d_final_models_VPAS_DRAGON.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Initialisations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-16T12:28:20.546609Z",
     "start_time": "2020-01-16T12:28:17.771964Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd ; pd.set_option('display.max_columns', 500) # dataframes\n",
    "import numpy as np # mathsy bits\n",
    "import ipywidgets as widgets # widgets\n",
    "\n",
    "from sklearn.model_selection import train_test_split #split data into train and test sets\n",
    "\n",
    "# feature selection + gridsearch\n",
    "from sklearn.feature_selection import SelectFromModel, SelectKBest\n",
    "from sklearn.feature_selection import f_regression, mutual_info_regression\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# models\n",
    "from sklearn.linear_model import Lars, ElasticNet, Lasso\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor, ExtraTreesRegressor\n",
    "from sklearn.ensemble import AdaBoostRegressor, GradientBoostingRegressor\n",
    "\n",
    "import joblib # saving models\n",
    "from datetime import datetime # get time for labelling saved models\n",
    "\n",
    "# visualization\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import mpld3 # hover-over labels for plots\n",
    "mpld3.disable_notebook()\n",
    "\n",
    "train_scores = dict() ; test_scores  = dict() #to hold scores for viewing at the end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-17T10:46:49.818926Z",
     "start_time": "2020-01-17T10:46:49.799892Z"
    }
   },
   "outputs": [],
   "source": [
    "def grid_search_return_hp_dict(model, X_train_gs, y_train_gs, param_grid):\n",
    "    \"\"\"Run GridSearchCV on given model with defined param_grid, return best params as dict.\n",
    "    Pre-assign sklearn model to variable. Pass param_grid as dict.\"\"\"\n",
    "    print(str(model).split(sep='(')[0], \"grid search.\")\n",
    "    GS = GridSearchCV(model, param_grid, n_jobs=2)\n",
    "    GS.fit(X_train_gs, y_train_gs)\n",
    "    print(GS.best_params_)\n",
    "    return(GS.best_params_)\n",
    "\n",
    "def produce_exp_vs_pred_df(features_list, model, codename):\n",
    "    \"\"\"Pass features_list as a string\"\"\"\n",
    "    pred_list = []\n",
    "    for i in range(0, len(dragon_features)):\n",
    "        prediction = model.predict([dragon_features[eval(features_list)].iloc[i]])\n",
    "        pred_list.append(prediction)\n",
    "    train_scores.update({codename : model.score(X_train, y_train)})\n",
    "    test_scores.update({codename : model.score(X_test, y_test)})\n",
    "    \n",
    "    print('Training Score:\\t', model.score(X_train, y_train))\n",
    "    print('Testing Score:\\t', model.score(X_test, y_test))\n",
    "    \n",
    "    exp_vs_calc = pd.DataFrame(constants_first)\n",
    "    exp_vs_calc['Predicted'] = pred_list\n",
    "    exp_vs_calc.rename({'Kh_first':'Experimental'}, inplace=True, axis=1)\n",
    "    \n",
    "    now = datetime.now()\n",
    "\n",
    "    dt_string = now.strftime(\"_%d_%m_%Y_%H_%M_%S\")\n",
    "    filename = \"models/DRAGON_noVPAS/\" + str(model).split(sep='(')[0] + \"_\" + features_list[16:] + dt_string + \".joblib\"\n",
    "    print(str(model).split(sep='(')[0], \"run at:\", now, \". Saving to\", filename)\n",
    "    \n",
    "    joblib.dump(model, filename)\n",
    "    return exp_vs_calc\n",
    "\n",
    "def prediction_plot_scores(model_func, pred_df):\n",
    "    \"\"\"Print train+test scores, then plot scatter of predicted vs actual HLCs. Uses {X/y}_{train/test},\n",
    "    redefining these variables will change the output.\"\"\"\n",
    "    #print('Training Score:\\t', model_func.score(X_train, y_train))\n",
    "    #print('Testing Score:\\t', model_func.score(X_test, y_test))\n",
    "    fig = plt.figure(figsize=(10, 10))\n",
    "    scatter = plt.scatter(pred_df['Experimental'], pred_df['Predicted'])\n",
    "    plt.xlabel('Experimental')\n",
    "    plt.ylabel('Predicted')\n",
    "    plt.title('%s predictions of HLCs' %str(model_func).split(sep='(')[0])\n",
    "    labels = ['{}'.format(i) for i in species_names]\n",
    "    tooltip = mpld3.plugins.PointLabelTooltip(scatter, labels=labels)\n",
    "    mpld3.plugins.connect(fig, tooltip)\n",
    "    plt.plot([-30, 10], [-30, 10], c='red')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DRAGON descriptors\n",
    "## Bringing in data and splitting it into parts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-16T12:28:21.369797Z",
     "start_time": "2020-01-16T12:28:20.572552Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input Shape (2075, 1480)\n",
      "Removed NaN, new shape (2068, 1480)\n"
     ]
    }
   ],
   "source": [
    "csv = pd.read_csv('filtered_organics_desc_kh.csv') # contains VP/AS HLCs\n",
    "\n",
    "print(\"Input Shape\", csv.shape) #input shape\n",
    "\n",
    "csv.dropna(axis=0, inplace=True)\n",
    "\n",
    "print(\"Removed NaN, new shape\", csv.shape) #removed NaN shape\n",
    "\n",
    "smiles_strings = csv.pop('Unnamed: 0')\n",
    "species_names = csv.pop('0')\n",
    "constants_mean = csv.pop('Kh_mean')\n",
    "constants_first = csv.pop('Kh_first')\n",
    "\n",
    "varying_columns = csv[['Varying_1', 'Varying_2', 'Varying_3', 'Varying_4',\n",
    " 'Varying_5', 'Varying_6', 'Varying_7', 'Varying_8', 'Varying_9']] # popping one-hot encoding columns\n",
    "\n",
    "dragon_features = csv.drop(['Varying_1', 'Varying_2', 'Varying_3', 'Varying_4',\n",
    " 'Varying_5', 'Varying_6', 'Varying_7', 'Varying_8', 'Varying_9'], axis=1) #seperating features\n",
    "\n",
    "X_train_main, X_test_main, y_train, y_test = train_test_split(dragon_features, constants_first, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-16T12:28:21.814209Z",
     "start_time": "2020-01-16T12:28:21.372811Z"
    }
   },
   "outputs": [
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       "      <th>F04[Cl-Br]</th>\n",
       "      <th>F04[Br-Br]</th>\n",
       "      <th>F04[I-I]</th>\n",
       "      <th>F05[C-C]</th>\n",
       "      <th>F05[C-N]</th>\n",
       "      <th>F05[C-O]</th>\n",
       "      <th>F05[C-S]</th>\n",
       "      <th>F05[C-F]</th>\n",
       "      <th>F05[C-Cl]</th>\n",
       "      <th>F05[C-Br]</th>\n",
       "      <th>F05[C-I]</th>\n",
       "      <th>F05[N-N]</th>\n",
       "      <th>F05[N-O]</th>\n",
       "      <th>F05[N-S]</th>\n",
       "      <th>F05[N-F]</th>\n",
       "      <th>F05[N-Cl]</th>\n",
       "      <th>F05[N-Br]</th>\n",
       "      <th>F05[N-I]</th>\n",
       "      <th>F05[O-O]</th>\n",
       "      <th>F05[O-S]</th>\n",
       "      <th>F05[O-F]</th>\n",
       "      <th>F05[O-Cl]</th>\n",
       "      <th>F05[O-Br]</th>\n",
       "      <th>F05[S-S]</th>\n",
       "      <th>F05[S-F]</th>\n",
       "      <th>F05[S-Cl]</th>\n",
       "      <th>F05[F-F]</th>\n",
       "      <th>F05[F-Cl]</th>\n",
       "      <th>F05[F-Br]</th>\n",
       "      <th>F05[Cl-Cl]</th>\n",
       "      <th>F05[Cl-Br]</th>\n",
       "      <th>F05[Br-Br]</th>\n",
       "      <th>F06[C-C]</th>\n",
       "      <th>F06[C-N]</th>\n",
       "      <th>F06[C-O]</th>\n",
       "      <th>F06[C-S]</th>\n",
       "      <th>F06[C-F]</th>\n",
       "      <th>F06[C-Cl]</th>\n",
       "      <th>F06[C-Br]</th>\n",
       "      <th>F06[C-I]</th>\n",
       "      <th>F06[N-N]</th>\n",
       "      <th>F06[N-O]</th>\n",
       "      <th>F06[N-S]</th>\n",
       "      <th>F06[N-F]</th>\n",
       "      <th>F06[N-Cl]</th>\n",
       "      <th>F06[N-Br]</th>\n",
       "      <th>F06[N-I]</th>\n",
       "      <th>F06[O-O]</th>\n",
       "      <th>F06[O-S]</th>\n",
       "      <th>F06[O-F]</th>\n",
       "      <th>F06[O-Cl]</th>\n",
       "      <th>F06[O-Br]</th>\n",
       "      <th>F06[O-I]</th>\n",
       "      <th>F06[S-S]</th>\n",
       "      <th>F06[S-F]</th>\n",
       "      <th>F06[S-Cl]</th>\n",
       "      <th>F06[F-F]</th>\n",
       "      <th>F06[F-Cl]</th>\n",
       "      <th>F06[Cl-Cl]</th>\n",
       "      <th>F06[Cl-Br]</th>\n",
       "      <th>F06[Br-Br]</th>\n",
       "      <th>F07[C-C]</th>\n",
       "      <th>F07[C-N]</th>\n",
       "      <th>F07[C-O]</th>\n",
       "      <th>F07[C-S]</th>\n",
       "      <th>F07[C-F]</th>\n",
       "      <th>F07[C-Cl]</th>\n",
       "      <th>F07[C-Br]</th>\n",
       "      <th>F07[C-I]</th>\n",
       "      <th>F07[N-N]</th>\n",
       "      <th>F07[N-O]</th>\n",
       "      <th>F07[N-S]</th>\n",
       "      <th>F07[N-F]</th>\n",
       "      <th>F07[N-Cl]</th>\n",
       "      <th>F07[N-Br]</th>\n",
       "      <th>F07[O-O]</th>\n",
       "      <th>F07[O-S]</th>\n",
       "      <th>F07[O-F]</th>\n",
       "      <th>F07[O-Cl]</th>\n",
       "      <th>F07[O-Br]</th>\n",
       "      <th>F07[S-S]</th>\n",
       "      <th>F07[S-F]</th>\n",
       "      <th>F07[S-Cl]</th>\n",
       "      <th>F07[F-F]</th>\n",
       "      <th>F07[F-Cl]</th>\n",
       "      <th>F07[Cl-Cl]</th>\n",
       "      <th>F07[Cl-Br]</th>\n",
       "      <th>F07[Br-Br]</th>\n",
       "      <th>F08[C-C]</th>\n",
       "      <th>F08[C-N]</th>\n",
       "      <th>F08[C-O]</th>\n",
       "      <th>F08[C-S]</th>\n",
       "      <th>F08[C-F]</th>\n",
       "      <th>F08[C-Cl]</th>\n",
       "      <th>F08[C-Br]</th>\n",
       "      <th>F08[C-I]</th>\n",
       "      <th>F08[N-N]</th>\n",
       "      <th>F08[N-O]</th>\n",
       "      <th>F08[N-S]</th>\n",
       "      <th>F08[N-F]</th>\n",
       "      <th>F08[N-Cl]</th>\n",
       "      <th>F08[N-Br]</th>\n",
       "      <th>F08[O-O]</th>\n",
       "      <th>F08[O-S]</th>\n",
       "      <th>F08[O-F]</th>\n",
       "      <th>F08[O-Cl]</th>\n",
       "      <th>F08[O-Br]</th>\n",
       "      <th>F08[S-Cl]</th>\n",
       "      <th>F08[F-F]</th>\n",
       "      <th>F08[F-Cl]</th>\n",
       "      <th>F08[Cl-Cl]</th>\n",
       "      <th>F08[Cl-Br]</th>\n",
       "      <th>F08[Br-Br]</th>\n",
       "      <th>F09[C-C]</th>\n",
       "      <th>F09[C-N]</th>\n",
       "      <th>F09[C-O]</th>\n",
       "      <th>F09[C-S]</th>\n",
       "      <th>F09[C-F]</th>\n",
       "      <th>F09[C-Cl]</th>\n",
       "      <th>F09[C-Br]</th>\n",
       "      <th>F09[C-I]</th>\n",
       "      <th>F09[N-N]</th>\n",
       "      <th>F09[N-O]</th>\n",
       "      <th>F09[N-S]</th>\n",
       "      <th>F09[N-F]</th>\n",
       "      <th>F09[N-Cl]</th>\n",
       "      <th>F09[N-Br]</th>\n",
       "      <th>F09[O-O]</th>\n",
       "      <th>F09[O-S]</th>\n",
       "      <th>F09[O-F]</th>\n",
       "      <th>F09[O-Cl]</th>\n",
       "      <th>F09[O-Br]</th>\n",
       "      <th>F09[F-F]</th>\n",
       "      <th>F09[F-Cl]</th>\n",
       "      <th>F09[Cl-Cl]</th>\n",
       "      <th>F09[Br-Br]</th>\n",
       "      <th>F10[C-C]</th>\n",
       "      <th>F10[C-N]</th>\n",
       "      <th>F10[C-O]</th>\n",
       "      <th>F10[C-S]</th>\n",
       "      <th>F10[C-F]</th>\n",
       "      <th>F10[C-Cl]</th>\n",
       "      <th>F10[C-Br]</th>\n",
       "      <th>F10[C-I]</th>\n",
       "      <th>F10[N-N]</th>\n",
       "      <th>F10[N-O]</th>\n",
