From 4b998c61d38b63ec4f12f3432d6bf6e1032e947a Mon Sep 17 00:00:00 2001 From: Logan Ward Date: Mon, 4 Dec 2023 07:18:29 -0500 Subject: [PATCH] Remove only notebook directory We can recreate these in the new directory structure if need be --- .../2_approximate-hessians.ipynb | 584 ------- notebooks/proof-of-concept/README.md | 3 - .../butanol_hf_def2-svpd-random-d=5.00e-03.db | Bin 1187840 -> 0 bytes ...caffeine_hf_def2-svpd-random-d=4.50e-03.db | Bin 69632 -> 0 bytes ...caffeine_hf_def2-svpd-random-d=5.00e-03.db | Bin 3473408 -> 0 bytes .../ethanol_b3lyp_6-31g*-random-d=5.00e-03.db | Bin 405504 -> 0 bytes .../ethanol_hf_def2-svpd-random-d=4.50e-02.db | Bin 401408 -> 0 bytes .../ethanol_hf_def2-svpd-random-d=4.50e-03.db | Bin 401408 -> 0 bytes .../ethanol_hf_def2-svpd-random-d=5.00e-03.db | Bin 716800 -> 0 bytes ...hanol_wb97x-d_cc-pvtz-random-d=5.00e-03.db | Bin 397312 -> 0 bytes ...thanol_wb97xd_cc-pvtz-random-d=5.00e-03.db | Bin 69632 -> 0 bytes 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/dev/null @@ -1,584 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "201346ec-3f5a-4235-b8ef-4a0051373865", - "metadata": {}, - "source": [ - "# Generate Approximate Hessians\n", - "Use the random displacement data to get the full Hessian matrix. We assume that many parameters of the Hessian matrix are zero, and therefore that we can fit the Hessian matrix with a reduced number of points using [sparse linear regression](https://scikit-learn.org/stable/modules/linear_model.html#lasso)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "ebbbc7f5-3007-420f-861a-9f65f84436be", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "from matplotlib import pyplot as plt\n", - "from jitterbug.model.linear import HarmonicModel\n", - "from jitterbug.model.linear import get_model_inputs\n", - "from sklearn.linear_model import LinearRegression, ElasticNetCV\n", - "from ase.vibrations import VibrationsData\n", - "from ase.db import connect\n", - "from pathlib import Path\n", - "from tqdm import tqdm\n", - "import numpy as np\n", - "import warnings" - ] - }, - { - "cell_type": "markdown", - "id": "85a147c1-2758-465b-bc54-dc373d73a0f3", - "metadata": {}, - "source": [ - "Configuration" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "99bd4c92-9a7b-4e88-ac45-dbf30fbfc9e0", - "metadata": { - "tags": [ - "parameters" - ] - }, - "outputs": [], - "source": [ - "molecule_name = 'water'\n", - "method = 'hf'\n", - "basis = 'def2-svpd'\n", - "step_size: float = 0.005 # Perturbation amount, used as maximum L2 norm\n", - "regressor = LinearRegression" - ] - }, - { - "cell_type": "markdown", - "id": "8505d400-8427-45b9-b626-3f9ca557d0c8", - "metadata": {}, - "source": [ - "Derived" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "a8be3c37-bf1f-4ba4-ba8f-afff6d6bed7d", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "run_name = f'{molecule_name}_{method}_{basis}'\n", - "out_dir = Path('data') / 'approx'\n", - "db_path = out_dir / f'{run_name}-random-d={step_size:.2e}.db'" - ] - }, - { - "cell_type": "markdown", - "id": "de1f6aac-b93e-45a7-98e6-ffd5205916a6", - "metadata": {}, - "source": [ - "## Read in the Data\n", - "Get all computations for the desired calculation and the exact solution" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "797b96d8-050c-4bdf-9815-586cfb5bc311", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loaded 46 structures\n" - ] - }, - { - "data": { - "text/plain": [ - "array([ 1.35768557e+03, 5.13074732e+02, -1.78611307e+03, -3.68960372e+02,\n", - " 4.06940665e+02, -5.02709874e+02, 9.81422728e+02, -7.49646372e+02,\n", - " 2.31747899e+03, 9.21655058e+05, 6.96594162e+05, -2.42497995e+06,\n", - " -5.00932174e+05, 5.52497470e+05, -6.82521944e+05, 1.33246348e+06,\n", - " -1.01778406e+06, 3.14640779e+06, 1.31622840e+05, -9.16409487e+05,\n", - " -1.89304244e+05, 2.08790973e+05, -2.57927734e+05, 5.03543203e+05,\n", - " -3.84624612e+05, 1.18903991e+06, 1.59509996e+06, 6.59004944e+05,\n", - " -7.26842042e+05, 8.97896679e+05, -1.75293196e+06, 1.33895319e+06,\n", - " -4.13927952e+06, 