bN0RHR0uYmIiMXuwd4OImzXTDUZJGIaKCJ2khU7p0acyYMQOnT5/GqVOn0Lx5c3Tq1AmXL18GAHzyySf4448/sHHjRhw4cAAPHjxA165dpYxMRMbu2HxAqIAyTQDPGlKnIaICJhNCCKlDvMzJyQnff/89unfvDhcXF4SGhqJ79+4AgKtXr6JSpUo4duwY6tWrl6f9xcfHQ6lUIi4uDg4OvPySqEhLfgLMrgykJwPvbQH8mkudiIjyKa/f30bTRkalUuGXX35BUlISgoODcfr0aaSnp6Nly5babSpWrAhvb28cO3Ysx/2kpqYiPj5e50ZExcS/izVFjHtVoGwzqdMQUSGQvJC5ePEi7OzsoFAo8NFHH2HLli0ICAhAVFQULC0t4ejoqLO9m5sboqKictzf9OnToVQqtTcvL68CfgZEZBTSkoATizTTDUcBMpmkcYiocEheyFSoUAHnzp3DiRMnMHToUPTr1w9XrlzJ9/7GjBmDuLg47e3u3bsGTEtERuvsWuDZE6CEL1Cpk9RpiKiQ5LmQGTZsGBITE7Xz69evR1JSknY+NjYWb731lt4BLC0t4e/vj6CgIEyfPh3VqlXD3Llz4e7ujrS0NMTGxupsHx0dDXd39xz3p1AotFdBZd6IqIhTpQNHf9JM1x8ByM2lzUNEhSbPhcyiRYuQnJysnR8yZIjOpdCpqanYsWPHGwdSq9VITU1FUFAQLCwssGfPHu26a9eu4c6dOwgODn7jxyGiIuTyFiDuDmDrAlTvK3UaIipEef635dWLmwxxsdOYMWPQrl07eHt7IyEhAaGhodi/fz927NgBpVKJQYMGYfTo0XBycoKDgwNGjBiB4ODgPF+xRETFgBAvOsCrOwSwsJY2DxEVKkmPv8bExOD9999HZGQklEolqlatih07dqBVq1YAgNmzZ8PMzAzdunVDamoq2rRpg59//lnKyERkbG7sBqIvAZZ2QO0PpE5DRIVM0kJm2bJlua63srLC/PnzMX/+/EJKREQm5/Aczc+g/oB1CSmTEJEE9CpkJkyYABsbGwBAWloapk6dCqVSCQA67WeIiArFvVPA7cOAmTlQb6jUaYhIAnkuZBo3boxr165p5+vXr4+bN29m2YaIqNAcnq35WaUnoCwtbRYikkSeC5n9+/cXYAwiIj09CgOu/qWZbvCxtFmISDJv3CFeRkaGTv8yRESF4shcAAIo3w5wrSh1GiKSSJ4LmT/++AMrV67UWTZ16lTY2dnB0dERrVu3xtOnTw2dj4goq/hI4MKvmumGoySNQkTSynMhM2vWLJ2efI8ePYoJEyZg/Pjx2LBhA+7evYtvv/22QEISEek4/jOgSgO86gHe7FeKqDjLcyFz+fJl1K9fXzu/adMmtGrVCl9//TW6du2KH374AX/88UeBhCQi0kqJA06t0EzzaAxRsZfnQiYhIQElS5bUzh8+fBgtWrTQzleuXBkPHjwwbDoioledWg6kJQAuFYFybaROQ0QSy3MhU6pUKfz3338AgMTERJw/f17nCM3jx4+1fcwQERWI9BTg+ALNdIOPAbM3vl6BiExcnj8FevTogVGjRmHNmjUYPHgw3N3ddcY8OnXqFCpUqFAgIYmIAAAXfgESowGHUkBgd6nTEJERyHM/MhMmTMD9+/cxcuRIuLu7Y+3atZDL5dr169evR8eOHQskJBER1CrgyDzNdHAIYG4pbR4iMgp5LmSsra2xevXqHNfv27fPIIGIiLJ19U/gSThg5QjU7Cd1GiIyEjzBTETGT4gXg0PWGQwo7CSNQ0TGI89HZJo3b56n7fbu3ZvvMERE2bp1CHhwBjC3AuoMkToNERkRvcZa8vHxQfv27WFhYVGQmYiIdGUejanxLmDnImkUIjIueS5kZs6ciRUrVmDjxo3o27cvBg4ciMDAwILMRkQERF4AwvcAMjMgeLjUaYjIyOS5jcznn3+OK1euYOvWrUhISECDBg1Qp04dLFy4EPHx8QWZkYiKsyNzNT8rdwGcykibhYiMjt6NfYODg7FkyRJERkYiJCQEy5cvh6enJ4sZIjK8p7eAy5s10w0+ljQKERmnfF+1dObMGRw4cAD//fcfAgMD2W6GiAzv6E+AUAN+zQGPalKnISIjpFch8+DBA0ybNg3ly5dH9+7d4eTkhBMnTuD48eOwtrYuqIxEVBwlPQLOrtVMNxglaRQiMl55buz71ltvYd++fWjdujW+//57tG/fHubmeb47EZF+TiwCMp4BnjWAMo2lTkNERkomhBB52dDMzAweHh5wdXWFTCbLcbszZ84YLJwhxMfHQ6lUIi4uDg4ODlLHIaK8SE0EZlcGUmKBHquAyp2lTkREhSyv3995PqQyceJEgwQjInqtM6s1RYxTWaASx3AjopyxkCEi46JKB47N10zXHwGYyXPfnoiKNY61RETG5eImIP4eYOsKVHtH6jREZORYyBCR8VCrX3SAV+8jwMJK2jxEZPRYyBCR8QjbCTz8D7C0B2oNkjoNEZkAFjJEZDyOzNH8rNUfsHaUMAgRmQoWMkRkHO6cAO4cA8wsgHrDpE5DRCYiXz3a7dmzB3v27EFMTAzUarXOuuXLlxskGBEVM5ltY6r1Ahw8pc1CRCZD70Jm0qRJmDx5MmrVqgUPD49cO8cjIsqTh9eAa38BkAH1OTgkEeWd3oXMwoULsXLlSrz33nsFkYeIiqMj8zQ/K7YHXMpLm4WITIrebWTS0tJQv379gshCRMVR3H3gwq+aaQ4OSUR60ruQ+eCDDxAaGloQWYioODr+M6BOB3waAF61pU5DRCZG71NLKSkpWLx4MXbv3o2qVavCwsJCZ/2sWbPyvK/p06dj8+bNuHr1KqytrVG/fn3MnDkTFSpU0G7TtGlTHDhwQOd+Q4YMwcKFC/WNTkTG5tlT4PRKzTSPxhBRPuhdyFy4cAHVq1cHAFy6dElnnb4Nfw8cOICQkBDUrl0bGRkZGDt2LFq3bo0rV67A1tZWu93gwYMxefJk7byNjY2+sYnIGJ1cBqQlAq6VgXKtpE5DRCZI70Jm3759Bnvwf/75R2d+5cqVcHV1xenTp9G4cWPtchsbG7i7uxvscYnICKQ/A048P7La4GOAV0ASUT4YVYd4cXFxAAAnJyed5evWrYOzszMCAwMxZswYJCcnSxGPiAzpXCiQ9BBQegGBXaVOQ0QmKk9HZLp27YqVK1fCwcEBXbvm/oGzefPmfAVRq9UYNWoUGjRogMDAQO3yd955Bz4+PvD09MSFCxfw5Zdf4tq1azk+TmpqKlJTU7Xz8fHx+cpDRAVIrQKO/qiZDh4OyC1y356IKAd5KmSUSqW2/YtSqSyQICEhIbh06RIOHz6ss/zDDz/UTlepUgUeHh5o0aIFwsPD4efnl2U/06dPx6RJkwokIxEZyJVtwNMIwNoJqMk+qYgo/2RCCCF1iOHDh2Pbtm04ePAgypQpk+u2SUlJsLOzwz///IM2bdpkWZ/dERkvLy/ExcXBwcHB4NmJSE9CAIubAJHngSZfAc3GSJ2IiIxQfHw8lErla7+/8zXWkqEIITBixAhs2bIF+/fvf20RAwDnzp0DAHh4eGS7XqFQQKFQGDImERnSzf2aIsbcGqjz4Ws3JyLKjaSFTEhICEJDQ7Ft2zbY29sjKioKgOb0lbW1NcLDwxEaGoq33noLJUuWxIULF/DJJ5+gcePGqFq1qpTRiSi/jszR/Kz5PmBbUtIoRGT6JD21lFO/MytWrED//v1x9+5dvPvuu7h06RKSkpLg5eWFLl26YNy4cXk+TZTXQ1NEVAgenAUWNwVkcmDkWaCEj9SJiMhImcyppdx4eXll6dWXiEzYkbman4HdWMQQkUG8UT8yKSkphspBREXdk5uaq5UATQd4REQGoHcho1ar8e2336JUqVKws7PDzZs3AQDjx4/HsmXLDB6QiIqIoz8CQg34twTcA1+/PRFRHuhdyEyZMgUrV67Ed999B0tLS+3ywMBALF261KDhiKiISIwBzq7TTHNwSCIyIL0LmdWrV2Px4sXo27cv5HK5dnm1atVw9epVg4YjoiLixEJAlQqUCgJ8G0qdhoiKEL0Lmfv378Pf3z/LcrVajfT0dIOEIqIiJDUBOPn8aG2DURwckogMSu9CJiAgAIcOHcqyfNOmTahRo4ZBQhFREXJ6JZASB5T0Byq2lzoNERUxel9+PWHCBPTr1w/379+HWq3G5s2bce3aNaxevRp//vlnQWQkIlOVkQYc+1kzXX8kYCbPfXsiIj3pfUSmU6dO+OOPP7B7927Y2tpiwoQJ+O+///DHH3+gVatWBZGRiEzVxY1AwgPAzh2o1lvqNERUBOWrQ7xGjRph165dhs5CREWJWv2iA7x6QwFzjoFGRIan9xGZkydP4sSJE1mWnzhxAqdOnTJIKCIqAq7/Azy6BigcgFoDpE5DREWU3oVMSEgI7t69m2X5/fv3ERISYpBQRFQEZA4OWWsgYKWUNAoRFV16FzJXrlxBzZo1syyvUaMGrly5YpBQRGTibh8D7p4A5Jaa00pERAVE70JGoVAgOjo6y/LIyEiYm0s6BiURGYvMozHV+gD27pJGIaKiTe9CpnXr1hgzZgzi4uK0y2JjYzF27FhetUREQPQVTfsYyDSXXBMRFSC9D6H873//Q+PGjeHj46PtAO/cuXNwc3PDmjVrDB6QiEzM0Xman5U6As5ZewEnIjIkvQuZUqVK4cKFC1i3bh3Onz8Pa2trDBgwAH369IGFhUVBZCQiUxF7V9N3DAA0HCVpFCIqHvLVqMXW1hYffvihobMQkak7/jOgzgB8G2kGiCQiKmD5KmTCwsKwb98+xMTEQK1W66ybMGGCQYIRkYlJfgKcXqWZ5tEYIiokehcyS5YswdChQ+Hs7Ax3d3fIXhrJViaTsZAhKq5OLgXSkwD3KoBfC6nTEFExoXchM2XKFEydOhVffvllQeQhIlOUlgycWKiZbjAKeOkfHCKigqT35ddPnz5Fjx49CiILEZmqc+uA5MeAow8Q0FnqNERUjOhdyPTo0QM7d+4siCxEZIpUGS8uua4/ApCzY0wiKjx6f+L4+/tj/PjxOH78OKpUqZLlkuuRI9kBFlGxcmUrEHsHsCkJVO8rdRoiKmZkQgihzx3KlCmT885kMty8efONQxlSfHw8lEol4uLi4ODgIHUcoqJFCGBhIyD6ItDsa6DJF1InIqIiIq/f33ofkYmIiHijYERUhITv0RQxFjZA7Q+kTkNExZDebWQypaWl4dq1a8jIyDBkHiIyJYfnaH7W7AfYOEkahYiKJ70LmeTkZAwaNAg2NjaoXLky7ty5AwAYMWIEZsyYYfCARGSEhAD+XQLcOgSYmQPBIVInIqJiSu9CZsyYMTh//jz2798PKysr7fKWLVvi119/NWg4IjJC8ZHAuu7A359p5oMGAI5e0mYiomJL7zYyW7duxa+//op69erp9OpbuXJlhIeHGzQcERmZi5uAvz4FUmIBcyugxUSg7kdSpyKiYkzvQubhw4dwdXXNsjwpKUmnsCGiIiT5iaaAubxZM+9RHei6GHCpIGksIiK9Ty3VqlULf/31l3Y+s3hZunQpgoODDZeMiIxD2C7g52BNESOTA03HAB/sZhFDREZB7yMy06ZNQ7t27XDlyhVkZGRg7ty5uHLlCo4ePYoDBw4UREYikkJqIrBzHHB6hWbeuTzQZRFQqqa0uYiIXqL3EZmGDRvi/PnzyMjIQJUqVbBz5064urri2LFjCAoKKoiMRFTY7hwHFjZ4UcTUGwYMOcgihoiMjl5HZNLT0zFkyBCMHz8eS5YsKahMRCSVjFRg3zTgyFwAAlB6AZ3mA2WbSJ2MiChbeh2RsbCwwG+//VZQWYhISlEXgcXNgCNzAAjNuElDj7CIISKjpveppc6dO2Pr1q0GefDp06ejdu3asLe3h6urKzp37oxr167pbJOSkoKQkBCULFkSdnZ26NatG6Kjow3y+EQEQK0CDs3SFDExlwEbZ6DXOqDzz4CVUup0RES50ruxb7ly5TB58mQcOXIEQUFBsLW11Vmvz+jXBw4cQEhICGrXro2MjAyMHTsWrVu3xpUrV7T7/eSTT/DXX39h48aNUCqVGD58OLp27YojR47oG52IXvU4HNg6FLh7QjNfoT3QcS5g5yJtLiKiPDKq0a8z+6g5cOAAGjdujLi4OLi4uCA0NBTdu3cHAFy9ehWVKlXCsWPHUK9evdfuk6NfE2VDCODUcs1VSenJgKU98NZ3QLU+APuDIiIjYJKjX8fFxQEAnJw0g8+dPn0a6enpaNmypXabihUrwtvbO8+FDBG9Iv4BsG24ZuRqAPBtpDmN5OgtbS4ionzQu5DJlJaWhoiICPj5+cHcPN+70VKr1Rg1ahQaNGiAwMBAAEBUVBQsLS3h6Oios62bmxuioqKy3U9qaipSU1O18/Hx8W+cjajIeHWIgZbfAHWGAGZ6N5cjIjIKRjP6dUhICC5duoRffvkl3/sANA2IlUql9ublxcHsiJD8BNg4APhtkKaI8ayh6Rem3lAWMURk0oxi9Ovhw4fjzz//xL59+1C6dGntcnd3d6SlpSE2NlZn++joaLi7u+eYLy4uTnu7e/duvjIRFRnZDTEwaBeHGCCiIkHS0a+FEBgxYgS2bNmC/fv3Z2lIHBQUBAsLC+zZswfdunUDAFy7dg137tzJcVwnhUIBhUKh57MiKoJSE4GdXwOnV2rmOcQAERVBko5+HRISgtDQUGzbtg329vbadi9KpRLW1tZQKpUYNGgQRo8eDScnJzg4OGDEiBEIDg5mQ1+i3Nw5DmwZAjy9pZmvNwxoMQGwsJY0FhGRoUk6+vWCBQsQFxeHpk2bwsPDQ3t7+RTV7Nmz0aFDB3Tr1g2NGzeGu7s7Nm/erG9souIhIxXYNQFY3lZTxCi9gH5/AG2ns4ghoiJJ0tGv89KFjZWVFebPn4/58+frG5WoeIm6CGweoumdF9AMMdB2OnvnJaIiLV+jX587d46jXxMZC1UGcOgHDjFARMVSno7IjB49Gt9++y1sbW1x8OBB1K9fn6NfExmDx+HAlo+Ae/9q5jnEABEVM3kaosDCwgL37t2Dm5sb5HI5IiMjs23wa4w4RAEVSUIAp5YBO8drhhhQOADtZnKIASIqMgw6RIGvry/mzZuH1q1bQwiBY8eOoUSJEtlu27hx4/wlJqK84RADRERaeTois3XrVnz00UeIiYmBTCbLsZGuTCaDSqUyeMg3wSMyVKRc3AT8NRpIieMQA0RUpOX1+1uv0a8TExPh4OCAa9eu5XhqSak0rsaFLGSoSEh+oilgLm/RzHvW0HRux955iaiIMuippczGvnZ2dti3bx/KlCljkIEiiSgPru8Efh8OJEZrhhho8gXQ6FNAbiF1MiIiybGxL5Gx4hADRFSMsbEvkSm7fQzY+hGHGCAieg029iUyJhmpwL6pwJF5AIRmiIHOPwNl+A8CERUvbOz7HAsZMhmRFzQDPcZc0cxziAEiKsYMemopExv7EhUAVQZwdC6wbzqgTtcMMdBxLlCpg9TJiIiMXp6qkfj4eG01VKNGDSQnJ+e4LY96EOnh1SEGKnYAOszhEANERHmUp0KmRIkS2iuVHB0dIcumC3QhhFG2kSEyShxigIjIIPJUyOzduxdOTk4AgH379hVoIKIi79UhBso0Bjr9DDh6SZuLiMgE6dXY1xSxsS8ZDSGAS7+9MsTAJKDOhxxigIjoFQXS2BcAwsLCsG3bNty6dQsymQxly5ZFp06dULZs2TcKTFSkcYgBIqICoVchM336dEyYMAFqtRqurq4QQuDhw4f48ssvMW3aNHz22WcFlZPIdF3fAfw+gkMMEBEVgDwfz963bx/GjRuHr7/+Go8ePUJkZCSioqLw8OFDfPXVV/jqq69w8ODBgsxKZFpSE4DfRwKhPTVFjHN54IPdQNOvWMQQERlIntvI9OrVC46Ojli0aFG26z/88EMkJCRg/fr1Bg34pthGhiRx+6jmsurY25r5eiFAi/EcYoCIKI8M3kbm33//xZo1a3Jc/9577+H999/XLyVRUZOeohli4OiP4BADREQFL8+FTHR0NHx9fXNcX6ZMGURFRRkiE5Fp4hADRESFLs+FTEpKCiwtLXNcb2FhgbS0NIOEIjIpqgzgyBxg/4wXQwy8PQ+o2F7qZERERZ5eVy0tXboUdnZ22a5LSEgwSCAik/I4XHMU5t5JzTyHGCAiKlR5LmS8vb2xZMmS125DVGxc3KS5rFo7xMB3QLXeHGKAiKgQ5bmQuXXrVgHGIDIxkeeBrUMBVRqHGCAikpDePfsSFXsp8cDG/poipnw7oHcohxggIpIIP32J9CEE8MfHwJObLy6tZhFDRCQZfgIT6eP0CuDyZsDMHOi+HLBxkjoREVGxxkKGKK+iLgLbv9JMt5gAeNWRNg8REbGQIcqT1ARgQz9AlQqUawMEj5A6ERERIZ+FTHh4OMaNG4c+ffogJiYGALB9+3ZcvnzZoOGIjIIQwB+jgCfhgEMpoMtCtoshIjISen8aHzhwAFWqVMGJEyewefNmJCYmAgDOnz+PiRMnGjwgkeTOrAIubQJkcraLISIyMnoXMl999RWmTJmCXbt26QxZ0Lx5cxw/ftyg4YgkF3UJ2P6lZrrFeMC7nrR5iIhIh96FzMWLF9GlS5csy11dXfHo0SODhCIyCqmJmv5iMlIA/1ZA/Y+lTkRERK/Qu5BxdHREZGRkluVnz55FqVKl9NrXwYMH0bFjR3h6ekImk2Hr1q066/v37w+ZTKZza9u2rb6RifQnBPDXaOBxGGDvyXYxRERGSu9P5t69e+PLL79EVFQUZDIZ1Go1jhw5gs8++wzvv/++XvtKSkpCtWrVMH/+/By3adu2LSIjI7W39evX6xuZSH9n1wIXfn3eLmYZYOssdSIiIsqG3kMUTJs2DSEhIfDy8oJKpUJAQABUKhXeeecdjBs3Tq99tWvXDu3atct1G4VCAXd3d31jEuVf9BXg7881083GAj71pc1DREQ50ruQsbS0xJIlSzB+/HhcunQJiYmJqFGjBsqVK1cQ+bB//364urqiRIkSaN68OaZMmYKSJUvmuH1qaipSU1O18/Hx8QWSi4qotKTn7WKeAX7NgYajpU5ERES50LuQOXz4MBo2bAhvb294e3sXRCattm3bomvXrihTpgzCw8MxduxYtGvXDseOHYNcLs/2PtOnT8ekSZMKNBcVYX99Bjy6Bti5A10Ws10MEZGRkwkhhD53sLS0RKlSpdCnTx+8++67CAgIMEwQmQxbtmxB586dc9zm5s2b8PPzw+7du9GiRYtst8nuiIyXlxfi4uLg4OBgkKxURJ1dB2wbBsjMgH5/AL4NpU5ERMWMEAIZaoF0lRrpKs3PDFXmvBoZaoG0DPVL22i2y3h5e7Ua6RkC6eqX75u5LzXSnm+foRZIe74s/eXHUAmkqwXSMzT7StPuP3OdZv8ZarU2y6S3K6NHLS+Dvhbx8fFQKpWv/f7W+4jMgwcP8Msvv2D9+vWYMWMGqlatir59+6JPnz4oXbr0G4V+nbJly8LZ2Rk3btzIsZBRKBRQKBQFmoOKoJirwN+faaabjmURQ1SMZajUSEpVITEtA0mpmTcVEp9PJ6dlIDFVhaTUDCSmZiA1Q639on9RALxSGOgUGpnFiGZd2kvbZKj1OrZgNFIz1JI9tt6FjLOzM4YPH47hw4cjIiICoaGhWLVqFcaMGYPGjRtj7969BZETAHDv3j08fvwYHh4eBfYYVAylJQEb+wHpyUDZpkAjtoshMiXpKrWm2Eh7UVxkFh+a5TktUyE5c/u0F+uk/FLOjrmZDBZyM5jLZbB8/tPczAyW5mbadRZyGcyf/9TMP19nbgYLs8x1L9Zr92Wmu1/dbTT31X1s3X1YyM1gYWYGJzvL1z+Rgnp93uTOZcqUwVdffYVq1aph/PjxOHDggF73T0xMxI0bN7TzEREROHfuHJycnODk5IRJkyahW7ducHd3R3h4OL744gv4+/ujTZs2bxKbSNffXwAPrwJ2bkDXJYBZ9u2viMgwMguPxMzC4qUjH4kvFRovHwl5+ShI0kuFR2JqBtIKqPCwlJvBViGHrcIctpbm2mk7hTlsLM1h93zeykL+UmGQWTyYwcJcU3DoFAavFhqvLnupmMi8r0wmK5DnV1Tku5A5cuQI1q1bh02bNiElJQWdOnXC9OnT9drHqVOn0KxZM+386NGa/4T79euHBQsW4MKFC1i1ahViY2Ph6emJ1q1b49tvv+WpIzKcc+uBc2s17WK6LQXsXKVORGR0hBBIzVAjISUDCSnpSEzNQGJKBuJTXhzpeHHaRaWzLDmboyRpqgIqPMzNnhcZctgpzDUFiMIctpYvChBbhfx5EWL+fJlm3Ytlcm2hYmnOxv6mQO/GvmPGjMEvv/yCBw8eoFWrVujbty86deoEGxubgsr4RvLaWIiKoYfXgMVNNaeUmo4Fmn4pdSIig0vLUCMxVVOAJDwvPDQ/03WKkYQUzXxi6vNlKRlISH2xLF1l+LYbCnOz58WGHLbPCwmbzOLC0vzFuswi5KWjIi8KkxeFioWchUdRUmCNfQ8ePIjPP/8cPXv2hLMzezslE5WWrOkvJj0ZKNMYaPyZ1ImIdGQ2OI3PPAKSXTHy/AhJQmrm9EtFyfNtDNneQyYD7BTmsFeYw87KHPZWFrB9Pp/dkQ4bS3OdIyHaAsTSHDYKOQsPMgi9C5kjR44URA6iwvXPl0DMFcDWFei6lO1iyGDUaoGktAydguPl0zEJKRlIeOUISHbLktNUBs1laymHnZWmqLC3soC9dtocdgrNvP1L6+2ez79ctNhYyGFmxvYaZFzyVMj8/vvvaNeuHSwsLPD777/nuu3bb79tkGBEBebCBuDMagAyoNsSwN5N6kRkgmISUrD4wE2cufNUW7QkpmQgMS0D+p2wz53C3ExbeGQWGi8XI9plz4sN+8zi5OWiRGEOOQsQKqLy1EbGzMwMUVFRcHV1hVkuPZ3KZDKoVIb9L+JNsY0M6XgUBixqAqQnAU2+1IylRKSHx4mpWHggHGuO30ZKes6nbSzkMm0R8fLRj8xlLx/xyLpMU6TYKtjglIovg7aRUavV2U4TmZT0Z8CGfpoixreRppAhyqOnSWlYfOgmVh29pT3tU9PbEf3q+8LFTqFzOsZOYQ6FuRkvmyUqBHq3kVm9ejV69eqV5RLotLQ0/PLLL3j//fcNFo7IoP75Coi5DNi6aC61ZrsYyoO4Z+lYdjgCyw9HIDE1AwBQtbQSo1uVR5PyLixWiCSm9+XXcrkckZGRcHXV7W/j8ePHcHV15aklMk4XNwG/DQIgA97brBnZmigXCSnpWHnkFpYcuon4FE0BE+DhgNGtyqNFJVcWMEQFrMAuvxZCZPsHfO/ePSiVSn13R1TwHt0A/vhYM934MxYxlKvktAysOnobiw6GIzY5HQBQ3s0On7QsjzaV3XnVDpGRyXMhU6NGDchkmq6SW7RoAXPzF3dVqVSIiIhA27ZtCyQkUb6lp2j6i0lLBHwaAE2+kjoRGamUdBXWHr+NBfvD8TgpDQBQ1sUWo1qWR4cqHixgiIxUnguZzp07AwDOnTuHNm3awM7OTrvO0tISvr6+6Natm8EDEr2RHWOA6IuATUlNuxj5Gw0vRkVQaoYKv/x7F/P33UBMQioAwKekDT5uUQ5vV/OEOTttIzJqef5UnzhxIgDA19cXvXr1gpWVVYGFIjKIS5uBU8s1010XAw6e0uYho5KWocbG03fx094biIxLAQCUcrTGyBb+6FqzNHudJTIRev972q9fv4LIQWRYj8OB30dqphuOBvxbSpuHjEaGSo3NZ+5j3t4w3Hv6DADg7mCF4c390bOWF/ttITIxehcyKpUKs2fPxoYNG3Dnzh2kpaXprH/y5InBwhHli7ZdTALgHQw0+1rqRGQEVGqBbefuY96eMNx6nAwAcLFXIKSpH3rX8YaVBS/HJzJFehcykyZNwtKlS/Hpp59i3Lhx+Prrr3Hr1i1s3boVEyZMKIiMRPrZOQ6IugBYOwHdlrFdTDGnVgv8dTESc3ZfR/jDJABASVtLfNTED+/W84G1JQsYIlOmdz8yfn5+mDdvHtq3bw97e3ucO3dOu+z48eMIDQ0tqKz5wn5kipnLW4GNz09/9t0ElGslaRySjlotsPNKFGbvCsO16AQAgKONBT5sXBb9gn1hq2CBS2TMCqwfmaioKFSpUgUAYGdnh7i4OABAhw4dMH78+HzGJTKAJzeB30dophuMYhFTTAkhsOe/GMzadR1XIuMBAPZW5hjcqCwGNPCFvZWFxAmJyJD0LmRKly6NyMhIeHt7w8/PDzt37kTNmjVx8uTJLMMWEBWajFRg4wAgNR7wqgs0Hyd1IipkQggcuP4Qs3ddx/l7mn+w7BTmGNjAF4MaloXShgUMUVGkdyHTpUsX7NmzB3Xr1sWIESPw7rvvYtmyZbhz5w4++eSTgshI9Ho7xwOR5wDrEkD35YCcX1rFhRACR8MfY9au6zh9+ykAwNpCjv4NfPFho7IoYWspcUIiKkh6t5F51bFjx3Ds2DGUK1cOHTt2NFQug2EbmWLgyu/Ahvc00+9sAMq3kTYPFZp/I57gh53XcCJCc7WkwtwM79XzwUdN/eBsxyPERKaswNrIvCo4OBjBwcFvuhui/Hl6C9g2XDNdfwSLmGLizJ2nmL3rOg6FPQIAWMrN8E5dbwxt6gc3B3bWSVSc5KmQ+f333/O8w7fffjvfYYj0kpH2vF1MHFC6NtBiotSJqIBduBeL2buuY9+1hwAAczMZetb2wvBm/vB0tJY4HRFJIU+FTOY4S68jk8mgUqneJA9R3u2eCDw4A1g5At1XsF1MEXblQTxm776OXVeiAQByMxm61SyFEc3LwcvJRuJ0RCSlPBUyarW6oHMQ6ee/P4HjP2umuywEHL2kzUMF4np0Aubsvo6/L0YBAMxkQOfqpTCyRTn4OttKnI6IjAF7hCLT8/Q2sG2YZjp4OFChnbR5yODCHyZi3p4w/H7+AYQAZDKgfRUPjGpZHv6udlLHIyIjonchM3ny5FzXc5gCKlAZacCmgUBKHFAqiO1iipjbj5Mwb88NbDl7D+rn11O2reyOUa3KoaI7rzokoqz0LmS2bNmiM5+eno6IiAiYm5vDz8+PhQwVrD2TgPunACulpl2MOfsIKQruPU3GT3tvYOPpe1A9r2BaVnLFqJblEVhKKXE6IjJmehcyZ8+ezbIsPj4e/fv3R5cuXQwSiihbV/8Gjv2kme70M1DCR9o89Mai4lLw074w/HryLtJVmgKmSXkXfNKqPKp7OUobjohMwht3iJfp4sWL6NixI27dumWI3RkMO8QrImLvAAsbASmxQL1hQNvpUieiNxCTkIKf94Uj9N87SMvQXEzQwL8kPmlZHrV8nSROR0TGoNA6xMsUFxenHUCSyKBU6c/bxcQCnjWBlpOkTkT59DgxFYsO3sTqY7eQkq4pYOr4OmF06/KoV7akxOmIyBTpXcjMmzdPZ14IgcjISKxZswbt2vHqESoAeyYD904CCiXQg+1iTFFschoWH7yJlUdvITlN09dUdS9HfNq6PBr6O0Mmk0mckIhMld6FzOzZs3XmzczM4OLign79+mHMmDEGC0YEALi+Azj6vHju9BNQwlfSOKSfuGfpWHY4AssPRyAxNQMAUKWUEqNblUfTCi4sYIjojeldyERERBREDqKs4u4BW4ZopusMAQI4/IWpSEzNwIrDEVhy6CbiUzQFTEV3e4xuVR6tAtxYwBCRwbBDPDJOme1inj0FPKoDrb+VOhHlQXJaBlYfu41FB8LxNDkdAFDO1Q6ftCqPtpXdYWbGAoaIDEvvQiYlJQU//vgj9u3bh5iYmCzDF5w5c8Zg4agY2zsFuHsCUDg8bxejkDoR5SIlXYW1x29j4YFwPEpMAwCUdbbFxy3LoUNVT8hZwBBRAdG7kBk0aBB27tyJ7t27o06dOjxETIYXtgs4Mkcz/faPgFNZSeNQzlIzVPjl37uYv+8GYhJSAQDeTjYY2aIcOlf3hLncTOKERFTU6V3I/Pnnn/j777/RoEGDN37wgwcP4vvvv8fp06cRGRmJLVu26Iy0LYTAxIkTsWTJEsTGxqJBgwZYsGABypUr98aPTUYq7j6w+UPNdO3BQOXOksYpLoQQSE5TIT4lHfHPMhD3LB3xz9Kfz6cj7lmGdjo+Jf35+gxExj3TnkIq5WiNEc390S2oNCxYwBBRIdG7kClVqhTs7e0N8uBJSUmoVq0aBg4ciK5du2ZZ/91332HevHlYtWoVypQpg/Hjx6NNmza4cuUKrKysDJKBjIgqA/htEPDsCeBeFWg9RepEJiUlXfVSofFS4fEsHfEpGVmKkBdFimZ95tAA+nJzUGB4M3/0rO0FhbncwM+KiCh3ehcyP/zwA7788kssXLgQPj5v1kV8u3btcux7RgiBOXPmYNy4cejUqRMAYPXq1XBzc8PWrVvRu3fvN3psMkL7pgJ3jgGW9kCPlYBF8SpW01VqnaIjTntEJOOlAiTnoiSzh9w3YW4mg9LaAg7WFnCwMtf8tLaAg5UFHKzNn/+00GxjZQ6ltQUqeTjAyoIFDBFJQ+9CplatWkhJSUHZsmVhY2MDCwsLnfVPnjwxSLCIiAhERUWhZcuW2mVKpRJ169bFsWPHcixkUlNTkZqaqp2Pj483SB4qYDd2A4dnaabfnguU9JM2Tz6o1AKJKa8WHS+dqkl5UYi8uj4+JV3bUdybkMmgU3QoXylCtEXKq/PPt7G2kLPdGxGZFL0LmT59+uD+/fuYNm0a3NwKrj+IqKgoAICbm5vOcjc3N+267EyfPh2TJrELe5MS/+BFu5haA4HAbtLmyYNHialYdjgCB6491BYlCc87fHtTdgrNkQ77zCMi2oLjxRGRzKMhLxchSmsL2Fqa8xJnIipW9C5kjh49imPHjqFatWoFkeeNjRkzBqNHj9bOx8fHw8vLS8JElCtVBvDbB0DyY8CtCtDGuAeDjElIweIDN7HuxB08S8/+CIq1hTzb0zA5FyUvltkpzHmlDxGRHvQuZCpWrIhnz54VRBYd7u7uAIDo6Gh4eHhol0dHR6N69eo53k+hUEChYJ8jJuPADOD2EcDSDui5ymjbxUTFpWDhgXCs//cOUp+3RalWWolBjcrC28lGW6jYW5mzwSsRUSHSu5CZMWMGPv30U0ydOhVVqlTJ0kYmt6G29VGmTBm4u7tjz5492sIlPj4eJ06cwNChQw3yGCSx8L3Awf9ppjsaZ7uY+7HPsHB/OH49eRdpKk0BU9PbER+3LI/G5TjYIRGR1PQuZNq2bQsAaNGihc5yIQRkMhlUqrw3WExMTMSNGze08xERETh37hycnJzg7e2NUaNGYcqUKShXrpz28mtPT0+dvmbIRCVEAb8NBiCAoP5Ale5SJ9Jx90kyft4fjk2n7yJdpbksuY6vEz5uWQ71/UqygCEiMhJ6FzL79u0z2IOfOnUKzZo1085ntm3p168fVq5ciS+++AJJSUn48MMPERsbi4YNG+Kff/5hHzKmTq163i7mEeAWCLSdIXUirVuPkvDz/hvYfOY+Mp73q1LfryRGtiiHemVLSpyOiIheJRNC5K8XLBMRHx8PpVKJuLg4g532oje0bxpwYCZgYQsMOQA4S99Tc/jDRMzfdwPbzj3QdgzXqJwzRrYoh9q+ThKnIyIqfvL6/a33EZmDBw/mur5x48b67pKKk5v7gQPfaaY7zpG8iAmLTsCPe2/gzwsPkNmxbbMKLhjRohxqepeQNBsREb2e3oVM06ZNsyx7ub2APm1kqJhJiH7RLqbGe0DVnpJF+S8yHj/tvYG/L0Ui85hky0puGNnCH1VLO0qWi4iI9KN3IfP06VOd+fT0dJw9exbjx4/H1KlTDRaMihi1Ctj8AZAUA7gGAO2+kyTGpftx+HFvGHZcjtYua1vZHcOb+yOwlFKSTERElH96FzJKZdYP+1atWsHS0hKjR4/G6dOnDRKMipiD/wMiDgIWNppxlCxtCvXhz9+NxY97w7D7vxgAmq7821fxwPDm/qjozrZTRESmSu9CJidubm64du2aoXZHRUnEQWD/8x57288CXCoU2kOfvv0UP+4Nw/5rDwEAZjLg7WqeGN7cH/6uhhnFnYiIpKN3IXPhwgWdeSEEIiMjMWPGjFx73KViKjFGc6k1BFD9XaB6n0J52H8jnmDenjAcvvEIACA3k6Fz9VIIaeaHsi52hZKBiIgKnt6FTPXq1SGTyfDqVdv16tXD8uXLDRaMigC1Ctg8GEiMBlwqAm8VbLsYIQSO3XyMeXvCcPymZhR2czMZutUsjWHN/OBT0rZAH5+IiAqf3oVMRESEzryZmRlcXFzYSR1ldWiW5nJrCxugxyrAsmAKCSEEDt94hHl7wnDylqYxuoVchh61vDC0iR+8nAq3PQ4RERUevQsZHx+fgshBRc2tw8D+aZrpt/4HuFY0+EMIIbD/+kPM2xOGs3diAQCW5mboXdsLHzXxg6ejtcEfk4iIjItZXjfcu3cvAgICEB8fn2VdXFwcKleujEOHDhk0HJmoxIfApkGAUAPV+gA1+hp090II7L4SjU7zj2DAipM4eycWCnMzDGxQBoe+aIbJnQJZxBARFRN5PiIzZ84cDB48ONtugpVKJYYMGYJZs2ahUaNGBg1IJkatBrZ8CCRGAc4VgPY/GHDXAjuvRGHenhu4EqkpqK0t5Hgv2AcfNCoDV3ue3iQiKm7yXMicP38eM2fOzHF969at8b///c8gociEHZkNhO8FzK2f9xfz5u1iVGqB7Zci8eOeG7gWnQAAsLWU4/36vvigYRmUtFO88WMQEZFpynMhEx0dDQsLi5x3ZG6Ohw8fGiQUmajbR4G9UzTTb30PuAW80e5UaoE/LzzAj3tv4EZMIgDAXmGO/g18MbBBGZSwtXzTxEREZOLyXMiUKlUKly5dgr+/f7brL1y4AA8PD4MFIxOT9OhFu5iqvYAa7+Z7VxkqNbade4D5+27g5qMkAICDlTkGNiyDAfXLQGmTc0FNRETFS54Lmbfeegvjx49H27Zts1xq/ezZM0ycOBEdOnQweEAyAWo1sGUIkPAAKFlO03vvSwOJ5lW6So0tZ+7jp303cOdJMgDA0cYCgxuVxXvBPnCwYgFDRES6ZOLVnu1yEB0djZo1a0Iul2P48OGoUEHTzfzVq1cxf/58qFQqnDlzBm5ubgUaWF/x8fFQKpWIi4vLtqEyGcDh2cDubwBzK+CDPYB7oF53T81Q4bfT9zF/3w3cj30GAChpa4nBjcvi3Xo+sFMYbCQNIiIyEXn9/s7zN4SbmxuOHj2KoUOHYsyYMdqefWUyGdq0aYP58+cbXRFDheDOcWDPt5rpdjP1KmJS0lXYcOouFuwPR2RcCgDA2U6Bj5qUxTt1vWFjyQKGiIhyp9c3hY+PD/7++288ffoUN27cgBAC5cqVQ4kSJQoqHxmz5CfApoGAUAFVegA1++XpbinpKoSeuIOFB8IRk5AKAHBzUOCjJn7oU8cbVhbygkxNRERFSL7+5S1RogRq165t6CxkKjLSgHsngQMzgfj7QEl/oMPs17aLSU7LwLrjd7Do4E08StQUMB5KKwxr6ocetbxYwBARkd547J5eT60Coi4ANw8AEQeBO8eAdE1jXMgVmv5iFPY53j0xNQNrjt3GkkM38SQpDQBQytEaIc380S2oFBTmLGCIiCh/WMhQVkIAj29oBnyMOABEHAJSYnW3sXUByjQGag0C3Ktku5v4lHSsPnoLSw9HIDY5HQDgU9IGIc380aVGKVjI8zxCBhERUbZYyJBG3H1N0ZJ51CXhge56S3vAtyFQtommgHENyPFUUlxyOpYficCKIxGIT8kAAJR1tsXw5v54u5onzFnAEBGRgbCQKa6SnwC3Dj0vXA5ojsC8TK4AvOo8L1yaAp41AHnub5enSWlYdjgCq47eQkKqpoDxd7XDiOb+6FDVE3Iz/fuWISIiyg0LmeIiLQm4fQyI2K854hJ5AcBLXQjJzDTFSpnnR1y86wEWeRtB+nFiKpYcisCaY7eQlKYCAFR0t8eI5uXQLtAdZixgiIiogLCQKaoy0oD7p1+cLrp3ElCn627jUlFTuJRtAvg0AKwd9XqImIQULDl4E2uP38GzdE0BE+DhgJEtyqF1gBsLGCIiKnAsZIoKtRqIvviijcvto0B6ku42Sm+gbOMXR13s3fP9cPuuxWBE6FkkPj+FVLW0EiObl0OLSq6Q5WN4AiIiovxgIWOqhACe3NS9sujZE91tbEpqCpbMoy4lyuRrDKRXhZ64g/HbLkGlFqhSSonRrcujaXkXFjBERFToWMiYkvjI50XLQc2Rl/h7uust7TSniMo01hQurpUBM8NdIaRWC3y34xoWHggHAHQPKo1pXarA0pxXIRERkTRYyBizZ0+BW4dfXFn06LruerklUDrzyqImQKmagLxgRohOSVfhs43n8eeFSADAJy3LY2QLfx6FISIiSbGQMSZpyZpeczMb6Eaeh86VRZABntVfurIoGLC0KfBYT5PS8OGaUzh56yks5DLM6FoV3YJKF/jjEhERvQ4LGSmp0oH7Z166suhfQJWmu41z+RdtXHwbAtaFO0Dn7cdJ6L/iJCIeJcHeyhyL3g1CfX/nQs1ARESUExYyhUmtBmIuvzhVdPsokJaou41D6Reniso0Ahw8pckK4Mydp/hg1Sk8SUpDKUdrrBhQG+Xdch5TiYiIqLCxkClIQgBPI14ULhEHgeTHuttYO2kKljJNgLJNAaeyBrmy6E1tvxiJUb+eQ2qGGlVKKbGsfy242ltJHYuIiEgHCxlDS4h6cVVRxAEg7q7uegtbwKf+izGL3KoY9MqiNyWEwLLDEZj6938QAmhR0RXz+tSArYJvFSIiMj78dnpTz2KB20deFC4Pr+quN7MAStd+6cqiIMDcUpKor6NSC0z+4zJWHbsNAHg/2AcTO1bmGElERGS0jLqQ+eabbzBp0iSdZRUqVMDVq1dzuEchOr4QuPArEHkOEOqXVsgAj6rP27g0AXyCAUtbqVLmWXJaBkauP4vd/8VAJgO+fqsSBjUsw8uriYjIqBl1IQMAlStXxu7du7Xz5uZGEvlxGPDgjGa6pP9LVxY1AmycpM2mp5iEFAxaeQoX78dBYW6GOb2qo10VD6ljERERvZaRVAU5Mzc3h7t7/scEKjDV39GcJirTBFCWkjpNvoVFJ6D/ipO4H/sMTraWWPJ+LQT5FO4l3kRERPll9IVMWFgYPD09YWVlheDgYEyfPh3e3t45bp+amorU1FTtfHx8fMEEKxWkuZmwo+GPMGTNaSSkZKCMsy1W9K8NX2fjPw1GRESUyXgul8lG3bp1sXLlSvzzzz9YsGABIiIi0KhRIyQkJOR4n+nTp0OpVGpvXl5ehZjYdGw+cw/9lv+LhJQM1PIpgc1D67OIISIikyMTQojXb2YcYmNj4ePjg1mzZmHQoEHZbpPdERkvLy/ExcXBwcGhsKIaLSEE5u25gdm7NeM2ta/qgR96VIOVhVziZERERC/Ex8dDqVS+9vvb6E8tvczR0RHly5fHjRs3ctxGoVBAoVAUYirTkZahxtgtF7HptGbU7I+a+OGLNhVgxsuriYjIRBn1qaVXJSYmIjw8HB4evKJGX/Ep6Ri48iQ2nb4HMxkwtUsgvmpXkUUMERGZNKM+IvPZZ5+hY8eO8PHxwYMHDzBx4kTI5XL06dNH6mgm5X7sMwxY8S+uRyfCxlKO+e/URLOKrlLHIiIiemNGXcjcu3cPffr0wePHj+Hi4oKGDRvi+PHjcHFxkTqaybh0Pw4DV55ETEIqXO0VWN6/NgJLKaWORUREZBBGXcj88ssvUkcwafuuxiAk9AyS01So4GaPFQNqw9PRWupYREREBmPUhQzl39rjtzFh2yWoBdConDPm960JBysLqWMREREZFAuZIkatFpj5z1UsOngTANAjqDSmda0CC7lJtesmIiLKExYyRUhKugqfbjiPvy5GAgA+bVUew5v7c+BHIiIqsljIFBFPktIwePUpnL79FBZyGb7rXhVdapSWOhYREVGBYiFTBNx6lIQBK08i4lESHKzMsei9Wgj2Kyl1LCIiogLHQsbEnb79BB+sOoWnyekoXcIaKwfUhr+rvdSxiIiICgULGRP298VIjPr1HNIy1KhaWoml/WrB1d5K6lhERESFhoWMCRJCYMmhm5j291UAQMtKbpjXpzpsLPnrJCKi4oXffCYmQ6XGpD+uYM3x2wCA/vV9Mb5DAOQcM4mIiIohFjImJCk1AyPWn8XeqzGQyYBx7QMwqGEZqWMRERFJhoWMiYiJT8HAVSdx6X48FOZmmNu7OtoGchRwIiIq3ljImIDr0QkYsOIk7sc+Q0lbSyztVws1vEtIHYuIiEhyLGSM3NEbjzBk7WkkpGSgrLMtVgyoDZ+StlLHIiIiMgosZIzYptP38NVvF5ChFqjj64TF7wfB0cZS6lhERERGg4WMERJCYO6eMMzZHQYA6FjNE993rworC7nEyYiIiIwLCxkjk5ahxpjNF/HbmXsAgGFN/fBZ6wow4+XVREREWbCQMSJxz9IxdO1pHA1/DLmZDFM6B6JPHW+pYxERERktFjJG4t7TZAxceRLXoxNhaynH/L410bSCq9SxiIiIjBoLGSNw8V4cBq46iYcJqXB3sMLy/rUR4OkgdSwiIiKjx0JGYnv+i8bw0LN4lq5CRXd7rBhQGx5Ka6ljERERmQQWMhJac+wWJv5+GWoBNCrnjJ/71oS9lYXUsYiIiEwGCxkJqNUCM/65isUHbwIAetf2wredA2EhN5M4GRERkWlhIVPIUtJVGL3hHP6+GAUA+LxNBQxr6geZjJdXExER6YuFTCF6nJiKwatP4cydWFjKzfB9j6roVL2U1LGIiIhMFguZQhLxKAkDVvyLW4+TobS2wOL3glC3bEmpYxEREZk0FjKF4NStJxi8+hSeJqfDy8kaK/rXgb+rndSxiIiITB4LmQL254UHGL3hPNIy1Kjm5Yil79eCi71C6lhERERFAguZAiKEwKKDNzFj+1UAQOsAN8ztXQPWlhz4kYiIyFBYyBSADJUaE3+/jHUn7gAABjTwxbj2AZBz4EciIiKDYiFjYEmpGRgeegb7rj2ETAaMbx+AgQ3LSB2LiIioSGIhY0DR8SkYuPIkLj+Ih5WFGeb2roE2ld2ljkVERFRksZAxkGtRCRiw4l88iEuBs50llvarjepejlLHIiIiKtJYyBjA4bBHGLr2NBJSM1DWxRarBtSBl5ON1LGIiIiKPBYyb2jjqbsYs/kiMtQCdcs4YdF7QXC0sZQ6FhERUbHAQiafhBCYvTsM8/aEAQA6VffEd92rQmHOy6uJiIgKi0kMtzx//nz4+vrCysoKdevWxb///itpHiEEPt90QVvEjGjujzm9qrOIISIiKmRGX8j8+uuvGD16NCZOnIgzZ86gWrVqaNOmDWJiYiTLJJPJEODhALmZDDO7VcGnrStw9GoiIiIJyIQQQuoQualbty5q166Nn376CQCgVqvh5eWFESNG4Kuvvnrt/ePj46FUKhEXFwcHBweDZrsRk8gxk4iIiApAXr+/jfqITFpaGk6fPo2WLVtql5mZmaFly5Y4duxYtvdJTU1FfHy8zq2gsIghIiKSllEXMo8ePYJKpYKbm5vOcjc3N0RFRWV7n+nTp0OpVGpvXl5ehRGViIiIJGDUhUx+jBkzBnFxcdrb3bt3pY5EREREBcSoL792dnaGXC5HdHS0zvLo6Gi4u2ff9b9CoYBCoSiMeERERCQxoz4iY2lpiaCgIOzZs0e7TK1WY8+ePQgODpYwGRERERkDoz4iAwCjR49Gv379UKtWLdSpUwdz5sxBUlISBgwYIHU0IiIikpjRFzK9evXCw4cPMWHCBERFRaF69er4559/sjQAJiIiouLH6PuReVMF2Y8MERERFYwi0Y8MERERUW5YyBAREZHJYiFDREREJouFDBEREZksFjJERERksljIEBERkcky+n5k3lTm1eUFOQo2ERERGVbm9/breokp8oVMQkICAHAUbCIiIhOUkJAApVKZ4/oi3yGeWq3GgwcPYG9vD5lMZrD9xsfHw8vLC3fv3i22He0V99eguD9/gK9BcX/+AF8DPv+Ce/5CCCQkJMDT0xNmZjm3hCnyR2TMzMxQunTpAtu/g4NDsXzzvqy4vwbF/fkDfA2K+/MH+Brw+RfM88/tSEwmNvYlIiIik8VChoiIiEwWC5l8UigUmDhxIhQKhdRRJFPcX4Pi/vwBvgbF/fkDfA34/KV//kW+sS8REREVXTwiQ0RERCaLhQwRERGZLBYyREREZLJYyBAREZHJYiGTDwcPHkTHjh3h6ekJmUyGrVu3Sh2p0EyfPh21a9eGvb09XF1d0blzZ1y7dk3qWIVqwYIFqFq1qrYDqODgYGzfvl3qWJKZMWMGZDIZRo0aJXWUQvPNN99AJpPp3CpWrCh1rEJ1//59vPvuuyhZsiSsra1RpUoVnDp1SupYhcbX1zfLe0AmkyEkJETqaIVCpVJh/PjxKFOmDKytreHn54dvv/32teMiFYQi37NvQUhKSkK1atUwcOBAdO3aVeo4herAgQMICQlB7dq1kZGRgbFjx6J169a4cuUKbG1tpY5XKEqXLo0ZM2agXLlyEEJg1apV6NSpE86ePYvKlStLHa9QnTx5EosWLULVqlWljlLoKleujN27d2vnzc2Lz8fp06dP0aBBAzRr1gzbt2+Hi4sLwsLCUKJECamjFZqTJ09CpVJp5y9duoRWrVqhR48eEqYqPDNnzsSCBQuwatUqVK5cGadOncKAAQOgVCoxcuTIQs1SfP7yDKhdu3Zo166d1DEk8c8//+jMr1y5Eq6urjh9+jQaN24sUarC1bFjR535qVOnYsGCBTh+/HixKmQSExPRt29fLFmyBFOmTJE6TqEzNzeHu7u71DEkMXPmTHh5eWHFihXaZWXKlJEwUeFzcXHRmZ8xYwb8/PzQpEkTiRIVrqNHj6JTp05o3749AM0RqvXr1+Pff/8t9Cw8tURvJC4uDgDg5OQkcRJpqFQq/PLLL0hKSkJwcLDUcQpVSEgI2rdvj5YtW0odRRJhYWHw9PRE2bJl0bdvX9y5c0fqSIXm999/R61atdCjRw+4urqiRo0aWLJkidSxJJOWloa1a9di4MCBBh2c2JjVr18fe/bswfXr1wEA58+fx+HDhyX5J59HZCjf1Go1Ro0ahQYNGiAwMFDqOIXq4sWLCA4ORkpKCuzs7LBlyxYEBARIHavQ/PLLLzhz5gxOnjwpdRRJ1K1bFytXrkSFChUQGRmJSZMmoVGjRrh06RLs7e2ljlfgbt68iQULFmD06NEYO3YsTp48iZEjR8LS0hL9+vWTOl6h27p1K2JjY9G/f3+poxSar776CvHx8ahYsSLkcjlUKhWmTp2Kvn37FnoWFjKUbyEhIbh06RIOHz4sdZRCV6FCBZw7dw5xcXHYtGkT+vXrhwMHDhSLYubu3bv4+OOPsWvXLlhZWUkdRxIv/9dZtWpV1K1bFz4+PtiwYQMGDRokYbLCoVarUatWLUybNg0AUKNGDVy6dAkLFy4sloXMsmXL0K5dO3h6ekodpdBs2LAB69atQ2hoKCpXroxz585h1KhR8PT0LPT3AAsZypfhw4fjzz//xMGDB1G6dGmp4xQ6S0tL+Pv7AwCCgoJw8uRJzJ07F4sWLZI4WcE7ffo0YmJiULNmTe0ylUqFgwcP4qeffkJqairkcrmECQufo6Mjypcvjxs3bkgdpVB4eHhkKdorVaqE3377TaJE0rl9+zZ2796NzZs3Sx2lUH3++ef46quv0Lt3bwBAlSpVcPv2bUyfPp2FDBk3IQRGjBiBLVu2YP/+/cWugV9O1Go1UlNTpY5RKFq0aIGLFy/qLBswYAAqVqyIL7/8stgVMYCm4XN4eDjee+89qaMUigYNGmTpduH69evw8fGRKJF0VqxYAVdXV22j1+IiOTkZZma6zWzlcjnUanWhZ2Ehkw+JiYk6/3lFRETg3LlzcHJygre3t4TJCl5ISAhCQ0Oxbds22NvbIyoqCgCgVCphbW0tcbrCMWbMGLRr1w7e3t5ISEhAaGgo9u/fjx07dkgdrVDY29tnaRNla2uLkiVLFpu2Up999hk6duwIHx8fPHjwABMnToRcLkefPn2kjlYoPvnkE9SvXx/Tpk1Dz5498e+//2Lx4sVYvHix1NEKlVqtxooVK9CvX79idfk9oLl6c+rUqfD29kblypVx9uxZzJo1CwMHDiz8MIL0tm/fPgEgy61fv35SRytw2T1vAGLFihVSRys0AwcOFD4+PsLS0lK4uLiIFi1aiJ07d0odS1JNmjQRH3/8sdQxCk2vXr2Eh4eHsLS0FKVKlRK9evUSN27ckDpWofrjjz9EYGCgUCgUomLFimLx4sVSRyp0O3bsEADEtWvXpI5S6OLj48XHH38svL29hZWVlShbtqz4+uuvRWpqaqFnkQkhQTd8RERERAbAfmSIiIjIZLGQISIiIpPFQoaIiIh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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot cumulative differences in mse vs number of features ablated\n", + "plt.plot(cumulative_diff_shap_test, label = 'SHAP')\n", + "plt.plot(cumulative_diff_lmdi_test, label = 'LMDI+')\n", + "plt.xlabel('# of Features Ablated')\n", + "plt.ylabel('Cumulative Difference in MSE')\n", + "# x ticks should be labeled 1-X_train.shape[1]\n", + "plt.xticks(np.arange(0, X_train.shape[1], 1), np.arange(1, X_train.shape[1]+1, 1))\n", + "plt.title('Testing Data: Abalone Dataset w/ Gender Subgroups')\n", + "plt.legend()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mdi", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/feature_importance/subgroup/legacy/agglomerative_dataset_eval.ipynb b/feature_importance/subgroup/legacy/agglomerative_dataset_eval.ipynb new file mode 100644 index 0000000..32d0619 --- /dev/null +++ b/feature_importance/subgroup/legacy/agglomerative_dataset_eval.ipynb @@ -0,0 +1,811 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib import font_manager\n", + "import math" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [], + "source": [ + "# read in all the files from the results folder\n", + "datasource = \"function\"\n", + "dataname = \"parkinsons\"\n", + "# dataname = \"ccle\"\n", + "seed = np.arange(1, 21)\n", + "task = \"regression\"\n", + "data_type = \"test\"\n", + "methods = ['shap', 'signed_normalized_l2_avg', 'signed_normalized_l2_noavg',\n", + " 'signed_nonnormalized_l2_avg', 'signed_nonnormalized_l2_noavg',\n", + " 'nonl2_avg', 'nonl2_noavg', 'l2_ranking', 'nonl2_ranking', 'baseline']\n", + "p_values = [0.1, 0.3, 0.5, 0.7, 0.9]\n", + "p_values = list(map(str, p_values))\n", + "values_results = {}\n", + "# rankings_results = {}\n", + "task_results = {}\n", + "for i in range(len(seed)):\n", + " values_dict = {}\n", + " rankings_dict = {}\n", + " for method in methods:\n", + " file = f\"results/{data_type}_data/{datasource}_{dataname}_seed{seed[i]}_{method}_values.csv\"\n", + " values_dict[method] = pd.read_csv(file)\n", + " # for p_value in p_values:\n", + " # if method == 'normalized_l2_ranking':\n", + " # continue\n", + " # file = f\"results/{data_type}_data/{datasource}_{dataname}_seed{seed[i]}_{method}_{p_value}_ranking.csv\"\n", + " # rankings_dict[method + \"_\" + p_value] = pd.read_csv(file)\n", + " values_results[datasource + \"_\" + dataname + \"_\" + str(seed[i])] = values_dict\n", + " # rankings_results[datasource + \"_\" + dataname + \"_\" + str(seed[i])] = rankings_dict\n", + " task_results[datasource + \"_\" + dataname + \"_\" + str(seed[i])] = task" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [], + "source": [ + "auc_dict = {}\n", + "auc_top_ten_percent = {}\n", + "viz_methods = ['shap', 'signed_nonnormalized_l2_noavg']\n", + "# metrics = ['AUROC', 'AUPRC', 'R^2', 'F1', 'Accuracy']\n", + "metrics = [\"R^2\"]\n", + "for metric in metrics:\n", + " metric_aucs = {}\n", + " metric_top_ten_aucs = {}\n", + " for method in methods:\n", + " aucs_per_seed = []\n", + " aucs_top_ten = []\n", + " for s in seed:\n", + " datastring = datasource + \"_\" + dataname + \"_\" + str(s)\n", + " vec = values_results[datastring][method].loc[:, metric]\n", + " vec_len = vec.shape[0]\n", + " aucs_per_seed.append(np.trapz(vec)/vec_len)\n", + " aucs_top_ten.append(np.trapz(vec[:int(vec_len * 0.1)])/vec[:int(vec_len * 0.1)].shape[0])\n", + " aucs_per_seed = np.array(aucs_per_seed)\n", + " aucs_top_ten = np.array(aucs_top_ten)\n", + " metric_aucs[method] = aucs_per_seed.mean()\n", + " metric_top_ten_aucs[method] = aucs_top_ten.mean()\n", + " auc_dict[metric] = metric_aucs\n", + " auc_top_ten_percent[metric] = metric_top_ten_aucs\n", + "aucs = pd.DataFrame(auc_dict)\n", + "aucs_top_ten = pd.DataFrame(auc_top_ten_percent)\n", + "aucs_top_ten = aucs_top_ten.sort_values(by=metrics[0], ascending=False)\n", + "# sort rows in aucs by highest auroc\n", + "aucs = aucs.sort_values(by=metrics[0], ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "auc_dict = {}\n", + "# metrics = ['AUROC', 'AUPRC', 'R^2', 'F1', 'Accuracy']\n", + "metrics = [\"R^2\"]\n", + "viz_methods = ['shap', 'nonl2_noavg']\n", + "method_names = {'shap': 'TreeSHAP', 'nonl2_noavg': 'LMDI+'}\n", + "method_colors = {'shap': 'orange', 'nonl2_noavg': 'black'}\n", + "for metric in metrics:\n", + " metric_aucs = {}\n", + " for method in viz_methods:\n", + " max_num = 0\n", + " for s in seed:\n", + " if values_results[datasource + \"_\" + dataname + \"_\" + str(s)][method].loc[:, metric].to_numpy().flatten().shape[0] > max_num:\n", + " max_num = values_results[datasource + \"_\" + dataname + \"_\" + str(s)][method].loc[:, metric].to_numpy().flatten().shape[0]\n", + " aucs_per_seed = np.empty((len(seed), max_num))\n", + " for s in seed:\n", + " # print(values_results[datasource + \"_\" + dataname + \"_\" + str(s)][method].loc[:, metric])\n", + " # if values_results[datasource + \"_\" + dataname + \"_\" + str(s)][method].loc[:, metric].to_numpy().flatten().shape[0] < max_num, repeat the last element to fill out space\n", + " if values_results[datasource + \"_\" + dataname + \"_\" + str(s)][method].loc[:, metric].to_numpy().flatten().shape[0] < max_num:\n", + " results = np.pad(values_results[datasource + \"_\" + dataname + \"_\" + str(s)][method].loc[:, metric].to_numpy().flatten(), (0, max_num - values_results[datasource + \"_\" + dataname + \"_\" + str(s)][method].loc[:, metric].to_numpy().flatten().shape[0]), 'edge')\n", + " else:\n", + " results = values_results[datasource + \"_\" + dataname + \"_\" + str(s)][method].loc[:, metric].to_numpy().flatten()\n", + " aucs_per_seed[s - 1, :] = results\n", + " aucs_per_seed = np.array(aucs_per_seed)\n", + " metric_aucs[method] = aucs_per_seed.mean(axis=0)\n", + " auc_dict[metric] = metric_aucs\n", + "auc_dict\n", + "\n", + "for metric, method_dict in auc_dict.items():\n", + " for method, vec in method_dict.items():\n", + " plt.plot(np.arange(vec.shape[0]), vec, label=method_names[method], color = method_colors[method])\n", + " # plt.plot(np.arange(vec.shape[0]), vec, label = method)\n", + " plt.xticks([0, 99, 199, 299, 399, 499, 599], [\"1\", \"100\", \"200\", \"300\", \"400\", \"500\", \"600\"])\n", + " plt.xlabel(\"Number of Subgroups\", fontsize=18)\n", + " # make xaxis label big\n", + " plt.tick_params(axis='both', labelsize=15)\n", + " # y-axis label should be Average Ranking\n", + " plt.ylabel(\"R^2\", fontsize=18)\n", + " plt.title(\"Subgroup Experiment: Parkinson's Data\", fontsize=20, fontweight='bold')\n", + " title_font_properties = font_manager.FontProperties(weight='bold', size=18)\n", + " plt.legend(title = \"Method\", fontsize=15, title_fontproperties = title_font_properties)\n", + " # plt.legend()\n", + " plt.gca().invert_xaxis()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "auc_dict = {}\n", + "# metrics = ['AUROC', 'AUPRC', 'R^2', 'F1', 'Accuracy']\n", + "metrics = [\"R^2\"]\n", + "p = 0.9\n", + "for metric in metrics:\n", + " metric_aucs = {}\n", + " for method in methods:\n", + " max_num = 0\n", + " for s in seed:\n", + " if rankings_results[datasource + \"_\" + dataname + \"_\" + str(s)][method + \"_\" + str(p)].loc[:, metric].to_numpy().flatten().shape[0] > max_num:\n", + " max_num = rankings_results[datasource + \"_\" + dataname + \"_\" + str(s)][method + \"_\" + str(p)].loc[:, metric].to_numpy().flatten().shape[0]\n", + " aucs_per_seed = np.empty((len(seed), max_num))\n", + " for s in seed:\n", + " if rankings_results[datasource + \"_\" + dataname + \"_\" + str(s)][method + \"_\" + str(p)].loc[:, metric].to_numpy().flatten().shape[0] < max_num:\n", + " results = np.pad(rankings_results[datasource + \"_\" + dataname + \"_\" + str(s)][method + \"_\" + str(p)].loc[:, metric].to_numpy().flatten(), (0, max_num - rankings_results[datasource + \"_\" + dataname + \"_\" + str(s)][method + \"_\" + str(p)].loc[:, metric].to_numpy().flatten().shape[0]), 'edge')\n", + " else:\n", + " results = rankings_results[datasource + \"_\" + dataname + \"_\" + str(s)][method + \"_\" + str(p)].loc[:, metric].to_numpy().flatten()\n", + " aucs_per_seed[s - 1, :] = results\n", + " aucs_per_seed = np.array(aucs_per_seed)\n", + " metric_aucs[method] = aucs_per_seed.mean(axis=0)\n", + " auc_dict[metric] = metric_aucs\n", + "auc_dict\n", + "\n", + "for metric, method_dict in auc_dict.items():\n", + " for method, vec in method_dict.items():\n", + " plt.plot(np.arange(vec.shape[0]), vec, label=method)\n", + " plt.legend()\n", + " plt.gca().invert_xaxis()\n", + " plt.title(metric)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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R^2
baseline_0.90.753399
shap_0.90.730412
signed_normalized_l2_avg_0.90.713246
signed_nonnormalized_l2_avg_0.90.697899
nonl2_avg_0.90.677963
signed_normalized_l2_noavg_0.90.666488
signed_nonnormalized_l2_noavg_0.90.654051
nonl2_noavg_0.90.618623
l2_ranking_0.90.615348
nonl2_ranking_0.90.593878
\n", + "
" + ], + "text/plain": [ + " R^2\n", + "baseline_0.9 0.753399\n", + "shap_0.9 0.730412\n", + "signed_normalized_l2_avg_0.9 0.713246\n", + "signed_nonnormalized_l2_avg_0.9 0.697899\n", + "nonl2_avg_0.9 0.677963\n", + "signed_normalized_l2_noavg_0.9 0.666488\n", + "signed_nonnormalized_l2_noavg_0.9 0.654051\n", + "nonl2_noavg_0.9 0.618623\n", + "l2_ranking_0.9 0.615348\n", + "nonl2_ranking_0.9 0.593878" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "auc_dict = {}\n", + "# metrics = ['AUROC', 'AUPRC', 'R^2', 'F1', 'Accuracy']\n", + "metrics = [\"R^2\"]\n", + "for metric in metrics:\n", + " metric_aucs = {}\n", + " for method in methods:\n", + " for p in p_values:\n", + " aucs_per_seed = []\n", + " for s in seed:\n", + " aucs_per_seed.append(np.trapz(rankings_results[datasource + \"_\" + dataname + \"_\" + str(s)][method + \"_\" + str(p)].loc[:, metric])/values_results[datasource + \"_\" + dataname + \"_\" + str(s)][method].shape[0])\n", + " aucs_per_seed = np.array(aucs_per_seed)\n", + " metric_aucs[method + \"_\" + p] = aucs_per_seed.mean()\n", + " auc_dict[metric] = metric_aucs\n", + "aucs = pd.DataFrame(auc_dict)\n", + "# sort rows in aucs by highest auroc\n", + "aucs = aucs.sort_values(by=metrics[0], ascending=False)\n", + "aucs" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results for dataset temperature from datasource function with seed 1.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results for dataset temperature from datasource function with seed 2.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results for dataset temperature from datasource function with seed 3.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results for dataset temperature from datasource function with seed 4.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results for dataset temperature from datasource function with seed 5.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results for dataset temperature from datasource function with seed 6.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results for dataset temperature from datasource function with seed 7.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results for dataset temperature from datasource function with seed 8.\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results for dataset temperature from datasource function with seed 9.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results for dataset temperature from datasource function with seed 10.\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/numpy/core/fromnumeric.py:42\u001b[0m, in \u001b[0;36m_wrapit\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m 41\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 42\u001b[0m wrap \u001b[38;5;241m=\u001b[39m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__array_wrap__\u001b[49m\n\u001b[1;32m 43\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m:\n", + "\u001b[0;31mAttributeError\u001b[0m: 'tuple' object has no attribute '__array_wrap__'", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[15], line 29\u001b[0m\n\u001b[1;32m 27\u001b[0m ax\u001b[38;5;241m.\u001b[39minvert_xaxis()\n\u001b[1;32m 28\u001b[0m plot_count \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m---> 29\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtight_layout\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 30\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n", + "File \u001b[0;32m/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/matplotlib/pyplot.py:2349\u001b[0m, in \u001b[0;36mtight_layout\u001b[0;34m(pad, h_pad, w_pad, rect)\u001b[0m\n\u001b[1;32m 2347\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Figure\u001b[38;5;241m.\u001b[39mtight_layout)\n\u001b[1;32m 2348\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtight_layout\u001b[39m(\u001b[38;5;241m*\u001b[39m, pad\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1.08\u001b[39m, h_pad\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, w_pad\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, rect\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m-> 2349\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgcf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtight_layout\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpad\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mh_pad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mh_pad\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mw_pad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mw_pad\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrect\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/matplotlib/figure.py:3543\u001b[0m, in \u001b[0;36mFigure.tight_layout\u001b[0;34m(self, pad, h_pad, w_pad, rect)\u001b[0m\n\u001b[1;32m 3541\u001b[0m previous_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_layout_engine()\n\u001b[1;32m 3542\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_layout_engine(engine)\n\u001b[0;32m-> 3543\u001b[0m \u001b[43mengine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3544\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m previous_engine \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\n\u001b[1;32m 3545\u001b[0m previous_engine, (TightLayoutEngine, PlaceHolderLayoutEngine)\n\u001b[1;32m 3546\u001b[0m ):\n\u001b[1;32m 3547\u001b[0m _api\u001b[38;5;241m.\u001b[39mwarn_external(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe figure layout has changed to tight\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "File \u001b[0;32m/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/matplotlib/layout_engine.py:184\u001b[0m, in \u001b[0;36mTightLayoutEngine.execute\u001b[0;34m(self, fig)\u001b[0m\n\u001b[1;32m 182\u001b[0m renderer \u001b[38;5;241m=\u001b[39m fig\u001b[38;5;241m.\u001b[39m_get_renderer()\n\u001b[1;32m 183\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(renderer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_draw_disabled\u001b[39m\u001b[38;5;124m\"\u001b[39m, nullcontext)():\n\u001b[0;32m--> 184\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m \u001b[43mget_tight_layout_figure\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 185\u001b[0m \u001b[43m \u001b[49m\u001b[43mfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mget_subplotspec_list\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maxes\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 186\u001b[0m \u001b[43m \u001b[49m\u001b[43mpad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minfo\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mpad\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mh_pad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minfo\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mh_pad\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mw_pad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minfo\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mw_pad\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 187\u001b[0m \u001b[43m \u001b[49m\u001b[43mrect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minfo\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mrect\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 188\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m kwargs:\n\u001b[1;32m 189\u001b[0m fig\u001b[38;5;241m.\u001b[39msubplots_adjust(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[0;32m/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/matplotlib/_tight_layout.py:266\u001b[0m, in \u001b[0;36mget_tight_layout_figure\u001b[0;34m(fig, axes_list, subplotspec_list, renderer, pad, h_pad, w_pad, rect)\u001b[0m\n\u001b[1;32m 261\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m {}\n\u001b[1;32m 262\u001b[0m span_pairs\u001b[38;5;241m.\u001b[39mappend((\n\u001b[1;32m 263\u001b[0m \u001b[38;5;28mslice\u001b[39m(ss\u001b[38;5;241m.\u001b[39mrowspan\u001b[38;5;241m.\u001b[39mstart \u001b[38;5;241m*\u001b[39m div_row, ss\u001b[38;5;241m.\u001b[39mrowspan\u001b[38;5;241m.\u001b[39mstop \u001b[38;5;241m*\u001b[39m div_row),\n\u001b[1;32m 264\u001b[0m \u001b[38;5;28mslice\u001b[39m(ss\u001b[38;5;241m.\u001b[39mcolspan\u001b[38;5;241m.\u001b[39mstart \u001b[38;5;241m*\u001b[39m div_col, ss\u001b[38;5;241m.\u001b[39mcolspan\u001b[38;5;241m.\u001b[39mstop \u001b[38;5;241m*\u001b[39m div_col)))\n\u001b[0;32m--> 266\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m \u001b[43m_auto_adjust_subplotpars\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 267\u001b[0m \u001b[43m \u001b[49m\u001b[43mshape\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mmax_nrows\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_ncols\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 268\u001b[0m \u001b[43m \u001b[49m\u001b[43mspan_pairs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mspan_pairs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 269\u001b[0m \u001b[43m \u001b[49m\u001b[43msubplot_list\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msubplot_list\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 270\u001b[0m \u001b[43m \u001b[49m\u001b[43max_bbox_list\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43max_bbox_list\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 271\u001b[0m \u001b[43m \u001b[49m\u001b[43mpad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpad\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mh_pad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mh_pad\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mw_pad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mw_pad\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 273\u001b[0m \u001b[38;5;66;03m# kwargs can be none if tight_layout fails...\u001b[39;00m\n\u001b[1;32m 274\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m rect \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m kwargs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 275\u001b[0m \u001b[38;5;66;03m# if rect is given, the whole subplots area (including\u001b[39;00m\n\u001b[1;32m 276\u001b[0m \u001b[38;5;66;03m# labels) will fit into the rect instead of the\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 280\u001b[0m \u001b[38;5;66;03m# auto_adjust_subplotpars twice, where the second run\u001b[39;00m\n\u001b[1;32m 281\u001b[0m \u001b[38;5;66;03m# with adjusted rect parameters.\u001b[39;00m\n", + "File \u001b[0;32m/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/matplotlib/_tight_layout.py:82\u001b[0m, in \u001b[0;36m_auto_adjust_subplotpars\u001b[0;34m(fig, renderer, shape, span_pairs, subplot_list, ax_bbox_list, pad, h_pad, w_pad, rect)\u001b[0m\n\u001b[1;32m 80\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ax \u001b[38;5;129;01min\u001b[39;00m subplots:\n\u001b[1;32m 81\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax\u001b[38;5;241m.\u001b[39mget_visible():\n\u001b[0;32m---> 82\u001b[0m bb \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m [\u001b[43mmartist\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_tightbbox_for_layout_only\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m]\n\u001b[1;32m 84\u001b[0m tight_bbox_raw \u001b[38;5;241m=\u001b[39m Bbox\u001b[38;5;241m.\u001b[39munion(bb)\n\u001b[1;32m 85\u001b[0m tight_bbox \u001b[38;5;241m=\u001b[39m fig\u001b[38;5;241m.\u001b[39mtransFigure\u001b[38;5;241m.\u001b[39minverted()\u001b[38;5;241m.\u001b[39mtransform_bbox(tight_bbox_raw)\n", + "File \u001b[0;32m/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/matplotlib/artist.py:1415\u001b[0m, in \u001b[0;36m_get_tightbbox_for_layout_only\u001b[0;34m(obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1409\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1410\u001b[0m \u001b[38;5;124;03mMatplotlib's `.Axes.get_tightbbox` and `.Axis.get_tightbbox` support a\u001b[39;00m\n\u001b[1;32m 1411\u001b[0m \u001b[38;5;124;03m*for_layout_only* kwarg; this helper tries to use the kwarg but skips it\u001b[39;00m\n\u001b[1;32m 1412\u001b[0m \u001b[38;5;124;03mwhen encountering third-party subclasses that do not support it.\u001b[39;00m\n\u001b[1;32m 1413\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1414\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1415\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_tightbbox\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfor_layout_only\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1416\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m 1417\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m obj\u001b[38;5;241m.\u001b[39mget_tightbbox(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[0;32m/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/matplotlib/axes/_base.py:4387\u001b[0m, in \u001b[0;36m_AxesBase.get_tightbbox\u001b[0;34m(self, renderer, call_axes_locator, bbox_extra_artists, for_layout_only)\u001b[0m\n\u001b[1;32m 4385\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m axis \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_axis_map\u001b[38;5;241m.\u001b[39mvalues():\n\u001b[1;32m 4386\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxison \u001b[38;5;129;01mand\u001b[39;00m axis\u001b[38;5;241m.\u001b[39mget_visible():\n\u001b[0;32m-> 4387\u001b[0m ba \u001b[38;5;241m=\u001b[39m \u001b[43mmartist\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_tightbbox_for_layout_only\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4388\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ba:\n\u001b[1;32m 4389\u001b[0m bb\u001b[38;5;241m.\u001b[39mappend(ba)\n", + "File \u001b[0;32m/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/matplotlib/artist.py:1415\u001b[0m, in \u001b[0;36m_get_tightbbox_for_layout_only\u001b[0;34m(obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1409\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1410\u001b[0m \u001b[38;5;124;03mMatplotlib's `.Axes.get_tightbbox` and `.Axis.get_tightbbox` support a\u001b[39;00m\n\u001b[1;32m 1411\u001b[0m \u001b[38;5;124;03m*for_layout_only* kwarg; this helper tries to use the kwarg but skips it\u001b[39;00m\n\u001b[1;32m 1412\u001b[0m \u001b[38;5;124;03mwhen encountering third-party subclasses that do not support it.\u001b[39;00m\n\u001b[1;32m 1413\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1414\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1415\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_tightbbox\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfor_layout_only\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1416\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m 1417\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m obj\u001b[38;5;241m.\u001b[39mget_tightbbox(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[0;32m/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/matplotlib/axis.py:1353\u001b[0m, in \u001b[0;36mAxis.get_tightbbox\u001b[0;34m(self, renderer, for_layout_only)\u001b[0m\n\u001b[1;32m 1351\u001b[0m \u001b[38;5;66;03m# take care of label\u001b[39;00m\n\u001b[1;32m 1352\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlabel\u001b[38;5;241m.\u001b[39mget_visible():\n\u001b[0;32m-> 1353\u001b[0m bb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlabel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_window_extent\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1354\u001b[0m \u001b[38;5;66;03m# for constrained/tight_layout, we want to ignore the label's\u001b[39;00m\n\u001b[1;32m 1355\u001b[0m \u001b[38;5;66;03m# width/height because the adjustments they make can't be improved.\u001b[39;00m\n\u001b[1;32m 1356\u001b[0m \u001b[38;5;66;03m# this code collapses the relevant direction\u001b[39;00m\n\u001b[1;32m 1357\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m for_layout_only:\n", + "File \u001b[0;32m/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/matplotlib/text.py:959\u001b[0m, in \u001b[0;36mText.get_window_extent\u001b[0;34m(self, renderer, dpi)\u001b[0m\n\u001b[1;32m 954\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 955\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot get window extent of text w/o renderer. You likely \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 956\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwant to call \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfigure.draw_without_rendering()\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m first.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 958\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m cbook\u001b[38;5;241m.\u001b[39m_setattr_cm(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure, dpi\u001b[38;5;241m=\u001b[39mdpi):\n\u001b[0;32m--> 959\u001b[0m bbox, info, descent \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_layout\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_renderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 960\u001b[0m x, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_unitless_position()\n\u001b[1;32m 961\u001b[0m x, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_transform()\u001b[38;5;241m.\u001b[39mtransform((x, y))\n", + "File \u001b[0;32m/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/matplotlib/text.py:507\u001b[0m, in \u001b[0;36mText._get_layout\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 504\u001b[0m xmin \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m=\u001b[39m offsetx\n\u001b[1;32m 505\u001b[0m ymin \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m=\u001b[39m offsety\n\u001b[0;32m--> 507\u001b[0m bbox \u001b[38;5;241m=\u001b[39m \u001b[43mBbox\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_bounds\u001b[49m\u001b[43m(\u001b[49m\u001b[43mxmin\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mymin\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwidth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheight\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 509\u001b[0m \u001b[38;5;66;03m# now rotate the positions around the first (x, y) position\u001b[39;00m\n\u001b[1;32m 510\u001b[0m xys \u001b[38;5;241m=\u001b[39m M\u001b[38;5;241m.\u001b[39mtransform(offset_layout) \u001b[38;5;241m-\u001b[39m (offsetx, offsety)\n", + "File \u001b[0;32m/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/matplotlib/transforms.py:807\u001b[0m, in \u001b[0;36mBbox.from_bounds\u001b[0;34m(x0, y0, width, height)\u001b[0m\n\u001b[1;32m 800\u001b[0m \u001b[38;5;129m@staticmethod\u001b[39m\n\u001b[1;32m 801\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfrom_bounds\u001b[39m(x0, y0, width, height):\n\u001b[1;32m 802\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 803\u001b[0m \u001b[38;5;124;03m Create a new `Bbox` from *x0*, *y0*, *width* and *height*.\u001b[39;00m\n\u001b[1;32m 804\u001b[0m \n\u001b[1;32m 805\u001b[0m \u001b[38;5;124;03m *width* and *height* may be negative.\u001b[39;00m\n\u001b[1;32m 806\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 807\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mBbox\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_extents\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx0\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mwidth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my0\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mheight\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/matplotlib/transforms.py:826\u001b[0m, in \u001b[0;36mBbox.from_extents\u001b[0;34m(minpos, *args)\u001b[0m\n\u001b[1;32m 809\u001b[0m \u001b[38;5;129m@staticmethod\u001b[39m\n\u001b[1;32m 810\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfrom_extents\u001b[39m(\u001b[38;5;241m*\u001b[39margs, minpos\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 811\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 812\u001b[0m \u001b[38;5;124;03m Create a new Bbox from *left*, *bottom*, *right* and *top*.\u001b[39;00m\n\u001b[1;32m 813\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 824\u001b[0m \u001b[38;5;124;03m scales where negative bounds result in floating point errors.\u001b[39;00m\n\u001b[1;32m 825\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 826\u001b[0m bbox \u001b[38;5;241m=\u001b[39m Bbox(\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreshape\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 827\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m minpos \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 828\u001b[0m bbox\u001b[38;5;241m.\u001b[39m_minpos[:] \u001b[38;5;241m=\u001b[39m minpos\n", + "File \u001b[0;32m/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/numpy/core/fromnumeric.py:285\u001b[0m, in \u001b[0;36mreshape\u001b[0;34m(a, newshape, order)\u001b[0m\n\u001b[1;32m 200\u001b[0m \u001b[38;5;129m@array_function_dispatch\u001b[39m(_reshape_dispatcher)\n\u001b[1;32m 201\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mreshape\u001b[39m(a, newshape, order\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mC\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m 202\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 203\u001b[0m \u001b[38;5;124;03m Gives a new shape to an array without changing its data.\u001b[39;00m\n\u001b[1;32m 204\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 283\u001b[0m \u001b[38;5;124;03m [5, 6]])\u001b[39;00m\n\u001b[1;32m 284\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 285\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_wrapfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mreshape\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnewshape\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morder\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/numpy/core/fromnumeric.py:56\u001b[0m, in \u001b[0;36m_wrapfunc\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m 54\u001b[0m bound \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(obj, method, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m 55\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bound \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m---> 56\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_wrapit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 58\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 59\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m bound(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n", + "File \u001b[0;32m/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/numpy/core/fromnumeric.py:42\u001b[0m, in \u001b[0;36m_wrapit\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_wrapit\u001b[39m(obj, method, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds):\n\u001b[1;32m 41\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 42\u001b[0m wrap \u001b[38;5;241m=\u001b[39m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__array_wrap__\u001b[49m\n\u001b[1;32m 43\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m:\n\u001b[1;32m 44\u001b[0m wrap \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for key, values_dict in values_results.items():\n", + " if task_results[key] == 'classification':\n", + " metrics = ['AUROC', 'AUPRC', 'R^2', 'F1', 'Accuracy']\n", + " else:\n", + " metrics = ['R^2', 'RMSE']\n", + " # print \"Results for \" + text of key after first underscore\n", + " parts = key.split('_')\n", + " print(f\"Results for dataset {parts[1]} from datasource {parts[0]} with seed {parts[-1]}.\")\n", + " # create new plot\n", + " if task_results[key] == 'classification':\n", + " height = 15\n", + " else:\n", + " height = 5\n", + " fig, axes = plt.subplots(math.ceil(len(metrics)/2.0), 2, figsize=(10, height))\n", + " axes = axes.flatten()\n", + " plot_count = 0\n", + " for metric in metrics:\n", + " ax = axes[plot_count]\n", + " # plt.figure()\n", + " for method, df in values_dict.items():\n", + " # plt.plot(df['nclust'], df[metric])\n", + " ax.plot(df['nclust'], df[metric])\n", + " ax.legend(list(values_dict.keys()))\n", + " ax.set_xlabel('Number of Clusters')\n", + " ax.set_ylabel('Cluster ' + metric)\n", + " ax.set_title('Cluster ' + metric + ' vs Number of Clusters')\n", + " ax.invert_xaxis()\n", + " plot_count += 1\n", + " plt.tight_layout()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for key, rankings_dict in rankings_results.items():\n", + " if task_results[key] == 'classification':\n", + " metrics = ['AUROC', 'AUPRC', 'R^2', 'F1', 'Accuracy']\n", + " else:\n", + " metrics = ['R^2', 'RMSE']\n", + " # print \"Results for \" + text of key after first underscore\n", + " parts = key.split('_')\n", + " print(f\"Results for dataset {parts[1]} from datasource {parts[0]} with seed {parts[-1]}.\")\n", + " for p_value in p_values:\n", + " print(f\"Results for RBO Matrix with parameter p = {p_value}.\")\n", + " # create new plot\n", + " if task_results[key] == 'classification':\n", + " height = 15\n", + " else:\n", + " height = 5\n", + " fig, axes = plt.subplots(math.ceil(len(metrics)/2.0), 2, figsize=(10, height))\n", + " axes = axes.flatten()\n", + " plot_count = 0\n", + " for metric in metrics:\n", + " # create new plot\n", + " ax = axes[plot_count]\n", + " method_list = []\n", + " for method, df in rankings_dict.items():\n", + " if method.endswith(p_value):\n", + " ax.plot(df['nclust'], df[metric])\n", + " method_list.append(method)\n", + " ax.legend(method_list)\n", + " ax.set_xlabel('Number of Clusters')\n", + " ax.set_ylabel('Cluster ' + metric)\n", + " ax.set_title('Cluster ' + metric + ' vs Number of Clusters')\n", + " ax.invert_xaxis()\n", + " plot_count += 1\n", + " plt.tight_layout()\n", + " plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mdi", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/feature_importance/subgroup/legacy/agglomerative_runner.sh b/feature_importance/subgroup/legacy/agglomerative_runner.sh new file mode 100644 index 0000000..2d09ad9 --- /dev/null +++ b/feature_importance/subgroup/legacy/agglomerative_runner.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +slurm_script="agglomerative_subgroups.sh" + +for rep in {1..20} +do + sbatch $slurm_script $rep # Submit SLURM job using the specified script +done \ No newline at end of file diff --git a/feature_importance/subgroup/legacy/agglomerative_subgroup.ipynb b/feature_importance/subgroup/legacy/agglomerative_subgroup.ipynb new file mode 100644 index 0000000..adced91 --- /dev/null +++ b/feature_importance/subgroup/legacy/agglomerative_subgroup.ipynb @@ -0,0 +1,906 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import math\n", + "from ucimlrepo import fetch_ucirepo\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def get_parkinsons_dataset():\n", + " # fetch dataset \n", + " parkinsons = fetch_ucirepo(id=320) \n", + " \n", + " # data (as pandas dataframes) \n", + " X = parkinsons.data.features \n", + " y = parkinsons.data.targets\n", + " cols = X.columns\n", + " \n", + " return X, y, cols" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "X, y, cols = get_parkinsons_dataset()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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shap0.978722
signed_normalized_l2_avg0.967216
signed_normalized_l2_noavg0.965957
signed_nonnormalized_l2_avg0.963637
signed_nonnormalized_l2_noavg0.964239
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nonl2_noavg0.974559
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nonl2_ranking0.768420
baseline0.955294
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" + ], + "text/plain": [ + " R^2\n", + "shap 0.978722\n", + "signed_normalized_l2_avg 0.967216\n", + "signed_normalized_l2_noavg 0.965957\n", + "signed_nonnormalized_l2_avg 0.963637\n", + "signed_nonnormalized_l2_noavg 0.964239\n", + "nonl2_avg 0.972153\n", + "nonl2_noavg 0.974559\n", + "l2_ranking 0.786176\n", + "nonl2_ranking 0.768420\n", + "baseline 0.955294" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "r2_aucs = {}\n", + "for method in methods:\n", + " aucs_per_seed = []\n", + " for s in seed:\n", + " aucs_per_seed.append(np.trapz(values_results[\"function\" + \"_\" + \"parkinsons\"][method].loc[:, \"R^2\"])/values_results[\"function\" + \"_\" + \"parkinsons\"][method].shape[0])\n", + " aucs_per_seed = np.array(aucs_per_seed)\n", + " r2_aucs[method] = aucs_per_seed.mean()\n", + "r2_aucs = pd.DataFrame(r2_aucs, index=[0]).T\n", + "r2_aucs.columns = [\"R^2\"]\n", + "r2_aucs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Results for Subgroup Experiments on Raw Feature Importance Scores" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results for dataset diabetes from datasource imodels.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results for dataset 183 from datasource openml.\n" + ] + }, + { + "ename": "KeyError", + "evalue": "'RMSE'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/pandas/core/indexes/base.py:3791\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3790\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3791\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3792\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", + "File \u001b[0;32mindex.pyx:152\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mindex.pyx:181\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7080\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7088\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'RMSE'", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[11], line 22\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;66;03m# plt.figure()\u001b[39;00m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m method, df \u001b[38;5;129;01min\u001b[39;00m values_dict\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m 21\u001b[0m \u001b[38;5;66;03m# plt.plot(df['nclust'], df[metric])\u001b[39;00m\n\u001b[0;32m---> 22\u001b[0m ax\u001b[38;5;241m.\u001b[39mplot(df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnclust\u001b[39m\u001b[38;5;124m'\u001b[39m], \u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[43mmetric\u001b[49m\u001b[43m]\u001b[49m)\n\u001b[1;32m 23\u001b[0m ax\u001b[38;5;241m.\u001b[39mlegend(\u001b[38;5;28mlist\u001b[39m(values_dict\u001b[38;5;241m.\u001b[39mkeys()))\n\u001b[1;32m 24\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_xlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNumber of Clusters\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "File \u001b[0;32m/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/pandas/core/frame.py:3893\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3891\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 3892\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 3893\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3894\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m 3895\u001b[0m indexer \u001b[38;5;241m=\u001b[39m [indexer]\n", + "File \u001b[0;32m/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/pandas/core/indexes/base.py:3798\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3793\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[1;32m 3794\u001b[0m \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[1;32m 3795\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[1;32m 3796\u001b[0m ):\n\u001b[1;32m 3797\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[0;32m-> 3798\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 3799\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m 3800\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m 3801\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m 3802\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n\u001b[1;32m 3803\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n", + "\u001b[0;31mKeyError\u001b[0m: 'RMSE'" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for key, values_dict in values_results.items():\n", + " if task_results[key] == 'classification':\n", + " metrics = ['AUROC', 'AUPRC', 'R^2', 'F1', 'Accuracy']\n", + " else:\n", + " metrics = ['R^2', 'RMSE']\n", + " # print \"Results for \" + text of key after first underscore\n", + " parts = key.split('_')\n", + " print(f\"Results for dataset {parts[1]} from datasource {parts[0]}.\")\n", + " # create new plot\n", + " if task_results[key] == 'classification':\n", + " height = 15\n", + " else:\n", + " height = 5\n", + " fig, axes = plt.subplots(math.ceil(len(metrics)/2.0), 2, figsize=(10, height))\n", + " axes = axes.flatten()\n", + " plot_count = 0\n", + " for metric in metrics:\n", + " ax = axes[plot_count]\n", + " # plt.figure()\n", + " for method, df in values_dict.items():\n", + " # plt.plot(df['nclust'], df[metric])\n", + " ax.plot(df['nclust'], df[metric])\n", + " ax.legend(list(values_dict.keys()))\n", + " ax.set_xlabel('Number of Clusters')\n", + " ax.set_ylabel('Cluster ' + metric)\n", + " ax.set_title('Cluster ' + metric + ' vs Number of Clusters')\n", + " ax.invert_xaxis()\n", + " plot_count += 1\n", + " plt.tight_layout()\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Results for Subgroup Experiments on Feature Rankings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results for dataset diabetes from datasource imodels.\n", + "Results for RBO Matrix with parameter p = 0.1.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results for RBO Matrix with parameter p = 0.3.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results for RBO Matrix with parameter p = 0.5.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results for RBO Matrix with parameter p = 0.7.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results for RBO Matrix with parameter p = 0.9.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results for dataset 183 from datasource openml.\n", + "Results for RBO Matrix with parameter p = 0.1.\n" + ] + }, + { + "data": { + "image/png": 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0L3+PJSUlARTYtPVVy+sjs3v3brKzs4u17ePHj/nzzz/p3r27/KT54cOHPHr0CB8fH65du6bWTAjg008/RVtbW+M8pkyZgrW1NXZ2dnz44YdERkYyb96819Z/KygoiN9++40vv/wSFxeXItOamZnRtm1bNm7cqNYca8OGDXzwwQdyTWneOd6xYwdKpfKFy/btt9+Sk5PD7NmzX3gfz2rZsqVajWbDhg0BVQ3o09df3vIbN26oba+jo8OQIUPk93p6egwZMoT4+HjCw8MBVY2Fm5sb1apVk6+Rhw8f8tFHHwFw6NAhtX02a9aM6tWrP7fsBw8eJCsri1GjRqk9vf/0008xMzN7oX5bpfHde5HrIiIigmvXrtG7d28ePXokn9PU1FRatmzJkSNH8u1z6NChau8NDQ3R09MjNDQ0XzNBQRBenLiHq6tRowa1atVi3bp1gOo+6+fnV2gt9sqVK1m8eDFOTk5s27aNsWPH4ubmRsuWLfOVB2Dy5MkEBwfnez3bdP1pCoWCjz/+mL1795KSkiIv37BhA+XLl5e74LzM+X3ayJEjKVOmjDyw1qtQvXp1tcHi8u7TH330kVpLrcLu36BqZp8nr3tXVlYWBw8eBFT3b3Nzc1q1aqV2//b09MTExCTf/dvJyQkfH5/nlv3MmTPEx8czbNgwtTFb2rdvT7Vq1d6a+3dpfu8sLCwICwt7brdD4flE0P0eMzMzA1RNpl63Zs2a0bVrV6ZNm0bZsmXx8/Nj5cqV+fraFiQ6OhpJkpg0aRLW1tZqr7w+tHkDYORxcnIqVvkGDx5McHAwu3btYvTo0aSnp7+26ZqOHj3KwIED8fHx4bvvvtNomx49enD79m15GrPr168THh5Ojx491NI0btyYQYMGYWtrS8+ePdm4cWOxAy1nZ2c++eQTli1bRlxcXLG2LcyzTajNzc0BqFixYoHLnw3OypUrl29Aj6pVqwL/dQW4du0aly5dyneN5KV70Wvk1q1bAPmar+np6eHs7CyvL46S/O69zHVx7do1AAICAvKd1+XLl5OZmUliYqLaNs+eV319fb7//nv++OMPbG1tadq0KT/88IPcvUIQhBcj7uH59e7dm02bNhEdHc2JEyfo3bt3oWm1tLQYPnw44eHhPHz4kB07dtC2bVv+/PNPevbsmS+9u7s73t7e+V56enpFlqlHjx6kp6fLTb5TUlLYu3cvH3/8sdws/WXO79PMzc0ZNWoUO3fuLLR/dXG97P1bS0sr37g0Bd2/ExMTsbGxyXeNpKSkvPL7N0C1atXe+Pv3m/C9++GHH7h48SIVK1akQYMGTJ06tcAHK8LziT7d7zEzMzPKlSv3UiMkF9aP6dmANW+e51OnTrFr1y7279/PgAEDmDdvHqdOnco3f+bT8oKDsWPHFvpk89npmYo7iqWLi4s8RUSHDh3Q1tbm66+/pkWLFmoDlb2s8+fP07FjR2rWrMnmzZvR0dHsK+jr64uRkREbN26kUaNGbNy4ES0tLbU+YoaGhhw5coRDhw6xZ88e9u3bx4YNG/joo484cOBAsWoNvvnmG37//Xe+//57OnXqlG+9pp97nsLyLmz50zX6mlIqlbi7uzN//vwC1z/7A6E0p2mrUqUKOjo68mByL0LTz+Blrou8796cOXMKnUrs2e9uQed11KhR+Pr6sn37dvbv38+kSZOYNWsWf/75J3Xq1CnqMAVBKIS4h+fXq1cvJkyYwKeffoqVlRWtW7fWaDsrKys6duxIx44dad68OYcPH+bWrVty3++X8cEHH+Do6MjGjRvp3bs3u3btIj09Xe2h+cuc32fl9e2eNm0agYGB+da/qfdvGxsb1q5dW+D6vMHC8pTm/ft9+951796dDz/8kG3btnHgwAHmzJnD999/z9atW2nbtm2RxymoEzXd77kOHTpw/fp1uQa1uPIGGnt6FGeg0KeHH3zwAd999x1nzpxh7dq1XLp0ifXr1wOF/xHKe0Kqq6tb4FNmb2/vV97M55tvvsHU1DTfnJ8v4/r167Rp0wYbGxv27t1brJuosbExHTp0YNOmTSiVSjZs2MCHH36Ybz5tLS0tWrZsyfz587l8+TLfffcdf/75Z76mWc9TuXJl+vTpw9KlSwus7S5Tpky+zxwK/9xf1t27d/MNpnP16lUAudl65cqVefz4MS1btizwGilqoJWi5P3oioqKUluelZVFTEzMC/0oMzIy4qOPPuLIkSPcvn37hcpVnM/geddFYd+9vIFkzMzMCv3uPT24TVEqV67Ml19+yYEDB7h48SJZWVnMmzdPw6MVBKEg4h6urlKlSjRu3JjQ0FA+/vhjjR9sPy3vQfuraukFqsBl3759JCUlsWHDBhwdHfPNAw5Fn19N5dV279ixo8Da7uJ+5i9LqVTmqxkt6P796NEjGjduXOD18fQ0cMVR2P07b9mLPlR537539vb2DBs2jO3btxMTE4OVlZXGLTWF/4ig+z03btw4jI2NGTRoEPfv38+3/vr16yxcuLDQ7fN+lB85ckRelpqayurVq9XSPXnyJN/Tz7yas7xmMnn9rp79I2RjY0Pz5s0LDQALmk/zZVlYWDBkyBD2799PRETES+/v3r17tG7dGi0tLfbv35/vqa0mevTowd27d1m+fDnnz59Xe0oOqv47z3r2HBfHt99+S3Z2Nj/88EO+dZUrVyYxMZG///5bXhYXF8e2bduKnY8mcnJy1KYEycrKYunSpVhbW+Pp6QmoftT8888//Prrr/m2T09Pf6FR7AG5+eCPP/6odg3/9ttvJCYm0r59+xfa75QpU5AkiU8++UStr1+e8PDwfN+jp2n6GWhyXeQ13X/2u+fp6UnlypWZO3dugWXU5LuXlpZGRkZGvrKbmpq+0HUpCMJ/xD08v//9739MmTKFL774otA09+7d4/Lly/mWZ2VlERISgpaWVr5awJfRo0cPMjMzWb16Nfv27aN79+5q6zU5v8UxatQoLCws1EYHz6PpZ/4qLV68WP6/JEksXrwYXV1dWrZsCaju37m5ucyYMSPftjk5OQU+YNZEvXr1sLGxYcmSJWrn8Y8//iAyMvKF79/vy/cuNzc3XxcyGxsbypUrJ+7fL0A0L3/PVa5cmaCgIHr06IGbmxt9+/alZs2aZGVlceLECTZt2lTkXIWtW7emUqVKDBw4kK+++gptbW1WrFiBtbU1sbGxcrrVq1fz888/07lzZypXrkxycjK//vorZmZmtGvXDlA1a6levTobNmygatWqWFpaUrNmTWrWrMlPP/1EkyZNcHd359NPP8XZ2Zn79+9z8uRJ7ty5w/nz51/5uRk5ciSBgYHMnj272E+an9WmTRtu3LjBuHHjOHbsGMeOHZPX2dra0qpVq+fuo127dpiamjJ27Fi0tbXp2rWr2vrp06dz5MgR2rdvj4ODA/Hx8fz8889UqFBBHqylOPJquwu6Effs2ZPx48fTuXNnRowYQVpaGr/88gtVq1Z9LXM3litXju+//56bN29StWpVNmzYQEREBMuWLZNrWj/55BM2btzI0KFDOXToEI0bNyY3N5crV66wceNGeU7P4rK2tmbChAlMmzaNNm3a0LFjR6Kiovj555+pX78+ffr0eaFjatSoET/99BPDhg2jWrVqfPLJJ7i4uJCcnExoaCg7d+7kf//7X6Hba/oZaHJdVK5cGQsLC5YsWYKpqSnGxsY0bNgQJycnli9fTtu2balRowb9+/enfPny/PPPPxw6dAgzMzN27dpV5HFevXqVli1b0r17d6pXr46Ojg7btm3j/v37BfabFARBc+Ienl+zZs1o1qxZkWnu3LlDgwYN+Oijj2jZsiV2dnbEx8ezbt06zp8/z6hRo/JN53n06NF8DxBBNT1TYdND5qlbty5VqlThm2++ITMzM99Dc03Ob3GYm5szcuTIAgdU0/Qzf1UMDAzYt28fAQEBNGzYkD/++IM9e/YwceJEuQKiWbNmDBkyhFmzZhEREUHr1q3R1dXl2rVrbNq0iYULF77QwLa6urp8//339O/fn2bNmtGrVy95yjBHR0dGjx79Qsf0vnzvkpOTqVChAt26dcPDwwMTExMOHjzI6dOnRUu1F1EqY6YLb5yrV69Kn376qeTo6Cjp6elJpqamUuPGjaVFixapTTXw7LQHkiRJ4eHhUsOGDSU9PT2pUqVK0vz58/NNe3D27FmpV69eUqVKlSR9fX3JxsZG6tChg3TmzBm1fZ04cULy9PSU9PT08k2BcP36dalv376SnZ2dpKurK5UvX17q0KGDtHnzZjmNplM+5CloOo2n9evXT9LW1paio6M12l9hKGK6kWbNmmm8H39/fwmQvL29860LCQmR/Pz8pHLlykl6enpSuXLlpF69ej13SjJJUp8y7GnXrl2TtLW1C5w+7cCBA1LNmjUlPT09ydXVVfq///u/QqcMe3a6icLOe0HTk+VNh3LmzBnJy8tLMjAwkBwcHKTFixfnK29WVpb0/fffSzVq1JD09fWlMmXKSJ6entK0adOkxMTEIsv0PIsXL5aqVasm6erqSra2ttJnn30mPXnyRC2NplOGPS08PFzq3bu3VK5cOUlXV1cqU6aM1LJlS2n16tVqU5Q9+32QJM0+A02vix07dkjVq1eXdHR08k0fdu7cOalLly6SlZWVpK+vLzk4OEjdu3eXQkJCnnvsDx8+lIYPHy5Vq1ZNMjY2lszNzaWGDRtKGzdu1PgcCYJQNHEPL3pKwmenDEtKSpIWLlwo+fj4SBUqVJB0dXUlU1NTycvLS/r111/Vpkh83pRhBU3VVJBvvvlGAqQqVarkW6fp+S3I01OGPe3JkyeSubl5gedHk89ckgr/baDpfT3vvF+/fl1q3bq1ZGRkJNna2kpTpkxRu7/lWbZsmeTp6SkZGhpKpqamkru7uzRu3Dh5CtOiylSUDRs2SHXq1JH09fUlS0tLyd/fX7pz545amuJee5L07n/vMjMzpa+++kry8PCQTE1NJWNjY8nDw0P6+eefNT5Hwn8UkvQCIx4IgiAIgiAIgiAIgvBcok+3IAiCIAiCIAiCILwmIugWBEEQBEEQBEEQhNdEBN2CIAiCIAiCIAiC8JqIoFsQBEEQBEEQBEEQXhMRdAuCIAiCIAiCIAjCayKCbkEQBEEQBEEQBEF4TXRKuwAlTalUcvfuXUxNTVEoFKVdHEEQBOE9J0kSycnJlCtXDi0t8Sy8KOIeLgiCILxJNL2Hv3dB9927d6lYsWJpF0MQBEEQ1Ny+fZsKFSqUdjHeaOIeLgiCILyJnncPf++CblNTU0B1YszMzEq5NIIgCML7LikpiYoVK8r3J6Fw4h4uCIIgvEk0vYe/d0F3XnM0MzMzccMWBEEQ3hiiufTziXu4IAiC8CZ63j1cdB4TBEEQBEEQBEEQhNdEBN2CIAiCIAiCIAiC8JqIoFsQBEEQBEEQBEEQXpP3rk+3IAiCIAjvttzcXLKzs0u7GIIgCBrR1dVFW1u7tIshvEYi6BYEQRAE4Z0gSRL37t0jISGhtIsiCIJQLBYWFtjZ2YlBNd9RIugWBEEQBOGdkBdw29jYYGRkJH68CoLwxpMkibS0NOLj4wGwt7cv5RIJr4MIugVBEARBeOvl5ubKAbeVlVVpF0cQBEFjhoaGAMTHx2NjYyOamr+DxEBqgiAIgiC89fL6cBsZGZVySQRBEIov72+XGI/i3SSCbkEQBEEQ3hmiSbkgCG8j8bfr3SaCbkEQBEEQBEEQBEF4TUTQLQiCIAiC8Abq168fnTp1Ku1iCIIgCC9JBN2CIAiCIAhCof7++28+/PBDDAwMqFixIj/88MNztxkxYgSenp7o6+tTu3bt119IQRCEN5gIugVBEARBEIQCJSUl0bp1axwcHAgPD2fOnDlMnTqVZcuWPXfbAQMG0KNHjxIopSAIwptNBN2CIAiCIAilaPPmzbi7u2NoaIiVlRXe3t6kpqbK6+fOnYu9vT1WVlYMHz5cbXTj33//nXr16mFqaoqdnR29e/eW5/sFCA0NRaFQsGfPHmrVqoWBgQEffPABFy9e1Khsa9euJSsrixUrVlCjRg169uzJiBEjmD9/fpHb/fjjjwwfPhxnZ+ding1BEIR3jwi6X0JmZiahoaFkZmaWdlEEQRAEQXiGJEmkZeWU+EuSJI3LGBcXR69evRgwYACRkZGEhobSpUsXeR+HDh3i+vXrHDp0iNWrV7Nq1SpWrVolb5+dnc2MGTM4f/4827dv5+bNm/Tr1y9fPl999RXz5s3j9OnTWFtb4+vrq9HURCdPnqRp06bo6enJy3x8fIiKiuLJkycaH6cgCMKb4HzIn6z+6jvWTphZovnqlGhu75igoCBu3brFxYsX+fDDD6lcuTImJialXSxBEARBEID07FyqT95f4vlenu6DkZ5mP7Hi4uLIycmhS5cuODg4AODu7i6vL1OmDIsXL0ZbW5tq1arRvn17QkJC+PTTTwFVE+48zs7O/Pjjj9SvX5+UlBS13yRTpkyhVatWAKxevZoKFSqwbds2unfvXmT57t27h5OTk9oyW1tbeV2ZMmU0Ok5BEIQ3QdTxcGKMszFS6pZoviLofgmNGzfm1q1bPHz4kG3btgFgZmaGlZUV1atXp3bt2ujqluwHKgiCIAjC28PDw4OWLVvi7u6Oj48PrVu3plu3bnIwW6NGDbS1teX09vb2XLhwQX4fHh7O1KlTOX/+PE+ePEGpVAIQGxtL9erV5XReXl7y/y0tLXF1dSUyMvJ1H54gCMIbJScrB3RATyrZMFgE3S+hatWq9O7dm+joaCIjI0lOTiYpKYmkpCRiYmLIzs6mUaNGpV1MQRAEQXgvGepqc3m6T6nkqyltbW2Cg4M5ceIEBw4cYNGiRXzzzTeEhYUB5Ht4r1Ao5MA6NTUVHx8ffHx8WLt2LdbW1sTGxuLj40NWVtYrORY7Ozvu37+vtizvvZ2d3SvJQxAEoaRk5ShVQbdS87/Tr4IIul9S1apVqVq1Ku3atSMxMZEnT56wc+dOHj9+zIEDB7h48SJeXl5qTcUEQRAEQXj9FAqFxs28S5NCoaBx48Y0btyYyZMn4+DgILegK8qVK1d49OgRs2fPpmLFigCcOXOmwLSnTp2iUqVKADx58oSrV6/i5ub23Dy8vLz45ptvyM7Olh8ABAcH4+rqKpqWC4Lw1sn9d7wMXalkhzYTA6m9Qubm5jg6OtK3b190dFQ3+bt377Jt2zbOnDnDo0ePSrmEgiAIgiC8ScLCwpg5cyZnzpwhNjaWrVu38uDBA40C4kqVKqGnp8eiRYu4ceMGO3fuZMaMGQWmnT59OiEhIVy8eJF+/fpRtmxZOnXq9Nw8evfujZ6eHgMHDuTSpUts2LCBhQsXMmbMGDnNtm3bqFatmtp20dHRREREcO/ePdLT04mIiCAiIuKV1cALgiC8iCzFv0G3smTD4Df/8e9byMLCgjFjxpCQkMDevXu5c+cOu3fvBsDa2poaNWrQuHFj0d9bEARBEN5zZmZmHDlyhMDAQJKSknBwcGDevHm0bduWDRs2FLmttbU1q1atYuLEifz444/UrVuXuXPn0rFjx3xpZ8+ezciRI7l27Rq1a9dm165daiOSF8bc3JwDBw4wfPhwPD09KVu2LJMnT2bw4MFymsTERKKiotS2GzRoEIcPH5bf16lTB4CYmBgcHR2fm68gCMKrlJuby/bt27lvkAuAjlJRovkrpOLMa/EOSEpKwtzcnMTERMzMzF57fllZWRw8eFDu852nTp06+Pn5vfb8BUEQhDdbSd+X3mZFnauMjAxiYmJwcnLCwMCglEr45gkNDaVFixY8efIECwuL0i6OIAiFEH/DXq+7d++ybNkyAEwkA2omWtImcPBztno+Te/hoqb7NdPT06Ndu3a0a9eOu3fvcujQIa5du8a5c+fQ1tambt26mJubY2hoiJaWaO0vCIIgCIIgCILwKuXm5sr/757ZiJvZ0SWavwi6S1C5cuXo2bMnq1evJjY2ljNnzsgDnujr61OhQgV69uwpmp0LgiAIglAi2rZty9GjRwtcN3HiRCZOnFjCJRIEQXj1UjKyATDI0UILBbnkPmeLV0tUrZYwbW1tBgwYwCeffIKTkxP6+voAZGZmcv36dS5fvlzKJRQEQRDed7/88gu1atXCzMwMMzMzvLy8+OOPP4rcZtOmTVSrVg0DAwPc3d3Zu3ev2npJkpg8eTL29vYYGhri7e3NtWvXXudhCEDz5s2RJKnQpuXLly+XBzl79jV06NCSLawgCMJrcj1e1c1XW6EKf3OlnBLNX9R0l5LKlStTuXJlALKzs/n999+JjY1l27Zt3Lt3j48++kjUeAuCIAilokKFCsyePRsXFxckSWL16tX4+flx7tw5atSokS/9iRMn6NWrF7NmzaJDhw4EBQXRqVMnzp49S82aNQH44Ycf+PHHH1m9ejVOTk5MmjQJHx8fLl++LPovlqLy5cuXdhEEQRBeu+QnaQDoKlQVnqKm+z2kq6tLt27d5PkuT548ycaNG0u5VIIgCML7ytfXl3bt2uHi4kLVqlX57rvvMDEx4dSpUwWmX7hwIW3atOGrr77Czc2NGTNmULduXRYvXgyoarkDAwP59ttv8fPzo1atWqxZs4a7d++yffv2EjwyQRAE4X2UczceAAUQlxbDP5n/lGj+Iuh+Q5iZmfHZZ5/x0UcfAXDt2jXu379fyqUSBEEQ3ne5ubmsX7+e1NRUvLy8Ckxz8uRJvL291Zb5+Phw8uRJQDVN1L1799TSmJub07BhQzlNQTIzM0lKSlJ7CYIgCEJxpGflkhJ1CQClMocj9zeTamxUomUQQfcbRE9Pj6ZNm+Lq6grAjh07uHDhAhkZGaVcMkEQBOF9c+HCBUxMTNDX12fo0KFs27aN6tWrF5j23r172Nraqi2ztbXl3r178vq8ZYWlKcisWbMwNzeXXxUrVnyZQxIEQRDeQ5N3XEQn+78+3PoWw8hxdCnRMoig+w30wQcfAKr55LZs2UJgYCBpaWmlXCpBEAThfeLq6kpERARhYWF89tlnBAQElPhgnxMmTCAxMVF+3b59u0TzFwRBEN5+f918jH7eG0lCodBDS0dRomUo9aD7p59+wtHREQMDAxo2bMhff/1VZPrAwEBcXV0xNDSkYsWKjB49+p2rCXZycqJPnz54eHgAkJGRwfXr10u5VIIgCML7RE9PjypVquDp6cmsWbPw8PBg4cKFBaa1s7PL1yXq/v372NnZyevzlhWWpiD6+vryCOp5L0EQBEEojofJmejKYa8EgJbWexR0b9iwgTFjxjBlyhTOnj2Lh4cHPj4+xMfHF5g+KCiIr7/+milTphAZGclvv/3Ghg0b3sk5JKtUqULnzp2pU6cOAFu2bOHBgwelXCpBEAThfaVUKsnMzCxwnZeXFyEhIWrLgoOD5T7gTk5O2NnZqaVJSkoiLCys0H7iAvTr149OnTqVdjGAN6ssr4OjoyOBgYHye4VC8VoH+QsNDUWhUJCQkPDctKtWrSp0yjdBEIqWlpVDalYuOgpt1QJVzI2WdsmGwaUadM+fP59PP/2U/v37U716dZYsWYKRkRErVqwoMP2JEydo3LgxvXv3xtHRkdatW9OrV6/n1o6/zRo0aCDP5f3TTz8RHh5eyiUSBEEQ3nUTJkzgyJEj3Lx5kwsXLjBhwgRCQ0Px9/cHoG/fvkyYMEFOP3LkSPbt28e8efO4cuUKU6dO5cyZM3z++eeAKoAZNWoU//vf/9i5cycXLlygb9++lCtX7p0O5F7WwoULWbVqVWkX470UFxdH27ZtS7sYBdq6dSutWrXC2toaMzMzvLy82L9/f2kXq1gyMjIYPnw4VlZWmJiY0LVr1+cOILx161Zat26NlZUVCoWCiIiIkims8NaKT85g/oGrOKOFrbGqD7fy36hb+31pXp6VlUV4eLjaSKZaWlp4e3sXOpJpo0aNCA8Pl4PsGzdusHfvXtq1a1doPm/7yKf29vb0798fHR3VlOq7du3i6tWrpVwqQRAE4V0WHx9P3759cXV1pWXLlpw+fZr9+/fTqlUrAGJjY4mLi5PTN2rUiKCgIJYtW4aHhwebN29m+/bt8hzdAOPGjeOLL75g8ODB1K9fn5SUFPbt2yfm6C6Cubm5qOH8V1ZWVonmZ2dnJ1d6vGmOHDlCq1at2Lt3L+Hh4bRo0QJfX1/OnTtX2kXT2OjRo9m1axebNm3i8OHD3L17ly5duhS5TWpqKk2aNOH7778voVIKb6vFf16j9vQDNPguhOXHYmiOLtK/wXa2UvW3xNHCoUTLVGpB98OHD8nNzS3WSKa9e/dm+vTpNGnSBF1dXSpXrkzz5s2LbF7+Lox8amdnx9dff025cuUAVTP7oKAgMbiaIAiC8Fr89ttv3Lx5k8zMTOLj4zl48KAccIOqaeyzNbAff/wxUVFRZGZmcvHixXwPxBUKBdOnT+fevXtkZGRw8OBBqlatWhKH88bbvHkz7u7uGBoaYmVlhbe3N6mpqfmadCcnJ+Pv74+xsTH29vYsWLCA5s2bM2rUKDmNo6MjM2fOZMCAAZiamlKpUiWWLVumlt/t27fp3r07FhYWWFpa4ufnx82bN+X1ubm5jBkzBgsLC6ysrBg3bhySJGl8PM2bN2fEiBGMGzcOS0tL7OzsmDp1qlqa2NhY/Pz8MDExwczMjO7du6vVdE6dOpXatWuzfPlynJyc5IczCoWCpUuX0qFDB4yMjHBzc+PkyZNER0fTvHlzjI2NadSokdpYONevX8fPzw9bW1tMTEyoX78+Bw8eLPIYnm5ePnXqVBQKRb5X3ndAqVQya9YsnJycMDQ0lB88PW3v3r1UrVoVQ0NDWrRooXa+iyswMJBx48ZRv359XFxcmDlzJi4uLuzatUuj7fft20eTJk3kz7dDhw5q56tRo0aMHz9ebZsHDx6gq6vLkSNHAFVLgPbt22NoaIiTkxNBQUH5mugXJjExkd9++4358+fz0Ucf4enpycqVKzlx4gSnTp0qdLtPPvmEyZMn55ueUBDy3EvMoNNPx5l74CoJadkAOFgZ0Vk7Ma9VOdn//i2zNi1bomUr9YHUiiM0NJSZM2fy888/c/bsWbZu3cqePXuYMWNGodu8KyOf6ujo0Lt3b8qXLw/A1atX+eGHH1ixYgXp6emlXDpBEARBeANJEmSllvyrGAFqXFwcvXr1YsCAAURGRhIaGkqXLl0KDHLHjBnD8ePH2blzJ8HBwRw9epSzZ8/mSzdv3jzq1avHuXPnGDZsGJ999hlRUVEAZGdn4+Pjg6mpKUePHuX48eOYmJjQpk0buTZ53rx5rFq1ihUrVnDs2DEeP37Mtm3binXqV69ejbGxMWFhYfzwww9Mnz6d4OBgQBWk+vn58fjxYw4fPkxwcDA3btygR48eavuIjo5my5YtbN26Va0p8YwZM+jbty8RERFUq1aN3r17M2TIECZMmMCZM2eQJEnu2gCQkpJCu3btCAkJ4dy5c7Rp0wZfX19iY2M1OpaxY8cSFxcnv+bOnYuRkRH16tUDVBU8a9asYcmSJVy6dInRo0fTp08fDh8+DKgecnTp0gVfX18iIiIYNGgQX3/9dbHOZ1GUSiXJyclYWlpqlD41NZUxY8Zw5swZQkJC0NLSonPnziiVSgD8/f1Zv3692jW4YcMGypUrx4cffgioupjcvXuX0NBQtmzZwrJlywodk+lZ4eHhZGdnqwXP1apVo1KlSoW2dhUETRy+Gk/E7QQAPvaswF8TW3JodFPMss3kmm4thRmSQsKhplWJlk2nRHN7StmyZdHW1i7WSKaTJk3ik08+YdCgQQC4u7uTmprK4MGD+eabb9DSyv8MQV9f/41tHlRcJiYmDBw4kIMHDxIREUFaWhqxsbFs374dZ2dnLCwsqFKlCtra2qVdVEEQBEEofdlpMLNcyec78S7oGWuUNC4ujpycHLp06YKDg6q5o7u7e750ycnJrF69mqCgIFq2bAnAypUr5VZwT2vXrh3Dhg0DYPz48SxYsIBDhw7h6urKhg0bUCqVLF++HIVCIe/HwsKC0NBQWrduTWBgIBMmTJCb+y5ZsqTYfYZr1arFlClTAHBxcWHx4sWEhITQqlUrQkJCuHDhAjExMXILxDVr1lCjRg1Onz5N/fr1AVWT8jVr1mBtba227/79+9O9e3f5+Ly8vJg0aRI+Pj6AaoyB/v37y+k9PDzkGWFAFbRv27aNnTt3qgXnhTExMcHExASAU6dO8e2337J69Wpq1qxJZmYmM2fO5ODBg/KggM7Ozhw7doylS5fSrFkzfvnlFypXrsy8efMA1XR8Fy5ceGXNpOfOnUtKSop8Tp6na9euau9XrFiBtbU1ly9fpmbNmnTv3p1Ro0Zx7NgxOcgOCgqiV69eKBQKrly5wsGDBzl9+rT84GH58uW4uGg27/G9e/fQ09PL13WiqNaugqCJpHTVXNz1HMow52PVdz4zJhFtLQOyJNVAoAqFHtnODylbwbREy1ZqNd16enp4enqqjWSqVCoJCQkpdCTTtLS0fIF1XoBZnGZPbzMtLS1at27N2LFjadGiBQBRUVH88ccfrFu3jl27dpGdnV3KpRQEQRAEQRMeHh60bNkSd3d3Pv74Y3799VeePHmSL92NGzfIzs6mQYMG8jJzc3NcXV3zpa1Vq5b8f4VCgZ2dnVwLef78eaKjozE1NZWDSUtLS3l60sTEROLi4mjYsKG8Dx0dHTm40tTTZQDVGDV5ZYiMjKRixYpqXf6qV6+OhYUFkZGR8jIHB4d8Afez+87rpvj0gwpbW1syMjLkcXxSUlIYO3Ysbm5uWFhYYGJiQmRkpMY13XliY2Pp1KkTY8eOlQPc6Oho0tLSaNWqlXw+TUxMWLNmjdxkOzIyUu18Aq9s1P6goCCmTZvGxo0bsbGx0Wiba9eu0atXL5ydnTEzM8PR0VE+PgBra2tat27N2rVrAYiJieHkyZPyQIpRUVHo6OhQt25deZ9VqlShTJkyr+SYBOFFJWeoYiA3ezMkSSL56B0eLP0bgKTsR6pEkgLpw7slXrZSq+kGVTOpgIAA6tWrR4MGDQgMDCQ1NVV+Otm3b1/Kly/PrFmzAPD19WX+/PnUqVOHhg0bEh0dzaRJk/D19X3vane1tLRo0qQJBgYGPHr0iNu3bxMXF0dERATXrl1j8ODBmJubl3YxBUEQBKH06Bqpap1LI18NaWtrExwczIkTJzhw4ACLFi3im2++ISws7MWz19VVe69QKOSmwykpKXh6esoB1dMKCnBfRxk0ZWxccGuBp/edV1tf0LK8/MaOHUtwcDBz586lSpUqGBoa0q1bt2INzpaamkrHjh3x8vJi+vTp8vKUlBQA9uzZI3cBzPO6W1quX7+eQYMGsWnTpmL1c/b19cXBwYFff/2VcuXKoVQqqVmzptr58Pf3Z8SIESxatIigoCDc3d0LbIHxIuzs7MjKyiIhIUGttruo1q6CoImkDFVNt4mBDinH/iFxT4y87knmfTDRJ0s7A3MDzVoivUqlGnT36NGDBw8eMHnyZO7du0ft2rXZt2+f/NQyNjZWrWb722+/RaFQ8O233/LPP/9gbW2Nr68v3333XWkdQqnS1taWn5xKksThw4cJDQ0lNTWVBQsWoKuri76+Pubm5hgYGGBjY0O9evWwsirZPgyCIAiCUCoUCo2beZcmhUJB48aNady4MZMnT8bBwSFfH2pnZ2d0dXU5ffo0lSpVAlQDUl29epWmTZtqnFfdunXZsGEDNjY2mJmZFZjG3t6esLAweb85OTmEh4er1Wy+DDc3N27fvs3t27fl2u7Lly+TkJBA9erVX0keTzt+/Dj9+vWjc+fOgCpQLs5AZpIk0adPH5RKJb///rsc1IOqhl5fX5/Y2FiaNWtW4PZubm7s3LlTbVlRA4ZpYt26dQwYMID169fTvn17jbd79OgRUVFR/Prrr3LT8WPHjuVL5+fnx+DBg9m3bx9BQUH07dtXXufq6kpOTg7nzp3D09MTUNX4F9RCoyCenp7o6uoSEhIiN3WPiooiNjb2lbUAEN5Pyf8G3aYGOmTeUrV0ycpNJ/juGrJMDYHySAoJfe2S73pcqkE3wOeff15of5rQ0FC19zo6OkyZMkXuIyT8R6FQ0Lx5c2xtbdm8eTO5ublkZ2eTnZ0tP4W9fv06J0+epGrVqvj6+mJqWrJ9GQRBEARBUBcWFkZISAitW7fGxsaGsLAwHjx4gJubG3///becztTUlICAAL766issLS2xsbFhypQpaGlpqQWBz+Pv78+cOXPw8/Nj+vTpVKhQgVu3brF161bGjRtHhQoVGDlyJLNnz8bFxYVq1aoxf/58EhISXtkxe3t74+7ujr+/P4GBgeTk5DBs2DCaNWtW7GbsmnBxcWHr1q34+vqiUCiYNGlSsWrdp06dysGDBzlw4AApKSny7ypzc3NMTU0ZO3Yso0ePRqlU0qRJExITEzl+/DhmZmYEBAQwdOhQ5s2bx1dffcWgQYMIDw9/qfnXg4KCCAgIYOHChTRs2FDuB21oaPjcVo5lypTBysqKZcuWYW9vT2xsbIGDuhkbG9OpUycmTZpEZGQkvXr1ktdVq1YNb29vBg8ezC+//IKuri5ffvklhoaGGl2L5ubmDBw4kDFjxmBpaYmZmRlffPEFXl5efPDBB2r5zJo1S35Y8vjxY2JjY7l7V9V6JW9wQDs7O1FDLgD/NS83NdAlPeYGCgz56+E+UnISsLauwiNU83TraemVeNneqtHLhedzc3Nj4sSJjB07lhEjRjB48GB69OghNyUC1cjneVM+CIIgCIJQeszMzDhy5Ajt2rWjatWqfPvtt8ybN4+2bdvmSzt//ny8vLzo0KED3t7eNG7cGDc3t2LNdW5kZMSRI0eoVKkSXbp0wc3NjYEDB5KRkSHXfH/55Zd88sknBAQE4OXlhampqRz4vAoKhYIdO3ZQpkwZmjZtire3N87OzmzYsOGV5fG0+fPnU6ZMGRo1aoSvry8+Pj7FqrU/fPgwKSkpNGrUCHt7e/mVV94ZM2YwadIkZs2ahZubG23atGHPnj04OTkBUKlSJbZs2cL27dvx8PBgyZIlzJw584WPZ9myZeTk5DB8+HC18owcOfK522ppabF+/XrCw8OpWbMmo0ePZs6cOQWm9ff35/z583z44Ydy64o8a9aswdbWlqZNm9K5c2c+/fRTTE1NNb4WFyxYQIcOHejatStNmzbFzs6OrVu3qqWJiooiMTFRfr9z507q1Kkj1+z37NmTOnXqsGTJEo3yFN59eTXdFR9mokg1BCA9J4muHdtgXl3VikJSKEulplshvS8jkP0rKSkJc3NzEhMTC21W9S47ffo0e/bsAaBevXrUrl2bChUqlHKpBEEQ3l/v+32pOIo6VxkZGcTExKjN6fyuS01NpXz58sybN4+BAweWdnGE99idO3eoWLEiBw8elEfXF4rnffwb9qr1+T6UsU8UlP23XlkpKTlzcx5dNuxky+9/cOF6GNm5utTrW53OLq/mQaKm9/BSb14ulKx69epx9uxZ4uLiOHPmDBERETRs2JAaNWoUOO2IIAiCIAhvhnPnznHlyhUaNGhAYmKiPKCXn59fKZdMeN/8+eefpKSk4O7uTlxcHOPGjcPR0bFY4wsIwquU9SSDb54oMP034L6XHsPlhJOUNVB1JVHm/vtvKdV0i+bl7xmFQsEnn3xCmzZtUCgU5OTkcPz4cZYtWyZPbSEIgiAIwptp7ty5eHh44O3tTWpqKkePHqVs2bIlln9sbKza1FjPvoo7DZcAbdu2LfR8FrcZekl9PtnZ2UycOJEaNWrQuXNnrK2tCQ0NRVdXl7Vr1xaaf40aNV5J/oLwrIuLdskBd/A/qzl8byMPMm5To4MvALm5uUDpNS8XNd3vISMjIz744APc3Nw4ffq0PGplUFAQ48ePR0+v5AcXEARBEAShaHXq1CE8PLxUy1CuXDkiIiKKXC8Uz/Lly0lPTy9wnaWlZbH2VVKfj4+PDz4+PgWu69ixY755yfM8O5WcILwqeg8zwAiuJZ0lN+UW5bShftvWOHw8BIDcHFVNt4SEvo4IuoUSZG5ujre3N1WrVmXFihXk5uZy//59efoOQRAEQRCEp+no6FClSpXSLsY75dn5vV/Gm/D5mJqaihlyhBIlSRJGetYAaGtFM2hbcL40KZmqWQckLYkaViXf4kI0LxeoVKkSVatWBSA4OJiMjIxSLpEgCIIgCIIgCELRclOzuT0zBD0dY3KUWdjXdy0wXUZWJgD6OvqUMShTkkUERE238K9mzZpx69YtYmNj+f7777G0tKR58+a4u7uXdtEEQRAEQRAEQRBkkiSRHHqHpP030ULVXPxu8mXqdeheYPrMVNV0Yrq6pRP+ippuAVA1berXrx9mZmZIksSjR4/Yvn07OTk5pV00QRAEQRAEQRAEWeIf0STtvwlAWk4yJ+I2s7eiIXpWtvnS5uYqkZJVwbZeGUVJFlMmgm5BZm9vz8iRIxk+fDigGuUvMTGxlEslCIIgCIIgCML7TpIkchIzST58h5Qj9wB4mPEPu2//wj8Gmfj3L7iW+89roYAEgKW5RckU9hmiebmgRltbG2tra6ytrXnw4AFPnjzBysqqtIslCIIgCIIgCMJ7SMrO5cmeaFJO30Yr97+Rx+PSYrgc+3/U6vgxLfsPQqHIX4t99clVph6eTkdFHwDsTPLXhJcEUdMtFChvzs+LFy+WckkEQRAE4f3Ur18/OnXqVNrFAN6ssrwOjo6OBAYGyu8VCgXbt29/bfmFhoaiUChISEh4btpVq1ZhYWHx2soiCG+y3Owc7q88TtqpeLRy9VFKShKyHvD341Au3/k/6i8KwnvApwUG3BHxEXxz7Bv0cgzJq+k21DUs4SNQEUG3UKC8+RUjIiLYtGkTMTExpVwiQRAEQXi/LFy4kFWrVpV2Md5LcXFxtG3btrSLUaCtW7fSqlUrrK2tMTMzw8vLi/3795d2sUrFTz/9hKOjIwYGBjRs2JC//vqryPSXLl2ia9euODo6olAo1B60CG+e098OYe/gb8m5oQqoryaeISR6Dn/fX80yPUP2+kyjesWyBW57J/kOA/YP4MrjK+jl/hd0a2mVTvgrgm6hQA4ODlSoUAFQ/YEKCgri3LlzKJXKUi6ZIAiCILwfzM3NRQ3nv7Kysko0Pzs7O/T19Z+fsBQcOXKEVq1asXfvXsLDw2nRogW+vr6cO3eutItWojZs2MCYMWOYMmUKZ8+excPDAx8fH+Lj4wvdJi0tDWdnZ2bPno2dnV0JllYorgtzxnPk2j9YGVcGICbhDA8f/kHHxb8xyWEIESZujG9brdDt993cR7YyGxtDG/q5DPg35KbAGvGSIIJuoUAKhYI+ffrQuXNnDAwMyM7OZseOHcyYMYMtW7aQm5tb2kUUBEEQhHfC5s2bcXd3x9DQECsrK7y9vUlNTc3XpDs5ORl/f3+MjY2xt7dnwYIFNG/enFGjRslpHB0dmTlzJgMGDMDU1JRKlSqxbNkytfxu375N9+7dsbCwwNLSEj8/P27evCmvz83NZcyYMVhYWGBlZcW4ceOQJAlNNW/enBEjRjBu3DgsLS2xs7Nj6tSpamliY2Px8/PDxMQEMzMzunfvzv379+X1U6dOpXbt2ixfvhwnJycMDAwA1e+TpUuX0qFDB4yMjHBzc+PkyZNER0fTvHlzjI2NadSoEdevX5f3df36dfz8/LC1tcXExIT69etz8ODBIo/h6eblU6dORaFQ5HvltUJQKpXMmjULJycnDA0N8fDwYPPmzWr727t3L1WrVsXQ0JAWLVqone/iCgwMZNy4cdSvXx8XFxdmzpyJi4sLu3bt0mj7V/H5PO+cTpw4UW41+TQPDw+mT58OQE5ODiNGjJCvs/HjxxMQEKBxN4b58+fz6aef0r9/f6pXr86SJUswMjJixYoVhW5Tv3595syZQ8+ePd/YhyoCSEqJu5duUM28AZVMVIG1+xd9sJ7xf/ituUSuUqKqrQl1KxU+3/bZ+2cB8FcMJ2mvCShETbfwhjIwMMDDw4NRo0ZRp04dtLS0kCSJCxcucPny5dIuniAIgiAUSZIk0rLTSvxVnAA1Li6OXr16MWDAACIjIwkNDaVLly4F7mPMmDEcP36cnTt3EhwczNGjRzl79my+dPPmzaNevXqcO3eOYcOG8dlnnxEVFQVAdnY2Pj4+mJqacvToUY4fP46JiQlt2rSRa5PnzZvHqlWrWLFiBceOHePx48ds27atWOd+9erVGBsbExYWxg8//MD06dMJDg4GVEGqn58fjx8/5vDhwwQHB3Pjxg169Oihto/o6Gi2bNnC1q1biYiIkJfPmDGDvn37EhERQbVq1ejduzdDhgxhwoQJnDlzBkmS+Pzzz+X0KSkptGvXjpCQEM6dO0ebNm3w9fUlNjZWo2MZO3YscXFx8mvu3LkYGRlRr149AGbNmsWaNWtYsmQJly5dYvTo0fTp04fDhw8DqoccXbp0wdfXl4iICAYNGsTXX39drPNZFKVSSXJyMpaWlhpv87Kfz/POqb+/P3/99Zfaw49Lly7x999/07t3bwC+//571q5dy8qVKzl+/DhJSUka96PPysoiPDwcb29veZmWlhbe3t6cPHlS4/MgvHmkXCXxP56lht0IPCxbAPBYB1qsO0P/Vae58yQdgA61yhW6jzvJdzhx9wTaubqkH7IgJ1tJXvPy0qrpFqOXC89lYGCAn58fHTp0YPfu3Zw7d47Dhw8THx9PlSpVcHBwKO0iCoIgCEI+6TnpNAzKX9v2uoX1DsNI10ijtHFxceTk5NClSxf5furu7p4vXXJyMqtXryYoKIiWLVsCsHLlSsqVy//Ds127dgwbNgyA8ePHs2DBAg4dOoSrqysbNmxAqVSyfPly+cfnypUrsbCwIDQ0lNatWxMYGMiECRPo0qULAEuWLCl2n+FatWoxZcoUAFxcXFi8eDEhISG0atWKkJAQLly4QExMDBUrVgRgzZo11KhRg9OnT1O/fn1AFVitWbMGa2trtX3379+f7t27y8fn5eXFpEmT8PHxAWDkyJH0799fTu/h4YGHh4f8fsaMGWzbto2dO3eqBeeFMTExwcTEBIBTp07x7bffsnr1amrWrElmZiYzZ87k4MGDeHl5AeDs7MyxY8dYunQpzZo145dffqFy5crMmzcPAFdXVy5cuMD3339frHNamLlz55KSkiKfE0287OfzvHNao0YNPDw8CAoKYtKkSQCsXbuWhg0bUqVKFQAWLVrEhAkT6Ny5MwCLFy9m7969GpX/4cOH5ObmYmurPhK1ra0tV65c0fg8CG+enMcZZN9LAyA1O5GbKRf5vIw72YChrjYeFc3p1aAS7d3tC9z+Ufoj/Pf6kyvl4kJ1+LdnbIUa5kTH3ENPT6+EjkSdqOkWNKatrU3t2rUB1R+7o0ePsnLlStatW0dqamrpFk4QBEEQ3kIeHh60bNkSd3d3Pv74Y3799VeePHmSL92NGzfIzs6mQYMG8jJzc3NcXV3zpa1Vq5b8f4VCgZ2dndzP9fz580RHR2NqaioHk5aWlmRkZHD9+nUSExOJi4tTaxqso6Mj1+pq6ukyANjb28tliIyMpGLFinJAB1C9enUsLCyIjIyUlzk4OOQLuJ/dd17Q9fSDCltbWzIyMkhKSgJUtbJjx47Fzc0NCwsLTExMiIyM1LimO09sbCydOnVi7NixcoAbHR1NWloarVq1ks+niYkJa9askWt5IyMj8zW1zgvQX1ZQUBDTpk1j48aN2NjYaLzdy34+mpxTf39/goKCAFWrk3Xr1uHv7w9AYmIi9+/fV7uetbW18fT0LOYZEN4l2blKzl1SXYepWY/YfWcJl3Ku0bV+RdZ9+gF/T23N+sFe+NUuj452/jD2bspd+u3rx+OMx9gY2tI9cTgA9pXNyVZmAGBqalpyB/QUUdMtFIuDgwNDhw4lNjaWK1eucOPGDaKioti9ezfdu3cvtSYbgiAIgvAsQx1DwnqHlUq+mtLW1iY4OJgTJ05w4MABFi1axDfffENY2IuXW1dXV+29QqGQB0JNSUnB09OTtWvX5tuuoAD3dZRBU8bGxs/dd97vjoKW5eU3duxYgoODmTt3LlWqVMHQ0JBu3boVa3C21NRUOnbsiJeXl9wnGVTnE2DPnj2UL19ebZvX3Wd4/fr1DBo0iE2bNqk1s9bEy34+mpzTXr16MX78eM6ePUt6ejq3b9/O14XgRZUtWxZtbW21fuYA9+/fFwOkvaX+t/syK47HMEoyoAt6JOeqHpp19WmAQ9daz9la1Yf7m2PfcCflDvaZjvRP+IpHUZkAONex5sjlZAC51UpJE0G3UGx2dnbY2dlRv359IiIi2LFjB5GRkfz88894enpiZmaGjY0NxsbG6Ovrl9qABYIgCML7TaFQaNzMuzQpFAoaN25M48aNmTx5Mg4ODvn6UDs7O6Orq8vp06epVKkSoKotvHr1Kk2bNtU4r7p167JhwwZsbGwwMzMrMI29vT1hYWHyfnNycggPD6du3boveITq3NzcuH37Nrdv35ZrUy9fvkxCQgLVq1d/JXk87fjx4/Tr109uxpySklKsgcwkSaJPnz4olUp+//13tQqG6tWro6+vT2xsLM2aNStwezc3N3bu3Km27NSpU8U/kKesW7eOAQMGsH79etq3b/9S+3qWJp+PJue0QoUKNGvWjLVr15Kenk6rVq3k2nhzc3NsbW05ffq0fJ3l5uZy9uxZuVVlUfT09PD09CQkJEQeeE2pVBISEqJRlwHhzaLMVXIn+AA/Z2ZS06IRAGk5SRjk5FC+Xa/nbh+fFs/g4MFk5mbimlaXFucDeEwOAG6N7Kn+oR3bjz0GRNAtvIUUCgV16tQhNTWVgwcP8uDBA/bt25cvnb6+PsbGxnIgXrFiRWrVqiWCcUEQBOG9FxYWRkhICK1bt8bGxoawsDAePHiAm5sbf//9t5zO1NSUgIAAvvrqKywtLbGxsWHKlCloaWkVq5WZv78/c+bMwc/Pj+nTp1OhQgVu3brF1q1bGTduHBUqVGDkyJHMnj0bFxcXqlWrxvz580lISHhlx+zt7Y27uzv+/v4EBgaSk5PDsGHDaNasWbGbsWvCxcWFrVu34uvri0KhYNKkScWq1Z06dSoHDx7kwIEDpKSkyLXb5ubmmJqaMnbsWEaPHo1SqaRJkyYkJiZy/PhxzMzMCAgIYOjQocybN4+vvvqKQYMGER4e/lLzrwcFBREQEMDChQtp2LAh9+7dA8DQ0BBzc/MX3m8eTT4fTc+pv78/U6ZMISsriwULFqit++KLL5g1axZVqlShWrVqLFq0iCdPnmh8PY8ZM4aAgADq1atHgwYNCAwMJDU1Va0/f9++fSlfvjyzZs0CVOME5A0GnJWVxT///ENERAQmJiZyX3Oh5Cgzc7jzyy6U/5jwraEn/NtI6EnmfW48Okkb39bomBT8cDBPZm4mn4d8TmZuJuWNy9P6XH+y/+3I3aJPNdwa28t/S3V1dUttGkYR9QgvrUmTJnz66afUqVMHZ2dnbG1t0dH573lOZmYmjx8/5sqVK4SHh7N9+3ZWr15Nenp6KZZaEARBEEqfmZkZR44coV27dlStWpVvv/2WefPm0bZt23xp58+fj5eXFx06dMDb25vGjRvj5uYmT6elCSMjI44cOUKlSpXo0qULbm5uDBw4kIyMDLnm+8svv+STTz4hICAALy8vTE1N5RrNV0GhULBjxw7KlClD06ZN8fb2xtnZmQ0bNryyPJ42f/58ypQpQ6NGjfD19cXHx6dYtfaHDx8mJSWFRo0aYW9vL7/yyjtjxgwmTZrErFmzcHNzo02bNuzZswcnJycAKlWqxJYtW9i+fTseHh4sWbKEmTNnvvDxLFu2jJycHIYPH65WnpEjR77wPp+myeej6Tnt1q0bjx49Ii0tLd9UYOPHj6dXr1707dsXLy8vTExM8PHx0fh67tGjB3PnzmXy5MnUrl2biIgI9u3bpza4WmxsLHFxcfL7u3fvUqdOHerUqSOPRF+nTh0GDRpUzLMkvKyEvTe4O+UkWvfKoqOt+sxTMx/yT0Ioafr76PPrz1TuN6bQ7U/ePcn/Tv2P9lvbE/k4En1tfRY0/pHsDFXA3Wl0Hao3KYdCoZBjjsqVK6vFKCVJIRVnXot3QFJSEubm5iQmJhbarEp4NXJycsjIyCAjI4NHjx6RmJhIdHQ0V69eBVRTOzRs2JCWLVuW2hdAEAShtIn7kuaKOlcZGRnExMSozen8rktNTaV8+fLMmzePgQMHlnZxBOGlKJVK3Nzc6N69OzNmzCjt4pS49+1v2J0phyFTVf8b+eBPjiofkNl+MN93e37/baWkpMn6JiRnJcvLPvP4jN72/Vg75RS6BtoMDvyvu8fx48cJDg7Gw8PjlT5ABM3v4SLSEV4bHR0deRTPsmXLAtCgQQMuXbrEjh07yMrK4uTJk0RFReHn5yemHhMEQRCEIpw7d44rV67QoEEDEhMT5QG9/Pz8SrlkglB8t27d4sCBAzRr1ozMzEwWL15MTEyMPI+38O6SJAkpQ0KhgMPRC5jq/AkGhkZsauKo0fYhsSEkZyVjoG3AaM/R2Brb0rR8Ux7GqGZTMjRVnxYsJ0fVv1tbW/uVHkdxiKBbKHE1atSgatWqREREsGfPHh4/fszRo0dF0C0IgiAIzzF37lyioqLkgaSOHj0qP9guCbGxsUUOdnb58mV5oDdBM23btuXo0aMFrps4cSITJ07UeF9v0+ejpaXFqlWrGDt2LJIkUbNmTQ4ePIibm9tbdRxC8UnpOSgUqgA4V0pH0tHnt371qWZXcE2xJEmciz9HdEI0p+JOEXwrGAA3Kzd6u/33kCYtWTV6vpGp+uj8ubm5AKXaslYE3UKp0NXVpX79+lhYWLB27Vri4uKQJElMOSYIgiAIhahTpw7h4eGlWoZy5coRERFR5HqheJYvX17oODeWlpbF2tfb9PlUrFiR48ePF7jubToOofguXLyPFZCVm4GxsR7bhzemZvnCBwGMeBBBwL4AtWVNyjdhZF31cQySH6nm4jY2V5+uT9R0C+89R0dHFAoFqampcp8IQRAEQRDeTDo6OmKU51fs2fm9X8a78vm8K8ch5Dd15yWcT9znI3TJlrIoZ2tZZMANcOXxFQDsjO1oWaklPo4+1LGpky9dQrzq4ZWFrfpUkW9CTbcYvVwoVbq6uvKcjU+PLikIgiAIgiAIwrsjVymx+uRN8npcP8m8h1EZi+dudzflLgDelbz5usHXBQbcAE/iVH26LezUg+43oaZbBN1CqbO3twdU0zgIgiAIgiAIgvBuScrIpvevp5AkKJ+jmus+NvUKxjb2RW73IO0B26O3A1DepPBWIQ9ik7l7LQEA60qmauvyarpF83LhvZbXbycqKgpnZ2ccHBxE325BEARBEARBeItJksSfV+IJCosl5Eq8vNxaWzVjdY4yE8eeQ4vcx6arm0jITADAq5xXgWluXXrE3p//BsDQTA9LO2O19Xk13WIgNeG9ljdq+f3791m1ahVVqlShWrVq2NraUrFixVIunSAIgiAIgiAImkjOyGbNyVv8FfOYq/eTiUvMkNeZGujwv041MdhwHiQwzk5Fx6Twua0B4tNUwXorh1ZUtqicb31meg4HV1xGmStRzsWCxt2qoNBSr7wTNd2CANja2tKvXz/2799PXFwc0dHRREdHA9C8eXOcnJwwMjLCysoKLS3RI0IQBEEQBEEQ3kSjN0RwMPK/Wm09bS38apejr5cjLrYmGOhqc2v9RdVKKfO5+8ur5W5g1yDfuuzMXDb87y8yUrPR0lLQdqg7Bsa6+dKJgdQE4V+Ojo4MHjyYrl27Uq9ePXl5aGgoK1eu5KeffmLhwoUkJSWVYikFQRDeD7NmzaJ+/fqYmppiY2NDp06diIqKKnKb5s2bo1Ao8r3at28vp+nXr1++9W3atHndh/PW6tevH506dSrtYgBvVlneZo6OjgQGBsrvFQoF27dvf235hYaGolAoSEhIeG7aVatWYWFh8drKIrzb4pMzGLPxv4B7REsXVg9owOlvvZnzsQfuFcwx0FXVNCskVWCsIOu5+32S8QSAMgZlAFWT9SPrr7Jp1mnWTDwhTxPWzN+1wIAbxEBqgqBGoVDg7u5Ohw4dGD16NDVr1qR8+fLyDSAxMZHAwEAOHDggf3kEQRCEV+/w4cMMHz6cU6dOERwcTHZ2Nq1btyY1NbXQbbZu3UpcXJz8unjxItra2nz88cdq6dq0aaOWbt26da/7cN5aCxcuZNWqVaVdDOE1iouLo23btqVdjAJt3bqVVq1aYW1tjZmZGV5eXuzfv7+0i1UsGRkZDB8+HCsrK0xMTOjatSv3798vcputW7fSunVrrKysUCgURc4XLqiC4J8ORdNu4VG2nv0HgM51yjOmVVWaVbXG3FA9EM5NykJLoVqmpch+7v6fZP4bdOurgu6Hd1K4EHqH+FvJZKRmY2Csi9/oOlRvXPjc7W9CTbdoXi68kczNzenWrZv8/sqVKxw4cIDHjx9z4sQJoqOj8fT0pH79+qLJuSAIwiu2b98+tferVq3CxsaG8PBwmjZtWuA2lpaWau/Xr1+PkZFRvqBbX18fOzu7V1vgd5S5edFz1wqvXlZWFnp6es9P+Iq8yd+FI0eO0KpVK2bOnImFhQUrV67E19eXsLAw6tQpeMqmN83o0aPZs2cPmzZtwtzcnM8//5wuXbpw/PjxQrdJTU2lSZMmdO/enU8//bQES/t2Cjx4jYUh1wAob2HImFZV6Vi78AA49fQ9AHKVOSi0i65EkySJh2kPAbA0sCQrI4eT267L6zuNqUPZiqboGxYd0oqabkHQULVq1Rg+fDidOnXCwMCA+Ph4/vjjD1atWsW+ffu4ceNGaRdREAThnZWYmAjkD6yL8ttvv9GzZ0+MjdVHkQ0NDcXGxgZXV1c+++wzHj169ErL+jbavHkz7u7uGBoaYmVlhbe3N6mpqfmadCcnJ+Pv74+xsTH29vYsWLCA5s2bM2rUKDmNo6MjM2fOZMCAAZiamlKpUiWWLVumlt/t27fp3r07FhYWWFpa4ufnx82bN+X1ubm5jBkzBgsLC6ysrBg3bhySJGl8PM2bN2fEiBGMGzcOS0tL7OzsmDp1qlqa2NhY/Pz8MDExwczMjO7du6vVQE6dOpXatWvz+++/4+joiLm5OT179iQ5Ofm15LN8+XKcnJwwMDAAVK3vli5dSocOHTAyMsLNzY2TJ08SHR1N8+bNMTY2plGjRly//l8AcP36dfz8/LC1tcXExIT69etz8ODBIs/V083Lp06dWmAXjbzWDkqlklmzZuHk5IShoSEeHh5s3rxZbX979+6latWqGBoa0qJFC7XPtbgCAwMZN24c9evXx8XFhZkzZ+Li4sKuXbs02n7fvn00adJEvo46dOigdr4aNWrE+PHj1bZ58OABurq6HDlyBFC1BGjfvj2GhoY4OTkRFBSUr4l+YRITE/ntt9+YP38+H330EZ6enqxcuZITJ05w6tSpQrf75JNPmDx5Mt7e3hod5/ssNCpeDriHNqvMgdFN6epZAV3tgkPM7Pg0koJvARCZeAo93aKD4EcZj0jOTkaBgopmFTn0f1e4ffkxALW9K1K+apnnBtzwZtR0i6BbeGtoa2tTu3ZtPvvsMxo3boyWlhaxsbGcOnWKdevWkZn532AMKZk5RN1LLmJvgiAIgiaUSiWjRo2icePG1KxZU6Nt/vrrLy5evMigQYPUlrdp04Y1a9YQEhLC999/z+HDh2nbtq38g+hZmZmZJCUlqb2KQ5IklGlpJf4qToAaFxdHr169GDBgAJGRkYSGhtKlS5cC9zFmzBiOHz/Ozp07CQ4O5ujRo5w9ezZfunnz5lGvXj3OnTvHsGHD+Oyzz+Q++dnZ2fj4+GBqasrRo0c5fvw4JiYmtGnThqysLHn7VatWsWLFCo4dO8bjx4/Ztm1bsc796tWrMTY2JiwsjB9++IHp06cTHBwMqK4pPz8/Hj9+zOHDhwkODubGjRv06NFDbR/Xr19n+/bt7N69m927d3P48GFmz579yvOJjo5my5YtbN26Va0p8YwZM+jbty8RERFUq1aN3r17M2TIECZMmMCZM2eQJInPP/9cTp+SkkK7du0ICQnh3LlztGnTBl9fX2JjYzU6Z2PHjlXrejF37lyMjIzksW5mzZrFmjVrWLJkCZcuXWL06NH06dOHw4cPA6qHKV26dMHX15eIiAgGDRrE119/rVHemlAqlSQnJ2v88C01NZUxY8Zw5swZQkJC0NLSonPnziiVSgD8/f1Zv3692rW+YcMGypUrx4cffghA3759uXv3LqGhoWzZsoVly5YRHx9fYH7PCg8PJzs7Wy14rlatGpUqVeLkyZOaHrZQhP87pbq2q9qaML6NK8b6hQe1OY/SuR/439+rO6lR6OoX3aokOkE1sHIlA0fCd9wm+ozqs3eubU1dHweNy/km1HSL5uXCW8fc3JxWrVpRq1YtoqOj5f6Gy5Yto0WLFliWd2LA6nBuPUpj0IdO1K5oQVVbUypaGpV20QVBEN46w4cP5+LFixw7dkzjbX777Tfc3d1p0EB9tNmePXvK/3d3d6dWrVpUrlyZ0NBQWrZsmW8/s2bNYtq0aS9cdik9nai6ni+8/YtyPRuOwkize05cXBw5OTl06dJFnkLT3d09X7rk5GRWr15NUFCQfK5WrlxJuXL5m3G2a9eOYcOGATB+/HgWLFjAoUOHcHV1ZcOGDSiVSpYvX45CoZD3Y2FhQWhoKK1btyYwMJAJEybQpUsXAJYsWVLsvry1atViypQpALi4uLB48WJCQkJo1aoVISEhXLhwgZiYGHlq0DVr1lCjRg1Onz5N/fr1AVWQt2rVKkxNTQFVDWRISAjffffdK80nKyuLNWvWYG1trXYM/fv3p3v37vJ59PLyYtKkSfj4+AAwcuRI+vfvL6f38PDAw8NDfj9jxgy2bdvGzp071YLzwpiYmGBiYgLAqVOn+Pbbb1m9ejU1a9YkMzOTmTNncvDgQby8VHMVOzs7c+zYMZYuXUqzZs345ZdfqFy5MvPmzQPA1dWVCxcu8P333z83b03MnTuXlJQU+Zw8T9euXdXer1ixAmtray5fvkzNmjXp3r07o0aN4tixY3KQHRQURK9evVAoFFy5coWDBw9y+vRp+cHD8uXLcXFx0Sj/e/fuoaenl29wOFtbW+7du6fRPoSC3X6cxonrDzkYqWo1MrtrLfnvSUGUWbnE/3IelBLp2Ukcjd9KYvZDqvgUfS39EfMHAE1vd+NctCrAN7bQx2dwTbS0Cs/vWW9CTbcIuoW3lq2trdyEa/v27Tx69EhuZlVbyxBLyZjVh1JZJKmaijVwsqRT7fI4WxtTs7w5JkU8jRMEQRDg888/Z/fu3Rw5coQKFSpotE1qairr169n+vTpz03r7OxM2bJliY6OLjDonjBhAmPGjJHfJyUlycHTu8LDw4OWLVvi7u6Oj48PrVu3plu3bpQpU0Yt3Y0bN8jOzlZ7kGFubo6rq2u+fdaqVUv+v0KhwM7OTq4dPH/+PNHR0XIgmycjI4Pr16+TmJhIXFwcDRs2lNfp6OhQr169YtXgP10GAHt7e7kMkZGRVKxYUe2zrF69OhYWFkRGRsrBsKOjo1o5n97Hq8zHwcEhX8D97L5tbW0B9Qcitra2ZGRkkJSUhJmZGSkpKUydOpU9e/bID1PS09M1runOExsbS6dOnRg7dqwc4EZHR5OWlkarVq3U0mZlZcn9qyMjI9U+N0AO0F9WUFAQ06ZNY8eOHdjY2Gi0zbVr15g8eTJhYWE8fPhQruGOjY2lZs2aWFtb07p1a9auXcuHH35ITEwMJ0+eZOnSpQBERUWho6ND3bp15X1WqVIl33dDKFm5SoluS05wP0nVwrS8hSEeFSyK3CYrNhllimrQtBMPdvIk6z5tP/Cggl/fIrc7e19VM17mUQWygJpNy1O3jUOxAm4QNd2C8Ep4eHhQsWJFIiIiOHXqFFlZWRgo06mqk05VnYec0q3NlWRd/op5zF8xqn4g7uXN2fl54yKfygmCILyvJEniiy++YNu2bYSGhuLk5KTxtps2bSIzM5M+ffo8N+2dO3d49OgR9vb2Ba7X19dHX19f47yfpTA0xPVs+Atv/zL5akpbW5vg4GBOnDjBgQMHWLRoEd988w1hYWEvnL+urvpowQqFQg54UlJS8PT0ZO3atfm2KyjwfB1leJX7eBX5PDvuQEH7zvu9UNCyvPzGjh1LcHAwc+fOpUqVKhgaGtKtWze52b4mUlNT6dixI15eXmoPrlJSUgDYs2cP5cuXV9vmZb4jmli/fj2DBg1i06ZNxern7Ovri4ODA7/++ivlypVDqVRSs2ZNtfPh7+/PiBEjWLRoEUFBQbi7uxfY0uNF2NnZkZWVRUJCglpt9/3799/oAezeZInp2cz+4wr3kzIx1NWmcRUr+jd2Qvs5QbAyTRVwZ2s95GHmP1hlZ1J99HeFpk/MTOS3v38j+7YeHW9/Qda/PUbrd3DCyKz4Ax3m1XSLoFsQXpKlpSUfffQRzZs3JyUlhevXr7Njxw4AvmxgjLFzHcJvPuFQVDxnYxO48E8ijWf/SZ1KZZjSsTo2pgalfASCIAhvjuHDhxMUFMSOHTswNTWVm2Kam5tj+G9A2bdvX8qXL8+sWbPUtv3tt9/o1KkTVlZWastTUlKYNm0aXbt2xc7OjuvXrzNu3DiqVKkiN9d91RQKhcbNvEuTQqGgcePGNG7cmMmTJ+Pg4JCvD7WzszO6urqcPn2aSpUqAaqBoq5evVroiPIFqVu3Lhs2bMDGxgYzM7MC09jb2xMWFibvNycnh/DwcLUax5fh5ubG7du3uX37tlwLffnyZRISEqhevforyaMk88lz/Phx+vXrR+fOnQHVNV+cgcwkSaJPnz4olUp+//13tYqB6tWro6+vT2xsLM2aNStwezc3N3bu3Km2rKgBwzSxbt06BgwYwPr162nfvr3G2z169IioqCh+/fVXuel4QV1U/Pz8GDx4MPv27SMoKIi+ff+r+XR1dSUnJ4dz587h6anqJhIdHc2TJ080KoOnpye6urqEhITITd2joqKIjY19ZS0A3id7/o7jq83nSctSBbCd65ZnZmfNHpAo01Q1zZkZCQCY6BYepO+L2cesk7P54PzH+CYOl5eXrWiCoWnB83A/T15Nt2heLgiviJaWFmZmZtSpUwctLS22bdvG9evXGdqqFY0ql+WLli5M2n6R30/d4m5iBncvxLHnQhzftHPD/4NKGOmJr4QgCMIvv/wCqEaHftrKlSvp168foGoi+uyUjVFRURw7dowDBw7k26e2tjZ///03q1evJiEhgXLlytG6dWtmzJjx2mvq3mRhYWGEhITQunVrbGxsCAsL48GDB7i5ufH333/L6UxNTQkICOCrr77C0tISGxsbpkyZgpaWVrFabfn7+zNnzhz8/PyYPn06FSpU4NatW2zdupVx48ZRoUIFRo4cyezZs3FxcaFatWrMnz+fhISEV3bM3t7euLu74+/vT2BgIDk5OQwbNoxmzZrJfXffpnzyuLi4sHXrVnx9fVEoFEyaNKlYte5Tp07l4MGDHDhwgJSUFLl229zcHFNTU8aOHcvo0aNRKpU0adKExMREjh8/jpmZGQEBAQwdOpR58+bx1VdfMWjQIMLDw19qnvegoCACAgJYuHAhDRs2lB++GRoaPnc6uzJlymBlZcWyZcuwt7cnNja2wEHdjI2N6dSpE5MmTSIyMpJevXrJ66pVq4a3tzeDBw/ml19+QVdXly+//BJDQ0ONrnlzc3MGDhzImDFjsLS0xMzMjC+++AIvLy8++OADtXxmzZolPyx5/PgxsbGx3L17F0AehNDOzu69riFf9Oc1OeAe5e3C0GaVNd5Wmaqq6Y7PTAXAzKTgh6GSJLFuxx/4XRuLfq4qjW0VUzxbO1GpuuULt1AVNd2C8BrlNYe8d+8eixcvxszMDAMDA1qULUu/L+px6k4G03ZeJitXyXd7I9ly9g47P2+Cno4Y1F8QhPebJn13Q0ND8y1zdXUtdFtDQ8NiD8b1PjAzM+PIkSMEBgaSlJSEg4MD8+bNo23btmzYsEEt7fz58xk6dCgdOnTAzMyMcePGcfv2bXmaK00YGRlx5MgRxo8fT5cuXUhOTqZ8+fK0bNlSrvn+8ssviYuLIyAgAC0tLQYMGEDnzp3lqeNelkKhYMeOHXzxxRc0bdoULS0t2rRpw6JFi17J/ks6nzzz589nwIABNGrUiLJlyzJ+/Phijbh/+PBhUlJSaNSokdryvIddM2bMwNramlmzZnHjxg0sLCyoW7cuEydOBKBSpUps2bKF0aNHs2jRIho0aCBPH/cili1bRk5ODsOHD2f48P9qHAMCAp4bzGtpabF+/XpGjBhBzZo1cXV15ccff8z3IA9UD4LatWtH06ZN5VYcedasWcPAgQNp2rQpdnZ2zJo1i0uXLml8zS9YsAAtLS26du1KZmYmPj4+/Pzzz2ppoqKi1K7tnTt3qg2QlzcA5JQpU/JNSfc+iIxLYsrOS1z5d1agkC+bUdnaROPtpWwlaX8/ACAzNx2Aqi1bF5j28qPLuN34UA64vftXx7Xhyz3okCTpjRhITSEVZ1SMd0BSUhLm5uYkJiYW2qxKeHfs3buXv/76q8B1VatWpUMHX5aHxfHjv3MM9m/syOQO1UVfb0EQSoy4L2muqHOVkZFBTEyM2lzL77rU1FTKly/PvHnzGDhwYGkXRxBeuzt37lCxYkUOHjxY4OCLb7M36W9YWlYOm87cYcPp21yO++/BUY1yZuwZ8aHG+8l5lM6joCtk/6NqtXH20UFuPf6LL7aoRiXPVmaz/+Z+IuIjuPzoMhceXqD/X7PQzzWi15SGWNoXPN5CceTk5PC///0PUM1CYFiMMTc0oek9XNR0C++0du3a8cEHH/D48WNSU1N58OABFy5ckPvBLVwYSIsWLehcuxzbIu6y8vhN/GqXp3ZFi9IuuiAIgiCoOXfuHFeuXKFBgwYkJibKA235+fmVcskE4fX4888/SUlJwd3dnbi4OMaNG4ejo2OxxjEQiifmYSqf/BbGnSfp8rJqdqbM6FQT9/JFdyt4WlpEPE+2XEPKVoIWZBrdJObmBYyUuSRkJPDZwc+49OgSEk/V/0qgq1R1N9IzeDVhal4tN4g+3YLwWllaWmJpaSm/9/b25tChQ1y+fJkHDx5w8OBBqtjaYaCwJ0PS4dr9ZBF0C4IgCG+kuXPnEhUVhZ6eHp6enhw9epSyZcuWWP6xsbFFDkJ2+fLlfE2EhTdD27ZtOXr0aIHrJk6cKDdT10RJXQfZ2dlMnDiRGzduYGpqSqNGjVi7di26urqsXbuWIUOGFLidg4MDly5deun830djNkZw50k6hrraDPrQiXbu9lSxMUFXW7Pul8q0bB6uvkzWLVUNuZ6TORbtnTgxcxE5UhaGWhIbr27k4qOLAJjomuBb2RcPaw9qlnFn96loAHQNXk3/67xB1ED06RaEEteiRQuaN2/O+fPn2b59Ow/u36Obkw3/d0OHS3eT+Li0CygIgiAIz6hTpw7h4SU/BdrTypUrR0RERJHrhTfT8uXLSU9PL3Dd05UTmiip68DHx6fQ2Q06duyYb17yPM9OJSc8X3xyBievP+JcbAIAOz5vTFVbU423z03KJOtOCqmn78kBt2mLipg0s+f4V/0If5gKCgXmhrqsi1bNMPRFnS8Y5D4ILYUqoM9IzQb+Dbr1Xs0YS3k13VpaWvkG/yxJIugW3lsKhYLatWuTmppKcHAwhukPgAqsOnGTr9tWw0C39J6GCYIgCMKbSEdHhypVqpR2MYQX8Oz83i/jTbgOTE1NMTXVPCgUCnc3IZ3WC46QkqmqFW5ZzUbjgFuZlUvCjuuknYsH5X9Nxa361eDG3rkcWnaWTB0dUCgwyk7np6ZJ/JOcAYBfZT854AbIzvw3QNZRoKVhzfrz5NV0l2YtN4igWxDkUc6zn8Shjx2Z6HDm5hOauJRccz1BEARBEARBKElKpcTsfVdYduSGvKxNDTvGtK6q0fa5yVk8Xn+FzOuq0d+1zfXRLW+CsacthtUsOffNaTJ1VH20002fsKpxImgp0NfW5/Pan2NrbKu2v7ygW1f/1QXIb8LI5SCCbkHA1tYWU1NTkpOT6Vo+maB/ytDntzA+qmbD7C7u2Ji9H6PgCoIgCIIgCO+P5cduyAF3GSNdln5SjwZOz+9qIOUqSdh1g9RTcaoFWgrKdKqCcQM7ri6dScjUUFJzJZ7oqgLu1Oo5bHJMAhR8Ve8rulXthpFu/rm6c/6dB1xX79UF3aKmWxDeENra2jRp0oQ//vgDvUfRdDIw4n6OMRejkmkwM55+jRz5snVVTA1E/yBBEARBEATh7ZaVo+T49YfM3HsFgEFNnPimvdtzp8yVJIncxxkk7Ikh4/Ij1UItBZa9XTm34ktivo8jXksPSUsP/m0dbpqdxf+Vvwso+KnlTzStUPjI89kZr76m+969ewCYm2s+8vrrIIJuQQBq1qzJ8ePHSUpKwoI0LHTScNV5wKUcW1adgIqWRgxs4lTaxRQEQRAEQRCEYsvIzmXD6dv8fuoWtx6lkp2r6n/9oUtZvu1Q+Ej0T0s5+g+Je2Pk91n6d7kZt4WYyU9I0NWHf5uS2+RkUrtBbSyquJLauCGL9vfGTM+syIAbIPpsPAAmlq+ulWl0tGpgNmdn51e2zxchgm5BAIyNjRk1ahSPHj3i/v37HDlyhPj4eGro3EcHJeuOKuhUuxxWJvqlXVRBEARBEARB0Njpm4+ZsPUC0fEp8jItBbiXN2e6X02N9pFyKk4t4L7z5BTHEw6r3ujqo5Ak3M30qFCrFlWHfI22viGXH11m67WtAJQ3KXogv4yUbC4dvQuAp49DcQ6v4P1lZHDkyBF56ri8MZxKiwi6BeFfWlpaWFtbY21tjZubG0uXLiU+Ph5XnQe4Zj7g+znnwKQso/r3pFzZ0m2iIgiCILz7+vXrR0JCAtu3by/torxRZXmbOTo6MmrUKEaNGgWoZlLZtm0bnTp1ei35hYaG0qJFC548eYKFhUWRaVetWsWoUaNISEh4LWURSs+YjRHcfqyaLm5os8p0r1cBBytjtLWKbk4OqiblqX/dI2G7qsZY20yPuPS1HE9Q9QUvn5uJva0Vrp0+xq5VV3m7sLgwhh4cSo5S1afa0dyx0Dyys3IJ33cTSSlRxs6I8q5lXvRQefDgAXFxcRw6dIgnT54Aqsq1VzFv/MsovcnKBOENpq2tTf/+/Wno5UWOnmrKBCNFNkapcfy8fncpl04QBEF4HyxcuJBVq1aVdjGE1yguLo62bduWdjEKtHXrVlq1aoW1tTVmZmZ4eXmxf//+0i5Wqfjpp59wdHTEwMCAhg0b8tdffxWZ/tKlS3Tt2hVHR0cUCgWBgYElU9ACxCdnyAH37i+a8HXbajhbmzw34JZyJRL33+T+3DMkbFMF3EkZ0ew5M5lDV1UBdx0LfXpuDqbZT+vVAu7YpFg+PfApOcocFCjoX6M/I+uOLDSvI+uvEnHwNgAVqxdvzvg8WVlZ7N69m59++omtW7fKAbeHhwcjR44Uo5cLwpvK0NCQtj4+tPXxISEhgZXbg0m8eYnHD+4xaftFejeshJu9WWkXs0iXj99FUkpYlTehjJ0R+kZiMDhBEIS3RWkP/PM+ysrKQk9Pr8Tys7OzK7G8iuvIkSO0atWKmTNnYmFhwcqVK/H19SUsLIw6deqUdvFKzIYNGxgzZgxLliyhYcOGBAYG4uPjQ1RUFDY2NgVuk5aWhrOzMx9//DGjR48u4RKr+yX0OgCutqbULF/w3xRJKZF9N4X0y4/IeZwB2UqyH6SSE58hp7mZfJEzj/aTq6v6LWmTk0nTuWvz7SstO43F5xYjIaGrpcu+rvuwMSr4PEmSxNW/7nP1lGqws0o1LGng+2J9r1evXs0///wDqL5XdnZ2ODk54eHh8UL7e9VETbcgaMDCwoLOH30AgI0imVOnz9Bx4SE+mhdKr2Wn+CchvZRLWLBT268TujaKLT+Es+abk2SkZJd2kQRBEIRnbN68GXd3dwwNDbGyssLb25vU1FT69eun1uw4OTkZf39/jI2Nsbe3Z8GCBTRv3lxuqgyq5sszZ85kwIABmJqaUqlSJZYtW6aW3+3bt+nevTsWFhZYWlri5+fHzZs35fW5ubmMGTMGCwsLrKysGDduHJIkaXw8zZs3Z8SIEYwbNw5LS0vs7OyYOnWqWprY2Fj8/PwwMTHBzMyM7t27c//+fXn91KlTqV27Nr///juOjo6Ym5vTs2dPkpOTX0s+y5cvx8nJCQMD1QBOCoWCpUuX0qFDB4yMjHBzc+PkyZNER0fTvHlzjI2NadSoEdevX5f3df36dfz8/LC1tcXExIT69etz8ODBIs+VQqGQm+xPnToVhUKR75XX2kGpVDJr1iycnJwwNDTEw8ODzZs3q+1v7969VK1aFUNDQ1q0aKH2uRZXYGAg48aNo379+ri4uDBz5kxcXFzYtWuXRtu/is/need04sSJNGzYMF/eHh4eTJ8+HVBNGTVixAj5eh4/fjwBAQEaN+mfP38+n376Kf3796d69eosWbIEIyMjVqxYUeg29evXZ86cOfTs2RN9/dIbDygxPZvfT94CoH0te3m5JEnkJGSQeTOR5GP/EDf7L+IXR5D8523SIx6QfukROfEZ5Eq5RDw+xK7bvxD2cA+GWWnUMtWlV9/e+G/ch47pf0G8UlIS9TiK9tva88fNPwAIqBFQaMANcHb/LQ6uvIxSKWFf2ZwOn3ugb1j8OuHMzEw54G7dujWDBw+mU6dOb0zADSLoFgSNlbOzRf/fm3Fj3Zv4G5yjftIxDG6fotcPW5i68xJPUrNKuZTqHN3LUs7FAoCs9BzO7r8lz4EoCILwrpMkiezM3BJ/FSdAjYuLo1evXgwYMIDIyEhCQ0Pp0qVLgfsYM2YMx48fZ+fOnQQHB3P06FHOnj2bL928efOoV68e586dY9iwYXz22WdERUUBkJ2djY+PD6amphw9epTjx49jYmJCmzZtyMrKkrdftWoVK1as4NixYzx+/Jht27YV69yvXr0aY2NjwsLC+OGHH5g+fTrBwcGAKnj08/Pj8ePHHD58mODgYG7cuEGPHj3U9nH9+nW2b9/O7t272b17N4cPH2b27NmvPJ/o6Gi2bNnC1q1biYiIkJfPmDGDvn37EhERQbVq1ejduzdDhgxhwoQJnDlzBkmS+Pzzz+X0KSkptGvXjpCQEM6dO0ebNm3w9fUlNjZWo3M2duxY4uLi5NfcuXMxMjKiXr16AMyaNYs1a9awZMkSLl26xOjRo+nTpw+HD6sGs7p9+zZdunTB19eXiIgIBg0axNdff61R3ppQKpUkJydjaal589+X/Xyed079/f3566+/1B5+XLp0ib///pvevXsD8P3337N27VpWrlwpz1Sj6dgEWVlZhIeH4+3tLS/T0tLC29ubkydPanweSkNqZg4Tt14gR6n6WzLoQycybybyZMs14maGcW/2aR4s+ZvE3TdQJqm++/ouFpi1ceBh1hHCHuxhb+wSUp8cw0E7Cf9B/Rmy9QCtlm+jXPveaD0173Vqdirtt7an265uPEx/iLWhNR0rd6RXtV4Fli0nK5fglZc4tV3VTN2lvi2+I2o/d8qywuQ1JTc0NKRRo0Zoab15Ia5oXi4IGtLT02P4sGEcOnSIixcvkp2djaEiBwftBCppJbDnpC5RcUmsG+JV2kWVfdTXDYBDv0dy+Xgc54JjuXbmPr2mNETPQHz9BUF4t+VkKVk28nCJ5zt4YTON55mNi4sjJyeHLl264OCgGrHX3d09X7rk5GRWr15NUFAQLVu2BGDlypWUK1cuX9p27doxbNgwAMaPH8+CBQs4dOgQrq6ubNiwAaVSyfLly+UfuCtXrsTCwoLQ0FBat25NYGAgEyZMoEuXLgAsWbKk2H15a9WqxZQpUwBwcXFh8eLFhISE0KpVK0JCQrhw4QIxMTFUrFgRgDVr1lCjRg1Onz5N/fr1AVVQtmrVKkxNVWOrfPLJJ4SEhPDdd9+90nyysrJYs2YN1tbWasfQv39/unfvLp9HLy8vJk2ahI+PDwAjR46kf//+cnoPDw+1mrUZM2awbds2du7cqRacF8bExAQTExMATp06xbfffsvq1aupWbMmmZmZzJw5k4MHD+Llpfqd4ezszLFjx1i6dCnNmjXjl19+oXLlysybNw8AV1dXLly4wPfff//cvDUxd+5cUlJS5HOiiZf9fJ53TmvUqIGHhwdBQUFMmjQJgLVr19KwYUOqVKkCwKJFi5gwYQKdO3cGYPHixezdu1ej8j98+JDc3FxsbW3Vltva2nLlyhWNz0NJu3Q3kb6//cWjfyuDOtSyx0hPh7iNV8l9/F+TcW1LAxQGcOvyQW4+DiflaiIZCm0y/u3/3MDOnA8XHnhufn8/+Js7KXcAqG1dm4UfLcTSIP/DGUmSyErP4e9Dd7gapmrRYOdshnc/N7S0XzxQzpsWrEyZFx+A7XUTv7oFoRjMzMzw8/PDz8+PrKws7ty5w85du0l48pj2+pHk3r3CV3Mu0rlLZxpVLlvaxZW5t6hIQnw6d68lkPIkk0d3UrCvYlHaxRIEQXjveXh40LJlS9zd3fHx8aF169Z069Yt34/HGzdukJ2dTYMGDeRl5ubmuLq65ttnrVq15P8rFArs7OyIj1fNf3v+/Hmio6PlQDZPRkYG169fJzExkbi4OLUmuzo6OtSrV69YNfhPlwHA3t5eLkNkZCQVK1aUAy2A6tWrY2FhQWRkpBwMOzo6qpXz6X28ynwcHBzyBdzP7jsv6Hr6gYitrS0ZGRkkJSVhZmZGSkoKU6dOZc+ePfLDlPT0dI1ruvPExsbSqVMnxo4dKwe40dHRpKWl0apVK7W0WVlZcv/qyMjIfE2t8wL0lxUUFMS0adPYsWNHof2YC/Kyn48m59Tf358VK1YwadIkJEli3bp1jBkzBoDExETu37+v9r3R1tbG09MTpVL5QufiTRf7KA3/5WEkpGWjp61FzwYVGdTEmdzETDngNmvjiEkDO7SMdNnXz5dL6RIoAF1VU3jtXCVVDBU0mrWsiJz+c/SfowA0Kd+EX7x/ybc+N0fJiS3R3Ih4QMqTTHl5tQ/s+CjA7YVruAEeP34sdzko6O/hm0IE3YLwgvT09HB2dqZL505s2bKFxMREtBUSxqn/sHzV/7GlQhW+698GA73S/5qVrWBC5y/rsnPhOW5HPuFO1BMRdAuC8M7T0dNi8MJmpZKvprS1tQkODubEiRMcOHCARYsW8c033xAWFvbC+evqqg+aqVAo5AAjJSUFT09P1q7NPwBSQYHn6yjDq9zHq8jH2Nj4ufnnBQUFLcvLb+zYsQQHBzN37lyqVKmCoaEh3bp1k5vtayI1NZWOHTvi5eUl90kG1ecGsGfPHsqXV5/v+HX3GV6/fj2DBg1i06ZNas2sNfGyn48m57RXr16MHz+es2fPkp6ezu3bt/N1IXhRZcuWRVtbW62fOcD9+/ff2EHwlh29TkJaNuaGuuwb9SH25oYAPAqKBEDbXB+z5qoHHednjlYF3IAD2bg19ETP1JxKHfugb1v0vNp5Lj+6zO+XfwfApYxLvvXpyVnsWBjBoztPzRGurcCtkT0f9qj6UgH3gwcPWLNmDaAaf6lx48YvvK/XrfSjAUF4y1WqVInRo0eTmJbJL8tXkvH4HpW0EyDuDHPmXaLJBw348MMPS32qglxlLuY2htyOfMJfu2JwrFUW64qmz99QEAThLaVQKDRu5l2aFAoFjRs3pnHjxkyePBkHB4d8faidnZ3R1dXl9OnT8nyziYmJXL16laZNm2qcV926ddmwYQM2NjaYmRU8A4e9vT1hYWHyfnNycggPD6du3boveITq3NzcuH37Nrdv35ZrOS9fvkxCQgLVq1d/JXmUZD55jh8/Tr9+/eRmzCkpKcUayEySJPr06YNSqeT3339XC0aqV6+Ovr4+sbGxNGtW8IMkNzc3du7cqbbs1KlTxT+Qp6xbt44BAwawfv162rdv/1L7epYmn48m57RChQo0a9aMtWvXkp6eTqtWreTaeHNzc2xtbTl9+rR8Pefm5nL27Flq16793DLq6enh6elJSEiIPPCaUqkkJCREoy4DpSE+SVWTPNrbRQ64ATJvJAJg8NQc2Nf/vgzoolBKfDTuayzrt9Aoj9TsVA7cPMCJuyfYd3MfABVMKvBx1Y/zpT248rIccDt5lOWjT9zQN9Z5qWAbVNfKxo0bVcdkYECvXr1K/bd2Ud7ckgnCW8bcSJ8xQwcQGXmFDX+eQUr4B/3MdA4fPszNmzcJCAgo1YEdQu+EMiVxJh2NhmGcVoaN352memN7DEx08WhZCSOzkpsiRRAEQVAJCwsjJCSE1q1bY2NjQ1hYGA8ePMDNzY2///5bTmdqakpAQABfffUVlpaW2NjYMGXKFLS0tIr149Xf3585c+bg5+fH9OnTqVChArdu3WLr1q2MGzeOChUqMHLkSGbPno2LiwvVqlVj/vz5JCQkvLJj9vb2xt3dHX9/fwIDA8nJyWHYsGE0a9ZMHjTsbconj4uLC1u3bsXX1xeFQsGkSZOKVas7depUDh48yIEDB0hJSZFrt83NzTE1NWXs2LGMHj0apVJJkyZNSExM5Pjx45iZmREQEMDQoUOZN28eX331FYMGDSI8PPyl5nkPCgoiICCAhQsX0rBhQ+7dU03rZGho+Eqms9Pk89H0nPr7+zNlyhSysrJYsGCB2rovvviCWbNmUaVKFapVq8aiRYt48uSJxt+bMWPGEBAQQL169WjQoAGBgYGkpqaq9efv27cv5cuXZ9asWYCq2f/ly5fl///zzz9ERERgYmIi9zV/XZIzcgAoY6z6XSdJEk82X0P57ww2ph+pHnA8Pn2YGEnVEqFV/RpFBtz3U++z6tIq7qfd51H6I/5+8Dc5Uo68XoGC2U1nU9G0IqkJmRxeF0VGajZZ6Tk8+icVgNaDalClrg2K58wP/jySJHH27Fl5AEFjY2OGDBlS6EPEN8WbN7SbILzF9PT08PCoxZef+rNHUY/T2RUAuHXrFnFxcaVatrTsNBIN49ntsgRJRzWC+eXjcZzdH8uRdVHcu5FIakJmsfrsCYIgCC/HzMyMI0eO0K5dO6pWrcq3337LvHnzaNu2bb608+fPx8vLiw4dOuDt7U3jxo1xc3OTp7nShJGREUeOHKFSpUp06dIFNzc3Bg4cSEZGhvyj9csvv+STTz4hICAALy8vTE1N5ZrGV0GhULBjxw7KlClD06ZN8fb2xtnZmQ0bNryyPEoynzzz58+nTJkyNGrUCF9fX3x8fIrVOuDw4cOkpKTQqFEj7O3t5VdeeWfMmMGkSZOYNWsWbm5utGnThj179uDk5ASoWt5t2bKF7du34+HhwZIlS5g5c+YLH8+yZcvIyclh+PDhauUZOXLkC+/zaZp8Ppqe027duvHo0SPS0tLyTQU2fvx4evXqRd++ffHy8sLExAQfHx+Nvzc9evRg7ty5TJ48mdq1axMREcG+ffvUBleLjY1V+5139+5d6tSpQ506deSR6OvUqcOgQYOKeZaKLzlTFVybGagC6qzbyaSFq5rHG3pYo22ujzI3lz9m/w8AndxcnLsPLHKf/zv1P/4v8v8IvhXM2fiz5Eg56Gnp0alKJwJbBLK3y148rD2QlBLBKy4Rc/4hcdGJcsBtX8Ucl3q2Lx1wA1y7do1du3aRlJSEtrb2WxFwAyik9+wXdlJSEubm5iQmJr4VH5Dw9opPyqDBzBA66l3EUisdl2pu+Pd8NX2MXkS2Mpt1keuYc2YOLqZVCdD/gqxIQ+KjU9XSKRRg62ROjablqPaBfSF7EwThVRH3Jc0Vda4yMjKIiYlRm2v5XZeamkr58uWZN28eAwcW/aNZEAQVpVKJm5sb3bt3Z8aMGaVdHNmr+hvWbM4hbj1KY/NQL+o5WpJ+5TGPVl1Cx8YIuzGeADw5d5wVs1W18l392uHYe1iR+2ywtgHpOen0dO1JPbt6WBlY4WHjga6WKrCXJIm46ARObrvOvRtJqnL0dsXYXA89Ax1snMzQ1Xs1XX2CgoK4evUqlStXxtfXFwsLi1ey3xel6T1cNC8XhNfExsyAnvUrcuXcP1hqpXPtSiTnzp2TRxnNvp/K/YXnUOhoYeBaBrPWDuhaG7228uhq6eJVTjWK6bXkq3yb/AWKsgra5vbDkyakJ2aTmpCFpJS4dyORezcSiQ6Pp27rStg6m6P9ElM5CIIgCC/v3LlzXLlyhQYNGpCYmCgPtOXn51fKJROEN9etW7c4cOAAzZo1IzMzk8WLFxMTEyPP4/2u+X/27js6iupt4Ph3e/qmVwJJIIHQe+8dVEBQpEgRxIbtVQS7KHaxITYsoPxAQLpKR3rvLRAgnfS6ySbZPu8fC4sxARJJqPdzTs5hd2buzATYu8/ce5/n0vRy94sj3ZLRPrtR7no5qV1xsr2uudpiuWbAbbVZKbWUAvBU86fwcipbWeHE1guc2ZtBVmKh470GHYNo3LVyidgqS5Ikjhw5wtmzZwHo37//TQ+4q0IE3YJQg6YPasQMyUrJiTW4yMysWrWKs2fPMnToUCSLBDYJyWSl9EQOpTG5+D3WFE2d6x/pyjuwDW3Ttig0zmXer+dZj2dbPMux7GPE6+JJKUphTeBcznruYPmg5disEllJRWyaF0NhdilJJ3JJOpFLcKQnQ15ocd1JLwRBEITrM3PmTGJjYx0Jnnbs2IGv740rUZmcnHzVJGQxMTGORG/CrWXAgAHs2LGjwm2vvvoqr776aqXbup3+HcjlcubNm8eUKVOQJInGjRuzadMmoqOjb6v7qAxJkigyXFy77WQP8yTTxaD7H1UVStJSANBI1845UGy5PCPSVVU2039eWjHbF511vK7d0JuQ+l606Fv9v7OFCxdy7tw5AAIDA6u12sKNIIJuQahBTioFbwxuSsuDiXRVxxMoL+L06dPMmTOHHt264zY+BH9nH3Sr4zGn6inamoJmXKPrOudvD/QhTaHhvp4diXq8bAcqk8mY1HSS4/XC0wv5YP8HnC84z+g1o/l1wK8E1dUy6q12xB/J5vSedFLP5JN2roCkk7mENbl1ao8LgiDcbVq0aMGhQ4du6jUEBwdz9OjRq24Xbk0//vgjpaWlFW7z9vauUlu307+D0NBQdu3aVeG22+k+KqPUbMVsta8c9nC+ONJ9MeiW/WN6d3GOvVa6hmuvMi422YNutVyNWlE26W5BZonjz0NeaEFIVNlR8OpSXFzsCLijo6Pp0aNyWdZvJSLoFoQa5qRS4OHhwebCegzTJuFkzCM7O5slS38H7E+emw+KJvvbYxjO5mMzWZFfx7oXJ5UCbPDH37upt30A/WZ+j1NQxU8cR0WPQm/W89WRrziRc4IEXQKRXpEolHIi2wQQ2SaATXNjiN2XwV9fH6dlvzo06BCIV2DFNU0FQRCEO5tSqazx7MtCzfh3fe/rcaf8O7hT7uOSXL29frmTSo7rxe+SNrN9NFumVmCzWslYt5g9R86ASoVTJb5u6s32LPpuardy2wpz7Q9x6rXyr7GAu7S0lG+//Raw1+KurhrsN5pYpCkIN0BkgBtmlCzWRbDLHEaRa6hj27p165i15HtWavaTLGWT9tZusn86gTGp8CotXlmT3r0cfz5vUfD180/x2wN9KIypeHTksaaPEeJm74hT9anltrfsXwdnd/vT0sPrk1g4fR/nD2X9p2sTBEEQBEEQakaO3l6j28dV41gSeGlNt0wJix/qz2+/LqRUZf9ep3V1rrihfyg220e6/z213GKyknAsBwAPv2u3818dPHjQUT6vU6dONXaemiaCbkG4Ad4Z3Jine9SjXYQP56x+LMsNJCu8L2onZyRJori4mBxZERvUx9iiPMWpuDNc+O4gpgtFVT5XvYkvMXz4EFr7uSG7WJwgTaFh4euvsHr0ALZNHkFxYmyZY6K9owF4a/dbnMk7g8V2ufaid5Ar4z/qTPshEY73kmNy/8uvQRAEQRAEQaghl0a6fd0uTwO/NL284MxB0hQaAHzMRtoGaun56Q/XbNMx0q10IyupkJ2/n+Ovb47z62u7STtXAEBE85pbX11QYD9HvXr1aNOmTY2dp6aJ6eWCcAOE+7oypV99AD5ad4Zvt8ax5nQ+choS6iZjSGMfrHG70RcVEafIIE6RgVJS8OCPEpFTuyJ3UV3jDGWFDnuU0GGP0iLmEAe/fJ8jBUaKVWrOWYAcPadeeJZANXR56ln8ut5DY9/GbEreRJ4hjwf/eJBIr0iGRQ6juX9zAl0C8XH2oVX/MLwCXVn73QniDmfjGeBC0x61UKqqpwSEIAiCIAiC8N8l5tpHpb1d/xF0X5xenhBnz1he22biweUbK93m/2L+R2R2azoefoDf1xwss02pUdB+UAQBYTVX7vLSKHeDBg1q7Bw3ggi6BeEGm9a/AfX83PjfviSOJBeQpIcv9+ahdYpmQjN3QmV5HD96BIvMSpopF/eZB3HvVgu3LrWQyauWPdyjYSt6fr+M8PlfkbJ/N/rCIs4W2yhVqUiQQP/FFzxUvznjG41HKVeyIWkDx7OPcy7/HB/u/9DRTpBrEC+1eYkejXriX8edrKQi9iyPIzO+kEZdg/EJccNVq6nuX5UgCIIgCIJQSX8cSwOgbbgPAJYCA8X7MwDQy+xZzYNDAyvd3tn8s+xL2c/4uPdRSvZAPiDcgwbtA3HzciKkvhcqTc0OvuTl5QHg6np75xMSQbcg3ATDWtViWKtaFJSY+HzjWdafyiSj0MDn+wrxd3flPv+6WLPiSFLnoDYo8V2rp5aHBtcW/v/pfOFjniF8zDMAdDp5gL2fvcvJYivZKg2/PzWeUUvWMa7ROMY1GkdKYQqLYxdzPOc4SYVJ5BnySC9OZ3HsYvrU6cPQqa3YsyyOY3+nEH80m/ij2QAERnjQdlAEoQ2qlgFVEARBEARBuD6SJHEuyz4q3DfI054f6OL0b0myklp8DmezmTZvfFrpNrembCW4MAqlpEbtrGDcB51QO9248DExMZHsbPv3TDe38oncbidiTbcg3ESeLmreHtyYXS/35OMHmuLupCSryMieVPuanESy2Kg+zm9Ou/hl4yJiYmKw2a5dU/FqtI3b0O/nVfRuYZ+mk6nUkLP9L8f2UI9QprSZwq8DfmXbQ9v4ptc3AMTkxiBJEgqFnM7DI+n/WGPCm/niGeACQEZ8IX9+daxM+QhBEATh1tC9e3eef/75m30ZgiDUkPicYkpMVuQycNue6gi4FZ4aUgr/pMRaRG0XJWqvyq2/Xh23mm8OfcvAM48BENUm8IYG3CUlJcybN8/xOiAg4IaduyaIoFsQbgEKuYzhrUPZ/EI3XupXnzirD2ctvhRp/HDT2KfTZJXksWTJEj777DM2bNiA1Wq9rnM2e3kmWpM9uM86vOeK+zXytdcNLzIV0XVxV7468hWSJFG3pT8Dn2zK6LfbM+KNtviHeWCzSpzcUT4DuiAIgnDrMJvNTJs2jSZNmuDq6kpwcDBjx44lLS3tZl9alRw/fpwuXbrg5OREaGgoH3/88TWPefbZZ2nVqhUajYbmzZvX/EUKwg1wPkvP/V/ba5FHBbhjy7dnMfe8vx5BL7clT38aAFUV8vDM2/8/Osc/6Hgd2ebGBL2ZmZnMmTOnzP/nyZMno1arr3LUre+mB91ff/01YWFhODk50a5dO/bv33/V/QsKCpg8eTJBQUFoNBqioqJYs2bNDbpaQahZ/h5OTO5Rj4c7R7HbEs4yXRjJtjaMNHSirtX+YafX69m9ezcHDx68RmvX5nHxgeXW3Uf4dWgf1o0fxJ4p4znx8VRMuZkAeGm86FunL3KZnAJjAXOOz+F4zvEy7fiEuNGyr70W+LFNKexbHY+h2Hzd1ycIgiBUv5KSEg4fPswbb7zB4cOHWb58ObGxsQwaNOhmX1qlFRYW0rdvX+rUqcOhQ4f45JNPmD59OnPmzLnmsRMmTLhta/0Kwj8VGy0s2p/MwFk7KDTYK8+8OjAa6eKfNRcTnFks9oEalfrqiXl1Rh0/n5jLlC8+oPv2R4nObg9Ak+61CI70rKG7uMxsNvP99987HgDK5XLuv/9+/PxqLjv6jXJTg+7Fixfzwgsv8NZbb3H48GGaNWtGv379yMqquAawyWSiT58+JCYmsnTpUmJjY/nhhx8ICQm5wVcuCDXrjXsb8u3olijlMjYUFeOKEz3MjXnY0JW6ymAAduzYwV9//UVpael/Pk9omP3/jlGpJFul4VSpjd0pOWw4FMP8SQ9jNZYik8n4tPun7Bu1jwbe9inpS2KXlGurVrS3I5nGwTWJzHt5F5t/iSE9Tod0sXSZIAiCUF737t159tlnmTp1Kt7e3gQGBjJ9+nTH9uTkZAYPHoybmxseHh4MHz6czMxMx/bp06fTvHlz5s+fT1hYGFqtlhEjRlBUVHHZSa1Wy8aNGxk+fDj169enffv2zJ49m0OHDpGcnFypa542bRpRUVG4uLgQERHBG2+8gdlsf9h69uxZZDIZZ86cKXPM559/Tt26dR2vV69eTWRkJE5OTvTo0YNffvkFmUzmKBF0NQsWLMBkMvHzzz/TqFEjRowYwbPPPstnn3121eNmzZrF5MmTiYiIuOp+gnArKzVZWXsinY4f/s3Ly09gstiI8HNlxpDGdI3yw3apNrfGPrpittqXJqqcnCpszybZWHp2KQOXD2TnyjOEn2mHQlKCs4VOD9Sj8/DIGruXkpISjh8/zjfffMN7773nWEY5ZswYXnvtNZo1a1Zj576Rbmoitc8++4xJkybxyCOPAPDdd9/x119/8fPPP/Pyyy+X2//nn38mLy+P3bt3o7pY1D0sLOxGXrIg3DADmgSxo7Ynzy86ynMJOmbighMqWuvDSNFkodfrOXDgADExMQwePJioqKgqn6PDJ3OJ3L2ewnOnyT59gozkNEpMFtIUGgpUGrL+XkXQgBEAOCmdeKP9G4xeM5p1Cet4qfVLeDp5OtrSOCt58JXWXDiTz5ENyRTlGTizJ4MzezJo2DmYHg/f3qUeBEG4/UiShMVovOHnVWo0yGRVqzbxyy+/8MILL7Bv3z727NnD+PHj6dSpE7169XIE3Nu2bcNisTB58mQeeughtm7d6jg+Li6OlStX8ueff5Kfn8/w4cP58MMPee+99yp1fp1Oh0wmw9PTs1L7u7u7M2/ePIKDgzlx4gSTJk3C3d2dqVOnEhUVRevWrVmwYAEzZsxwHLNgwQJGjRoFQEJCAg888ADPPfccjz76KEeOHGHKlCmV/n3t2bOHrl27lply2q9fPz766CPy8/Px8vKqdFuCcLsoMpj5aWcC32yJw3QxkHZSyXm4XR2e6RWJ1lmFZLGB1T7YIXeyD4ZYbBIoKg669SY9Mw/OZNm5ZaisGpqn9wIgpK0r941tg0JZ/WO0JpOJrKws9uzZw+nTp8vkK3J2dqZHjx5lHtDdCW5a0G0ymTh06BCvvPKK4z25XE7v3r3Zs6fi9aWrV6+mQ4cOTJ48mVWrVuHn58eoUaOYNm0aCoWoFSzceYK0zix+vAOn0nR8vOEstrMFTLU5McLYiX3Kc8Qq0yguLmbhwoU0bdqUrl274u7ujkqlQi6v3Iekb8d++Hbsxz+f+f88tA/5Kg1bfvqJpqeO4tu8HYG976eJbxMivSI5l3+OdYnrGNFgRJm2vAJd8Qp0pWGnYJJO5bL/zwRyL+i5cCavGn8rgiAIlWMxGpk17oEbft5nf1l6xRGlK2natClvvfUWAJGRkcyePZvNmzcDcOLECRISEggNDQXg119/pVGjRhw4cIA2bdoAYLPZmDdvHu7u7oB9lGjz5s2VCroNBgPTpk1j5MiReHhUrt7u66+/7vhzWFgYU6ZMYdGiRUydOhWA0aNHM3v2bEfQffbsWQ4dOsT//vc/AL7//nvq16/PJ598AkD9+vU5efJkpR8SZGRkEB4eXua9S4mWMjIyRNAt3HHWnEjnleUn0JXaZ5Q4qeT0bxTIB0Ob4qy+HAfZLk4tB5BdfN9sAxSgdnEu0+ZH+z9i4ZmF2CR70NvLsz8KmxKNq5LBj7St8sPDikiSREJCAocPHyYrK4vCwkIMBkOZffz8/IiKiqJVq1Z4enpW+jvs7eSmBd05OTlYrdZymegCAgLKTUe6JD4+nr///pvRo0ezZs0azp8/z1NPPYXZbHZ0VP9mNBox/uMpd2FhYfXdhCDcII2CtXw7vg16o4WX5h9CcV7HNEsDatm8iXPNIdGcwfHjxzl+/PJaa39/fyZOnIhGU/X62V5qBfkSpCs0pB84CftPMLwwn9ChE+gU3Ilz+ed4b997dArpRKh7aLnjFSo5Ec398A11Y/5re9AXGJFsUpXrjAuCINwtmjZtWuZ1UFAQWVlZnD59mtDQUEfADdCwYUM8PT05ffq0I+gOCwtzBNz/PP5azGYzw4cPR5Ikvv3220pf7+LFi5k1axZxcXHo9XosFkuZgH3EiBFMmTKFvXv30r59exYsWEDLli1p0MA+6yk2NtZx7Ze0bdu20ucXhLtFYk4xH6w9zfpT9iUlIZ7OjOlQh8e7RlQYFEuXpparFY7vXZaLq/zUrpfLblltVn4/+zs2yYa72p0x0WMYoHqAvzacwM2z6rN1KmK1Wlm+fDmnTp0qt83JyYnw8HA6duxY5vPtTnVb1em22Wz4+/szZ84cFAoFrVq1IjU1lU8++eSKQfcHH3zA22+/fYOvVBBqhptGyZP96zP4610kSza+sQUQXhTAfqWG84oMzCobZov9CWhWVhY7duygd+/eVT5Ph0cfR/rxO0rNNvJRYFQqWf+/33h06ASGRQ5jcexiSi2lDFs9jCmtp9DIp5Ejy/k/uXpqQAY2i8SFM/mENhQ1vAVBuHGUGg3P/rL0ppy3qi4tm7tEJpNVqUTkfzn+UsCdlJTE33//XelR7j179jB69Gjefvtt+vXrh1arZdGiRXz66eX6v4GBgfTs2ZOFCxfSvn17Fi5cyJNPPlnp+7mWwMDAMuvaAcfrwMDAajuPINxs326NcwTcw1rW4v2hjdEorzzDt+TIxYdtKtg79RGyUjPIV9s/k9Su9gdzJ3NOMuf4HIxW+8Dkjod2oJAriN2bDoCLR/VkCt+yZYsj4K5Xrx5NmzYlMDAQDw8PnKo4G+h2d9PG7n19fVEoFBV+YF7pwzIoKIioqKgyU8mjo6PJyMjAdLH00b+98sor6HQ6x09KSkr13YQg3ARNa3kyZ0xr4jUyPqGUvVhobanLKGNnBpm7MXD0kzRu3BiAnTt3smjRIvbt21elL2+Bve9n6KL1jF62kfYNwwDQqTQUxhwiTBvGsvuW4anxpNRSyoy9Mxjx1wjmnpxLkals0h6FQo7W1z6VafWsoyx+bz8H1ySQdDIXi+n6Sp4JglBzPvjgA9q0aYO7uzv+/v4MGTKE2NjYqx4zb948ZDJZmZ9/f6mSJIk333yToKAgnJ2d6d27N+fOnaux+5DJZKicnG74T3WMEF0SHR1NSkpKme8vMTExFBQU0LBhw//c7qWA+9y5c2zatAkfH59KH7t7927q1KnDa6+9RuvWrYmMjCQpKancfqNHj2bx4sXs2bOH+Ph4Roy4vCSpfv365apwHDhwoNLX0KFDB7Zv3+5I3gawceNG6tevL6aWC7c9q03it/3JPPDtbhYftP/ff7ZnPWY+2PSKAbdkk9DvSqVwkz0ZYnH+BXYlZXPOYt9fY7EQcs8I1iWuY9zacWxJ2QJAq4BWKOT2fYoulhpz8aj6g8OKXJq93LJlSx5++GGaNm2Kv7//XRdww00MutVqNa1atXKsVwL7SPbmzZvp0KFDhcd06tSJ8+fPlwkezp49S1BQ0BVrt2k0Gjw8PMr8CMLtrk/DAP5+sRv+XWqxOsqVYYpijEh4GSWcfzrDwcPOyJX2D8wzZ86wdu1aFixYUG4NTWW0fGOW48+600cACPUI5c/7/+Sh+pdLrnx26DM6L+rMuLXjSNNfrvXa77HGRLUNABnkpOjZtzqBP2cfY/7re9Bll/zXX4EgCDVo27ZtTJ48mb1797Jx40bMZjN9+/aluLj4qsd5eHiQnp7u+Pl3IPbxxx8za9YsvvvuO/bt24erqyv9+vX7T59Nd4vevXvTpEkTRo8ezeHDh9m/fz9jx46lW7dutG7d+j+1aTabeeCBBzh48CALFizAarWSkZFx1UGMf4qMjCQ5OZlFixYRFxfHrFmzWLFiRbn9hg4dSlFREU8++SQ9evQgODjYse3xxx/nzJkzTJs2jbNnz7JkyRLmzZsHUKmHFqNGjUKtVjNx4kROnTrF4sWL+fLLL3nhhRcc+6xYscIxnf2S8+fPc/ToUTIyMigtLeXo0aMcPXq0UvctCDeCJEm8ueokryw/wcGkfAB8XNU8WsF0cskmYS0yUXoql6xZhyn4I96xbUfyQgBczSba+Lsz/OWpzM5YykvbXsJkM1FXW5dpbabxUZePACgtMnF8ywUAfEPduF6xsbHk5OQgk8no1avXdbd3u7up08tfeOEFxo0bR+vWrWnbti1ffPEFxcXFjmzmY8eOJSQkhA8++ACAJ598ktmzZ/Pcc8/xzDPPcO7cOd5//32effbZm3kbgnBT+Hs48do99lEOSZI4uOY8fjsy8ELO0zYPDhnbUX9EECdPn+LkyZPExcXx+eef07VrV9q1a4dSWbn//nK1Gq3ZiE6loSg5wfG+VqPl9fav83+t/o9Zh2exK20XSYVJHM46TL9l/ehduzetA1sT4BJA8KAgWt3TljM7M8hN05N2XkdJoYmVnx/hnqea4Vvr+j/cBUGoPuvWrSvzet68efj7+3Po0CG6du16xeNkMtkVZ6tJksQXX3zB66+/zuDBgwF7QrCAgABWrlxZZhRUuEwmk7Fq1SqeeeYZunbtilwup3///nz11Vf/uc3U1FRWr14NQPPmzcts27JlC927d7/q8YMGDeL//u//ePrppzEajdxzzz288cYbZcqcgT3D+X333ceSJUv4+eefy2wLDw9n6dKlvPjii3z55Zd06NCB1157jSeffLJSuUi0Wi0bNmxg8uTJtGrVCl9fX958800ee+wxxz46na7cDI1HH32Ubdu2OV63aNECsGdTFxVxhJvJbLVRWGpmwrwD7Emyzxwc1rIWD7evTf1Ad1zUZb+3WYvNZM06jFX3jwdGchmu7QLJSlpCQYI9odrQZ54mtVEoj+x8neQi+yh4u6B2fNXzK5yVlxOr7V0ZR2mhCVetmoadgrkeGRkZ/PbbbwC0bt0aV1fX62rvTiCTbnIB3dmzZ/PJJ5+QkZFB8+bNmTVrFu3atQPsdSvDwsIcTz7Bvo7o//7v/zh69CghISFMnDixStnLCwsL0Wq16HQ6Meot3HFspRYu/C8GeZwOAOcOQXgPqsuhQ4dYu3YtVqt9SrdWq+WJJ57A2dn5as05LBjWhwylhiiVjY6T/w+vtj2RV/B/7kDGAZ75+xmKzeVHw9RyNZ1COvFBlw8w5cLidw9gtdhQOym4f0orEXgLd63boV86f/48kZGRnDhxwrF85d/mzZvHo48+SkhICDabjZYtW/L+++/TqJE930N8fDx169blyJEjZQK9bt260bx5c7788strXsfVflcGg4GEhATCw8PvyqmLd4L33nuP7777TiwFFO46FquNs6l5pF5IZvqWLDKKbUy/ryFjOoRVuL8kSeTMPYXxrH0kXKHVoA73QNs/HKWnhmUP9SMRFTJJot47j/LB4Y/Rm/UoZArGNRrHsy2edUwpB/so988v7QSg0wP1aN679n++l0OHDvHHH384Xj/xxBN3dJ6FyvbhNz3ovtFuhy83gnA9rEYLG6bvpIlk/zBdEqZBG+XFvU19iTt7mg0bNgBwzz33lMsceyV/jrmHWNPlKU0uZjOhLkqcXZxoMelZvNt0c2wzWAzsTd/L/oz9JOoSyTfkcyb/DBab/YnrE82eYHLzyWQnF7Hhp1MUZNqnmPeZ0JCotnfuh7IgXMmt3i/ZbDYGDRpEQUEBO3fuvOJ+e/bs4dy5czRt2hSdTsfMmTPZvn07p06dolatWuzevZtOnTqRlpZGUFCQ47jhw4cjk8lYvHhxuTYrqkASGhoqgu47xDfffEObNm3w8fFh165dPPPMMzz99NO8++67N/vSBOGGis/WU1RcQm76BfRqbzrWD8LX7cozPkpP55L7SwwAnoPr4tbh8sj03mkT2ZVoz5l1ISibTS3s37OcFE4sH7ScUI/ymcLPH8pi/Q8nAZj0eVfUzlWfDK3X69m7d6+jn/D29mbYsGGEhIRUua3bSWX78DuvCJog3OUUGiVxvYNJw577YHiikX4bMtj01XHkLuHUr18fgL/++oszZ86QnZ1d5kttRXq+9yUtPDV4mu37lahUxJplHNUZmTvzEy6smOfY10npRPfQ7kxtM5Vven/Db/f+xoHRB3ip9UsAfHfsO17Y+gKHrLu47/+aonayPxzYsfgcFrNIriYIt5rJkydz8uRJFi1adNX9OnTowNixY2nevDndunVj+fLl+Pn58f333//nc3/wwQdotVrHz91QVuZW8P777+Pm5lbhz4ABA6rtPOfOnWPw4ME0bNiQGTNm8OKLLzqmqA8YMOCK1/D+++9X2zUIws1islgpMpjJKjSgN9oHJrxd1fRtFHjVgLtgTbwj4HZp4Y9r+8sPMY1WI7Hn7bk0VBYrW5oU4+3kzcgGI/lr6F9XDLg3/mTPMN6wU9B/CriLi4v59ttvHQF3s2bNePrpp+/4gLsqxEi3INyh9GdyydycjJRRjJP58n/zvcpcTiqPlts/ODiYRx55pFzZmX8rOnOM43M+JTUlgxT55QSGTdyU9P1p5RWPKzGXMGTVENKL0x3vuavdmd7wfc59a39AoFDKaTc4grot/HD3qd4MwIJwq7qV+6Wnn36aVatWsX37dsLDw6t8/IMPPohSqeS33377T9PLxUj3zZGXl0deXl6F25ydnW/IF+nU1FRKS0sr3Obt7Y23tyhBKdyeCkvN5OiNjkD7EleFDVNB5lU/w4oPZpK/9Kz9hUJGwDMtUAW6YraaWXpuKb+e+pV7FlgwqjScr5VBvZEj+b9W/4dcduVx1uUzD5F+3r4sccx7HfDwqdzSw386cuQIq1atAqBr1650794dufzuGNsV08uv4Fb+ciMINUGSJNL2pZO8IYHgEisKZKTJ8zmnSCfXtRS9rRSTyf6lVqFQEB0djbOzMw0aNKBu3bpXbfvkzJfZvfswRSp78O1rNhKodSasdRvqjn0Wpbu2zP5mm5m9aXvZk76H+THzAYjQRvC+zzdsWxjLPz+Najf05p7JTZEr7o4PbeHudSv2S5Ik8cwzz7BixQq2bt1KZGRklduwWq00atSIgQMH8tlnnyFJEsHBwUyZMoUXX3wRsN+7v78/8+bNq1QiNbGmWxCE21WJ0ULBxYD7Eo1SgVIuw0mtQKuSSElOqvAzTLJJ6NYnot9mzy6u8NQQ8GwLTGorK8+v5NeYX0kpSqHffmeCcvwBCLi3IaMe/vCqAXd+RjELp+8D4MFXWuNfp+p9kCRJ/Pzzz6SkpNC1a1d69uxZ5TZuZ5Xtw29q9nJBEGqeTCYjpH0wIe2DKc4uYf0PR2hb6EWwzQsKYB8W9vql4FoUj9Vq5eRJ+5qeAwcOcN9999GqVasrtt14yoc0tFrZ/PgDHC8yk6PSkFNi4+T2fTj9/RD1PDV0nfElzsFhAKjkKrrU6kKXWl2Y1GQSXRd3JV4Xj9Q5n/EfdWbvqjgyEwrJSysmOSaPC2fyqd2o8rVjBUGoHpMnT2bhwoWsWrUKd3d3MjIyAHsSxksJGP9dYeSdd96hffv21KtXj4KCAj755BOSkpJ49NFHAftn0fPPP8+7775LZGQk4eHhvPHGGwQHBzNkyJCbcp+CIAg3gt5oIT5b73jtplESpHXGWX05mdmVSidaCgzkLz2H8XwBAJpIT5T9Anj76LtsSNyA3mxvV2tWE5Lli00OzmYzI+5//aoB97mDmfz9y2kA1M5KfP5DQtucnBz27dtHSkoKSqXyP5cxvBuIoFsQ7iKufi7cN60Dr/7vCB3PFtHGqqAdShpm1yG5VX1qRbqgKykiKSmJ2NhYtm7detWgG0CuUNDnxxU0+3slp5YuJCUjnwK5EoNSyUm9lVPPT0ZrNtGkfh3afvij4zgvJy8GhA1gbeJa/oz/k5fbvkzPMdEArPriCBfO5FNSJOqmCsLN8O233wKUKx01d+5cxo8fD0BycnKZ6YP5+flMmjSJjIwMvLy8aNWqFbt376Zhw4aOfaZOnUpxcTGPPfYYBQUFdO7cmXXr1omRaUEQ7lgmi42UPHsyM2eVAh83DV4uqkotoZPMVrK/O461wAgKGS4t/PG6P5JF5xaxInYZ9VNUdDnhjcaqRq/SYJMDksTEb39G6VbxqKvZZGXL/DOcO2BPtubu7UTfRxuhqOLMQp1Ox+zZsx2v27Zte8vM1roVienlgnAXKzmeTeaiM6hsl9/TRHriOqIuH3/yMQCvvvoqarX6Ci1UzJSfza7XJnMqsxDjP+qB92vTmMZTPnS83n5hO5M3Twbg615f07WWvf7v2u9PEH8km64jomjSvdZ/vT1BuC2IfqnyxPRyQRBuJ0azlbNZei6FW5H+7mVGt//p359hNqOF7DknMKfqUWg1+D7aGJWfCzEb5rN23hxURlfMyrLjpyqrlZa1vOn8xYIKzyFJEpvnnSZ2n332Ut0WfnQbVR9n96p9z4uJiWHJkiWO1127dqVLly7XzAt0JxLTywVBuCaXpn4ofJ34a9Z+uqDCAxnGcwVoiyU0Gg1GoxGdToefn1+V2lV7+dHjmyV0s1rJ3bWOPz7/gny1hh27j9DQanXU+O4c0tlxzNaUrY6gW62xbzcbRTZzQRAEQRBuT6VmqyPg9nfXXDHgrkjR1guYU+1Txz361kHl50JRRhKbvl8ASi3mi1Gcu9lE8/p1CG7fheABI5BXMFAi2SRyLujZuuAMWUlFAPSd2IjINgFVuh+9Xs+BAwfYtm2b471BgwbRsmXLKrVzNxJBtyDc5UKD3JinhQ90RWzR+qLSmbDpTHh7e5Oens53331HVFQUHh4edOnSBTe3yq/5kSsU+HW9h4dq1+O7qS9QolIR9/NMIidNs2+XyXmj/RvM2DuD7NJsx3EqJ/tHk8lgqbBdQRAEQRCEW53Jap9K6OWiJlBbtazghotruLMaG5ld/DnaKQfwiJdjVmlAkvALlDHsrR9w9Qkqd2xJoQlTqYXSIhOFOaUc35pKVmKhY3vtRt7Ua+VfpevZsmVLmWC7Tp06PPjgg1X6Xng3E0G3INzlZDIZbcK8WX0sjVwFBGIvSdGmZWtW//UHVquV06ftiTb27dtHr1696NKlS5XO4RpWn3oqG+ctCvas3eQIugH8Xewf+tkl/wy6L450G8RItyAIQnXo3r07zZs354svvrjZlyIIdw2TxR50q6q4XtqiN2FOtY9Iv2SYQcCmIlolB2FQ2deBhwaqGT5rRZljJEni2OYUDq1NwlBsLtemXCHDN9SdRl2CadgpuFLXYbPZyM7OJiYmxhFw+/n5UbduXXr27Fnl5Yd3MxF0C4JA23B70L0nT8/9qCk9lo3/MXjAuwumECWpqnwOxRwFYPPmzRQVFTFgwIAq1dHu+PjTnP/6W7KVaixFOkc5MT8X+9T1U7mnmHtyLv3C+qG+GHSbxPRyQRCEamc2m3n99ddZs2YN8fHxaLVaevfuzYcffkhwcOW+jN8p8vLyeOaZZ/jjjz+Qy+UMGzaML7/88qqjd3PmzGHhwoUcPnyYoqIi8vPz8fT0vHEXLdzyzFYbOXojecX2hLBOqkoG3ZJE0qJDuJ6xB+tZyjwanyihdkYwyEBus9G9S3NaPPt+mcNMpRb+nn+GuMNZ9jdkoHFWotIo8PB1xjfUjYadgvEJufK/65KSEnbs2EFKSgpGoxGDwUBxcTE22+XEP40aNeLBBx+swm9CuEQE3YIg0LmeL3IZLJFMaJHREiWeyPDMU0Me+ONDY68+7PA+R3xqIvv378fZ2ZkePXpU+hw+nfoj/+prbHI5+cd249d5AAB1tXUJdg0mrTiNzw59xqwjs/ghaBkA6ecKkCSpSsG9IAiCcHUlJSUcPnyYN954g2bNmpGfn89zzz3HoEGDOHjw4M2+vBtq9OjRpKens3HjRsxmM4888giPPfYYCxcuvOIxJSUl9O/fn/79+/PKK6/cwKsVbhcZOgP5JfaAW62U4+F07QRjlkIjFp0J91T7GnCbZCMz9xBRmV4YVKC02hj72mt4tehU7th9q+MdAXeH++vSpEctVNdYP240GikoKKC4uJjs7Gw2btyIxVJ+WZ9cLickJITIyEjat29/zfsQKla1uQ6CINyRwnxdWfBoe+pH+/Gxxsy9FDGUIj6llF1KK5JChirfRue4OtR2sifd2LZtG6tWraKyBRDkCgXOVvuHecKay1OinJROLLxnIVNaTwHAYrPw5elPAdBll3JqR1p13qogCMItp3v37jz77LNMnToVb29vAgMDmT59umN7cnIygwcPxs3NDQ8PD4YPH05mZqZj+/Tp02nevDnz588nLCwMrVbLiBEjKCoqqvB8Wq2WjRs3Mnz4cOrXr0/79u2ZPXs2hw4dIjk5+ZrXm5iYiEwmY/ny5fTo0QMXFxeaNWvGnj17yuy3bNkyGjVqhEajISwsjE8//bTM9vnz59O6dWvc3d0JDAxk1KhRZGXZAwebzUatWrUc5esuOXLkCHK5nKSkJADOnDlD586dcXJyomHDhmzatAmZTMbKlSuveR+nT59m3bp1/Pjjj7Rr147OnTvz1VdfsWjRItLSrtz3PP/887z88ssiABGuqKDUPr3by0VNlL87cvnVBw8km4StxAKShFWysiP9d5YmziQm7zAGlX0K98inHqsw4N7/ZwLHt1wAoFmvUFr2q3PNgPvMmTN89NFHfPvtt/z666+sXbvWEXDfc889jB07lkmTJvH888/z2muvMXHiRLp27Sqmk18HEXQLggBAh7o+/DiuNUff7MPqpzvRpWUIKzAzzVLMR2EqFKHuqFHSsyCaMJt9HfaRI0eYO3cu6enplTqHm8weoO+IS2PNuHtJXPgNAD7OPoxrNI43O7wJwHG3XeQ529vMTiqsuDFBEIRrkCQJm8l6w3/+SzXWX375BVdXV/bt28fHH3/MO++8w8aNG7HZbAwePJi8vDy2bdvGxo0biY+P56GHHipzfFxcHCtXruTPP//kzz//ZNu2bXz44YdXOFt5Op0OmUxWpWnSr732GlOmTOHo0aNERUUxcuRIxxf3Q4cOMXz4cEaMGMGJEyeYPn06b7zxBvPmzXMcbzabmTFjBseOHWPlypUkJiY66sDL5XJGjhxZbsR5wYIFdOrUiTp16mC1WhkyZAguLi7s27ePOXPm8Nprr1X6+vfs2YOnpyetW7d2vNe7d2/kcjn79u2rdDuC8E+ZhQbHZ0Cgh1OFAbdkk7CWmLEWmbDkGbBklTi2bUqbT5ohHm+zgfoqiabuKvq1bYJ/zyGOfSwmK6d3p/HX18c48GcCAFFtA+g4rN41r+/MmTMsWbLEMW3cz8+PyMhIOnTowBNPPEGbNm2IiIggJCQET09PFIrKZ1wXrkxMLxcEoQylQk7TWp58OtyT+1uE8PBP+/gzLoe/gL4uzrxaqqKXqTGbVMdJUuSQnJzMsmXLePrpp6/Zdqchg1j+53oAThvg9Ko1BC5bQVidICLvHcqwzoPx1Hgy88BMjgZvpmfcw6RdyKvhOxYE4U4lmW2kvbn7hp83+J2OyKpQGgigadOmvPXWWwBERkYye/ZsNm/eDMCJEydISEggNDQUgF9//ZVGjRpx4MAB2rRpA9hHhufNm4e7uzsAY8aMYfPmzbz33nvXPLfBYGDatGmMHDmySrXip0yZwj333APA22+/TaNGjTh//jwNGjTgs88+o1evXrzxxhsAREVFERMTwyeffOIIrCdMmOBoKyIiglmzZtGmTRv0ej1ubm6MHj2aTz/9lOTkZGrXro3NZmPRokW8/vrrAGzcuJG4uDi2bt1KYGAgAO+99x59+vSp1PVnZGTg7182g7NSqcTb25uMjIxK/x4E4Z8urePWOqtQKcuPb9oMFiw5pRUea7IZsEkW6rhJDJ2zzlFi9ZL08wXs/P2co+zXJeHNfOkzoVGFbebk5HD48GHi4uLQ6XQYDAb79Wm1PPnkkzg5OVX5HoWqEyPdgiBcUad6PjzeNQIfVzUSsL6klOFSEWdU0MPcmOaWMMD+gX5pqt/VhI95hkdfe4PO4QEEWowAZCg17E3NY/73PzJr9BD85v/FmqFrcPG2r38qSDIyZ8fcmrpFQRCEW0LTpk3LvA4KCiIrK4vTp08TGhrqCLgBGjZsiKenp6OyBEBYWJgj4P7n8ddiNpsZPnw4kiSVm8pdlWsOCrKXLbp0ztOnT9OpU9mpsJ06deLcuXNYrfYkmYcOHeK+++6jdu3auLu7061bNwDHFPfmzZsTHR3tGO3etm0bWVlZjkROsbGxhIaGOgJugLZt21bpHgShOtlsEuaLZcJCPCsuEWb7R5LYUqWRPKWOLGUuenM+FpuJyIf78sBPf5ULuK1WG+t/POUIuOVKGZFtAhjwRBP6P9a4wnOdOXOG2bNns3v3bjIzMx0Bd926dZk0aZIIuG8gMdItCMIVyWQyXhkYzSsDo8nVG5m5IZbf9qfwnLmIb3CltaUu55WZ6Cllzbr13HfPQAIDA1Eqr/zRom3ajnZN29EOiP32XRL27ydZZ6BIpcYql7P5xHlynxjOS2+/zY4D9i9vp44nQtWqlAmCICBTyQl+p+NNOW9VqVRlEy3JZLIyWYNr4vhLAXdSUhJ///13lUa5/33OSwkvK3vNxcXF9OvXj379+rFgwQL8/PxITk6mX79+mEwmx36jR49m4cKFvPzyyyxcuJD+/fvj4+NTpeu8ksDAwHIPJiwWC3l5eWUCeUGoDEmSSNPZR7AVMhmKK6zjtlnsQXeOsoB8pX0JnY/kilmyL83o3GpohW0f3ZhMcYERtbOSh15rg7u3E7KrrBU/efIkS5cudbzu2bMnUVFReHh44OLi8t9uUvjPRNAtCEKl+Lhp+GBoU4a1rMX32+N5OiaTFbjTyRTFevUxMtPT+PHHH3F1deXBBx+kTp0618w6Xv/J16n/pP3PRWeOMect+1q8ozojTxgUpHT2IHFnIU6pvqTF5RMU4SkymQuCUGkymazK07xvNdHR0aSkpJCSkuIY7Y6JiaGgoICGDRv+53YvBdznzp1jy5Yt1RbIXhIdHc2uXbvKvLdr1y6ioqJQKBScOXOG3NxcPvzwQ8d9VZQ5fdSoUbz++uscOnSIpUuX8t133zm21a9fn5SUFDIzMwkIsCf5PHDgQKWvsUOHDhQUFHDo0CFatWoFwN9//43NZqNdu3ZVvmfh7lRqspBRaERvsCBhX8vtrFZc8fuKxWJGiRyZQkagayCuKlfMqakUADLK54MwG62s+PQw2cn2Ee6W/Wrj4VvxKPo/nThxAoCAgACGDx9e7f/HhaoR08sFQaiS1mHe/DC2NV9PaMvRpl6E2HzpYI5Ca/UE7KMX8+bN43//+1+VRmncGzTj6a/mILuYfOSHl17EPe8MALXzGrHikyP89s5+jCXmar8nQRCEW1Xv3r1p0qQJo0eP5vDhw+zfv5+xY8fSrVu3MgnAqsJsNvPAAw9w8OBBFixYgNVqJSMjg4yMjDKjzNfjxRdfZPPmzcyYMYOzZ8/yyy+/MHv2bKZMsVeqqF27Nmq1mq+++or4+HhWr17NjBkzyrUTFhZGx44dmThxIlarlUGDBjm29enTh7p16zJu3DiOHz/Orl27HOu9K/OANjo6mv79+zNp0iT279/Prl27ePrppxkxYoSjXnlqaioNGjRg//79juMyMjI4evQo58+fB+zBzdGjR8nLEzlI7haSJJFTZOR8VhHns4spMpiRkJAhw8dVQx0f1/LHWG0UFepQmu3hl5uTO95O3pQkJVNssX9fqigwO7s/wxFwt74njBZ961TqGgsKCgD7Z4gIuG8+EXQLgvCfdIvy48FRTQic3JxG1lAeNLeiqbElWVb7msK4uDj+/PPPKmXx1fgH08LX3lFZFXIO7FqEWf8bae72Lzb56cUsfHsfMbtEGTFBEO4OMpmMVatW4eXlRdeuXenduzcREREsXrz4P7eZmprK6tWruXDhAs2bNycoKMjxs3t39SSea9myJUuWLGHRokU0btyYN998k3feeceRRM3Pz4958+bx+++/07BhQz788ENmzpxZYVujR4/m2LFj3H///Tg7Xx7hUygUrFy5Er1eT5s2bXj00Ucd2csru1Z1wYIFNGjQgF69ejFw4EA6d+7MnDlzHNvNZjOxsbGUlFzOLv3dd9/RokULJk2aBEDXrl1p0aIFq1evrtLvSLh9JeeVkKYrpeRitQJ3JxURfm40DPYgxMu53NRyySZhzipBU3g59JIX6CiIP4cJ+74qJJy1ngBYzTZObk9lx5Kz7F0ZD0Db+8Jpd1/EVcuPmc1mzp07x/79+x1lBatSkUCoOTLpv9S1uI0VFhai1WrR6XRVXrskCELFirZdQLfWXrKiEIlYbTbHjPZpTQMGDKjyNL0Drz/OjtgLSBc7luTATLrd/y66ta6U6OyjMA07BdFtVH3kCvHsULi9iX6p8q72uzIYDCQkJBAeHi6SA93Fdu3aRefOnTl//jx169a92Zcj3IH0Rgvx2XoAfN00eLqocFZVPJ1cstiwFpvtNbgvJlgzWUsx2gyYbUbHfk4KJbj7kJCYSPy2UjLOFpdpx8lNxejp7XFyK5u7Aex5FNLS0sjMzGTHjh2OEW6w512YOnVquZwPQvWpbB8u1nQLgnDd3LvVwqm+F/Gzj+BhgTY6f5wUkexTnWPt2rX8/fff+Pn54efnh4+PDyEhIYSHh1+xvTbvfk+jlDhW/d8TpCk01M4IQP/zTB7+cR1b5p/h3IFMYnal41PLjaY9Qq/YjiAIgnBnW7FiBW5ubkRGRnL+/Hmee+45OnXqJAJuocakF9iTpblqlAT/I0O5JEnYSi1IBitIEpLVhmQqu8yu2FKI0WqfNSGXJDQKGUqNM8VmD8wFJsylVgoy7e1rXJREtQnAw8+Zui39Kwy4jUYjixcvJj4+vsz7ERERBAUF0bRpUxFw3yJE0C0IQrVQBbri+kwzfl10km7pJhpbQymUlXBamYrRaOTChQtcuHDBsX+7du3o27cvCkXFSY5cQuty/1c/MX/yBApVTmQb1GzYPIv+45/D3duJw+uT2LH4HP51PAiM0N6o2xQEQbjjvf/++7z//vsVbuvSpQtr1669wVd0ZUVFRUybNo3k5GR8fX3p3bs3n376KXB73YdwezBbbZSa7dnHfd00ANhKzVj1ZiSL5BjN/iebZMVoK8Vqs2CyGVBINjy8vFB7+iDJ5OSnFwP241QaBW3vCyc00g8PX6erzubLzc3l999/JyMjA5lMRlhYGLVr16Zly5ZoteJ70a1GTC8XBKHaWaw2Nm9LpNGGVAyY2C8rYbGsAGSlNFBm4Syzl8XwCA4nIqoRXVo3wedi5/VvsXmxbHx0MkaVE3IKeeinRfgo/Jk3bScWkw2FSk7Xh6IIb+6LQilHpblyxlBBuBWJfqnyxPTyGyMvL++KScGcnZ0JCQm5wVf039wp9yHcGjIKDWQV2utcK+VyooPckclkmNKLywTbBmsJNsmeydxqM2OVTNjkEhISFoWEm28A3i72xGa67FJHgli1G6RnpVbqMywvL4+FCxeSk5MDwIgRI2jQoEFN3LZwDZXtw0XQLQhCjSk6kIFu2TnH69OBGl7V5RJiTqGZIo1LsbERJRFhdejfq7ujdMw//fnaw8SeL0BjNrJ5nBvf9f4Osp1Z9/0J9PnGMvsqVXICIjzoPqoBngGiDqVw6xP9UuWJoFsQhBvNZpMoMVlIyivBapPso8o+Lrg7qbAWl2DNt498F5nzsUpmbJINmSRhUlspcrYhyewJET3UHvg5+6FRarDZJAqzSzEZ7IMQbl5OyNW2K36GGQwG4uLiSElJIScnh/j4eEeFmEGDBtGyZcsb+0sRHMSabkEQbjrXxr4YTuRgPJsPQHSGkb9e6MqWpFxOnz6DPvUsLoZcNDILqYlx/PRTHB07dqRv375l2mndYxix53/CqNLQZHU692b3oWfUAF56eRrx2ws4sTUVQ7H9SbHFbCM1toDF7+3nvmebE1zP80bftiAIgiAId4jE3GL0RovjdaNAdySDFWN6LlhVyJBhtpkw24zIAL+QWiSUXsB4cfQ70DUQLycv5LLLU8VNpRZHwK1xUeLiocZgMJQ79759+9i1axeFhYXltgUFBdGnTx8iIiKq+Y6FmiCCbkEQaozcWYnfhMZIFhupb+0Gq4Tpi8MMm9IGedNgZOo+XMjR8ei3Gwkxp1JbUcDu3bvx9PSkbdu2jnZ82/dC8d0PWBVytHpfxq0xsC5nPT0S1uCucmfK4y8xpN4QrGYbaecK2LPiPLmpxayYeZj2QyJo1T/s5v0SBEEQBEG47dhsEvklJvRGCzJkuGgUeLmoseYZkIxWZKgBsEoWSs35KCUJFw83zpckY7HZA+p6nvXQKMsvn7NdDMgVSjkevs7ltptMJo4cOVIm74Cbmxu1a9emVq1a1KtXDz8/P7Gc7jYippcLgnBDFG5OpnBjUpn3ZGoFHn1qc8pfw4i5+2mvTKSBMhsA31oRPDR4AH5+fgCc/fY9zu7ezXmDhFUux8ls5LvBGfZ9nX3Z+MBGlHL7c0RjiZnVXx4lK6kIgL6PNqJeK3/ROQm3JNEvVZ6YXi4Iwo1wIb+E/BIzl8Ikb1c1wR5OmDN0yKSL3zVspVhtZtRqGy7B9qVxhcZCUopSAHBRuRDmEVbhdw99noGSIhPO7mrcve2fV6WlpZw7d44jR46QkJDg2LdOnToMHToUd3d35HJRJvVWU9k+XPzNCYJwQ3j0qo3n/fWQu6rgYv1tyWRF91cCoYvO88WgxpxS1CXFas+4mXMhnq+//pqZ3/7M7nNZRD35GvfO/4sBPTsCYFBpWNv1V/u+pTkcyTqC2WafYq5xUfHAy60JbegNwIYfT7Hu+5PYbHfVM0ZBEARBEKrIbLGhKzajlEArk1NbpSLADJb0YkfAXWwpxGgqAFsJzgFBjmMvjXC7qd2uGHBLkoTZfGmk+/L2oqIiiouLyc62Dz4oFAoaN27MmDFj0Gq1IuC+zYmRbkEQbjhJkpDMNvS70ihcnwiA0scJy8SG/Hk8nQV/H6EZCfjK7bUst5jq8vTQ7jzY2v4kefaw/hiVSu6/tw/vB5xgX/o+R9vh2nDm9Z+Ht5M3hTml/PHVMQoy7e24aNX41/HAv447wfU8CQj3QKmuuGSZINwool+qPDHSLQhCTTLnlCIZLFfcbpWsGCyFuHho0PgElNueWZxJTmkO3k7eBLkFVdAC6PMNlBSaAPAKdEWlUWAwGMjKyiI1NZX9+/dz7733EhERIWbo3QbESLcgCLcsmUyGXK3Ao0co3qPsJS4suQb49AhDzxTz16N9uX/kWGwe9g7LU1bKS0uPM23pcWLSCvGR2TOFrluxhgfdu+Lr7IsMe8eUoEvgr/i/APDwdWb02+3pOiIKjYuSEp2JxOM57P8jgZWfH+GnKTvISiqfnEQQBEEQhLuHZLFhyTOUCbglGaCQI3dRofByotCUg86Ujau3W4UBN1we6b603K3cdpPVEXC7+2iwSmaKiorQ6XQAyOVyHn30UerWrSsC7juMCLoFQbipXJr64dr+4tNgq4QpsZCin0/RLciTfu0aA1BbpUeNhSUHkxg4awc2V/sU9FKVCun7pWwZvoUjY47wUuuXAJh3ah4l5hLHOZp0r8UjH3Xmvmeb0W5QBCFRngBYTDZ2/n7OkflcEAThZujevTvPP//8TTv/+PHjGTJkyC1zPYJwI9lMVswZxdgu1ssuQUKnVaEJcUcd5IrS2wmZBiySPaBWOJVPfHbJpWVuKoWqzPtWs438jGLy0ouRZFYktZGCInsd+aKiIqxWKzKZDDc3NxQKMQPvTlTloPvChQvo9fpy75vNZrZv314tFyUIwt3Fc3Bdgl5pi9+TzVAFuyIZrOh3pznKYPhQyCinI4xxPkq0IpNP/e4n0GwvrRFXaMJmMqGQK3iowUOEuIWQVZLFI+sfcXR+AAqVnNoNfWg9MIwhL7Rk2LRWAKSf13FobeINv2dBqC5ZWVlX3W6xWNi/f/8NuhrhTrB8+XJmzJhxQ8/5+++/06BBA5ycnGjSpAlr1qy56v7p6emMGjWKqKgo5HK5eEgg/CeSJGHKv1yqKwuJXKUMX7eyGccLk+2JzWSAXH3l5SuOoFt+OeiWJAldXjEGSwkWZTFWZSlWyZ6kTS6X4+zsjJubG15eXmLd9h2s0n+z6enptG3bljp16uDp6cnYsWPLBN95eXn06NGjRi5SEIQ7m0wmQ6HVoKnjgXvXWgDot6fifKCEbl26OtZnyiUr7VTJ3Ot6nnUN7kVhtWJQKTk/dyYAGoWGl9rYR7tjcmPYcWHHFc8ZGK6lYedgAHLTimvy9gShRgUFBZUJvJs0aUJKSorjdW5uLh06dLgZlybcpry9vXF3d79h59u9ezcjR45k4sSJHDlyhCFDhjBkyBBOnjx5xWOMRiN+fn68/vrrNGvW7IZdq3BnkCSJnIJSilP1yC8mNctEAlclob6uZaZ2F8SfwyDZXysk6YrTvo1WIyarfeq4Sq7CarVRkK0nIy0Lg7UISW4Gmf14FxcXfHx8CAgIwMvLCw8PD5RKUcn5TlbpoPvll19GLpezb98+1q1bR0xMDD169CA/P9+xz12Wk00QhBqgifRC5mTvePS702hwyJ0XH3+e119/nR49eqBUKpHLJOqqC5B7+SEBRzdvJV1XisVqo1ftXkR7RwOUSbBWkfrtAgEoyCi56n6CcCv7d9+bmJiI2Wy+6j7CrcdisfD000+j1Wrx9fXljTfecPy9zZ8/n9atW+Pu7k5gYCCjRo0q86AlPz+f0aNH4+fnh7OzM5GRkcydO9exPSUlheHDh+Pp6Ym3tzeDBw8mMTHxitfy7+nlYWFhvP/++0yYMAF3d3dq167NnDlzyhxT1XP805dffkn//v156aWXiI6OZsaMGbRs2ZLZs2df8ZiwsDC+/PJLxo4di1arrdR5BAHs67dLMovx0FsuVtqGUoUMb19nanm5oFZeDo8kmw3jxf+HKknCOyyiwjZNVhPxBfHIJTkuVheKCorIysqixFyIdDEPjUKmxNPTk4CAADw9PdFoNGLd9l2k0kH3pk2bmDVrFq1bt6Z3797s2rWLoKAgevbsSV5eHoD4hyMIwnVTuKoIerkNrhcDYmuegaxZRzCdyKNbt268+uqraJxdAMgLicAQEkGKTM3GcffS/a2VvL/mNMPqjgdg4ZmF7E+/8rRa72BXZDIoyjOwce4pclPLL50RhDvB3do/S5KEyWS64T//5SHHL7/8glKpZP/+/Xz55Zd89tln/Pjjj4B9Cd+MGTM4duwYK1euJDExkfHjxzuOfeONN4iJiWHt2rWcPn2ab7/9Fl9fX8ex/fr1w93dnR07drBr1y7c3Nzo378/JpOp0tf36aef0rp1a44cOcJTTz3Fk08+SWxsbLWcY8+ePfTu3bvMe/369WPPnj2Vvj5BuBpJkrCWmDFnl2DOKEZlsf8ftchl4KlBG+SGq5Oq3HGlGReQLiZq9Q6vi7yC0Wij1UhcQRxqixqtWYvGqsFoNCJJNpBAIVPh4+NLQJA/Li4uYgr5XarS8xh0Oh1eXl6O1xqNhuXLl/Pggw/So0cP/ve//9XIBQqCcPeROynxuj8Sdag7ujUJ2Eos5C2OxZJbiluXWjw+6VHmz59Pfn4+VjdPJGRka5zpeWEdc7ZrYGcJXtEqLDYzU7ZNYeODG9EoNOXO4+SqIrShD8mncjm7L5Oz+zJp2b8Oka0D8K3ldhPuXBCE6mQ2m3n//fdv+HlfffVV1Gr1tXf8h9DQUD7//HNkMhn169fnxIkTfP7550yaNIkJEyY49ouIiGDWrFm0adMGvV6Pm5sbycnJtGjRgtatWwP2UeBLFi9ejM1m48cff3Q8fJk7dy6enp5s3bqVvn37Vur6Bg4cyFNPPQXAtGnT+Pzzz9myZQv169e/7nNkZGQQEFA2G3RAQAAZGRmVujZBuBqb2YpVZyqTmdwApGEjMtADhbz8Q0mLvhB9VubFUW4ZznKQVZDgTJIkMouyUJudcbKq7Yu+JTlymwqZJEetUaH1c0VewTmEu0ulH7VERERw/PjxMu8plUp+//13IiIiuPfee6v94gRBuLu5tg4k8KU2jteFm5LR707D29ubZ555Bo1GgySXYw2sjcXFnU6+Eu4aJdhc0MVNAiDfmM+hjENXPEevcdF0GxmFxtX+DPLwuiQWv7ufk9suYDZaa/YGBaEayGQyioqKKCwsRKfTIZPJ0Ov1FBYWOn6EW1/79u3LzEjo0KED586dw2q1cujQIe677z5q166Nu7s73bp1AyA5ORmAJ598kkWLFtG8eXOmTp3K7t27He0cO3aM8+fP4+7ujpubG25ubnh7e2MwGIiLi6v09TVt2tTxZ5lMRmBgoGOKe3WdQxCqi81sxZxVgildjyWzxBFw25yV5DvJScSKTS4rF3BbigspSo4nJzMTgwQSMmSShFtQrXLn0Ol1pGWkoyxW4WxVI5OBzKZAaXXBWeOMb7AWrwA3EXALQBVGugcMGMCcOXMYNmxY2QYuBt7Dhg3jwoUL1X6BgiDc3eTOSgJfak32DyewFhgxXayrLZfLqV+/PsePH6fUyw+8/DhqNTE5Io/FsWYSDKFYC5uj8DjKhqQNdAzpWGH7Lh5qGnerRUQLf2J2pbFvVTwA2347y97V8bTsW4cWfWvftdNzhVufJElERUWVed2iRYsyr+/Wf78qlYpXX331ppy3uhgMBvr160e/fv1YsGABfn5+JCcn069fP8fU7QEDBpCUlMSaNWvYuHEjvXr1YvLkycycORO9Xk+rVq1YsGBBubb9/Pz+8z3JZDJsNnsCqus9R2BgIJmZmWXey8zMJDAwsNLXJwiXSDYJa74RyXT5wblNJSfTakVXanS8p1HJsZmMGPOyMRkMmC02LP/6rHRRyHDxCyxTJsxgMZBemI7GoLFPPJfZLm6R4e7ujou7swi0hXIqHXS/9957lJRUnGxIqVSybNkyUlNTq+3CBEEQLlH6OOM9qgHZ3xzDlFLkCCIGDRpE3bp12Tr/J/JdPSlVqClNiKWbGoIthewrqo+zx1GWn1uOj7MP7QLb0SawTYUBiIuHmtYDwqjbwo+T21KJ3ZeBsdjCnhVxZCUW0mpAGH61b1w2X0GorC1bttzsS7hlyWSyKk/zvln27Sub+HHv3r1ERkZy5swZcnNz+fDDDwkNDQXg4MGD5Y738/Nj3LhxjBs3ji5duvDSSy8xc+ZMWrZsyeLFi/H398fDw6NGrv16z9GhQwc2b95cJnnbxo0bRdZ9oUokScJaYMRW/I9yoR5q5K4qTmfqsVx8SOSmUeKsUuCSn0pW4T/yL1z8bqCUJDRqJU5e3qjcPcucQ2/Sk5WfhZP1ctkwuc0JL39XVEqVWK8tXFGlg26lUnnVD1KlUkmdOnWq5aIEQRD+TR3kBgoZtmIzluxSVP4uKJVKmjVrRu63aRxKv4DV2RWFhyfFHj5EKnMoKQkit7gl+S6HmXN8DnOOz+GTbp/QP6z/Fc/jFehKl4ei6HB/Xbb9FsuZPRnEHckm7kg2PiFuNO1Zi6g2ASjV5dd2CcLNcGmqsXB7S05O5oUXXuDxxx/n8OHDfPXVV3z66afUrl0btVrNV199xRNPPMHJkyfL1dB+8803adWqFY0aNcJoNPLnn38SHW2v4jB69Gg++eQTBg8ezDvvvEOtWrVISkpi+fLlTJ06lVq1yk+brarrPcdzzz1Ht27d+PTTT7nnnntYtGgRBw8eLJMh/ZVXXiE1NZVff/3V8d7Ro0cB+0h7dnY2R48eRa1W07Bhw+u+J+H2YyuxXA64ZTJwUVIgh2KdAYvNhkwmo0GgOyqFHFNBLnk2e8AtA1RIKJUKnLRa1J6+FbafUZxBQUkBHtZL8ZAMuVWNk8YJjbp83hhB+KcqF4TLyclxZMQUBEG4UWQqOepa7piSCsn+4QQBTzdHobV3cvUHDub0LwswWExIRQXIndywqTU0U6ZDVjhuUc34XVpMiaWEd/e+iwwZ/cL6XfV8SrWCnmOjCY325tSONNLPF5CbqmfL/DNsXRCLV6ALvqFuBIRpie4UhEoE4cJNYrFYsFqtaDSXv/RlZmby3XffUVxczKBBg+jcufNNvEKhMsaOHUtpaSlt27ZFoVDw3HPP8dhjjyGTyZg3bx6vvvoqs2bNomXLlsycOZNBgwY5jlWr1bzyyiskJibi7OxMly5dWLRoEQAuLi5s376dadOmMXToUIqKiggJCaFXr17VNvJ9vefo2LEjCxcu5PXXX+fVV18lMjKSlStX0rhxY8c+6enpjjXsl/xzGcWhQ4dYuHAhderUqXSpMuHOINkkbAYL1nwDADJ3NRdMZoqKDWX283VTo5SBMTeL/AIdYK+77Vs3slJLcEpKS/Aw2/89q9VqbCUakCRcPG6P2TTCzSWTqlDXIjExkX79+jlKRNyOCgsL0Wq16HS6GptmJQhCzTCl6cn6+ihYJRRaDS4t/XFp5ocq0BWAFSP7E29TYlMoMfmHYHb3BoUCJBsDhvXkiWP/h8VmT6bSLqgdQ+oNoaF3Q8K14dfscAtzSjm6OYXzBzMpLSpbA7nj0Hq06Fu7Ru5ZuPNdb7/0yCOPoFar+f777wEoKiqiUaNGGAwGgoKCiImJYdWqVQwcOLC6L/2Gu9rvymAwkJCQQHh4OE5OTldoQRCEO4UkSWCTMGeXguXiumqZjBKtiuSCUgBc1Pap5K4aBVonBYVJ8ZTaLrfhqXXHybfi3AGSJGEwGCguLsZsNpcpBejm6o6hQEKukOET4lYteTPEZ9jtqbJ9eKVHuk+ePEn//v0d5SIEQRBuNHWwG94PRpG3KBarzkjRlhSKtqYQ8FxLVIGu3P/bOooTYznxzYfExZ2l2CYjM6IhVndPNv+6im9HfsjcvOXsTtvNvvR97Eu3r6H0d/YnyjuKh6MfplNIpwrP7eHrTNeHougyPJLiAiM5KXpO704n/mg2GQm6G/lrEIQydu3axezZsx2vf/31V6xWK+fOnUOr1TJt2jQ++eSTOyLoFgRBALDojNj0JrgUB8tkyF2V4KREV2xPlubrpiHY0xmzXkdheiZZyC7vjoSnpxaNT0C5tiVJori4GL1e70gWCCAhYZQbCfYKRmZVYKAUuUJ+1yaqFKqmUqv9d+/eTdeuXRk7duxNyQIqCIJwiUtzfwKntsHz/noo/ZxBAmP85aDXNaw+7T+ey+hlG9E9MQtNgQ4kCZOblnV/7OF+6/3M6zaPYZHDiPaORoaMrNIsdqbu5IlNT/DjiR/JLM7kSpOAZDIZbl5OhDX1JbpTEADxR7I5uU1UbxBujtTUVCIjIx2vN2/ezLBhw9BqtQCMGzeOU6dO3azLEwRHGbGKfnbs2HGzL0+4zVj1JmxF/wi45TLQqsmw2YjJ0aMrtc9G0zqrMGSlkZeZhfliwC0DVJKEX53wCgNum81Gbm4uhYWFjoDbprChV+opUBdgUptw0jhhu7geXGQpFyqrUiPdffv2ZeLEibz//vs1fT2CIAjXpPR2wq1dENZCE0Wbk9GtS8BWYsapgTeqIDdkCnsnOKpdHTpteYCXE+aSERYNcgV79+7F+ZgzLz71Iu7u7mSVZBGTG8OS2CXsSN3Bl4e/5MvDX+Lj5MMjjR9hTMMxyGUVP58MrueJXCHDZpU4uSONxt2uPyGRIFSVk5MTpaWljtd79+7lk08+KbNdr9ffjEsTBOBywrOKhISE3LgLEW57kiRhLbSXypO7q1F4qMkrNpFacLnCklwmI1irwXghHpO9qBcKScJd647G2x+Z4so5WPR6vaMUn1FhpFRRinQxulfKlfg520vg2awXg26FCLqFyqnUSLerqyvp6elXHPkRBEG4GVya+oIcJJONwk3JZM0+Suqbu8iZexLJKhHi6cyMIY35ImAEwSf3o8lIAkmitLSU3377jaKiIvxd/Oke2p0ve37JmIZjqOVmD5xzDbnMPDiTr458dcXzq52VjH3fXv8794Kenb+fozC3VHxWCjdU8+bNmT9/PgA7duwgMzOTnj17OrbHxcURHBxcpTY/+OAD2rRpg7u7O/7+/gwZMuSa+Vx++OEHunTpgpeXF15eXvTu3Zv9+/eX2Wf8+PHIZLIyP/37X7magHBnqFev3hV/nJ2dr92AIFwkWWxgk0AmQ+GhRiaTUWiwOLb7uKppFOyBlJHsCLhVkoRPWAROfkEVBtw2mw2DwUBJSQnFxcUAOLk7UaIoQULCTe1GbY/aRHlF4e3sbb+OS5nPxUi3UEmVCrp37drFwYMHmTBhQk1fjyAIQqWpAlzxHd8Y9x6hqIJc7fPGrBKG2HwKNyQCMKZ9HX54rBuF/vVQ52fjkngGgLS0NL766iuOHTtGUVERChRMbTOVtcPWsv2h7TzW9DEAfjzxIwOWDeCLQ19QYi4pdw2uWo0jidqxzSnMf20P/3tzL2nn8m/I70AQ3nzzTb788kvq1q1Lv379GD9+PEFBQY7tK1asoFOninMVXMm2bduYPHkye/fuZePGjZjNZvr27ev4QlqRrVu3MnLkSLZs2cKePXsIDQ2lb9++pKamltmvf//+pKenO35+++23qt3wNYiHXoJw55LM9infMtXltdSmi0nUInxdCfFywazLw3xxm5MMvOtGIleWndxrtVopKSkhPz+fzMxM8vLyKCgoQJIkVCoV6cZ0ALQaLXU86uCudi+zdrsmRrrFZ9edrdLZy9PS0ujfvz9dunTh66+/runrqjEie7kg3Lkkm4R+xwV0axMB8OgXhkePUMf22cP6Y1QqsfiFUupbdi2XUqnkvvvuo1mzZo735hyfU2akO8wjjLc7vk3LgJblzp1wPIf9f8STk2KfxitXyhg9vT0evmIUR7i66uiXTp8+zYYNGwgMDOTBBx9ELr/8TH3OnDm0bduW5s2b/+drzM7Oxt/fn23bttG1a9dKHWO1WvHy8mL27NmMHTsWsI90FxQUsHLlyv90HVf7XVmtVs6ePYu/vz8+Pj7/qX1BEG5tlgJ7AjW5mwqlpxP5xSZS8u0PxC/V4M6LO4dZJkMpSfjWiypzvNlsRq/Xl1mSAyCXy+1L0+RQJCvCaLMnYwtxC8HTybPcdeSl6bGYbWj9nNG4qKrl3nJzc8nKyiIqKgrFVabAC7eWas9eHhwczLZt27j33nur5QIFQRCqm0wuw71bKIY4Hcaz+RSuT0Tl54xzY18AQlQQL4EqK5lD/p0Z2dSNxIR4dDodFouFFStWsGPHDlq2bEnHjh15rOljDK47mBXnV/D98e9JLExk3LpxuKpcaeDdgFfavkJ97/oAhDf1JbypL6VFJpZ+fIjC7FKWfnSQoS+1wtPf5Wb+WoS7QHR0NNHR0RVue+yxx667fZ3OnqzQ29u70seUlJRgNpvLHbN161b8/f3x8vKiZ8+evPvuu1cMko1GI0aj0fG6sLDwiudTKBR4enqSlZUF2GtHi6zCgnBnMReXIllsKGygL7SQkmdfc+0qs1CQkI5JupyhXKVUYDBcrtVdVFRUJthWKpWo1WpUKhV55jz05su5L5QyJYGugTjhVKYNAJPRQmmp/T2LTYlksF7XPUmSRElJCVlZWXh6eoqA+w5VpTrdAKWlpbf1+hsx0i0Idz7JJpG//BwlBzMBUNfxwPuh+ux+6xEOZBUB4GYyUcvXgzajxuLRoS+//fYbSUlJjjbGjRtHeHi443VuaS4vbX+JAxkHHO8Fuwazbti6cl/s8zOK+f2Dg5iNVuRKGVGtA2jSoxZ+td1FECCUc7390vbt2yu1X2VHqP/NZrMxaNAgCgoK2LlzZ6WPe+qpp1i/fj2nTp1y1JxdtGgRLi4uhIeHExcXx6uvvoqbmxt79uyp8Ivm9OnTefvtt8u9f6XflSRJZGRkUFBQUPkbFAThlibZJCSjFclkdaylVnhoyCs1YTEZcbMa+HfYq5KBk5c3kkyOzWbDYrE4gmeFQoGzszNKpRKjxYjJZqLIZP9uoFFqUMqUuKncUMgrDn5LdCasFhtKjQJnt+oZ5Qbw9PQkMDBQfE+4zVS2D69y0H27E0G3INwdLPkGsr46gq3EnmDFo38YmoYKVk0eT4pc7dhPabUyfvo7aBu3QafTMWvWLKxWe/fdtWtX2rVrh6urK2D/Qq8z6jiec5zJmycD0C6oHd/3/r5c55wck8uBPxPJ+Ec5s4BwD/pNaoy7t1ON3rtwe7nefkkuv7y28Wql7i79u66qJ598krVr17Jz505q1apchv4PP/yQjz/+mK1bt9K0adMr7hcfH0/dunXZtGkTvXr1Kre9opHu0NDQa/6urFYrZrO5UtcqCMKty5JvIHfBaSTjxc8vGahbBvBKSib+53ZSVxeDdHE5jcpqIcrHjebPv4nMxZ3FixeXewAXHR3t+KzZk7qHD/Z/cHmbTzQfdf3oitdSUmQi4Wg2RzelADDk/1rg6qmplvtUqVRihPs2dcOD7vT0dN577z1mz55dHc3VGBF0C8LdQ7LaKNyQRNG2C7i09Md7eH1sVisbpz1BRnwiBXIlFoWCdkFaOn+xALB/ln3//feONoKCghg/fjwaTdmO9YWtL7AxaSMA3/T6hi61upQ/vyQRdzibY5tTygTf4c18adGnNkH1PGvgroXbzfX2Sz4+Pri7uzN+/HjGjBmDr69vhftdqttdFU8//TSrVq1i+/btZWZ+XM3MmTN599132bRpE61bt77m/n5+frz77rs8/vjj19xX9OGCcHcp3JJC4fpE5C5KPPqG4dLUl82JeUz69SAzYj8jS22ffdva143On81FoXHm9OnTbN68mZycHMBeJ97Hx4eAgAB69uyJk5MTCboEHvzjQYxWIy38W9A6oDWD6w2mjkedctdgLLWwc8lZzuzJcLwXEO7BA9Ou/fkm3PlqJOg+deoUW7ZsQa1WM3z4cDw9PcnJyeG9997ju+++IyIiglOnTlXLDdQU0WELwt2l9GQOuf87DYBblxBc2wWRbLPQ67PtvBb/HYUy+0dguMxMn+kf496gGTqdjj179rB3715HO1qtFl9fXwYMGICvry9mm5mnNz/N7rTdBLkG0TKgJYPqDqJjcMcKryN2XwZ7V8ahz7eP2mlclIz/sBNKtXiyfbe73n7JZDKxYsUKfv75Z3bs2MHAgQOZOHEi/fv3/8/TFCVJ4plnnmHFihVs3bqVyMjISh338ccf895777F+/Xrat29/zf0vXLhA7dq1WblyJYMGDbrm/qIPF4S7gyRJmJIK0a1PxJRQiEe/Onj0qE1hzCHWvPkyeouETmOfNTakXw/qTngRgN27d7NhwwZHO926daNHjx5l2rZJNvou7UtmSSZymZz1w9YT6BpY7hqKC4zsWRFH7L7Lwba7jxMN2gfSqGsIrtrqGeUWbm/VHnSvXr2aBx54AIvFPlUzIiKCH374geHDh9OqVSuef/7526LWpuiwBeHuIpmtZH1zDHP65VJHTtHefOZuZceuQ4yK/40S9cXp5pJEpMpGjzc+xL1BM5YvX05MTIzjcw+gVq1aTJw4EZlMRoIugaGrh2Kx2bd7O3mzZfgW5LKKqzHabBJpZ/P58+vjWM02Bj3fnNAGlU9MJdyZqrNfSk5OZt68efzyyy8YjUbGjRvH22+/jVJZ6bypgH099sKFC1m1ahX169d3vK/Vah15XcaOHUtISAgffGCfnvnRRx/x5ptvsnDhwjIlytzc3HBzc0Ov1/P2228zbNgwAgMDiYuLY+rUqRQVFXHixIlys0kqIvpwQbg7lBzPJm/hGcdrn/GNyIlZydLflmL7R3UGP7ORhxf9RYFeT0xMDJs2bQLs66Pvu+8+wsPDHdUcUgpTOJx1mA1JG9h+wZ4L452O73B/5P1lzi1JElsXxHJmdzq2i2vIXbRqWvSpTdOeochFbW7hH6o96G7bti2dOnVixowZ/Pjjj7zwwgs0atSIn3/+mTZt2lTbhdc00WELwt1HstooOZqNfnca5lR7dlLfR5vwaWwa87bFMi3nd0oK8zH/Yz1VHUz0ffsjPBq0QKfTkZmZyaJFi7DZbHh5eXHPPfdQr149UgpTOJ13mjd3v0mxuZhmfs2o71WfUdGjqOtZt8Lr2fzrac7sTicwwoP7p7QSHfhdrib6pYSEBCZOnMi2bdvIzs6uUtZx4Ioj5HPnzmX8+PEAdO/enbCwMObNmwdAWFhYmWSEl7z11ltMnz6d0tJShgwZwpEjRygoKCA4OJi+ffsyY8YMAgICyh1XEdGHC8Kdz5hUSN5vZ7AWGJFpFLh3rYV791CWjepHEvaH5D4WE83vuQdt1wHEJSaXSfIok8l49dVXUansSc5sko1FZxbxycFPHA/JAe6NuJcPunzAvx3dlMyupecB+zTylv3qENHcryZvWbiNVXvQrdVqOXToEPXq1cNqtaLRaFi3bh29e/eutou+EUSHLQh3t7zFsZQcycK9V22M7QPo/ek2Cg0W6qjNvJExn9jcYgwXRwWjNTDw1z8dx+7bt4+1a9c6Xvv6+tKwYUPq1q3LypyV/HjyR8c2D7UH3/T+hmZ+l+t+X6LLLmXhW3ux2SSGvNCCkCivGrxj4VZXXf2S0Whk2bJl/Pzzz+zZs4d77rmHCRMm3Baz0CpL9OGCcGezFprImHkAyWRD7qYi4NmWKDzsgfbP9/chX63Bx2ohvvvDqAsSy8xE8/DwoEmTJjRr1gx/f3+yS7JZem4pS2OXklVqLyXoqfGkQ1AHWga05MGoB8skQS3WGTl3INMRcDfvHUrHYfVENnHhqqq9TndRUZGjoUup9iMiIq7/SgVBEG4gVYgbHMnCnF6Mv7sTs0a2YPzcAySZVHi88gOTw7358+F7iDXLSNcbyxzbrl07GjRowI4dOzh8+DA5OTls376d7du34+3tzfM+z2NUGdmj3sNR3VEeXvMwL7d9mdHRo8u0o/VzpnYjbxJP5JKXViyCbuG67N+/n7lz57Jo0SLCwsJ45JFHWLJkSZVHtwVBEG4WU0oRpjQ9ujUJSCYbAP5PNkPhocZmk9i8fgv5ag2SXEF8VEvkOeexYI9JQkND8fT0pFevXri7uwOQU5rDmLVjSNWnAiCXyXmy2ZNMaDwBtUJd5twFWSX8/etp0s9fTnhaq4GXCLiFalWlRV7r1693ZD+12Wxs3ryZkydPltmnMolQBEEQbhZVkL38lznDvsa7e31/Otb1YXdcLhfyS2gb7k3nl14n9r13KVBp2PTYUHrPWe44XqvVcu+999KtWzfi4uI4d+4c58+fJy8vj7y8PAC61OpCgksCOrOOjUkbywXdAN7BriSeyCX/H2vNBeG/aN++PbVr1+bZZ5+lVatWABXW0xb9syAItxKb0UruL6cwXdAjmS6XNJSp5PhPbo7Sx5mUvBJe/P0Y9657H0nthCGoDnKFfY1227Zt6d+/v2PNdk5pDvuT93M06yir41aTZ8jD19mX0dGj6RfWj1D30HLXYCg2s/TDgxgvlhf1r+NO3Zb+NOsVKgJuoVpVKegeN25cmdf/Lu9xPXVABUEQbgRVoD3otuYZsBksyJ2UhHjaE0O9svwE+SVmJnRqTy3JxAWZhmM6E0Uj+zP4l9XI1Zefjru7u9O8eXOaN29OSUkJZ8+epbi4mO3bt5N2IY3X+rzG1PNTOZR5iM8OfsbIBiMJcgtyHH+ptmdJkekG3r1wp0pOTmbGjBlX3C76Z0EQbjX6PWkYL5XTlNlnoqlD3XFu6IMq0BVJkhj27W76n1vMhaBwzF5+SCp7PzxmzBjq1r2cN2VP2h6e/ftZDFaD4z0/Zz/m9Z9HbY/aFZ5fsknsWHzWEXCPmt4Or4vfEQShulU66LbZbDV5HYIgCDeEwlWFwkuDNd9IybFs3NoF0SzUk98PXcBosTHjzxj+OJbG6+/9QuHL4ylUqYm3KVk6+l7u+eBzXOs1Ktemi4sLzZs3B8BisbBlyxaO7zxOhFcE8Zp45p6ay9xTc+lTpw8j6o+gbVBbnFztCV4MxeYbefvCHUj0z4Ig3G4kScJ4vgAAt07BuPesjeJiv3jJ11vOE5R5AmWgFyYnF8f7Xbt2Re2vZkvyFrZd2MbpvNPE5MYA4OvsS+uA1jT2bcz9kffjob7yGtukU7mc3Z8JQJPutUTALdSoqtUQEQRBuAO4tg2icH0iBSvOU3oql4FaNZH3NeG9I8kcv6DjaEoBY+YfZ/lXS9B9+X/sSswiRa7mp5enECCzEhLiR4ePfkShcS7Xdvv27Tlx4gQ5OTm0KG1BG482nHQ9yWn1aTYmbWTHhR3sHrkbzaWgW28p14YgCIIg3IlsBgv5K85jPF+A7eJDZ5cW/mUCbmNmKhfWLsZtx0E6ObtT4hQEkkTDOrWo26cl38Z8y77l+8q13S6wHbN6zsJF5VJuW0WyEgsdf249MOz6bkwQrkEE3YIg3HXcu9bCmKDDeDYf49l8AEI91Cwe24y9+hK+2xbPvoQ85u9P4f2P5+L59dts+nsPRqWSCyi4kK7jwuhBDPrsW1xq1yvTtkajYcKECfz555/2Gt+FFhoUNqChqiFJ6iTOe5wnpzQHJ1f7E3Ux0i0IgiDcDWwGC5mzjmDNs08Bl6nkuLTwRxXsBkDM569xdv8hks0yDC5uWDy8Mfnal2W1czLye8geDvw9y9FeuDacBt4NaBvYlia+TajvXf+q58/PKCb+aDY5F/TkXtBTkFkCQKcH6uHiob7qsYJwvSpdMuxOIcqNCIIA9trdhrP5WHIN6P6Md7yv9HfmWPcgHl9ylCCtE2/c25C24d646LNJWTaXnZu3U6Cyr8dWWa08+vlsXEIrrsedmZnJ7t27OXbs2OXzIuHl50XvTgPY9l0qCpWcx2d1Ewlb7mKiX6o88bsShNtX7sLTlB7PAcBrWCQuzf2QqRQUJ5xm17uvcOLizC+Dfy3MPoGO42r5+3CyQRybUzYDEO0dzfud36eeV73yJ/kXq9VGwtEczuxJJ+lkbrntHr5ODHquOVq/yo2OC8K/VXud7juF6LAFQfg3Y1IhhesTHQldVEMi6PLncUyWy2tlg7VOfDmyBY3keWx8+RnOW+21PYOtRkYu3XjV9ouLi0lOTuanDT/hlm9/ol+vXiRFe4OxWSSRvOUuJ/qlyhO/K0G4PdmMFtKm7wEJfMZE49zI17Ft/cTBnNRbkQCLhzeGEHtJYp1Ng97VyEm/9egVegCebv40jzV9rFIPqpNjcln7/UksxstJJAPCPYho7odvLTd8arnhqtVU740Kd53K9kvyqjRqtVrZvn07BQUF13t9giAItwxNHQ/8HmuKew97ORFpZzob+jTi6U7hjszmaToDTy04jFOtugxeuJa2gfbyiWkKDbtfHIsxM/WK7bu6uhIdHY2suYxDPocASEiOx7OODAmJo5tSavgOhTud6J8FQbiVmS7oQQKFVl0m4LZYLKRKTpSERlIc2cwRcJeg4U+5K/sCl6NX6PFx8qFfWD/GNBxz1YBbsknkXChiy//O8MesY1iMVuQKGY27hfDAy615YFprWvarQ+1GPiLgFm6oKo90Ozk5cfr0acLDw2vqmmqUeEouCMKV2ErMZHx2CJvevs5a5qzE/6lmlLiqaPPeJkxWG8/2imRi53C0zirmD+1D1sWp5jJJws1ipraHhtaPPY1vx37l2k/QJfDytpepfaw2rlb7yLbS7EaIpR2PfNTlxt2ocEuprn7pdu+fK0P04YJw+5EkiYLVcRTvSce5sQ8+DzcEYO/evWzatAmLpWxC0TTJk7+NEahqLUTpfor2Qe35oe8P1zxPsc7Iqs+PkJ9R4njPr7Y7g55r7qgYIgjVrUZGugEaN25MfHz8tXcUBEG4zchdVPg8HI1LC38ApFIL2d8fRx2vY3DDAABmbT5Hs7c30Puzbbj2GUodzGgsFiSZjCKVmlOlEr98MYtfh/Zh79RHsP2jNnK4Npz/3fs/TC1NZGuyAbCo9KTJD7Nw2e8kJibe8HsW7hyifxYE4VaU+2sMxXvSAVCH2oMSq9XqCLhlVgvKghy6ecAO1y5sMEZiQYaLRzIAjzd9/Krtm41W1n1/gnnTdjkC7qB6WvpNasywaa1EwC3cEqo80r1u3TpeeeUVZsyYQatWrXB1LbsO8VZ/8iyekguCUBn6fekUrDh/+Q2VnFOBGt7X6UgqtGde9XBScnx6P2wmE1nb/iD573WcjE0kX3V5ypqv2UjbHp2IfmZ6mfZ3pu5k8W+L8dH7lHm/fmBr+tzbDd9a7jV2b8Ktpbr6pdu9f64M0YcLwu3DWmwm//ezGM7kAaAKdMFnfCOszjLWr1/P4cOHkUkSrmcOIQNSfJux0r0jyA0ENvqcYosON5UbW4ZvwUnpVOE5tv0Wy8ltl5d3uXs70bxPbZr2qHUjblEQai6Rmlx+eXD8n2sqJElCJpNh/ceozq1IdNiCIFSGZLFRtDUFc06pvZ7oxSnn6nAP9PU9GbDuJEbg2Ft90TqXfYqes3s9qz6ZSYH6YvAtSTz44BBqPzipzH46nY5la1eTciwHhcyGRV2EzKrCJ7stXgHu9BzTgKB6njfgboWbqbr6pdu9f64M0YcLwu3BcDafnJ9POl67tgnEa1gkAP9b+Bvnz8YCoNDrcEk5h5PZzA/h43D1C2RghwwWJX4AwP+1+j8mNJ5Q4TlKi0zMnbYLySahclLQemAYLfvWqeE7E4SyKtsvVblO95YtW67rwgRBEG4HMqUcj972zluyShRtS6FwUxKmhELUCYX8LnPnFamElLwStCHaMsf6duzHuF/bc/i9FzkWm0yhWs3uxUvKBd1arZYJI8bwbYNv+WX//+id1hOlAnL991FaFMqiWRfw9fNjyOR2uHtX/JRfEC4R/bMgCDebzWghf8lZSk9dLs/lPaoBLk39uHDhArt27XYE3E56HYqMZLxNBsyjX+WLJo3pEunHO3unAzCywcgrBtwAGfE6JJuE1s+Zh2d0qNH7EoTrVeWgu1u3bjVxHYIgCLcsmUKGR8/aONX3Rr8zlZLj2Xhb4Xtcyf75FLm96uDTMaTMMUp3LW0//JGg5T+zZPFyUhUaCo7twbNZ+S8GDzd8mA1JGzhoPkD77PZIcgvFHgkA6Kzw4zeneXDcfdQKDSl3rCBcIvpnQRButtITOY6AW+6hxjjIm63JByiOLeb06dPYbPZSnDqbBq+kWCxyOR17d6P+Az0BOJVziuXnlgPQtVbXq54rO8VeRsyvjliOJdz6qpxIDWDHjh08/PDDdOzYkdRU+zqK+fPns3Pnzmq9OEEQhFuJOsQN74fqE/x6e3KD7aXE/IqtlK6OZ8Pn+4hNzC93TOjQCXiYTQCsfvvNCtt1V7vzy4BfKPUtZVWdVcR6xRIRFYGbs32aks6SwY8//cDsT38kLi7ujpgmLNQM0T8LgnCzSJJE6Ul7wK2J9EQ5MZz5y39j//79nDp1CpvNRoakZac5jC5Zp7DI5WgsFuo9OgUAm2Tjnb3vAOCsdKZVQKsrnkuXXcKBP+0Ppz0DXGr4zgTh+lU56F62bBn9+vXD2dmZw4cPYzQaAfvaxPfff7/aL1AQBOFWI3dW0vSZVmzs5Esy9qf2DTNNLPnhMPP3JlFsLFv+JMzL/oUgW6Xhf8P6lMlofomH2oOF9yzEIrdw0vMk0T2jeebZZ2ka0Ae1xROAnKILzJ8/ny+++II9e/ZQWFhYszcq3FZE/ywIws1QfCSL7B9PkPHRAXvSNBloB0Zw8tRJbDYbNqUzR2x1+NtUj/XGSIamrCchvwCACDclCo39IfYfcX8QkxuDk8KJ1UNW46x0LneuojwDp3en8ds7+wFw8VAT1Sbght2rIPxXVQ663333Xb777jt++OEHVKrLyYM6derE4cOHq/XiBEEQblUymYxH7oum9fROFPrYE6Y9YlXz7cpTTF5wqMy+veYsI1xmT8SWqdRQmny2wjZD3EJoHdAagJzSHDTOSoY+2Ymp058hwqMtaoMPMpuCoqIi1q9fz5dffsnq1auJiYkhLS3NMW1PuDuJ/lkQhBvNmFRI/uJYjOcLsBYYQQ5eQyNRBrhwNtE+2+ZAqS/HTP4UOvkzuUckHsXZjuNrjXyAzw59xus7X+erI18B8FjTxwh0DXTsk5uq5+/5p/n5pR38+upu/v71DFazvb/rM6EhXoFlKzUIwq2oymu6Y2Nj6dq1/BoLrVZLQUFBdVyTIAjCbUPtpKTeiGiyvj4KwBLc+ftsMX8eT+PepsEAyBUKhi5az6fD7wGZjMLYE7iGR1fYnp+zHwBZJVmO95RKBfc81JUVn7ph0BkpcUkF7wJKzAUcPnzYEVCFhYUxcuRINBpNhW0LdzbRPwuCcCOZs0vI/vaY47XvI40o9VDz8b4ETq9ZS0MpCQCd5EzXKD9+GtcahdXCrB/UADw04gFek7Zw+OTlh4IquYr76t5nb99k5eBfiRxen1TmvF5BrgSEexDexJdaDbxr+jYFoVpUOegODAzk/PnzhIWFlXl/586dREREVNd1CYIg3DbUoe54PRBJ4ZYUrLkGeqJi1e+xpOVY0Hpq0ERoUXo64WYxo1epKUqOI+gKbfm52IPunNKcMu/7hLgx9r2OnN6dzs7fFVBamwGPBpGSkUBycjIZGRkkJiayefNmBg4cWMN3LNyKRP8sCEJNMqXqKT2Zg63UgrXA6Ki/DSCNqs/EHWfJTjhNU2U6DWX2XCZmuZreraN5bVBTVAo5GZtWY1XIkdts+PYZwskV9tHtSU0mEeIWQiPfRo5R7n0r4zn2dwoAHr5O9BgTjW8tN5xcVQjC7abKQfekSZN47rnn+Pnnn5HJZKSlpbFnzx6mTJnCG2+88Z8u4uuvv+aTTz4hIyODZs2a8dVXX9G2bdtrHrdo0SJGjhzJ4MGDWbly5X86tyAIQnVwbR2Ia+tAUj4+gCzPwGCzEtuGJC6lVlP6OtMycBDnS8+Tfvw4UVdop6KR7kvUzkqa9Qol6WQOKafzOfFHIcOm9kXjrOTYsWOsWLGC/fv3ExAQQMuWLcvUahbufDXRPwuCIIB9Gvk/R7UvUYd5YG0XSL/lR/AzZdBFbR+VlmQK2rRpTfeuXXBzc8OiLyRt25+s+OlXUClxtpoZvmksJpsJT40nz7R4xtFnSTaJI5uSHQF3VNsA2g+pK0pnCre1KgfdL7/8MjabjV69elFSUkLXrl3RaDRMmTKFZ555psoXsHjxYl544QW+++472rVrxxdffEG/fv2IjY3F39//isclJiYyZcoUunTpUuVzCoIg1JSAh6M5ty2ZLcfS8UJGB09X1AUmLDmlBHk0I8ijGdmGCyx+cCD3fvxpuWnmVxrp/qeeY6NZ+uFB8tOLObU9lZb96hAdHc369espKSnhjz/+4NSpU7Rv356goCDc3UU5lbtBdffPgiAIADajlew5xx2v3brVQuGqQuHlhCLKiyHf7sLTlE1HdSIA9evXZ8iQITg72xOhmXIz+WnSWEpUKlApkdtsxIYUkKDLAOD+yPsdAbfNamP3sjhHwB0c6UnPcdEoFP+p4JIg3DJkkiRJ/+VAk8nE+fPn0ev1NGzYEDc3t/90Ae3ataNNmzbMnj0bAJvNRmhoKM888wwvv/xyhcdYrVa6du3KhAkT2LFjBwUFBZUe6S4sLESr1aLT6fDw8PhP1ywIgnAtXT/eQnJeCQANPJz5rFM9tEdjkdLtT+ozS5PYmrEIV7OZIY9PIrDfAwDsT9/PxA0TAdj20Da8nSper3bgrwT2/5FAw05B9BhjD9zz8/OZN28eOp3OsZ9Go2HChAkEBIjsrreq6u6Xqqt/vhWJPlwQbhzJJmE4k0fxgQwMp+1TyX3GN8KpvheJWYUcOBnLgSPHsRZm4yG3V0vw9/fn8ccfR6FQONpJ/O0blq1cA4DGbGBLy1yy67kzsclEfCyB+CRFYDFIWMw2Eo7lUFpon5repHstOj1QD4VSBNzCrauy/VKV/xVPmDCBoqIi1Go1DRs2pG3btri5uVFcXMyECROq1JbJZOLQoUP07t378gXJ5fTu3Zs9e/Zc8bh33nkHf39/Jk6cWNXLFwRBuCE+GtaU3tEBaJRyzhSW8tKxJIKf7Y33+IbYJAsBznUYEPIoDQIHcmbZX47jLo10A/Rf1p98Q/na32AvkwJQcvHLCYCXlxfPP/8899xzD3Xr1kWhUGA0Gvnpp5+Ij493lJAS7kzV2T8LgiCUHMsm99cYR8Atb+DFWydTaD/9D775ZjYxO9bgqr9gD7hlMlq2bMm4cePKBNwAuadPAqAxl/L94Ez6DXqSFYNX0DilG8nfqzm8JoXjf18gZkeaI+Du9EA9uo6IEgG3cMeo8ki3QqEgPT293NTvnJwcAgMDsVgsVziyvLS0NEJCQti9ezcdOnRwvD916lS2bdvGvn37yh2zc+dORowYwdGjR/H19WX8+PFXHek2Go1lvmgWFhYSGhoqnpILgnBDZBUa6PrJFgxmGw0C3enfOJAJaCjclOzYx2Q1sP3Cr1gMmTRvUo8lvdxYm7gWgK97fU3XWuUzUiccz2HNN8eRyeCJr3sgl5dfv52Tk8P333+P2WwvV6ZQKKhduzZhYWFERkYSFBQk1n3fAqpr9LY6++dblRjpFoQbQ5Ik4mbsxanE/rmxCBMrKMVXkUYzZTpymYRFpsSs8SSiQWNG9GiBVqst105GcQbLxo3EoHBFTiH9v/meSI8oVn52hMyEQgD8wzyoVd8LhUqOq1ZNnca+uHmJKhzC7aGy/VKl13QXFhYiSRKSJFFUVIST0+VkBlarlTVr1lx1DXZ1KCoqYsyYMfzwww/4+vpW6pgPPviAt99+u0avSxAE4Ur8PZx4uF0dftyZwJmMIs5kFDHy1Z74N/Qhf+c+8vcW46b2ol3wA6y58APbzyQx/dEvQQZrE9YyefNkXm/3Og81eKhMu65a+0i3JMGqz48w+P9alAu8fX19GT16NJs3byY7OxuDwUBCQgIJCQls2bKFgIAA+vfvT3h4+A37fQjV71bonwVBuP1JksSBhDxSdqTgGqsjQNJzWJnOflkxVlkpvWQGLnUzzi4uPDZpEl5eXldtc9PbkzEo7HW0i5ppifaJJu5wliPgbtY7lE7D6okHwMIdr9Ij3XK5/Kr/IWQyGW+//TavvfZapU9uMplwcXFh6dKlDBkyxPH+uHHjKCgoYNWqVWX2P3r0KC1atCgzbcVmszmuLzY2lrp165Y5Rox0C4Jws9lsEmezinhqwWHis4tRKWQsebwDLWp7Yc7VkfnpcbDBhqTvybcVAOBuNrIvKo/90fapdoGugQyNHMojjR7BSemEzWpj/Y+niD+SDUDXEVE06V7ritdgtVpJTk4mISGBrKwszp07h9VqBWDkyJHUr1+/Zn8JwhVd7+htTfTPtyox0i0INUOySqydexS38zoiUJAn07NafQCLzFZmP5VKRe3atRk1alS5aeT/duSdZ/n7VDwA7mYDk5ZtJDkmj83zYigtMtOgYxC9xkZftQ1BuNVVtl+qdNC9bds2JEmiZ8+eLFu2DG/vy8l91Go1derUITg4uMoX2q5dO9q2bctXX9nr9NlsNmrXrs3TTz9dLpGawWDg/PnzZd57/fXXKSoq4ssvvyQqKgq1Wn3V84kOWxCEm2XW5nN8tvEsAO4aJYfe6INaKSfzqyOYU/UAlFgKOZL7NznGC5htJtSmQlJ9i8nyMpPvZqPI34kX+71N37C+AGyZf5qYXekA9H6kIfXbBVbqWvLz8/ntt9/IyrKXJhs7dqyo5XyTXG+/VFP9861I9OGCUP2sxWbyl8RiiLXnENFjYKXLEQy2Ery8vGjXrh1+fn74+fnh7u5+zVFpm9XKjudGczDb3q9pzKU0euIhfFQD2bsyDrPRipOrihFvtsVVK6aRC7e3ag+6L0lKSqJ27drVNg1k8eLFjBs3ju+//562bdvyxRdfsGTJEs6cOUNAQABjx44lJCSEDz74oMLjr7Wm+99Ehy0Iws10MlXHvV/tBGDFUx1pUduL4gMZFPwZj2S0ltu/2KIjSX+KMwX7MEsmkCTULsU8M28rAKV6Ewve2oux2L7ubti0VvjVckehunbymczMTH755RdKSkrQarV0796diIiICtflCTWnuvql6u6fb0WiDxeE6mUrMZP62SFkejMWJObK8tH6xFGot/9fe/zxx3Fxcal0e7rj+1g4/U17eTBAbTazqruVMblvoc+05xdxdlcx/NW2Yt22XdKj/QAAx3ZJREFUcEeosezlp0+fZteuXY7XX3/9Nc2bN2fUqFHk51ecZfdqHnroIWbOnMmbb75J8+bNOXr0KOvWrXOUt0lOTiY9Pb3K7QqCINyKGodoaR9hH4l8bYU9o6trm0BC3u5I0BvtcW0fhMxJCRfjJlelloaeHRka9n8MDn0ab00wplI3fr6/D9ufGYnCrGP4q20c7S/76BA/vridv745zsE1iRhLzFe8loCAAJ566ik8PDzQ6XSsWrWKL774ggULFnDhwgVMJtMVjxVuPdXdPwuCcGeTLDZyV5wHvYk8DHylOopMc4RCfSFKpZLRo0dXKeC2lhTz69tvOQJud6scm+9E7o951R5wy6DD0Lo8PKODCLiFu06VR7qbNGnCRx99xMCBAzlx4gStW7fmxRdfZMuWLTRo0IC5c+fW1LVWC/GUXBCEm23JwRSmLj2OSiHjwGu98XQpvyxGsklIJivFBzMp3JSEZLCPgmebU9iWugSrdDETtSThaTZRq9048i1R6LJKsJjKrsFz1aqJahdIh/vrVjgKqtPp2LlzJ2lpaaSmpjrel8vlNG7cmMGDB19z7Z7w31VXv3S798+VIfpwQbh++cUm3lx1koGn9dQ3W1mlPoBOXuLYHhgYSN++fau85GjrU8M5lGtvp2OoD5nRT5J6oASrzIJvoAet+odVegmUINwuamx6uZubGydPniQsLIzp06dz8uRJli5dyuHDhxk4cCAZGRnXffE1SXTYgiDcbJIk0eXjLVzIL8XTRcXItrUZ1bY2od4VjyhIkoQls4TMLw4DYMNGgfEsuy78QYncHmC7mU2M/24eKt8g0s7mk3q2gJidaWXqeA98qinhTa9e+SEmJoZdu3b9P3t3Hh7T9T9w/D1L9n3fJJIQkRCxhcauQlD71qK20o22SlWrrVK+RVtUq/1Zi2gFRaiWqkjFvkQWa4RESJAEkX1PZn5/jAzTREwIsZzX88zD3Hvuuefemcy9n3s20tPTKSwsBODNN9/EycmpJg5dqERNXZee9euzNsQ1XBAeXmFJGXN3xhJ05ArtkDMPQ/7ROUmy7BagmvHCz8+P1q1bVyvfm/t3ELV6GWfyVdcjm5IiRoaEsmTqbhTZci423ceid8RMQsLzqcanDCunq6tLfr7qKdaePXsYOXIkAJaWlmRnZz9kcQVBEF4cEomE+YN9efvXSDLzS1gSnkDwsSTGd6pH2/rWeDmYIrtn+i+JRIKOvREmLzuTEX4ZuUKGpV5DBn3SFWXOKTb8vIRcHV2WjR9LYJe2eL77BXUaWuL3iit5WcUc2ZrAxYg0onZdwdXHqso+v97e3nh7e6NQKFi0aBHZ2dlkZ2eLoPsZIK7PgiD8V5lCyeX0PKKuZPC/HbFkFZTQCBnzMKSEUq7K0gHo3LkzHTt21DrfvPiz7P1qKqm5JWTdM4ixRKmk8+tDWf7vWhTZqhk1LGyNavagBOEZVO2gu127dkyePJm2bdty/PhxNm7cCMCFCxeoU+f+09UIgiAId73kbsWuD9uzJfIqf59J5ez1bOb+fR4AbwdT1ozxw9ZUX2Mbs26uFLY14NiP22me1ZBD8Qfo99oIXo46wu6jpyiRydgbuh/Pd1XppTIpJpb6NOvmwsWINFIvZbFr2Rm6jPZCV7/qn3+pVIqTk5M66BaefuL6LAjCvTLzixm09AjxN3LVyzogZ6bciIyyHP7ROYkSVYPXtm3bapVn7I8ziD18lORSGaUyKdwJuI1KSrCSKek242tuOlnw74r5tGcIAAPa9qzhIxOEZ0+1B1L76aefkMvlbN68mSVLlqhrP/7++2+6d+9e4wUUBEF4XjmYGfDeyx5sesefSQENaOykapZ0LiWb/v93mMp6/9gZ2eHu5AFAcmoi2+K3Yf3OREa8p4q082U6KMo0R0G3rmOMT2dV0HUp5iaHtmhOvXg/5c2kjh49ys2bNx/uIIUnRlyfBUG41/L9l9QBdwtbExY4WDMHQygt4W+9GHKlqi5Evr6+yOUProc78MEwdh6KJFGpQ6lMin5pKS85WTLy/Qm8E/IPg37/h5/zQum/vT9GReYANO7ohI9d48d2jILwrKh2n+5nnegPJgjC0ywk6iqTfz8JwMkZ3TAz0KmQ5tbfFyncl8p2i3CW2P+OXCLnbffh5C0OB+CdbxZg5OpZYbvEkzfZueQ0ch0pY75th65B1TdZV65cISgoCIVC1U9v2LBheHh4PNdTUtUGcV3SnjhXglC1jLxijl5K58d/44lNUbVS+vYlN9qdzkaRV0KOpIC/9WPIVqq6ogQGBtKiRQt0dSsO6Plf/zcgkAIdHSRKJV1bNsLzzY/RtbBRr5+6byp/X/4bw2JThkfNQKaU49+/Hs0D6z6egxWEp8Bj69OdlJRU5XoXF5fqZikIgiDcMaB5Hf63I5bbecVcyyioNOjWMzGkEOiT0Yk8s2KOEs2Ki8GMKLOjTCYjIvIvOlUSdLs2scbczpDMtHx+m3EUK0cjGvo74OFnh1RaMZCuW7cuI0aMICgoCIDg4GB0dHQwNzencePGtG/fHqm02g2mhMdEXJ8F4cWkVCrZEnWNNYcTOXMtGxngipSx6NFZXx/Xo+kogHyDUnbrnSG7UBVw9+vXj6ZNm2q1j7KiAgrv1Ia//u7b2Hbuo153KesS2+O3s+vyLgD6lo5CplSldahnVmPHKQjPsmoH3a6urlXWcpT9p1njs6qsrIySkvvPbysIgvC4NHEw4GJaGeHnruFuWbH2QWElp9RE9Ts8OD2QwcpApIVKcp1vEpH+D2fW7cJOaoJX/7c0tpNIJLQdVJ/dv5ylILuYq9nFXD2fwb+/xtIswIWX+tWrsC83NzfGjRvH/v37uXjxIiUlJdy8eZO9e/dy4cIFxo4dKwLvp8SLcn0WBEHT/ou3mLLpJN7I+A4DmiFHnzu/BYVKkIDSzYDdRUfJSFfVfo8YMYJ69Sr+5t9PzvkYlBIJKJVYte6iXn4j/wav/fUaBaUFAPhY+9CxoDMxJOPR0haH+uY1dpyC8CyrdtAdHR2t8b6kpITo6GgWLlzI119/XWMFqy1KpZLU1FQyMzNruyiCILyg3m5mQm6RIRIKuJhwCXkltdDK3hYoi8tU83mXKaFUgc4lOS1P9mBX0Up2BW+jLCkZ7/dmIr1njm1XH2tGzWlD6qVszuy7yuXT6ShKlUTuukIdL0vqeFpU2FedOnUYNmwYJSUlZGRkcPDgQU6dOsW1a9fYvHkzzZs3x8LCAnNzczGfdy163q/PgiBUbs+5NADelOjjp1T9BislSgodZRS76HCpLIXIU3d/H3r37l2tgBvgxrFwAPTLypAZ3h2N/OTNkxSUFqAn02OG/wxednmZiN+TAbBwEKOWC0K5agfdvr6+FZa1bNkSR0dHvvvuOwYMGFAjBast5QG3ra0thoaGou+iIAhPXKlCQcKdwW8czA0w1q/YxPxeCoWCvNw80nSvI8cOn6wWnM2L4Z/D0ew+2AddRRlmlDFw0VIMneuhZ6hD3cZW1G1shUKhZPsP0VyLy+TPH2IY/FlLrOuYVLofHR0dbG1tGTBgABYWFuzbt49z585x7tw59XpfX1969uwpar9rQU1en+fOnUtISAjnz5/HwMCANm3a8M033+DpWbHbwr02bdrE9OnTuXz5Mh4eHnzzzTf07Hl35GKlUsmMGTNYsWIFmZmZtG3bliVLluDh4aH9gQqCoGHfBdVAl01MDSCrGNOudTljcJXdu3dDumbaPn360Lx582rvIzr0X5DqYS/XHArqVoFqju/2Tu3pXa83AMWFqlY1OnriIawglKt20H0/np6eRERE1FR2taKsrEwdcFtZWdV2cQRBeIFZmEBmQTGpeQrqGcox0K3659rQ0BBlbglpxak0vBiAWaYjZzKPkV1yiyKphBvIOfHtF3RYvF5jO6lUQqdhDdn+Yww56YXs+PkUw2e9hFyn6pultm3bUlpaSmpqKjk5Ody8eZOSkhJOnDhBeno67u7u6OnpYWJigrm5Oba2tqIWvJY8zPV53759TJgwAT8/P0pLS/nss8/o1q0b586dw8io8tqrw4cPM3ToUObOnUuvXr0IDg6mX79+REVF0bixavTib7/9lh9//JGgoCDc3NyYPn06gYGBnDt3Dn19/UrzFQTh/lKyCki6nY9cKkHvTrBr0MSaw0G/q9PUq1cPc3Nzunbt+lB/Z1mnjnFVqgeA/8hRGutu5qsCfmsDa/WykiIRdAvCf1U76P7vfK1KpZKUlBRmzpz5zD+pLu/DbWhoWMslEQThRWekJyOzABRKJfE38mhgZ4zeAwJhIytTJLduotCX4GTsjZOxN+d0znPhwm8UYULEjRycf12M24j3NbYztzPk5ZFe/PF9NLkZRexeeZYOrzXA0Eyv0gHWAHR1denatav6vUKh4K+//iIqKorExEQSExM10hsaGtKwYUP8/f2xsbH5b3ZCDajJ6/OuXbs03q9ZswZbW1siIyPp0KFDpdv88MMPdO/enY8//hiA2bNnExoayk8//cTSpUtRKpUsWrSIL774gr59+wKwdu1a7Ozs2LZtG6+99lq1yigIAsRdz+ZVdAmU6cGdYLdMX0Jurqq11CeffIKBgcEj7WPnrC9Apod5cRGOrwxTL1coFaw4vQIQQbcgPEi1g25zc/MKTa6VSiXOzs5s2LChxgpWm0STckEQapulkS56chmXbuWiRElmQQl2Dwi6ZXpyZCa6SNtZkbEvEYsCEzxLPFjSNJ9OkYaUymSE/PUPXVOTaTzxf0jvmSKmjqcFL4/0Ijz4PIknb5F48hYSqQQjM12MzPVwrG9OwzYOWN6nj55UKqVPnz40btyYixcvUlBQQGFhITk5OaSnp5Ofn09UVBQpKSm89dZb4nf2MXic1+esrCwALC0t75vmyJEjTJ48WWNZYGAg27ZtAyAxMZHU1FQCAgLU683MzGjdujVHjhypNOguKiqiqKhI/f6/DxYE4UVWciMf880JvI8+3Bn7V6eOMTmFqoBbT0/voQPuq1vXcGR9MDfKpBTKVbXcLVtrdmHZl7xP/X9vK++75SoPuvVrrEGtIDzzqv3XsHfvXo33UqkUGxsb6tevj1wu/rgEQRBqgkQiwVhfjr2ZPqlZhRSXKrTe1vYlN5w7NiRlzjHIAV1zRzZ3TGTgvjqUyaSEnjjHuff689ryHRrbebVxwNBUl4ObLpJ1Ix+lQkluRhG5GUWkJWZz8t9k6jWzoV5zW+o1t6103+7u7ri7u2ssKysr49y5c2zZsoWUlBR++eUXPD09ad26tVZzwwraeVzXZ4VCwYcffkjbtm3VzcQrk5qaip2dncYyOzs7UlNT1evLl90vzX/NnTuXr7766qHLLgjPo3/OpHAjMpV253OxVoICJWmNLWns54jEyYATMZGA6qFWdWREHyJu/UqSL18jSaILEl1VpKBU4qkLvp/O10j/16W/AOjl3ov2ddqrl4uabkGoqNpX4Y4dOz6OcgiPyejRo8nMzFTXNAiC8GzRuTMgWUmZ9kE3qIJ23TomFMbeZqrTh/xmsp1b12KwvyCnSEeP6xkQfXATzdoN1thOPcBamYL87BLyMovIuplPxI7LZKblc/HEDS6euIFvF2faDqqvVY21TCbDx8eHW7dusW/fPq5evcrVq1e5ceMGffr0QUen6oHiBO08ruvzhAkTOHPmDAcPHnws+Vdl2rRpGrXn2dnZODs7P/FyCMLTYvX2WJwOp9EZVUCbhoJ15uk0yD1H2NZMCgsL1Wnr16+vVZ6Xghax748d3NZR1WgjUT0MtSgpok2XdtTtPwoDR1eNbQpKCzhw7QAAr3u/DsCtq7mkJmSSkZIHiKBbEO6lVdC9fft2rTPs06fPQxdGeH6dOnWKCRMmEBERgY2NDe+//z5Tp06tcpsPPviAQ4cOcebMGby8vIiJiXkyhRWEp4iOTBXU5haVUlhShv4DmpjfqzzotjssZ+6bM9CfZ05+5k1WjBtJqUzGv4uDOPPDMlwd7Wg9cxG6Fnf7WktlUowt9DC20MPOzZR6LWy5cjqd8HXnKcgp4WRYMu5NrXH0qDjF2P106tSJRo0aceTIEaKjozl9+jSJiYmMHj0aa2vrB2cgVPC4r8/vvfcef/31F/v376dOnTpVprW3tyctLU1jWVpaGvb29ur15cscHBw00jRt2rTSPPX09NDT06t2uQXheaIsVXBpfzJpUal0vVUMyMijkGSDPC7Wk+OYdpnU1LtdL3R0dGjZsiUvv/zyA/OOnDGB/ecSUdwJuPVLS3HShWb9+lH31Xcq3aawtJBP9n9CQWkBTsZO1JXW48/FJ0k6e3eodCMzXawcxZRhglBOq6C7X79+WmUmkUgoKyt7lPIIz6Hs7Gy6detGQEAAS5cu5fTp07zxxhuYm5vz1ltvVbntG2+8wbFjxzh16tQTKq0gPF105Hen3rp4IxdPOxN05dpNx2XU0o7ssCuggOzdV9B/1xxDcxu8Wrpy4XgCRTq63JDqcyM1i5PjRmAnVVC3nguNJ0zD0FlzDleZTIp7UxucPC34fU4E2TcL2L/hAt3GNb5vP+//kkgk2Nra0qdPH4yMjDh48CC5ubkEBQUxadIkMc3YQ3hc12elUsn777/P1q1bCQ8Px83N7YHb+Pv7ExYWxocffqheFhoair+/PwBubm7Y29sTFhamDrKzs7M5duwY7777rtZlE4QXgVKhpCyjkMKETK7/mYBhiRIXII9C/tE7y21JJiiB+LvbjB49GicnJ61aD+UnJ/DbxHfJ0dEFqRSTkmIGT5+BRbO2lZdHqWTzxc3suLSDqLQolKimDhvoMoQt30aSm6Eae8HUWh/3ZrY07uCEXFfUdAtCOa3ucBQKhVYvEXDXns2bN+Pj44OBgQFWVlYEBASQl5enXj9//nwcHBywsrJiwoQJ6pHaAX799VdatmyJiYkJ9vb2DBs2jBs3bqjXh4eHI5FI2LFjB02aNEFfX5+XXnqJM2fOaFW2devWUVxczKpVq2jUqBGvvfYaH3zwAQsXLqxyux9//JEJEyZU6B8qCC8SPbkMezPVFC9KpZLMgmKtt5WZ6WH/SSsAiq9kk/XPZZQKJd0+XYLt3HHEuaQgU+aAUkmRXE6SVJcDiamsnTgeRXHl+9EzkPPK+CboGshJv5bHhlnHuHC88v649yORSAgICKBbt24A5OTkMHfuXJKTk6uVj/D4rs8TJkzgt99+Izg4GBMTE1JTU0lNTaWgoECdZuTIkUybNk39fuLEiezatYsFCxZw/vx5Zs6cyYkTJ3jvvfcA1ef+4Ycf8r///Y/t27dz+vRpRo4ciaOjo9YPDwThRZBXVMrZ/4sm9bsTZIbEY1iiJBsl+8ylHPDIVgXcqFqPNGrUiLZt2zJ69GhcXV2rDLhTQ7cQPn4IKwZ0Y8mUiaqAG7AvLWL4t9/fN+AGOJ56nFlHZhGZFokSJZb6lkxpOQWvpHbqgLv9qw14fbY/bQfWx8zm0UZMF4TnjahWeAClUkl+cWmtvJRKpVZlTElJYejQobzxxhvExsYSHh7OgAED1Nvv3buXhIQE9u7dS1BQEGvWrGHNmjXq7UtKSpg9ezYnT55k27ZtXL58mdGjR1fYz8cff8yCBQvUTcR79+6tEbzfz5EjR+jQoYPGgEmBgYHExcWRkZGh1TEKwovM1kQfJwvVDcyN7CKSb+dTptDu90Fupodp17oA5OxNJmXOMUozi/Cx9uFI42J+eeU2/7a4Rr5jLs46pQDk6egQt2zOffO0dDBi0CctMLXWR6mE0FXniNx1udrH1aZNG1q1Uj0UKCkp4e+//9b6d094vJYsWUJWVhadOnXCwcFB/dq4caM6TVJSEikpKer3bdq0ITg4mOXLl+Pr68vmzZvZtm2bxuBrU6dO5f333+ett97Cz8+P3Nxcdu3aJeboFoQ7yhRKRn5/APOrqoqTqyjYISkhuoMdr05pzdXk0wC0bNmSd955h8GDB9O1a1dcXV3vm+eFJV+zsn831q1cTWR6Ptk6d+/H/GxNGL4lFKP6jSrdtqisiIjUCBZHLwZAJpGxqfcm/h38L20KunMy9CoATbu60KRzHTEzhSDch9YDqf3777+89957HD16FFNTU411WVlZtGnThiVLltx3/s5nVUFJGd5f/lMr+z43KxBD3Qd/RCkpKZSWljJgwADq1lXdXPv4+KjXW1hY8NNPPyGTyWjYsCGvvPIKYWFhvPnmm4CqCXc5d3d3fvzxR/XNkLGxsXrdjBkz1PPyBgUFUadOHbZu3cqQIUOqLF9qamqFponlo9empqZiYaF9n1BBeFGZ6etwQ1ZESZmCjPxi8opKcbY0xEjvwb8Rpl1ckFvqc3tjHIrcEtKDzuLetx5ruq9h4/mN/M3fJNmr+uK9v92WHLkBOw9E4NjxGGZNWleap4W9EUNntOavn05yLS6To9su4dHSDlPr6tVu9OzZk/bt2/P9999z/fp1rly5UuXNo1DR47g+a/PwIzw8vMKywYMHM3jw4IqJ75BIJMyaNYtZs2ZpXRZBeJGkxN/mh0zV7/pVUxklgxsyxNGMm9cus379enW65s2ba53n0dBwsnT1kCiUWJYV42xtimeP3ph4NMKssZ9G2rjbcexI3EFkWiSJmYnklORorP8l8Bc8LTw5sfMyEX8lAmDtbEzTLmKAQ0GoitY13YsWLeLNN9+scEEH1ZQEb7/9Nt9//32NFk7Qjq+vL126dMHHx4fBgwezYsUKjRrkRo0aIZPd7Vfj4OCg0Xw8MjKS3r174+LigomJiXoE3KSkJI39lPfLA9VcrZ6ensTGxj6uwxIE4R5ymRRPOxPqWhkik0goLlOQcDOXi2k5XEnPIyWrgNIqRjg3bGaLzbuqOVZLUvK4vT6O5rbN+bbjt/zR7w861ekEQKjvbdUGEgl7/15JmeL+zZLlOjJ6v99U/f729bz7pq2KiYmJuo9vdHT0Q+XxIhPXZ0F4PpTcyKdw4wX1e98+DfB3s2DvP38RHBxMQkICAC+99BKOjo5a51uAqva5Z6dWjA4JpcvyLdTpP1oj4C5TlPHL6V8Y9OcgVp9Zzambp9QBt4mOCe2d2jOv/Txa2LUgPvIGx/9MRKkEpwbmDJnmh5G5GPBQEKqidU33yZMn+eabb+67vlu3bsyfP/++659VBjoyzs0KrLV9a0MmkxEaGsrhw4fZvXs3ixcv5vPPP+fYsWMAFfr3SCQSFArVzXleXh6BgYEEBgaybt06bGxsSEpKIjAwkOL79OmsrvuNaFu+ThAE7UilEswMdNGxkXIju4jswhIKSsooKFEFxvmyqqcV06tris07Tbi59BRlWUVcn3UUm7ea4O7gzo8v/0jolVB+jvkZvZM5FMkNOXbpFGXx2xjYYOB985TJpdRvaUv8iRvs+L9TjFvYHj3D6k8B5u3tTVRUFKdPn8bHx4d69eqJZopaelGvz4LwvFAqlRxbe5o6sVmUd7RYZ6+gj+wmR9ZuV1eCODs70717d5ycnKqVf6FUdT9pXkkT8hv5N5gfMZ9D1w+RXawaAd1Yx5i3m7xNK4dW2BraYqFnQc7NImLCkgkJiSQlPgsA1ybWBI5rhEQqfqsF4UG0DrrT0tKqHJxBLpdz8+bNGinU00QikWjVxLu2SSQS2rZtS9u2bfnyyy+pW7cuW7dufeB258+fJz09nXnz5qnnPj1x4kSlaY8ePYqLiwsAGRkZXLhwAS8vrwfuw9/fn88//5ySkhL1dyg0NBRPT0/RtFwQHoKhrhxXazmFJWUUlZRRUKLgRk4huUWlSB/Q11vP1QyjVvbkHU9FWVBKfkQqun1UAW431250dO7Ipg2vkKYEp5smzDwyk3Pp5xjuNRx388oHNXT3tSH+hKr1zMHN8XQZ+eDfhf9ydnZGT0+PoqIifvvtN/r06VOt5pMvshf1+iwIz7K07EKCjyVx+loWF+PTWV+qmgXiDKX8aVyAbdYJNm68+yC1bdu2BAQEVPthZHF6GqV3WjuaeN7tephRmMHyU8v5LfY39TITHRO6unbl01afYiBXdRW6dTWXsH/OczFCs/LEtq4JXd/wFiOUC4KWtG5e7uTkVOVo1adOndKYd1N4co4dO8acOXM4ceIESUlJhISEcPPmTa0CYhcXF3R1dVm8eDGXLl1i+/btzJ49u9K0s2bNIiwsjDNnzqjn1dVmxNlhw4ahq6vL2LFjOXv2LBs3buSHH35g8uTJ6jRbt26lYcOGGtvFx8cTExOjHjE3JiaGmJiYGquBF4Rnnb6ODDNDXezN9DG683Dwdl7xA/vjWgzwwGKgBwC5R1MovparXqcn08P5zpzZRTr6vHROl98v/E7fP/ryzfHKa1M9/Ozo/rZqsKzrFx5ucEQ9PT1GjBihfrC3fft2MVWglsT1WRCeLScu36brwn1EhiXicz6LX0sNAUgzleP1cSs6eeahVKoC7iZNmjBo0CC6du36UK1/9n+qmo5PqlBg4FwfgOTsZPr/0V8j4P6u43fsf20/X7X5Sh1wl5Up2Do/Uh1wW9Uxpt1gDwZObcGgT1qiq//0V0oJwtNC66C7Z8+eTJ8+ncLCwgrrCgoKmDFjBr169arRwgnaMTU1Zf/+/fTs2ZMGDRrwxRdfsGDBAnr06PHAbW1sbFizZg2bNm3C29ubefPm3bcZ4rx585g4cSItWrQgNTWVP//8U2NE8vsxMzNj9+7dJCYm0qJFCz766CO+/PJLjTm6s7KyiIuL09hu3LhxNGvWjGXLlnHhwgWaNWtGs2bNuH79+gP3KQgvmvJpxYpLFUzcEMOvR69QWHL//tiGzWyRmemCQsmtNWcouZmP8k4t+UtfLsSq5M4UMJctaGbbDIDfYn/jx6gfK83P0cMcgOxbhVw+feuhjqFOnTqMHj2aevVUc4SHhITwxx9/aDVLwotMXJ8F4dkRnZTBq8uP0qBQyTwMGYwe8bLr/KtzhtNWsez8fS1nz54FoHv37gwYMEBjBgBt5Fw8Q8QXb3Pgg2EkpKseqlqXlSCVyUjNS2XkrpGkF6ZjKDfk45YfE9wzmO6u3ZFLNYPo/KxiigvLkEolDJjSnNe+aIVvF2fs3c1Ek3JBqCaJUsv5WdLS0mjevDkymYz33nsPT09PQNU8+eeff6asrIyoqCj1qNRPq+zsbMzMzMjKyqow6ExhYSGJiYm4ubmJ6UvuER4eTufOncnIyMDc3Ly2iyMIwn2kpGdzNi6eGXtvcC2njPYe1qwa7YeOrPLnq0WXsri5/G5tstREB8vBnug3sODoJ2M5dFlVu/HmjK+Yl76FPy/9CYCbmRu/dPsFG0MbjfyCPjtE7u0iPPzs6Da28ulntFFWVsaaNWvU83b37NlTPbXY86iq65I2npfrszYe9VwJQm2buvkkf5y4yjapKYYKBRfNb3Cw8GyFdMbGxkycOLHKriP3s2lwN5KkmpUiw98YjX3gIGYfmc3vF34HIKRPCB4WHvfNJzUxiy3fRGJsoceoufefw1sQXmTaXpe0bhdiZ2fH4cOHeffdd5k2bZq6+aJEIiEwMJCff/75ubigC4IgPKssjHSxM9PjjXZufL0rngMXbzF0+VG+6OVNQ3sT9P8zOKOumynGHZwoupBJSVoeipwSbq06g35DS7xefY9D30wHIHnXFmZ9OAt7I3tWnF5BYlYir/71Klv6bMFC/+64DF1GefPH99EkRN4gq48bZjaGD3UcMpmMsWPHEhQURGJiIrGxsc910P2oxPVZEJ5uSqWSQ/Hp7Dx6Ba+zWXyAKTeV2YToRVFSqGqRZGpqSufOnTExMUFPTw9ra+uHCrgB0sskIAW90lLcjOQ4NaiPfeAgAKJuRAHwYfMPqwy4ARJjVK2WxMjkgvDoqtUZo27duuzcuZOMjAzi4+NRKpV4eHiIwbBecD169ODAgQOVrvvss8/47LPPnnCJBOHFJZdKGd66LhYmRkz+/SQnrmTQ7+dDmOrL+WdSBxzM7s6jLZFIMO/pDj1BUVhKxqYLFJxNp/D8bYoSpLhL9LikLOLAwUik8tm8NXgsTW2bMiFsAjcLbjLy75F87PcxrexboS/Xx9HDHFtXU25czuZ0+DXaDa76hu5BXnnlFX7++WcSExNJTU0Vsx1UQVyfBeHpdOZaFov/jMX+cg7j0UeBnOPyeE7Jr6jTtGjRgh49eiCXP1of6dKcLDKiD5J3J1gf9uk0LP06qtfHZ8QTnxmPVCKlv0f/SvNQKpSc3neNM/uvkZGimgbS1Nqg0rSCIGjvof66LSws8PPze3BC4bnQqVOnKgdmWrlyJQUFBZWus7S0fFzFEgShCgOa18FYT87KA4kcv3yb7MJSDly8xZCWzpWml+rLsRrhTe6xFDK3xqMsUdDEeSSXklaQp6PDzkORGIUfZeSin1nTfQ3v7nmXy9mXmRA2gUZWjdjQawNSqYQmnZzYsyabW1dzHvkYrK2tcXNz49KlS0RHR2s1TsWLTlyfBeHpoFAomb87jv8LT2AuBrS/MxnYRVmKOuCWSCS8/fbbD/1A8fbRMKJ++Zm8vAJuFZaSKdeFO4Ot6ZSVYe7rr5F++enlAHR27oylvub9mVKh5HBIPFfOpJORmq8qn1RC/Ra2tOrl9lDlEwThLjHsoPDIqjtfpCAIT0a3RvZ0a2TP//46x8qDiUzdfApLQ10CvO/f1Ni4tWqU68yt8ejJLGliokvK7Rxu6uiRp6PD+dWLaDHjJ37u8jNLTi4hIjWCs+lnyS/Jx1DHEENTVTPEgpyaGfzM2dmZS5cucezYMVxdXWnYsKGYv1sQhKdaRl4xQ1cc5XxqDo2Q0R4dylCQUr+IqFtXoBC8vLzo1asXRkZGD72fPQu+IVmqC8jgTvchiVKJQWkpbfwaI70z2G1eSR7RN6L5O/FvAEY3Gl0hr2sXMojZoxpHQyqT4PuyM75dnEXTckGoISLoFgRBeM51b2zPb8euUFii4N11kXw7qAl9fZ2Q3mf0WaPmtmRujQegy8+/I9WTs2FQV67J9CjKUY2E62fvh5+9Hx02dCCjKINNFzYx3Gs4+saqZo0FuTUTdLdu3ZrTp09z+/ZtNm7ciIGBAV5eXlhaWmJjY4OLiwsGBqLpoyAITwelUsnApYe5dFPVNHusqzVXk65yQCeWvKuqWSEkEgkdO3Z8pIAbIKsMkIKbpJS6Ddxx6z0Yc9+26mAbVAF3z5Ce3C68DYC5njmNrSuOhn41TjXdo6m1PgM+boGRmQi2BaEmiaBbEAThOdfS1ZKIzwMYvy6KAxdvMWnjSRaHxfPDa83wqWNWcQO5FCSAEpTFCtADuUwVoJcUFWkkrWdejxNpJ5h/Yj7rz6/n59bLACjMLUGpVD5yrbShoSGjR49mx44dxMXFUVBQQFRUlHq9VCqldevWdOjQQQTfgiDUqks3c/lpbzzpt27RWecqHpZyTqee4JTu3S56Pj4+tGrV6pHHqFCUlZEnU93Gt3vrHWxf7ldpuosZF9UBdw/XHrzu/XqFqcFSErI4fzgFgFa93ETALQiPgQi6BUEQXgAm+jqsGu3H/H/iWHUokUu38uj900Ea2puwYmRLnC3vjjQukUiQ6MpQFpWhLCoDE9UAbSigtLhYI99PW31K0Nkg/rz0J9dyr/Hm/jcYyOcoFUoK80owMNb9b1GqzdTUlKFDh5KRkcGVK1dIT08nLS2N9PR00tPTOXLkCMePH8fNzY3OnTtjZ2f3yAMSCYIgaEupVLI1+hqfhpymuFRBa/kN6soyKc5SrZcppXg6uvPK6/0fuXa7XOSX4ymTSpEolZi36HDfdMk5qibjfvZ+fNvxW/XyiyfSiD2cQmZaPjnphQAYmOri1tSm0nwEQXg04q5EEAThBaEjkzKtpxeDWtThs62nibicwfnUHNp/u5e3O7ozoXN9TPVVzcPLg25FsWo6G7lcBsVQUlqqkaenpSdz2s9hUINBjNo1ipslN8gyuIlZgQ1//3KSPu82R64rq1CWh2FhYVFhNO4zZ86wf/9+bty4QXx8PPHx8ejq6tKuXTs6dLj/jaggCMKjUiqVbIhIZkNEMieTMwFwsTTEV19O8W3wlrhQv9AWS6Ux9l180a+BgFtRXEzMnMnsj78GgKu0DF2zu4Oi5RTnEJ4cTkRqBEk5SUSmRQLgZOykLnPEjstE/JV4N1MJ1PG0oN1gD3T1RWggCI+D+MsSBEF4wXjYmbDpnTbE38hh1KoIrmUWsGzfJQ7Hp/NV30Y0d7FAqitFASjLg26ZDFBQWlJWaZ7N7ZoT3DOYKfumEOUYSueEYaTE5rBiRhj9xrbCob75YzmWxo0b06hRI1JTU9m9ezdXrlyhuLiYf//9l4YNG2Jra/tY9isIwovr0s1ctsVcJyTqKlczVLO36MmlTOjoTifHMkI27QOgbpEVtjJzTF92Qa+e+UPvT1FcTO6ls9yK2E9YyA6y7/TZti8totfKYACSspP4O/Fvgs8Hq5uTl9OT6dHZuTOKMgX/rj1P3LFUADz87PB8yR67uqbq8TgEQXg8RND9nBs9ejSZmZls27attovyVJXlcXB1deXDDz/kww8/BFRNdLdu3Uq/fv0ey/7Cw8Pp3LkzGRkZmJubV5l2zZo1fPjhh2RmZj6WsgjPpvq2JoR91JGt0df4bOtpTl/LYuCSw+x4vz1Wd2qnlcUKAOS6cigoprSs8qAbwMfGh5C+IQSdWcsZZTjelzqgyJATMj+Kzq83xLud42M5DolEgoODA6NGjaKsrIy5c+dSWlrK8uXL8fHxwc/PD0fHx7NvQRBeLEcS0nljTQQFdx5ASiTwSmN72utf5Ur0H4QcujPdllKCpdwUhyl+yEwfvo906j+b2bJsJYU6d27Z7wTcJiXFvPzWm+ha2LArcRcf7/9YvY2prildXLrQyqEVdYzr0MiqEToyHS5GpBF3LBWJBDxfsqf9qw1EzbYgPCHS2i6A8Hj98MMPrFmzpraL8UJKSUl5aucVDgkJoWvXrtjY2GBqaoq/vz///PNPbRerWgoLC5kwYQJWVlYYGxszcOBA0tLSqtwmJCSEbt26YWVlhUQiISYm5skU9immryNjaCsX1oxpBYBSCV/vPIfkTtCtKFLdWOroqG70SksVVeZnpGPE+Gbv8vPHX3G09UZuGKnmo93723nysoqq3LYmyGQyhg4deqespURHR7N69WrKqnhYIAiCoI3EW3kMXXGUgpIyrI10ebthKd+0UtAw4wixp6PJz89HVyLHvcyWHiXNqDPE56ED7rz4s+wY2Yt1q9aoA255WRlWJUUMHTmMt0J249B9CACbLmxSbzex+US29t3KrLaz6OXei6a2TdGR6VBWpuDkv6r+3Y06ONFllLcIuAXhCRJB93POzMzsgbWgL4ri/wwA9bjZ29ujp/d0jgC6f/9+unbtys6dO4mMjKRz58707t2b6Ojo2i6a1iZNmsSff/7Jpk2b2LdvH9evX2fAgAFVbpOXl0e7du345ptvnlApnx0dG9iwbUJbAA7Fp6OQq0YdzztyndLbhcj17gTdCuV987iXRCLBp1F9/vT+Wb3s1J0bvsetXr16TJo0ifbt2wNQUlJCaGjoE9m3IAjPn5/3xtPq6z10nh8OgKm+jO+7mFJ0OZpzJyO5desWAO5ldgwraMfLpT54tPZGv5FVtfeVlxjLjpG9+G3qJM6XP6dUKhnYrycTN//N6JBQHF8ZprFNWr7qgfOqwFWM8xmHraFmt5rLp28RNO0waYnZANg4m1S7XIIgPBoRdD8nNm/ejI+PDwYGBlhZWREQEEBeXh6jR4/WaN6ck5PD8OHDMTIywsHBge+//55OnTqpm0SDqpn0nDlzeOONNzAxMcHFxYXly5dr7C85OZkhQ4Zgbm6OpaUlffv25fLly+r1ZWVlTJ48GXNzc6ysrJg6dSpKpXY36wCdOnXigw8+YOrUqVhaWmJvb8/MmTM10iQlJdG3b1+MjY0xNTVlyJAhGjWdM2fOpGnTpqxcuRI3Nzf09fUBVTCwbNkyevXqhaGhIV5eXhw5coT4+Hg6deqEkZERbdq0ISEhQZ1XQkICffv2xc7ODmNjY/z8/NizZ0+VxyCRSNRN6WfOnKkaEfo/r/JWCAqFgrlz5+Lm5oaBgQG+vr5s3rxZI7+dO3fSoEEDDAwM6Ny5s8b5rq5FixYxdepU/Pz88PDwYM6cOXh4ePDnn39qtf2uXbto166d+vPt1auXxvlq06YNn3zyicY2N2/eREdHh/379wOqlgCvvPIKBgYGuLm5ERwcjKurK4sWLXrg/rOysvjll19YuHAhL7/8Mi1atGD16tUcPnyYo0eP3ne7ESNG8OWXXxIQEKDVcb5omjqbY2ag6teXX0c14E/RpSxSv43AWlcVwJZV4+/43abv4u3QkFMO4QBcv5hZo+WtipmZGV26dMHDwwOAo0ePcuPGjSe2f0EQnh8rD1ziRk4RhhQRYHCZ4UZnCdv9N6C61ge81InuxU3pVOKNeQcXbCc0xaJv/YeaMvHo3C84XwS5d1oX1ZOWMnTUcFyHjq80fUlZCVeyVS2K7I0qTkN2Zv81dvx8ioLsYqRSCe7NbKjXXIxQLghPmgi6H0SphOK82nlpeXObkpLC0KFDeeONN4iNjSU8PJwBAwZUGuROnjyZQ4cOsX37dkJDQzlw4IDGnLflFixYQMuWLYmOjmb8+PG8++67xMXFAapao8DAQExMTDhw4ACHDh3C2NiY7t27q2uTFyxYwJo1a1i1ahUHDx7k9u3bbN26tVqnPigoCCMjI44dO8a3337LrFmz1LVVCoWCvn37cvv2bfbt20doaCiXLl3i1Vdf1cgjPj6eLVu2EBISotGUePbs2YwcOZKYmBgaNmzIsGHDePvtt5k2bRonTpxAqVTy3nvvqdPn5ubSs2dPwsLCiI6Opnv37vTu3ZukpCStjmXKlCmkpKSoX/Pnz8fQ0JCWLVsCMHfuXNauXcvSpUs5e/YskyZN4vXXX2ffPtVgLMnJyQwYMIDevXsTExPDuHHj+PTTT6t1PquiUCjIycnB0tLywYlR1RhPnjyZEydOEBYWhlQqpX///igUqqbHw4cPZ8OGDRrfwY0bN+Lo6KiufRw5ciTXr18nPDycLVu2sHz5cq2DosjISEpKSjSC54YNG+Li4sKRI0e0PWyhEk7mqrmur9Q1xuwVd6SGquaHRjTERr8OaRIditKuaZWXqa4p7/i+w2l71fc49VI2xYWlD9iqZg0ePFj9/9TU1Ce6b0EQnn35xaVk5JdgLCniNaOz1FHepCAvBwBDQ0Peeecdmlk1pI7CCoN6Fpj3dEe3zsPXJF+5paqNNi8uYvCgvvRbv6tCzfa93t7zNgASJBVquAHO7FP9Xhtb6DHi6zb0eNsHPUMxaJogPGmiM8eDlOTDnFoagOez66D74OklUlJSKC0tZcCAAdStWxcAHx+fCulycnIICgoiODiYLl26ALB69epKBxjq2bMn48ernqp+8sknfP/99+zduxdPT082btyIQqFg5cqV6qe4q1evxtzcnPDwcLp168aiRYuYNm2aurnv0qVLq91nuEmTJsyYMQMADw8PfvrpJ8LCwujatSthYWGcPn2axMREnJ2dAVi7di2NGjUiIiICPz8/QNWkfO3atdjYaD7VHTNmDEOGDFEfn7+/P9OnTycwMBCAiRMnMmbMGHV6X19ffH191e9nz57N1q1b2b59u0Zwfj/GxsYYGxsDqhq3L774gqCgIBo3bkxRURFz5sxhz549+Pv7A+Du7s7BgwdZtmwZHTt2ZMmSJdSrV48FCxYA4OnpyenTp2usmfT8+fPJzc1Vn5MHGThwoMb7VatWYWNjw7lz52jcuDFDhgzhww8/5ODBg+ogOzg4mKFDhyKRSDh//jx79uwhIiJC/eBh5cqV6lrJB0lNTUVXV7dC1wk7OzsRWD0iF0tDzqVkc+xKBp17NMS4jSMpc46hyCvBwaA+Nwuvsv/z9+i6UruHaD7WPuTqZZCjm4FJsQWrphzEo5Udzbu5YGKlj1ynZqYTux9dXV0aNWrE2bNnyc/Pf6z7EgTh+XM9sxADiummexHFnbEh2rZtS8eOHdHV1aU0vYBb4WcA0HmEYBugrKgAE7mE3LIyBs6chbmvf5XpF0cvJiI1AoB3fN9BT6bq0qZUKLlwPJWYsGTSr+UilUt49YtW6BuJYFsQaosIup8Dvr6+dOnSBR8fHwIDA+nWrRuDBg2qMJ/tpUuXKCkpoVWrVuplZmZmeHp6VsizSZMm6v9LJBLs7e3VtZAnT54kPj4eExPNi0thYSEJCQlkZWWRkpJC69at1evkcjktW7asVhPze8sA4ODgoC5DbGwszs7O6oAbwNvbG3Nzc2JjY9VBd926dSsE3P/N287ODtB8UGFnZ0dhYSHZ2dmYmpqSm5vLzJkz2bFjh/ohR0FBgdY13eWSkpLo168fU6ZMUQe48fHx5Ofn07VrV420xcXFNGvWTH28955PQB2gP6rg4GC++uor/vjjD62nV7p48SJffvklx44d49atW+oa7qSkJBo3boyNjQ3dunVj3bp1tG/fnsTERI4cOcKyZcsAiIuLQy6X07x5c3We9evXr/CdFZ687o3t2XU2lYPxN4GGSGQSjNs4kh16BdM7l4y0jFyt8zPTM6Ora1eO3fqTlxOGQymcP5zC+cMpSGUSXLwtaTfEAzMbw8d0RGBgoKq9LygoeGz7EATh+aNUKtkWfZVeeucwkpQA0KFDBzp37kzhudtcDY6FMtV9jURfhnFrh0fan0zPgMG/76asqACZnkGl5fnn8j/EZcRx9tZZjqSoWnZ1q9uN8U1VFSVFBaXs+OkkKQlZ6u08WtqJgFsQapkIuh9Ex1BV41xb+9aCTCYjNDSUw4cPs3v3bhYvXsznn3/OsWPHHn7XOpo/zhKJRB1Y5ebm0qJFC9atW1dhu8oC3MdRBm0ZGVXeUuDevMtr6ytbVr6/KVOmEBoayvz586lfvz4GBgYMGjSoWoOz5eXl0adPH/z9/Zk1a5Z6eW6uKoDZsWMHTk5OGts87oHYNmzYwLhx49i0aVO1+jn37t2bunXrsmLFChwdHVEoFDRu3FjjfAwfPpwPPviAxYsXExwcjI+PT6UtMB6Gvb09xcXFZGZmatR2p6WlYW9fsU+boD1Xa9XfTEZeiXqZREfVE8mhrhtkwC2pDtd3BFfZ5PFebzV5i0FXBpHjcJ3Fbms4HX6NW8k5KMqUXD6dzrULmQyd0RoTS/2aPyBUTUABUdMtCEK1zP4rltPH9tFIrvo9HDJkCN7e3qqVUtQBt56HORb96iOvod+wygJugJibMRpTgwG0KupCn7S3+HdtLLmZRSSfuztHt5uvNc261cXOzbRGyiUIwsMTQfeDSCRaNfGubRKJhLZt29K2bVu+/PJL6tatW6EPtbu7Ozo6OkRERODi4gKoBqS6cOECHTp00HpfzZs3Z+PGjdja2mJqWvkPuYODA8eOHVPnW1paSmRkpEbN5qPw8vIiOTmZ5ORkdW33uXPnyMzMvHtBrEGHDh1i9OjR9O/fH1AFytUZyEypVPL666+jUCj49ddfNQZX8fb2Rk9Pj6SkJDp27Fjp9l5eXmzfvl1jWVUDhmlj/fr1vPHGG2zYsIFXXnlF6+3S09OJi4tjxYoV6qbjBw8erJCub9++vPXWW+zatYvg4GBGjhypXufp6amezqlFixaAqsY/IyNDqzK0aNECHR0dwsLC1E3d4+LiSEpKqrEWAC8q8zsDqWXm332AUh50G9jVRa+0lCK5nPVrgzFbuYaurw2g7qvvVJmnoVwV9GaTiVcbR7zaOKJUKrl2IZPdK89QkFNC3LFUWvZwfSzHVF7THRERQffu3ZHJHm+TdkEQnn1lCiUbIpLoLFU9GK/v6a1xf6Ff3xz7aa2Q6kiRPqE+0udvnwfA1dSV/h79qZfvw5lVOZxBc5wNmVxKix518XvF7YmUSxCEBxMDqT0Hjh07xpw5czhx4gRJSUmEhIRw8+ZNvLy8NNKZmJgwatQoPv74Y/bu3cvZs2cZO3YsUqm0WiNsDh8+HGtra/r27cuBAwdITEwkPDycDz74gKtXrwKqPtHz5s1j27ZtnD9/nvHjx5OZmVljxxwQEICPjw/Dhw8nKiqK48ePM3LkSDp27KjuI1yTPDw81IOxnTx5kmHDhlWr1n3mzJns2bOHZcuWkZubS2pqKqmpqRQUFGBiYsKUKVOYNGkSQUFBJCQkEBUVxeLFiwkKCgLgnXfe4eLFi3z88cfExcURHBz8SPOvlwfBCxYsoHXr1uryZGVlPXBbCwsLrKysWL58OfHx8fz7779Mnjy5QjojIyP69evH9OnTiY2NVc+dDKpBzwICAnjrrbc4fvw40dHRvPXWWxgYGGj1XTQzM2Ps2LFMnjyZvXv3EhkZyZgxY/D39+ell17S2M+9D59u375NTEwM586dA1SBekxMjOgHfg/zOzePecVlFN+Zk7s86JZI5HQL7Ih5sWoemyxdXQ78vuWBeRroqILewtJCFMo7eUok1PG0oMWdQPvG5ewaPY57OTjcbfK5fv36x7YfQRCeD0qlknd+i6SsuBAbaR4AgQGdNdJIdGTIzfSeWMB98uZJ1sWuQ6qQEpA1GN3fvYhdqyqbqbU+rfu60/n1hvT5oClvft9BBNyC8JQRQfdzwNTUlP3799OzZ08aNGjAF198wYIFC+jRo0eFtAsXLsTf359evXoREBBA27Zt8fLyUk+npQ1DQ0P279+Pi4sLAwYMwMvLi7Fjx1JYWKiu+f7oo48YMWIEo0aNwt/fHxMTE3UtcU2QSCT88ccfWFhY0KFDBwICAnB3d2fjxo01to97LVy4EAsLC9q0aUPv3r0JDAysVq39vn37yM3NpU2bNjg4OKhf5eWdPXs206dPZ+7cuXh5edG9e3d27NiBm5vqouni4sKWLVvYtm0bvr6+LF26lDlz5jz08SxfvpzS0lImTJigUZ6JEyc+cFupVMqGDRuIjIykcePGTJo0ie+++67StMOHD+fkyZO0b99e3bqi3Nq1a7Gzs6NDhw7079+fN998ExMTE62/i99//z29evVi4MCBdOjQAXt7e0JCQjTSxMXFaTxI2L59O82aNVPX7L/22ms0a9aMpUuXarXPF4Gpvg7lzz2yClRNKsuDbmWJggZvTWPs1lD866hGui/UYpiG8ppuJUoKSws11pU3Kc/LLKqwXU1xdXWlbVvVHOSXLl2itPTJjqAuCMKzZdeZVKJjEwjUVc3aoqenp/XsHo/LidQTXMm+QpOUTsiP1iHrRgFlpQokUgldRnnRsocr3u0ccfa2RKYjbu8F4WkjUVZnZKvnQHZ2NmZmZmRlZVVoGl1YWEhiYqLGnM7Pu7y8PJycnFiwYAFjx46t7eIIL7CrV6/i7OzMnj171KPrC9VTU79hTWftJjO/hNBJHfCwM6HgbDrpv55D19kE2wlNAUj8dTEhf/2DYUkJ74ZUPTOBQqnAd61q9P/wIeFYGVip1924ks2muScAeOvHjujoPp6m30qlkjlz5qgHk+zatWuFcSNqS1XXJUGTOFfC45SZmUlEVAx7Dh7HQKEaA0IqlTJkyBAaNmxYq2U7fO0we/efwHx/I1BKcPa2pMOrDdA31hGDpAlCLdL2uiT6dL9goqOjOX/+PK1atSIrK0s9oFffvn1ruWTCi+bff/8lNzcXHx8fUlJSmDp1Kq6urtUaX0B4PEz05WTml5BTpKoRlujerekuZ2CvmmqwWPrgGhWpRIqB3ICC0gLyS/Ox4m7QbWR+d7DAXz8/TOcRXjh5mKNrULOXJ4lEgqurKxcvXuT48eOkpKSIB42CIKhl5Zfwf78EUZyTgQGgVIJz/Yb07tZZPctJbWrj1Ib0W4YkKW+joy+j5zs+yB/TQ0pBEGqeaH/yApo/fz6+vr4EBASQl5fHgQMHsLa2fmL7T0pKUs9bXdmrutNwCdCjR4/7ns/qNkN/Up9PSUkJn332GY0aNaJ///7Y2NgQHh6Ojo4O69atu+/+GzVqVCP7F+7PSFcV8OYXqeakvdu8vEydxsBB1V2gVCpFocUo/gbyO9N2lWpO22Voqkv9Fqqp6gpyStj5f6dYMWk/t67mPOJRVDRw4ECaNm0KQHJyslZjGAiC8HxLSkpi1apVzF+wgOIc1WCeMaWO2Pr3Y9yI156KgLtc9i1V95zubzUWAbcgPGNETfcLplmzZkRGRtZqGRwdHYmJialyvVA9K1euvO8cxNXth/akPp/AwEACAwMrXdenT58K85KXe1qaBD/PjPRUl4bc8ppueSU13c71VP+RSCi4noiRq2eVeZYH3fklmtN2SSQSAt9sTIehxYT/FselmJsAbJpzgjcXdajRG0t9fX369evHrVu3uHr1KvHx8erR8wVBeDGFhYWRlJRE+S9NnokzMwYPoJmLRa2W614FucVcOJZGZprq99PMRrspZQVBeHqIoFt44uRyOfXr16/tYjxX/ju/96N4Gj4fExMTTExMarUMLzLDO4FufnF583LVe0XR3ZpuXTNL5GVllMpkxPz4NW0Xrq0yT2MdYwB+Of0Lfev3xcbQBm8rb3SkqocoBsa69HjHh7TL2WyedwKFQsmJvy/jeKepubWTcY0F4B4eHly9epUDBw7g7u6OhcXTc3MtCMLjlZ6ezrlz57h16xaXrySRlamq3d5T7IGTkxMbJ3Sq3QJW4nBIAucPpwCgoy/D2FLvAVsIgvC0EUG3IAiCoMH4Tk13XrEqyJbe6V+tLCrj9sY4LAY3QCKV0NBUlzN5ZUQk38I7cj8WLe7fH7+dUzviMuIIvxpO+NVwAIx0jBjhPYKhDYdiqa9qkWHnaop//3oc2ZpA5N9XiPz7CgAW9oYM/bI1Eqn20xveT5MmTQgPDyczM5PFixfzzjvvYGtr+8j5CoLw9FIqlezdu5f9+/dXWHe+1IZiQ1um92tWCyWrWlF+CReOq6a1tHIypssoL2Qy0TtUEJ414q9WEARB0GB4p0933p3m5TITXYzbqVpT5EffoOR6LgDtvvwWlErKpFLiNq6uMs83m7zJOJ9xdK3blSY2TVT5l+Sx9ORSOm7sSLfN3Th/+zwAjTs4Ua+5DbZ1TbByMgIgIzWf2yl5NXJ8FhYWDB06FIlEgkKhYMuWLSgUigdvKAjCM+v69evqgDtHoUdMqSPhxe6sL2xKqw4BHPj0ZRo7mdVyKe8qKS5j5Uf7WTn5AIpS1URDvd7zxcZFtAIThGeRCLoFQRAEDUZ6d5qXF92dz9rsFTekhqpgvOROv0IjV088dVXrryReQ1FWxv0Y6RgxsflEFnZayLqe69j36j7GNB6Ds4kzACl5Kfx67lcAdA3kdH/Lh8HT/HhtemucvVW14Dt+PsX1i5k1cowNGjRQz9qQlpbGtm3bKKui/IIgPNsuJl1X//+P4kakGLozuGsbFg1vzaSuDdCTP30DkxXlqX6DDUx08G7niJG5bi2XSBCEhyWCbkEQBEFD+UBq17MK1cskEgkGjVSzHGRsvqBebu2oapZ9VarL8WnjtN6Hpb4lk1tMZueAnfzY+UcAtidsZ13sugppW/VyAyDndiFbF0Rx7tB10i5nU1b2aLXTTZo0oWvXrgCcOnWKsLCwR8pPEISn16n4qwBcl9vz3avNOTD1ZSZ0rk8PHwckkkfvtlLT5HIpQ79szYiv/Xnju/Z0fr3hU1lOQRC0I4JuQRAEQUN9G9WgZ5sjr3IkIV29XN/rzkj4SlDcqQX3eXcq0jtNs8/GX+X4tDc5/d0nZERW7Dd5Px3qdMDWUBW8H75+uMJ6e3czhnzmp36/99fzbJ53gk1zIsjLKqrewd1DKpXStm1bOnfurNr34cPcvn37ofMTBOHpoVAouJR4hYW/rGf67LncTjgJgJuTPf2b1UFX/nTfAkukEiwdjTC1MqjtogiCUAOe7l8c4ZGNHj2afv361XYxgKerLI+Dq6srixYtUr+XSCRs27btse0vPDwciURCZmbmA9OuWbMGc3Pzx1YW4fnSv5kTLeuqRvQeuuIo8/4+z7XMAgy8rZAa3envfTQFpUKJkZsX/fv1BCBTR48Dl1LYfeIsq779ls2vBlKa8+C5sGVSGbPbzAbgeu71StPYuJgw8JMWeLd3xMLeECSQfi2PQ5vjH/l4O3TogLW1qhb/wIEDj5yfIAi1799//2Vt0Gqyk+OQlakezuXKTBjVp3Mtl0wQhBeRCLqfcz/88ANr1qyp7WK8kFJSUujRo0dtF6NSISEhdO3aFRsbG0xNTfH39+eff/6p7WLVip9//hlXV1f09fVp3bo1x48frzL92bNnGThwIK6urkgkEo0HLc8LqVTCT8Oa42SuqmFZui+BTzafAkBurZofNuvvy6T87ygZWy9iYduL1hbO1FEUYV9ahElJMQBX0OHwZ+9otU8HYwcA4jPjWRi5kMLSwgpp7N3M6Dy8IcNmvkTPd1WDsV2MSOPftbEoFMqHPl6JREKXLl1U+V28+ND5CILwdLhw4QIHDx4EIFOhz8ESV9oPGMO3n0/C0erpGSxNEIQXhwi6n3NmZmaihvOO4uLiJ7o/e3t79PSezrk09+/fT9euXdm5cyeRkZF07tyZ3r17Ex0dXdtFe6I2btzI5MmTmTFjBlFRUfj6+hIYGMiNGzfuu01+fj7u7u7MmzcPe3v7J1jaJ8veTJ+wjzrSx9cRgNPXslAqlZh2q4uehzkAivxS8o6lkhOWjKv5MDo0+x8Dv93EWyG7ae2gSnPqegYZkftRPODvz8nYCTM91c3w6jOrmX10Nml5afdN7+JtiZOnah+xh1O4fPLWIx2vs7NqQLfc3FwxoBqq34jevXvj6OioVaud0aNHI5FIKrwaNWqkTjNz5swK6xs2bPiYj0R4UeTk5HDq1Cl27txJcHAwAAVKOduLGzG8Z0e6NKmLVCpuewVBqB3i1+c5sXnzZnx8fDAwMMDKyoqAgADy8vIqNOnOyclh+PDhGBkZ4eDgwPfff0+nTp348MMP1WlcXV2ZM2cOb7zxBiYmJri4uLB8+XKN/SUnJzNkyBDMzc2xtLSkb9++XL58Wb2+rKyMyZMnY25ujpWVFVOnTkWp1L4mqlOnTnzwwQdMnToVS0tL7O3tmTlzpkaapKQk+vbti7GxMaampgwZMoS0tLs36TNnzqRp06asXLkSNzc39PX1AVWt1rJly+jVqxeGhoZ4eXlx5MgR4uPj6dSpE0ZGRrRp04aEhAR1XgkJCfTt2xc7OzuMjY3x8/Njz549VR7DvTeqld1sSiQSdSsEhULB3LlzcXNzw8DAAF9fXzZv3qyR386dO2nQoAEGBgZ07txZ43xX16JFi5g6dSp+fn54eHgwZ84cPDw8+PPPP7XaviY+nwed088++4zWrVtX2Levry+zZs0CoLS0lA8++ED9Pfvkk08YNWqU1t0YFi5cyJtvvsmYMWPw9vZm6dKlGBoasmrVqvtu4+fnx3fffcdrr7321D5UqSn6OjK+HdQEiQSyCkr4+0wq+vXMsRnrg8PnrbEY0gCTl53Rc1cFy2XZxdxacZrrXx+jvvNEAuxeo751J9Z8t4Afhvfjz9d7khF9qNJRznVlumzuvZkR3iMA1aBqAZsD2HRhU6Vlk8ml9JvUnHrNbAD4e9lpEk/efOhjNTQ0VN+Q5+TkPHQ+z4u8vDx8fX35+eeftUr/ww8/kJKSon4lJydjaWnJ4MGDNdI1atRII115baQgPIrr16+zcOFCQkJC1K2VcpW6/FnkjQIpjRxF7bYgCLVLBN0PoFQqyS/Jr5WXtkFqSkoKQ4cO5Y033iA2Npbw8HAGDBhQ6faTJ0/m0KFDbN++ndDQUA4cOEBUVFSFdAsWLKBly5ZER0czfvx43n33XeLi4gAoKSkhMDAQExMTDhw4wKFDhzA2NqZ79+7q2uQFCxawZs0aVq1axcGDB7l9+zZbt26t1rkPCgrCyMiIY8eO8e233zJr1ixCQ0MBVZDat29fbt++zb59+wgNDeXSpUu8+uqrGnnEx8ezZcsWQkJCiImJUS+fPXs2I0eOJCYmhoYNGzJs2DDefvttpk2bxokTJ1Aqlbz33nvq9Lm5ufTs2ZOwsDCio6Pp3r07vXv3JikpSatjmTJlisaN5vz58zE0NKRly5YAzJ07l7Vr17J06VLOnj3LpEmTeP3119m3bx+gesgxYMAAevfuTUxMDOPGjePTTz+t1vmsikKhICcnB0tLS623edTP50HndPjw4Rw/flzj4cfZs2c5deoUw4YNA+Cbb75h3bp1rF69mkOHDpGdna11P/ri4mIiIyMJCAhQL5NKpQQEBHDkyBGtz8PzTl9HRr07A6ttOpGsXi4z0cWouR1m3VyxeasJ1m80Rt/TAqQSFDnFlN4sxMqwLo0t2tLebhA2hu5cKJGyat5cfhvcnYsrvqE4S3PQMnsje6b6TWVOuznqZf8m/Vtl+ToM9cTMVtUM/p+VZ8nPfrgWLVKpFBMT1fy3mzdvfuItY542PXr04H//+x/9+/fXKr2ZmRn29vbq14kTJ8jIyGDMmDEa6eRyuUa68r70gvAoLly4oL7nuYwt4cX12FLUBAtzc34Z1ZJWbtpf2wRBEB4HeW0X4GlXUFpA6+CKtW1PwrFhxzDUMXxgupSUFEpLSxkwYAB169YFwMfHp0K6nJwcgoKCCA4OVvdfXL16NY6OjhXS9uzZk/HjxwPwySef8P3337N37148PT3ZuHEjCoWClStXqqevWL16Nebm5oSHh9OtWzcWLVrEtGnTGDBgAABLly6tdp/hJk2aMGPGDAA8PDz46aefCAsLo2vXroSFhXH69GkSExPVzULXrl1Lo0aNiIiIwM9PNdJxcXExa9euxcbGRiPvMWPGMGTIEPXx+fv7M336dAIDAwGYOHGixs2ir68vvr6+6vezZ89m69atbN++XSM4vx9jY2OMjVWBy9GjR/niiy8ICgqicePGFBUVMWfOHPbs2YO/vz8A7u7uHDx4kGXLltGxY0eWLFlCvXr1WLBgAQCenp6cPn2ab775plrn9H7mz59Pbm6u+pxo41E/nwed00aNGuHr60twcDDTp08HYN26dbRu3Zr69esDsHjxYqZNm6YODH766Sd27typVflv3bpFWVkZdnZ2Gsvt7Ow4f/681ufhRfDDa0155ceDHLmUTplCiUxacdoa/QYW6DewoCy3mLKMIhRFpZRczyNrZyL2Bq7YG7iSlpfAwZvbuakD2/ccwOTvMEb8tBwDR1eNvHrX600dkzqM/HskB68dJDUvFXujypvyG5rqMmxGazZ+HcHt63n8sSia/h81R99Ip9rH2bJlS8LCwrh69SpLly6lXr16uLi40LhxYzFVTzX98ssvBAQEqK9J5S5evIijoyP6+vr4+/szd+5cXFxcaqmUwvOgtLSUEydOAJBj05jwZNVDuMBGdnz/alMMdcWtriAItU/UdD8HfH196dKlCz4+PgwePJgVK1aQkZFRId2lS5coKSmhVatW6mVmZmZ4enpWSNukSRP1/yUSCfb29up+ridPniQ+Ph4TExN1MGlpaUlhYSEJCQlkZWWRkpKi0TRYLpera3W1dW8ZABwcHNRliI2NxdnZWR3QAXh7e2Nubk5sbKx6Wd26dSsE3P/NuzzouvdBhZ2dHYWFhWRnZwOqWtkpU6bg5eWFubk5xsbGxMbGal3TXS4pKYl+/foxZcoUdYAbHx9Pfn4+Xbt2VZ9PY2Nj1q5dq67ljY2NrdDUujxAf1TBwcF89dVX/P7779ja2mq93aN+Ptqc0+HDh6v75imVStavX8/w4cMByMrKIi0tTeP7LJPJaNGiRTXPgPAgDe1N0ZNLKSxRcCU9r8q0MmNddJ1N0K9vgUmHOli97qXuA25nVI/eDQdjU6IaSThHR5eDX06qNB8Pcw/0ZaouIX229SEqLYoSRUmlaaUyKfVbqL67t6/ncfD3hxsMrX379vTt2xeJRMLt27eJiIhgy5Yt7N27t1rdY150169f5++//2bcOM1521u3bs2aNWvYtWsXS5YsITExkfbt21fZnL+oqIjs7GyNlyDc69q1a+Tm5gKwI1kVYP82tjXLRrQUAbcgCE8N8Wv0AAZyA44NO1Zr+9aGTCYjNDSUw4cPs3v3bhYvXsznn3/OsWMPX24dHc1aIolEguLOXLy5ubm0aNGCdevWVdiusgD3cZRBW0ZGRg/Mu7wGq7Jl5fubMmUKoaGhzJ8/n/r162NgYMCgQYOq1QQ1Ly+PPn364O/vr+6TDNy9WdixAycnJ41tHnef4Q0bNjBu3Dg2bdqk0cxaG4/6+WhzTocOHconn3xCVFQUBQUFJCcnV+hC8LCsra2RyWQa/cwB0tLSnusB0h6GTCqhvq0xZ69n0/2HAxyd1gVLI12ttjVobI1BY2vyTqSSsfkiuoV16D3i/7hy5mfCzsRzJquIhltW4thjKDLDu3+vxrrGrAxcyYidIygoLWDUrlH0cO3Btx2/rXQ/zQProihTcmLnZa7GVXzoqK1mzZrh6upKYmIi//77L7m5uezfv5/U1FSGDh0qary1EBQUhLm5eYWxFe6dzaFJkya0bt2aunXr8vvvvzN27NhK85o7dy5fffXV4yyu8Iwr/w3XtXSi8LoOOjIJfm4WtVwqQRAETaKm+wEkEgmGOoa18qrOzZ1EIqFt27Z89dVXREdHo6urW6EPtbu7Ozo6OkRERKiXZWVlceHChWqdk+bNm3Px4kVsbW2pX7++xsvMzAwzMzMcHBw0gv7S0lIiIyOrtZ+qeHl5kZycTHLy3T6m586dIzMzE29v7xrbT7lDhw4xevRo+vfvj4+PD/b29tUayEypVPL666+jUCj49ddfNT5bb29v9PT0SEpKqnA+y2uKvby8KkxldfTo0Uc6pvXr1zNmzBjWr1/PK6+88kh5/Zc2n48257ROnTp07NiRdevWsW7dOrp27aqujTczM8POzk7j+1xWVlbpGAWV0dXVpUWLFoSFhamXKRQKwsLCaqwVwfNkxEt1kUiguFRB89mhzP7rHKHn0riZU6TV9oYt7DBsqnool3csFff2kzApKUYhlfL779tYMmIgO0f1ojj97kMQXxtfvm73NT7WqlYop2+dvm/+MrmUZt1UzZTzMovIzdCuXJWxsLCgefPmvPnmm+qWMBcuXKh2y5YXkVKpZNWqVYwYMQJd3aofzJibm9OgQQPi4+8/1/q0adPIyspSv+79TREEgCtXrgCQI1E9tPN2MEVPLqvNIgmCIFQggu7nwLFjx5gzZw4nTpwgKSmJkJAQbt68iZeXl0Y6ExMTRo0axccff8zevXs5e/YsY8eORSqVVivAHz58ONbW1vTt25cDBw6QmJhIeHg4H3zwAVevXgVUfaLnzZvHtm3bOH/+POPHjyczM7PGjjkgIAAfHx+GDx9OVFQUx48fZ+TIkXTs2LHazdi14eHhoR6M7eTJkwwbNqxatbozZ85kz549LFu2jNzcXFJTU0lNTaWgoAATExOmTJnCpEmTCAoKIiEhgaioKBYvXkxQUBAA77zzDhcvXuTjjz8mLi6O4ODgR5p/PTg4mJEjR7JgwQJat26tLk9WVtZD53kvbT4fbc/p8OHD2bBhA5s2bVI3LS/3/vvvM3fuXP744w/i4uKYOHEiGRkZWn+fJ0+ezIoVKwgKCiI2NpZ3332XvLw8jf78I0eOZNq0aer3xcXFxMTEEBMTQ3FxMdeuXSMmJqbKwOF58ForF4LGtMLcUNXC4ZeDiby59gR+X+8hJOrqA7eXSCRYDPFEbq1qwZO9K4keTaZiq1DdHBfJ5cQWQvCbr3PwwxHkJ6nOZ+96vfmmg2rsgpsFN6ts5q2rL8f+zkjqZw9ce/iDvcPMzIy3335b/b789024v3379hEfH3/fmut75ebmkpCQgIODw33T6OnpYWpqqvEShHJnzpzh7NmzANyUqP72R7d1rcUSCYIgVE4E3c8BU1NT9u/fT8+ePWnQoAFffPEFCxYs0GjKV27hwoX4+/vTq1cvAgICaNu2LV5eXurptLRhaGjI/v37cXFxYcCAAXh5eTF27FgKCwvVN0QfffQRI0aMYNSoUfj7+2NiYqL1KLjakEgk/PHHH1hYWNChQwcCAgJwd3dn48aNNbaPey1cuBALCwvatGlD7969CQwMpHnz5lpvv2/fPnJzc2nTpg0ODg7qV3l5Z8+ezfTp05k7dy5eXl50796dHTt24ObmBoCLiwtbtmxh27Zt+Pr6snTpUubMmVPVLqu0fPlySktLmTBhgkZ5Jk6c+NB53kubz0fbczpo0CDS09PJz8+v0Fz1k08+YejQoYwcORJ/f3+MjY0JDAzU+vv86quvMn/+fL788kuaNm1KTEwMu3bt0hhcLSkpiZSUFPX769ev06xZM5o1a6Yeib5Zs2YV+q8+jzo0sOHotC5M69GQwEZ2lD/b+CHsImWKB/d5lkglWI3wUgfekkI9ugf+jwkLl/KSkyVShYJ0HT2OpWSw7sPxXNm4lLz4s9gaqlo3FJUVkV1cdZ9e3y6q1iHnj6RUmU5b5SPag6rv6IsiNzdX/XAJIDExkZiYGHVt/7Rp0xg5cmSF7X755Rdat25N48aNK6ybMmUK+/bt4/Llyxw+fJj+/fsjk8kYOnToYz0W4fkTFxfHH3/8oZ5a09OzIZHpqh6Tdiba388IgiA8KRLlCzY6THZ2NmZmZmRlZVV4Yl5YWEhiYqLGnM7Pu7y8PJycnFiwYIFWNROC8DRTKBR4eXkxZMgQZs+eXdvFeeKe9G9YfnEpL80JI7uwlJm9vRnd1k3rbXMOXiPrr0sASAzk2LzRmJSjwZz64w8SC5WUyO40D1UqsSsrZlXvfG5KstnQawONrBrdN9/sWwX8+sUR5DpS3l7c6VEOTy0xMZGgoCDMzMyYNKnygd8eRVXXpdoSHh5O586dKywfNWoUa9asYfTo0Vy+fJnw8HD1uqysLBwcHPjhhx948803K2z72muvsX//ftLT07GxsaFdu3Z8/fXX1KtXT+tyPY3nSnhySkpK2Ldvn8b87i1btuRQkQvrI1RdD/79qCPud6Y5FARBeNy0vS6JgdReMNHR0Zw/f55WrVqRlZWlHtCrb9++tVwyQai+K1eusHv3bjp27EhRURE//fQTiYmJ6nm8hcfLUFfOB108+N+OWGb+eY5bucW81dEdU/0HT9dl3MaRktQ88k+koSwoJX1dLHXeG0PdwW8Tv/JbDu4MJUcio1guJ02uR/dLevxaL5sxu8YQ0ieEOiZ1Ks1XpqNqwFVaokCpVNbIwGfl4whkZWVRVlaGTPb89xft1KlTlU35K+veYmZmRn5+/n232bBhQ00UTXiB5OTkqOd8z8rKIikpSf291NPT49VXX8XV1ZVps0IBaFffGjfrygdQFQRBqE2iefkLaP78+fj6+hIQEEBeXh4HDhzA2tr6ie0/KSlJY2qs/77EYEXV16NHj/uez+o2Q3+WPh+pVMqaNWvw8/Ojbdu2nD59mj179uDl5fVMHcezbHQbV9xtVDe5P+2N57OQ+w92di+JVILloAZYjW6ERE9GWWYRKf87RvHVHOqPm8rokFDe37ILizvTi9mcKUSiUFJQWkDPkJ7suLQDhbLiGAA6uncD4rLS6s12cD9y+d3n02VlZTWSpyAIVSsuLmblypXs27ePU6dOceXKFXXA3bp1ayZMmICxtSM9fzxITmEpMqmElaNaihkGBEF4Koma7hdMs2bNanQU8Yfh6Oio7id4v/VC9axcuZKCgoJK11laWlYrr2fp83F2dubQoUOVrnuWjuNZJpdJ+fO9diwJT+CnvfH8dSqFE5fDmNazIX2bOj1we4OGlti81YQbi6MBuPFTDFITHYxa2mPatS4uViZkZBdzW0efSX/Z83NgGkV6Sj498Cm/x/3OL4G/IJfevZTJdO8+Sy4tViDXefRa6Xtrtqs7baEgCA/n+vXr6sE9u3Tpgrm5OYaGhjg5OaGvr8/vJ5KZuvmUOv1H3RqgXwN/74IgCI+DCLqFJ04ul1O/fv3aLsZz5b/zez+K5+XzeV6O41lgpCfno24NiE3JJuz8DVKzC/kh7KJWQTeArpMxNu/6krHlIqU38lHklJCzNxm5pT6tP5pBwf+mEV8Et+X6vLXLnusDXdhSepyoG1FE34jGz95PnZdMJkUilaBUKCkrqZkA+d6gW9R0C8KTUT5woZeXF+3bt1cvX7g7jg0Rydy4M12hXCph24S2NHYyq5VyCoIgaEM0LxcEQRAemUQi4ZfRfhz/rAtyqYRLN/O4kp6n9fZ6dU2xn9wCh+kvYdxB1V87I+Qi8rI69P5tJ68EtEOqUJCjo0fj7cl0cekCwJlbZyrkJVf3666ZAFkikaibrIqgWxAev7KyMo4dOwbcfaicX1zK7rOp/PhvvDrgbuZizskZ3UTALQjCU0/UdAuCIAg1xtZUn5auFhy9dJuPfj/JmjdaYayn/aVGZqSD6cvOFJy9RVl6Iem/nsNicAMavP0ZWUlvsz/+Ghkl0My2GWFJYWy5uAWAgQ0GYqqrGjVUriulpKiM0uKaawouk8koLS0VzcsF4TFRKBQkJSWRmppKREQE2dmq6QHLDCyYuCGa8LibZBWUACCTSjg5o1u1flsEQRBqk/i1EgRBEGrU2x3qcfTSbU5cyaDjt3sZ296N1m5WtKhrodX2Un05dhObk/7rOYouZpKx6QLFV3Ow820J8dcokkh5pW5XFkUu4kr2FRZGLmTJySW85PASrR1aI5XXBVQjmNeU8qBb1HQLQs1LSkpi7969JCYmaiy/LLFn9KZElKhamtib6uNmbcTYdm4i4BYE4ZkifrEEQRCEGtW5oS0/Dm3G9G1nSM8r5ttdccikEha92pTODW21ulmW6sqwHtOYzO0J5B1NIe9ICnJrD4zkZhQoMrE3sGNNjzUcvn6Yjec3kl6Yzt7kvexN3strhZ9jji2lxTUXIEulqibrIugWhJp18OBB9uzZo35f182dPVdKOFtgSqbSEIB6Nka893J9ejdxRC4TPSMFQXj2iKBbEARBqHF9fB3p4GHNqkOX+eXAJfKKy3h/fTT2pvrsm9oJPfmDRxmWSCVY9KuPzFiH7D1JKG7J8DT1I+r2HopvXMPXwRdfG1/GNR7HudvniEiNYM3ZNZRKigEoLiqtseMpH0xNNC8XhJpTVFREWFgYAG5ubvTq1Yu0Ih1Wrj0BlLJ0QBM6NLDGUFfcrgqC8GwTjwufc6NHj6Zfv361XQzg6SrLs8zV1ZVFixap30skErZt2/bY9hceHo5EIiEzM/OBadesWYO5ufljK4vwbDE31GVy1wbsm9qZXk0cAEjNLiT+Rm618jENqItRK3sA9KQGAFzduVG9Xkemowq+fcYRPiScUqmq3+f+3+O4naL9YG5VKQ+6RU23IDy6goICVq1axdy5c1Eqlejq6vL6669jZWWFt6Mpuz5sz5Z329C9sb0IuAVBeC48FUH3zz//jKurK/r6+rRu3Zrjx4/fN+2KFSto3749FhYWWFhYEBAQUGX6F90PP/zAmjVrarsYwmOUkpJCjx49arsYlQoJCaFr167Y2NhgamqKv78///zzT20Xq1oKCwuZMGECVlZWGBsbM3DgQNLS0qrcJiQkhG7dumFlZYVEIqlyvvAXgbWxHj8Na46fq6pP95ydsZQplNXKQ9fZRPWvVBeAs//+W2k6uVROlsNVAHJvFrN1fhS3ruagVFZvf/8lmpcLwqNTKpVERUWxbNkykpKSANDR0cHf319jaj4TfR3q2RjXVjEFQRBqXK0H3Rs3bmTy5MnMmDGDqKgofH19CQwM5MaNG5WmDw8PZ+jQoezdu5cjR47g7OxMt27d1PM5CprMzMxEzeMTVlxc/ET3Z29vj56e3hPdp7b2799P165d2blzJ5GRkXTu3JnevXsTHR1d20XT2qRJk/jzzz/ZtGkT+/bt4/r16wwYMKDKbfLy8mjXrh3ffPPNEyrls6GpszkAh+LT6fDtXuJSc7TeVqKvqu0y0dEHoKCKpuMW/gpCGi+kUJZHYV4JG/8XwT8rzpJ1s+Chyy6alwvCo1EoFPz1119s376dzMxMdHR0GDNmDJ999hmdO3eu7eIJgiA8VrUedC9cuJA333yTMWPG4O3tzdKlSzE0NGTVqlWVpl+3bh3jx4+nadOmNGzYkJUrV6JQKNR9gl5UmzdvxsfHBwMDA6ysrAgICCAvL69Ck+6cnByGDx+OkZERDg4OfP/993Tq1IkPP/xQncbV1ZU5c+bwxhtvYGJigouLC8uXL9fYX3JyMkOGDMHc3BxLS0v69u3L5cuX1evLysqYPHky5ubmWFlZMXXq1GrVNHXq1IkPPviAqVOnYmlpib29PTNnztRIk5SURN++fTE2NsbU1JQhQ4Zo1EDOnDmTpk2b8uuvv+Lq6oqZmRmvvfYaOTk5j2U/K1euxM3NDX19VVAgkUhYtmwZvXr1wtDQEC8vL44cOUJ8fDydOnXCyMiINm3akJCQoM4rISGBvn37Ymdnh7GxMX5+fhoDzFTm3ublM2fOVM8pfO+rvLWDQqFg7ty5uLm5YWBggK+vL5s3b9bIb+fOnTRo0AADAwM6d+6s8blW16JFi5g6dSp+fn54eHgwZ84cPDw8+PPPP7XafteuXbRr1079PerVq5fG+WrTpg2ffPKJxjY3b95ER0eH/fv3A6qWAK+88goGBga4ubkRHBxcoYn+/WRlZfHLL7+wcOFCXn75ZVq0aMHq1as5fPgwR48eve92I0aM4MsvvyQgIECr43xRfNTNk9dfcgHgWmYBUzef1Hpbqb4q6NU3UE0Lll9F7PvFS1/Qy78Luz1XkaeTBUBC1A02/O84xQUP189bNC8XhId37do1li5dSmRkJKD67f7ggw+oW7cuEomklksnCILw+NVq0F1cXExkZKTGjalUKiUgIIAjR45olUd+fj4lJSVYWlpWur6oqIjs7GyNV3UolUoU+fm18tI2SE1JSWHo0KG88cYbxMbGEh4ezoABAyrdfvLkyRw6dIjt27cTGhrKgQMHiIqKqpBuwYIFtGzZkujoaMaPH8+7775LXFwcACUlJQQGBmJiYsKBAwc4dOgQxsbGdO/eXV3Lu2DBAtasWcOqVas4ePAgt2/fZuvWrdU690FBQRgZGXHs2DG+/fZbZs2aRWhoKKAKHvv27cvt27fZt28foaGhXLp0iVdffVUjj4SEBLZt28Zff/3FX3/9xb59+5g3b16N7yc+Pp4tW7YQEhKi0ZR49uzZjBw5kpiYGBo2bMiwYcN4++23mTZtGidOnECpVPLee++p0+fm5tKzZ0/CwsKIjo6me/fu9O7dW90M70GmTJlCSkqK+jV//nwMDQ1p2bIlAHPnzmXt2rUsXbqUs2fPMmnSJF5//XX27dsHqB6mDBgwgN69exMTE8O4ceP49NNPtdq3NhQKBTk5Off9e/2vvLw8Jk+ezIkTJwgLC0MqldK/f391bePw4cPZsGGDxnd948aNODo60r59ewBGjhzJ9evXCQ8PZ8uWLSxfvvy+LWn+KzIykpKSEo3fqIYNG+Li4qL1b5Rwl76OjNl9G7NypOr7eOpaFkvCE4i/8eAab+mdmm6p9E5NN/cfiE1HqsOkFpP45rUZ/NZiBlFOuwEoLSojIy3/ocoumpcLgvaUSiU3b97k+PHj/P3336xYsUL9uxsQEEC3bt0wMTGp5VIKgiA8ObU6OsWtW7coKyvDzs5OY7mdnR3nz5/XKo9PPvkER0fH+9YozZ07l6+++uqhy6gsKCCueYuH3v5ReEZFIjE0fGC6lJQUSktLGTBgAHXrquan9fHxqZAuJyeHoKAggoOD6dKlCwCrV6/G0dGxQtqePXsyfvx4QHWOv//+e/bu3YunpycbN25EoVCwcuVK9RPq1atXY25uTnh4ON26dWPRokVMmzZN3Qx36dKl1e7L26RJE2bMmAGAh4cHP/30E2FhYXTt2pWwsDBOnz5NYmIizs7OAKxdu5ZGjRoRERGBn58foAry1qxZo764jxgxgrCwML7++usa3U9xcTFr167FxsZG4xjGjBnDkCFD1OfR39+f6dOnExgYCMDEiRMZM2aMOr2vry++vr7q97Nnz2br1q1s375dIzi/H2NjY4yNVf3gjh49yhdffEFQUBCNGzemqKiIOXPmsGfPHvz9/QFwd3fn4MGDLFu2jI4dO7JkyRLq1avHggULAPD09OT06dM11kx6/vz55Obmqs/JgwwcOFDj/apVq7CxseHcuXM0btyYIUOG8OGHH3Lw4EF1kB0cHMzQoUORSCScP3+ePXv2EBERoX7wsHLlSjw8PLTaf2pqKrq6uhW6aNjZ2ZGamqpVHoImiURCgLcdrlaGXE7P55td59kQkUT4lE5V1nhJ7tR0S0p0kSKlUEdORvQhLJq1ve82TW2b0syuGcclO/CXdKHoqozsmwXYuZpWu9yiebkgaO/QoUOVttKaMGFCheukIAjCi6DWm5c/innz5rFhwwa2bt2qbtL7X9OmTSMrK0v9Sk5OfsKlfPx8fX3p0qULPj4+DB48mBUrVpCRkVEh3aVLlygpKaFVq1bqZWZmZnh6elZI26RJE/X/JRIJ9vb26qfUJ0+eJD4+HhMTE3WQZ2lpSWFhIQkJCWRlZZGSkkLr1q3VecjlcnXQo617ywDg4OCgLkNsbCzOzs7qQBjA29sbc3NzYmNj1ctcXV01nqbfm0dN7qdu3bqV3kjcm3f5w6V7H4jY2dlRWFioboGRm5vLlClT8PLywtzcHGNjY2JjY7Wu6S6XlJREv379mDJlijrAjY+PJz8/n65du6o/N2NjY9auXatush0bG6vxuQHqAP1RBQcH89VXX/H7779ja2ur1TYXL15k6NChuLu7Y2pqiqurq/r4AGxsbOjWrRvr1q0DIDExkSNHjjB8+HAA4uLikMvlNG/eXJ1n/fr1sbCwqJFjEh7e/MG+DGpRB4Ar6fmcvV51KySZqR7IVZesdtavALBh9qwH7sfNzA2APINMAPZtiCNq95Vql1c0LxeEqpWUlLB7925WrlypDrj19PRo0aIFL730kgi4BUF4odVqTbe1tTUymazCSMBpaWnY29tXue38+fOZN28ee/bsqRA03UtPT++RBpmSGBjgGRX50Ns/ComBgVbpZDIZoaGhHD58mN27d7N48WI+//xzjh079tD71tHR0SyLRKKu4cnNzaVFixbqQOdeNXlBraoMNZlHTezHyMjogfsvr8WrbFn5/qZMmUJoaCjz58+nfv36GBgYMGjQoGoNzpaXl0efPn3w9/dn1qy7QUlurmqaph07duDk5KSxzeMeiG3Dhg2MGzeOTZs2Vaufc+/evalbty4rVqzA0dERhUJB48aNNc7H8OHD+eCDD1i8eDHBwcH4+PhU2tLjYdjb21NcXExmZqZGbbc2v1FC1Vq6WtLS1ZIbOUXsv3CTiMu3aexkdt/0Uj0Z5r3dydwaj4OJN3ULEriSd45Dk0fSduHa+27n7+jPlotbiLD8hw76IyjKK+VISAL1mtliZqPdbyyI5uWCUJVbt26xdu1ajS58pqamTJgw4akd6FMQBOFJqtWabl1dXVq0aKExCFr5oGhV1a59++23zJ49m127dlW79rS6JBIJUkPDWnlVZ3ARiURC27Zt+eqrr4iOjkZXV7dCH2p3d3d0dHSIiIhQL8vKyuLChQvVOifNmzfn4sWL2NraUr9+fY2XmZkZZmZmODg4aAT9paWl6gFUaoKXlxfJyckaLRfOnTtHZmYm3t7ez9x+yh06dIjRo0fTv39/fHx8sLe3r9ZAZkqlktdffx2FQsGvv/6q8R3y9vZGT0+PpKSkCp9beU2+l5dXhSn4qhowTBvr169nzJgxrF+/nldeeUXr7dLT04mLi+OLL76gS5cueHl5VdqCo2/fvhQWFrJr1y6Cg4PVtdygah5fWlqqMVp6fHx8pflUpkWLFujo6Gj8RsXFxZGUlFRjLQBedI0cVU29F+25SElZ1Q+7jFs7oFffHICXbHvjaFif9LSbVW7TzLYZAGdkJxjyxd2uQuHrzlNUjUHVRPNyQajcjRs3WLJkiTrgfumll3j77bd5//33RcAtCIJwR603L588eTIrVqwgKCiI2NhY3n33XfLy8tT9XEeOHMm0adPU6b/55humT5/OqlWrcHV1JTU1ldTUVHUt3ovo2LFjzJkzhxMnTpCUlERISAg3b97Ey8tLI52JiQmjRo3i448/Zu/evZw9e5axY8cilUqrFeAPHz4ca2tr+vbty4EDB0hMTCQ8PJwPPviAq1dV8+NOnDiRefPmsW3bNs6fP8/48ePJzMyssWMOCAjAx8eH4cOHExUVxfHjxxk5ciQdO3as0QcxT2o/5Tw8PNSDsZ08eZJhw4ZV6yZ/5syZ7Nmzh2XLlpGbm6v++ygoKMDExIQpU6YwadIkgoKCSEhIICoqisWLFxMUFATAO++8w8WLF/n444+Ji4sjODj4keZ5Dw4OZuTIkSxYsIDWrVury5OVlfXAbS0sLLCysmL58uXEx8fz77//Mnny5ArpjIyM6NevH9OnTyc2NpahQ4eq1zVs2JCAgADeeustjh8/TnR0NG+99RYGBgZafefNzMwYO3YskydPZu/evURGRjJmzBj8/f156aWXNPZz70Ou27dvExMTw7lz5wBVoB4TEyP6gVeiX1NVq4usghLCYh88wJ3VSG+kxqrWIlZ6DpQ+IFC3NrBGR6pDmbKMfP1sfLuoHjBdPZ/BoU0XtS6naF4uCJW7cOECZWVl2NraMnz4cLp3746Dg0OFVmSCIAgvsloPul999VXmz5/Pl19+SdOmTYmJiWHXrl3q/q9JSUmkpKSo0y9ZsoTi4mIGDRqEg4OD+jV//vzaOoRaZ2pqyv79++nZsycNGjTgiy++YMGCBfTo0aNC2oULF+Lv70+vXr0ICAigbdu2eHl53bdPfGUMDQ3Zv38/Li4uDBgwAC8vL8aOHUthYSGmpqpaq48++ogRI0YwatQo/P39MTExoX///jV2zBKJhD/++AMLCws6dOhAQEAA7u7ubNy4scb28ST3U27hwoVYWFjQpk0bevfuTWBgoEZ/5AfZt28fubm5tGnTRuPvo7y8s2fPZvr06cydOxcvLy+6d+/Ojh07cHNT9Xt1cXFhy5YtbNu2DV9fX5YuXcqcOXMe+niWL19OaWkpEyZM0CjPxIkTH7itVCplw4YNREZG0rhxYyZNmsR3331Xadrhw4dz8uRJ2rdvj4uLi8a6tWvXYmdnR4cOHejfvz9vvvkmJiYmWn/nv//+e3r16sXAgQPp0KED9vb2hISEaKSJi4vTeJCwfft2mjVrpq7Zf+2112jWrBlLly7Vap8vEk97E97u4A7AO79FsupgYpXppboyTDqrAmdX48aUPiAGlkqk2BupugJcz71Oq15uOHup+vTfTNZ+nnDRvFwQKpeeng6oWlNpO0ilIAjCi0airM7kyc+B7OxszMzMyMrKUgeI5QoLC0lMTNSYa/l5l5eXh5OTEwsWLGDs2LG1XRxBeOyuXr2Ks7Mze/bsUY/i/7x4Vn/D8otLGfB/hzmfqgqCuzS0ZWafRjhbVj57Q8mtAtLmnwDgxJX/o9/69VXmP+rvUUTdiGJ+x/kEugZyOyWP9V+pur+0G+yBnZsp9u73708OsHnzZs6cOUP37t01WjnUhKquS4Imca6ePr/88gvJyckMHDiwxsbTEARBeFZoe12q1YHUhCcvOjqa8+fP06pVK7KystQDbfXt27eWSyYIj8e///5Lbm4uPj4+pKSkMHXqVFxdXenQoUNtF024w1BXzh/vtWXh7gss23+JsPM3uJpRwK4P21faDUDH2oAyCpBhAJIH9xk11VVdBHOKVUG9qbU+MrmUslIFB+80Me84zJPGHZzum4doXi4IlSuv6baysqrlkgiCIDy9ar15ufDkzZ8/H19fXwICAsjLy+PAgQNYW1s/sf0nJSVpTFn131d1p8cSnpwePXrc93OrbjP0J/U9KCkp4bPPPqNRo0b0798fGxsbwsPD0dHRYd26dffdf6NGjWpk/4J29OQyPu3RkJ+GqQY+i0vL4e8zVfSBl5ao/pU8uN+oia5q2sDcYtXYH3IdGYFvNqJxByecPFVNzfcFx3H59K375iGCbkGoqKCggPz8fEAE3YIgCFURNd0vmGbNmtXoKOIPw9HRkZiYmCrXC0+nlStXUlBQUOk6S0vLauX1pL4HgYGBBAYGVrquT58+FeYlLycGAXryJBIJvZo48veZVHacSmH8uiisjfXo5GnDWx3caWBncjexDFCApBpBd3bx3emM3HxtcPO1QalQsm7mUbJuFPDP8jOMmtcWfaOKeYo+3YJQ0a1bqgdVJiYmYqRyQRCEKoigW3ji5HI59evXr+1iCA/hv/N7P4qn4XtgYmKCiYnJgxMKT9RnPb24lVPEscTb3MotYnPkVbZFX2Nsezc6NbDlJXdLJHKgBJDqPjA/Y11j4G7z8ntJpBJ6TfBl3cyjlJYouH4hE/dmNhXSiSnDBKGi8qBb1HILgiBUTTQvFwRBEJ4qTuYGbHzbn8gvAlg5siWNHE0pVShZtu8SQ1ccZcb2s0h0VH29JVoE3eV9uv+5/E+l683tDPG505/7wO8XuBRTce5v0bxcEDTdvn2bP/74A+CJdlETBEF4FomgWxAEQXgqWRnrEeBtx7YJbflpWDNc7oxmvvbIFa7nq/p0K+V6FKenVZmPpb6q60NGUQaRaZV3r2n5ihum1vrkZhTxz/IzFBeUaqwXzcsFQUWpVHLkyBFWrlwJqLriNG7cuJZLJQiC8HQTQbcgCILwVNORSenVxJF/P+pIYCM7AC4Xqmq6ZVIdzv5f1YP4dXHpgrOJam7v/Vf3V5rG0FSXIZ/5AaBQKLl1LVdjvWheLgiq739wcDD//PMP+fn5GBkZ8e677+Lq6lrbRRMEQXiqiaBbEARBeCbIZVKWjWiJRAI35EaqZRId8jMyqtzOUMeQgR4DAbhVcP8RyvUMdajro+qbmn618qBb1HQLL6KsrCwuX75MUFAQFy+qptnz9vZmwoQJ1R5EUxAE4UUkBlITBEEQnimt3SwpupQHgLd5G+KzHjwjg7WBqs9pVUE3gLmNIVdIJye9UGO5aF4uPM8UCgXJyckUFBRw69Ytbt68SU5ODvn5+eTl5ZGTozkI4csvv0yHDh1qqbSCIAjPHlHT/ZwbPXo0/fr1q+1iAE9XWZ5lrq6uLFq0SP1eIpGwbdu2x7a/8PBwJBIJmZmZD0y7Zs0azM3NH1tZBAHgo26eFOirap4lSCguLH7gNuVB9/nb51l9ZjVpeZX3Aze2VE17lJOhGXSL5uXC8yojI4O1a9eyevVqNmzYwJ49ezh58iSXLl0iNTVVHXBbWFjg5ubGyy+/TPv27Wu51IIgCM8WUdP9nPvhhx9QKpW1XQzhMUpJScHCwqK2i1GpkJAQlixZQkxMDEVFRTRq1IiZM2fed97s59nPP//Md999R2pqKr6+vixevJhWrVrdN/3Zs2f58ssviYyM5MqVK3z//fd8+OGHT67ATzE/V0s8JvuTOycCiURC0YNjblxMXAC4XXibhZEL+Sn6Jz5/6XP61e+HVHL3+bOxhT4AubcrD7pFTbfwPFEqlWzZsoWrV68CYGZmhrW1NXXq1MHS0hJDQ0MMDQ0xMzPD2Ni4lksrCILw7BI13c85MzMzUfP4hBUXaxEB1CB7e3v09PSe6D61tX//frp27crOnTuJjIykc+fO9O7dm+jo6Nou2hO1ceNGJk+ezIwZM4iKisLX15fAwEBu3Lhx323y8/Nxd3dn3rx52NvbP8HSPhvMTPRQKFUjjBcVyxm+8ig3c4rum97Z1JllAct4x/cdXE1dKVYUM+PwDD7c+6FGOiNz1d9Sfrbm37FoXi48jy5duqQOuAcMGMCkSZMYMWIEnTt3xtfXFw8PD5ycnETALQiC8IhE0P2c2Lx5Mz4+PhgYGGBlZUVAQAB5eXkVmnTn5OQwfPhwjIyMcHBw4Pvvv6dTp04aNWiurq7MmTOHN954AxMTE1xcXFi+fLnG/pKTkxkyZAjm5uZYWlrSt29fLl++rF5fVlbG5MmTMTc3x8rKiqlTp1arxr1Tp0588MEHTJ06FUtLS+zt7Zk5c6ZGmqSkJPr27YuxsTGmpqYMGTKEtLS7TUZnzpxJ06ZN+fXXX3F1dcXMzIzXXntNo29aTe5n5cqVuLm5oa+vqimTSCQsW7aMXr16YWhoiJeXF0eOHCE+Pp5OnTphZGREmzZtSEhIUOeVkJBA3759sbOzw9jYGD8/P/bs2VPlubq3efnMmTORSCQVXmvWrAFUTWPnzp2Lm5sbBgYG+Pr6snnzZo38du7cSYMGDTAwMKBz584an2t1LVq0iKlTp+Ln54eHhwdz5szBw8ODP//8U6vta+LzedA5/eyzz2jdunWFffv6+jJr1iwASktL+eCDD9Tf508++YRRo0Zp3V1i4cKFvPnmm4wZMwZvb2+WLl2KoaEhq1atuu82fn5+fPfdd7z22mtP7UOV2iSRSChTqgJjqcyAQ/HprD6UWOU2bZzaMKHpBP7o9wf96/cH4PD1wyiUd5uM6xmqGoAV5WtOGSaalwvPm9jYWH799VcAWrVqRZMmTWq5RIIgCM8vEXQ/gFKppKSorFZe2gapKSkpDB06lDfeeIPY2FjCw8MZMGBApdtPnjyZQ4cOsX37dkJDQzlw4ABRUVEV0i1YsICWLVsSHR3N+PHjeffdd4mLiwOgpKSEwMBATExMOHDgAIcOHcLY2Jju3bura3kXLFjAmjVrWLVqFQcPHuT27dts3bq1Wuc+KCgIIyMjjh07xrfffsusWbMIDQ0FVDe+ffv25fbt2+zbt4/Q0FAuXbrEq6++qpFHQkIC27Zt46+//uKvv/5i3759zJs3r8b3Ex8fz5YtWwgJCSEmJka9fPbs2YwcOZKYmBgaNmzIsGHDePvtt5k2bRonTpxAqVTy3nvvqdPn5ubSs2dPwsLCiI6Opnv37vTu3ZukpCStztmUKVNISUlRv+bPn4+hoSEtW7YEYO7cuaxdu5alS5dy9uxZJk2axOuvv86+ffsA1cOUAQMG0Lt3b2JiYhg3bhyffvqpVvvWhkKhICcnp1qj3T7q5/Ogczp8+HCOHz+u8fDj7NmznDp1imHDhgHwzTffsG7dOlavXs2hQ4fIzs7Wuh99cXExkZGRBAQEqJdJpVICAgI4cuSI1udBqEgqV9U6l8mNMC7NZem+BPbG3b/1gHo7iZQv/b9ELpFTVFbEzsSdxGfEo1Qq7wbdBaUoFXd/Q0XzcuF5UFRUxN69e/ntt9/YuHEjAIaGhrRt27aWSyYIgvB8E326H6C0WMHyiftqZd9v/dARHT3ZA9OlpKRQWlrKgAEDqFu3LgA+Pj4V0uXk5BAUFERwcDBdunQBYPXq1Tg6OlZI27NnT8aPHw/AJ598wvfff8/evXvx9PRk48aNKBQKVq5ciUQiUedjbm5OeHg43bp1Y9GiRUybNo0BAwYAsHTpUv75559qHX+TJk2YMWMGAB4eHvz000+EhYXRtWtXwsLCOH36NImJiTg7q+bfXbt2LY0aNSIiIgI/v/L5dhWsWbMGExMTAEaMGEFYWBhff/11je6nuLiYtWvXYmNjo3EMY8aMYciQIerz6O/vz/Tp09V9midOnMiYMWPU6X19ffH19VW/nz17Nlu3bmX79u0awfn9GBsbq5sBHj16lC+++IKgoCAaN25MUVERc+bMYc+ePfj7+wPg7u7OwYMHWbZsGR07dmTJkiXUq1ePBQsWAODp6cnp06f55ptvHrhvbcyfP5/c3Fz1OdHGo34+DzqnjRo1wtfXl+DgYKZPnw7AunXraN26NfXr1wdg8eLFTJs2jf79VbWjP/30Ezt37tSq/Ldu3aKsrAw7OzuN5XZ2dpw/f17r8yBUpGdrRWlqMS0cBzAzeS2rbFqz4bgdnT1tH7itXCqnrmldErISmHZgGgBv+rzJBJ87f2dKKC4sRc9QBxDNy4XnQ2RkpPohK0CDBg0YNGgQurq6tVgqQRCE55+o6X4O+Pr60qVLF3x8fBg8eDArVqwgo5J5ay9dukRJSYnG4E1mZmZ4enpWSHtvMzOJRIK9vb26/+nJkyeJj4/HxMREHeRZWlpSWFhIQkICWVlZpKSkaDTZlcvl6tpWbf23qZuDg4O6DLGxsTg7O6sDLVDNGWpubk5sbKx6maurqzrg/m8eNbmfunXrVgi4/5t3edB17wMROzs7CgsLyc7OBlS1slOmTMHLywtzc3OMjY2JjY3Vuqa7XFJSEv369WPKlCnqADc+Pp78/Hy6du2q/tyMjY1Zu3atupY3Nja2QlPr8gD9UQUHB/PVV1/x+++/Y2v74KCo3KN+Ptqc0+HDhxMcHAyoWresX7+e4cOHA6r5adPS0jT+bmQyGS1atKjmGRBqmp6LqsWEvsyIl1zf5bPiUhpE7KWs6P59u+811W8qnZw74W7mDsD2hO0gUyLXUV0a721iLpqXC8+DlJQUAKytrRk/fjzDhg0TAbcgCMITIGq6H0CuK+WtHzrW2r61IZPJCA0N5fDhw+zevZvFixfz+eefc+zYsYfet46OjsZ7iUSivtnMzc2lRYsWrFu3rsJ2lQWej6MMNZlHTezHyMjogfsvbxVQ2bLy/U2ZMoXQ0FDmz59P/fr1MTAwYNCgQdUanC0vL48+ffrg7++v7pMMqs8NYMeOHTg5OWls87j7DG/YsIFx48axadMmjWbW2njUz0ebczp06FA++eQToqKiKCgoIDk5uUIXgodlbW2NTCbT6GcOkJaWJgZIe0Tmfeuj18CMtKAIdGSmuFq0whU4/+lfJN7YROt3R2HTrsd9t2/j1IY2Tm3ILc6l0++dSMtPY83ZNegZelKaVcyhzfH0eEf1kKw86C4oKHgCRyYINSsrK4sLFy5w4cIFALp161ath5+CIAjCoxE13Q8gkUjQ0ZPVyqs8INO2nG3btuWrr74iOjoaXV3dCn2o3d3d0dHRISIiQr2s/EJcHc2bN+fixYvY2tpSv359jZeZmRlmZmY4ODhoBP2lpaVERkZWaz9V8fLyIjk5meTkZPWyc+fOkZmZibe39zO3n3KHDh1i9OjR9O/fHx8fH+zt7as1kJlSqeT1119HoVDw66+/anyHvL290dPTIykpqcLnVl5T7OXlxfHjxzXyPHr06CMd0/r16xkzZgzr16/nlVdeeaS8/kubz0ebc1qnTh06duzIunXrWLduHV27dlXfkJqZmWFnZ6fxd1NWVlbpWAiV0dXVpUWLFoSFhamXKRQKwsLCaqwVwYtKIpNg2NgW17k9UDilcStX1S/bTM8WT4dR/LVsCxdXPLhrhLGuMVP9pgLwfzH/h5Gd6kHPpZibRO9OQqlUqlvM3Lp1S6s56wXhaZGWlsbixYvZsWMHRUVFGBsb4+rqWtvFEgRBeKGIoPs5cOzYMebMmcOJEydISkoiJCSEmzdv4uXlpZHOxMTk/9u787gqqv9/4K/hsu8gyKJsKiIoKoIL7hSGZqgfK1z4CC4tGqaGmlqpqF+FyiW3XPJXWqmopWimFuJCmqKgmAsgIIobYi4sIgL3zu8PdD7dQL2sl+X1fDzuQ2bmzMx7jnDPfd85cw6Cg4Mxffp0HD58GBcvXsS4ceOgoaFRoQQ/MDAQFhYWGDx4MP744w9kZGTgyJEjmDRpkjT1yOTJkxEREYGoqCgkJyfjgw8+qNYPqr6+vnB3d0dgYCDOnDmDU6dOISgoCH369KlwN/a6cJ5nnJ2dpcHYzp07h5EjR1borm5YWBgOHjyIdevWIT8/H1lZWcjKysLjx49hZGSEadOm4aOPPsKmTZuQnp6OM2fOYOXKldi0aRMAYPz48UhNTcX06dORkpKCLVu2SCOfV8aWLVsQFBSEJUuWoGvXrlI8OTk5lT7mP6ny/6NqnQYGBiIyMhI7duyQupY/8+GHHyI8PBy7d+9GSkoKJk+ejAcPHqj8dxMaGopvvvkGmzZtQlJSEiZMmIBHjx4pPc8fFBSEWbNmSctFRUVITExEYmIiioqKcPPmTSQmJiItLa0yVdWgCZoasP/wLXzcsgWmldwEAOhpGqKdWS/s++0IinLuv/QYQ52HwlLPEkWKIqyzmg0949LE+8+dabhw9Casra1hYmICALh//+XHI6oL/v77b6xbtw4lJaWPSnh5eSE4OJhdyomIahmT7gbA2NgYsbGxeP3119G6dWt89tlnWLJkCQYMKNutcunSpfD29sYbb7wBX19f9OjRA66urtI0V6rQ19dHbGws7O3tMXToULi6umLcuHEoLCyEsbExAGDq1KkYNWoUgoOD4e3tDSMjI2kQquogCAJ2794NMzMz9O7dG76+vmjRooU0Gmt9O88zS5cuhZmZGbp37w5/f3/4+fmhU6dOKu9/9OhR5Ofno3v37rCxsZFez+JdsGABZs+ejfDwcLi6uqJ///749ddf4eTkBACwt7fHzz//jKioKHTo0AFr167FokWLKn0969evR0lJCUJCQpTimTx5cqWP+U+q/P+oWqdvvfUW7t27h4KCgjJTgc2YMQMjRoxAUFAQvL29YWhoCD8/P5X/boYNG4bFixdjzpw56NixIxITE3HgwAGlwdUyMzOl5y0B4NatW/Dw8ICHh4c0Er2HhwfeeeedCtZS4+HpYIY4LWOsRiGA0sS7RCZDxo+rX7qvpoYmlvksQxPdJrhTchtnu0XBpnXp+1ls5GXcSn0IU1NTAKVzqDd0sbGx8Pf3h62trdK0hM9z5MiRcqcrzMrKUiq3evVqODo6QldXF127di3Ts4aqhyiK+Pnnn7Fq1SooFAoYGBhg8uTJeOONN6r1MTAiIlKNIFZk8uQGIDc3FyYmJsjJyZESxGcKCwuRkZGhNNdyQ/fo0SM0a9YMS5Yswbhx49QdDlG9oFAo4OrqioCAACxYsEDd4Uga43vYPxXLFTiW9jf+ir+FN8/n4UlJHqKufw3vFrboHr5epWPEZ8Vj3O/joBAVGNZyOKx+7oWixyWwdTbFk2aXkZycjIEDB0ozF1SHF7VL6rJ//34cP34cnp6eGDp0KHbt2vXCeemPHDkCHx8fpKSkKF1D06ZNpZHft23bhqCgIKxduxZdu3bFV199hR07diAlJUXl54vrYl3VRffu3cPKlSsBAGZmZujXr1+NPBJFRNTYqdou8U53I3P27Fls3bpV6lr8rBvt4MGD1RwZUd117do1fPPNN7h8+TLOnz+PCRMmICMjQ5rHm+oGLZkGfFyaImSg69NlfQDAxStZmLbjHNLv5uNl3zN7WXthUc/S3h07M36Ga3DpMe7ffgQ9PT0AjWMwtQEDBuD//u//KtxDqWnTprC2tpZezxJuoLTXybvvvosxY8bAzc0Na9euhb6+Pr799tvqDr/RS01NBQDY2dlh8uTJTLiJiNSMSXcjtHjxYnTo0AG+vr549OgR/vjjD1hYWNTa+TMzM5WmrPr3q6LTY1HtGTBgwHP/3yraDb0+/R5oaGhg48aN6Ny5M3r06IHz58/j4MGDcHV1rVfX0VjIDJ7OrS3I0Nd6GMxkZvgp4QZeXXIUr684hqXRl/Gw4PkzAvR37A8XMxcUK4ox53zpc/aF+cXQ1SntPdAYupdXVseOHWFjY4N+/frh+PHj0vqioiIkJCQozV6goaEBX19fnDhxQh2hNmh//fUXAKBdu3ZqjoSIiABOGdboeHh4VOso4pVha2uLxMTEF26numnDhg3Pvctnbm5eoWPVp98DOzs7pQTin+rTdTQWgpYGZE10Ib9XCCs9R1jZv4MPDRRY+SgfSbdzkXQ7F+uOpmPtKE/4uJTt1izTkGHDaxvwyo5XcFeeBQ1tQFEEJP2RDWgBT1ScB7wxsbGxwdq1a+Hl5YUnT55gw4YN6Nu3L+Li4tCpUyf8/fffkMvlSuMYAICVlRWSk5Ofe9wnT54o1Xdubm6NXUND8mywSnt7ezVHQkREAJNuUgNNTU20atVK3WFQJfx7fu+qaCi/Bw3lOhqapuM7IOGTOdCReaCJri2GPdLAf97ywDeZ97D1VCaelCiw+lBauUk3AJjqmqJHsx44cv0IZHaFUKTrorhQDmiVW7zRc3FxgYuLi7TcvXt3pKenY9myZfjhhx8qfdzw8HDMmzevOkJsNBQKhdQbw9DQUM3REBERwO7lRETUAMmMtCHXuY2Dt3/AE3lp7wzLQhHhQ92xY3zp/Oh/3cxBUcnzp+RzMi4d1f9uz7PoM9IFQOkUcY1s/NFK69KlizTFnYWFBWQyGe7cuaNU5s6dO7C2tn7uMWbNmoWcnBzpdf369RqNuSEoKCiQfkf19fXVHA0REQFMuomIqIFy8PEDAFzJOwcAuLxlG05+PAadbA1gpq+FohIFzt98/pzxbczbAACO3z4OM3td4GmuzaRbNYmJibCxsQEAaGtrw9PTEzExMdJ2hUKBmJgYeHt7P/cYOjo6MDY2VnpR+eRyOU6ePIl169YBKE24ZTKZmqMiIiKASTcRETVQtgNHoldLW+QW/Q0AMDFoi8SbRbj01Wx4OpgBANYcSX/u/p2tO0OAgOt51xEcHYTGdKc7Pz8fiYmJ0ngFGRkZSExMlAYGnDVrFoKCgqTyX331FXbv3o20tDRcuHABU6ZMwaFDhxASEiKVCQ0NxTfffINNmzYhKSkJEyZMwKNHjzBmzJhavbaG6ujRozhw4ADy8vIAlA5qR0REdQOf6SYiogary6L1aHslFXc3JEEXZnjV5r/489x2+D75BPYG9vhR9haelMiho1n2jqClviWmeE7BsoRlyBf/d0dcoXh+l/SGIj4+Hj4+PtJyaGgoACA4OBgbN27E7du3lUbmLyoqwtSpU3Hz5k3o6+ujffv2OHjwoNIxhg0bhrt372LOnDnIyspCx44dceDAgTKDq1HFpaSkIDY2FgDg7u4OT09PODo6qjcoIiKSCGJj+Mr+H140gXlhYSEyMjLg5OQEXV1dNUVIRFQ5fA97vqLrechenSgt5xXfR0beefx99zCazd2AVzs6PXffk7dPImTfhxh+cRIeGafDza0tAgLerrbYXtQukTLWVfk2bNiAGzduwMPDA4MGDYIgCOoOiYioUVC1XWL3clJZ3759MWXKFHWHQURUYdp2Rmgyyg0y69IOXkZa5mhv3ge21q/j5qIQ5BQUP3ffNmZtINcswbM05lHho1qImEh1f/9d+ghFt27dmHATEdVBTLqpUoqLizFjxgy4u7vDwMAAtra2CAoKwq1bt9QdWoX89ddf6NWrF3R1dWFnZ4cvvvjipftMmjQJnp6e0NHR4TNzRPWIXtsmsJniDZvZ3WDQpXTE7DYmXWBg1B5RI/sj4XxaufuZ6ppiqc8SKFDarTyn8PmDrxHVtoyMDBQWFgIAzMzM1BwNERGVh0k3VUpBQQHOnDmD2bNn48yZM9i5cydSUlIwaNAgdYemstzcXLz22mtwcHBAQkICvvzyS4SFhWH9+vUv3Xfs2LEYNmxYLURJRNVNZqAF0yGtIGta2gR2tRyIR/pNcXH2+/jueEa5+7xi/wqEp499P3pSUFuhEr1QcXExvv/+ewCAkZERtLW11RwRERGVh0l3A9G3b19MmjQJH3/8MczNzWFtbY2wsDBpe2ZmJgYPHgxDQ0MYGxsjICBAab7UsLAwdOzYET/88AMcHR1hYmKC4cOHS6Og/puJiQmio6MREBAAFxcXdOvWDatWrUJCQoLS4DovMmPGDLRu3Rr6+vpo0aIFZs+ejeLi0i6ely9fhiAISE5OVtpn2bJlaNmypbS8Z88eODs7Q1dXFz4+Pti0aRMEQcDDhw9fev7NmzejqKgI3377Ldq2bYvhw4dj0qRJWLp06Qv3W7FiBUJCQtCiRQuVrpOI6h5BQ4BFUCdpeaDdeygxcsKpXT89dx8NzdJuu0VPnt8Vnai2xMXFYeHChdJo+kOGDFFvQERE9FxMul9CFEUUFxaq5VXRMe42bdoEAwMDxMXF4YsvvsD8+fMRHR0NhUKBwYMH4/79+zh69Ciio6Nx5cqVMndq09PTERUVhb1792Lv3r04evQoIiIiVD5/Tk4OBEGAqampSuWNjIywceNGXLp0CcuXL8c333yDZcuWAQBat24NLy8vbN68WWmfzZs3Y+TIkQBKu9S99dZbGDJkCM6dO4f3338fn376qcrxnjhxAr1791a6M+Dn54eUlBQ8ePBA5eMQUf2kZaEHy/HtAQAyQROvNQvGsIdPUJhT/t+/TpOnz8o+5sQfpD5FRUVISEjA/v37pXU9evRQ+kKaiIjqFn5yeImSJ0+wIvgttZx70qafoFWBEYjbt2+PuXPnAgCcnZ2xatUqxMTEAADOnz+PjIwM2NnZAQC+//57tG3bFqdPn0bnzp0BlE6Ds3HjRhgZGQEARo0ahZiYGCxcuPCl5y4sLMSMGTMwYsQIlUeU/eyzz6SfHR0dMW3aNERGRuLjjz8GAAQGBmLVqlVYsGABgNK73wkJCfjxxx8BAOvWrYOLiwu+/PJLAICLiwsuXLigUrwAkJWVBScn5RGLn01dk5WVxWfjiBoBHUcT2M7phpsRf0Ao0oKDuTcu/b/l6BQaVqasnok2kA+I8kY16QfVIaIo4vvvv8eNGzcAAObm5ggODuZI7kREdRzvdDcg7du3V1q2sbFBdnY2kpKSYGdnJyXcAODm5gZTU1MkJSVJ6xwdHaWE+5/7v0xxcTECAgIgiiLWrFmjcrzbtm1Djx49YG1tDUNDQ3z22WdKXdOHDx+Oq1ev4uTJkwBK73J36tQJbdq0AVA6L+mzLwye6dKli8rnJyICAA19LTSf0xcAoKWhjcc3y3+sRk+r9EtQReOaaZPqkOzsbCnhbteuHd58802YmJhwxHIiojqOd7pfQlNHB5M2Pf8Zv5o+d0VoaWkpLQuCAIVCUaP7P0u4r127hkOHDqn8bfuJEycQGBiIefPmwc/PDyYmJoiMjMSSJUukMtbW1njllVewZcsWdOvWDVu2bMGECRNUvp6Xsba2VnquHYC0bG1tXW3nIaK6T9DUQM7jazDRc4A8X1ZuGT1t/dIfRBFXcq6ghQnHdaDadf78eQBAq1at8NZb6umFR0REFcek+yUEQahQF++6yNXVFdevX8f169elu92XLl3Cw4cP4ebmVunjPku4U1NTcfjwYTRp0kTlff/88084ODgoPYN97dq1MuUCAwPx8ccfY8SIEbhy5QqGDx8ubXNxccG+ffuUyp8+fVrlGLy9vfHpp5+iuLhY+sIhOjoaLi4u7FpO1AgVKfIBALkKQ5wLnwqrTt5o6jMIGk/HfdDVKv1XgICEOwlMuqnWKBQKJCQk4NixYwBKk24iIqo/2L28EfD19YW7uzsCAwNx5swZnDp1CkFBQejTpw+8vLwqdczi4mK89dZbiI+Px+bNmyGXy5GVlYWsrCwUFRW9dH9nZ2dkZmYiMjIS6enpWLFiBXbt2lWm3NChQ5GXl4cJEybAx8cHtra20rb3338fycnJmDFjBi5fvozt27dj48aNAKBSV7uRI0dCW1sb48aNw8WLF7Ft2zYsX74coaGhUpldu3ZJ3dmfSUtLQ2JiIrKysvD48WMkJiYiMTFRpesmorpLYVLau6hjEx8cTEzB5m83YtvIgdJ2DdnTJlMUUCTn3zvVjLt37+LixYs4deoUYmNjsWvXLqxduxa//vorgNKxR9zd3dUcJRERVQTvdDcCgiBg9+7d+PDDD9G7d29oaGigf//+WLlyZaWPefPmTezZswcA0LFjR6Vthw8fRt++fV+4/6BBg/DRRx9h4sSJePLkCQYOHIjZs2crTXMGlI5w7u/vj+3bt+Pbb79V2ubk5ISffvoJU6dOxfLly6U71xMmTICOCl3zTUxM8PvvvyMkJASenp6wsLDAnDlz8N5770llcnJykJKSorTfO++8g6NHj0rLHh4eAEpHU3d0dHzpeYmobmo97A3kbC6dprCljhPSn2TglkwHJ6aNhvfijVLSLUBAiaJEnaFSAxUfH4+9e/eWu00QBPTp0wc9e/aEpiY/vhER1SeCWNF5qeq53NxcmJiYICcnp8zzx4WFhcjIyICTkxN063mX8sZq4cKFWLt2La5fv67uUIhqHd/Dqu7nuUfR9Ulpcp2ZcxanHxyBtTwfw3b8jkN7/0Rs/O8Q5dqwC2iKd9zfqZZzvqhdImUNua5u3LiBDRs2SMuurq7Q0tKCqakpzMzM4OjoyEefiIjqGFXbJX5VSvXa119/jc6dO6NJkyY4fvw4vvzyS0ycOFHdYRFRPfW7lRbaZ5ZADwLsTTxgadAKp6+tA6DcvbxYXqzGKKmhycnJwXfffSctT506VWk2ESIiqt/4TDfViEWLFsHQ0LDc14ABA6rtPKmpqRg8eDDc3NywYMECTJ06VeqiPmDAgOfGsGjRomqLgYgaDm0zPbyGPFy3Kx2pXE/TCLo6pbMZ/K97OVCsYNJNVSOXy5Geno4TJ05g165dkMvl0NbWxvjx45lwExE1MLzTTTVi/PjxCAgIKHebnp5etZ1n2bJlWLZsWbnbNmzYgMePH5e7zdzcvNpiIKKGw1BHBhHA6TbGsMu9D+ToQq5tjPunD0Om+SwR4jPdVHUJCQllZuDw9/fnlJVERA0Qk26qEebm5mpPbJs1a6bW8xNR/aOnVdosPi6WQ8vMEMU5JdCVGeC7LxejZdvOAHinm6quoKAAZ8+elZb79OmDZs2awdnZWY1RERFRTWHSTURE9JS+tgwA8LhIDm0bSxRfvQ0DmTEgCMi6mgo4OAMQmHRTpSgUChw5cgSxsbHSurFjx8Le3l6NURERUU3jM91ERERP6T1NuguKSiAz0AIAtDbtjKa69ihQKADwTjdV3sGDB5US7m7dusHOzk6NERERUW3gnW4iIqKn9LSeJd1y6La1QO7BTACAj80I/PHoKHJRAojg6OVUYenp6fjzzz8BlI5tMmHChAY37RkREZWPd7qJiIieeta9vLBYDm0bA1i+3x7QEAAAZtpNn5Zi93KqGIVCgaioKACAkZERJk6cyISbiKgRYdJNRET01P+6l8sBADpOJmj6QQcAgIGmKYDS7uWyO5zSiVRTUlKC3377DXl5edDQ0EBISAgMDAzUHRYREdUiJt2ksr59+2LKlCnqDoOIqMY8615+J7dQWqdlpQ9RVEBHpiOts4rrCFEUaz0+qn9Onz6NuLg4AECHDh2gq6ur5oiIiKi2MemmSikuLsaMGTPg7u4OAwMD2NraIigoCLdu3VJ3aLXu/v37CAwMhLGxMUxNTTFu3Djk5+e/cJ/169ejb9++MDY2hiAIePjwYe0ES0QvpK9dOtRJ+t1HWHs0HQAgaMlQovEAgig8LSWH9iNDZKXnqClKqi/y8/Nx8uRJAICVlRX8/PzUHBEREakDk26qlIKCApw5cwazZ8/GmTNnsHPnTqSkpGDQoEHqDq3WBQYG4uLFi4iOjsbevXsRGxuL995774X7FBQUoH///vjkk09qKUoiUkV7OxO0amoIAIjYn4yQLWdwOCUbmk1kEFCadIviIwBAany22uKk+uHw4cPIycmBpqYmRo4cybvcRESNFJPuBqJv376YNGkSPv74Y5ibm8Pa2hphYWHS9szMTAwePBiGhoYwNjZGQEAA7ty5I20PCwtDx44d8cMPP8DR0REmJiYYPnw48vLyyj2fiYkJoqOjERAQABcXF3Tr1g2rVq1CQkICMjMzXxrv1atXIQgCdu7cCR8fH+jr66NDhw44ceKEUrmff/4Zbdu2hY6ODhwdHbFkyRKl7T/88AO8vLxgZGQEa2trjBw5EtnZpR+EFQoFmjdvjjVr1ijtc/bsWWhoaODatWsAgOTkZPTs2RO6urpwc3PDwYMHIQiCNOjNiyQlJeHAgQPYsGEDunbtip49e2LlypWIjIx84V3/KVOmYObMmejWrdtLz0FEtcdYVwsHQ/vA17V00LRf/7qNMd+dxp4iUwhSqdKpw0qK5WqJkeqPrKwsAMBrr70GExMTNUdDRETqwqT7JURRhKJIrpZXRZ8X3LRpEwwMDBAXF4cvvvgC8+fPR3R0NBQKBQYPHoz79+/j6NGjiI6OxpUrVzBs2DCl/dPT0xEVFYW9e/di7969OHr0KCIiIlQ+f05ODgRBgKmpqcr7fPrpp5g2bRoSExPRunVrjBgxAiUlJQCAhIQEBAQEYPjw4Th//jzCwsIwe/ZsbNy4Udq/uLgYCxYswLlz5xAVFYWrV69i9OjRAAANDQ2MGDECW7ZsUTrn5s2b0aNHDzg4OEAul2PIkCHQ19dHXFwc1q9fj08//VTl+E+cOAFTU1N4eXlJ63x9faGhoSE9w0dE9c8Xb3VAmL8bfFwsIQhAQm6BdKdbU0MbAHA3/646Q6Q67tGjR7h58yYAwN7eXs3REBGROnGe7pcQixW4NedPtZzbdn53CE9H0lVF+/btMXfuXACAs7MzVq1ahZiYGADA+fPnkZGRATs7OwDA999/j7Zt2+L06dPo3LkzgNI7wxs3boSRUemovKNGjUJMTAwWLlz40nMXFhZixowZGDFiRIWmQZk2bRoGDhwIAJg3bx7atm2LtLQ0tGnTBkuXLsWrr76K2bNnAwBat26NS5cu4csvv5QS67Fjx0rHatGiBVasWIHOnTsjPz8fhoaGCAwMxJIlS5CZmQl7e3soFApERkbis88+AwBER0cjPT0dR44cgbW1NQBg4cKF6Nevn0rxZ2VloWnTpkrrNDU1YW5uLt3hIKL6x9xAG6N7OGF0DyfcznmMfuGHpG0aQun78s28xjeGBb2cXC7H/v37ER8fDwAQBAFmZmZqjoqIiNSJd7obkPbt2yst29jYIDs7G0lJSbCzs5MSbgBwc3ODqakpkpKSpHWOjo5Swv3P/V+muLgYAQEBEEWxTFfuisRsY2MDANI5k5KS0KNHD6XyPXr0QGpqKuTy0m6dCQkJ8Pf3h729PYyMjNCnTx8AkLq4d+zYEa6urtLd7qNHjyI7Oxtvv/02ACAlJQV2dnZSwg0AXbp0qdA1EFHDZmOiB1FbhpS8RKX1ooKjl1NZZ8+elRJuHR0d+Pv7Q0dH5yV7ERFRQ8Y73S8haGnAdn53tZ27IrS0tJT3FwQoFIoa3f9Zwn3t2jUcOnSoQne5/31OQSjtuqlqzI8ePYKfnx/8/PywefNmWFpaIjMzE35+figqKpLKBQYGYsuWLZg5cya2bNmC/v37o0mTJhWK83msra3LfDFRUlKC+/fvKyXyRFS/mehp4cm/cmxR9bdXakQuX74MoPRLX39/f8hkqvdYIyKihol3ul9CEARoaMvU8nqWhFaVq6srrl+/juvXr0vrLl26hIcPH8LNza3Sx32WcKempuLgwYPVlsg+4+rqiuPHjyutO378OFq3bg2ZTIbk5GTcu3cPERER6NWrF9q0aVPunfmRI0fiwoULSEhIwE8//YTAwEBpm4uLC65fv640qNzp06dVjtHb2xsPHz5EQkKCtO7QoUNQKBTo2rVrRS6XiOowYz0tPHn6lvxs5jAm3fRvoihKbW3nzp2ZcBMREQAm3Y2Cr68v3N3dERgYiDNnzuDUqVMICgpCnz59lAYAq4ji4mK89dZbiI+Px+bNmyGXy5GVlYWsrCylu8xVMXXqVMTExGDBggW4fPkyNm3ahFWrVmHatGkASgem0dbWxsqVK3HlyhXs2bMHCxYsKHMcR0dHdO/eHePGjYNcLlea1qxfv35o2bIlgoOD8ddff+H48ePS896qfOnh6uqK/v37491338WpU6dw/PhxTJw4EcOHD4etrS0A4ObNm2jTpg1OnTol7ZeVlYXExESkpaUBKH3mPjExEffv3698hRFRjTHR00KhoNxkMummf8vKysLjx48hk8lgZWWl7nCIiKiOYNLdCAiCgN27d8PMzAy9e/eGr68vWrRogW3btlX6mDdv3sSePXtw48YNdOzYETY2NtLrzz+rZ+C5Tp06Yfv27YiMjES7du0wZ84czJ8/XxpEzdLSEhs3bsSOHTvg5uaGiIgILF68uNxjBQYG4ty5c/jPf/4DPT09ab1MJkNUVBTy8/PRuXNnvPPOO9Lo5arOp7p582a0adMGr776Kl5//XX07NkT69evl7YXFxcjJSUFBQUF0rq1a9fCw8MD7777LgCgd+/e8PDwwJ49eypUR0RUO5oYauPxv5tMPtJN/5CUlIR169YBAJycnKCpySf4iIiolCBWdF6qei43NxcmJibIyckp8/xxYWEhMjIy4OTkpHLCRQ3P8ePH0bNnT6SlpaFly5bqDodIZXwPqzl/pN7Fbys3QtP8EbQVMphk98Ajqzv4eN6IKh/7Re0SKaurdXX37l2sW7cOJSUl0NDQwMSJE2Fubq7usIiIqIap2i7xa1hq9Hbt2gVDQ0M4OzsjLS0NkydPRo8ePZhwE5Gkl7MlMlvaIPVBGiAARXk7oKndWt1hUR3xxx9/oKSkBDKZDOPGjWPCTURESti9nGrEokWLYGhoWO5rwIAB6g5PSV5eHkJCQtCmTRuMHj0anTt3xu7duwHUr+sgoppl4OAo/awouQ7Z/XT1BUN1hkKhkEYsHzp0qDSeBxER0TO80001Yvz48QgICCh32z+fqa4LgoKCEBQUVO62+nQdRFSz9C1KZ2gQAQy2/xDx90+9eAdqsIqKinD69Gmkp6cjOzsbhYWFAMCpIomIqFxMuqlGmJubN4judQ3lOoio6vRNSp+TFwHoyvTRTK+5egMitSgsLMSWLVuQmZmptN7FxQVmZmZqioqIiOoyJt1EREQqMNbXBgAoIAcAaAh8QquxKSgowKpVq6TZKHx8fNC8eXM0a9aMgxcSEdFzMekmIiJSgZmBDoD/zRSmwWFRGp2rV69KCfeoUaM44CYREamEnxiIiIhUYKynpbTckO90x8bGwt/fH7a2thAEAVFRUS8sv3PnTvTr1w+WlpYwNjaGt7c3fvvtN6UyYWFhEARB6dWmTZsavIrqo1AocOPGDWzfvh0A0KlTJybcRESksob7iYGIiKgaCYIA4H93umUNOOl+9OgROnTogNWrV6tUPjY2Fv369cO+ffuQkJAAHx8f+Pv74+zZs0rl2rZti9u3b0uvY8eO1UT41er27dv49ttvsWHDBmldu3bt1BgRERHVN+xeTkREpAINjadJtgA8FB5BQ5CpN6AaNGDAgApNi/jVV18pLS9atAi7d+/GL7/8Ag8PD2m9pqZmvRrhOyUlBVu3bpWW9fX1MXDgQLRo0UKNURERUX3TcL+mb2T69u2LKVOmqO38o0ePxpAhQ+pMPERE1U1LS0tKvH/SOYnbWnlqjqjuUigUyMvLKzP7Q2pqKmxtbdGiRQsEBgaWGQH83548eYLc3FylV20RRREHDx4EAJiZmeGVV17B9OnT0bZt21qLgYiIGgYm3VQjdu7ciQULFtTqOXfs2IE2bdpAV1cX7u7u2Ldv3wvL3759GyNHjkTr1q2hoaHBLwmI6IV0dHQwYsQIaTlbK1+N0dRtixcvRn5+PgICAqR1Xbt2xcaNG3HgwAGsWbMGGRkZ6NWrF/Lynv/lRXh4OExMTKSXnZ1djcdeXFyMgwcPYs2aNbh79y60tLTw/vvvo3fv3tIjBkRERBXBpJtqhLm5OYyMjGrtfH/++SdGjBiBcePG4ezZsxgyZAiGDBmCCxcuPHefJ0+ewNLSEp999hk6dOhQa7ESUf3l7OwMB0Xp1GElgkLN0dRNW7Zswbx587B9+3Y0bdpUWj9gwAC8/fbbaN++Pfz8/LBv3z48fPhQGpysPLNmzUJOTo70un79eo3Hf+nSJRw7dgzZ2dkAAE9PT04HRkREVcKkuwEpKSnBxIkTYWJiAgsLC8yePRuiWDrkzw8//AAvLy8YGRnB2toaI0eOlD5QAMCDBw8QGBgIS0tL6OnpwdnZGd999520/fr16wgICICpqSnMzc0xePBgXL169bmx/Lt7uaOjIxYtWoSxY8fCyMgI9vb2WL9+vdI+FT3HPy1fvhz9+/fH9OnT4erqigULFqBTp05YtWrVc/dxdHTE8uXLERQUBBMTE5XOQ0SkqVk6HAqT7rIiIyPxzjvvYPv27fD19X1hWVNTU7Ru3RppaWnPLaOjowNjY2OlV027e/cuAEBXVxdjxozBa6+9VuPnJCKihq1OJN2rV6+Go6MjdHV10bVrV5w6deqF5SvajbgqRFFEUVGRWl7PEmZVbdq0CZqamjh16hSWL1+OpUuXSqOtFhcXY8GCBTh37hyioqJw9epVjB49Wtp39uzZuHTpEvbv34+kpCSsWbMGFhYW0r5+fn4wMjLCH3/8gePHj8PQ0BD9+/dHUVGRyvEtWbIEXl5eOHv2LD744ANMmDABKSkp1XKOEydOlPmA5+fnhxMnTqgcHxGRKrQ0SwdQY9KtbOvWrRgzZgy2bt2KgQMHvrR8fn4+0tPTYWNjUwvRvdyDBw8QFxcnjajet29fODg4/G8APSIiokpS++jl27ZtQ2hoKNauXYuuXbviq6++gp+fH1JSUpS6pT3zrBtxeHg43njjDWzZsgVDhgzBmTNnamQKj+LiYixatKjaj6uKTz75BNra2iqXt7Ozw7JlyyAIAlxcXHD+/HksW7YM7777LsaOHSuVa9GiBVasWIHOnTsjPz8fhoaGyMzMhIeHB7y8vACU3gV+Ztu2bVAoFNiwYYP0PNt3330HU1NTHDlyROW7AK+//jo++OADAMCMGTOwbNkyHD58GC4uLlU+R1ZWFqysrJTWWVlZISsrS6XYiIhUpaWrAxTlNeikOz8/X+kOdEZGBhITE2Fubg57e3vMmjULN2/exPfffw+gtEt5cHAwli9fjq5du0rvvXp6elJPomnTpsHf3x8ODg64desW5s6dC5lMpvScvDqkpaUhJiYGt2/fltbJZDI4OTmpMSoiImpI1P717dKlS/Huu+9izJgxcHNzw9q1a6Gvr49vv/223PKV6UbcWHTr1k1pkBdvb2+kpqZCLpcjISEB/v7+sLe3h5GREfr06QMA0sixEyZMQGRkJDp27IiPP/4Yf/75p3Scc+fOIS0tDUZGRjA0NIShoSHMzc1RWFiI9PR0leNr37699LMgCLC2tpa6uFfXOYiIapqObumXocWQqzmSmhMfHw8PDw9puq/Q0FB4eHhgzpw5AEoHovznyOPr169HSUkJQkJCYGNjI70mT54slblx4wZGjBgBFxcXBAQEoEmTJjh58iQsLS1r9+Keys3NxZo1a/Djjz9KCbeFhQU8PT0RGhpa5otcIiKiylLrne6ioiIkJCRg1qxZ0joNDQ34+vo+t1vwiRMnEBoaqrTOz88PUVFR5ZZ/8uQJnjx5Ii1XdLoRLS0tfPLJJxXap7poaWlVy3EKCwvh5+cHPz8/bN68GZaWlsjMzISfn5/UdXvAgAG4du0a9u3bh+joaLz66qsICQmRRqD19PTE5s2byxy7Ih+W/n09giBAoSi9U1TVc1hbW+POnTtK6+7cuVOv5oMlovpBx0AfAFAsNNyku2/fvi98xGnjxo1Ky0eOHHnpMSMjI6sYVfU6c+aM1G60atUKPj4+aNasmZqjIiKihkitSffff/8NuVxebrfg5OTkcvepaDfi8PBwzJs3r9IxCoJQoS7e6hQXF6e0fPLkSTg7OyM5ORn37t1DRESENN1KfHx8mf0tLS0RHByM4OBg9OrVC9OnT8fixYvRqVMnbNu2DU2bNq2xQWyqeg5vb2/ExMQoDd4WHR0Nb2/vaoySiAgwePoeVSzIUVxUBK160kaQsosXLwIo/eKebQUREdUktXcvr2nqmG5EXTIzMxEaGoqUlBRs3boVK1euxOTJk2Fvbw9tbW2sXLkSV65cwZ49e8rMoT1nzhzs3r0baWlpuHjxIvbu3QtXV1cAQGBgICwsLDB48GD88ccfyMjIwJEjRzBp0iTcuHGjWmKv6jkmT56MAwcOYMmSJUhOTkZYWBji4+MxceJEqcysWbMQFBSktF9iYiISExORn5+Pu3fvIjExEZcuXaqWayKihsmxvRsc87XRIk8bJcUN9253QxcQEIA+ffqgY8eO6g6FiIgaOLXe6bawsIBMJqtQt+CKdiPW0dGBjo5O9QRcxwUFBeHx48fo0qULZDIZJk+ejPfeew+CIGDjxo345JNPsGLFCnTq1AmLFy/GoEGDpH21tbUxa9YsXL16FXp6eujVq5fUFVBfXx+xsbGYMWMGhg4diry8PDRr1gyvvvpqtd35ruo5unfvji1btuCzzz7DJ598AmdnZ0RFRSkNrvfvZxABSM8rAkBCQgK2bNkCBwcHlacqI6LGx7ZlK4xerJ7Hjqj6WFpawsfHR91hEBFRIyCIFZ2Xqpp17doVXbp0wcqVKwEACoUC9vb2mDhxImbOnFmm/LBhw1BQUIBffvlFWte9e3e0b98ea9eufen5cnNzYWJigpycnDLJXGFhITIyMuDk5ARdXd0qXhkRUe3ie1j99KJ2iZSxroiIqC5RtV1S+5RhoaGhCA4OhpeXF7p06YKvvvoKjx49wpgxYwCU3r1t1qwZwsPDAZR2I+7Tpw+WLFmCgQMHIjIyEvHx8Vi/fr06L4OIiIiIiIioDLUn3cOGDcPdu3cxZ84cZGVloWPHjjhw4IA0WFpmZiY0NP736Lkq3Yip4TE0NHzutv3796NXr161GA0REREREZFq1N69vLaxe3n9lJaW9txtzZo1g56eXi1GQ1Q38T2sfmKXadWxroiIqC6pN93LiVTRqlUrdYdARERERERUYQ1+yjAiIiIiIiIidWHSXY5G1uOeiBoIvncRERER1T1Muv9BS0sLAFBQUKDmSIiIKu7Ze9ez9zIiIiIiUj8+0/0PMpkMpqamyM7OBgDo6+tDEAQ1R0VE9GKiKKKgoADZ2dkwNTWFTCZTd0hERERE9BST7n+xtrYGACnxJiKqL0xNTaX3MCIiIiKqG5h0/4sgCLCxsUHTpk1RXFys7nCIiFSipaXFO9xEREREdRCT7ueQyWT8AEtERERERERVwoHUiIiIiIiIiGoIk24iIiIiIiKiGsKkm4iIiIiIiKiGNLpnukVRBADk5uaqORIiIqL/tUfP2id6PrbhRERUl6jahje6pDsvLw8AYGdnp+ZIiIiI/icvLw8mJibqDqNOYxtORER10cvacEFsZF+tKxQK3Lp1C0ZGRhAEQVqfm5sLOzs7XL9+HcbGxmqMsP5h3VUN66/yWHdVw/qrvOqsO1EUkZeXB1tbW2ho8KmvFymvDefvcdWw/qqG9Vd5rLuqYf1Vnjra8EZ3p1tDQwPNmzd/7nZjY2P+4lYS665qWH+Vx7qrGtZf5VVX3fEOt2pe1Ibz97hqWH9Vw/qrPNZd1bD+Kq8223B+pU5ERERERERUQ5h0ExEREREREdUQJt1P6ejoYO7cudDR0VF3KPUO665qWH+Vx7qrGtZf5bHu6g7+X1QN669qWH+Vx7qrGtZf5amj7hrdQGpEREREREREtYV3uomIiIiIiIhqCJNuIiIiIiIiohrCpJuIiIiIiIiohjSopDs2Nhb+/v6wtbWFIAiIiopS2i6KIubMmQMbGxvo6enB19cXqampSmXu37+PwMBAGBsbw9TUFOPGjUN+fr5Smb/++gu9evWCrq4u7Ozs8MUXX9T0pdU6uVyO2bNnw8nJCXp6emjZsiUWLFiAfw4BUF312RDdvHkT//3vf9GkSRPo6enB3d0d8fHx0nbWneoiIiIgCAKmTJkirSssLERISAiaNGkCQ0NDvPnmm7hz547SfpmZmRg4cCD09fXRtGlTTJ8+HSUlJbUcfe0LDw9H586dYWRkhKZNm2LIkCFISUlRKsP6q7rVq1fD0dERurq66Nq1K06dOqXukOo1tt/Vi2141bANrz5swyuGbXjtUEsbLjYg+/btEz/99FNx586dIgBx165dStsjIiJEExMTMSoqSjx37pw4aNAg0cnJSXz8+LFUpn///mKHDh3EkydPin/88YfYqlUrccSIEdL2nJwc0crKSgwMDBQvXLggbt26VdTT0xPXrVtXW5dZKxYuXCg2adJE3Lt3r5iRkSHu2LFDNDQ0FJcvXy6VqY76bIju378vOjg4iKNHjxbj4uLEK1euiL/99puYlpYmlWHdqebUqVOio6Oj2L59e3Hy5MnS+vHjx4t2dnZiTEyMGB8fL3br1k3s3r27tL2kpERs166d6OvrK549e1bct2+faGFhIc6aNUsNV1G7/Pz8xO+++068cOGCmJiYKL7++uuivb29mJ+fL5Vh/VVNZGSkqK2tLX777bfixYsXxXfffVc0NTUV79y5o+7Q6i2239WLbXjlsQ2vPmzDK45teM1TVxveoJLuf/p3o61QKERra2vxyy+/lNY9fPhQ1NHREbdu3SqKoiheunRJBCCePn1aKrN//35REATx5s2boiiK4tdffy2amZmJT548kcrMmDFDdHFxqeErql0DBw4Ux44dq7Ru6NChYmBgoCiK1VefDdGMGTPEnj17Pnc76041eXl5orOzsxgdHS326dNHarAfPnwoamlpiTt27JDKJiUliQDEEydOiKJY+gFeQ0NDzMrKksqsWbNGNDY2VvrbbQyys7NFAOLRo0dFUWT9VYcuXbqIISEh0rJcLhdtbW3F8PBwNUbVcLD9rjq24ZXHNrx6sA2vHmzDq5+62vAG1b38RTIyMpCVlQVfX19pnYmJCbp27YoTJ04AAE6cOAFTU1N4eXlJZXx9faGhoYG4uDipTO/evaGtrS2V8fPzQ0pKCh48eFBLV1PzunfvjpiYGFy+fBkAcO7cORw7dgwDBgwAUH312RDt2bMHXl5eePvtt9G0aVN4eHjgm2++kbaz7lQTEhKCgQMHKtUTACQkJKC4uFhpfZs2bWBvb69Uf+7u7rCyspLK+Pn5ITc3FxcvXqydC6gjcnJyAADm5uYAWH9VVVRUhISEBKX609DQgK+vr1R/VL3Yflcc2/DKYxtePdiGVw+24dVLnW24Zo0evQ7JysoCAKVfwGfLz7ZlZWWhadOmSts1NTVhbm6uVMbJyanMMZ5tMzMzq5H4a9vMmTORm5uLNm3aQCaTQS6XY+HChQgMDARQffXZEF25cgVr1qxBaGgoPvnkE5w+fRqTJk2CtrY2goODWXcqiIyMxJkzZ3D69Oky27KysqCtrQ1TU1Ol9f+uv/Lq99m2xkKhUGDKlCno0aMH2rVrB4D1V1V///035HJ5ufWTnJyspqgaNrbfFcc2vPLYhlcd2/DqwTa8+qmzDW80STdVzPbt27F582Zs2bIFbdu2RWJiIqZMmQJbW1sEBwerO7w6TaFQwMvLC4sWLQIAeHh44MKFC1i7di3rTgXXr1/H5MmTER0dDV1dXXWHU6+FhITgwoULOHbsmLpDIaJaxDa88tiGVw3b8OrDNrxhaTTdy62trQGgzOh+d+7ckbZZW1sjOztbaXtJSQnu37+vVKa8Y/zzHA3B9OnTMXPmTAwfPhzu7u4YNWoUPvroI4SHhwOovvpsiGxsbODm5qa0ztXVFZmZmQBYdy+TkJCA7OxsdOrUCZqamtDU1MTRo0exYsUKaGpqwsrKCkVFRXj48KHSfv+uv8bwd/oiEydOxN69e3H48GE0b95cWm9tbc36qwILCwvIZLIX/v1S9WL7XXFswyuPbXjVsA2vHmzDa4Y62/BGk3Q7OTnB2toaMTEx0rrc3FzExcXB29sbAODt7Y2HDx8iISFBKnPo0CEoFAp07dpVKhMbG4vi4mKpTHR0NFxcXBpU17SCggJoaCj/eshkMigUCgDVV58NUY8ePcpM73D58mU4ODgAYN29zKuvvorz588jMTFRenl5eSEwMFD6WUtLS6n+UlJSkJmZqVR/58+fV/rQEx0dDWNj4zIfphoaURQxceJE7Nq1C4cOHSrTndbT05P1VwXa2trw9PRUqj+FQoGYmBip/qh6sf2uOLbhlcc2vGrYhlcN2/CapdY2vEaHaatleXl54tmzZ8WzZ8+KAMSlS5eKZ8+eFa9duyaKYukUD6ampuLu3bvFv/76Sxw8eHC5Uzx4eHiIcXFx4rFjx0RnZ2elKR4ePnwoWllZiaNGjRIvXLggRkZGivr6+g1uypHg4GCxWbNm0nQjO3fuFC0sLMSPP/5YKlMd9dkQnTp1StTU1BQXLlwopqamips3bxb19fXFH3/8USrDuquYf458Koql02XY29uLhw4dEuPj40Vvb2/R29tb2v5suozXXntNTExMFA8cOCBaWlo2iukyJkyYIJqYmIhHjhwRb9++Lb0KCgqkMqy/qomMjBR1dHTEjRs3ipcuXRLfe+890dTUVGmkWKoYtt/Vi2145bENr35sw1XHNrzmqasNb1BJ9+HDh0UAZV7BwcGiKJZO8zB79mzRyspK1NHREV999VUxJSVF6Rj37t0TR4wYIRoaGorGxsbimDFjxLy8PKUy586dE3v27Cnq6OiIzZo1EyMiImrrEmtNbm6uOHnyZNHe3l7U1dUVW7RoIX766adKUw1UV302RL/88ovYrl07UUdHR2zTpo24fv16pe2su4r5d4P9+PFj8YMPPhDNzMxEfX198T//+Y94+/ZtpX2uXr0qDhgwQNTT0xMtLCzEqVOnisXFxbUcee0r7z0QgPjdd99JZVh/Vbdy5UrR3t5e1NbWFrt06SKePHlS3SHVa2y/qxfb8KphG1692Iarjm147VBHGy6IoijW7L10IiIiIiIiosap0TzTTURERERERFTbmHQTERERERER1RAm3UREREREREQ1hEk3ERERERERUQ1h0k1ERERERERUQ5h0ExEREREREdUQJt1ERERERERENYRJNxEREREREVENYdJNVA9dvXoVgiAgMTFR3aFIkpOT0a1bN+jq6qJjx45VOpYgCIiKiqqWuIiIiOoStuFEjQ+TbqJKGD16NARBQEREhNL6qKgoCIKgpqjUa+7cuTAwMEBKSgpiYmKeWy4rKwsffvghWrRoAR0dHdjZ2cHf3/+F+1TFkSNHIAgCHj58WCPHJyKi+oVteFlsw4lqFpNuokrS1dXF559/jgcPHqg7lGpTVFRU6X3T09PRs2dPODg4oEmTJuWWuXr1Kjw9PXHo0CF8+eWXOH/+PA4cOAAfHx+EhIRU+ty1QRRFlJSUqDsMIiKqBmzDlbENJ6pZTLqJKsnX1xfW1tYIDw9/bpmwsLAy3bS++uorODo6SsujR4/GkCFDsGjRIlhZWcHU1BTz589HSUkJpk+fDnNzczRv3hzfffddmeMnJyeje/fu0NXVRbt27XD06FGl7RcuXMCAAQNgaGgIKysrjBo1Cn///be0vW/fvpg4cSKmTJkCCwsL+Pn5lXsdCoUC8+fPR/PmzaGjo4OOHTviwIED0nZBEJCQkID58+dDEASEhYWVe5wPPvgAgiDg1KlTePPNN9G6dWu0bdsWoaGhOHnyZLn7lPctd2JiIgRBwNWrVwEA165dg7+/P8zMzGBgYIC2bdti3759uHr1Knx8fAAAZmZmEAQBo0ePlq4pPDwcTk5O0NPTQ4cOHfDTTz+VOe/+/fvh6ekJHR0dHDt2DOfOnYOPjw+MjIxgbGwMT09PxMfHlxs7ERHVTWzD2YazDafaxKSbqJJkMhkWLVqElStX4saNG1U61qFDh3Dr1i3ExsZi6dKlmDt3Lt544w2YmZkhLi4O48ePx/vvv1/mPNOnT8fUqVNx9uxZeHt7w9/fH/fu3QMAPHz4EK+88go8PDwQHx+PAwcO4M6dOwgICFA6xqZNm6CtrY3jx49j7dq15ca3fPlyLFmyBIsXL8Zff/0FPz8/DBo0CKmpqQCA27dvo23btpg6dSpu376NadOmlTnG/fv3ceDAAYSEhMDAwKDMdlNT08pUHQAgJCQET548QWxsLM6fP4/PP/8choaGsLOzw88//wwASElJwe3bt7F8+XIAQHh4OL7//nusXbsWFy9exEcffYT//ve/ZT70zJw5ExEREUhKSkL79u0RGBiI5s2b4/Tp00hISMDMmTOhpaVV6diJiKj2sQ1nG842nGqVSEQVFhwcLA4ePFgURVHs1q2bOHbsWFEURXHXrl3iP/+s5s6dK3bo0EFp32XLlokODg5Kx3JwcBDlcrm0zsXFRezVq5e0XFJSIhoYGIhbt24VRVEUMzIyRABiRESEVKa4uFhs3ry5+Pnnn4uiKIoLFiwQX3vtNaVzX79+XQQgpqSkiKIoin369BE9PDxeer22trbiwoULldZ17txZ/OCDD6TlDh06iHPnzn3uMeLi4kQA4s6dO196PgDirl27RFEUxcOHD4sAxAcPHkjbz549KwIQMzIyRFEURXd3dzEsLKzcY5W3f2Fhoaivry/++eefSmXHjRsnjhgxQmm/qKgopTJGRkbixo0bX3oNRERUN7ENZxtOVNs0azvJJ2poPv/8c7zyyivlfjOsqrZt20JD438dT6ysrNCuXTtpWSaToUmTJsjOzlbaz9vbW/pZU1MTXl5eSEpKAgCcO3cOhw8fhqGhYZnzpaeno3Xr1gAAT0/PF8aWm5uLW7duoUePHkrre/TogXPnzql4haXPU9WUSZMmYcKECfj999/h6+uLN998E+3bt39u+bS0NBQUFKBfv35K64uKiuDh4aG0zsvLS2k5NDQU77zzDn744Qf4+vri7bffRsuWLavvYoiIqNawDVcN23CiqmH3cqIq6t27N/z8/DBr1qwy2zQ0NMo0VMXFxWXK/btrkyAI5a5TKBQqx5Wfnw9/f38kJiYqvVJTU9G7d2+pXHndxGqCs7MzBEFAcnJyhfZ79kHmn/X47zp85513cOXKFYwaNQrnz5+Hl5cXVq5c+dxj5ufnAwB+/fVXpbq5dOmS0jNhQNn6CQsLw8WLFzFw4EAcOnQIbm5u2LVrV4WuiYiI6ga24aphG05UNUy6iapBREQEfvnlF5w4cUJpvaWlJbKyspQam+qcl/OfA5eUlJQgISEBrq6uAIBOnTrh4sWLcHR0RKtWrZReFWmkjY2NYWtri+PHjyutP378ONzc3FQ+jrm5Ofz8/LB69Wo8evSozPbnTQdiaWkJoPSZs2fKq0M7OzuMHz8eO3fuxNSpU/HNN98AALS1tQEAcrlcKuvm5gYdHR1kZmaWqRs7O7uXXkvr1q3x0Ucf4ffff8fQoUPLHSCHiIjqB7bhL8c2nKhqmHQTVQN3d3cEBgZixYoVSuv79u2Lu3fv4osvvkB6ejpWr16N/fv3V9t5V69ejV27diE5ORkhISF48OABxo4dC6B0YJL79+9jxIgROH36NNLT0/Hbb79hzJgxSo2XKqZPn47PP/8c27ZtQ0pKCmbOnInExERMnjy5wvHK5XJ06dIFP//8M1JTU5GUlIQVK1YodbP7p2eNaFhYGFJTU/Hrr79iyZIlSmWmTJmC3377DRkZGThz5gwOHz4sfXBxcHCAIAjYu3cv7t69i/z8fBgZGWHatGn46KOPsGnTJqSnp+PMmTNYuXIlNm3a9Nz4Hz9+jIkTJ+LIkSO4du0ajh8/jtOnT0vnIiKi+odtuOrxsg0nqhwm3UTVZP78+WW6jrm6uuLrr7/G6tWr0aFDB5w6dapKz439W0REBCIiItChQwccO3YMe/bsgYWFBQBI32zL5XK89tprcHd3x5QpU2Bqaqr07JkqJk2ahNDQUEydOhXu7u44cOAA9uzZA2dn5wodp0WLFjhz5gx8fHwwdepUtGvXDv369UNMTAzWrFlT7j5aWlrYunUrkpOT0b59e3z++ef4v//7P6UycrkcISEhcHV1Rf/+/dG6dWt8/fXXAIBmzZph3rx5mDlzJqysrDBx4kQAwIIFCzB79myEh4dL+/36669wcnJ6bvwymQz37t1DUFAQWrdujYCAAAwYMADz5s2rUD0QEVHdwjb85diGE1WeINbkyAhEREREREREjRjvdBMRERERERHVECbdRERERERERDWESTcRERERERFRDWHSTURERERERFRDmHQTERERERER1RAm3UREREREREQ1hEk3ERERERERUQ1h0k1ERERERERUQ5h0ExEREREREdUQJt1ERERERERENYRJNxEREREREVENYdJNREREREREVEP+P7FDX5LG8GjmAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results for RBO Matrix with parameter p = 0.3.