       "      <th>F10[N-S]</th>\n",
       "      <th>F10[N-F]</th>\n",
       "      <th>F10[N-Cl]</th>\n",
       "      <th>F10[O-O]</th>\n",
       "      <th>F10[O-S]</th>\n",
       "      <th>F10[O-F]</th>\n",
       "      <th>F10[O-Cl]</th>\n",
       "      <th>F10[O-Br]</th>\n",
       "      <th>F10[S-F]</th>\n",
       "      <th>F10[F-F]</th>\n",
       "      <th>F10[F-Cl]</th>\n",
       "      <th>F10[Cl-Cl]</th>\n",
       "      <th>F10[Br-Br]</th>\n",
       "      <th>Uc</th>\n",
       "      <th>Ui</th>\n",
       "      <th>Hy</th>\n",
       "      <th>TPSA(NO)</th>\n",
       "      <th>TPSA(Tot)</th>\n",
       "      <th>MLOGP</th>\n",
       "      <th>MLOGP2</th>\n",
       "      <th>SAtot</th>\n",
       "      <th>SAacc</th>\n",
       "      <th>SAdon</th>\n",
       "      <th>Vx</th>\n",
       "      <th>VvdwMG</th>\n",
       "      <th>VvdwZAZ</th>\n",
       "      <th>PDI</th>\n",
       "      <th>BLTF96</th>\n",
       "      <th>BLTD48</th>\n",
       "      <th>BLTA96</th>\n",
       "      <th>cRo5</th>\n",
       "      <th>DLS_01</th>\n",
       "      <th>DLS_02</th>\n",
       "      <th>DLS_03</th>\n",
       "      <th>DLS_04</th>\n",
       "      <th>DLS_05</th>\n",
       "      <th>DLS_06</th>\n",
       "      <th>DLS_07</th>\n",
       "      <th>DLS_cons</th>\n",
       "      <th>LLS_01</th>\n",
       "      <th>LLS_02</th>\n",
       "      <th>Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1185</th>\n",
       "      <td>383.40</td>\n",
       "      <td>8.157</td>\n",
       "      <td>29.825</td>\n",
       "      <td>48.669</td>\n",
       "      <td>30.012</td>\n",
       "      <td>53.922</td>\n",
       "      <td>0.635</td>\n",
       "      <td>1.036</td>\n",
       "      <td>0.639</td>\n",
       "      <td>1.147</td>\n",
       "      <td>47.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.188</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>42.6</td>\n",
       "      <td>40.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>8.5</td>\n",
       "      <td>6.4</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.444</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.429</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>212.263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8226.0</td>\n",
       "      <td>21.045</td>\n",
       "      <td>9.611</td>\n",
       "      <td>6.760</td>\n",
       "      <td>7.491</td>\n",
       "      <td>0.578</td>\n",
       "      <td>0.310</td>\n",
       "      <td>0.140</td>\n",
       "      <td>0.076</td>\n",
       "      <td>5.696</td>\n",
       "      <td>4.768</td>\n",
       "      <td>48.800</td>\n",
       "      <td>77.983</td>\n",
       "      <td>77.917</td>\n",
       "      <td>2.886</td>\n",
       "      <td>18.066</td>\n",
       "      <td>13.873</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.386</td>\n",
       "      <td>10.417</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.367</td>\n",
       "      <td>4.890</td>\n",
       "      <td>5.687</td>\n",
       "      <td>6.535</td>\n",
       "      <td>7.353</td>\n",
       "      <td>8.203</td>\n",
       "      <td>9.030</td>\n",
       "      <td>9.882</td>\n",
       "      <td>10.712</td>\n",
       "      <td>11.566</td>\n",
       "      <td>4.043</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.342</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.819</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.371</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.963</td>\n",
       "      <td>3.367</td>\n",
       "      <td>3.664</td>\n",
       "      <td>3.784</td>\n",
       "      <td>3.892</td>\n",
       "      <td>4.078</td>\n",
       "      <td>4.094</td>\n",
       "      <td>4.159</td>\n",
       "      <td>4.220</td>\n",
       "      <td>4.205</td>\n",
       "      <td>4.234</td>\n",
       "      <td>3.584</td>\n",
       "      <td>4.094</td>\n",
       "      <td>4.516</td>\n",
       "      <td>4.947</td>\n",
       "      <td>5.475</td>\n",
       "      <td>5.560</td>\n",
       "      <td>5.845</td>\n",
       "      <td>6.174</td>\n",
       "      <td>6.437</td>\n",
       "      <td>6.739</td>\n",
       "      <td>12.126</td>\n",
       "      <td>6.837</td>\n",
       "      <td>9.091</td>\n",
       "      <td>2.253</td>\n",
       "      <td>4.000</td>\n",
       "      <td>3.338</td>\n",
       "      <td>261.066</td>\n",
       "      <td>81.162</td>\n",
       "      <td>1.727</td>\n",
       "      <td>3.924</td>\n",
       "      <td>8.154</td>\n",
       "      <td>4.282</td>\n",
       "      <td>4.720</td>\n",
       "      <td>1377.157</td>\n",
       "      <td>19211.120</td>\n",
       "      <td>1.632</td>\n",
       "      <td>4.655</td>\n",
       "      <td>119.627</td>\n",
       "      <td>3.706</td>\n",
       "      <td>1.693</td>\n",
       "      <td>59.018</td>\n",
       "      <td>0.222</td>\n",
       "      <td>0.364</td>\n",
       "      <td>0.566</td>\n",
       "      <td>1.727</td>\n",
       "      <td>3.512</td>\n",
       "      <td>4.410</td>\n",
       "      <td>4.868</td>\n",
       "      <td>4.953</td>\n",
       "      <td>4.953</td>\n",
       "      <td>81.162</td>\n",
       "      <td>165.044</td>\n",
       "      <td>207.291</td>\n",
       "      <td>228.801</td>\n",
       "      <td>232.801</td>\n",
       "      <td>232.801</td>\n",
       "      <td>0.311</td>\n",
       "      <td>0.632</td>\n",
       "      <td>0.794</td>\n",
       "      <td>0.876</td>\n",
       "      <td>0.892</td>\n",
       "      <td>0.892</td>\n",
       "      <td>3.828</td>\n",
       "      <td>2.043</td>\n",
       "      <td>1.144</td>\n",
       "      <td>0.686</td>\n",
       "      <td>0.601</td>\n",
       "      <td>0.601</td>\n",
       "      <td>0.299</td>\n",
       "      <td>0.607</td>\n",
       "      <td>0.763</td>\n",
       "      <td>0.842</td>\n",
       "      <td>0.857</td>\n",
       "      <td>0.857</td>\n",
       "      <td>3.558</td>\n",
       "      <td>3.977</td>\n",
       "      <td>3.931</td>\n",
       "      <td>3.814</td>\n",
       "      <td>3.697</td>\n",
       "      <td>3.648</td>\n",
       "      <td>3.471</td>\n",
       "      <td>3.498</td>\n",
       "      <td>3.425</td>\n",
       "      <td>3.725</td>\n",
       "      <td>3.763</td>\n",
       "      <td>3.686</td>\n",
       "      <td>3.651</td>\n",
       "      <td>3.597</td>\n",
       "      <td>3.472</td>\n",
       "      <td>3.391</td>\n",
       "      <td>3.948</td>\n",
       "      <td>4.507</td>\n",
       "      <td>4.683</td>\n",
       "      <td>4.660</td>\n",
       "      <td>4.527</td>\n",
       "      <td>4.560</td>\n",
       "      <td>4.469</td>\n",
       "      <td>4.361</td>\n",
       "      <td>3.401</td>\n",
       "      <td>3.699</td>\n",
       "      <td>3.747</td>\n",
       "      <td>3.672</td>\n",
       "      <td>3.682</td>\n",
       "      <td>3.660</td>\n",
       "      <td>3.538</td>\n",
       "      <td>3.428</td>\n",
       "      <td>4.040</td>\n",
       "      <td>4.654</td>\n",
       "      <td>4.865</td>\n",
       "      <td>4.849</td>\n",
       "      <td>4.716</td>\n",
       "      <td>4.763</td>\n",
       "      <td>4.688</td>\n",
       "      <td>4.570</td>\n",
       "      <td>8.112</td>\n",
       "      <td>18.879</td>\n",
       "      <td>24.564</td>\n",
       "      <td>25.313</td>\n",
       "      <td>20.448</td>\n",
       "      <td>22.080</td>\n",
       "      <td>20.669</td>\n",
       "      <td>18.296</td>\n",
       "      <td>5.158</td>\n",
       "      <td>8.397</td>\n",
       "      <td>9.832</td>\n",
       "      <td>8.481</td>\n",
       "      <td>9.030</td>\n",
       "      <td>9.978</td>\n",
       "      <td>9.193</td>\n",
       "      <td>7.958</td>\n",
       "      <td>0.213</td>\n",
       "      <td>1.104</td>\n",
       "      <td>1.012</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.807</td>\n",
       "      <td>3.000</td>\n",
       "      <td>-0.696</td>\n",
       "      <td>57.65</td>\n",
       "      <td>57.65</td>\n",
       "      <td>3.865</td>\n",
       "      <td>14.936</td>\n",
       "      <td>481.887</td>\n",
       "      <td>119.810</td>\n",
       "      <td>0.000</td>\n",
       "      <td>441.013</td>\n",
       "      <td>184.120</td>\n",
       "      <td>336.63</td>\n",
       "      <td>0.915</td>\n",
       "      <td>-4.68</td>\n",
       "      <td>-4.99</td>\n",
       "      <td>-5.09</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.83</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1113</th>\n",
       "      <td>153.15</td>\n",
       "      <td>8.508</td>\n",
       "      <td>11.746</td>\n",
       "      <td>18.735</td>\n",
       "      <td>11.653</td>\n",
       "      <td>20.372</td>\n",
       "      <td>0.653</td>\n",
       "      <td>1.041</td>\n",
       "      <td>0.647</td>\n",
       "      <td>1.132</td>\n",
       "      <td>18.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.056</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.9</td>\n",
       "      <td>38.9</td>\n",
       "      <td>5.6</td>\n",
       "      <td>16.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.545</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.545</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.381</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>399.0</td>\n",
       "      <td>7.856</td>\n",
       "      <td>2.782</td>\n",
       "      <td>1.609</td>\n",
       "      <td>1.987</td>\n",
       "      <td>0.587</td>\n",
       "      <td>0.326</td>\n",
       "      <td>0.159</td>\n",
       "      <td>0.067</td>\n",
       "      <td>2.477</td>\n",
       "      <td>3.257</td>\n",
       "      <td>18.821</td>\n",
       "      <td>20.660</td>\n",
       "      <td>35.000</td>\n",
       "      <td>3.182</td>\n",
       "      <td>7.024</td>\n",
       "      <td>5.237</td>\n",
       "      <td>0.005</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>-0.008</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.682</td>\n",
       "      <td>3.998</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.011</td>\n",
       "      <td>2.485</td>\n",
       "      <td>3.970</td>\n",
       "      <td>4.762</td>\n",
       "      <td>5.609</td>\n",
       "      <td>6.422</td>\n",
       "      <td>7.272</td>\n",
       "      <td>8.088</td>\n",
       "      <td>8.939</td>\n",
       "      <td>9.756</td>\n",
       "      <td>10.607</td>\n",
       "      <td>3.135</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.419</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.475</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.094</td>\n",
       "      <td>2.485</td>\n",
       "      <td>2.773</td>\n",
       "      <td>2.890</td>\n",
       "      <td>2.996</td>\n",
       "      <td>2.944</td>\n",
       "      <td>2.773</td>\n",
       "      <td>1.792</td>\n",
       "      <td>1.099</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.833</td>\n",
       "      <td>3.450</td>\n",
       "      <td>3.876</td>\n",
       "      <td>4.355</td>\n",
       "      <td>4.707</td>\n",
