docs/notebooks/arfs_large_data_sampling.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ARFS - How to use with large data? \n",
    "\n",
    "It might be than the data set is too large for All Relevant Feature Selection to run in a reasonable time. Usually random sampling (stratified, grouped, etc) solves this issues. If extreme sampling is needed, ARFS provide two methods for decreasing drastically the number of rows. The sampling methods and outcomes are illustrated below.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from IPython.core.display import display, HTML\n",
    "# display(HTML(\"<style>.container { width:95% !important; }</style>\"))\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import gc\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "import arfs.feature_selection as arfsfs\n",
    "import arfs.feature_selection.allrelevant as arfsgroot\n",
    "from arfs.preprocessing import OrdinalEncoderPandas\n",
    "from arfs.benchmark import highlight_tick, compare_varimp, sklearn_pimp_bench\n",
    "from arfs.utils import load_data\n",
    "from arfs.sampling import sample\n",
    "\n",
    "# plt.style.use('fivethirtyeight')\n",
    "rng = np.random.RandomState(seed=42)\n",
    "\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run with ARFS 2.3.1\n"
     ]
    }
   ],
   "source": [
    "import arfs\n",
    "\n",
    "print(f\"Run with ARFS {arfs.__version__}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc.enable()\n",
    "gc.collect()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sampling data\n",
    "\n",
    "A fairly large data set for illustration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.3252</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>37.88</td>\n",
       "      <td>-122.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.3014</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2.109842</td>\n",
       "      <td>37.86</td>\n",
       "      <td>-122.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.2574</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>496.0</td>\n",
       "      <td>2.802260</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.6431</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>558.0</td>\n",
       "      <td>2.547945</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.8462</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.181467</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
       "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
       "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
       "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
       "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
       "\n",
       "   Longitude  \n",
       "0    -122.23  \n",
       "1    -122.22  \n",
       "2    -122.24  \n",
       "3    -122.25  \n",
       "4    -122.25  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# X, y, w = _generated_corr_dataset_regr(size=100_000)\n",
    "\n",
    "housing = load_data(name=\"housing\")\n",
    "X, y = housing.data, housing.target\n",
    "\n",
    "X = pd.DataFrame(X)\n",
    "X.columns = housing.feature_names\n",
    "y = pd.Series(y)\n",
    "X.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "basic_fs_pipeline = Pipeline(\n",
    "    [\n",
    "        (\"missing\", arfsfs.MissingValueThreshold(threshold=0.05)),\n",
    "        (\"unique\", arfsfs.UniqueValuesThreshold(threshold=1)),\n",
    "        (\"cardinality\", arfsfs.CardinalityThreshold(threshold=10)),\n",
    "        (\"collinearity\", arfsfs.CollinearityThreshold(threshold=0.75)),\n",
    "    ]\n",
    ")\n",
    "\n",
    "X_trans = basic_fs_pipeline.fit_transform(X=X, y=y)\n",
    "#   collinearity__sample_weight=w,\n",
    "#   lowimp__sample_weight=w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = basic_fs_pipeline.named_steps[\"collinearity\"].plot_association(figsize=(4, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = X_trans.copy()\n",
    "data[\"target\"] = y.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dataset shape is (20640, 8)\n"
     ]
    }
   ],
   "source": [
    "print(f\"The dataset shape is {data.shape}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random sampling\n",
    "You can sample using pandas or scikit-learn (providing more advanced random sampling, as stratified sampling, depending on the task)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The sampled dataset shape is (1000, 8)\n"
     ]
    }
   ],
   "source": [
    "data_rnd_samp = data.sample(n=1_000)\n",
    "print(f\"The sampled dataset shape is {data_rnd_samp.shape}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sampling by clustering the rows\n",
    "Using the Gower distance (handling mixed-type data), the rows are clustered and the average/mode for each column is returned. The larger the number of clusters, the closer to the original data. As it requires the computation of a distance matrix, you might need to use random sampling first to avoid a computational bottleneck. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "data_dum = data.sample(n=10_000)\n",