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results for RBO Matrix with parameter p = 0.5.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results for RBO Matrix with parameter p = 0.7.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results for RBO Matrix with parameter p = 0.9.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for key, rankings_dict in rankings_results.items():\n", + " if task_results[key] == 'classification':\n", + " metrics = ['AUROC', 'AUPRC', 'R^2', 'F1', 'Accuracy']\n", + " else:\n", + " metrics = ['R^2', 'MSE']\n", + " # print \"Results for \" + text of key after first underscore\n", + " parts = key.split('_')\n", + " print(f\"Results for dataset {parts[1]} from datasource {parts[0]}.\")\n", + " for p_value in p_values:\n", + " print(f\"Results for RBO Matrix with parameter p = {p_value}.\")\n", + " # create new plot\n", + " if task_results[key] == 'classification':\n", + " height = 15\n", + " else:\n", + " height = 5\n", + " fig, axes = plt.subplots(math.ceil(len(metrics)/2.0), 2, figsize=(10, height))\n", + " axes = axes.flatten()\n", + " plot_count = 0\n", + " for metric in metrics:\n", + " # create new plot\n", + " ax = axes[plot_count]\n", + " method_list = []\n", + " for method, df in rankings_dict.items():\n", + " if method.endswith(p_value):\n", + " ax.plot(df['nclust'], df[metric])\n", + " method_list.append(method)\n", + " ax.legend(method_list)\n", + " ax.set_xlabel('Number of Clusters')\n", + " ax.set_ylabel('Cluster ' + metric)\n", + " ax.set_title('Cluster ' + metric + ' vs Number of Clusters')\n", + " ax.invert_xaxis()\n", + " plot_count += 1\n", + " plt.tight_layout()\n", + " plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mdi", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/feature_importance/subgroup/legacy/agglomerative_subgroups.py b/feature_importance/subgroup/legacy/agglomerative_subgroups.py new file mode 100644 index 0000000..3b64128 --- /dev/null +++ b/feature_importance/subgroup/legacy/agglomerative_subgroups.py @@ -0,0 +1,709 @@ +# import required packages +from imodels import get_clean_dataset +import numpy as np +import pandas as pd +from sklearn.model_selection import train_test_split +from sklearn.metrics import roc_auc_score, average_precision_score, f1_score, \ + accuracy_score, r2_score, f1_score, log_loss, root_mean_squared_error +from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier +from imodels.tree.rf_plus.rf_plus.rf_plus_models import RandomForestPlusRegressor, RandomForestPlusClassifier +from imodels.tree.rf_plus.feature_importance.rfplus_explainer import RFPlusMDI, AloRFPlusMDI +import shap +from subgroup_detection import * +import warnings +from sklearn.linear_model import RidgeCV, LogisticRegressionCV, LogisticRegression, LinearRegression +import argparse +import os +from os.path import join as oj +import scipy.cluster.hierarchy as sch +from scipy.cluster.hierarchy import fcluster, cut_tree +from scipy import cluster +from scipy.spatial.distance import squareform +import time +from joblib import Parallel, delayed +from ucimlrepo import fetch_ucirepo +from sklearn.preprocessing import OneHotEncoder, LabelEncoder +from sklearn.impute import SimpleImputer + +# global variable for classification/regression status +TASK = None + +def preprocessing_data_X(X): + categorical_cols = X.select_dtypes(include=["object", "category"]).columns + numerical_cols = X.select_dtypes(include=["number"]).columns + if X[numerical_cols].isnull().any().any(): + num_imputer = SimpleImputer(strategy="mean") + X[numerical_cols] = num_imputer.fit_transform(X[numerical_cols]) + if len(categorical_cols) > 0 and X[categorical_cols].isnull().any().any(): + # Convert categorical columns to string to ensure consistent types + X[categorical_cols] = X[categorical_cols].astype(str) + cat_imputer = SimpleImputer(strategy="most_frequent") + X[categorical_cols] = cat_imputer.fit_transform(X[categorical_cols]) + if len(categorical_cols) > 0: + encoder = OneHotEncoder(handle_unknown="ignore", sparse_output=False) + X_categorical = encoder.fit_transform(X[categorical_cols]) + X_categorical_df = pd.DataFrame( + X_categorical, + columns=encoder.get_feature_names_out(categorical_cols), + index=X.index + ) + X = pd.concat([X[numerical_cols], X_categorical_df], axis=1) + else: + X = X[numerical_cols] + X = X.to_numpy() + if X.shape[0]>2000: + X = X[:2000,:] + return X + +def preprocessing_data_y(y): + if y.to_numpy().shape[1] > 1: + y = y.iloc[:, 0].to_numpy().flatten() + else: + y = y.to_numpy().flatten() + if y.shape[0]>2000: + y = y[:2000] + + if np.all(np.vectorize(isinstance)(y, str)): + encoder = LabelEncoder() + y = encoder.fit_transform(y) + return y + +def get_parkinsons_dataset(): + # fetch dataset + parkinsons = fetch_ucirepo(id=189) + + # data (as pandas dataframes) + X = parkinsons.data.features + y = parkinsons.data.targets + cols = X.columns + + X = preprocessing_data_X(X) + y = preprocessing_data_y(y) + + return X, y, cols + +def get_performance_data(): + performance = fetch_ucirepo(id=320) + X = performance.data.features + y = performance.data.targets + cols = X.columns + + X = preprocessing_data_X(X) + y = preprocessing_data_y(y) + + return X, y, cols + +def get_temperature_data(): + temperature = fetch_ucirepo(id=925) + X = temperature.data.features + y = temperature.data.targets + cols = X.columns + + X = preprocessing_data_X(X) + y = preprocessing_data_y(y) + + return X, y, cols + +def get_ccle_data(): + + X = pd.read_csv('X_ccle_rnaseq_PD-0325901_top500.csv') + y = pd.read_csv('y_ccle_rnaseq_PD-0325901.csv') + cols = X.columns + X = X.to_numpy() + y = y.to_numpy().flatten() + return X, y, cols + +def get_adult_dataset(num_samples): + + # fetch dataset + adult = fetch_ucirepo(id=2) + + # data (as pandas dataframes) + X = adult.data.features + y = adult.data.targets + + X = X.dropna() + + # drop the same ones in y, which is a dataframe + y = y.loc[X.index] + + # one hot encode adult dataset + X_encoded = pd.get_dummies(X, drop_first=True) + + # convert y to 1 (>50K) and 0 (<=50K) + y = y.replace({'<=50K' : 0, '<=50K.' : 0, '>50K' : 1, ">50K." : 1}) + y = y['income'] + + # replace trues and falses in X_encoded with 1s and 0s + X_encoded = X_encoded.replace({True : 1, False : 0}) + + # return the first num_samples samples, make all return values numpy arrays + return X_encoded.iloc[:num_samples].values, y.iloc[:num_samples].values, X_encoded.columns + +def split_data(X, y, seed): + # split data into train and test sets + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, + random_state=seed) + return X_train, X_test, y_train, y_test + +def fit_models(X_train, y_train): + # fit models + if TASK == "classification": + rf = RandomForestClassifier(n_estimators=100, min_samples_leaf=5, + random_state=42) + rf.fit(X_train, y_train) + # rf_plus = RandomForestPlusClassifier(rf_model=rf, + # prediction_model=LogisticRegressionCV()) + rf_plus = RandomForestPlusClassifier(rf_model=rf) + rf_plus.fit(X_train, y_train) + elif TASK == "regression": + rf = RandomForestRegressor(n_estimators=100, min_samples_leaf=5, + random_state=42) + rf.fit(X_train, y_train) + # rf_plus = RandomForestPlusRegressor(rf_model=rf, + # prediction_model=RidgeCV()) + rf_plus = RandomForestPlusRegressor(rf_model=rf) + rf_plus.fit(X_train, y_train) + else: + raise ValueError("Task must be either 'classification' or 'regression'.") + return rf, rf_plus + +def get_shap(X, shap_explainer): + if TASK == "classification": + # the shap values are an array of shape + # (# of samples, # of features, # of classes), and in this binary + # classification case, we want the shap values for the positive class. + # check_additivity=False is used to speed up computation. + shap_values = \ + shap_explainer.shap_values(X, check_additivity=False)[:, :, 1] + else: + # check_additivity=False is used to speed up computation. + shap_values = shap_explainer.shap_values(X, check_additivity=False) + # get the rankings of the shap values. negative absolute value is taken + # because np.argsort sorts from smallest to largest. + shap_rankings = np.argsort(-np.abs(shap_values), axis = 1) + return shap_values, shap_rankings + +def get_lmdi(X, y, lmdi_explainer, l2norm, sign, normalize, leaf_average, ranking=False): + # get feature importances + lmdi = lmdi_explainer.explain_linear_partial(X, y, l2norm=l2norm, sign=sign, + normalize=normalize, + leaf_average=leaf_average, + ranking=ranking) + mdi_rankings = lmdi_explainer.get_rankings(np.abs(lmdi)) + return lmdi, mdi_rankings + +if __name__ == '__main__': + + # start time + start = time.time() + + # store command-line arguments + parser = argparse.ArgumentParser() + parser.add_argument('--seed', type=int, default=None) + parser.add_argument('--datasource', type=str, default=None) + parser.add_argument('--dataname', type=str, default=None) + parser.add_argument('--use_test', type=int, default=0) + parser.add_argument('--njobs', type=int, default=1) + args = parser.parse_args() + + # convert namespace to a dictionary + args_dict = vars(args) + + # assign the arguments to variables + seed = args_dict['seed'] + datasource = args_dict['datasource'] + dataname = args_dict['dataname'] + use_test = bool(args_dict['use_test']) # convert from 0/1 to boolean + njobs = args_dict['njobs'] + + # if the datasource is openml, we need to make the dataname an integer + if datasource == "openml": + dataname = int(dataname) + + # if the datasource is a file, we need to read the file rather than call + # get_clean_dataset + if datasource == "function": + if dataname == "adult": + X, y, feature_names = get_adult_dataset(5000) + elif dataname == "parkinsons": + X, y, feature_names = get_parkinsons_dataset() + elif dataname == "performance": + X, y, feature_names = get_performance_data() + elif dataname == "temperature": + X, y, feature_names = get_temperature_data() + elif dataname == "ccle": + X, y, feature_names = get_ccle_data() + else: + raise ValueError("Unknown function dataset.") + else: + # obtain data + X, y, feature_names = get_clean_dataset(dataname, data_source = datasource) + # if y is not a float (abalone), convert it + if y.dtype != np.float64: + y = y.astype(np.float64) + + # end time + end = time.time() + + # print progress message + print(f"Progress Message 1/15: Obtained {dataname} from {datasource}.") + print(f"Step #1 took {end-start} seconds.") + + # start time + start = time.time() + + # check if task is regression or classification + if len(np.unique(y)) == 2: + TASK = 'classification' + else: + TASK = 'regression' + # convert y to float, if it is not already (ints will cause errors) + y = y.astype(float) + + # end time + end = time.time() + + print(f"Progress Message 2/15: Task is identified as {TASK}.") + print(f"Step #2 took {end-start} seconds.") + + # start time + start = time.time() + + # split data + X_train, X_test, y_train, y_test = split_data(X, y, seed) + + # end time + end = time.time() + + print(f"Training Data Shape: {X_train.shape}") + print(f"Testing Data Shape: {X_test.shape}") + + print(f"Progress Message 3/15: Data split with seed {seed}.") + print(f"Step #3 took {end-start} seconds.") + + # start time + start = time.time() + + # fit the prediction models + rf, rf_plus = fit_models(X_train, y_train) + + # fit baseline model + if TASK == "classification": + rf_plus_baseline = RandomForestPlusClassifier(rf_model=rf, include_raw=False, fit_on="inbag", prediction_model=LogisticRegression()) + elif TASK == "regression": + rf_plus_baseline = RandomForestPlusRegressor(rf_model=rf, include_raw=False, fit_on="inbag", prediction_model=LinearRegression()) + rf_plus_baseline.fit(X_train, y_train) + + # end time + end = time.time() + + print(f"Progress Message 4/15: RF and RF+ models fit.") + print(f"Step #4 took {end-start} seconds.") + + # start time + start = time.time() + + # obtain shap feature importances + shap_explainer = shap.TreeExplainer(rf) + if use_test: + shap_values, shap_rankings = get_shap(X_test, shap_explainer) + else: + shap_values, shap_rankings = get_shap(X_train, shap_explainer) + + # end time + end = time.time() + + print(f"Progress Message 5/15: SHAP values/rankings obtained.") + print(f"Step #5 took {end-start} seconds.") + + # start time + start = time.time() + + # obtain lmdi feature importances + lmdi_explainer_signed_normalized_l2_avg = AloRFPlusMDI(rf_plus, mode = "only_k") + lmdi_explainer_signed_normalized_l2_noavg = AloRFPlusMDI(rf_plus, mode = "only_k") + lmdi_explainer_signed_nonnormalized_l2_avg = AloRFPlusMDI(rf_plus, mode = "only_k") + lmdi_explainer_signed_nonnormalized_l2_noavg = AloRFPlusMDI(rf_plus, mode = "only_k") + lmdi_explainer_nonl2_avg = AloRFPlusMDI(rf_plus, mode = "only_k") + lmdi_explainer_nonl2_noavg = AloRFPlusMDI(rf_plus, mode = "only_k") + lmdi_explainer_l2_ranking = AloRFPlusMDI(rf_plus, mode = "only_k") + lmdi_explainer_nonl2_ranking = AloRFPlusMDI(rf_plus, mode = "only_k") + lmdi_explainer_normalized_l2_ranking = AloRFPlusMDI(rf_plus, mode = "only_k") + lmdi_explainer_nonnormalized_l2_ranking = AloRFPlusMDI(rf_plus, mode = "only_k") + lmdi_baseline_explainer = RFPlusMDI(rf_plus_baseline, mode = "only_k", evaluate_on = "inbag") + lmdi_values_signed_normalized_l2_avg, \ + lmdi_rankings_signed_normalized_l2_avg = \ + get_lmdi(X_train, y_train, lmdi_explainer_signed_normalized_l2_avg, + l2norm=True, sign=True, normalize=True, leaf_average=True) + lmdi_values_signed_normalized_l2_noavg, \ + lmdi_rankings_signed_normalized_l2_noavg = \ + get_lmdi(X_train, y_train,lmdi_explainer_signed_normalized_l2_noavg, + l2norm=True, sign=True, normalize=True, leaf_average=False) + lmdi_values_signed_nonnormalized_l2_avg, \ + lmdi_rankings_signed_nonnormalized_l2_avg = \ + get_lmdi(X_train,y_train,lmdi_explainer_signed_nonnormalized_l2_avg, + l2norm=True, sign=True, normalize=False, leaf_average=True) + lmdi_values_signed_nonnormalized_l2_noavg, \ + lmdi_rankings_signed_nonnormalized_l2_noavg = \ + get_lmdi(X_train, y_train, + lmdi_explainer_signed_nonnormalized_l2_noavg, l2norm=True, + sign=True, normalize=False, leaf_average=False) + lmdi_values_nonl2_avg, lmdi_rankings_nonl2_avg = \ + get_lmdi(X_train, y_train, lmdi_explainer_nonl2_avg, l2norm=False, + sign=False, normalize=False, leaf_average=True) + lmdi_values_nonl2_noavg, lmdi_rankings_nonl2_noavg = \ + get_lmdi(X_train, y_train, lmdi_explainer_nonl2_noavg, l2norm=False, + sign=False, normalize=False, leaf_average=False) + lmdi_values_l2_ranking, lmdi_rankings_l2_ranking = \ + get_lmdi(X_train, y_train, lmdi_explainer_l2_ranking, l2norm=True, + sign=False, normalize=False, leaf_average=False, ranking=True) + lmdi_values_nonl2_ranking, lmdi_rankings_nonl2_ranking = \ + get_lmdi(X_train, y_train, lmdi_explainer_nonl2_ranking, l2norm=False, + sign=False, normalize=False, leaf_average=False, ranking=True) + lmdi_values_normalized_l2_ranking, lmdi_rankings_normalized_l2_ranking = \ + get_lmdi(X_train, y_train, lmdi_explainer_normalized_l2_ranking, l2norm=True, + sign=False, normalize=True, leaf_average=False, ranking=True) + lmdi_values_baseline, lmdi_rankings_baseline = \ + get_lmdi(X_train, y_train, lmdi_baseline_explainer, l2norm=False, + sign=False, normalize=False, leaf_average=False) + if use_test: + lmdi_values_signed_normalized_l2_avg, \ + lmdi_rankings_signed_normalized_l2_avg = \ + get_lmdi(X_test, None, lmdi_explainer_signed_normalized_l2_avg, l2norm=True, sign=True, + normalize=True, leaf_average=True) + lmdi_values_signed_normalized_l2_noavg, \ + lmdi_rankings_signed_normalized_l2_noavg = \ + get_lmdi(X_test, None, lmdi_explainer_signed_normalized_l2_noavg, l2norm=True, sign=True, + normalize=True, leaf_average=False) + lmdi_values_signed_nonnormalized_l2_avg, \ + lmdi_rankings_signed_nonnormalized_l2_avg = \ + get_lmdi(X_test, None, lmdi_explainer_signed_nonnormalized_l2_avg, l2norm=True, sign=True, + normalize=False, leaf_average=True) + lmdi_values_signed_nonnormalized_l2_noavg, \ + lmdi_rankings_signed_nonnormalized_l2_noavg = \ + get_lmdi(X_test, None, lmdi_explainer_signed_nonnormalized_l2_noavg, l2norm=True, sign=True, + normalize=False, leaf_average=False) + lmdi_values_nonl2_avg, lmdi_rankings_nonl2_avg = \ + get_lmdi(X_test, None, lmdi_explainer_nonl2_avg, l2norm=False, sign=False, + normalize=False, leaf_average=True) + lmdi_values_nonl2_noavg, lmdi_rankings_nonl2_noavg = \ + get_lmdi(X_test, None, lmdi_explainer_nonl2_noavg, l2norm=False, sign=False, + normalize=False, leaf_average=False) + lmdi_values_l2_ranking, lmdi_rankings_l2_ranking = \ + get_lmdi(X_test, None, lmdi_explainer_l2_ranking, l2norm=True, + sign=False, normalize=False, leaf_average=False, ranking=True) + lmdi_values_nonl2_ranking, lmdi_rankings_nonl2_ranking = \ + get_lmdi(X_test, None, lmdi_explainer_nonl2_ranking, l2norm=False, + sign=False, normalize=False, leaf_average=False, ranking=True) + lmdi_values_normalized_l2_ranking, lmdi_rankings_normalized_l2_ranking = \ + get_lmdi(X_test, None, lmdi_explainer_l2_ranking, l2norm=True, + sign=False, normalize=True, leaf_average=False, ranking=True) + lmdi_values_baseline, lmdi_rankings_baseline = \ + get_lmdi(X_test, None, lmdi_baseline_explainer, l2norm=True, sign=False, + normalize=False, leaf_average=False) + + # end time + end = time.time() + + print(f"Progress Message 6/15: LMDI+ values/rankings obtained.") + print(f"Step #6 took {end-start} seconds.") + + # start time + start = time.time() + + # create storage for iteration purposes + lfi_values = \ + {'shap': shap_values, + 'signed_normalized_l2_avg': lmdi_values_signed_normalized_l2_avg, + 'signed_normalized_l2_noavg': lmdi_values_signed_normalized_l2_noavg, + 'signed_nonnormalized_l2_avg': lmdi_values_signed_nonnormalized_l2_avg, + 'signed_nonnormalized_l2_noavg': + lmdi_values_signed_nonnormalized_l2_noavg, + 'nonl2_avg': lmdi_values_nonl2_avg, + 'nonl2_noavg': lmdi_values_nonl2_noavg, + 'l2_ranking': lmdi_values_l2_ranking, + 'nonl2_ranking': lmdi_values_nonl2_ranking, + 'normalized_l2_ranking': lmdi_values_normalized_l2_ranking, + # 'normalized_nonl2_ranking': lmdi_values_normalized_nonl2_ranking, + 'baseline': lmdi_values_baseline} + lfi_rankings = \ + {'shap': shap_rankings, + 'signed_normalized_l2_avg': lmdi_rankings_signed_normalized_l2_avg, + 'signed_normalized_l2_noavg': lmdi_rankings_signed_normalized_l2_noavg, + 'signed_nonnormalized_l2_avg': lmdi_rankings_signed_nonnormalized_l2_avg, + 'signed_nonnormalized_l2_noavg': + lmdi_rankings_signed_nonnormalized_l2_noavg, + 'nonl2_avg': lmdi_rankings_nonl2_avg, + 'nonl2_noavg': lmdi_rankings_nonl2_noavg, + 'l2_ranking': lmdi_rankings_l2_ranking, + 'nonl2_ranking': lmdi_rankings_nonl2_ranking, + # 'normalized_l2_ranking': lmdi_values_normalized_l2_ranking, + # 'normalized_nonl2_ranking': lmdi_values_normalized_nonl2_ranking, + 'baseline': lmdi_rankings_baseline} + + # get rbo matrices for rankings + # rbo_matrices = {} + # for method, ranking in lfi_rankings.items(): + # for p in [0.1, 0.3, 0.5, 0.7, 0.9]: + # rbo_matrices[method + "_" + str(p)] = \ + # compute_rbo_matrix(ranking, 'distance', p=p) + + # def compute_rbo_for_method_and_p(method, ranking, p): + # """ + # Helper function to compute the RBO matrix for a given method and p value. + # """ + # # print("method:") + # # print(method) + # return (method + "_" + str(p), compute_rbo_matrix(ranking, 'distance', p=p)) + + # # parallelize the computation of RBO matrices + # rbo_matrices = dict(Parallel(n_jobs=njobs)( + # delayed(compute_rbo_for_method_and_p)(method, ranking, p) + # for method, ranking in lfi_rankings.items() + # for p in [0.1, 0.3, 0.5, 0.7, 0.9] + # )) + + # end time + end = time.time() + + print(f"Progress Message 7/15: RBO matrices computed.") + print(f"Step #7 took {end-start} seconds.") + + # start time + start = time.time() + + # get linkages for values + values_linkage = {} + for method, values in lfi_values.items(): + # values_linkage[method] = sch.linkage(values, method="ward") + values_linkage[method] = cluster.hierarchy.ward(values) + + # end time + end = time.time() + + print(f"Progress Message 8/15: Linkages for values computed.") + print(f"Step #8 took {end-start} seconds.") + + # start time + start = time.time() + + # # get linkages for rankings + # rankings_linkage = {} + # for method, rbo_mat in rbo_matrices.items(): + # # rankings_linkage[method] = sch.linkage(squareform(rbo_mat), + # # method="ward") + # rankings_linkage[method] = cluster.hierarchy.ward(squareform(rbo_mat)) + + # end time + end = time.time() + + print(f"Progress Message 9/15: Linkages for rankings computed.") + print(f"Step #9 took {end-start} seconds.") + + # start time + start = time.time() + + # get clusters for values + value_clusters = {} + for method, link in values_linkage.items(): + # maximum number of clusters is the number of unique feature importances + max_num_clusters = np.unique(lfi_values[method], axis = 0).shape[0] + print(f"The Number of Unique Values (Maximum # of Clusters) for {method} is {max_num_clusters}.") + num_cluster_map = {} + for num_clusters in np.arange(1, max_num_clusters + 1): + # num_cluster_map[num_clusters] = fcluster(link, num_clusters, + # criterion = "maxclust") + num_cluster_map[num_clusters] = cut_tree(link, n_clusters=num_clusters).flatten() + value_clusters[method] = num_cluster_map + + # end time + end = time.time() + + print(f"Progress Message 10/15: Clusters for values computed.") + print(f"Step #10 took {end-start} seconds.") + + # start time + start = time.time() + + # # get clusters for rankings + # ranking_clusters = {} + # for method, link in rankings_linkage.items(): + # # maximum number of clusters is the number of unique rankings + # max_num_clusters = np.unique(rbo_matrices[method], axis = 0).shape[0] + # print(f"The Number of Unique Rankings (Maximum # of Clusters) for {method} is {max_num_clusters}.") + # num_cluster_map = {} + # for num_clusters in np.arange(1, max_num_clusters + 1): + # # num_cluster_map[num_clusters] = fcluster(link, num_clusters, + # # criterion = "maxclust") + # num_cluster_map[num_clusters] = cut_tree(link, n_clusters=num_clusters).flatten() + # ranking_clusters[method] = num_cluster_map + + # end time + end = time.time() + + print(f"Progress Message 11/15: Clusters for rankings computed.") + print(f"Step #11 took {end-start} seconds.") + + # start time + start = time.time() + + # get predictions and performance metrics for each methods clusters + if TASK == "classification": + metrics = {"AUROC": roc_auc_score, "AUPRC": average_precision_score, + "F1": f1_score, "Accuracy": accuracy_score, + "R^2": r2_score, "Cross-Entropy": log_loss} + elif TASK == "regression": + metrics = {"R^2": r2_score, "RMSE": root_mean_squared_error} + + # maps method to future dataframe (dict for now) where the columns of the + # dataframe are the metrics and the rows are the number of clusters, and + # the values are the performance of the method on the metric for the number + # of clusters. + # method_values_results = {} + # for method, cluster_map in value_clusters.items(): + # metric_results = {} + # max_num_clusters = np.max(list(cluster_map.keys())) + # metric_results["nclust"] = np.arange(1, max_num_clusters + 1) + # for metric, metric_func in metrics.items(): + # cluster_results = np.repeat(np.nan, max_num_clusters) + # for num_clusters, clusters in cluster_map.items(): + # cluster_predictions = np.repeat(np.nan, len(clusters)) + # cluster_truths = np.repeat(np.nan, len(clusters)) + # for i in range(num_clusters): + # cluster_indices = np.where(clusters == i + 1)[0] + # if y_train[cluster_indices].shape[0] == 0: + # continue + # cluster_predictions[cluster_indices] = \ + # np.mean(y_train[cluster_indices]) + # if metric in ["Accuracy", "F1"]: + # cluster_predictions[cluster_indices] = \ + # cluster_predictions[cluster_indices] > 0.5 + # cluster_truths[cluster_indices] = y_train[cluster_indices] + # cluster_results[num_clusters-1] = metric_func(cluster_truths, + # cluster_predictions) + # metric_results[metric] = cluster_results + # method_values_results[method] = metric_results + + def evaluate_method(method, cluster_map, metrics, y_data): + metric_results = {} + max_num_clusters = np.max(list(cluster_map.keys())) + metric_results["nclust"] = np.arange(1, max_num_clusters + 1) + for metric, metric_func in metrics.items(): + cluster_results = np.repeat(np.nan, max_num_clusters) + for num_clusters, clusters in cluster_map.items(): + cluster_predictions = np.repeat(np.nan, len(clusters)) + cluster_truths = np.repeat(np.nan, len(clusters)) + for i in range(num_clusters): + cluster_indices = np.where(clusters == i)[0] + if y_data[cluster_indices].shape[0] == 0: + print("ERROR: Empty cluster!") + continue + cluster_predictions[cluster_indices] = \ + np.mean(y_data[cluster_indices]) + if metric in ["Accuracy", "F1"]: + cluster_predictions[cluster_indices] = \ + cluster_predictions[cluster_indices] > 0.5 + cluster_truths[cluster_indices] = y_data[cluster_indices] + cluster_results[num_clusters-1] = metric_func(cluster_truths, + cluster_predictions) + metric_results[metric] = cluster_results + return method, metric_results + + if use_test: + method_values_results = dict(Parallel(n_jobs=njobs)( + delayed(evaluate_method)(method, cluster_map, metrics, y_test) + for method, cluster_map in value_clusters.items())) + else: + method_values_results = dict(Parallel(n_jobs=njobs)( + delayed(evaluate_method)(method, cluster_map, metrics, y_train) + for method, cluster_map in value_clusters.items())) + + # end time + end = time.time() + + print(f"Progress Message 12/15: Performance metrics computed for values.") + print(f"Step #12 took {end-start} seconds.") + + # start time + start = time.time() + + # maps method to future dataframe (dict for now) where the columns of the + # dataframe are the metrics and the rows are the number of clusters, and + # the values are the performance of the method on the metric for the number + # of clusters. + # method_rankings_results = {} + # for method, cluster_map in ranking_clusters.items(): + # metric_results = {} + # max_num_clusters = np.max(list(cluster_map.keys())) + # metric_results["nclust"] = np.arange(1, max_num_clusters + 1) + # for metric, metric_func in metrics.items(): + # cluster_results = np.repeat(np.nan, max_num_clusters) + # for num_clusters, clusters in cluster_map.items(): + # cluster_predictions = np.repeat(np.nan, len(clusters)) + # cluster_truths = np.repeat(np.nan, len(clusters)) + # for i in range(num_clusters): + # cluster_indices = np.where(clusters == i + 1)[0] + # if y_train[cluster_indices].shape[0] == 0: + # continue + # cluster_predictions[cluster_indices] = \ + # np.mean(y_train[cluster_indices]) + # if metric in ["Accuracy", "F1"]: + # cluster_predictions[cluster_indices] = \ + # cluster_predictions[cluster_indices] > 0.5 + # cluster_truths[cluster_indices] = y_train[cluster_indices] + # cluster_results[num_clusters-1] = metric_func(cluster_truths, + # cluster_predictions) + # metric_results[metric] = cluster_results + # method_rankings_results[method] = metric_results + + # if use_test: + # method_rankings_results = dict(Parallel(n_jobs=njobs)( + # delayed(evaluate_method)(method, cluster_map, metrics, y_test) + # for method, cluster_map in ranking_clusters.items())) + # else: + # method_rankings_results = dict(Parallel(n_jobs=njobs)( + # delayed(evaluate_method)(method, cluster_map, metrics, y_train) + # for method, cluster_map in ranking_clusters.items())) + + # end time + end = time.time() + + print(f"Progress Message 13/15: Performance metrics computed for rankings.") + print(f"Step #13 took {end-start} seconds.") + + # start time + start = time.time() + + if use_test: + result_dir = oj(os.path.dirname(os.path.realpath(__file__)), 'results/test_data') + else: + result_dir = oj(os.path.dirname(os.path.realpath(__file__)), 'results/train_data') + + # get result dataframes + for method, metric_results in method_values_results.items(): + df = pd.DataFrame(metric_results) + df.to_csv(oj(result_dir, + f'{datasource}_{dataname}_seed{seed}_{method}_values.csv'), + index=False) + + # end time + end = time.time() + + print(f"Progress Message 14/15: Value results saved to {result_dir}.") + print(f"Step #14 took {end-start} seconds.") + + # start time + start = time.time() + + # for method, metric_results in method_rankings_results.items(): + # df = pd.DataFrame(metric_results) + # df.to_csv(oj(result_dir, + # f'{datasource}_{dataname}_seed{seed}_{method}_ranking.csv'), + # index=False) + + # end time + end = time.time() + + print(f"Progress Message 15/15: Ranking results saved to {result_dir}.") + print(f"Step #15 took {end-start} seconds.") \ No newline at end of file diff --git a/feature_importance/subgroup/legacy/agglomerative_subgroups.sh b/feature_importance/subgroup/legacy/agglomerative_subgroups.sh new file mode 100644 index 0000000..b866e9d --- /dev/null +++ b/feature_importance/subgroup/legacy/agglomerative_subgroups.sh @@ -0,0 +1,15 @@ +#!/bin/bash +#SBATCH --cpus-per-task=32 + +datasource="function" +dataname="ccle" +# seed=1 +njobs=32 +use_test=1 + +source activate mdi +# command="agglomerative_subgroups.py --seed $seed --datasource $datasource --dataname $dataname --use_test $use_test --njobs $njobs" +command="agglomerative_subgroups.py --seed ${1} --datasource $datasource --dataname $dataname --use_test $use_test --njobs $njobs" + +# Execute the command +python $command \ No newline at end of file diff --git a/feature_importance/subgroup/analysis.ipynb b/feature_importance/subgroup/legacy/analysis.ipynb similarity index 100% rename from feature_importance/subgroup/analysis.ipynb rename to feature_importance/subgroup/legacy/analysis.ipynb diff --git a/feature_importance/subgroup/legacy/auroc_eval.ipynb b/feature_importance/subgroup/legacy/auroc_eval.ipynb new file mode 100644 index 0000000..5598fcf --- /dev/null +++ b/feature_importance/subgroup/legacy/auroc_eval.ipynb @@ -0,0 +1,1208 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# import required packages\n", + "from imodels import get_clean_dataset\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import roc_auc_score, average_precision_score, f1_score, \\\n", + " accuracy_score, r2_score, f1_score, root_mean_squared_error, log_loss\n", + "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n", + "from sklearn.linear_model import RidgeCV, LogisticRegressionCV, LogisticRegression, LinearRegression\n", + "from imodels.tree.rf_plus.rf_plus.rf_plus_models import RandomForestPlusClassifier, RandomForestPlusRegressor\n", + "from imodels.tree.rf_plus.feature_importance.rfplus_explainer import AloRFPlusMDI, RFPlusMDI\n", + "from sklearn.svm import SVC\n", + "import shap\n", + "from subgroup_detection import *\n", + "import warnings\n", + "import matplotlib.pyplot as plt\n", + "warnings.filterwarnings('ignore', category=DeprecationWarning)\n", + "import numpy as np\n", + "import scipy.cluster.hierarchy as sch\n", + "import matplotlib.pyplot as plt\n", + "from scipy import cluster\n", + "from scipy.cluster.hierarchy import fcluster, cut_tree\n", + "from joblib import Parallel, delayed" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fetching diabetes from sklearn\n" + ] + } + ], + "source": [ + "# get abalone data\n", + "X, y, feature_names = get_clean_dataset(\"diabetes_regr\", data_source='imodels')\n", + "task = 'regression'\n", + "data_type = \"test\"\n", + "# convert y to float\n", + "y = y.astype(float)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# split data into train, validation, and test sets\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,\n", + " random_state=3)\n", + "if data_type == \"test\":\n", + " X_data = X_test\n", + " y_data = y_test\n", + "else:\n", + " X_data = X_train\n", + " y_data = y_train" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 6.5s\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 8.6s finished\n", + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 52 tasks | elapsed: 1.1s\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 2.0s finished\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RF+ Test Set R^2: 0.4274633423340176\n" + ] + } + ], + "source": [ + "# fit rf\n", + "if task == 'classification':\n", + " rf = RandomForestClassifier(n_estimators=100, min_samples_leaf=5, random_state=42)\n", + "else:\n", + " rf = RandomForestRegressor(n_estimators=100, min_samples_leaf=5, random_state=42)\n", + "rf.fit(X_train, y_train)\n", + "\n", + "# fit rf+\n", + "if task == 'classification':\n", + " rf_plus = RandomForestPlusClassifier(rf_model = rf, prediction_model = LogisticRegressionCV())\n", + "else:\n", + " rf_plus = RandomForestPlusRegressor(rf_model = rf, prediction_model = RidgeCV())\n", + "rf_plus.fit(X_train, y_train)\n", + "\n", + "# fit baseline model\n", + "if task == \"classification\":\n", + " rf_plus_baseline = RandomForestPlusClassifier(rf_model=rf, include_raw=False, fit_on=\"inbag\", prediction_model=LogisticRegression())\n", + "elif task == \"regression\":\n", + " rf_plus_baseline = RandomForestPlusRegressor(rf_model=rf, include_raw=False, fit_on=\"inbag\", prediction_model=LinearRegression())\n", + "rf_plus_baseline.fit(X_train, y_train)\n", + "\n", + "# check performance on test set\n", + "y_pred_rf_plus = rf_plus.predict(X_test)\n", + "\n", + "# accuracy\n", + "if task == 'classification':\n", + " accuracy_rf_plus = accuracy_score(y_test, y_pred_rf_plus)\n", + "else:\n", + " r2_rf_plus = r2_score(y_test, y_pred_rf_plus)\n", + "\n", + "if task == 'classification':\n", + " print(f'RF+ Test Set Accuracy: {accuracy_rf_plus}')\n", + "else:\n", + " print(f'RF+ Test Set R^2: {r2_rf_plus}')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# # get feature importances\n", + "# mdi_explainer = RFPlusMDI(rf_plus)\n", + "# mdi = mdi_explainer.explain_linear_partial(np.asarray(X_train), y_train, l2norm = True, sign = True, leaf_average=True)\n", + "# if data_type == \"test\":\n", + "# mdi = mdi_explainer.explain_linear_partial(np.asarray(X_test), l2norm = True, sign = True, leaf_average=True)\n", + "# mdi_rankings = mdi_explainer.get_rankings(mdi)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def get_lmdi(X, y, lmdi_explainer, l2norm, sign, normalize, leaf_average):\n", + " # get feature importances\n", + " lmdi = lmdi_explainer.explain_linear_partial(X, y, l2norm=l2norm, sign=sign,\n", + " normalize=normalize,\n", + " leaf_average=leaf_average)\n", + " mdi_rankings = lmdi_explainer.get_rankings(np.abs(lmdi))\n", + " return lmdi, mdi_rankings" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def get_shap(X, shap_explainer):\n", + " if task == \"classification\":\n", + " # the shap values are an array of shape\n", + " # (# of samples, # of features, # of classes), and in this binary\n", + " # classification case, we want the shap values for the positive class.\n", + " # check_additivity=False is used to speed up computation.\n", + " shap_values = \\\n", + " shap_explainer.shap_values(X, check_additivity=False)[:, :, 1]\n", + " else:\n", + " # check_additivity=False is used to speed up computation.\n", + " shap_values = shap_explainer.shap_values(X, check_additivity=False)\n", + " # get the rankings of the shap values. negative absolute value is taken\n", + " # because np.argsort sorts from smallest to largest.\n", + " shap_rankings = np.argsort(-np.abs(shap_values), axis = 1)\n", + " return shap_values, shap_rankings" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# obtain shap feature importances\n", + "shap_explainer = shap.TreeExplainer(rf)\n", + "if data_type == \"test\":\n", + " shap_values, shap_rankings = get_shap(X_test, shap_explainer)\n", + "else:\n", + " shap_values, shap_rankings = get_shap(X_train, shap_explainer)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# obtain lmdi feature importances\n", + "lmdi_explainer = RFPlusMDI(rf_plus)\n", + "lmdi_baseline_explainer = RFPlusMDI(rf_plus_baseline, mode = \"only_k\", evaluate_on = \"inbag\")\n", + "lmdi_values_signed_normalized_l2_avg, \\\n", + " lmdi_rankings_signed_normalized_l2_avg = \\\n", + " get_lmdi(X_train, y_train, lmdi_explainer, l2norm=True, sign=True,\n", + " normalize=True, leaf_average=True)\n", + "lmdi_values_signed_normalized_l2_noavg, \\\n", + " lmdi_rankings_signed_normalized_l2_noavg = \\\n", + " get_lmdi(X_train, y_train, lmdi_explainer, l2norm=True, sign=True,\n", + " normalize=True, leaf_average=False)\n", + "lmdi_values_signed_nonnormalized_l2_avg, \\\n", + " lmdi_rankings_signed_nonnormalized_l2_avg = \\\n", + " get_lmdi(X_train, y_train, lmdi_explainer, l2norm=True, sign=True,\n", + " normalize=False, leaf_average=True)\n", + "lmdi_values_signed_nonnormalized_l2_noavg, \\\n", + " lmdi_rankings_signed_nonnormalized_l2_noavg = \\\n", + " get_lmdi(X_train, y_train, lmdi_explainer, l2norm=True, sign=True,\n", + " normalize=False, leaf_average=False)\n", + "lmdi_values_nonl2_avg, lmdi_rankings_nonl2_avg = \\\n", + " get_lmdi(X_train, y_train, lmdi_explainer, l2norm=False, sign=False,\n", + " normalize=False, leaf_average=True)\n", + "lmdi_values_nonl2_noavg, lmdi_rankings_nonl2_noavg = \\\n", + " get_lmdi(X_train, y_train, lmdi_explainer, l2norm=False, sign=False,\n", + " normalize=False, leaf_average=False)\n", + "lmdi_values_baseline, lmdi_rankings_baseline = \\\n", + " get_lmdi(X_train, y_train, lmdi_baseline_explainer, l2norm=False, sign=False,\n", + " normalize=False, leaf_average=False)\n", + "if data_type == \"test\":\n", + " lmdi_values_signed_normalized_l2_avg, \\\n", + " lmdi_rankings_signed_normalized_l2_avg = \\\n", + " get_lmdi(X_test, None, lmdi_explainer, l2norm=True, sign=True,\n", + " normalize=True, leaf_average=True)\n", + " lmdi_values_signed_normalized_l2_noavg, \\\n", + " lmdi_rankings_signed_normalized_l2_noavg = \\\n", + " get_lmdi(X_test, None, lmdi_explainer, l2norm=True, sign=True,\n", + " normalize=True, leaf_average=False)\n", + " lmdi_values_signed_nonnormalized_l2_avg, \\\n", + " lmdi_rankings_signed_nonnormalized_l2_avg = \\\n", + " get_lmdi(X_test, None, lmdi_explainer, l2norm=True, sign=True,\n", + " normalize=False, leaf_average=True)\n", + " lmdi_values_signed_nonnormalized_l2_noavg, \\\n", + " lmdi_rankings_signed_nonnormalized_l2_noavg = \\\n", + " get_lmdi(X_test, None, lmdi_explainer, l2norm=True, sign=True,\n", + " normalize=False, leaf_average=False)\n", + " lmdi_values_nonl2_avg, lmdi_rankings_nonl2_avg = \\\n", + " get_lmdi(X_test, None, lmdi_explainer, l2norm=False, sign=False,\n", + " normalize=False, leaf_average=True)\n", + " lmdi_values_nonl2_noavg, lmdi_rankings_nonl2_noavg = \\\n", + " get_lmdi(X_test, None, lmdi_explainer, l2norm=False, sign=False,\n", + " normalize=False, leaf_average=False)\n", + " lmdi_values_baseline, lmdi_rankings_baseline = \\\n", + " get_lmdi(X_test, None, lmdi_baseline_explainer, l2norm=False, sign=False,\n", + " normalize=False, leaf_average=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# create storage for iteration purposes\n", + "lfi_values = \\\n", + " {'shap': shap_values,\n", + " 'signed_normalized_l2_avg': lmdi_values_signed_normalized_l2_avg,\n", + " 'signed_normalized_l2_noavg': lmdi_values_signed_normalized_l2_noavg,\n", + " 'signed_nonnormalized_l2_avg': lmdi_values_signed_nonnormalized_l2_avg,\n", + " 'signed_nonnormalized_l2_noavg':\n", + " lmdi_values_signed_nonnormalized_l2_noavg,\n", + " 'nonl2_avg': lmdi_values_nonl2_avg,\n", + " 'nonl2_noavg': lmdi_values_nonl2_noavg,\n", + " 'baseline': lmdi_values_baseline}\n", + "lfi_rankings = \\\n", + " {'shap': shap_rankings,\n", + " 'signed_normalized_l2_avg': lmdi_rankings_signed_normalized_l2_avg,\n", + " 'signed_normalized_l2_noavg': lmdi_rankings_signed_normalized_l2_noavg,\n", + " 'signed_nonnormalized_l2_avg': lmdi_rankings_signed_nonnormalized_l2_avg,\n", + " 'signed_nonnormalized_l2_noavg':\n", + " lmdi_rankings_signed_nonnormalized_l2_noavg,\n", + " 'nonl2_avg': lmdi_rankings_nonl2_avg,\n", + " 'nonl2_noavg': lmdi_rankings_nonl2_noavg,\n", + " 'baseline': lmdi_rankings_baseline}" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def compute_rbo_for_method_and_p(method, ranking, p):\n", + " \"\"\"\n", + " Helper function to compute the RBO matrix for a given method and p value.\n", + " \"\"\"\n", + " # print(\"method:\")\n", + " # print(method)\n", + " return (method + \"_\" + str(p), compute_rbo_matrix(ranking, 'distance', p=p))\n", + "\n", + "# parallelize the computation of RBO matrices\n", + "rbo_matrices = dict(Parallel(n_jobs=2)(\n", + " delayed(compute_rbo_for_method_and_p)(method, ranking, p)\n", + " for method, ranking in lfi_rankings.items()\n", + " for p in [0.1, 0.3, 0.5, 0.7, 0.9]\n", + "))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# get linkages for values\n", + "values_linkage = {}\n", + "for method, values in lfi_values.items():\n", + " values_linkage[method] = cluster.hierarchy.ward(values)\n", + "\n", + "# get linkages for rankings\n", + "rankings_linkage = {}\n", + "for method, rbo_mat in rbo_matrices.items():\n", + " rankings_linkage[method] = cluster.hierarchy.ward(squareform(rbo_mat))" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The Number of Unique Values (Maximum # of Clusters) for shap is 133.\n", + "The Number of Unique Values (Maximum # of Clusters) for signed_normalized_l2_avg is 133.\n", + "The Number of Unique Values (Maximum # of Clusters) for signed_normalized_l2_noavg is 133.\n", + "The Number of Unique Values (Maximum # of Clusters) for signed_nonnormalized_l2_avg is 133.\n", + "The Number of Unique Values (Maximum # of Clusters) for signed_nonnormalized_l2_noavg is 133.\n", + "The Number of Unique Values (Maximum # of Clusters) for nonl2_avg is 133.\n", + "The Number of Unique Values (Maximum # of Clusters) for nonl2_noavg is 133.\n", + "The Number of Unique Values (Maximum # of Clusters) for baseline is 133.\n", + "The Number of Unique Rankings (Maximum # of Clusters) for shap_0.1 is 131.\n", + "The Number of Unique Rankings (Maximum # of Clusters) for shap_0.3 is 131.\n", + "The Number of Unique Rankings (Maximum # of Clusters) for shap_0.5 is 131.\n", + "The Number of Unique Rankings (Maximum # of Clusters) for shap_0.7 is 131.\n", + "The Number of Unique Rankings (Maximum # of Clusters) for shap_0.9 is 131.\n", + "The Number of Unique Rankings (Maximum # of Clusters) for signed_normalized_l2_avg_0.1 is 131.\n", + "The Number of Unique Rankings (Maximum # of Clusters) for signed_normalized_l2_avg_0.3 is 131.\n", + "The Number of Unique Rankings (Maximum # of Clusters) for signed_normalized_l2_avg_0.5 is 131.\n", + "The Number of Unique Rankings (Maximum # of Clusters) for signed_normalized_l2_avg_0.7 is 131.\n", + "The Number of Unique Rankings (Maximum # of Clusters) for signed_normalized_l2_avg_0.9 is 131.\n", + "The Number of Unique Rankings (Maximum # of Clusters) for signed_normalized_l2_noavg_0.1 is 132.\n", + "The Number of Unique Rankings (Maximum # of Clusters) for signed_normalized_l2_noavg_0.3 is 132.\n", + "The Number of Unique Rankings (Maximum # of Clusters) for signed_normalized_l2_noavg_0.5 is 132.\n", + "The Number of Unique Rankings (Maximum # of Clusters) for signed_normalized_l2_noavg_0.7 is 132.\n", + "The Number of Unique Rankings (Maximum # of Clusters) for signed_normalized_l2_noavg_0.9 is 132.\n", + "The Number of Unique Rankings (Maximum # of Clusters) for signed_nonnormalized_l2_avg_0.1 is 131.\n", + "The Number of Unique Rankings (Maximum # of Clusters) for signed_nonnormalized_l2_avg_0.3 is 131.\n", + "The Number of Unique Rankings (Maximum # of Clusters) for signed_nonnormalized_l2_avg_0.5 is 131.\n", + "The Number of Unique Rankings (Maximum # of Clusters) for signed_nonnormalized_l2_avg_0.7 is 131.\n", + "The Number of Unique Rankings (Maximum # of Clusters) for signed_nonnormalized_l2_avg_0.9 is 131.\n", + "The Number of Unique Rankings (Maximum # of Clusters) for signed_nonnormalized_l2_noavg_0.1 is 132.\n", + "The Number of Unique Rankings (Maximum # of Clusters) for signed_nonnormalized_l2_noavg_0.3 is 132.\n", + "The Number of Unique Rankings (Maximum # of Clusters) for signed_nonnormalized_l2_noavg_0.5 is 132.\n", + "The Number of Unique Rankings (Maximum # of Clusters) for signed_nonnormalized_l2_noavg_0.7 is 132.\n", + "The Number of Unique Rankings (Maximum # of Clusters) for signed_nonnormalized_l2_noavg_0.9 is 132.\n", + "The Number of Unique Rankings (Maximum # of Clusters) for nonl2_avg_0.1 is 131.\n", + "The Number of Unique Rankings (Maximum # of Clusters) for nonl2_avg_0.3 is 131.\n", + "The Number of Unique Rankings (Maximum # of Clusters) for nonl2_avg_0.5 is 131.\n", + "The Number of Unique Rankings (Maximum # of Clusters) for nonl2_avg_0.7 is 131.\n", + "The Number of Unique Rankings (Maximum # of Clusters) for nonl2_avg_0.9 is 131.\n", + "The Number of Unique Rankings (Maximum # of Clusters) for nonl2_noavg_0.1 is 131.\n", + "The Number of Unique Rankings (Maximum # of Clusters) for nonl2_noavg_0.3 is 131.\n", + "The Number of Unique Rankings (Maximum # of Clusters) for nonl2_noavg_0.5 is 131.\n", + "The Number of Unique Rankings (Maximum # of Clusters) for nonl2_noavg_0.7 is 131.\n", + "The Number of Unique Rankings (Maximum # of Clusters) for nonl2_noavg_0.9 is 131.\n", + "The Number of Unique Rankings (Maximum # of Clusters) for baseline_0.1 is 133.\n", + "The Number of Unique Rankings (Maximum # of Clusters) for baseline_0.3 is 133.\n", + "The Number of Unique Rankings (Maximum # of Clusters) for baseline_0.5 is 133.\n", + "The Number of Unique Rankings (Maximum # of Clusters) for baseline_0.7 is 133.\n", + "The Number of Unique Rankings (Maximum # of Clusters) for baseline_0.9 is 133.\n" + ] + } + ], + "source": [ + "# get clusters for values\n", + "value_clusters = {}\n", + "for method, link in values_linkage.items():\n", + " # maximum number of clusters is the number of unique feature importances\n", + " max_num_clusters = np.unique(lfi_values[method], axis = 0).shape[0]\n", + " print(f\"The Number of Unique Values (Maximum # of Clusters) for {method} is {max_num_clusters}.\")\n", + " num_cluster_map = {}\n", + " for num_clusters in np.arange(1, max_num_clusters + 1):\n", + " # num_cluster_map[num_clusters] = fcluster(link, num_clusters,\n", + " # criterion = \"maxclust\")\n", + " num_cluster_map[num_clusters] = cut_tree(link, n_clusters=num_clusters).flatten()\n", + " value_clusters[method] = num_cluster_map\n", + " \n", + "# get clusters for rankings\n", + "ranking_clusters = {}\n", + "for method, link in rankings_linkage.items():\n", + " # maximum number of clusters is the number of unique rankings\n", + " max_num_clusters = np.unique(rbo_matrices[method], axis = 0).shape[0]\n", + " print(f\"The Number of Unique Rankings (Maximum # of Clusters) for {method} is {max_num_clusters}.\")\n", + " num_cluster_map = {}\n", + " for num_clusters in np.arange(1, max_num_clusters + 1):\n", + " # num_cluster_map[num_clusters] = fcluster(link, num_clusters,\n", + " # criterion = \"maxclust\")\n", + " num_cluster_map[num_clusters] = cut_tree(link, n_clusters=num_clusters).flatten()\n", + " ranking_clusters[method] = num_cluster_map\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# get predictions and performance metrics for each methods clusters\n", + "if task == \"classification\":\n", + " metrics = {\"AUROC\": roc_auc_score, \"AUPRC\": average_precision_score,\n", + " \"F1\": f1_score, \"Accuracy\": accuracy_score,\n", + " \"R^2\": r2_score, \"Cross-Entropy\": log_loss}\n", + "elif task == \"regression\":\n", + " metrics = {\"R^2\": r2_score, \"RMSE\": root_mean_squared_error}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "value_clusters" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "here3\n", + "Max Num Clusters: 133\n", + "here1\n", + "here1\n", + "Max Num Clusters: 133\n", + "here1\n", + "here1\n", + "Max Num Clusters: 133\n", + "here1\n", + "here1\n", + "Max Num Clusters: 133\n", + "here1\n", + "here1\n", + "Max Num Clusters: 133\n", + "here1\n", + "here1\n", + "Max Num Clusters: 133\n", + "here1\n", + "here1\n", + "Max Num Clusters: 133\n", + "here1\n", + "here1\n", + "Max Num Clusters: 133\n", + "here1\n", + "here1\n" + ] + } + ], + "source": [ + "def evaluate_method(method, cluster_map, metrics, y_data):\n", + " metric_results = {}\n", + " max_num_clusters = np.max(list(cluster_map.keys()))\n", + " print(\"Max Num Clusters:\", max_num_clusters)\n", + " metric_results[\"nclust\"] = np.arange(1, max_num_clusters + 1)\n", + " printed = False\n", + " for metric, metric_func in metrics.items():\n", + " cluster_results = np.repeat(np.nan, max_num_clusters)\n", + " for num_clusters, clusters in cluster_map.items():\n", + " if num_clusters == max_num_clusters:\n", + " print(\"here1\")\n", + " cluster_predictions = np.repeat(np.nan, len(clusters))\n", + " cluster_truths = np.repeat(np.nan, len(clusters))\n", + " for i in range(num_clusters):\n", + " if i == max_num_clusters:\n", + " print(\"here2\")\n", + " print(\"clusters:\", clusters)\n", + " print(\"clusters length:\", len(clusters))\n", + " print(\"number of unique clusters:\", np.unique(clusters).shape[0])\n", + " print(\"biggest cluster number:\", np.max(clusters))\n", + " cluster_indices = np.where(clusters == i)[0]\n", + " if i == max_num_clusters:\n", + " print(\"cluster indices\", cluster_indices)\n", + " if y_data[cluster_indices].shape[0] == 0:\n", + " continue\n", + " cluster_predictions[cluster_indices] = \\\n", + " np.mean(y_data[cluster_indices])\n", + " if metric in [\"Accuracy\", \"F1\"]:\n", + " cluster_predictions[cluster_indices] = \\\n", + " cluster_predictions[cluster_indices] > 0.5\n", + " cluster_truths[cluster_indices] = y_data[cluster_indices]\n", + " if i == max_num_clusters and printed == False:\n", + " print(method)\n", + " print(cluster_predictions - cluster_truths)\n", + " printed = True\n", + " cluster_results[num_clusters-1] = metric_func(cluster_truths,\n", + " cluster_predictions)\n", + " metric_results[metric] = cluster_results\n", + " return method, metric_results\n", + "\n", + "if data_type == \"test\":\n", + " # method_values_results = dict(Parallel(n_jobs=2)(\n", + " # delayed(evaluate_method)(method, cluster_map, metrics, y_test)\n", + " # for method, cluster_map in value_clusters.items()))\n", + " print(\"here3\")\n", + " method_values_results = dict(evaluate_method(method, cluster_map, metrics, y_test) for method, cluster_map in value_clusters.items())\n", + "else:\n", + " # method_values_results = dict(Parallel(n_jobs=2)(\n", + " # delayed(evaluate_method)(method, cluster_map, metrics, y_train)\n", + " # for method, cluster_map in value_clusters.items()))\n", + " method_values_results = dict(evaluate_method(method, cluster_map, metrics, y_train) for method, cluster_map in value_clusters.items())\n", + "# if data_type == \"test\":\n", + "# method_rankings_results = dict(Parallel(n_jobs=2)(\n", + "# delayed(evaluate_method)(method, cluster_map, metrics, y_test)\n", + "# for method, cluster_map in ranking_clusters.items()))\n", + "# else:\n", + "# method_rankings_results = dict(Parallel(n_jobs=2)(\n", + "# delayed(evaluate_method)(method, cluster_map, metrics, y_train)\n", + "# for method, cluster_map in ranking_clusters.items()))" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "R^2 for shap at 133 clusters is 1.0\n", + "R^2 for signed_normalized_l2_avg at 133 clusters is 1.0\n", + "R^2 for signed_normalized_l2_noavg at 133 clusters is 1.0\n", + "R^2 for signed_nonnormalized_l2_avg at 133 clusters is 1.0\n", + "R^2 for signed_nonnormalized_l2_noavg at 133 clusters is 1.0\n", + "R^2 for nonl2_avg at 133 clusters is 1.0\n", + "R^2 for nonl2_noavg at 133 clusters is 1.0\n", + "R^2 for baseline at 133 clusters is 1.0\n" + ] + } + ], + "source": [ + "for method in method_values_results.keys():\n", + " print(f\"R^2 for {method} at 133 clusters is {method_values_results[method]['R^2'][132]}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'shap': {'nclust': array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", + " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", + " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", + " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", + " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", + " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", + " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", + " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", + " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", + " 131, 132, 133]),\n", + " 'R^2': array([0. , 0.2927174 , 0.31298573, 0.3555322 , 0.40046554,\n", + " 0.40537322, 0.41733588, 0.42427932, 0.45783226, 0.47039441,\n", + " 0.4760174 , 0.47941986, 0.48391261, 0.48459165, 0.48586485,\n", + " 0.49350402, 0.4951161 , 0.54870113, 0.55914736, 0.59063613,\n", + " 0.59360127, 0.59478502, 0.61411791, 0.61893762, 0.62783303,\n", + " 0.62783305, 0.62784837, 0.62828402, 0.63331129, 0.63529491,\n", + " 0.63543672, 0.63543684, 0.63666211, 0.63666212, 0.63797269,\n", + " 0.65145094, 0.65206812, 0.66448446, 0.6655677 , 0.66570821,\n", + " 0.66757198, 0.66757723, 0.67448569, 0.67562995, 0.6972981 ,\n", + " 0.69834729, 0.70003981, 0.70191877, 0.70845624, 0.70977407,\n", + " 0.71292062, 0.72131413, 0.72209288, 0.72545029, 0.72622962,\n", + " 0.73530565, 0.73934153, 0.74039072, 0.74733456, 0.75006494,\n", + " 0.75204986, 0.76076285, 0.76152769, 0.76644576, 0.76923391,\n", + " 0.77812933, 0.78563407, 0.78713774, 0.79567646, 0.79573648,\n", + " 0.798025 , 0.80182434, 0.80876818, 0.81030457, 0.81059965,\n", + " 0.81468387, 0.81612185, 0.81753794, 0.82234097, 0.83263931,\n", + " 0.8341757 , 0.83421666, 0.83857315, 0.84347624, 0.84681168,\n", + " 0.84934458, 0.85699295, 0.8580115 , 0.85938425, 0.8627063 ,\n", + " 0.86270903, 0.86636871, 0.8705101 , 0.87589128, 0.87589146,\n", + " 0.88058081, 0.89206766, 0.89795505, 0.89795573, 0.89972624,\n", + " 0.89979431, 0.90053559, 0.90164741, 0.9020075 , 0.90213819,\n", + " 0.90528576, 0.9060486 , 0.91024535, 0.91064561, 0.91121286,\n", + " 0.92592215, 0.93084022, 0.9315815 , 0.9334936 , 0.96045008,\n", + " 0.96133227, 0.96134316, 0.9646786 , 0.96468949, 0.96523428,\n", + " 0.97015235, 0.98387803, 0.98409857, 0.98463224, 0.98566759,\n", + " 0.98959933, 0.99053121, 0.99314783, 0.99551735, 0.99796788,\n", + " 0.99850155, 0.99934585, 1. ]),\n", + " 'RMSE': array([74.31571492, 62.49958714, 61.59756425, 59.65972815, 57.5423685 ,\n", + " 57.3063693 , 56.72699765, 56.38798498, 54.72017756, 54.0825228 ,\n", + " 53.79465073, 53.61970942, 53.38783178, 53.3526979 , 53.28675944,\n", + " 52.88940236, 52.80516702, 49.92439203, 49.34320835, 47.54834585,\n", + " 47.37582984, 47.3067818 , 46.16448273, 45.87527705, 45.33666535,\n", + " 45.33666405, 45.33573117, 45.30918787, 45.00175303, 44.87986826,\n", + " 44.8711418 , 44.87113436, 44.79566699, 44.79566633, 44.71480321,\n", + " 43.87454452, 43.83568267, 43.0464141 , 42.97686876, 42.96783915,\n", + " 42.84789339, 42.84755497, 42.39998496, 42.32539634, 40.88728105,\n", + " 40.81636027, 40.70169271, 40.57401381, 40.12661627, 40.03582318,\n", + " 39.81820336, 39.23179105, 39.17693834, 38.93957039, 38.88426426,\n", + " 38.23428688, 37.94168183, 37.8652444 , 37.35541607, 37.15303143,\n", + " 37.00520691, 36.34920959, 36.29105919, 35.91488999, 35.69987153,\n", + " 35.0050426 , 34.40792989, 34.28704023, 33.59230914, 33.5873753 ,\n", + " 33.39869281, 33.08307118, 32.49830726, 32.36749545, 32.34231072,\n", + " 31.99169692, 31.86733347, 31.74438754, 31.32379007, 30.40236643,\n", + " 30.26249612, 30.25875875, 29.85853866, 29.40158811, 29.08663492,\n", + " 28.84516613, 28.10343409, 28.00317293, 27.86747639, 27.5363237 ,\n", + " 27.53605065, 27.16656595, 26.74229095, 26.18073389, 26.18071475,\n", + " 25.68134203, 24.41498472, 23.73976498, 23.7396858 , 23.53283846,\n", + " 23.52484955, 23.43767398, 23.30631268, 23.26360868, 23.2480899 ,\n", + " 22.87116581, 22.77887603, 22.26430435, 22.21460605, 22.14398089,\n", + " 20.22669146, 19.54373029, 19.43870902, 19.1651574 , 14.7792868 ,\n", + " 14.61352585, 14.61146767, 13.96688708, 13.9647336 , 13.85658733,\n", + " 12.83913386, 9.4360398 , 9.37127537, 9.21267787, 8.89693141,\n", + " 7.57899088, 7.23149267, 6.15169557, 4.97562983, 3.35008323,\n", + " 2.87674977, 1.90073195, 0. ])},\n", + " 'signed_normalized_l2_avg': {'nclust': array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", + " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", + " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", + " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", + " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", + " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", + " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", + " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", + " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", + " 131, 132, 133]),\n", + " 'R^2': array([0. , 0.29769971, 0.30963245, 0.34613173, 0.34907152,\n", + " 0.38745279, 0.38999954, 0.39293254, 0.40548813, 0.41516373,\n", + " 0.42432326, 0.42780867, 0.4537385 , 0.48075072, 0.48179335,\n", + " 0.50047789, 0.50579588, 0.51436425, 0.51918396, 0.51919153,\n", + " 0.54152179, 0.54275842, 0.54278664, 0.54443163, 0.54712204,\n", + " 0.56307992, 0.56489365, 0.56785879, 0.57831437, 0.57831449,\n", + " 0.57879855, 0.58816768, 0.58817494, 0.60141567, 0.60325356,\n", + " 0.60779781, 0.60857937, 0.61044313, 0.62102941, 0.63099557,\n", + " 0.65975205, 0.66683408, 0.6718715 , 0.70033032, 0.70071197,\n", + " 0.70193723, 0.70222086, 0.71024815, 0.71030061, 0.71144487,\n", + " 0.71798233, 0.71861164, 0.71861327, 0.73188288, 0.7335754 ,\n", + " 0.73504299, 0.7351165 , 0.74029007, 0.74143433, 0.74396722,\n", + " 0.74789215, 0.75659805, 0.75668518, 0.76783781, 0.76927817,\n", + " 0.7756829 , 0.77634182, 0.78014115, 0.78047061, 0.7818867 ,\n", + " 0.782406 , 0.78991074, 0.79095992, 0.79132297, 0.796126 ,\n", + " 0.79891415, 0.80139917, 0.81650259, 0.81715674, 0.82030431,\n", + " 0.82434019, 0.84688776, 0.84876673, 0.85555764, 0.85764876,\n", + " 0.8580709 , 0.8588578 , 0.85967464, 0.86215965, 0.86219233,\n", + " 0.8622604 , 0.86227742, 0.86719549, 0.87155198, 0.87224902,\n", + " 0.87416111, 0.87614604, 0.88300888, 0.8833195 , 0.88332223,\n", + " 0.88628737, 0.88664746, 0.90037313, 0.90626052, 0.90727908,\n", + " 0.94282194, 0.94495662, 0.95289633, 0.95329658, 0.95383025,\n", + " 0.95874832, 0.96366639, 0.96405848, 0.96551064, 0.96573119,\n", + " 0.96581356, 0.96582445, 0.96656573, 0.96669915, 0.96744043,\n", + " 0.97479202, 0.97532569, 0.97585936, 0.97670365, 0.98700199,\n", + " 0.98937152, 0.99330325, 0.99543794, 0.99675577, 0.99687081,\n", + " 0.99752497, 0.99754947, 1. ]),\n", + " 'RMSE': array([74.31571492, 62.27906519, 61.74770855, 60.09326371, 59.95802214,\n", + " 58.16348805, 58.04245063, 57.90274308, 57.30083159, 56.83263729,\n", + " 56.38583329, 56.21488132, 54.92637718, 53.55112648, 53.49733549,\n", + " 52.52402706, 52.24368974, 51.78881617, 51.53118531, 51.53078001,\n", + " 50.31992762, 50.25201964, 50.25046873, 50.15998984, 50.0116581 ,\n", + " 49.12263597, 49.02057167, 48.85325483, 48.25863805, 48.25863113,\n", + " 48.23092502, 47.6914879 , 47.69106748, 46.91813838, 46.80984227,\n", + " 46.54099537, 46.49460005, 46.38377519, 45.74919054, 45.14362726,\n", + " 43.34893354, 42.89542234, 42.56990082, 40.68197812, 40.65606443,\n", + " 40.57275758, 40.55344919, 40.00311123, 39.99948979, 39.92041627,\n", + " 39.46560999, 39.42155258, 39.42143814, 38.48069738, 38.35904841,\n", + " 38.25325257, 38.24794526, 37.87258422, 37.7890604 , 37.60351532,\n", + " 37.31417523, 36.66423966, 36.65767678, 35.80769864, 35.69644793,\n", + " 35.19750295, 35.14576954, 34.84597586, 34.81985762, 34.70737217,\n", + " 34.66603106, 34.0629761 , 33.97781415, 33.9482966 , 33.55533529,\n", + " 33.3250961 , 33.11854042, 31.83432401, 31.77752985, 31.50282439,\n", + " 31.14704595, 29.07941119, 28.90043187, 28.24411354, 28.03892077,\n", + " 27.99731459, 27.91959454, 27.83868677, 27.59108861, 27.58781831,\n", + " 27.58100396, 27.57930011, 27.0823954 , 26.63448873, 26.56212277,\n", + " 26.36259121, 26.15384885, 25.41892118, 25.38515344, 25.38485725,\n", + " 25.0602282 , 25.0205179 , 23.45680721, 22.75317048, 22.62921681,\n", + " 17.7703253 , 17.43545203, 16.12902239, 16.06034984, 15.96832705,\n", + " 15.09389991, 14.16559778, 14.08895833, 13.80140198, 13.75720363,\n", + " 13.74066099, 13.73847204, 13.5886581 , 13.56151861, 13.40972789,\n", + " 11.79912277, 11.67355686, 11.54662554, 11.34291207, 8.47264506,\n", + " 7.66154346, 6.08152632, 5.01951081, 4.23288238, 4.15715707,\n", + " 3.69718446, 3.67883604, 0. ])},\n", + " 'signed_normalized_l2_noavg': {'nclust': array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", + " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", + " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", + " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", + " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", + " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", + " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", + " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", + " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", + " 131, 132, 133]),\n", + " 'R^2': array([0. , 0.29769971, 0.3367896 , 0.37506979, 0.40682507,\n", + " 0.41328323, 0.41541414, 0.43870928, 0.44557102, 0.44601605,\n", + " 0.46292034, 0.468761 , 0.46900498, 0.46954378, 0.47000393,\n", + " 0.49475002, 0.5033184 , 0.50813811, 0.50814567, 0.50820875,\n", + " 0.50824809, 0.51588727, 0.52679246, 0.52984382, 0.53681085,\n", + " 0.54750362, 0.5668365 , 0.56833688, 0.568337 , 0.57365499,\n", + " 0.5744249 , 0.57936679, 0.58089463, 0.58091369, 0.58478233,\n", + " 0.58499183, 0.58542766, 0.59601394, 0.59663112, 0.60235015,\n", + " 0.60598245, 0.61078643, 0.6108246 , 0.61083186, 0.61103199,\n", + " 0.61217625, 0.61513257, 0.6157452 , 0.61898545, 0.61971739,\n", + " 0.6198592 , 0.62160253, 0.62327542, 0.63021926, 0.63270428,\n", + " 0.64020902, 0.64840013, 0.64881836, 0.65535582, 0.65871322,\n", + " 0.66258999, 0.70009951, 0.72757809, 0.72790755, 0.73308111,\n", + " 0.73335907, 0.7333754 , 0.73493852, 0.74165115, 0.76060734,\n", + " 0.78051175, 0.78080683, 0.78279176, 0.78882028, 0.78882096,\n", + " 0.79696234, 0.79742249, 0.79911501, 0.80331176, 0.80342204,\n", + " 0.81457466, 0.81461913, 0.81653122, 0.81809956, 0.82186357,\n", + " 0.82519901, 0.82519969, 0.82542024, 0.83449626, 0.83645873,\n", + " 0.85136883, 0.85751216, 0.85790425, 0.85823371, 0.8631368 ,\n", + " 0.86693614, 0.86946903, 0.87438711, 0.87456137, 0.8784931 ,\n", + " 0.88021051, 0.88299867, 0.88714006, 0.89205813, 0.89210918,\n", + " 0.89230591, 0.89734038, 0.8975371 , 0.90643251, 0.9104684 ,\n", + " 0.91197207, 0.91280593, 0.91300265, 0.91708686, 0.91763165,\n", + " 0.92198814, 0.92234824, 0.92323043, 0.93352877, 0.93382896,\n", + " 0.93461585, 0.93554773, 0.93590782, 0.95061711, 0.9525292 ,\n", + " 0.96401605, 0.96638557, 0.96674566, 0.96727933, 0.96972986,\n", + " 0.9704269 , 0.97304352, 1. ]),\n", + " 'RMSE': array([74.31571492, 62.27906519, 60.52103198, 58.74845073, 57.23636612,\n", + " 56.92393439, 56.82046911, 55.67684175, 55.33547243, 55.31325959,\n", + " 54.46280608, 54.16585885, 54.15341921, 54.12593776, 54.10245636,\n", + " 52.82430735, 52.3744767 , 52.11974087, 52.11934015, 52.11599812,\n", + " 52.11391337, 51.7075441 , 51.12184145, 50.95675169, 50.57779019,\n", + " 49.9905847 , 48.9110051 , 48.82622327, 48.82621643, 48.52452071,\n", + " 48.48068714, 48.19837956, 48.11076618, 48.1096722 , 47.88710386,\n", + " 47.87502147, 47.84987632, 47.23499231, 47.19889741, 46.863106 ,\n", + " 46.64858173, 46.36333248, 46.36105933, 46.36062685, 46.34870504,\n", + " 46.28048087, 46.1037489 , 46.06704033, 45.87239737, 45.82831532,\n", + " 45.8197695 , 45.71458418, 45.61342027, 45.19108856, 45.03898505,\n", + " 44.57648265, 44.06614018, 44.03992434, 43.62808354, 43.41505864,\n", + " 43.16777256, 40.69764203, 38.78838316, 38.76492125, 38.39461469,\n", + " 38.37461856, 38.37344296, 38.26079315, 37.77321287, 36.36102145,\n", + " 34.81659532, 34.79318339, 34.63528843, 34.15126232, 34.15120728,\n", + " 33.48643884, 33.44847131, 33.30844873, 32.95868341, 32.94944293,\n", + " 32.00112193, 31.99728411, 31.83184004, 31.69549473, 31.36584792,\n", + " 31.07081208, 31.07075158, 31.05114424, 30.23323082, 30.05345071,\n", + " 28.65072486, 28.05237002, 28.01374752, 27.98125267, 27.4931183 ,\n", + " 27.10882644, 26.84957634, 26.33890839, 26.32063239, 25.90485289,\n", + " 25.7211283 , 25.42003039, 24.96609229, 24.41606255, 24.4102879 ,\n", + " 24.38802348, 23.81115659, 23.78833142, 22.73228744, 22.23662336,\n", + " 22.04910219, 21.94442171, 21.91965278, 21.39894359, 21.32852585,\n", + " 20.75682694, 20.70886629, 20.59089513, 19.16008931, 19.11677608,\n", + " 19.00276988, 18.86686637, 18.81408847, 16.51463238, 16.19175507,\n", + " 14.09727217, 13.62521987, 13.55204393, 13.44286154, 12.92968258,\n", + " 12.77994787, 12.20147286, 0. ])},\n", + " 'signed_nonnormalized_l2_avg': {'nclust': array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", + " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", + " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", + " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", + " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", + " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", + " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", + " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", + " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", + " 131, 132, 133]),\n", + " 'R^2': array([0. , 0.29769971, 0.30963245, 0.34613173, 0.34907152,\n", + " 0.38745279, 0.38999954, 0.39293254, 0.40548813, 0.41516373,\n", + " 0.42432326, 0.42780867, 0.4537385 , 0.48075072, 0.48179335,\n", + " 0.50047789, 0.50579588, 0.51436425, 0.51918396, 0.51919153,\n", + " 0.54152179, 0.54275842, 0.54278664, 0.54443163, 0.54712204,\n", + " 0.56307992, 0.56489365, 0.56785879, 0.57831437, 0.57831449,\n", + " 0.57879855, 0.58816768, 0.58817494, 0.60141567, 0.60325356,\n", + " 0.60779781, 0.60857937, 0.61044313, 0.62102941, 0.63099557,\n", + " 0.65975205, 0.66683408, 0.6718715 , 0.70033032, 0.70071197,\n", + " 0.70193723, 0.70222086, 0.71024815, 0.71030061, 0.71144487,\n", + " 0.71798233, 0.71861164, 0.71861327, 0.73188288, 0.7335754 ,\n", + " 0.73504299, 0.7351165 , 0.74029007, 0.74143433, 0.74396722,\n", + " 0.74789215, 0.75659805, 0.75668518, 0.76783781, 0.76927817,\n", + " 0.7756829 , 0.77634182, 0.78014115, 0.78047061, 0.7818867 ,\n", + " 0.782406 , 0.78991074, 0.79095992, 0.79132297, 0.796126 ,\n", + " 0.79891415, 0.80139917, 0.81650259, 0.81715674, 0.82030431,\n", + " 0.82434019, 0.84688776, 0.84876673, 0.85555764, 0.85764876,\n", + " 0.8580709 , 0.8588578 , 0.85967464, 0.86215965, 0.86219233,\n", + " 0.8622604 , 0.86227742, 0.86719549, 0.87155198, 0.87224902,\n", + " 0.87416111, 0.87614604, 0.88300888, 0.8833195 , 0.88332223,\n", + " 0.88628737, 0.88664746, 0.90037313, 0.90626052, 0.90727908,\n", + " 0.94282194, 0.94495662, 0.95289633, 0.95329658, 0.95383025,\n", + " 0.95874832, 0.96366639, 0.96405848, 0.96551064, 0.96573119,\n", + " 0.96581356, 0.96582445, 0.96656573, 0.96669915, 0.96744043,\n", + " 0.97479202, 0.97532569, 0.97585936, 0.97670365, 0.98700199,\n", + " 0.98937152, 0.99330325, 0.99543794, 0.99675577, 0.99687081,\n", + " 0.99752497, 0.99754947, 1. ]),\n", + " 'RMSE': array([74.31571492, 62.27906519, 61.74770855, 60.09326371, 59.95802214,\n", + " 58.16348805, 58.04245063, 57.90274308, 57.30083159, 56.83263729,\n", + " 56.38583329, 56.21488132, 54.92637718, 53.55112648, 53.49733549,\n", + " 52.52402706, 52.24368974, 51.78881617, 51.53118531, 51.53078001,\n", + " 50.31992762, 50.25201964, 50.25046873, 50.15998984, 50.0116581 ,\n", + " 49.12263597, 49.02057167, 48.85325483, 48.25863805, 48.25863113,\n", + " 48.23092502, 47.6914879 , 47.69106748, 46.91813838, 46.80984227,\n", + " 46.54099537, 46.49460005, 46.38377519, 45.74919054, 45.14362726,\n", + " 43.34893354, 42.89542234, 42.56990082, 40.68197812, 40.65606443,\n", + " 40.57275758, 40.55344919, 40.00311123, 39.99948979, 39.92041627,\n", + " 39.46560999, 39.42155258, 39.42143814, 38.48069738, 38.35904841,\n", + " 38.25325257, 38.24794526, 37.87258422, 37.7890604 , 37.60351532,\n", + " 37.31417523, 36.66423966, 36.65767678, 35.80769864, 35.69644793,\n", + " 35.19750295, 35.14576954, 34.84597586, 34.81985762, 34.70737217,\n", + " 34.66603106, 34.0629761 , 33.97781415, 33.9482966 , 33.55533529,\n", + " 33.3250961 , 33.11854042, 31.83432401, 31.77752985, 31.50282439,\n", + " 31.14704595, 29.07941119, 28.90043187, 28.24411354, 28.03892077,\n", + " 27.99731459, 27.91959454, 27.83868677, 27.59108861, 27.58781831,\n", + " 27.58100396, 27.57930011, 27.0823954 , 26.63448873, 26.56212277,\n", + " 26.36259121, 26.15384885, 25.41892118, 25.38515344, 25.38485725,\n", + " 25.0602282 , 25.0205179 , 23.45680721, 22.75317048, 22.62921681,\n", + " 17.7703253 , 17.43545203, 16.12902239, 16.06034984, 15.96832705,\n", + " 15.09389991, 14.16559778, 14.08895833, 13.80140198, 13.75720363,\n", + " 13.74066099, 13.73847204, 13.5886581 , 13.56151861, 13.40972789,\n", + " 11.79912277, 11.67355686, 11.54662554, 11.34291207, 8.47264506,\n", + " 7.66154346, 6.08152632, 5.01951081, 4.23288238, 4.15715707,\n", + " 3.69718446, 3.67883604, 0. ])},\n", + " 'signed_nonnormalized_l2_noavg': {'nclust': array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", + " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", + " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", + " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", + " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", + " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", + " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", + " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", + " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", + " 131, 132, 133]),\n", + " 'R^2': array([0. , 0.29769971, 0.29772861, 0.35020421, 0.37379086,\n", + " 0.37808942, 0.37837583, 0.41950775, 0.43376646, 0.45486461,\n", + " 0.4555194 , 0.46383326, 0.4664704 , 0.46909156, 0.46961695,\n", + " 0.48652124, 0.51353346, 0.51642815, 0.52604058, 0.52746503,\n", + " 0.53278301, 0.56069361, 0.56833279, 0.5696779 , 0.57308603,\n", + " 0.57308762, 0.57790733, 0.58505964, 0.590199 , 0.59020581,\n", + " 0.59020857, 0.5905192 , 0.60367105, 0.60399125, 0.60618246,\n", + " 0.61562304, 0.61563071, 0.61733247, 0.6184437 , 0.63219423,\n", + " 0.63281141, 0.63364527, 0.63856334, 0.65085001, 0.65085701,\n", + " 0.65085714, 0.65409224, 0.65417937, 0.65436525, 0.65550951,\n", + " 0.66200892, 0.67108495, 0.67112311, 0.69077107, 0.71555412,\n", + " 0.71625531, 0.72279277, 0.72294593, 0.72811949, 0.73520151,\n", + " 0.73548693, 0.74274877, 0.74363822, 0.74374725, 0.7440252 ,\n", + " 0.7526914 , 0.75438392, 0.7617355 , 0.76387018, 0.76387585,\n", + " 0.77241458, 0.7748996 , 0.77993407, 0.78184616, 0.78411517,\n", + " 0.78444463, 0.78453539, 0.7845903 , 0.78463125, 0.78843059,\n", + " 0.81527759, 0.82563107, 0.82564808, 0.82580147, 0.83695409,\n", + " 0.83700514, 0.83736523, 0.83886891, 0.84062602, 0.84322859,\n", + " 0.84758509, 0.85037324, 0.85059379, 0.86089213, 0.8658102 ,\n", + " 0.86708493, 0.86728165, 0.88172479, 0.88204387, 0.88300252,\n", + " 0.88317678, 0.88552929, 0.88582948, 0.88986536, 0.92581097,\n", + " 0.93953664, 0.94555133, 0.94555201, 0.9480849 , 0.95280625,\n", + " 0.9530243 , 0.95304132, 0.95637676, 0.95673685, 0.95679493,\n", + " 0.95719519, 0.96053063, 0.96115188, 0.96508362, 0.96578066,\n", + " 0.96837844, 0.96963706, 0.9697521 , 0.97166419, 0.97613028,\n", + " 0.97701247, 0.97701247, 0.97780231, 0.97993699, 0.98230651,\n", + " 0.98284018, 0.99754947, 1. ]),\n", + " 'RMSE': array([74.31571492, 62.27906519, 62.27778365, 59.90583234, 58.80853484,\n", + " 58.60634451, 58.59284773, 56.62117445, 55.92145395, 54.86973323,\n", + " 54.83677007, 54.41649898, 54.28250989, 54.14900451, 54.1222044 ,\n", + " 53.25273297, 51.83309566, 51.67865095, 51.16243876, 51.08549881,\n", + " 50.79722323, 49.25659879, 48.82645466, 48.7503215 , 48.55688792,\n", + " 48.5567976 , 48.28192386, 47.87110997, 47.57372591, 47.5733308 ,\n", + " 47.57317024, 47.5551363 , 46.7852076 , 46.76630442, 46.6367403 ,\n", + " 46.07436235, 46.0739027 , 45.97179585, 45.90499788, 45.07024629,\n", + " 45.03241632, 44.9812545 , 44.67831244, 43.91234974, 43.91190929,\n", + " 43.91190168, 43.70798774, 43.70248264, 43.69073612, 43.61835478,\n", + " 43.20492751, 42.62089215, 42.61841938, 41.3257473 , 39.63514803,\n", + " 39.58626555, 39.12757541, 39.11676486, 38.74982079, 38.24180735,\n", + " 38.22119214, 37.69288579, 37.62766746, 37.61966544, 37.59925718,\n", + " 36.95730287, 36.83062258, 36.27524302, 36.11237713, 36.11194337,\n", + " 35.45299083, 35.25890311, 34.86238284, 34.71059761, 34.52961422,\n", + " 34.50325651, 34.49599189, 34.49159605, 34.48831697, 34.18275772,\n", + " 31.94040765, 31.03238936, 31.03087502, 31.01722242, 30.00790066,\n", + " 30.00320228, 29.97004213, 29.83117353, 29.66807465, 29.42483841,\n", + " 29.01311712, 28.74652098, 28.72532721, 27.71765653, 27.22327735,\n", + " 27.09366585, 27.07360824, 25.55803877, 25.52354066, 25.41961136,\n", + " 25.40067392, 25.14362254, 25.1106324 , 24.66281383, 20.24186461,\n", + " 18.27371258, 17.34100707, 17.34089868, 16.93275067, 16.14443734,\n", + " 16.10709771, 16.10417994, 15.5217125 , 15.45751717, 15.44713679,\n", + " 15.37541898, 14.76422894, 14.64757244, 13.88657959, 13.74727132,\n", + " 13.2151561 , 12.94948737, 12.92493268, 12.50974557, 11.48165148,\n", + " 11.26748154, 11.26748154, 11.0722168 , 10.52637218, 9.88524381,\n", + " 9.7350232 , 3.67883604, 0. ])},\n", + " 'nonl2_avg': {'nclust': array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", + " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", + " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", + " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", + " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", + " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", + " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 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23.18264086,\n", + " 21.33281445, 20.94547762, 20.93650144, 20.86154981, 20.83233582,\n", + " 20.77921291, 20.63515262, 20.62531233, 20.55373787, 20.45390318,\n", + " 20.38172679, 14.27355372, 14.26037854, 14.25088486, 13.26371234,\n", + " 12.95785534, 12.95553413, 9.30639686, 8.86871667, 8.5896423 ,\n", + " 8.58176069, 7.69386046, 6.1221894 , 5.87654955, 4.58257569,\n", + " 2.8131004 , 2.69781309, 0. ])},\n", + " 'baseline': {'nclust': array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", + " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", + " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", + " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", + " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", + " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", + " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", + " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", + " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", + " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", + " 131, 132, 133]),\n", + " 'R^2': array([0. , 0.29769971, 0.30963245, 0.34613173, 0.34907152,\n", + " 0.35200451, 0.39313643, 0.41423458, 0.42679017, 0.43646577,\n", + " 0.43995118, 0.44911071, 0.47612292, 0.4798305 , 0.49808259,\n", + " 0.50340058, 0.50340814, 0.53501257, 0.53983228, 0.54924267,\n", + " 0.54928357, 0.56162818, 0.57758606, 0.58166028, 0.58278572,\n", + " 0.59324131, 0.59505504, 0.59505516, 0.59950241, 0.60277707,\n", + " 0.61151803, 0.61335593, 0.61336319, 0.61867471, 0.62028128,\n", + " 0.6253187 , 0.63528486, 0.63641756, 0.64360668, 0.64360857,\n", + " 0.64360891, 0.68111843, 0.69435915, 0.69893438, 0.70547184,\n", + " 0.71255386, 0.71496105, 0.72403708, 0.72517523, 0.72641774,\n", + " 0.73037125, 0.73151551, 0.73157433, 0.73913046, 0.74082298,\n", + " 0.74092508, 0.74234117, 0.75104707, 0.7510493 , 0.75314042,\n", + " 0.75794345, 0.76311701, 0.76363699, 0.77114173, 0.77367462,\n", + " 0.78877804, 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59.82278831, 57.8930185 , 56.87776547, 56.26489002, 55.78800238,\n", + " 55.61521281, 55.15854811, 53.78923374, 53.59855742, 52.64980757,\n", + " 52.37014356, 52.36974475, 50.67587628, 50.41255812, 49.89442941,\n", + " 49.8921656 , 49.20417745, 48.300295 , 48.06679981, 48.00210038,\n", + " 47.39680664, 47.29101778, 47.29101072, 47.03061015, 46.83794307,\n", + " 46.31973776, 46.21003928, 46.20960539, 45.89109983, 45.79432537,\n", + " 45.48955303, 44.88048664, 44.81073964, 44.36550604, 44.36538835,\n", + " 44.36536717, 41.96578705, 41.08529024, 40.77662202, 40.33147208,\n", + " 39.84363009, 39.67644607, 39.03966008, 38.95907154, 38.870903 ,\n", + " 38.58902063, 38.50705084, 38.50283305, 37.95704084, 37.83370817,\n", + " 37.82625495, 37.72273534, 37.07996169, 37.07979609, 36.9237377 ,\n", + " 36.56276918, 36.16992409, 36.13020407, 35.551994 , 35.35471051,\n", + " 34.15467789, 33.88415199, 33.78928672, 33.71651845, 33.40389997,\n", + " 32.46885505, 32.23085559, 32.1017685 , 31.96087022, 31.61024757,\n", + " 31.39241056, 31.09391921, 31.04724954, 30.14412955, 30.14179107,\n", + " 29.85203695, 29.44628941, 28.51996618, 28.11068349, 28.1073399 ,\n", + " 27.93810291, 27.90738352, 27.87766418, 27.80853352, 27.61800836,\n", + " 27.61120146, 27.33966703, 27.31031643, 26.80842646, 26.72239634,\n", + " 22.75498789, 22.65564379, 22.29130448, 22.28919628, 21.98350373,\n", + " 21.88443674, 21.8338738 , 21.20274489, 20.42159037, 17.68447346,\n", + " 17.52470508, 17.46787241, 17.46658105, 17.46615058, 16.34400705,\n", + " 16.32144982, 16.23090743, 16.21688838, 13.68148228, 13.24362049,\n", + " 13.24134938, 13.19527522, 13.08311583, 12.77293505, 12.65703367,\n", + " 12.30278001, 12.13525488, 11.5835204 , 11.55606363, 8.75595036,\n", + " 7.88768273, 6.61774027, 4.69895738, 4.68453436, 4.15715707,\n", + " 3.69718446, 3.67883604, 0. ])}}" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "method_values_results" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "metrics = [\"R^2\", \"RMSE\"]\n", + "for metric in metrics:\n", + " plt.figure()\n", + " for method, results in method_values_results.items():\n", + " plt.plot(results[\"nclust\"], results[metric], label=method)\n", + " plt.xlabel(\"Number of Clusters\")\n", + " plt.gca().invert_xaxis()\n", + " plt.ylabel(metric)\n", + " plt.title(f\"{metric} vs. Number of Clusters\")\n", + " plt.legend()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "task_results = {}\n", + "task_results['diabetes_regr'] = 'regression'\n", + "for key, values_dict in method_values_results.items():\n", + " if task_results[key] == 'classification':\n", + " metrics = ['AUROC', 'AUPRC', 'R^2', 'F1', 'Accuracy']\n", + " else:\n", + " metrics = ['R^2', 'RMSE']\n", + " # print \"Results for \" + text of key after first underscore\n", + " parts = key.split('_')\n", + " print(f\"Results for dataset {parts[1]} from datasource {parts[0]}.\")\n", + " # create new plot\n", + " if task_results[key] == 'classification':\n", + " height = 15\n", + " else:\n", + " height = 5\n", + " fig, axes = plt.subplots(math.ceil(len(metrics)/2.0), 2, figsize=(10, height))\n", + " axes = axes.flatten()\n", + " plot_count = 0\n", + " for metric in metrics:\n", + " ax = axes[plot_count]\n", + " # plt.figure()\n", + " for method, df in values_dict.items():\n", + " # plt.plot(df['nclust'], df[metric])\n", + " ax.plot(df['nclust'], df[metric])\n", + " ax.legend(list(values_dict.keys()))\n", + " ax.set_xlabel('Number of Clusters')\n", + " ax.set_ylabel('Cluster ' + metric)\n", + " ax.set_title('Cluster ' + metric + ' vs Number of Clusters')\n", + " ax.invert_xaxis()\n", + " plot_count += 1\n", + " plt.tight_layout()\n", + " plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mdi", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/feature_importance/subgroup/legacy/compas-eda-ablate.ipynb b/feature_importance/subgroup/legacy/compas-eda-ablate.ipynb new file mode 100644 index 0000000..4c2fc69 --- /dev/null +++ b/feature_importance/subgroup/legacy/compas-eda-ablate.ipynb @@ -0,0 +1,605 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# import required packages\n", + "from imodels import get_clean_dataset\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n", + "from imodels.tree.rf_plus.rf_plus.rf_plus_models import RandomForestPlusRegressor, RandomForestPlusClassifier\n", + "from imodels.tree.rf_plus.feature_importance.rfplus_explainer import RFPlusMDI\n", + "from sklearn.linear_model import RidgeCV, LogisticRegression\n", + "import shap\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# set seed\n", + "np.random.seed(0)\n", + "# get pre-cleaned compas dataset from imodels\n", + "X, y, feature_names = get_clean_dataset('compas_two_year_clean', data_source='imodels')\n", + "X = pd.DataFrame(X, columns=feature_names)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Proportion of data with 0 priors: 0.3378159429682437\n", + "Proportion of data with 1-3 priors: 0.36876215165262477\n", + "Proportion of data with 4+ priors: 0.29342190537913154\n" + ] + } + ], + "source": [ + "# get proportion of X that has zero priors, 1-3 prior counts, and 4+ prior counts\n", + "bins = [-1, 0, 3, float('inf')]\n", + "labels = [0, 1, 2]\n", + "priors = pd.cut(X['priors_count'], bins=bins, labels=labels)\n", + "zero_priors = sum(priors == 0) / len(priors)\n", + "one_to_three_priors = sum(priors == 1) / len(priors)\n", + "four_plus_priors = sum(priors == 2) / len(priors)\n", + "print(f\"Proportion of data with 0 priors: {zero_priors}\")\n", + "print(f\"Proportion of data with 1-3 priors: {one_to_three_priors}\")\n", + "print(f\"Proportion of data with 4+ priors: {four_plus_priors}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/IPython/core/pylabtools.py:77: DeprecationWarning: backend2gui is deprecated since IPython 8.24, backends are managed in matplotlib and can be externally registered.\n", + " warnings.warn(\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/IPython/core/pylabtools.py:77: DeprecationWarning: backend2gui is deprecated since IPython 8.24, backends are managed in matplotlib and can be externally registered.\n", + " warnings.warn(\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/IPython/core/pylabtools.py:77: DeprecationWarning: backend2gui is deprecated since IPython 8.24, backends are managed in matplotlib and can be externally registered.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot proportion of y for each group of priors\n", + "df = pd.DataFrame({'priors': priors, 'recidivism': y})\n", + "df_prop = df.groupby('priors', observed=False)['recidivism'].mean().reset_index()\n", + "ax = sns.barplot(data=df_prop, x='priors', y='recidivism')\n", + "plt.xlabel('# of Priors')\n", + "plt.ylabel('Proportion of Recidivism')\n", + "plt.title('Proportion of Recidivism for Each Group of Priors')\n", + "ax.set_xticks([0, 1, 2])\n", + "ax.set_xticklabels([\"0\", \"1-3\", \"4+\"])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# split data into train, validation, and test sets\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,\n", + " random_state=2)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 16 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 18 tasks | elapsed: 17.0s\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 49.4s finished\n" + ] + } + ], + "source": [ + "# fit RF model\n", + "rf = RandomForestClassifier(n_estimators=100, random_state=0)\n", + "rf.fit(X_train, y_train)\n", + "\n", + "# fit RF+ model\n", + "rf_plus = RandomForestPlusClassifier(rf_model = rf, prediction_model = LogisticRegression())\n", + "rf_plus.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# get TreeSHAP importances and rankings\n", + "explainer = shap.TreeExplainer(rf)\n", + "shap_values = np.abs(explainer.shap_values(X_train, check_additivity=False))[:,:,1]\n", + "shap_rankings = np.argsort(-shap_values, axis = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# get LMDI+ importances and rankings\n", + "rfplus_explainer = RFPlusMDI(rf_plus)\n", + "lmdi_values = np.abs(rfplus_explainer.explain_linear_partial(np.asarray(X_train), y_train, l2norm=True, njobs = 1))\n", + "lmdi_rankings = rfplus_explainer.get_rankings(lmdi_values)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# sort based highest y to lowest y\n", + "sorted_indices = np.argsort(-y_train)\n", + "sorted_lmdi_values = lmdi_values[sorted_indices]\n", + "sorted_lmdi_rankings = lmdi_rankings[sorted_indices]\n", + "sorted_shap_values = shap_values[sorted_indices]\n", + "sorted_shap_rankings = shap_rankings[sorted_indices]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot heatmap of LMDI+ importances based on true y\n", + "plt.figure(figsize=(10, 6))\n", + "sns.heatmap(sorted_shap_values, cmap='viridis')\n", + "plt.title('TreeSHAP Importances')\n", + "plt.xlabel('Features')\n", + "plt.ylabel('Samples')\n", + "plt.xticks(ticks = np.arange(X_train.shape[1]) + 0.5, labels = X_train.columns, rotation = 90)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot heatmap of LMDI+ importances based on true y\n", + "plt.figure(figsize=(10, 6))\n", + "sns.heatmap(sorted_lmdi_values, cmap='viridis')\n", + "plt.title('LMDI+ Importances')\n", + "plt.xlabel('Features')\n", + "plt.ylabel('Samples')\n", + "plt.xticks(ticks = np.arange(X_train.shape[1]) + 0.5, labels = X_train.columns, rotation = 90)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "lmdi_sub_imp = pd.concat([X_train[\"priors_count\"].reset_index(drop=True), pd.DataFrame(lmdi_rankings)], axis=1)\n", + "# define subgroup bins and labels\n", + "bins = [-1, 0, 3, float('inf')]\n", + "labels = [0, 1, 2]\n", + "lmdi_sub_imp[\"priors_count\"] = pd.cut(X['priors_count'], bins=bins, labels=labels)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "shap_sub_imp = pd.concat([X_train[\"priors_count\"].reset_index(drop=True), pd.DataFrame(shap_rankings)], axis=1)\n", + "# define subgroup bins and labels\n", + "bins = [-1, 0, 3, float('inf')]\n", + "labels = [0, 1, 2]\n", + "shap_sub_imp[\"priors_count\"] = pd.cut(X['priors_count'], bins=bins, labels=labels)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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agepriors_countdays_b_screening_arrestc_jail_timejuv_fel_countjuv_other_countjuv_misd_countc_charge_degree:Fc_charge_degree:Mrace:African-Americanrace:Asianrace:Caucasianrace:Hispanicrace:Native_Americanrace:Otherage_cat:25_-_45age_cat:Greater_than_45age_cat:Less_than_25sex:Femalesex:Male
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" + ], + "text/plain": [ + " age priors_count days_b_screening_arrest c_jail_time juv_fel_count \\\n", + "0 1.0 2.0 3.0 4.0 9.0 \n", + "1 1.0 2.0 3.0 4.0 8.0 \n", + "2 1.0 2.0 3.0 4.0 8.0 \n", + "\n", + " juv_other_count juv_misd_count c_charge_degree:F c_charge_degree:M \\\n", + "0 11.0 8.0 10.0 13.0 \n", + "1 10.0 9.0 12.0 13.0 \n", + "2 9.0 10.0 12.0 13.0 \n", + "\n", + " race:African-American race:Asian race:Caucasian race:Hispanic \\\n", + "0 15.0 18.0 20.0 19.0 \n", + "1 15.0 18.0 20.0 19.0 \n", + "2 15.0 18.0 19.0 20.0 \n", + "\n", + " race:Native_American race:Other age_cat:25_-_45 age_cat:Greater_than_45 \\\n", + "0 16.0 14.0 12.0 7.0 \n", + "1 16.0 14.0 11.0 6.0 \n", + "2 16.0 14.0 11.0 7.0 \n", + "\n", + " age_cat:Less_than_25 sex:Female sex:Male \n", + "0 5.0 6.0 17.0 \n", + "1 5.0 7.0 17.0 \n", + "2 5.0 6.0 17.0 " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lmdi_mean_rankings = lmdi_sub_imp.groupby('priors_count', observed=False).mean().reset_index()\n", + "# rank features for each row\n", + "lmdi_ranked_mean_rankings = lmdi_mean_rankings.drop(columns=[\"priors_count\"]).rank(axis=1)\n", + "lmdi_ranked_mean_rankings.columns = X.columns\n", + "lmdi_ranked_mean_rankings" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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agepriors_countdays_b_screening_arrestc_jail_timejuv_fel_countjuv_other_countjuv_misd_countc_charge_degree:Fc_charge_degree:Mrace:African-Americanrace:Asianrace:Caucasianrace:Hispanicrace:Native_Americanrace:Otherage_cat:25_-_45age_cat:Greater_than_45age_cat:Less_than_25sex:Femalesex:Male
01.02.03.04.07.012.016.018.017.019.015.014.011.09.06.05.08.010.013.020.0
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21.02.03.04.05.012.014.017.018.019.016.015.011.010.09.06.07.08.013.020.0