       "      <td>4.780</td>\n",
       "      <td>4.239</td>\n",
       "      <td>3.446</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>11.171</td>\n",
       "      <td>4.736</td>\n",
       "      <td>6.233</td>\n",
       "      <td>1.497</td>\n",
       "      <td>3.102</td>\n",
       "      <td>2.523</td>\n",
       "      <td>75.059</td>\n",
       "      <td>31.001</td>\n",
       "      <td>1.722</td>\n",
       "      <td>2.328</td>\n",
       "      <td>5.613</td>\n",
       "      <td>3.278</td>\n",
       "      <td>3.433</td>\n",
       "      <td>128.041</td>\n",
       "      <td>841.906</td>\n",
       "      <td>1.573</td>\n",
       "      <td>3.322</td>\n",
       "      <td>36.054</td>\n",
       "      <td>2.284</td>\n",
       "      <td>1.430</td>\n",
       "      <td>19.070</td>\n",
       "      <td>0.544</td>\n",
       "      <td>0.768</td>\n",
       "      <td>1.727</td>\n",
       "      <td>1.722</td>\n",
       "      <td>3.266</td>\n",
       "      <td>3.530</td>\n",
       "      <td>3.795</td>\n",
       "      <td>3.795</td>\n",
       "      <td>3.795</td>\n",
       "      <td>31.001</td>\n",
       "      <td>58.794</td>\n",
       "      <td>63.549</td>\n",
       "      <td>68.304</td>\n",
       "      <td>68.304</td>\n",
       "      <td>68.304</td>\n",
       "      <td>0.413</td>\n",
       "      <td>0.783</td>\n",
       "      <td>0.847</td>\n",
       "      <td>0.910</td>\n",
       "      <td>0.910</td>\n",
       "      <td>0.910</td>\n",
       "      <td>2.448</td>\n",
       "      <td>0.904</td>\n",
       "      <td>0.639</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.381</td>\n",
       "      <td>0.722</td>\n",
       "      <td>0.780</td>\n",
       "      <td>0.839</td>\n",
       "      <td>0.839</td>\n",
       "      <td>0.839</td>\n",
       "      <td>2.655</td>\n",
       "      <td>2.960</td>\n",
       "      <td>2.949</td>\n",
       "      <td>2.754</td>\n",
       "      <td>1.976</td>\n",
       "      <td>1.545</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.512</td>\n",
       "      <td>2.817</td>\n",
       "      <td>2.843</td>\n",
       "      <td>2.611</td>\n",
       "      <td>1.994</td>\n",
       "      <td>0.970</td>\n",
       "      <td>0.320</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.019</td>\n",
       "      <td>3.417</td>\n",
       "      <td>3.585</td>\n",
       "      <td>3.679</td>\n",
       "      <td>3.451</td>\n",
       "      <td>2.392</td>\n",
       "      <td>1.253</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.494</td>\n",
       "      <td>2.804</td>\n",
       "      <td>2.836</td>\n",
       "      <td>2.596</td>\n",
       "      <td>2.008</td>\n",
       "      <td>0.925</td>\n",
       "      <td>0.297</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.106</td>\n",
       "      <td>3.536</td>\n",
       "      <td>3.718</td>\n",
       "      <td>3.879</td>\n",
       "      <td>3.651</td>\n",
       "      <td>2.656</td>\n",
       "      <td>1.366</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.964</td>\n",
       "      <td>5.053</td>\n",
       "      <td>6.102</td>\n",
       "      <td>8.795</td>\n",
       "      <td>8.753</td>\n",
       "      <td>3.403</td>\n",
       "      <td>0.779</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.752</td>\n",
       "      <td>2.936</td>\n",
       "      <td>3.112</td>\n",
       "      <td>3.139</td>\n",
       "      <td>1.933</td>\n",
       "      <td>0.957</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.149</td>\n",
       "      <td>0.222</td>\n",
       "      <td>0.232</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.170</td>\n",
       "      <td>2.585</td>\n",
       "      <td>-0.031</td>\n",
       "      <td>66.05</td>\n",
       "      <td>66.05</td>\n",
       "      <td>1.701</td>\n",
       "      <td>2.893</td>\n",
       "      <td>212.923</td>\n",
       "      <td>93.430</td>\n",
       "      <td>42.683</td>\n",
       "      <td>181.096</td>\n",
       "      <td>77.597</td>\n",
       "      <td>133.21</td>\n",
       "      <td>0.851</td>\n",
       "      <td>-2.84</td>\n",
       "      <td>-2.94</td>\n",
       "      <td>-2.93</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1110</th>\n",
       "      <td>153.15</td>\n",
       "      <td>8.508</td>\n",
       "      <td>11.746</td>\n",
       "      <td>18.735</td>\n",
       "      <td>11.653</td>\n",
       "      <td>20.372</td>\n",
       "      <td>0.653</td>\n",
       "      <td>1.041</td>\n",
       "      <td>0.647</td>\n",
       "      <td>1.132</td>\n",
       "      <td>18.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.056</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.9</td>\n",
       "      <td>38.9</td>\n",
       "      <td>5.6</td>\n",
       "      <td>16.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.545</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.545</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.381</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>399.0</td>\n",
       "      <td>7.856</td>\n",
       "      <td>2.782</td>\n",
       "      <td>1.609</td>\n",
       "      <td>1.987</td>\n",
       "      <td>0.587</td>\n",
       "      <td>0.328</td>\n",
       "      <td>0.160</td>\n",
       "      <td>0.084</td>\n",
       "      <td>2.609</td>\n",
       "      <td>3.220</td>\n",
       "      <td>18.806</td>\n",
       "      <td>20.928</td>\n",
       "      <td>35.000</td>\n",
       "      <td>3.182</td>\n",
       "      <td>7.024</td>\n",
       "      <td>5.237</td>\n",
       "      <td>0.005</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>-0.008</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.682</td>\n",
       "      <td>4.006</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.011</td>\n",
       "      <td>2.485</td>\n",
       "      <td>3.970</td>\n",
       "      <td>4.762</td>\n",
       "      <td>5.609</td>\n",
       "      <td>6.428</td>\n",
       "      <td>7.272</td>\n",
       "      <td>8.099</td>\n",
       "      <td>8.939</td>\n",
       "      <td>9.770</td>\n",
       "      <td>10.608</td>\n",
       "      <td>3.135</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.419</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.475</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.096</td>\n",
       "      <td>2.485</td>\n",
       "      <td>2.773</td>\n",
       "      <td>2.890</td>\n",
       "      <td>2.996</td>\n",
       "      <td>3.045</td>\n",
       "      <td>2.485</td>\n",
       "      <td>2.079</td>\n",
       "      <td>1.099</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.833</td>\n",
       "      <td>3.450</td>\n",
       "      <td>3.876</td>\n",
       "      <td>4.355</td>\n",
       "      <td>4.785</td>\n",
       "      <td>4.523</td>\n",
       "      <td>4.495</td>\n",
       "      <td>3.446</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>11.176</td>\n",
       "      <td>4.736</td>\n",
       "      <td>6.237</td>\n",
       "      <td>1.501</td>\n",
       "      <td>3.102</td>\n",
       "      <td>2.524</td>\n",
       "      <td>75.059</td>\n",
       "      <td>31.001</td>\n",
       "      <td>1.722</td>\n",
       "      <td>2.240</td>\n",
       "      <td>5.624</td>\n",
       "      <td>2.914</td>\n",
       "      <td>3.435</td>\n",
       "      <td>123.186</td>\n",
       "      <td>832.382</td>\n",
       "      <td>1.573</td>\n",
       "      <td>3.322</td>\n",
       "      <td>36.054</td>\n",
       "      <td>2.255</td>\n",
       "      <td>1.433</td>\n",
       "      <td>19.006</td>\n",
       "      <td>0.554</td>\n",
       "      <td>0.776</td>\n",
       "      <td>1.790</td>\n",
       "      <td>1.722</td>\n",
       "      <td>3.266</td>\n",
       "      <td>3.530</td>\n",
       "      <td>3.795</td>\n",
       "      <td>3.795</td>\n",
       "      <td>3.795</td>\n",
       "      <td>31.001</td>\n",
       "      <td>58.794</td>\n",
       "      <td>63.549</td>\n",
       "      <td>68.304</td>\n",
       "      <td>68.304</td>\n",
       "      <td>68.304</td>\n",
       "      <td>0.413</td>\n",
       "      <td>0.783</td>\n",
       "      <td>0.847</td>\n",
       "      <td>0.910</td>\n",
       "      <td>0.910</td>\n",
       "      <td>0.910</td>\n",
       "      <td>2.448</td>\n",
       "      <td>0.904</td>\n",
       "      <td>0.639</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.381</td>\n",
       "      <td>0.722</td>\n",
       "      <td>0.780</td>\n",
       "      <td>0.839</td>\n",
       "      <td>0.839</td>\n",
       "      <td>0.839</td>\n",
       "      <td>2.655</td>\n",
       "      <td>2.960</td>\n",
       "      <td>2.967</td>\n",
       "      <td>2.788</td>\n",
       "      <td>2.184</td>\n",
       "      <td>0.840</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.512</td>\n",
       "      <td>2.817</td>\n",
       "      <td>2.834</td>\n",
       "      <td>2.563</td>\n",
       "      <td>2.028</td>\n",
       "      <td>1.204</td>\n",
       "      <td>0.189</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.019</td>\n",
       "      <td>3.417</td>\n",
       "      <td>3.587</td>\n",
       "      <td>3.690</td>\n",
       "      <td>3.262</td>\n",
       "      <td>2.753</td>\n",
       "      <td>1.298</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.494</td>\n",
       "      <td>2.804</td>\n",
       "      <td>2.828</td>\n",
       "      <td>2.552</td>\n",
       "      <td>1.989</td>\n",
       "      <td>1.189</td>\n",
       "      <td>0.361</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.106</td>\n",
       "      <td>3.536</td>\n",
       "      <td>3.718</td>\n",
       "      <td>3.885</td>\n",
       "      <td>3.487</td>\n",
       "      <td>2.905</td>\n",
       "      <td>1.682</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.964</td>\n",
       "      <td>5.053</td>\n",
       "      <td>6.047</td>\n",
       "      <td>9.472</td>\n",
       "      <td>6.678</td>\n",
       "      <td>4.466</td>\n",
       "      <td>1.169</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.752</td>\n",
       "      <td>2.936</td>\n",
       "      <td>3.193</td>\n",
       "      <td>3.323</td>\n",
       "      <td>1.819</td>\n",
       "      <td>0.402</td>\n",
       "      <td>0.454</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.149</td>\n",
       "      <td>0.222</td>\n",
       "      <td>0.237</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.170</td>\n",
       "      <td>2.585</td>\n",
       "      <td>-0.031</td>\n",
       "      <td>66.05</td>\n",
       "      <td>66.05</td>\n",
       "      <td>1.701</td>\n",
       "      <td>2.893</td>\n",
       "      <td>212.923</td>\n",
       "      <td>93.430</td>\n",
       "      <td>42.683</td>\n",
       "      <td>181.096</td>\n",
       "      <td>77.597</td>\n",
       "      <td>133.21</td>\n",
       "      <td>0.851</td>\n",
       "      <td>-2.84</td>\n",
       "      <td>-2.94</td>\n",
       "      <td>-2.93</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635</th>\n",