6.80658780e+04, -1.50144979e+05, 1.85480022e+05,\n", - " -3.62106095e+05, 2.76589804e+05, -8.55057910e+05, 8.28003524e+04,\n", - " -2.04573091e+05, 3.99380817e+05, -3.05061593e+05, 9.43076441e+05,\n", - " 1.26358609e+05, -4.93370896e+05, 3.76854634e+05, -1.16501957e+06,\n", - " 4.81595285e+05, -7.35719987e+05, 2.27442655e+06, 2.80984842e+05,\n", - " -1.73728972e+06, 2.68535444e+06])" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "with connect(db_path) as db:\n", - " data = [a.toatoms() for a in db.select('')]\n", - "print(f'Loaded {len(data)} structures')\n", - "get_model_inputs(data[1], data[0])" - ] - }, - { - "cell_type": "markdown", - "id": "cb1a8e03-b045-49a4-95fd-61636a48fbad", - "metadata": {}, - "source": [ - "Read in the exact Hessian" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "7389208d-9323-492c-8fc5-d05a372206c6", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "with open(f'data/exact/{run_name}-ase.json') as fp:\n", - " exact_vibs = VibrationsData.read(fp)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "a9965595-532c-4067-ba24-7620bd977007", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "exact_hess = exact_vibs.get_hessian_2d()\n", - "exact_zpe = exact_vibs.get_zero_point_energy()\n", - "exact_freqs = exact_vibs.get_frequencies()" - ] - }, - { - "cell_type": "markdown", - "id": "04c60da8-4a1d-4ae3-b45d-b77e71fd598f", - "metadata": {}, - "source": [ - "## Fit a Hessian with All Data\n", - "Fit a model which explains the energy data by fitting a Hessian matrix using compressed sensing (i.e., Lasso)." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "d6a7d756-37d3-44e0-b5e2-348d07c9d296", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/elliott/Packages/faster-molecular-hessians/jitterbug/model/linear.py:27: RuntimeWarning: divide by zero encountered in reciprocal\n", - " disp_matrix = np.reciprocal((atoms.positions - reference.positions).flatten())\n" - ] - }, - { - "ename": "ValueError", - "evalue": "Input X contains infinity or a value too large for dtype('float64').", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[7], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m model \u001b[38;5;241m=\u001b[39m HarmonicModel(reference\u001b[38;5;241m=\u001b[39mdata[\u001b[38;5;241m0\u001b[39m], regressor\u001b[38;5;241m=\u001b[39mregressor)\n\u001b[0;32m----> 2\u001b[0m hess_model \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Packages/faster-molecular-hessians/jitterbug/model/linear.py:75\u001b[0m, in \u001b[0;36mHarmonicModel.train\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 73\u001b[0m y \u001b[38;5;241m=\u001b[39m [atoms\u001b[38;5;241m.\u001b[39mget_potential_energy() \u001b[38;5;241m-\u001b[39m ref_energy \u001b[38;5;28;01mfor\u001b[39;00m atoms \u001b[38;5;129;01min\u001b[39;00m data]\n\u001b[1;32m 74\u001b[0m \u001b[38;5;66;03m# Fit the ARD model and ensure it captures the data well\u001b[39;00m\n\u001b[0;32m---> 75\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mregressor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfit_intercept\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\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\n\u001b[1;32m 76\u001b[0m pred \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mpredict(x)\n\u001b[1;32m 77\u001b[0m max_error \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mabs(pred \u001b[38;5;241m-\u001b[39m y)\u001b[38;5;241m.\u001b[39mmax()\n", - "File \u001b[0;32m~/miniconda3/envs/jitterbug/lib/python3.10/site-packages/sklearn/linear_model/_base.py:648\u001b[0m, in \u001b[0;36mLinearRegression.fit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m 644\u001b[0m n_jobs_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_jobs\n\u001b[1;32m 646\u001b[0m accept_sparse \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpositive \u001b[38;5;28;01melse\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcsr\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcsc\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcoo\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m--> 