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" + ], + "text/plain": [ + " age priors_count days_b_screening_arrest c_jail_time juv_fel_count \\\n", + "0 1.0 2.0 3.0 4.0 7.0 \n", + "1 1.0 2.0 3.0 4.0 5.0 \n", + "2 1.0 2.0 3.0 4.0 5.0 \n", + "\n", + " juv_other_count juv_misd_count c_charge_degree:F c_charge_degree:M \\\n", + "0 12.0 16.0 18.0 17.0 \n", + "1 11.0 15.0 16.0 17.0 \n", + "2 12.0 14.0 17.0 18.0 \n", + "\n", + " race:African-American race:Asian race:Caucasian race:Hispanic \\\n", + "0 19.0 15.0 14.0 11.0 \n", + "1 19.0 18.0 14.0 12.0 \n", + "2 19.0 16.0 15.0 11.0 \n", + "\n", + " race:Native_American race:Other age_cat:25_-_45 age_cat:Greater_than_45 \\\n", + "0 9.0 6.0 5.0 8.0 \n", + "1 10.0 7.0 6.0 8.0 \n", + "2 10.0 9.0 6.0 7.0 \n", + "\n", + " age_cat:Less_than_25 sex:Female sex:Male \n", + "0 10.0 13.0 20.0 \n", + "1 9.0 13.0 20.0 \n", + "2 8.0 13.0 20.0 " + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "shap_mean_rankings = shap_sub_imp.groupby('priors_count', observed=False).mean().reset_index()\n", + "# rank features for each row\n", + "shap_ranked_mean_rankings = shap_mean_rankings.drop(columns=[\"priors_count\"]).rank(axis=1)\n", + "shap_ranked_mean_rankings.columns = X.columns\n", + "shap_ranked_mean_rankings" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mdi", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/feature_importance/subgroup/legacy/compas.ipynb b/feature_importance/subgroup/legacy/compas.ipynb new file mode 100644 index 0000000..94984a8 --- /dev/null +++ b/feature_importance/subgroup/legacy/compas.ipynb @@ -0,0 +1,849 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# import required packages\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mimodels\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m get_clean_dataset\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n", + "File \u001b[0;32m~/research/imodels/imodels/__init__.py:8\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Python `imodels` package for interpretable models compatible with scikit-learn.\u001b[39;00m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m# Github repo available [here](https://github.com/csinva/imodels)\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01malgebraic\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mslim\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m SLIMRegressor, SLIMClassifier\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01malgebraic\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtree_gam\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m TreeGAMClassifier, TreeGAMRegressor\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m 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3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbase\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m BaseEstimator\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlinear_model\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ElasticNetCV, LinearRegression, RidgeCV\n", + "File \u001b[0;32m/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/pandas/__init__.py:49\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;66;03m# let init-time option registration happen\u001b[39;00m\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m 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\u001b[0;32m:945\u001b[0m, in \u001b[0;36m_find_spec\u001b[0;34m(name, path, target)\u001b[0m\n", + "File \u001b[0;32m:1439\u001b[0m, in \u001b[0;36mfind_spec\u001b[0;34m(cls, fullname, path, target)\u001b[0m\n", + "File \u001b[0;32m:1411\u001b[0m, in \u001b[0;36m_get_spec\u001b[0;34m(cls, fullname, path, target)\u001b[0m\n", + "File \u001b[0;32m:1577\u001b[0m, in \u001b[0;36mfind_spec\u001b[0;34m(self, fullname, target)\u001b[0m\n", + "File \u001b[0;32m:161\u001b[0m, in \u001b[0;36m_path_isfile\u001b[0;34m(path)\u001b[0m\n", + "File \u001b[0;32m:153\u001b[0m, in \u001b[0;36m_path_is_mode_type\u001b[0;34m(path, mode)\u001b[0m\n", + "File \u001b[0;32m:147\u001b[0m, in \u001b[0;36m_path_stat\u001b[0;34m(path)\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "# import required packages\n", + "from imodels import get_clean_dataset\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import r2_score\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from imodels.tree.rf_plus.rf_plus.rf_plus_models import RandomForestPlusClassifier\n", + "from imodels.tree.rf_plus.feature_importance.rfplus_explainer import AloRFPlusMDI, RFPlusLime\n", + "# from subgroup_detection import detect_subgroups, compute_rbo_matrix\n", + "import warnings\n", + "warnings.filterwarnings('ignore', category=DeprecationWarning)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# get pre-cleaned compas dataset from imodels\n", + "X, y, feature_names = get_clean_dataset('compas_two_year_clean', data_source='imodels')\n", + "X = pd.DataFrame(X, columns=feature_names)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# the propublica study narrowed the dataset to only African-American and\n", + "# Caucasian defendants, and doing so keeps the vast majority of the data,\n", + "# so we will do the same.\n", + "y = y[(X['race:African-American'] == 1) | (X['race:Caucasian'] == 1)]\n", + "X = X[(X['race:African-American'] == 1) | (X['race:Caucasian'] == 1)]\n", + "\n", + "# now that we have narrowed the dataset, we should remove the one-hot encodings\n", + "# of variables that are consistently zero, such as the other ethnicities.\n", + "# we also drop age because the binned 'age category' is preferred here.\n", + "X = X.drop([\"race:Asian\", \"race:Hispanic\", \"race:Native_American\",\n", + " \"race:Other\", \"age\"], axis = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " priors_count days_b_screening_arrest c_jail_time juv_fel_count \\\n", + "1 0.0 -1.0 10.0 0.0 \n", + "2 4.0 -1.0 1.0 0.0 \n", + "4 14.0 -1.0 6.0 0.0 \n", + "6 0.0 -1.0 2.0 0.0 \n", + "7 0.0 -1.0 1.0 0.0 \n", + "\n", + " juv_other_count juv_misd_count c_charge_degree:F c_charge_degree:M \\\n", + "1 0.0 0.0 1.0 0.0 \n", + "2 1.0 0.0 1.0 0.0 \n", + "4 0.0 0.0 1.0 0.0 \n", + "6 0.0 0.0 0.0 1.0 \n", + "7 0.0 0.0 1.0 0.0 \n", + "\n", + " race:African-American race:Caucasian age_cat:25_-_45 \\\n", + "1 1.0 0.0 1.0 \n", + "2 1.0 0.0 0.0 \n", + "4 0.0 1.0 1.0 \n", + "6 0.0 1.0 1.0 \n", + "7 0.0 1.0 1.0 \n", + "\n", + " age_cat:Greater_than_45 age_cat:Less_than_25 sex:Female sex:Male \n", + "1 0.0 0.0 0.0 1.0 \n", + "2 0.0 1.0 0.0 1.0 \n", + "4 0.0 0.0 0.0 1.0 \n", + "6 0.0 0.0 1.0 0.0 \n", + "7 0.0 0.0 0.0 1.0 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# split data into training and testing sets\n", + "# we won't actually use the test set here though, since 'discovery' would be\n", + "# a post-hoc analysis in real life\n", + "# proportion of training data is small so rf+ can fit without taking hours\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,\n", + " random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total Proportion of Recidivism: 0.5015157256536567\n", + "Proportion of Recidivism in Training Data: 0.5064970221981592\n", + "Total Proportion of African-American Defendants: 0.6015536\n", + "Proportion of African-American Defendants in Training Data: 0.5979968\n", + "Total Proportion of Caucasian Defendants: 0.39844638\n", + "Proportion of Caucasian Defendants in Training Data: 0.40200326\n", + "Total Proportion of Male Defendants: 0.80466086\n", + "Proportion of Male Defendants in Training Data: 0.80915\n", + "Total Proportion of Female Defendants: 0.19533914\n", + "Proportion of Female Defendants in Training Data: 0.19085003\n" + ] + } + ], + "source": [ + "print(\"Total Proportion of Recidivism:\", y.mean())\n", + "print(\"Proportion of Recidivism in Training Data:\", y_train.mean())\n", + "print(\"Total Proportion of African-American Defendants:\",\n", + " X[\"race:African-American\"].mean())\n", + "print(\"Proportion of African-American Defendants in Training Data:\",\n", + " X_train[\"race:African-American\"].mean())\n", + "print(\"Total Proportion of Caucasian Defendants:\",\n", + " X[\"race:Caucasian\"].mean())\n", + "print(\"Proportion of Caucasian Defendants in Training Data:\",\n", + " X_train[\"race:Caucasian\"].mean())\n", + "print(\"Total Proportion of Male Defendants:\",\n", + " X[\"sex:Male\"].mean())\n", + "print(\"Proportion of Male Defendants in Training Data:\",\n", + " X_train[\"sex:Male\"].mean())\n", + "print(\"Total Proportion of Female Defendants:\",\n", + " X[\"sex:Female\"].mean())\n", + "print(\"Proportion of Female Defendants in Training Data:\",\n", + " X_train[\"sex:Female\"].mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 5.6min\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 16.6min finished\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RF+ Test Set Accuracy: 0.6167929292929293\n", + "RF+ Test Set # Misclassified: 607\n", + "RF+ Test Set # of Points: 1584\n" + ] + } + ], + "source": [ + "# fit RF+ model\n", + "rf = RandomForestClassifier(n_estimators=100, random_state=1)\n", + "rf_plus = RandomForestPlusClassifier(rf)\n", + "rf_plus.fit(X_train, y_train)\n", + "y_pred = rf_plus.predict(X_test)\n", + "\n", + "# compute accuracy on the test set\n", + "accuracy = np.mean(y_pred == y_test)\n", + "misclassified = np.sum(y_pred != y_test)\n", + "\n", + "print(f'RF+ Test Set Accuracy: {accuracy}')\n", + "print(f'RF+ Test Set # Misclassified: {misclassified}')\n", + "print(f'RF+ Test Set # of Points: {y_test.shape[0]}')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 11.0min\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 31.7min finished\n" + ] + } + ], + "source": [ + "# fit RF+ model\n", + "rf = RandomForestClassifier(n_estimators=100, random_state=1)\n", + "rf.fit(X, y)\n", + "rf_plus = RandomForestPlusClassifier(rf)\n", + "rf_plus.fit(X, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# get feature importances\n", + "mdi_explainer = AloRFPlusMDI(rf_plus, evaluate_on='oob')\n", + "mdi, partial_preds = mdi_explainer.explain(np.asarray(X), y)\n", + "mdi_rankings = mdi_explainer.get_rankings(mdi)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['priors_count', 'days_b_screening_arrest', 'c_jail_time',\n", + " 'juv_fel_count', 'juv_other_count', 'juv_misd_count',\n", + " 'c_charge_degree:F', 'c_charge_degree:M', 'race:African-American',\n", + " 'race:Caucasian', 'age_cat:25_-_45', 'age_cat:Greater_than_45',\n", + " 'age_cat:Less_than_25', 'sex:Female', 'sex:Male'],\n", + " dtype='object')" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mdi_copy = pd.DataFrame(mdi, columns=X.columns).copy()\n", + "num_clusters = 5\n", + "clusters = detect_subgroups(mdi_copy, mdi_rankings, num_clusters)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "X['cluster'] = clusters\n", + "\n", + "# split each cluster into its own set\n", + "cluster1X = X[X['cluster']==1]\n", + "cluster2X = X[X['cluster']==2]\n", + "cluster3X = X[X['cluster']==3]\n", + "cluster4X = X[X['cluster']==4]\n", + "cluster5X = X[X['cluster']==5]\n", + "cluster1y = y[X['cluster']==1]\n", + "cluster2y = y[X['cluster']==2]\n", + "cluster3y = y[X['cluster']==3]\n", + "cluster4y = y[X['cluster']==4]\n", + "cluster5y = y[X['cluster']==5]\n", + "\n", + "# remove cluster column from cluster X's\n", + "cluster1X = cluster1X.drop(columns='cluster')\n", + "cluster2X = cluster2X.drop(columns='cluster')\n", + "cluster3X = cluster3X.drop(columns='cluster')\n", + "cluster4X = cluster4X.drop(columns='cluster')\n", + "cluster5X = cluster5X.drop(columns='cluster')\n", + "X = X.drop(columns='cluster')\n", + "\n", + "# split each cluster into train/test\n", + "cluster1_trainX, cluster1_testX, cluster1_trainy, cluster1_testy = \\\n", + " train_test_split(cluster1X, cluster1y, test_size=0.3, random_state=0)\n", + "cluster2_trainX, cluster2_testX, cluster2_trainy, cluster2_testy = \\\n", + " train_test_split(cluster2X, cluster2y, test_size=0.3, random_state=1)\n", + "cluster3_trainX, cluster3_testX, cluster3_trainy, cluster3_testy = \\\n", + " train_test_split(cluster3X, cluster3y, test_size=0.3, random_state=1)\n", + "cluster4_trainX, cluster4_testX, cluster4_trainy, cluster4_testy = \\\n", + " train_test_split(cluster4X, cluster4y, test_size=0.3, random_state=1)\n", + "cluster5_trainX, cluster5_testX, cluster5_trainy, cluster5_testy = \\\n", + " train_test_split(cluster5X, cluster5y, test_size=0.3, random_state=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 22.9s\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 42.6s finished\n", + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 4.7s\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 11.4s finished\n", + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 100 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 10.1s\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 101 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 26.8s finished\n", + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 97 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 7.6s\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 100 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 89 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 97 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 25.6s finished\n", + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 17.8s\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 52.9s finished\n" + ] + } + ], + "source": [ + "# fit RF+ on each training set, predict test\n", + "rf1 = RandomForestClassifier(n_estimators=100, random_state=0)\n", + "rf_plus1 = RandomForestPlusClassifier(rf1)\n", + "rf_plus1.fit(cluster1_trainX, cluster1_trainy)\n", + "\n", + "rf2 = RandomForestClassifier(n_estimators=100, random_state=0)\n", + "rf_plus2 = RandomForestPlusClassifier(rf2)\n", + "rf_plus2.fit(cluster2_trainX, cluster2_trainy)\n", + "\n", + "rf3 = RandomForestClassifier(n_estimators=100, random_state=0)\n", + "rf_plus3 = RandomForestPlusClassifier(rf3)\n", + "rf_plus3.fit(cluster3_trainX, cluster3_trainy)\n", + "\n", + "rf4 = RandomForestClassifier(n_estimators=100, random_state=0)\n", + "rf_plus4 = RandomForestPlusClassifier(rf4)\n", + "rf_plus4.fit(cluster4_trainX, cluster4_trainy)\n", + "\n", + "rf5 = RandomForestClassifier(n_estimators=100, random_state=0)\n", + "rf_plus5 = RandomForestPlusClassifier(rf5)\n", + "rf_plus5.fit(cluster5_trainX, cluster5_trainy)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---------------------------------------------\n", + "RF+ Cluster #1 Test Set # of Points: 680\n", + "RF+ Cluster #1 Test Set # Misclassified: 2\n", + "---------------------------------------------\n", + "RF+ Cluster #2 Test Set # of Points: 274\n", + "RF+ Cluster #2 Test Set # Misclassified: 167\n", + "---------------------------------------------\n", + "RF+ Cluster #3 Test Set # of Points: 162\n", + "RF+ Cluster #3 Test Set # Misclassified: 11\n", + "---------------------------------------------\n", + "RF+ Cluster #4 Test Set # of Points: 126\n", + "RF+ Cluster #4 Test Set # Misclassified: 5\n", + "---------------------------------------------\n", + "RF+ Cluster #5 Test Set # of Points: 345\n", + "RF+ Cluster #5 Test Set # Misclassified: 35\n", + "---------------------------------------------\n" + ] + } + ], + "source": [ + "print(\"---------------------------------------------\")\n", + "y_pred = rf_plus1.predict(cluster1_testX)\n", + "mis1 = np.sum(cluster1_testy != y_pred)\n", + "print(f'RF+ Cluster #1 Test Set # of Points: {cluster1_testy.shape[0]}')\n", + "print(f'RF+ Cluster #1 Test Set # Misclassified: {mis1}')\n", + "print(\"---------------------------------------------\")\n", + "y_pred = rf_plus1.predict(cluster2_testX)\n", + "mis2 = np.sum(cluster2_testy != y_pred)\n", + "print(f'RF+ Cluster #2 Test Set # of Points: {cluster2_testy.shape[0]}')\n", + "print(f'RF+ Cluster #2 Test Set # Misclassified: {mis2}')\n", + "print(\"---------------------------------------------\")\n", + "y_pred = rf_plus3.predict(cluster3_testX)\n", + "mis3 = np.sum(cluster3_testy != y_pred)\n", + "print(f'RF+ Cluster #3 Test Set # of Points: {cluster3_testy.shape[0]}')\n", + "print(f'RF+ Cluster #3 Test Set # Misclassified: {mis3}')\n", + "print(\"---------------------------------------------\")\n", + "y_pred = rf_plus4.predict(cluster4_testX)\n", + "mis4 = np.sum(cluster4_testy != y_pred)\n", + "print(f'RF+ Cluster #4 Test Set # of Points: {cluster4_testy.shape[0]}')\n", + "print(f'RF+ Cluster #4 Test Set # Misclassified: {mis4}')\n", + "print(\"---------------------------------------------\")\n", + "y_pred = rf_plus5.predict(cluster5_testX)\n", + "mis5 = np.sum(cluster5_testy != y_pred)\n", + "print(f'RF+ Cluster #5 Test Set # of Points: {cluster5_testy.shape[0]}')\n", + "print(f'RF+ Cluster #5 Test Set # Misclassified: {mis5}')\n", + "print(\"---------------------------------------------\")" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---------------------------------------------\n", + "Total # of Observations Predicted by Global Model: 1584\n", + "Total # of Observations Predicted by Cluster Models: 1587\n", + "---------------------------------------------\n", + "Difference in # Misclassified (Global - Sum of Clusters): 387\n", + "Percent Improvement Over Global Model: 63.76%\n", + "---------------------------------------------\n" + ] + } + ], + "source": [ + "print(\"---------------------------------------------\")\n", + "print(\"Total # of Observations Predicted by Global Model:\", X_test.shape[0])\n", + "print(\"Total # of Observations Predicted by Cluster Models:\",\n", + " cluster1_testX.shape[0] + cluster2_testX.shape[0] + cluster3_testX.shape[0] + cluster4_testX.shape[0] + cluster5_testX.shape[0])\n", + "print(\"---------------------------------------------\")\n", + "print(\"Difference in # Misclassified (Global - Sum of Clusters):\", round(misclassified - (mis1 + mis2 + mis3 + mis4 + mis5), 2))\n", + "print(f\"Percent Improvement Over Global Model: {round(100*(misclassified - (mis1 + mis2 + mis3 + mis4 + mis5))/misclassified, 2)}%\")\n", + "print(\"---------------------------------------------\")" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "import shap\n", + "explainer = shap.TreeExplainer(rf)\n", + "shap_values = np.abs(explainer.shap_values(X, check_additivity=False))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "shap_rankings = mdi_explainer.get_rankings(shap_values)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "shap_copy = pd.DataFrame(shap_values, columns=X.columns).copy()\n", + "num_clusters = 5\n", + "clusters = detect_subgroups(shap_copy, shap_rankings, num_clusters)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# split each cluster into its own set\n", + "cluster1X = X[X['cluster']==1]\n", + "cluster2X = X[X['cluster']==2]\n", + "cluster3X = X[X['cluster']==3]\n", + "cluster4X = X[X['cluster']==4]\n", + "cluster5X = X[X['cluster']==5]\n", + "# cluster6X = X[X['cluster']==6]\n", + "# cluster7X = X[X['cluster']==7]\n", + "# cluster8X = X[X['cluster']==8]\n", + "cluster1y = y[X['cluster']==1]\n", + "cluster2y = y[X['cluster']==2]\n", + "cluster3y = y[X['cluster']==3]\n", + "cluster4y = y[X['cluster']==4]\n", + "cluster5y = y[X['cluster']==5]\n", + "# cluster6y = y[X['cluster']==6]\n", + "# cluster7y = y[X['cluster']==7]\n", + "# cluster8y = y[X['cluster']==8]\n", + "\n", + "# remove cluster column from cluster X's\n", + "cluster1X = cluster1X.drop(columns='cluster')\n", + "cluster2X = cluster2X.drop(columns='cluster')\n", + "cluster3X = cluster3X.drop(columns='cluster')\n", + "cluster4X = cluster4X.drop(columns='cluster')\n", + "cluster5X = cluster5X.drop(columns='cluster')\n", + "# cluster6X = cluster6X.drop(columns='cluster')\n", + "# cluster7X = cluster7X.drop(columns='cluster')\n", + "# cluster8X = cluster8X.drop(columns='cluster')\n", + "\n", + "# split each cluster into train/test\n", + "cluster1_trainX, cluster1_testX, cluster1_trainy, cluster1_testy = \\\n", + " train_test_split(cluster1X, cluster1y, test_size=0.3, random_state=0)\n", + "cluster2_trainX, cluster2_testX, cluster2_trainy, cluster2_testy = \\\n", + " train_test_split(cluster2X, cluster2y, test_size=0.3, random_state=1)\n", + "cluster3_trainX, cluster3_testX, cluster3_trainy, cluster3_testy = \\\n", + " train_test_split(cluster3X, cluster3y, test_size=0.3, random_state=0)\n", + "cluster4_trainX, cluster4_testX, cluster4_trainy, cluster4_testy = \\\n", + " train_test_split(cluster4X, cluster4y, test_size=0.3, random_state=0)\n", + "cluster5_trainX, cluster5_testX, cluster5_trainy, cluster5_testy = \\\n", + " train_test_split(cluster5X, cluster5y, test_size=0.3, random_state=0)\n", + "# cluster6_trainX, cluster6_testX, cluster6_trainy, cluster6_testy = \\\n", + "# train_test_split(cluster6X, cluster6y, test_size=0.3, random_state=1)\n", + "# cluster7_trainX, cluster7_testX, cluster7_trainy, cluster7_testy = \\\n", + "# train_test_split(cluster7X, cluster7y, test_size=0.3, random_state=0)\n", + "# cluster8_trainX, cluster8_testX, cluster8_trainy, cluster8_testy = \\\n", + "# train_test_split(cluster8X, cluster8y, test_size=0.3, random_state=0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# fit RF+ on each training set, predict test\n", + "rf1 = RandomForestRegressor(n_estimators=100, random_state=0)\n", + "rf_plus1 = RandomForestPlusRegressor(rf1)\n", + "rf_plus1.fit(cluster1_trainX, cluster1_trainy)\n", + "\n", + "rf2 = RandomForestRegressor(n_estimators=100, random_state=0)\n", + "rf_plus2 = RandomForestPlusRegressor(rf2)\n", + "rf_plus2.fit(cluster2_trainX, cluster2_trainy)\n", + "\n", + "rf3 = RandomForestRegressor(n_estimators=100, random_state=0)\n", + "rf_plus3 = RandomForestPlusRegressor(rf3)\n", + "rf_plus3.fit(cluster3_trainX, cluster3_trainy)\n", + "\n", + "rf4 = RandomForestRegressor(n_estimators=100, random_state=0)\n", + "rf_plus4 = RandomForestPlusRegressor(rf4)\n", + "rf_plus4.fit(cluster4_trainX, cluster4_trainy)\n", + "\n", + "rf5 = RandomForestRegressor(n_estimators=100, random_state=0)\n", + "rf_plus5 = RandomForestPlusRegressor(rf5)\n", + "rf_plus5.fit(cluster5_trainX, cluster5_trainy)\n", + "\n", + "# rf6 = RandomForestRegressor(n_estimators=100, random_state=0)\n", + "# rf_plus6 = RandomForestPlusRegressor(rf6)\n", + "# rf_plus6.fit(cluster6_trainX, cluster6_trainy)\n", + "\n", + "# rf7 = RandomForestRegressor(n_estimators=100, random_state=0)\n", + "# rf_plus7 = RandomForestPlusRegressor(rf7)\n", + "# rf_plus7.fit(cluster7_trainX, cluster7_trainy)\n", + "\n", + "# rf8 = RandomForestRegressor(n_estimators=100, random_state=0)\n", + "# rf_plus8 = RandomForestPlusRegressor(rf8)\n", + "# rf_plus8.fit(cluster8_trainX, cluster8_trainy)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# compute r^2 on the test set\n", + "print(\"---------------------------------------------\")\n", + "y_pred = rf_plus1.predict(cluster1_testX)\n", + "r2 = r2_score(cluster1_testy, y_pred)\n", + "tse1 = np.sum((cluster1_testy - y_pred)**2)\n", + "print(f'RF+ Cluster #1 Test Set R^2: {r2}')\n", + "print(f'RF+ Cluster #1 Test Set TSE: {tse1}')\n", + "print(\"---------------------------------------------\")\n", + "y_pred = rf_plus2.predict(cluster2_testX)\n", + "r2 = r2_score(cluster2_testy, y_pred)\n", + "tse2 = np.sum((cluster2_testy - y_pred)**2)\n", + "print(f'RF+ Cluster #2 Test Set R^2: {r2}')\n", + "print(f'RF+ Cluster #2 Test Set TSE: {tse2}')\n", + "print(\"---------------------------------------------\")\n", + "y_pred = rf_plus3.predict(cluster3_testX)\n", + "r2 = r2_score(cluster3_testy, y_pred)\n", + "tse3 = np.sum((cluster3_testy - y_pred)**2)\n", + "print(f'RF+ Cluster #3 Test Set R^2: {r2}')\n", + "print(f'RF+ Cluster #3 Test Set TSE: {tse3}')\n", + "print(\"---------------------------------------------\")\n", + "y_pred = rf_plus4.predict(cluster4_testX)\n", + "r2 = r2_score(cluster4_testy, y_pred)\n", + "tse4 = np.sum((cluster4_testy - y_pred)**2)\n", + "print(f'RF+ Cluster #4 Test Set R^2: {r2}')\n", + "print(f'RF+ Cluster #4 Test Set TSE: {tse4}')\n", + "print(\"---------------------------------------------\")\n", + "y_pred = rf_plus5.predict(cluster5_testX)\n", + "r2 = r2_score(cluster5_testy, y_pred)\n", + "tse5 = np.sum((cluster5_testy - y_pred)**2)\n", + "print(f'RF+ Cluster #5 Test Set R^2: {r2}')\n", + "print(f'RF+ Cluster #5 Test Set TSE: {tse5}')\n", + "print(\"---------------------------------------------\")\n", + "# y_pred = rf_plus6.predict(cluster6_testX)\n", + "# r2 = r2_score(cluster6_testy, y_pred)\n", + "# tse6 = np.sum((cluster6_testy - y_pred)**2)\n", + "# print(f'RF+ Cluster #6 Test Set R^2: {r2}')\n", + "# print(f'RF+ Cluster #6 Test Set TSE: {tse6}')\n", + "# print(\"---------------------------------------------\")\n", + "# y_pred = rf_plus7.predict(cluster7_testX)\n", + "# r2 = r2_score(cluster7_testy, y_pred)\n", + "# tse7 = np.sum((cluster7_testy - y_pred)**2)\n", + "# print(f'RF+ Cluster #7 Test Set R^2: {r2}')\n", + "# print(f'RF+ Cluster #7 Test Set TSE: {tse7}')\n", + "# print(\"---------------------------------------------\")\n", + "# y_pred = rf_plus8.predict(cluster8_testX)\n", + "# r2 = r2_score(cluster8_testy, y_pred)\n", + "# tse8 = np.sum((cluster8_testy - y_pred)**2)\n", + "# print(f'RF+ Cluster #8 Test Set R^2: {r2}')\n", + "# print(f'RF+ Cluster #8 Test Set TSE: {tse8}')\n", + "# print(\"---------------------------------------------\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"---------------------------------------------\")\n", + "print(\"Total # of Observations Predicted by Global Model:\", X_test.shape[0])\n", + "print(\"Total # of Observations Predicted by Cluster Models:\",\n", + " cluster1_testX.shape[0] + cluster2_testX.shape[0] + \\\n", + " cluster3_testX.shape[0] + cluster4_testX.shape[0] + \\\n", + " cluster5_testX.shape[0])\n", + "print(\"---------------------------------------------\")\n", + "print(\"Difference in TSE (Global - Sum of Clusters):\", round(tse - (tse1 + tse2 + tse3 + tse4 + tse5), 2))\n", + "print(f\"Percent Improvement Over Global Model: {round(100*(tse - (tse1 + tse2 + tse3 + tse4 + tse5))/tse, 2)}%\")\n", + "print(\"---------------------------------------------\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mdi", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/feature_importance/subgroup/legacy/compas.py b/feature_importance/subgroup/legacy/compas.py new file mode 100644 index 0000000..ed5ac13 --- /dev/null +++ b/feature_importance/subgroup/legacy/compas.py @@ -0,0 +1,876 @@ +# import required packages +from imodels import get_clean_dataset +import numpy as np +import pandas as pd +from sklearn.model_selection import train_test_split +from sklearn.metrics import roc_auc_score, average_precision_score, f1_score +from sklearn.ensemble import RandomForestClassifier +from sklearn.linear_model import LogisticRegression +from imodels.tree.rf_plus.rf_plus.rf_plus_models import RandomForestPlusClassifier +from imodels.tree.rf_plus.feature_importance.rfplus_explainer import RFPlusMDI +from subgroup_detection import * +import warnings +import shap +warnings.filterwarnings('ignore', category=DeprecationWarning) + +print("line 16") + +def load_data(): + print("began load_data") + # get pre-cleaned compas dataset from imodels + X, y, feature_names = get_clean_dataset('compas_two_year_clean', data_source='imodels') + X = pd.DataFrame(X, columns=feature_names) + + # the propublica study narrowed the dataset to only African-American and + # Caucasian defendants, and doing so keeps the vast majority of the data, + # so we will do the same. + y = y[(X['race:African-American'] == 1) | (X['race:Caucasian'] == 1)] + X = X[(X['race:African-American'] == 1) | (X['race:Caucasian'] == 1)] + + # now that we have narrowed the dataset, we should remove the one-hot encodings + # of variables that are consistently zero, such as the other ethnicities. + # we also drop age because the binned 'age category' is preferred here. + X = X.drop(["race:Asian", "race:Hispanic", "race:Native_American", + "race:Other", "age"], axis = 1) + + # we dont want y as a pandas series + y = np.asarray(y) + print("data loaded") + return X, y + +def split_data(X, y, random_state): + print("began split_data") + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, + random_state=random_state) + print("data split") + return X_train, X_test, y_train, y_test + +def train_models(X_train, y_train): + print("began train_models") + log = LogisticRegression(random_state=0, max_iter=1000) + log.fit(X_train, y_train) + rf = RandomForestClassifier(n_estimators=100, random_state=0) + rf.fit(X_train, y_train) + rf_plus = RandomForestPlusClassifier(rf) + rf_plus.fit(X_train, y_train) + print("models trained") + return log, rf, rf_plus + +def lmdi_plus(X_train, y_train, X_test, y_test, log, rf, rf_plus): + print("began lmdi_plus") + # get feature importances + mdi_explainer = RFPlusMDI(rf_plus, prediction_model = LogisticRegression()) + mdi, partial_preds = mdi_explainer.explain(np.asarray(X_train), y_train) + mdi_rankings = mdi_explainer.get_rankings(mdi) + + # get rbo distance matrix + rbo_train = compute_rbo_matrix(mdi_rankings, form = 'distance') + + mdi_copy = pd.DataFrame(mdi, columns=X_train.columns).copy() + num_clusters = 4 + clusters = assign_training_clusters(mdi_copy, rbo_train, num_clusters) + + # get mdi rankings assignments for test points + mdi_test, partial_preds_test = mdi_explainer.explain(np.asarray(X_test)) + mdi_test_rankings = mdi_explainer.get_rankings(mdi_test) + + test_clust = assign_testing_clusters(method = "centroid", median_approx = False, + rbo_distance_matrix = rbo_train, + lfi_train_ranking = mdi_rankings, + lfi_test_ranking = mdi_test_rankings, + clusters = clusters) + print("testing clusters assigned") + + cluster1_trainX = X_train[clusters == 1] + cluster2_trainX = X_train[clusters == 2] + cluster3_trainX = X_train[clusters == 3] + cluster4_trainX = X_train[clusters == 4] + + cluster1_trainy = y_train[clusters == 1] + cluster2_trainy = y_train[clusters == 2] + cluster3_trainy = y_train[clusters == 3] + cluster4_trainy = y_train[clusters == 4] + + cluster1_testX = X_test[test_clust == 1] + cluster2_testX = X_test[test_clust == 2] + cluster3_testX = X_test[test_clust == 3] + cluster4_testX = X_test[test_clust == 4] + + cluster1_testy = y_test[test_clust == 1] + cluster2_testy = y_test[test_clust == 2] + cluster3_testy = y_test[test_clust == 3] + cluster4_testy = y_test[test_clust == 4] + + # fit RF+ on each training set, predict test + rf1 = RandomForestClassifier(n_estimators=100, random_state=0) + rf_plus1 = RandomForestPlusClassifier(rf1) + rf_plus1.fit(cluster1_trainX, cluster1_trainy) + + rf2 = RandomForestClassifier(n_estimators=100, random_state=1) + rf_plus2 = RandomForestPlusClassifier(rf2) + rf_plus2.fit(cluster2_trainX, cluster2_trainy) + + rf3 = RandomForestClassifier(n_estimators=100, random_state=0) + rf_plus3 = RandomForestPlusClassifier(rf3) + rf_plus3.fit(cluster3_trainX, cluster3_trainy) + + rf4 = RandomForestClassifier(n_estimators=100, random_state=0) + rf_plus4 = RandomForestPlusClassifier(rf4) + rf_plus4.fit(cluster4_trainX, cluster4_trainy) + + # fit RF on each training set, predict test + rf1.fit(cluster1_trainX, cluster1_trainy) + + rf2.fit(cluster2_trainX, cluster2_trainy) + + rf3.fit(cluster3_trainX, cluster3_trainy) + + rf4.fit(cluster4_trainX, cluster4_trainy) + + # fit log model on each training set, predict test + log1 = LogisticRegression(random_state=0, max_iter=1000) + log1.fit(cluster1_trainX, cluster1_trainy) + + log2 = LogisticRegression(random_state=0, max_iter=1000) + log2.fit(cluster2_trainX, cluster2_trainy) + + log3 = LogisticRegression(random_state=0, max_iter=1000) + log3.fit(cluster3_trainX, cluster3_trainy) + + log4 = LogisticRegression(random_state=0, max_iter=1000) + log4.fit(cluster4_trainX, cluster4_trainy) + print("local models fit") + + cols = ['global_log_accuracy', 'global_log_misclassified', + 'global_log_auroc', 'global_log_auprc', 'global_log_f1', + 'global_rf_accuracy', 'global_rf_misclassified', 'global_rf_auroc', + 'global_rf_auprc', 'global_rf_f1', 'global_rf_plus_accuracy', + 'global_rf_plus_misclassified', 'global_rf_plus_auroc', + 'global_rf_plus_auprc', 'global_rf_plus_f1', 'local_log_accuracy', + 'local_log_misclassified', 'local_log_auroc', 'local_log_auprc', + 'local_log_f1', 'local_rf_accuracy', 'local_rf_misclassified', + 'local_rf_auroc', 'local_rf_auprc', 'local_rf_f1', + 'local_rf_plus_accuracy', 'local_rf_plus_misclassified', + 'local_rf_plus_auroc', 'local_rf_plus_auprc', 'local_rf_plus_f1'] + + # c1_results = pd.DataFrame(columns=cols) + # c2_results = pd.DataFrame(columns=cols) + # c3_results = pd.DataFrame(columns=cols) + # c4_results = pd.DataFrame(columns=cols) + + local_log_preds1 = log1.predict(cluster1_testX) + local_log_acc1 = np.mean(cluster1_testy == local_log_preds1) + local_log_mis1 = np.sum(cluster1_testy != local_log_preds1) + local_log_auroc1 = roc_auc_score(cluster1_testy, + log1.predict_proba(cluster1_testX)[:, 1]) + local_log_auprc1 = average_precision_score(cluster1_testy, + log1.predict_proba(cluster1_testX)[:, 1]) + local_log_f1score1 = f1_score(cluster1_testy, local_log_preds1) + + global_log_preds1 = log.predict(cluster1_testX) + global_log_acc1 = np.mean(cluster1_testy == global_log_preds1) + global_log_mis1 = np.sum(cluster1_testy != global_log_preds1) + global_log_auroc1 = roc_auc_score(cluster1_testy, + log.predict_proba(cluster1_testX)[:, 1]) + global_log_auprc1 = average_precision_score(cluster1_testy, + log.predict_proba(cluster1_testX)[:, 1]) + global_log_f1score1 = f1_score(cluster1_testy, global_log_preds1) + + local_rf_preds1 = rf1.predict(cluster1_testX) + local_rf_acc1 = np.mean(cluster1_testy == local_rf_preds1) + local_rf_mis1 = np.sum(cluster1_testy != local_rf_preds1) + local_rf_auroc1 = roc_auc_score(cluster1_testy, + rf1.predict_proba(cluster1_testX)[:, 1]) + local_rf_auprc1 = average_precision_score(cluster1_testy, + rf1.predict_proba(cluster1_testX)[:, 1]) + local_rf_f1score1 = f1_score(cluster1_testy, local_rf_preds1) + + global_rf_preds1 = rf.predict(cluster1_testX) + global_rf_acc1 = np.mean(cluster1_testy == global_rf_preds1) + global_rf_mis1 = np.sum(cluster1_testy != global_rf_preds1) + global_rf_auroc1 = roc_auc_score(cluster1_testy, + rf.predict_proba(cluster1_testX)[:, 1]) + global_rf_auprc1 = average_precision_score(cluster1_testy, + rf.predict_proba(cluster1_testX)[:, 1]) + global_rf_f1score1 = f1_score(cluster1_testy, global_rf_preds1) + + local_rf_plus_probpreds1 = rf_plus1.predict_proba(cluster1_testX)[:, 1] + local_rf_plus_preds1 = local_rf_plus_probpreds1 > 0.5 + local_rf_plus_acc1 = np.mean(cluster1_testy == local_rf_plus_preds1) + local_rf_plus_mis1 = np.sum(cluster1_testy != local_rf_plus_preds1) + local_rf_plus_auroc1 = roc_auc_score(cluster1_testy, + local_rf_plus_probpreds1) + local_rf_plus_auprc1 = average_precision_score(cluster1_testy, + local_rf_plus_probpreds1) + local_rf_plus_f1score1 = f1_score(cluster1_testy, local_rf_plus_preds1) + + global_rf_plus_probpreds1 = rf_plus.predict_proba(cluster1_testX)[:, 1] + global_rf_plus_preds1 = global_rf_plus_probpreds1 > 0.5 + global_rf_plus_acc1 = np.mean(cluster1_testy == global_rf_plus_preds1) + global_rf_plus_mis1 = np.sum(cluster1_testy != global_rf_plus_preds1) + global_rf_plus_auroc1 = roc_auc_score(cluster1_testy, + global_rf_plus_probpreds1) + global_rf_plus_auprc1 = average_precision_score(cluster1_testy, + global_rf_plus_probpreds1) + global_rf_plus_f1score1 = f1_score(cluster1_testy, global_rf_plus_preds1) + + # add row to cluster 1 results data + c1_results = np.asarray([global_log_acc1, global_log_mis1, global_log_auroc1, + global_log_auprc1, global_log_f1score1, global_rf_acc1, + global_rf_mis1, global_rf_auroc1, global_rf_auprc1, + global_rf_f1score1, global_rf_plus_acc1, global_rf_plus_mis1, + global_rf_plus_auroc1, global_rf_plus_auprc1, + global_rf_plus_f1score1, + local_log_acc1, local_log_mis1, + local_log_auroc1, local_log_auprc1, local_log_f1score1, + local_rf_acc1, local_rf_mis1, local_rf_auroc1, + local_rf_auprc1, local_rf_f1score1, local_rf_plus_acc1, + local_rf_plus_mis1, local_rf_plus_auroc1, + local_rf_plus_auprc1, local_rf_plus_f1score1]) + print("cluster 1 results complete") + + local_log_preds2 = log2.predict(cluster2_testX) + local_log_acc2 = np.mean(cluster2_testy == local_log_preds2) + local_log_mis2 = np.sum(cluster2_testy != local_log_preds2) + local_log_auroc2 = roc_auc_score(cluster2_testy, + log2.predict_proba(cluster2_testX)[:, 1]) + local_log_auprc2 = average_precision_score(cluster2_testy, + log2.predict_proba(cluster2_testX)[:, 1]) + local_log_f1score2 = f1_score(cluster2_testy, local_log_preds2) + + global_log_preds2 = log.predict(cluster2_testX) + global_log_acc2 = np.mean(cluster2_testy == global_log_preds2) + global_log_mis2 = np.sum(cluster2_testy != global_log_preds2) + global_log_auroc2 = roc_auc_score(cluster2_testy, + log.predict_proba(cluster2_testX)[:, 1]) + global_log_auprc2 = average_precision_score(cluster2_testy, + log.predict_proba(cluster2_testX)[:, 1]) + global_log_f1score2 = f1_score(cluster2_testy, global_log_preds2) + + local_rf_preds2 = rf2.predict(cluster2_testX) + local_rf_acc2 = np.mean(cluster2_testy == local_rf_preds2) + local_rf_mis2 = np.sum(cluster2_testy != local_rf_preds2) + local_rf_auroc2 = roc_auc_score(cluster2_testy, + rf2.predict_proba(cluster2_testX)[:, 1]) + local_rf_auprc2 = average_precision_score(cluster2_testy, + rf2.predict_proba(cluster2_testX)[:, 1]) + local_rf_f1score2 = f1_score(cluster2_testy, local_rf_preds2) + + global_rf_preds2 = rf.predict(cluster2_testX) + global_rf_acc2 = np.mean(cluster2_testy == global_rf_preds2) + global_rf_mis2 = np.sum(cluster2_testy != global_rf_preds2) + global_rf_auroc2 = roc_auc_score(cluster2_testy, + rf.predict_proba(cluster2_testX)[:, 1]) + global_rf_auprc2 = average_precision_score(cluster2_testy, + rf.predict_proba(cluster2_testX)[:, 1]) + global_rf_f1score2 = f1_score(cluster2_testy, global_rf_preds2) + + local_rf_plus_probpreds2 = rf_plus2.predict_proba(cluster2_testX)[:, 1] + local_rf_plus_preds2 = local_rf_plus_probpreds2 > 0.5 + local_rf_plus_acc2 = np.mean(cluster2_testy == local_rf_plus_preds2) + local_rf_plus_mis2 = np.sum(cluster2_testy != local_rf_plus_preds2) + local_rf_plus_auroc2 = roc_auc_score(cluster2_testy, + local_rf_plus_probpreds2) + local_rf_plus_auprc2 = average_precision_score(cluster2_testy, + local_rf_plus_probpreds2) + local_rf_plus_f1score2 = f1_score(cluster2_testy, local_rf_plus_preds2) + + global_rf_plus_probpreds2 = rf_plus.predict_proba(cluster2_testX)[:, 1] + global_rf_plus_preds2 = global_rf_plus_probpreds2 > 0.5 + global_rf_plus_acc2 = np.mean(cluster2_testy == global_rf_plus_preds2) + global_rf_plus_mis2 = np.sum(cluster2_testy != global_rf_plus_preds2) + global_rf_plus_auroc2 = roc_auc_score(cluster2_testy, + global_rf_plus_probpreds2) + global_rf_plus_auprc2 = average_precision_score(cluster2_testy, + global_rf_plus_probpreds2) + global_rf_plus_f1score2 = f1_score(cluster2_testy, global_rf_plus_preds2) + + # add row to cluster 2 results data + c2_results = np.asarray([global_log_acc2, global_log_mis2, global_log_auroc2, + global_log_auprc2, global_log_f1score2, global_rf_acc2, + global_rf_mis2, global_rf_auroc2, global_rf_auprc2, + global_rf_f1score2, global_rf_plus_acc2, global_rf_plus_mis2, + global_rf_plus_auroc2, global_rf_plus_auprc2, + global_rf_plus_f1score2, + local_log_acc2, local_log_mis2, + local_log_auroc2, local_log_auprc2, local_log_f1score2, + local_rf_acc2, local_rf_mis2, local_rf_auroc2, + local_rf_auprc2, local_rf_f1score2, local_rf_plus_acc2, + local_rf_plus_mis2, local_rf_plus_auroc2, + local_rf_plus_auprc2, local_rf_plus_f1score2]) + print("cluster 2 results complete") + + local_log_preds3 = log3.predict(cluster3_testX) + local_log_acc3 = np.mean(cluster3_testy == local_log_preds3) + local_log_mis3 = np.sum(cluster3_testy != local_log_preds3) + local_log_auroc3 = roc_auc_score(cluster3_testy, + log3.predict_proba(cluster3_testX)[:, 1]) + local_log_auprc3 = average_precision_score(cluster3_testy, + log3.predict_proba(cluster3_testX)[:, 1]) + local_log_f1score3 = f1_score(cluster3_testy, local_log_preds3) + + global_log_preds3 = log.predict(cluster3_testX) + global_log_acc3 = np.mean(cluster3_testy == global_log_preds3) + global_log_mis3 = np.sum(cluster3_testy != global_log_preds3) + global_log_auroc3 = roc_auc_score(cluster3_testy, + log.predict_proba(cluster3_testX)[:, 1]) + global_log_auprc3 = average_precision_score(cluster3_testy, + log.predict_proba(cluster3_testX)[:, 1]) + global_log_f1score3 = f1_score(cluster3_testy, global_log_preds3) + + local_rf_preds3 = rf3.predict(cluster3_testX) + local_rf_acc3 = np.mean(cluster3_testy == local_rf_preds3) + local_rf_mis3 = np.sum(cluster3_testy != local_rf_preds3) + local_rf_auroc3 = roc_auc_score(cluster3_testy, + rf3.predict_proba(cluster3_testX)[:, 1]) + local_rf_auprc3 = average_precision_score(cluster3_testy, + rf3.predict_proba(cluster3_testX)[:, 1]) + local_rf_f1score3 = f1_score(cluster3_testy, local_rf_preds3) + + global_rf_preds3 = rf.predict(cluster3_testX) + global_rf_acc3 = np.mean(cluster3_testy == global_rf_preds3) + global_rf_mis3 = np.sum(cluster3_testy != global_rf_preds3) + global_rf_auroc3 = roc_auc_score(cluster3_testy, + rf.predict_proba(cluster3_testX)[:, 1]) + global_rf_auprc3 = average_precision_score(cluster3_testy, + rf.predict_proba(cluster3_testX)[:, 1]) + global_rf_f1score3 = f1_score(cluster3_testy, global_rf_preds3) + + local_rf_plus_probpreds3 = rf_plus3.predict_proba(cluster3_testX)[:, 1] + local_rf_plus_preds3 = local_rf_plus_probpreds3 > 0.5 + local_rf_plus_acc3 = np.mean(cluster3_testy == local_rf_plus_preds3) + local_rf_plus_mis3 = np.sum(cluster3_testy != local_rf_plus_preds3) + local_rf_plus_auroc3 = roc_auc_score(cluster3_testy, + local_rf_plus_probpreds3) + local_rf_plus_auprc3 = average_precision_score(cluster3_testy, + local_rf_plus_probpreds3) + local_rf_plus_f1score3 = f1_score(cluster3_testy, local_rf_plus_preds3) + + global_rf_plus_probpreds3 = rf_plus.predict_proba(cluster3_testX)[:, 1] + global_rf_plus_preds3 = global_rf_plus_probpreds3 > 0.5 + global_rf_plus_acc3 = np.mean(cluster3_testy == global_rf_plus_preds3) + global_rf_plus_mis3 = np.sum(cluster3_testy != global_rf_plus_preds3) + global_rf_plus_auroc3 = roc_auc_score(cluster3_testy, + global_rf_plus_probpreds3) + global_rf_plus_auprc3 = average_precision_score(cluster3_testy, + global_rf_plus_probpreds3) + global_rf_plus_f1score3 = f1_score(cluster3_testy, global_rf_plus_preds3) + + # add row to cluster 3 results data + c3_results = np.asarray([global_log_acc3, global_log_mis3, global_log_auroc3, + global_log_auprc3, global_log_f1score3, global_rf_acc3, + global_rf_mis3, global_rf_auroc3, global_rf_auprc3, + global_rf_f1score3, global_rf_plus_acc3, global_rf_plus_mis3, + global_rf_plus_auroc3, global_rf_plus_auprc3, + global_rf_plus_f1score3, + local_log_acc3, local_log_mis3, + local_log_auroc3, local_log_auprc3, local_log_f1score3, + local_rf_acc3, local_rf_mis3, local_rf_auroc3, + local_rf_auprc3, local_rf_f1score3, local_rf_plus_acc3, + local_rf_plus_mis3, local_rf_plus_auroc3, + local_rf_plus_auprc3, local_rf_plus_f1score3]) + + print("cluster 3 results complete") + + local_log_preds4 = log4.predict(cluster4_testX) + local_log_acc4 = np.mean(cluster4_testy == local_log_preds4) + local_log_mis4 = np.sum(cluster4_testy != local_log_preds4) + local_log_auroc4 = roc_auc_score(cluster4_testy, + log4.predict_proba(cluster4_testX)[:, 1]) + local_log_auprc4 = average_precision_score(cluster4_testy, + log4.predict_proba(cluster4_testX)[:, 1]) + local_log_f1score4 = f1_score(cluster4_testy, local_log_preds4) + + global_log_preds4 = log.predict(cluster4_testX) + global_log_acc4 = np.mean(cluster4_testy == global_log_preds4) + global_log_mis4 = np.sum(cluster4_testy != global_log_preds4) + global_log_auroc4 = roc_auc_score(cluster4_testy, + log.predict_proba(cluster4_testX)[:, 1]) + global_log_auprc4 = average_precision_score(cluster4_testy, + log.predict_proba(cluster4_testX)[:, 1]) + global_log_f1score4 = f1_score(cluster4_testy, global_log_preds4) + + local_rf_preds4 = rf4.predict(cluster4_testX) + local_rf_acc4 = np.mean(cluster4_testy == local_rf_preds4) + local_rf_mis4 = np.sum(cluster4_testy != local_rf_preds4) + local_rf_auroc4 = roc_auc_score(cluster4_testy, + rf4.predict_proba(cluster4_testX)[:, 1]) + local_rf_auprc4 = average_precision_score(cluster4_testy, + rf4.predict_proba(cluster4_testX)[:, 1]) + local_rf_f1score4 = f1_score(cluster4_testy, local_rf_preds4) + + global_rf_preds4 = rf.predict(cluster4_testX) + global_rf_acc4 = np.mean(cluster4_testy == global_rf_preds4) + global_rf_mis4 = np.sum(cluster4_testy != global_rf_preds4) + global_rf_auroc4 = roc_auc_score(cluster4_testy, + rf.predict_proba(cluster4_testX)[:, 1]) + global_rf_auprc4 = average_precision_score(cluster4_testy, + rf.predict_proba(cluster4_testX)[:, 1]) + global_rf_f1score4 = f1_score(cluster4_testy, global_rf_preds4) + + local_rf_plus_probpreds4 = rf_plus4.predict_proba(cluster4_testX)[:, 1] + local_rf_plus_preds4 = local_rf_plus_probpreds4 > 0.5 + local_rf_plus_acc4 = np.mean(cluster4_testy == local_rf_plus_preds4) + local_rf_plus_mis4 = np.sum(cluster4_testy != local_rf_plus_preds4) + local_rf_plus_auroc4 = roc_auc_score(cluster4_testy, + local_rf_plus_probpreds4) + local_rf_plus_auprc4 = average_precision_score(cluster4_testy, + local_rf_plus_probpreds4) + local_rf_plus_f1score4 = f1_score(cluster4_testy, local_rf_plus_preds4) + + global_rf_plus_probpreds4 = rf_plus.predict_proba(cluster4_testX)[:, 1] + global_rf_plus_preds4 = global_rf_plus_probpreds4 > 0.5 + global_rf_plus_acc4 = np.mean(cluster4_testy == global_rf_plus_preds4) + global_rf_plus_mis4 = np.sum(cluster4_testy != global_rf_plus_preds4) + global_rf_plus_auroc4 = roc_auc_score(cluster4_testy, + global_rf_plus_probpreds4) + global_rf_plus_auprc4 = average_precision_score(cluster4_testy, + global_rf_plus_probpreds4) + global_rf_plus_f1score4 = f1_score(cluster4_testy, global_rf_plus_preds4) + + # add row to cluster 4 results data + c4_results = np.asarray([global_log_acc4, global_log_mis4, global_log_auroc4, + global_log_auprc4, global_log_f1score4, global_rf_acc4, + global_rf_mis4, global_rf_auroc4, global_rf_auprc4, + global_rf_f1score4, global_rf_plus_acc4, global_rf_plus_mis4, + global_rf_plus_auroc4, global_rf_plus_auprc4, + global_rf_plus_f1score4, + local_log_acc4, local_log_mis4, + local_log_auroc4, local_log_auprc4, local_log_f1score4, + local_rf_acc4, local_rf_mis4, local_rf_auroc4, + local_rf_auprc4, local_rf_f1score4, local_rf_plus_acc4, + local_rf_plus_mis4, local_rf_plus_auroc4, + local_rf_plus_auprc4, local_rf_plus_f1score4]) + + print("cluster 4 results complete") + + # combine cluster results into one array where the clusters are the rows + result = np.vstack((c1_results, c2_results, c3_results, c4_results)) + + # Convert the NumPy array to a pandas DataFrame + cluster_results = pd.DataFrame(result) + + # Set the column names + cluster_results.columns = cols + + + # row names + cluster_results.index = ['cluster1', 'cluster2', 'cluster3', 'cluster4'] + return cluster_results + +def tree_shap(X_train, y_train, X_test, y_test, log, rf, rf_plus): + print("began tree_shap") + # get feature importances + shap_explainer = shap.TreeExplainer(rf) + shap_values = np.abs(shap_explainer.shap_values(X_train, check_additivity=False)) + mdi_explainer = AloRFPlusMDI(rf_plus, evaluate_on='oob') + shap_rankings = mdi_explainer.get_rankings(shap_values)[:,:,0] + + # get rbo distance matrix + rbo_train = compute_rbo_matrix(shap_rankings, form = 'distance') + + shap_copy = pd.DataFrame(shap_values[:,:,0], columns=X_train.columns).copy() + num_clusters = 4 + clusters = assign_training_clusters(shap_copy, rbo_train, num_clusters) + + # get mdi rankings assignments for test points + shap_test_values = np.abs(shap_explainer.shap_values(X_test, check_additivity=False)) + shap_test_rankings = mdi_explainer.get_rankings(shap_test_values)[:,:,0] + + test_clust = assign_testing_clusters(method = "centroid", median_approx = False, + rbo_distance_matrix = rbo_train, + lfi_train_ranking = shap_rankings, + lfi_test_ranking = shap_test_rankings, + clusters = clusters) + print("testing clusters assigned") + + cluster1_trainX = X_train[clusters == 1] + cluster2_trainX = X_train[clusters == 2] + cluster3_trainX = X_train[clusters == 3] + cluster4_trainX = X_train[clusters == 4] + + cluster1_trainy = y_train[clusters == 1] + cluster2_trainy = y_train[clusters == 2] + cluster3_trainy = y_train[clusters == 3] + cluster4_trainy = y_train[clusters == 4] + + cluster1_testX = X_test[test_clust == 1] + cluster2_testX = X_test[test_clust == 2] + cluster3_testX = X_test[test_clust == 3] + cluster4_testX = X_test[test_clust == 4] + + cluster1_testy = y_test[test_clust == 1] + cluster2_testy = y_test[test_clust == 2] + cluster3_testy = y_test[test_clust == 3] + cluster4_testy = y_test[test_clust == 4] + + # fit RF+ on each training set, predict test + rf1 = RandomForestClassifier(n_estimators=100, random_state=0) + rf_plus1 = RandomForestPlusClassifier(rf1) + rf_plus1.fit(cluster1_trainX, cluster1_trainy) + + rf2 = RandomForestClassifier(n_estimators=100, random_state=1) + rf_plus2 = RandomForestPlusClassifier(rf2) + rf_plus2.fit(cluster2_trainX, cluster2_trainy) + + rf3 = RandomForestClassifier(n_estimators=100, random_state=0) + rf_plus3 = RandomForestPlusClassifier(rf3) + rf_plus3.fit(cluster3_trainX, cluster3_trainy) + + rf4 = RandomForestClassifier(n_estimators=100, random_state=0) + rf_plus4 = RandomForestPlusClassifier(rf4) + rf_plus4.fit(cluster4_trainX, cluster4_trainy) + + # fit RF on each training set, predict test + rf1.fit(cluster1_trainX, cluster1_trainy) + + rf2.fit(cluster2_trainX, cluster2_trainy) + + rf3.fit(cluster3_trainX, cluster3_trainy) + + rf4.fit(cluster4_trainX, cluster4_trainy) + + # fit log model on each training set, predict test + log1 = LogisticRegression(random_state=0, max_iter=1000) + log1.fit(cluster1_trainX, cluster1_trainy) + + log2 = LogisticRegression(random_state=0, max_iter=1000) + log2.fit(cluster2_trainX, cluster2_trainy) + + log3 = LogisticRegression(random_state=0, max_iter=1000) + log3.fit(cluster3_trainX, cluster3_trainy) + + log4 = LogisticRegression(random_state=0, max_iter=1000) + log4.fit(cluster4_trainX, cluster4_trainy) + print("local models fit") + + cols = ['global_log_accuracy', 'global_log_misclassified', + 'global_log_auroc', 'global_log_auprc', 'global_log_f1', + 'global_rf_accuracy', 'global_rf_misclassified', 'global_rf_auroc', + 'global_rf_auprc', 'global_rf_f1', 'global_rf_plus_accuracy', + 'global_rf_plus_misclassified', 'global_rf_plus_auroc', + 'global_rf_plus_auprc', 'global_rf_plus_f1', 'local_log_accuracy', + 'local_log_misclassified', 'local_log_auroc', 'local_log_auprc', + 'local_log_f1', 'local_rf_accuracy', 'local_rf_misclassified', + 'local_rf_auroc', 'local_rf_auprc', 'local_rf_f1', + 'local_rf_plus_accuracy', 'local_rf_plus_misclassified', + 'local_rf_plus_auroc', 'local_rf_plus_auprc', 'local_rf_plus_f1'] + + # c1_results = pd.DataFrame(columns=cols) + # c2_results = pd.DataFrame(columns=cols) + # c3_results = pd.DataFrame(columns=cols) + # c4_results = pd.DataFrame(columns=cols) + + local_log_preds1 = log1.predict(cluster1_testX) + local_log_acc1 = np.mean(cluster1_testy == local_log_preds1) + local_log_mis1 = np.sum(cluster1_testy != local_log_preds1) + local_log_auroc1 = roc_auc_score(cluster1_testy, + log1.predict_proba(cluster1_testX)[:, 1]) + local_log_auprc1 = average_precision_score(cluster1_testy, + log1.predict_proba(cluster1_testX)[:, 1]) + local_log_f1score1 = f1_score(cluster1_testy, local_log_preds1) + + global_log_preds1 = log.predict(cluster1_testX) + global_log_acc1 = np.mean(cluster1_testy == global_log_preds1) + global_log_mis1 = np.sum(cluster1_testy != global_log_preds1) + global_log_auroc1 = roc_auc_score(cluster1_testy, + log.predict_proba(cluster1_testX)[:, 1]) + global_log_auprc1 = average_precision_score(cluster1_testy, + log.predict_proba(cluster1_testX)[:, 1]) + global_log_f1score1 = f1_score(cluster1_testy, global_log_preds1) + + local_rf_preds1 = rf1.predict(cluster1_testX) + local_rf_acc1 = np.mean(cluster1_testy == local_rf_preds1) + local_rf_mis1 = np.sum(cluster1_testy != local_rf_preds1) + local_rf_auroc1 = roc_auc_score(cluster1_testy, + rf1.predict_proba(cluster1_testX)[:, 1]) + local_rf_auprc1 = average_precision_score(cluster1_testy, + rf1.predict_proba(cluster1_testX)[:, 1]) + local_rf_f1score1 = f1_score(cluster1_testy, local_rf_preds1) + + global_rf_preds1 = rf.predict(cluster1_testX) + global_rf_acc1 = np.mean(cluster1_testy == global_rf_preds1) + global_rf_mis1 = np.sum(cluster1_testy != global_rf_preds1) + global_rf_auroc1 = roc_auc_score(cluster1_testy, + rf.predict_proba(cluster1_testX)[:, 1]) + global_rf_auprc1 = average_precision_score(cluster1_testy, + rf.predict_proba(cluster1_testX)[:, 1]) + global_rf_f1score1 = f1_score(cluster1_testy, global_rf_preds1) + + local_rf_plus_probpreds1 = rf_plus1.predict_proba(cluster1_testX)[:, 1] + local_rf_plus_preds1 = local_rf_plus_probpreds1 > 0.5 + local_rf_plus_acc1 = np.mean(cluster1_testy == local_rf_plus_preds1) + local_rf_plus_mis1 = np.sum(cluster1_testy != local_rf_plus_preds1) + local_rf_plus_auroc1 = roc_auc_score(cluster1_testy, + local_rf_plus_probpreds1) + local_rf_plus_auprc1 = average_precision_score(cluster1_testy, + local_rf_plus_probpreds1) + local_rf_plus_f1score1 = f1_score(cluster1_testy, local_rf_plus_preds1) + + global_rf_plus_probpreds1 = rf_plus.predict_proba(cluster1_testX)[:, 1] + global_rf_plus_preds1 = global_rf_plus_probpreds1 > 0.5 + global_rf_plus_acc1 = np.mean(cluster1_testy == global_rf_plus_preds1) + global_rf_plus_mis1 = np.sum(cluster1_testy != global_rf_plus_preds1) + global_rf_plus_auroc1 = roc_auc_score(cluster1_testy, + global_rf_plus_probpreds1) + global_rf_plus_auprc1 = average_precision_score(cluster1_testy, + global_rf_plus_probpreds1) + global_rf_plus_f1score1 = f1_score(cluster1_testy, global_rf_plus_preds1) + + # add row to cluster 1 results data + c1_results = np.asarray([global_log_acc1, global_log_mis1, global_log_auroc1, + global_log_auprc1, global_log_f1score1, global_rf_acc1, + global_rf_mis1, global_rf_auroc1, global_rf_auprc1, + global_rf_f1score1, global_rf_plus_acc1, global_rf_plus_mis1, + global_rf_plus_auroc1, global_rf_plus_auprc1, + global_rf_plus_f1score1, + local_log_acc1, local_log_mis1, + local_log_auroc1, local_log_auprc1, local_log_f1score1, + local_rf_acc1, local_rf_mis1, local_rf_auroc1, + local_rf_auprc1, local_rf_f1score1, local_rf_plus_acc1, + local_rf_plus_mis1, local_rf_plus_auroc1, + local_rf_plus_auprc1, local_rf_plus_f1score1]) + print("cluster 1 results complete") + + local_log_preds2 = log2.predict(cluster2_testX) + local_log_acc2 = np.mean(cluster2_testy == local_log_preds2) + local_log_mis2 = np.sum(cluster2_testy != local_log_preds2) + local_log_auroc2 = roc_auc_score(cluster2_testy, + log2.predict_proba(cluster2_testX)[:, 1]) + local_log_auprc2 = average_precision_score(cluster2_testy, + log2.predict_proba(cluster2_testX)[:, 1]) + local_log_f1score2 = f1_score(cluster2_testy, local_log_preds2) + + global_log_preds2 = log.predict(cluster2_testX) + global_log_acc2 = np.mean(cluster2_testy == global_log_preds2) + global_log_mis2 = np.sum(cluster2_testy != global_log_preds2) + global_log_auroc2 = roc_auc_score(cluster2_testy, + log.predict_proba(cluster2_testX)[:, 1]) + global_log_auprc2 = average_precision_score(cluster2_testy, + log.predict_proba(cluster2_testX)[:, 1]) + global_log_f1score2 = f1_score(cluster2_testy, global_log_preds2) + + local_rf_preds2 = rf2.predict(cluster2_testX) + local_rf_acc2 = np.mean(cluster2_testy == local_rf_preds2) + local_rf_mis2 = np.sum(cluster2_testy != local_rf_preds2) + local_rf_auroc2 = roc_auc_score(cluster2_testy, + rf2.predict_proba(cluster2_testX)[:, 1]) + local_rf_auprc2 = average_precision_score(cluster2_testy, + rf2.predict_proba(cluster2_testX)[:, 1]) + local_rf_f1score2 = f1_score(cluster2_testy, local_rf_preds2) + + global_rf_preds2 = rf.predict(cluster2_testX) + global_rf_acc2 = np.mean(cluster2_testy == global_rf_preds2) + global_rf_mis2 = np.sum(cluster2_testy != global_rf_preds2) + global_rf_auroc2 = roc_auc_score(cluster2_testy, + rf.predict_proba(cluster2_testX)[:, 1]) + global_rf_auprc2 = average_precision_score(cluster2_testy, + rf.predict_proba(cluster2_testX)[:, 1]) + global_rf_f1score2 = f1_score(cluster2_testy, global_rf_preds2) + + local_rf_plus_probpreds2 = rf_plus2.predict_proba(cluster2_testX)[:, 1] + local_rf_plus_preds2 = local_rf_plus_probpreds2 > 0.5 + local_rf_plus_acc2 = np.mean(cluster2_testy == local_rf_plus_preds2) + local_rf_plus_mis2 = np.sum(cluster2_testy != local_rf_plus_preds2) + local_rf_plus_auroc2 = roc_auc_score(cluster2_testy, + local_rf_plus_probpreds2) + local_rf_plus_auprc2 = average_precision_score(cluster2_testy, + local_rf_plus_probpreds2) + local_rf_plus_f1score2 = f1_score(cluster2_testy, local_rf_plus_preds2) + + global_rf_plus_probpreds2 = rf_plus.predict_proba(cluster2_testX)[:, 1] + global_rf_plus_preds2 = global_rf_plus_probpreds2 > 0.5 + global_rf_plus_acc2 = np.mean(cluster2_testy == global_rf_plus_preds2) + global_rf_plus_mis2 = np.sum(cluster2_testy != global_rf_plus_preds2) + global_rf_plus_auroc2 = roc_auc_score(cluster2_testy, + global_rf_plus_probpreds2) + global_rf_plus_auprc2 = average_precision_score(cluster2_testy, + global_rf_plus_probpreds2) + global_rf_plus_f1score2 = f1_score(cluster2_testy, global_rf_plus_preds2) + + # add row to cluster 2 results data + c2_results = np.asarray([global_log_acc2, global_log_mis2, global_log_auroc2, + global_log_auprc2, global_log_f1score2, global_rf_acc2, + global_rf_mis2, global_rf_auroc2, global_rf_auprc2, + global_rf_f1score2, global_rf_plus_acc2, global_rf_plus_mis2, + global_rf_plus_auroc2, global_rf_plus_auprc2, + global_rf_plus_f1score2, + local_log_acc2, local_log_mis2, + local_log_auroc2, local_log_auprc2, local_log_f1score2, + local_rf_acc2, local_rf_mis2, local_rf_auroc2, + local_rf_auprc2, local_rf_f1score2, local_rf_plus_acc2, + local_rf_plus_mis2, local_rf_plus_auroc2, + local_rf_plus_auprc2, local_rf_plus_f1score2]) + print("cluster 2 results complete") + + local_log_preds3 = log3.predict(cluster3_testX) + local_log_acc3 = np.mean(cluster3_testy == local_log_preds3) + local_log_mis3 = np.sum(cluster3_testy != local_log_preds3) + local_log_auroc3 = roc_auc_score(cluster3_testy, + log3.predict_proba(cluster3_testX)[:, 1]) + local_log_auprc3 = average_precision_score(cluster3_testy, + log3.predict_proba(cluster3_testX)[:, 1]) + local_log_f1score3 = f1_score(cluster3_testy, local_log_preds3) + + global_log_preds3 = log.predict(cluster3_testX) + global_log_acc3 = np.mean(cluster3_testy == global_log_preds3) + global_log_mis3 = np.sum(cluster3_testy != global_log_preds3) + global_log_auroc3 = roc_auc_score(cluster3_testy, + log.predict_proba(cluster3_testX)[:, 1]) + global_log_auprc3 = average_precision_score(cluster3_testy, + log.predict_proba(cluster3_testX)[:, 1]) + global_log_f1score3 = f1_score(cluster3_testy, global_log_preds3) + + local_rf_preds3 = rf3.predict(cluster3_testX) + local_rf_acc3 = np.mean(cluster3_testy == local_rf_preds3) + local_rf_mis3 = np.sum(cluster3_testy != local_rf_preds3) + local_rf_auroc3 = roc_auc_score(cluster3_testy, + rf3.predict_proba(cluster3_testX)[:, 1]) + local_rf_auprc3 = average_precision_score(cluster3_testy, + rf3.predict_proba(cluster3_testX)[:, 1]) + local_rf_f1score3 = f1_score(cluster3_testy, local_rf_preds3) + + global_rf_preds3 = rf.predict(cluster3_testX) + global_rf_acc3 = np.mean(cluster3_testy == global_rf_preds3) + global_rf_mis3 = np.sum(cluster3_testy != global_rf_preds3) + global_rf_auroc3 = roc_auc_score(cluster3_testy, + rf.predict_proba(cluster3_testX)[:, 1]) + global_rf_auprc3 = average_precision_score(cluster3_testy, + rf.predict_proba(cluster3_testX)[:, 1]) + global_rf_f1score3 = f1_score(cluster3_testy, global_rf_preds3) + + local_rf_plus_probpreds3 = rf_plus3.predict_proba(cluster3_testX)[:, 1] + local_rf_plus_preds3 = local_rf_plus_probpreds3 > 0.5 + local_rf_plus_acc3 = np.mean(cluster3_testy == local_rf_plus_preds3) + local_rf_plus_mis3 = np.sum(cluster3_testy != local_rf_plus_preds3) + local_rf_plus_auroc3 = roc_auc_score(cluster3_testy, + local_rf_plus_probpreds3) + local_rf_plus_auprc3 = average_precision_score(cluster3_testy, + local_rf_plus_probpreds3) + local_rf_plus_f1score3 = f1_score(cluster3_testy, local_rf_plus_preds3) + + global_rf_plus_probpreds3 = rf_plus.predict_proba(cluster3_testX)[:, 1] + global_rf_plus_preds3 = global_rf_plus_probpreds3 > 0.5 + global_rf_plus_acc3 = np.mean(cluster3_testy == global_rf_plus_preds3) + global_rf_plus_mis3 = np.sum(cluster3_testy != global_rf_plus_preds3) + global_rf_plus_auroc3 = roc_auc_score(cluster3_testy, + global_rf_plus_probpreds3) + global_rf_plus_auprc3 = average_precision_score(cluster3_testy, + global_rf_plus_probpreds3) + global_rf_plus_f1score3 = f1_score(cluster3_testy, global_rf_plus_preds3) + + # add row to cluster 3 results data + c3_results = np.asarray([global_log_acc3, global_log_mis3, global_log_auroc3, + global_log_auprc3, global_log_f1score3, global_rf_acc3, + global_rf_mis3, global_rf_auroc3, global_rf_auprc3, + global_rf_f1score3, global_rf_plus_acc3, global_rf_plus_mis3, + global_rf_plus_auroc3, global_rf_plus_auprc3, + global_rf_plus_f1score3, + local_log_acc3, local_log_mis3, + local_log_auroc3, local_log_auprc3, local_log_f1score3, + local_rf_acc3, local_rf_mis3, local_rf_auroc3, + local_rf_auprc3, local_rf_f1score3, local_rf_plus_acc3, + local_rf_plus_mis3, local_rf_plus_auroc3, + local_rf_plus_auprc3, local_rf_plus_f1score3]) + + print("cluster 3 results complete") + + local_log_preds4 = log4.predict(cluster4_testX) + local_log_acc4 = np.mean(cluster4_testy == local_log_preds4) + local_log_mis4 = np.sum(cluster4_testy != local_log_preds4) + local_log_auroc4 = roc_auc_score(cluster4_testy, + log4.predict_proba(cluster4_testX)[:, 1]) + local_log_auprc4 = average_precision_score(cluster4_testy, + log4.predict_proba(cluster4_testX)[:, 1]) + local_log_f1score4 = f1_score(cluster4_testy, local_log_preds4) + + global_log_preds4 = log.predict(cluster4_testX) + global_log_acc4 = np.mean(cluster4_testy == global_log_preds4) + global_log_mis4 = np.sum(cluster4_testy != global_log_preds4) + global_log_auroc4 = roc_auc_score(cluster4_testy, + log.predict_proba(cluster4_testX)[:, 1]) + global_log_auprc4 = average_precision_score(cluster4_testy, + log.predict_proba(cluster4_testX)[:, 1]) + global_log_f1score4 = f1_score(cluster4_testy, global_log_preds4) + + local_rf_preds4 = rf4.predict(cluster4_testX) + local_rf_acc4 = np.mean(cluster4_testy == local_rf_preds4) + local_rf_mis4 = np.sum(cluster4_testy != local_rf_preds4) + local_rf_auroc4 = roc_auc_score(cluster4_testy, + rf4.predict_proba(cluster4_testX)[:, 1]) + local_rf_auprc4 = average_precision_score(cluster4_testy, + rf4.predict_proba(cluster4_testX)[:, 1]) + local_rf_f1score4 = f1_score(cluster4_testy, local_rf_preds4) + + global_rf_preds4 = rf.predict(cluster4_testX) + global_rf_acc4 = np.mean(cluster4_testy == global_rf_preds4) + global_rf_mis4 = np.sum(cluster4_testy != global_rf_preds4) + global_rf_auroc4 = roc_auc_score(cluster4_testy, + rf.predict_proba(cluster4_testX)[:, 1]) + global_rf_auprc4 = average_precision_score(cluster4_testy, + rf.predict_proba(cluster4_testX)[:, 1]) + global_rf_f1score4 = f1_score(cluster4_testy, global_rf_preds4) + + local_rf_plus_probpreds4 = rf_plus4.predict_proba(cluster4_testX)[:, 1] + local_rf_plus_preds4 = local_rf_plus_probpreds4 > 0.5 + local_rf_plus_acc4 = np.mean(cluster4_testy == local_rf_plus_preds4) + local_rf_plus_mis4 = np.sum(cluster4_testy != local_rf_plus_preds4) + local_rf_plus_auroc4 = roc_auc_score(cluster4_testy, + local_rf_plus_probpreds4) + local_rf_plus_auprc4 = average_precision_score(cluster4_testy, + local_rf_plus_probpreds4) + local_rf_plus_f1score4 = f1_score(cluster4_testy, local_rf_plus_preds4) + + global_rf_plus_probpreds4 = rf_plus.predict_proba(cluster4_testX)[:, 1] + global_rf_plus_preds4 = global_rf_plus_probpreds4 > 0.5 + global_rf_plus_acc4 = np.mean(cluster4_testy == global_rf_plus_preds4) + global_rf_plus_mis4 = np.sum(cluster4_testy != global_rf_plus_preds4) + global_rf_plus_auroc4 = roc_auc_score(cluster4_testy, + global_rf_plus_probpreds4) + global_rf_plus_auprc4 = average_precision_score(cluster4_testy, + global_rf_plus_probpreds4) + global_rf_plus_f1score4 = f1_score(cluster4_testy, global_rf_plus_preds4) + + # add row to cluster 4 results data + c4_results = np.asarray([global_log_acc4, global_log_mis4, global_log_auroc4, + global_log_auprc4, global_log_f1score4, global_rf_acc4, + global_rf_mis4, global_rf_auroc4, global_rf_auprc4, + global_rf_f1score4, global_rf_plus_acc4, global_rf_plus_mis4, + global_rf_plus_auroc4, global_rf_plus_auprc4, + global_rf_plus_f1score4, + local_log_acc4, local_log_mis4, + local_log_auroc4, local_log_auprc4, local_log_f1score4, + local_rf_acc4, local_rf_mis4, local_rf_auroc4, + local_rf_auprc4, local_rf_f1score4, local_rf_plus_acc4, + local_rf_plus_mis4, local_rf_plus_auroc4, + local_rf_plus_auprc4, local_rf_plus_f1score4]) + + print("cluster 4 results complete") + + # combine cluster results into one array where the clusters are the rows + result = np.vstack((c1_results, c2_results, c3_results, c4_results)) + + # Convert the NumPy array to a pandas DataFrame + cluster_results = pd.DataFrame(result) + + # Set the column names + cluster_results.columns = cols + + + # row names + cluster_results.index = ['cluster1', 'cluster2', 'cluster3', 'cluster4'] + return cluster_results + + +if __name__ == '__main__': + print("HERE") + X, y = load_data() + X_train, X_test, y_train, y_test = split_data(X, y, random_state=0) + log, rf, rf_plus = train_models(X_train, y_train) + lmdi_results = lmdi_plus(X_train, y_train, X_test, y_test, log, rf, rf_plus) + lmdi_results.to_csv('compas_output/compas_lmdi_results.csv') + shap_results = tree_shap(X_train, y_train, X_test, y_test, log, rf, rf_plus) + shap_results.to_csv('compas_output/compas_shap_results.csv') + print(lmdi_results) + print(shap_results) + \ No newline at end of file diff --git a/feature_importance/subgroup/legacy/compas.sh b/feature_importance/subgroup/legacy/compas.sh new file mode 100644 index 0000000..7c6a229 --- /dev/null +++ b/feature_importance/subgroup/legacy/compas.sh @@ -0,0 +1,9 @@ +#!/bin/bash +#SBATCH --mail-user=zachrewolinski@berkeley.edu +#SBATCH --mail-type=ALL + +source activate mdi +command="compas.py --nreps 1" + +# Execute the command +python $command \ No newline at end of file diff --git a/feature_importance/subgroup/legacy/compas_short.ipynb b/feature_importance/subgroup/legacy/compas_short.ipynb new file mode 100644 index 0000000..d079936 --- /dev/null +++ b/feature_importance/subgroup/legacy/compas_short.ipynb @@ -0,0 +1,2876 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# import required packages\n", + "from imodels import get_clean_dataset\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import roc_auc_score, average_precision_score, f1_score\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.linear_model import LogisticRegression\n", + "from imodels.tree.rf_plus.rf_plus.rf_plus_models import RandomForestPlusClassifier\n", + "from imodels.tree.rf_plus.feature_importance.rfplus_explainer import RFPlusMDI\n", + "from subgroup_detection import *\n", + "import warnings\n", + "import shap\n", + "warnings.filterwarnings('ignore', category=DeprecationWarning)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# get pre-cleaned compas dataset from imodels\n", + "X, y, feature_names = get_clean_dataset('compas_two_year_clean', data_source='imodels')\n", + "X = pd.DataFrame(X, columns=feature_names)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# the propublica study narrowed the dataset to only African-American and\n", + "# Caucasian defendants, and doing so keeps the vast majority of the data,\n", + "# so we will do the same.\n", + "y = y[(X['race:African-American'] == 1) | (X['race:Caucasian'] == 1)]\n", + "X = X[(X['race:African-American'] == 1) | (X['race:Caucasian'] == 1)]\n", + "\n", + "# now that we have narrowed the dataset, we should remove the one-hot encodings\n", + "# of variables that are consistently zero, such as the other ethnicities.\n", + "# we also drop age because the binned 'age category' is preferred here.\n", + "X = X.drop([\"race:Asian\", \"race:Hispanic\", \"race:Native_American\",\n", + " \"race:Other\", \"age\"], axis = 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introduction\n", + "## About the Dataset\n", + "Throughout this report, we will be looking at the pre-cleaned version of `COMPAS` dataset from the `imodels` package. Each row in this dataset represents an incarcerated individual in Broward County, FL. The task is to predict whether each individual will recommit a crime within two years of release from custody. We take a peek at the data below:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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priors_countdays_b_screening_arrestc_jail_timejuv_fel_countjuv_other_countjuv_misd_countc_charge_degree:Fc_charge_degree:Mrace:African-Americanrace:Caucasianage_cat:25_-_45age_cat:Greater_than_45age_cat:Less_than_25sex:Femalesex:Male
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60.0-1.02.00.00.00.00.01.00.01.01.00.00.01.00.0
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" + ], + "text/plain": [ + " priors_count days_b_screening_arrest c_jail_time juv_fel_count \\\n", + "1 0.0 -1.0 10.0 0.0 \n", + "2 4.0 -1.0 1.0 0.0 \n", + "4 14.0 -1.0 6.0 0.0 \n", + "6 0.0 -1.0 2.0 0.0 \n", + "7 0.0 -1.0 1.0 0.0 \n", + "\n", + " juv_other_count juv_misd_count c_charge_degree:F c_charge_degree:M \\\n", + "1 0.0 0.0 1.0 0.0 \n", + "2 1.0 0.0 1.0 0.0 \n", + "4 0.0 0.0 1.0 0.0 \n", + "6 0.0 0.0 0.0 1.0 \n", + "7 0.0 0.0 1.0 0.0 \n", + "\n", + " race:African-American race:Caucasian age_cat:25_-_45 \\\n", + "1 1.0 0.0 1.0 \n", + "2 1.0 0.0 0.0 \n", + "4 0.0 1.0 1.0 \n", + "6 0.0 1.0 1.0 \n", + "7 0.0 1.0 1.0 \n", + "\n", + " age_cat:Greater_than_45 age_cat:Less_than_25 sex:Female sex:Male \n", + "1 0.0 0.0 0.0 1.0 \n", + "2 0.0 1.0 0.0 1.0 \n", + "4 0.0 0.0 0.0 1.0 \n", + "6 0.0 0.0 1.0 0.0 \n", + "7 0.0 0.0 0.0 1.0 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dimensions of the COMPAS Dataset (Covariates): (5278, 15)\n", + "Dimensions of the Response: (5278,)\n" + ] + } + ], + "source": [ + "print(\"Dimensions of the COMPAS Dataset (Covariates):\", X.shape)\n", + "print(\"Dimensions of the Response:\", y.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before we begin analyzing our data, we first perform some simple exploratory data analysis." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---------------------------------------------\n", + "The Average # of Priors for Non-Recidivists is: 2.04\n", + "The Average # of Priors for Recidivists is: 4.87\n", + "---------------------------------------------\n", + "The Average # of Days Before Screening Arrest for Non-Recidivists is: -2.11\n", + "The Average # of Days Before Screening Arrest for Recidivists is: -1.36\n", + "---------------------------------------------\n", + "The Average # of Days in Jail for Non-Recidivists is: 9.92\n", + "The Average # of Days in Jail for Recidivists is: 20.29\n", + "---------------------------------------------\n", + "The Average # of Juvenile Felony Counts for Non-Recidivists is: 0.02\n", + "The Average # of Juvenile Felony Counts for Recidivists is: 0.1\n", + "---------------------------------------------\n", + "The Average # of Other Juvenile Counts for Non-Recidivists is: 0.05\n", + "The Average # of Other Juvenile Counts for Recidivists is: 0.18\n", + "---------------------------------------------\n", + "The Average # of Juvenile Misdemeanor Counts for Non-Recidivists is: 0.04\n", + "The Average # of Juvenile Misdemeanor Counts for Recidivists is: 0.15\n", + "---------------------------------------------\n", + "The Probability of Felony Charge for Non-Recidivists is: 0.6\n", + "The Probability of Felony Charge for Recidivists is: 0.7\n", + "---------------------------------------------\n", + "The Probability of Being a Recidivist Given African-American Race: 0.56\n", + "The Probability of Being a Recidivist Given Caucasian Race: 0.42\n", + "---------------------------------------------\n", + "The Probability of Being a Recidivist Given Age Between 25 & 45: 0.52\n", + "The Probability of Being a Recidivist Given Age Over 45: 0.36\n", + "The Probability of Being a Recidivist Given Age Under 25: 0.6\n", + "---------------------------------------------\n", + "The Probability of Recidivism Given Male Gender: 0.53\n", + "The Probability of Recidivism Given Female Gender: 0.38\n", + "---------------------------------------------\n" + ] + } + ], + "source": [ + "print(\"---------------------------------------------\")\n", + "print(\"The Average # of Priors for Non-Recidivists is:\",\n", + " round(X['priors_count'][y==0].mean(), 2))\n", + "print(\"The Average # of Priors for Recidivists is:\",\n", + " round(X['priors_count'][y==1].mean(), 2))\n", + "print(\"---------------------------------------------\")\n", + "print(\"The Average # of Days Before Screening Arrest for Non-Recidivists is:\",\n", + " round(X['days_b_screening_arrest'][y==0].mean(), 2))\n", + "print(\"The Average # of Days Before Screening Arrest for Recidivists is:\",\n", + " round(X['days_b_screening_arrest'][y==1].mean(), 2))\n", + "print(\"---------------------------------------------\")\n", + "print(\"The Average # of Days in Jail for Non-Recidivists is:\",\n", + " round(X['c_jail_time'][y==0].mean(), 2))\n", + "print(\"The Average # of Days in Jail for Recidivists is:\",\n", + " round(X['c_jail_time'][y==1].mean(), 2))\n", + "print(\"---------------------------------------------\")\n", + "print(\"The Average # of Juvenile Felony Counts for Non-Recidivists is:\",\n", + " round(X['juv_fel_count'][y==0].mean(), 2))\n", + "print(\"The Average # of Juvenile Felony Counts for Recidivists is:\",\n", + " round(X['juv_fel_count'][y==1].mean(), 2))\n", + "print(\"---------------------------------------------\")\n", + "print(\"The Average # of Other Juvenile Counts for Non-Recidivists is:\",\n", + " round(X['juv_other_count'][y==0].mean(), 2))\n", + "print(\"The Average # of Other Juvenile Counts for Recidivists is:\",\n", + " round(X['juv_other_count'][y==1].mean(), 2))\n", + "print(\"---------------------------------------------\")\n", + "print(\"The Average # of Juvenile Misdemeanor Counts for Non-Recidivists is:\",\n", + " round(X['juv_misd_count'][y==0].mean(), 2))\n", + "print(\"The Average # of Juvenile Misdemeanor Counts for Recidivists is:\",\n", + " round(X['juv_misd_count'][y==1].mean(), 2))\n", + "print(\"---------------------------------------------\")\n", + "print(\"The Probability of Felony Charge for Non-Recidivists is:\",\n", + " round(X['c_charge_degree:F'][y==0].mean(), 2))\n", + "print(\"The Probability of Felony Charge for Recidivists is:\",\n", + " round(X['c_charge_degree:F'][y==1].mean(), 2))\n", + "print(\"---------------------------------------------\")\n", + "print(\"The Probability of Being a Recidivist Given African-American Race:\",\n", + " round(y[X['race:African-American']==1].mean(), 2))\n", + "print(\"The Probability of Being a Recidivist Given Caucasian Race:\",\n", + " round(y[X['race:Caucasian']==1].mean(), 2))\n", + "print(\"---------------------------------------------\")\n", + "print(\"The Probability of Being a Recidivist Given Age Between 25 & 45:\",\n", + " round(y[X['age_cat:25_-_45']==1].mean(), 2))\n", + "print(\"The Probability of Being a Recidivist Given Age Over 45:\",\n", + " round(y[X['age_cat:Greater_than_45']==1].mean(), 2))\n", + "print(\"The Probability of Being a Recidivist Given Age Under 25:\",\n", + " round(y[X['age_cat:Less_than_25']==1].mean(), 2))\n", + "print(\"---------------------------------------------\")\n", + "print(\"The Probability of Recidivism Given Male Gender:\", round(y[X['sex:Male']==1].mean(), 2))\n", + "print(\"The Probability of Recidivism Given Female Gender:\", round(y[X['sex:Female']==1].mean(), 2))\n", + "print(\"---------------------------------------------\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is apparent that while multiple variables might impact the medical expenses of an individual, the most eye-popping difference is the average expense difference between smokers and non-smokers.\n", + "\n", + "Now, we split the data into training and testing datasets using a 70/30 split. We check for covariate balance in our train/test split below. We do not include `region` in this covariate balance check for the sake of brevity." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# split data into training and testing sets\n", + "# we won't actually use the test set here though, since 'discovery' would be\n", + "# a post-hoc analysis in real life\n", + "# proportion of training data is small so rf+ can fit without taking hours\n", + "y = np.asarray(y)\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,\n", + " random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---------------------------------------------\n", + "Number of Observations in Training Data: 3694\n", + "Number of Observations in Testing Data: 1584\n", + "---------------------------------------------\n", + "Average # of Priors in Training Data: 3.4948566\n", + "Average # of Priors in Testing Data: 3.3838384\n", + "---------------------------------------------\n", + "Average # of Days Before Screening Arrest in Training Data: -1.7818084\n", + "Average # of Days Before Screening Arrest in Testing Data: -1.6085858\n", + "---------------------------------------------\n", + "Average # of Days in Jail in Training Data: 14.9269085\n", + "Average # of Days in Jail in Testing Data: 15.573233\n", + "---------------------------------------------\n", + "Average # of Juvenile Felony Counts in Training Data: 0.06578235\n", + "Average # of Juvenile Felony Counts in Testing Data: 0.051136363\n", + "---------------------------------------------\n", + "Average # of Other Juvenile Counts in Training Data: 0.123443425\n", + "Average # of Other Juvenile Counts in Testing Data: 0.109217174\n", + "---------------------------------------------\n", + "Average # of Juvenile Misdemeanor Counts in Training Data: 0.10043313\n", + "Average # of Juvenile Misdemeanor Counts in Testing Data: 0.09406566\n", + "---------------------------------------------\n", + "Probability of Felony Charge in Training Data: 0.6583649\n", + "Probability of Felony Charge in Testing Data: 0.6363636\n", + "---------------------------------------------\n", + "Proportion of African-American Defendants in Training Data: 0.5979968\n", + "Proportion of African-American Defendants in Testing Data: 0.6098485\n", + "---------------------------------------------\n", + "Proportion of Caucasian Defendants in Training Data: 0.40200326\n", + "Proportion of Caucasian Defendants in Testing Data: 0.3901515\n", + "---------------------------------------------\n", + "Proportion of Defendants Between Ages 25 & 45 in Training Data: 0.5722794\n", + "Proportion of Defendants Between Ages 25 & 45 in Testing Data: 0.57575756\n", + "---------------------------------------------\n", + "Proportion of Defendants Over Age 45 in Training Data: 0.2019491\n", + "Proportion of Defendants Over Age 45 in Testing Data: 0.22095959\n", + "---------------------------------------------\n", + "Proportion of Defendants Under Age 25 in Training Data: 0.22577152\n", + "Proportion of Defendants Under Age 25 in Testing Data: 0.20328283\n", + "---------------------------------------------\n", + "Proportion of Women in Training Data: 0.19085003\n", + "Proportion of Women in Testing Data: 0.20580809\n", + "---------------------------------------------\n", + "Probability of Recidivism in Training Data: 0.5064970221981592\n", + "Probability of Recidivism in Testing Data: 0.4898989898989899\n", + "---------------------------------------------\n" + ] + } + ], + "source": [ + "print(\"---------------------------------------------\")\n", + "print(\"Number of Observations in Training Data:\", X_train.shape[0])\n", + "print(\"Number of Observations in Testing Data:\", X_test.shape[0])\n", + "print(\"---------------------------------------------\")\n", + "print(\"Average # of Priors in Training Data:\", X_train['priors_count'].mean())\n", + "print(\"Average # of Priors in Testing Data:\", X_test['priors_count'].mean())\n", + "print(\"---------------------------------------------\")\n", + "print(\"Average # of Days Before Screening Arrest in Training Data:\", X_train['days_b_screening_arrest'].mean())\n", + "print(\"Average # of Days Before Screening Arrest in Testing Data:\", X_test['days_b_screening_arrest'].mean())\n", + "print(\"---------------------------------------------\")\n", + "print(\"Average # of Days in Jail in Training Data:\", X_train['c_jail_time'].mean())\n", + "print(\"Average # of Days in Jail in Testing Data:\", X_test['c_jail_time'].mean())\n", + "print(\"---------------------------------------------\")\n", + "print(\"Average # of Juvenile Felony Counts in Training Data:\", X_train['juv_fel_count'].mean())\n", + "print(\"Average # of Juvenile Felony Counts in Testing Data:\", X_test['juv_fel_count'].mean())\n", + "print(\"---------------------------------------------\")\n", + "print(\"Average # of Other Juvenile Counts in Training Data:\", X_train['juv_other_count'].mean())\n", + "print(\"Average # of Other Juvenile Counts in Testing Data:\", X_test['juv_other_count'].mean())\n", + "print(\"---------------------------------------------\")\n", + "print(\"Average # of Juvenile Misdemeanor Counts in Training Data:\", X_train['juv_misd_count'].mean())\n", + "print(\"Average # of Juvenile Misdemeanor Counts in Testing Data:\", X_test['juv_misd_count'].mean())\n", + "print(\"---------------------------------------------\")\n", + "print(\"Probability of Felony Charge in Training Data:\", X_train['c_charge_degree:F'].mean())\n", + "print(\"Probability of Felony Charge in Testing Data:\", X_test['c_charge_degree:F'].mean())\n", + "print(\"---------------------------------------------\")\n", + "print(\"Proportion of African-American Defendants in Training Data:\", X_train['race:African-American'].mean())\n", + "print(\"Proportion of African-American Defendants in Testing Data:\", X_test['race:African-American'].mean())\n", + "print(\"---------------------------------------------\")\n", + "print(\"Proportion of Caucasian Defendants in Training Data:\", X_train['race:Caucasian'].mean())\n", + "print(\"Proportion of Caucasian Defendants in Testing Data:\", X_test['race:Caucasian'].mean())\n", + "print(\"---------------------------------------------\")\n", + "print(\"Proportion of Defendants Between Ages 25 & 45 in Training Data:\", X_train['age_cat:25_-_45'].mean())\n", + "print(\"Proportion of Defendants Between Ages 25 & 45 in Testing Data:\", X_test['age_cat:25_-_45'].mean())\n", + "print(\"---------------------------------------------\")\n", + "print(\"Proportion of Defendants Over Age 45 in Training Data:\", X_train['age_cat:Greater_than_45'].mean())\n", + "print(\"Proportion of Defendants Over Age 45 in Testing Data:\", X_test['age_cat:Greater_than_45'].mean())\n", + "print(\"---------------------------------------------\")\n", + "print(\"Proportion of Defendants Under Age 25 in Training Data:\", X_train['age_cat:Less_than_25'].mean())\n", + "print(\"Proportion of Defendants Under Age 25 in Testing Data:\", X_test['age_cat:Less_than_25'].mean())\n", + "print(\"---------------------------------------------\")\n", + "print(\"Proportion of Women in Training Data:\", X_train['sex:Female'].mean())\n", + "print(\"Proportion of Women in Testing Data:\", X_test['sex:Female'].mean())\n", + "print(\"---------------------------------------------\")\n", + "print(\"Probability of Recidivism in Training Data:\", y_train.mean())\n", + "print(\"Probability of Recidivism in Testing Data:\", y_test.mean())\n", + "print(\"---------------------------------------------\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The train and test datasets seem reasonable, so we continue with our analysis.\n", + "# Baseline 'Global' Model\n", + "We begin by fitting a RF+ to the training data. The accuracy, AUROC, AUPRC, and F1 on the test data is reported below. The total number misclassified is also reported, as we will use this to compare the 'global' model fit on all of the data to the 'local' models fit on each cluster. We also report the results of a simple logistic regression model, as we will fit logistic regression models to the clusters to determine if splitting into subgroups makes the decision rule easier. However, we will see that logistic regression outperforms RF+ on this task, so perhaps this is not the best dataset to do this with." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 16 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 18 tasks | elapsed: 9.4s\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 25.3s finished\n" + ] + } + ], + "source": [ + "# fit RF+ model\n", + "rf = RandomForestClassifier(n_estimators=100, random_state=0)\n", + "rf.fit(X_train, y_train)\n", + "rf_plus = RandomForestPlusClassifier(rf, prediction_model = LogisticRegression())\n", + "rf_plus.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
LogisticRegression(max_iter=1000, random_state=0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "LogisticRegression(max_iter=1000, random_state=0)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# fit logistic model\n", + "log = LogisticRegression(random_state=0, max_iter=1000)\n", + "log.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RF+ Test Set Accuracy: 0.6275252525252525\n", + "RF+ Test Set # Misclassified: 590\n", + "RF+ Test Set AUROC: 0.6820367523221395\n", + "RF+ Test Set AUPRC: 0.6682569037944643\n", + "RF+ Test Set F1 Score: 0.6103038309114928\n", + "---------------------------------------------\n", + "Logistic Test Set Accuracy: 0.6628787878787878\n", + "Logistic Test Set # Misclassified: 534\n", + "Logistic Test Set AUROC: 0.7149868901194243\n", + "Logistic Test Set AUPRC: 0.7064967979499193\n", + "Logistic Test Set F1 Score: 0.6103038309114928\n" + ] + } + ], + "source": [ + "# compute accuracy and # misclassified on the test set\n", + "y_pred = rf_plus.predict(X_test)\n", + "global_acc = np.mean(y_test == y_pred)\n", + "print(f'RF+ Test Set Accuracy: {np.mean(y_test == y_pred)}')\n", + "global_misclassified = np.sum(y_test != y_pred)\n", + "print(f'RF+ Test Set # Misclassified: {np.sum(y_test != y_pred)}')\n", + "# compute auroc, auprc, and f1 score on test set\n", + "y_pred_prob = rf_plus.predict_proba(X_test)[:, 1]\n", + "auroc = roc_auc_score(y_test, y_pred_prob)\n", + "auprc = average_precision_score(y_test, y_pred_prob)\n", + "f1 = f1_score(y_test, y_pred)\n", + "print(f'RF+ Test Set AUROC: {auroc}')\n", + "print(f'RF+ Test Set AUPRC: {auprc}')\n", + "print(f'RF+ Test Set F1 Score: {f1}')\n", + "print('---------------------------------------------')\n", + "print(\"Logistic Test Set Accuracy:\", log.score(X_test, y_test))\n", + "print(\"Logistic Test Set # Misclassified:\", np.sum(y_test != log.predict(X_test)))\n", + "# compute auroc, auprc, and f1 score on test set\n", + "y_pred_prob = log.predict_proba(X_test)[:, 1]\n", + "auroc = roc_auc_score(y_test, y_pred_prob)\n", + "auprc = average_precision_score(y_test, y_pred_prob)\n", + "f1 = f1_score(y_test, y_pred)\n", + "print(f'Logistic Test Set AUROC: {auroc}')\n", + "print(f'Logistic Test Set AUPRC: {auprc}')\n", + "print(f'Logistic Test Set F1 Score: {f1}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the following sections, we will compute subgroups using the RF/RF+ fit on the training data. We will then assign each test point to these subgroups using various methods. This will give us clusters that are composed of both training and testing points, allowing us to fit separate RF/RF+'s to the training data in each of these subgroups and predict the testing data. This will allow us to compare the TSE of the global model to the summed TSEs of the 'local' models fit on the clusters. Intuitively, the drop in TSE acheived in the local models will be directly related to how 'accurate' the subgroups determined by local feature importance are.\n", + "\n", + "When clustering our data, we will compute the ranking-based overlap (RBO) between each pair of points, and then use this as the distance matrix for hierarchical clustering with Ward linkage. The number of clusters will be chosen based on the appearance of the heatmap. It may be worth trying this with a range of cluster amounts and checking how it changes model performance - we could perhaps make a train/validate/test split, where we choose the number of clusters that results in the lowest total squared error in the 'local' models (or perhaps make an elbow plot, since the performance should only improve as # of clusters increases)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Local MDI+\n", + "\n", + "To detect subgroups using Local MDI+, we convert the Local MDI+ scores to feature rankings, and then compute RBO, as described above. We compute LMDI+ for the training points by using $metric(y_i, \\hat{y}^{(k)}_i)$. The resulting clusters can be visualized below." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# get feature importances\n", + "mdi_explainer = RFPlusMDI(rf_plus)\n", + "mdi = mdi_explainer.explain_linear_partial(np.asarray(X_train), y_train)\n", + "mdi_rankings = mdi_explainer.get_rankings(mdi)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# get rbo distance matrix\n", + "rbo_train = compute_rbo_matrix(mdi_rankings, form = 'distance')" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0. , 0.55742876, 0.44754199, ..., 0.52776019, 0.47897967,\n", + " 0.50295596],\n", + " [0.55742876, 0. , 0.56299939, ..., 0.38079048, 0.26492448,\n", + " 0.3456257 ],\n", + " [0.44754199, 0.56299939, 0. , ..., 0.58235487, 0.5821001 ,\n", + " 0.59286178],\n", + " ...,\n", + " [0.52776019, 0.38079048, 0.58235487, ..., 0. , 0.25694213,\n", + " 0.15314445],\n", + " [0.47897967, 0.26492448, 0.5821001 , ..., 0.25694213, 0. ,\n", + " 0.23225884],\n", + " [0.50295596, 0.3456257 , 0.59286178, ..., 0.15314445, 0.23225884,\n", + " 0. ]])" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "The matrix argument must be square.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[16], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m mdi_copy \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(mdi, columns\u001b[38;5;241m=\u001b[39mX_train\u001b[38;5;241m.\u001b[39mcolumns)\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m 2\u001b[0m num_clusters \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m4\u001b[39m\n\u001b[0;32m----> 3\u001b[0m clusters \u001b[38;5;241m=\u001b[39m \u001b[43massign_training_clusters\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmdi_copy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrbo_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_clusters\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/research/imodels-experiments/feature_importance/subgroup/subgroup_detection.py:66\u001b[0m, in \u001b[0;36massign_training_clusters\u001b[0;34m(rbo_distance_matrix, num_clusters, linkage_method)\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21massign_training_clusters\u001b[39m(rbo_distance_matrix, num_clusters,\n\u001b[1;32m 63\u001b[0m linkage_method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mward\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m 64\u001b[0m \n\u001b[1;32m 65\u001b[0m \u001b[38;5;66;03m# convert to condensed distance matrix for scipy compatibility\u001b[39;00m\n\u001b[0;32m---> 66\u001b[0m condensed_distance_matrix \u001b[38;5;241m=\u001b[39m \u001b[43msquareform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrbo_distance_matrix\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 68\u001b[0m \u001b[38;5;66;03m# perform hierarchical clustering\u001b[39;00m\n\u001b[1;32m 69\u001b[0m linkage_matrix \u001b[38;5;241m=\u001b[39m linkage(condensed_distance_matrix, method\u001b[38;5;241m=\u001b[39mlinkage_method)\n", + "File \u001b[0;32m/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/scipy/spatial/distance.py:2324\u001b[0m, in \u001b[0;36msquareform\u001b[0;34m(X, force, checks)\u001b[0m\n\u001b[1;32m 2322\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(s) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m 2323\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m s[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m!=\u001b[39m s[\u001b[38;5;241m1\u001b[39m]:\n\u001b[0;32m-> 2324\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe matrix argument must be square.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 2325\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m checks:\n\u001b[1;32m 2326\u001b[0m is_valid_dm(X, throw\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "\u001b[0;31mValueError\u001b[0m: The matrix argument must be square." + ] + } + ], + "source": [ + "mdi_copy = pd.DataFrame(mdi, columns=X_train.columns).copy()\n", + "num_clusters = 4\n", + "clusters = assign_training_clusters(mdi_copy, rbo_train, num_clusters)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is easy to see that these subgroups differ in feature importance, as shown above. However, just because they differ in feature importance does not necessarily imply that they differ in the values of those features themselves. We now check some useful summary statistics for each cluster to better understand how we have grouped the data." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---------------------------------------------\n", + "Proportion of Data in Cluster #1: 0.4648077964266378\n", + "Proportion of Data in Cluster #2: 0.17569030860855442\n", + "Proportion of Data in Cluster #3: 0.10341093665403357\n", + "Proportion of Data in Cluster #4: 0.2560909583107742\n", + "---------------------------------------------\n", + "Average # of Priors in Cluster #1: 4.2312174\n", + "Average # of Priors in Cluster #2: 2.890601\n", + "Average # of Priors in Cluster #3: 3.0183246\n", + "Average # of Priors in Cluster #4: 2.7653277\n", + "---------------------------------------------\n", + "Average # of Days Before Screening Arrest in Cluster #1: -1.3750728\n", + "Average # of Days Before Screening Arrest in Cluster #2: -2.3066256\n", + "Average # of Days Before Screening Arrest in Cluster #3: -1.9371728\n", + "Average # of Days Before Screening Arrest in Cluster #4: -2.0972517\n", + "---------------------------------------------\n", + "Average Jail Time (Days) in Cluster #1: 13.998253\n", + "Average Jail Time (Days) in Cluster #2: 12.080123\n", + "Average Jail Time (Days) in Cluster #3: 15.83246\n", + "Average Jail Time (Days) in Cluster #4: 18.19979\n", + "---------------------------------------------\n", + "Average # of Juvenile Felony Counts in Cluster #1: 0.0832848\n", + "Average # of Juvenile Felony Counts in Cluster #2: 0.018489985\n", + "Average # of Juvenile Felony Counts in Cluster #3: 0.015706806\n", + "Average # of Juvenile Felony Counts in Cluster #4: 0.08668076\n", + "---------------------------------------------\n", + "Average # of Juvenile 'Other' Counts in Cluster #1: 0.089108914\n", + "Average # of Juvenile 'Other' Counts in Cluster #2: 0.0030816642\n", + "Average # of Juvenile 'Other' Counts in Cluster #3: 0.041884817\n", + "Average # of Juvenile 'Other' Counts in Cluster #4: 0.3012685\n", + "---------------------------------------------\n", + "Average # of Juvenile Misdemeanor Counts in Cluster #1: 0.10075714\n", + "Average # of Juvenile Misdemeanor Counts in Cluster #2: 0.012326657\n", + "Average # of Juvenile Misdemeanor Counts in Cluster #3: 0.034031413\n", + "Average # of Juvenile Misdemeanor Counts in Cluster #4: 0.1871036\n", + "---------------------------------------------\n", + "Probability of Felony Charge in Cluster #1: 0.6499709\n", + "Probability of Felony Charge in Cluster #2: 0.6101695\n", + "Probability of Felony Charge in Cluster #3: 0.67277485\n", + "Probability of Felony Charge in Cluster #4: 0.70084566\n", + "---------------------------------------------\n", + "Proportion of African-American Defendants in Cluster #1: 0.613279\n", + "Proportion of African-American Defendants in Cluster #2: 0.51309705\n", + "Proportion of African-American Defendants in Cluster #3: 0.5837696\n", + "Proportion of African-American Defendants in Cluster #4: 0.63424945\n", + "---------------------------------------------\n", + "Proportion of Defendants Aged 25-45 in Cluster #1: 0.63715786\n", + "Proportion of Defendants Aged 25-45 in Cluster #2: 0.5362095\n", + "Proportion of Defendants Aged 25-45 in Cluster #3: 0.73036647\n", + "Proportion of Defendants Aged 25-45 in Cluster #4: 0.4154334\n", + "---------------------------------------------\n", + "Proportion of Defendants Over Age 45 in Cluster #1: 0.16773441\n", + "Proportion of Defendants Over Age 45 in Cluster #2: 0.45762712\n", + "Proportion of Defendants Over Age 45 in Cluster #3: 0.17015707\n", + "Proportion of Defendants Over Age 45 in Cluster #4: 0.10147992\n", + "---------------------------------------------\n", + "Proportion of Defendants Under Age 25 in Cluster #1: 0.19510774\n", + "Proportion of Defendants Under Age 25 in Cluster #2: 0.0061633284\n", + "Proportion of Defendants Under Age 25 in Cluster #3: 0.09947644\n", + "Proportion of Defendants Under Age 25 in Cluster #4: 0.48308668\n", + "---------------------------------------------\n", + "Proportion of Men in Cluster #1: 0.7815958\n", + "Proportion of Men in Cluster #2: 0.8412943\n", + "Proportion of Men in Cluster #3: 0.7905759\n", + "Proportion of Men in Cluster #4: 0.8446089\n", + "Probability of Recidivism in Cluster #1: 0.41292952824694235\n", + "Probability of Recidivism in Cluster #2: 0.5423728813559322\n", + "Probability of Recidivism in Cluster #3: 0.4109947643979058\n", + "Probability of Recidivism in Cluster #4: 0.6902748414376322\n", + "---------------------------------------------\n" + ] + } + ], + "source": [ + "print(\"---------------------------------------------\")\n", + "# calculate average charge for each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Proportion of Data in Cluster #{i+1}:\",\n", + " X_train[clusters==i+1].shape[0]/X_train.shape[0])\n", + "print(\"---------------------------------------------\")\n", + "# get average number of priors in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Average # of Priors in Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['priors_count'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get average # of days before screening arrest in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Average # of Days Before Screening Arrest in Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['days_b_screening_arrest'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get average jail time in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Average Jail Time (Days) in Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['c_jail_time'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get average # of juvenile felony counts in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Average # of Juvenile Felony Counts in Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['juv_fel_count'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get average number juvenile 'other' counts in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Average # of Juvenile 'Other' Counts in Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['juv_other_count'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get average number of juvenile misdemeanor counts in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Average # of Juvenile Misdemeanor Counts in Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['juv_misd_count'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get probability of felony charge in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Probability of Felony Charge in Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['c_charge_degree:F'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get proportion of African-American defendants in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Proportion of African-American Defendants in Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['race:African-American'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get proportion of age 25-45 in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Proportion of Defendants Aged 25-45 in Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['age_cat:25_-_45'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get proportion of age over 45 in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Proportion of Defendants Over Age 45 in Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['age_cat:Greater_than_45'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get proportion of age under 25 in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Proportion of Defendants Under Age 25 in Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['age_cat:Less_than_25'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get proportion of men in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Proportion of Men in Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['sex:Male'].mean())\n", + "# get proportion of smokers in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Probability of Recidivism in Cluster #{i+1}:\",\n", + " y_train[clusters==i+1].mean())\n", + "print(\"---------------------------------------------\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evaluating Cluster Performance - Centroid Method w/ Exact Mean\n", + "Now that we have a good idea of what our clustering has done, we can check if this helps improve our predictions. We compute LMDI+ of test points using $metric(\\hat{y}_i, \\hat{y}^{(k)}_i)$. We will take the test points and determine their cluster membership based on their RBO similarity to the mean point in each cluster (in RBO embedding). We will then fit a RF+ on the training data and using it to predict the test data for that cluster. We can then compute the R^2 and total squared error for each cluster's model. By summing the TSE across cluster models and comparing this to the original TSE reported above, we can get a good idea of how well these clusters improve model accuracy." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# get mdi rankings assignments for test points\n", + "mdi_test, partial_preds_test = mdi_explainer.explain(np.asarray(X_test))\n", + "mdi_test_rankings = mdi_explainer.get_rankings(mdi_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "test_clust = assign_testing_clusters(method = \"centroid\", median_approx = False,\n", + " rbo_distance_matrix = rbo_train,\n", + " lfi_train_ranking = mdi_rankings,\n", + " lfi_test_ranking = mdi_test_rankings,\n", + " clusters = clusters)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "cluster1_trainX = X_train[clusters == 1]\n", + "cluster2_trainX = X_train[clusters == 2]\n", + "cluster3_trainX = X_train[clusters == 3]\n", + "cluster4_trainX = X_train[clusters == 4]\n", + "\n", + "cluster1_trainy = y_train[clusters == 1]\n", + "cluster2_trainy = y_train[clusters == 2]\n", + "cluster3_trainy = y_train[clusters == 3]\n", + "cluster4_trainy = y_train[clusters == 4]\n", + "\n", + "cluster1_testX = X_test[test_clust == 1]\n", + "cluster2_testX = X_test[test_clust == 2]\n", + "cluster3_testX = X_test[test_clust == 3]\n", + "cluster4_testX = X_test[test_clust == 4]\n", + "\n", + "cluster1_testy = y_test[test_clust == 1]\n", + "cluster2_testy = y_test[test_clust == 2]\n", + "cluster3_testy = y_test[test_clust == 3]\n", + "cluster4_testy = y_test[test_clust == 4]" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---------------------------------------------\n", + "Proportion of Train Data in Cluster #1: 0.4648077964266378\n", + "Proportion of Test Data in Cluster #1: 0.5195707070707071\n", + "Proportion of Train Data in Cluster #2: 0.17569030860855442\n", + "Proportion of Test Data in Cluster #2: 0.09848484848484848\n", + "Proportion of Train Data in Cluster #3: 0.10341093665403357\n", + "Proportion of Test Data in Cluster #3: 0.28914141414141414\n", + "Proportion of Train Data in Cluster #4: 0.2560909583107742\n", + "Proportion of Test Data in Cluster #4: 0.0928030303030303\n", + "---------------------------------------------\n", + "Average # of Priors in Train Cluster #1: 4.2312174\n", + "Average # of Priors in Test Cluster #1: 4.1907654\n", + "Average # of Priors in Train Cluster #2: 2.890601\n", + "Average # of Priors in Test Cluster #2: 2.3012822\n", + "Average # of Priors in Train Cluster #3: 3.0183246\n", + "Average # of Priors in Test Cluster #3: 2.617904\n", + "Average # of Priors in Train Cluster #4: 2.7653277\n", + "Average # of Priors in Test Cluster #4: 2.4013605\n", + "---------------------------------------------\n", + "Average # of Days Before Screening Arrest in Train Cluster #1: -1.3750728\n", + "Average # of Days Before Screening Arrest in Test Cluster #1: -1.3207777\n", + "Average # of Days Before Screening Arrest in Train Cluster #2: -2.3066256\n", + "Average # of Days Before Screening Arrest in Test Cluster #2: -2.0320513\n", + "Average # of Days Before Screening Arrest in Train Cluster #3: -1.9371728\n", + "Average # of Days Before Screening Arrest in Test Cluster #3: -1.4344978\n", + "Average # of Days Before Screening Arrest in Train Cluster #4: -2.0972517\n", + "Average # of Days Before Screening Arrest in Test Cluster #4: -3.312925\n", + "---------------------------------------------\n", + "Average Jail Time (Days) in Train Cluster #1: 13.998253\n", + "Average Jail Time (Days) in Test Cluster #1: 12.244228\n", + "Average Jail Time (Days) in Train Cluster #2: 12.080123\n", + "Average Jail Time (Days) in Test Cluster #2: 5.6858974\n", + "Average Jail Time (Days) in Train Cluster #3: 15.83246\n", + "Average Jail Time (Days) in Test Cluster #3: 26.657206\n", + "Average Jail Time (Days) in Train Cluster #4: 18.19979\n", + "Average Jail Time (Days) in Test Cluster #4: 10.170068\n", + "---------------------------------------------\n", + "Average # of Juvenile Felony Counts in Train Cluster #1: 0.0832848\n", + "Average # of Juvenile Felony Counts in Test Cluster #1: 0.07533415\n", + "Average # of Juvenile Felony Counts in Train Cluster #2: 0.018489985\n", + "Average # of Juvenile Felony Counts in Test Cluster #2: 0.012820513\n", + "Average # of Juvenile Felony Counts in Train Cluster #3: 0.015706806\n", + "Average # of Juvenile Felony Counts in Test Cluster #3: 0.019650655\n", + "Average # of Juvenile Felony Counts in Train Cluster #4: 0.08668076\n", + "Average # of Juvenile Felony Counts in Test Cluster #4: 0.054421768\n", + "---------------------------------------------\n", + "Average # of Juvenile 'Other' Counts in Train Cluster #1: 0.089108914\n", + "Average # of Juvenile 'Other' Counts in Test Cluster #1: 0.13122721\n", + "Average # of Juvenile 'Other' Counts in Train Cluster #2: 0.0030816642\n", + "Average # of Juvenile 'Other' Counts in Test Cluster #2: 0.070512824\n", + "Average # of Juvenile 'Other' Counts in Train Cluster #3: 0.041884817\n", + "Average # of Juvenile 'Other' Counts in Test Cluster #3: 0.05458515\n", + "Average # of Juvenile 'Other' Counts in Train Cluster #4: 0.3012685\n", + "Average # of Juvenile 'Other' Counts in Test Cluster #4: 0.19727892\n", + "---------------------------------------------\n", + "Average # of Juvenile Misdemeanor Counts in Train Cluster #1: 0.10075714\n", + "Average # of Juvenile Misdemeanor Counts in Test Cluster #1: 0.13244228\n", + "Average # of Juvenile Misdemeanor Counts in Train Cluster #2: 0.012326657\n", + "Average # of Juvenile Misdemeanor Counts in Test Cluster #2: 0.070512824\n", + "Average # of Juvenile Misdemeanor Counts in Train Cluster #3: 0.034031413\n", + "Average # of Juvenile Misdemeanor Counts in Test Cluster #3: 0.048034936\n", + "Average # of Juvenile Misdemeanor Counts in Train Cluster #4: 0.1871036\n", + "Average # of Juvenile Misdemeanor Counts in Test Cluster #4: 0.04761905\n", + "---------------------------------------------\n", + "Probability of Felony Charge in Train Cluster #1: 0.6499709\n", + "Probability of Felony Charge in Test Cluster #1: 0.636695\n", + "Probability of Felony Charge in Train Cluster #2: 0.6101695\n", + "Probability of Felony Charge in Test Cluster #2: 0.5\n", + "Probability of Felony Charge in Train Cluster #3: 0.67277485\n", + "Probability of Felony Charge in Test Cluster #3: 0.6528384\n", + "Probability of Felony Charge in Train Cluster #4: 0.70084566\n", + "Probability of Felony Charge in Test Cluster #4: 0.72789115\n", + "---------------------------------------------\n", + "Proportion of African-American Defendants in Train Cluster #1: 0.613279\n", + "Proportion of African-American Defendants in Test Cluster #1: 0.6597813\n", + "Proportion of African-American Defendants in Train Cluster #2: 0.51309705\n", + "Proportion of African-American Defendants in Test Cluster #2: 0.41025642\n", + "Proportion of African-American Defendants in Train Cluster #3: 0.5837696\n", + "Proportion of African-American Defendants in Test Cluster #3: 0.5480349\n", + "Proportion of African-American Defendants in Train Cluster #4: 0.63424945\n", + "Proportion of African-American Defendants in Test Cluster #4: 0.7346939\n", + "---------------------------------------------\n", + "Proportion of Defendants Aged 25-45 in Train Cluster #1: 0.63715786\n", + "Proportion of Defendants Aged 25-45 in Test Cluster #1: 0.6925881\n", + "Proportion of Defendants Aged 25-45 in Train Cluster #2: 0.5362095\n", + "Proportion of Defendants Aged 25-45 in Test Cluster #2: 0.23717949\n", + "Proportion of Defendants Aged 25-45 in Train Cluster #3: 0.73036647\n", + "Proportion of Defendants Aged 25-45 in Test Cluster #3: 0.6113537\n", + "Proportion of Defendants Aged 25-45 in Train Cluster #4: 0.4154334\n", + "Proportion of Defendants Aged 25-45 in Test Cluster #4: 0.17006803\n", + "---------------------------------------------\n", + "Proportion of Defendants Over Age 45 in Train Cluster #1: 0.16773441\n", + "Proportion of Defendants Over Age 45 in Test Cluster #1: 0.16889429\n", + "Proportion of Defendants Over Age 45 in Train Cluster #2: 0.45762712\n", + "Proportion of Defendants Over Age 45 in Test Cluster #2: 0.6858974\n", + "Proportion of Defendants Over Age 45 in Train Cluster #3: 0.17015707\n", + "Proportion of Defendants Over Age 45 in Test Cluster #3: 0.15938865\n", + "Proportion of Defendants Over Age 45 in Train Cluster #4: 0.10147992\n", + "Proportion of Defendants Over Age 45 in Test Cluster #4: 0.21088435\n", + "---------------------------------------------\n", + "Proportion of Defendants Under Age 25 in Train Cluster #1: 0.19510774\n", + "Proportion of Defendants Under Age 25 in Test Cluster #1: 0.13851762\n", + "Proportion of Defendants Under Age 25 in Train Cluster #2: 0.0061633284\n", + "Proportion of Defendants Under Age 25 in Test Cluster #2: 0.07692308\n", + "Proportion of Defendants Under Age 25 in Train Cluster #3: 0.09947644\n", + "Proportion of Defendants Under Age 25 in Test Cluster #3: 0.22925764\n", + "Proportion of Defendants Under Age 25 in Train Cluster #4: 0.48308668\n", + "Proportion of Defendants Under Age 25 in Test Cluster #4: 0.61904764\n", + "---------------------------------------------\n", + "Proportion of Men in Train Cluster #1: 0.7815958\n", + "Proportion of Men in Test Cluster #1: 0.7764277\n", + "Proportion of Men in Train Cluster #2: 0.8412943\n", + "Proportion of Men in Test Cluster #2: 0.8333333\n", + "Proportion of Men in Train Cluster #3: 0.7905759\n", + "Proportion of Men in Test Cluster #3: 0.8122271\n", + "Proportion of Men in Train Cluster #4: 0.8446089\n", + "Proportion of Men in Test Cluster #4: 0.79591835\n", + "Probability of Recidivism in Train Cluster #1: 0.41292952824694235\n", + "Probability of Recidivism in Test Cluster #1: 0.47144592952612396\n", + "Probability of Recidivism in Train Cluster #2: 0.5423728813559322\n", + "Probability of Recidivism in Test Cluster #2: 0.3782051282051282\n", + "Probability of Recidivism in Train Cluster #3: 0.4109947643979058\n", + "Probability of Recidivism in Test Cluster #3: 0.5589519650655022\n", + "Probability of Recidivism in Train Cluster #4: 0.6902748414376322\n", + "Probability of Recidivism in Test Cluster #4: 0.4965986394557823\n", + "---------------------------------------------\n" + ] + } + ], + "source": [ + "print(\"---------------------------------------------\")\n", + "# calculate average charge for each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Proportion of Train Data in Cluster #{i+1}:\",\n", + " X_train[clusters==i+1].shape[0]/X_train.shape[0])\n", + " print(f\"Proportion of Test Data in Cluster #{i+1}:\",\n", + " X_test[test_clust==i+1].shape[0]/X_test.shape[0])\n", + "print(\"---------------------------------------------\")\n", + "# get average number of priors in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Average # of Priors in Train Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['priors_count'].mean())\n", + " print(f\"Average # of Priors in Test Cluster #{i+1}:\",\n", + " X_test[test_clust==i+1]['priors_count'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get average # of days before screening arrest in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Average # of Days Before Screening Arrest in Train Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['days_b_screening_arrest'].mean())\n", + " print(f\"Average # of Days Before Screening Arrest in Test Cluster #{i+1}:\",\n", + " X_test[test_clust==i+1]['days_b_screening_arrest'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get average jail time in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Average Jail Time (Days) in Train Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['c_jail_time'].mean())\n", + " print(f\"Average Jail Time (Days) in Test Cluster #{i+1}:\",\n", + " X_test[test_clust==i+1]['c_jail_time'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get average # of juvenile felony counts in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Average # of Juvenile Felony Counts in Train Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['juv_fel_count'].mean())\n", + " print(f\"Average # of Juvenile Felony Counts in Test Cluster #{i+1}:\",\n", + " X_test[test_clust==i+1]['juv_fel_count'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get average number juvenile 'other' counts in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Average # of Juvenile 'Other' Counts in Train Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['juv_other_count'].mean())\n", + " print(f\"Average # of Juvenile 'Other' Counts in Test Cluster #{i+1}:\",\n", + " X_test[test_clust==i+1]['juv_other_count'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get average number of juvenile misdemeanor counts in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Average # of Juvenile Misdemeanor Counts in Train Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['juv_misd_count'].mean())\n", + " print(f\"Average # of Juvenile Misdemeanor Counts in Test Cluster #{i+1}:\",\n", + " X_test[test_clust==i+1]['juv_misd_count'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get probability of felony charge in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Probability of Felony Charge in Train Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['c_charge_degree:F'].mean())\n", + " print(f\"Probability of Felony Charge in Test Cluster #{i+1}:\",\n", + " X_test[test_clust==i+1]['c_charge_degree:F'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get proportion of African-American defendants in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Proportion of African-American Defendants in Train Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['race:African-American'].mean())\n", + " print(f\"Proportion of African-American Defendants in Test Cluster #{i+1}:\",\n", + " X_test[test_clust==i+1]['race:African-American'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get proportion of age 25-45 in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Proportion of Defendants Aged 25-45 in Train Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['age_cat:25_-_45'].mean())\n", + " print(f\"Proportion of Defendants Aged 25-45 in Test Cluster #{i+1}:\",\n", + " X_test[test_clust==i+1]['age_cat:25_-_45'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get proportion of age over 45 in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Proportion of Defendants Over Age 45 in Train Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['age_cat:Greater_than_45'].mean())\n", + " print(f\"Proportion of Defendants Over Age 45 in Test Cluster #{i+1}:\",\n", + " X_test[test_clust==i+1]['age_cat:Greater_than_45'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get proportion of age under 25 in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Proportion of Defendants Under Age 25 in Train Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['age_cat:Less_than_25'].mean())\n", + " print(f\"Proportion of Defendants Under Age 25 in Test Cluster #{i+1}:\",\n", + " X_test[test_clust==i+1]['age_cat:Less_than_25'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get proportion of men in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Proportion of Men in Train Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['sex:Male'].mean())\n", + " print(f\"Proportion of Men in Test Cluster #{i+1}:\",\n", + " X_test[test_clust==i+1]['sex:Male'].mean())\n", + "# get proportion of smokers in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Probability of Recidivism in Train Cluster #{i+1}:\",\n", + " y_train[clusters==i+1].mean())\n", + " print(f\"Probability of Recidivism in Test Cluster #{i+1}:\",\n", + " y_test[test_clust==i+1].mean())\n", + "print(\"---------------------------------------------\")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 32 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 34 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 36 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 39 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 33 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 37 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 37 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 34 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 31 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 37 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 33 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 33 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 39 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 36 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 37 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 34 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 32 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 33 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 35 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 34 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 36 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 97 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n" + ] + } + ], + "source": [ + "%%capture\n", + "# fit RF+ on each training set, predict test\n", + "rf1 = RandomForestClassifier(n_estimators=100, random_state=0)\n", + "rf_plus1 = RandomForestPlusClassifier(rf1)\n", + "rf_plus1.fit(cluster1_trainX, cluster1_trainy)\n", + "\n", + "rf2 = RandomForestClassifier(n_estimators=100, random_state=1)\n", + "rf_plus2 = RandomForestPlusClassifier(rf2)\n", + "rf_plus2.fit(cluster2_trainX, cluster2_trainy)\n", + "\n", + "rf3 = RandomForestClassifier(n_estimators=100, random_state=0)\n", + "rf_plus3 = RandomForestPlusClassifier(rf3)\n", + "rf_plus3.fit(cluster3_trainX, cluster3_trainy)\n", + "\n", + "rf4 = RandomForestClassifier(n_estimators=100, random_state=0)\n", + "rf_plus4 = RandomForestPlusClassifier(rf4)\n", + "rf_plus4.fit(cluster4_trainX, cluster4_trainy)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---------------------------------------------\n", + "Local RF+ Cluster #1 Test Set Accuracy: 0.6670716889428918\n", + "Local RF+ Cluster #1 Test Set # Misclassified: 274\n", + "Local RF+ Cluster #1 Test Set AUROC: 0.7038185804005214\n", + "Local RF+ Cluster #1 Test Set AUPRC: 0.6871291256779989\n", + "Local RF+ Cluster #1 Test Set F1 Score: 0.6256830601092896\n", + "Global RF+ Cluster #1 Test Set Accuracy: 0.6865127582017011\n", + "Global RF+ Cluster #1 Test Set # Misclassified: 258\n", + "Global RF+ Cluster #1 Test Set AUROC: 0.704950231070032\n", + "Global RF+ Cluster #1 Test Set AUPRC: 0.667177026826716\n", + "Global RF+ Cluster #1 Test Set F1 Score: 0.6426592797783933\n", + "---------------------------------------------\n", + "Local RF+ Cluster #2 Test Set Accuracy: 0.6794871794871795\n", + "Local RF+ Cluster #2 Test Set # Misclassified: 50\n", + "Local RF+ Cluster #2 Test Set AUROC: 0.6037917176306135\n", + "Local RF+ Cluster #2 Test Set AUPRC: 0.4487923045844864\n", + "Local RF+ Cluster #2 Test Set F1 Score: 0.5370370370370371\n", + "Global RF+ Cluster #2 Test Set Accuracy: 0.6794871794871795\n", + "Global RF+ Cluster #2 Test Set # Misclassified: 50\n", + "Global RF+ Cluster #2 Test Set AUROC: 0.6539402411322733\n", + "Global RF+ Cluster #2 Test Set AUPRC: 0.5666661344714743\n", + "Global RF+ Cluster #2 Test Set F1 Score: 0.5370370370370371\n", + "---------------------------------------------\n", + "Local RF+ Cluster #3 Test Set Accuracy: 0.5807860262008734\n", + "Local RF+ Cluster #3 Test Set # Misclassified: 192\n", + "Local RF+ Cluster #3 Test Set AUROC: 0.4783705909653465\n", + "Local RF+ Cluster #3 Test Set AUPRC: 0.5223780859167565\n", + "Local RF+ Cluster #3 Test Set F1 Score: 0.5534883720930233\n", + "Global RF+ Cluster #3 Test Set Accuracy: 0.517467248908297\n", + "Global RF+ Cluster #3 Test Set # Misclassified: 221\n", + "Global RF+ Cluster #3 Test Set AUROC: 0.538685411509901\n", + "Global RF+ Cluster #3 Test Set AUPRC: 0.6078467551307416\n", + "Global RF+ Cluster #3 Test Set F1 Score: 0.5462012320328542\n", + "---------------------------------------------\n", + "Local RF+ Cluster #4 Test Set Accuracy: 0.5102040816326531\n", + "Local RF+ Cluster #4 Test Set # Misclassified: 72\n", + "Local RF+ Cluster #4 Test Set AUROC: 0.555905220288782\n", + "Local RF+ Cluster #4 Test Set AUPRC: 0.5853340167854038\n", + "Local RF+ Cluster #4 Test Set F1 Score: 0.6363636363636364\n", + "Global RF+ Cluster #4 Test Set Accuracy: 0.564625850340136\n", + "Global RF+ Cluster #4 Test Set # Misclassified: 64\n", + "Global RF+ Cluster #4 Test Set AUROC: 0.6533691225472047\n", + "Global RF+ Cluster #4 Test Set AUPRC: 0.6412040372413288\n", + "Global RF+ Cluster #4 Test Set F1 Score: 0.6559139784946236\n", + "---------------------------------------------\n" + ] + } + ], + "source": [ + "print(\"---------------------------------------------\")\n", + "y_pred1 = rf_plus1.predict(cluster1_testX)\n", + "acc1l = np.mean(cluster1_testy == y_pred1)\n", + "mis1l = np.sum(cluster1_testy != y_pred1)\n", + "y_pred1g = rf_plus.predict(cluster1_testX)\n", + "acc1g = np.mean(cluster1_testy == y_pred1g)\n", + "mis1g = np.sum(cluster1_testy != y_pred1g)\n", + "y_pred_prob1 = rf_plus1.predict_proba(cluster1_testX)[:, 1]\n", + "auroc1l = roc_auc_score(cluster1_testy, y_pred_prob1)\n", + "auprc1l = average_precision_score(cluster1_testy, y_pred_prob1)\n", + "f1_1l = f1_score(cluster1_testy, y_pred1)\n", + "y_pred_prob1g = rf_plus.predict_proba(cluster1_testX)[:, 1]\n", + "auroc1g = roc_auc_score(cluster1_testy, y_pred_prob1g)\n", + "auprc1g = average_precision_score(cluster1_testy, y_pred_prob1g)\n", + "f1_1g = f1_score(cluster1_testy, y_pred1g)\n", + "print(f'Local RF+ Cluster #1 Test Set Accuracy: {acc1l}')\n", + "print(f'Local RF+ Cluster #1 Test Set # Misclassified: {mis1l}')\n", + "print(f'Local RF+ Cluster #1 Test Set AUROC: {auroc1l}')\n", + "print(f'Local RF+ Cluster #1 Test Set AUPRC: {auprc1l}')\n", + "print(f'Local RF+ Cluster #1 Test Set F1 Score: {f1_1l}')\n", + "print(f'Global RF+ Cluster #1 Test Set Accuracy: {acc1g}')\n", + "print(f'Global RF+ Cluster #1 Test Set # Misclassified: {mis1g}')\n", + "print(f'Global RF+ Cluster #1 Test Set AUROC: {auroc1g}')\n", + "print(f'Global RF+ Cluster #1 Test Set AUPRC: {auprc1g}')\n", + "print(f'Global RF+ Cluster #1 Test Set F1 Score: {f1_1g}')\n", + "print(\"---------------------------------------------\")\n", + "y_pred2 = rf_plus2.predict(cluster2_testX)\n", + "acc2l = np.mean(cluster2_testy == y_pred2)\n", + "mis2l = np.sum(cluster2_testy != y_pred2)\n", + "y_pred2g = rf_plus.predict(cluster2_testX)\n", + "acc2g = np.mean(cluster2_testy == y_pred2g)\n", + "mis2g = np.sum(cluster2_testy != y_pred2g)\n", + "y_pred_prob2 = rf_plus2.predict_proba(cluster2_testX)[:, 1]\n", + "auroc2l = roc_auc_score(cluster2_testy, y_pred_prob2)\n", + "auprc2l = average_precision_score(cluster2_testy, y_pred_prob2)\n", + "f1_2l = f1_score(cluster2_testy, y_pred2)\n", + "y_pred_prob2g = rf_plus.predict_proba(cluster2_testX)[:, 1]\n", + "auroc2g = roc_auc_score(cluster2_testy, y_pred_prob2g)\n", + "auprc2g = average_precision_score(cluster2_testy, y_pred_prob2g)\n", + "f1_2g = f1_score(cluster2_testy, y_pred2g)\n", + "print(f'Local RF+ Cluster #2 Test Set Accuracy: {acc2l}')\n", + "print(f'Local RF+ Cluster #2 Test Set # Misclassified: {mis2l}')\n", + "print(f'Local RF+ Cluster #2 Test Set AUROC: {auroc2l}')\n", + "print(f'Local RF+ Cluster #2 Test Set AUPRC: {auprc2l}')\n", + "print(f'Local RF+ Cluster #2 Test Set F1 Score: {f1_2l}')\n", + "print(f'Global RF+ Cluster #2 Test Set Accuracy: {acc2g}')\n", + "print(f'Global RF+ Cluster #2 Test Set # Misclassified: {mis2g}')\n", + "print(f'Global RF+ Cluster #2 Test Set AUROC: {auroc2g}')\n", + "print(f'Global RF+ Cluster #2 Test Set AUPRC: {auprc2g}')\n", + "print(f'Global RF+ Cluster #2 Test Set F1 Score: {f1_2g}')\n", + "print(\"---------------------------------------------\")\n", + "y_pred3 = rf_plus1.predict(cluster3_testX)\n", + "acc3l = np.mean(cluster3_testy == y_pred3)\n", + "mis3l = np.sum(cluster3_testy != y_pred3)\n", + "y_pred3g = rf_plus.predict(cluster3_testX)\n", + "acc3g = np.mean(cluster3_testy == y_pred3g)\n", + "mis3g = np.sum(cluster3_testy != y_pred3g)\n", + "y_pred_prob3 = rf_plus3.predict_proba(cluster3_testX)[:, 1]\n", + "auroc3l = roc_auc_score(cluster3_testy, y_pred_prob3)\n", + "auprc3l = average_precision_score(cluster3_testy, y_pred_prob3)\n", + "f1_3l = f1_score(cluster3_testy, y_pred3)\n", + "y_pred_prob3g = rf_plus.predict_proba(cluster3_testX)[:, 1]\n", + "auroc3g = roc_auc_score(cluster3_testy, y_pred_prob3g)\n", + "auprc3g = average_precision_score(cluster3_testy, y_pred_prob3g)\n", + "f1_3g = f1_score(cluster3_testy, y_pred3g)\n", + "print(f'Local RF+ Cluster #3 Test Set Accuracy: {acc3l}')\n", + "print(f'Local RF+ Cluster #3 Test Set # Misclassified: {mis3l}')\n", + "print(f'Local RF+ Cluster #3 Test Set AUROC: {auroc3l}')\n", + "print(f'Local RF+ Cluster #3 Test Set AUPRC: {auprc3l}')\n", + "print(f'Local RF+ Cluster #3 Test Set F1 Score: {f1_3l}')\n", + "print(f'Global RF+ Cluster #3 Test Set Accuracy: {acc3g}')\n", + "print(f'Global RF+ Cluster #3 Test Set # Misclassified: {mis3g}')\n", + "print(f'Global RF+ Cluster #3 Test Set AUROC: {auroc3g}')\n", + "print(f'Global RF+ Cluster #3 Test Set AUPRC: {auprc3g}')\n", + "print(f'Global RF+ Cluster #3 Test Set F1 Score: {f1_3g}')\n", + "print(\"---------------------------------------------\")\n", + "y_pred4 = rf_plus4.predict(cluster4_testX)\n", + "acc4l = np.mean(cluster4_testy == y_pred4)\n", + "mis4l = np.sum(cluster4_testy != y_pred4)\n", + "y_pred4g = rf_plus.predict(cluster4_testX)\n", + "acc4g = np.mean(cluster4_testy == y_pred4g)\n", + "mis4g = np.sum(cluster4_testy != y_pred4g)\n", + "y_pred_prob4 = rf_plus4.predict_proba(cluster4_testX)[:, 1]\n", + "auroc4l = roc_auc_score(cluster4_testy, y_pred_prob4)\n", + "auprc4l = average_precision_score(cluster4_testy, y_pred_prob4)\n", + "f1_4l = f1_score(cluster4_testy, y_pred4)\n", + "y_pred_prob4g = rf_plus.predict_proba(cluster4_testX)[:, 1]\n", + "auroc4g = roc_auc_score(cluster4_testy, y_pred_prob4g)\n", + "auprc4g = average_precision_score(cluster4_testy, y_pred_prob4g)\n", + "f1_4g = f1_score(cluster4_testy, y_pred4g)\n", + "print(f'Local RF+ Cluster #4 Test Set Accuracy: {acc4l}')\n", + "print(f'Local RF+ Cluster #4 Test Set # Misclassified: {mis4l}')\n", + "print(f'Local RF+ Cluster #4 Test Set AUROC: {auroc4l}')\n", + "print(f'Local RF+ Cluster #4 Test Set AUPRC: {auprc4l}')\n", + "print(f'Local RF+ Cluster #4 Test Set F1 Score: {f1_4l}')\n", + "print(f'Global RF+ Cluster #4 Test Set Accuracy: {acc4g}')\n", + "print(f'Global RF+ Cluster #4 Test Set # Misclassified: {mis4g}')\n", + "print(f'Global RF+ Cluster #4 Test Set AUROC: {auroc4g}')\n", + "print(f'Global RF+ Cluster #4 Test Set AUPRC: {auprc4g}')\n", + "print(f'Global RF+ Cluster #4 Test Set F1 Score: {f1_4g}')\n", + "print(\"---------------------------------------------\")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---------------------------------------------\n", + "Total # of Observations Predicted by Global Model: 1584\n", + "Total # of Observations Predicted by Cluster Models: 1584\n", + "---------------------------------------------\n", + "Difference in # Misclassified (Global - Sum of Clusters): 5\n", + "Percent Improvement Over Global Model: 0.84%\n", + "---------------------------------------------\n" + ] + } + ], + "source": [ + "print(\"---------------------------------------------\")\n", + "print(\"Total # of Observations Predicted by Global Model:\", X_test.shape[0])\n", + "print(\"Total # of Observations Predicted by Cluster Models:\",\n", + " cluster1_testX.shape[0] + cluster2_testX.shape[0] + \\\n", + " cluster3_testX.shape[0] + cluster4_testX.shape[0])\n", + "print(\"---------------------------------------------\")\n", + "print(\"Difference in # Misclassified (Global - Sum of Clusters):\", round(global_misclassified - (mis1l + mis2l + mis3l + mis4l), 2))\n", + "print(f\"Percent Improvement Over Global Model: {round(100*(global_misclassified - (mis1l + mis2l + mis3l + mis4l))/global_misclassified, 2)}%\")\n", + "print(\"---------------------------------------------\")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# order rows of mdi_test by cluster assignment\n", + "mdi_test_clust = mdi_test[np.argsort(test_clust)]" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "# order test clust by cluster assignment\n", + "test_clust_org = test_clust[np.argsort(test_clust)]" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# get indexes where mdi_test_clust changes clusters\n", + "cluster_changes = np.where(np.diff(test_clust_org) != 0)[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# create new heatmap\n", + "plt.figure(figsize=(10, 10))\n", + "sns.heatmap(mdi_test_clust, cmap='viridis')\n", + "plt.xlabel('Features')\n", + "plt.ylabel('Observations')\n", + "plt.title('LMDI+ Heatmap of Test Data Ordered by Cluster Assignment')\n", + "plt.yticks([])\n", + "plt.xticks(np.arange(len(X.columns)) + 0.5, X.columns, rotation = 90)\n", + "# put horizontal lines where cluster membership changes\n", + "for i in cluster_changes:\n", + " plt.axhline(i, color='red', linewidth=2, linestyle='--')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The most important feature for Train Cluster #1 is: priors_count\n", + "The most important feature for Test Cluster #1 is: priors_count\n", + "The most important feature for Train Cluster #2 is: age_cat:Greater_than_45\n", + "The most important feature for Test Cluster #2 is: age_cat:Greater_than_45\n", + "The most important feature for Train Cluster #3 is: c_jail_time\n", + "The most important feature for Test Cluster #3 is: c_jail_time\n", + "The most important feature for Train Cluster #4 is: age_cat:Less_than_25\n", + "The most important feature for Test Cluster #4 is: age_cat:Less_than_25\n" + ] + } + ], + "source": [ + "# get most important feature on average for each cluster\n", + "for i in range(num_clusters):\n", + " print(f'The most important feature for Train Cluster #{i+1} is:', X.columns[np.argmax(np.mean(mdi[clusters==i+1], axis=0))])\n", + " print(f'The most important feature for Test Cluster #{i+1} is:', X.columns[np.argmax(np.mean(mdi_test[test_clust==i+1], axis=0))])" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The feature ranking for Train Cluster #1 is: ['priors_count', 'juv_fel_count', 'sex:Female', 'c_charge_degree:F', 'race:Caucasian', 'race:African-American', 'c_charge_degree:M', 'juv_misd_count', 'sex:Male', 'juv_other_count', 'age_cat:25_-_45', 'age_cat:Less_than_25', 'age_cat:Greater_than_45', 'days_b_screening_arrest', 'c_jail_time']\n", + "The feature ranking for Test Cluster #1 is: ['priors_count', 'juv_other_count', 'juv_fel_count', 'race:Caucasian', 'c_charge_degree:F', 'juv_misd_count', 'race:African-American', 'c_charge_degree:M', 'sex:Female', 'age_cat:25_-_45', 'sex:Male', 'age_cat:Less_than_25', 'c_jail_time', 'age_cat:Greater_than_45', 'days_b_screening_arrest']\n", + "The feature ranking for Train Cluster #2 is: ['age_cat:Greater_than_45', 'age_cat:25_-_45', 'juv_misd_count', 'juv_fel_count', 'juv_other_count', 'race:Caucasian', 'age_cat:Less_than_25', 'c_charge_degree:F', 'race:African-American', 'c_charge_degree:M', 'sex:Male', 'sex:Female', 'days_b_screening_arrest', 'c_jail_time', 'priors_count']\n", + "The feature ranking for Test Cluster #2 is: ['age_cat:Greater_than_45', 'age_cat:Less_than_25', 'juv_other_count', 'days_b_screening_arrest', 'race:Caucasian', 'age_cat:25_-_45', 'juv_fel_count', 'race:African-American', 'c_charge_degree:F', 'c_charge_degree:M', 'juv_misd_count', 'c_jail_time', 'priors_count', 'sex:Male', 'sex:Female']\n", + "The feature ranking for Train Cluster #3 is: ['c_jail_time', 'juv_misd_count', 'juv_fel_count', 'juv_other_count', 'sex:Male', 'c_charge_degree:M', 'race:Caucasian', 'sex:Female', 'race:African-American', 'c_charge_degree:F', 'age_cat:25_-_45', 'age_cat:Less_than_25', 'days_b_screening_arrest', 'age_cat:Greater_than_45', 'priors_count']\n", + "The feature ranking for Test Cluster #3 is: ['c_jail_time', 'juv_fel_count', 'juv_other_count', 'sex:Female', 'juv_misd_count', 'sex:Male', 'c_charge_degree:F', 'c_charge_degree:M', 'age_cat:25_-_45', 'race:Caucasian', 'age_cat:Less_than_25', 'race:African-American', 'days_b_screening_arrest', 'age_cat:Greater_than_45', 'priors_count']\n", + "The feature ranking for Train Cluster #4 is: ['age_cat:Less_than_25', 'juv_other_count', 'days_b_screening_arrest', 'age_cat:25_-_45', 'race:African-American', 'race:Caucasian', 'sex:Male', 'c_charge_degree:F', 'juv_fel_count', 'c_charge_degree:M', 'sex:Female', 'juv_misd_count', 'age_cat:Greater_than_45', 'c_jail_time', 'priors_count']\n", + "The feature ranking for Test Cluster #4 is: ['age_cat:Less_than_25', 'days_b_screening_arrest', 'race:African-American', 'age_cat:Greater_than_45', 'race:Caucasian', 'sex:Female', 'juv_other_count', 'age_cat:25_-_45', 'sex:Male', 'c_charge_degree:F', 'c_charge_degree:M', 'juv_fel_count', 'juv_misd_count', 'c_jail_time', 'priors_count']\n" + ] + } + ], + "source": [ + "for i in range(num_clusters):\n", + " # negative is taken because argsort goes in the wrong order\n", + " print(f'The feature ranking for Train Cluster #{i+1} is:', list(X.columns[np.argsort(-np.mean(mdi[clusters==i+1], axis=0))]))\n", + " print(f'The feature ranking for Test Cluster #{i+1} is:', list(X.columns[np.argsort(-np.mean(mdi_test[test_clust==i+1], axis=0))]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TreeSHAP\n", + "\n", + "To detect subgroups using TreeSHAP, we obtain the TreeSHAP scores from the fitted random forest that was given to the RF+ constructor. We then convert these importance scores to feature rankings and compute RBO, which is the same process we underwent for Local MDI+. The resulting clusters can be visualized below. It is worth noting that we evaluate the RF here instead of the RF+. This is due to two reasons: TreeSHAP is made for RFs, not RF+s, and the clusters made by TreeSHAP are too small to fit RF+s (due to cross-validation)." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RF Test Set Accuracy: 0.6193181818181818\n", + "RF Test Set Misclassified: 603\n" + ] + } + ], + "source": [ + "# compute r^2 on the test set\n", + "y_pred = rf.predict(X_test)\n", + "global_acc = np.mean(y_test == y_pred)\n", + "print(f'RF Test Set Accuracy: {np.mean(y_test == y_pred)}')\n", + "global_misclassified = np.sum(y_test != y_pred)\n", + "print(f'RF Test Set Misclassified: {np.sum(y_test != y_pred)}')" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "explainer = shap.TreeExplainer(rf)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "shap_values = np.abs(explainer.shap_values(X_train, check_additivity=False))" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "shap_rankings = mdi_explainer.get_rankings(shap_values)[:,:,0]" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(3694, 15)" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "shap_rankings.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "# get rbo distance matrix\n", + "rbo_train = compute_rbo_matrix(shap_rankings, form = 'distance')" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "shap_copy = pd.DataFrame(shap_values[:,:,0], columns=X.columns).copy()\n", + "num_clusters = 4\n", + "clusters = assign_training_clusters(shap_copy, rbo_train, num_clusters)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now check some summary statistics of the above clusters. It is worth noting that the subgroups that are 'discovered' by TreeSHAP either are composed entirely of smokers or of non-smokers." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---------------------------------------------\n", + "Proportion of Data in Cluster #1: 0.14076881429344884\n", + "Proportion of Data in Cluster #2: 0.10205739036275041\n", + "Proportion of Data in Cluster #3: 0.5720086626962642\n", + "Proportion of Data in Cluster #4: 0.18516513264753653\n", + "---------------------------------------------\n", + "Average # of Priors in Cluster #1: 2.7576923\n", + "Average # of Priors in Cluster #2: 3.1803713\n", + "Average # of Priors in Cluster #3: 4.3932796\n", + "Average # of Priors in Cluster #4: 1.4532164\n", + "---------------------------------------------\n", + "Average # of Days Before Screening Arrest in Cluster #1: -2.125\n", + "Average # of Days Before Screening Arrest in Cluster #2: -5.3501325\n", + "Average # of Days Before Screening Arrest in Cluster #3: -1.3166114\n", + "Average # of Days Before Screening Arrest in Cluster #4: -0.99122804\n", + "---------------------------------------------\n", + "Average Jail Time (Days) in Cluster #1: 23.948076\n", + "Average Jail Time (Days) in Cluster #2: 9.787799\n", + "Average Jail Time (Days) in Cluster #3: 13.917179\n", + "Average Jail Time (Days) in Cluster #4: 14.020468\n", + "---------------------------------------------\n", + "Average # of Juvenile Felony Counts in Cluster #1: 0.034615386\n", + "Average # of Juvenile Felony Counts in Cluster #2: 0.01591512\n", + "Average # of Juvenile Felony Counts in Cluster #3: 0.09086607\n", + "Average # of Juvenile Felony Counts in Cluster #4: 0.039473683\n", + "---------------------------------------------\n", + "Average # of Juvenile 'Other' Counts in Cluster #1: 0.06153846\n", + "Average # of Juvenile 'Other' Counts in Cluster #2: 0.00795756\n", + "Average # of Juvenile 'Other' Counts in Cluster #3: 0.11310932\n", + "Average # of Juvenile 'Other' Counts in Cluster #4: 0.26608187\n", + "---------------------------------------------\n", + "Average # of Juvenile Misdemeanor Counts in Cluster #1: 0.057692308\n", + "Average # of Juvenile Misdemeanor Counts in Cluster #2: 0.01591512\n", + "Average # of Juvenile Misdemeanor Counts in Cluster #3: 0.101277806\n", + "Average # of Juvenile Misdemeanor Counts in Cluster #4: 0.17690058\n", + "---------------------------------------------\n", + "Probability of Felony Charge in Cluster #1: 0.6788462\n", + "Probability of Felony Charge in Cluster #2: 0.61538464\n", + "Probability of Felony Charge in Cluster #3: 0.63984853\n", + "Probability of Felony Charge in Cluster #4: 0.7236842\n", + "---------------------------------------------\n", + "Proportion of African-American Defendants in Cluster #1: 0.5692308\n", + "Proportion of African-American Defendants in Cluster #2: 0.40583554\n", + "Proportion of African-American Defendants in Cluster #3: 0.56176054\n", + "Proportion of African-American Defendants in Cluster #4: 0.8377193\n", + "---------------------------------------------\n", + "Proportion of Defendants Aged 25-45 in Cluster #1: 0.6269231\n", + "Proportion of Defendants Aged 25-45 in Cluster #2: 0.27320954\n", + "Proportion of Defendants Aged 25-45 in Cluster #3: 0.68338853\n", + "Proportion of Defendants Aged 25-45 in Cluster #4: 0.35233918\n", + "---------------------------------------------\n", + "Proportion of Defendants Over Age 45 in Cluster #1: 0.15769231\n", + "Proportion of Defendants Over Age 45 in Cluster #2: 0.6816976\n", + "Proportion of Defendants Over Age 45 in Cluster #3: 0.19119735\n", + "Proportion of Defendants Over Age 45 in Cluster #4: 0.004385965\n", + "---------------------------------------------\n", + "Proportion of Defendants Under Age 25 in Cluster #1: 0.21538462\n", + "Proportion of Defendants Under Age 25 in Cluster #2: 0.04509284\n", + "Proportion of Defendants Under Age 25 in Cluster #3: 0.1254141\n", + "Proportion of Defendants Under Age 25 in Cluster #4: 0.64327484\n", + "---------------------------------------------\n", + "Proportion of Men in Cluster #1: 0.8403846\n", + "Proportion of Men in Cluster #2: 0.8302387\n", + "Proportion of Men in Cluster #3: 0.7699953\n", + "Proportion of Men in Cluster #4: 0.8947368\n", + "Probability of Recidivism in Cluster #1: 0.6557692307692308\n", + "Probability of Recidivism in Cluster #2: 0.22281167108753316\n", + "Probability of Recidivism in Cluster #3: 0.47231424514907716\n", + "Probability of Recidivism in Cluster #4: 0.6549707602339181\n", + "---------------------------------------------\n" + ] + } + ], + "source": [ + "print(\"---------------------------------------------\")\n", + "# calculate average charge for each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Proportion of Data in Cluster #{i+1}:\",\n", + " X_train[clusters==i+1].shape[0]/X_train.shape[0])\n", + "print(\"---------------------------------------------\")\n", + "# get average number of priors in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Average # of Priors in Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['priors_count'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get average # of days before screening arrest in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Average # of Days Before Screening Arrest in Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['days_b_screening_arrest'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get average jail time in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Average Jail Time (Days) in Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['c_jail_time'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get average # of juvenile felony counts in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Average # of Juvenile Felony Counts in Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['juv_fel_count'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get average number juvenile 'other' counts in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Average # of Juvenile 'Other' Counts in Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['juv_other_count'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get average number of juvenile misdemeanor counts in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Average # of Juvenile Misdemeanor Counts in Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['juv_misd_count'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get probability of felony charge in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Probability of Felony Charge in Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['c_charge_degree:F'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get proportion of African-American defendants in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Proportion of African-American Defendants in Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['race:African-American'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get proportion of age 25-45 in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Proportion of Defendants Aged 25-45 in Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['age_cat:25_-_45'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get proportion of age over 45 in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Proportion of Defendants Over Age 45 in Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['age_cat:Greater_than_45'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get proportion of age under 25 in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Proportion of Defendants Under Age 25 in Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['age_cat:Less_than_25'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get proportion of men in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Proportion of Men in Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['sex:Male'].mean())\n", + "# get proportion of smokers in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Probability of Recidivism in Cluster #{i+1}:\",\n", + " y_train[clusters==i+1].mean())\n", + "print(\"---------------------------------------------\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evaluating Cluster Performance - Centroid Method w/ Exact Mean\n", + "Now that we have a good idea of what our clustering has done, we can check if this helps improve our predictions. We will take the test points and determine their cluster membership based on their RBO similarity to the mean point in each cluster (in RBO embedding). We will then fit a RF+ on the training data and using it to predict the test data for that cluster. We can then compute the R^2 and total squared error for each cluster's model. By summing the TSE across cluster models and comparing this to the original TSE reported above, we can get a good idea of how well these clusters improve model accuracy." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "# get mdi rankings assignments for test points\n", + "shap_test_values = np.abs(explainer.shap_values(X_test, check_additivity=False))\n", + "shap_test_rankings = mdi_explainer.get_rankings(shap_test_values)[:,:,0]" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "test_clust = assign_testing_clusters(method = \"centroid\", median_approx = False,\n", + " rbo_distance_matrix = rbo_train,\n", + " lfi_train_ranking = shap_rankings,\n", + " lfi_test_ranking = shap_test_rankings,\n", + " clusters = clusters)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "cluster1_trainX = X_train[clusters == 1]\n", + "cluster2_trainX = X_train[clusters == 2]\n", + "cluster3_trainX = X_train[clusters == 3]\n", + "cluster4_trainX = X_train[clusters == 4]\n", + "\n", + "cluster1_trainy = y_train[clusters == 1]\n", + "cluster2_trainy = y_train[clusters == 2]\n", + "cluster3_trainy = y_train[clusters == 3]\n", + "cluster4_trainy = y_train[clusters == 4]\n", + "\n", + "cluster1_testX = X_test[test_clust == 1]\n", + "cluster2_testX = X_test[test_clust == 2]\n", + "cluster3_testX = X_test[test_clust == 3]\n", + "cluster4_testX = X_test[test_clust == 4]\n", + "\n", + "cluster1_testy = y_test[test_clust == 1]\n", + "cluster2_testy = y_test[test_clust == 2]\n", + "cluster3_testy = y_test[test_clust == 3]\n", + "cluster4_testy = y_test[test_clust == 4]" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---------------------------------------------\n", + "Proportion of Train Data in Cluster #1: 0.14076881429344884\n", + "Proportion of Test Data in Cluster #1: 0.13952020202020202\n", + "Proportion of Train Data in Cluster #2: 0.10205739036275041\n", + "Proportion of Test Data in Cluster #2: 0.09911616161616162\n", + "Proportion of Train Data in Cluster #3: 0.5720086626962642\n", + "Proportion of Test Data in Cluster #3: 0.5858585858585859\n", + "Proportion of Train Data in Cluster #4: 0.18516513264753653\n", + "Proportion of Test Data in Cluster #4: 0.1755050505050505\n", + "---------------------------------------------\n", + "Average # of Priors in Train Cluster #1: 2.7576923\n", + "Average # of Priors in Test Cluster #1: 2.3755655\n", + "Average # of Priors in Train Cluster #2: 3.1803713\n", + "Average # of Priors in Test Cluster #2: 2.3057325\n", + "Average # of Priors in Train Cluster #3: 4.3932796\n", + "Average # of Priors in Test Cluster #3: 4.380388\n", + "Average # of Priors in Train Cluster #4: 1.4532164\n", + "Average # of Priors in Test Cluster #4: 1.4676259\n", + "---------------------------------------------\n", + "Average # of Days Before Screening Arrest in Train Cluster #1: -2.125\n", + "Average # of Days Before Screening Arrest in Test Cluster #1: -2.3484163\n", + "Average # of Days Before Screening Arrest in Train Cluster #2: -5.3501325\n", + "Average # of Days Before Screening Arrest in Test Cluster #2: -3.057325\n", + "Average # of Days Before Screening Arrest in Train Cluster #3: -1.3166114\n", + "Average # of Days Before Screening Arrest in Test Cluster #3: -1.518319\n", + "Average # of Days Before Screening Arrest in Train Cluster #4: -0.99122804\n", + "Average # of Days Before Screening Arrest in Test Cluster #4: -0.50359714\n", + "---------------------------------------------\n", + "Average Jail Time (Days) in Train Cluster #1: 23.948076\n", + "Average Jail Time (Days) in Test Cluster #1: 22.62896\n", + "Average Jail Time (Days) in Train Cluster #2: 9.787799\n", + "Average Jail Time (Days) in Test Cluster #2: 8.038217\n", + "Average Jail Time (Days) in Train Cluster #3: 13.917179\n", + "Average Jail Time (Days) in Test Cluster #3: 16.05819\n", + "Average Jail Time (Days) in Train Cluster #4: 14.020468\n", + "Average Jail Time (Days) in Test Cluster #4: 12.600719\n", + "---------------------------------------------\n", + "Average # of Juvenile Felony Counts in Train Cluster #1: 0.034615386\n", + "Average # of Juvenile Felony Counts in Test Cluster #1: 0.013574661\n", + "Average # of Juvenile Felony Counts in Train Cluster #2: 0.01591512\n", + "Average # of Juvenile Felony Counts in Test Cluster #2: 0.0\n", + "Average # of Juvenile Felony Counts in Train Cluster #3: 0.09086607\n", + "Average # of Juvenile Felony Counts in Test Cluster #3: 0.06573276\n", + "Average # of Juvenile Felony Counts in Train Cluster #4: 0.039473683\n", + "Average # of Juvenile Felony Counts in Test Cluster #4: 0.06115108\n", + "---------------------------------------------\n", + "Average # of Juvenile 'Other' Counts in Train Cluster #1: 0.06153846\n", + "Average # of Juvenile 'Other' Counts in Test Cluster #1: 0.05882353\n", + "Average # of Juvenile 'Other' Counts in Train Cluster #2: 0.00795756\n", + "Average # of Juvenile 'Other' Counts in Test Cluster #2: 0.0\n", + "Average # of Juvenile 'Other' Counts in Train Cluster #3: 0.11310932\n", + "Average # of Juvenile 'Other' Counts in Test Cluster #3: 0.09913793\n", + "Average # of Juvenile 'Other' Counts in Train Cluster #4: 0.26608187\n", + "Average # of Juvenile 'Other' Counts in Test Cluster #4: 0.24460432\n", + "---------------------------------------------\n", + "Average # of Juvenile Misdemeanor Counts in Train Cluster #1: 0.057692308\n", + "Average # of Juvenile Misdemeanor Counts in Test Cluster #1: 0.022624435\n", + "Average # of Juvenile Misdemeanor Counts in Train Cluster #2: 0.01591512\n", + "Average # of Juvenile Misdemeanor Counts in Test Cluster #2: 0.006369427\n", + "Average # of Juvenile Misdemeanor Counts in Train Cluster #3: 0.101277806\n", + "Average # of Juvenile Misdemeanor Counts in Test Cluster #3: 0.1174569\n", + "Average # of Juvenile Misdemeanor Counts in Train Cluster #4: 0.17690058\n", + "Average # of Juvenile Misdemeanor Counts in Test Cluster #4: 0.12230216\n", + "---------------------------------------------\n", + "Probability of Felony Charge in Train Cluster #1: 0.6788462\n", + "Probability of Felony Charge in Test Cluster #1: 0.54751134\n", + "Probability of Felony Charge in Train Cluster #2: 0.61538464\n", + "Probability of Felony Charge in Test Cluster #2: 0.6242038\n", + "Probability of Felony Charge in Train Cluster #3: 0.63984853\n", + "Probability of Felony Charge in Test Cluster #3: 0.6314655\n", + "Probability of Felony Charge in Train Cluster #4: 0.7236842\n", + "Probability of Felony Charge in Test Cluster #4: 0.73021585\n", + "---------------------------------------------\n", + "Proportion of African-American Defendants in Train Cluster #1: 0.5692308\n", + "Proportion of African-American Defendants in Test Cluster #1: 0.5294118\n", + "Proportion of African-American Defendants in Train Cluster #2: 0.40583554\n", + "Proportion of African-American Defendants in Test Cluster #2: 0.3821656\n", + "Proportion of African-American Defendants in Train Cluster #3: 0.56176054\n", + "Proportion of African-American Defendants in Test Cluster #3: 0.5969828\n", + "Proportion of African-American Defendants in Train Cluster #4: 0.8377193\n", + "Proportion of African-American Defendants in Test Cluster #4: 0.84532374\n", + "---------------------------------------------\n", + "Proportion of Defendants Aged 25-45 in Train Cluster #1: 0.6269231\n", + "Proportion of Defendants Aged 25-45 in Test Cluster #1: 0.61538464\n", + "Proportion of Defendants Aged 25-45 in Train Cluster #2: 0.27320954\n", + "Proportion of Defendants Aged 25-45 in Test Cluster #2: 0.28025478\n", + "Proportion of Defendants Aged 25-45 in Train Cluster #3: 0.68338853\n", + "Proportion of Defendants Aged 25-45 in Test Cluster #3: 0.6939655\n", + "Proportion of Defendants Aged 25-45 in Train Cluster #4: 0.35233918\n", + "Proportion of Defendants Aged 25-45 in Test Cluster #4: 0.31654677\n", + "---------------------------------------------\n", + "Proportion of Defendants Over Age 45 in Train Cluster #1: 0.15769231\n", + "Proportion of Defendants Over Age 45 in Test Cluster #1: 0.18099548\n", + "Proportion of Defendants Over Age 45 in Train Cluster #2: 0.6816976\n", + "Proportion of Defendants Over Age 45 in Test Cluster #2: 0.6942675\n", + "Proportion of Defendants Over Age 45 in Train Cluster #3: 0.19119735\n", + "Proportion of Defendants Over Age 45 in Test Cluster #3: 0.21659483\n", + "Proportion of Defendants Over Age 45 in Train Cluster #4: 0.004385965\n", + "Proportion of Defendants Over Age 45 in Test Cluster #4: 0.0\n", + "---------------------------------------------\n", + "Proportion of Defendants Under Age 25 in Train Cluster #1: 0.21538462\n", + "Proportion of Defendants Under Age 25 in Test Cluster #1: 0.20361991\n", + "Proportion of Defendants Under Age 25 in Train Cluster #2: 0.04509284\n", + "Proportion of Defendants Under Age 25 in Test Cluster #2: 0.025477707\n", + "Proportion of Defendants Under Age 25 in Train Cluster #3: 0.1254141\n", + "Proportion of Defendants Under Age 25 in Test Cluster #3: 0.08943965\n", + "Proportion of Defendants Under Age 25 in Train Cluster #4: 0.64327484\n", + "Proportion of Defendants Under Age 25 in Test Cluster #4: 0.68345326\n", + "---------------------------------------------\n", + "Proportion of Men in Train Cluster #1: 0.8403846\n", + "Proportion of Men in Test Cluster #1: 0.8235294\n", + "Proportion of Men in Train Cluster #2: 0.8302387\n", + "Proportion of Men in Test Cluster #2: 0.83439493\n", + "Proportion of Men in Train Cluster #3: 0.7699953\n", + "Proportion of Men in Test Cluster #3: 0.7521552\n", + "Proportion of Men in Train Cluster #4: 0.8947368\n", + "Proportion of Men in Test Cluster #4: 0.8884892\n", + "Probability of Recidivism in Train Cluster #1: 0.6557692307692308\n", + "Probability of Recidivism in Test Cluster #1: 0.5203619909502263\n", + "Probability of Recidivism in Train Cluster #2: 0.22281167108753316\n", + "Probability of Recidivism in Test Cluster #2: 0.37579617834394907\n", + "Probability of Recidivism in Train Cluster #3: 0.47231424514907716\n", + "Probability of Recidivism in Test Cluster #3: 0.47844827586206895\n", + "Probability of Recidivism in Train Cluster #4: 0.6549707602339181\n", + "Probability of Recidivism in Test Cluster #4: 0.5683453237410072\n", + "---------------------------------------------\n" + ] + } + ], + "source": [ + "print(\"---------------------------------------------\")\n", + "# calculate average charge for each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Proportion of Train Data in Cluster #{i+1}:\",\n", + " X_train[clusters==i+1].shape[0]/X_train.shape[0])\n", + " print(f\"Proportion of Test Data in Cluster #{i+1}:\",\n", + " X_test[test_clust==i+1].shape[0]/X_test.shape[0])\n", + "print(\"---------------------------------------------\")\n", + "# get average number of priors in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Average # of Priors in Train Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['priors_count'].mean())\n", + " print(f\"Average # of Priors in Test Cluster #{i+1}:\",\n", + " X_test[test_clust==i+1]['priors_count'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get average # of days before screening arrest in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Average # of Days Before Screening Arrest in Train Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['days_b_screening_arrest'].mean())\n", + " print(f\"Average # of Days Before Screening Arrest in Test Cluster #{i+1}:\",\n", + " X_test[test_clust==i+1]['days_b_screening_arrest'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get average jail time in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Average Jail Time (Days) in Train Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['c_jail_time'].mean())\n", + " print(f\"Average Jail Time (Days) in Test Cluster #{i+1}:\",\n", + " X_test[test_clust==i+1]['c_jail_time'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get average # of juvenile felony counts in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Average # of Juvenile Felony Counts in Train Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['juv_fel_count'].mean())\n", + " print(f\"Average # of Juvenile Felony Counts in Test Cluster #{i+1}:\",\n", + " X_test[test_clust==i+1]['juv_fel_count'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get average number juvenile 'other' counts in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Average # of Juvenile 'Other' Counts in Train Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['juv_other_count'].mean())\n", + " print(f\"Average # of Juvenile 'Other' Counts in Test Cluster #{i+1}:\",\n", + " X_test[test_clust==i+1]['juv_other_count'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get average number of juvenile misdemeanor counts in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Average # of Juvenile Misdemeanor Counts in Train Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['juv_misd_count'].mean())\n", + " print(f\"Average # of Juvenile Misdemeanor Counts in Test Cluster #{i+1}:\",\n", + " X_test[test_clust==i+1]['juv_misd_count'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get probability of felony charge in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Probability of Felony Charge in Train Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['c_charge_degree:F'].mean())\n", + " print(f\"Probability of Felony Charge in Test Cluster #{i+1}:\",\n", + " X_test[test_clust==i+1]['c_charge_degree:F'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get proportion of African-American defendants in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Proportion of African-American Defendants in Train Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['race:African-American'].mean())\n", + " print(f\"Proportion of African-American Defendants in Test Cluster #{i+1}:\",\n", + " X_test[test_clust==i+1]['race:African-American'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get proportion of age 25-45 in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Proportion of Defendants Aged 25-45 in Train Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['age_cat:25_-_45'].mean())\n", + " print(f\"Proportion of Defendants Aged 25-45 in Test Cluster #{i+1}:\",\n", + " X_test[test_clust==i+1]['age_cat:25_-_45'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get proportion of age over 45 in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Proportion of Defendants Over Age 45 in Train Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['age_cat:Greater_than_45'].mean())\n", + " print(f\"Proportion of Defendants Over Age 45 in Test Cluster #{i+1}:\",\n", + " X_test[test_clust==i+1]['age_cat:Greater_than_45'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get proportion of age under 25 in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Proportion of Defendants Under Age 25 in Train Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['age_cat:Less_than_25'].mean())\n", + " print(f\"Proportion of Defendants Under Age 25 in Test Cluster #{i+1}:\",\n", + " X_test[test_clust==i+1]['age_cat:Less_than_25'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get proportion of men in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Proportion of Men in Train Cluster #{i+1}:\",\n", + " X_train[clusters==i+1]['sex:Male'].mean())\n", + " print(f\"Proportion of Men in Test Cluster #{i+1}:\",\n", + " X_test[test_clust==i+1]['sex:Male'].mean())\n", + "# get proportion of smokers in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Probability of Recidivism in Train Cluster #{i+1}:\",\n", + " y_train[clusters==i+1].mean())\n", + " print(f\"Probability of Recidivism in Test Cluster #{i+1}:\",\n", + " y_test[test_clust==i+1].mean())\n", + "print(\"---------------------------------------------\")" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "%%capture\n", + "# fit RF+ on each training set, predict test\n", + "rf1 = RandomForestClassifier(n_estimators=100, random_state=0)\n", + "rf1.fit(cluster1_trainX, cluster1_trainy)\n", + "\n", + "rf2 = RandomForestClassifier(n_estimators=100, random_state=0)\n", + "rf2.fit(cluster2_trainX, cluster2_trainy)\n", + "\n", + "rf3 = RandomForestClassifier(n_estimators=100, random_state=0)\n", + "rf3.fit(cluster3_trainX, cluster3_trainy)\n", + "\n", + "rf4 = RandomForestClassifier(n_estimators=100, random_state=0)\n", + "rf4.fit(cluster4_trainX, cluster4_trainy)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---------------------------------------------\n", + "Local RF Cluster #1 Test Set Accuracy: 0.4479638009049774\n", + "Local RF Cluster #1 Test Set # Misclassified: 122\n", + "Local RF Cluster #1 Test Set AUROC: 0.44040196882690735\n", + "Local RF Cluster #1 Test Set AUPRC: 0.4942416166089245\n", + "Local RF Cluster #1 Test Set F1 Score: 0.5378787878787878\n", + "Global RF Cluster #1 Test Set Accuracy: 0.4796380090497738\n", + "Global RF Cluster #1 Test Set # Misclassified: 115\n", + "Global RF Cluster #1 Test Set AUROC: 0.508777686628384\n", + "Global RF Cluster #1 Test Set AUPRC: 0.5739333314313575\n", + "Global RF Cluster #1 Test Set F1 Score: 0.5106382978723404\n", + "---------------------------------------------\n", + "Local RF Cluster #2 Test Set Accuracy: 0.5796178343949044\n", + "Local RF Cluster #2 Test Set # Misclassified: 66\n", + "Local RF Cluster #2 Test Set AUROC: 0.48970944309927367\n", + "Local RF Cluster #2 Test Set AUPRC: 0.41647824148913437\n", + "Local RF Cluster #2 Test Set F1 Score: 0.2826086956521739\n", + "Global RF Cluster #2 Test Set Accuracy: 0.5859872611464968\n", + "Global RF Cluster #2 Test Set # Misclassified: 65\n", + "Global RF Cluster #2 Test Set AUROC: 0.5180733310273262\n", + "Global RF Cluster #2 Test Set AUPRC: 0.39175920886155335\n", + "Global RF Cluster #2 Test Set F1 Score: 0.2696629213483146\n", + "---------------------------------------------\n", + "Local RF Cluster #3 Test Set Accuracy: 0.5344827586206896\n", + "Local RF Cluster #3 Test Set # Misclassified: 432\n", + "Local RF Cluster #3 Test Set AUROC: 0.666340927704564\n", + "Local RF Cluster #3 Test Set AUPRC: 0.6211849042207942\n", + "Local RF Cluster #3 Test Set F1 Score: 0.39154929577464787\n", + "Global RF Cluster #3 Test Set Accuracy: 0.6530172413793104\n", + "Global RF Cluster #3 Test Set # Misclassified: 322\n", + "Global RF Cluster #3 Test Set AUROC: 0.6853524495569951\n", + "Global RF Cluster #3 Test Set AUPRC: 0.6421761015584979\n", + "Global RF Cluster #3 Test Set F1 Score: 0.6184834123222749\n", + "---------------------------------------------\n", + "Local RF Cluster #4 Test Set Accuracy: 0.6294964028776978\n", + "Local RF Cluster #4 Test Set # Misclassified: 103\n", + "Local RF Cluster #4 Test Set AUROC: 0.6770569620253164\n", + "Local RF Cluster #4 Test Set AUPRC: 0.722143682990643\n", + "Local RF Cluster #4 Test Set F1 Score: 0.6906906906906907\n", + "Global RF Cluster #4 Test Set Accuracy: 0.6366906474820144\n", + "Global RF Cluster #4 Test Set # Misclassified: 101\n", + "Global RF Cluster #4 Test Set AUROC: 0.6618407172995782\n", + "Global RF Cluster #4 Test Set AUPRC: 0.7141680149331786\n", + "Global RF Cluster #4 Test Set F1 Score: 0.691131498470948\n", + "---------------------------------------------\n" + ] + } + ], + "source": [ + "print(\"---------------------------------------------\")\n", + "y_pred1 = rf1.predict(cluster1_testX)\n", + "acc1l = np.mean(cluster1_testy == y_pred1)\n", + "mis1l = np.sum(cluster1_testy != y_pred1)\n", + "y_pred1g = rf.predict(cluster1_testX)\n", + "acc1g = np.mean(cluster1_testy == y_pred1g)\n", + "mis1g = np.sum(cluster1_testy != y_pred1g)\n", + "y_pred_prob1 = rf1.predict_proba(cluster1_testX)[:, 1]\n", + "auroc1l = roc_auc_score(cluster1_testy, y_pred_prob1)\n", + "auprc1l = average_precision_score(cluster1_testy, y_pred_prob1)\n", + "f1_1l = f1_score(cluster1_testy, y_pred1)\n", + "y_pred_prob1g = rf.predict_proba(cluster1_testX)[:, 1]\n", + "auroc1g = roc_auc_score(cluster1_testy, y_pred_prob1g)\n", + "auprc1g = average_precision_score(cluster1_testy, y_pred_prob1g)\n", + "f1_1g = f1_score(cluster1_testy, y_pred1g)\n", + "print(f'Local RF Cluster #1 Test Set Accuracy: {acc1l}')\n", + "print(f'Local RF Cluster #1 Test Set # Misclassified: {mis1l}')\n", + "print(f'Local RF Cluster #1 Test Set AUROC: {auroc1l}')\n", + "print(f'Local RF Cluster #1 Test Set AUPRC: {auprc1l}')\n", + "print(f'Local RF Cluster #1 Test Set F1 Score: {f1_1l}')\n", + "print(f'Global RF Cluster #1 Test Set Accuracy: {acc1g}')\n", + "print(f'Global RF Cluster #1 Test Set # Misclassified: {mis1g}')\n", + "print(f'Global RF Cluster #1 Test Set AUROC: {auroc1g}')\n", + "print(f'Global RF Cluster #1 Test Set AUPRC: {auprc1g}')\n", + "print(f'Global RF Cluster #1 Test Set F1 Score: {f1_1g}')\n", + "print(\"---------------------------------------------\")\n", + "y_pred2 = rf2.predict(cluster2_testX)\n", + "acc2l = np.mean(cluster2_testy == y_pred2)\n", + "mis2l = np.sum(cluster2_testy != y_pred2)\n", + "y_pred2g = rf.predict(cluster2_testX)\n", + "acc2g = np.mean(cluster2_testy == y_pred2g)\n", + "mis2g = np.sum(cluster2_testy != y_pred2g)\n", + "y_pred_prob2 = rf2.predict_proba(cluster2_testX)[:, 1]\n", + "auroc2l = roc_auc_score(cluster2_testy, y_pred_prob2)\n", + "auprc2l = average_precision_score(cluster2_testy, y_pred_prob2)\n", + "f1_2l = f1_score(cluster2_testy, y_pred2)\n", + "y_pred_prob2g = rf.predict_proba(cluster2_testX)[:, 1]\n", + "auroc2g = roc_auc_score(cluster2_testy, y_pred_prob2g)\n", + "auprc2g = average_precision_score(cluster2_testy, y_pred_prob2g)\n", + "f1_2g = f1_score(cluster2_testy, y_pred2g)\n", + "print(f'Local RF Cluster #2 Test Set Accuracy: {acc2l}')\n", + "print(f'Local RF Cluster #2 Test Set # Misclassified: {mis2l}')\n", + "print(f'Local RF Cluster #2 Test Set AUROC: {auroc2l}')\n", + "print(f'Local RF Cluster #2 Test Set AUPRC: {auprc2l}')\n", + "print(f'Local RF Cluster #2 Test Set F1 Score: {f1_2l}')\n", + "print(f'Global RF Cluster #2 Test Set Accuracy: {acc2g}')\n", + "print(f'Global RF Cluster #2 Test Set # Misclassified: {mis2g}')\n", + "print(f'Global RF Cluster #2 Test Set AUROC: {auroc2g}')\n", + "print(f'Global RF Cluster #2 Test Set AUPRC: {auprc2g}')\n", + "print(f'Global RF Cluster #2 Test Set F1 Score: {f1_2g}')\n", + "print(\"---------------------------------------------\")\n", + "y_pred3 = rf1.predict(cluster3_testX)\n", + "acc3l = np.mean(cluster3_testy == y_pred3)\n", + "mis3l = np.sum(cluster3_testy != y_pred3)\n", + "y_pred3g = rf.predict(cluster3_testX)\n", + "acc3g = np.mean(cluster3_testy == y_pred3g)\n", + "mis3g = np.sum(cluster3_testy != y_pred3g)\n", + "y_pred_prob3 = rf3.predict_proba(cluster3_testX)[:, 1]\n", + "auroc3l = roc_auc_score(cluster3_testy, y_pred_prob3)\n", + "auprc3l = average_precision_score(cluster3_testy, y_pred_prob3)\n", + "f1_3l = f1_score(cluster3_testy, y_pred3)\n", + "y_pred_prob3g = rf.predict_proba(cluster3_testX)[:, 1]\n", + "auroc3g = roc_auc_score(cluster3_testy, y_pred_prob3g)\n", + "auprc3g = average_precision_score(cluster3_testy, y_pred_prob3g)\n", + "f1_3g = f1_score(cluster3_testy, y_pred3g)\n", + "print(f'Local RF Cluster #3 Test Set Accuracy: {acc3l}')\n", + "print(f'Local RF Cluster #3 Test Set # Misclassified: {mis3l}')\n", + "print(f'Local RF Cluster #3 Test Set AUROC: {auroc3l}')\n", + "print(f'Local RF Cluster #3 Test Set AUPRC: {auprc3l}')\n", + "print(f'Local RF Cluster #3 Test Set F1 Score: {f1_3l}')\n", + "print(f'Global RF Cluster #3 Test Set Accuracy: {acc3g}')\n", + "print(f'Global RF Cluster #3 Test Set # Misclassified: {mis3g}')\n", + "print(f'Global RF Cluster #3 Test Set AUROC: {auroc3g}')\n", + "print(f'Global RF Cluster #3 Test Set AUPRC: {auprc3g}')\n", + "print(f'Global RF Cluster #3 Test Set F1 Score: {f1_3g}')\n", + "print(\"---------------------------------------------\")\n", + "y_pred4 = rf4.predict(cluster4_testX)\n", + "acc4l = np.mean(cluster4_testy == y_pred4)\n", + "mis4l = np.sum(cluster4_testy != y_pred4)\n", + "y_pred4g = rf.predict(cluster4_testX)\n", + "acc4g = np.mean(cluster4_testy == y_pred4g)\n", + "mis4g = np.sum(cluster4_testy != y_pred4g)\n", + "y_pred_prob4 = rf4.predict_proba(cluster4_testX)[:, 1]\n", + "auroc4l = roc_auc_score(cluster4_testy, y_pred_prob4)\n", + "auprc4l = average_precision_score(cluster4_testy, y_pred_prob4)\n", + "f1_4l = f1_score(cluster4_testy, y_pred4)\n", + "y_pred_prob4g = rf.predict_proba(cluster4_testX)[:, 1]\n", + "auroc4g = roc_auc_score(cluster4_testy, y_pred_prob4g)\n", + "auprc4g = average_precision_score(cluster4_testy, y_pred_prob4g)\n", + "f1_4g = f1_score(cluster4_testy, y_pred4g)\n", + "print(f'Local RF Cluster #4 Test Set Accuracy: {acc4l}')\n", + "print(f'Local RF Cluster #4 Test Set # Misclassified: {mis4l}')\n", + "print(f'Local RF Cluster #4 Test Set AUROC: {auroc4l}')\n", + "print(f'Local RF Cluster #4 Test Set AUPRC: {auprc4l}')\n", + "print(f'Local RF Cluster #4 Test Set F1 Score: {f1_4l}')\n", + "print(f'Global RF Cluster #4 Test Set Accuracy: {acc4g}')\n", + "print(f'Global RF Cluster #4 Test Set # Misclassified: {mis4g}')\n", + "print(f'Global RF Cluster #4 Test Set AUROC: {auroc4g}')\n", + "print(f'Global RF Cluster #4 Test Set AUPRC: {auprc4g}')\n", + "print(f'Global RF Cluster #4 Test Set F1 Score: {f1_4g}')\n", + "print(\"---------------------------------------------\")" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---------------------------------------------\n", + "Total # of Observations Predicted by Global Model: 1584\n", + "Total # of Observations Predicted by Cluster Models: 1584\n", + "---------------------------------------------\n", + "Difference in # Misclassified (Global - Sum of Clusters): -120\n", + "Percent Improvement Over Global Model: -19.9%\n", + "---------------------------------------------\n" + ] + } + ], + "source": [ + "print(\"---------------------------------------------\")\n", + "print(\"Total # of Observations Predicted by Global Model:\", X_test.shape[0])\n", + "print(\"Total # of Observations Predicted by Cluster Models:\",\n", + " cluster1_testX.shape[0] + cluster2_testX.shape[0] + \\\n", + " cluster3_testX.shape[0] + cluster4_testX.shape[0])\n", + "print(\"---------------------------------------------\")\n", + "print(\"Difference in # Misclassified (Global - Sum of Clusters):\", round(global_misclassified - (mis1l + mis2l + mis3l + mis4l), 2))\n", + "print(f\"Percent Improvement Over Global Model: {round(100*(global_misclassified - (mis1l + mis2l + mis3l + mis4l))/global_misclassified, 2)}%\")\n", + "print(\"---------------------------------------------\")" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "# order rows of mdi_test by cluster assignment\n", + "shap_test_clust = shap_test_values[np.argsort(test_clust)]" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "# order test clust by cluster assignment\n", + "shap_test_clust_org = test_clust[np.argsort(test_clust)]" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "# get indexes where mdi_test_clust changes clusters\n", + "cluster_changes = np.where(np.diff(shap_test_clust_org) != 0)[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# create new heatmap\n", + "plt.figure(figsize=(10, 10))\n", + "sns.heatmap(shap_test_clust[:,:,0], cmap='viridis')\n", + "plt.xlabel('Features')\n", + "plt.ylabel('Observations')\n", + "plt.title('TreeSHAP Heatmap of Test Data Ordered by Cluster Assignment')\n", + "plt.yticks([])\n", + "plt.xticks(np.arange(len(X.columns)) + 0.5, X.columns, rotation=90)\n", + "# put horizontal lines where cluster membership changes\n", + "for i in cluster_changes:\n", + " plt.axhline(i, color='red', linewidth=2, linestyle='--')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The most important feature for Train Cluster #1 is: c_jail_time\n", + "The most important feature for Test Cluster #1 is: c_jail_time\n", + "The most important feature for Train Cluster #2 is: age_cat:Greater_than_45\n", + "The most important feature for Test Cluster #2 is: age_cat:Greater_than_45\n", + "The most important feature for Train Cluster #3 is: priors_count\n", + "The most important feature for Test Cluster #3 is: priors_count\n", + "The most important feature for Train Cluster #4 is: priors_count\n", + "The most important feature for Test Cluster #4 is: priors_count\n" + ] + } + ], + "source": [ + "# get most important feature on average for each cluster\n", + "for i in range(num_clusters):\n", + " print(f'The most important feature for Train Cluster #{i+1} is:', X.columns[np.argmax(np.mean(shap_values[:,:,0][clusters==i+1], axis=0))])\n", + " print(f'The most important feature for Test Cluster #{i+1} is:', X.columns[np.argmax(np.mean(shap_test_values[:,:,0][test_clust==i+1], axis=0))])" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The feature ranking for Train Cluster #1 is: ['c_jail_time', 'priors_count', 'age_cat:Less_than_25', 'age_cat:Greater_than_45', 'days_b_screening_arrest', 'race:Caucasian', 'race:African-American', 'c_charge_degree:F', 'c_charge_degree:M', 'juv_other_count', 'age_cat:25_-_45', 'sex:Male', 'sex:Female', 'juv_misd_count', 'juv_fel_count']\n", + "The feature ranking for Test Cluster #1 is: ['c_jail_time', 'priors_count', 'age_cat:Less_than_25', 'age_cat:Greater_than_45', 'days_b_screening_arrest', 'c_charge_degree:M', 'c_charge_degree:F', 'race:Caucasian', 'race:African-American', 'juv_other_count', 'sex:Female', 'sex:Male', 'age_cat:25_-_45', 'juv_misd_count', 'juv_fel_count']\n", + "The feature ranking for Train Cluster #2 is: ['age_cat:Greater_than_45', 'days_b_screening_arrest', 'priors_count', 'c_jail_time', 'age_cat:Less_than_25', 'race:Caucasian', 'race:African-American', 'c_charge_degree:F', 'c_charge_degree:M', 'age_cat:25_-_45', 'juv_other_count', 'sex:Female', 'sex:Male', 'juv_misd_count', 'juv_fel_count']\n", + "The feature ranking for Test Cluster #2 is: ['age_cat:Greater_than_45', 'priors_count', 'c_jail_time', 'days_b_screening_arrest', 'age_cat:Less_than_25', 'race:Caucasian', 'race:African-American', 'age_cat:25_-_45', 'c_charge_degree:M', 'c_charge_degree:F', 'juv_other_count', 'sex:Female', 'sex:Male', 'juv_misd_count', 'juv_fel_count']\n", + "The feature ranking for Train Cluster #3 is: ['priors_count', 'c_jail_time', 'age_cat:Greater_than_45', 'age_cat:Less_than_25', 'race:Caucasian', 'days_b_screening_arrest', 'juv_other_count', 'race:African-American', 'c_charge_degree:F', 'c_charge_degree:M', 'sex:Male', 'sex:Female', 'age_cat:25_-_45', 'juv_misd_count', 'juv_fel_count']\n", + "The feature ranking for Test Cluster #3 is: ['priors_count', 'c_jail_time', 'age_cat:Greater_than_45', 'age_cat:Less_than_25', 'days_b_screening_arrest', 'race:Caucasian', 'juv_other_count', 'c_charge_degree:F', 'c_charge_degree:M', 'race:African-American', 'sex:Male', 'sex:Female', 'age_cat:25_-_45', 'juv_misd_count', 'juv_fel_count']\n", + "The feature ranking for Train Cluster #4 is: ['priors_count', 'age_cat:Less_than_25', 'juv_other_count', 'c_jail_time', 'age_cat:Greater_than_45', 'age_cat:25_-_45', 'race:Caucasian', 'days_b_screening_arrest', 'juv_misd_count', 'race:African-American', 'sex:Female', 'sex:Male', 'c_charge_degree:M', 'c_charge_degree:F', 'juv_fel_count']\n", + "The feature ranking for Test Cluster #4 is: ['priors_count', 'age_cat:Less_than_25', 'juv_other_count', 'age_cat:25_-_45', 'age_cat:Greater_than_45', 'c_jail_time', 'days_b_screening_arrest', 'race:Caucasian', 'race:African-American', 'sex:Female', 'sex:Male', 'c_charge_degree:M', 'c_charge_degree:F', 'juv_misd_count', 'juv_fel_count']\n" + ] + } + ], + "source": [ + "for i in range(num_clusters):\n", + " # negative is taken because argsort goes in the wrong order\n", + " print(f'The feature ranking for Train Cluster #{i+1} is:', list(X.columns[np.argsort(-np.mean(shap_values[:,:,0][clusters==i+1], axis=0))]))\n", + " print(f'The feature ranking for Test Cluster #{i+1} is:', list(X.columns[np.argsort(-np.mean(shap_test_values[:,:,0][test_clust==i+1], axis=0))]))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mdi", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/feature_importance/subgroup/legacy/compascompute copy 2.ipynb b/feature_importance/subgroup/legacy/compascompute copy 2.ipynb new file mode 100644 index 0000000..0c3fd8e --- /dev/null +++ b/feature_importance/subgroup/legacy/compascompute copy 2.ipynb @@ -0,0 +1,77 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "line 16\n", + "began load_data\n", + "data loaded\n", + "began split_data\n", + "data split\n", + "began train_models\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 15.3min\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 4\u001b[0m\n\u001b[1;32m 2\u001b[0m X, y \u001b[38;5;241m=\u001b[39m load_data()\n\u001b[1;32m 3\u001b[0m X_train, X_test, y_train, y_test \u001b[38;5;241m=\u001b[39m split_data(X, y, random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m123\u001b[39m)\n\u001b[0;32m----> 4\u001b[0m log, rf, rf_plus \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_models\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 5\u001b[0m shap_results \u001b[38;5;241m=\u001b[39m tree_shap(X_train, y_train, X_test, y_test, log, rf, rf_plus)\n\u001b[1;32m 6\u001b[0m shap_results\u001b[38;5;241m.\u001b[39mto_csv(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcompas_output/compas_shap_results123.csv\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "File \u001b[0;32m~/research/imodels-experiments/feature_importance/subgroup/compas.py:55\u001b[0m, in \u001b[0;36mtrain_models\u001b[0;34m(X_train, y_train)\u001b[0m\n\u001b[1;32m 53\u001b[0m rf\u001b[38;5;241m.\u001b[39mfit(X_train, y_train)\n\u001b[1;32m 54\u001b[0m rf_plus \u001b[38;5;241m=\u001b[39m RandomForestPlusClassifier(rf)\n\u001b[0;32m---> 55\u001b[0m \u001b[43mrf_plus\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 56\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodels trained\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m log, rf, rf_plus\n", + "File \u001b[0;32m~/research/imodels/imodels/tree/rf_plus/rf_plus/rf_plus_models.py:148\u001b[0m, in \u001b[0;36m_RandomForestPlus.fit\u001b[0;34m(self, X, y, sample_weight, n_jobs, **kwargs)\u001b[0m\n\u001b[1;32m 146\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_oob_indices[result[\u001b[38;5;241m3\u001b[39m][\u001b[38;5;241m0\u001b[39m]] \u001b[38;5;241m=\u001b[39m result[\u001b[38;5;241m3\u001b[39m][\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m 147\u001b[0m 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This starts the asynchronous\u001b[39;00m\n\u001b[1;32m 2004\u001b[0m \u001b[38;5;66;03m# dispatch of the tasks to the workers.\u001b[39;00m\n\u001b[1;32m 2005\u001b[0m \u001b[38;5;28mnext\u001b[39m(output)\n\u001b[0;32m-> 2007\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m output \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreturn_generator \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/joblib/parallel.py:1650\u001b[0m, in \u001b[0;36mParallel._get_outputs\u001b[0;34m(self, iterator, pre_dispatch)\u001b[0m\n\u001b[1;32m 1647\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m\n\u001b[1;32m 1649\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backend\u001b[38;5;241m.\u001b[39mretrieval_context():\n\u001b[0;32m-> 1650\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_retrieve()\n\u001b[1;32m 1652\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mGeneratorExit\u001b[39;00m:\n\u001b[1;32m 1653\u001b[0m \u001b[38;5;66;03m# The generator has been garbage collected before being fully\u001b[39;00m\n\u001b[1;32m 1654\u001b[0m \u001b[38;5;66;03m# consumed. This aborts the remaining tasks if possible and warn\u001b[39;00m\n\u001b[1;32m 1655\u001b[0m \u001b[38;5;66;03m# the user if necessary.\u001b[39;00m\n\u001b[1;32m 1656\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n", + "File \u001b[0;32m/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/joblib/parallel.py:1762\u001b[0m, in \u001b[0;36mParallel._retrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1757\u001b[0m \u001b[38;5;66;03m# If the next job is not ready for retrieval yet, we just wait for\u001b[39;00m\n\u001b[1;32m 1758\u001b[0m \u001b[38;5;66;03m# async callbacks to progress.\u001b[39;00m\n\u001b[1;32m 1759\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ((\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jobs) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m\n\u001b[1;32m 1760\u001b[0m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jobs[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mget_status(\n\u001b[1;32m 1761\u001b[0m timeout\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeout) \u001b[38;5;241m==\u001b[39m TASK_PENDING)):\n\u001b[0;32m-> 1762\u001b[0m \u001b[43mtime\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msleep\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0.01\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1763\u001b[0m \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[1;32m 1765\u001b[0m \u001b[38;5;66;03m# We need to be careful: the job list can be filling up as\u001b[39;00m\n\u001b[1;32m 1766\u001b[0m \u001b[38;5;66;03m# we empty it and Python list are not thread-safe by\u001b[39;00m\n\u001b[1;32m 1767\u001b[0m \u001b[38;5;66;03m# default hence the use of the lock\u001b[39;00m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "from compas import *\n", + "X, y = load_data()\n", + "X_train, X_test, y_train, y_test = split_data(X, y, random_state=123)\n", + "log, rf, rf_plus = train_models(X_train, y_train)\n", + "shap_results = tree_shap(X_train, y_train, X_test, y_test, log, rf, rf_plus)\n", + "shap_results.to_csv('compas_output/compas_shap_results123.csv')\n", + "print(shap_results)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mdi", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.1.-1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/feature_importance/subgroup/legacy/compascompute copy.ipynb b/feature_importance/subgroup/legacy/compascompute copy.ipynb new file mode 100644 index 0000000..523d4ba --- /dev/null +++ b/feature_importance/subgroup/legacy/compascompute copy.ipynb @@ -0,0 +1,203 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "line 16\n", + "began load_data\n", + "data loaded\n", + "began split_data\n", + "data split\n", + "began train_models\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 8.1min\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 24.5min finished\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "models trained\n", + "began tree_shap\n", + "testing clusters assigned\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 30 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 33 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 33 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 32 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 35 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 29 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 34 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 31 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 30 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 3.9min\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 34 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 33 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 35 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 29 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 28 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 29 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 33 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 35 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 33 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 33 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 31 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 33 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 31 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 10.9min finished\n", + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 11.4s\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 30.7s finished\n", + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 13.0s\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 36.9s finished\n", + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 12.7s\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 97 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 89 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 87 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 36.1s finished\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "local models fit\n", + "cluster 1 results complete\n", + "cluster 2 results complete\n", + "cluster 3 results complete\n", + "cluster 4 results complete\n", + " global_log_accuracy global_log_misclassified global_log_auroc \\\n", + "cluster1 0.670397 324.0 0.720703 \n", + "cluster2 0.575221 96.0 0.645455 \n", + "cluster3 0.622857 66.0 0.643348 \n", + "cluster4 0.665000 67.0 0.724479 \n", + "\n", + " global_log_auprc global_log_f1 global_rf_accuracy \\\n", + "cluster1 0.705619 0.665289 0.629705 \n", + "cluster2 0.699269 0.515152 0.535398 \n", + "cluster3 0.527348 0.214286 0.634286 \n", + "cluster4 0.786423 0.772881 0.660000 \n", + "\n", + " global_rf_misclassified global_rf_auroc global_rf_auprc \\\n", + "cluster1 364.0 0.672615 0.628146 \n", + "cluster2 105.0 0.525738 0.565170 \n", + "cluster3 64.0 0.602391 0.510110 \n", + "cluster4 68.0 0.731198 0.786609 \n", + "\n", + " global_rf_f1 ... local_rf_accuracy local_rf_misclassified \\\n", + "cluster1 0.615222 ... 0.626653 367.0 \n", + "cluster2 0.578313 ... 0.486726 116.0 \n", + "cluster3 0.360000 ... 0.594286 71.0 \n", + "cluster4 0.771812 ... 0.590000 82.0 \n", + "\n", + " local_rf_auroc local_rf_auprc local_rf_f1 local_rf_plus_accuracy \\\n", + "cluster1 0.663863 0.622194 0.612460 0.629705 \n", + "cluster2 0.459543 0.503623 0.550388 0.486726 \n", + "cluster3 0.553257 0.461294 0.202247 0.588571 \n", + "cluster4 0.692240 0.736900 0.742138 0.640000 \n", + "\n", + " local_rf_plus_misclassified local_rf_plus_auroc \\\n", + "cluster1 364.0 0.669741 \n", + "cluster2 116.0 0.456946 \n", + "cluster3 72.0 0.527213 \n", + "cluster4 72.0 0.687083 \n", + "\n", + " local_rf_plus_auprc local_rf_plus_f1 \n", + "cluster1 0.642563 0.618449 \n", + "cluster2 0.498783 0.546875 \n", + "cluster3 0.433811 0.307692 \n", + "cluster4 0.732144 0.716535 \n", + "\n", + "[4 rows x 30 columns]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from compas import *\n", + "X, y = load_data()\n", + "X_train, X_test, y_train, y_test = split_data(X, y, random_state=0)\n", + "log, rf, rf_plus = train_models(X_train, y_train)\n", + "shap_results = tree_shap(X_train, y_train, X_test, y_test, log, rf, rf_plus)\n", + "shap_results.to_csv('compas_output/compas_shap_results.csv')\n", + "print(shap_results)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mdi", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/feature_importance/subgroup/legacy/compascompute.ipynb b/feature_importance/subgroup/legacy/compascompute.ipynb new file mode 100644 index 0000000..93672a2 --- /dev/null +++ b/feature_importance/subgroup/legacy/compascompute.ipynb @@ -0,0 +1,77 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "line 16\n", + "began load_data\n", + "data loaded\n", + "began split_data\n", + "data split\n", + "began train_models\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 13.7min\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 4\u001b[0m\n\u001b[1;32m 2\u001b[0m X, y \u001b[38;5;241m=\u001b[39m load_data()\n\u001b[1;32m 3\u001b[0m X_train, X_test, y_train, y_test \u001b[38;5;241m=\u001b[39m split_data(X, y, random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m123\u001b[39m)\n\u001b[0;32m----> 4\u001b[0m log, rf, rf_plus \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_models\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 5\u001b[0m lmdi_results \u001b[38;5;241m=\u001b[39m lmdi_plus(X_train, y_train, X_test, y_test, log, rf, rf_plus)\n\u001b[1;32m 6\u001b[0m lmdi_results\u001b[38;5;241m.\u001b[39mto_csv(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcompas_output/compas_lmdi_results123.csv\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "File \u001b[0;32m~/research/imodels-experiments/feature_importance/subgroup/compas.py:55\u001b[0m, in \u001b[0;36mtrain_models\u001b[0;34m(X_train, y_train)\u001b[0m\n\u001b[1;32m 53\u001b[0m rf\u001b[38;5;241m.\u001b[39mfit(X_train, y_train)\n\u001b[1;32m 54\u001b[0m rf_plus \u001b[38;5;241m=\u001b[39m RandomForestPlusClassifier(rf)\n\u001b[0;32m---> 55\u001b[0m \u001b[43mrf_plus\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 56\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodels trained\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m log, rf, rf_plus\n", + "File \u001b[0;32m~/research/imodels/imodels/tree/rf_plus/rf_plus/rf_plus_models.py:148\u001b[0m, in \u001b[0;36m_RandomForestPlus.fit\u001b[0;34m(self, X, y, sample_weight, n_jobs, **kwargs)\u001b[0m\n\u001b[1;32m 146\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_oob_indices[result[\u001b[38;5;241m3\u001b[39m][\u001b[38;5;241m0\u001b[39m]] 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\u001b[38;5;66;03m# The first item from the output is blank, but it makes the interpreter\u001b[39;00m\n\u001b[1;32m 2002\u001b[0m \u001b[38;5;66;03m# progress until it enters the Try/Except block of the generator and\u001b[39;00m\n\u001b[1;32m 2003\u001b[0m \u001b[38;5;66;03m# reaches the first `yield` statement. This starts the asynchronous\u001b[39;00m\n\u001b[1;32m 2004\u001b[0m \u001b[38;5;66;03m# dispatch of the tasks to the workers.\u001b[39;00m\n\u001b[1;32m 2005\u001b[0m \u001b[38;5;28mnext\u001b[39m(output)\n\u001b[0;32m-> 2007\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m output \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreturn_generator \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/joblib/parallel.py:1650\u001b[0m, in \u001b[0;36mParallel._get_outputs\u001b[0;34m(self, iterator, pre_dispatch)\u001b[0m\n\u001b[1;32m 1647\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m\n\u001b[1;32m 1649\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backend\u001b[38;5;241m.\u001b[39mretrieval_context():\n\u001b[0;32m-> 1650\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_retrieve()\n\u001b[1;32m 1652\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mGeneratorExit\u001b[39;00m:\n\u001b[1;32m 1653\u001b[0m \u001b[38;5;66;03m# The generator has been garbage collected before being fully\u001b[39;00m\n\u001b[1;32m 1654\u001b[0m \u001b[38;5;66;03m# consumed. This aborts the remaining tasks if possible and warn\u001b[39;00m\n\u001b[1;32m 1655\u001b[0m \u001b[38;5;66;03m# the user if necessary.\u001b[39;00m\n\u001b[1;32m 1656\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n", + "File \u001b[0;32m/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/joblib/parallel.py:1762\u001b[0m, in \u001b[0;36mParallel._retrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1757\u001b[0m \u001b[38;5;66;03m# If the next job is not ready for retrieval yet, we just wait for\u001b[39;00m\n\u001b[1;32m 1758\u001b[0m \u001b[38;5;66;03m# async callbacks to progress.\u001b[39;00m\n\u001b[1;32m 1759\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ((\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jobs) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m\n\u001b[1;32m 1760\u001b[0m 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load_data()\n", + "X_train, X_test, y_train, y_test = split_data(X, y, random_state=123)\n", + "log, rf, rf_plus = train_models(X_train, y_train)\n", + "lmdi_results = lmdi_plus(X_train, y_train, X_test, y_test, log, rf, rf_plus)\n", + "lmdi_results.to_csv('compas_output/compas_lmdi_results123.csv')\n", + "print(lmdi_results)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mdi", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/feature_importance/subgroup/legacy/competing_methods_local.py b/feature_importance/subgroup/legacy/competing_methods_local.py new file mode 100644 index 0000000..53e1f57 --- /dev/null +++ b/feature_importance/subgroup/legacy/competing_methods_local.py @@ -0,0 +1,1616 @@ +import os +import sys +import pandas as pd +import numpy as np +import sklearn.base +from sklearn.base import RegressorMixin, ClassifierMixin +from sklearn.metrics import mean_squared_error, log_loss +from functools import reduce + +import shap +import lime +import lime.lime_tabular +from imodels.tree.rf_plus.rf_plus.rf_plus_models import RandomForestPlusRegressor, RandomForestPlusClassifier +from sklearn.ensemble import RandomForestRegressor +from imodels.tree.rf_plus.feature_importance.rfplus_explainer import * +from sklearn.metrics import r2_score, mean_absolute_error, accuracy_score, roc_auc_score, mean_squared_error + +# ### Helper function that mask the matrix +# def feature_importance_mask(feature_importance, mask_matrix, mode, mask_to = "zero"): +# assert mode in ["positive", "negative"] +# assert mask_to in ["zero", "inf"] +# masked_feature_importance = feature_importance.copy() +# if mode == "positive": +# mask = mask_matrix > 0 +# elif mode == "negative": +# mask = mask_matrix < 0 +# if mask_to == "zero": +# masked_feature_importance[~mask] = 0 +# else: +# masked_feature_importance[~mask] = sys.maxsize - 1 +# return masked_feature_importance + +def random_retrain(X_train, y_train, fit=None, mode="absolute"): + local_fi_score_train = np.random.randn(*X_train.shape) + if mode == "absolute": + return np.abs(local_fi_score_train) + + +def tree_shap_evaluation_RF_retrain(X_train, y_train, fit=None, mode="absolute"): + """ + Compute average treeshap value across observations. + Larger absolute values indicate more important features. + :param X: design matrix + :param y: response + :param fit: fitted model of interest (tree-based) + :return: dataframe of shape: (n_samples, n_features) + """ + explainer = shap.TreeExplainer(fit) + local_fi_score_train = explainer.shap_values(X_train, check_additivity=False) + if sklearn.base.is_classifier(fit): + if mode == "absolute": + return np.abs(local_fi_score_train[:,:,1]) + if mode == "absolute": + return np.abs(local_fi_score_train) + + +def lime_evaluation_RF_retrain(X_train, y_train, fit=None, mode="absolute"): + result = np.zeros((X_train.shape[0], X_train.shape[1])) + if sklearn.base.is_classifier(fit): + task = "classification" + else: + task = "regression" + if task == "classification": + explainer = lime.lime_tabular.LimeTabularExplainer(X_train,verbose=False,mode=task) + num_features = X_train.shape[1] + for i in range(X_train.shape[0]): + exp = explainer.explain_instance(X_train[i,:], fit.predict_proba, num_features=num_features) + original_feature_importance = exp.as_map()[1] + sorted_feature_importance = sorted(original_feature_importance,key = lambda x: x[0]) + for j in range(num_features): + result[i,j] = sorted_feature_importance[j][1] #abs(sorted_feature_importance[j][1]) + elif task == "regression": + explainer = lime.lime_tabular.LimeTabularExplainer(X_train,verbose=False,mode=task) + num_features = X_train.shape[1] + for i in range(X_train.shape[0]): + exp = explainer.explain_instance(X_train[i,:], fit.predict, num_features=num_features) + original_feature_importance = exp.as_map()[1] + sorted_feature_importance = sorted(original_feature_importance,key = lambda x: x[0]) + for j in range(num_features): + result[i,j] = sorted_feature_importance[j][1] + if mode == "absolute": + lime_values = np.abs(result) + return lime_values + + +def LFI_evaluation_RFPlus_inbag_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = RFPlusMDI(fit, mode = 'only_k', evaluate_on="inbag") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train) + if mode == "absolute": + return np.abs(local_fi_score_train) + + +def LFI_evaluation_RFPlus_oob_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="oob") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train) + if mode == "absolute": + return np.abs(local_fi_score_train) + +def LFI_evaluation_RFPlus_all_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="all") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train) + if mode == "absolute": + return np.abs(local_fi_score_train) + + +def LFI_evaluation_RFPlus_inbag_error_metric_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = RFPlusMDI(fit, mode = 'keep_rest_zero', evaluate_on="inbag") + local_fi_score_train = rf_plus_mdi.explain_linear_partial_error_metric(X=X_train, y=y_train) + if mode == "absolute": + return np.abs(local_fi_score_train) + + +def LFI_evaluation_RFPlus_oob_error_metric_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, mode = 'keep_rest_zero', evaluate_on="oob") + local_fi_score_train = rf_plus_mdi.explain_linear_partial_error_metric(X=X_train, y=y_train) + if mode == "absolute": + return np.abs(local_fi_score_train) + +def LFI_evaluation_RFPlus_all_error_metric_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, mode = 'keep_rest_zero', evaluate_on="all") + local_fi_score_train = rf_plus_mdi.explain_linear_partial_error_metric(X=X_train, y=y_train) + if mode == "absolute": + return np.abs(local_fi_score_train) + + +def LFI_evaluation_RFPlus_inbag_error_metric_average_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = RFPlusMDI(fit, mode = 'keep_rest_zero', evaluate_on="inbag") + local_fi_score_train = rf_plus_mdi.explain_linear_partial_error_metric(X=X_train, y=y_train, leaf_average=True) + if mode == "absolute": + return np.abs(local_fi_score_train) + + +def LFI_evaluation_RFPlus_oob_error_metric_average_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, mode = 'keep_rest_zero', evaluate_on="oob") + local_fi_score_train = rf_plus_mdi.explain_linear_partial_error_metric(X=X_train, y=y_train, leaf_average=True) + if mode == "absolute": + return np.abs(local_fi_score_train) + +def LFI_evaluation_RFPlus_all_error_metric_average_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, mode = 'keep_rest_zero', evaluate_on="all") + local_fi_score_train = rf_plus_mdi.explain_linear_partial_error_metric(X=X_train, y=y_train, leaf_average=True) + if mode == "absolute": + return np.abs(local_fi_score_train) + + +def LFI_evaluation_RFPlus_inbag_error_metric_ranking_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = RFPlusMDI(fit, mode = 'keep_rest_zero', evaluate_on="inbag") + local_fi_score_train = rf_plus_mdi.explain_linear_partial_error_metric(X=X_train, y=y_train, ranking=True) + if mode == "absolute": + return np.abs(local_fi_score_train) + + +def LFI_evaluation_RFPlus_oob_error_metric_ranking_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, mode = 'keep_rest_zero', evaluate_on="oob") + local_fi_score_train = rf_plus_mdi.explain_linear_partial_error_metric(X=X_train, y=y_train, ranking=True) + if mode == "absolute": + return np.abs(local_fi_score_train) + +def LFI_evaluation_RFPlus_all_error_metric_ranking_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, mode = 'keep_rest_zero', evaluate_on="all") + local_fi_score_train = rf_plus_mdi.explain_linear_partial_error_metric(X=X_train, y=y_train, ranking=True) + if mode == "absolute": + return np.abs(local_fi_score_train) + + +def LFI_evaluation_RFPlus_inbag_average_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = RFPlusMDI(fit, mode = 'only_k', evaluate_on="inbag") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, leaf_average = True) + if mode == "absolute": + return np.abs(local_fi_score_train) + + +def LFI_evaluation_RFPlus_oob_average_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="oob") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, leaf_average = True) + if mode == "absolute": + return np.abs(local_fi_score_train) + +def LFI_evaluation_RFPlus_all_average_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="all") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, leaf_average = True) + if mode == "absolute": + return np.abs(local_fi_score_train) + + +def LFI_evaluation_RFPlus_inbag_ranking_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = RFPlusMDI(fit, mode = 'only_k', evaluate_on="inbag") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, ranking = True) + if mode == "absolute": + return np.abs(local_fi_score_train) + + +def LFI_evaluation_RFPlus_oob_ranking_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="oob") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, ranking = True) + if mode == "absolute": + return np.abs(local_fi_score_train) + +def LFI_evaluation_RFPlus_all_ranking_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="all") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, ranking = True) + if mode == "absolute": + return np.abs(local_fi_score_train) + +def LFI_evaluation_RFPlus_inbag_ranking_ridge_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = RFPlusMDI(fit, mode = 'only_k', evaluate_on="inbag") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, ranking = True) + if mode == "absolute": + return np.abs(local_fi_score_train) + + +def LFI_evaluation_RFPlus_oob_ranking_ridge_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = RFPlusMDI(fit, mode = 'only_k', evaluate_on="oob") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, ranking = True) + if mode == "absolute": + return np.abs(local_fi_score_train) + +def LFI_evaluation_RFPlus_all_ranking_ridge_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = RFPlusMDI(fit, mode = 'only_k', evaluate_on="all") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, ranking = True) + if mode == "absolute": + return np.abs(local_fi_score_train) + + + +# def LFI_evaluation_RFPlus_inbag_l2_norm_sign_retrain(X_train, y_train, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = RFPlusMDI(fit, mode = 'only_k', evaluate_on="inbag") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=True) +# if mode == "absolute": +# return np.abs(local_fi_score_train) + + +# def LFI_evaluation_RFPlus_oob_l2_norm_sign_retrain(X_train, y_train, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="oob") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=True) +# if mode == "absolute": +# return np.abs(local_fi_score_train) + +# def LFI_evaluation_RFPlus_all_l2_norm_sign_retrain(X_train, y_train, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="all") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=True) +# if mode == "absolute": +# return np.abs(local_fi_score_train) + + +def LFI_evaluation_RFPlus_inbag_l2_norm_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = RFPlusMDI(fit, mode = 'only_k', evaluate_on="inbag") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=False) + if mode == "absolute": + return np.abs(local_fi_score_train) + + +def LFI_evaluation_RFPlus_oob_l2_norm_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="oob") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=False) + if mode == "absolute": + return np.abs(local_fi_score_train) + +def LFI_evaluation_RFPlus_all_l2_norm_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="all") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=False) + if mode == "absolute": + return np.abs(local_fi_score_train) + +def LFI_evaluation_RFPlus_inbag_l2_norm_average_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = RFPlusMDI(fit, mode = 'only_k', evaluate_on="inbag") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=False, leaf_average= True) + if mode == "absolute": + return np.abs(local_fi_score_train) + +def LFI_evaluation_RFPlus_oob_l2_norm_average_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="oob") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=False, leaf_average= True) + if mode == "absolute": + return np.abs(local_fi_score_train) + +def LFI_evaluation_RFPlus_all_l2_norm_average_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="all") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=False, leaf_average= True) + if mode == "absolute": + return np.abs(local_fi_score_train) + +def LFI_evaluation_RFPlus_inbag_l2_norm_ranking_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = RFPlusMDI(fit, mode = 'only_k', evaluate_on="inbag") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=False, ranking=True) + if mode == "absolute": + return np.abs(local_fi_score_train) + + +def LFI_evaluation_RFPlus_oob_l2_norm_ranking_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="oob") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=False, ranking=True) + if mode == "absolute": + return np.abs(local_fi_score_train) + +def LFI_evaluation_RFPlus_all_l2_norm_ranking_retrain(X_train, y_train, fit=None, mode="absolute"): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, mode = 'only_k', evaluate_on="all") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=False, ranking=True) + if mode == "absolute": + return np.abs(local_fi_score_train) + + + + + + + + + + + + + + + + + +#### Baseline Methods +def random(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute", train_only=False): + local_fi_score_train = np.random.randn(*X_train.shape) + local_fi_score_train_subset = np.random.randn(*X_train_subset.shape) + local_fi_score_test = np.random.randn(*X_test.shape) + local_fi_score_test_subset = np.random.randn(*X_test_subset.shape) + if mode == "absolute": + return np.abs(local_fi_score_train), np.abs(local_fi_score_train_subset), np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) + else: + return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset + # local_fi_score_train_subset = feature_importance_mask(local_fi_score_train_subset, local_fi_score_train_subset, mode, mask_to = "zero") + # local_fi_score_test = feature_importance_mask(local_fi_score_test, local_fi_score_test, mode, mask_to = "zero") + # local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset, mode, mask_to = "zero") + # return local_fi_score_train, np.abs(local_fi_score_train_subset), np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) + + +def tree_shap_evaluation_RF(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute", train_only=False): + """ + Compute average treeshap value across observations. + Larger absolute values indicate more important features. + :param X: design matrix + :param y: response + :param fit: fitted model of interest (tree-based) + :return: dataframe of shape: (n_samples, n_features) + """ + explainer = shap.TreeExplainer(fit) + local_fi_score_train = explainer.shap_values(X_train, check_additivity=False) + local_fi_score_train_subset = explainer.shap_values(X_train_subset, check_additivity=False) + local_fi_score_test = explainer.shap_values(X_test, check_additivity=False) + local_fi_score_test_subset = explainer.shap_values(X_test_subset, check_additivity=False) + if sklearn.base.is_classifier(fit): + if mode == "absolute": + #return None, np.sum(np.abs(local_fi_score_train_subset),axis=-1), np.sum(np.abs(local_fi_score_test),axis=-1), np.sum(np.abs(local_fi_score_test_subset),axis=-1) + return np.abs(local_fi_score_train[:,:,1]), np.abs(local_fi_score_train_subset[:,:,1]), np.abs(local_fi_score_test[:,:,1]), np.abs(local_fi_score_test_subset[:,:,1]) + else: + return local_fi_score_train[:,:,1], local_fi_score_train_subset[:,:,1], local_fi_score_test[:,:,1], local_fi_score_test_subset[:,:,1] + else: + if mode == "absolute": + return np.abs(local_fi_score_train), np.abs(local_fi_score_train_subset), np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) + else: + # local_fi_score_train_subset = feature_importance_mask(local_fi_score_train_subset, local_fi_score_train_subset, mode, mask_to = "zero") + # local_fi_score_test = feature_importance_mask(local_fi_score_test, local_fi_score_test, mode, mask_to = "zero") + # local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset, mode, mask_to = "zero") + return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset + + +def lime_evaluation_RF(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute", train_only=False): + if train_only: + result = np.zeros((X_train.shape[0], X_train.shape[1])) + if sklearn.base.is_classifier(fit): + task = "classification" + else: + task = "regression" + + if task == "classification": + explainer = lime.lime_tabular.LimeTabularExplainer(X_train,verbose=False,mode=task) + num_features = X_train.shape[1] + for i in range(X_train.shape[0]): + exp = explainer.explain_instance(X_train[i,:], fit.predict_proba, num_features=num_features) + original_feature_importance = exp.as_map()[1] + sorted_feature_importance = sorted(original_feature_importance,key = lambda x: x[0]) + for j in range(num_features): + result[i,j] = sorted_feature_importance[j][1] #abs(sorted_feature_importance[j][1]) + elif task == "regression": + explainer = lime.lime_tabular.LimeTabularExplainer(X_train,verbose=False,mode=task) + num_features = X_train.shape[1] + for i in range(X_train.shape[0]): + exp = explainer.explain_instance(X_train[i,:], fit.predict, num_features=num_features) + original_feature_importance = exp.as_map()[1] + sorted_feature_importance = sorted(original_feature_importance,key = lambda x: x[0]) + for j in range(num_features): + result[i,j] = sorted_feature_importance[j][1] + if mode == "absolute": + lime_values = np.abs(result) + else: + lime_values = result + + return lime_values, None, None, None + + + +# def kernel_shap_evaluation_RF_plus(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_kernel_shap = RFPlusKernelSHAP(fit) +# local_fi_score_train = None +# local_fi_score_train_subset = rf_plus_kernel_shap.explain(X_train=X_train, X_test=X_train_subset) +# local_fi_score_test = None +# local_fi_score_test_subset = rf_plus_kernel_shap.explain(X_train=X_train, X_test=X_test_subset) +# if sklearn.base.is_classifier(fit): +# if mode == "absolute": +# #return None, np.sum(np.abs(local_fi_score_train_subset),axis=-1), np.sum(np.abs(local_fi_score_test),axis=-1), np.sum(np.abs(local_fi_score_test_subset),axis=-1) +# return None, np.abs(local_fi_score_train_subset[:,:,1]), None, np.abs(local_fi_score_test_subset[:,:,1]) +# else: +# return None, local_fi_score_train_subset[:,:,1], None, local_fi_score_test_subset[:,:,1] +# else: +# if mode == "absolute": +# return None, np.abs(local_fi_score_train_subset), None, np.abs(local_fi_score_test_subset) +# else: +# # local_fi_score_train_subset = feature_importance_mask(local_fi_score_train_subset, local_fi_score_train_subset, mode, mask_to = "zero") +# # local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset, mode, mask_to = "zero") +# return None, local_fi_score_train_subset, None, local_fi_score_test_subset + + +# def lime_evaluation_RF_plus(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_lime = RFPlusLime(fit) +# local_fi_score_train = None +# local_fi_score_train_subset = rf_plus_lime.explain(X_train=X_train, X_test=X_train_subset).values +# local_fi_score_test = None +# local_fi_score_test_subset = rf_plus_lime.explain(X_train=X_train, X_test=X_test_subset).values +# if mode == "absolute": +# return None, np.abs(local_fi_score_train_subset), None, np.abs(local_fi_score_test_subset) +# else: +# return None, local_fi_score_train_subset, None, local_fi_score_test_subset +# # local_fi_score_train_subset = feature_importance_mask(local_fi_score_train_subset, local_fi_score_train_subset, mode, mask_to = "zero") +# # local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset, mode, mask_to = "zero") +# # return local_fi_score_train, np.abs(local_fi_score_train_subset), local_fi_score_test, np.abs(local_fi_score_test_subset) + + + + + + + + + + + +### Feature Importance Methods for RF+ + +# def LFI_evaluation_RFPlus_inbag(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = RFPlusMDI(fit, evaluate_on="inbag") +# local_fi_score_train = np.abs(rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train)) +# local_fi_score_train_subset = None +# local_fi_score_test = np.abs(rf_plus_mdi.explain_linear_partial(X=X_test, y=None)) +# local_fi_score_test_subset = np.abs(rf_plus_mdi.explain_linear_partial(X=X_test_subset, y=None)) +# return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset + + +def LFI_evaluation_RFPlus_oob(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute", train_only=False): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="oob") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train) + local_fi_score_train_subset = None + local_fi_score_test = rf_plus_mdi.explain_linear_partial(X=X_test, y=None) + local_fi_score_test_subset = rf_plus_mdi.explain_linear_partial(X=X_test_subset, y=None) + if mode == "absolute": + return np.abs(local_fi_score_train), None, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) + else: + return local_fi_score_train, None, local_fi_score_test, local_fi_score_test_subset + + +def LFI_evaluation_RFPlus_all(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute", train_only=False): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="all") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train) + local_fi_score_train_subset = None + local_fi_score_test = rf_plus_mdi.explain_linear_partial(X=X_test, y=None) + local_fi_score_test_subset = rf_plus_mdi.explain_linear_partial(X=X_test_subset, y=None) + if mode == "absolute": + return np.abs(local_fi_score_train), None, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) + else: + return local_fi_score_train, None, local_fi_score_test, local_fi_score_test_subset + + + +def LFI_evaluation_RFPlus_oob_error_metric(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute", train_only=False): + assert train_only == True + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="oob") + local_fi_score_train = rf_plus_mdi.explain(X=X_train, y=y_train)[0] + if mode == "absolute": + return np.abs(local_fi_score_train), None, None, None + else: + return local_fi_score_train, None, None, None + +def LFI_evaluation_RFPlus_all_error_metric(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute", train_only=False): + assert train_only == True + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="all") + local_fi_score_train = rf_plus_mdi.explain(X=X_train, y=y_train)[0] + if mode == "absolute": + return np.abs(local_fi_score_train), None, None, None + else: + return local_fi_score_train, None, None, None + + +# ##### Average Leaf +# def LFI_evaluation_RFPlus_inbag_avg_leaf(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = RFPlusMDI(fit, evaluate_on="inbag") +# local_fi_score_train = np.abs(rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, leaf_average=True)) +# local_fi_score_train_subset = None +# local_fi_score_test = np.abs(rf_plus_mdi.explain_linear_partial(X=X_test, y=None,leaf_average=True)) +# local_fi_score_test_subset = np.abs(rf_plus_mdi.explain_linear_partial(X=X_test_subset, y=None, leaf_average=True)) +# return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset + + +# def LFI_evaluation_RFPlus_oob_avg_leaf(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="oob") +# local_fi_score_train = np.abs(rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train,leaf_average=True)) +# local_fi_score_train_subset = None +# local_fi_score_test = np.abs(rf_plus_mdi.explain_linear_partial(X=X_test, y=None,leaf_average=True)) +# local_fi_score_test_subset = np.abs(rf_plus_mdi.explain_linear_partial(X=X_test_subset, y=None,leaf_average=True)) +# return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset + + +# def LFI_evaluation_RFPlus_all_avg_leaf(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="all") +# local_fi_score_train = np.abs(rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train,leaf_average=True)) +# local_fi_score_train_subset = None +# local_fi_score_test = np.abs(rf_plus_mdi.explain_linear_partial(X=X_test, y=None,leaf_average=True)) +# local_fi_score_test_subset = np.abs(rf_plus_mdi.explain_linear_partial(X=X_test_subset, y=None,leaf_average=True)) +# return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset + + + +##### l2 norm with sign +# def LFI_evaluation_RFPlus_inbag_l2_norm_sign(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = RFPlusMDI(fit, evaluate_on="inbag") +# local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=True) +# local_fi_score_train_subset = None +# local_fi_score_test = rf_plus_mdi.explain_linear_partial(X=X_test, y=None, l2norm=True, sign=True) +# local_fi_score_test_subset = rf_plus_mdi.explain_linear_partial(X=X_test_subset, y=None, l2norm=True, sign=True) +# if mode == "absolute": +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) +# else: +# return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset + + +def LFI_evaluation_RFPlus_oob_l2_norm_sign(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute", train_only=False): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="oob") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=True) + local_fi_score_train_subset = None + local_fi_score_test = rf_plus_mdi.explain_linear_partial(X=X_test, y=None, l2norm=True, sign=True) + local_fi_score_test_subset = rf_plus_mdi.explain_linear_partial(X=X_test_subset, y=None, l2norm=True, sign=True) + if mode == "absolute": + return np.abs(local_fi_score_train), None, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) + else: + return local_fi_score_train, None, local_fi_score_test, local_fi_score_test_subset + + +def LFI_evaluation_RFPlus_all_l2_norm_sign(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute", train_only=False): + assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) + rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="all") + local_fi_score_train = rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, sign=True) + local_fi_score_train_subset = None + local_fi_score_test = rf_plus_mdi.explain_linear_partial(X=X_test, y=None, l2norm=True, sign=True) + local_fi_score_test_subset = rf_plus_mdi.explain_linear_partial(X=X_test_subset, y=None, l2norm=True, sign=True) + if mode == "absolute": + return np.abs(local_fi_score_train), None, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) + else: + return local_fi_score_train, None, local_fi_score_test, local_fi_score_test_subset + + +# ##### l2 norm +# def LFI_evaluation_RFPlus_inbag_l2_norm(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = RFPlusMDI(fit, evaluate_on="inbag") +# local_fi_score_train = np.abs(rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True)) +# local_fi_score_train_subset = None +# local_fi_score_test = np.abs(rf_plus_mdi.explain_linear_partial(X=X_test, y=None, l2norm=True)) +# local_fi_score_test_subset = np.abs(rf_plus_mdi.explain_linear_partial(X=X_test_subset, y=None, l2norm=True)) +# return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset + + +# def LFI_evaluation_RFPlus_oob_l2_norm(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="oob") +# local_fi_score_train = np.abs(rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True)) +# local_fi_score_train_subset = None +# local_fi_score_test = np.abs(rf_plus_mdi.explain_linear_partial(X=X_test, y=None, l2norm=True)) +# local_fi_score_test_subset = np.abs(rf_plus_mdi.explain_linear_partial(X=X_test_subset, y=None, l2norm=True)) +# return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset + + +# def LFI_evaluation_RFPlus_all_l2_norm(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="all") +# local_fi_score_train = np.abs(rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True)) +# local_fi_score_train_subset = None +# local_fi_score_test = np.abs(rf_plus_mdi.explain_linear_partial(X=X_test, y=None, l2norm=True)) +# local_fi_score_test_subset = np.abs(rf_plus_mdi.explain_linear_partial(X=X_test_subset, y=None, l2norm=True)) +# return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset + +# ##### Average Leaf and l2 norm +# def LFI_evaluation_RFPlus_inbag_l2_norm_avg_leaf(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = RFPlusMDI(fit, evaluate_on="inbag") +# local_fi_score_train = np.abs(rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, leaf_average=True)) +# local_fi_score_train_subset = None +# local_fi_score_test = np.abs(rf_plus_mdi.explain_linear_partial(X=X_test, y=None, l2norm=True ,leaf_average=True)) +# local_fi_score_test_subset = np.abs(rf_plus_mdi.explain_linear_partial(X=X_test_subset, y=None, l2norm=True ,leaf_average=True)) +# return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset + + +# def LFI_evaluation_RFPlus_oob_l2_norm_avg_leaf(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="oob") +# local_fi_score_train = np.abs(rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, leaf_average=True)) +# local_fi_score_train_subset = None +# local_fi_score_test = np.abs(rf_plus_mdi.explain_linear_partial(X=X_test, y=None, l2norm=True, leaf_average=True)) +# local_fi_score_test_subset = np.abs(rf_plus_mdi.explain_linear_partial(X=X_test_subset, y=None, l2norm=True, leaf_average=True)) +# return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset + + +# def LFI_evaluation_RFPlus_all_l2_norm_avg_leaf(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="all") +# local_fi_score_train = np.abs(rf_plus_mdi.explain_linear_partial(X=X_train, y=y_train, l2norm=True, leaf_average=True)) +# local_fi_score_train_subset = None +# local_fi_score_test = np.abs(rf_plus_mdi.explain_linear_partial(X=X_test, y=None, l2norm=True, leaf_average=True)) +# local_fi_score_test_subset = np.abs(rf_plus_mdi.explain_linear_partial(X=X_test_subset, y=None, l2norm=True, leaf_average=True)) +# return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset + + + + + +# ### Feature Importance Methods for RF+ avg leaf +# def LFI_evaluation_RFPlus_inbag_avg_leaf(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi_train = RFPlusMDI(fit, evaluate_on="inbag") +# rf_plus_mdi_test = RFPlusMDI(fit, evaluate_on="all") +# local_fi_score_train = np.abs(rf_plus_mdi_train.explain(X=X_train, y=y_train, leaf_average=True)[0]) +# local_fi_score_train_subset = None +# local_fi_score_test = np.abs(rf_plus_mdi_test.explain(X=X_test, y=None, leaf_average=True)[0]) +# local_fi_score_test_subset = np.abs(rf_plus_mdi_test.explain(X=X_test_subset, y=None, leaf_average=True)[0]) +# if mode != "absolute": +# local_fi_score_train_mask = rf_plus_mdi_train.explain_subtract_intercept(X=X_train, y=y_train) +# local_fi_score_test_mask = rf_plus_mdi_test.explain_subtract_intercept(X=X_test, y=None) +# local_fi_score_test_subset_mask = rf_plus_mdi_test.explain_subtract_intercept(X=X_test_subset, y=None) +# local_fi_score_train = feature_importance_mask(local_fi_score_train, local_fi_score_train_mask, mode, mask_to = "inf") +# local_fi_score_test = feature_importance_mask(local_fi_score_test, local_fi_score_test_mask, mode, mask_to = "inf") +# local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset_mask, mode, mask_to = "inf") +# return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset + + +# def LFI_evaluation_RFPlus_oob_avg_leaf(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi_train = AloRFPlusMDI(fit, evaluate_on="oob") +# rf_plus_mdi_test = AloRFPlusMDI(fit, evaluate_on="all") +# local_fi_score_train = np.abs(rf_plus_mdi_train.explain(X=X_train, y=y_train, leaf_average=True)[0]) +# local_fi_score_train_subset = None +# local_fi_score_test = np.abs(rf_plus_mdi_test.explain(X=X_test, y=None, leaf_average=True)[0]) +# local_fi_score_test_subset = np.abs(rf_plus_mdi_test.explain(X=X_test_subset, y=None, leaf_average=True)[0]) +# if mode != "absolute": +# local_fi_score_train_mask = rf_plus_mdi_train.explain_subtract_intercept(X=X_train, y=y_train) +# local_fi_score_test_mask = rf_plus_mdi_test.explain_subtract_intercept(X=X_test, y=None) +# local_fi_score_test_subset_mask = rf_plus_mdi_test.explain_subtract_intercept(X=X_test_subset, y=None) +# local_fi_score_train = feature_importance_mask(local_fi_score_train, local_fi_score_train_mask, mode, mask_to = "inf") +# local_fi_score_test = feature_importance_mask(local_fi_score_test, local_fi_score_test_mask, mode, mask_to = "inf") +# local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset_mask, mode, mask_to = "inf") +# return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset + + +# def LFI_evaluation_RFPlus_all_avg_leaf(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="all") +# local_fi_score_train = np.abs(rf_plus_mdi.explain(X=X_train, y=y_train, leaf_average=True)[0]) +# local_fi_score_train_subset = None +# local_fi_score_test = np.abs(rf_plus_mdi.explain(X=X_test, y=None, leaf_average=True)[0]) +# local_fi_score_test_subset = np.abs(rf_plus_mdi.explain(X=X_test_subset, y=None, leaf_average=True)[0]) +# if mode != "absolute": +# local_fi_score_train_mask = rf_plus_mdi.explain_subtract_intercept(X=X_train, y=y_train) +# local_fi_score_test_mask = rf_plus_mdi.explain_subtract_intercept(X=X_test, y=None) +# local_fi_score_test_subset_mask = rf_plus_mdi.explain_subtract_intercept(X=X_test_subset, y=None) +# local_fi_score_train = feature_importance_mask(local_fi_score_train, local_fi_score_train_mask, mode, mask_to = "inf") +# local_fi_score_test = feature_importance_mask(local_fi_score_test, local_fi_score_test_mask, mode, mask_to = "inf") +# local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset_mask, mode, mask_to = "inf") +# return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset + + +# ### No intercept +# def LFI_evaluation_RFPlus_inbag_subtract_intercept(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi_train = RFPlusMDI(fit, evaluate_on="inbag") +# rf_plus_mdi_test = RFPlusMDI(fit, evaluate_on="all") +# local_fi_score_train = rf_plus_mdi_train.explain_subtract_intercept(X=X_train, y=y_train) +# local_fi_score_train_subset = None +# local_fi_score_test = rf_plus_mdi_test.explain_subtract_intercept(X=X_test, y=None) +# local_fi_score_test_subset = rf_plus_mdi_test.explain_subtract_intercept(X=X_test_subset, y=None) +# if mode == "absolute": +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) +# else: +# local_fi_score_train = feature_importance_mask(local_fi_score_train, local_fi_score_train, mode, mask_to = "zero") +# local_fi_score_test = feature_importance_mask(local_fi_score_test, local_fi_score_test, mode, mask_to = "zero") +# local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset, mode, mask_to = "zero") +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) + + +# def LFI_evaluation_RFPlus_oob_subtract_intercept(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi_train = AloRFPlusMDI(fit, evaluate_on="oob") +# rf_plus_mdi_test = AloRFPlusMDI(fit, evaluate_on="all") +# local_fi_score_train = rf_plus_mdi_train.explain_subtract_intercept(X=X_train, y=y_train) +# local_fi_score_train_subset = None +# local_fi_score_test = rf_plus_mdi_test.explain_subtract_intercept(X=X_test, y=None) +# local_fi_score_test_subset = rf_plus_mdi_test.explain_subtract_intercept(X=X_test_subset, y=None) +# if mode == "absolute": +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) +# else: +# local_fi_score_train = feature_importance_mask(local_fi_score_train, local_fi_score_train, mode, mask_to = "zero") +# local_fi_score_test = feature_importance_mask(local_fi_score_test, local_fi_score_test, mode, mask_to = "zero") +# local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset, mode, mask_to = "zero") +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) + + + +# def LFI_evaluation_RFPlus_all_subtract_intercept(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="all") +# local_fi_score_train = rf_plus_mdi.explain_subtract_intercept(X=X_train, y=y_train) +# local_fi_score_train_subset = None +# local_fi_score_test = rf_plus_mdi.explain_subtract_intercept(X=X_test, y=None) +# local_fi_score_test_subset = rf_plus_mdi.explain_subtract_intercept(X=X_test_subset, y=None) +# if mode == "absolute": +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) +# else: +# local_fi_score_train = feature_importance_mask(local_fi_score_train, local_fi_score_train, mode, mask_to = "zero") +# local_fi_score_test = feature_importance_mask(local_fi_score_test, local_fi_score_test, mode, mask_to = "zero") +# local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset, mode, mask_to = "zero") +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) + + + + + +# ### No intercept and average leaf +# def LFI_evaluation_RFPlus_inbag_subtract_intercept_avg_leaf(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi_train = RFPlusMDI(fit, evaluate_on="inbag") +# rf_plus_mdi_test = RFPlusMDI(fit, evaluate_on="all") +# local_fi_score_train = rf_plus_mdi_train.explain_subtract_intercept(X=X_train, y=y_train, leaf_average=True) +# local_fi_score_train_subset = None +# local_fi_score_test = rf_plus_mdi_test.explain_subtract_intercept(X=X_test, y=None, leaf_average=True) +# local_fi_score_test_subset = rf_plus_mdi_test.explain_subtract_intercept(X=X_test_subset, y=None, leaf_average=True) +# if mode == "absolute": +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) +# else: +# local_fi_score_train = feature_importance_mask(local_fi_score_train, local_fi_score_train, mode, mask_to = "zero") +# local_fi_score_test = feature_importance_mask(local_fi_score_test, local_fi_score_test, mode, mask_to = "zero") +# local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset, mode, mask_to = "zero") +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) + + +# def LFI_evaluation_RFPlus_oob_subtract_intercept_avg_leaf(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi_train = AloRFPlusMDI(fit, evaluate_on="oob") +# rf_plus_mdi_test = AloRFPlusMDI(fit, evaluate_on="all") +# local_fi_score_train = rf_plus_mdi_train.explain_subtract_intercept(X=X_train, y=y_train, leaf_average=True) +# local_fi_score_train_subset = None +# local_fi_score_test = rf_plus_mdi_test.explain_subtract_intercept(X=X_test, y=None, leaf_average=True) +# local_fi_score_test_subset = rf_plus_mdi_test.explain_subtract_intercept(X=X_test_subset, y=None, leaf_average=True) +# if mode == "absolute": +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) +# else: +# local_fi_score_train = feature_importance_mask(local_fi_score_train, local_fi_score_train, mode, mask_to = "zero") +# local_fi_score_test = feature_importance_mask(local_fi_score_test, local_fi_score_test, mode, mask_to = "zero") +# local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset, mode, mask_to = "zero") +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) + + + +# def LFI_evaluation_RFPlus_all_subtract_intercept_avg_leaf(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="all") +# local_fi_score_train = rf_plus_mdi.explain_subtract_intercept(X=X_train, y=y_train, leaf_average=True) +# local_fi_score_train_subset = None +# local_fi_score_test = rf_plus_mdi.explain_subtract_intercept(X=X_test, y=None, leaf_average=True) +# local_fi_score_test_subset = rf_plus_mdi.explain_subtract_intercept(X=X_test_subset, y=None, leaf_average=True) +# if mode == "absolute": +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) +# else: +# local_fi_score_train = feature_importance_mask(local_fi_score_train, local_fi_score_train, mode, mask_to = "zero") +# local_fi_score_test = feature_importance_mask(local_fi_score_test, local_fi_score_test, mode, mask_to = "zero") +# local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset, mode, mask_to = "zero") +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) + + + + + + +# ### Subtract train mean +# def LFI_evaluation_RFPlus_inbag_subtract_train_mean(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi_train = RFPlusMDI(fit, evaluate_on="inbag") +# rf_plus_mdi_test = RFPlusMDI(fit, evaluate_on="all") +# constant = np.mean(y_train) +# local_fi_score_train = rf_plus_mdi_train.explain_subtract_constant(X=X_train,constant=constant, y=y_train) +# local_fi_score_train_subset = None +# local_fi_score_test = rf_plus_mdi_test.explain_subtract_constant(X=X_test,constant=constant, y=None) +# local_fi_score_test_subset = rf_plus_mdi_test.explain_subtract_constant(X=X_test_subset,constant=constant, y=None) +# if mode == "absolute": +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) +# else: +# local_fi_score_train = feature_importance_mask(local_fi_score_train, local_fi_score_train, mode, mask_to = "zero") +# local_fi_score_test = feature_importance_mask(local_fi_score_test, local_fi_score_test, mode, mask_to = "zero") +# local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset, mode, mask_to = "zero") +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) + + +# def LFI_evaluation_RFPlus_oob_subtract_train_mean(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi_train = AloRFPlusMDI(fit, evaluate_on="oob") +# rf_plus_mdi_test = AloRFPlusMDI(fit, evaluate_on="all") +# constant = np.mean(y_train) +# local_fi_score_train = rf_plus_mdi_train.explain_subtract_constant(X=X_train, constant=constant,y=y_train) +# local_fi_score_train_subset = None +# local_fi_score_test = rf_plus_mdi_test.explain_subtract_constant(X=X_test,constant=constant, y=None) +# local_fi_score_test_subset = rf_plus_mdi_test.explain_subtract_constant(X=X_test_subset, constant=constant,y=None) +# if mode == "absolute": +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) +# else: +# local_fi_score_train = feature_importance_mask(local_fi_score_train, local_fi_score_train, mode, mask_to = "zero") +# local_fi_score_test = feature_importance_mask(local_fi_score_test, local_fi_score_test, mode, mask_to = "zero") +# local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset, mode, mask_to = "zero") +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) + + + +# def LFI_evaluation_RFPlus_all_subtract_train_mean(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="all") +# constant = np.mean(y_train) +# local_fi_score_train = rf_plus_mdi.explain_subtract_constant(X=X_train,constant=constant, y=y_train) +# local_fi_score_train_subset = None +# local_fi_score_test = rf_plus_mdi.explain_subtract_constant(X=X_test,constant=constant, y=None) +# local_fi_score_test_subset = rf_plus_mdi.explain_subtract_constant(X=X_test_subset,constant=constant, y=None) +# if mode == "absolute": +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) +# else: +# local_fi_score_train = feature_importance_mask(local_fi_score_train, local_fi_score_train, mode, mask_to = "zero") +# local_fi_score_test = feature_importance_mask(local_fi_score_test, local_fi_score_test, mode, mask_to = "zero") +# local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset, mode, mask_to = "zero") +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) + + +# ### subtract pred mean +# def LFI_evaluation_RFPlus_inbag_subtract_pred_mean(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi_train = RFPlusMDI(fit, evaluate_on="inbag") +# rf_plus_mdi_test = RFPlusMDI(fit, evaluate_on="all") +# constant = np.mean(y_train_pred) +# local_fi_score_train = rf_plus_mdi_train.explain_subtract_constant(X=X_train,constant=constant, y=y_train) +# local_fi_score_train_subset = None +# local_fi_score_test = rf_plus_mdi_test.explain_subtract_constant(X=X_test,constant=constant, y=None) +# local_fi_score_test_subset = rf_plus_mdi_test.explain_subtract_constant(X=X_test_subset,constant=constant, y=None) +# if mode == "absolute": +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) +# else: +# local_fi_score_train = feature_importance_mask(local_fi_score_train, local_fi_score_train, mode, mask_to = "zero") +# local_fi_score_test = feature_importance_mask(local_fi_score_test, local_fi_score_test, mode, mask_to = "zero") +# local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset, mode, mask_to = "zero") +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) + + +# def LFI_evaluation_RFPlus_oob_subtract_pred_mean(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi_train = AloRFPlusMDI(fit, evaluate_on="oob") +# rf_plus_mdi_test = AloRFPlusMDI(fit, evaluate_on="all") +# constant = np.mean(y_train_pred) +# local_fi_score_train = rf_plus_mdi_train.explain_subtract_constant(X=X_train, constant=constant,y=y_train) +# local_fi_score_train_subset = None +# local_fi_score_test = rf_plus_mdi_test.explain_subtract_constant(X=X_test,constant=constant, y=None) +# local_fi_score_test_subset = rf_plus_mdi_test.explain_subtract_constant(X=X_test_subset, constant=constant,y=None) +# if mode == "absolute": +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) +# else: +# local_fi_score_train = feature_importance_mask(local_fi_score_train, local_fi_score_train, mode, mask_to = "zero") +# local_fi_score_test = feature_importance_mask(local_fi_score_test, local_fi_score_test, mode, mask_to = "zero") +# local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset, mode, mask_to = "zero") +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) + + + +# def LFI_evaluation_RFPlus_all_subtract_pred_mean(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="all") +# constant = np.mean(y_train_pred) +# local_fi_score_train = rf_plus_mdi.explain_subtract_constant(X=X_train,constant=constant, y=y_train) +# local_fi_score_train_subset = None +# local_fi_score_test = rf_plus_mdi.explain_subtract_constant(X=X_test,constant=constant, y=None) +# local_fi_score_test_subset = rf_plus_mdi.explain_subtract_constant(X=X_test_subset,constant=constant, y=None) +# if mode == "absolute": +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) +# else: +# local_fi_score_train = feature_importance_mask(local_fi_score_train, local_fi_score_train, mode, mask_to = "zero") +# local_fi_score_test = feature_importance_mask(local_fi_score_test, local_fi_score_test, mode, mask_to = "zero") +# local_fi_score_test_subset = feature_importance_mask(local_fi_score_test_subset, local_fi_score_test_subset, mode, mask_to = "zero") +# return np.abs(local_fi_score_train), local_fi_score_train_subset, np.abs(local_fi_score_test), np.abs(local_fi_score_test_subset) + + +### +# def LFI_evaluation_RFPlus_inbag_2(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# assert mode == "absolute" +# rf_plus_mdi_train = RFPlusMDI(fit, evaluate_on="inbag") +# rf_plus_mdi_test = RFPlusMDI(fit, evaluate_on="all") +# local_fi_score_train = np.abs(rf_plus_mdi_train.explain(X=X_train, y=y_train)[1]) +# local_fi_score_train_subset = None +# local_fi_score_test = np.abs(rf_plus_mdi_test.explain(X=X_test, y=None)[1]) +# local_fi_score_test_subset = np.abs(rf_plus_mdi_test.explain(X=X_test_subset, y=None)[1]) +# return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset + + +# def LFI_evaluation_RFPlus_oob_2(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# assert mode == "absolute" +# rf_plus_mdi_train = AloRFPlusMDI(fit, evaluate_on="oob") +# rf_plus_mdi_test = AloRFPlusMDI(fit, evaluate_on="all") +# local_fi_score_train = np.abs(rf_plus_mdi_train.explain(X=X_train, y=y_train)[1]) +# local_fi_score_train_subset = None +# local_fi_score_test = np.abs(rf_plus_mdi_test.explain(X=X_test, y=None)[1]) +# local_fi_score_test_subset = np.abs(rf_plus_mdi_test.explain(X=X_test_subset, y=None)[1]) +# return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset + + + +# def LFI_evaluation_RFPlus_all_2(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# assert mode == "absolute" +# rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="all") +# local_fi_score_train = np.abs(rf_plus_mdi.explain(X=X_train, y=y_train)[1]) +# local_fi_score_train_subset = None +# local_fi_score_test = np.abs(rf_plus_mdi.explain(X=X_test, y=None)[1]) +# local_fi_score_test_subset = np.abs(rf_plus_mdi.explain(X=X_test_subset, y=None)[1]) +# return local_fi_score_train, local_fi_score_train_subset, local_fi_score_test, local_fi_score_test_subset + + +# def LFI_evaluation_oracle_RF_plus(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None, mode="absolute"): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# subsets = [(None, None),(None, None),(X_test, y_test), (X_test_subset, y_test_subset)] +# result_tables = [] +# rf_plus_mdi = RFPlusMDI(fit, evaluate_on="all") + +# for X_data, y_data in subsets: +# if isinstance(X_data, np.ndarray): +# local_feature_importances, partial_preds = rf_plus_mdi.explain(X=X_data, y=y_data) +# abs_local_feature_importances = np.abs(local_feature_importances) +# result_tables.append(abs_local_feature_importances) +# else: +# result_tables.append(None) + +# return tuple(result_tables) + +# def fast_r2_score(y_true, y_pred, multiclass=False): +# """ +# Evaluates the r-squared value between the observed and estimated responses. +# Equivalent to sklearn.metrics.r2_score but without the robust error +# checking, thus leading to a much faster implementation (at the cost of +# this error checking). For multi-class responses, returns the mean +# r-squared value across each column in the response matrix. + +# Parameters +# ---------- +# y_true: array-like of shape (n_samples, n_targets) +# Observed responses. +# y_pred: array-like of shape (n_samples, n_targets) +# Predicted responses. +# multiclass: bool +# Whether or not the responses are multi-class. + +# Returns +# ------- +# Scalar quantity, measuring the r-squared value. +# """ +# numerator = ((y_true - y_pred) ** 2).sum(axis=0, dtype=np.float64) +# denominator = ((y_true - np.mean(y_true, axis=0)) ** 2). \ +# sum(axis=0, dtype=np.float64) +# if multiclass: +# return np.mean(1 - numerator / denominator) +# else: +# return 1 - numerator / denominator + + +# def neg_log_loss(y_true, y_pred): +# """ +# Evaluates the negative log-loss between the observed and +# predicted responses. + +# Parameters +# ---------- +# y_true: array-like of shape (n_samples, n_targets) +# Observed responses. +# y_pred: array-like of shape (n_samples, n_targets) +# Predicted probabilies. + +# Returns +# ------- +# Scalar quantity, measuring the negative log-loss value. +# """ +# return -log_loss(y_true, y_pred) + +# def neg_mae(y_true, y_pred): +# return -mean_absolute_error(y_true, y_pred) + +# def partial_preds_to_scores(partial_preds, y_test, scoring_fn): +# scores = [] +# for k in range(partial_preds.shape[1]): +# y_pred = partial_preds[:,k] +# scores.append(scoring_fn(y_test, y_pred)) +# return scores + +# def LFI_global_MDI_plus_RF_Plus(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None): +# if isinstance(fit, RandomForestPlusRegressor): +# scoring_fn = fast_r2_score +# elif isinstance(fit, RandomForestPlusClassifier): +# scoring_fn = neg_log_loss +# test_classification_scoring_fn = neg_mae +# y_test_subset_hat = fit.predict(X_test_subset) +# y_test_hat = fit.predict(X_test) +# subsets = [(X_train, y_train, y_train),(None, None, None),(X_test, None, y_test_hat), (X_test_subset, None, y_test_subset_hat)] +# result_tables = [] +# rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="all") + +# for X_data, y_data, y_hat in subsets: +# if isinstance(X_data, np.ndarray): +# if isinstance(fit, RandomForestPlusClassifier) and (np.array_equal(X_data, X_test) or np.array_equal(X_data, X_test_subset)): +# local_feature_importances, partial_preds = rf_plus_mdi.explain(X=X_data, y=y_data) +# scores = partial_preds_to_scores(partial_preds, y_hat, test_classification_scoring_fn) +# result_tables.append(np.tile(scores, (X_data.shape[0], 1))) +# else: +# local_feature_importances, partial_preds = rf_plus_mdi.explain(X=X_data, y=y_data) +# scores = partial_preds_to_scores(partial_preds, y_hat, scoring_fn) +# result_tables.append(np.tile(scores, (X_data.shape[0], 1))) +# else: +# result_tables.append(None) + +# return tuple(result_tables) + + + +# ########## Pos_Neg +# # Feature Importance Methods for RF +# def tree_shap_evaluation_RF_pos_neg(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None): +# """ +# Compute average treeshap value across observations. +# Larger absolute values indicate more important features. +# :param X: design matrix +# :param y: response +# :param fit: fitted model of interest (tree-based) +# :return: dataframe of shape: (n_samples, n_features) +# """ +# def add_abs(a, b): +# return abs(a) + abs(b) + +# subsets = [(None, None), (X_train_subset, None), (X_test, None), (X_test_subset, None)] +# result_tables = [] + +# explainer = shap.TreeExplainer(fit) + +# for X_data, _ in subsets: +# if isinstance(X_data, np.ndarray): +# shap_values = explainer.shap_values(X_data, check_additivity=False) +# if sklearn.base.is_classifier(fit): +# # Shape values are returned as a list of arrays, one for each class +# #results = np.sum(np.abs(shap_values), axis=-1) +# results = np.sum(shap_values, axis=-1) +# else: +# results = shap_values + +# result_tables.append(results) +# result_tables.append(np.abs(results)) +# else: +# result_tables.append(None) +# result_tables.append(None) +# return tuple(result_tables) + +# # Feature Importance Methods for RF+ +# def LFI_evaluation_RFPlus_inbag_pos_neg(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# subsets = [(X_train, y_train), (None, None), (X_test, None), (X_test_subset, None)] +# result_tables = [] + +# for X_data, y_data in subsets: +# if isinstance(X_data, np.ndarray): +# if np.array_equal(X_data, X_train): +# rf_plus_mdi = RFPlusMDI(fit, evaluate_on="inbag") +# else: +# rf_plus_mdi = RFPlusMDI(fit, evaluate_on="all") +# partial_preds_subtract_intercept = rf_plus_mdi.explain_subtract_intercept(X=X_data, y=y_data) +# local_feature_importances, _ = rf_plus_mdi.explain(X=X_data, y=y_data) +# abs_local_feature_importances = np.abs(local_feature_importances) +# result_tables.append(partial_preds_subtract_intercept) +# result_tables.append(abs_local_feature_importances) +# else: +# result_tables.append(None) +# result_tables.append(None) +# return tuple(result_tables) + +# def LFI_evaluation_RFPlus_oob_pos_neg(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# subsets = [(X_train, y_train), (None, None), (X_test, None), (X_test_subset, None)] +# result_tables = [] + +# for X_data, y_data in subsets: +# if isinstance(X_data, np.ndarray): +# if np.array_equal(X_data, X_train): +# rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="oob") +# else: +# rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="all") +# partial_preds_subtract_intercept = rf_plus_mdi.explain_subtract_intercept(X=X_data, y=y_data) +# local_feature_importances, _ = rf_plus_mdi.explain(X=X_data, y=y_data) +# abs_local_feature_importances = np.abs(local_feature_importances) +# result_tables.append(partial_preds_subtract_intercept) +# result_tables.append(abs_local_feature_importances) +# else: +# result_tables.append(None) +# result_tables.append(None) +# return tuple(result_tables) + +# def LFI_evaluation_RFPlus_all_pos_neg(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# subsets = [(X_train, y_train), (None, None), (X_test, None), (X_test_subset, None)] +# result_tables = [] +# rf_plus_mdi = AloRFPlusMDI(fit, evaluate_on="all") + +# for X_data, y_data in subsets: +# if isinstance(X_data, np.ndarray): +# partial_preds_subtract_intercept = rf_plus_mdi.explain_subtract_intercept(X=X_data, y=y_data) +# local_feature_importances, _ = rf_plus_mdi.explain(X=X_data, y=y_data) +# abs_local_feature_importances = np.abs(local_feature_importances) +# result_tables.append(partial_preds_subtract_intercept) +# result_tables.append(abs_local_feature_importances) +# else: +# result_tables.append(None) +# result_tables.append(None) + +# return tuple(result_tables) + +# def lime_evaluation_RF_plus_pos_neg(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# subsets = [(None, None), (X_train_subset, None), (None, None), (X_test_subset, None)] +# result_tables = [] + +# for X_data, _ in subsets: +# if isinstance(X_data, np.ndarray): +# rf_plus_lime = RFPlusLime(fit) +# lime_values = rf_plus_lime.explain(X_train=X_train, X_test=X_data).values +# result_tables.append(lime_values) +# result_tables.append(np.abs(lime_values)) +# else: +# result_tables.append(None) +# result_tables.append(None) + +# return tuple(result_tables) + + +# def kernel_shap_evaluation_RF_plus_pos_neg(X_train, y_train, X_train_subset, y_train_subset, X_test, y_test, X_test_subset, y_test_subset, fit=None): +# assert isinstance(fit, RandomForestPlusRegressor) or isinstance(fit, RandomForestPlusClassifier) +# subsets = [(None, None), (X_train_subset, None), (None, None), (X_test_subset, None)] +# result_tables = [] + +# for X_data, _ in subsets: +# if isinstance(X_data, np.ndarray): +# rf_plus_kernel_shap = RFPlusKernelSHAP(fit) +# kernel_shap_scores = rf_plus_kernel_shap.explain(X_train=X_train, X_test=X_data) +# result_tables.append(kernel_shap_scores) +# result_tables.append(np.abs(kernel_shap_scores)) +# else: +# result_tables.append(None) +# result_tables.append(None) + +# return tuple(result_tables) + + + + + + + + + + + + + + + + +# result_table = pd.DataFrame(kernel_shap_scores, columns=[f'Feature_{i}' for i in range(num_features)]) +# result_tables.append(result_table) + +# def MDI_local_sub_stumps(X, y, fit, scoring_fns="auto", return_stability_scores=False, **kwargs): +# """ +# Compute local MDI importance for each feature and sample. +# :param X: design matrix +# :param y: response +# :param fit: fitted model of interest (tree-based) +# :return: dataframe of shape: (n_samples, n_features) + +# """ +# num_samples, num_features = X.shape +# if isinstance(fit, RegressorMixin): +# RFPlus = RandomForestPlusRegressor +# elif isinstance(fit, ClassifierMixin): +# RFPlus = RandomForestPlusClassifier +# else: +# raise ValueError("Unknown task.") +# rf_plus_model = RFPlus(rf_model=fit, **kwargs) +# rf_plus_model.fit(X, y) + +# try: +# mdi_plus_scores = rf_plus_model.get_mdi_plus_scores(X=X, y=y, local_scoring_fns=mean_squared_error, version = "sub", lfi=False)["local"].values +# if return_stability_scores: +# raise NotImplementedError +# stability_scores = rf_plus_model.get_mdi_plus_stability_scores(B=25) +# except ValueError as e: +# if str(e) == 'Transformer representation was empty for all trees.': +# mdi_plus_scores = np.zeros((num_samples, num_features)) +# stability_scores = None +# else: +# raise +# result_table = pd.DataFrame(mdi_plus_scores, columns=[f'Feature_{i}' for i in range(num_features)]) + +# return result_table + +# def MDI_local_all_stumps(X, y, fit, scoring_fns="auto", return_stability_scores=False, **kwargs): +# """ +# Wrapper around MDI+ object to get feature importance scores + +# :param X: ndarray of shape (n_samples, n_features) +# The covariate matrix. If a pd.DataFrame object is supplied, then +# the column names are used in the output +# :param y: ndarray of shape (n_samples, n_targets) +# The observed responses. +# :param rf_model: scikit-learn random forest object or None +# The RF model to be used for interpretation. If None, then a new +# RandomForestRegressor or RandomForestClassifier is instantiated. +# :param kwargs: additional arguments to pass to +# RandomForestPlusRegressor or RandomForestPlusClassifier class. +# :return: dataframe - [Var, Importance] +# Var: variable name +# Importance: MDI+ score +# """ +# num_samples, num_features = X.shape +# if isinstance(fit, RegressorMixin): +# RFPlus = RandomForestPlusRegressor +# elif isinstance(fit, ClassifierMixin): +# RFPlus = RandomForestPlusClassifier +# else: +# raise ValueError("Unknown task.") +# rf_plus_model = RFPlus(rf_model=fit, **kwargs) +# rf_plus_model.fit(X, y) + +# try: +# mdi_plus_scores = rf_plus_model.get_mdi_plus_scores(X=X, y=y, local_scoring_fns=mean_squared_error, version = "all", lfi=False)["local"].values +# if return_stability_scores: +# raise NotImplementedError +# stability_scores = rf_plus_model.get_mdi_plus_stability_scores(B=25) +# except ValueError as e: +# if str(e) == 'Transformer representation was empty for all trees.': +# mdi_plus_scores = np.zeros((num_samples, num_features)) +# stability_scores = None +# else: +# raise +# result_table = pd.DataFrame(mdi_plus_scores, columns=[f'Feature_{i}' for i in range(num_features)]) + +# return result_table + + +# def LFI_absolute_sum(X, y, fit, scoring_fns="auto", return_stability_scores=False, **kwargs): +# num_samples, num_features = X.shape +# if isinstance(fit, RegressorMixin): +# RFPlus = RandomForestPlusRegressor +# elif isinstance(fit, ClassifierMixin): +# RFPlus = RandomForestPlusClassifier +# else: +# raise ValueError("Unknown task.") +# rf_plus_model = RFPlus(rf_model=fit, **kwargs) +# rf_plus_model.fit(X, y) + +# try: +# mdi_plus_scores = rf_plus_model.get_mdi_plus_scores(X=X, y=y, lfi=True, lfi_abs="outside")["lfi"].values +# if return_stability_scores: +# raise NotImplementedError +# stability_scores = rf_plus_model.get_mdi_plus_stability_scores(B=25) +# except ValueError as e: +# if str(e) == 'Transformer representation was empty for all trees.': +# mdi_plus_scores = np.zeros((num_samples, num_features)) +# stability_scores = None +# else: +# raise +# result_table = pd.DataFrame(mdi_plus_scores, columns=[f'Feature_{i}' for i in range(num_features)]) + +# return result_table + +# def lime_local(X, y, fit): +# """ +# Compute LIME local importance for each feature and sample. +# Larger values indicate more important features. +# :param X: design matrix +# :param y: response +# :param fit: fitted model of interest (tree-based) +# :return: dataframe of shape: (n_samples, n_features) + +# """ + +# np.random.seed(1) +# num_samples, num_features = X.shape +# result = np.zeros((num_samples, num_features)) +# explainer = lime.lime_tabular.LimeTabularExplainer(X, verbose=False, mode='regression') +# for i in range(num_samples): +# exp = explainer.explain_instance(X[i], fit.predict, num_features=num_features) +# original_feature_importance = exp.as_map()[1] +# sorted_feature_importance = sorted(original_feature_importance, key=lambda x: x[0]) +# for j in range(num_features): +# result[i,j] = abs(sorted_feature_importance[j][1]) +# # Convert the array to a DataFrame +# result_table = pd.DataFrame(result, columns=[f'Feature_{i}' for i in range(num_features)]) + +# return result_table + +# def tree_shap_local(X, y, fit): +# """ +# Compute average treeshap value across observations. +# Larger absolute values indicate more important features. +# :param X: design matrix +# :param y: response +# :param fit: fitted model of interest (tree-based) +# :return: dataframe of shape: (n_samples, n_features) +# """ +# explainer = shap.TreeExplainer(fit) +# shap_values = explainer.shap_values(X, check_additivity=False) +# if sklearn.base.is_classifier(fit): +# # Shape values are returned as a list of arrays, one for each class +# def add_abs(a, b): +# return abs(a) + abs(b) +# results = np.sum(np.abs(shap_values),axis=-1) +# else: +# results = abs(shap_values) +# result_table = pd.DataFrame(results, columns=[f'Feature_{i}' for i in range(X.shape[1])]) + +# return result_table + +# def permutation_local(X, y, fit, num_permutations=100): +# """ +# Compute local permutation importance for each feature and sample. +# Larger values indicate more important features. +# :param X: design matrix +# :param y: response +# :param fit: fitted model of interest (tree-based) +# :num_permutations: Number of permutations for each feature (default is 100) +# :return: dataframe of shape: (n_samples, n_features) +# """ + +# # Get the number of samples and features +# num_samples, num_features = X.shape + +# # Initialize array to store local permutation importance +# lpi = np.zeros((num_samples, num_features)) + +# # For each feature +# for k in range(num_features): +# # Permute X_k num_permutations times +# for b in range(num_permutations): +# X_permuted = X.copy() +# X_permuted[:, k] = np.random.permutation(X[:, k]) + +# # Feed permuted data through the fitted model +# y_pred_permuted = fit.predict(X_permuted) + +# # Calculate MSE for each sample +# for i in range(num_samples): +# lpi[i, k] += (y[i]-y_pred_permuted[i])**2 + +# lpi /= num_permutations + +# # Convert the array to a DataFrame +# result_table = pd.DataFrame(lpi, columns=[f'Feature_{i}' for i in range(num_features)]) + +# return result_table + + +# def MDI_local_sub_stumps_evaluate(X_train, y_train, X_test, y_test, fit, scoring_fns="auto", return_stability_scores=False, **kwargs): +# """ +# Compute local MDI importance for each feature and sample. +# :param X: design matrix +# :param y: response +# :param fit: fitted model of interest (tree-based) +# :return: dataframe of shape: (n_samples, n_features) + +# """ +# num_samples, num_features = X_test.shape +# if isinstance(fit, RegressorMixin): +# RFPlus = RandomForestPlusRegressor +# elif isinstance(fit, ClassifierMixin): +# RFPlus = RandomForestPlusClassifier +# else: +# raise ValueError("Unknown task.") +# rf_plus_model = RFPlus(rf_model=fit, **kwargs) +# rf_plus_model.fit(X_train, y_train) + +# try: +# mdi_plus_scores = rf_plus_model.get_mdi_plus_scores(X=X_test, y=y_test, local_scoring_fns=scoring_fns, version = "sub", lfi=False, sample_split=None)["local"].values +# if return_stability_scores: +# raise NotImplementedError +# stability_scores = rf_plus_model.get_mdi_plus_stability_scores(B=25) +# except ValueError as e: +# if str(e) == 'Transformer representation was empty for all trees.': +# mdi_plus_scores = np.zeros((num_samples, num_features)) +# stability_scores = None +# else: +# raise +# result_table = pd.DataFrame(mdi_plus_scores, columns=[f'Feature_{i}' for i in range(num_features)]) + +# return result_table + +# def lime_local(X_train, y_train, X_test, y_test, fit): +# """ +# Compute LIME local importance for each feature and sample. +# Larger values indicate more important features. +# :param X: design matrix +# :param y: response +# :param fit: fitted model of interest (tree-based) +# :return: dataframe of shape: (n_samples, n_features) + +# """ +# if isinstance(fit, RegressorMixin): +# mode='regression' +# elif isinstance(fit, ClassifierMixin): +# mode='classification' +# np.random.seed(1) +# num_samples, num_features = X_test.shape +# result = np.zeros((num_samples, num_features)) +# explainer = lime.lime_tabular.LimeTabularExplainer(X_train, verbose=False, mode=mode) +# for i in range(num_samples): +# exp = explainer.explain_instance(X_test[i], fit.predict, num_features=num_features) +# original_feature_importance = exp.as_map()[1] +# sorted_feature_importance = sorted(original_feature_importance, key=lambda x: x[0]) +# for j in range(num_features): +# result[i,j] = abs(sorted_feature_importance[j][1]) +# # Convert the array to a DataFrame +# result_table = pd.DataFrame(result, columns=[f'Feature_{i}' for i in range(num_features)]) + +# return result_table + +# def MDI_local_all_stumps_evaluate(X_train, y_train, X_test, y_test, fit, scoring_fns="auto", return_stability_scores=False, **kwargs): +# """ +# Wrapper around MDI+ object to get feature importance scores + +# :param X: ndarray of shape (n_samples, n_features) +# The covariate matrix. If a pd.DataFrame object is supplied, then +# the column names are used in the output +# :param y: ndarray of shape (n_samples, n_targets) +# The observed responses. +# :param rf_model: scikit-learn random forest object or None +# The RF model to be used for interpretation. If None, then a new +# RandomForestRegressor or RandomForestClassifier is instantiated. +# :param kwargs: additional arguments to pass to +# RandomForestPlusRegressor or RandomForestPlusClassifier class. +# :return: dataframe - [Var, Importance] +# Var: variable name +# Importance: MDI+ score +# """ +# num_samples, num_features = X_test.shape +# if isinstance(fit, RegressorMixin): +# RFPlus = RandomForestPlusRegressor +# elif isinstance(fit, ClassifierMixin): +# RFPlus = RandomForestPlusClassifier +# else: +# raise ValueError("Unknown task.") +# rf_plus_model = RFPlus(rf_model=fit, **kwargs) +# rf_plus_model.fit(X_train, y_train) + +# try: +# mdi_plus_scores = rf_plus_model.get_mdi_plus_scores(X=X_test, y=y_test, local_scoring_fns=scoring_fns, version = "all", lfi=False, sample_split=None)["local"].values +# if return_stability_scores: +# raise NotImplementedError +# stability_scores = rf_plus_model.get_mdi_plus_stability_scores(B=25) +# except ValueError as e: +# if str(e) == 'Transformer representation was empty for all trees.': +# mdi_plus_scores = np.zeros((num_samples, num_features)) +# stability_scores = None +# else: +# raise +# result_table = pd.DataFrame(mdi_plus_scores, columns=[f'Feature_{i}' for i in range(num_features)]) + +# return result_table + +# def LFI_ablation_test_evaluation(X_train, y_train, X_test, y_test, fit, scoring_fns="auto", return_stability_scores=False, **kwargs): +# num_samples, num_features = X_test.shape +# if isinstance(fit, RegressorMixin): +# RFPlus = RandomForestPlusRegressor +# elif isinstance(fit, ClassifierMixin): +# RFPlus = RandomForestPlusClassifier +# else: +# raise ValueError("Unknown task.") +# rf_plus_model = RFPlus(rf_model=fit, **kwargs) +# rf_plus_model.fit(X_train, y_train) + +# try: +# mdi_plus_scores = rf_plus_model.get_mdi_plus_scores(X=X_test, y=y_test, lfi=True, lfi_abs="none", sample_split=None, train_or_test = "test")["lfi"].values +# mdi_plus_scores = np.abs(mdi_plus_scores) +# if return_stability_scores: +# raise NotImplementedError +# stability_scores = rf_plus_model.get_mdi_plus_stability_scores(B=25) +# except ValueError as e: +# if str(e) == 'Transformer representation was empty for all trees.': +# mdi_plus_scores = np.zeros((num_samples, num_features)) +# stability_scores = None +# else: +# raise +# result_table = pd.DataFrame(mdi_plus_scores, columns=[f'Feature_{i}' for i in range(num_features)]) + +# return result_table + +######################## Considering not using these methods +# def LFI_sum_absolute_evaluate(X_train, y_train, X_test, y_test, fit, scoring_fns="auto", return_stability_scores=False, **kwargs): +# num_samples, num_features = X_test.shape +# if isinstance(fit, RegressorMixin): +# RFPlus = RandomForestPlusRegressor +# elif isinstance(fit, ClassifierMixin): +# RFPlus = RandomForestPlusClassifier +# else: +# raise ValueError("Unknown task.") +# rf_plus_model = RFPlus(rf_model=fit, **kwargs) +# rf_plus_model.fit(X_train, y_train) + +# try: +# mdi_plus_scores = rf_plus_model.get_mdi_plus_scores(X=X_test, y=y_test, lfi=True, lfi_abs="inside", sample_split=None)["lfi"].values +# if return_stability_scores: +# raise NotImplementedError +# stability_scores = rf_plus_model.get_mdi_plus_stability_scores(B=25) +# except ValueError as e: +# if str(e) == 'Transformer representation was empty for all trees.': +# mdi_plus_scores = np.zeros((num_samples, num_features)) +# stability_scores = None +# else: +# raise +# result_table = pd.DataFrame(mdi_plus_scores, columns=[f'Feature_{i}' for i in range(num_features)]) + +# return result_table + +# def LFI_sum_absolute(X, y, fit, scoring_fns="auto", return_stability_scores=False, **kwargs): +# num_samples, num_features = X.shape +# if isinstance(fit, RegressorMixin): +# RFPlus = RandomForestPlusRegressor +# elif isinstance(fit, ClassifierMixin): +# RFPlus = RandomForestPlusClassifier +# else: +# raise ValueError("Unknown task.") +# rf_plus_model = RFPlus(rf_model=fit, **kwargs) +# rf_plus_model.fit(X, y) + +# try: +# mdi_plus_scores = rf_plus_model.get_mdi_plus_scores(X=X, y=y, lfi=True, lfi_abs="inside")["lfi"].values +# if return_stability_scores: +# raise NotImplementedError +# stability_scores = rf_plus_model.get_mdi_plus_stability_scores(B=25) +# except ValueError as e: +# if str(e) == 'Transformer representation was empty for all trees.': +# mdi_plus_scores = np.zeros((num_samples, num_features)) +# stability_scores = None +# else: +# raise +# result_table = pd.DataFrame(mdi_plus_scores, columns=[f'Feature_{i}' for i in range(num_features)]) + +# return result_table \ No newline at end of file diff --git a/feature_importance/subgroup/legacy/evaluate_subgroups.py b/feature_importance/subgroup/legacy/evaluate_subgroups.py new file mode 100644 index 0000000..0f9b56f --- /dev/null +++ b/feature_importance/subgroup/legacy/evaluate_subgroups.py @@ -0,0 +1,375 @@ +# import required packages +from imodels import get_clean_dataset +import numpy as np +import pandas as pd +from sklearn.model_selection import train_test_split +from sklearn.metrics import roc_auc_score, average_precision_score, f1_score +from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor +from sklearn.linear_model import RidgeCV, LogisticRegression +from imodels.tree.rf_plus.rf_plus.rf_plus_models import RandomForestPlusClassifier, RandomForestPlusRegressor +from imodels.tree.rf_plus.feature_importance.rfplus_explainer import AloRFPlusMDI, RFPlusMDI +import shap +from subgroup_detection import * +import warnings +import argparse +import os +from os.path import join as oj +warnings.filterwarnings('ignore', category=DeprecationWarning) + +def split_data(X, y, seed): + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, + random_state=seed) + X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, + test_size=0.25, + random_state=seed) + return X_train, X_valid, X_test, y_train, y_valid, y_test + +def evaluate_model(X_train, X_valid, X_test, y_train, y_valid, y_test, task): + + # fit RF model + if task == 'regression': + rf = RandomForestRegressor(n_estimators=100, random_state=0) + else: + rf = RandomForestClassifier(n_estimators=100, random_state=0) + + rf.fit(X_train, y_train) + + print("Initial Random Forest fit for", task, "task.") + + # check performance on test set + y_pred = rf.predict(X_test) + + # compute accuracy on the test set + if task == 'regression': + global_error_rf = np.sum((y_pred - y_test)**2) + else: + global_error_rf = np.sum(y_pred != y_test) + + # get feature importances + explainer = shap.TreeExplainer(rf) + if task == 'regression': + shap_values = np.abs(explainer.shap_values(X_train, check_additivity=False)) + else: + shap_values = np.abs(explainer.shap_values(X_train, check_additivity=False))[:,:,0] + shap_rankings = np.argsort(-shap_values, axis = 1) + + # get rbo distance matrix + shap_rbo_train = compute_rbo_matrix(shap_rankings, form = 'distance') + shap_copy = pd.DataFrame(shap_values, columns=X_train.columns).copy() + if task == "regression": + shap_valid_values = np.abs(explainer.shap_values(X_valid, + check_additivity=False)) + else: + shap_valid_values = np.abs(explainer.shap_values(X_valid, + check_additivity=False))[:,:,0] + shap_valid_rankings = np.argsort(-shap_valid_values, axis = 1) + best_error = np.inf + opt_num_clusters = -1 + time_since_king = 0 + if task == 'regression': + print("Total Squared Error for Zero Clusters:", np.sum((rf.predict(X_valid) - y_valid)**2)) + else: + print("Total Error (# Misclassified) for Zero Clusters:", np.sum(rf.predict(X_valid) != y_valid)) + + no_valid = False + for num_clusters in np.arange(2, X_train.shape[0]//30): + print("Now Calculating for", num_clusters, "Clusters") + shap_train_clusters = assign_training_clusters(shap_rbo_train, num_clusters) + valid_clusters = assign_testing_clusters(method="centroid", + median_approx=True, + rbo_distance_matrix=shap_rbo_train, + lfi_train_ranking=shap_rankings, + lfi_test_ranking=shap_valid_rankings, + clusters = shap_train_clusters) + total_error = 0 + for cluster in np.arange(1, num_clusters + 1): + + # if the cluster has zero validation points, break out of both loops + if np.sum(valid_clusters == cluster) == 0: + no_valid = True + break + + if task == 'regression': + local_rf = RandomForestRegressor(n_estimators=100, random_state=0) + else: + local_rf = RandomForestClassifier(n_estimators=100, random_state=0) + local_rf.fit(X_train[shap_train_clusters == cluster], y_train[shap_train_clusters == cluster]) + local_preds = local_rf.predict(X_valid[valid_clusters == cluster]) + + if task == 'regression': + local_error = np.sum((local_preds - y_valid[valid_clusters == cluster])**2) + else: + local_error = np.sum(local_preds != y_valid[valid_clusters == cluster]) + + total_error += local_error + + if no_valid: + break + + if total_error < best_error: + best_error = total_error + opt_num_clusters = num_clusters + time_since_king = 0 + else: + time_since_king += 1 + if task == 'regression': + print("Total Squared Error w/", num_clusters, "Clusters:", total_error) + else: + print("Total Error (# Misclassified) w/", num_clusters, "Clusters:", total_error) + if time_since_king > 2: + break + print(f'Optimal Number of Clusters: {opt_num_clusters}') + + # fit rf+ + if task == 'regression': + rf_plus = RandomForestPlusRegressor(rf, prediction_model = RidgeCV()) + else: + rf_plus = RandomForestPlusClassifier(rf, prediction_model = LogisticRegression()) + rf_plus.fit(X_train, y_train) + + # check performance on test set + y_pred_rf_plus = rf_plus.predict(X_test) + + # compute accuracy on the test set + if task == 'regression': + global_error_rf_plus = np.sum((y_pred_rf_plus - y_test)**2) + else: + global_error_rf_plus = np.sum(y_pred_rf_plus != y_test) + + # compute auroc on test set + if task == 'classification': + auroc_rf_plus = roc_auc_score(y_test, rf_plus.predict_proba(X_test)[:,1]) + auprc_rf_plus = average_precision_score(y_test, rf_plus.predict_proba(X_test)[:,1]) + f1_rf_plus = f1_score(y_test, rf_plus.predict(X_test)) + + # get feature importances + mdi_explainer = RFPlusMDI(rf_plus, evaluate_on='oob') + mdi = np.abs(mdi_explainer.explain_linear_partial(np.asarray(X_train), y_train, l2norm = True)) + mdi_rankings = mdi_explainer.get_rankings(mdi) + + # get rbo distance matrix + mdi_rbo_train = compute_rbo_matrix(mdi_rankings, form = 'distance') + mdi_copy = pd.DataFrame(mdi, columns=X_train.columns).copy() + mdi_train_clusters = assign_training_clusters(mdi_rbo_train, opt_num_clusters) + + # get mdi rankings assignments for test points + mdi_test = np.abs(mdi_explainer.explain_linear_partial(np.asarray(X_test), l2norm=True)) + mdi_test_rankings = mdi_explainer.get_rankings(mdi_test) + + mdi_test_clusters = assign_testing_clusters(method = "centroid", median_approx = False, + rbo_distance_matrix = mdi_rbo_train, + lfi_train_ranking = mdi_rankings, + lfi_test_ranking = mdi_test_rankings, + clusters = mdi_train_clusters) + + total_local_error_rf_plus = 0 + if task == 'classification': + local_rf_plus_aurocs = [] + local_rf_plus_auprcs = [] + local_rf_plus_f1s = [] + for cluster in np.arange(1, opt_num_clusters + 1): + if task == 'classification': + local_rf_plus = RandomForestPlusClassifier(RandomForestClassifier(n_estimators=100, random_state=0), prediction_model = LogisticRegression()) + else: + local_rf_plus = RandomForestPlusRegressor(RandomForestRegressor(n_estimators=100, random_state=0), prediction_model = RidgeCV()) + local_rf_plus.fit(X_train[mdi_train_clusters == cluster], y_train[mdi_train_clusters == cluster]) + local_preds = local_rf_plus.predict(X_test[mdi_test_clusters == cluster]) + if task == 'regression': + # if there are no test points in the cluster, the local error is zero + if len(y_test[mdi_test_clusters == cluster]) == 0: + local_error = 0 + else: + local_error = np.sum((local_preds - y_test[mdi_test_clusters == cluster])**2) + else: + if len(y_test[mdi_test_clusters == cluster]) == 0: + local_error = 0 + else: + local_error = np.sum(local_preds != y_test[mdi_test_clusters == cluster]) + local_rf_plus_aurocs.append(roc_auc_score(y_test[mdi_test_clusters == cluster], + local_rf_plus.predict_proba(X_test[mdi_test_clusters == cluster])[:,1])) + local_rf_plus_auprcs.append(average_precision_score(y_test[mdi_test_clusters == cluster], + local_rf_plus.predict_proba(X_test[mdi_test_clusters == cluster])[:,1])) + local_rf_plus_f1s.append(f1_score(y_test[mdi_test_clusters == cluster], + local_rf_plus.predict(X_test[mdi_test_clusters == cluster])) + ) + total_local_error_rf_plus += local_error + + # get feature importances + mdi_explainer_intercept = RFPlusMDI(rf_plus, evaluate_on='oob') + mdi_intercept = np.abs(mdi_explainer_intercept.explain_linear_partial(np.asarray(X_train), y_train, l2norm = True)) + mdi_intercept_rankings = mdi_explainer_intercept.get_rankings(mdi_intercept) + + # get rbo distance matrix + mdi_rbo_train_int = compute_rbo_matrix(mdi_intercept_rankings, form = 'distance') + mdi_copy_int = pd.DataFrame(mdi_intercept, columns=X_train.columns).copy() + mdi_train_clusters_int = assign_training_clusters(mdi_rbo_train_int, opt_num_clusters) + + # get mdi rankings assignments for test points + mdi_test_int = np.abs(mdi_explainer_intercept.explain_linear_partial(np.asarray(X_test), l2norm=True)) + mdi_test_rankings_int = mdi_explainer_intercept.get_rankings(mdi_test_int) + + mdi_test_clusters_int = assign_testing_clusters(method = "centroid", median_approx = False, + rbo_distance_matrix = mdi_rbo_train_int, + lfi_train_ranking = mdi_intercept_rankings, + lfi_test_ranking = mdi_test_rankings_int, + clusters = mdi_train_clusters_int) + + total_local_int_error_rf_plus = 0 + if task == 'classification': + local_rf_plus_aurocs = [] + local_rf_plus_auprcs = [] + local_rf_plus_f1s = [] + for cluster in np.arange(1, opt_num_clusters + 1): + if task == 'classification': + local_rf_plus = RandomForestPlusClassifier(RandomForestClassifier(n_estimators=100, random_state=0), prediction_model = LogisticRegression()) + else: + local_rf_plus = RandomForestPlusRegressor(RandomForestRegressor(n_estimators=100, random_state=0), prediction_model = RidgeCV()) + local_rf_plus.fit(X_train[mdi_train_clusters == cluster], y_train[mdi_train_clusters == cluster]) + local_preds = local_rf_plus.predict(X_test[mdi_test_clusters_int == cluster]) + if task == 'regression': + # if there are no test points in the cluster, the local error is zero + if len(y_test[mdi_test_clusters_int == cluster]) == 0: + local_error = 0 + else: + local_error = np.sum((local_preds - y_test[mdi_test_clusters_int == cluster])**2) + else: + if len(y_test[mdi_test_clusters_int == cluster]) == 0: + local_error = 0 + else: + local_error = np.sum(local_preds != y_test[mdi_test_clusters_int == cluster]) + local_rf_plus_aurocs.append(roc_auc_score(y_test[mdi_test_clusters_int == cluster], + local_rf_plus.predict_proba(X_test[mdi_test_clusters_int == cluster])[:,1])) + local_rf_plus_auprcs.append(average_precision_score(y_test[mdi_test_clusters_int == cluster], + local_rf_plus.predict_proba(X_test[mdi_test_clusters_int == cluster])[:,1])) + local_rf_plus_f1s.append(f1_score(y_test[mdi_test_clusters_int == cluster], + local_rf_plus.predict(X_test[mdi_test_clusters_int == cluster])) + ) + total_local_int_error_rf_plus += local_error + + if task == 'classification': + auroc_rf = roc_auc_score(y_test, rf.predict_proba(X_test)[:,1]) + auprc_rf = average_precision_score(y_test, rf.predict_proba(X_test)[:,1]) + f1_rf = f1_score(y_test, rf.predict(X_test)) + local_rf_aurocs = [] + local_rf_auprcs = [] + local_rf_f1s = [] + + shap_copy = pd.DataFrame(shap_values, columns=X_train.columns).copy() + shap_train_clusters = assign_training_clusters(shap_rbo_train, opt_num_clusters) + if task == "regression": + shap_test_values = np.abs(explainer.shap_values(X_test, + check_additivity=False)) + else: + shap_test_values = np.abs(explainer.shap_values(X_test, + check_additivity=False))[:,:,0] + shap_test_rankings = np.argsort(-shap_test_values, axis = 1) + shap_test_clusters = assign_testing_clusters(method="centroid", median_approx=False, + rbo_distance_matrix=shap_rbo_train, + lfi_train_ranking=shap_rankings, + lfi_test_ranking=shap_test_rankings, + clusters = shap_train_clusters) + + total_local_error_rf = 0 + for cluster in np.arange(1, opt_num_clusters + 1): + if task == "classification": + local_rf = RandomForestClassifier(n_estimators=100, random_state=0) + else: + local_rf = RandomForestRegressor(n_estimators=100, random_state=0) + local_rf.fit(X_train[shap_train_clusters == cluster], y_train[shap_train_clusters == cluster]) + local_preds = local_rf.predict(X_test[shap_test_clusters == cluster]) + if task == 'regression': + if len(y_test[shap_test_clusters == cluster]) == 0: + local_error = 0 + else: + local_error = np.sum((local_preds - y_test[shap_test_clusters == cluster])**2) + else: + if len(y_test[shap_test_clusters == cluster]) == 0: + local_error = 0 + else: + local_error = np.sum(local_preds != y_test[shap_test_clusters == cluster]) + local_rf_aurocs.append(roc_auc_score(y_test[shap_test_clusters == cluster], + local_rf.predict_proba(X_test[shap_test_clusters == cluster])[:,1])) + local_rf_auprcs.append(average_precision_score(y_test[shap_test_clusters == cluster], + local_rf.predict_proba(X_test[shap_test_clusters == cluster])[:,1])) + local_rf_f1s.append(f1_score(y_test[shap_test_clusters == cluster], + local_rf.predict(X_test[shap_test_clusters == cluster])) + ) + total_local_error_rf += local_error + + if task == 'classification': + rf_plus_weighted_auroc = weighted_metric(local_rf_plus_aurocs, np.bincount(mdi_test_clusters.astype(int))[1:]) + rf_plus_weighted_auprc = weighted_metric(local_rf_plus_auprcs, np.bincount(mdi_test_clusters.astype(int))[1:]) + rf_plus_weighted_f1 = weighted_metric(local_rf_plus_f1s, np.bincount(mdi_test_clusters.astype(int))[1:]) + rf_weighted_auroc = weighted_metric(local_rf_aurocs, np.bincount(shap_test_clusters.astype(int))[1:]) + rf_weighted_auprc = weighted_metric(local_rf_auprcs, np.bincount(shap_test_clusters.astype(int))[1:]) + rf_weighted_f1 = weighted_metric(local_rf_f1s, np.bincount(shap_test_clusters.astype(int))[1:]) + return {'global_error_rf': global_error_rf, + 'global_error_rf_plus': global_error_rf_plus, + 'total_local_error_rf': total_local_error_rf, + 'total_local_error_rf_plus': total_local_error_rf_plus, + 'total_local_int_error_rf_plus': total_local_int_error_rf_plus, + 'auroc_rf': auroc_rf, + 'auprc_rf': auprc_rf, + 'f1_rf': f1_rf, + 'auroc_rf_plus': auroc_rf_plus, + 'auprc_rf_plus': auprc_rf_plus, + 'f1_rf_plus': f1_rf_plus, + 'rf_plus_weighted_auroc': rf_plus_weighted_auroc, + 'rf_plus_weighted_auprc': rf_plus_weighted_auprc, + 'rf_plus_weighted_f1': rf_plus_weighted_f1, + 'rf_weighted_auroc': rf_weighted_auroc, + 'rf_weighted_auprc': rf_weighted_auprc, + 'rf_weighted_f1': rf_weighted_f1} + + return {'global_error_rf': global_error_rf, + 'global_error_rf_plus': global_error_rf_plus, + 'total_local_error_rf': total_local_error_rf, + 'total_local_error_rf_plus': total_local_error_rf_plus, + 'total_local_int_error_rf_plus': total_local_int_error_rf_plus} + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + default_dir = os.getenv("SCRATCH") + if default_dir is not None: + default_dir = oj(default_dir, "feature_importance", "subgroup", "results") + else: + default_dir = oj(os.path.dirname(os.path.realpath(__file__)), 'results') + parser.add_argument('--seed', type=int, default=None) + parser.add_argument('--datasource', type=str, default=None) + parser.add_argument('--dataname', type=str, default=None) + args = parser.parse_args() + + # Convert Namespace to a dictionary + args_dict = vars(args) + + # Assign each key-value pair to a variable + seed = args_dict['seed'] + datasource = args_dict['datasource'] + dataname = args_dict['dataname'] + print("Obtaining", dataname, "from", datasource, "with seed", seed) + if datasource == "openml": + dataname = int(dataname) + X, y, feature_names = get_clean_dataset(dataname, data_source = datasource) + X = pd.DataFrame(X, columns=feature_names) + print("Data obtained!") + + # check if task is regression or classification + if len(np.unique(y)) == 2: + task = 'classification' + else: + task = 'regression' + # convert y to float + y = y.astype(float) + + X_train, X_valid, X_test, y_train, y_valid, y_test = split_data(X, y, seed) + print("data split") + eval_dict = evaluate_model(X_train, X_valid, X_test, y_train, y_valid, y_test, task) + eval_dict['seed'] = seed + eval_dict['datasource'] = datasource + eval_dict['dataname'] = dataname + eval_dict['task'] = task + # convert eval_dict to dataframe and save dataframe to csv + eval_df = pd.DataFrame([eval_dict]) + print(default_dir) + eval_df.to_csv(oj(default_dir, f'new_{datasource}_{dataname}_{seed}.csv'), index=False) + print("ran experiment successfully") \ No newline at end of file diff --git a/feature_importance/subgroup/legacy/insurance-eda-ablate.ipynb b/feature_importance/subgroup/legacy/insurance-eda-ablate.ipynb new file mode 100644 index 0000000..1694af2 --- /dev/null +++ b/feature_importance/subgroup/legacy/insurance-eda-ablate.ipynb @@ -0,0 +1,1026 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# import required packages\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n", + "from imodels.tree.rf_plus.rf_plus.rf_plus_models import RandomForestPlusRegressor, RandomForestPlusClassifier\n", + "from imodels.tree.rf_plus.feature_importance.rfplus_explainer import RFPlusMDI\n", + "from sklearn.linear_model import RidgeCV, LogisticRegression\n", + "import shap\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "from joypy import joyplot\n", + "from pycaret.datasets import get_data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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agesexbmichildrensmokerregioncharges
019female27.9000yessouthwest16884.92400
118male33.7701nosoutheast1725.55230
228male33.0003nosoutheast4449.46200
333male22.7050nonorthwest21984.47061
432male28.8800nonorthwest3866.85520
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" + ], + "text/plain": [ + " age sex bmi children smoker region charges\n", + "0 19 female 27.900 0 yes southwest 16884.92400\n", + "1 18 male 33.770 1 no southeast 1725.55230\n", + "2 28 male 33.000 3 no southeast 4449.46200\n", + "3 33 male 22.705 0 no northwest 21984.47061\n", + "4 32 male 28.880 0 no northwest 3866.85520" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# set seed\n", + "np.random.seed(0)\n", + "# get pre-cleaned insurance dataset from pycaret\n", + "data = get_data('insurance')\n", + "# remove last feature from X\n", + "X = data.iloc[:, :-1]\n", + "# get y\n", + "y = data.iloc[:, -1]\n", + "# convert y to (n,) np array\n", + "y = y.to_numpy().reshape(-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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agesexbmichildrensmokerregion
019027.90001southwest
118133.77010southeast
228133.00030southeast
333122.70500northwest
432128.88000northwest
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" + ], + "text/plain": [ + " age sex bmi children smoker region\n", + "0 19 0 27.900 0 1 southwest\n", + "1 18 1 33.770 1 0 southeast\n", + "2 28 1 33.000 3 0 southeast\n", + "3 33 1 22.705 0 0 northwest\n", + "4 32 1 28.880 0 0 northwest" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# switch sex and smoker to binary features\n", + "X['sex'] = X['sex'].map({'male': 1, 'female': 0})\n", + "X['smoker'] = X['smoker'].map({'yes': 1, 'no': 0})\n", + "X.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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agesexbmichildrensmokerregion_northeastregion_northwestregion_southeastregion_southwest
019027.900010.00.00.01.0
118133.770100.00.01.00.0
228133.000300.00.01.00.0
333122.705000.01.00.00.0
432128.880000.01.00.00.0
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" + ], + "text/plain": [ + " age sex bmi children smoker region_northeast region_northwest \\\n", + "0 19 0 27.900 0 1 0.0 0.0 \n", + "1 18 1 33.770 1 0 0.0 0.0 \n", + "2 28 1 33.000 3 0 0.0 0.0 \n", + "3 33 1 22.705 0 0 0.0 1.0 \n", + "4 32 1 28.880 0 0 0.0 1.0 \n", + "\n", + " region_southeast region_southwest \n", + "0 0.0 1.0 \n", + "1 1.0 0.0 \n", + "2 1.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# switch region to one-hot encoding\n", + "X = pd.get_dummies(X, columns=['region'], dtype = 'float')\n", + "X.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Proportion of data that smokes: 0.20478325859491778\n", + "Proportion of data that doesn't smoke: 0.7952167414050823\n" + ] + } + ], + "source": [ + "# get proportion of X that smokes\n", + "smoker = X[\"smoker\"]\n", + "print(\"Proportion of data that smokes: \", sum(smoker == 1)/len(smoker))\n", + "print(\"Proportion of data that doesn't smoke: \", sum(smoker == 0)/len(smoker))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot demonstrating subgroup impact on outcome:\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"Plot demonstrating subgroup impact on outcome:\")\n", + "# Create the joyplot\n", + "plt.figure(figsize=(10, 6))\n", + "joyplot(\n", + " data=X.assign(y=y),\n", + " by='smoker',\n", + " column='y',\n", + " fade=True,\n", + " grid=True,\n", + " linecolor='k',\n", + " linewidth=0.5\n", + ")\n", + "\n", + "plt.title('Distribution of Outcome by Smoking Status')\n", + "plt.xlabel('Outcome')\n", + "plt.ylabel('Density')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# split data into train, validation, and test sets\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,\n", + " random_state=2)\n", + "X_train = X_train.reset_index(drop=True)\n", + "X_test = X_test.reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 16 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 18 tasks | elapsed: 6.1s\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 12.7s finished\n" + ] + } + ], + "source": [ + "# fit RF model\n", + "rf = RandomForestRegressor(n_estimators=100, random_state=0)\n", + "rf.fit(X_train, y_train)\n", + "\n", + "# fit RF+ model\n", + "rf_plus = RandomForestPlusRegressor(rf_model = rf, prediction_model = RidgeCV())\n", + "rf_plus.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# get TreeSHAP importances and rankings\n", + "explainer = shap.TreeExplainer(rf)\n", + "shap_values = np.abs(explainer.shap_values(X_train, check_additivity=False))\n", + "shap_rankings = np.argsort(-shap_values, axis = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# get LMDI+ importances and rankings\n", + "rfplus_explainer = RFPlusMDI(rf_plus)\n", + "lmdi_values = np.abs(rfplus_explainer.explain_linear_partial(np.asarray(X_train), y_train, l2norm=True, njobs = 1))\n", + "lmdi_rankings = rfplus_explainer.get_rankings(lmdi_values)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# sort lmdi_values based highest y to lowest y\n", + "sorted_indices = np.argsort(-y_train)\n", + "sorted_ytrain = y_train[sorted_indices]\n", + "sorted_lmdi_values = lmdi_values[sorted_indices]\n", + "sorted_lmdi_rankings = lmdi_rankings[sorted_indices]\n", + "sorted_shap_values = shap_values[sorted_indices]\n", + "sorted_shap_rankings = shap_rankings[sorted_indices]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0, 46, 92, 138, 184, 230, 276, 322, 368, 414, 460, 506, 552,\n", + " 598, 644, 690, 736, 782, 828, 874, 920])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.arange(0, len(y_train), len(y_train)//20)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([63770.42801, 40003.33225, 34806.4677 , 25309.489 , 20773.62775,\n", + " 17179.522 , 13887.204 , 12629.1656 , 11552.904 , 10713.644 ,\n", + " 9583.8933 , 8604.48365, 7731.85785, 6770.1925 , 5972.378 ,\n", + " 5031.26955, 4428.88785, 3597.596 , 2727.3951 , 1981.5819 ,\n", + " 1534.3045 ])" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sorted_ytrain[::len(y_train)//20]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot heatmap of LMDI+ importances based on true y\n", + "plt.figure(figsize=(10, 6))\n", + "sns.heatmap(sorted_shap_values, cmap='viridis')\n", + "plt.title('TreeSHAP Importances')\n", + "plt.xlabel('Features')\n", + "plt.ylabel('Outcome (Insurance Expenditures)')\n", + "plt.yticks(ticks = np.arange(0, len(y_train), len(y_train)//20), labels = sorted_ytrain[::len(y_train)//20])\n", + "plt.xticks(ticks = np.arange(X_train.shape[1]) + 0.5, labels = X_train.columns, rotation = 90)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot heatmap of LMDI+ importances based on true y\n", + "plt.figure(figsize=(10, 6))\n", + "sns.heatmap(sorted_lmdi_values, cmap='viridis')\n", + "plt.title('LMDI+ Importances')\n", + "plt.xlabel('Features')\n", + "plt.ylabel('Outcome (Insurance Expenditures)')\n", + "plt.yticks(ticks = np.arange(0, len(y_train), len(y_train)//20), labels = sorted_ytrain[::len(y_train)//20])\n", + "plt.xticks(ticks = np.arange(X_train.shape[1]) + 0.5, labels = X_train.columns, rotation = 90)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "lmdi_sub_imp = pd.concat([X_train[\"smoker\"].reset_index(drop=True), pd.DataFrame(lmdi_rankings)], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "shap_sub_imp = pd.concat([X_train[\"smoker\"].reset_index(drop=True), pd.DataFrame(shap_rankings)], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Subgroup feature rankings for LMDI+:\n" + ] + }, + { + "data": { + "text/html": [ + "
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agesexbmichildrensmokerregion_northeastregion_northwestregion_southeastregion_southwest
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" + ], + "text/plain": [ + " age sex bmi children smoker region_northeast region_northwest \\\n", + "0 4.0 1.0 2.0 3.0 7.0 5.0 6.0 \n", + "1 5.0 2.0 1.0 3.0 6.0 4.0 7.0 \n", + "\n", + " region_southeast region_southwest \n", + "0 9.0 8.0 \n", + "1 8.0 9.0 " + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lmdi_mean_rankings = lmdi_sub_imp.groupby('smoker', observed=False).mean().reset_index()\n", + "# rank features for each row\n", + "lmdi_ranked_mean_rankings = lmdi_mean_rankings.drop(columns=[\"smoker\"]).rank(axis=1)\n", + "lmdi_ranked_mean_rankings.columns = X.columns\n", + "print(\"Subgroup feature rankings for LMDI+:\")\n", + "lmdi_ranked_mean_rankings" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Subgroup feature rankings for TreeSHAP:\n" + ] + }, + { + "data": { + "text/html": [ + "
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agesexbmichildrensmokerregion_northeastregion_northwestregion_southeastregion_southwest
03.01.02.04.06.05.07.08.09.0
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" + ], + "text/plain": [ + " age sex bmi children smoker region_northeast region_northwest \\\n", + "0 3.0 1.0 2.0 4.0 6.0 5.0 7.0 \n", + "1 5.0 2.0 1.0 3.0 6.0 4.0 7.0 \n", + "\n", + " region_southeast region_southwest \n", + "0 8.0 9.0 \n", + "1 8.0 9.0 " + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "shap_mean_rankings = shap_sub_imp.groupby('smoker', observed=False).mean().reset_index()\n", + "# rank features for each row\n", + "shap_ranked_mean_rankings = shap_mean_rankings.drop(columns=[\"smoker\"]).rank(axis=1)\n", + "shap_ranked_mean_rankings.columns = X.columns\n", + "print(\"Subgroup feature rankings for TreeSHAP:\")\n", + "shap_ranked_mean_rankings" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "def mask_feature(row, i, ranked_mean_rankings):\n", + " return ranked_mean_rankings.iloc[int(row['smoker'])].sort_values().index[i]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "X_train_copy = X_train.copy()\n", + "mse_list = []\n", + "mse_list.append(np.mean((rf.predict(X_train_copy) - y_train)**2))\n", + "for i in range(X_train.shape[1]):\n", + " # if the gender is 0, mask the feature with importance == i in the 0th row of shap_ranked_mean_rankings\n", + " for j in range(X_train.shape[0]):\n", + " feature_to_mask = mask_feature(X_train.iloc[j,], i, shap_ranked_mean_rankings)\n", + " X_train_copy.loc[j, feature_to_mask] = X_train[feature_to_mask].mean()\n", + " mse = np.mean((rf.predict(X_train_copy) - y_train)**2)\n", + " mse_list.append(mse)\n", + "mse_arr_shap = np.array(mse_list)\n", + "# get difference between elements of mse_list\n", + "diff_shap = np.abs(np.diff(mse_arr_shap))\n", + "# get cumulative sum of differences\n", + "cumulative_diff_shap = np.cumsum(diff_shap)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "X_train_copy = X_train.copy()\n", + "mse_list = []\n", + "mse_list.append(np.mean((rf_plus.predict(X_train_copy) - y_train)**2))\n", + "for i in range(X_train.shape[1]):\n", + " # if the gender is 0, mask the feature with importance == i in the 0th row of shap_ranked_mean_rankings\n", + " for j in range(X_train.shape[0]):\n", + " feature_to_mask = mask_feature(X_train.iloc[j,], i, lmdi_ranked_mean_rankings)\n", + " X_train_copy.loc[j, feature_to_mask] = X_train[feature_to_mask].mean()\n", + " mse = np.mean((rf_plus.predict(X_train_copy) - y_train)**2)\n", + " mse_list.append(mse)\n", + "mse_arr_lmdi = np.array(mse_list)\n", + "# get difference between elements of mse_list\n", + "diff_lmdi = np.abs(np.diff(mse_arr_lmdi))\n", + "# get cumulative sum of differences\n", + "cumulative_diff_lmdi = np.cumsum(diff_lmdi)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot cumulative differences in mse vs number of features ablated\n", + "plt.plot(cumulative_diff_shap, label = 'SHAP')\n", + "plt.plot(cumulative_diff_lmdi, label = 'LMDI+')\n", + "plt.xlabel('# of Features Ablated')\n", + "plt.ylabel('Cumulative Difference in MSE')\n", + "# x ticks should be labeled 1-X_train.shape[1]\n", + "plt.xticks(np.arange(0, X_train.shape[1], 1), np.arange(1, X_train.shape[1]+1, 1))\n", + "plt.title('Training Data: Insurance Dataset w/ Smoking Status Subgroups')\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "X_test_copy = X_test.copy()\n", + "mse_list = []\n", + "mse_list.append(np.mean((rf.predict(X_test_copy) - y_test)**2))\n", + "for i in range(X_test.shape[1]):\n", + " # if the gender is 0, mask the feature with importance == i in the 0th row of shap_ranked_mean_rankings\n", + " for j in range(X_test.shape[0]):\n", + " feature_to_mask = mask_feature(X_test.iloc[j,], i, shap_ranked_mean_rankings)\n", + " X_test_copy.loc[j, feature_to_mask] = X_test[feature_to_mask].mean()\n", + " mse = np.mean((rf.predict(X_test_copy) - y_test)**2)\n", + " mse_list.append(mse)\n", + "mse_arr_shap = np.array(mse_list)\n", + "# get difference between elements of mse_list\n", + "diff_shap = np.abs(np.diff(mse_arr_shap))\n", + "# get cumulative sum of differences\n", + "cumulative_diff_shap_test = np.cumsum(diff_shap)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "X_test_copy = X_test.copy()\n", + "mse_list = []\n", + "mse_list.append(np.mean((rf_plus.predict(X_test_copy) - y_test)**2))\n", + "for i in range(X_test.shape[1]):\n", + " # if the gender is 0, mask the feature with importance == i in the 0th row of shap_ranked_mean_rankings\n", + " for j in range(X_test.shape[0]):\n", + " feature_to_mask = mask_feature(X_test.iloc[j,], i, lmdi_ranked_mean_rankings)\n", + " X_test_copy.loc[j, feature_to_mask] = X_test[feature_to_mask].mean()\n", + " mse = np.mean((rf_plus.predict(X_test_copy) - y_test)**2)\n", + " mse_list.append(mse)\n", + "mse_arr_lmdi = np.array(mse_list)\n", + "# get difference between elements of mse_list\n", + "diff_lmdi = np.abs(np.diff(mse_arr_lmdi))\n", + "# get cumulative sum of differences\n", + "cumulative_diff_lmdi_test = np.cumsum(diff_lmdi)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot cumulative differences in mse vs number of features ablated\n", + "plt.plot(cumulative_diff_shap_test, label = 'SHAP')\n", + "plt.plot(cumulative_diff_lmdi_test, label = 'LMDI+')\n", + "plt.xlabel('# of Features Ablated')\n", + "plt.ylabel('Cumulative Difference in MSE')\n", + "# x ticks should be labeled 1-X_train.shape[1]\n", + "plt.xticks(np.arange(0, X_train.shape[1], 1), np.arange(1, X_train.shape[1]+1, 1))\n", + "plt.title('Testing Data: Insurance Dataset w/ Smoking Status Subgroups')\n", + "plt.legend()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mdi", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/feature_importance/subgroup/legacy/insurance.ipynb b/feature_importance/subgroup/legacy/insurance.ipynb new file mode 100644 index 0000000..3d8ae14 --- /dev/null +++ b/feature_importance/subgroup/legacy/insurance.ipynb @@ -0,0 +1,2038 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "# import required packages\n", + "from imodels import get_clean_dataset\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import r2_score\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "from imodels.tree.rf_plus.rf_plus.rf_plus_models import RandomForestPlusRegressor\n", + "from imodels.tree.rf_plus.feature_importance.rfplus_explainer import AloRFPlusMDI, RFPlusLime\n", + "from subgroup_detection import *\n", + "import warnings\n", + "import shap\n", + "warnings.filterwarnings('ignore', category=DeprecationWarning)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "# get pre-cleaned compas dataset from imodels\n", + "X, _, feature_names = get_clean_dataset(43463, data_source='openml')\n", + "X = pd.DataFrame(X, columns=feature_names)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introduction\n", + "## About the Dataset\n", + "Throughout this report, we will be looking at the `insurance` dataset, which can be found on OpenML [here](https://www.openml.org/search?type=data&status=active&id=43463). Each row in this dataset represents an individual on some (unknown) health insurance plan. The task is to predict someone's medical expenses using demographic information such as age, sex, smoker status, and more. We take a peek at the data below:" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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agesexbmichildrensmokerregioncharges
019.00.027.9000.01.03.016884.92400
118.01.033.7701.00.02.01725.55230
228.01.033.0003.00.02.04449.46200
333.01.022.7050.00.01.021984.47061
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" + ], + "text/plain": [ + " age sex bmi children smoker region charges\n", + "0 19.0 0.0 27.900 0.0 1.0 3.0 16884.92400\n", + "1 18.0 1.0 33.770 1.0 0.0 2.0 1725.55230\n", + "2 28.0 1.0 33.000 3.0 0.0 2.0 4449.46200\n", + "3 33.0 1.0 22.705 0.0 0.0 1.0 21984.47061\n", + "4 32.0 1.0 28.880 0.0 0.0 1.0 3866.85520" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dimensions of the Insurance Dataset (Covariates): (1338, 6)\n", + "Dimensions of the Response: (1338,)\n" + ] + } + ], + "source": [ + "y = X.pop('charges')\n", + "print(\"Dimensions of the Insurance Dataset (Covariates):\", X.shape)\n", + "print(\"Dimensions of the Response:\", y.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before we begin analyzing our data, we first perform some simple exploratory data analysis." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---------------------------------------------\n", + "The Pearson Correlation Coefficient Between Age and Charges is: 0.3\n", + "---------------------------------------------\n", + "The Average Charge for Men is: 13956.75\n", + "The Average Charge for Women is: 12569.58\n", + "---------------------------------------------\n", + "The Pearson Correlation Coefficient Between BMI and Charges is: 0.2\n", + "---------------------------------------------\n", + "The Pearson Correlation Coefficient Between # of Children and Charges is: 0.07\n", + "---------------------------------------------\n", + "The Average Charge for Smokers is: 32050.23\n", + "The Average Charge for Non-Smokers is: 8434.27\n", + "---------------------------------------------\n", + "The Average Charge for Region #1 is: 13406.38\n", + "The Average Charge for Region #2 is: 12417.58\n", + "The Average Charge for Region #3 is: 14735.41\n", + "The Average Charge for Region #4 is: 12346.94\n", + "---------------------------------------------\n" + ] + } + ], + "source": [ + "print(\"---------------------------------------------\")\n", + "# get correlation between age and charges\n", + "print(\"The Pearson Correlation Coefficient Between Age and Charges is:\",\n", + " round(X['age'].corr(y), 2))\n", + "print(\"---------------------------------------------\")\n", + "# get the average charge by sex\n", + "print(\"The Average Charge for Men is:\", round(y[X['sex']==1].mean(), 2))\n", + "print(\"The Average Charge for Women is:\", round(y[X['sex']==0].mean(), 2))\n", + "print(\"---------------------------------------------\")\n", + "# get correlation between bmi and charges\n", + "print(\"The Pearson Correlation Coefficient Between BMI and Charges is:\",\n", + " round(X['bmi'].corr(y), 2))\n", + "print(\"---------------------------------------------\")\n", + "# get the correlation between children and charges\n", + "print(\"The Pearson Correlation Coefficient Between # of \\\n", + "Children and Charges is:\", round(X['children'].corr(y), 2))\n", + "print(\"---------------------------------------------\")\n", + "# get the average charge by smoker status\n", + "print(\"The Average Charge for Smokers is:\", round(y[X['smoker']==1].mean(), 2))\n", + "print(\"The Average Charge for Non-Smokers is:\",\n", + " round(y[X['smoker']==0].mean(), 2))\n", + "print(\"---------------------------------------------\")\n", + "# get the average charge by region\n", + "print(\"The Average Charge for Region #1 is:\",\n", + " round(y[X['region']==0].mean(), 2))\n", + "print(\"The Average Charge for Region #2 is:\",\n", + " round(y[X['region']==1].mean(), 2))\n", + "print(\"The Average Charge for Region #3 is:\",\n", + " round(y[X['region']==2].mean(), 2))\n", + "print(\"The Average Charge for Region #4 is:\",\n", + " round(y[X['region']==3].mean(), 2))\n", + "print(\"---------------------------------------------\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is apparent that while multiple variables might impact the medical expenses of an individual, the most eye-popping difference is the average expense difference between smokers and non-smokers.\n", + "\n", + "Now, we split the data into training and testing datasets using a 70/30 split. We check for covariate balance in our train/test split below. We do not include `region` in this covariate balance check for the sake of brevity." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "# split data into training and testing sets\n", + "# we won't actually use the test set here though, since 'discovery' would be\n", + "# a post-hoc analysis in real life\n", + "# proportion of training data is small so rf+ can fit without taking hours\n", + "y = np.asarray(np.log(y+1))\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,\n", + " random_state=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---------------------------------------------\n", + "Number of Observations in Training Data: 936\n", + "Number of Observations in Testing Data: 402\n", + "---------------------------------------------\n", + "Average of Age in Training Data: 38.822649572649574\n", + "Average of Age in Testing Data: 40.101990049751244\n", + "---------------------------------------------\n", + "Proportion of Men in Training Data: 0.5117521367521367\n", + "Proportion of Men in Testing Data: 0.4900497512437811\n", + "---------------------------------------------\n", + "Proportion of Women in Training Data: 0.4882478632478633\n", + "Proportion of Women in Testing Data: 0.5099502487562189\n", + "---------------------------------------------\n", + "Average of # of Children in Training Data: 1.1047008547008548\n", + "Average of # of Children in Testing Data: 1.072139303482587\n", + "---------------------------------------------\n", + "Proportion of Smokers in Training Data: 0.20619658119658119\n", + "Proportion of Smokers in Testing Data: 0.20149253731343283\n", + "---------------------------------------------\n", + "Average (Log) Charge in Training Data: 9.095506078918\n", + "Average (Log) Charge in Testing Data: 9.106562557748349\n", + "---------------------------------------------\n" + ] + } + ], + "source": [ + "print(\"---------------------------------------------\")\n", + "print(\"Number of Observations in Training Data:\", X_train.shape[0])\n", + "print(\"Number of Observations in Testing Data:\", X_test.shape[0])\n", + "print(\"---------------------------------------------\")\n", + "print(\"Average of Age in Training Data:\", X_train['age'].mean())\n", + "print(\"Average of Age in Testing Data:\", X_test['age'].mean())\n", + "print(\"---------------------------------------------\")\n", + "print(\"Proportion of Men in Training Data:\", X_train['sex'].mean())\n", + "print(\"Proportion of Men in Testing Data:\", X_test['sex'].mean())\n", + "print(\"---------------------------------------------\")\n", + "print(\"Proportion of Women in Training Data:\", 1-X_train['sex'].mean())\n", + "print(\"Proportion of Women in Testing Data:\", 1-X_test['sex'].mean())\n", + "print(\"---------------------------------------------\")\n", + "print(\"Average of # of Children in Training Data:\", X_train['children'].mean())\n", + "print(\"Average of # of Children in Testing Data:\", X_test['children'].mean())\n", + "print(\"---------------------------------------------\")\n", + "print(\"Proportion of Smokers in Training Data:\", X_train['smoker'].mean())\n", + "print(\"Proportion of Smokers in Testing Data:\", X_test['smoker'].mean())\n", + "print(\"---------------------------------------------\")\n", + "print(\"Average (Log) Charge in Training Data:\", y_train.mean())\n", + "print(\"Average (Log) Charge in Testing Data:\", y_test.mean())\n", + "print(\"---------------------------------------------\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The train and test datasets seem reasonable, so we continue with our analysis.\n", + "# Baseline 'Global' Model\n", + "We begin by fitting a RF+ to the training data. The R^2 on the test data is reported below. The total squared error (TSE) is also reported, as we will use this to compare the 'global' model fit on all of the data to the 'local' models fit on each cluster." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 2.5min\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 6.2min finished\n" + ] + } + ], + "source": [ + "# fit RF+ model\n", + "rf = RandomForestRegressor(n_estimators=100, random_state=0)\n", + "rf.fit(X_train, y_train)\n", + "rf_plus = RandomForestPlusRegressor(rf)\n", + "rf_plus.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RF+ Test Set R^2: 0.8535953274461104\n", + "RF+ Test Set TSE: 49.49116893683127\n" + ] + } + ], + "source": [ + "# compute r^2 on the test set\n", + "y_pred = rf_plus.predict(X_test)\n", + "r2 = r2_score(y_test, y_pred)\n", + "tse = np.sum((y_test - y_pred)**2)\n", + "print(f'RF+ Test Set R^2: {r2}')\n", + "print(f'RF+ Test Set TSE: {tse}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the following sections, we will compute subgroups using the RF/RF+ fit on the training data. We will then assign each test point to these subgroups using various methods. This will give us clusters that are composed of both training and testing points, allowing us to fit separate RF/RF+'s to the training data in each of these subgroups and predict the testing data. This will allow us to compare the TSE of the global model to the summed TSEs of the 'local' models fit on the clusters. Intuitively, the drop in TSE acheived in the local models will be directly related to how 'accurate' the subgroups determined by local feature importance are.\n", + "\n", + "When clustering our data, we will compute the ranking-based overlap (RBO) between each pair of points, and then use this as the distance matrix for hierarchical clustering with Ward linkage. The number of clusters will be chosen based on the appearance of the heatmap. It may be worth trying this with a range of cluster amounts and checking how it changes model performance - we could perhaps make a train/validate/test split, where we choose the number of clusters that results in the lowest total squared error in the 'local' models (or perhaps make an elbow plot, since the performance should only improve as # of clusters increases)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Local MDI+\n", + "\n", + "To detect subgroups using Local MDI+, we convert the Local MDI+ scores to feature rankings, and then compute RBO, as described above. We compute LMDI+ for the training points by using $metric(y_i, \\hat{y}^{(k)}_i)$. The resulting clusters can be visualized below." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "# get feature importances\n", + "mdi_explainer = AloRFPlusMDI(rf_plus, evaluate_on='oob')\n", + "mdi, partial_preds = mdi_explainer.explain(np.asarray(X_train), y_train)\n", + "mdi_rankings = mdi_explainer.get_rankings(mdi)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "# get rbo distance matrix\n", + "rbo_train = compute_rbo_matrix(mdi_rankings, form = 'distance')" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mdi_copy = pd.DataFrame(mdi, columns=feature_names[:-1]).copy()\n", + "num_clusters = 3\n", + "clusters = assign_training_clusters(mdi_copy, rbo_train, num_clusters)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is easy to see that these subgroups differ in feature importance, as shown above. However, just because they differ in feature importance does not necessarily imply that they differ in the values of those features themselves. We now check some useful summary statistics for each cluster to better understand how we have grouped the data." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---------------------------------------------\n", + "Proportion of Data in Cluster #1: 0.33547008547008544\n", + "Proportion of Data in Cluster #2: 0.2670940170940171\n", + "Proportion of Data in Cluster #3: 0.3974358974358974\n", + "---------------------------------------------\n", + "Average Age in Cluster #1: 47.4140127388535\n", + "Average Age in Cluster #2: 30.228\n", + "Average Age in Cluster #3: 37.346774193548384\n", + "---------------------------------------------\n", + "Proportion of Women in Cluster #1: 0.5222929936305732\n", + "Proportion of Women in Cluster #2: 0.516\n", + "Proportion of Women in Cluster #3: 0.4408602150537635\n", + "---------------------------------------------\n", + "Proportion of Men in Cluster #1: 0.47770700636942676\n", + "Proportion of Men in Cluster #2: 0.484\n", + "Proportion of Men in Cluster #3: 0.5591397849462365\n", + "---------------------------------------------\n", + "Average BMI in Cluster #1: 30.49052547770701\n", + "Average BMI in Cluster #2: 30.115599999999997\n", + "Average BMI in Cluster #3: 31.224731182795693\n", + "---------------------------------------------\n", + "Average # of Children in Cluster #1: 1.1464968152866242\n", + "Average # of Children in Cluster #2: 0.844\n", + "Average # of Children in Cluster #3: 1.2446236559139785\n", + "---------------------------------------------\n", + "Proportion of Smokers in Cluster #1: 0.044585987261146494\n", + "Proportion of Smokers in Cluster #2: 0.0\n", + "Proportion of Smokers in Cluster #3: 0.48118279569892475\n", + "---------------------------------------------\n", + "Average Medical Expenses in Cluster #1: 9.317014554106203\n", + "Average Medical Expenses in Cluster #2: 8.29435323632679\n", + "Average Medical Expenses in Cluster #3: 9.44694303977474\n", + "---------------------------------------------\n" + ] + } + ], + "source": [ + "print(\"---------------------------------------------\")\n", + "# calculate average charge for each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Proportion of Data in Cluster #{i+1}:\", X_train[clusters==i+1].shape[0]/X_train.shape[0])\n", + "print(\"---------------------------------------------\")\n", + "# get average age in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Average Age in Cluster #{i+1}:\", X_train[clusters==i+1]['age'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get percentage women in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Proportion of Women in Cluster #{i+1}:\", 1-X_train[clusters==i+1]['sex'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get percentage men in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Proportion of Men in Cluster #{i+1}:\", X_train[clusters==i+1]['sex'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get average bmi in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Average BMI in Cluster #{i+1}:\", X_train[clusters==i+1]['bmi'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get average number of children in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Average # of Children in Cluster #{i+1}:\", X_train[clusters==i+1]['children'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get proportion of smokers in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Proportion of Smokers in Cluster #{i+1}:\", X_train[clusters==i+1]['smoker'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get proportion of smokers in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Average Medical Expenses in Cluster #{i+1}:\", y_train[clusters==i+1].mean())\n", + "print(\"---------------------------------------------\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evaluating Cluster Performance - Centroid Method w/ Exact Mean\n", + "Now that we have a good idea of what our clustering has done, we can check if this helps improve our predictions. We compute LMDI+ of test points using $metric(\\hat{y}_i, \\hat{y}^{(k)}_i)$. We will take the test points and determine their cluster membership based on their RBO similarity to the mean point in each cluster (in RBO embedding). We will then fit a RF+ on the training data and using it to predict the test data for that cluster. We can then compute the R^2 and total squared error for each cluster's model. By summing the TSE across cluster models and comparing this to the original TSE reported above, we can get a good idea of how well these clusters improve model accuracy." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "# get mdi rankings assignments for test points\n", + "mdi_test, partial_preds_test = mdi_explainer.explain(np.asarray(X_test))\n", + "mdi_test_rankings = mdi_explainer.get_rankings(mdi_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "test_clust = assign_testing_clusters(method = \"centroid\", median_approx = False,\n", + " rbo_distance_matrix = rbo_train,\n", + " lfi_train_ranking = mdi_rankings,\n", + " lfi_test_ranking = mdi_test_rankings,\n", + " clusters = clusters)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "cluster1_trainX = X_train[clusters == 1]\n", + "cluster2_trainX = X_train[clusters == 2]\n", + "cluster3_trainX = X_train[clusters == 3]\n", + "# cluster4_trainX = X_train[clusters == 4]\n", + "# cluster5_trainX = X_train[clusters == 5]\n", + "# cluster6_trainX = X_train[clusters == 6]\n", + "# cluster7_trainX = X_train[clusters == 7]\n", + "\n", + "cluster1_trainy = y_train[clusters == 1]\n", + "cluster2_trainy = y_train[clusters == 2]\n", + "cluster3_trainy = y_train[clusters == 3]\n", + "# cluster4_trainy = y_train[clusters == 4]\n", + "# cluster5_trainy = y_train[clusters == 5]\n", + "# cluster6_trainy = y_train[clusters == 6]\n", + "# cluster7_trainy = y_train[clusters == 7]\n", + "\n", + "cluster1_testX = X_test[test_clust == 1]\n", + "cluster2_testX = X_test[test_clust == 2]\n", + "cluster3_testX = X_test[test_clust == 3]\n", + "# cluster4_testX = X_test[test_clust == 4]\n", + "# cluster5_testX = X_test[test_clust == 5]\n", + "# cluster6_testX = X_test[test_clust == 6]\n", + "# cluster7_testX = X_test[test_clust == 7]\n", + "\n", + "cluster1_testy = y_test[test_clust == 1]\n", + "cluster2_testy = y_test[test_clust == 2]\n", + "cluster3_testy = y_test[test_clust == 3]\n", + "# cluster4_testy = y_test[test_clust == 4]\n", + "# cluster5_testy = y_test[test_clust == 5]\n", + "# cluster6_testy = y_test[test_clust == 6]\n", + "# cluster7_testy = y_test[test_clust == 7]" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---------------------------------------------\n", + "Proportion of Train Data in Cluster #1: 0.33547008547008544; Proportion of Test Data in Cluster #1: 0.27860696517412936\n", + "Proportion of Train Data in Cluster #2: 0.2670940170940171; Proportion of Test Data in Cluster #2: 0.3706467661691542\n", + "Proportion of Train Data in Cluster #3: 0.3974358974358974; Proportion of Test Data in Cluster #3: 0.35074626865671643\n", + "---------------------------------------------\n", + "Average Age in Train Cluster #1:, 47.4140127388535; Average Age in Test Cluster #1:, 49.205357142857146\n", + "Average Age in Train Cluster #2:, 30.228; Average Age in Test Cluster #2:, 34.77181208053691\n", + "Average Age in Train Cluster #3:, 37.346774193548384; Average Age in Test Cluster #3:, 38.50354609929078\n", + "---------------------------------------------\n", + "Proportion of Women in Train Cluster #1:, 0.5222929936305732; Proportion of Women in Test Cluster #1:, 0.5178571428571428\n", + "Proportion of Women in Train Cluster #2:, 0.516; Proportion of Women in Test Cluster #2:, 0.5704697986577181\n", + "Proportion of Women in Train Cluster #3:, 0.4408602150537635; Proportion of Women in Test Cluster #3:, 0.43971631205673756\n", + "---------------------------------------------\n", + "Proportion of Men in Train Cluster #1:, 0.47770700636942676; Proportion of Men in Test Cluster #1:, 0.48214285714285715\n", + "Proportion of Men in Train Cluster #2:, 0.484; Proportion of Men in Test Cluster #2:, 0.42953020134228187\n", + "Proportion of Men in Train Cluster #3:, 0.5591397849462365; Proportion of Men in Test Cluster #3:, 0.5602836879432624\n", + "---------------------------------------------\n", + "Average BMI in Train Cluster #1:, 30.49052547770701; Average BMI in Test Cluster #1:, 31.132053571428575\n", + "Average BMI in Train Cluster #2:, 30.115599999999997; Average BMI in Test Cluster #2:, 30.157147651006717\n", + "Average BMI in Train Cluster #3:, 31.224731182795693; Average BMI in Test Cluster #3:, 30.70138297872341\n", + "---------------------------------------------\n", + "Average # of Children in Train Cluster #1:, 1.1464968152866242; Average # of Children in Test Cluster #1:, 1.0357142857142858\n", + "Average # of Children in Train Cluster #2:, 0.844; Average # of Children in Test Cluster #2:, 0.9328859060402684\n", + "Average # of Children in Train Cluster #3:, 1.2446236559139785; Average # of Children in Test Cluster #3:, 1.24822695035461\n", + "---------------------------------------------\n", + "Proportion of Smokers in Train Cluster #1:, 0.044585987261146494; Proportion of Smokers in Test Cluster #1:, 0.05357142857142857\n", + "Proportion of Smokers in Train Cluster #2:, 0.0; Proportion of Smokers in Test Cluster #2:, 0.0\n", + "Proportion of Smokers in Train Cluster #3:, 0.48118279569892475; Proportion of Smokers in Test Cluster #3:, 0.5319148936170213\n", + "---------------------------------------------\n", + "Average Medical Expenses in Train Cluster #1:, 9.317014554106203; Average Medical Expenses in Test Cluster #1:, 9.337061672271831\n", + "Average Medical Expenses in Train Cluster #2:, 8.29435323632679; Average Medical Expenses in Test Cluster #2:, 8.488956914996146\n", + "Average Medical Expenses in Train Cluster #3:, 9.44694303977474; Average Medical Expenses in Test Cluster #3:, 9.576118160184153\n", + "---------------------------------------------\n" + ] + } + ], + "source": [ + "print(\"---------------------------------------------\")\n", + "# calculate average charge for each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Proportion of Train Data in Cluster #{i+1}: {X_train[clusters==i+1].shape[0]/X_train.shape[0]};\",\n", + " f\"Proportion of Test Data in Cluster #{i+1}: {X_test[test_clust==i+1].shape[0]/X_test.shape[0]}\")\n", + "print(\"---------------------------------------------\")\n", + "# get average age in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Average Age in Train Cluster #{i+1}:, {X_train[clusters==i+1]['age'].mean()};\",\n", + " f\"Average Age in Test Cluster #{i+1}:, {X_test[test_clust==i+1]['age'].mean()}\")\n", + "print(\"---------------------------------------------\")\n", + "# get percentage women in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Proportion of Women in Train Cluster #{i+1}:, {1-X_train[clusters==i+1]['sex'].mean()};\",\n", + " f\"Proportion of Women in Test Cluster #{i+1}:, {1-X_test[test_clust==i+1]['sex'].mean()}\") \n", + "print(\"---------------------------------------------\")\n", + "# get percentage men in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Proportion of Men in Train Cluster #{i+1}:, {X_train[clusters==i+1]['sex'].mean()};\",\n", + " f\"Proportion of Men in Test Cluster #{i+1}:, {X_test[test_clust==i+1]['sex'].mean()}\")\n", + "print(\"---------------------------------------------\")\n", + "# get average bmi in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Average BMI in Train Cluster #{i+1}:, {X_train[clusters==i+1]['bmi'].mean()};\",\n", + " f\"Average BMI in Test Cluster #{i+1}:, {X_test[test_clust==i+1]['bmi'].mean()}\")\n", + "print(\"---------------------------------------------\")\n", + "# get average number of children in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Average # of Children in Train Cluster #{i+1}:, {X_train[clusters==i+1]['children'].mean()};\",\n", + " f\"Average # of Children in Test Cluster #{i+1}:, {X_test[test_clust==i+1]['children'].mean()}\")\n", + "print(\"---------------------------------------------\")\n", + "# get proportion of smokers in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Proportion of Smokers in Train Cluster #{i+1}:, {X_train[clusters==i+1]['smoker'].mean()};\",\n", + " f\"Proportion of Smokers in Test Cluster #{i+1}:, {X_test[test_clust==i+1]['smoker'].mean()}\")\n", + "print(\"---------------------------------------------\")\n", + "# get proportion of smokers in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Average Medical Expenses in Train Cluster #{i+1}:, {y_train[clusters==i+1].mean()};\",\n", + " f\"Average Medical Expenses in Test Cluster #{i+1}:, {y_test[test_clust==i+1].mean()}\")\n", + "print(\"---------------------------------------------\")" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 18.1s\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 47.2s finished\n", + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 8.3s\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 22.9s finished\n", + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 12.7s\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 34.6s finished\n" + ] + } + ], + "source": [ + "# fit RF+ on each training set, predict test\n", + "rf1 = RandomForestRegressor(n_estimators=100, random_state=0)\n", + "rf_plus1 = RandomForestPlusRegressor(rf1)\n", + "rf_plus1.fit(cluster1_trainX, cluster1_trainy)\n", + "\n", + "rf2 = RandomForestRegressor(n_estimators=100, random_state=1)\n", + "rf_plus2 = RandomForestPlusRegressor(rf2)\n", + "rf_plus2.fit(cluster2_trainX, cluster2_trainy)\n", + "\n", + "rf3 = RandomForestRegressor(n_estimators=100, random_state=0)\n", + "rf_plus3 = RandomForestPlusRegressor(rf3)\n", + "rf_plus3.fit(cluster3_trainX, cluster3_trainy)\n", + "\n", + "# rf4 = RandomForestRegressor(n_estimators=100, random_state=0)\n", + "# rf_plus4 = RandomForestPlusRegressor(rf4)\n", + "# rf_plus4.fit(cluster4_trainX, cluster4_trainy)\n", + "\n", + "# rf5 = RandomForestRegressor(n_estimators=100, random_state=1)\n", + "# rf_plus5 = RandomForestPlusRegressor(rf5)\n", + "# rf_plus5.fit(cluster5_trainX, cluster5_trainy)\n", + "\n", + "# rf6 = RandomForestRegressor(n_estimators=100, random_state=0)\n", + "# rf_plus6 = RandomForestPlusRegressor(rf6)\n", + "# rf_plus6.fit(cluster6_trainX, cluster6_trainy)\n", + "\n", + "# rf7 = RandomForestRegressor(n_estimators=100, random_state=1)\n", + "# rf_plus7 = RandomForestPlusRegressor(rf7)\n", + "# rf_plus7.fit(cluster7_trainX, cluster7_trainy)" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---------------------------------------------\n", + "Local RF+ Cluster #1 Test Set R^2: -0.11724094675708985\n", + "Local RF+ Cluster #1 Test Set TSE: 14.949604098796454\n", + "Global RF+ Cluster #1 Test Set R^2: 0.029493163020803292\n", + "Global RF+ Cluster #1 Test Set TSE: 12.986180850359272\n", + "% Improvement in TSE: -13.13\n", + "---------------------------------------------\n", + "Local RF+ Cluster #2 Test Set R^2: 0.6772757620329343\n", + "Local RF+ Cluster #2 Test Set TSE: 38.95704027567547\n", + "Global RF+ Cluster #2 Test Set R^2: 0.781584506318687\n", + "Global RF+ Cluster #2 Test Set TSE: 26.365609344293446\n", + "% Improvement in TSE: -32.32\n", + "---------------------------------------------\n", + "Local RF+ Cluster #3 Test Set R^2: 0.9078883801833639\n", + "Local RF+ Cluster #3 Test Set TSE: 6.596004099534014\n", + "Global RF+ Cluster #3 Test Set R^2: 0.781584506318687\n", + "Global RF+ Cluster #3 Test Set TSE: 10.139378742178563\n", + "% Improvement in TSE: 53.72\n", + "---------------------------------------------\n" + ] + } + ], + "source": [ + "# compute r^2 on the test set\n", + "print(\"---------------------------------------------\")\n", + "y_pred1 = rf_plus1.predict(cluster1_testX)\n", + "r2 = r2_score(cluster1_testy, y_pred1)\n", + "tse1 = np.sum((cluster1_testy - y_pred1)**2)\n", + "y_pred_g1 = rf_plus.predict(cluster1_testX)\n", + "r2g = r2_score(cluster1_testy, y_pred_g1)\n", + "tse1g = np.sum((cluster1_testy - y_pred_g1)**2)\n", + "print(f'Local RF+ Cluster #1 Test Set R^2: {r2}')\n", + "print(f'Local RF+ Cluster #1 Test Set TSE: {tse1}')\n", + "print(f'Global RF+ Cluster #1 Test Set R^2: {r2g}')\n", + "print(f'Global RF+ Cluster #1 Test Set TSE: {tse1g}')\n", + "print(f'% Improvement in TSE: {(tse1g - tse1)/tse1*100:.2f}')\n", + "print(\"---------------------------------------------\")\n", + "y_pred2 = rf_plus2.predict(cluster2_testX)\n", + "r2 = r2_score(cluster2_testy, y_pred2)\n", + "tse2 = np.sum((cluster2_testy - y_pred2)**2)\n", + "y_pred_g2 = rf_plus.predict(cluster2_testX)\n", + "r2g = r2_score(cluster2_testy, y_pred_g2)\n", + "tse2g = np.sum((cluster2_testy - y_pred_g2)**2)\n", + "print(f'Local RF+ Cluster #2 Test Set R^2: {r2}')\n", + "print(f'Local RF+ Cluster #2 Test Set TSE: {tse2}')\n", + "print(f'Global RF+ Cluster #2 Test Set R^2: {r2g}')\n", + "print(f'Global RF+ Cluster #2 Test Set TSE: {tse2g}')\n", + "print(f'% Improvement in TSE: {(tse2g - tse2)/tse2*100:.2f}')\n", + "print(\"---------------------------------------------\")\n", + "y_pred3 = rf_plus3.predict(cluster3_testX)\n", + "r2 = r2_score(cluster3_testy, y_pred3)\n", + "tse3 = np.sum((cluster3_testy - y_pred3)**2)\n", + "y_pred_g3 = rf_plus.predict(cluster3_testX)\n", + "r2 = r2_score(cluster3_testy, y_pred_g3)\n", + "tse3g = np.sum((cluster3_testy - y_pred_g3)**2)\n", + "print(f'Local RF+ Cluster #3 Test Set R^2: {r2}')\n", + "print(f'Local RF+ Cluster #3 Test Set TSE: {tse3}')\n", + "print(f'Global RF+ Cluster #3 Test Set R^2: {r2g}')\n", + "print(f'Global RF+ Cluster #3 Test Set TSE: {tse3g}')\n", + "print(f'% Improvement in TSE: {(tse3g - tse3)/tse3*100:.2f}')\n", + "print(\"---------------------------------------------\")\n", + "# y_pred4 = rf_plus4.predict(cluster4_testX)\n", + "# r2 = r2_score(cluster4_testy, y_pred4)\n", + "# tse4 = np.sum((cluster4_testy - y_pred4)**2)\n", + "# y_pred_g4 = rf_plus.predict(cluster4_testX)\n", + "# r2g = r2_score(cluster4_testy, y_pred_g4)\n", + "# tse4g = np.sum((cluster4_testy - y_pred_g4)**2)\n", + "# print(f'Local RF+ Cluster #4 Test Set R^2: {r2}')\n", + "# print(f'Local RF+ Cluster #4 Test Set TSE: {tse4}')\n", + "# print(f'Global RF+ Cluster #4 Test Set R^2: {r2g}')\n", + "# print(f'Global RF+ Cluster #4 Test Set TSE: {tse4g}')\n", + "# print(f'% Improvement in TSE: {(tse4g - tse4)/tse4*100:.2f}')\n", + "# print(\"---------------------------------------------\")\n", + "# y_pred5 = rf_plus5.predict(cluster5_testX)\n", + "# r2 = r2_score(cluster5_testy, y_pred5)\n", + "# tse5 = np.sum((cluster5_testy - y_pred5)**2)\n", + "# y_pred_g5 = rf_plus.predict(cluster5_testX)\n", + "# r2g = r2_score(cluster5_testy, y_pred_g5)\n", + "# tse5g = np.sum((cluster5_testy - y_pred_g5)**2)\n", + "# print(f'Local RF+ Cluster #5 Test Set R^2: {r2}')\n", + "# print(f'Local RF+ Cluster #5 Test Set TSE: {tse5}')\n", + "# print(f'Global RF+ Cluster #5 Test Set R^2: {r2g}')\n", + "# print(f'Global RF+ Cluster #5 Test Set TSE: {tse5g}')\n", + "# print(f'% Improvement in TSE: {(tse5g - tse5)/tse5*100:.2f}')\n", + "# print(\"---------------------------------------------\")\n", + "# y_pred6 = rf_plus6.predict(cluster6_testX)\n", + "# r2 = r2_score(cluster6_testy, y_pred6)\n", + "# tse6 = np.sum((cluster6_testy - y_pred6)**2)\n", + "# y_pred_g6 = rf_plus.predict(cluster6_testX)\n", + "# r2g = r2_score(cluster6_testy, y_pred_g6)\n", + "# tse6g = np.sum((cluster6_testy - y_pred_g6)**2)\n", + "# print(f'Local RF+ Cluster #6 Test Set R^2: {r2}')\n", + "# print(f'Local RF+ Cluster #6 Test Set TSE: {tse6}')\n", + "# print(f'Global RF+ Cluster #6 Test Set R^2: {r2g}')\n", + "# print(f'Global RF+ Cluster #6 Test Set TSE: {tse6g}')\n", + "# print(f'% Improvement in TSE: {(tse6g - tse6)/tse6*100:.2f}')\n", + "# print(\"---------------------------------------------\")\n", + "# y_pred7 = rf_plus7.predict(cluster7_testX)\n", + "# r2 = r2_score(cluster7_testy, y_pred7)\n", + "# tse7 = np.sum((cluster7_testy - y_pred7)**2)\n", + "# y_pred_g7 = rf_plus.predict(cluster7_testX)\n", + "# r2g = r2_score(cluster7_testy, y_pred_g7)\n", + "# tse7g = np.sum((cluster7_testy - y_pred_g7)**2)\n", + "# print(f'Local RF+ Cluster #7 Test Set R^2: {r2}')\n", + "# print(f'Local RF+ Cluster #7 Test Set TSE: {tse7}')\n", + "# print(f'Global RF+ Cluster #7 Test Set R^2: {r2g}')\n", + "# print(f'Global RF+ Cluster #7 Test Set TSE: {tse7g}')\n", + "# print(f'% Improvement in TSE: {(tse7g - tse7)/tse7*100:.2f}')\n", + "# print(\"---------------------------------------------\")" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---------------------------------------------\n", + "Total # of Observations Predicted by Global Model: 402\n", + "Total # of Observations Predicted by Cluster Models: 402\n", + "---------------------------------------------\n", + "Difference in TSE (Global - Sum of Clusters): -11.01\n", + "Percent Improvement Over Global Model: -22.25%\n", + "---------------------------------------------\n" + ] + } + ], + "source": [ + "print(\"---------------------------------------------\")\n", + "print(\"Total # of Observations Predicted by Global Model:\", X_test.shape[0])\n", + "print(\"Total # of Observations Predicted by Cluster Models:\",\n", + " cluster1_testX.shape[0] + cluster2_testX.shape[0] + \\\n", + " cluster3_testX.shape[0])\n", + "print(\"---------------------------------------------\")\n", + "print(\"Difference in TSE (Global - Sum of Clusters):\", round(tse - (tse1 + tse2 + tse3), 2))\n", + "print(f\"Percent Improvement Over Global Model: {round(100*(tse - (tse1 + tse2 + tse3))/tse, 2)}%\")\n", + "print(\"---------------------------------------------\")" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [], + "source": [ + "# order rows of mdi_test by cluster assignment\n", + "mdi_test_clust = mdi_test[np.argsort(test_clust)]" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [], + "source": [ + "# order test clust by cluster assignment\n", + "test_clust_org = test_clust[np.argsort(test_clust)]" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "# get indexes where mdi_test_clust changes clusters\n", + "cluster_changes = np.where(np.diff(test_clust_org) != 0)[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# create new heatmap\n", + "plt.figure(figsize=(10, 10))\n", + "sns.heatmap(mdi_test_clust, cmap='viridis')\n", + "plt.xlabel('Features')\n", + "plt.ylabel('Observations')\n", + "plt.title('LMDI+ Heatmap of Test Data Ordered by Cluster Assignment')\n", + "plt.yticks([])\n", + "plt.xticks(np.arange(len(feature_names[:-1])) + 0.5, feature_names[:-1])\n", + "# put horizontal lines where cluster membership changes\n", + "for i in cluster_changes:\n", + " plt.axhline(i, color='red', linewidth=2, linestyle='--')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The most important feature for Train Cluster #1 is: region\n", + "The most important feature for Test Cluster #1 is: sex\n", + "The most important feature for Train Cluster #2 is: age\n", + "The most important feature for Test Cluster #2 is: age\n", + "The most important feature for Train Cluster #3 is: smoker\n", + "The most important feature for Test Cluster #3 is: smoker\n" + ] + } + ], + "source": [ + "# get most important feature on average for each cluster\n", + "for i in range(num_clusters):\n", + " print(f'The most important feature for Train Cluster #{i+1} is:', X.columns[np.argmax(np.mean(mdi[clusters==i+1], axis=0))])\n", + " print(f'The most important feature for Test Cluster #{i+1} is:', X.columns[np.argmax(np.mean(mdi_test[test_clust==i+1], axis=0))])" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The feature ranking for Train Cluster #1 is: ['region', 'sex', 'children', 'bmi', 'age', 'smoker']\n", + "The feature ranking for Test Cluster #1 is: ['sex', 'region', 'children', 'bmi', 'age', 'smoker']\n", + "The feature ranking for Train Cluster #2 is: ['age', 'smoker', 'children', 'region', 'bmi', 'sex']\n", + "The feature ranking for Test Cluster #2 is: ['age', 'smoker', 'children', 'bmi', 'sex', 'region']\n", + "The feature ranking for Train Cluster #3 is: ['smoker', 'age', 'bmi', 'region', 'sex', 'children']\n", + "The feature ranking for Test Cluster #3 is: ['smoker', 'bmi', 'children', 'region', 'sex', 'age']\n" + ] + } + ], + "source": [ + "for i in range(num_clusters):\n", + " # negative is taken because argsort goes in the wrong order\n", + " print(f'The feature ranking for Train Cluster #{i+1} is:', list(X.columns[np.argsort(-np.mean(mdi[clusters==i+1], axis=0))]))\n", + " print(f'The feature ranking for Test Cluster #{i+1} is:', list(X.columns[np.argsort(-np.mean(mdi_test[test_clust==i+1], axis=0))]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TreeSHAP\n", + "\n", + "To detect subgroups using TreeSHAP, we obtain the TreeSHAP scores from the fitted random forest that was given to the RF+ constructor. We then convert these importance scores to feature rankings and compute RBO, which is the same process we underwent for Local MDI+. The resulting clusters can be visualized below. It is worth noting that we evaluate the RF here instead of the RF+. This is due to two reasons: TreeSHAP is made for RFs, not RF+s, and the clusters made by TreeSHAP are too small to fit RF+s (due to cross-validation)." + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RF Test Set R^2: 0.8457991205543834\n", + "RF Test Set TSE: 52.126627120059155\n" + ] + } + ], + "source": [ + "# compute r^2 on the test set\n", + "y_pred = rf.predict(X_test)\n", + "r2 = r2_score(y_test, y_pred)\n", + "tse = np.sum((y_test - y_pred)**2)\n", + "print(f'RF Test Set R^2: {r2}')\n", + "print(f'RF Test Set TSE: {tse}')" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [], + "source": [ + "explainer = shap.TreeExplainer(rf)" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [], + "source": [ + "shap_values = np.abs(explainer.shap_values(X_train, check_additivity=False))" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [], + "source": [ + "shap_rankings = mdi_explainer.get_rankings(shap_values)" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# get rbo distance matrix\n", + "rbo_train = compute_rbo_matrix(shap_rankings, form = 'distance')\n", + "shap_copy = pd.DataFrame(shap_values, columns=feature_names[:-1]).copy()\n", + "num_clusters = 3\n", + "clusters = assign_training_clusters(shap_copy, rbo_train, num_clusters)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now check some summary statistics of the above clusters. It is worth noting that the subgroups that are 'discovered' by TreeSHAP either are composed entirely of smokers or of non-smokers." + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---------------------------------------------\n", + "Proportion of Data in Cluster #1: 0.4935897435897436\n", + "Proportion of Data in Cluster #2: 0.09294871794871795\n", + "Proportion of Data in Cluster #3: 0.41346153846153844\n", + "---------------------------------------------\n", + "Average Age in Cluster #1: 40.577922077922075\n", + "Average Age in Cluster #2: 36.59770114942529\n", + "Average Age in Cluster #3: 37.22739018087855\n", + "---------------------------------------------\n", + "Proportion of Women in Cluster #1: 0.5\n", + "Proportion of Women in Cluster #2: 0.5517241379310345\n", + "Proportion of Women in Cluster #3: 0.4599483204134367\n", + "---------------------------------------------\n", + "Proportion of Men in Cluster #1: 0.5\n", + "Proportion of Men in Cluster #2: 0.4482758620689655\n", + "Proportion of Men in Cluster #3: 0.5400516795865633\n", + "---------------------------------------------\n", + "Average BMI in Cluster #1: 30.515238095238097\n", + "Average BMI in Cluster #2: 30.319540229885064\n", + "Average BMI in Cluster #3: 30.96301033591731\n", + "---------------------------------------------\n", + "Average # of Children in Cluster #1: 0.8896103896103896\n", + "Average # of Children in Cluster #2: 1.9540229885057472\n", + "Average # of Children in Cluster #3: 1.1705426356589148\n", + "---------------------------------------------\n", + "Proportion of Smokers in Cluster #1: 0.0\n", + "Proportion of Smokers in Cluster #2: 0.0\n", + "Proportion of Smokers in Cluster #3: 0.49870801033591733\n", + "---------------------------------------------\n", + "Average Medical Expenses in Cluster #1: 8.741192353571316\n", + "Average Medical Expenses in Cluster #2: 8.999729423305789\n", + "Average Medical Expenses in Cluster #3: 9.540016441058643\n", + "---------------------------------------------\n" + ] + } + ], + "source": [ + "print(\"---------------------------------------------\")\n", + "# calculate average charge for each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Proportion of Data in Cluster #{i+1}:\", X_train[clusters==i+1].shape[0]/X_train.shape[0])\n", + "print(\"---------------------------------------------\")\n", + "# get average age in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Average Age in Cluster #{i+1}:\", X_train[clusters==i+1]['age'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get percentage women in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Proportion of Women in Cluster #{i+1}:\", 1-X_train[clusters==i+1]['sex'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get percentage men in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Proportion of Men in Cluster #{i+1}:\", X_train[clusters==i+1]['sex'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get average bmi in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Average BMI in Cluster #{i+1}:\", X_train[clusters==i+1]['bmi'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get average number of children in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Average # of Children in Cluster #{i+1}:\", X_train[clusters==i+1]['children'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get proportion of smokers in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Proportion of Smokers in Cluster #{i+1}:\", X_train[clusters==i+1]['smoker'].mean())\n", + "print(\"---------------------------------------------\")\n", + "# get proportion of smokers in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Average Medical Expenses in Cluster #{i+1}:\", y_train[clusters==i+1].mean())\n", + "print(\"---------------------------------------------\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evaluating Cluster Performance - Centroid Method w/ Exact Mean\n", + "Now that we have a good idea of what our clustering has done, we can check if this helps improve our predictions. We will take the test points and determine their cluster membership based on their RBO similarity to the mean point in each cluster (in RBO embedding). We will then fit a RF+ on the training data and using it to predict the test data for that cluster. We can then compute the R^2 and total squared error for each cluster's model. By summing the TSE across cluster models and comparing this to the original TSE reported above, we can get a good idea of how well these clusters improve model accuracy." + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [], + "source": [ + "# get mdi rankings assignments for test points\n", + "shap_test_values = np.abs(explainer.shap_values(X_test, check_additivity=False))\n", + "shap_test_rankings = mdi_explainer.get_rankings(shap_test_values)" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [], + "source": [ + "test_clust = assign_testing_clusters(method = \"centroid\", median_approx = False,\n", + " rbo_distance_matrix = rbo_train,\n", + " lfi_train_ranking = shap_rankings,\n", + " lfi_test_ranking = shap_test_rankings,\n", + " clusters = clusters)" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [], + "source": [ + "cluster1_trainX = X_train[clusters == 1]\n", + "cluster2_trainX = X_train[clusters == 2]\n", + "cluster3_trainX = X_train[clusters == 3]\n", + "# cluster4_trainX = X_train[clusters == 4]\n", + "# cluster5_trainX = X_train[clusters == 5]\n", + "# cluster6_trainX = X_train[clusters == 6]\n", + "# cluster7_trainX = X_train[clusters == 7]\n", + "\n", + "cluster1_trainy = y_train[clusters == 1]\n", + "cluster2_trainy = y_train[clusters == 2]\n", + "cluster3_trainy = y_train[clusters == 3]\n", + "# cluster4_trainy = y_train[clusters == 4]\n", + "# cluster5_trainy = y_train[clusters == 5]\n", + "# cluster6_trainy = y_train[clusters == 6]\n", + "# cluster7_trainy = y_train[clusters == 7]\n", + "\n", + "cluster1_testX = X_test[test_clust == 1]\n", + "cluster2_testX = X_test[test_clust == 2]\n", + "cluster3_testX = X_test[test_clust == 3]\n", + "# cluster4_testX = X_test[test_clust == 4]\n", + "# cluster5_testX = X_test[test_clust == 5]\n", + "# cluster6_testX = X_test[test_clust == 6]\n", + "# cluster7_testX = X_test[test_clust == 7]\n", + "\n", + "cluster1_testy = y_test[test_clust == 1]\n", + "cluster2_testy = y_test[test_clust == 2]\n", + "cluster3_testy = y_test[test_clust == 3]\n", + "# cluster4_testy = y_test[test_clust == 4]\n", + "# cluster5_testy = y_test[test_clust == 5]\n", + "# cluster6_testy = y_test[test_clust == 6]\n", + "# cluster7_testy = y_test[test_clust == 7]" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---------------------------------------------\n", + "Proportion of Train Data in Cluster #1: 0.4935897435897436; Proportion of Test Data in Cluster #1: 0.5298507462686567\n", + "Proportion of Train Data in Cluster #2: 0.09294871794871795; Proportion of Test Data in Cluster #2: 0.07711442786069651\n", + "Proportion of Train Data in Cluster #3: 0.41346153846153844; Proportion of Test Data in Cluster #3: 0.39303482587064675\n", + "---------------------------------------------\n", + "Average Age in Train Cluster #1:, 40.577922077922075; Average Age in Test Cluster #1:, 42.15023474178404\n", + "Average Age in Train Cluster #2:, 36.59770114942529; Average Age in Test Cluster #2:, 37.45161290322581\n", + "Average Age in Train Cluster #3:, 37.22739018087855; Average Age in Test Cluster #3:, 37.860759493670884\n", + "---------------------------------------------\n", + "Proportion of Women in Train Cluster #1:, 0.5; Proportion of Women in Test Cluster #1:, 0.5258215962441315\n", + "Proportion of Women in Train Cluster #2:, 0.5517241379310345; Proportion of Women in Test Cluster #2:, 0.32258064516129037\n", + "Proportion of Women in Train Cluster #3:, 0.4599483204134367; Proportion of Women in Test Cluster #3:, 0.5253164556962026\n", + "---------------------------------------------\n", + "Proportion of Men in Train Cluster #1:, 0.5; Proportion of Men in Test Cluster #1:, 0.47417840375586856\n", + "Proportion of Men in Train Cluster #2:, 0.4482758620689655; Proportion of Men in Test Cluster #2:, 0.6774193548387096\n", + "Proportion of Men in Train Cluster #3:, 0.5400516795865633; Proportion of Men in Test Cluster #3:, 0.47468354430379744\n", + "---------------------------------------------\n", + "Average BMI in Train Cluster #1:, 30.515238095238097; Average BMI in Test Cluster #1:, 30.3324647887324\n", + "Average BMI in Train Cluster #2:, 30.319540229885064; Average BMI in Test Cluster #2:, 29.356129032258067\n", + "Average BMI in Train Cluster #3:, 30.96301033591731; Average BMI in Test Cluster #3:, 31.254715189873412\n", + "---------------------------------------------\n", + "Average # of Children in Train Cluster #1:, 0.8896103896103896; Average # of Children in Test Cluster #1:, 0.7981220657276995\n", + "Average # of Children in Train Cluster #2:, 1.9540229885057472; Average # of Children in Test Cluster #2:, 2.161290322580645\n", + "Average # of Children in Train Cluster #3:, 1.1705426356589148; Average # of Children in Test Cluster #3:, 1.2278481012658229\n", + "---------------------------------------------\n", + "Proportion of Smokers in Train Cluster #1:, 0.0; Proportion of Smokers in Test Cluster #1:, 0.0\n", + "Proportion of Smokers in Train Cluster #2:, 0.0; Proportion of Smokers in Test Cluster #2:, 0.0\n", + "Proportion of Smokers in Train Cluster #3:, 0.49870801033591733; Proportion of Smokers in Test Cluster #3:, 0.5126582278481012\n", + "---------------------------------------------\n", + "Average Medical Expenses in Train Cluster #1:, 8.741192353571316; Average Medical Expenses in Test Cluster #1:, 8.827239088076178\n", + "Average Medical Expenses in Train Cluster #2:, 8.999729423305789; Average Medical Expenses in Test Cluster #2:, 8.91868734032258\n", + "Average Medical Expenses in Train Cluster #3:, 9.540016441058643; Average Medical Expenses in Test Cluster #3:, 9.519980474079814\n", + "---------------------------------------------\n" + ] + } + ], + "source": [ + "print(\"---------------------------------------------\")\n", + "# calculate average charge for each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Proportion of Train Data in Cluster #{i+1}: {X_train[clusters==i+1].shape[0]/X_train.shape[0]};\",\n", + " f\"Proportion of Test Data in Cluster #{i+1}: {X_test[test_clust==i+1].shape[0]/X_test.shape[0]}\")\n", + "print(\"---------------------------------------------\")\n", + "# get average age in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Average Age in Train Cluster #{i+1}:, {X_train[clusters==i+1]['age'].mean()};\",\n", + " f\"Average Age in Test Cluster #{i+1}:, {X_test[test_clust==i+1]['age'].mean()}\")\n", + "print(\"---------------------------------------------\")\n", + "# get percentage women in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Proportion of Women in Train Cluster #{i+1}:, {1-X_train[clusters==i+1]['sex'].mean()};\",\n", + " f\"Proportion of Women in Test Cluster #{i+1}:, {1-X_test[test_clust==i+1]['sex'].mean()}\") \n", + "print(\"---------------------------------------------\")\n", + "# get percentage men in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Proportion of Men in Train Cluster #{i+1}:, {X_train[clusters==i+1]['sex'].mean()};\",\n", + " f\"Proportion of Men in Test Cluster #{i+1}:, {X_test[test_clust==i+1]['sex'].mean()}\")\n", + "print(\"---------------------------------------------\")\n", + "# get average bmi in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Average BMI in Train Cluster #{i+1}:, {X_train[clusters==i+1]['bmi'].mean()};\",\n", + " f\"Average BMI in Test Cluster #{i+1}:, {X_test[test_clust==i+1]['bmi'].mean()}\")\n", + "print(\"---------------------------------------------\")\n", + "# get average number of children in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Average # of Children in Train Cluster #{i+1}:, {X_train[clusters==i+1]['children'].mean()};\",\n", + " f\"Average # of Children in Test Cluster #{i+1}:, {X_test[test_clust==i+1]['children'].mean()}\")\n", + "print(\"---------------------------------------------\")\n", + "# get proportion of smokers in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Proportion of Smokers in Train Cluster #{i+1}:, {X_train[clusters==i+1]['smoker'].mean()};\",\n", + " f\"Proportion of Smokers in Test Cluster #{i+1}:, {X_test[test_clust==i+1]['smoker'].mean()}\")\n", + "print(\"---------------------------------------------\")\n", + "# get proportion of smokers in each cluster\n", + "for i in range(num_clusters):\n", + " print(f\"Average Medical Expenses in Train Cluster #{i+1}:, {y_train[clusters==i+1].mean()};\",\n", + " f\"Average Medical Expenses in Test Cluster #{i+1}:, {y_test[test_clust==i+1].mean()}\")\n", + "print(\"---------------------------------------------\")" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
RandomForestRegressor(random_state=0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "RandomForestRegressor(random_state=0)" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# fit RF+ on each training set, predict test\n", + "rf1 = RandomForestRegressor(n_estimators=100, random_state=0)\n", + "rf1.fit(cluster1_trainX, cluster1_trainy)\n", + "\n", + "rf2 = RandomForestRegressor(n_estimators=100, random_state=3)\n", + "rf2.fit(cluster2_trainX, cluster2_trainy)\n", + "\n", + "rf3 = RandomForestRegressor(n_estimators=100, random_state=0)\n", + "rf3.fit(cluster3_trainX, cluster3_trainy)\n", + "\n", + "# rf4 = RandomForestRegressor(n_estimators=100, random_state=0)\n", + "# rf4.fit(cluster4_trainX, cluster4_trainy)\n", + "\n", + "# rf5 = RandomForestRegressor(n_estimators=100, random_state=1)\n", + "# rf5.fit(cluster5_trainX, cluster5_trainy)\n", + "\n", + "# rf6 = RandomForestRegressor(n_estimators=100, random_state=0)\n", + "# rf6.fit(cluster6_trainX, cluster6_trainy)\n", + "\n", + "# rf7 = RandomForestRegressor(n_estimators=100, random_state=1)\n", + "# rf7.fit(cluster7_trainX, cluster7_trainy)" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---------------------------------------------\n", + "RF+ Cluster #1 Test Set R^2: 0.7954807176710599\n", + "RF+ Cluster #1 Test Set TSE: 33.97710269064372\n", + "Global RF+ Cluster #1 Test Set R^2: 0.7971226721797012\n", + "Global RF+ Cluster #1 Test Set TSE: 33.70432226467027\n", + "---------------------------------------------\n", + "RF+ Cluster #2 Test Set R^2: -0.6955215097355831\n", + "RF+ Cluster #2 Test Set TSE: 9.273467030510359\n", + "Global RF+ Cluster #2 Test Set R^2: -0.2848002996970307\n", + "Global RF+ Cluster #2 Test Set TSE: 7.027072880890977\n", + "---------------------------------------------\n", + "RF+ Cluster #3 Test Set R^2: 0.8716513090625141\n", + "RF+ Cluster #3 Test Set TSE: 15.623300295525235\n", + "Global RF+ Cluster #3 Test Set R^2: 0.9063857777044271\n", + "Global RF+ Cluster #3 Test Set TSE: 11.39523197449791\n", + "---------------------------------------------\n" + ] + } + ], + "source": [ + "# compute r^2 on the test set\n", + "print(\"---------------------------------------------\")\n", + "y_pred1 = rf1.predict(cluster1_testX)\n", + "r2 = r2_score(cluster1_testy, y_pred1)\n", + "tse1 = np.sum((cluster1_testy - y_pred1)**2)\n", + "print(f'RF+ Cluster #1 Test Set R^2: {r2}')\n", + "print(f'RF+ Cluster #1 Test Set TSE: {tse1}')\n", + "y_pred_g1 = rf.predict(cluster1_testX)\n", + "r2g = r2_score(cluster1_testy, y_pred_g1)\n", + "tse1g = np.sum((cluster1_testy - y_pred_g1)**2)\n", + "print(f'Global RF+ Cluster #1 Test Set R^2: {r2g}')\n", + "print(f'Global RF+ Cluster #1 Test Set TSE: {tse1g}')\n", + "print(\"---------------------------------------------\")\n", + "y_pred2 = rf2.predict(cluster2_testX)\n", + "r2 = r2_score(cluster2_testy, y_pred2)\n", + "tse2 = np.sum((cluster2_testy - y_pred2)**2)\n", + "print(f'RF+ Cluster #2 Test Set R^2: {r2}')\n", + "print(f'RF+ Cluster #2 Test Set TSE: {tse2}')\n", + "y_pred_g2 = rf.predict(cluster2_testX)\n", + "r2g = r2_score(cluster2_testy, y_pred_g2)\n", + "tse2g = np.sum((cluster2_testy - y_pred_g2)**2)\n", + "print(f'Global RF+ Cluster #2 Test Set R^2: {r2g}')\n", + "print(f'Global RF+ Cluster #2 Test Set TSE: {tse2g}')\n", + "print(\"---------------------------------------------\")\n", + "y_pred3 = rf3.predict(cluster3_testX)\n", + "r2 = r2_score(cluster3_testy, y_pred3)\n", + "tse3 = np.sum((cluster3_testy - y_pred3)**2)\n", + "print(f'RF+ Cluster #3 Test Set R^2: {r2}')\n", + "print(f'RF+ Cluster #3 Test Set TSE: {tse3}')\n", + "y_pred_g3 = rf.predict(cluster3_testX)\n", + "r2g = r2_score(cluster3_testy, y_pred_g3)\n", + "tse3g = np.sum((cluster3_testy - y_pred_g3)**2)\n", + "print(f'Global RF+ Cluster #3 Test Set R^2: {r2g}')\n", + "print(f'Global RF+ Cluster #3 Test Set TSE: {tse3g}')\n", + "print(\"---------------------------------------------\")\n", + "# y_pred4 = rf4.predict(cluster4_testX)\n", + "# r2 = r2_score(cluster4_testy, y_pred4)\n", + "# tse4 = np.sum((cluster4_testy - y_pred4)**2)\n", + "# print(f'RF+ Cluster #4 Test Set R^2: {r2}')\n", + "# print(f'RF+ Cluster #4 Test Set TSE: {tse4}')\n", + "# print(\"---------------------------------------------\")\n", + "# y_pred5 = rf5.predict(cluster5_testX)\n", + "# r2 = r2_score(cluster5_testy, y_pred5)\n", + "# tse5 = np.sum((cluster5_testy - y_pred5)**2)\n", + "# print(f'RF+ Cluster #5 Test Set R^2: {r2}')\n", + "# print(f'RF+ Cluster #5 Test Set TSE: {tse5}')\n", + "# print(\"---------------------------------------------\")\n", + "# y_pred6 = rf6.predict(cluster6_testX)\n", + "# r2 = r2_score(cluster6_testy, y_pred6)\n", + "# tse6 = np.sum((cluster6_testy - y_pred6)**2)\n", + "# print(f'RF+ Cluster #6 Test Set R^2: {r2}')\n", + "# print(f'RF+ Cluster #6 Test Set TSE: {tse6}')\n", + "# print(\"---------------------------------------------\")\n", + "# y_pred7 = rf7.predict(cluster7_testX)\n", + "# r2 = r2_score(cluster7_testy, y_pred7)\n", + "# tse7 = np.sum((cluster7_testy - y_pred7)**2)\n", + "# print(f'RF+ Cluster #7 Test Set R^2: {r2}')\n", + "# print(f'RF+ Cluster #7 Test Set TSE: {tse7}')\n", + "# print(\"---------------------------------------------\")" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---------------------------------------------\n", + "Total # of Observations Predicted by Global Model: 402\n", + "Total # of Observations Predicted by Cluster Models: 402\n", + "---------------------------------------------\n", + "Difference in TSE (Global - Sum of Clusters): -6.75\n", + "Percent Improvement Over Global Model: -12.94%\n", + "---------------------------------------------\n" + ] + } + ], + "source": [ + "print(\"---------------------------------------------\")\n", + "print(\"Total # of Observations Predicted by Global Model:\", X_test.shape[0])\n", + "print(\"Total # of Observations Predicted by Cluster Models:\",\n", + " cluster1_testX.shape[0] + cluster2_testX.shape[0] + \\\n", + " cluster3_testX.shape[0])\n", + "print(\"---------------------------------------------\")\n", + "print(\"Difference in TSE (Global - Sum of Clusters):\", round(tse - (tse1 + tse2 + tse3), 2))\n", + "print(f\"Percent Improvement Over Global Model: {round(100*(tse - (tse1 + tse2 + tse3))/tse, 2)}%\")\n", + "print(\"---------------------------------------------\")" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [], + "source": [ + "# order rows of mdi_test by cluster assignment\n", + "shap_test_clust = shap_test_values[np.argsort(test_clust)]" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [], + "source": [ + "# order test clust by cluster assignment\n", + "shap_test_clust_org = test_clust[np.argsort(test_clust)]" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [], + "source": [ + "# get indexes where mdi_test_clust changes clusters\n", + "cluster_changes = np.where(np.diff(shap_test_clust_org) != 0)[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# create new heatmap\n", + "plt.figure(figsize=(10, 10))\n", + "sns.heatmap(shap_test_clust, cmap='viridis')\n", + "plt.xlabel('Features')\n", + "plt.ylabel('Observations')\n", + "plt.title('TreeSHAP Heatmap of Test Data Ordered by Cluster Assignment')\n", + "plt.yticks([])\n", + "plt.xticks(np.arange(len(feature_names[:-1])) + 0.5, feature_names[:-1])\n", + "# put horizontal lines where cluster membership changes\n", + "for i in cluster_changes:\n", + " plt.axhline(i, color='red', linewidth=2, linestyle='--')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The most important feature for Train Cluster #1 is: age\n", + "The most important feature for Test Cluster #1 is: age\n", + "The most important feature for Train Cluster #2 is: smoker\n", + "The most important feature for Test Cluster #2 is: smoker\n", + "The most important feature for Train Cluster #3 is: smoker\n", + "The most important feature for Test Cluster #3 is: smoker\n" + ] + } + ], + "source": [ + "# get most important feature on average for each cluster\n", + "for i in range(num_clusters):\n", + " print(f'The most important feature for Train Cluster #{i+1} is:', X.columns[np.argmax(np.mean(shap_values[clusters==i+1], axis=0))])\n", + " print(f'The most important feature for Test Cluster #{i+1} is:', X.columns[np.argmax(np.mean(shap_test_values[test_clust==i+1], axis=0))])" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The feature ranking for Train Cluster #1 is: ['age', 'smoker', 'children', 'bmi', 'region', 'sex']\n", + "The feature ranking for Test Cluster #1 is: ['age', 'smoker', 'children', 'region', 'bmi', 'sex']\n", + "The feature ranking for Train Cluster #2 is: ['smoker', 'children', 'age', 'region', 'bmi', 'sex']\n", + "The feature ranking for Test Cluster #2 is: ['smoker', 'children', 'age', 'region', 'bmi', 'sex']\n", + "The feature ranking for Train Cluster #3 is: ['smoker', 'age', 'bmi', 'children', 'region', 'sex']\n", + "The feature ranking for Test Cluster #3 is: ['smoker', 'age', 'bmi', 'children', 'region', 'sex']\n" + ] + } + ], + "source": [ + "for i in range(num_clusters):\n", + " # negative is taken because argsort goes in the wrong order\n", + " print(f'The feature ranking for Train Cluster #{i+1} is:', list(X.columns[np.argsort(-np.mean(shap_values[clusters==i+1], axis=0))]))\n", + " print(f'The feature ranking for Test Cluster #{i+1} is:', list(X.columns[np.argsort(-np.mean(shap_test_values[test_clust==i+1], axis=0))]))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mdi", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.1.undefined" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/feature_importance/subgroup/metrics.py b/feature_importance/subgroup/legacy/metrics.py similarity index 100% rename from feature_importance/subgroup/metrics.py rename to feature_importance/subgroup/legacy/metrics.py diff --git a/feature_importance/subgroup/ranking.ipynb b/feature_importance/subgroup/legacy/ranking.ipynb similarity index 100% rename from feature_importance/subgroup/ranking.ipynb rename to feature_importance/subgroup/legacy/ranking.ipynb diff --git a/feature_importance/subgroup/legacy/sandbox.ipynb b/feature_importance/subgroup/legacy/sandbox.ipynb new file mode 100644 index 0000000..4156bcc --- /dev/null +++ b/feature_importance/subgroup/legacy/sandbox.ipynb @@ -0,0 +1,185 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# imodels imports\n", + "from imodels import get_clean_dataset\n", + "from imodels.tree.rf_plus.rf_plus.rf_plus_models import RandomForestPlusClassifier, RandomForestPlusRegressor\n", + "from imodels.tree.rf_plus.feature_importance.rfplus_explainer import AloRFPlusMDI, RFPlusMDI\n", + "\n", + "# sklearn imports\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n", + "from sklearn.linear_model import LogisticRegressionCV, LinearRegression\n", + "from sklearn.metrics import accuracy_score\n", + "\n", + "# other important libraries\n", + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# get abalone data\n", + "X, y, feature_names = get_clean_dataset(\"compas_two_year_clean\", data_source='imodels')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# split data into train, validation, and test sets\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,\n", + " random_state=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 26.1s\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 1.0min finished\n", + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 3.3s\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 8.8s finished\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RF+ Test Set Accuracy: 0.7046436285097192\n", + "RF+ Baseline Test Set Accuracy: 0.6868250539956804\n" + ] + } + ], + "source": [ + "# fit rf\n", + "rf = RandomForestClassifier(n_estimators=100, max_features='sqrt',\n", + " min_samples_leaf=5, random_state=1)\n", + "rf_baseline = RandomForestRegressor(n_estimators=100, max_features='sqrt',\n", + " min_samples_leaf=5, random_state=1)\n", + "rf.fit(X_train, y_train)\n", + "rf_baseline.fit(X_train, y_train)\n", + "\n", + "# fit rf+\n", + "rf_plus = RandomForestPlusClassifier(rf_model = rf,\n", + " prediction_model = LogisticRegressionCV())\n", + "rf_plus_baseline = RandomForestPlusRegressor(rf_model = rf_baseline,\n", + " include_raw=False, fit_on='inbag',\n", + " prediction_model = LinearRegression())\n", + "rf_plus.fit(X_train, y_train)\n", + "rf_plus_baseline.fit(X_train, y_train)\n", + "\n", + "# check performance on test set\n", + "yhat_rfplus = rf_plus.predict(X_test)\n", + "yhat_rfplus_baseline = rf_plus_baseline.predict(X_test)\n", + "\n", + "# evaluate accuracy on test set\n", + "accuracy_rf_plus = accuracy_score(y_test, yhat_rfplus)\n", + "accuracy_rf_plus_baseline = accuracy_score(y_test, yhat_rfplus_baseline > 0.5)\n", + "print(f'RF+ Test Set Accuracy: {accuracy_rf_plus}')\n", + "print(f'RF+ Baseline Test Set Accuracy: {accuracy_rf_plus_baseline}')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# create explainers\n", + "mdi_explainer = RFPlusMDI(rf_plus)\n", + "baseline_explainer = RFPlusMDI(rf_plus_baseline, evaluate_on='inbag')\n", + "\n", + "# get feature importances for train and test sets\n", + "mdi_train_values = mdi_explainer.explain_linear_partial(X_train, y_train, l2norm = True, sign = True, leaf_average=False)\n", + "mdi_test_values = mdi_explainer.explain_linear_partial(X_test, y=None, l2norm = True, sign = True, leaf_average=False)\n", + "baseline_train_values = baseline_explainer.explain_linear_partial(X=X_train, y=y_train)\n", + "baseline_test_values = baseline_explainer.explain_linear_partial(X=X_test, y=None)\n", + "\n", + "# get feature rankings for train and test sets\n", + "mdi_train_rankings = mdi_explainer.get_rankings(mdi_train_values)\n", + "mdi_test_rankings = mdi_explainer.get_rankings(mdi_test_values)\n", + "baseline_train_rankings = baseline_explainer.get_rankings(baseline_train_values)\n", + "baseline_test_rankings = baseline_explainer.get_rankings(baseline_test_values)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train Data Shape: (4320, 20); Test Data Shape: (1852, 20)\n", + "Number of unique rows of MDI+ training values: (1738, 20)\n", + "Number of unique rows of baseline MDI+ training values: (1737, 20)\n", + "Number of unique rows of MDI+ test values: (1738, 20)\n", + "Number of unique rows of baseline MDI+ test values: (1737, 20)\n", + "Number of unique rows of MDI+ training rankings: (4318, 20)\n", + "Number of unique rows of baseline MDI+ training rankings: (4313, 20)\n", + "Number of unique rows of MDI+ test rankings: (1671, 20)\n", + "Number of unique rows of baseline MDI+ test rankings: (1708, 20)\n" + ] + } + ], + "source": [ + "# get unique number of points for raw feature importance values\n", + "print(f'Train Data Shape: {X_train.shape}; Test Data Shape: {X_test.shape}')\n", + "\n", + "print(f'Number of unique rows of MDI+ training values: {np.unique(mdi_test_values, axis=0, return_counts=True)[0].shape}')\n", + "print(f'Number of unique rows of baseline MDI+ training values: {np.unique(baseline_test_values, axis=0, return_counts=True)[0].shape}')\n", + "\n", + "print(f'Number of unique rows of MDI+ test values: {np.unique(mdi_test_values, axis=0, return_counts=True)[0].shape}')\n", + "print(f'Number of unique rows of baseline MDI+ test values: {np.unique(baseline_test_values, axis=0, return_counts=True)[0].shape}')\n", + "\n", + "# get unique number of points for feature rankings\n", + "print(f'Number of unique rows of MDI+ training rankings: {np.unique(mdi_train_rankings, axis=0, return_counts=True)[0].shape}')\n", + "print(f'Number of unique rows of baseline MDI+ training rankings: {np.unique(baseline_train_rankings, axis=0, return_counts=True)[0].shape}')\n", + "\n", + "print(f'Number of unique rows of MDI+ test rankings: {np.unique(mdi_test_rankings, axis=0, return_counts=True)[0].shape}')\n", + "print(f'Number of unique rows of baseline MDI+ test rankings: {np.unique(baseline_test_rankings, axis=0, return_counts=True)[0].shape}')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mdi", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/feature_importance/subgroup/legacy/subgroup.ipynb b/feature_importance/subgroup/legacy/subgroup.ipynb new file mode 100644 index 0000000..d8ab26e --- /dev/null +++ b/feature_importance/subgroup/legacy/subgroup.ipynb @@ -0,0 +1,756 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# import required packages\n", + "from imodels import get_clean_dataset\n", + "from evaluate_subgroups import split_data\n", + "import numpy as np\n", + "import pandas as pd\n", + "import os\n", + "import warnings\n", + "import matplotlib.pyplot as plt\n", + "warnings.simplefilter(action='ignore', category=FutureWarning)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "X, y, feature_names = get_clean_dataset(\"diabetes\", data_source = \"imodels\")\n", + "X_train, X_valid, X_test, y_train, y_valid, y_test = split_data(X, y, seed = 0)\n", + "num_observations = X_test.shape[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " global_error_rf global_error_rf_plus total_local_error_rf \\\n", + "0 61 60 62 \n", + "1 56 54 65 \n", + "2 52 55 63 \n", + "3 58 55 63 \n", + "4 43 43 58 \n", + "\n", + " total_local_error_rf_plus total_local_int_error_rf_plus auroc_rf \\\n", + "0 65 65 0.817121 \n", + "1 56 56 0.804673 \n", + "2 49 49 0.831917 \n", + "3 53 53 0.808484 \n", + "4 49 49 0.839909 \n", + "\n", + " auprc_rf f1_rf auroc_rf_plus auprc_rf_plus ... \\\n", + "0 0.698455 0.616352 0.813472 0.712390 ... \n", + "1 0.648322 0.621622 0.828286 0.712327 ... \n", + "2 0.743165 0.679012 0.831917 0.748162 ... \n", + "3 0.604525 0.597222 0.823242 0.651716 ... \n", + "4 0.638347 0.661417 0.848542 0.654354 ... \n", + "\n", + " rf_plus_weighted_auroc rf_plus_weighted_auprc rf_plus_weighted_f1 \\\n", + "0 0.816112 0.676384 0.513959 \n", + "1 0.797601 0.685487 0.603430 \n", + "2 0.783505 0.703600 0.695710 \n", + "3 0.792401 0.653876 0.620250 \n", + "4 0.793245 0.610373 0.590312 \n", + "\n", + " rf_weighted_auroc rf_weighted_auprc rf_weighted_f1 seed datasource \\\n", + "0 0.793368 0.637686 0.616565 1 imodels \n", + "1 0.763039 0.624491 0.576842 2 imodels \n", + "2 0.782772 0.662042 0.661140 3 imodels \n", + "3 0.780718 0.615987 0.628436 4 imodels \n", + "4 0.771071 0.564776 0.619397 5 imodels \n", + "\n", + " dataname task \n", + "0 diabetes classification \n", + "1 diabetes classification \n", + "2 diabetes classification \n", + "3 diabetes classification \n", + "4 diabetes classification \n", + "\n", + "[5 rows x 21 columns]\n" + ] + } + ], + "source": [ + "# initialize empty dataframe\n", + "results = pd.read_csv(\"results/new_imodels_diabetes_1.csv\")\n", + "for seed in range(2, 6):\n", + " # read in results/imodels_compas_two_year_clean_{seed}.csv\n", + " # add data to results dataframe\n", + " results = pd.concat([results, pd.read_csv(f\"results/new_imodels_diabetes_{seed}.csv\")], ignore_index=True)\n", + "print(results)\n", + "# for col in range(4):\n", + "# # make the column a float\n", + "# for row in range(compas_results.shape[0]):\n", + "# compas_results.iloc[row, col] = float(compas_results.iloc[row, col])/float(num_observations)\n", + "# # take mean of columns 1-4\n", + "# names = compas_results.columns[0:4]\n", + "# compas_results = compas_results.iloc[:, 0:4].mean(axis = 1)\n", + "# compas_results = pd.DataFrame(np.array(compas_results).reshape(1,4), columns = names)\n", + "# print(1-compas_results)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# get dataframe with cols auroc_rf, auroc_rf_plus, rf_weighted_auroc, rf_plus_weighted_auroc\n", + "subset = results[['auroc_rf', 'auroc_rf_plus', 'rf_weighted_auroc', 'rf_plus_weighted_auroc']]\n", + "# calculate mean of each column\n", + "subset = subset.mean(axis = 0)\n", + "# make barplot of these values\n", + "\n", + "labels = ['Global RF', 'Global RF+', 'Local RF', 'Local RF+']\n", + "\n", + "\n", + "# Create the bar plot\n", + "plt.bar(labels, subset)\n", + "\n", + "# Add titles and labels\n", + "plt.title('Subgroup Detection Results: Diabetes')\n", + "plt.xlabel('')\n", + "plt.ylabel('AUROC')\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "X, y, feature_names = get_clean_dataset(\"compas_two_year_clean\", data_source = \"imodels\")\n", + "X_train, X_valid, X_test, y_train, y_valid, y_test = split_data(X, y, seed = 0)\n", + "num_observations = X_test.shape[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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global_error_rfglobal_error_rf_plustotal_local_error_rftotal_local_error_rf_plustotal_local_int_error_rf_plusauroc_rfauprc_rff1_rfauroc_rf_plusauprc_rf_plus...rf_plus_weighted_aurocrf_plus_weighted_auprcrf_plus_weighted_f1rf_weighted_aurocrf_weighted_auprcrf_weighted_f1seeddatasourcedatanametask
06536236766306300.6933040.6460500.6433640.7218050.691117...0.6541390.6313900.5807100.6440370.6177860.6007531imodelscompas_two_year_cleanclassification
16546216586156150.6913520.6662540.6232720.7156990.697154...0.6852420.6565180.5887710.6679770.6258870.6232562imodelscompas_two_year_cleanclassification
26976507036516510.6713100.6504450.6138500.7087210.701431...0.6686650.6578770.5942560.6518560.6163210.6137383imodelscompas_two_year_cleanclassification
36265976516036030.6999890.6670060.6506700.7265330.718634...0.6886330.6638110.6231100.6784940.6264410.6381784imodelscompas_two_year_cleanclassification
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4 rows × 21 columns

\n", + "
" + ], + "text/plain": [ + " global_error_rf global_error_rf_plus total_local_error_rf \\\n", + "0 653 623 676 \n", + "1 654 621 658 \n", + "2 697 650 703 \n", + "3 626 597 651 \n", + "\n", + " total_local_error_rf_plus total_local_int_error_rf_plus auroc_rf \\\n", + "0 630 630 0.693304 \n", + "1 615 615 0.691352 \n", + "2 651 651 0.671310 \n", + "3 603 603 0.699989 \n", + "\n", + " auprc_rf f1_rf auroc_rf_plus auprc_rf_plus ... \\\n", + "0 0.646050 0.643364 0.721805 0.691117 ... \n", + "1 0.666254 0.623272 0.715699 0.697154 ... \n", + "2 0.650445 0.613850 0.708721 0.701431 ... \n", + "3 0.667006 0.650670 0.726533 0.718634 ... \n", + "\n", + " rf_plus_weighted_auroc rf_plus_weighted_auprc rf_plus_weighted_f1 \\\n", + "0 0.654139 0.631390 0.580710 \n", + "1 0.685242 0.656518 0.588771 \n", + "2 0.668665 0.657877 0.594256 \n", + "3 0.688633 0.663811 0.623110 \n", + "\n", + " rf_weighted_auroc rf_weighted_auprc rf_weighted_f1 seed datasource \\\n", + "0 0.644037 0.617786 0.600753 1 imodels \n", + "1 0.667977 0.625887 0.623256 2 imodels \n", + "2 0.651856 0.616321 0.613738 3 imodels \n", + "3 0.678494 0.626441 0.638178 4 imodels \n", + "\n", + " dataname task \n", + "0 compas_two_year_clean classification \n", + "1 compas_two_year_clean classification \n", + "2 compas_two_year_clean classification \n", + "3 compas_two_year_clean classification \n", + "\n", + "[4 rows x 21 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# initialize empty dataframe\n", + "compas_results = pd.read_csv(\"results/new_imodels_compas_two_year_clean_1.csv\")\n", + "for seed in range(2, 5):\n", + " # read in results/imodels_compas_two_year_clean_{seed}.csv\n", + " # add data to results dataframe\n", + " compas_results = pd.concat([compas_results, pd.read_csv(f\"results/new_imodels_compas_two_year_clean_{seed}.csv\")], ignore_index=True)\n", + "# for col in range(4):\n", + "# # make the column a float\n", + "# for row in range(_results.shape[0]):\n", + "# enhancer_results.iloc[row, col] = float(enhancer_results.iloc[row, col])/float(num_observations)\n", + "compas_results" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# get dataframe with cols auroc_rf, auroc_rf_plus, rf_weighted_auroc, rf_plus_weighted_auroc\n", + "subset = compas_results[['auroc_rf', 'auroc_rf_plus', 'rf_weighted_auroc', 'rf_plus_weighted_auroc']]\n", + "# calculate mean of each column\n", + "subset = subset.mean(axis = 0)\n", + "# make barplot of these values\n", + "\n", + "labels = ['Global RF', 'Global RF+', 'Local RF', 'Local RF+']\n", + "\n", + "\n", + "# Create the bar plot\n", + "plt.bar(labels, subset)\n", + "\n", + "# Add titles and labels\n", + "plt.title('Subgroup Detection Results: COMPAS')\n", + "plt.xlabel('')\n", + "plt.ylabel('AUROC')\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "X, y, feature_names = get_clean_dataset(183, data_source = \"openml\")\n", + "X_train, X_valid, X_test, y_train, y_valid, y_test = split_data(X, y, seed = 0)\n", + "num_observations = X_test.shape[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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global_error_rfglobal_error_rf_plustotal_local_error_rftotal_local_error_rf_plustotal_local_int_error_rf_plusseeddatasourcedatanametask
06071.70495869.8436576708.43645734.2774125734.2774121openml183regression
16360.68666129.6733576964.55136193.8256866193.8256862openml183regression
25910.68415675.9128056625.22525681.6457095681.6457093openml183regression
35719.21925430.1492246393.74505510.2478535510.2478534openml183regression
46422.63766222.0920696953.11336336.0188776336.0188775openml183regression
\n", + "
" + ], + "text/plain": [ + " global_error_rf global_error_rf_plus total_local_error_rf \\\n", + "0 6071.7049 5869.843657 6708.4364 \n", + "1 6360.6866 6129.673357 6964.5513 \n", + "2 5910.6841 5675.912805 6625.2252 \n", + "3 5719.2192 5430.149224 6393.7450 \n", + "4 6422.6376 6222.092069 6953.1133 \n", + "\n", + " total_local_error_rf_plus total_local_int_error_rf_plus seed datasource \\\n", + "0 5734.277412 5734.277412 1 openml \n", + "1 6193.825686 6193.825686 2 openml \n", + "2 5681.645709 5681.645709 3 openml \n", + "3 5510.247853 5510.247853 4 openml \n", + "4 6336.018877 6336.018877 5 openml \n", + "\n", + " dataname task \n", + "0 183 regression \n", + "1 183 regression \n", + "2 183 regression \n", + "3 183 regression \n", + "4 183 regression " + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# initialize empty dataframe\n", + "abalone_results = pd.read_csv(\"results/new_openml_183_1.csv\")\n", + "for seed in range(2, 6):\n", + " # add data to results dataframe\n", + " abalone_results = pd.concat([abalone_results, pd.read_csv(f\"results/new_openml_183_{seed}.csv\")], ignore_index=True)\n", + "# for col in range(4):\n", + "# # make the column a float\n", + "# for row in range(fico_results.shape[0]):\n", + " # fico_results.iloc[row, col] = float(fico_results.iloc[row, col])/float(num_observations)\n", + "abalone_results" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# get dataframe with cols auroc_rf, auroc_rf_plus, rf_weighted_auroc, rf_plus_weighted_auroc\n", + "subset = compas_results[['global_error_rf', 'global_error_rf_plus', 'total_local_error_rf', 'total_local_error_rf_plus']]\n", + "# calculate mean of each column\n", + "subset = subset.mean(axis = 0)/num_observations\n", + "# make barplot of these values\n", + "\n", + "labels = ['Global RF', 'Global RF+', 'Local RF', 'Local RF+']\n", + "\n", + "\n", + "# Create the bar plot\n", + "plt.bar(labels, subset)\n", + "\n", + "# Add titles and labels\n", + "plt.title('Subgroup Detection Results: Abalone')\n", + "plt.xlabel('')\n", + "plt.ylabel('MSE')\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "X, y, feature_names = get_clean_dataset(8, data_source = \"openml\")\n", + "X_train, X_valid, X_test, y_train, y_valid, y_test = split_data(X, y, seed = 0)\n", + "num_observations = X_test.shape[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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411.06431911.23808513.63627313.9867035openml8regression
\n", + "
" + ], + "text/plain": [ + " global_error_rf global_error_rf_plus total_local_error_rf \\\n", + "0 12.549004 12.158141 14.291584 \n", + "1 10.162031 10.143755 12.740303 \n", + "2 11.035154 11.573886 14.117602 \n", + "3 10.258891 10.793834 12.711956 \n", + "4 11.064319 11.238085 13.636273 \n", + "\n", + " total_local_error_rf_plus seed datasource dataname task \n", + "0 15.384509 1 openml 8 regression \n", + "1 16.358263 2 openml 8 regression \n", + "2 16.029488 3 openml 8 regression \n", + "3 20.621230 4 openml 8 regression \n", + "4 13.986703 5 openml 8 regression " + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "openml_results = pd.read_csv(\"results/openml_8_1.csv\")\n", + "for seed in range(2, 6):\n", + " # add data to results dataframe\n", + " openml_results = pd.concat([openml_results, pd.read_csv(f\"results/openml_8_{seed}.csv\")], ignore_index=True)\n", + "for col in range(4):\n", + " # make the column a float\n", + " for row in range(openml_results.shape[0]):\n", + " openml_results.iloc[row, col] = float(openml_results.iloc[row, col])/float(num_observations)\n", + "openml_results" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mdi", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/feature_importance/subgroup/legacy/subgroup.sh b/feature_importance/subgroup/legacy/subgroup.sh new file mode 100644 index 0000000..78fd90d --- /dev/null +++ b/feature_importance/subgroup/legacy/subgroup.sh @@ -0,0 +1,13 @@ +#!/bin/bash +#SBATCH --mail-user=zachrewolinski@berkeley.edu +#SBATCH --mail-type=ALL +#SBATCH --cpus-per-task=16 + +datasource="imodels" +dataname="compas_two_year_clean" + +source activate mdi +command="evaluate_subgroups.py --seed ${1} --datasource $datasource --dataname $dataname" + +# Execute the command +python $command \ No newline at end of file diff --git a/feature_importance/subgroup/legacy/subgroup_detection.py b/feature_importance/subgroup/legacy/subgroup_detection.py new file mode 100644 index 0000000..a34211c --- /dev/null +++ b/feature_importance/subgroup/legacy/subgroup_detection.py @@ -0,0 +1,426 @@ +import numpy as np +import pandas as pd +import rbo +from sklearn.cluster import AgglomerativeClustering +from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor +from scipy.cluster.hierarchy import linkage, dendrogram, fcluster +from scipy.spatial.distance import pdist, squareform +import matplotlib.pyplot as plt +import seaborn as sns + +def weighted_metric(metrics, sample_sizes): + """ + Calculate the weighted average of a set of metrics. + + Args: + sample_sizes (np.ndarray): the number of samples in each subgroup + metrics (np.ndarray): the metrics of each subgroup + + Returns: + float: the weighted average of the metrics + """ + + print("Sample sizes: ", sample_sizes) + print("Metrics: ", metrics) + print("Sample sizes type: ", type(sample_sizes)) + print("Metrics type: ", type(metrics)) + + # calculate the total number of samples + total_samples = np.sum(sample_sizes) + + # calculate the weighted average + weighted_metric = np.sum(sample_sizes * metrics) / total_samples + + return weighted_metric + +def compute_rbo_matrix(rankings, form, p=0.9, k=None, ext=False): + """ + Compute the distance matrix based on Rank-based Overlap (RBO). + Inputs: + * rankings: numpy array of shape (n, p) where the (i,j)th entry denotes that + the jth feature is ranked (j-1) in terms of importance for the ith instance. + * p: float between 0 and 1 + * k: int (optional) evaluation depth for extrapolation + * ext: bool (optional) whether to use extrapolation + Outputs: + * numpy array of shape (n, n) where the (i,j)th entry is the RBO between the + ith and jth rankings. + """ + # ensure form is either "distance" or "similarity" + if form not in ['distance', 'similarity']: + raise ValueError('form must be either "distance" or "similarity"') + n = rankings.shape[0] + rbo_matrix = np.zeros((n, n)) + for i in range(n): + for j in range(i, n): + rbo_matrix[i, j] = rbo.RankingSimilarity(rankings[i,:], + rankings[j,:]).rbo(p=p,k=k, + ext=ext) + rbo_matrix[j, i] = rbo_matrix[i, j] + + if form == "distance": + # since rbo is a similarity metric, 1 means the rankings are identical + return rbo_matrix.max()-rbo_matrix + else: + return rbo_matrix + +def assign_training_clusters(rbo_distance_matrix, num_clusters, + linkage_method='ward'): + + # convert to condensed distance matrix for scipy compatibility + condensed_distance_matrix = squareform(rbo_distance_matrix) + + # perform hierarchical clustering + linkage_matrix = linkage(condensed_distance_matrix, method=linkage_method) + + # Determine cluster memberships using fcluster + clusters = fcluster(linkage_matrix, num_clusters, criterion='maxclust') + + return clusters + +def plot_training_clusters(lfi, rbo_distance_matrix, + clusters, linkage_method='ward'): + + # convert to condensed distance matrix for scipy compatibility + condensed_distance_matrix = squareform(rbo_distance_matrix) + + # perform hierarchical clustering + linkage_matrix = linkage(condensed_distance_matrix, method=linkage_method) + clustergrid = sns.clustermap(lfi, row_linkage=linkage_matrix, + col_cluster=False, cmap='viridis', + cbar_pos = (1, 0.2, 0.05, 0.5)) + + # Get the reordered row indices + reordered_indices = clustergrid.dendrogram_row.reordered_ind + + # determine number of clusters + num_clusters = np.unique(clusters).shape[0] + + # Reorder clusters to match the heatmap + ordered_clusters = clusters[reordered_indices] + + # Create a new DataFrame for annotations + annotations = pd.DataFrame(data=np.zeros_like(lfi, dtype=object), + index=lfi.index, columns=lfi.columns) + + # Add cluster numbers as annotations + for i, cluster in enumerate(ordered_clusters): + annotations.iloc[i, :] = cluster + + # Find the boundaries where clusters change + boundaries = np.where(np.diff(ordered_clusters))[0] + 1 + + # Plot the horizontal dashed red lines + for boundary in boundaries: + clustergrid.ax_heatmap.hlines(boundary, + *clustergrid.ax_heatmap.get_xlim(), + colors='red', linestyles='dashed') + + ax = clustergrid.ax_heatmap + total_obs = 0 + for cluster in range(1, num_clusters + 1): + num_obs = np.sum(clusters == cluster) + x_position = lfi.shape[1]//2 # for alignment + y_position = total_obs + num_obs//2 # for alignment + ax.text(x_position, y_position, "Cluster #" + str(cluster), color='red', + ha='center', va='center', fontsize=10, fontweight='bold') + total_obs += num_obs + + + plt.suptitle('Heatmap with Hierarchical Clustering', fontsize=24) + return + +def find_geometric_median(distance_matrix): + + # calculate the sum of distances for each point + distance_sums = np.sum(distance_matrix, axis=1) + + # find the index of the point with the minimum sum of distances + geometric_median_index = np.argmin(distance_sums) + + return geometric_median_index + +def assign_testing_centroid_approx(rbo_distance_matrix: np.ndarray, + lfi_train_ranking: np.ndarray, + lfi_test_ranking: np.ndarray, + clusters: np.ndarray) -> np.ndarray: + """ + Assigns testing points to clusters based on similarity to median ranking. + + Args: + rbo_distance_matrix (np.ndarray): distance matrix based on the rbo + lfi_train_ranking (np.ndarray): the local feature importance rankings of + the training points + lfi_test_ranking (np.ndarray): the local feature importance rankings of + the testing points + clusters (np.ndarray): the cluster labels for the training points + + Returns: + np.ndarray: cluster lables for the testing points + """ + + # initialize array to store cluster assignments for testing points + test_clust = np.zeros(lfi_test_ranking.shape[0]) + + # iterate over testing points and + # assign to cluster with most similar median ranking + for i in range(lfi_test_ranking.shape[0]): + king_of_hill = float('-inf') # start at negative infinity + # loop through clusters, calculate median ranking, and compare to point + for j in range(np.unique(clusters).shape[0]): + # get distance matrix for just this cluster + rbo_distance_clust = \ + rbo_distance_matrix[clusters == j+1, :][:, clusters == j+1] + median_index = find_geometric_median(rbo_distance_clust) + median_ranking = lfi_train_ranking[clusters==j+1,:][median_index,:] + similarity = rbo.RankingSimilarity(median_ranking, + lfi_test_ranking[i, :]).rbo(p=0.9, k=None) + if similarity > king_of_hill: + king_of_hill = similarity + test_clust[i] = j+1 + return test_clust + +def assign_testing_centroid_exact(rbo_distance_matrix: np.ndarray, + lfi_train_ranking: np.ndarray, + lfi_test_ranking: np.ndarray, + clusters: np.ndarray) -> np.ndarray: + """ + Assigns testing points to clusters based on similarity to average ranking. + + Args: + rbo_distance_matrix (np.ndarray): distance matrix based on the rbo + lfi_train_ranking (np.ndarray): the local feature importance rankings of + the training points + lfi_test_ranking (np.ndarray): the local feature importance rankings of + the testing points + clusters (np.ndarray): the cluster labels for the training points + + Returns: + np.ndarray: cluster lables for the testing points + """ + + # initialize array to store cluster assignments for testing points + test_clust = np.zeros(lfi_test_ranking.shape[0]) + + # iterate over testing points and calculate rbo similarity to each cluster + # assign to cluster with most similar points (on average) + for i in range(lfi_test_ranking.shape[0]): + king_of_hill = float('-inf') # start at negative infinity + # loop through clusters + for j in range(np.unique(clusters).shape[0]): + # get distance matrix for just this cluster + curr_clust = lfi_train_ranking[clusters == j+1, :] + similarity = np.zeros(curr_clust.shape[0]) + # compute similarity to each point in cluster + for k in range(curr_clust.shape[0]): + similarity[k] = rbo.RankingSimilarity(curr_clust[k, :], + lfi_test_ranking[i, :]).rbo(p=0.9, k=None) + # assign to cluster with most similar rankings on average + if similarity.mean() > king_of_hill: + king_of_hill = similarity.mean() + test_clust[i] = j+1 + + return test_clust + +def within_cluster_variance(rbo_distance_matrix: np.ndarray): + """ + Calculates the variance of a set of points given a distance matrix. + + Args: + rbo_distance_matrix (numpy.ndarray): a square matrix representing the + pairwise distances between points. + + Returns: + float: The variance of the set of points. + """ + n = rbo_distance_matrix.shape[0] + + # calculate the mean distance + mean_distance = np.mean(rbo_distance_matrix) + + # calculate the squared differences + squared_diffs = (rbo_distance_matrix - mean_distance)**2 + + # sum squared differences, divide by the total number of pairwise distances + variance = np.sum(squared_diffs) / (n * (n - 1)) + + return variance + +def rbo_distance_offset(num_features): + return rbo.RankingSimilarity(np.arange(1, num_features+1), + np.arange(1, num_features+1)).rbo(p=0.9,k=None) + +def assign_testing_variance_exact(rbo_distance_matrix: np.ndarray, + lfi_train_ranking: np.ndarray, + lfi_test_ranking: np.ndarray, + clusters: np.ndarray) -> np.ndarray: + """ + Assigns testing points to clusters based on smallest variance increase. + + Args: + rbo_distance_matrix (np.ndarray): distance matrix based on the rbo + lfi_train_ranking (np.ndarray): the local feature importance rankings of + the training points + lfi_test_ranking (np.ndarray): the local feature importance rankings of + the testing points + clusters (np.ndarray): the cluster labels for the training points + + Returns: + np.ndarray: cluster lables for the testing points + """ + + # initialize array to store cluster assignments for testing points + test_clust = np.zeros(lfi_test_ranking.shape[0]) + + # iterate over testing points and + # assign to cluster with smallest variance increase + for i in range(lfi_test_ranking.shape[0]): + king_of_hill = float('inf') # start at negative infinity + # loop through clusters, calculate median ranking, and compare to point + for j in range(np.unique(clusters).shape[0]): + # get distance matrix for just this cluster + rbo_distance_clust = \ + rbo_distance_matrix[clusters == j+1, :][:, clusters == j+1] + current_variance = within_cluster_variance(rbo_distance_clust) + distance_to_point = np.zeros(rbo_distance_clust.shape[0]) + for k in range(rbo_distance_clust.shape[0]): + distance_to_point[k] = \ + rbo_distance_offset(lfi_test_ranking.shape[1]) - \ + rbo.RankingSimilarity( + lfi_train_ranking[clusters == j+1, :][k, :], + lfi_test_ranking[i,:]).rbo(p=0.9, k=None) + # add distance_to_point as new row and column, diagonal elem is zero + extended_rbo_distance_clust = np.zeros((rbo_distance_clust.shape[0]+1, + rbo_distance_clust.shape[1]+1)) + extended_rbo_distance_clust[:-1, :-1] = rbo_distance_clust + # add distance_to_point as last row and column + extended_rbo_distance_clust[-1, :-1] = distance_to_point + extended_rbo_distance_clust[:-1, -1] = distance_to_point + + new_variance = within_cluster_variance(extended_rbo_distance_clust) + variance_increase = new_variance - current_variance + if variance_increase < king_of_hill: + king_of_hill = variance_increase + test_clust[i] = j+1 + return test_clust + +def assign_testing_clusters(method: str, median_approx: bool, + rbo_distance_matrix: np.ndarray, + lfi_train_ranking: np.ndarray, + lfi_test_ranking: np.ndarray, + clusters: np.ndarray) -> np.ndarray: + """ + Assigns testing points to clusters based on the method specified. + + Args: + method (str): the method to use for assigning testing points to clusters + median_approx (bool): whether to use the median approximation + rbo_distance_matrix (np.ndarray): distance matrix based on the rbo + lfi_train_ranking (np.ndarray): the local feature importance rankings of + the training points + lfi_test_ranking (np.ndarray): the local feature importance rankings of + the testing points + clusters (np.ndarray): the cluster labels for the training points + + Raises: + ValueError: if the method is not centroid or variance + + Returns: + np.ndarray: cluster lables for the testing points + """ + + # ensure the method is either centroid or variance + if method not in ['centroid', 'variance']: + raise ValueError('method must be either centroid or variance') + + # compute the testing point cluster assignments as specified by arguments + if method == "centroid": + if median_approx: + return assign_testing_centroid_approx(rbo_distance_matrix, + lfi_train_ranking, + lfi_test_ranking, clusters) + else: + return assign_testing_centroid_exact(rbo_distance_matrix, + lfi_train_ranking, + lfi_test_ranking, clusters) + else: + if median_approx: + return assign_testing_variance_exact(rbo_distance_matrix, + lfi_train_ranking, + lfi_test_ranking, clusters) + else: + return assign_testing_variance_exact(rbo_distance_matrix, + lfi_train_ranking, + lfi_test_ranking, clusters) + +def match_subgroups(cluster1, cluster2): + """ + Match subgroups between two clusterings. + + Args: + cluster1 (np.ndarray): cluster assignments for the first clustering + cluster2 (np.ndarray): cluster assignments for the second clustering + + Returns: + np.ndarray: an array of shape (num_clusters1,) where the ith entry is + the cluster number in the second clustering that best matches the ith + cluster in the first clustering + """ + + # create storage dictionaries + dict1to2 = {} + dict2to1 = {} + + # find the number of clusters in each clustering + num_clusters1 = np.unique(cluster1).shape[0] + num_clusters2 = np.unique(cluster2).shape[0] + + # check that each cluster has at least one point + if num_clusters1 != num_clusters2: + print("Warning: Number of clusters in clusterings do not match. Returning None.") + return None + + for clust1 in range(1, num_clusters1 + 1): + # get the points in cluster 1 + points1 = np.where(cluster1 == clust1)[0] + # for each cluster in the second clustering, find the percentage of its points in points1 + best_percentage = 0 + best_match = None + for clust2 in range(1, num_clusters2 + 1): + # get the points in cluster 2 + points2 = np.where(cluster2 == clust2)[0] + # calculate the percentage of points in points2 that are in points1 + percentage = len(np.intersect1d(points1, points2)) / len(points2) + if percentage > best_percentage: + best_percentage = percentage + best_match = clust2 + dict1to2[clust1] = best_match + for clust2 in range(1, num_clusters2 + 1): + # get the points in cluster 2 + points2 = np.where(cluster2 == clust2)[0] + # for each cluster in the first clustering, find the percentage of its points in points2 + best_percentage = 0 + best_match = None + for clust1 in range(1, num_clusters1 + 1): + # get the points in cluster 1 + points1 = np.where(cluster1 == clust1)[0] + # calculate the percentage of points in points1 that are in points2 + percentage = len(np.intersect1d(points1, points2)) / len(points1) + if percentage > best_percentage: + best_percentage = percentage + best_match = clust1 + dict2to1[clust2] = best_match + print("dict1to2") + print(dict1to2) + print("dict2to1") + print(dict2to1) + # check that the dictionaries agree with each other + for clust1 in range(1, num_clusters1 + 1): + if dict2to1[dict1to2[clust1]] != clust1: + print("Warning: Dictionaries do not agree with each other. Returning None.") + return None + + converted_membership = [dict2to1[cls] for cls in cluster2] + return converted_membership + + \ No newline at end of file diff --git a/feature_importance/subgroup/legacy/subgroup_experiment.py b/feature_importance/subgroup/legacy/subgroup_experiment.py new file mode 100644 index 0000000..c19a025 --- /dev/null +++ b/feature_importance/subgroup/legacy/subgroup_experiment.py @@ -0,0 +1,135 @@ +# import required packages +from imodels import get_clean_dataset +import numpy as np +import pandas as pd +from sklearn.model_selection import train_test_split +from sklearn.metrics import roc_auc_score, average_precision_score, f1_score +from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier +from imodels.tree.rf_plus.rf_plus.rf_plus_models import RandomForestPlusRegressor, RandomForestPlusClassifier +from imodels.tree.rf_plus.feature_importance.rfplus_explainer import AloRFPlusMDI, RFPlusMDI +import shap +from subgroup_detection import * +import warnings +import matplotlib.pyplot as plt +from sklearn.linear_model import RidgeCV, LogisticRegression + +global_task = None + +def split_data(X, y, seed = 1): + # split data into train, validation, and test sets + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, + random_state=seed) + X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, + test_size=0.25, + random_state=seed) + return X_train, X_valid, X_test, y_train, y_valid, y_test + +def fit_models(X_train, y_train, task): + # fit models + if task == 'classification': + global_task = 'classification' + rf = RandomForestClassifier(n_estimators=100) + rf.fit(X_train, y_train) + rf_plus = RandomForestPlusClassifier(rf_model=rf, + prediction_model=LogisticRegression()) + rf_plus.fit(X_train, y_train) + elif task == 'regression': + global_task = 'regression' + rf = RandomForestRegressor(n_estimators=100) + rf.fit(X_train, y_train) + rf_plus = RandomForestPlusRegressor(rf_model=rf, + prediction_model=RidgeCV()) + rf_plus.fit(X_train, y_train) + return rf, rf_plus + +def get_shap(X, shap_explainer): + if global_task == 'classification': + shap_values = shap_explainer.shap_values(X, check_additivity=False)[:,:,1] + else: + shap_values = shap_explainer.shap_values(X, check_additivity=False) + shap_rankings = np.argsort(-np.abs(shap_values), axis = 1) + return shap_values, shap_rankings + +def get_lmdi(X, y, lmdi_explainer): + # get feature importances + lmdi = np.abs(lmdi_explainer.explain_linear_partial(np.asarray(X), y, + l2norm=True)) + mdi_rankings = lmdi_explainer.get_rankings(lmdi) + return lmdi, mdi_rankings + +def get_num_clusters(X_train, y_train, X_valid, y_valid, shap_explainer, + shap_rbo_train, shap_train_rankings): + if global_task == 'classification': + shap_valid_values = np.abs(shap_explainer.shap_values(X_valid, + check_additivity=False))[:,:,1] + else: + shap_valid_values = np.abs(shap_explainer.shap_values(X_valid, + check_additivity=False)) + shap_valid_rankings = np.argsort(-shap_valid_values, axis = 1) + + + lowest_error = np.inf + opt_num_clusters = -1 + error_lst = [] + time_since_king = 0 + opt_clusters = None + for num_clusters in np.arange(2, 21): + shap_train_clusters = assign_training_clusters(shap_rbo_train, num_clusters) + valid_clusters = assign_testing_clusters(method="centroid", + median_approx=True, + rbo_distance_matrix=shap_rbo_train, + lfi_train_ranking=shap_train_rankings, + lfi_test_ranking=shap_valid_rankings, + clusters = shap_train_clusters) + total_error = 0 + for cluster in np.arange(1, num_clusters + 1): + if global_task == 'classification': + local_rf = RandomForestClassifier(n_estimators=100, random_state=0) + else: + local_rf = RandomForestRegressor(n_estimators=100, random_state=0) + local_rf.fit(X_train[shap_train_clusters == cluster], y_train[shap_train_clusters == cluster]) + local_preds = local_rf.predict(X_valid[valid_clusters == cluster]) + if global_task == 'classification': + local_error = np.sum(local_preds != y_valid[valid_clusters == cluster]) + else: + local_error = np.sum((local_preds - y_valid[valid_clusters == cluster])**2) + total_error += local_error + if total_error < lowest_error: + lowest_error = total_error + opt_num_clusters = num_clusters + time_since_king = 0 + opt_clusters = shap_train_clusters + else: + time_since_king += 1 + if time_since_king > 2: + break + return opt_num_clusters, opt_clusters + +def run_experiment(X, y, task, seed = 1): + # split data + X_train, X_valid, X_test, y_train, y_valid, y_test = split_data(X, y, seed) + # fit models + rf, rf_plus = fit_models(X_train, y_train, task) + # get shap values + shap_explainer = shap.TreeExplainer(rf) + shap_values, shap_rankings = get_shap(X_train, shap_explainer) + # get lmdi values + lmdi_explainer = RFPlusMDI(rf_plus) + lmdi_train, lmdi_train_rankings = get_lmdi(X_train, y_train, lmdi_explainer) + # get shap rbo + shap_rbo_train = compute_rbo_matrix(shap_rankings) + # get optimal number of clusters + opt_num_clusters, opt_clusters = get_num_clusters(X_train, y_train, X_valid, + y_valid, shap_explainer, + shap_rbo_train, shap_rankings) + lmdi_rbo_train = compute_rbo_matrix(lmdi_train_rankings) + lmdi_train_clusters = assign_training_clusters(lmdi_rbo_train, opt_num_clusters) + lmdi_test, lmdi_test_rankings = get_lmdi(X_test, y_test, lmdi_explainer) + lmdi_test_clusters = assign_testing_clusters(method="centroid", + median_approx=False, + rbo_distance_matrix=lmdi_rbo_train, + lfi_train_ranking=lmdi_train_rankings, + lfi_test_ranking=lmdi_test_rankings, + clusters=lmdi_train_clusters) + + return opt_num_clusters, opt_clusters \ No newline at end of file diff --git a/feature_importance/subgroup/legacy/subgroup_master.sh b/feature_importance/subgroup/legacy/subgroup_master.sh new file mode 100644 index 0000000..e5b9a89 --- /dev/null +++ b/feature_importance/subgroup/legacy/subgroup_master.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +slurm_script="subgroup.sh" + +for rep in {1..5} +do + sbatch $slurm_script $rep # Submit SLURM job using the specified script +done \ No newline at end of file diff --git a/feature_importance/subgroup/subgroups_analysis.ipynb b/feature_importance/subgroup/legacy/subgroups_analysis.ipynb similarity index 100% rename from feature_importance/subgroup/subgroups_analysis.ipynb rename to feature_importance/subgroup/legacy/subgroups_analysis.ipynb diff --git a/feature_importance/subgroup/legacy/test.py b/feature_importance/subgroup/legacy/test.py new file mode 100644 index 0000000..8d1b017 --- /dev/null +++ b/feature_importance/subgroup/legacy/test.py @@ -0,0 +1,11 @@ +# write function that just stalls for one minute +import time + +def stall(): + time.sleep(60) + return + +if __name__ == '__main__': + print("test started") + stall() + print("test complete") \ No newline at end of file diff --git a/feature_importance/subgroup/legacy/test.sh b/feature_importance/subgroup/legacy/test.sh new file mode 100644 index 0000000..b38c88d --- /dev/null +++ b/feature_importance/subgroup/legacy/test.sh @@ -0,0 +1,9 @@ +#!/bin/bash +#SBATCH --mail-user=zachrewolinski@berkeley.edu +#SBATCH --mail-type=ALL + +source activate mdi +command="test.py --nreps 1" + +# Execute the command +python $command \ No newline at end of file diff --git a/feature_importance/subgroup/legacy/treeshap_subgroup_figs.ipynb b/feature_importance/subgroup/legacy/treeshap_subgroup_figs.ipynb new file mode 100644 index 0000000..2a78057 --- /dev/null +++ b/feature_importance/subgroup/legacy/treeshap_subgroup_figs.ipynb @@ -0,0 +1,96 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from imodels import get_clean_dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " age workclass fnlwgt ... hours-per-week native-country income\n", + "0 39 State-gov 77516 ... 40 United-States <=50K\n", + "1 50 Self-emp-not-inc 83311 ... 13 United-States <=50K\n", + "2 38 Private 215646 ... 40 United-States <=50K\n", + "3 53 Private 234721 ... 40 United-States <=50K\n", + "4 28 Private 338409 ... 40 Cuba <=50K\n", + "\n", + "[5 rows x 15 columns]\n" + ] + } + ], + "source": [ + "# Load the dataset from a URL\n", + "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\"\n", + "column_names = [\n", + " 'age', 'workclass', 'fnlwgt', 'education', 'education-num',\n", + " 'marital-status', 'occupation', 'relationship', 'race', 'sex',\n", + " 'capital-gain', 'capital-loss', 'hours-per-week', 'native-country', 'income'\n", + "]\n", + "\n", + "# Read the dataset\n", + "data = pd.read_csv(url, header=None, names=column_names, na_values=' ?', skipinitialspace=True)\n", + "\n", + "# Display the first few rows\n", + "print(data.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['age', 'workclass', 'fnlwgt', 'education', 'education-num',\n", + " 'marital-status', 'occupation', 'relationship', 'race', 'sex',\n", + " 'capital-gain', 'capital-loss', 'hours-per-week', 'native-country',\n", + " 'income'],\n", + " dtype='object')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.columns" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mdi", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/feature_importance/subgroup/legacy/validation.ipynb b/feature_importance/subgroup/legacy/validation.ipynb new file mode 100644 index 0000000..5a9a8ba --- /dev/null +++ b/feature_importance/subgroup/legacy/validation.ipynb @@ -0,0 +1,1056 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# import required packages\n", + "from imodels import get_clean_dataset\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import roc_auc_score, average_precision_score, f1_score\n", + "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n", + "from imodels.tree.rf_plus.rf_plus.rf_plus_models import RandomForestPlusRegressor, RandomForestPlusClassifier\n", + "from imodels.tree.rf_plus.feature_importance.rfplus_explainer import AloRFPlusMDI, RFPlusMDI\n", + "import shap\n", + "from subgroup_detection import *\n", + "import warnings\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.linear_model import RidgeCV\n", + "warnings.filterwarnings('ignore', category=DeprecationWarning)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# get pre-cleaned compas dataset from imodels\n", + "X, y, feature_names = get_clean_dataset(183, data_source='openml')\n", + "X = pd.DataFrame(X, columns=feature_names)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "y = y.astype(float)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# split data into train, validation, and test sets\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,\n", + " random_state=2)\n", + "X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train,\n", + " test_size=0.25,\n", + " random_state=2)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([15., 7., 9., ..., 9., 10., 12.])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