       "      <td>152.16</td>\n",
       "      <td>8.008</td>\n",
       "      <td>12.252</td>\n",
       "      <td>19.516</td>\n",
       "      <td>12.409</td>\n",
       "      <td>21.289</td>\n",
       "      <td>0.645</td>\n",
       "      <td>1.027</td>\n",
       "      <td>0.653</td>\n",
       "      <td>1.120</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.105</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.1</td>\n",
       "      <td>42.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.545</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.545</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.943</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>397.0</td>\n",
       "      <td>7.920</td>\n",
       "      <td>3.280</td>\n",
       "      <td>1.638</td>\n",
       "      <td>2.362</td>\n",
       "      <td>0.560</td>\n",
       "      <td>0.342</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.073</td>\n",
       "      <td>2.192</td>\n",
       "      <td>3.852</td>\n",
       "      <td>15.763</td>\n",
       "      <td>17.079</td>\n",
       "      <td>31.500</td>\n",
       "      <td>2.864</td>\n",
       "      <td>7.180</td>\n",
       "      <td>5.196</td>\n",
       "      <td>0.007</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>-0.008</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.364</td>\n",
       "      <td>4.012</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.011</td>\n",
       "      <td>2.485</td>\n",
       "      <td>3.932</td>\n",
       "      <td>4.727</td>\n",
       "      <td>5.549</td>\n",
       "      <td>6.365</td>\n",
       "      <td>7.192</td>\n",
       "      <td>8.012</td>\n",
       "      <td>8.841</td>\n",
       "      <td>9.661</td>\n",
       "      <td>10.490</td>\n",
       "      <td>3.135</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.369</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.832</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.392</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.996</td>\n",
       "      <td>2.485</td>\n",
       "      <td>2.708</td>\n",
       "      <td>2.890</td>\n",
       "      <td>2.944</td>\n",
       "      <td>2.890</td>\n",
       "      <td>2.398</td>\n",
       "      <td>2.079</td>\n",
       "      <td>1.609</td>\n",
       "      <td>0.693</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.773</td>\n",
       "      <td>3.277</td>\n",
       "      <td>3.778</td>\n",
       "      <td>4.188</td>\n",
       "      <td>4.533</td>\n",
       "      <td>4.227</td>\n",
       "      <td>4.163</td>\n",
       "      <td>3.663</td>\n",
       "      <td>2.151</td>\n",
       "      <td>0.000</td>\n",
       "      <td>11.066</td>\n",
       "      <td>4.710</td>\n",
       "      <td>6.060</td>\n",
       "      <td>1.351</td>\n",
       "      <td>3.108</td>\n",
       "      <td>2.523</td>\n",
       "      <td>80.711</td>\n",
       "      <td>27.956</td>\n",
       "      <td>1.471</td>\n",
       "      <td>2.336</td>\n",
       "      <td>5.616</td>\n",
       "      <td>2.914</td>\n",
       "      <td>3.433</td>\n",
       "      <td>128.486</td>\n",
       "      <td>853.559</td>\n",
       "      <td>1.539</td>\n",
       "      <td>3.356</td>\n",
       "      <td>38.054</td>\n",
       "      <td>2.355</td>\n",
       "      <td>1.431</td>\n",
       "      <td>19.024</td>\n",
       "      <td>0.535</td>\n",
       "      <td>0.755</td>\n",
       "      <td>1.707</td>\n",
       "      <td>1.471</td>\n",
       "      <td>3.156</td>\n",
       "      <td>3.471</td>\n",
       "      <td>3.892</td>\n",
       "      <td>3.998</td>\n",
       "      <td>3.998</td>\n",
       "      <td>27.956</td>\n",
       "      <td>59.956</td>\n",
       "      <td>65.956</td>\n",
       "      <td>73.956</td>\n",
       "      <td>75.956</td>\n",
       "      <td>75.956</td>\n",
       "      <td>0.346</td>\n",
       "      <td>0.743</td>\n",
       "      <td>0.817</td>\n",
       "      <td>0.916</td>\n",
       "      <td>0.941</td>\n",
       "      <td>0.941</td>\n",
       "      <td>2.777</td>\n",
       "      <td>1.092</td>\n",
       "      <td>0.777</td>\n",
       "      <td>0.356</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.325</td>\n",
       "      <td>0.698</td>\n",
       "      <td>0.767</td>\n",
       "      <td>0.860</td>\n",
       "      <td>0.884</td>\n",
       "      <td>0.884</td>\n",
       "      <td>2.641</td>\n",
       "      <td>2.902</td>\n",
       "      <td>2.943</td>\n",
       "      <td>2.774</td>\n",
       "      <td>2.147</td>\n",
       "      <td>1.208</td>\n",
       "      <td>0.337</td>\n",
       "      <td>0.021</td>\n",
       "      <td>2.557</td>\n",
       "      <td>2.804</td>\n",
       "      <td>2.822</td>\n",
       "      <td>2.586</td>\n",
       "      <td>2.202</td>\n",
       "      <td>1.721</td>\n",
       "      <td>0.985</td>\n",
       "      <td>0.189</td>\n",
       "      <td>3.052</td>\n",
       "      <td>3.464</td>\n",
       "      <td>3.547</td>\n",
       "      <td>3.607</td>\n",
       "      <td>3.291</td>\n",
       "      <td>3.009</td>\n",
       "      <td>2.546</td>\n",
       "      <td>1.298</td>\n",
       "      <td>2.538</td>\n",
       "      <td>2.798</td>\n",
       "      <td>2.808</td>\n",
       "      <td>2.557</td>\n",
       "      <td>2.245</td>\n",
       "      <td>1.927</td>\n",
       "      <td>1.342</td>\n",
       "      <td>0.361</td>\n",
       "      <td>3.125</td>\n",
       "      <td>3.581</td>\n",
       "      <td>3.674</td>\n",
       "      <td>3.739</td>\n",
       "      <td>3.469</td>\n",
       "      <td>3.258</td>\n",
       "      <td>2.942</td>\n",
       "      <td>1.682</td>\n",
       "      <td>3.411</td>\n",
       "      <td>6.258</td>\n",
       "      <td>6.564</td>\n",
       "      <td>8.390</td>\n",
       "      <td>6.367</td>\n",
       "      <td>5.155</td>\n",
       "      <td>3.833</td>\n",
       "      <td>1.019</td>\n",
       "      <td>1.957</td>\n",
       "      <td>2.975</td>\n",
       "      <td>3.316</td>\n",
       "      <td>2.761</td>\n",
       "      <td>2.169</td>\n",
       "      <td>2.061</td>\n",
       "      <td>1.851</td>\n",
       "      <td>0.436</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.249</td>\n",
       "      <td>0.150</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.000</td>\n",
       "      <td>2.322</td>\n",
       "      <td>-0.119</td>\n",
       "      <td>46.53</td>\n",
       "      <td>46.53</td>\n",
       "      <td>1.977</td>\n",
       "      <td>3.907</td>\n",
       "      <td>220.391</td>\n",
       "      <td>78.828</td>\n",
       "      <td>42.683</td>\n",
       "      <td>187.924</td>\n",
       "      <td>80.395</td>\n",
       "      <td>139.51</td>\n",
       "      <td>0.853</td>\n",
       "      <td>-3.07</td>\n",
       "      <td>-3.20</td>\n",
       "      <td>-3.21</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>228.42</td>\n",
       "      <td>5.191</td>\n",
       "      <td>22.805</td>\n",
       "      <td>43.025</td>\n",
       "      <td>25.569</td>\n",
       "      <td>50.232</td>\n",
       "      <td>0.518</td>\n",
       "      <td>0.978</td>\n",
       "      <td>0.581</td>\n",
       "      <td>1.142</td>\n",
       "      <td>44.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.279</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63.6</td>\n",
       "      <td>31.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>647.0</td>\n",
       "      <td>15.636</td>\n",
       "      <td>12.705</td>\n",
       "      <td>12.636</td>\n",
       "      <td>12.416</td>\n",
       "      <td>0.496</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.120</td>\n",
       "      <td>0.052</td>\n",
       "      <td>1.703</td>\n",
       "      <td>4.028</td>\n",
       "      <td>11.297</td>\n",
       "      <td>56.029</td>\n",
       "      <td>32.667</td>\n",
       "      <td>2.042</td>\n",
       "      <td>12.083</td>\n",
       "      <td>8.930</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.542</td>\n",
       "      <td>5.980</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.773</td>\n",
       "      <td>4.111</td>\n",
       "      <td>4.779</td>\n",
       "      <td>5.476</td>\n",
       "      <td>6.159</td>\n",
       "      <td>6.864</td>\n",
       "      <td>7.552</td>\n",
       "      <td>8.260</td>\n",
       "      <td>8.952</td>\n",
       "      <td>9.662</td>\n",
       "      <td>3.434</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.511</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.727</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.009</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.329</td>\n",
       "      <td>2.773</td>\n",
       "      <td>2.773</td>\n",
       "      <td>2.708</td>\n",
       "      <td>2.639</td>\n",
       "      <td>2.485</td>\n",
       "      <td>2.398</td>\n",
       "      <td>2.303</td>\n",
       "      <td>2.197</td>\n",
       "      <td>2.079</td>\n",
       "      <td>1.946</td>\n",
       "      <td>2.833</td>\n",
       "      <td>2.890</td>\n",
       "      <td>2.833</td>\n",
       "      <td>2.773</td>\n",
       "      <td>2.565</td>\n",
       "      <td>2.485</td>\n",
       "      <td>2.398</td>\n",
       "      <td>2.303</td>\n",
       "      <td>2.197</td>\n",
       "      <td>2.079</td>\n",
       "      <td>10.344</td>\n",
       "      <td>4.920</td>\n",
       "      <td>5.024</td>\n",
       "      <td>0.104</td>\n",
       "      <td>3.455</td>\n",
       "      <td>2.845</td>\n",
       "      <td>240.215</td>\n",
       "      <td>50.306</td>\n",
       "      <td>1.143</td>\n",
       "      <td>3.580</td>\n",
       "      <td>6.613</td>\n",
       "      <td>3.500</td>\n",
       "      <td>3.967</td>\n",
       "      <td>429.638</td>\n",
       "      <td>4278.323</td>\n",
       "      <td>1.014</td>\n",
       "      <td>3.948</td>\n",
       "      <td>64.000</td>\n",
       "      <td>3.456</td>\n",
       "      <td>1.536</td>\n",
       "      <td>61.781</td>\n",
       "      <td>0.484</td>\n",
       "      <td>0.817</td>\n",
       "      <td>1.174</td>\n",
       "      <td>1.143</td>\n",
       "      <td>1.706</td>\n",
       "      <td>2.864</td>\n",
       "      <td>3.586</td>\n",
       "      <td>4.108</td>\n",
       "      <td>4.527</td>\n",
       "      <td>50.306</td>\n",
       "      <td>75.080</td>\n",
       "      <td>125.995</td>\n",
       "      <td>157.793</td>\n",
       "      <td>180.731</td>\n",
       "      <td>199.195</td>\n",
       "      <td>0.209</td>\n",
       "      <td>0.313</td>\n",
       "      <td>0.525</td>\n",
       "      <td>0.657</td>\n",
       "      <td>0.752</td>\n",
       "      <td>0.829</td>\n",
       "      <td>4.316</td>\n",
       "      <td>3.753</td>\n",
       "      <td>2.596</td>\n",
       "      <td>1.873</td>\n",
       "      <td>1.352</td>\n",
       "      <td>0.932</td>\n",
       "      <td>0.209</td>\n",
       "      <td>0.313</td>\n",
       "      <td>0.525</td>\n",
       "      <td>0.657</td>\n",