648\u001b[0m X, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_data\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 649\u001b[0m \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[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maccept_sparse\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_numeric\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[43mmulti_output\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[1;32m 650\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 652\u001b[0m sample_weight \u001b[38;5;241m=\u001b[39m _check_sample_weight(\n\u001b[1;32m 653\u001b[0m sample_weight, X, dtype\u001b[38;5;241m=\u001b[39mX\u001b[38;5;241m.\u001b[39mdtype, only_non_negative\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 654\u001b[0m )\n\u001b[1;32m 656\u001b[0m X, y, X_offset, y_offset, X_scale \u001b[38;5;241m=\u001b[39m _preprocess_data(\n\u001b[1;32m 657\u001b[0m X,\n\u001b[1;32m 658\u001b[0m y,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 661\u001b[0m sample_weight\u001b[38;5;241m=\u001b[39msample_weight,\n\u001b[1;32m 662\u001b[0m )\n", - "File \u001b[0;32m~/miniconda3/envs/jitterbug/lib/python3.10/site-packages/sklearn/base.py:584\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m 582\u001b[0m y \u001b[38;5;241m=\u001b[39m check_array(y, input_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_y_params)\n\u001b[1;32m 583\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 584\u001b[0m X, y \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_X_y\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[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcheck_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 585\u001b[0m out \u001b[38;5;241m=\u001b[39m X, y\n\u001b[1;32m 587\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m check_params\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mensure_2d\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m):\n", - "File \u001b[0;32m~/miniconda3/envs/jitterbug/lib/python3.10/site-packages/sklearn/utils/validation.py:1106\u001b[0m, in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[1;32m 1101\u001b[0m estimator_name \u001b[38;5;241m=\u001b[39m _check_estimator_name(estimator)\n\u001b[1;32m 1102\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 1103\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mestimator_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m requires y to be passed, but the target y is None\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1104\u001b[0m )\n\u001b[0;32m-> 1106\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_array\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1107\u001b[0m \u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1108\u001b[0m \u001b[43m \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maccept_sparse\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1109\u001b[0m \u001b[43m \u001b[49m\u001b[43maccept_large_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maccept_large_sparse\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1110\u001b[0m \u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1111\u001b[0m \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\u001b[1;32m 1112\u001b[0m \u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1113\u001b[0m \u001b[43m \u001b[49m\u001b[43mforce_all_finite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_all_finite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1114\u001b[0m \u001b[43m \u001b[49m\u001b[43mensure_2d\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mensure_2d\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1115\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_nd\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mallow_nd\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1116\u001b[0m \u001b[43m \u001b[49m\u001b[43mensure_min_samples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mensure_min_samples\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1117\u001b[0m \u001b[43m \u001b[49m\u001b[43mensure_min_features\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mensure_min_features\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1118\u001b[0m \u001b[43m \u001b[49m\u001b[43mestimator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1119\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mX\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1120\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1122\u001b[0m y \u001b[38;5;241m=\u001b[39m _check_y(y, multi_output\u001b[38;5;241m=\u001b[39mmulti_output, y_numeric\u001b[38;5;241m=\u001b[39my_numeric, estimator\u001b[38;5;241m=\u001b[39mestimator)\n\u001b[1;32m 1124\u001b[0m check_consistent_length(X, y)\n", - "File \u001b[0;32m~/miniconda3/envs/jitterbug/lib/python3.10/site-packages/sklearn/utils/validation.py:921\u001b[0m, in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[1;32m 915\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 916\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFound array with dim \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m expected <= 2.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 917\u001b[0m \u001b[38;5;241m%\u001b[39m (array\u001b[38;5;241m.\u001b[39mndim, estimator_name)\n\u001b[1;32m 918\u001b[0m )\n\u001b[1;32m 920\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m force_all_finite:\n\u001b[0;32m--> 921\u001b[0m \u001b[43m_assert_all_finite\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 922\u001b[0m \u001b[43m \u001b[49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 923\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 924\u001b[0m \u001b[43m \u001b[49m\u001b[43mestimator_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mestimator_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 925\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_nan\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_all_finite\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mallow-nan\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 926\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 928\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ensure_min_samples \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 929\u001b[0m n_samples \u001b[38;5;241m=\u001b[39m _num_samples(array)\n", - "File \u001b[0;32m~/miniconda3/envs/jitterbug/lib/python3.10/site-packages/sklearn/utils/validation.py:161\u001b[0m, in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype, estimator_name, input_name)\u001b[0m\n\u001b[1;32m 144\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m estimator_name \u001b[38;5;129;01mand\u001b[39;00m input_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 \u001b[38;5;129;01mand\u001b[39;00m has_nan_error:\n\u001b[1;32m 145\u001b[0m \u001b[38;5;66;03m# Improve the error message on how to handle missing values in\u001b[39;00m\n\u001b[1;32m 146\u001b[0m \u001b[38;5;66;03m# scikit-learn.\u001b[39;00m\n\u001b[1;32m 147\u001b[0m msg_err \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 148\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mestimator_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m does not accept missing values\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 149\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m encoded as NaN natively. For supervised learning, you might want\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 159\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m#estimators-that-handle-nan-values\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 160\u001b[0m )\n\u001b[0;32m--> 161\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg_err)\n", - "\u001b[0;31mValueError\u001b[0m: Input X contains infinity or a value too large for dtype('float64')." - ] - } - ], - "source": [ - "model = HarmonicModel(reference=data[0], regressor=regressor)\n", - "hess_model = model.train(data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "35baa4a3-5e0e-4067-92ea-f29b3c080477", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "print(f'Trained