RandomForestRegressor(random_state=0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "RandomForestRegressor(random_state=0)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# fit RF model\n", + "rf = RandomForestRegressor(n_estimators=100, random_state=0)\n", + "rf.fit(X_train, y_train)\n", + "\n", + "# # check performance on test set\n", + "# y_pred = rf.predict(X_test)\n", + "\n", + "# # compute accuracy on the test set\n", + "# accuracy = np.mean(y_pred == y_test)\n", + "\n", + "# print(f'RF Test Set Accuracy: {accuracy}')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# get feature importances\n", + "explainer = shap.TreeExplainer(rf)\n", + "shap_values = np.abs(explainer.shap_values(X_train, check_additivity=False))\n", + "shap_rankings = np.argsort(-shap_values, axis = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# get rbo distance matrix\n", + "shap_rbo_train = compute_rbo_matrix(shap_rankings, form = 'distance')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Now Calculating for 2 Clusters\n", + "Number of Misclassified w/ 2 Clusters: 78\n", + "Accuracy w/ 2 Clusters: 0.00%\n", + "Now Calculating for 3 Clusters\n", + "Number of Misclassified w/ 3 Clusters: 78\n", + "Accuracy w/ 3 Clusters: 0.00%\n", + "Now Calculating for 4 Clusters\n", + "Number of Misclassified w/ 4 Clusters: 78\n", + "Accuracy w/ 4 Clusters: 0.00%\n", + "Now Calculating for 5 Clusters\n", + "Number of Misclassified w/ 5 Clusters: 78\n", + "Accuracy w/ 5 Clusters: 0.00%\n", + "Optimal Number of Clusters: 2\n", + "Lowest Number of Misclassified: 78\n", + "Accuracy: 0.0\n" + ] + } + ], + "source": [ + "shap_copy = pd.DataFrame(shap_values, columns=X_train.columns).copy()\n", + "shap_valid_values = np.abs(explainer.shap_values(X_valid,\n", + " check_additivity=False))\n", + "shap_valid_rankings = np.argsort(-shap_valid_values, axis = 1)\n", + "lowest_misclassified = np.inf\n", + "opt_num_clusters = -1\n", + "acc_lst = []\n", + "time_since_king = 0\n", + "opt_clusters = None\n", + "for num_clusters in np.arange(2, 21):\n", + " print(\"Now Calculating for\", num_clusters, \"Clusters\")\n", + " shap_train_clusters = assign_training_clusters(shap_rbo_train, num_clusters)\n", + " valid_clusters = assign_testing_clusters(method=\"centroid\",\n", + " median_approx=True,\n", + " rbo_distance_matrix=shap_rbo_train,\n", + " lfi_train_ranking=shap_rankings,\n", + " lfi_test_ranking=shap_valid_rankings,\n", + " clusters = shap_train_clusters)\n", + " cluster_trainX = []\n", + " cluster_trainy = []\n", + " cluster_validX = []\n", + " cluster_validy = []\n", + " total_misclassified = 0\n", + " for cluster in np.arange(1, num_clusters + 1):\n", + " local_rf = RandomForestRegressor(n_estimators=100, random_state=0)\n", + " local_rf.fit(X_train[shap_train_clusters == cluster], y_train[shap_train_clusters == cluster])\n", + " local_preds = local_rf.predict(X_valid[valid_clusters == cluster])\n", + " local_misclassified = np.sum(local_preds != y_valid[valid_clusters == cluster])\n", + " total_misclassified += local_misclassified\n", + " if total_misclassified < lowest_misclassified:\n", + " lowest_misclassified = total_misclassified\n", + " opt_num_clusters = num_clusters\n", + " time_since_king = 0\n", + " opt_clusters = shap_train_clusters\n", + " else:\n", + " time_since_king += 1\n", + " print(\"Number of Misclassified w/\", num_clusters, \"Clusters:\", total_misclassified)\n", + " print(\"Accuracy w/\", num_clusters, \"Clusters:\", f\"{100*(1 - (total_misclassified / len(y_valid))):.2f}%\")\n", + " acc_lst.append(100*(1 - (total_misclassified / len(y_valid))))\n", + " if time_since_king > 2:\n", + " break\n", + "print(f'Optimal Number of Clusters: {opt_num_clusters}')\n", + "print(f'Lowest Number of Misclassified: {lowest_misclassified}')\n", + "print(f'Accuracy: {1 - lowest_misclassified / len(y_valid)}')" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot elbow plot of accuracy\n", + "plt.plot(np.arange(2, len(acc_lst)+2), acc_lst)\n", + "plt.xlabel('Number of Clusters')\n", + "plt.ylabel('Accuracy')\n", + "plt.title('Accuracy vs. Number of Clusters')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_training_clusters(shap_copy, shap_rbo_train, opt_clusters)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 26.6s\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 52.4s finished\n" + ] + } + ], + "source": [ + "# fit rf+\n", + "rf_plus = RandomForestPlusRegressor(rf, prediction_model=RidgeCV())\n", + "rf_plus.fit(X_train, y_train)\n", + "\n", + "# check performance on test set\n", + "# y_pred_rf_plus = rf_plus.predict(X_test)\n", + "# y_pred_proba_rf_plus = rf_plus.predict_proba(X_test)[:, 1]\n", + "\n", + "# compute accuracy on the test set\n", + "# accuracy_rf_plus = np.mean(y_pred_rf_plus == y_test)\n", + "# misclassified_rf_plus = np.sum(y_pred_rf_plus != y_test)\n", + "# auroc_rf_plus = roc_auc_score(y_test, y_pred_proba_rf_plus)\n", + "# auprc_rf_plus = average_precision_score(y_test, y_pred_proba_rf_plus)\n", + "# f1_rf_plus = f1_score(y_test, y_pred_rf_plus)\n", + "\n", + "# print(f'RF+ Test Set Accuracy: {accuracy_rf_plus}')\n", + "# print(f'RF+ Test Set # Misclassified: {misclassified_rf_plus}')\n", + "# print(f'RF+ Test Set AUROC: {auroc_rf_plus}')\n", + "# print(f'RF+ Test Set AUPRC: {auprc_rf_plus}')\n", + "# print(f'RF+ Test Set F1: {f1_rf_plus}')" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "# get feature importances\n", + "mdi_explainer = RFPlusMDI(rf_plus, evaluate_on='oob')\n", + "mdi, _ = mdi_explainer.explain(np.asarray(X_train), y_train)\n", + "mdi_rankings = mdi_explainer.get_rankings(mdi)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# get rbo distance matrix\n", + "mdi_rbo_train = compute_rbo_matrix(mdi_rankings, form = 'distance')" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "mdi_copy = pd.DataFrame(mdi, columns=X_train.columns).copy()\n", + "mdi_train_clusters = assign_training_clusters(mdi_rbo_train, opt_num_clusters)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_training_clusters(mdi_copy, mdi_rbo_train, mdi_train_clusters)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "# get feature importances\n", + "mdi_explainer = AloRFPlusMDI(rf_plus, evaluate_on='oob')\n", + "mdi = np.abs(mdi_explainer.explain_subtract_intercept(np.asarray(X_train), y_train, leaf_average=True))\n", + "mdi_rankings = mdi_explainer.get_rankings(mdi)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "# get rbo distance matrix\n", + "mdi_rbo_train = compute_rbo_matrix(mdi_rankings, form = 'distance')" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "mdi_copy = pd.DataFrame(mdi, columns=X_train.columns).copy()\n", + "mdi_train_clusters = assign_training_clusters(mdi_rbo_train, opt_num_clusters)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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91Thx4oTffV511VWetnfccYfPNvPnz/f6u7pixQq/+1uzZk2J4UUUOgAEGoUO1AjF3zRX9Mffh6Ki33iMGTPGVKai490/+OCDSj2+1atXe/Y1btw4v+0qW+goOg7Yl0cffdSr/UcffVRq+xtvvNHT9sUXXzSVqTRmnofij+nGG28sc79Dhw71tL/uuusqlfFf//qXZ1/33XdfiesvuOACzxv5gwcPej7A+Xpd9e3bt8wCTFUUOjp37lxm+/POO8/Tft68eabuw+z9B+OYNoyyn7v169d7rj/rrLP89koq6t133/XcZvTo0SWuL/4B5OWXXy7P02JKRY6TBx54oMz9Fi2gdO/e3dTz4U8wXl9mCgUdOnTwtCltHp1AS05ONqSCQk16errPNoEsdJx11lmeffnrkVIZVhQ6vv7664A9pvKeE6vTuaAihY6y3oN89tlnnrZnn322zzZFiyF/+ctfysx5yy23eGWg0AEg0JijAwiAomNWzY517tevn2fM+4oVKzR06FC/bXNzc7V69Wr98MMP2rdvn06cOKG8vDzP9SdOnPD8vn79+nKmN++qq64q9frOnTt7fo+JidHFF19cavvTTz/d8/v27dvLvP9gPA833XRTmW2GDx+uDz/8UJL/+TLMKm2ejpMnT+rbb7/1tGvUqJFOP/10/fTTTyXaulwuz2oNUVFR6tGjR6VyVcbVV19dZpuzzjrL89hCZXb9osf99ddfb2qcfL9+/Ty/r1ixosz21157bblzBeM48TeHTlFFVxK66667AjbXRFW9vnbu3OmZl0EqeAyBsmvXLn377bfatGmTjh07ppMnT3rNkVF4/jMMQz/88IMuuOCCgN23L0VfA3Xq1AnqfVWVovMnLVmyRJs2bVK7du2q5L6r67nAjOjoaF122WWltjnrrLM8v/s7vpYtW+b5/a9//WuZ9/vXv/5V06dPNxcSACqAQgdqnPHjx5teWlAqWJKw6ORmvhROCClJr732mtfSdP7s3r3b8/vvv//us83Jkyf15JNP6pVXXtGhQ4dM5TXbrrxiY2PVrFmzUts0aNDA83u7du0UERFRavu4uDjP78ePH/fbLljPg8PhULdu3cpsV7SIsH//fqWlpSkxMdFUjuLOOecc1a1bVydOnND333+v9PR0xcbGSip4HRUuZdu3b1/Pvz/99JN+/PFHHTlyxPOcrV69WidPnpRUMCleTExMhfIEQtEClz8NGzb0/F7a/3VFBOOYNqPocb9kyRLt3LmzzNsU/XDr77gv1KpVK69jpCzBOk4iIiLK/D/ev3+/1wegwtdvIFTV66voMq+nnnpqmec7M1auXKmHHnpIX331VZnLiRYK1jm8qKITQZpZbtgOmjdvru7du2vVqlVKT0/XOeecoxtvvFFXXnmlevXqpVq1agXtvqvbuaA82rdvX+bf6rKOrz179nhNQGrm72rXrl3lcDhMHxcAUF4UOoBKysjI8Pp27D//+U+593H06FGf2/r161fuHhpFswRS4Yfx0oSH/3lKKW/7wlnqiwvm89CgQQNTM783btxY0dHRntUVDh48WOFCR+EKNAsWLJDb7dby5cs936YtXbrU066w50efPn3073//W4ZhaNmyZbryyiv9trWKmf/rom+k/f1f283evXs9v8+fP7/ct/d13BfVuHHjcu0rmMdJ0WPVl/3793t+j4qKUtOmTcuVozRV9foq+hhat25doX0U9cYbb+i2224r9we5YJ3Diyr6ofnYsWNBv7+qMm3aNPXr10/79+9XRkaGpk6dqqlTpyo8PFxnnnmmevfurUGDBql///4KCwsL2P1Wp3NBeZX3+CraO6xQ0SJHrVq1TBVl6tatq9jY2JB6/QGoXkquaQmgXNLT0yu9D19vHEaNGuX50BIZGanbbrtNc+fO1aZNmzxd0Y2CeXa8hn243e5K5/GlvN3QA9VtPZjPQ3m+4atdu7bn98p+ECn6bXfRISmFxYtWrVqpRYsWkqQLL7zQ81z6alt8f1aoiuUwq6PKHvu+lrcsqjy9dIJ5nJjJEcyhEFX1+grkY/jll1/0t7/9zVPkOO200/TCCy/o22+/1f79+z1DVwp/hg8f7rltsM7hRbVs2dIra6jo1KmTfvjhB40ePdrrA3xeXp7Wrl2r559/XoMGDVKLFi0q9KWEP9XpXFBegTi+ivYKKs/f1VAZNgWgeqJHB1BJRT8AS9KRI0e8hnBUxJ49ezR79mxJktPp1IIFC0r9MFsV3wBaIdjPQ1ZWlum2mZmZnt/N9AIpja95Ok6ePKnVq1dL8i5cxMXF6YwzztAPP/zgaetyuTxdpa2en6MmK3rsf/jhh57eNlWtOpwvQmEoRCAfw+TJkz0F7EGDBmnevHmKjIz0276qz+Hnn3++pk2bJkk6cOCAduzY4VX8qI7MFoCaNGmif//733rmmWe0atUqffXVV/rmm2/09ddfe4Zd7NmzR7fffrt+/PFH/fvf/650tupyLrBK0YJFRf+uAkCg0aMDqKT69esrKirKc3nfvn2V3ufixYs93wQmJyeX+Y29mfHAdhTs5+Ho0aOmPtAcOnTIM2xFkho1alSu+ymucJ4OSfrhhx905MgRffPNN8rJyZFUsodGYWHk559/1sGDB7Vq1SpPnm7duik6OrpSeVAxTZo08fweiOO+oqrD+aLoc+FyuZSWlhbw+wi2oo/BzOTIpVm0aJHn98cff7zUIodU9efwvn37en2TP2vWrCq9f6ns4RDFlbfXRFRUlC688EI98sgj+uyzz3To0CHNnz9f559/vqfNiy++qDVr1pRrv75Ul3OBVYr+TczKyipzKI5UUExk2AqAYKLQAQTAeeed5/n966+/rvT+io73NTMR3/Llyyt9n9VRsJ8HwzA8vShKU3SiuSZNmlR6/oGwsDDPm23DMLR8+fJS59wo/OBaOE9HoOfnqKlDTyqr6IR7gTjuK6o6nC+aNGni1SNg8eLFAb+PYOvevbvn902bNnlNGF1e5fk/SU9P148//ljh+6qIFi1aKDk52XP51VdfLdc38YFQr149z++HDx8us/1PP/1UqfuLiIjQ4MGD9eWXX3qt+PXxxx+XaFvec2J1ORdYpVmzZl7FDjN/V9euXctEpACCikIHEACXXnqp5/epU6dW+o+30/nnoVnWm8+srCy9+eabpvZb9Jt/O0wIGaznoai33nqrzDZF9xuo+TCKD18pLF60bdu2xGoPvXv39jwXRdsW309F2e11UV0UPe4//PBDr8ksq1JVHCdmFP3gPGXKFNt9iGnRooU6duzouTxlypQK76s8/yf/+c9/LDnuHnroIc/vu3bt0oMPPlih/SxcuLBCtytaGPvhhx/KfL3873//q9D9FBcVFaWBAwd6Lvs6bst7Tqwu5wIrXXjhhZ7f33nnnTLbv/3228GMAwAUOoBA+Nvf/qb69etLkr777rtyLV156NChEhORFZ3x/7PPPit1orJ7773X9JuqokvE7dmzx3RGqwTreSjq7bffLvXbpyVLluiDDz7wXL7tttvKfR++FC2YzJ8/X99++22J7YUaNGigLl26SJI+//xzzzKYgZqfw26vi+rivPPO8xSaTp48qRtvvNEz/KgsOTk5prp3m1EVx4kZY8aM8XzAX7lypZ566qmg3E8wjRs3zvP7c889p6+++qpC+yn6fzJv3jy/7TZv3hyQpY4r4oILLtCdd97pufzSSy/p0UcfNX37w4cP6+qrr9YTTzxRofvv2LGjZwhfWlqavvjiC79tP/30U3366ael7u/o0aOm5/EoupxrfHx8ievLe06sLucCK40YMcLz+6xZs7yWay7uu+++08yZM6siFoAajEIHEACxsbGaNGmS5/LEiRM1fPhw7dq1y2d7wzD09ddf684771RSUpJOnjzpdX2/fv08M5dv2bJFw4cPLzGW9fjx4xo5cqReeeWVEhOi+lO0u+57771n6jZWCtbzUCgiIkL5+fm69NJL9eWXX5a4/tNPP9WVV17p+abxoosuUv/+/Sv2YIo5++yzPV23N2/e7HlT7K+HRuH2rVu3eubn6N69e0Dm5yj6upg7d67pN+goGONfOBHfwoUL1bt371ILZ5s2bdJjjz2mli1bBqyLe7CPE7PatWune++913M5JSVFo0eP1pEjR3y2//bbb3XzzTfr559/Dkqeirj55pvVs2dPSQXf5A8ePFgvv/yyz2/1c3Jy9PHHH/uceLJwyWipoHjy+eefl2izaNEi9enTRydOnAja/0lZnn/+ec/jlQrmEzn//PO1ePFivwWzbdu2afz48WrVqpXef//9Ct93eHi4rrnmGs/l22+/vcQKMIZh6K233tI111zjNReWL3PnzlW7du307LPPaseOHT7buFwuvfTSS165i/ZEKlSRc2J1OBdYKTk52TMk0+1267LLLvP5d3Xp0qW6+OKLlZ+fX+bcNQBQGay6AgTIzTffrG3btumxxx6TVDDc4Z133tGZZ56pDh06qE6dOsrIyNDu3bu1fv36UidWa9Cgge677z7985//lFTQDXT+/Pnq1q2bTjnlFKWlpWnp0qXKzMxUeHi4Xn75Za/lCf0ZNmyY5w33gw8+qPnz5+u0007zegP597//vdKrxgRKsJ6HQk2bNtWVV16pyZMn66KLLlKXLl105plnyjAMrVu3zusDWGJiol5//fWAPbbCeTo+++wzr+3+hsb07dvXq5gmBWbYilTwBjUmJkYnT57U+vXr1bFjR/Xp00f169f3jFUfOHCgV3dvFDj99NP17rvv6tprr1VWVpZWr16t7t27q02bNjr77LMVFxen7OxsHThwQD/++GNQeswE+zgpjyeffFK//fabZ96Dl156Sa+99pp69OihVq1aKTw8XPv27dO6des8E5aOGTMmKFkqIjw8XP/973/Vr18/bd68WVlZWRo1apT+/ve/q1evXkpMTFReXp527typdevW6fjx417LmBYaM2aM/vOf/+jgwYM6cuSIBg8erLP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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_training_clusters(mdi_copy, mdi_rbo_train, mdi_train_clusters)" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [], + "source": [ + "# get mdi rankings assignments for test points\n", + "mdi_test, _ = mdi_explainer.explain(np.asarray(X_test), leaf_average=True)\n", + "mdi_test_rankings = mdi_explainer.get_rankings(mdi_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [], + "source": [ + "mdi_test_clusters = assign_testing_clusters(method = \"centroid\", median_approx = False,\n", + " rbo_distance_matrix = mdi_rbo_train,\n", + " lfi_train_ranking = mdi_rankings,\n", + " lfi_test_ranking = mdi_test_rankings,\n", + " clusters = mdi_train_clusters)" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 2.7s\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 6.8s finished\n", + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 3.8s\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 95 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "/scratch/users/zachrewolinski/conda/envs/mdi/lib/python3.10/site-packages/glmnet/errors.py:66: RuntimeWarning: Model did not converge for smaller values of lambda, returning solution for the largest 95 values.\n", + " warnings.warn(\"Model did not converge for smaller values of lambda, \"\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 9.7s finished\n" + ] + }, + { + "data": { + "text/plain": [ + "0.7272727272727273" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cluster_trainX = []\n", + "cluster_trainy = []\n", + "cluster_testX = []\n", + "cluster_testy = []\n", + "total_misclassified = 0\n", + "for cluster in np.arange(1, opt_num_clusters + 1):\n", + " local_rf_plus = RandomForestPlusRegressor(RandomForestRegressor(n_estimators=100, random_state=0))\n", + " local_rf_plus.fit(X_train[mdi_train_clusters == cluster], y_train[mdi_train_clusters == cluster])\n", + " local_preds = local_rf_plus.predict(X_test[mdi_test_clusters == cluster])\n", + " local_misclassified = np.sum(local_preds != y_test[mdi_test_clusters == cluster])\n", + " total_misclassified += local_misclassified\n", + "1-(total_misclassified / len(y_test))" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [], + "source": [ + "shap_copy = pd.DataFrame(shap_values, columns=X_train.columns).copy()\n", + "shap_train_clusters = assign_training_clusters(shap_rbo_train, opt_num_clusters)" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [], + "source": [ + "shap_copy = pd.DataFrame(shap_values, columns=X_train.columns).copy()\n", + "shap_train_clusters = assign_training_clusters(shap_rbo_train, opt_num_clusters)\n", + "shap_test_values = np.abs(explainer.shap_values(X_test,\n", + " check_additivity=False))[:,:,0]\n", + "shap_test_rankings = np.argsort(-shap_test_values, axis = 1)\n", + "shap_test_clusters = assign_testing_clusters(method=\"centroid\", median_approx=False,\n", + " rbo_distance_matrix=shap_rbo_train,\n", + " lfi_train_ranking=shap_rankings,\n", + " lfi_test_ranking=shap_test_rankings,\n", + " clusters = shap_train_clusters)" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.7186147186147186" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cluster_trainX = []\n", + "cluster_trainy = []\n", + "cluster_testX = []\n", + "cluster_testy = []\n", + "total_misclassified = 0\n", + "for cluster in np.arange(1, opt_num_clusters + 1):\n", + " local_rf = RandomForestRegressor(n_estimators=100, random_state=0)\n", + " local_rf.fit(X_train[shap_train_clusters == cluster], y_train[shap_train_clusters == cluster])\n", + " local_preds = local_rf.predict(X_test[shap_test_clusters == cluster])\n", + " local_misclassified = np.sum(local_preds != y_test[shap_test_clusters == cluster])\n", + " total_misclassified += local_misclassified\n", + "1-(total_misclassified / len(y_test))" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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OiMwxHcFK7+T0MB3BOm/91NN0BOuM7/SW6QhWYmIfnMBkaVgjJaXqDvwej8eBJAAAAADqktfrVe/evZWQkKCUlBRdfvnlWr9+fYX7+v1+DRkyRC6XS2+//XbIdRkZGbr44ovVoEEDpaSk6L777lNxMaOh6ztGlMBaZT1JysvOzlZWCS8LpzWNiDQdwUo/lzQwHcE6i3NONh3BOlc1Xmk6gpVyStymI1inX/PNpiNY5/uCVNMRrHReNfdbtGiRRo4cqd69e6u4uFh//vOfNXDgQH3zzTdq2LBhyL7PPPOMXK7Dm9yXlJTo4osvVmpqqj777DNlZmbqpptuUnR0tMaPH18Ljwbhir8IYa38/PzDVsFhRAkAAABw/JszZ07I5VdffVUpKSlatWqVzjnnnOD2NWvW6KmnntLKlSvVokWLkNt8+OGH+uabb/TRRx+pefPm6tmzpx599FE98MADGjt2rGJiYhx5LHAehRJYw+12B5cBlkpHjxzK6/Xq/AUFTsaCpJtaLTMdwUr//PEs0xGss2Nli6p3Qq16o/VppiNY6eTRWaYjWOe7xxnd4DR/US/TEaw0vG2BCgpCP6/HxsYqNjb2iLfbt2+fJCkpKSm4LS8vTzfccINeeOEFpaYe/hpaunSpunXrpubNmwe3DRo0SLfffru+/vpr9erFz0B9RaEEFapoWkptqqhIUdfKN2yVFFI0CXm8lx4+7A4AAACAeV6vV+PGjQvZNmbMGI0dO7bS2/h8Pt19990688wzdcoppwS3p6en64wzztBll11W4e2ysrJCiiSSgpezsigI12cUSlChiqal1KbyRQpTyo8wyc7O1rRp0yRJW7ZyxtdpyZEMWzTh1E7TTUewzkcndDEdwTqnuH8yHcFKc2d1Nx3BOmdFbTIdwTq/avCD6QhW6n/9ao0aNSpkW1WjSUaOHKl169ZpyZIlwW3vvvuu5s+fr9WrV9dJThzfKJTAWuVHmKSnpwdHlfzpHoOhAAAAAFSqOtNsyrvzzjs1a9YsLV68WK1atQpunz9/vjZt2qTGjRuH7H/VVVfp7LPP1sKFC5WamqrPP/885PodO3ZIUoVTdVB/UCgBVDq6JCMjQ9OmTdNXP80wHcc+JYWmE1jJ5+dXgNPOarjBdATr5Pv4OTdhfxGr3jgtQj7TEazTOOKg6Qg4Ar/fr7vuukszZ87UwoUL1a5du5DrR48erT/84Q8h27p166aJEyfqkksukST169dPjz32mLKzs5WSkiJJmjdvnho1aqQuXRglWp9FmA4AhAOv16u0tLSwmBIEAAAAoGZGjhypf//735o+fboSEhKUlZWlrKwsHTxYWuBKTU3VKaecEvIlSWlpacGiysCBA9WlSxcNHTpUa9eu1dy5c/Xggw9q5MiRRzWqBccfTrMAAWVTcdb/9IbhJPahR4kZXxUmmo5gnZNjdpqOYJ0iV5HpCFYanvyJ6QjWaRzBqn1OS4hgFE84mzRpkiSpf//+IdunTJmi4cOHV+sYkZGRmjVrlm6//Xb169dPDRs21LBhw/TII4/UclqEGwolgEJXvRkxqoqdAQAAAIQ1v99fK7dp06aNPvjgg9qIhOMIhRJYq3xxpPyqN9u3MaLEaRHMAjQiLWqP6QjWidTRf2hDDbHiuxH8rDtvS3ET0xGs0zNmt+kIAOoIhRJYq/wSyB6PJ9ifZNS9fLhzWpG/xHQEK31XSLd2p20v4g8Zp/06YZ3pCFb6395fmY5gnUsSWeLUaZzmAeovXt+ASvuTuN106AcAAAAA2zGiBFbyeDzKzs4O2VY2wqT37DhDqez1cKdZpiNY6YFPrzYdwT4MWHPcczH9TUew0okv0OTSaTOuP910BOu07JRd9U6odZ+eYDoBbEChBFbKz88ProVe1qvk0MIJAAAAAMA+FEpgvbKRJGV9SgrPjzcdyTrf5nNqwAgfXS6dFrWTX7tO87emB5IJkbmFpiNYp+nqBqYjWKdZz1zTEQDUET6xwVput1vp6enBkSRer1eS1GPWQyZjAQAAAAAMolACa5UVRspWuykzq9e/TMSxWqIr2nQEK5173nemI1incUSB6QjWKaRvvRG732Z0g9PaR+eYjgA45HnTAWABCiWwRlkvEkmH9SMpf92oex2PBgAAAAAIExRKYI2yXiRS6CgSt9utjIwMTZs2TZL01U8zjOSzWbOoSNMRAEdkldADyWmFft5fTFhf0MJ0BOtEKMN0BOukRuWZjoAj8Hq9euutt/Tdd98pLi5OZ5xxhv72t7+pU6dOpqPhOMB4VFjP6/UqLS1N6enph03DAQAAAHD8WbRokUaOHKlly5Zp3rx5Kioq0sCBA5WbSxNeVI0RJYB+6VciSb1nxxlMYqfUeOZVm7Dh52TTEaxTtLWh6Qj2aUZfGBNaJO8zHcE6/5swyHQE60T9Oct0BCvNa1W9/ebMmRNy+dVXX1VKSopWrVqlc845pw6SoT6hUAJrle9LEmIghRIAAACgPtm3r7SAm5SUZDgJjgcUSmClQ/uSlPfVT9UsU6PWdI52m45gpa/bcKbdaWu6tTYdwTpnxG0xHcFKa+hR4rj8F1hBzmn9G/xoOoKVCgoeV0FB6GeY2NhYxcbGVnobn8+nu+++W2eeeaZOOeWUuo6IeoAeJbCS1+tVSkpK8LLH46FHCQAAABDmvF6vEhMTQ77KT6OvyMiRI7Vu3Tq9/vrrDqXE8Y4RJQg7lU6JqaFDlwR2u93Bwkh2dnZwdMn6n96o9fvGkWWX0DXeDFYDQf0XKb/pCFaKlM90BOucHseqN04r4e3FCI/Ho1GjRoVsO9JokjvvvFOzZs3S4sWL1aoVI8dRPRRKEHbKL+Nbmw4dLVK+8lw2okSSzrurWa3fN47svDia/pnwQW6a6QjW6RK7zXQE6xT5XaYjWKlxJAVwp/n4o91xPt5ejKhqmk0Zv9+vu+66SzNnztTChQvVrl07B9KhvqBQAgAAAACoV0aOHKnp06frnXfeUUJCgrKySlcpSkxMVFwcizfgyCiUAAodxfJFBmfZnRbFW5ERsa4i0xGs0ziCBrpOyyqJNx3BSlnFjU1HsM4JkTmmI1iHZo/hbdKkSZKk/v37h2yfMmWKhg8f7nwgHFf46wRWqaz/SXZ2dvC6ofRzBQAAAI5rfj/z0XDsKJTAKpX1P/F4PMHlgj/78R0DyexWrBLTEax0mvsn0xGsQ2NR5yVE1H5zcFRtb0kD0xGsE0G/DMfRshiovxgxBqi0sWtaWhrLAwMAAACA5RhRAmu43W5lZFS+dF7ZKjjt/5PsVCQETOz7X9MRrHTfF1eZjmCdwh2cZXeaK7HQdAQrdbx5rekI1nl62vmmI1inQRzvLyZ8WY9W+PVlnWQ6Qq2ISN1gOkKto1ACa3i93sNGjFTYs6QPhRIAAAAAsBWFEljF7XaHFEuys7M1bdq0kH3O3tTL6VjWuyBuj+kIVnq774umI1hnU1FT0xGskxq133QEK331dT065XucyPHtNB3BOlcmfG06gqUeMR2g1vjqSaeb+tjPg0IJrFI2vaZMRSNMev/ByUQAAAAAgHBCoQRWq2iESZOCVIOJ7HRu3M+mI1gp1x9tOoJ1cnxxpiNYp5GPVW9MiHYVm45gnSW7O5qOYJ3BDb8xHQHHuRJ//RhRUh+LCvXxMQHVdugIE4/HI2m3mTAAAAAAAOMolADleL1ePfHNENMxrFNQT6rpx5vIejIv9ngyf29n0xGsM6r5PNMRrPRdQQvTEazTNPaA6QjWyShONB3BSp1MB6hFPvlNR0AlKJQACl39psUthsMAAAAAAIyhUALrVLQkcPnVb9q88oSJWFbrNWCL6QhWumXxcNMRrBO5i74wTlvUuoPpCFYqOsjPuuNyI00nsM4cdzfTEay0pV319vN6vXrrrbf03XffKS4uTmeccYb+9re/qVOn0jEpW7ZsUbt2FR/sv//9r6655hpJUkZGhm6//XYtWLBA8fHxGjZsmLxer6Kiav6ndH1Z9aY+olAC6+Tn52vixIkh2zweT7Cp67/+OMdELKv1ZriwEVf0WG06gnUuSlxrOoJ12kbvMx3BSn/dzjRWp/0plWlmTmtI02JD7q/WXosWLdLIkSPVu3dvFRcX689//rMGDhyob775Rg0bNlTr1q2VmZkZcpuXX35ZEyZM0JAhpe9hJSUluvjii5WamqrPPvtMmZmZuummmxQdHa3x48fX+iND+KBQAiMOXW2mvOzs7Dq5z7KRJIcev6IRJgAAAACOX3PmhJ78fPXVV5WSkqJVq1bpnHPOUWRkpFJTQ1e7nDlzpq699lrFx8dLkj788EN98803+uijj9S8eXP17NlTjz76qB544AGNHTtWMTExNcpY4qdHSbiiUAIjDl1tprzKCig1VTaS5NDjHzrCpPPMxnVy/6hc+6a7TEew0rdbWQrbabN+7Gs6gnXiTtljOoKVGk1pZDqCdW7seorpCNZxncaINRNWpxSooKAgZFtsbKxiY2OPeLt9+0r/v5KSkiq8ftWqVVqzZo1eeOGF4LalS5eqW7duat68eXDboEGDdPvtt+vrr79Wr169jvVhIMxRKIF1Dh3NUjbCJDiy5JzGhpIBAAAAOBKv16tx48aFbBszZozGjh1b6W18Pp/uvvtunXnmmTrllIqLipMnT1bnzp11xhlnBLdlZWWFFEkkBS9nZWUd4yMol4tVb8IWhRJY59DRLGX9Scoaul6+5SRDyezVK4apTyZ824bGf057sWN/0xGsM4q+DUZ84u1oOoJ1OsbU/I82HJ3GkQdNR7BS9yHrNWrUqJBtVY0mGTlypNatW6clS5ZUeP3Bgwc1ffp0PfTQQ7WWE8c3CiUIe7XVQ6Sy3idlhZOygsnlf6rxXQEAAACoA9WZZlPenXfeqVmzZmnx4sVq1apVhfu8+eabysvL00033RSyPTU1VZ9//nnIth07dgSvq6kSRpSELQolCHsVrVJzLKrqfVJWMHn+O1alcFqf2M2mI1gpo7jiObqoO8OTPzUdAXBEctR+0xGs0yiC0ZlO+yq/4j+6Ubd6V3M/v9+vu+66SzNnztTChQsrXQpYKp12c+mllyo5OTlke79+/fTYY48pOztbKSkpkqR58+apUaNG6tKly7E+BBwHKJTAOlWNUDnxNgfDAAAAAKh1I0eO1PTp0/XOO+8oISEh2FMkMTFRcXFxwf02btyoxYsX64MPPjjsGAMHDlSXLl00dOhQPfHEE8rKytKDDz6okSNHHtWolsrQoyR8USiBdaoaofJFRpqDaSBJ+3zFpiNY6YQoVgNxWrTLZzqCdSL5EGpEowh6Nzht+u7TTUewzkWNGYUcziZNmiRJ6t+/f8j2KVOmaPjw4cHLr7zyilq1aqWBAwcedozIyEjNmjVLt99+u/r166eGDRtq2LBheuSRR+oyOsIAhRJAoaNMhtbN6sQAAAAAHOL3V69QPn78eI0fP77S69u0aVPhaJPaUFLNjHAehRJYo2xZ4Ozs7MOm35SteCNJ3299w1REaxVxxteIXF/Nh4zi6PxQmGI6gnUGNdxgOoKV9vviqt4JtWrlTkbEOq1vwg+mIwCoIxRKYI2yZq3p6emHTb9JT08PFk/uuMdUQgAAAAC2YEJu+KJQAqh0tElGRoamTZumDzbPMx3HOmfE7jMdwUo5PrfpCNbJYxSP4wr8LtMRrLSvpIHpCNY5L5XRU05LoBcPaojlgcNXhOkAgNPcbreys7NDtnm9XqWlpVW5hDAAAAAAoH5jRAms4/V6KyyIlE3NGbvuMqcjWe+s2NWmI1gpIaLyZbJRN/o22Gg6gnUiGFBiROvoXaYjWGf2zm6mI1inV4MtpiPgOFfCgJKwRaEE1ju0sesto1YaTGOnPDp+GxHJUrWOa0pxCpbYW9LQdATr+Jhm5rgTovaajgCgjlAogZXKVsCRQle88Xg8JmMBAAAAsASnrMIXhRKEnfJFDEmH9ROpDWXTbKTS4kj5okmOL6bW7w9HdmIUZ8FMaOgqNB3BOoW0BnOcm4+hRvxUlGQ6gnUaRTNizWn7aYoO1FsUShB2yhcxJNV5g9VDiybSZ3V6fwAAAABQIk4WhisKJUA5Xq9Xn2yZYTqGdQ76C0xHsNJ+X7zpCNZJiuRn3Wl7GSVoRKfYTNMRrFPgizYdwTppUftMR0AVFi9erAkTJmjVqlXKzMzUzJkzdfnll1e474gRI/TSSy9p4sSJuvvuu4Pb27Ztqx9//DFkX6/Xq9GjR9dhcphGoQQIKGvqeuWfTCcBAAAAUFO5ubnq0aOHfve73+nKK6+sdL+ZM2dq2bJlatmyZYXXP/LII7rllluClxMSEmoln4/1DMIWhRJYrfyKN2VNXdf/9IbhVPaJdnEWzIRGrMDiuEjxichpDV1FpiNYyUc/Hsed1XCD6QjWiXbxnh7uhgwZoiFDhhxxn23btumuu+7S3LlzdfHFF1e4T0JCglJTU+siIsIUv8Vgtfz8fE2cOFETJ05UWlpanfdDAQAAABAefD6fhg4dqvvuu09du3atdL/HH39cTZs2Va9evTRhwgQVFxfXyv2XyFUvvuojRpSg3is/auRQ5VfUKWvqOm/zHEdy4Rdto+jbYMKWoqamI1ineeRW0xGsw0pmZuwvYTUQpzWOyDMdwTo/83NuxAkFBSooCP3sGBsbq9jY2KM+1t/+9jdFRUXpj3/8Y6X7/PGPf9Spp56qpKQkffbZZ/J4PMrMzNTTTz991PeH4weFEtR7ZaNGKpKenn5YIeWiyt8nAQAAABjk9Xo1bty4kG1jxozR2LFjj+o4q1at0rPPPqsvvvhCLlfloyJGjRoV/L579+6KiYnRbbfdJq/Xe0zFmfLq62iM+oBCCazmdruVkZGhadOmBbd9v5UeJU7LppOVEal063ecz3QACyVEFJqOYKVGkfRAclrjiIOmIwCO8Hg8IcULScdUsPjkk0+UnZ2ttLS04LaSkhLdc889euaZZ7Rly5YKb9e3b18VFxdry5Yt6tSp01HfL44PFEpgNa/Xq8GDB4f0JrnjHoOBAAAAAFTqWKfZHGro0KG64IILQrYNGjRIQ4cO1c0331zp7dasWaOIiAilpKTUOIPPz4iScEWhBNZr2rRpyNScRVveN5jGTqfEMK/aBIZ7wgY/lzQ0HcFKr2f3MR3BOoU+PtY77cbmS01HsFKPo9j3wIED2rhxY/Dy5s2btWbNGiUlJSktLU1Nm4b2a4uOjlZqampwpMjSpUu1fPlyDRgwQAkJCVq6dKnS09P129/+Vk2aNKmNh4MwxTsqrHfoajeX/8lgGAAAAAC1YuXKlRowYEDwctmUnWHDhunVV1+t8vaxsbF6/fXXNXbsWBUUFKhdu3ZKT08/bOrPseKkVfiiUIKw53a7a7Rsb/mVbSpSttpNWVPXz/I6HPN94dj0if3WdAQr5fujTUewThFDbB3XgB4lRlzSbK3pCNY5OSbTdATr/CP7PNMRrHTlUezbv39/+f3V74V3aF+SU089VcuWLTuKe0R9QaEEYa+skHGsqiqylBVIsrOzNW3aNP1h5fAa3R+OQRMKJSbsKo43HcE6raP2mo5gnd0lDUxHsNLWwiTTEazTMWaH6QjWubLpStMRcJwrUYTpCKgE/zOwXtnywYdOwQEAAAAA2IcRJbBW+ZEk0i8jV9LXXGcylpWiXZGmI1ipjzvDdATrxLpYCttpDVxMvTGheTTLjzstn2auwHGHVW/CF++osFbZSJL09PRg0USSNMxsLgAAAACAORRKYD23262MjAxNmzZNktTt3olV3AK17ffXsnynCYu+7mQ6gnWSmu83HcE6+76jV4YJkQc5S+q0orb5piNYx3+ApugmbLnNdILaw6o34YtCCeq9ylbNKT/lxuPx/LJPZJqT8QAAAAAAYYRCCeq9ylbNKV88Kb9P2xefrPNMCJUce8B0BCvFbuVMmNN6nLLNdATrLMhobDqClVr3yDIdwTrNG/C71GnL13QwHQHHuRI/a6uEKwolsFpIb5IynU4wEwYAAAAAYByFEljr0N4kZT54lB4lTot1FZmOYCUWSHDe5v1NTUewD9O/jSjysZqZ09Zlp5qOYJ2o/fyco2Z8YkRJuOJjMqzl9XqD029CRpYk0aMEAAAAAGxFoQRWK2v0mp2dHRxZsvGTuwynss+fk1eZjmCltCt3mY5gnd5xm01HsE7uiTGmI1hpr6+B6QjWaRTBqjdO29Q5xXQESx2+SENFJk2apEmTJmnLli2SpK5du+rhhx/WkCFDJEkvv/yypk+fri+++EI5OTnas2ePGjduHHKMDRs26L777tOnn36qwsJCde/eXY8++qgGDBhQK4+EVW/CF2N9YDWv16uJEycqLS1N6enpFa6OAwAAAOD40qpVKz3++ONatWqVVq5cqfPOO0+XXXaZvv76a0lSXl6eBg8erD//+c+VHuPXv/61iouLNX/+fK1atUo9evTQr3/9a2Vl0bC6vnP5/X6/6RAIP+np6Zo40c5eHT9sbWE6gnWaRbD6iglrCznT7rTUqDzTEayzvTjedAQrnRLDz7rTcnwlpiNYZ0eJ23QEK/Vtc+yjM5OSkjRhwgT9/ve/D25buHChBgwYcNiIkp07dyo5OVmLFy/W2WefLUnKyclRo0aNNG/ePF1wwQXHnKPMvM2da3yMcHBhu29NR6h1jCgByvF4PKYjAAAAAKhEQUGB9u/fH/JVUFBwxNuUlJTo9ddfV25urvr161et+2natKk6deqk1157Tbm5uSouLtZLL72klJQUnXbaabXxUBDG6FEC6Jdmrnv27KF6aECBOAtmQkokZ3ydFikGcTot2lVsOoKVfPysO87toteB0yJc/Jyb4PV6NW7cuJBtY8aM0dixYw/b96uvvlK/fv2Un5+v+Ph4zZw5U126dKnW/bhcLn300Ue6/PLLlZCQoIiICKWkpGjOnDlq0qRJbTwU+ehRErYolACS8vPzg1ONzvrofsNp7NMx8WfTEay04OuTTUewTnQW052cVpJ20HQEK3VtnWk6gnV8fv7gctrPeUztM+ETj0ejRo0K2RYbG1vhvp06ddKaNWu0b98+vfnmmxo2bJgWLVpUrWKJ3+/XyJEjlZKSok8++URxcXH617/+pUsuuUQrVqxQixZM16/PKJTASiHLAUvKzs7+ZdvF9MsAAAAAwlFsbGylhZFDxcTEqEOHDpKk0047TStWrNCzzz6rl156qcrbzp8/X7NmzdKePXvUqFEjSdI//vEPzZs3T1OnTtXo0aOP/UEElDCWPWxRKIGVyo8gkUqb15Zt20IzV8cl0czViI3NPzYdwToJEUwDcVoRZ9mNSI40ncA++X6f6QjWyef9xZDxx3xLn89XZT+TMnl5pVOUIyJCixkRERHy+Xi91XcUSgAAAAAA9YrH49GQIUOUlpamnJwcTZ8+XQsXLtTcuXMlSVlZWcrKytLGjRsllfYzSUhIUFpampKSktSvXz81adJEw4YN08MPP6y4uDj985//1ObNm3XxxRfXSsYSPyNKwhWFEkCS2+1WRkaGPB6P3up9u+k41nm+139MR7DSbe/eYjqCdXyJRaYj2CefoQ0mdO2SYTqCdbbsSTIdwTrFJfyRa8L6VtXbLzs7WzfddJMyMzOVmJio7t27a+7cubrwwgslSS+++GJIU9hzzjlHkjRlyhQNHz5czZo105w5c/SXv/xF5513noqKitS1a1e988476tGjR60/LoQXCiWASrtnl02/keJMxwEAAABQA5MnTz7i9WPHjq1wpZzyfvWrXwVHoNQFHz1KwhaFEiCgbFTJ3D6vm45iHXqUmPHGFX83HcE6jVyFpiNYZ5fPbTqCldpH5Ve9E2pVEUsyO44eJaY8bDoALEAJCwjwer1KSUkxHQMAAACABUr8rnrxVR8xogQox+12ix7Wzot01c832HDnq6e/2MIaT7njaJRnRrSL591pPla9cZyPQTyoIZYHDl8USlDveTyeQO+RX2RnZ1e4r9fr1Q9bX3UgFQAAAACgMrt379Zdd92l9957TxEREbrqqqv07LPPKj4+vtLb9O/fX4sWLQrZdtttt+nFF188qvumUIJ6Lz8/XxMnTgzZlp6eXun+0ZzxdVyJn1MyJkS7SkxHsE4E7y+Oc0cUm44AOILz0s7jPR015WPU4xHdeOONyszM1Lx581RUVKSbb75Zt956q6ZPn37E291yyy165JFHgpcbNGhw1PdNoQQAAAAAAISNb7/9VnPmzNGKFSv0q1/9SpL03HPP6aKLLtKTTz6pli1bVnrbBg0aKDU1tUb3T6EEOEQkTQQcF+virciE/9vdx3QE69zVbFHVO6FW5ft4fzEhmo5fjssTowSdRq8v1FR96VFSUFCggoKCkG2xsbGKjY095mMuXbpUjRs3DhZJJOmCCy5QRESEli9friuuuKLS206bNk3//ve/lZqaqksuuUQPPfTQUY8qqdVPDxX1gsDxqbIeHvVZ2c/vffeaTgIAAAAAxwev16tx48aFbBszZozGjh17zMfMyso6bEXSqKgoJSUlKSsrq9Lb3XDDDWrTpo1atmypL7/8Ug888IDWr1+vt95666juv1YLJRX1gsDx6Ug9POojj8ejjIwMTZs2TT9sfd10HOvs8VFgNeGixLWmI1hnl+/Yz6zg2OT5Y0xHsNJuX67pCNYpYHSD47YUNzYdwUqdTAeoRfVlaV2Px6NRo0aFbKtsNMno0aP1t7/97YjH+/bbb485y6233hr8vlu3bmrRooXOP/98bdq0SSeeeGK1j8N4VEClRb60tDSlp6drFCNKHNeAqTdGnBC133QE60S7aFzsNIbGm5EQEWk6gnUi/Uy9cVpqZI7pCEBYOJppNvfcc4+GDx9+xH3at2+v1NTUw2Y5FBcXa/fu3UfVf6Rv376SpI0bNx5VoaR+TIoCaoHX62VEFAAAAFAPTJo0Sd27d1ejRo3UqFEj9evXT7Nnzw5ef9ttt+nEE09UXFyckpOTddlll+m7774LOYbL5Trs6/XXa2/0uU8R9eLraCQnJ+vkk08+4ldMTIz69eunvXv3atWqVcHbzp8/Xz6fL1j8qI41a9ZIklq0aHFUOTmNCwCWyvdzxtdp0S6WqnVaBKN4jKAxuvOK+FF3XIMIRvGEs1atWunxxx9Xx44d5ff7NXXqVF122WVavXq1unbtqtNOO0033nij0tLStHv3bo0dO1YDBw7U5s2bFRn5y2ekKVOmaPDgwcHLjRs3NvBo7NO5c2cNHjxYt9xyi1588UUVFRXpzjvv1HXXXRdc8Wbbtm06//zz9dprr6lPnz7atGmTpk+frosuukhNmzbVl19+qfT0dJ1zzjnq3r37Ud0/hRJYye12h/RhsbF5LQAAAFBfXXLJJSGXH3vsMU2aNEnLli1T165dQ3pZtG3bVn/961/Vo0cPbdmyJWSKRuPGjWu81GxlSvxM8DiSadOm6c4779T555+viIgIXXXVVfr73/8evL6oqEjr169XXl6eJCkmJkYfffSRnnnmGeXm5qp169a66qqr9OCDDx71fVMogZW8Xm/I5fT09HKr3nAWDHYoYkSJ4yJVZDqCdYr4EGpEvr/QdATrRPPxxXHRYhiPCceyFG1JSYlmzJih3Nxc9evX77Drc3NzNWXKFLVr106tW7cOuW7kyJH6wx/+oPbt22vEiBG6+eab5XLxgnNCUlKSpk+fXun1bdu2ld//y+uwdevWWrRoUa3cN58egABWbQIAAADCm9frVWJiYsjXoSdBy3z11VeKj49XbGysRowYoZkzZ6pLly7B6//xj38oPj5e8fHxmj17tubNm6eYmF9WS3vkkUf03//+V/PmzdNVV12lO+64Q88991ytPRafXPXiqz5iRAmg0qk4GRkZkqQSzg7AEtEu5lY77ecSt+kI1nHTF8aISM62Oi7fx+cXp7EksxlHsxRtp06dtGbNGu3bt09vvvmmhg0bpkWLFgWLJTfeeKMuvPBCZWZm6sknn9S1116rTz/9VG536e/rhx56KHisXr16KTc3VxMmTNAf//jHOnp0CBeMKAFUWplOSUmRx+MxHQUAAABAJWJjY4Mr2ZR9VVYoiYmJUYcOHXTaaafJ6/WqR48eevbZZ4PXJyYmqmPHjjrnnHP05ptv6rvvvtPMmTMrve++fftq69ath039OVYl/oh68VUfMaIECCgbVXLdNzeZjmKdy0740nQEK70490LTEazjS6JHidNcUT7TEazU/6QNpiNY59vdddNsEpX7eU+C6QhW2nTdsd/W5/NVWuTw+/3y+/1HLIKsWbNGTZo0OWI/FNQPFEpwXCprvFod1V3Rxuv1yuPxaIv4UA0AAAAczzwej4YMGaK0tDTl5ORo+vTpWrhwoebOnasffvhBb7zxhgYOHKjk5GRt3bpVjz/+uOLi4nTRRRdJkt577z3t2LFDp59+utxut+bNm6fx48fr3nvvrbWMJUzwCFsUSnBcOprGq+WXAa6K1+vVT9taHGssHKPoetoEKtz1vWKT6QjWaRl1wHQE69C2wYwGETzxTstJ5Q8up7Gqlil/rtZe2dnZuummm5SZmanExER1795dc+fO1YUXXqjt27frk08+0TPPPKM9e/aoefPmOuecc/TZZ58pJSVFkhQdHa0XXnhB6enp8vv96tChg55++mndcsstdfngECYolAABZaNURtVekRgAAACAAZMnT670upYtW+qDDz444u0HDx6swYMH13asED4aAoctCiWASoskGRkZmjZtms75mJeF07o0zjIdwUofru9sOoJ1IuiX4bgmsxuYjmClhr/dbjqCdbbuamw6gnWK86NNR7DSFtoJwgH8RQiodCpPcNWbC0ynAQAAAFDf0aMkfFEoAQLcbrcyMzM17eS5pqNYJykixnQEK33RdKnpCNZJjcozHcE6eadHmo5gpTZ8wnTcTyX0hXFaVjGr3pjxgOkAsAC/xmCdilbMyc7O1rRp0yRJW7bSzNVp+f4S0xGs1DiS5x313y4fU29MaCkaFzstx8dypU5rHHnQdAQc53w0BA5bFEpgnYpWzElPTw8WUP50j6FgAAAAAADjKJQAAWUFlJ+2vW46inXcLobGm1DkZ5i20xJcjOJx2gmROaYjWCmCefeOc7uKTUewTuOIItMRcJwrEavehCt+iwEAAAAAAAQwogRQaSPXZcuWKT09XffdS2XXaSWMbDBiry/OdATrJHH20XGFnBMyIpKzpI7bVJRsOoJ1usZkmY6A4xw9SsIX/zOAJK/Xq+jo6MN6lwAAAAAA7MKIEiAgObn0TAzVQ+dF06PEiIauQtMRrFPEWXbH5fmiTUewUyT9MpzWPSbTdATrRPCWHvYWL16sCRMmaNWqVcrMzNTMmTN1+eWXB68fPny4pk6dGnKbQYMGac6cOcHLX3zxhR544AGtWLFCkZGRuuqqq/T0008rPj6+xvnoURK++JsQCEhJSZHH4zEdAwAAAEAtyM3NVY8ePfTCCy9Uus/gwYOVmZkZ/PrPf/4TvG779u264IIL1KFDBy1fvlxz5szR119/reHDhzuQHiYxogQIcLvdysjIMB3DSkV+VgIxwe2iN4zTeM5hi3ze1x3HmWnnxcpnOgKqMGTIEA0ZMuSI+8TGxio1NbXC62bNmqXo6Gi98MILiogoHWPw4osvqnv37tq4caM6dOhQo3z0KAlf/M8AAV6vVykpKaZjAAAAAHDIwoULlZKSok6dOun222/Xrl27gtcVFBQoJiYmWCSRpLi40mb4S5YscTwrnMOIEtR7brdb6enpwcvZ2dlH3JdzA86jR4kZES5WYHFaicUDSpql71X8m/nKPz1aWTOaOna/DVhpyAg37+uOixSjeJzGZ0YzCgoKVFBQELItNjZWsbGxR32swYMH68orr1S7du20adMm/fnPf9aQIUO0dOlSRUZG6rzzztOoUaM0YcIE/elPf1Jubq5Gjx4tScrMrHlfoBJGlIQt/mdQ73m9Xk2cODH4daRRI16v18FkAFA/uPL9avRyrlpcsktpnXcorWOWTjjnZzUdvU9RP9Z9U89m6XvVtnWWUq/ZVfXOtazFw3vVuft2qdivBssL1C1tm+IX54fsk/z3/Trxkmx17bBN3dJKv1z5FlfNAKAGvF6vEhMTQ76O9TP8ddddp0svvVTdunXT5ZdfrlmzZmnFihVauHChJKlr166aOnWqnnrqKTVo0ECpqalq166dmjdvHjLKBPUPI0qAQ5SID69Oo0eJGT4/89md5quHT3nEXp9Sr9+t2HWlBRFfvEvFbaIUtb1ECdMO6uCp0SpsExV8Z/Wr9s/CHunY+f5j+6jjKvTLH1PFf5jfr0Zz8rXnwgbKj4xWyvs5KkqK0M4zGkrlXl+NPshXzNZiFSdFKiarJJjLX49fg/l+VtVyGiuwOI8/k83weDwaNWpUyLZjGU1Skfbt26tZs2bauHGjzj//fEnSDTfcoBtuuEE7duxQw4YN5XK59PTTT6t9+/Y1vj8fvYXCFoUSoByPx6M77jSdwj4+ilNG0PjPefXxOW/20P5gkWT3iIba9UCCFFX6ON3LCqRol0rkkj/w2P1yBZ+Hjq1Lhy1nPZWonGsbSJJOuGaXGiwr1P6r47RjYmNJUuOXDijxP3mK2lYif7RLxa0ilXdOrHY+2Eht+2Uremtp8SFuWZHat86SJG39b5IO9ouVsnxKe3KPmiw6qKg9JSpMjVL2NfHaekfjYM6uv8lU4vJ8ZV/RUEXJUUr5X45K4iL0xZLWFT7m5Bk56njfznKXc5U8Izd4uXf7DGVfFa+NT5UuO//N5FQVpkaq9TN71frZvZKkIr9L/no85LqI93XHfVVQcTNK1J1usTtMR7DSsU6zqY6tW7dq165datGixWHXNW/eXJL0yiuvyO1268ILL6yTDAgPFEpgPY/Ho/z80mHSa9asoVACANUUsd+nhFml758FXaK0688JkuuXYlD+6TX/INvww3wl/zWn9D5OipLL51f05mLF5/i188FGKugaJVeeX1G7fSqJd6mwY+lHG198hCL2+NT9iu2K3V6i4niXDp4Yo7iNhUp7eq9ifyrWpgnJIffV7P1cyS8dbB8d8jgOVdw0Ujk9YxXzc7Fit5XoQPcYqViK/6ZQ+WlRKkqKVH6bXz5iFbbg4xYAmHDgwAFt3LgxeHnz5s1as2aNkpKSlJSUpHHjxumqq65SamqqNm3apPvvv18dOnTQoEGDgrd5/vnndcYZZyg+Pl7z5s3Tfffdp8cff1yNGzeucT56lIQvfnPDevn5+Zo4caIkaeDAgYqsh2d8w10Ez7kRJQwaRg1F/1AsV6AFycE+MUcsLhzzfWwuvYO8s2O0bXppE1hXgV+xX5U2ac38V5Kap+9VozcPquCUaG0r1yg2aWJOaZEkOUJbPmymkqaRiv8wXyf8YY9S3jyg3LviVNQ2StGuXybsZLzXTAVdoqUSv5pGhPYaCbogQtsvSFLz0fsUOfugts1qqgaLCxT/293KfqKRDp5RWiBqqtDbx7l+6dfSNKJA/no8VyKa93XHdY1hdIPTIhk5FfZWrlypAQMGBC+XTdkZNmyYJk2apC+//FJTp07V3r171bJlSw0cOFCPPvpoyIiVzz//XGPGjNGBAwd08skn66WXXtLQoUMdfyxwFoUSoJyiIlZHAIBqK/83Qh39XZx3bqx8T+SowSeFatdjh4pOjFR+t2jtD0zVORL3mtL39KifferQK3TFM5dfcq8uUlHbXz4KHewXW1okkaTIqh+Qe02h8nvEBL/3R0j53aOr+9AAAHWsf//+8vsrL2jNnTu3ymO89tprtRkpBP3iwheFEqCcli1bSlpvOgYAHBeKToySP0pyFUvuFYWS339Mo0rKDehQRE5oO9bCk6OV8VGyEt45qNh1RYr9tlhNVuQp8T8H9eOCZBWfUPUytCXxLuV3PLyAUeCOVL4/MtgAtqBZ6eUjif6pWF3OyvplwzfF6pT2yxKRJ3XZocJWkfr208PntxeXqybl+yPrdTPXRJaqdVxWSbzpCNZpGXXAdAQAdYRCCVBOWlqa6QgAcNzwNYpQzq/davR2vtzritX0bznade8vzVzjPimQP86l/F/FVHj74mYRitrpU/QPpVNSojcWK/a70OWEozcXSy5p990JpRsK/Wrfc4cic/xyry3UgRPi5Isrvb+IvNCzhvk9otVwfoH8kdKPzyWpqHXpx56IAz4lzj2o/YPjQgNVo27hj3Ept1eMIvf75N5UrPyOUSqJj1CDtYUqbhahwhOiVJzCtDYAQNWYBh2+KJTAOm63W+np6cHL2dm/DMf2er06c15tL1yJqjzc4T3TEaw0YtbvTUewjqu4/o0gSDwtT/9eMUldt21X0gu5ip5coq1NmqjF3r1qfPCg7rvuN/rf5j564sf/6Gqt1Jc/t9YNM+6QJD3T+t+6dOdqNXrpoNZ/mKou27fL53MpUn59+GNX3T/jel27bJke/+8M7WjUSD8nJKhZTo4ic/wqjojQTT/cqk0zmmtYzicao7fl/rJIvt5+5cXE6IY77lDDJgV6L+kptdi9Xyf2/1mbTkhWw4OFarFrn2JKSjSgxb2SpGn7/qXTtVkLsjvp/s+urvpB3y/d+u5ijdoyT/3uH63E3INaePdTGn31lXr77F6l+3z2y+5PP/9f9dz4k9wHfikCJZ59UHJJj98wWB/26Vpr/x/h4uSWWVXvhFr1zU+Hj2JC3WqWlGM6gpVWtDKdADagUALreL3ekMvliyYAgKOzr2EDXfXHP2rop5/q16vX6MTsbLX/+WdlJSZqTvfu+vzEEyu97V8vu1TxBfnqs+kHtdm1S/84/3yd98036vvDD8F9vjnhBM3t1k1dt25Vxx07VBAVpS/atNHLAwZoU2Cpxhl9+6jPDz/ozA0b1Cmr9A/0SL9Pu+PjdfUjI3T3jI90ztrv1XFrtnYnNNTKk9vo41NPrtHjHrB6vb7omKb98XG6YslqlbhcWtTjpAr3bb57v9rs2B2yrU126eWEgwU1ygEAOH7RoyR8ufxH6m5zlNLT04Orh+D4Fu7/l7WZLz09XW63O7hE8JKBcVXcArXt4U6zTEew0l1zbzIdwTqRuQyxdZq/ZSUr16BO9Wq71XQE66z6up3pCNaJSeL9xYQNVz1kOkKtuXftb0xHqBVP9njDdIRax4gSQKFLBPee/WfDaQAAAAAAplAogfXcbrcyMjKCl9/qPsVgGjs1i3CbjmClGRc/bzqCdfJ9/Np1mjuiuOqdUOvaRhWZjmCdn1ubTgA4pf6MKClh6k3YYgwwrOf1epWSkiKPx0O/EgAAAACwHKe2AP0yqmTatGn6advrpuNYxydWGjLB56dW7rQTonJNR7BOrj/SdAQrldReCzxUU0IEzzlwvKGZa/jiUzKgX0aVAAAAAADsxogSIMDtdis9PV1/usd0EgD1VRFnjhxXxMgpIyJdjG5wWg6DMx0XKX7Ow93ixYs1YcIErVq1SpmZmZo5c6Yuv/zy4PUuV8W/l5944gndd999dZ6P0b3hi0IJqs3j8QSX0DUtOzu71o/p9XolST9sZeoN7PBZXkfTEaxzWcJXpiNYZ28JS76bkOrPMR3BOvzR7rx8/sgNe7m5uerRo4d+97vf6corrzzs+szMzJDLs2fP1u9//3tdddVVTkVEmKJQgmorv4SuaXXRdLWsEHQXI0oAAACA496QIUM0ZMiQSq9PTU0NufzOO+9owIABat++fV1HkySViJGm4YpCCRBQVgj6ZMt7pqNYp0VkiekIVlqf19x0BOuUJKwzHcE60eL9xQTOs8MGTO0zo6CgQAUFBSHbYmNjFRsbW6Pj7tixQ++//76mTp1ao+OgfqBQAquVn06UnZ0tj8eji24zHAoAAABAhbxer8aNGxeybcyYMRo7dmyNjjt16lQlJCRUOEWnrrDqTfiiUAKrlZ9OlJ6ervz8fN0063bDqezz4sWTTUew0idvnmo6gnVWnJNmOoJ1dm1tbDqClU7rutl0BOvsLXCbjmCdTRtbmI5gpfWeP2rUqFEh22o6mkSSXnnlFd14441yu3ktgUIJEOR2u5WRkSGd2Mp0FAAAAAAVqI1pNof65JNPtH79er3xxhu1etyqsOpN+KJQAgR4vV55PB49evmTpqNYp0UklXsT3h45wXQE67hZMtVxJd1MJ7BT4wg+YjqtSKwP7LT8k3jOzaj9ZXsnT56s0047TT169Kj1Y+P4xG8xWKmsN8mhywx7vV79sPVVM6EAAAAA1JoDBw5o48aNwcubN2/WmjVrlJSUpLS00umw+/fv14wZM/TUU085ns/Hqjdhi0IJrFTWm6SiZYaHTLnfQCK7jbn+ddMRrPTnD68xHcE+cZx9dJrrQKTpCFYadMYa0xGs8/3+ZNMRrJNXFGM6gpWWnlD9fVeuXKkBAwYEL5f1Nhk2bJheffVVSdLrr78uv9+v66+/vjZj4jhHoQQAAAAAUO/0799ffv+Rp73eeuutuvXWWx1KFKqEVW/CFoUSQKHLBL/8p/cNp7FPn9gi0xGs1PqiF01HsE7jyHzTEayzt4QeSCacGJ1nOoJ1SlLpgeS0Ip5yQx43HQAWoFACKHSZ4EVbKJQAAAAAqFusehO+KJTAam63W+np6SFNXYct/L3BRHaaPuAl0xGsNPzNO0xHsE5JM0ZPOS0modB0BCv1arnNdATr7C6IMx3BOj/uSjIdwUobWplOABtQKIHVvF6vJFXY1BUAAAAA6oqPHiVhi0IJoF9GlkiSv2tLw2ns88XBtqYjWMkXy+Rqp/l9fCByWsG+WNMR7MSvUse5I4tNR7BOcSGragH1FYUSQL+MLJGk9/420WASO7WM3mM6gpVc/NHuuJjsaNMRrBPVab/pCFYqZt6943IKKQo6LXoj051QMz7xWSxcUShBvVN+BZvKlO9Jcuht1TilLmIBAAAAAI4DFEpQ75RfwaYy5XuSlC+sZGdnKyaCQonT2kbvMh3BSr4mNLl0WmQeZ3ydlp8XYzqCldIa7DYdwTprMuhw6TQXby+oIXqUhC8KJbBe+cKKx+PRasN5AAAAAADmUCiB1TweT8g0HK/Xq7cepUeJ07YVNzYdwUrR2xnd4LSiVJYHdpr/IB91TNhd2NB0BOuc1X6T6QjW+SSrq+kIqMLixYs1YcIErVq1SpmZmZo5c6Yuv/zy4PUHDhzQ6NGj9fbbb2vXrl1q166d/vjHP2rEiBEhx1m6dKn+8pe/aPny5YqMjFTPnj01d+5cxcXVrE+Nj35OYYtPD7Bafn6+UlJKp9oEp+AkpRlOBQAAAKCmcnNz1aNHD/3ud7/TlVdeedj1o0aN0vz58/Xvf/9bbdu21Ycffqg77rhDLVu21KWXXiqptEgyePBgeTwePffcc4qKitLatWsVEUGRoz6jUAIElE3BmfsgI0qctrEg1XQEK5U08JmOYJ98PlQ5LTKRXjwmHChmxJrTtuUkmo5gHV8Mv0fD3ZAhQzRkyJBKr//ss880bNgw9e/fX5J066236qWXXtLnn38eLJSkp6frj3/8o0aPHh28XadOnWolHz1Kwhef2GA9t9ut9PR0ZWdnl656AwAAAKDeO+OMM/Tuu+9q27Zt8vv9WrBggTZs2KCBAwdKKl3oYfny5UpJSdEZZ5yh5s2b69xzz9WSJUsMJ0ddY0QJrOf1eiWVTr3JyMhQfp9kw4nsc1bDDaYjWOnvEYNMRwDqnK8w0nQEK333MyvIOc3n48y001w850YUFBSooKAgZFtsbKxiY49+JNtzzz2nW2+9Va1atVJUVJQiIiL0z3/+U+ecc44k6YcffpAkjR07Vk8++aR69uyp1157Teeff77WrVunjh071uix+MTPULhiRAkQ4PV6lZZGfxIAAAAgXHm9XiUmJoZ8lZ34PFrPPfecli1bpnfffVerVq3SU089pZEjR+qjjz6SJPl8pdOrbrvtNt18883q1auXJk6cqE6dOumVV16ptceE8MOIElip/HSb8rxer87b3NlQKnu1jyo2HcFK/7v0WdMRrJMQwc+6034ucZuOYKWO0QVV74RaVeL3m45gnbWFjU1HsFL/lqs1atSokG3HMprk4MGD+vOf/6yZM2fq4osvliR1795da9as0ZNPPqkLLrhALVq0kCR16dIl5LadO3dWRkbGMT6CX9CjJHwxogRW8nq9mjhxYnDFGwAAAADhLzY2Vo0aNQr5OpZCSVFRkYqKig5bvSYyMjI4kqRt27Zq2bKl1q9fH7LPhg0b1KZNm2N/EKiWxx57TGeccYYaNGigxo0bV+s2fr9fDz/8sFq0aKG4uDhdcMEF+v7774/6vhlRApTj8Xj0n9a/Nx3DOs8Nfs10BCv9cen1piNYp3mz/aYjWCdrU1PTEazUu+cm0xGsszO/gekI1slYc4LpCFbaNKrqfcocOHBAGzduDF7evHmz1qxZo6SkJKWlpencc8/Vfffdp7i4OLVp00aLFi3Sa6+9pqefflqS5HK5dN9992nMmDHq0aOHevbsqalTp+q7777Tm2++WePHwoiSIyssLNQ111yjfv36afLkydW6zRNPPKG///3vmjp1qtq1a6eHHnpIgwYN0jfffCO3u/qjTCmUAOXk5+ebjgAAAACgFqxcuVIDBgwIXi6bsjNs2DC9+uqrev311+XxeHTjjTdq9+7datOmjR577DGNGDEieJu7775b+fn5Sk9P1+7du9WjRw/NmzdPJ554ouOPxzbjxo2TJL366qvV2t/v9+uZZ57Rgw8+qMsuu0yS9Nprr6l58+Z6++23dd1111X7vimUwHoejydYIMnOzlZkX1a9cZqPWYBGREaXmI5gnd05nPF1WtJXrHpjgvu0ItMRrLMrt6HpCNaJzGc0QLjr37+//Efo35OamqopU6ZUeZzRo0dr9OjRtRlNEiNKatvmzZuVlZWlCy64ILgtMTFRffv21dKlSymUAEcjPz9fEydOlCQNHz5cJfl8qHbaydHZVe+EWlfCsqmOa5SUazqCdXafSjNXE4r9FMCdltQgz3QE62xJTDQdAQgLtblkc01kZWVJkpo3bx6yvXnz5sHrqovfYrCa2+0OWfmmrLM1AAAAANQln99VL76OZsnm0aNHy+VyHfHru+++c/h/4nCMKIHVvF6v0tPTQy6/9ehEg4nslF3CdAQT/Az3dNz+dTQWdVwjppiZkBh90HQE62wpSTIdwTox+zjnDEilrQyqu2TzPffco+HDhx/xeO3btz+mHKmpqZKkHTt2hJwA37Fjh3r27HlUx6JQguOS2+0OKXCUV36EyNEI9ipJSqtJNAAAAACokk/146TV0UyzSU5OVnJy3fSEbNeunVJTU/Xxxx8HCyP79+/X8uXLdfvttx/VsSiU4LhU2VAuSZUWUCpTVnTJzs7WtGnTNPOFp2oaD0dp+UG6hpvgL6BHidOKGd3gOFdR/fgQerzZlNPMdATrFJbwnu60wiTe01EzNHM9soyMDO3evVsZGRkqKSnRmjVrJEkdOnRQfHy8JOnkk0+W1+vVFVdcIZfLpbvvvlt//etf1bFjx+DywC1bttTll19+VPdNoQTWKyu6eDye0iJLh1aGEwEAAACA3R5++GFNnTo1eLlXr16SpAULFqh///6SpPXr12vfvn3Bfe6//37l5ubq1ltv1d69e3XWWWdpzpw5cruPrrk7hRIgoKxgcstPJxhOYp82UbwVmdD7gs2mI1gnNfKA6QjW2etztuM+SnVm+XHHZZYUm45gnYhuphPY6l7TAWoNI0qO7NVXX9Wrr756xH0OXf7Z5XLpkUce0SOPPFKj++avE0Dl+pNIumVUFTsDAAAAAOotCiWwWlmBpKw/iSR19NLM1Wnjr/236QhW8rx1o+kI1ilqwll22OHkjttMR7DO5p2squW0hAb5piNYaVU9miXPiJLwxZpWsFp+fr4mTpyolJQUSaWFEwAAAACAvRhRAih05ZuSPnWzXBUq92Z2b9MRrFTi9le9E2pVzE5+7TrN1ZG+MCY0iCoyHcE6Lhfv6U5LisszHQFVWLx4sSZMmKBVq1YpMzNTM2fODFn95K233tKLL76oVatWaffu3Vq9enVwWVlJ2r17t8aMGaMPP/xQGRkZSk5O1uWXX65HH31UiYmJNc7HiJLwxSc21Gvle49UJDs7W1LoyjdLHUkGAAAAoC7l5uaqR48e+t3vfqcrr7yywuvPOussXXvttbrlllsOu3779u3avn27nnzySXXp0kU//vijRowYoe3bt+vNN9904iHAEAolqNfKptZUJj09PeSy1+vVwg9H13UsHOLeE+aYjmClP7zeyXQE6+Sc6DMdwT4FkaYTWGlPfpzpCNZxL0wwHcE6BZflmI6AKgwZMkRDhgyp9PqhQ4dKkrZs2VLh9aeccor+97//BS+feOKJeuyxx/Tb3/5WxcXFiqrhyo1+RpSELQolgA4ZeTKEpSQBAACAcFRQUKCCgoKQbbGxsYqNdeYz/L59+9SoUaMaF0kQ3vjfhdXK9yZJS0tTfn6+stbTo8RpLyX1Nx3BSvtOZgUWp/mj6SHgNNfeGNMRrPRTRJLpCNZpuo/3F6f9lMXPuQler1fjxo0L2TZmzBiNHTu2zu97586devTRR3XrrbfWyvF8YkRJuKJQAquV702SkZGhadOm6a1XnjCcyj6jms8zHcFKSzN6mI5gnbxTWErScW7TAex0csss0xGs87OrrekI1ol2F5uOYCWPx6NRo0aFbHNiNMn+/ft18cUXq0uXLo4UZWAWhRJApQUTj8dT2rOkWwvTcQAAAABUwMlpNmVycnI0ePBgJSQkaObMmYqOjq6V47LqTfiiUAIElI0ueefvTxlOYp93chjZYMKBEzkT5rT2LXaajmCdH9e1NB3BSimdaHLptMxrd5uOYJ3ijU1MR4AD9u/fr0GDBik2Nlbvvvuu3G6GKtqAQgkQEGzo2q6V6SgAAAAAaujAgQPauHFj8PLmzZu1Zs0aJSUlKS0tTbt371ZGRoa2b98uSVq/fr0kKTU1Vampqdq/f78GDhyovLw8/fvf/9b+/fu1f/9+SVJycrIiI2u2shqr3oQvCiVAQNlSwhdt7mw6inX6xuaajmClcy/8znQE67SOOmg6gnX2dmR5YBPaREaYjmCdHS0YJei0om78kWvGPdXec+XKlRowYEDwcllvk2HDhunVV1/Vu+++q5tvvjl4/XXXXSfpl+awX3zxhZYvXy5J6tChQ8ixN2/erLZt2x7rg0CYo1CCeqdsJRtJys7OrtZtPB6PsrOz5fF4dF7tNLEGAAAAYFD//v3l91e+ItTw4cM1fPjwY759TdGjJHxRKEGFyhcbylS36GBaWa8RSYc9hsrk5+crLS1NGRkZahhRUPUNUKsiXfySMCEhotB0BABALXG7WB4YAGoLhRJUqHyxoUx1iw7hLtiLpJzs7GxNmzZNHo9H0lIzwQAAAABYgx4l4YtCCaxT1oukvLIikNfr1dDlP5uIZbXnWs81HcFKD2dcZjqCdSa2ect0BOvk+GJMR7DSvgj68ThtezErcThtxcF2piNYqaPpALAChRIgIDjS5DrTSQAAAADUd/QoCV8USgCV9mTJyMjQtGnT1H7i06bjWGf6RZtMR7DS2hUnmo5gnZsLbjAdwTo/ZjU1HcFK15+y0nQE68z4vpfpCNZxfRNvOoKV7nzIdALYgLXbAJVOuUlJSQn0KAEAAACAuuX314+v+ogRJUBA2agStWhmOop1OsZkmY5gpahWuaYjWCe3MNZ0BOv4CiNNR7DS1vwmpiNYp3FD+sI4bWejhqYjAKgjjCgBArxer9LS0kzHAAAAAGABn1z14qs+YkQJ6jW3233YssbZ2dmH7Vd+yeCGbfc7kg2/+GBfD9MRrOTbzJkwpxV2KTQdwToRMSWmI1iJBoXO2/UNI2KdFreDn3OgvqJQgnrN6/Uetu3QwokUumRwp7ceqfNcCHVlY5r+mTCz4a9MR7BOI3e+6QjW2be3gekIVsrOp8ml01yt8kxHsE5JW5/pCDjO+Skqhy2m3sBaHo9H6enpSk9PV3Z2dvAyAAAAgOPbpEmT1L17dzVq1EiNGjVSv379NHv2bEnSli1b5HK5KvyaMWNG8BgZGRm6+OKL1aBBA6WkpOi+++5TcXGxqYcEBzGiBNYqP4okPT09eHl4RmvDyezTKqqetssOc08PmmY6gnV6xtK42GlZHeJMR7BSp2immTltbRumUzqtaQSjeMx4uFp7tWrVSo8//rg6duwov9+vqVOn6rLLLtPq1at18sknKzMzM2T/l19+WRMmTNCQIUMkSSUlJbr44ouVmpqqzz77TJmZmbrpppsUHR2t8ePH18ojYZpi+KJQAqjcijcAAAAAjnuXXHJJyOXHHntMkyZN0rJly9S1a1elpqaGXD9z5kxde+21io8vnTr44Ycf6ptvvtFHH32k5s2bq2fPnnr00Uf1wAMPaOzYsYqJiXHsscB5FEoAlfYyKZt6c8c9NP5zmtsVbTqClVpH7zEdwTol9bQzfDhzuxgibUIkP+uOaxzB8sBOc7v4zGhCQUGBCgoKQrbFxsYqNja20tuUlJRoxowZys3NVb9+/Q67ftWqVVqzZo1eeOGF4LalS5eqW7duat68eXDboEGDdPvtt+vrr79Wr169avxY/AyqDlv0KIF1ylbCOXT1G6/XG5yKAwAAACD8eL1eJSYmhnxVtICDJH311VeKj49XbGysRowYoZkzZ6pLly6H7Td58mR17txZZ5xxRnBbVlZWSJFEUvByVhZTaes7RpTAOmVvpJU1br3wnXucjANJUy9+yXQEK9389gjTEaxTEs/ZR6dFxDGixIRmSTmmI1jH/XKS6QjWyewXaTqClb723KFRo0aFbKtsNEmnTp20Zs0a7du3T2+++aaGDRumRYsWhRRLDh48qOnTp+uhhx6q09wVYdWb8EWhBFbzeDzKzz9kuc4OrcyEAQAAAHBEVU2zKS8mJkYdOnSQJJ122mlasWKFnn32Wb300i8n6d58803l5eXppptuCrltamqqPv/885BtO3bsCF6H+o1CCaxWfuWbMu89wfQb2MHXgNENTottnF/1TqhVUWviTUewku/MA6YjWOfnHoxucFpxAr9Hjzc+n++w/iaTJ0/WpZdequTk5JDt/fr102OPPabs7GylpKRIkubNm6dGjRpVOH3nWDCiJHxRKIG1yla6OWxUSYs0c6EAAAAA1JjH49GQIUOUlpamnJwcTZ8+XQsXLtTcuXOD+2zcuFGLFy/WBx98cNjtBw4cqC5dumjo0KF64oknlJWVpQcffFAjR46s9ogWHL8olMBaZSvdZGRkaNq0acHt1/zY3mAqO/WI4YyMCe8NedZ0BOsksEKC4wr6crbOhESWC3Dc2pMbm45gHVbVMuXeau2VnZ2tm266SZmZmUpMTFT37t01d+5cXXjhhcF9XnnlFbVq1UoDBw487PaRkZGaNWuWbr/9dvXr108NGzbUsGHD9Mgjj9TaI/ExoiRsUSiB1bxeb0hTV4/Ho0vobwkAAAAc1yZPnlzlPuPHj9f48eMrvb5NmzYVjjZB/UehBNYrWy5YKq08ezMuNpzIPq93eNd0BCvl+aJNR7BOQiQjSpy23x9jOoKVklxFpiNYZ0thctU7AfXAuaYD1CK/33QCVIZCCaxXft11j8ejdaIBHQAAAADYikIJrFbR8sDrF9OjxGlvpbYwHcFKL/14jukI1tn2bXPTEawTe0Ku6QhWcs9PMB3BOtEX/2w6gnWytzc2HcFKt3UynaD2sOpN+KJQAuuUL45kZ2eHNHKVpKEZrHrjtPZRjDs0oftJ/zEdwTovND7PdATrjEhZYDqClRZ1Ptl0BOv0iPvRdATrNOxSaDqCpe43HaDWUCgJXxRKYJ38/HxNnDhRUmnRpHwzV0kaml7RrQAAAAAANqBQAquV709Spv0zrQ0ksduDF71lOoKVPs9pZzqCdRZ+2NN0BOss784oQRO6J2eajmCdL/bzs+60z5Z0MR3BSpvuMZ2g9jCmOnxRKMFxo6J+IsciOzv7yMdvQ6EEAAAAAGxFoQTHjfJTZmqibKrNoYWXsn4l7zz7dI3vA0dnd0lD0xGstCO/kekI1olgOrvjcnLiTEew0vaGvL84LSrCZzqCdfwRphPgeEePkvBFoQTWOrTwEuxX0pYRJQAAAABgKwolsI7b7VZ6evphU3DK+pXkrBxuIJXdbk782nQEKw2K53l3Ws7NMaYjWOenoqamI1jJHVFkOoJ1GrgKTEewzvrmLUxHsNSoau01adIkTZo0SVu2bJEkde3aVQ8//LCGDBkiSdq0aZPuvfdeLVmyRAUFBRo8eLCee+45NW/e/LBjFRQUqG/fvlq7dq1Wr16tnj171s5DoUlJ2KJQAuuUFUTKr3YTMg3nRhOpAAAAANSWVq1a6fHHH1fHjh3l9/s1depUXXbZZVq9erXatm2rgQMHqkePHpo/f74k6aGHHtIll1yiZcuWKSIidF7V/fffr5YtW2rt2rUmHgoMoFACa5WNLJF+6U8iSW9s7G0ylpWK/MyrNoPJ1aj/cn2xpiNYKcLF+7rTkiMZUeK0FftZPS6cXXLJJSGXH3vsMU2aNEnLli3Ttm3btGXLFq1evVqNGpX2VJo6daqaNGmi+fPn64ILLgjebvbs2frwww/1v//9T7Nnz67VjPQoCV8USmCt8ksDp6enB0eVnD7SYCgAAAAAtaqkpEQzZsxQbm6u+vXrp02bNsnlcik29pdiutvtVkREhJYsWRIslOzYsUO33HKL3n77bTVo0MBUfBhAoQRQ6RtjRkaGpk2bpis+pVLitEvbf2A6gpXy/DVfbhtHZy9LJDgu2lVsOoKV8hjJ47iGLvrCOK1bwlbTEaxUUFCggoLQEVSxsbEhRY8yX331lfr166f8/HzFx8dr5syZ6tKli5KTk9WwYUM98MADGj9+vPx+v0aPHq2SkhJlZmZKkvx+v4YPH64RI0boV7/6VbDXSW3y06MkbPGJDVDp6JK0tLSQviUAAAAAwovX61ViYmLIV/mR4uV16tRJa9as0fLly3X77bdr2LBh+uabb5ScnKwZM2bovffeU3x8vBITE7V3716deuqpwf4kzz33nHJycuTxeJx8eAgTjCgBAsreYLdspYO504r80aYjWCnHx68Ap6082N50BOv0bbDRdAQrNY1gxJrTEjn96bizG2wwHcFKPT3vatSo0JVvKhpNIkkxMTHq0KGDJOm0007TihUr9Oyzz+qll17SwIEDtWnTJu3cuVNRUVFq3LixUlNT1b596e/q+fPna+nSpYcd+1e/+pVuvPFGTZ06tcaPhR4l4YtPyUBAWY+SP91jOgkAAACAilQ2zaY6fD7fYdN2mjVrJqm0MJKdna1LL71UkvT3v/9df/3rX4P7bd++XYMGDdIbb7yhvn37HmN6HC8olMBq5ZcF3rNnj5o0aaK7Nl9tOJV9Lkj+1nQEK016/WLTEaxzsDU9BJzmijnPdAQrdUrLMh3BOq0b7jUdwTrf7U0xHcFKS9pUbz+Px6MhQ4YoLS1NOTk5mj59uhYuXKi5c+dKkqZMmaLOnTsrOTlZS5cu1Z/+9Celp6erU6dOkqS0tLSQ48XHx0uSTjzxRLVq1ap2HgwjSsIWhRJYLT8/XxMnTgzZ9gNTbxyXFtXQdAQrXfj7J01HsE6J+EAEOyS4SkxHAOpcTvNI0xEs9US19srOztZNN92kzMxMJSYmqnv37po7d64uvPBCSdL69evl8Xi0e/dutW3bVn/5y1/oV4ggCiWwSvkRJFLpG+ih193F1BsAAADguDZ58uQjXv/444/r8ccfr/bx2rZtK38tL1PDqjfhi0IJrHLoCJKyqrHH4wkuD9z/Y84OOG1Qi29MR7DSv748y3QE67h+cpuOYJ2iJoxsMKHDiZmmI1gnwsVfXE77aXcT0xGs9F1r0wlgA/pjAyotoLA8MAAAAADH+OvJVz3EiBJYze12Kz09XdnZ2Zo2bZokqcvbY82GslCXdttNR7BSyb4Y0xGsE9GKJVOdFrWdUTwm7M/neXdaSsMDpiNYJ39nnOkIAOoIhRJYzev1SiqdghPsX3JuouFUAAAAAOo7P6vehC0KJYBKR5aU9Sg5fa7HdBzrvJ7dx3QEO0XU07GSYSwyymc6gnUi2nGW3YSGMYWmI1inUQwj1pwW1bjAdAQAdYQeJYBKR5bQowQAAACAY0z3FqFHSaUYUQIElE3DaTvpScNJ7PPo4LdNR7DS0lWdTEewzglJe01HsM7mLSmmI1ip9QkZpiNYp3H0QdMRrBO7qqHpCHa61nQA2IBCCRAQ7FFy0gmmowAAAACo5+hREr4olMB6ZQWSspVvPhw90XQk66w82M50BCs12M7sS6dl7qYQ6zRXGr0yTFib3dJ0BOuc33qD6QjWcdF2Cqi3+JQM6+Xn52vixIn0KAEAAADgHNO9RehRUilGlKDa3G630UJCdnb2YduC02VqcIwyZT1K5m+hb4PTukTnmI5gpb63bDIdwTpuV5HpCNbJ9cWajmClRhGswOK0aFeJ6QjWufCOdaYjWKr6f48sXrxYEyZM0KpVq5SZmamZM2fq8ssvD17/1ltv6cUXX9SqVau0e/durV69Wj179qzwWH6/XxdddJHmzJlz2HFQNx577DG9//77WrNmjWJiYrR3794qbzN8+HBNnTo1ZNugQYM0Z86co7pvCiWotrJCgikVFWnKRoMc7THKF1iys7ODl/fs2aOFfW+pncCotjvO/dh0BCtNfW2g6QjWiT57t+kI1in8LMl0BCs1P3+r6QjW6ZRY+ckg1I15C3qZjmCli+6t/r65ubnq0aOHfve73+nKK6+s8PqzzjpL1157rW655ch/AzzzzDNyuWq7pwg9So6ksLBQ11xzjfr166fJkydX+3aDBw/WlClTgpdjY4/+pAmFElipfIElPT09eNnj8RhOBgAAAKA2DBkyREOGDKn0+qFDh0qStmzZcsTjrFmzRk899ZRWrlypFi1a1GZEHMG4ceMkSa+++upR3S42Nlapqak1um8KJUA5Xq9Xg7ecZDrG/7N35+FNVun7wO8kTZPuG91Yyi6bgMCggA4CsuogjCjugrsOKqbKQFQEVAyKio6DqL9BQC2iKIjbwBdFQAQcQKogglIphdIStpZuSdskvz9qQlKatKXJe9Kc+3NdvTRvTtK7pU2TJ8/7HOn0DueWhiIM/8cvoiNIJ17NU2+Ult+D23eK0CasTHQE6YT7/Z1uqs+DEzeKjiAlq3UKrFarxzGdTndBXQMNUV5ejltuuQULFy5s8ovv84TofA/RNm7ciJSUFCQkJGDYsGF47rnnkJSU1Kj74DBXkp5er3edfsNhrkREREREwctkMiEuLs7jI5AjAgwGAwYNGoRx48YF7HM0d1arFWfPnvX4qF3MUsro0aPx7rvv4ptvvsELL7yATZs2YcyYMbDZGjfHiR0lJD2TyeRx+k27Rdy+U2mvj3pXdAQpPbz5VtERpKONYkeJ0qI2saNEhIhrj4uOIB19GB9flPZHbqroCFL6zTgVmZmZHscC1U3y2WefYcOGDdi9e3dA7j9UOkpMJpPrNBmnWbNmYfbs2eetnTFjBl544QWf9/frr7+ia9euF5Tlpptucv1/z5490atXL3Ts2BEbN27EVVdd1eD7YaGEiIiIiIiImoVAnmZT24YNG5CTk4P4+HiP4xMmTMBf//pXbNy4UZEcwc5oNDa4ePXYY49h8uTJPu+vQ4cO/oqGDh06oEWLFjh48CALJUSNpdfrsX37dhgMBugSMkTHkU6P8BOiI0hJVcI/AUqrDrOLjiAd3dkQebuumam28exupfVKPiY6gnTyt7YWHYECbMaMGbjnnns8jvXs2RMLFizA2LFjm/4JHKExW6gxxavk5GQkJycHONE5R48exalTpxo9hJd/xYhQ0y6m1WobtdUwEREREREFr9LSUmRnZyM7OxsAcOjQIWRnZyMvLw8AcPr0aWRnZ2Pfvn0AgAMHDiA7OxuFhYUAgLS0NFx88cUeHwCQkZGB9u3bK/8FSSYvL8/172Wz2Vz/lqWlpa41Xbt2xerVqwHU/HtPmzYN27dvR25uLr755huMGzcOnTp1wqhRoxr1ufl2ItGfnJXN6ki++6i0NSU9RUeQkipJzJAtmak0fHxR2vFhnNsgwmXxp0VHkE6ZTZlTEegcbmQW/Hbu3ImhQ4e6LjtPD5k0aRKWLl2Kzz77DHfeeafreud8C2/zNfzNwacFPj399NNYtmyZ63KfPn0AAN9++y2GDBkCoKa4VVxcDADQaDT4+eefsWzZMhQVFaFly5YYOXIknn322UafrsVCCUlFr9fDYDDAbDafd11KSgqMRiOQpFwrGBERERERBcaQIUPg8FGNmDx5cr3zMmrzdX/kX0uXLsXSpUt9rnH/94iIiMC6dev88rlZKCGpOLcOq2sbYL1ej7y8PNhbtVA6lvSujNovOoKUXq0YLjqCdFJaFomOIB3Hej6mi1DQJkZ0BOmoozgDSWlVcXzBTE3EH6GgxRklRH8ymUw4deqU6BhEREREREQkEDtKiNwkJSXBHmkTHUM6aZpq0RGkxHkZymsRVSY6gnQO9EoSHUFK6eEW0RGkE6ZiR4nSNJbQ2LGEBAqRXW9CETtKiNxkZHBrYCIiIiIiIpmxo4TIjclkworXXxYdQzpvn+kvOoKUVCX8E6C03PXtREeQjr1zpegIUiqv1oqOIJ1YdvEorrpdhegI1Myp2NwbtPgsmaRlNBphsXg+qThz5gzUF/cSlEhel0f9JjqClN61XSk6gnTK23EvSaWp1TwdQYTWUcWiI0hHp+ZprEpLbXFWdARq7lgoCVoslJC0LBYLFixY4HHMaDQKSkNERERERETBgIUSIjcmkwkr3jeJjiGdcodOdAQpaTNKRUeQjvogt0xVmq0VB3SL8L98zvxSWock7tyntIJD3H6cmojDXIMWh7mSlPR6Pcxm83nH2VFCREREREQkN3aUkJRMJhMMBoPrsnNeidlshrpFP4HJ5LTP0lJ0BCmlxpWIjiCd41Z2lCityqIRHUFK3dsfFx1BOknh3H5caXvDOAMpmC1atAiLFi1Cbm4uAKBHjx54+umnMWbMGADAkCFDsGnTJo/b3H///XjzzTcBAD/99BPmzZuHLVu24OTJk2jXrh0eeOABTJ061X8hOaMkaLFQQoRz80qMRiO2iw5DRERERERN0rp1a8ybNw+dO3eGw+HAsmXLMG7cOOzevRs9evQAANx777145plnXLeJjIx0/f+uXbuQkpKC999/H23atMHWrVtx3333QaPR4KGHHlL86yFlsVBC0tLr9a6uEudpOCaTCR/+i9sDK+362GzREaT01uZhoiPIJ4nzMpSmKWNHiQiWaj7FVNopRImOIJ3YX7kNdjAbO3asx+W5c+di0aJF2L59u6tQEhkZibS0tDpvf9ddd3lc7tChA7Zt24ZVq1b5r1DCjpKgxb9i1Gy4Fzac6poz0lAm07mhrQaD4dx2we1bX/B9EhERERFRcLHZbFi5ciXKysowcOBA1/GsrCy8//77SEtLw9ixYzFz5kyPrpLaiouLkZiYqERkEoyFEmo23AsbTrULJxdKr9cjLy8PWVlZuCWvnV/ukxouWc13fEX46poF9S8iv9pfmSI6gnT06irREaTUMey06AjSiVTzrWmlbX+QM9ZEsFr/AavV6nFMp9NBpzt/F8U9e/Zg4MCBsFgsiI6OxurVq9G9e3cAwC233IK2bduiZcuW+PnnnzF9+nQcOHAAq1atqvPzbt26FR9++CG+/PJL/30x/LUNWtz1hgg1RZiMjAy/FV6IiIiIiMj/TCYT4uLiPD7qekMVALp06YLs7Gz88MMPePDBBzFp0iTs27cPAHDfffdh1KhR6NmzJ2699Va8++67WL16NXJycs67n71792LcuHGYNWsWRo4cGdCvj4IDO0qI/uR8gO3ybIbgJPJ5f9JroiNI6cZVLAwqrXOfPNERpHNieVvREaR0egA7eZQ2sucvoiNI55uNvUVHkNI+4xRkZmZ6HKurmwQAwsPD0alTJwBAv379sGPHDrz22mt46623zlt72WWXAQAOHjyIjh07nvt8+/bhqquuwn333YennnrKX19GDYfKv/dHfsNCCRHObQ8MAEhkoYSIiIiIKBh5O82mIex2+3mn7ThlZ2cDANLT013HfvnlFwwbNgyTJk3C3LlzL+hzUvPEQglJz2g0uuaTAEDc3nGCE8mnQ1i16AhSeu5vH4qOIJ1+uiOiI0jHbPQ+lI8CJ1Zd9wsRChy9irtqKW30338WHUFSmfUvQc1z/DFjxiAjIwMlJSVYvnw5Nm7ciHXr1iEnJwfLly/H1VdfjaSkJPz8888wGAwYPHgwevXqBaDmdJthw4Zh1KhRyMzMRGFhIQBAo9EgOTnZL1+JijNKghYLJSQ9i8WClJQUV1dJ3N2iExERERERUVOYzWbccccdKCgoQFxcHHr16oV169ZhxIgROHLkCL7++mu8+uqrKCsrQ5s2bTBhwgSPU2s+/vhjnDhxAu+//z7ef/991/G2bdsiNzdXwFdESmKhhAieu960W/iy6DjSaT2CuyOIYPrs76IjSCfp4pOiI0jnxO9JoiNIKbETH9eVFhPOLh6l5f+vlegIUrrO2LB1ixcv9npdmzZtsGnTJp+3nz17NmbPnt2IZBeAHSVBi4USkpL7TBKz2YysrCwYjUYYDAasy+TpCEpL1/ChSITeE/8lOoJ0knk6guL2dWGhRITOWhZKlKZnD7/ibF1EJ5AVh9FT4PHVCUnJYrFgwYIFAACDweA5zJWIiIiIiIikxUIJSc/9tBsAOHCEHSVK06o0oiNIqcQeLjqCdJLYUaK4JHWZ6AhSsrO5QXEc5aq8codadAQiChD+dpP0TCYTMjIyYDAYYDCwlY+IiIiIiAJP5QiNj1DEjhIKOQ05jcZsNntcNplMrv9nR4nySh2VoiNIqcoRITqCdKocKtERpJOoYRePCKdtOtERpNM2jD/rSrM6QvQVIhGxUEKhx33+iDfOzpG6iioPNGxrdiIiIiIiogvHN1CCFgslJLW6iiov/bpPUBp5PRi/X3QEKVU6+CdAaZU841VxRdV60RGk9EdViugI0mkblis6gnQWn75cdAQpvdhGdAKSAZ8lk5T0ej0MBsN5p+AQEREREREpgmdvBS0WSkhKzpkkdW0N3PNB7pBAcrCzu4EkoFdXi45ARCEq3xIvOgIRBQgLJSS92qffvPPbFQLTEBERERGRFNhRErRYKCGp6fV65OXleRzrocsXlEZeJfYq0RGk1EF7UnQE6YTDLjqCfFTsKBEhUVMqOoJ0ivnworinW34pOgI1wrx582A0GjF16lS8+uqrAGreMH3sscewYsUKWK1WjBo1Cm+88QZSU1Ndt8vLy8ODDz6Ib7/9FtHR0Zg0aRJMJhPCwvhSOpSx75qkZjKZkJJybuCc0WgUmIaIiIiIiPxtx44deOutt9CrVy+P4waDAZ9//jlWrlyJTZs24dixY7juuutc19tsNlxzzTWorKzE1q1bsWzZMixduhRPP/20X3KpHKHxEYpYBiPpOQe7AoDZbMb+ynTBieTTK7xAdAQpfWdJFB1BOj3CC0VHkE6JPVx0BCmdtkWLjiAdbfgJ0RGkU2LnS6nmoLS0FLfeeiv+3//7f3juuedcx4uLi7F48WIsX74cw4YNAwAsWbIE3bp1w/bt2zFgwAD83//9H/bt24evv/4aqampuOSSS/Dss89i+vTpmD17NsLD+TcmVLGjhKRnMpmwYMECLFiwABkZGaLjEBERERGRn0yZMgXXXHMNhg8f7nF8165dqKqq8jjetWtXZGRkYNu2bQCAbdu2oWfPnh6n4owaNQpnz57FL7/80vRwjhD5CEEsgxK5MZlMuGfnZNExpDMs8pDoCFK6SMvtsZX2cXE/0RGk85eoP0RHkFKMukJ0BOnw3U/lfV/RSXQEKXW1WmG1Wj2O6XQ66HS689auWLECP/74I3bs2HHedYWFhQgPD0d8fLzH8dTUVBQWFrrWuBdJnNc7r6PQxUIJ0Z+c2wRvaNer/sXkV1GDrfUvIr/76mAP0RGkYyuIEB1BOu/EXS46gpQiDrIdXWnqvxSLjiCditxY0RGkZD5jwpw5czyOzZo1C7Nnz/Y4duTIEUydOhXr16+HXq9XMGEjhGg3RihgoYSaNff5Ik5mc8PfJXcWRwAgOzsbl1xyiT/jERERERGRHxmNRmRmZnocq6ubZNeuXTCbzejbt6/rmM1mw+bNm/Hvf/8b69atQ2VlJYqKijy6So4fP460tDQAQFpaGv73v/953O/x48dd11HoYqGEmjWTyXTesdqFE18sFgsWLFgAABg5ciQWLFiA/gcv81s+aphxUWxdFMGQvFF0BOn8YGktOoJ0ksPOio4gpaIrIkVHkM6wCG75rrSZbQaLjiAlb6fZ1HbVVVdhz549HsfuvPNOdO3aFdOnT0ebNm2g1WrxzTffYMKECQCAAwcOIC8vDwMHDgQADBw4EHPnzoXZbHbtlLl+/XrExsaie/fuTf5aQnXHmFDAQgnRn5KTk2E0GtHzbtFJiIiIiIioKWJiYnDxxRd7HIuKikJSUpLr+N13343MzEwkJiYiNjYWDz/8MAYOHIgBAwYAqHkjtXv37rj99tvx4osvorCwEE899RSmTJnSoGINNV8slBD9KSMjA3l5eejq0IiOIp1yR7XoCFIq5raGimujPSU6gnT0Kj6+iHCK2wMrrsjOn3Wl9Y7OEx2BmmjBggVQq9WYMGECrFYrRo0ahTfeeMN1vUajwRdffIEHH3wQAwcORFRUFCZNmoRnnnnGPwEcKv/cD/kdnyVTSHKfPeKL+zwTk8kEo9EIgDuwEBERERGFmo0bN3pc1uv1WLhwIRYuXOj1Nm3btsVXX30V4GQUbFgooZDkPnvEl9rzTEwmE9otfSFQsciL3/r+IDqClJbv6S86gnRUJ9imqzR1y3LREaTU/qafREeQzjOf/k10BOmUHIsRHUFK91wkOoEfcUZJ0GKhhKR3XvdJb06wJiIiIiIikhULJSS92t0nq/4zX2AaOf3vdFvREaSk4qh1xdnD7aIjSMd+Wi86gpTKbhggOoJ0yivqP+WY/EtVzfkS1DR8Kha8WCghqen1euTl5Xl2lfRoKTYUERERERERCcNCCUnNOcA1Ly8PWVlZAIDPXnpFcCr5PN3uc9ERpDS58C7REaQTVsJdtZRWncCdQETQlrJ7SmlarU10BOlURPJ7Tk3EjpKgxUIJSc9kMsFgMJzrKmnVRnQkIiIiIiIKcTz1JnixUEKEc6fgZGVlYWxuF9FxpNNZy10pRPj08jdER5DOpktCaVR/8zAo8qDoCFLKuSpZdATp9NMdEx1BOqfs3MlMjH+KDkASUIsOQORver0eZrO5UbcxmUzIyMg4b7tgIiIiIiKigHCEyEcIYkcJhRznqTQXcjuj0YgkNbsblMYz2cWocrBWrjR2NyjPxveEhDhRHSs6gnTU7ChRXLk9XHQEIgoQFkqIANd8ErPZjDg1B/8pLUUTJTqClErsLAoqTc2dJBVXYudTHRGSw86KjkAUcMmaMtERqLkL0W6MUMC3WYgAWCwWLFiwABkZGaKjEBERERFRE5lMJvTv3x8xMTFISUnB+PHjceDAAY81Q4YMgUql8vh44IEHPNZ88803GDRoEGJiYpCWlobp06ejuppvrIY6vs1C0nHtbuPGOdPEZDLh96PLRMSSWrm9UnQEKVkc3KpWaTEqPrFSWhlb44U4VpUgOoJ0YiKOi44gndPVfM85mG3atAlTpkxB//79UV1djSeeeAIjR47Evn37EBV1rpv53nvvxTPPPOO6HBkZ6fr/n376CVdffTWefPJJvPvuu8jPz8cDDzwAm82Gl156qckZuetN8GKhhKTj7B5x57498D8eExSMiIiIiIj8Yu3atR6Xly5dipSUFOzatQuDBw92HY+MjERaWlqd9/Hhhx+iV69eePrppwEAnTp1wosvvoiJEydi1qxZiImJCdwXQEKxUEIEz+2B9x35SHQc6WhV7GwQodAWITqCdPSqItERiBQRr+EMJKWV2G2iI0jnF2tr0RGk1MNqhdVq9Tim0+mg0/nerrm4uBgAkJiY6HE8KysL77//PtLS0jB27FjMnDnT1VVitVqh1+s91kdERMBisWDXrl0YMmRIE78aClbsFyMCtwcmIiIiImoOTCYT4uLiPD5MJpPP29jtdjz66KO4/PLLcfHFF7uO33LLLXj//ffx7bffwmg04r333sNtt93mun7UqFHYunUrPvjgA9hsNuTn57tO0ykoKAjMF0hBgR0lRH9yPsAey/9QcBL5qMGtQERI1pSKjiAd7nqjvLQwdjaIUGTn44vS9Co+wChtRGSe6AhSmmg0IjMz0+NYfd0kU6ZMwd69e7FlyxaP4/fdd5/r/3v27In09HRcddVVyMnJQceOHTFy5EjMnz8fDzzwAG6//XbodDrMnDkT3333HdRqP/QccEZJ0GKhhEKSXq/32h3iHNzqVHu467THAxqNiIiIiIguUENOs3H30EMP4YsvvsDmzZvRurXv06Uuu+wyAMDBgwfRsWNHAEBmZiYMBgMKCgqQkJCA3NxcGI1GdOjQ4cK/CAp6LJRQSPLVfle7gFJ7uOvvR9lRojStw1r/IvK7MjtnlCjtlC2q/kXkV/GaCtERpPRzRYboCNLppzspOoJ0ytgNIETdY1fP53A48PDDD2P16tXYuHEj2rdvX+9tsrOzAQDp6ekex1UqFVq2bAkA+OCDD9CmTRv07du3MbHrxF1vghcLJURERERERBRSpkyZguXLl2PNmjWIiYlBYWEhACAuLg4RERHIycnB8uXLcfXVVyMpKQk///wzDAYDBg8ejF69ernuZ/78+Rg9ejTUajVWrVqFefPm4aOPPoJGw80IQhkLJUR/cp6Cw1NvlBeranj7JPkPZzco77SNP+tKS1azY02EodH7REeQjpYzShQXxQETQW3RokUAcN7ONEuWLMHkyZMRHh6Or7/+Gq+++irKysrQpk0bTJgwAU899ZTH+v/+97+YO3curFYrevfujTVr1mDMmDH+CckfoaDFQglJp/b8EufMEucpOBzmSkRERETUvDkcvqsQbdq0waZNm+q9nw0bNvgrEjUjLJSQdGrPLzEYDDAajecKJvU8qBKFityqONERiALulJ1zYUQosXEGktK6aLlVqdIsDrvoCNTc8WVH0PLDnkZEzZ/FYkFKSgqMRqPoKERERERERCQQO0pIenq9Hnl5ecjIyEBBAd+NEaEaNtERpJQWViI6gnRO2yJFR5COHlWiI0ipSs2nmEorsfNvqdKqHJwLQ03DXW+CF/+KkfRMJhMMBoPrlJw/jqbXcwui0BAOtgwrTauqFh1BOl+XXCw6gpT6ROaKjkAUcGUOvpQiCpTc3Fw8++yz2LBhAwoLC9GyZUvcdtttePLJJxEeHu71dhaLBY899hhWrFgBq9WKUaNG4Y033kBqamqjPj9/u4kA/Prrr64Brw8/JjgMERERERGFPnaUeLV//37Y7Xa89dZb6NSpE/bu3Yt7770XZWVleOmll7zezmAw4Msvv8TKlSsRFxeHhx56CNdddx2+//77Rn1+FkqIACQlJWHBggUAgEO/Lfe+UK2CQ3+uzVJV7uMdeZUKjogLXFvhALwNlQ3UWgCOSPWFrbU4APuFr7Wo3VrjI1SAc4tDqwOw+fgL0pi1ehWg/nNtpQOo9tNanQrQXMDaKkfNhzfhKiDsAtZWO2pyeKNV1XwAeLPwrwir8v5zWR2mhl1b82+nstmhrfS+1hamgk2rafxauwNaq/d28Uat1ahgC69ZC4cD4Rb/rLWrVajWaVyXwyu8d4XUt/aKmAOu/3eoVbDrz/1uaMq9ZzhvbYXd5++9LeLC1qotdqh8/C7bIjUXttZqh8rH72ej1kaoXb/3DVnr3KZWZXX4XGt3+71XVTqg8vG73Ki1br/3jVpb5YDKx++93e33vjFrUe2A2sdjhEOrgkN7AWttDqit59YWlMZ7rg1TwR7ufDxxQG31/hjhvhZ2BzQWP63VqGDX/bnW4aj53fD3WtTzu9yItVCrYGvgYwTUKsDtDVbZnkd4rK313MDX732j1tZ6bqCqdqCwMrbOtTaPxwg71D6aCW0ejxGNWFvlgLqe33tH2AWsref33u72e9+YtbA5oLH6WBsGOJy/y/WsJTmMHj0ao0ePdl3u0KEDDhw4gEWLFnktlBQXF2Px4sVYvnw5hg0bBqBmO+hu3bph+/btGDBgQIM/PwslRAAyMjJcHSULXjV7XVc+TIfCZQmuy20vOQF1Rd0P5BUDtChYmXTucww8Ac3putdaeoXh2JctXJdbDzsB7dG6n5RVXhSGo9+cW9vqb6cQ/lvdf1WrWqtxZFuK63L69aeg/7nutbZEFQ7/dK4lLe2O04jYXve5/fYIFXJ/O7c29f4iRG6w1rkWAP44kub6/+RHixD9pfe1p35PByJr/qhGTS+CfmW517Wnf06DI6nmBVbUnGLol5V5XXtmeyrsbWoe8iJfOIuIN0u9ri3akAJbFy0AIOL1EkS+4n2WR9GXybBdUvPsVL+4FFHPnfW6tnhlC1QP0gEAdFlliH6y2Ovas8uSUDVcX7N2VTmiM4u8ri15MxGVY2t2mAj/rwUxD5z2urb0lXhYb6zZBaTrtkLcP3Wz17Urp/fDlhs7AwA67j6Bh+/71uvaNVN7Y8OkbgCANvvP4LHb13td+9/7emDtAz0BAKmHzsJ4w3+9rv3m9q74zHAJACChsAyz/vaF17Xf3dAJHxv/AgCIKrLi+as+9br2h7HtsHxOzR/LcIsN8y//2Ova3cPbYOmLl7su+1r7yxXpePtfV7ouP3fVaui8FGFOXBqF75Z3dl0efeU+6M7UvfZMzwh8u7qL6/Lw0b8iKr/u38+znfT4em1X1+Whf/8NsQctda4ta6XFuk09XJevvPl3JOypqHOtNUGDL3f0dF2+/K4cJP+v7t+56gg1PtvTy3V5wJRcpG30/rux6uAlrv//y2OH0Xqt99+NNT/3dBVW+sw8grarznhd+8UPFwN/PgR2eO4kWr7vPcP/vsuAtXXN7327l06h9f/znmHXujYov6jm977NG2fQ9jXvGXZ/2gqlvWt+l1suKUKHed5/P3/+oCWKB9T8Lqd9cBadZp30unbv4jScGVbzu5y8pgRdpp3wuvbXf6fi5DXRAIAW68rQ7aHjXtcemJ8M8/U1L/4SNpfj4rsLva49OKcFCu6o2T0rbocFvW4+5nVt9j9b48C9NX8LEn4px4gJv3pdu/fhdPzySCsAQGyOBWOu/sXr2v13p+KnGW0AAJHHKjF26B6va3+/NRk/zm4LANCdrsb4AT95XXvo70n434vtAdQUG6/vvdvr2iOjE7D19Y6uy77WHrsyDt/959zv/fgBPyHMSxHGfGk0vs0697v8tyF7oD9T99/w0z0jcfarcy/aWw07gTAfzyOOuT2PSPfxPKK6tRpH3Z5HpF1/CjofzyOOuD2PSL3jNPQ+nkfkuT2PSK7neUSu2/OIFo8WIcrH84jDB1Lg+PN5RIsZxYj+uO7HPwDIy06BPalmbeIzJYh91/tzjqNbW6D6z+cRCS+WIO6tcrRF3c8Zv/1vZ5ReVPN733nRCXT5l/fnlptXd0Rxr5r5VR2WnkL3F7z/zm3Nao9TA2p+l9uuOI2es73/zv3wn7YwD635mWi1pgh9ph/1unbn6xkouLrmdznt/87iLw/neV27+4XWOHp9zXPh5O9KcNk9h72u3TO7JXJvr3kunLSjDINuPeR17b7paci5LxkAEPdLBQb/Pcfr2lDqwgiVGSVWqxVWq+fvpU6ng06n8+vnKS4uRmJiotfrd+3ahaqqKgwfPtx1rGvXrsjIyMC2bdtYKCFqLI8tg1991eu6fRWpmHv4b67LHzjehh51P2HIsSRj5uHxrstL7e8gDnX/sT5SmYB/Hr7Odfmt6veQgrpfnBdWxuAxt7WvVX6ADNT9JP1MdZTH2hcrV6Iz6n4yXWrXeax91vIpLkbdf4ArHRqPtU9WfIG/wPsfVfe108rWYRC8//Gzw4FzfwF9//WwwwHHn2scDVhrD8Bah8da3y50bX2TROwea+v72s6tydCd8rk2PbwY3SNrfgbSdUU+16aGn3WtTdb7HhKbrC1xrU3Qey9uAUALbalrbbTe+5NdAEjUlrnW6i2VPtfGaypca8PqGSYc67a2PtEaq8datY9nQDHqSvTVnXtCHKby/i8dqaryWKtTec+sV1d7rNX7eHtSp7J5rI1UeR98Gqaye6yNUXv/Hqvh8Fgbq/b+ogaAx9oEje9/50t0x+H48x35JHXdRR2nXjozjqtrCg8aH99foOZ7ofrzHWBNPc9cw9XVsKtrMvj6dwMAndqG6j//DbT1rA1X2Vz/Xv5cq1W7rVX7/nnXquyuteE+fs4au7ZreAk6/PniVa33/fPQWVuGjMjjf671PYy3vbYcLf9cq4rwPQOobVgFUp1rK3znbRVWgTGRzoKS7+9vmsbitta3ZI3VY63Gx2N2oqbKY224j3/nOHUVLCqV2xHvQ0ZVAMLd1voeR6ryXKvytbrW2nru2X1tfdtweq6t/34dzq4zn3lrGjLtKufvve8MWpXKdX+aejIUVsfjTFVNITPN5r1ACwDm6jiYq2oKGkl2338TzdWxOFZVU6SIs/l+rDxZHYsjVTVFikib77+Jp2zRrrXhNt+/G6dtUa61vrpfAOCM21pbte9/5SJ7pGuttVrr+44p6JhMJsyZM8fj2KxZszB79my/fY6DBw/i9ddf93naTWFhIcLDwxEfH+9xPDU1FYWF3ouQdVE5HD564hrJYDC4Tl8gao6MRiMOXeX9xaNdrUKV7lx9UVfh/QmcQ61C5QWuDbdUea0wO1RApV57YWut1T5b5a0RF7ZWa62GuglrX2/3+bkLPPWmhgKn3qw+0xEaH6fI2LUqt1NvHND4aJW312qrb+ha2B0I89Eqf8FrHQ6v79I2dq1Do4LNrVU+rJ62el9rh0f9fu4CT8+7sLWNbKs/4ah5V5en3ih76k3nsFo/z2GqmseqP9fCV1u9+1q7A7D4aa1GVfM4DNT87HrpCG30WrWq5u+Gk6/f5casVQFwOzWuvrWl+nPXB+oxAhUOqBr4e9+otRaHz+ccjVqr4Kk3n5deVOfaar3atVZdaYfax++9TaeGQ3MBa6vsPk+nsYWr4AhTN3qtqtoOTT2n07ieGzRmrR+fR9zXx3s3bHPT/cnQeO28++l/NLijZMaMGXjhhRd83t+vv/6Krl3PddTl5+fjyiuvxJAhQ/Cf//zH6+2WL1+OO++887wsl156KYYOHVrv53XHjhIiNxaLBTd3+J/oGNJR6zV1X6FT1X28qWvDPc/lFrJWqwIa+oZJY9aGqRr8yD4w9o8G3umfYgK0NjpAa6MCtLYxO/zWWlup8vKzDgCRPq6rLaIRP+/BsFYfoLUN+L2vsv/5Aqshv5vO5/ta1P87F+i1Yaj/d/lC1moARARgrdpzrV1b15sDjnP3G1nfv53j3P0GYq0qQGshbq3a/fVqhAr1dZUE1Vp9gNbqArQ2vKZA1zrM+6l059bWvyTo1jb071xj1gL1P55c6FofSkpKMHPmTKxevRpmsxl9+vTBa6+9hv79+7vW/Prrr5g+fTo2bdqE6upqdO/eHZ988gkyMjL8E0ICjTnN5rHHHsPkyZN9runQoYPr/48dO4ahQ4di0KBBePvtt33eLi0tDZWVlSgqKvLoKjl+/DjS0tK837AOLJSQ1IxGIyyWc62L2dnZGCIuDhERERER+ck999yDvXv34r333kPLli3x/vvvY/jw4di3bx9atWqFnJwcXHHFFbj77rsxZ84cxMbG4pdffoFer1cmYIjMKGmM5ORkJCcnN2htfn4+hg4din79+mHJkiVQq32fwtWvXz9otVp88803mDBhAgDgwIEDyMvLw8CBAxuVk4USkprFYvE4XezWW2/FYx9fKzCRnCK6FomOIKWSo41p+yC/aESzBPmHurK+6QcUCDEdvQ+4pcAoK/fv0ESqnyqnMe2I5C9XGxu2rqKiAp988gnWrFmDwYMHAwBmz56Nzz//HIsWLcJzzz2HJ598EldffTVefPFF1+06duzo7S5JQfn5+RgyZAjatm2Ll156CSdOnJuz6OwOyc/Px1VXXYV3330Xl156KeLi4nD33XcjMzMTiYmJiI2NxcMPP4yBAwc2apArwEIJSaR29wgAmM2eU8gzMjLwg5KhiIiIiIjI76qrq2Gz2c7rDomIiMCWLVtgt9vx5Zdf4p///CdGjRqF3bt3o3379jAajRg/frwiGUNl15tAWL9+PQ4ePIiDBw+idevWHtc5x6xWVVXhwIEDKC8/t2PVggULoFarMWHCBFitVowaNQpvvPFGoz8/h7mSNOr6+azr2L4jrZSMRQBaaxoxm4H85v0SvmOitNFR+0VHkM4Jm0Lt0+Th4vD69usifztt972zCfmfrznrFDitknMbPDh00KBBCA8Px/Lly5GamooPPvgAkyZNQqdOnbBp0yakp6cjMjISzz33HIYOHYq1a9fiiSeewLfffosrr7wy4F9LD2NovHb+xWQQHcHv2I9KhJpuE4PBAIMh9H7JiYiIiIhChclkQlxcnMeHyWSqc+17770Hh8OBVq1aQafT4V//+hduvvlmqNVq2O01Bd1x48bBYDDgkksuwYwZM/C3v/0Nb775pjJfjCNEPkIQT70hqen1ehgMBpjNZmRlZQEAen/RmG01yB/SYkpER5DSgUPpoiNI55XIq0RHkA5nCIjR9YpG7qpFTVZa1ZitTcgfDhcmiY4gpV+NjyEzM9PjmLcdVzp27IhNmzahrKwMZ8+eRXp6Om688UZ06NABLVq0QFhYGLp37+5xm27dumHLli0By0/NAwslJDVn9dnZUQIAuIqFEiIiIiKiYNSYrWidoqKiEBUVhTNnzmDdunV48cUXER4ejv79++PAgQMea3/77Te0bdvWn5G9C9FujFDAQgk1K3UNZG2o2oNbvfmiz38u6P7pwmm4FYgQOW0jRUeQTjttmegI0vnxL6miI0jpCv1J0RGk8zHnTimue4d80REk9USDV65btw4OhwNdunTBwYMHMW3aNHTt2hV33nknAGDatGm48cYbMXjwYNeMks8//xwbN24MUHZqLlgooWal9na+jeFr/oj7/eYeXXFB908XTsM6iRDhKpvoCNLh4D/lJWvOio4gJS3H4CluWNTvoiNIx+Lgz3mwKy4uhtFoxNGjR5GYmIgJEyZg7ty50Gq1AIC///3vePPNN2EymfDII4+gS5cu+OSTT3DFFVcoko+73gQvFkpISrU7U8xms+vY1McEBiMiIiIiIr+YOHEiJk6c6HPNXXfdhbvuukuhRNRcsFBCUqrdmWIwGFzH9hxZKTCZnJJU3EZShDIHB/8pTW3nW0dKs/EdXyFKHdWiI0jnSHWM6AjSWX36L6IjSOn1NqIT+BGfFgQtPnsgQs3uN86uEiIiIiIiIpIXO0pIGs6tgIHzB7uaTCYYjUbk5eUhXs25DUrTqlizFUGvqhIdQTqxqkrREeTDhxcholV8iqm0NE2p6AjSuT5xh+gI1MxxRknw4l8xkoZzK2Cg7sGuzmIJERERERERyYuFEiI3JpMJHV9JFh1DOum9CkVHkFL+r9w2VWm6VtweWGnVOdGiI0ip3xW/iY4gnV8/6iI6gnQq40UnkNP+WaIT+BE7SoIWCyUkvdo74KBNKE2IIiIiIiKioMRCSdBioYSkVHteSVZWluu663I7iYolrRRNuegIUjJ3ihQdQTqxaqvoCNLJ6d1CdAQp9dWxU1BpH9/XS3QE6VwdvVd0BEmdfwo9kb+xUEJScp9XYjQaPWaWXDdVRCIiIiIiIpKJSnQA8oqFEgoq550GU0vt3Wr8wb1oAgC5R1f4/XOQb/fn3Cg6gpT+3fEj0RGkU+XgUyKlaVXcyUyEGLVGdATpjIvZIzqCdPZVpoiOIKXuogOQFFgooaBisViwYMECr9fXtVtNU9Uuzkx9zO+fgoiIiIiIyBNnlAQtFkpIOrULI7VnlFzyJXdIUFr86/yeizDixkdFRyAKOH2c9y5FCpz2LU6JjiCdX/dyGL3S1JVq0RGkNO7Rhq0zmUxYtWoV9u/fj4iICAwaNAgvvPACunQ5t0NUYWEhpk2bhvXr16OkpARdunTBk08+iQkTJrjWtGvXDocPHz7vvmfMmOGPL4eCFAslJJ3aXSu1Z5RgOF+0ExERERE1Z5s2bcKUKVPQv39/VFdX44knnsDIkSOxb98+REVFAQDuuOMOFBUV4bPPPkOLFi2wfPlyTJw4ETt37kSfPn1c9/XMM8/g3nvvdV2OiYnxS0YVO0qCFgslFHIaO+fEZDJ53GZV7/8ENB+d745Inu8kwgt//VB0BOn01eeLjiCd3Ko40RGkdInurOgI0vkqva3oCNLpq88THUFSmQ1atXbtWo/LS5cuRUpKCnbt2oXBgwcDALZu3YpFixbh0ksvBQA89dRTWLBgAXbt2uVRKImJiUFaWpqf8lNzwEIJhZzGzDlxFkjcT785ls8Xj0o7Olx0AjnFaCpER5AOX7Qrr1UYX7CLYHPwbVKlxaj5mK60eDWHRYtgtVphtVo9jul0Ouh0Op+3Ky4uBgAkJia6jg0aNAgffvghrrnmGsTHx+Ojjz6CxWLBkCFDPG47b948PPvss8jIyMAtt9wCg8GAsDA/vJTmQ2XQ4ol1JDVnUSUjIwMGgyEgw2KJiIiIiMg/TCYT4uLiPD5q72JZm91ux6OPPorLL78cF198sev4Rx99hKqqKiQlJUGn0+H+++/H6tWr0alTJ9eaRx55BCtWrMC3336L+++/H88//zz++c9/Buzro+DAjhKSjl6vdxVEnKfhuD+4sqNEeevGviI6gpRO2fWiI0inJbt4FHfC7vsdRgqM1iq76AjS2VvBYa5KS9aUiI4gJaPRiMxMz9Nv6usmmTJlCvbu3YstW7Z4HJ85cyaKiorw9ddfo0WLFvj0008xceJEfPfdd+jZsycAeHyuXr16ITw8HPfffz9MJlO9n7de7CgJWiyUkHTciyIGg+G8mSbTHheRioiIiIiI6tOQ02zcPfTQQ/jiiy+wefNmtG7d2nU8JycH//73v7F371706NEDANC7d2989913WLhwId5888067++yyy5DdXU1cnNzPXbQodDCQglJTa/XIy8vz2N74CP5KwQmktPPlS1FR5BSOd9pV1ybyEOiIxApotRRLTqCdK6M3i86gnSqoBEdgXxwOBx4+OGHsXr1amzcuBHt27f3uL68vBwAoFZ7TqPQaDSw2713xWVnZ0OtViMlJaXJGbnrTfBioYSk5tzxxn02SSY7SoiIiIiImrUpU6Zg+fLlWLNmDWJiYlBYWAgAiIuLQ0REBLp27YpOnTrh/vvvx0svvYSkpCR8+umnWL9+Pb744gsAwLZt2/DDDz9g6NChiImJwbZt22AwGHDbbbchISFB5JdHAcZCCTUr7vNFvKm9/W99ag9/+uMoO0qU1k93THQEKRXaIkRHkE65XSU6gnRO2yJFR5BS5zDuBqK0tmGcl6G0Egc7SoLZokWLAOC8HWyWLFmCyZMnQ6vV4quvvsKMGTMwduxYlJaWolOnTli2bBmuvvpqADWn+axYsQKzZ8+G1WpF+/btYTAYzpuRcsHYURK0WCihZqW+idYAGrVzTe35JADw8GONjkVEREREREHE0YBtyjt37oxPPvnE6/V9+/bF9u3b/RmLmgkWSkgadRVFzGazx3wSAMhlR4niiu18KBKhyM532pWWpLbUv4j8Ss2364SwONhRorRwFTvWFMeHF2oizigJXnx1QtKwWCxYsGCBx7Ha3SdGoxH3T1EyFREREREREQUTFkpIarVnnpjNZoz/6R6BieRk+zZRdAQplfWtEB1BOjGx/J4rrTgvTnQEKV35l19FR5COxaYVHUE62QXctU+EA21EJ/AjdpQELRZKSGq1Z54YjUb8IigLERERERERicdCCYUcbzvjNGQ3HJPJhKn5fHdAaZF9+FAkwrcWdvIorXf4cdERpFPci48vIrQP47wMpZU6qkVHkI4tg+0AYjwtOoDfcEZJ8OKzBwo53nbGcS+e1B7seubMGdde6JmP8xFLaaftVaIjSCleXS46gnTsogNIqBLcvlMEDZ/9K67Szu85EZG/sFBCUqo92LWuHXGIiIiIiIgChvXNoMVCCRE8u1D2HflIYBI5VTnUoiNISatim7bSCm0RoiNIR6tiH48I3B5YecV2Pq1X2lm7TnQEKbUTHYCkwEdUkob77BL3eSW1u0nuzVQ8GhERERERyYYdJUGLhRKShnvXiPu8ktqn4UzYalU0FwG7fmsrOoKUNKe4laTSbJHsblCaqppDRUWYMPh/oiNI538nM0RHkE7hmVjREaT0W7uGrVu0aBEWLVqE3NxcAECPHj3w9NNPY8yYMQCAnJwcPP7449iyZQusVitGjx6N119/HampqYEJTs0K+91JSs7uEoPB0KDdcIiIiIiIqPlo3bo15s2bh127dmHnzp0YNmwYxo0bh19++QVlZWUYOXIkVCoVNmzYgO+//x6VlZUYO3Ys7Hbl3tRQOULjIxSxo4Sk5N5dYjQaPTpMXn18tYhIUqvim2BCFNnZUaI0zstQnt3BjhIREjWcgaS0qiTRCYiUMrNBq8aOHetxee7cuVi0aBG2b9+O/Px85ObmYvfu3YiNrekMWrZsGRISErBhwwYMHz7c76mpeWGhhKRXezvhI/krBCUhIiIiIiJ/s9lsWLlyJcrKyjBw4EDk5ORApVJBpzs3kFev10OtVmPLli3KFUpCtBsjFLBQQlTLkA+niY4gHf1xvuMrQlmPStERpKPWcicQpdmrNKIjSKlbu2OiI0inoopdgko7URotOoKUfky2wmr1nCmo0+k8ih5Oe/bswcCBA2GxWBAdHY3Vq1eje/fuSE5ORlRUFKZPn47nn38eDocDM2bMgM1mQ0FBgVJfCgUxzighwrnTb9xPwSEiIiIiouBiMpkQFxfn8VG7Q9ypS5cuyM7Oxg8//IAHH3wQkyZNwr59+5CcnIyVK1fi888/R3R0NOLi4lBUVIS+fftCrVbuJbLK4QiJj1DEjpJGqL2NLPmfUoNVa/9bms1mZGVlAQBuONxBkQx0TryaOw2JkF/Naf1KywgrFh1BOrnV8aIjSKmvjj/rSrM4OANJaRbOQBIiY/QhZGZmehyrq5sEAMLDw9GpUycAQL9+/bBjxw689tpreOuttzBy5Ejk5OTg5MmTCAsLQ3x8PNLS0tChA18LEAsljVJ7G1nyP6U6Omr/W7oPdL3hUUUiEBERERFRI3k7zaYh7Hb7eafttGjRAgCwYcMGmM1mXHvttU3O2GCh2YwRElgooZDlqwOodueKe7veukPrApqLzjfj4ATREaQ0Ku1X0RGk0zlul+gI0jllixEdQUpalIiOIJ09VZyXobQYdsQGNaPRiDFjxiAjIwMlJSVYvnw5Nm7ciHXrap7rL1myBN26dUNycjK2bduGqVOnwmAwoEuXLoKTUzBgoYRClq8OIGf3SF3FlNGPBDwaEREREREFkNlsxh133IGCggLExcWhV69eWLduHUaMGAEAOHDgAIxGI06fPo127drhySefVHxeoYodJUGLhRKSkl6vh8Fg8JhN4rTnSGtBqeTVNvqM6AhS6hWRJzqCdDhBQHmdw4+LjiAlrYr7BSits/as6AhEQWXx4sU+r583bx7mzZunUBpqblgoISk5T7Vxn03iVHKrQvumk8vIxL2iI0jpIu1J0RGk81VpN9ERpHNl1G+iI0jJ5mChRGk2vjOtuC/4mC7EQ6ID+BN/b4MW/4qR1EwmExYsWAC9Xi86ChEREREREQUBdpRQs9HY7Zkbs9Ww+zyTLs9yZyOl7fm9l+gIUrJM5ClPSjtbHCk6gnRexgjREaR0UWue8qS0YkuE6AjSKTzYQnQEKT3UVXQC/+GMkuDFQgk1G43dntnbMKa6Ci5ms/nc8cSMJuUkIiIiIiKi5ouFEpJOXQUXo9GIvLw8ZGVlIX3fGEHJ5LV4/0DREaT0Qa9loiNIp8qhER2BSBFdtNWiI0jntJ3fc6V90Lqf6AiSekx0AP9hR0nQ4owSItTMKsnIyFB8SzAiIiIiIiIKLuwoIfqTcyec9Yc4wVxpb/fNER2BSBGJGqvoCNLZX8kZAiJ00XIGktL0KpXoCNLpps8XHYGaOc4oCV4slFDI0uv1dXaI+BryajQaMey+QKYiIiIiIiICT70JYiyUUMhydojU5iyeeBvqus/aKuDZyNNfI38THUFKWpVddATpxPANX8XFq8tFRyBSBM+nV16ypkR0BCIKEBZKSFrehroC3NKQiIiIiIgCi6feBC8WSkg6zlNy6joFx2QyYc+R1gJSye3W7LtER5DSm73eFx1BOuGw1L+I/KoK3GmIiALjWHWC6AhUj82bN2P+/PnYtWsXCgoKsHr1aowfP951/ezZs7FixQocOXIE4eHh6NevH+bOnYvLLrvMtebHH3/E9OnTsWPHDmg0GkyYMAGvvPIKoqOjBXxFpBR26ZF0TCYTFixYgJSUFNcxo9EIg8HAXW+IiIiIiEJEWVkZevfujYULF9Z5/UUXXYR///vf2LNnD7Zs2YJ27dph5MiROHHiBADg2LFjGD58ODp16oQffvgBa9euxS+//ILJkyf7J6DDERofIYgdJSQt92GvZrMZWVlZAIBuT2eIjCWltp9xdwQRbvnHg6IjSCc8kR0lSnM4OBhGhK5pPI1VaactkaIjSOd4dqroCFK6cVrD144ZMwZjxozxev0tt9zicfmVV17B4sWL8fPPP+Oqq67CF198Aa1Wi4ULF0KtrukxePPNN9GrVy8cPHgQnTp1uqCvgYIfCyUkLfdhrwaD4dxw1xgWSoiIiIiIZFJZWYm3334bcXFx6N27NwDAarUiPDzcVSQBgIiICADAli1bmlwo4YyS4MVCCRFqukvy8vKQlZWFv+b0ER1HOmv+1ld0BCn9Pe6g6AjSGRDxh+gI0sm3xYmOIKUrdGdFR5BOqaNadATpfJrRRXQEKVmtU2C1Wj2O6XQ66HS6C7q/L774AjfddBPKy8uRnp6O9evXo0WLFgCAYcOGITMzE/Pnz8fUqVNRVlaGGTNmAAAKCgqa9oVQUGOhhAg13SXOOSXXTeVWb0rb/DvbFkWYdPkW0RGko1fZREeQTpK6THQEKdnBt0mVZuP3XHGdwwtFR5CSyWTCnDlzPI7NmjULs2fPvqD7Gzp0KLKzs3Hy5En8v//3/zBx4kT88MMPSElJQY8ePbBs2TJkZmbCaDRCo9HgkUceQWpqqkeXyQXjr23QYqGE6E/OU3G+y/1ccBIiIiIiIqqL0WhEZmamx7EL7SYBgKioKHTq1AmdOnXCgAED0LlzZyxevBhGoxFAzRyTW265BcePH0dUVBRUKhVeeeUVdOjQoUlfBwU3FkqIakkLKxcdQTotvrnwP2504ZIH8512pak5V1Rxkeoq0RGkVO6wi44gnSI7t8JW2mkbt4cVoSmn2TSE3W4/79QeAEhNrRne+84770Cv12PEiBFN/lwqPlR6lZubi2effRYbNmxAYWEhWrZsidtuuw1PPvkkwsPDvd5uyJAh2LRpk8ex+++/H2+++WajPj8LJUR/cg5z/cdjopMQEREREVFTlZaW4uDBczPZDh06hOzsbCQmJiIpKQlz587Ftddei/T0dJw8eRILFy5Efn4+brjhBtdt/v3vf2PQoEGIjo7G+vXrMW3aNMybNw/x8fECviJ57N+/H3a7HW+99RY6deqEvXv34t5770VZWRleeukln7e999578cwzz7guR0Y2flcwFkpIaq6dbnBui+AdeasFp5KP7hae4yvCSwWjREeQzuPp60RHkM5pG7dMFSEcpaIjSKfE7v0dVgqMPyqTRUegeuzcuRNDhw51XXaesjNp0iS8+eab2L9/P5YtW4aTJ08iKSkJ/fv3x3fffYcePXq4bvO///0Ps2bNQmlpKbp27Yq33noLt99+u38CckaJV6NHj8bo0aNdlzt06IADBw5g0aJF9RZKIiMjkZaW1qTPz0IJSc1isWDBggUA4BrmeotBcCgiIiIiImqyIUOGwOHwXo1YtWpVvffx7rvv+jMSNUFxcTESExPrXZeVlYX3338faWlpGDt2LGbOnNnorhIWSkhKzk4Ss9nsOuYc5pp7dIWoWNI6eZbn+IrwYuePRUeQTgx3vVHcEQffZRehhcYPu0FQo7xf1Ft0BOm0150QHYGaOVWIdJRYrVa/btlcl4MHD+L111+vt5vklltuQdu2bdGyZUv8/PPPmD59Og4cONCgopg7/hUjKTk7SVJSUlzHnB0lRERERERE1DAmkwlxcXEeH843oWubMWMGVCqVz4/9+/d73CY/Px+jR4/GDTfcgHvvvddnlvvuuw+jRo1Cz549ceutt+Ldd9/F6tWrkZOT06iviR0lJDW9Xu8qjjhnlKw7xBkCSuvX6ojoCFKKUVeKjiAdDrdXns3B94REqOKuN4qL4659ijttixIdgZo7H6cFNSeN2bL5sccew+TJk33en/vWy8eOHcPQoUMxaNAgvP32243OdtlllwGo6Ujp2LFjg2/HQgkFFffCRW3up8n4i3ul09lRMvoRv38aIiIiIiKikNSY02ySk5ORnNywQcj5+fkYOnQo+vXrhyVLlkCtbvybH9nZ2QCA9PT0Rt2OhRIKKt5atABc0Gkx7rvauKur6OL83P/57a+N/jzUNPmlcaIjSKmK77QrrsShEh1BOn9UptS/iPyun+6k6AjSaaM9LTqCdGycYkBNFCozSgIhPz8fQ4YMQdu2bfHSSy/hxIlzM4GcO9rk5+fjqquuwrvvvotLL70UOTk5WL58Oa6++mokJSXh559/hsFgwODBg9GrV69GfX4WSiikue9q485b0cVoNKLjnYFORURERERERN6sX78eBw8exMGDB9G6dWuP65w7GVVVVeHAgQMoL6859TA8PBxff/01Xn31VZSVlaFNmzaYMGECnnrqqUZ/fhZKSGq1O07MZjM+/O/fBSaSU9JPohPI6daxd4uOIJ2LkrlDgtJ+3dzw85HJfzYM7io6gnR++qON6AjSCSvkrloi3PJP0Qn8iB0lXk2ePLneWSbt2rXz2P65TZs22LRpk18+PwslJLXaHSdGoxHbBOYhIiIiIiIisVgoISk5h8bWnlViMpnQav8wQank9fHFfUVHkNKDbbaLjiCdIZGN25qOmm5HWuv6F5HfjYk8LjqCdI62tYmOIB3jYXYhU9NwRknwYqGEpOQc3GowGM47/Sb6Lg4WVdqpr1qJjiClYQa+aKfQ10Z7SnQEKVVxM2zF7bG2FB1BOv9otUF0BCIKEI5qJqnp9Xrk5eVBr9eLjkJERERERH6yaNEi9OrVC7GxsYiNjcXAgQPx3//+12PNtm3bMGzYMERFRSE2NhaDBw9GRUWFciEdjtD4CEHsKCGpmUwmGI1G5OXlISsrCwDw4cH+glPJ57frD4iOIKU/qmNFR5BOm7AS0RGko1dVi44gpWI7TwNR2ilbtOgI0tld3lZ0BCmNauC61q1bY968eejcuTMcDgeWLVuGcePGYffu3ejRowe2bduG0aNHw2g04vXXX0dYWBh++uknqNXsJSAWSqiZqX2aTH1qzyCpi7NY4twyeMCUC45HRERERERBYOzYsR6X586di0WLFmH79u3o0aMHDAYDHnnkEcyYMcO1pkuXLopm5IyS4MVCCTUrtXepqY+z+FEf58wSAHjp14bWqclfHm+1VnQEKe2xcMil0tqFnRUdQTo2nmUshF6lEh1BOvnWBNERpJOq5WO6CFarFVar1eOYTqeDTqfzehubzYaVK1eirKwMAwcOhNlsxg8//IBbb70VgwYNQk5ODrp27Yq5c+fiiiuuCPSXQM0Anz0QERERERFRs2AymRAXF+fx4f6mp7s9e/YgOjoaOp0ODzzwAFavXo3u3bvjjz/+AADMnj0b9957L9auXYu+ffviqquuwu+//67cF+MIkY8QxI4Skl7t03lS7tUITCOnKFWV6AhSaqU9LToCUcBVOfiekAg6Fb/vStt7lrveKG1I0m+iI0jJaDQiMzPT45i3bpIuXbogOzsbxcXF+PjjjzFp0iRs2rQJdnvNzlz3338/7rzzTgBAnz598M033+Cdd97xWnghebBQQtKrfTqPad/VAtMQEREREZE39Z1m4y48PBydOnUCAPTr1w87duzAa6+95ppL0r17d4/13bp1Q15enn8D+8AZJcGLhRKSSl3DYGsPfL0yar+SkQiAxcEuHhFi1A0fjEz+UeXg3AalFdkjRUeQkhbcbUhp0Vpr/YvIr2I0Cm4jS35ht9thtVrRrl07tGzZEgcOeO68+Ntvv2HMmDGC0lEwYaGEpFLXMFiDweBRQLnhUQHBiIiIiIjIb4xGI8aMGYOMjAyUlJRg+fLl2LhxI9atWweVSoVp06Zh1qxZ6N27Ny655BIsW7YM+/fvx8cff6xcSDtbSoIVCyUkPb1ej7y8PGRlZQEAfspT8MGRSKATtljREaSTxncfFRevLhcdQUr2UJ3uF8SuSvhVdATplNvDRUcgH8xmM+644w4UFBQgLi4OvXr1wrp16zBixAgAwKOPPgqLxQKDwYDTp0+jd+/eWL9+PTp27Cg4OQUDFkpIeiaTCUaj0bWV8OSG7ShMRERERERBavHixfWumTFjhmteiRCsKQctFkoopOn1elcBBDh/HomT+2TrHXmrA56LPFnsfCgSYZ+FOyQorbP2hOgI0imx60VHkBR3M1Na/4hc0RGkc8IWJToCEQUIX51QSKu9tZd70aQuRqMR1z0YyERERERERETc9SaYsVBC0qprB5wzZ85AA7ugRETKuiwyR3QE6Wj5jEhxavY1C1HFv6WK0/PxRXEWh1Z0BCIKEBZKqNlwDl1t6n04u0rMZrNrgKu7Ls8sOO8YBVZVJw64FMFexW2Zldajfb7oCNL55TBPMROhd/ujoiNI5/eTLURHkE7F0RjREaR06CHRCfzIwQJnsGKhhJoNk8lU76kzDbkPJ/f78uguScho0ucgIiIiIiKi5ouFEpKWt+6SQTn9RMaS0iW6AtERpLShrLPoCNIZEfWb6AjSKWwbITqClFpyK2zFncjQiY4gnSM9E0RHkNRjogP4Dc+YC14slJC03LtL3LcHHhRK7XxERERERBScWCgJWiyUEMGzaNJu4csCk8jp8r/sFx1BSuHqatERpDNv/bWiI0gnudMp0RGkNKIlH9eV9vX8K0RHkM6J/nyVK8J1j4hOQDJgoYSkVNeONy6dWisbhoiIiIiIpKPiMNegxUIJSclisWDBgrp3t1n91ksKp6Hdn3UXHUFKcVceFx1BOo4YdvEo7VRRtOgIUtqgvkh0BOmUtFWJjiAdTRm/58GsXbt2OHz48HnH//GPf2DhwoUCElFzwkIJhRSfnSJuzGaz99t0bRWIaEREREREpJAdO3bAZrO5Lu/duxcjRozADTfcIDBVLXbRAcgbFkoopPjqFHHnvjVw7dtcfahbQLKRd/8pvFJ0BClNb/WV6AjSqeqiER1BOmftetERpJQRViw6gnTWZ3QVHUE6cZpy0REkZah/CYDk5GSPy/PmzUPHjh1x5ZV83kn1Y6GEpFR7a2AiIiIiIgpNlZWVeP/995GZmQmVKnhOmeKMkuDFQglJydvWwAAw2VAqIpLU+sWdf/4oBV6iukp0BOmUONhjq7QyR7joCFJqoVGLjiCdSHWl6AhEirBarbBarR7HdDoddDqd19t8+umnKCoqwuTJkwOcjkIFCyUkPfeiCQD8lPexoCREREREROSLyWTCnDlzPI7NmjULs2fP9nqbxYsXY8yYMWjZsmWA0zUSG0qCFgslRLWM+2qq6AjSafsZ32UXYfE9A0VHkI7lDOdlKE1lZWeDCD17slNQaft+aC86gnQcWtEJ5PSrcQoyMzM9jvnqJjl8+DC+/vprrFq1KtDRKISwUELNivtskbpw3ggRERERUeiq7zSb2pYsWYKUlBRcc801AUx1gTijJGixUELNSu3TZGrzVUSpzdtWwh8++mljY1ETaa+21b+I/C7bkiE6gnS66o6JjiCdeLW1/kXkdy05o0RxO1pHi44gnSRNmegIksqsf8mf7HY7lixZgkmTJiEsjC99qeH400LS8raVcPv3UwSkkZtKw2q6CGFaFqiUVlnKwaJKS07lNrUiROtYoFJa0SetRUeQTvlVJaIjSOlAI97n+frrr5GXl4e77rorcIGaQMWnwEGLhRKSjrOTxHmaznmdJf1YKCEiIiIiau5GjhwJB09voQvAQglJx9lJ4twW2Gw2Iysry3X92NwuAtPJyeZgi7YIGhWH6CptxckBoiNI56YW20VHkJKGWzko7vepqaIjSCdOUy46gqSeFh3Af1jECVoslFBIqW/YK3Bu4Ktz3omzYOI0lpveEBERERERSYuFEgop9Q17Bc4f+Fr7Nm8cyPZnJGqAyyMOio4gJYuDfwKU9mjq16IjSCeSnVNCxKv5+KK0s3bO41Ha8eo40RGomeOfqODFv2IkLW+73nR+QEAYIiIiIiIiCgoslJB0nKfn1J5N4vTivjECUsntvTODREeQ0tVx2aIjSKeoOkJ0BOkka0pFR5BSvJrn3SstXFUtOoJ0dKoq0RGoueOMkqDFQglJx9tsEqf0e5VORERERERERMGChRKSlrd5Jr0yFyichKojRSeQ08cX9RUdQTqOKpXoCPLht1yIFumcl6G0oS0570tpn+y5RHQEKd3eWXQCP2JDSdBioYSkVuecElWGmDBEREREREQkHAslJJXahZG65pQs+53zMpSWFlYkOoKU4tUVoiNIp8wRLjqCdKJUlaIjSClRYxUdQTpavjWtuM79C0VHkJRRdAC/UXFGSdBioYSkYrFYsGDBuVNrnDNK3Asol/xDSDQiIiIiIiIKAiyUkNTq2gGny7OcUaK02D9YTRehbNxZ0RGkU17EXW8UV6ERnUBKrTqdEB1BOvow7sCitEPZrUVHkNL9XRq+dvPmzZg/fz527dqFgoICrF69GuPHj3ddv2rVKrz55pvYtWsXTp8+jd27d+OSSy5xXZ+bm4v27dvXed8fffQRbrjhhgv8Kv7EjpKgpRYdgEgkk8mEBQsWICUlRXQUIiIiIiLyo7KyMvTu3RsLFy70ev0VV1yBF154oc7r27Rpg4KCAo+POXPmIDo6GmPGjAlkdBKMHSVEONdZAgCqOA5zVVr875yVIcKJYnY3KI7dDYpT2bjtjQgqFd8lVVqfxHzREaSTZ20jOgLVY8yYMT4LGrfffjuAms6Rumg0GqSlpXkcW716NSZOnIjo6OimB7Q3/S4oMFgooWajzh1qGslsNtd53H2r4M3/fbJJn4Mar3xvgugIcioTHUBCMdWiE0jHweKUEBVVWtERpJNT2kJ0BPl05B9SEaxWK6xWz4HROp0OOp0u4J97165dyM7O9tqhQqGDhRJqNmoPYr0Qzq6R2jyKMCMjm/Q5iIiIiIgoMEwmE+bMmeNxbNasWZg9e3bAP/fixYvRrVs3DBrkn10yuetN8GKhhKTka5vgG7c9ICqWtA4m8l0wEdQJPOVJaSmJJaIjSKfwUJLoCFK6KIHDXJUWE8YtmZVWWRz4DgY6n9E4DZmZmR7HlOgmqaiowPLlyzFz5syAfy4Sj4USklLt7hSj0Xiu22SioFBEREREROSTUqfZ1Pbxxx+jvLwcd9xxh//ulB0lQYuFEiJ4zijp+MorApPIKX1igegIUjr1G3d7Ulyi6ADy0Zk5o0SEaju/70orruKAbqJAWrx4Ma699lokJyeLjkIKYKGEpOLc3cZsNnsfDtuGE8yJiIiIiJq70tJSHDx40HX50KFDyM7ORmJiIjIyMnD69Gnk5eXh2LFjAIADBw4AANLS0jx2uzl48CA2b96Mr776yr8B2VEStFgoIak4O0cMBoPX4bCf/vtlpWNJr3ppWv2LyO8cw7gDi9Jsdm5Vq7TKBO69KMJJCwejK61l1FnREaQTkchZX8Fu586dGDp0qOuyc7bJpEmTsHTpUnz22We48847XdffdNNNAM4fDvvOO++gdevWGDlypDLBSTi/Fkqc79aHKm9by1JocHWYdGwtOgoRERERETXRkCFD4PDRtTF58mRMnjy53vt5/vnn8fzzz/sx2Z9Yyw9afi2UuM95CEWhXASSjV6vR15eHoBzBRLnzjdrPgjAgyD5FP/VIdERpHT8sq6iI0gnMbJcdATpnNBwMIwIiXq+0640i00rOoJ0NBq+yiUKVWrRAYhEMJlMSEmpGWTpPAUnIyODxTAiIiIiIlKEyuEIiY9QxBklJC33wa6AW0dU9k0CU8npj89aiI4gpfsSvxEdQTpXRu0XHUE6trZ8T0iEruHsKKHQtzaZGwCIMVt0AJIACyUkLffBrh474EwSGIqIiIiIiOQQot0YoYCFEhLK6xa9dQjUMF3nvJKsrCwAQOd55++EQ4FVHcVzfEX4JZW7DSntg4i/iI4gnbP5saIjSKlz13zREaRzcXyB6AjSWf2/fqIjSOn2zqITkAxYKCGhvG3RW5dAzQ8xmUyeXSWpGQH5PERERERERC7sKAlaLJQQoaarpKCgAAkJCXh6woei40jnuRU3io4gpY/Hvyk6gnSiVDbREaRT1Js7gYiQqK4SHUE6epVKdATpjB/5o+gIkpomOoD/sFAStFgoIYLn1tYfHuwvMImcrIk89UaEHRXtREeQzrDIHNERpKMBn4SKMO/4cNERpDMvfaPoCNIx22JERyCiAGGhhKTkazbKgCkKhyEiIiIiIvnwvcKgxUIJScnXbJS3DlypcBqaP3q56AhSStGUiI4gnRKHRnQE6eRUcftxEa5N3C06gnTKHTy1T2lRaqvoCOSDyWTCqlWrsH//fkRERGDQoEF44YUX0KVLl/PWOhwOXH311Vi7di1Wr16N8ePHAwCWLl2KO++8s877P378OFJSUgL5JZBALJRQyPLVNVJ7Bx33tV0fCHg0IiIiIiIKoE2bNmHKlCno378/qqur8cQTT2DkyJHYt28foqKiPNa++uqrUNUx5+fGG2/E6NGjPY5NnjwZFovFL0USFWeU+HTttdciOzsbZrMZCQkJGD58OF544QW0bNnS620sFgsee+wxrFixAlarFaNGjcIbb7yB1NTURn1uFkooZPnqGjEajR676JjNZtf2wG8cGKpIPjqnVdgZ0RGkFKOuFB2BKOAsjnDREeTEdnLFVfIFl+KSNaWiI5APa9eu9bi8dOlSpKSkYNeuXRg8eLDreHZ2Nl5++WXs3LkT6enpHreJiIhARESE6/KJEyewYcMGLF68OLDhCQAwdOhQPPHEE0hPT0d+fj4ef/xxXH/99di6davX2xgMBnz55ZdYuXIl4uLi8NBDD+G6667D999/36jPzUIJScl9eCvgWTjpzI4SIiIiIqKQUlxcDABITEx0HSsvL8ctt9yChQsXIi0trd77ePfddxEZGYnrr7/eP6FY4PTJ/Y3ttm3bYsaMGRg/fjyqqqqg1Z6/q11xcTEWL16M5cuXY9iwYQCAJUuWoFu3bti+fTsGDBjQ4M/NQgkRPAsn//zpBoFJ5NRVd0x0BCklq+o+NY0C50g1d0hQWtfwAtERpPSL1XtbNAUGm3iUN+/o1aIjSGl5qhVWq+d8GJ1OB51O5/U2drsdjz76KC6//HJcfPHFruMGgwGDBg3CuHHjGvS5Fy9ejFtuucWjy4QAq7Xx/yaNdfr0aWRlZWHQoEF1FkkAYNeuXaiqqsLw4ed2XuvatSsyMjKwbdu2RhVK1E1OTBQCnB0l7lVLIiIiIiIKLiaTCXFxcR4ftbvFa5syZQr27t2LFStWuI599tln2LBhA1599dUGfd5t27bh119/xd13392U+J7sjpD4uJB/k4aaPn06oqKikJSUhLy8PKxZs8br2sLCQoSHhyM+Pt7jeGpqKgoLCxv1edlRQtJyH+DqPqNkwtZ/iIwlpY+qLxUdQUqPpX4jOoJ0YrhDguK0Kr7PLkJBVYLoCNKJizoqOoJ0JqbsEB1BSuONW5CZmelxzFfnwkMPPYQvvvgCmzdvRuvWrV3HN2zYgJycnPNeVE+YMAF//etfsXHjRo/j//nPf3DJJZegX79+Tf4aQo3RaGzwv8mMGTPwwgsv+Ly/X3/9FV27dgUATJs2DXfffTcOHz6MOXPm4I477sAXX3xR5/Bdf2KhhJoNvV7fqI6P2jvb1OY+7NV5v0ajERh74RmJiIiIiChwGnpKh8PhwMMPP4zVq1dj48aNaN++vcf1M2bMwD333ONxrGfPnliwYAHGjvV8QVBaWoqPPvrIb10SbiH9e3+CNOY0m8ceewyTJ0/2uaZDhw6u/2/RogVatGiBiy66CN26dUObNm2wfft2DBw48LzbpaWlobKyEkVFRR4FsOPHjzdoBo07Fkqo2WjsA1NjiirOIozZbMau3X0bG42aKO43ngUowrVDOomOIJ2KE5GiI0hHm8AuHhHi1vFnXWlZ4/8iOoJ0ivPiREeQ0o0NfPoyZcoULF++HGvWrEFMTIzr1Iu4uDhEREQgLS2tzhfPGRkZ5xVVPvzwQ1RXV+O2225rcn7ZJScnIzk5+YJua7fXdInWnofi1K9fP2i1WnzzzTeYMGECAODAgQPIy8urs7DiCwslJA33U20Az44TZxHGaDSicRtHERERERFRsFm0aBEAYMiQIR7HlyxZUm9HQ22LFy/Gddddd95pOk0WIh0lgfDDDz9gx44duOKKK5CQkICcnBzMnDkTHTt2dBU98vPzcdVVV+Hdd9/FpZdeiri4ONx9993IzMxEYmIiYmNj8fDDD2PgwIGNGuQKsFBCEnE/1Qaou+PEZDIh+pe/KRmLAOy8pK3oCFKa1vq/oiNIp8rBP7tKK7Kzs0GEsj7+2+mAGuavEXmiI0hnR7fGtfKTvzzeoFWOCyhCeLvN1q1bG31f1DSRkZFYtWoVZs2ahbKyMqSnp2P06NF46qmnXKf5VFVV4cCBAygvL3fdbsGCBVCr1ZgwYQKsVitGjRqFN954o9Gfn8/YiODZbdLinnoWk99dl7JLdAQpxagrRUeQTpGNp5kpTauqFh1BSiU2npKgtBI7H1+UlqwpER2Bmjt2lHjVs2dPbNiwweeadu3anVfc0uv1WLhwIRYuXNikz89CCUnLfTis2WxGRkaGx6k5REREREREJB8WSkha7sNhjUYj8vLykJWVhbcOXCkwlZwuCj8uOoKUolQ20RGko9FUiI4gnaIqnnojgpaPL4pLVPOdaaWV2PlzTk1k5+9tsGKhhEJW7e2EfW0XbDKZYDQaYTAY0PUBJdIRERERERFRMGKhhEJW7e2E69su2Ln+j6PpActEdZt66HrREaT0WvuPRUeQzvaK9vUvIr/qocsXHUFKB60ccqk0C2cdKE6t4vecmshhF52AvGChhAiew1wffkxwGCIiIiIiIhKGhRKSknthBKg5LScrKwsA8F3u56JiSeuOltxyTQTuj6C8juHeTwGkwDhr14uOIKUSG7/vFPpiVdw9jpqInWBBi4USkpLFYsGCBQtcl53zSQDguqmiUhEREREREZFoLJSQNGpvB+zOfZ7Jjdu4K4XSbktlR4kIPCtWeTaoREeQTjh3XxHitvgdoiNIR6/i44vS/qiOER1BSl1EB/An7noTtFgoIWm4F0PcB7vWPg0HE5VMRURERERERMGEhRIBznthLjFfW/YGUu3uEud8EgDYd6SVkEwy21h+kegIUuqoPSU6AlHAmW18x1eEZE2Z6AjSieQOLIorsUeIjkD12Lx5M+bPn49du3ahoKAAq1evxvjx413XHz9+HNOnT8f//d//oaioCIMHD8brr7+Ozp07u9bk5OTg8ccfx5YtW2C1WjF69Gi8/vrrSE1NbXpAzigJWiyUCFB7PobM6tuyN1Dcu0vc55MAwL2ZIhIREREREZE/lZWVoXfv3rjrrrtw3XXXeVzncDgwfvx4aLVarFmzBrGxsXjllVcwfPhw7Nu3D1FRUSgrK8PIkSPRu3dvbNiwAQAwc+ZMjB07Ftu3b4dazdH4oYqFEpKee9EEAF76dZ+gJPL6a+RvoiNIKbc6QXQE6WjBeRlKywg7LTqClLKtrUVHkM6IyKOiI0jnSGWi6AhUjzFjxmDMmDF1Xvf7779j+/bt2Lt3L3r06AEAWLRoEdLS0vDBBx/gnnvuwffff4/c3Fzs3r0bsbGxAIBly5YhISEBGzZswPDhw5sWkB0lQYuFEiJ4ng7V6j7BYYiIiIiIKKCsViuAmlPyndRqNXQ6HbZs2YJ77rkHVqsVKpUKOp3OtUav10OtVmPLli1NL5RQ0GKhhKTlXhxxn1OyI6+dwFRyStNYRUeQktbG7gal2cAWXaXFqipFRyBShJq7aimutz5PdAQpWa1WV5HDSafTeRQzGqJr167IyMiA0WjEW2+9haioKCxYsABHjx5FQUEBAGDAgAGIiorC9OnT8fzzz8PhcGDGjBmw2WyuNU3CjpKgxUIJSct9VszkyZNdc0oy7uNgUaWNi9kjOoKUNOAfZ6V9cbaX6AjSuTl+p+gIRIqocnDTd6Xts3IDABG+MZkwZ84cj2OzZs3C7NmzG3U/Wq0Wq1atwt13343ExERoNBoMHz4cY8aMgePPAkZycjJWrlyJBx98EP/617+gVqtx8803o2/fvpxPEuJYKCECkJ6ezp2IiIiIiIiCnNFoRGam5+4Lje0mcerXrx+ys7NRXFyMyspKJCcn47LLLsNf/vIX15qRI0ciJycHJ0+eRFhYGOLj45GWloYOHTo06esAANhZ4AxWLJRQSPO1FbP71sTuA13n/vK3gOciT5EqtguLUCI6gIRaaPldV9oJm77+ReR3Jfy+K06r4rvbSovR8E02ES7kNJv6xMXFAagZ8Lpz5048++yz561p0aIFAGDDhg0wm8249tpr/ZqBggsLJRTSfG3F7DzVpnYxpcU9ikQjIiIiIqIAKi0txcGDB12XDx06hOzsbCQmJiIjIwMrV65EcnIyMjIysGfPHkydOhXjx4/HyJEjXbdZsmQJunXrhuTkZGzbtg1Tp06FwWBAly5dmh6QM0qCFgslJC29Xg+DweAxyBUA2v5nvsBUctrYqbPoCFL6LSdddATpaE9pRUeQTnUU25pFcOj4fVfa2m65oiNIJ/tHP5x6QY12y8MNX7tz504MHTrUddl5ys6kSZOwdOlSFBQUIDMzE8ePH0d6ejruuOMOzJw50+M+Dhw4AKPRiNOnT6Ndu3Z48sknXW+4UuhioYSk5Tzdxmg0ej7Y9WgpKBERUeh64aMPMGHXDvzQoSNuvX+K6DhERCSBIUOGuAaz1uWRRx7BI4884vM+5s2bh3nz5vk7Wg12lAQtFkpIeu7zSQAgM5/vsitNyy0Nhfi9XaToCNIpcYTm3Aa11Y62751G+pdnEZVjhbraAUuaFqcGRiHn/haoyAhHr//lA7uATskF+NcNi/36+XtNy0frT4pw6rJI/PBBe4/rktRlfv1ctbWdfQpJn5bhx51tELPLiu43FuLX91Jx9q8RAIDwgmq0fL0IMbusCC+wQVXtgLV1GE5cH43jd8bCoQ3Nx7+WYZzdoDT+LVXejvQWoiNI6jHRAUgCLJRQs+BrKKsv7gNbG3rfmY83+tMQEUkrrNiGy27LRdwvNY+jVdFqlGeEQ3+sChkfnMGZPhHIzwgXnLLxVJUOOMLreeHpcCBxbTnOjIgEwlRI/G8ZqhLVODvoXEFMl1uF1OWlsEWpYGmrhe5IFSJ/q0Lb589An1eN3OeSAvyVEBFR0LKzoyRYsVBCzYKvoay+NOT8wdr3vefIykZ/HmqaKJVNdAQpHahk95TS+uoPi47gd+1mn3IVSY7fH4P8f8YBYTUFhugfLNCHVSNNcxYRqkoAQLjKhjTNWQBA37ZHAAC5LyXi9A1RAIDON5oRs92KU9dH4vDLNUWElLfPosWKMoTn2+DQAtbWYSj5qx75T8ajx+XHoDta8xiS9EM5ru7wCwDgtxXJKB2oh+54NdLnFyNmkwWaM3ZUpWlw+oYomKfEuHJ2vNGM6O2VOP33SFQnq5HwSTnsESrs/77u35GElWXIePyM63LKylKkrCx1Xb6s42Gcvj4SR15OhCbBgSPzEnDmukg4dCpoiu3ofM1x6I7Y0GJNKQrnxvnhXyH4LDx1uegI0pnWYrvoCNL5X1lH0RGkNFZ0AJICCyUklbo6U8xms8fxuzLruiUREdWmPmtHwpflAIDy7lrkG+MAt+2+Sy9r+qlGcesr0HpuMQCgonMYVHZAf6gampJy5D8Zj4oe4VCXW6E9bYctWgVL55qBubZoNTRnbOg83ozwYzbYolWwdtJC/3sV0l85C92Rahx5KdHjc8V/WQ44AGuHMEDtvZukOlGNsj7h0JptCM+3obyXFqpqIGJfFawZGlQnaWDNqHmKZekWDku3cx01tjg1LF200B2xwV5fxwoREYU0h4ODr4MVCyUklbo6UwwGg8fx3KMrRESTWriKLxZE6BBe/6lp5F+5VaF1mkXC7+VQVdf8f0G/OORWez9fP8leiiSUw2IPc30f+qKmo+SkLQp5fx5rYz+NGFhRatchtyoJnQ7W/Jwevzwa3y/rBKBmJkr8LxU4XRWF3IVJ6PfPw2i76gxOd4/Ed8vP7aLV9V+FCD9mQ2ULDXauzUBVUhiS/q8UF99fgISPy/H7P5JhaReOaofmz1uo8OOnbVDWXQfYHIC97semoqERODI0EZ2fOI7ktaXYsaYdEr4rQ687jmH/vDQUDfxz/k8dz38jcioRvdUKADh2YxyK7BH1f6OboX5Rh0RHkE4VX3ApbkzsT6IjEFGAsFBC0tPr9cjLy3N1lUzlfCgiooZxm9bvCFC90zw4BraXVUj9vhTX9N+Dkg56FF0cgcMTEuu9beLPNd0u4SdtGPQXzxfuKgcQm22Bpd25bo+iARE1RRIA0NT/BcVmW1DSq6ZrJibbCocaKOnpvYsm5icLLr7nGDTlDpwYHYVcQ2gVzoiIqJE4oyRosVBC0jOZTDAajcjLy0NWVhaO5X8oOpJ0dCq16AhSSlKXi44gHS1Cax6PppMN9jBAXQ2k7TqLAvVZj1Nv3On/bD3RqmxI1pR4XBfrsLiORZRWudYna0qAbsCP/9cGKZ+VIOqXSkT9akWLZWVo/9Ep7FrfBtZWWq/3Hf7ncXu0CpWdz3/KkxBlhS6sBDrn7VPsaBNWct46d2FHqpEx6OS5A79W4sr2v7suXtEzB1Wt1TiyLcXjdpHrLEh5uBjqCgfO3hqBkrnRaKMpRagqczS/Ab7NnV6lqX8R+VWapkJ0BCIKEBZKKKTp9XqPga7edsExmUwwGAwwGo14+CGl0hERNW+2WA1OXhONlDWliP6lEu3mn0ZuZqJrSGr8lnLYItQo6Vd3l0Vlkgbhp2yIOFRTHInIqUTUgUqPNfpDlYBahbxHajpIVJUODPhLLsJK7Ij+2QprKy1sETWfT1Ph+c5cSS8dEr8th0MDmBfGobpNzdMeVakdUWutKB/T+BkqjnAVLH20UJfYEX7QhsqLwmCPUkH3UxVsLdSobqWBLcWz+Bu7uAxJz5QADuDUE9EofjC60Z+XiIhCkIMdJcGKhRIKaSaTyeOyr11wnKfgVPIBS3GVjtB6l725yKlKEx1BOj9XZIiO4Hdbp3XGHQe2In3/WbRZVITk90pR1DIScYUViDhbhU+fvQTZXTIQU1WJVJSgyBaFb0p7AADiL7Wg53/z0eo/xaj+UYO0/WfhcAAqAAVV8fimtAf6bj6Ma2f/hJJkHUpb6BF1yoqwEjvsGhU2tOqOk6UxKG39B1piL2J+tuKiEWZURYZh6eJBCL+uGoYP10NbaEfrISdR1SkMqlIHtAU2qKqA4utrZok4oHL91wbfp9zYUsNw5LMWSHijFEnzS3DkkySoi+1of8UJnHwiBiUTIj3W63dVosXsmi4VW7QKkf+1IvK/Vtf1Bf8vAbbU0OsEOFLJ04qUVhJ+SnQE6RTZtaIjUHNn52yhYMVCCdGfnKfg8OFKeSV2PhSJ0EZ7pv5F5FeWUDwdQQ9s+vgiXPT+cbT96jRicyqQnFuC8tRwHBydDNUgOy7SFyL2zxb1CHUlLtIXAgB+n5mMxIpSpOwoQcrREux/MA0tvy1C6o4SxGoqcJG+ENre1TgyIgEJ+8qQknMWNp0aJy+Jwr5705HYvQyJKEPJTTrk7U5A2tazSD1YU5S4SFuI6pYa/G9VW7RfcApJm8qg/a0aVYkanOkfgZNXReNodQwAoIWjCJEAyhxaHPnzWH2Svy5CUd8IHI6OQ+uVZ+BQA7//tQWqqj2LHvEV5Wjz5/9rSh2I2F3lcX1hRRQs1aH3YitNWyw6gnT0PI1VcTlVyaIjSKmv6AAkBb46IenVtWUwERE1nF2nxv6707H/7nSva7a/2AHbX+zgccySHI5N/6+Lx7Ff7/W8jzMXR+G7NzrDl+ooDbb8u+411nQt9r/ou3tq94o2Pq+vy48fnbvN0TsTcPTOhDrXFQ2IxIZDFzX6/omIqGlMJhNWrVqF/fv3IyIiAoMGDcILL7yALl3O/d25//778fXXX+PYsWOIjo52renatavHfS1duhSvvPIKfvvtN8TGxuKGG27AwoULmx6SnexBi4USkl7tLYO5PbDy4tTVoiNIaVNF418cUtPoVVX1LyK/StaUiY4gJZuD3Q1KU9dz2hj5X9fw46IjkA+bNm3ClClT0L9/f1RXV+OJJ57AyJEjsW/fPkRFRQEA+vXrh1tvvRUZGRk4ffo0Zs+ejZEjR+LQoUPQaGo6BF955RW8/PLLmD9/Pi677DKUlZUhNzdX4FdGSmChhKRSe7gr4Dng1Wg04v4pSqciIiIiIiJ/Wrt2rcflpUuXIiUlBbt27cLgwYMBAPfdd5/r+nbt2uG5555D7969kZubi44dO+LMmTN46qmn8Pnnn+Oqq65yre3Vq5dfMjo4oyRosVBCUqk93BWoKY44iydmsxnhXra2pMAp4d8IIRJDeGvSYJWi8b31LPmfBmxrFmHlqUtFR5BO/5YbREeQTpWDzxlFsFqtsFqtHsd0Oh10Op3P2xUX18xOSkxMrPP6srIyLFmyBO3bt0ebNjVdt+vXr4fdbkd+fj66deuGkpISDBo0CC+//LJrDYUmFkpIqLo6POribVtff3AvnhiNxoB9HiIiIiIiahqTyYQ5c+Z4HJs1axZmz57t9TZ2ux2PPvooLr/8clx88cUe173xxhv45z//ibKyMnTp0gXr169HeHjN8PU//vgDdrsdzz//PF577TXExcXhqaeewogRI/Dzzz+71l0wzigJWiyUkFB1dXjUpSHFFH8wmUzo8EHDdjwg/2n/Nt+REeHg7aG3JWmw0xWG3u4mwU7dlV08IliOR9a/iPzqSI940RGkc9aqFx1BShuMRmRmZnocq6+bZMqUKdi7dy+2bNly3nW33norRowYgYKCArz00kuYOHEivv/+e+j1etjtdlRVVeFf//oXRo4cCQD44IMPkJaWhm+//RajRo3y3xdGQYWFEiLU2vnmUm71RkREREQUjBpymo27hx56CF988QU2b96M1q1bn3d9XFwc4uLi0LlzZwwYMAAJCQlYvXo1br75ZqSn1+zE1r17d9f65ORktGjRAnl5eU3/YuzsKAlWLJSQdOraDthsNiMrKwsA8OUnz4qIJbX8wdGiI0gprIh/nJVm0/N7rjRbJZ/qiBCVx441pcVeYq1/EfnV8VJ2IQczh8OBhx9+GKtXr8bGjRvRvn37Bt3G4XC4ZqBcfvnlAIADBw64iiynT5/GyZMn0bZt28CFJ+H47IGkU3s7YMBzoCuuqHvAExERERERNQ9TpkzB8uXLsWbNGsTExKCwsBBATQdJREQE/vjjD3z44YcYOXIkkpOTcfToUcybNw8RERG4+uqrAQAXXXQRxo0bh6lTp+Ltt99GbGwsjEYjunbtiqFDhzY9pIM7GgQrFkooJNTVJeJNXYNh3Wel3J6X4bdc1DAl/XiOrwhp3PWGJJBTzeK3CDsv7iA6gnQeTNwlOoJ0itqwS1CM5xq0atGiRQCAIUOGeBxfsmQJJk+eDL1ej++++w6vvvoqzpw5g9TUVAwePBhbt25FSkqKa/27774Lg8GAa665Bmq1GldeeSXWrl0LrZZzx0IZCyUUEurqEvHG2TnirbhyuzJzY4mIiIiIKEAc9ewo07JlS3z11Vf13k9sbCwWL16MxYsX+yuai4MzSoIWCyUkHeeWxO5zSdzlHl0hIJXcYtTVoiNIyeJQi44gHQ34hEhpUapK0RGkNCb2Z9ERpFPFbUYVp+VjOlHIYqGEpOM8zcZgMNTZVTL1MRGpiIiIiIhIKpxRErRYKCFp6fV65OXlnddV8mPeJ4ISyUuvsomOIKUiG2fDKK3QFic6gnT0qirREaTUMowzkJTGHkHlaVSiExBRoLBQQtIymUyeu938acQjsYISyeuRHTeJjiClzwYuEh1BOvusrURHkM5fIw+KjiClI9XcNlVp3bUloiNIJ7cqSnQEKbURHcCPOKMkeLFQQlJqzC45REREREREJA8WSqhZcA5g9aauLX998bVLzr4jfMdXaTd345aGIuyxthQdQTo99UdER5AOB+iKEaO2io4gHa2KJ98orQoa0RGoueOMkqDFQgk1C84BrN7Ut+Vvbe6Fldq3uTfzAkMSERERERFRs8dCCYUUX50i7ty7U2rfpt27qQHJRt61/pQPRSK8N5bvYijOwnd8laZJ4PbAItiKw0VHkM5lvTiPR2k7frhIdAQp/fGo6AT+s96+UnQE8oKvTkhK7qfymM1mz66SPiyUEBERERERyYqFEpKS+6k8BoPBo6vkXs4oUVzZEK3oCFLSgB0lSiu0cVctpXXWnhYdQUrZ1nTREaQzJKJQdATp/NGSnVNiNPw8+ZKSEsycOROrV6+G2WxGnz598Nprr6F///4BzEehgIUSkp5er0deXp6rq4QzSoiIiIiImr977rkHe/fuxXvvvYeWLVvi/fffx/Dhw7Fv3z60asU3R8k7FkpIeiaTCUajEXl5ecjKysIPhz8VHUk6uyztREeQUg9dvugI0olScV6G0srt3JVChJ/LM0RHkE6v8ALREaRTZI8QHYF8qKiowCeffII1a9Zg8ODBAIDZs2fj888/x6JFi/Dcc88JTkjBjFPlKCQ4Z440dptgJ5PJhIyMDJ9bEBMRERERUfNQXV0Nm80GvV7vcTwiIgJbtmwRlIqaC3aUUEhwzhxpSqHDeR9/HF3hl0zUcPN3jBQdQUprr3xddATpmG2RoiNI5/uKTqIjSOkvUYdER5BODN/+VFyiplx0BClZrVZYrVaPYzqdDjqdzuNYTEwMBg4ciGeffRbdunVDXVKbgQAANs5JREFUamoqPvjgA2zbtg2dOvFvA/nGh1QiAEajEQaDgR0lRERERERBzGQyIS4uzuPDfaMGd++99x4cDgdatWoFnU6Hf/3rX7j55puhVvNlMPnGjhKSjsdWwH8ym83IysoCAPyU97GIWFL7ZPAi0RGkZHFwdoPSLA7u8KS0duEnREeQ0oazPURHkE5nLX/WlVZi19W/iPzOaDQiM9Nz94Xa3SROHTt2xKZNm1BWVoazZ88iPT0dN954Izp06KBEVGrGWCgh6bhvBezk7CgBgMlsKiEiIiIiCkp1nWZTn6ioKERFReHMmTNYt24dXnzxxQClo1DBQglJra7ukut/uF9QGnlpd0eJjiClih6W+heRXznsKtERpKPR2kVHkJPKITqBdLYlthMdQTr5fySLjiCl3EY8VV+3bh0cDge6dOmCgwcPYtq0aejatSvuvPPOwAWkkMBCCUmtru6Smw63F5RGXk+njBMdQUr/aLNRdATpWOw89UZpyWFnRUeQUm4lX0Aq7a+Rf4iOIJ3t7bgNthiPN3hlcXExjEYjjh49isTEREyYMAFz586FVsu/x+QbCyUUUpzbBPtiNptdnSTO7YTdO0tuejTQKYmIiIiIKNAmTpyIiRMnio5BzRALJRRSvE28dmcwGFydJM7ZJO7DXLutjg9wSqpNtSNWdAQpPdr9RtERpKM6ycF/StNmlIqOIKWET3hKpdLeu/uY6AjSyT3WQnQEKd3eWXQCkgELJSQ1Z2HFfZgrBseLC0RERERERERCsVBCBM9OlGn5LQUmkdNfK6eIjiCll//CrbCVFqW2io4gHb2qWnQEKSX3LxMdQTrxag7QVZqtC7/nYhhFByAJsFDSCA2Zf9EQzrkYJIZer0deXp7HMfcZJdMaPh+KiIiIiIiIQozK4XCwFKowg8Fw3k4rpCyDwQC9Xu8qjrjPKBmxsenFMGqcgq84NV6E8j4VoiNIR5WvFx1BOtUJ7CgRQXecO0oord+wX0VHkE7u2UTREaS0beQ80RFIAuwoIWm5bw3sMaOEO9USERERERFJi4USklLt02/cZ5TkHk0XEUlqU6+dIDqClDJb/5/oCNJJ1nBug9JK7OGiI0jpF2sr0RGkMzrqD9ERpLO+vJ3oCEQUICyUkJRMJpPHvBn3GSVTHxOVioiIiIiIiERjoYSk5T6c131Gye9HPxQZS0o/H+Y7jyKcbcl5GUpjR4nyztr5cy5CXmWS6AjSsUXliI4gnQH6XNERiChAWCghabmfbmMwGFxdJf9gRwkREREREZG0WCghwrmZJVlZWTiSv0J0HOlM6r1ddAQpldgjREcgCrgotVV0BCllhJ8SHUE6GqhER5BOJdSiI1A9Nm/ejPnz52PXrl0oKCjA6tWrMX78eNf1KlXdvzcvvvgipk2b5rr85Zdf4plnnsHPP/8MvV6PK6+8Ep9++mmA05NI/O0mQk13SUZGhsfcEiIiIiIiar7KysrQu3dvLFy4sM7rCwoKPD7eeecdqFQqTJhwbqOBTz75BLfffjvuvPNO/PTTT/j+++9xyy23KPUlkCDsKCGpuQ9xdbp9/62C0sjryMkE0RGkZKvgnwClhZm5A4vSovPqX0P+d/byCtERpPPOSj6+KE095bjoCFLa3Kbha8eMGYMxY8Z4vT4tLc3j8po1azB06FB06NABAFBdXY2pU6di/vz5uPvuu13runfv3rjQ1OzwWTJJzWKxYMGCBR7H7ts5SVAaednZLizEsV0tRUeQTnVylegI0ilKFp1ATrqDPLVPafYwu+gI0mFrvhhWqxVWq+dplTqdDjqdrkn3e/z4cXz55ZdYtmyZ69iPP/6I/Px8qNVq9OnTB4WFhbjkkkswf/58XHzxxU36fBTcWCghadTVPWI2m8+/jg0lRERERERByWQyYc6cOR7HZs2ahdmzZzfpfpctW4aYmBhcd911rmN//PEHAGD27Nl45ZVX0K5dO7z88ssYMmQIfvvtNyQmJjbpc1LwYqGEpFFX94hzJon7dffsnKx0NOkdOxUnOoKU7FqH6AhEAefgm+xCaEtEJ5BP4d85uFhpKdV8KSWC0WhEZmamx7GmdpMAwDvvvINbb70Vev25beXt9po/Ik8++aRrbsmSJUvQunVrrFy5Evfff3+TPy8FJ/52k9T0ej0MBgPMZvO5rhJ2lBARERERBSV/nGZT23fffYcDBw7gww8/9Dienp4OwHMmiU6nQ4cOHZCXxyFYoYyFEpKayWQCUFOZPrc9cLrgVPLJTF0vOoKUyhxa0RGk81lxH9ERpPP3uF2iI0hp6+WdREeQzrCoA6IjSCdSxZY1MUx+v8fFixejX79+6N27t8fxfv36QafT4cCBA7jiiisAAFVVVcjNzUXbtm39noOCBwsl1KzVNXfEG+c8krqYTCYYjUYYDAZkPu6vdEREREREJEppaSkOHjzounzo0CFkZ2cjMTERGRkZAICzZ89i5cqVePnll8+7fWxsLB544AHMmjULbdq0Qdu2bTF//nwAwA033KDMF0FCsFBCzVpdc0e8cc4j8cbZXXLrD6VNzkWNc9oaKTqClH7bwXdClGaLsYmOIJ33Iy8VHUFK7VqeFB1BOp+AHWtKO13O5y8i/NS64Wt37tyJoUOHui47Z5tMmjQJS5cuBQCsWLECDocDN998c533MX/+fISFheH2229HRUUFLrvsMmzYsAEJCQkX/DVQ8GOhhKRTbxfKTcplISIiIiKiwBgyZAgcDt/D6++77z7cd999Xq/XarV46aWX8NJLL/k7HgUxFkpIGu6DW7Oysryu44wSkkVuRpToCNKxQy06gnSSNWWiI0hph4Uda0ors/t3uCXV7+qo/aIjSOpZ0QFIAiyUkDScp9bUdQqOe5cJZ5QQERERERHJi4USko6zs8Sde5dJ7tEVImJJjTPjxSh3hIuOIJ00TYnoCNIpsfPnXIQYdYXoCNLpHF4oOoJ0+PyFKHSxUELScXaWuHPueAMAUx9TOhEREREREREFCxZKiOBZPGFHifKmHrpedAQp3dPqO9ERpKNV8f1HpVXC9xA/CowiG2cgKa2n7rjoCNKxODh3iihUsVAimXp3fGlmzGZzk++j9veEHSVERERERETyYqFEMhaLBQsWLBAdw29qzxq5kEKQ2WxGRkaG63blfHdAcZ1iToiOIKVIlVV0BOkU2qJFR5BOPGdlCFFsixQdQTrldo3oCNL5oOgy0RGkNK+N6AQkAxZKKKRcSCHIaDQiLy/PNcz1WP6HgYhGPqSHF4uOIKVj1QmiI0ina3iB6AjSiVdXiY4gpcSwUtERpJOoqRYdQTp3JmwVHYGIAoRvnZP0TCYTMjIyYDAY6tw6mIiIiIiIiOTBjhIieA5znbCVpyMoreC1jqIjSCl/lE10BOmoVKITyEel5c+5COGR7ORR2tz9MaIjSEfdlVu+i7C/gafeLFq0CIsWLUJubi4AoEePHnj66acxZswYj3UOhwNXX3011q5di9WrV2P8+PGu63bs2IEZM2Zg165dUKlUuPTSS/Hiiy+id+/efvpqKFixUELSO2+uyQ3ishARERERUdO1bt0a8+bNQ+fOneFwOLBs2TKMGzcOu3fvRo8ePVzrXn31VajqeCejtLQUo0ePxrXXXos33ngD1dXVmDVrFkaNGoUjR45Aq9Uq+eWQwlgoIenVnmtyJD9dYBo5XXPbvaIjSOnj3u+KjiCdcLC7geSQW50kOoJ0frooQ3QE6dwYt1N0BEk93aBVY8eO9bg8d+5cLFq0CNu3b3cVSrKzs/Hyyy9j586dSE/3fA2wf/9+nD59Gs888wzatKlpY5k1axZ69eqFw4cPo1OnTn74WihYsVBCUnLvIjGbzR6XMx8XmYyIiIiIiPzJZrNh5cqVKCsrw8CBAwEA5eXluOWWW7Bw4UKkpaWdd5suXbogKSkJixcvxhNPPAGbzYbFixejW7duaNeuncJfASmNhRKSknsXSe1db/44ukJkNCmdLeB51SJEXcIZAkqzgUNKlKZX2UVHkNL/SjuIjiCd3Ap28ShNG+8QHUFKVqsVVqvnTEGdTgedTnfe2j179mDgwIGwWCyIjo7G6tWr0b17dwCAwWDAoEGDMG7cuDo/T0xMDDZu3Ijx48fj2WefBQB07twZ69atQ1gYX0aHOv4LU7Om1+s9dqoxm80A6pg7UotzHVAzyNVoNLru5+HHAhSWiIiIiIiaxGQyYc6cOR7HZs2ahdmzZ5+3tkuXLsjOzkZxcTE+/vhjTJo0CZs2bcLBgwexYcMG7N692+vnqaiowN13343LL78cH3zwAWw2G1566SVcc8012LFjByIiIvz9pVEQYaGEmjX33WoAuIodteeO1FZ7G2D3+zmW/6EfE1JDvD78PdERpFTm4BAypelV1aIjSGeHpYHbI5BfXRX7i+gI0tHGcgaS0tglKIbRaERmZqbHsbq6SQAgPDzcNUukX79+2LFjB1577TVEREQgJycH8fHxHusnTJiAv/71r9i4cSOWL1+O3NxcbNu2DWq1GgCwfPlyJCQkYM2aNbjpppv8/8VR0GChhKRWV+fJNM4oISIiIiIKSt5Os2kIu90Oq9WKOXPm4J577vG4rmfPnliwYIFrCGx5eTnUarXHjjjOy3Y7T+sMdSyUkJScp+yYzWbXbBKnE8c+EpRKXseqEkRHkFLX8JOiI0inyB4uOoJ09KpK0RGklKYpFR1BOhvLLxIdQTptwvaLjkA+GI1GjBkzBhkZGSgpKcHy5cuxceNGrFu3DmlpaXUOcM3IyED79u0BACNGjMC0adMwZcoUPPzww7Db7Zg3bx7CwsIwdOhQpb8cUhgLJSQl56k2BoPhvK6SJ6aJSkVERERERP5gNptxxx13oKCgAHFxcejVqxfWrVuHESNGNOj2Xbt2xeeff445c+Zg4MCBUKvV6NOnD9auXXveVsIUelgoIanp9XqPHW8A4PejnFGitHk7RouOIKX+gw+JjiCdI+yeUlyM2vtgbwqcrRXc9UZpPXT5oiNIp9jOl1LBbPHixY1a73Ccv4vRiBEjGlxYodDC326SWu0dbwDgH9z1hoiIiIiISFoslFBIcZ89Uh9vWwhP/vX2QEQjHzT5etERpHTduodFR5COyqIWHUE64a3KREeQkrWU83iUFnHwwoZb0oWrSOVATxFyp4hOQDJgoYRCivvskfp420L4SD7POVRaUVeN6AhS+q68s+gI0rHauSWz0v4Ws1d0BCk9ffRvoiNI5/JLD4qOIJ1R0RzmKgbbvynwWCihkOWtY8TJ2XVSe10mtwcmIiIiIiKSFgslFLK8dYw4ObtOaq8b+111wLORpz2/ZoiOICVNKTt5lGZPtYqOIJ3X1UNER5CSw64SHUE6u3/vJjqCdF7pMlx0BCn9cbPoBCQDFkpIWu7zTDy6Sq4Tm4uIiIiIiIjEYaGEpOU+z8S9q+TGbQ+IjCWl8BN8KBKhMoXdU4or4YwSpSW2PSM6gpTsn7cQHUE6VdGiE8jHamPnFFGo4qsTkp5er0deXt65rpKJohMRERERERGRKCyUkPRMJhOMRiPy8vKQlZWFId9wmqvSIk6ITiCnqrbsKFGaw87tgZVWWsEtU0WwDqgUHUE6Eb9zS2alqTQO0RGIKED4jI1Ckl6vd+1q0xAmkwkZGRkN2laYiIiIiIiIQhc7SigkmUymRhc9nDNLfj/aMhCRyIeSznwoEuGULUp0BOm0CjsrOoJ0yhycCyPCt6XcgUVpB7uniI4gnYdTvxEdQVJPNHhlSUkJZs6cidWrV8NsNqNPnz547bXX0L9/fwCASlX3nJkXX3wR06ZN80taap746oSk5LHLTS3/eEzhMERERERE5Hf33HMP9u7di/feew8tW7bE+++/j+HDh2Pfvn1o1aoVCgoKPNb/97//xd13340JEyYISkzBgoUSCnl1FUXMZjOysrLqXJ97dIUSscjNXXsmiY4gpWe7rREdQTp6lV10BAlViQ4gpSuiDoiOIB2dmj/rSvuhor3oCFLq2cB1FRUV+OSTT7BmzRoMHjwYADB79mx8/vnnWLRoEZ577jmkpaV53GbNmjUYOnQoOnTo4OfU1NywUEIhz33rXyej0ej11Jyp7CghIiIiImrWqqurYbPZoNfrPY5HRERgy5Yt560/fvw4vvzySyxbtkypiBTEWCihkKXX62EwGOoc6uqcR1KXI/nsKFHaNW1+ER1BShdpT4qOIJ39VUmiI0gnTcO5MCKkaSpER5DOL6WtREeQzpSUDaIjSMlqtcJqtXoc0+l00Ok8dzmLiYnBwIED8eyzz6Jbt25ITU3FBx98gG3btqFTp07n3e+yZcsQExOD6667LqD5qXlgoYRClrMY4t454ms2iRM7SpR3U8L/REeQkrru+WUUQHzRrjwbN/gT4pfKZNERpHNTi+2iI0inEhrREaRkMpkwZ84cj2OzZs3C7Nmzz1v73nvv4a677kKrVq2g0WjQt29f3Hzzzdi1a9d5a9955x3ceuut53WgkJxYKKGQ5+wsAc6fTdKQwgkREREREQUHo9GIzMxMj2O1u0mcOnbsiE2bNqGsrAxnz55Feno6brzxxvNmkHz33Xc4cOAAPvzww4DlpuaFhRIKee6n2dSeTVLXUNd7dp5RLBvV+H5Nb9ERpKS9lD/rSjtbECM6gnQiU8pER5BS7Er+rCutqDO7p5Tm6FUiOoKUDlx3/mk29YmKikJUVBTOnDmDdevW4cUXX/S4fvHixejXrx969+ZzUqrBQokA7h0OSqtrXodMas8mqXOo660KBiIiIiIiooBYt24dHA4HunTpgoMHD2LatGno2rUr7rzzTteas2fPYuXKlXj55ZcFJqVgw0KJAL4GiQaaqAJNsKrr36LT/FcEJJFbcg63TBXhWFu+46u0AT1zREeQzg+/cftOEbr84w/REaSTbGdHidL25HKAbrArLi6G0WjE0aNHkZiYiAkTJmDu3LnQarWuNStWrIDD4cDNN98sMCkFGxZKiIiIiIiIKORMnDgREydO9Lnmvvvuw3333adQImouWCghgudQ17C4DMFp5FOWxu1XRNAV8t1HpUX2tta/iPwq7Hi46AhSUnd2iI4gnZLKxs1sID84q61/DRE1SyyUEAGwWCxYsGABAOC/sxcITkNERERERESisFBCUnN2krgPubUkc16G4lTsbBDBzjfaFVdezXd8lWZrxS3gSQ5llXxQV1xslegERBQgfHVCUnN2kqSkpNS9Aw4RERERERFJhR0lRKjZsjkvLw9ZWVn4/EWeeqO09ivk3rZalF8fThIdQTpqFTvWlBYXXy46gpTyiuNFR5COHZz3pTTtUXYJEoUqFkqo2XIfwHqhnKfcmEymcx0l6RzmSkREREREJCsWSqjZch/AeqHcT7UxmUwAgNVvvNSk+6TGyxufIjqCnNQ20Qmkk18WJzqCdM7mxIuOIKWULidFR5BO0dlI0RGkY9dxdyeiUMVCCRFqdad0biU2DBEREREREQnDQglJTa/Xw2AwwGw2IysrCwDw5XOcUaK0Vt+UiI4gpcMzRCeQT6uoYtERpFNY0lJ0BCm1iSkSHUE6hQXxoiNIR8OGEqKQxUIJSc15uo37jjefZn4kMpKUrtU+LjqClN7u+6boCNJpF3ZWdATpHLl1o+gIUjplixIdQTq3pW0THUE67cJOiY4gqUzRAUgC3B6YpOMsirh/NHUoLBERERERBZfNmzdj7NixaNmyJVQqFT799FPXdVVVVZg+fTp69uyJqKgotGzZEnfccQeOHTvmcR/t2rWDSqXy+Jg3b57CXwkpjR0l5JM/dpYJFOeONY1V3xDY73I/v9BIdIEW37hIdAQpaVXVoiNIh+9OKE8NbsksQmF1vOgI0rlEf1h0BOnoVRyKHuzKysrQu3dv3HXXXbjuuus8risvL8ePP/6ImTNnonfv3jhz5gymTp2Ka6+9Fjt37vRY+8wzz+Dee+91XY6JiVEkP4nDQgn55I+dZQLFfcea2nwVeMxms8/rr5vql3hERERERCTQmDFjMGbMmDqvi4uLw/r16z2O/fvf/8all16KvLw8ZGRkuI7HxMQgLS0toFkpuLBQQiHJV4HHeaqNt+u3Hl4TyGhUhyhVpegIUiq0xYqOIJ1kTXB26IWyWLVVdAQp7S3jDnJKO1nFd7iV9ve4H0VHkJLVaoXV6vnYrtPpoNPpmnzfxcXFUKlUiI+P9zg+b948PPvss8jIyMAtt9wCg8GAsDC+lA5l/Ncl6ej1euTl5QGou/PkhkcFhCIiIiIionqZTCbMmTPH49isWbMwe/bsJt2vxWLB9OnTcfPNNyM29tybSY888gj69u2LxMREbN26FUajEQUFBXjllVea9PkouLFQQtIxmUyuga7u2wI7fXhwi6Bk8orkO75CxKvLRUeQjgbcS1JpnCEght2hEh1BOsescaIjSKfcrhUdQUpGoxGZmZ473zS1m6SqqgoTJ06Ew+HAokWes/PcP1evXr0QHh6O+++/HyaTyS9dLBScWCihZkuv13udU1LfoNe6tgV2GjDFP/mIiIiIiMi//HWajZOzSHL48GFs2LDBo5ukLpdddhmqq6uRm5uLLl26+C0HBRcWSqjZchY76uJr0Gt99/H4TzdecCa6MP9I2iw6gpRK7PwToDTueqO8Mgd/zkUYHPeb6AjS6ai9sN0A6cL9VpkqOoKULvPjfTmLJL///ju+/fZbJCUl1Xub7OxsqNVqpKSk+DEJBRs+eyCC56wSzWSxWYiIiIiIqOlKS0tx8OBB1+VDhw4hOzsbiYmJSE9Px/XXX48ff/wRX3zxBWw2GwoLCwEAiYmJCA8Px7Zt2/DDDz9g6NChiImJwbZt22AwGHDbbbchISFB1JdFCmChhKRR35bBzlklP+W1UTIWkTCRas5uUJpddAAihRyrihcdQTpZx/z5Pjs1RP/Ew6IjUD127tyJoUOHui47541MmjQJs2fPxmeffQYAuOSSSzxu9+2332LIkCHQ6XRYsWIFZs+eDavVivbt28NgMJw3I4VCDwslJA1fWwK7zyqZ3LCzdoiIiIiIKIgNGTIEDof3Qeq+rgOAvn37Yvv27f6ORc0ACyUUkuoa9OprwKv7rJJNuV8GLBfV7dGDY0VHkNKLnT4RHUE64WAXj9IsnFEihJW7gSguWlspOoJ0tNxViyhk8dkDhaS6hrQ6Cye+TsEBgPFTAxaLiIiIiIiIghwLJSQNZ5eJ+zySuvzjxxMKpiKA74KJoldVi44gnRi++6i4s9X+20KSGq5PZK7oCNK5PvZH0RGk831FB9ERiChAWCghaTi7TNznkdTlq66XKJSInNp+4fv8UAqMsdc9IjqCdDQRLE4pLTLSKjqClPTaKtERpKN/O1F0BOnop+aLjiCluy4SnYBkwEIJSaeu03LcrX7rJYWSEBERERERUbBhoYSkV3tmiapDa4Fp5GSNU4uOICWHg90NSnNwf2DFVVZrREeQUkQ4O0qUVtKaP+tKq67kqX1EoYqFEpJe7W2DP339ZYFpiIiIiIiISCQWSohqUSfzfHalneirFx1BSqpKdvIozV7Bdx+VVsWxDUIUOSJER5BOSj6HRSut2MLHdKJQxUIJhYT6tvz1xWw2e97+smQ/JiMiIiIiIqLmhIUSyTi3yG0os9kcwDT+U/v0mcYwGAwet/9sxfP+jEYNEH1YJTqClIr78t1HxXGDJ8XpI7j9uAgaNX/YlXayN2eUKC0yjH9Hg5nJZMKqVauwf/9+REREYNCgQXjhhRfQpUsX15qcnBw8/vjj2LJlC6xWK0aPHo3XX38dqamprjVz587Fl19+iezsbISHh6OoqEjAV0NKY6FEMvXt+FJbY4oqzZVer0deXt65rhJ2lBARERERNWubNm3ClClT0L9/f1RXV+OJJ57AyJEjsW/fPkRFRaGsrAwjR45E7969sWHDBgDAzJkzMXbsWGzfvh1qdc0pypWVlbjhhhswcOBALF68WOSXRApioYSkZzKZYDQakZeXh6ysLKx650XRkaRTkVr/GgoAvuGruLATWtERpFOu4Q+6CJpwvtOuNC1/1BXXJrZIdATyYe3atR6Xly5dipSUFOzatQuDBw/G999/j9zcXOzevRuxsbEAgGXLliEhIQEbNmzA8OHDAQBz5sxx3Z7kwUl+RKgplqSkpIiOQUREREREAVBcXAwASEysmfJttVqhUqmg050byqvX66FWq7FlyxYhGSl4sKOE6E/O+S3qv7BgojR1Fd9lF0HNd3wVVx3P9yeUxs4GMWKiL2zAOl04dWGk6AhEirBarbBaPXep1Ol0HgWP2ux2Ox599FFcfvnluPjiiwEAAwYMQFRUFKZPn47nn38eDocDM2bMgM1mQ0FBQUC/Bgp+fMZG9CeTyXTBA2GJiIiIiCjwTCYT4uLiPD7qm8M4ZcoU7N27FytWrHAdS05OxsqVK/H5558jOjoacXFxKCoqQt++fV3zSUhe7CihRmnKNryB5M/dedYOft1v90UN81q3YaIjSOmh5G9FR5DO1ooOoiNIp6/+sOgIUvq6tIfoCNLZPbmN6AjSmd3qC9ERpNTOeAiZmZkex3x1kzz00EP44osvsHnzZrRu3drjupEjRyInJwcnT55EWFgY4uPjkZaWhg4d+PdadiyUUKM0ZRveQLrQ3XnqKvw8kOllMRERERERCVXfaTZODocDDz/8MFavXo2NGzeiffv2Xte2aNECALBhwwaYzWZce+21fstLzRMLJSQd9+KI2WxGVlaWx/X7jnwkIpbUhsf9IjoCkSL663NFR5BOpJozSkQYE7NHdATpaFX8WVeaTsWthoLZlClTsHz5cqxZswYxMTEoLCwEAMTFxSEiIgIAsGTJEnTr1g3JycnYtm0bpk6dCoPBgC5durjuJy8vD6dPn0ZeXh5sNhuys7MBAJ06dUJ0dLTiXxcpg4USClneThNyL44YjcbzulFGPJKgSD46J1lTIjqClIrs9b8bQ/5lyrtGdATpvNL+E9ERpPRSwSjREaSTqjsrOgJRUFm0aBEAYMiQIR7HlyxZgsmTJwMADhw4AKPRiNOnT6Ndu3Z48sknz3tt8PTTT2PZsmWuy3369AEAfPvtt+fdN4UOFkooZHk7Tcj54Bes81aIiIiIiKhpHI76O37mzZuHefPm+VyzdOlSLF261E+pqLlgoYSk49wGuK7TbgDgpzwOQ1NakT1CdAQpxaqs9S8iv4oPrxAdQTpHqmNER5BSUSUf15XWM+ao6AjSKXdwZxSiUMVCCYUEZ/HDnbedcJzbh9V12g0ATL6wubBEREREREQUAlgooZBQ197p9e2E422/9bHfVfslEzXcr9u9TyEnCiWRXYtER5DO/d8+IDqClC4adEh0BOlsOnmR6AjS4fdcjM/Z/E0KYKGEQl6jZ5FcF7gsREREREREFNxYKKGQ522oqzftlr4QwDRUl54D+M6jCAfXdxAdQTpniyJFR5BPHLdMFWHv761FR5BO6mY+rVda2/t/Fx2BiAKEj6gUstyHtvpyXsdJ77QAJyMiIiIiIqJgxUIJhaz6hrY61d795tsvZwY8G3niTiBi2LWiE0jIphKdQDq61HLREaQUvpW7DSlNf4Yz1pQWrW3Eqd1E1KywUEIhz9vQVqfzCinDowOciIiIiIiIiIIVCyUkvdqFlB/zMgQlkVeUiu+CiVA26f9ER5DOJ0V/ER1BOncnbhMdQUr3JtwsOoJ0et12THQE6fSP5ow1olDFQgn55Jzz4VTfvI9g1tDdb273vaswERERERERhTCVw+FwiA5BzYfBYGjUDjIi1S6M1J5F4k2Hf70cyFhUB1Ul5zaIYEtiJ4/S1CUa0RGko2tTKjqClCpKdaIjSEebz++50lSd+Pgiwm/XN2ye4KJFi7Bo0SLk5uYCAHr06IGnn34aY8aMAQC8/fbbWL58OX788UeUlJTgzJkziI+P97iPuXPn4ssvv0R2djbCw8NRVFTkx6+Eghk7Sihk1d4W2L0zxmd3SXtuaUhERERE1Jy1bt0a8+bNQ+fOneFwOLBs2TKMGzcOu3fvRo8ePVBeXo7Ro0dj9OjRMBqNdd5HZWUlbrjhBgwcOBCLFy9W+CsgkVgoIWm4n0ZUu7vEvXDy/riFQvLJ7Nbv7hUdQUrrrnxddATpfF/RQXQE6YyIyhEdQUqvnLhSdATptOjL7galjYzZKzqCpBrWUTJ27FiPy3PnzsWiRYuwfft29OjRA48++igAYOPGjV7vY86cOQCApUuXXkhQasZYKCFpuA9trb3TjXvh5Me8TxTPJrvoWG6vJ4LFwdNAlNYxvPnOeWquSuxq0RGk1ELLF+1Kax1+WnQE6eg5jF4Iq9UKq9XqcUyn00Gn8376mc1mw8qVK1FWVoaBAwcGOiKFABZKSEq1d7pxL5xwmCsRERERUXAymUyuTg+nWbNmYfbs2eet3bNnDwYOHAiLxYLo6GisXr0a3bt3VygpNWcslFBANXSnmUBozA497oWTO3cUBSANUfDRquyiI0gnUVMuOoJ04tWcWS9CurZIdATp9NQdFR1BOn9UtRAdQUpGoxGZmZkex7x1k3Tp0gXZ2dkoLi7Gxx9/jEmTJmHTpk0sllC9WCihgKo9UFVJ7qfWeFNnIeeWAAUiIiIiIvr/7d1/UFd1vsfxFz+UH4IgKtstBG+RpN5E19au/VjMpvx9zbWRpctag/f2Q2vvtZu7kZVmY8zc3Uon19wSJa+VMrW2jlsZdrW11TVz0FDTyNzBAvveJFBWQYTP/SNgRIHvF4XzgXOej5nvjH7PYeb1fc+Zw3fevM/ng8vi7zGb8/Xs2VPJycmSpJEjR2r37t1aunSpfv/733dmRLgAjRK4mr+Jlpa2DF64f2pnx8IF/vHqE7YjeFJFPVtJwv3Cg1gDyYZq08N2BM/5W21f2xE8p/D0QNsRPOlyvqnX19dftL4J0BIaJXA1fxMtFy7qKkkxszo7FQAAAIDOlJ2drQkTJigxMVGnTp3SG2+8oW3btmnz5s2SpOPHj+v48eP68ssvJf2wnkl0dLQSExMVFxcnSSopKVF5eblKSkpUV1envXv3SpKSk5MVFRVl5XPBGTRK4Frh4eEqKSlp85wLF3WVpH/dxVa1TnvwR1ttR/Ck4+dibEfwnDqxA4vTegWx05AN1/T81nYEz4kOZnrKaSGs9dWl+Xw+zZw5U2VlZYqJidGwYcO0efNm3XHHHZKkFStWNFsU9qc//akkafXq1brvvvskSU8//bRee+21pnNGjBghSdq6davGjBnjzAeBFTRK4Fo5OTmtrlPS5iM5P+/EUAAAAAA6XW5ubpvHFy5c2OJOOefLy8tTXl5ex4VCt0GjBK4WHh7eYrOkpbVJGh08dlVnx8IFMvZl2Y7gSWtTV9uO4DkVdeG2I3hO/5A62xE8qeDvA2xH8Jz03p/ZjuA5M2I+tR0BQCehUQJXa+nRGqnltUka/fujLb4NAAAAAPCAIGOMsR0C3Ye/XWQu1NbkRlf1v39LsR3Bc2pNiO0InnRFyCnbETzn5f8bYzuC50yL22M7gicVnLzedgTPqTNBtiN4zpTYQtsRPGnswMO2I8ADmChBu7Q2odGa1qY2nNaeBs+U/+jkMAAAAACALotGCVyltYZIeyZbir9e39Gx4MeUlfNsR/Ckd//tv21H8JxJffbZjuA5A0MrbEfwpG+ro21H8JyHr/jQdgTPOVIbbzsCgE5CowSuUl1drRdffPGi9/1NtpzfYJn9X50SDQAAAADQDdAogSe0tvtNI5/Pp8TERFVXV6uWZ3wdd64XSyXZEGw7gAf1DamyHcFz/m74qmPD9dHf2I7gOZtODrcdwXNu6HXUdgQAnYRvD/AEf2urzJ07t2ka5fAxHr1xWsgZmlM21NAUdNyJuijbETxnUI/vbEfwpJiQ07YjeE75uV62I3jO1dxfANfiD4qAfpg48fl8ys7Oth0FAAAAAGAREyWAfpg4yc7OVklJiR4+km47jucE1dlO4E2rv7/JdgTPyd822nYEz4m+usJ2BE+Kfy7MdgTPOTqViRKnrfvRT2xH8KS/3RfYeQsXLtQzzzzT7L2UlBQdOnRIknT8+HHNmzdPBQUFOnXqlFJSUjR//nxNnz69gxOjO2KiBGiQk5OjxMRE2zEAAAAAdIChQ4eqrKys6fXxxx83HZs5c6YOHz6sjRs3qqioSD/72c80Y8YMFRYWWkyMroKJEnQqf4uodjSfz3dZP5+Tk6OiYwkdlAaBCrmGxVxtiAum7k67bcpB2xE8J461Mqz4KDfFdgTPmRi133YEz1lcOtF2BPgRGhqqK664osVjO3bs0Msvv6xRo0ZJkp588km9+OKL2rNnj0aMGOFkTHRBNErQqfwtotrRAmnKnL8VcEuyHu3IRAAAAABsKC4u1pVXXqnw8HCNHj262QT5TTfdpPXr12vSpEmKjY1Vfn6+qqurNWbMGLuh0SXQKIHnNO5u05q5e8scTANJ+oeelbYjeNLdvffajgB0up5iESQbjlXH2Y7gOT2imRJ02ojeJbYjeFJNTY1qamqavRcWFqawsOZrI914443Ky8tTSkqKysrK9Mwzz+jWW2/V/v37FR0drfz8fKWnp6tv374KDQ1VZGSkNmzYoOTkZCc/DrooGiVwrdYmRxofz2l1suTezk4GAAAA4FLk5ORctEjrggULtHDhwmbvTZgwoenfw4YN04033qikpCTl5+dr1qxZeuqpp1RRUaEtW7aoX79+eueddzRjxgxt375d119/vRMfBV0YjRK4VmuTI9nZ2Zo7d658Pp9ef/31i47/T/E/OxEP5xnY4zvbETypTkG2I3jOwNAK2xE856TpaTuCJw0MP2E7guecqudrvdMig8/ajuBJj2Rn69FHmz8rf+E0SUtiY2M1aNAgffnllzpy5IiWLVum/fv3a+jQoZKk1NRUbd++Xb/73e+0YsWKTsmO7oM7Kjyncd2UxobJhX482+lEAAAAAALR0mM2gaiqqtKRI0f0i1/8QqdP/7DQd3Bw801gQ0JCVF9f3yE50b3RKIGrnL/Ljr8dcFpbaPbqJS90eC60rd9e2wm8qfJfqmxH8JzaGn7tOi2IwSkrQnuesx3Bc5Z/McH/SehQtTGsgWTD7AA31Xrsscc0ZcoUJSUlqbS0VAsWLFBISIgyMjIUGxur5ORkPfDAA/rtb3+rvn376p133lFBQYE2bdrUuR8A3QLf2OAq5zc/LnkHnKQBHR0LAAAAgIO+/vprZWRk6MSJE+rfv79uueUW/fWvf1X//v0lSe+++64ef/xxTZkyRVVVVUpOTtZrr72miRPZ9hk0SuARbS3setE6JXt/7lAqNBo+iVXjbUiLOGo7gueU1/ewHcFzqg1fdWzoH9LCYunoVKdvCLEdwXP2VCfZjuBRjwV01rp169o8fu211+rtt9/uiEBwIb49wBP8LezaDLveAAAAAIBn0SiBa7W0XkmrWwKf5/rIrzs9G5p76/hI2xE86ccDmeRxWq3hL75OCw9irQwbDp6Ntx3Bc47XxtiO4DnXhZXZjgCgkwQZY4ztEEBna2yQtLYl8PmGzL948gSd66Zp+2xH8KReITW2I3jOpg9/YjuC5/RKqbAdwZOMYRVdp4VtpFHitPJ/sp3Am776z0f9nwRcJiZK4An+tgRuJjLRgUQAAAAAgK6IiRLAJWpqapSTk6Ps7OxL2lse7UfNnUfN7aDuzqPmzqPmdlB351FzwD8aJYBLnDx5UjExMaqsrFTv3r1tx/EEau48am4HdXceNXceNbeDujuPmgP+BdsOAAAAAAAA0FXQKAEAAAAAAGhAowQAAAAAAKABjRLAJcLCwrRgwQIW5XIQNXceNbeDujuPmjuPmttB3Z1HzQH/WMwVAAAAAACgARMlAAAAAAAADWiUAAAAAAAANKBRAgAAAAAA0IBGCQAAAAAAQAMaJUA3tHPnToWEhGjSpEkXHfvlL3+pkSNHKiwsTMOHD3c+nEu1VvN9+/YpIyNDAwYMUEREhAYPHqylS5daSukurdX8xIkTGj9+vK688kqFhYVpwIABevjhh3Xy5ElLSd2lrftLoxMnTighIUFBQUGqqKhwLpxLtVXzoKCgi17r1q2zkNJd/F3neXl5GjZsmMLDwxUfH685c+Y4nNCdWqt7Xl5ei9d6UFCQfD6fpbTu0Na1vnv3bt1+++2KjY1Vnz59NG7cOO3bt89CSqDroVECdEO5ubl65JFH9Oc//1mlpaUXHc/KylJ6erqFZO7VWs337Nmj+Ph4rV27VgcOHND8+fOVnZ2tZcuWWUzrDq3VPDg4WFOnTtXGjRv1xRdfKC8vT1u2bNGDDz5oMa17+Lu/SNKsWbM0bNgwh5O5l7+ar169WmVlZU2vu+66y/mQLtNWzV944QXNnz9fjz/+uA4cOKAtW7Zo3LhxlpK6S2t1T09Pb3aNl5WVady4cUpLS1N8fLzFxN1fazWvqqrS+PHjlZiYqF27dunjjz9WdHS0xo0bp9raWouJgS7CAOhWTp06ZaKiosyhQ4dMenq6Wbx4cYvnLViwwKSmpjobzqUCrXmj2bNnm9tuu82hdO7U3povXbrUJCQkOJTOvQKp+/Lly01aWpr58MMPjSTz/fffOx/URfzVXJLZsGGDnXAu1VbNy8vLTUREhNmyZYvFhO7Unvu6z+czPXr0MGvWrHEwofu0VfPdu3cbSaakpKTpvc8++8xIMsXFxTbiAl0KEyVAN5Ofn6/rrrtOKSkpyszM1KpVq2SMsR3L1dpb88rKSsXFxTmY0H3aU/PS0lL94Q9/UFpamsMp3cdf3Q8ePKhFixZpzZo1Cg7mK0RHCORanzNnjvr166dRo0Zxz+8AbdW8oKBA9fX1+uabbzR48GAlJCRoxowZOnbsmOXU3V977utr1qxRZGSk7r77bodTuktbNU9JSVHfvn2Vm5urs2fP6syZM8rNzdXgwYM1cOBAu8GBLoBvOUA3k5ubq8zMTEnS+PHjVVlZqY8++shyKndrT8137Nih9evX6/7773cyousEUvOMjAxFRkbqqquuUu/evbVy5UobUV2lrbrX1NQoIyNDv/nNb5SYmGgzpqv4u9YXLVqk/Px8FRQUaPr06Zo9e7ZeeuklW3Fdoa2af/XVV6qvr9dzzz2nJUuW6K233lJ5ebnuuOMOnT171mbsbq89v0tzc3N1zz33KCIiwsmIrtNWzaOjo7Vt2zatXbtWERERioqK0vvvv6/33ntPoaGhNmMDXYO1WRYA7Xbo0CETGhpqvv3226b35syZYzIzMy86l0dvOkZ7al5UVGT69etnnn32WScjuk6gNS8rKzOff/65+eMf/2iGDBliHnroIaejuoq/us+dO9ekp6c3Hdu6dSuP3lym9txfGj311FM8ZnYZ/NV88eLFRpLZvHlz03Gfz2eCg4PN+++/73het2jPtb5jxw4jyXz66adORnQdfzU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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_training_clusters(mdi_copy, mdi_rbo_train, mdi_train_clusters)" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [], + "source": [ + "# # get training error of rf+\n", + "# y_pred_rf_plus_train = rf_plus.predict(X_train)\n", + "# y_pred_proba_rf_plus_train = rf_plus.predict_proba(X_train)[:, 1]\n", + "\n", + "# # compute accuracy on the train set\n", + "# accuracy_rf_plus_train = np.mean(y_pred_rf_plus_train == y_train)\n", + "# misclassified_rf_plus_train = np.sum(y_pred_rf_plus_train != y_train)\n", + "# auroc_rf_plus_train = roc_auc_score(y_train, y_pred_proba_rf_plus_train)\n", + "# auprc_rf_plus_train = average_precision_score(y_train, y_pred_proba_rf_plus_train)\n", + "# f1_rf_plus_train = f1_score(y_train, y_pred_rf_plus_train)\n", + "\n", + "# print(f'RF+ Train Set Accuracy: {accuracy_rf_plus_train}')\n", + "# print(f'RF+ Train Set # Misclassified: {misclassified_rf_plus_train}')\n", + "# print(f'RF+ Train Set AUROC: {auroc_rf_plus_train}')\n", + "# print(f'RF+ Train Set AUPRC: {auprc_rf_plus_train}')\n", + "# print(f'RF+ Train Set F1: {f1_rf_plus_train}')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+0.1675 +1.4826 +2.8683 +1.0105 +0.753 +2.7097 +0.9762 +0.3764 +0.283 +0.8026 +1.3425 +4.2844 +1.6836 +0.3997 +1.683 +1.3091 +1.5143 +4.2295 +4.916 +1.0812 +0.9588 +0.0 +1.8358 +2.0602 +3.8317 +4.8727 +3.0477 +6.2069 +6.3027 +0.8524 +1.5942 +0.0 +0.6933 +0.9822 +0.4013 diff --git a/feature_importance/subgroup/subgroup_detection.py b/feature_importance/subgroup/subgroup_detection.py deleted file mode 100644 index e435dcf..0000000 --- a/feature_importance/subgroup/subgroup_detection.py +++ /dev/null @@ -1,128 +0,0 @@ -import numpy as np -import pandas as pd -import rbo -from sklearn.cluster import AgglomerativeClustering -from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor -from scipy.cluster.hierarchy import linkage, dendrogram, fcluster -from scipy.spatial.distance import pdist, squareform -import matplotlib.pyplot as plt -import seaborn as sns - -def compute_rbo_matrix(rankings, p, k=None): - """ - Compute the Rank-based Overlap (RBO) matrix for a set of rankings. - Inputs: - * rankings: numpy array of shape (n, p) where the (i,j)th entry denotes that - the jth feature is ranked (j-1) in terms of importance for the ith instance. - * p: float between 0 and 1 - * k: int (optional) evaluation depth for extrapolation - Outputs: - * numpy array of shape (n, n) where the (i,j)th entry is the RBO between the - ith and jth rankings. - """ - n, _ = rankings.shape - rbo_matrix = np.zeros((n, n)) - for i in range(n): - for j in range(i, n): - rbo_matrix[i, j] = rbo.RankingSimilarity(rankings[i, :], - rankings[j,:]).rbo(p=p,k=k) - rbo_matrix[j, i] = rbo_matrix[i, j] - return rbo_matrix - -def detect_subgroups(mdi, rankings, num_clusters, p = 0.9, k = None, - linkage_method = 'ward'): - - # compute rbo matrix - rbo_matrix = compute_rbo_matrix(rankings, 0.9, k=k) - - # since rbo is a similarity metric, 1 means the rankings are identical - distance_matrix = rbo_matrix.max()-rbo_matrix - - # convert to condensed distance matrix for scipy compatibility - condensed_distance_matrix = squareform(distance_matrix) - - # perform hierarchical clustering - linkage_matrix = linkage(condensed_distance_matrix, method=linkage_method) - clustergrid = sns.clustermap(mdi, row_linkage=linkage_matrix, - col_cluster=False, cmap='viridis', - cbar_pos = (1, 0.2, 0.05, 0.5)) - - # Get the reordered row indices - reordered_indices = clustergrid.dendrogram_row.reordered_ind - - # Determine cluster memberships using fcluster - clusters = fcluster(linkage_matrix, num_clusters, criterion='maxclust') - - # Reorder clusters to match the heatmap - ordered_clusters = clusters[reordered_indices] - - # Create a new DataFrame for annotations - annotations = pd.DataFrame(data=np.zeros_like(mdi, dtype=object), index=mdi.index, columns=mdi.columns) - - # Add cluster numbers as annotations - for i, cluster in enumerate(ordered_clusters): - annotations.iloc[i, :] = cluster - - # Find the boundaries where clusters change - boundaries = np.where(np.diff(ordered_clusters))[0] + 1 - - # Plot the horizontal dashed red lines - for boundary in boundaries: - clustergrid.ax_heatmap.hlines(boundary, - *clustergrid.ax_heatmap.get_xlim(), - colors='red', linestyles='dashed') - - ax = clustergrid.ax_heatmap - total_obs = 0 - for cluster in range(1, num_clusters + 1): - num_obs = np.sum(clusters == cluster) - x_position = mdi.shape[1] - 3 # Adjust this value for correct alignment - y_position = total_obs + num_obs//2 # Adjust this value for correct alignment - ax.text(x_position, y_position, "Cluster #" + str(cluster), color='red', ha='center', va='center', fontsize=10, fontweight='bold') - total_obs += num_obs - - plt.suptitle('Heatmap with Hierarchical Clustering', fontsize=24) - plt.show() - - return clusters - -# def one_hot_encode(rankings): -# """ -# Obtain a one-hot encoded representation of the local feature importance -# rankings. -# Inputs: -# * rankings: numpy array of shape (n, p) where the (i,j)th entry denotes that -# the jth feature is ranked (j-1) in terms of importance for the ith instance. -# Outputs: -# * numpy array of shape (n, p^2) which encodes the same information as the -# input, but formatted as a matrix with binary entries. -# """ -# n, p = rankings.shape -# one_hot_matrix = np.full(shape = (n, p**2), fill_value = -1, dtype = int) -# for i in range(n): -# one_hot_matrix[i, :] = np.array(pd.get_dummies(rankings[0,:]), -# dtype = int).flatten() -# return one_hot_matrix - -# def decision_tree_grouper(rankings, y): -# """ -# Group features based on the decision tree feature importance rankings. -# Inputs: -# * rankings: numpy array of shape (n, p) where the (i,j)th entry denotes that -# the jth feature is ranked (j-1) in terms of importance for the ith instance. -# * y: length n array of the target labels. -# Outputs: -# * length n array where the ith entry is the group # of the ith instance. -# """ -# encoded_rankings = one_hot_encode(rankings) -# # check if y is binary or continuous -# if len(np.unique(y)) == 2: -# clf = DecisionTreeClassifier() -# print("classification tree fit") -# else: -# clf = DecisionTreeRegressor() -# print("regression tree fit") -# clf.fit(encoded_rankings, y) - -# # get which leaf each instance falls into -# return clf.apply(encoded_rankings) \ No newline at end of file diff --git a/feature_importance/test.ipynb b/feature_importance/test.ipynb index c5e67e5..4c1b7fd 100644 --- a/feature_importance/test.ipynb +++ b/feature_importance/test.ipynb @@ -1,9 +1,390 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from imodels.tree.rf_plus.rf_plus.rf_plus_models import RandomForestPlusClassifier, RandomForestPlusRegressor\n", + "from imodels.tree.rf_plus.feature_importance.rfplus_explainer import AloRFPlusMDI, RFPlusMDI\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n", + "from sklearn.linear_model import SGDRegressor\n", + "from imodels import get_clean_dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fetching diabetes from sklearn\n", + "X_train shape: (309, 10)\n" + ] + } + ], + "source": [ + "# X = np.load('X.npy')\n", + "# y = np.load('y.npy')\n", + "# sample train and test data from diabetes dataset\n", + "X, y, feature_names = get_clean_dataset('diabetes_regr')\n", + "# train-test split\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n", + "# print shape of X_train\n", + "print(\"X_train shape: \", X_train.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 7.1s\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 9.0s finished\n" + ] + } + ], + "source": [ + "# initialize RF model\n", + "rf = RandomForestRegressor(n_estimators = 100, min_samples_leaf = 5,\n", + " max_depth = 5, max_features = 0.33,\n", + " random_state = 42)\n", + "\n", + "# fit RF+ model\n", + "rf_plus = RandomForestPlusRegressor(rf_model=rf,\n", + " prediction_model=SGDRegressor(alpha=0.001))\n", + "rf_plus.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.886222839355469e-05" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rf_plus.check_data_time" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.19572162628173828" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rf_plus.fit_rf_time" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9.045751571655273" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rf_plus.fit_forest_time" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "lst = []" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "init_transformer 0.000505\n", + "get_transformed_data 0.014320\n", + "fit_prediction_model 0.004274\n", + "total_ith_tree 0.020401\n", + "dtype: float64" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rf_plus.fit_trees_time.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# get feature importances\n", + "mdi_explainer = RFPlusMDI(rf_plus)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.015544652938842773" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdi_explainer.init_ppm_time" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# train_lmdi = mdi_explainer.explain_r2(X_train, y_train, l2norm=True)\n", + "test_lmdi = mdi_explainer.explain_linear_partial(X_train, y_train, leaf_average=True, l2norm=True, njobs = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total partial preds time: 0.0034155869483947754\n", + "Partial preds time: 0.0003127286434173584\n" + ] + } + ], + "source": [ + "lst = list()\n", + "lst2 = list()\n", + "for explainer in mdi_explainer.tree_explainers:\n", + " lst.append(explainer._total_partial_preds_time)\n", + " lst2.append(explainer._partial_preds_time)\n", + "print(\"Total partial preds time: \", np.array(lst).mean())\n", + "print(\"Partial preds time: \", np.array(lst2).mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.76837158203125e-06" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdi_explainer.get_leafs_in_test_samples_time" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "12.056845903396606" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdi_explainer.partial_predictions_time" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.1785750389099121" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdi_explainer.leaf_average_time" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "12.23560905456543" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdi_explainer.get_lfi_time" + ] + }, { "cell_type": "code", "execution_count": 12, "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'RFPlusMDI' object has no attribute '_partial_preds_time'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[12], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmdi_explainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_partial_preds_time\u001b[49m\n", + "\u001b[0;31mAttributeError\u001b[0m: 'RFPlusMDI' object has no attribute '_partial_preds_time'" + ] + } + ], + "source": [ + "mdi_explainer.rf_plus_model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "data = np.load(\"X.npy\")\n", + "data.shape\n", + "n_samples = 100\n", + "n_features = 5" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# randomly sample n_samples and n_features from X and y\n", + "sampled_rows = np.random.choice(X.shape[0], n_samples, replace=False)\n", + "\n", + "# Randomly sample m columns\n", + "sampled_cols = np.random.choice(X.shape[1], n_features, replace=False)\n", + "\n", + "# Create the sampled array\n", + "X_train = X[sampled_rows][:, sampled_cols]" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(174368, 367)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -17,16 +398,81 @@ "output_type": "stream", "text": [ "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 3.5s\n", - "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 9.3s finished\n" + "[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 10.2s\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 15.4s finished\n" ] } ], "source": [ "from scripts.simulations_util import *\n", - "from scripts.competing_methods_local import *\n", + "from sklearn.ensemble import RandomForestRegressor\n", "from util import apply_splitting_strategy\n", - "from sklearn.metrics import roc_auc_score, f1_score, recall_score, precision_score, mean_squared_error, r2_score, average_precision_score\n", + "from imodels.tree.rf_plus.rf_plus.rf_plus_models import RandomForestPlusRegressor\n", + "from imodels.tree.rf_plus.feature_importance.rfplus_explainer import AloRFPlusMDI\n", + "\n", + "# get data\n", + "X = sample_real_data_X(source = \"imodels\", data_name = \"diabetes_regr\", sample_row_n = 400)\n", + "y = linear_model(X, beta = 1, sigma = None, heritability = 0.8, s = 5)\n", + "X_train, X_tune, X_test, y_train, y_tune, y_test = apply_splitting_strategy(X, y, \"train-test\", 1)\n", + "\n", + "# initialize RF model\n", + "rf = RandomForestRegressor(n_estimators = 100, min_samples_leaf = 5, max_features = 0.33, random_state = 42)\n", + "\n", + "# fit RF+ model\n", + "rf_plus = RandomForestPlusRegressor(rf_model=rf)\n", + "rf_plus.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# get feature importances\n", + "mdi_explainer = AloRFPlusMDI(rf_plus, evaluate_on='oob')\n", + "mdi, partial_preds = mdi_explainer.explain(np.asarray(X_train), y_train, leaf_average=True)\n", + "# mdi_rankings = mdi_explainer.get_rankings(mdi)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.15401222, -0.12353409, -0.09846435, ..., -0.1649551 ,\n", + " -0.16848943, -0.16695095],\n", + " [-0.04393247, -0.08856134, -0.01087691, ..., -0.02182093,\n", + " -0.03546607, -0.03541808],\n", + " [-0.04454344, -0.01119317, -0.05182914, ..., -0.06075052,\n", + " -0.05162437, -0.04708101],\n", + " ...,\n", + " [-0.12245341, -0.05899384, -0.06858969, ..., -0.09567499,\n", + " -0.11051321, -0.10448243],\n", + " [-0.06144286, -0.06667639, -0.01863273, ..., -0.02394332,\n", + " -0.0196163 , -0.0212131 ],\n", + " [-0.0227569 , -0.01432783, -0.01247537, ..., -0.06030641,\n", + " -0.06521143, -0.06407156]])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdi" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "X = sample_real_data_X(source = \"imodels\", data_name = \"diabetes_regr\", sample_row_n = 400)\n", "y = linear_model(X, beta = 1, sigma = None, heritability = 0.8, s = 5)\n", "X_train, X_tune, X_test, y_train, y_tune, y_test = apply_splitting_strategy(X, y, \"train-test\", 1)\n", @@ -101,7 +547,7 @@ ], "metadata": { "kernelspec": { - "display_name": "base", + "display_name": "mdi", "language": "python", "name": "python3" }, diff --git a/feature_importance/treeshap_time_test.ipynb b/feature_importance/treeshap_time_test.ipynb new file mode 100644 index 0000000..7c6e447 --- /dev/null +++ b/feature_importance/treeshap_time_test.ipynb @@ -0,0 +1,82 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import shap\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "import time" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time to create explainer: 0.008701086044311523\n", + "Time to explain: 5.225969314575195\n" + ] + } + ], + "source": [ + "# generate normally distributed data with n_samples and n_features\n", + "X = np.random.normal(size=(5000, 100))\n", + "# make y the sum of half of the features plus noise\n", + "y = np.sum(X[:, :100//2], axis=1) + np.random.normal(size=5000)\n", + "\n", + "# initialize RF model\n", + "rf = RandomForestRegressor(n_estimators = 100, max_depth = 5, min_samples_leaf = 5, max_features = 0.33, random_state = 42)\n", + "\n", + "# fit rf\n", + "rf.fit(X, y)\n", + "\n", + "start_create_explainer = time.time()\n", + "\n", + "# explain the model's predictions using SHAP\n", + "explainer = shap.TreeExplainer(rf)\n", + "\n", + "end_create_explainer = time.time()\n", + "\n", + "print(\"Time to create explainer: \", end_create_explainer - start_create_explainer)\n", + "\n", + "start_explanation = time.time()\n", + "\n", + "shap_values = explainer.shap_values(X)\n", + "\n", + "end_explanation = time.time()\n", + "\n", + "print(\"Time to explain: \", end_explanation - start_explanation)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mdi", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}