       "      <td>0.752</td>\n",
       "      <td>0.829</td>\n",
       "      <td>2.963</td>\n",
       "      <td>3.098</td>\n",
       "      <td>3.024</td>\n",
       "      <td>2.969</td>\n",
       "      <td>2.838</td>\n",
       "      <td>2.736</td>\n",
       "      <td>2.643</td>\n",
       "      <td>2.541</td>\n",
       "      <td>3.114</td>\n",
       "      <td>3.362</td>\n",
       "      <td>3.388</td>\n",
       "      <td>3.331</td>\n",
       "      <td>3.255</td>\n",
       "      <td>3.154</td>\n",
       "      <td>3.050</td>\n",
       "      <td>2.933</td>\n",
       "      <td>3.769</td>\n",
       "      <td>4.389</td>\n",
       "      <td>4.663</td>\n",
       "      <td>4.562</td>\n",
       "      <td>4.530</td>\n",
       "      <td>4.455</td>\n",
       "      <td>4.353</td>\n",
       "      <td>4.240</td>\n",
       "      <td>3.220</td>\n",
       "      <td>3.545</td>\n",
       "      <td>3.615</td>\n",
       "      <td>3.552</td>\n",
       "      <td>3.492</td>\n",
       "      <td>3.405</td>\n",
       "      <td>3.296</td>\n",
       "      <td>3.174</td>\n",
       "      <td>3.921</td>\n",
       "      <td>4.632</td>\n",
       "      <td>4.979</td>\n",
       "      <td>4.864</td>\n",
       "      <td>4.839</td>\n",
       "      <td>4.780</td>\n",
       "      <td>4.677</td>\n",
       "      <td>4.562</td>\n",
       "      <td>10.935</td>\n",
       "      <td>18.063</td>\n",
       "      <td>21.194</td>\n",
       "      <td>19.544</td>\n",
       "      <td>18.629</td>\n",
       "      <td>16.818</td>\n",
       "      <td>15.220</td>\n",
       "      <td>13.623</td>\n",
       "      <td>6.506</td>\n",
       "      <td>9.973</td>\n",
       "      <td>11.340</td>\n",
       "      <td>10.531</td>\n",
       "      <td>10.008</td>\n",
       "      <td>9.265</td>\n",
       "      <td>8.282</td>\n",
       "      <td>7.299</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.236</td>\n",
       "      <td>0.212</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.856</td>\n",
       "      <td>26.30</td>\n",
       "      <td>26.30</td>\n",
       "      <td>4.701</td>\n",
       "      <td>22.095</td>\n",
       "      <td>420.515</td>\n",
       "      <td>36.145</td>\n",
       "      <td>0.000</td>\n",
       "      <td>358.073</td>\n",
       "      <td>150.128</td>\n",
       "      <td>265.70</td>\n",
       "      <td>0.852</td>\n",
       "      <td>-5.39</td>\n",
       "      <td>-5.79</td>\n",
       "      <td>-5.93</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.75</td>\n",
       "      <td>604</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1467 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          MW    AMW      Sv      Se      Sp      Si     Mv     Me     Mp  \\\n",
       "1185  383.40  8.157  29.825  48.669  30.012  53.922  0.635  1.036  0.639   \n",
       "1113  153.15  8.508  11.746  18.735  11.653  20.372  0.653  1.041  0.647   \n",
       "1110  153.15  8.508  11.746  18.735  11.653  20.372  0.653  1.041  0.647   \n",
       "635   152.16  8.008  12.252  19.516  12.409  21.289  0.645  1.027  0.653   \n",
       "604   228.42  5.191  22.805  43.025  25.569  50.232  0.518  0.978  0.581   \n",
       "\n",
       "         Mi   nAT   nSK   nBT   nBO   nBM  SCBO   RBN    RBF  nDB  nTB   nAB  \\\n",
       "1185  1.147  47.0  27.0  48.0  28.0  13.0  35.0   9.0  0.188  1.0  0.0  12.0   \n",
       "1113  1.132  18.0  11.0  18.0  11.0   8.0  16.0   1.0  0.056  2.0  0.0   6.0   \n",
       "1110  1.132  18.0  11.0  18.0  11.0   8.0  16.0   1.0  0.056  2.0  0.0   6.0   \n",
       "635   1.120  19.0  11.0  19.0  11.0   7.0  15.0   2.0  0.105  1.0  0.0   6.0   \n",
       "604   1.142  44.0  16.0  43.0  15.0   1.0  16.0  12.0  0.279  1.0  0.0   0.0   \n",
       "\n",
       "        nH    nC   nN   nO   nS   nF  nCL  nBR   nI  nHM  nHet   nX    H%  \\\n",
       "1185  20.0  19.0  1.0  4.0  0.0  3.0  0.0  0.0  0.0  0.0   8.0  3.0  42.6   \n",
       "1113   7.0   7.0  1.0  3.0  0.0  0.0  0.0  0.0  0.0  0.0   4.0  0.0  38.9   \n",
       "1110   7.0   7.0  1.0  3.0  0.0  0.0  0.0  0.0  0.0  0.0   4.0  0.0  38.9   \n",
       "635    8.0   8.0  0.0  3.0  0.0  0.0  0.0  0.0  0.0  0.0   3.0  0.0  42.1   \n",
       "604   28.0  14.0  0.0  2.0  0.0  0.0  0.0  0.0  0.0  0.0   2.0  0.0  63.6   \n",
       "\n",
       "        C%   N%    O%   X%  nCsp3  nCsp2  nCsp  nCIC  nCIR   TRS  Rperim  \\\n",
       "1185  40.4  2.1   8.5  6.4    7.0   12.0   0.0   2.0   2.0  12.0    12.0   \n",
       "1113  38.9  5.6  16.7  0.0    1.0    6.0   0.0   1.0   1.0   6.0     6.0   \n",
       "1110  38.9  5.6  16.7  0.0    1.0    6.0   0.0   1.0   1.0   6.0     6.0   \n",
       "635   42.1  0.0  15.8  0.0    1.0    7.0   0.0   1.0   1.0   6.0     6.0   \n",
       "604   31.8  0.0   4.5  0.0   13.0    1.0   0.0   0.0   0.0   0.0     0.0   \n",
       "\n",
       "      Rbrid    MCD  RFD  RCI  NRS  NNRS  nR03  nR04  nR05  nR06  nR07  nR08  \\\n",
       "1185    0.0  0.444  0.0  1.0  2.0   1.0   0.0   0.0   0.0   2.0   0.0   0.0   \n",
       "1113    0.0  0.545  0.0  1.0  1.0   1.0   0.0   0.0   0.0   1.0   0.0   0.0   \n",
       "1110    0.0  0.545  0.0  1.0  1.0   1.0   0.0   0.0   0.0   1.0   0.0   0.0   \n",
       "635     0.0  0.545  0.0  1.0  1.0   1.0   0.0   0.0   0.0   1.0   0.0   0.0   \n",
       "604     0.0  0.000  0.0  0.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "      nR09  nR10  nR11  nR12  nBnz    ARR  D/Dtr03  D/Dtr04  D/Dtr05  D/Dtr06  \\\n",
       "1185   0.0   0.0   0.0   0.0   1.0  0.429      0.0      0.0      0.0  212.263   \n",
       "1113   0.0   0.0   0.0   0.0   1.0  0.545      0.0      0.0      0.0   34.381   \n",
       "1110   0.0   0.0   0.0   0.0   1.0  0.545      0.0      0.0      0.0   34.381   \n",
       "635    0.0   0.0   0.0   0.0   1.0  0.545      0.0      0.0      0.0   34.943   \n",
       "604    0.0   0.0   0.0   0.0   0.0  0.000      0.0      0.0      0.0    0.000   \n",
       "\n",
       "      D/Dtr07  D/Dtr08  D/Dtr09  D/Dtr10  D/Dtr11  D/Dtr12     Wap     S1K  \\\n",
       "1185      0.0      0.0      0.0      0.0      0.0      0.0  8226.0  21.045   \n",
       "1113      0.0      0.0      0.0      0.0      0.0      0.0   399.0   7.856   \n",
       "1110      0.0      0.0      0.0      0.0      0.0      0.0   399.0   7.856   \n",
       "635       0.0      0.0      0.0      0.0      0.0      0.0   397.0   7.920   \n",
       "604       0.0      0.0      0.0      0.0      0.0      0.0   647.0  15.636   \n",
       "\n",
       "         S2K     S3K     PHI    PW2    PW3    PW4    PW5  MAXDN  MAXDP  \\\n",
       "1185   9.611   6.760   7.491  0.578  0.310  0.140  0.076  5.696  4.768   \n",
       "1113   2.782   1.609   1.987  0.587  0.326  0.159  0.067  2.477  3.257   \n",
       "1110   2.782   1.609   1.987  0.587  0.328  0.160  0.084  2.609  3.220   \n",
       "635    3.280   1.638   2.362  0.560  0.342  0.166  0.073  2.192  3.852   \n",
       "604   12.705  12.636  12.416  0.496  0.250  0.120  0.052  1.703  4.028   \n",
       "\n",
       "        DELS     TIE  Psi_i_s  Psi_i_A  Psi_i_0  Psi_i_1  Psi_i_t  Psi_i_0d  \\\n",
       "1185  48.800  77.983   77.917    2.886   18.066   13.873    0.000     0.000   \n",
       "1113  18.821  20.660   35.000    3.182    7.024    5.237    0.005    -0.005   \n",
       "1110  18.806  20.928   35.000    3.182    7.024    5.237    0.005    -0.005   \n",
       "635   15.763  17.079   31.500    2.864    7.180    5.196    0.007    -0.005   \n",
       "604   11.297  56.029   32.667    2.042   12.083    8.930    0.008     0.000   \n",
       "\n",
       "      Psi_i_1d  Psi_i_1s  Psi_e_A  Psi_e_0  Psi_e_t  Psi_e_0d  MWC01  MWC02  \\\n",
       "1185     0.000       0.0    8.386   10.417      0.0     0.000  3.367  4.890   \n",
       "1113    -0.008       0.0    8.682    3.998      0.0    -0.011  2.485  3.970   \n",
       "1110    -0.008       0.0    8.682    4.006      0.0    -0.011  2.485  3.970   \n",
       "635     -0.008       0.0    8.364    4.012      0.0    -0.011  2.485  3.932   \n",
       "604     -0.001       0.0    7.542    5.980      0.0     0.000  2.773  4.111   \n",
       "\n",
       "      MWC03  MWC04  MWC05  MWC06  MWC07  MWC08   MWC09   MWC10  SRW02  SRW03  \\\n",
       "1185  5.687  6.535  7.353  8.203  9.030  9.882  10.712  11.566  4.043    0.0   \n",
       "1113  4.762  5.609  6.422  7.272  8.088  8.939   9.756  10.607  3.135    0.0   \n",
       "1110  4.762  5.609  6.428  7.272  8.099  8.939   9.770  10.608  3.135    0.0   \n",
       "635   4.727  5.549  6.365  7.192  8.012  8.841   9.661  10.490  3.135    0.0   \n",
       "604   4.779  5.476  6.159  6.864  7.552  8.260   8.952   9.662  3.434    0.0   \n",
       "\n",
       "      SRW04  SRW05  SRW06  SRW07  SRW08  SRW09  SRW10  MPC01  MPC02  MPC03  \\\n",
       "1185  5.342    0.0  6.819    0.0  8.371    0.0  9.963  3.367  3.664  3.784   \n",
       "1113  4.419    0.0  5.900    0.0  7.475    0.0  9.094  2.485  2.773  2.890   \n",
       "1110  4.419    0.0  5.900    0.0  7.475    0.0  9.096  2.485  2.773  2.890   \n",
       "635   4.369    0.0  5.832    0.0  7.392    0.0  8.996  2.485  2.708  2.890   \n",
       "604   4.511    0.0  5.727    0.0  7.009    0.0  8.329  2.773  2.773  2.708   \n",
       "\n",
       "      MPC04  MPC05  MPC06  MPC07  MPC08  MPC09  MPC10  piPC01  piPC02  piPC03  \\\n",
       "1185  3.892  4.078  4.094  4.159  4.220  4.205  4.234   3.584   4.094   4.516   \n",
       "1113  2.996  2.944  2.773  1.792  1.099  0.000  0.000   2.833   3.450   3.876   \n",
       "1110  2.996  3.045  2.485  2.079  1.099  0.000  0.000   2.833   3.450   3.876   \n",
       "635   2.944  2.890  2.398  2.079  1.609  0.693  0.000   2.773   3.277   3.778   \n",
       "604   2.639  2.485  2.398  2.303  2.197  2.079  1.946   2.833   2.890   2.833   \n",
       "\n",
       "      piPC04  piPC05  piPC06  piPC07  piPC08  piPC09  piPC10     TWC    TPC  \\\n",
       "1185   4.947   5.475   5.560   5.845   6.174   6.437   6.739  12.126  6.837   \n",
       "1113   4.355   4.707   4.780   4.239   3.446   0.000   0.000  11.171  4.736   \n",
       "1110   4.355   4.785   4.523   4.495   3.446   0.000   0.000  11.176  4.736   \n",
       "635    4.188   4.533   4.227   4.163   3.663   2.151   0.000  11.066  4.710   \n",
       "604    2.773   2.565   2.485   2.398   2.303   2.197   2.079  10.344  4.920   \n",
       "\n",
       "       piID    PCD    CID    BID     ISIZ     IAC    AAC    IDE    IDM   IDDE  \\\n",
       "1185  9.091  2.253  4.000  3.338  261.066  81.162  1.727  3.924  8.154  4.282   \n",
       "1113  6.233  1.497  3.102  2.523   75.059  31.001  1.722  2.328  5.613  3.278   \n",
       "1110  6.237  1.501  3.102  2.524   75.059  31.001  1.722  2.240  5.624  2.914   \n",
       "635   6.060  1.351  3.108  2.523   80.711  27.956  1.471  2.336  5.616  2.914   \n",
       "604   5.024  0.104  3.455  2.845  240.215  50.306  1.143  3.580  6.613  3.500   \n",
       "\n",
       "       IDDM      IDET       IDMT   IVDE   IVDM      S0K  HVcpx  HDcpx  Uindex  \\\n",
       "1185  4.720  1377.157  19211.120  1.632  4.655  119.627  3.706  1.693  59.018   \n",