    "data_dum = data.copy()\n",
    "# Compute a distance matrix, 10_000x10_000 and use it for clustering (1_000 clusters)\n",
    "data_g_samp = sample(df=data_dum, n=1_000, sample_weight=None, method=\"gower\")\n",
    "print(f\"The sampled dataset shape is {data_g_samp.shape}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sampling by removing outliers\n",
    "\n",
    "This method is adapted from BorutaShap. It uses `IsolationForest` to remove the less similar samples and iterates till the 2-sample KS statistics is > $95\\%$ (for having a similar distribution than the original data). There is no guarantee of the output size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The sampled dataset shape is (2064, 8)\n",
      "CPU times: user 428 ms, sys: 6.97 ms, total: 435 ms\n",
      "Wall time: 434 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "data_isof_samp = sample(df=data, sample_weight=None, method=\"isoforest\")\n",
    "print(f\"The sampled dataset shape is {data_isof_samp.shape}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Impact on the feature selection\n",
    "Let's perform the feature selection on this toy data set to see what is the impact of the different sampling strategies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1b15f8c70ad042fc8cf5574535e99d12",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Cross Validation:   0%|          | 0/25 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['MedInc' 'HouseAge' 'AveRooms' 'AveBedrms' 'Population' 'AveOccup'\n",
      " 'Longitude']\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x140 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 13min 9s, sys: 2.56 s, total: 13min 11s\n",
      "Wall time: 4min 15s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# No Sampling\n",
    "X = data.drop(\"target\", axis=1)\n",
    "y = data.target\n",
    "\n",
    "# GrootCV\n",
    "feat_selector = arfsgroot.GrootCV(\n",
    "    objective=\"rmse\", cutoff=1, n_folds=5, n_iter=5, silent=True\n",
    ")\n",
    "feat_selector.fit(X, y, sample_weight=None)\n",
    "print(feat_selector.get_feature_names_out())\n",
    "fig = feat_selector.plot_importance(n_feat_per_inch=5)\n",
    "\n",
    "# highlight synthetic random variable\n",
    "fig = highlight_tick(figure=fig, str_match=\"random\")\n",
    "fig = highlight_tick(figure=fig, str_match=\"genuine\", color=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "66036749fa9145839700f8b7060dcad5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Cross Validation:   0%|          | 0/25 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['MedInc' 'HouseAge' 'AveRooms' 'AveBedrms' 'Population' 'AveOccup'\n",
      " 'Longitude']\n"
     ]
    },
    {
     "data": {
      "image/png": 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fL7zwgq3YCSmfq65du3L27Nl0n/O/f7+FlCk6Vq1axaxZs9i7dy9msznLsYiIiORH6uEpIiIid+Xn50eBAgXSFdsAPvjgA+Lj44mNjaVjx47pXvfy8qJQoUJ2265cuUJAQEC6Ypa/vz+urq62YZZXr17F1dWV4sWL2+1nMBgICAjgypUrD3Q9qb3TTp48mW649d3861//omLFiqxcuZLw8HAOHz7MwYMHGT9+/EMtCFKsWDG756lD8f9ecEzd/vcFhFJ7m6aVWnBLvY+Zvd+pUocoZ9aYMWNYsmQJw4YNo1GjRhQtWhSj0ciAAQMyXPDo79fs4eEBpCywAva9Se8mtSA5btw4xo0bl+E+9/p8/Pjjjzz33HM0btyYd955hxIlSuDu7s6mTZuYPXt2urj/HnNq3Gn3u3r1ql2xM1VG2/7OYDDwxRdfMHPmTObPn8/YsWMpVqwYnTt3ZtKkSfj4+NiuuXHjxhme4169WTN7bGbu/b1ktp3Uz9zd7ldMTEym2rvX/b7f5zo11m7dut31/NeuXcNgMGCxWB74vb18+bLte05WXb9+HavVmmFOpvaCzUz+rlixglmzZrF8+XKmTJlCwYIFadu2LdOmTctyvouIiOQnKniKiIjIXbm4uPDEE0/wzTffcP78ebvhpqnz8d2tQJFRMdDX15cff/wRq9Wabj7KpKQkfH19gZQiU1JSEpcuXbIrelqtVi5cuEDt2rUf6HpatGhBREQEn3/+OU8//XSmj+vRowcTJ07kxx9/ZO3atRiNxnS9T3NbRnMIXrhwAfi/Il1m73eqrBZw16xZQ1hYGJMnT7bbfuXKFYoUKZKlc8H/zV149uzZu+6TGvOIESPS9dxLVbFixbsev379etzc3Pjoo4/seu1t2rQpy/GmKlasmO3ep5XRtoyULl2axYsXAyk9LTdu3Mj06dNJTExk4cKFtmtetWoVpUuXzlJsmT02M/c+O9pJ/Ww+zP263/F/L1L//XOdGutbb71116kw/P39bSu6P2isfn5+nDt37r77ZaRIkSIYjUZiY2PTvXb+/HmATOWvn58fc+bMYc6cOZw+fZovvviCiRMncunSJT799NMHik1ERCQ/0JB2ERERuacRI0aQnJxMeHj4Qw+HbNq0Kbdv305XXFq9erXt9bT/pp1fD+CTTz4hLi7Obii5h4eHrYfg/dSqVYunn36aqKiouw6L379/f7oerd26dcPV1ZVly5axbt06mjZtmuXCU3a7desWX3zxhd22devWYTQabXNLZvZ+38vfe2GmZTAYbK+n2rJlywMXeerXr0/hwoVZtmxZuhXTU1WqVIkKFSpw+PBhateuneGXj4/PXdswGAy4urraFklKvba0UxZk1RNPPMH27dvtimDJycmZXjU+rYoVKzJ69GiqVatmG5rdokULXF1dOX78+F2v+W4ye2zFihUpV64cK1euxGQy3fV8d/s8ZLadSpUqERgYyPr16+3e41OnTrFnz55M36ejR49y6NAhu23r1q3Dx8eHmjVr3vPYBg0aUKRIEX799de7xuru7o63tzd169bl008/tevRe+vWLb788sv7xvj000/zxx9/3HMKjrvdT29vbx577DE+/fRTu9csFgtr1qyhRIkS9yzsZ6RUqVK89tprNGvWLN2wfxEREWejHp4iIiJyTw0aNGD+/PkMHz6chg0b0qdPH6pUqWLrfZTaS+jvw9czEhYWxpIlS+jXrx8xMTFUq1aN3bt3M2fOHFq2bGmb66558+a0aNGCCRMmcOvWLerXr29bpb1GjRp2i7tUq1aNDRs2sGHDBsqWLYuHh4fd4j5/9/7779OuXTteeOEFXnrpJZ5++mmKFClCbGwsmzdvZv369fznP/+xm7cxMDCQli1bsnLlSqxW60MtVpRdfH19CQ8P5/Tp01SoUIGvvvqKDz74gFdeecUWe2bv9734+PhQunRpvvjiC5o2bUqxYsXw9fUlJCSEVq1a8eGHH1KpUiWqV6/OgQMHWLBgwQMPiy5YsCAzZ85kwIABtGnTht69e+Pv78/x48c5fPiwbfX5hQsX8sILL/Dcc8/RvXt3goODuXbtGr/++iv/+9//7Bao+btWrVrx9ttv07t3b3r37s3Vq1eJjIxMV7jNilGjRvHFF1/Qpk0bxowZg5eXF0uWLCEuLu6+xx4+fJjhw4fzwgsvUL58edzd3dmxYwdHjhxh+PDhAISEhPDGG28wefJkTp48yVNPPUWRIkW4ePEiP/74I97e3rzxxhsZnj8rx86bN4+OHTvStGlTBg0aRKlSpTh9+jTbtm3jgw8+AFLyDWDRokV069YNNzc3KlasmOl2jEYjEydOZMCAAXTp0oXevXtz/fp1ZsyYkalh4qmCgoLo1KkT48aNIzAwkDVr1vDtt98ydepUuwWLMlKwYEHmzp1Lv379uHbtGu3ataN48eJcvnyZw4cPc/nyZducxBMmTKBdu3a0bduWIUOGkJyczLx58/Dy8rrvSucDBw5kw4YNdO7cmeHDh1OnTh0SEhL4/vvvad26NU888cQ982vy5Mm0bduW1q1bEx4ejru7O0uXLuWXX35h+fLl9+2RfePGDVq3bk2nTp2oXLkyBQsW5KeffuLrr7/mueeey/S9FhERyY9U8BQREZH7evnll6lXrx6LFi3inXfe4fz58xgMBkqUKMHjjz9u+2X9fjw9Pfnyyy+ZPHkyCxYs4PLlywQHBxMeHm43H6PBYGDt2rVMnz6dlStXMnv2bHx9fenatSsRERF2xak33niD2NhYBg0axK1btyhdujRHjx69awx+fn62As769etZt24dd+7coXjx4tSrV49169alWwwFUhYv+uKLLyhWrBht27bN2g3MAf7+/syfP59x48bx888/U7RoUUaOHGlX+Mrs/b6fRYsWMX78eDp16oTJZKJbt24sXbqUOXPm4Obmxty5c4mLi6NmzZpER0czZcqUB76unj17EhgYyPz58xk4cCBWq5WQkBDCwsJs+zzxxBPs2LGDOXPmMGrUKK5fv06xYsV45JFH7rtQUNOmTVm8eDHz58+nY8eOBAcH06tXL4oXL86AAQMeKOZq1arx+eefM3bsWPr160eRIkXo2rUr7dq1Y9CgQfc8NiAggLJly/L+++9z5swZDAYDZcqUYebMmfTv39+238iRI6lSpQrvvvsu69evx2QyERAQQJ06dejbt+8928jssU899RRbt25l5syZjBw5koSEBEqUKMEzzzxj26dJkyaMGDGCVatW8cEHH2CxWPjyyy9p0qRJpttJ/YPBvHnz6Nq1KyEhIYwYMYL//Oc/fP/995m656GhofTo0YNp06Zx7NgxgoKCePPNNxk8eHCmju/atSulSpVi/vz5DBkyhFu3blG8eHFCQ0Ptpqto3rw5a9euZfLkybz00ksEBATwyiuvkJCQwIwZM+7Zho+PD9u2bWP69On8+9//ZsaMGRQpUoQ6derQp08f2353y6/GjRuzefNmpk2bxquvvorFYuHRRx9l/fr1mVo0ydPTk8cee4zo6GhOnTqF2WymVKlSDBs2jNdffz1T90lERCS/MsTFxWU8XkhERERE8qRWrVpx+fJlfvzxR0eHIpLrqlSpQtWqVR9oygARERH5Z9AcniIiIiIiIiIiIuI0VPAUERERERERERERp6Eh7SIiIiIiIiIiIuI01MNTREREREREREREnIYKniIiIiIiIiIiIuI0VPAUERERERERERERp6GCp4iIiIiIiIiIiDgNFTydnMVi4fjx41gsFkeH8tCS78TxfZWSfF+lJMl34hwdjojDOFNei0gK5bWI81Feizgf5bVI/uHq6AAk51mtVkeHkG2Sb99ydAgieYIz5bWIpFBeizgf5bWI83GGvLZaISHJ0VHkXZ6uYDA4Ogp5WCp4ioiIiIiIiIj8QyQkQYPlbo4OI8/a3ctMAd2efO8fNaS9fv36rFy50tFhiIiIiIiIiEgeZLVaSUxMJDEx0Sl6c+ZnFlMcfwx154+h7lhMmtbufvTZtZcnC56tWrXC29ubb7/91m77/Pnz8fb2ZuTIkQ/dxs6dOwkODn7o84iIiIiIiIiIczCbzURGRhIZGYnZbHZ0OCKZps+uvTw7pL1SpUqsXLmSZs2a2batWrWKypUrOzAqEREREREREXFWaXvG/b1oZLVaSUpKwmw2Y8jHkzyak8Dlr+tMxk0TVgJYrbiQ8n6bzWZc8mEHybSfV/XwzMMFzw4dOrB48WJu3LhB4cKF2bdvH1arlbp169r2OX78OKNGjWLfvn0UKFCA3r17M3LkSIzGlI6r7733HvPmzSM+Pp6+ffves71WrVpRr149Dh48yJ49e6hQoQJLly6levXqANy8eZOIiAi++OILbty4QcWKFYmOjqZkyZLpzmUymTCZTHbbChUq5JCV3FLbdIZV5NJeg8ViweAE1yTyIJwpr0UkhfJaxPkor0Xyp7RFo0WLFjkwkpzV5a9/1xQeQTLuDo0lL3DBTJcbcwFY8q6Dg8kGZrMZNzfnnIg0teZ3P3m24Fm4cGFatGjB+vXrefnll1mxYgXdu3fn6NGjAMTHx9OmTRsGDBjA6tWruXDhAi+88AKBgYH07NmT7du3M3nyZD755BNq1arFjBkz+OWXX+7Z5urVq9mwYQPVq1dn6NChjBgxgi1btgDw6quvcufOHbZv305AQACHDh3C09Mzw/PMnTuXGTNm2J77+PgQGxvLyZMnHVZlj4mJcUi72cmamIhXy7YAxJw+g8Fd35Tln80Z8lpE7CmvRZyP8lokf0lISHB0CCIPLSYm5q41q/zMYDBQrly5TO2bZwueAD169GDKlCl069aNTz/9lL179zJx4kQAvvzyS4oUKcLgwYMBKFWqFAMHDmTt2rX07NmTtWvX0rlzZx5//HEAxo8fz5IlS+7ZXteuXalZsyYA3bp1o127dgBcuHCBzz77jF9//ZWgoCAA234ZGTFihC2utMqUKZOFq88eFouFmJgYQkJCMl0Fz8vKLV3h6BBEHM7Z8lpElNcizkh5LZI/JSYm2h7379/frpecxWLh1KlTlC5dOl/ndbwZmq9O6UCUjHP2AsyqZNxYU3gEAN+EJebLVdrNZjOLFy8GoHz58rj/wzuJ5emC55NPPsnAgQN58803qVevHoGBgbbXYmJi+OWXX+wWHrJYLLYh5ufPn6dx48a219zc3OyOz0hAQIDtsZeXF7dv3wbg9OnTeHh4UKpUqUzF7eHhgYeHR7rtjvyGaDQa8/U3ZBFJT3kt4nyU1yLOR3ktkr+kzVd3d3e7opHFYsHV1RUPD498ndcWIyQb8mFFLycZDLah/R4eBjzy4e1JO6+sfvbk8YKn0WgkLCyM2bNns2rVKrvXSpYsSa1atdi+fXuGxwYFBXH69Gnbc7PZTGxs7APFUapUKUwmE2fOnMlwzk7JHVarFUv8HQCMBbzy9STRIiIiIiIikve4ubkRHh5ueyySX+izay/Pl3sHDRrEZ599xjPPPGO3vXXr1ly8eJGlS5eSkJBAcnIyv//+Ozt37gSgY8eOrF27ln379pGYmMjMmTOJi4t7oBgCAgJ49tlnGTJkCOfPn8disXDw4EGuXLny0NcnmWeJv8P3lYP5vnKwrfApIiIiIiIikl0MBoOtZ6c62TiW0cObigsSqbggEaOHt6PDyfP02bWXp3t4AhQrVoxmzZql216wYEE+//xz3njjDWbOnInJZKJs2bIMHToUgGbNmjFhwgTCwsJISEigb9++VK1a9YHjWLp0KRMmTKBx48bcvn2bypUrs3r16gc+n4iIiIiIiIhIbvN0hd29zPff8R/KM89XyiQzDHFxcY5ZNlxyhcVi4cSJE5QtWzbfz9+QfCeO7yunzNna+LdzuHjpLzzyz+RMeS0iKZTXIs5HeS3ifJTXIvmHMlRERERERERERESchgqeIiIiIiIiIiIi4jRU8BQRERERERERERGnoalYRUREJM+wWiEhyXHte7qCFrUUEREREcnfVPCU/MPoQvE2z9sei4iI80lIggbL3RzW/u5eZgo4rnkREREREckGOTKk/bXXXsPb25tff/012865fv16/vWvf1G8eHFKlixJ9+7dOXbsWLadX/I+F09Pqr23gmrvrcDF09PR4YiIOCWr1UpiYiKJiYlYrVZHh3NfFlMcfwx154+h7lhMcY4OJ8tS77fZbM4X91tEREREJD/I9oLn7du32bhxI8WKFSMqKipbzvnee+8xfPhwRo8ezenTp9m/fz8lSpSgWbNmnDp1KlvaEBERETCbzURGRhIZGYnZbHZ0OE4v9X4vXLiQ5ORkR4cjIiIiIuIUsr3guWHDBry8vJg6dSqrV6/GbDbTsWNHZs6cabdfeHg4Q4YMAVL+sz916lSqV69OqVKl6NixI+fPnwfg1q1bTJo0idmzZ/Pcc8/h6emJv78/s2bNonr16kybNs12zj///JOOHTsSEhJCyZIl6dq1631f27lzJ8HBwXaxde7cmenTp9u9vnjxYsqVK0fZsmWZNm2aemGIiIhTSvvzzWw223p75taX2ZyIizXli9z6WWu12to0m3P7es1pwtD/LUREREREskO2z+EZFRVF586d6dChA6NGjWLz5s2EhYUxadIkxo4dC0BiYiIbN25k3bp1AERERHDgwAG+/vprfH19mTRpEj179mTr1q3s2bOH+Ph4XnzxxXRtderUiSlTpgAQFxfHs88+S+fOnVm+fDlubm7s3r37vq9lxq1btzh48CBHjhzh9OnTtG3bljJlytC9e/cM9zeZTJhMJrtthQoVwmKxZLrN7JLapiPazm7Jd+LYVaUkAP86egYXL28HRyTiGM6U15L3pC3ALVq0yCExdPnr3zWFR5CMe46354KZLjfmArDk3Rxv7q6Sk5OV1yJORD+vRZyP8lrE8YzGzPXdzNaC59GjR9m7dy+RkZEULFiQtm3bEhUVRXR0NIMHD2bv3r3Uq1ePLVu2UKRIERo0aIDVauX9999n27ZtBAUFATBp0iSKFy/OmTNnuHLlCr6+vri5pV9BIDAwkMuXLwPw5Zdf4ubmRkREBIa/lld94okn7vtaZlgsFqZOnYqXlxeVK1fm1VdfJTo6+q4Fz7lz5zJjxgzbcx8fH2JjYzl58qTDem/ExMQ4pN3sZImPtz0+eTIGY4ECDoxGxPGcIa8l70lISHB0CP9oymsR56O8FnE+ymsRxzAYDJQrVy5T+2ZrwTMqKopHH32U0NBQALp160a7du24cuUK7du3Z/Xq1dSrV4/Vq1fbhpRfvnyZuLg4WrZsaStGAri7u3PmzBl8fX25cuUKZrM5XdEzNjYWPz8/AE6dOkXZsmXtzpHqXq9lRuow+lSlS5e2DbnPyIgRIxg8eHC67WXKlHmg9h+GxWIhJiaGkJCQTFfB86rkO3Gc/etxmTIh6uEp/1jOlNeS9yQmJtoe9+/fP8M/OOakeDM0X53SqzOZ3Gk7GTfWFB4BwDdhibm6SrvZbGbx4sUAuLi4KK9FnIh+Xos4H+W1SP6RbQVPs9lMdHQ0cXFxlC1bFkiZiyo5OZkPP/yQrl270qlTJ8aMGcPWrVttPSB9fX3x8vJi+/btVK5cOd15b968SYECBfjoo4/o0qWL3Wvr1q2jWbNmQEoR8sSJE1it1nSFzXu9VrBgQeLj4+1ei42NtRVtIaW3y8WLF21Fz9OnT9t6o2bEw8MDDw+PdNsd+Q3RaDTm+2/I1jTxO8P1iDws5YHkhLSfKXd3d9zdc35IeVoWIyQbcrfIisFgGzrv4WHAIxebT/v/EoPBoLwWcULKaxHno7wWyfuyLUO/+OILbt26xa5du9i9eze7d+9mz549jBkzhhUrVlC/fn2KFCnCq6++Su3atW1dUI1GI3379mXs2LGcOXMGgCtXrrBhwwYgZe7LiRMnMmrUKDZt2mQrPo4ePZpDhw4xfvx4AFq1aoXJZGLq1KnExcWRmJjIjh077vtahQoVcHNzY+3atSQnJ7N+/Xr+97//2d8ko5FJkyYRHx/P77//ztKlS+ncuXN23ToREZE8w83NjfDwcMLDw3O9d+eDMHp4U3FBIhUXJGL0yH89/1Pv95AhQ3BxcXF0OCIiIiIiTiHbenhGRUXRsWPHdL00+/fvz4IFC9ixYwddu3Zl+vTpvP3223b7TJkyhfnz5/PMM89w4cIFihUrRtOmTenQoQMAAwcOxM/Pj5kzZ9KnTx/c3d154okn+Pbbb23DxAsWLMjnn3/O6NGjeeSRRwBo0qQJTzzxxD1fK1SoEO+88w5vvPEGw4YNo0uXLrRo0cIuPh8fH0JDQ6lWrRoWi4U+ffrcdf5OERGR/MxgMOR6r860PF1hdy/z/XfMwfZzU+r9tlgsDzz1joiIiIiI2DPExcU5ZhWdfGLnzp106dKFc+fOOTqUB2KxWDhx4gRly5bN913uk+/E8X3lYAAa/3ZOc3jKP5Yz5bWIpFBeizgf5bWI81Fei+QfudyPQeQhGF0o1uxp22MREREREREREZG/U8FT8g0XT09Co9Y7OgwREREREREREcnD1Af7Ppo0aZJvh7OLiIiIiIiIiIj806iHp4jkCVYrJCQ5rn1PV9B6ISIiIiIiIvlHcnIySUkO/EVSsp2rqysuLg8/jaEKnpJvJN+JY1fNCgD86+CfWrTIySQkQYPlbg5rf3cvMwUc17yIiIiIiIhkktVq5cqVK9y6dcvRoUgO8PHxwdfXF8ND9EpSwVPyFUv8HUeH8I9gtVoxm80AuLm5PdQ3mZxgMcVxbHRRAMrPuobRI28Uv/P6fRMREREREXEGqcXOokWL4unpqd+9nITVaiUhIYFr164B4Ofn98DnytaCZ6tWrXj22WcZNGiQ3XZvb29++OEHatSokZ3NZdnMmTOZNm0aGzdupGXLlg6NRSQvM5vNREZGAhAeHo67u7uDI8ofdN9ERERERERyVnJysq3YWaRIEUeHI9nM09MTgGvXrlG0aNEHHt7+j+nhabVaWblyJcWKFWPFihUqeIrcg9VqtT1O7bGY08xJ4PJXu8m45c6EmlYrLqRcn9lsxsV6n/3vI+29SnsPRUREREREJHukztmZWhgT55P63iYlJeWPgqfVamXhwoW8//77XL9+nTp16rBgwQLKli0LpO8J+s477/D555+zZcsWrFYrEyZMYNWqVcTHxxMQEMCbb75J69atAVi/fj1z5szhzJkzlC9fnjlz5lC/fn1b29999x3nzp3jgw8+oHfv3ly6dInixYvbXv/444+ZOHEily9fpn379sTGxlK7dm3Gjx8PwIEDBxg3bhyHDx+maNGiDBs2jN69e2d4nSaTCZPJZLetUKFCWCyW7LuZmZTapiPazm5pr8FisWBwgmvKq9IW7hYtWpRr7Xb56981hUeQTM73jnTBTJcbcwFY8m72nttsNuPmljOTgjpTXotICuW1iPNRXos4H+V13mCxWGwdTNTRxHlZrVYsFku6fDMajZk6PlcLnqtXr+btt9/mk08+oUKFCkRERNChQwf++9//4up671C++eYb1q1bxw8//EBQUBCnT58mISEBgC1btjBu3DjWrVtHjRo12LRpEx07duTgwYP4+voCEBUVRevWrWnXrh3jx48nOjqaIUOGAPDHH3/w8ssvEx0dTbNmzVi5ciVDhw6ldu3aAMTGxvLcc8+xYMEC2rVrx6+//spzzz1HmTJlePLJJ9PFOnfuXGbMmGF77uPjQ2xsLCdPnnRYMsbExDik3exkiY+3PT55MgZjgQIOjMa5peaWPLiYmJgc/4ujM+S1iNhTXos4H+W1iPNRXjtWaiEsMTHR0aFIDklMTCQpKYmzZ8/azc9qMBgoV65cps6R7QXPSZMm2RX70oqOjqZ///5Ur14dgMmTJ7N8+XJ+/PFHu96YGXFzc8NkMvHLL7/g5+dHqVKlbK8tXbqUoUOHUqtWLQCef/55Fi5cyFdffUVYWBjXrl1j06ZNrFixAoPBQJcuXVixYoWt4PnRRx/RtGlTnn76aQB69+7Nu+/+X3ev6Oho/vWvf/Hiiy8CUK1aNXr06MG6desyLHiOGDGCwYMHp9tepkyZe15jTrBYLMTExBASEpLpKnhelXwnjrN/PS5TJkSrtOegtD84+vfvn2M9FdOKN0Pz1Sm9OpPJneXSk3FjTeERAHwTlvjQq7SbzWYWL14MQPny5XNsDk9nymsRSaG8FnE+ymsR56O8zhtMJhPnz5/H3d0dDw8PR4cjOcTV1ZWgoKAHfo+zveA5efLkDBctAjh37hwhISG27R4eHgQFBXH27Fnu54knnmD8+PFMnTqV3377jSeffJIZM2ZQpkwZTp06RUREBNOnT7ftbzabOXfuHABr1qzBx8fHNm9nWFgYs2bNYu/evdSrV4/z589TsmRJu/bSPj916hRfffUVwcHBtm3Jyck0bNgww1g9PDwyfEMc+Q3RaDTm+2/IVhdXCtdvBIDRxTXfX09elvbeuru758riOxYjJBtyp9BpYzDYhs57eBjweMjm0/7lKTdyzhnyWkTsKa9FnI/yWsT5KK8dy2g0YjAYbF9i786dO7z88st8++233Lp1i7Nnz9KgQQMGDhyYrl7nKNOnT2fTpk3s2bMnw9dT39uHybVcHdIeHBxs1/U7MTGR8+fPU6JECSClMBqfZthybGys3fH9+vWjX79+3Lhxg/DwcEaMGMGGDRsoUaIEr732Gi+//HKG7UZFRXHz5k0qVapk22YwGIiKiqJevXoEBQWxb98+u2POnDnDY489BkCJEiV47rnniIqKergbIA/FpUABaq3/wtFh/CO4ubkRHh5ueyyZo/smIiIiIiIijrRq1Sp27drFN998g6+vL4ULF2bnzp22zoj/FLla8OzSpQtTpkzhmWeeoVy5ckyZMoXg4GDq1q0LQM2aNYmOjqZu3br8/PPPREdHU7FiRQB++uknzGYztWvXpkCBAnh7e9vmGXzttdcYOXIkderUoWbNmsTHx7Nnzx4qV67MxYsXOXz4MF999RUVKlSwxfLll18yZswYZs+eTfv27ZkzZw7btm2jadOmrFq1ij/++MO2b1hYGA0aNOCTTz6hTZs2APzyyy8kJSVRp06d3Lp9IrnGYDDkSq/OB2X08Kbigrw3X0tev28iIiIiIiLOpuFyV8zJjo4C3Fzgh15Jjg6D48eP88gjj1CtWjXbtrSLdmckJxfddZRcLXh269aNixcv0qFDB9sq7evXr7ctWDR37lz69etHcHAw9evXp1u3buzduxeAmzdvMnbsWE6cOIGbmxv16tUjMjISgNatWxMfH8/AgQM5efIk7u7u1K1bl/nz5xMVFUWTJk1o1KiRXSzdu3dn2rRpbNiwgZ49e7JkyRKGDh3KlStXaN++PU888YStcBEcHMynn37KhAkTGDJkCBaLhcqVK/PGG2/k4t0TcW6errC7l/n+O+Zg+yIiIiIiIpK/mJPBbMkLQ9szt0i1xWJh/vz5LF++nDNnzuDv70/fvn0ZNWoUR44cYeTIkezduxcvLy+ef/553nzzTQoWLAhgG/XcoEEDFi5ciNlspkOHDsyePRs3NzdatWrF999/D6SMom7cuDFbtmyhSpUqdkPavb29iYyMZOvWrXz33XeEh4djMBjYtGkT/fv3Z8aMGVy7do2uXbsyb948IiMjefvtt7FYLAwcOJBRo0bZrufGjRuMHz+eTZs2YTKZqFWrFrNmzSI0NNS2z9y5c3nnnXeIj4+nffv2+Pn5ZddNv6ts/RV/y5YtGW6Pi4uzPR42bBjDhg3LcL/Q0NC7jt9/8skn7/oaQPv27Wnfvn267QsWLMhwfzc3N44dO2Z73qFDBzp06GB7XrNmTbt5PGvWrMmmTZvu2r7kvOQ7cexp8CgA9Xcf1qJFTsZg4KEXDRIRERERERHJyyZOnMjy5ct58803adiwIbGxsfz+++/cuXOHdu3aUa9ePXbu3MmlS5cYOHAgw4YNY+nSpbbjd+7cSWBgIF9++SXHjx/npZdeIjQ0lN69e7N69WomTpzIL7/8wurVq+85AnHatGlMnjyZWbNm4eLiwsqVKzlx4gRff/01n3zyCSdOnKBbt26cPHmSihUr8tVXX7Fnzx769+9P06ZNqVevHlarlRdffJGiRYvy8ccfU6hQIZYtW8azzz7LwYMHKVasGB999BHTp09n/vz5NGzYkOjoaBYvXpzjC3urT9NfNm/eTOPGjXF3d+e9997j/PnzPPXUU44OS/7GfPWKo0MQEREREREREcmyW7dusWjRIubNm0f37t0BKFeuHA0bNuSDDz4gISGB999/3zbf5ltvvUXHjh2ZOnUqAQEBABQpUoR58+bh4uJC5cqVadWqFdu3b6d3794UK1aMAgUK4O7uTmBg4D1j6dSpEz179rTbZrFYWLx4MT4+PlSpUoUmTZrwxx9/8PHHH2M0GqlUqRLz589n586d1KtXjx07dvDzzz9z8uRJ2+LdM2fO5PPPP+eTTz6hT58+vPvuu7z00kv06tULgEmTJvHdd9/ZpqnMKVpW7C/btm2jSpUqlC5dmvXr17N27dpc6WIrIiIiIiIiIiLO77fffsNkMtG0adN0r/366688+uijdosLNWjQAIvFYrfOTJUqVXBxcbE9DwgI4OLFi1mOpXbt2um2hYSE4OPjY3vu7+/PI488YrdSur+/P5cuXQLgwIED3L59m1KlSuHv72/7OnnyJMePH7ddc7169eza+fvznKAenn+ZN28e8+bNc3QYIiIiIiIiIiLihDw9Pe/6mtV69zlADYb/m6P074sLGQyGex57Nxmt2p66xk7ac2fUnsViAVJ6hAYGBmY4xWXhwoWzHFN2UsFTRHKd1QoJubR4nadryvygIiIiIiIi4nzcXCCzCwblJDeX++9ToUIFChQowPbt221DvFNVqVKF1atXExcXZytG7t69G6PRSIUKFXIg4odXs2ZNLly4gKurKyEhIRnuU7lyZfbt20e3bt1s2/bt25fjsangKSKZZrVaMZtTVlJ3c3Oz+ytTViQkQYPlubNC0e5e5rsuhpRd1yMiIiIiIiKO8UOvXOpNkw08PT0ZNmwYb7zxBm5ubjRo0IDLly9z9OhROnfuzLRp0+jXrx/jxo3j8uXLjBgxgq5du9rm78xrmjVrxuOPP07nzp2ZOnUqlSpV4vz583z11Ve0bduW2rVrM2DAAPr160etWrVo2LAha9eu5ejRozm+aFG2zuH52muv4e3tza+//pot56tSpQq+vr4EBARQunRp2rdvb7eyuojkLrPZTGRkJJGRkbZCoaNYTHH8MdSdP4a6YzHFPdA58tL1iIiIiIiIiPMbM2YMQ4YMYdq0adSuXZuXXnqJixcv4uXlxaeffsrVq1dp0qQJ3bt3p2nTpnl6+kWDwcDGjRtp1KgR/fv3p0aNGvTs2ZOYmBj8/f0B6NChA2PHjmXixIk0atSIU6dO8fLLL+d8bHFxcdnS7/f27duUK1cODw8PunfvzsyZMx/6nFWqVGH27Nm0bduW27dvM3jwYM6ePcvWrVuzIeJ/BovFwokTJyhbtqzdJLP5UXJ8PAc7tAag5oYvcSlQwMER/fOYTCYWLlwIwIABA9LN5ZFZCUnw5Icpxybj9kBjzi2mOI6NLgpA+VnXMHqkmX/EasWFlALmd93NeN6lL7vZbGbRokUADBkyxLaqXF7nTHktIimU1yLOR3kt4nyU13mDyWTi3LlzBAcH55vf4SRrsuM9zrYh7Rs2bMDLy4uIiAgmTZrElClTCAsLo3bt2owdO9a2X3h4OFarlYULF2I2m3nzzTdZu3YtN27coH79+ixcuJCgoKB05y9YsCCdO3emR48etm23bt1i7NixbN68GYBnn32WmTNn2uY62L9/PyNHjuTo0aMEBQUxevRoOnXqBMD06dM5cOAAwcHBrF+/nqJFi7J48WJu3LjB+PHjuXr1Kq+88goREREAnDx5koEDB7J//35cXFyoXLkymzZtwsvLK12sJpMJk8lkt61QoUK2SV1zU9qJZPM7g4cHtTZ9a3vuDNeU36TtBZlaKHxQXf76d03hESTj/lDn+jsXzHS5MReAJe9m7hiz2fzABdzc5kx5LSIplNcizkd5LeJ8lNd5g8ViwWq12r7E+aS+txaLJV2+ZfaPDdlW8IyKiqJz58506NCBUaNGsXnzZsLCwpg0aZKt4JmYmMjGjRtZt24dABERERw4cICvv/4aX19fJk2aRM+ePTPswXnjxg2io6PtJmodOXIkMTEx7Nu3D6vVSvfu3Rk9ejTvvPMO169fp127dowdO5aXX36ZPXv28OKLL1KqVCkaNGgAwLZt2/jggw+YP38+06ZNo2/fvjz55JPs2bOHmJgY/vWvf/H8889Tq1YtIiIiKF++PJ988gkAP/30U7rVq1LNnTuXGTNm2J77+PgQGxvLyZMnHZaMMTExDmlXnEtCQoKjQ8gxMTEx91wxLy9SXos4H+W1iPNRXos4H+W1Y6UWwhITEx0diuSQxMREkpKSOHv2rN1aGwaDgXLlymXqHNkypP3o0aPUrVuX3bt3ExoaSt++fbl27RrR0dGUL1+ejRs3Uq9ePT777DPGjx/P4cOHsVqtBAQEsG3bNkJDQ4GUYkrx4sU5evQoJUuWpEqVKly+fBlXV1du3rxJxYoViY6OpkqVKlgsFvz8/Pjqq6947LHHANizZw/PPPMMly9fZt26dcyePZv9+/fb4hw0aBAA77zzDtOnT+frr79m+/btdtewf/9+KleuDECTJk3o06cPvXr14pVXXuHGjRvMmDHjvqtj5bUenjExMYSEhKjLvTy0xMRE3n77bQD69+//wD0i483QfHVKr86cHtL+TVjiXRctMpvNLF68GIDBgwfj7p69PU1zivJaxPkor0Wcj/JaxPkor/MGk8nE+fPnNaTdiaUOaQ8KCkr3HudqD8+oqCgeffRRW+GyW7dutGvXjitXrtC+fXtWr15NvXr1WL16NV27dgXg8uXLxMXF0bJlS7tqrbu7O2fOnKFkyZIA/Pvf/6Zt27b8/vvvtG/fnuPHj1OlShUuXbqEyWSidOnStmPLlCmDyWTi8uXLnD171u611Nd37dple552lavUoel/33b79m0gZQj8jBkzePbZZzEYDHTv3p2xY8dmeKM9PDwyTDpHfkM0Go35/htycvwd9jZ7HIB63/4XlwLppxOQnJX2M+Tu7v7ABUKLEZINOTh83GCwDZP38DDgcZem0n7vyY85kh9jFpF7U16LOB/ltYjzUV47ltFoxGAw2L7E+aS+tw+Taw9d8DSbzURHRxMXF0fZsmWBlO7FycnJfPjhh3Tt2pVOnToxZswYtm7dahvq7evri5eXF9u3b7f1qLyXSpUqMWPGDMLDw2nWrBnFixfH3d2dU6dO2YqUMTExeHh44OfnR4kSJTh16pTdOWJiYihRosQDXae/vz8LFiwA4Oeff+bZZ5+lWrVqtGvX7oHOJw/AasV05pTtseQ+Nzc3wsPDbY/zO2e7HhERERERERGBh/6TxBdffMGtW7fYtWsXu3fvZvfu3ezZs4cxY8awYsUK6tevT5EiRXj11VepXbu2bay90Wikb9++jB07ljNnzgBw5coVNmzYcNe22rZtS7FixViyZAlGo5FOnToRERHB1atXuXLlChEREXTt2hWj0UjLli25dOkSS5cuJSkpiV27drFu3TrCwsIe6Do/+ugjTp8+jdVqpXDhwri4uNx1Dk8RZ2UwGGw9Ox39lzSjhzcVFyRScUGi/XD2LMhL1yMiIiIiIiIi2eOhK3ZRUVF07NgxXS/N/v37s2DBAnbs2EHXrl2ZPn26be6/VFOmTGH+/Pk888wzXLhwgWLFitG0aVM6dOiQYVsGg4ERI0YwcuRIXnnlFebMmcOYMWOoU6cOAG3atGHmzJkAFC1alI8//phRo0YxadIkgoKCWLBgAQ0bNnyg6zxw4ABjxozh+vXrFClShJ49e9KmTZsHOpfIP52nK+zuZb7/jtnUloiIiIiIiIj8c2TLokWSd1ksFk6cOEHZsmXz/RwjyXfi+L5yMACNfzuHi9eD9eoTye+cKa9FJIXyWsT5KK9FnI/yOm9IXdBGixY5r+x4j5WhIiIiIiIiIiIi4jRU8BQREREREREREcmD+vXrR+fOnR0dRobycmya3U7yD4MBr0qP2B6LiIiIiIiIiDizOXPmYLVm32yU/fr148aNG6xduzbbzpkXqeAp+YZLAS/qffNfR4chd2G1QkJSzp3f01V1bhEREREREbG3YMECLBaLo8PAaDQydOjQLB2TmJiIu7v7PfcpXLjwQ0T1z6WCp4hki4QkaLDcLcfOv7uXmQI5d3oRERERERHJhywWC8nJyY4OI1NatWpF1apVcXNzIzo6mipVqrBgwQLGjRvHrl278PLyonnz5syaNQs/Pz8gfY9Mq9XK/PnzWbZsGbGxsVSoUIExY8bwwgsv2Nr55ZdfeOONN/jhhx+wWq2EhoayZMkSoqOjWbVqFQDe3ikLQX/55Zc0adKEc+fOMWbMGL755huMRiMNGjRgzpw5hISEAJCcnMy4ceNYuXIlLi4uvPTSS9na8zS7OcUcnkOGDOGNN97I1nPWr1+flStXZus5RfIKq9VKYmIiiYmJDvsGZTHF8cdQd/4Y6o7FFOeQGNLKC/dEREREREREnNuqVatwdXVl27ZtTJkyhVatWhEaGsr333/PJ598wsWLF+nRo8ddj588eTIffvghCxYs4Mcff2TQoEH07duX77//HoBz587RsmVLPD092bx5M//5z3/o0aMHSUlJhIeH8+KLL/LUU09x7Ngxjh07Rv369blz5w6tW7fG29ubr776iq+//hpvb2/atWtHYmIiAJGRkaxcuZJFixbx9ddfc+3aNTZt2pQr9+xBZHsPz1atWvHss88yaNCg7D71XS1cuND2OCYmhqpVq3L27FmKFCmSazFIzkuOv8NPzz4JQJ3Pv8OlgJeDI8q/zGYzkZGRAISHh9+3C/0/ge6JiIiIiIiI5LRy5coxffp0AKZOnUqNGjWYPHmy7fX33nuPSpUq8ccff1CxYkW7Y+Pi4nj77bfZvHkzjz/+OABly5Zl9+7dLFu2jMaNG7NkyRIKFSpEVFQUbm4pwyTTnsfT0xOTyURgYKBtW3R0NEajkUWLFmH4ay65JUuWEBwczM6dO2nRogXvvvsuw4cPp127dkBKLW7btm3Zf4OyiYa0S/5htXLn919tj+XBpe3BaDabs+Wc5iRw+eu8ybhlz4SbVisupMRnNptxycG3Pe19UA9PERERERERyQm1a9e2PT5w4AA7d+7E398/3X7Hjx9PV/D89ddfSUhIoG3btnbbExMTqVGjBgCHDh2iYcOGtmJnZhw8eJBjx44REBBgtz0hIYETJ05w48YNYmNjbUVWAFdXV2rXrp1nf3/OtYLn/v37GTlyJEePHiUoKIjRo0fTqVMnAKZPn86BAwcoXbo0a9aswcfHh+nTp9OhQwcATCYTI0eOZOPGjRQqVIjRo0czYMAAfvnlF0JCQujXrx+FCxdmzpw5NGnSBIBKlSoBKRVns9nMu+++y549e2zx1K9fn4EDB9q6Cb/33nvMmzeP+Ph4+vbtmy7+b7/9loiICP7880+Cg4OZPHkybdq0yfBaTSYTJpPJbluhQoUcMoluapt5YQLfh5X2GiwWCwYnuCZHSVvcW7RoUbadt8tf/64pPIJkHr6HpAtmutyYC8CSdx/6dJlmNpuz9MMhtzlTXotICuW1iPNRXos4H+V13mCxWLBarbavvOR+8VitVry8vGz7WSwWWrduzdSpU9PtGxgYaHc+q9Vqm6t0w4YNBAcH2+3v4eGB1WrF09Mz07GkSk5OplatWixbtizdfn5+frZ9/37P027PTqntWCyWdPlmNGZuds5cKXhev36ddu3aMXbsWF5++WX27NnDiy++SKlSpWjQoAEA27ZtY+nSpcyZM4c1a9YwcOBAWrZsiY+PD7NmzWL//v3s27cPLy8v+vTpc9e2du7cSdWqVfn9999tQ9rvNxfn9u3bmTx5Mp988gm1atVixowZ/PLLL7bXDx8+TI8ePVi1ahVNmjSxxb9jxw5bYTWtuXPnMmPGDNtzHx8fYmNjOXnypMOSMSYmxiHtZidLfLzt8cmTMRgLFHBgNPlbQkKCo0PI02JiYmw/JPIyZ8hrEbGnvBZxPsprEeejvHas1EJY6tySRqMxTxQ+jUZjus5vf5datEzdr3r16mzatInAwEBcXdOX6EwmE8nJybZjypYti4eHBydOnKBevXoZ7l+lShXWrFnD7du3M+zI4+Ligtlstou1evXqfPTRRxQqVIhChQplGHtAQAC7d+/mscceAyApKYkDBw4QGhp63+vOqsTERJKSkjh79qxtiD2AwWCgXLlymTpHrhQ8t2zZgp+fH/379wegcePGdOrUiVWrVtkKnjVr1rT1+AwLC2PQoEH8+eef1KpVi3Xr1jF16lSCgoIAGDduHFu2bMm2+NauXUvnzp1tXXPHjx/PkiVLbK//+9//plu3bjRt2hSAhg0b0qpVKzZu3MiYMWPSnW/EiBEMHjw43fYyZcpkW8yZZbFYiImJISQkJNNV8Lwq+U4cZ/96XKZMCC5e3g6NJz9L/cEA0L9//2zpzRhvhuarU3p1JpM9vSOTcWNN4REAfBOWmKOrtJvNZhYvXgxA+fLl8/Qcns6U1yKSQnkt4nyU1yLOR3mdN5hMJs6fP4+7uzseHh6Eh4c7OqRMMxgMuLi44OHhAcCAAQNYuXIlr776KkOHDsXX15fjx4+zYcMG3nnnHVxcXGxfHh4eeHh42BbudnFxoUGDBty6dYs9e/ZQsGBBunXrxoABA3j//ffp168fI0aMoFChQuzbt486depQqVIlypYty3fffUdMTAzFihWjcOHCdOvWjXfffZeXXnqJN954gxIlSnD69Gk+++wzhg4dSokSJRg4cCDz58+ncuXKVK5cmbfffpubN2/aXU92cnV1JSgo6IHPnSsFz7Nnz1K6dGm7bWXKlGHXrl2252nnCTAYDBQoUIBbt24BcP78eUqWLGl7Pe3j7HD+/HkaN25se+7m5mY3eWtMTAw7duzgww8/tG1LSkq6a9U79UP4d478hmg0GvP9N2Rrmvid4XocKe29c3d3z5binsUIyYZsrkgaDLah8R4eBjxysOCZ9q9G+eXzlV/iFJHMU16LOB/ltYjzUV47ltFoxGAw2L7yk9R4U/8NDg7mm2++YcKECbRr1w6TyUTp0qVp0aIFLi4u6Xo3AkyaNAl/f3/eeustTpw4QeHChalZsyYjR47EYDDg5+fH5s2bGT9+PK1atcLFxYXQ0FAaNGiAwWCgd+/efP/99zRp0oTbt2/z5Zdf0qRJE7766ismTJhAt27duHXrFsHBwTRt2pRChQphMBgIDw8nNjaW1157DaPRSI8ePWjbti03b97M9vch9b19mFzLlYJniRIlOHXqlN22mJgYSpQokanjg4KCOHPmjK3b7JkzZ+66b0Y3omDBgsSnGQ4NcOHCBbvznz592vbcbDYTGxtre16yZEkGDBiQ4ZwKIvmRm5ub7a9geXmuytykeyIiIiIiIiI5KaPRyhUqVCA6Ovqux5hMJry9/2+Eq8FgYMCAAQwYMOCuxzz66KN89tlnGb5WvHhxNm3alG57YGAg77///l3P6erqypw5c5gzZ85d98lLcuRPEklJSSQkJNi+nnzySS5dusTSpUtJSkpi165drFu3jrCwsEydr2PHjsyfP5/Y2Fhu3LjBm2++edd9/fz8MBqNnDhxwrYtNDSUEydOsGvXLpKSkpg3bx5Xr161O//atWvZt28fiYmJzJw5k7i4ONvrffr0YeXKlezYscM2b8J///tffv311we4O/LADAY8SpbGo2Tp7FkB/B/MYDDYenY66i9iRg9vKi5IpOKCRIwejp+eIC/cExERERERERFIqa0dPXqUvXv3UqVKFUeHk+/kSA/P8ePHM378eNvz0qVL8/HHHzNq1CgmTZpEUFAQCxYsoGHDhpk63+jRo7l48SJ169alUKFCDBs2jM2bN2c4DLdAgQKMGzeOdu3aYTabmT9/Pp07d2batGl069YNq9VK//797T4szZo1Y8KECYSFhZGQkEDfvn2pWrWq7fWaNWuyfPlypkyZwm+//YbRaOTRRx+1W5hIcp5LAS8a7D7s6DDkLjxdYXcv8/13fIjzi4iIiIiIiPwT/PzzzzRv3pwmTZrw8ssvOzqcfMcQFxfn+KWssmjPnj20atWKq1evat6M+7BYLJw4cYKyZcvqXok4CeW1iPNRXos4H+W1iPNRXucNJpOJc+fOERwcnCOL5YjjZcd7nC8y9OLFi7bh5OfPnyciIoLnn39e32BERERERERERETETr6oGCYnJzN69GiCgoKoX78+AQEBvPXWW44OS3JZcnw8P7Vpyk9tmpL8t0WoREREREREREREIJdWaX9YQUFB7Nmzx9FhiKNZLdw6dMD2OD+wWiEhKWfb8HTVGk4iIiIiIiIiIqnyRcFTJL9KSIIGy91ytI3dvcwUyNkmRERERERERETyjXwxpD2rrl+/jre3NzExMY4ORfIBq9VKYmIiiYmJWK2OX8PLYorjj6Hu/DHUHYspLtfazWv3QURERERERETkQTxQwfO1117D29ubX3/9NVuCqFKlCr6+vvj7+xMYGEijRo3YsWNHtpxb5H7MZjORkZFERkZiNpsdHY7D6D6IiIiIiIiIiDPIcsHz9u3bbNy4kWLFihEVFZVtgSxfvpyLFy9y7tw5evXqRefOnUlISMi286eVlJTDkypKvpK2N6PZbLb1csyOL7M5ERdryhfZ2WvSarWd12zOrljNaU6vHp4iIiIiIiIiOWnnzp14e3tz/fr1XG135cqVBAcHP9Q5YmJi8Pb25n//+99d93HU9cEDzOG5YcMGvLy8iIiIYNKkSUyZMoWwsDBq167N2LFjbfuFh4djtVpZuHAhZrOZN998k7Vr13Ljxg3q16/PwoULCQoKSnd+o9FIWFgY4eHhnD59mooVKwLw7bffEhERwZ9//klwcDCTJ0+mTZs2AJhMJkaNGsVHH31EoUKFGDVqlN05+/Xrh4uLC7dv3+brr79m0qRJfPrpp9StW5f9+/ezb98+qlWrxurVq/nggw9YunQp7u7uvPXWWzz33HMAfPPNN4wdO5aYmBgKFCjA888/T2RkZIb3yGQyYTKZ7LYVKlQIiyX3F9pJbdMRbWe3tNdgsVgwZNM1pS30LVq0KFvOmVaXv/5dU3gEybhnyzldMNPlxlwAlrybLae0YzabcXPTxKB5lTPltYikUF6LOB/ltYjzUV7nDRaLBavVavvKT1q1akVoaCizZ88G/q+zkaOu5WHaTHvs3c7zoNeXur/FYkmXb0Zj5vpuZrngGRUVRefOnenQoQOjRo1i8+bNhIWFMWnSJFvBMzExkY0bN7Ju3ToAIiIiOHDgAF9//TW+vr5MmjSJnj17snXr1nTnT0pKYuXKlZQsWZKQkBAADh8+TI8ePVi1ahVNmjRhz549vPjii+zYsYNKlSoxe/Zs/vvf/7Jv3z4KFChA79690513/fr1REdHExUVRUJCAp9++ilr167lk08+oXz58rRv356nnnqK1157jWPHjrFy5UoGDRpE69atcXNzo1+/fkydOpWwsDDi4uI4fPjwXe/R3LlzmTFjhu25j48PsbGxnDx50mHJ6AzzmVri4zEWLgLAyZMxGAsUyJbz5lRP4vwsJiYGT09PR4ch9+EMeS0i9pTXIs5HeS3ifJTXjpVaCEtMTHR0KFlmtVpJTk62dZJL7YCVmJiYruPc3WRHByWz2YzVas10mxlJvf/3iv1Bri91/6SkJM6ePYvBYLBtNxgMlCtXLlPnyFLB8+jRo+zdu5fIyEgKFixI27ZtiYqKIjo6msGDB7N3717q1avHli1bKFKkCA0aNMBqtfL++++zbds2W4/OSZMmUbx4cc6cOUPJkiUB6NOnD25ubsTHx2MwGFi0aBHu7im94f7973/TrVs3mjZtCkDDhg1p1aoVGzduZMyYMaxdu5bJkyfbzj9u3Lh0xdTmzZvz1FNPAeDl5QVAly5dqFatGgDt2rXjzTffZPDgwQB07tyZQYMGcerUKcqXL4+bmxvHjx/n0qVLFC9enPr169/1Po0YMcJ2nrTKlCmTldudLSwWCzExMYSEhGS6Cp6XlT90ItvPmfabZP/+/bO1Z2O8GZqvTvkcJ5N9503GjTWFRwDwTVhitqzSbjabWbx4MQDly5e35Z/kPc6W1yKivBZxRsprEeejvM4bTCYT58+fx93dHQ8PD0eHk2mvvvoqu3btYteuXSxZsgSA9957D4Cff/6ZiRMn8uuvvxIaGsrixYupVKkSANOnT+fzzz+nf//+zJ49m5iYGG7evMnNmzcZP348n3/+OSaTiVq1ajFr1iweffRRIKXz4KhRozhw4AAGg4Hy5cuzcOFCateujZubGwaDge+//55Ro0Zx9uxZGjRowHvvvUdgYCCQ8nmfNWsWH3zwAZcvX6Zy5cpMmTLFVltLrRmkfR+++uorRo8ezZkzZ3jsscfo1q1bun0yy9XVlaCgoAd+j7NU8IyKiuLRRx8lNDQUgG7dutGuXTuuXLlC+/btWb16NfXq1WP16tV07doVgMuXLxMXF0fLli3tqrLu7u52Bc9///vftG3bFqvVyv/+9z/at29PsWLFePrpp4mJiWHHjh18+OGHtuOTkpIoVKgQAOfPn6d06dK210qVKpUu9oy2BQQE2B4XKFAAf39/2/PUoujt27cBiI6OZs6cOdSqVYtSpUoxYsQIXnzxxQzvk4eHR4ZviCO/IRqNRn1Dvou098Xd3T1bC30WIyQbcmBouMFgGx7v4WHAIxuaSJuf+rzkD3qfRJyP8lrE+SivRZyP8tqxjEYjBoPB9pXW6aXvcPr9+8/75lO9Bo9+sMZu2+HeXbh15O7zUaYq9cpASvUblLWggTlz5vDnn39StWpV3njjDSClYyHAlClTmDlzJn5+foSHhzNgwAC++eYbIOV39ePHj7Nx40ZWrVqFi4sLBoOBDh06ULRoUT7++GMKFSrEsmXLePbZZzl48CDFihWjT58+1KhRg8jISFxcXDh06JCt0Alw584dIiMjWbZsGQaDgZdffplx48bxwQcfAClT/r399tssXLiQGjVqsGLFCjp16sSPP/5IhQoV0vW8PHPmDGFhYfTt25dXXnmF/fv320aCZ/Re3Uvq/g+Ta5kueJrNZqKjo4mLi6Ns2bLA/3XF/fDDD+natSudOnVizJgxbN261Tak29fXFy8vL7Zv307lypUzdVE1a9akQYMGfPXVVzz99NOULFmSAQMGMHXq1AyPCQoK4tSpUzz22GMAnDlzJsPzPoxatWqxevVqLBYLmzZtokePHjRq1MiuaCr5k5ubG+Hh4bbH/1S6DyIiIiIiIpKfJd2+RWLsufvuZw4ukX7b1cuZOjbp9q0Hiq1w4cK4u7tToEABWy/K33//HUgZCd24cWMAhg0bxosvvkhCQoJtqrnExET+3//7fxQvXhyA7du38/PPP3Py5Elbh7uZM2fy+eef88knn9CnTx/OnDnD0KFDbbW4ChUq2F+v2czChQttQ8RfffVVZs6caXs9MjKSYcOG0bFjRwCmTZvGzp07effdd5k/f36663v//fcpW7Yss2fPxmAwUKlSJX7++WfmzZv3QPfrYWW64PnFF19w69Ytdu/eTeHChW3b33//fVasWMHIkSMpUqQIr776KrVr17bdMKPRSN++fRk7diwLFy6kZMmSXLlyhe+++44OHTpk2NaRI0fYtWsXEydOBFKGu7dr144WLVrQqFEjkpKSOHjwIIULF+aRRx6hY8eOvPXWWzRs2JACBQrYvUHZITExkQ0bNtC6dWuKFi1qu35X1yxPgSoPITk+nkMvpXxmQldswCWb5vA0GAx5avi20cObigtyfy6SvHYfRERERERERLLCtaAP7oH3X33crZhfhtsyc6xrQZ8Hiu1eqlevbnucWgy9dOmSbbRy6dKlbcVOgAMHDnD79u10o5nj4+M5fvw4AIMHD2bgwIFER0fz5JNP0r59e7v5L728vOyeBwYGcunSJQBu3rzJ+fPn003nWL9+/buuafPbb7/x2GOP2XU4fPzxxzN/E7JZpit2UVFRdOzYMV0vzf79+7NgwQJ27NhB165dmT59Om+//bbdPlOmTGH+/Pk888wzXLhwgWLFitG0aVO7gmevXr1wcXEBwM/Pj1deecW2+FDNmjVZvnw5U6ZM4bfffsNoNPLoo4/aepGOHj2aS5cu8dhjj+Hj48Po0aPZsmXLg92Ru1i3bh2jR48mMTGRUqVK8cEHH+Dr65utbch9WC3c2PMf2+P8wNMVdvcy33/Hh2xDRERERERE5J+uVL9BDzTcHEg3xD03pR1lmVowTLs6eeq0i6ksFguBgYEZ1r5SO+mNHz+eTp06sWXLFrZu3cr06dOJioriueeeS9dmart/X2j776OlrVbrXUdQO2qR7rvJdKnk448/znC7n58fV65cAaBp06aMGzcu3T7u7u6MHj2a0aNHZ3iO1DkL7qVp06a2RYv+ztPTk3feeYd33nnHtq1Xr162x0uXLk13zN8/FD169KBHjx522+Li4myPP/nkk/vGKPJ3BgPZsqCQiIiIiIiIiORvbm5udoXMB1WzZk0uXLiAq6srISEhd92vYsWKVKxYkcGDB9OzZ09WrlxpK3jeS6FChQgKCmL37t00atTItv2///0vdevWzfCYRx55hM8//9xu2969ezN5RdlPs+yKiIiIiIiIiIjksJCQEPbt20dMTAyXL19+4OJns2bNePzxx+ncuTNff/01MTEx7Nmzh8mTJ7N//37i4+MZNmwYO3fu5NSpU+zevZv9+/dnam2dVEOHDmXevHls2LCB33//nQkTJnDo0CEGDBiQ4f4vv/wyx48fZ/To0fz++++sXbvWbvHx3KaCp4iIiIiIiIiISA4LDw/HxcWFOnXqEBISwunTpx/oPAaDgY0bN9KoUSP69+9PjRo16NmzJzExMfj7++Pi4sKVK1d45ZVXqFGjBj169ODpp5+2rQ6fGQMGDGDIkCGMHTuWevXq8fXXX7Nu3bp0ix+lKlWqFKtXr+bLL7+kfv36LFu2jIiIiAe6vuxgiIuLy1uD7CVbWSwWTpw4QdmyZTEa83d9O/lOHN9XTplAuPFv53Dx8nZwRCKO4Ux5LSIplNcizkd5LeJ8lNd5g8lk4ty5cwQHB9tWKBfnkh3vsZY7kTzFaoWEpIxfS06z9k+8GVwysRaQp2vKPJoiIiIiIiIiIvLPkG8LntOnT+fQoUOsXbv2gY5fs2YNS5cu5dtvv83myORhJCRBg+UZr/LjnujGVLeUlcmafehGovv9VwPa3cusRYNERERERERERP5BsqUPdqtWrShatCj+/v6UKFGCli1b8tNPP2XHqbPFzp07CQ4OttvWpUsXFTtzkNVqJTExkcTERKzW7Jk1IdHdm9FjLzN67GUSrPDHUHf+GOqOxRSXLeeHnIlbRERERERERERyT7ZNOjF16lQuXrzIsWPHqFGjBp07d86uU0s+ZDabiYyMJDIyErM5E2PP84j8GreIiIiIiIiIiKTI9ll2PT096dmzJ+fPn+fChQv06NGDkJAQKleuTEREBElJKRM0pva6XLx4MeXKlaNs2bJMmzbN1qtu+vTp6YqmwcHB7Ny5M8N2x48fzyOPPEJAQAB16tRh48aNAFy5coUXXniBGzdu4O/vj7+/P7t27WLlypXUr1/fdnxmYl2+fDmVKlWiVKlSjB8/PrtvnVNJ2zvSbDbbek3e78tsTsTFmvLFg/awtFpt5zCbM9fu/7VvTnMa9fAUERERERERyUsMfy3Uod/ZnVfqe2t4iEVZsn0Ozzt37rB8+XJKly5N7969CQgI4Oeff+bq1au0b98eb29vRo4cCcCtW7c4ePAgR44c4fTp07Rt25YyZcrQvXv3LLf76KOPEh4ejq+vLxs3buTll1+mdu3alClTho8//pguXbpw7tw52/7Hjx+3Oz4zsR45coRDhw5x8uRJGjduTMuWLWnSpEm6WEwmEyaTyW5boUKFsFgsWb6uh5XaZm63nbZwuGjRoiwd2+Wvf9cUHkEy7rbtrkkJ9F7XFYBlz/+/ux7vgpkuN+YCsOTdLDVtx2w24+amCUAl73FUXotIzlFeizgf5bWI81Fe5w0uLi4YDAYuXbpEkSJF9Hu7kzGbzVy7dg2DwYCLi0u6fDMaM9d3M9sKnpMmTWLGjBl4eHgQGhrK0qVLadWqFceOHaNgwYIULFiQkSNHMmPGDFsR0WKxMHXqVLy8vKhcuTKvvvoq0dHRD1Tw7NKli+1xx44deeutt9izZw9lypS577Hnzp1jx44d94zVarUyZcoUPD09eeSRR3j88cc5cOBAhgXPuXPnMmPGDNtzHx8fYmNjOXnypMP+AhETE5Or7SUkJGT7OY2WZKr9+VXKY2tytp//72JiYvD09MzxdkQeVG7ntYjkPOW1iPNRXos4H+W141mtVkwmE/Hx8Y4ORXKA1WrFxcWFkydP2m03GAyUK1cuU+fItoLn5MmTGTRokO35vn378PT0JDAw0LatbNmynD171vbc09MTf39/2/PSpUtz/vz5B2r/7bffJioqirNnz2IwGLh9+zZXrlzJ1LFnz569b6yFChXCy8vL9tzb25vbt29neL4RI0YwePDgdNszU3zNbhaLhZiYGEJCQjJdBc8OiYmJtsf9+/fP9F9c4s3QfHVKr85kHuyvNMm4sabwCAC+CUvM0irtZrOZxYsXA1C+fHnc3d3vc4RI7nNUXotIzlFeizgf5bWI81Fe5y1Wq5Xk5GQsFouGtzsJg8GA0Wi09eJ9GNk+pD1ViRIlSEhI4MKFCwQEBABw8uRJSpQoYdsnISGBixcv2oqep0+fJigoCEgpKN65c8e27507d7h582aGbf3www/MmDGDzZs3U6NGDYxGI/Xr17d94O/3jSgzsWaFh4cHHh4e6bY78hui0WjM1fbTtuXu7p7pwqHFCMmGh+yObjDYhsJ7eBjwyMLp0iZUbt8zkazSZ1TE+SivRZyP8lrE+Siv8w4XFxdHhyB5VI5laHBwME2aNGHcuHHExcVx+vRp5syZQ7du3f6vcaORSZMmER8fz++//87SpUttCxXVrFmTvXv38ttvv5GQkMDEiRPvWt29efMmrq6u+Pn5YbFYiIqK4pdffrG97u/vz61bt7h06dIDxypZ4+bmRnh4OOHh4Tkyn4bR3ZuKCxKpuCARo4d3tp03p+MWEREREREREZGclWM9PAE++OADhg8fTpUqVfD09KRz5868/vrrttd9fHwIDQ2lWrVqWCwW+vTpY5u/s2nTpvTp04fmzZtToEABxo8fj4+PT4btPP300zz//PPUq1cPd3d3unbtSoMGDWyvV6pUiZ49e1KnTh2SkpLYsGFDlmOVrDEYDA80HNzTFXb3Mmf4WvIdMz++mfL42+5mXLwy3u/v58uKB41bRERERERERETyBkNcXJxDJjrYuXNnupXTJftZLBZOnDhB2bJl832X++Q7cXxfORiAxr+dw8Ur+3p2iuQnzpTXIpJCeS3ifJTXIs5HeS2Sf+RoD0/JGx52ote8xKVgxr18Rf5pnCmvRSSF8lrE+SivRZyP8lokf1APTxEREREREREREXEaDuuD3aRJExU7c8HNmzcJDAy86wr3IpL/KK9FnI/yWsT5KK9FnI/yWiT/0KQT/wC3bt1ydAgiks2U1yLOR3kt4nyU1yLOR3ktkj+o4CkiIiIiIiIiIiJOQwVPERERERERERERcRoqeDo5Dw8Pxo0bh4eHh6NDEZFsorwWcT7KaxHno7wWcT7Ka5H8w2GrtIuIiIiIiIiIiIhkN/XwFBEREREREREREaehgqeIiIiIiIiIiIg4DRU8RURERERERERExGmo4CkiIiIiIiIiIiJOQwVPERERERERERERcRoqeDoBs9nMsGHDKFmyJCVLlmT48OEkJSU99L4i4jhZydX33nuPRo0aUbRoUTp37pzLkYpIZmU2r00mEwMHDqRq1aoEBARQq1YtoqKiHBCxiNxPVn5eDx8+nEqVKhEYGEiFChUYOXIkiYmJuRyxiNzPg/zOHB8fz6OPPkpwcHAuRSki96OCpxOYNWsWP/zwA/v27WPfvn3s2rWLOXPmPPS+IuI4WcnVoKAgRo8eTe/evXM5ShHJiszmdVJSEoGBgXz++efExsayZMkSxo0bx7Zt2xwQtYjcS1Z+Xr/yyiscOHCA2NhYdu/ezZEjR5g/f34uRywi9/MgvzNPnTqVEiVK5FKEIpIZKng6gRUrVjB69GiCgoIICgpi1KhRd+0JkpV9RcRxspKrzz//PG3btsXX1zeXoxSRrMhsXnt7ezNhwgTKlSuHwWCgXr16NGnShN27dzsgahG5l6z8vH7kkUfw9va2PTcYDPz555+5FaqIZFJWf2c+cOAAW7duZfjw4bkYpYjcjwqe+dy1a9c4e/YsoaGhtm2hoaGcPn2aGzduPPC+IuI4ylUR5/MweZ2QkMCPP/5I9erVczpMEcmCB8nruXPnEhAQQJkyZThy5Aj9+/fPrXBFJBOymtdJSUkMGjSIefPm4eHhkZuhish9qOCZz8XFxQFQuHBh27bUx7dv337gfUXEcZSrIs7nQfPaarUyYMAAKlSowPPPP5+zQYpIljxIXo8YMYILFy7w008/0bdvXwICAnI+UBHJtKzmdWRkJNWrV6dJkya5E6CIZJoKnvlc6rCYmzdv2ralPi5YsOAD7ysijqNcFXE+D5LXVquV8PBw/vjjD9asWYPRqP+2ieQlD/Pz+pFHHuHRRx+lX79+ORegiGRZVvL6+PHjLF26lBkzZuRegCKSafqfcz5XtGhRSpQowaFDh2zbDh06RMmSJe3+KpXVfUXEcZSrIs4nq3lttVp5/fXX+fHHH/nss8+U+yJ50MP+vDabzRw7diwnQxSRLMpKXu/atYvLly9Tt25dypYtS1hYGDdv3qRs2bL8+OOPuR26iPyNCp5OoEePHsyePZvY2FhiY2OZM2cOvXr1euh9RcRxspKrSUlJJCQkkJSUhMViISEhgcTExNwNWETuKyt5PWzYMHbv3s2mTZsoWrRo7gYqIpmW2by+ffs2K1as4Pr161itVo4cOcLs2bNp0aJF7gctIveU2bzu0KEDP//8M7t372b37t28++67+Pj4sHv3bmrUqJH7gYuIHVdHByAPb8yYMVy9epU6deoA0LlzZ0aOHAnAkCFDAFi4cOF99xWRvCMreT1r1iy7oTS+vr40btyYLVu25HLUInIvmc3rU6dOsXTpUjw8PKhSpYrt+C5dutjyXkTyhszmtcFgYN26dYwfPx6TyUTx4sV5/vnneeONNxwWu4hkLLN5XaBAAQoUKGA7rmjRohgMBgIDA3M/aBFJxxAXF2d1dBAiIiIiIiIiIiIi2UFD2kVERERERERERMRpqOApIiIiIiIiIiIiTkMFTxEREREREREREXEaKniKiIiIiIiIiIiI01DBU0RERERERERERJyGCp4iIiIiIiIiIiLiNFTwFBEREREREREREaehgqeIiIiIiIiIiIg4DRU8RURERERERERExGmo4CkiIiIiIiIiIiJOQwVPERERERERERERcRr/H6vDirrHiR86AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1600x140 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 25.5 s, sys: 261 ms, total: 25.8 s\n",
      "Wall time: 7.98 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "X = data_rnd_samp.drop(\"target\", axis=1)\n",
    "y = data_rnd_samp.target\n",
    "\n",
    "# GrootCV\n",
    "feat_selector = arfsgroot.GrootCV(\n",
    "    objective=\"rmse\", cutoff=1, n_folds=5, n_iter=5, silent=True\n",
    ")\n",
    "feat_selector.fit(X, y, sample_weight=None)\n",
    "print(feat_selector.get_feature_names_out())\n",
    "fig = feat_selector.plot_importance(n_feat_per_inch=5)\n",
    "\n",
    "# highlight synthetic random variable\n",
    "fig = highlight_tick(figure=fig, str_match=\"random\")\n",
    "fig = highlight_tick(figure=fig, str_match=\"genuine\", color=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data_g_samp' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "File \u001b[0;32m<timed exec>:1\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'data_g_samp' is not defined"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "X = data_g_samp.drop(\"target\", axis=1)\n",
    "y = data_g_samp.target\n",
    "\n",
    "# GrootCV\n",
    "feat_selector = arfsgroot.GrootCV(\n",
    "    objective=\"rmse\", cutoff=1, n_folds=5, n_iter=5, silent=True\n",
    ")\n",
    "feat_selector.fit(X, y, sample_weight=None)\n",
    "print(feat_selector.get_feature_names_out())\n",
    "fig = feat_selector.plot_importance(n_feat_per_inch=5)\n",
    "\n",
    "# highlight synthetic random variable\n",
    "fig = highlight_tick(figure=fig, str_match=\"random\")\n",
    "fig = highlight_tick(figure=fig, str_match=\"genuine\", color=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f9b1d11f36994d32aa4f83fd87ace987",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Cross Validation:   0%|          | 0/25 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['MedInc' 'HouseAge' 'AveRooms' 'AveBedrms' 'Population' 'AveOccup'\n",
      " 'Longitude']\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x140 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 38.6 s, sys: 316 ms, total: 38.9 s\n",
      "Wall time: 11.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "X = data_isof_samp.drop(\"target\", axis=1)\n",
    "y = data_isof_samp.target\n",
    "\n",
    "# GrootCV\n",
    "feat_selector = arfsgroot.GrootCV(\n",
    "    objective=\"rmse\", cutoff=1, n_folds=5, n_iter=5, silent=True\n",
    ")\n",
    "feat_selector.fit(X, y, sample_weight=None)\n",
    "print(feat_selector.get_feature_names_out())\n",
    "fig = feat_selector.plot_importance(n_feat_per_inch=5)\n",
    "\n",
    "# highlight synthetic random variable\n",
    "fig = highlight_tick(figure=fig, str_match=\"random\")\n",
    "fig = highlight_tick(figure=fig, str_match=\"genuine\", color=\"green\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "arfs-env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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