a model with {len(hess_model.coef_)} terms. {(np.abs(hess_model.coef_) > 1e-7).sum()} are nonzero')" - ] - }, - { - "cell_type": "markdown", - "id": "aa509659-701d-4001-8cc7-980c9d999976", - "metadata": {}, - "source": [ - "Compare the forces estimated at a zero displacement to the true value" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "70d80f87-9983-4bd5-a6ae-b9c966b0d838", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "actual_forces = data[0].get_forces()\n", - "get_model_inputs(data[1], data[0])\n", - "len(data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f548b145-0aa8-47f7-802b-6b7232a74bd3", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "pred_forces = -hess_model.coef_[:actual_forces.size].reshape((-1, 3))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d7cd7762-6e12-4dcd-b564-67a33b18d9e0", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "print(f'Maximum force: {np.abs(pred_forces).max():.2e} eV/Angstrom')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "425b77a9-7fd7-40da-af6f-eaed197c9ab6", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(1, 2, figsize=(4, 2))\n", - "\n", - "for ax, l, h in zip(axs, ['Actual', 'Estimated'], [actual_forces, pred_forces]):\n", - " ax.matshow(h, vmin=-0.05, vmax=0.05, aspect='auto', cmap='RdBu')\n", - "\n", - " ax.set_xticklabels([])\n", - " ax.set_yticklabels([])\n", - " \n", - " ax.set_title(l, fontsize=10)\n", - "\n", - "fig.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "id": "46a0f2f8-f863-4de3-bd97-97ebd92676d4", - "metadata": {}, - "source": [ - "Get the mean Hessian" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "00a10907-667a-413c-851d-d47f0eff092b", - "metadata": {}, - "outputs": [], - "source": [ - "approx_hessian = model.mean_hessian(hess_model)" - ] - }, - { - "cell_type": "markdown", - "id": "f4de2e78-00c2-427f-b9bd-eb3ca881564b", - "metadata": {}, - "source": [ - "Compare to exact answer" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d48893fd-df0d-4fa8-bfbe-0d04b71fbf1a", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "exact_hess[:3, :3]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9b311dea-5744-4211-81cb-40aa1183301e", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "approx_hessian[:3, :3]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "addd7bef-854a-4b9f-96e9-2aa01b652495", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(1, 2, figsize=(4, 2))\n", - "\n", - "for ax, l, h in zip(axs, ['Exact', 'Approximate'], [exact_hess, approx_hessian]):\n", - " ax.matshow(h, vmin=-100, vmax=100, cmap='RdBu')\n", - "\n", - " ax.set_xticklabels([])\n", - " ax.set_yticklabels([])\n", - " \n", - " ax.set_title(l, fontsize=10)\n", - "\n", - "fig.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "id": "b516bb4e-d27d-4ad6-ad4b-b873c81670ff", - "metadata": {}, - "source": [ - "Get the zero point energy" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "abbbbfd6-7d17-4b93-880a-3352903b56c4", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "approx_vibs = VibrationsData.from_2d(data[0], approx_hessian)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fdd80af3-8c18-40d8-b971-4a473bc91498", - "metadata": {}, - "outputs": [], - "source": [ - "approx_vibs.get_zero_point_energy()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6b1af348-4bc9-4ced-9a12-44b3e49abe9c", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "exact_zpe" - ] - }, - { - "cell_type": "markdown", - "id": "ab6a6645-bf0e-4ed7-874e-6a345063e0b5", - "metadata": {}, - "source": [ - "The two differ, but I'm not sure how important the difference is." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b2ad9116", - "metadata": {}, - "outputs": [], - "source": [ - "freq_diff = [app_freq.real-ex_freq.real for app_freq, ex_freq in zip(approx_vibs.get_frequencies(), exact_freqs)]\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b70a0581", - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(3.5, 2))\n", - "\n", - "ax.plot(exact_freqs, freq_diff)\n", - "\n", - "ax.set_xlabel('Exact frequency')\n", - "ax.set_ylabel('Frequency difference (cm-1)')\n", - "\n", - "fig.