       "1113  3.433   128.041    841.906  1.573  3.322   36.054  2.284  1.430  19.070   \n",
       "1110  3.435   123.186    832.382  1.573  3.322   36.054  2.255  1.433  19.006   \n",
       "635   3.433   128.486    853.559  1.539  3.356   38.054  2.355  1.431  19.024   \n",
       "604   3.967   429.638   4278.323  1.014  3.948   64.000  3.456  1.536  61.781   \n",
       "\n",
       "      Vindex  Xindex  Yindex    IC0    IC1    IC2    IC3    IC4    IC5  \\\n",
       "1185   0.222   0.364   0.566  1.727  3.512  4.410  4.868  4.953  4.953   \n",
       "1113   0.544   0.768   1.727  1.722  3.266  3.530  3.795  3.795  3.795   \n",
       "1110   0.554   0.776   1.790  1.722  3.266  3.530  3.795  3.795  3.795   \n",
       "635    0.535   0.755   1.707  1.471  3.156  3.471  3.892  3.998  3.998   \n",
       "604    0.484   0.817   1.174  1.143  1.706  2.864  3.586  4.108  4.527   \n",
       "\n",
       "        TIC0     TIC1     TIC2     TIC3     TIC4     TIC5   SIC0   SIC1  \\\n",
       "1185  81.162  165.044  207.291  228.801  232.801  232.801  0.311  0.632   \n",
       "1113  31.001   58.794   63.549   68.304   68.304   68.304  0.413  0.783   \n",
       "1110  31.001   58.794   63.549   68.304   68.304   68.304  0.413  0.783   \n",
       "635   27.956   59.956   65.956   73.956   75.956   75.956  0.346  0.743   \n",
       "604   50.306   75.080  125.995  157.793  180.731  199.195  0.209  0.313   \n",
       "\n",
       "       SIC2   SIC3   SIC4   SIC5   CIC0   CIC1   CIC2   CIC3   CIC4   CIC5  \\\n",
       "1185  0.794  0.876  0.892  0.892  3.828  2.043  1.144  0.686  0.601  0.601   \n",
       "1113  0.847  0.910  0.910  0.910  2.448  0.904  0.639  0.375  0.375  0.375   \n",
       "1110  0.847  0.910  0.910  0.910  2.448  0.904  0.639  0.375  0.375  0.375   \n",
       "635   0.817  0.916  0.941  0.941  2.777  1.092  0.777  0.356  0.250  0.250   \n",
       "604   0.525  0.657  0.752  0.829  4.316  3.753  2.596  1.873  1.352  0.932   \n",
       "\n",
       "       BIC0   BIC1   BIC2   BIC3   BIC4   BIC5  ATS1m  ATS2m  ATS3m  ATS4m  \\\n",
       "1185  0.299  0.607  0.763  0.842  0.857  0.857  3.558  3.977  3.931  3.814   \n",
       "1113  0.381  0.722  0.780  0.839  0.839  0.839  2.655  2.960  2.949  2.754   \n",
       "1110  0.381  0.722  0.780  0.839  0.839  0.839  2.655  2.960  2.967  2.788   \n",
       "635   0.325  0.698  0.767  0.860  0.884  0.884  2.641  2.902  2.943  2.774   \n",
       "604   0.209  0.313  0.525  0.657  0.752  0.829  2.963  3.098  3.024  2.969   \n",
       "\n",
       "      ATS5m  ATS6m  ATS7m  ATS8m  ATS1v  ATS2v  ATS3v  ATS4v  ATS5v  ATS6v  \\\n",
       "1185  3.697  3.648  3.471  3.498  3.425  3.725  3.763  3.686  3.651  3.597   \n",
       "1113  1.976  1.545  0.202  0.000  2.512  2.817  2.843  2.611  1.994  0.970   \n",
       "1110  2.184  0.840  0.021  0.000  2.512  2.817  2.834  2.563  2.028  1.204   \n",
       "635   2.147  1.208  0.337  0.021  2.557  2.804  2.822  2.586  2.202  1.721   \n",
       "604   2.838  2.736  2.643  2.541  3.114  3.362  3.388  3.331  3.255  3.154   \n",
       "\n",
       "      ATS7v  ATS8v  ATS1e  ATS2e  ATS3e  ATS4e  ATS5e  ATS6e  ATS7e  ATS8e  \\\n",
       "1185  3.472  3.391  3.948  4.507  4.683  4.660  4.527  4.560  4.469  4.361   \n",
       "1113  0.320  0.000  3.019  3.417  3.585  3.679  3.451  2.392  1.253  0.000   \n",
       "1110  0.189  0.000  3.019  3.417  3.587  3.690  3.262  2.753  1.298  0.000   \n",
       "635   0.985  0.189  3.052  3.464  3.547  3.607  3.291  3.009  2.546  1.298   \n",
       "604   3.050  2.933  3.769  4.389  4.663  4.562  4.530  4.455  4.353  4.240   \n",
       "\n",
       "      ATS1p  ATS2p  ATS3p  ATS4p  ATS5p  ATS6p  ATS7p  ATS8p  ATS1i  ATS2i  \\\n",
       "1185  3.401  3.699  3.747  3.672  3.682  3.660  3.538  3.428  4.040  4.654   \n",
       "1113  2.494  2.804  2.836  2.596  2.008  0.925  0.297  0.000  3.106  3.536   \n",
       "1110  2.494  2.804  2.828  2.552  1.989  1.189  0.361  0.000  3.106  3.536   \n",
       "635   2.538  2.798  2.808  2.557  2.245  1.927  1.342  0.361  3.125  3.581   \n",
       "604   3.220  3.545  3.615  3.552  3.492  3.405  3.296  3.174  3.921  4.632   \n",
       "\n",
       "      ATS3i  ATS4i  ATS5i  ATS6i  ATS7i  ATS8i  ATSC1m  ATSC2m  ATSC3m  \\\n",
       "1185  4.865  4.849  4.716  4.763  4.688  4.570   8.112  18.879  24.564   \n",
       "1113  3.718  3.879  3.651  2.656  1.366  0.000   2.964   5.053   6.102   \n",
       "1110  3.718  3.885  3.487  2.905  1.682  0.000   2.964   5.053   6.047   \n",
       "635   3.674  3.739  3.469  3.258  2.942  1.682   3.411   6.258   6.564   \n",
       "604   4.979  4.864  4.839  4.780  4.677  4.562  10.935  18.063  21.194   \n",
       "\n",
       "      ATSC4m  ATSC5m  ATSC6m  ATSC7m  ATSC8m  ATSC1v  ATSC2v  ATSC3v  ATSC4v  \\\n",
       "1185  25.313  20.448  22.080  20.669  18.296   5.158   8.397   9.832   8.481   \n",
       "1113   8.795   8.753   3.403   0.779   0.000   1.752   2.936   3.112   3.139   \n",
       "1110   9.472   6.678   4.466   1.169   0.000   1.752   2.936   3.193   3.323   \n",
       "635    8.390   6.367   5.155   3.833   1.019   1.957   2.975   3.316   2.761   \n",
       "604   19.544  18.629  16.818  15.220  13.623   6.506   9.973  11.340  10.531   \n",
       "\n",
       "      ATSC5v  ATSC6v  ATSC7v  ATSC8v  ATSC1e  ATSC2e  ATSC3e  ...  F02[Cl-Cl]  \\\n",
       "1185   9.030   9.978   9.193   7.958   0.213   1.104   1.012  ...         0.0   \n",
       "1113   1.933   0.957   0.048   0.000   0.149   0.222   0.232  ...         0.0   \n",
       "1110   1.819   0.402   0.454   0.000   0.149   0.222   0.237  ...         0.0   \n",
       "635    2.169   2.061   1.851   0.436   0.080   0.249   0.150  ...         0.0   \n",
       "604   10.008   9.265   8.282   7.299   0.051   0.236   0.212  ...         0.0   \n",
       "\n",
       "      F02[Cl-Br]  F02[Cl-I]  F02[Br-Br]  F02[I-I]  F03[C-C]  F03[C-N]  \\\n",
       "1185         0.0        0.0         0.0       0.0      16.0       3.0   \n",
       "1113         0.0        0.0         0.0       0.0       5.0       3.0   \n",
       "1110         0.0        0.0         0.0       0.0       5.0       2.0   \n",
       "635          0.0        0.0         0.0       0.0       6.0       0.0   \n",
       "604          0.0        0.0         0.0       0.0      11.0       0.0   \n",
       "\n",
       "      F03[C-O]  F03[C-S]  F03[C-F]  F03[C-Cl]  F03[C-Br]  F03[C-I]  F03[N-N]  \\\n",
       "1185      10.0       0.0       6.0        0.0        0.0       0.0       0.0   \n",
       "1113       6.0       0.0       0.0        0.0        0.0       0.0       0.0   \n",
       "1110       6.0       0.0       0.0        0.0        0.0       0.0       0.0   \n",
       "635        8.0       0.0       0.0        0.0        0.0       0.0       0.0   \n",
       "604        3.0       0.0       0.0        0.0        0.0       0.0       0.0   \n",
       "\n",
       "      F03[N-O]  F03[N-S]  F03[N-F]  F03[N-Cl]  F03[N-Br]  F03[O-O]  F03[O-S]  \\\n",
       "1185       0.0       0.0       0.0        0.0        0.0       2.0       0.0   \n",
       "1113       0.0       0.0       0.0        0.0        0.0       0.0       0.0   \n",
       "1110       1.0       0.0       0.0        0.0        0.0       0.0       0.0   \n",
       "635        0.0       0.0       0.0        0.0        0.0       0.0       0.0   \n",
       "604        0.0       0.0       0.0        0.0        0.0       0.0       0.0   \n",
       "\n",
       "      F03[O-F]  F03[O-Cl]  F03[O-Br]  F03[O-I]  F03[S-S]  F03[S-F]  F03[S-Cl]  \\\n",
       "1185       0.0        0.0        0.0       0.0       0.0       0.0        0.0   \n",
       "1113       0.0        0.0        0.0       0.0       0.0       0.0        0.0   \n",
       "1110       0.0        0.0        0.0       0.0       0.0       0.0        0.0   \n",
       "635        0.0        0.0        0.0       0.0       0.0       0.0        0.0   \n",
       "604        0.0        0.0        0.0       0.0       0.0       0.0        0.0   \n",
       "\n",
       "      F03[F-F]  F03[F-Cl]  F03[F-Br]  F03[Cl-Cl]  F03[Cl-Br]  F03[Br-Br]  \\\n",
       "1185       0.0        0.0        0.0         0.0         0.0         0.0   \n",
       "1113       0.0        0.0        0.0         0.0         0.0         0.0   \n",
       "1110       0.0        0.0        0.0         0.0         0.0         0.0   \n",
       "635        0.0        0.0        0.0         0.0         0.0         0.0   \n",
       "604        0.0        0.0        0.0         0.0         0.0         0.0   \n",
       "\n",
       "      F04[C-C]  F04[C-N]  F04[C-O]  F04[C-S]  F04[C-F]  F04[C-Cl]  F04[C-Br]  \\\n",
       "1185      16.0       2.0       8.0       0.0       3.0        0.0        0.0   \n",
       "1113       1.0       1.0       8.0       0.0       0.0        0.0        0.0   \n",
       "1110       1.0       2.0       5.0       0.0       0.0        0.0        0.0   \n",
       "635        3.0       0.0       5.0       0.0       0.0        0.0        0.0   \n",
       "604       10.0       0.0       3.0       0.0       0.0        0.0        0.0   \n",
       "\n",
       "      F04[C-I]  F04[N-N]  F04[N-O]  F04[N-S]  F04[N-F]  F04[N-Cl]  F04[N-Br]  \\\n",
       "1185       0.0       0.0       0.0       0.0       3.0        0.0        0.0   \n",
       "1113       0.0       0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "1110       0.0       0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "635        0.0       0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "604        0.0       0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "\n",
       "      F04[N-I]  F04[O-O]  F04[O-S]  F04[O-F]  F04[O-Cl]  F04[O-Br]  F04[O-I]  \\\n",
       "1185       0.0       0.0       0.0       0.0        0.0        0.0       0.0   \n",
       "1113       0.0       0.0       0.0       0.0        0.0        0.0       0.0   \n",
       "1110       0.0       2.0       0.0       0.0        0.0        0.0       0.0   \n",
       "635        0.0       2.0       0.0       0.0        0.0        0.0       0.0   \n",
       "604        0.0       0.0       0.0       0.0        0.0        0.0       0.0   \n",
       "\n",
       "      F04[S-S]  F04[S-F]  F04[S-Cl]  F04[F-F]  F04[F-Cl]  F04[F-Br]  \\\n",
       "1185       0.0       0.0        0.0       0.0        0.0        0.0   \n",
       "1113       0.0       0.0        0.0       0.0        0.0        0.0   \n",
       "1110       0.0       0.0        0.0       0.0        0.0        0.0   \n",
       "635        0.0       0.0        0.0       0.0        0.0        0.0   \n",
       "604        0.0       0.0        0.0       0.0        0.0        0.0   \n",
       "\n",
       "      F04[Cl-Cl]  F04[Cl-Br]  F04[Br-Br]  F04[I-I]  F05[C-C]  F05[C-N]  \\\n",
       "1185         0.0         0.0         0.0       0.0      17.0       2.0   \n",
       "1113         0.0         0.0         0.0       0.0       0.0       0.0   \n",
       "1110         0.0         0.0         0.0       0.0       0.0       0.0   \n",
       "635          0.0         0.0         0.0       0.0       2.0       0.0   \n",
       "604          0.0         0.0         0.0       0.0       9.0       0.0   \n",
       "\n",