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "id": "6489882c-acaf-4a07-bbe9-d643f7c5c882", - "metadata": {}, - "source": [ - "## Plot as a Function of Data\n", - "See what happens as we add more data to the training" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bce41a81-6c88-4b0c-9d8d-0891d1832fd6", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "steps = np.linspace(5, len(data), 16, dtype=int)\n", - "print(f'Plotting at {len(steps)} steps: {\", \".join(map(str, steps[:5]))}, ...')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fe39ce86-1806-4367-8c86-e3ef58f81f84", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "zpes = []\n", - "freqs = []\n", - "freq_diffs = []\n", - "for count in tqdm(steps):\n", - " with warnings.catch_warnings():\n", - " warnings.simplefilter(\"ignore\")\n", - " hess_model = model.train(data[:count])\n", - " \n", - " approx_hessian = model.mean_hessian(hess_model)\n", - " approx_vibs = VibrationsData.from_2d(data[0], approx_hessian)\n", - " zpes.append(approx_vibs.get_zero_point_energy()*23.060541945329334)\n", - " freqs.append(approx_vibs.get_frequencies())\n", - " freq_diffs.append([app_freq.real-ex_freq.real for app_freq, ex_freq in zip(approx_vibs.get_frequencies(), exact_freqs)])" - ] - }, - { - "cell_type": "markdown", - "id": "c179c3ae-695f-44ad-b548-10002c4ff30b", - "metadata": {}, - "source": [ - "Plot it" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1c6706a9-a27f-448f-81d4-957939bb2ca8", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(3.5, 2))\n", - "\n", - "ax.plot(steps, zpes)\n", - "\n", - "ax.set_xlim([0, steps.max()])\n", - "ax.plot(ax.get_xlim(), [exact_zpe*23.060541945329334]*2, 'k--')\n", - "\n", - "ax.set_xlabel('Energies')\n", - "ax.set_ylabel('ZPE (eV)')\n", - "\n", - "fig.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "id": "e8788f74-c208-4939-aa9b-3bbdfd8310ee", - "metadata": {}, - "source": [ - "We consistently underestimate the ZPE. Is it because we have too few oscillators?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "384af4b3-5eb3-4eac-b176-160f19944853", - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pylab as pl\n", - "\n", - "fig, ax = plt.subplots(figsize=(5, 5))\n", - "colors = pl.cm.jet(np.linspace(0,1,len(freq_diffs)))\n", - "\n", - "for i, freq_diff in enumerate(freq_diffs):\n", - " ax.plot(exact_freqs, freq_diff, color=colors[i])\n", - "\n", - "ax.set_xlabel('Exact frequency')\n", - "ax.set_ylabel('Frequency difference (cm-1)')\n", - "\n", - "fig.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6cee624f", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/proof-of-concept/README.md b/notebooks/proof-of-concept/README.md deleted file mode 100644 index 3ca3018..0000000 --- a/notebooks/proof-of-concept/README.md +++ /dev/null @@ -1,3 +0,0 @@ -# Proof of Concept - -The goal behind these notebooks are to illustrate a proof-of-concept for the larger application. diff --git a/notebooks/proof-of-concept/data/approx/butanol_hf_def2-svpd-random-d=5.00e-03.db b/notebooks/proof-of-concept/data/approx/butanol_hf_def2-svpd-random-d=5.00e-03.db deleted file mode 100644 index 3121c517c66f832ee38e6ae527bf2c13c78fcedf..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1187840 zcmeFa1y~j9*EUY0?y^vj5D`UGba!ApAfliGk`@9x2`Om-g9a0eunW68c`ykRL2N8M zh-@0{_|~AlM}M#9|Mz+?uj_j~-|TZ-vuF13?3sJtvu4$^#?^U(e}vpv5fT<8jxgq^ zbXHMSRk1QQR#DNgQc+PgQTh8thqoGkud1ki_x>N=!Wct>kJA`$R61+zSD_+CF{O^; z)v2b_sE#W;3OhXQkki3dTdBQCTc%Bi9mp4n0EqyJ0EqyJz&{}1*;%V+zoDw({=qVN zOmJk7L>}h-{reg6S>93Nz({#`7aJE_Yd2eCd&kkXla0UM)!51L+xp+T4}iDkZVauS zeFmw{Xdf{`9ux6p?Dltm@7no?k^kO}823MRZ69?wU8`qr9aY!%QHf!`y$NUhFw%d0 zL<|Hg;q8BIN7L$Qrms4^{g1*!e5%Lwk@LAyz@}O|q4}<;ZPVM7;*>S+%pP4&Tw0fHBs|K|X92qVT3l;~- zzaHb4y{SJ8`{OR(5BlvBbGuGjJ^SjbioYCdg#UkxNBLnq>EEA!KUn)_bGMFKJqPKl z&irz4!TvKN;m~A199^d$MvMG$pYKQfuaC^9b%5jRt2#GAED4nR2Y<1j{6m7jd)x8r zsBYGyCfFK(_kr<%PR8&eKr(i-o$NNaeYKsn?d#?V1GTk#4jrnRsNO#OSML5z!2h

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