       "      F05[C-O]  F05[C-S]  F05[C-F]  F05[C-Cl]  F05[C-Br]  F05[C-I]  F05[N-N]  \\\n",
       "1185       7.0       0.0       3.0        0.0        0.0       0.0       0.0   \n",
       "1113       2.0       0.0       0.0        0.0        0.0       0.0       0.0   \n",
       "1110       5.0       0.0       0.0        0.0        0.0       0.0       0.0   \n",
       "635        3.0       0.0       0.0        0.0        0.0       0.0       0.0   \n",
       "604        2.0       0.0       0.0        0.0        0.0       0.0       0.0   \n",
       "\n",
       "      F05[N-O]  F05[N-S]  F05[N-F]  F05[N-Cl]  F05[N-Br]  F05[N-I]  F05[O-O]  \\\n",
       "1185       0.0       0.0       0.0        0.0        0.0       0.0       1.0   \n",
       "1113       1.0       0.0       0.0        0.0        0.0       0.0       0.0   \n",
       "1110       0.0       0.0       0.0        0.0        0.0       0.0       0.0   \n",
       "635        0.0       0.0       0.0        0.0        0.0       0.0       0.0   \n",
       "604        0.0       0.0       0.0        0.0        0.0       0.0       0.0   \n",
       "\n",
       "      F05[O-S]  F05[O-F]  F05[O-Cl]  F05[O-Br]  F05[S-S]  F05[S-F]  F05[S-Cl]  \\\n",
       "1185       0.0       0.0        0.0        0.0       0.0       0.0        0.0   \n",
       "1113       0.0       0.0        0.0        0.0       0.0       0.0        0.0   \n",
       "1110       0.0       0.0        0.0        0.0       0.0       0.0        0.0   \n",
       "635        0.0       0.0        0.0        0.0       0.0       0.0        0.0   \n",
       "604        0.0       0.0        0.0        0.0       0.0       0.0        0.0   \n",
       "\n",
       "      F05[F-F]  F05[F-Cl]  F05[F-Br]  F05[Cl-Cl]  F05[Cl-Br]  F05[Br-Br]  \\\n",
       "1185       0.0        0.0        0.0         0.0         0.0         0.0   \n",
       "1113       0.0        0.0        0.0         0.0         0.0         0.0   \n",
       "1110       0.0        0.0        0.0         0.0         0.0         0.0   \n",
       "635        0.0        0.0        0.0         0.0         0.0         0.0   \n",
       "604        0.0        0.0        0.0         0.0         0.0         0.0   \n",
       "\n",
       "      F06[C-C]  F06[C-N]  F06[C-O]  F06[C-S]  F06[C-F]  F06[C-Cl]  F06[C-Br]  \\\n",
       "1185      15.0       1.0       8.0       0.0       0.0        0.0        0.0   \n",
       "1113       0.0       0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "1110       0.0       0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "635        1.0       0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "604        8.0       0.0       2.0       0.0       0.0        0.0        0.0   \n",
       "\n",
       "      F06[C-I]  F06[N-N]  F06[N-O]  F06[N-S]  F06[N-F]  F06[N-Cl]  F06[N-Br]  \\\n",
       "1185       0.0       0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "1113       0.0       0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "1110       0.0       0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "635        0.0       0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "604        0.0       0.0       0.0       0.0       0.0        0.0        0.0   \n",
       "\n",
       "      F06[N-I]  F06[O-O]  F06[O-S]  F06[O-F]  F06[O-Cl]  F06[O-Br]  F06[O-I]  \\\n",
       "1185       0.0       0.0       0.0       3.0        0.0        0.0       0.0   \n",
       "1113       0.0       2.0       0.0       0.0        0.0        0.0       0.0   \n",
       "1110       0.0       0.0       0.0       0.0        0.0        0.0       0.0   \n",
       "635        0.0       0.0       0.0       0.0        0.0        0.0       0.0   \n",
       "604        0.0       0.0       0.0       0.0        0.0        0.0       0.0   \n",
       "\n",
       "      F06[S-S]  F06[S-F]  F06[S-Cl]  F06[F-F]  F06[F-Cl]  F06[Cl-Cl]  \\\n",
       "1185       0.0       0.0        0.0       0.0        0.0         0.0   \n",
       "1113       0.0       0.0        0.0       0.0        0.0         0.0   \n",
       "1110       0.0       0.0        0.0       0.0        0.0         0.0   \n",
       "635        0.0       0.0        0.0       0.0        0.0         0.0   \n",
       "604        0.0       0.0        0.0       0.0        0.0         0.0   \n",
       "\n",
       "      F06[Cl-Br]  F06[Br-Br]  F07[C-C]  F07[C-N]  F07[C-O]  F07[C-S]  \\\n",
       "1185         0.0         0.0      13.0       0.0       6.0       0.0   \n",
       "1113         0.0         0.0       0.0       0.0       0.0       0.0   \n",
       "1110         0.0         0.0       0.0       0.0       0.0       0.0   \n",
       "635          0.0         0.0       0.0       0.0       0.0       0.0   \n",
       "604          0.0         0.0       7.0       0.0       2.0       0.0   \n",
       "\n",
       "      F07[C-F]  F07[C-Cl]  F07[C-Br]  F07[C-I]  F07[N-N]  F07[N-O]  F07[N-S]  \\\n",
       "1185       3.0        0.0        0.0       0.0       0.0       1.0       0.0   \n",
       "1113       0.0        0.0        0.0       0.0       0.0       0.0       0.0   \n",
       "1110       0.0        0.0        0.0       0.0       0.0       0.0       0.0   \n",
       "635        0.0        0.0        0.0       0.0       0.0       0.0       0.0   \n",
       "604        0.0        0.0        0.0       0.0       0.0       0.0       0.0   \n",
       "\n",
       "      F07[N-F]  F07[N-Cl]  F07[N-Br]  F07[O-O]  F07[O-S]  F07[O-F]  F07[O-Cl]  \\\n",
       "1185       0.0        0.0        0.0       0.0       0.0       0.0        0.0   \n",
       "1113       0.0        0.0        0.0       0.0       0.0       0.0        0.0   \n",
       "1110       0.0        0.0        0.0       0.0       0.0       0.0        0.0   \n",
       "635        0.0        0.0        0.0       0.0       0.0       0.0        0.0   \n",
       "604        0.0        0.0        0.0       0.0       0.0       0.0        0.0   \n",
       "\n",
       "      F07[O-Br]  F07[S-S]  F07[S-F]  F07[S-Cl]  F07[F-F]  F07[F-Cl]  \\\n",
       "1185        0.0       0.0       0.0        0.0       0.0        0.0   \n",
       "1113        0.0       0.0       0.0        0.0       0.0        0.0   \n",
       "1110        0.0       0.0       0.0        0.0       0.0        0.0   \n",
       "635         0.0       0.0       0.0        0.0       0.0        0.0   \n",
       "604         0.0       0.0       0.0        0.0       0.0        0.0   \n",
       "\n",
       "      F07[Cl-Cl]  F07[Cl-Br]  F07[Br-Br]  F08[C-C]  F08[C-N]  F08[C-O]  \\\n",
       "1185         0.0         0.0         0.0      12.0       1.0       2.0   \n",
       "1113         0.0         0.0         0.0       0.0       0.0       0.0   \n",
       "1110         0.0         0.0         0.0       0.0       0.0       0.0   \n",
       "635          0.0         0.0         0.0       0.0       0.0       0.0   \n",
       "604          0.0         0.0         0.0       6.0       0.0       2.0   \n",
       "\n",
       "      F08[C-S]  F08[C-F]  F08[C-Cl]  F08[C-Br]  F08[C-I]  F08[N-N]  F08[N-O]  \\\n",
       "1185       0.0       6.0        0.0        0.0       0.0       0.0       0.0   \n",
       "1113       0.0       0.0        0.0        0.0       0.0       0.0       0.0   \n",
       "1110       0.0       0.0        0.0        0.0       0.0       0.0       0.0   \n",
       "635        0.0       0.0        0.0        0.0       0.0       0.0       0.0   \n",
       "604        0.0       0.0        0.0        0.0       0.0       0.0       0.0   \n",
       "\n",
       "      F08[N-S]  F08[N-F]  F08[N-Cl]  F08[N-Br]  F08[O-O]  F08[O-S]  F08[O-F]  \\\n",
       "1185       0.0       0.0        0.0        0.0       2.0       0.0       0.0   \n",
       "1113       0.0       0.0        0.0        0.0       0.0       0.0       0.0   \n",
       "1110       0.0       0.0        0.0        0.0       0.0       0.0       0.0   \n",
       "635        0.0       0.0        0.0        0.0       0.0       0.0       0.0   \n",
       "604        0.0       0.0        0.0        0.0       0.0       0.0       0.0   \n",
       "\n",
       "      F08[O-Cl]  F08[O-Br]  F08[S-Cl]  F08[F-F]  F08[F-Cl]  F08[Cl-Cl]  \\\n",
       "1185        0.0        0.0        0.0       0.0        0.0         0.0   \n",
       "1113        0.0        0.0        0.0       0.0        0.0         0.0   \n",
       "1110        0.0        0.0        0.0       0.0        0.0         0.0   \n",
       "635         0.0        0.0        0.0       0.0        0.0         0.0   \n",
       "604         0.0        0.0        0.0       0.0        0.0         0.0   \n",
       "\n",
       "      F08[Cl-Br]  F08[Br-Br]  F09[C-C]  F09[C-N]  F09[C-O]  F09[C-S]  \\\n",
       "1185         0.0         0.0      10.0       2.0       4.0       0.0   \n",
       "1113         0.0         0.0       0.0       0.0       0.0       0.0   \n",
       "1110         0.0         0.0       0.0       0.0       0.0       0.0   \n",
       "635          0.0         0.0       0.0       0.0       0.0       0.0   \n",
       "604          0.0         0.0       5.0       0.0       2.0       0.0   \n",
       "\n",
       "      F09[C-F]  F09[C-Cl]  F09[C-Br]  F09[C-I]  F09[N-N]  F09[N-O]  F09[N-S]  \\\n",
       "1185       6.0        0.0        0.0       0.0       0.0       0.0       0.0   \n",
       "1113       0.0        0.0        0.0       0.0       0.0       0.0       0.0   \n",
       "1110       0.0        0.0        0.0       0.0       0.0       0.0       0.0   \n",
       "635        0.0        0.0        0.0       0.0       0.0       0.0       0.0   \n",
       "604        0.0        0.0        0.0       0.0       0.0       0.0       0.0   \n",
       "\n",
       "      F09[N-F]  F09[N-Cl]  F09[N-Br]  F09[O-O]  F09[O-S]  F09[O-F]  F09[O-Cl]  \\\n",
       "1185       0.0        0.0        0.0       0.0       0.0       0.0        0.0   \n",
       "1113       0.0        0.0        0.0       0.0       0.0       0.0        0.0   \n",
       "1110       0.0        0.0        0.0       0.0       0.0       0.0        0.0   \n",
       "635        0.0        0.0        0.0       0.0       0.0       0.0        0.0   \n",
       "604        0.0        0.0        0.0       0.0       0.0       0.0        0.0   \n",
       "\n",
       "      F09[O-Br]  F09[F-F]  F09[F-Cl]  F09[Cl-Cl]  F09[Br-Br]  F10[C-C]  \\\n",
       "1185        0.0       0.0        0.0         0.0         0.0       9.0   \n",
       "1113        0.0       0.0        0.0         0.0         0.0       0.0   \n",
       "1110        0.0       0.0        0.0         0.0         0.0       0.0   \n",
       "635         0.0       0.0        0.0         0.0         0.0       0.0   \n",
       "604         0.0       0.0        0.0         0.0         0.0       4.0   \n",
       "\n",
       "      F10[C-N]  F10[C-O]  F10[C-S]  F10[C-F]  F10[C-Cl]  F10[C-Br]  F10[C-I]  \\\n",
       "1185       0.0       4.0       0.0       3.0        0.0        0.0       0.0   \n",
       "1113       0.0       0.0       0.0       0.0        0.0        0.0       0.0   \n",
       "1110       0.0       0.0       0.0       0.0        0.0        0.0       0.0   \n",
       "635        0.0       0.0       0.0       0.0        0.0        0.0       0.0   \n",
       "604        0.0       2.0       0.0       0.0        0.0        0.0       0.0   \n",
       "\n",
       "      F10[N-N]  F10[N-O]  F10[N-S]  F10[N-F]  F10[N-Cl]  F10[O-O]  F10[O-S]  \\\n",
       "1185       0.0       2.0       0.0       0.0        0.0       0.0       0.0   \n",
       "1113       0.0       0.0       0.0       0.0        0.0       0.0       0.0   \n",
       "1110       0.0       0.0       0.0       0.0        0.0       0.0       0.0   \n",
       "635        0.0       0.0       0.0       0.0        0.0       0.0       0.0   \n",
       "604        0.0       0.0       0.0       0.0        0.0       0.0       0.0   \n",
       "\n",
       "      F10[O-F]  F10[O-Cl]  F10[O-Br]  F10[S-F]  F10[F-F]  F10[F-Cl]  \\\n",
       "1185       0.0        0.0        0.0       0.0       0.0        0.0   \n",
       "1113       0.0        0.0        0.0       0.0       0.0        0.0   \n",
       "1110       0.0        0.0        0.0       0.0       0.0        0.0   \n",
       "635        0.0        0.0        0.0       0.0       0.0        0.0   \n",
       "604        0.0        0.0        0.0       0.0       0.0        0.0   \n",
       "\n",
       "      F10[Cl-Cl]  F10[Br-Br]     Uc     Ui     Hy  TPSA(NO)  TPSA(Tot)  MLOGP  \\\n",
       "1185         0.0         0.0  3.807  3.000 -0.696     57.65      57.65  3.865   \n",
       "1113         0.0         0.0  3.170  2.585 -0.031     66.05      66.05  1.701   \n",
       "1110         0.0         0.0  3.170  2.585 -0.031     66.05      66.05  1.701   \n",
       "635          0.0         0.0  3.000  2.322 -0.119     46.53      46.53  1.977   \n",
       "604          0.0         0.0  1.000  1.000 -0.856     26.30      26.30  4.701   \n",
       "\n",
       "      MLOGP2    SAtot    SAacc   SAdon       Vx   VvdwMG  VvdwZAZ    PDI  \\\n",
       "1185  14.936  481.887  119.810   0.000  441.013  184.120   336.63  0.915   \n",
       "1113   2.893  212.923   93.430  42.683  181.096   77.597   133.21  0.851   \n",
       "1110   2.893  212.923   93.430  42.683  181.096   77.597   133.21  0.851   \n",
       "635    3.907  220.391   78.828  42.683  187.924   80.395   139.51  0.853   \n",
       "604   22.095  420.515   36.145   0.000  358.073  150.128   265.70  0.852   \n",
       "\n",
       "      BLTF96  BLTD48  BLTA96  cRo5  DLS_01  DLS_02  DLS_03  DLS_04  DLS_05  \\\n",
       "1185   -4.68   -4.99   -5.09   1.0    1.00    1.00    0.83     1.0     1.0   \n",
       "1113   -2.84   -2.94   -2.93   1.0    1.00    0.83    0.83     0.6     0.0   \n",
       "1110   -2.84   -2.94   -2.93   1.0    1.00    0.83    0.83     0.6     0.0   \n",
       "635    -3.07   -3.20   -3.21   1.0    1.00    0.83    0.83     0.6     0.0   \n",
       "604    -5.39   -5.79   -5.93   1.0    0.75    0.67    0.83     0.4     1.0   \n",
       "\n",
       "      DLS_06  DLS_07  DLS_cons  LLS_01  LLS_02  Index  \n",
       "1185    1.00     1.0      0.98    0.33    1.00   1188  \n",
       "1113    1.00     1.0      0.75    0.67    1.00   1116  \n",
       "1110    1.00     1.0      0.75    0.67    1.00   1113  \n",
       "635     1.00     1.0      0.75    1.00    1.00    635  \n",
       "604     0.83     0.5      0.71    0.67    0.75    604  \n",
       "\n",
       "[5 rows x 1467 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_main.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection\n",
    "Made aware of features selection methods in scikitlearn via https://towardsdatascience.com/feature-selection-techniques-in-machine-learning-with-python-f24e7da3f36e\n",
    "\n",
    "See also [scikit-learn docs](https://scikit-learn.org/stable/modules/feature_selection.html)\n",
    "### Importances via 'dummy' GradientBoostingRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-16T12:28:29.831027Z",
     "start_time": "2020-01-16T12:28:21.816216Z"
    }
   },
   "outputs": [],
   "source": [
    "fs_model = GradientBoostingRegressor()\n",
    "fs_model.fit(X_train_main, y_train)\n",
    "importances = pd.Series(fs_model.feature_importances_, index = X_train_main.columns)\n",
    "top_15_features_model = list(importances.nlargest(15).index.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-16T12:28:29.839582Z",
     "start_time": "2020-01-16T12:28:29.833593Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['TPSA(NO)',\n",
       " 'P_VSA_p_2',\n",
       " 'IC0',\n",
       " 'Me',\n",
       " 'H-050',\n",
       " 'nHDon',\n",
       " 'F01[C-N]',\n",
       " 'Index',\n",
       " 'CATS2D_02_DA',\n",
       " 'T(O..O)',\n",
       " 'SAdon',\n",
       " 'GATS1v',\n",
       " 'MATS1e',\n",
       " 'LLS_02',\n",
       " 'SAacc']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_15_features_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importances via 'dummy' LASSO model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-16T12:28:48.091336Z",
     "start_time": "2020-01-16T12:28:29.842595Z"
    }
   },
   "outputs": [],
   "source": [
    "dummy_lasso = Lasso(max_iter=10000, alpha=0.5)\n",
    "selector = SelectFromModel(dummy_lasso).fit(X_train_main, y_train)\n",
    "coefs = selector.estimator_.coef_\n",
    "importances_lasso = pd.Series(coefs, index = X_train_main.columns)\n",
    "top_15_features_lasso = list(importances_lasso.nlargest(15).index.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SelectKBest using f_regression scoring function\n",
    "F-test estimates degree of linear dependency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-16T12:28:48.312235Z",
     "start_time": "2020-01-16T12:28:48.095353Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\feature_selection\\univariate_selection.py:299: RuntimeWarning: invalid value encountered in true_divide\n",
      "  corr /= X_norms\n",
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:901: RuntimeWarning: invalid value encountered in greater\n",
      "  return (a < x) & (x < b)\n",
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:901: RuntimeWarning: invalid value encountered in less\n",
      "  return (a < x) & (x < b)\n",
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:1892: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= _a)\n"
     ]
    }
   ],
   "source": [
    "fs_kbest = SelectKBest(score_func= f_regression, k=15)\n",
    "fit = fs_kbest.fit(X_train_main, y_train)\n",
    "scores_f = pd.Series(fit.scores_, index=X_train_main.columns)\n",
    "top_15_features_kbest = list(scores_f.nlargest(15).index.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SelectKBest using mutual_info_regression\n",
    "Catch any statistical dependancy, but requires more samples than f_reg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-16T12:29:31.882609Z",
     "start_time": "2020-01-16T12:28:48.316258Z"
    }
   },
   "outputs": [],
   "source": [
    "fs_mut_info = SelectKBest(score_func= mutual_info_regression, k=15)\n",
    "fit = fs_mut_info.fit(X_train_main, y_train)\n",
    "scores_mutual = pd.Series(fit.scores_, index=X_train_main.columns)\n",
    "top_15_features_mutual_info = list(scores_mutual.nlargest(15).index.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Selection results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-16T12:29:32.763398Z",
     "start_time": "2020-01-16T12:29:31.884618Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, imp_plot = plt.subplots(nrows=1, ncols=4, figsize=(15, 10))\n",
    "imp_plot[0].barh(importances.nlargest(15).index.values, importances.nlargest(15))\n",
    "imp_plot[0].set_title('Dummy Model')\n",
    "imp_plot[1].barh(scores_mutual.nlargest(15).index.values, scores_mutual.nlargest(15))\n",
    "imp_plot[1].set_title('Mutual Information')\n",
    "imp_plot[2].barh(scores_f.nlargest(15).index.values, scores_f.nlargest(15))\n",
    "imp_plot[2].set_title('F Regression')\n",
    "imp_plot[3].barh(importances_lasso.nlargest(15).index.values, importances_lasso.nlargest(15))\n",
    "imp_plot[3].set_title('LASSO')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GradientBoosting\n",
    "### Model-chosen features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-16T12:29:32.771430Z",
     "start_time": "2020-01-16T12:29:32.765407Z"
    }
   },
   "outputs": [],
   "source": [
    "gbr_param_grid = {\n",
    "    'n_estimators': [500, 1000, 2000],\n",
    "    'max_depth': [2, 4, 6],\n",
    "    'min_samples_leaf': [3, 5, 9, 17],\n",
    "    'learning_rate': [0.1, 0.05, 0.02],\n",
    "    'max_features': [1.0, 0.3, 0.1],\n",
    "    'loss': ['ls', 'lad', 'huber']\n",
    "} #hyperparameters for each gbr grid search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-16T13:31:54.733466Z",
     "start_time": "2020-01-16T12:29:32.774442Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GradientBoostingRegressor grid search.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'learning_rate': 0.02, 'loss': 'lad', 'max_depth': 6, 'max_features': 0.1, 'min_samples_leaf': 9, 'n_estimators': 2000}\n",
      "Training Score:\t 0.9136506916481263\n",
      "Testing Score:\t 0.8532764923340206\n",
      "GradientBoostingRegressor run at: 2020-01-16 13:31:54.501366 . Saving to models/DRAGON_noVPAS/GradientBoostingRegressor_model_16_01_2020_13_31_54.joblib\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test = X_train_main[top_15_features_model], X_test_main[top_15_features_model]\n",
    "\n",
    "\n",
    "best_hp = grid_search_return_hp_dict(GradientBoostingRegressor(),\n",
    "                                     X_train, y_train, gbr_param_grid)\n",
    "\n",
    "gbr_model = GradientBoostingRegressor(**best_hp)\n",
    "gbr_model.fit(X_train, y_train)\n",
    "\n",
    "exp_vs_calc_gbr_model = produce_exp_vs_pred_df('top_15_features_model', gbr_model,\n",
    "                                         \"GradBoost, model-chosen features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### F-regression features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-16T14:33:39.603510Z",
     "start_time": "2020-01-16T13:31:54.739984Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GradientBoostingRegressor grid search.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'learning_rate': 0.05, 'loss': 'lad', 'max_depth': 6, 'max_features': 0.1, 'min_samples_leaf': 3, 'n_estimators': 500}\n",
      "Training Score:\t 0.9317395172085381\n",
      "Testing Score:\t 0.8411981283092483\n",
      "GradientBoostingRegressor run at: 2020-01-16 14:33:39.559393 . Saving to models/DRAGON_noVPAS/GradientBoostingRegressor_kbest_16_01_2020_14_33_39.joblib\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test = X_train_main[top_15_features_kbest], X_test_main[top_15_features_kbest]\n",
    "\n",
    "best_hp = grid_search_return_hp_dict(GradientBoostingRegressor(),\n",
    "                                     X_train, y_train, gbr_param_grid)\n",
    "\n",
    "gbr_freg = GradientBoostingRegressor(**best_hp)\n",
    "gbr_freg.fit(X_train, y_train)\n",
    "\n",
    "exp_vs_calc_gbr_freg = produce_exp_vs_pred_df('top_15_features_kbest', gbr_freg,\n",
    "                                                    \"GradBoost, f_reg features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mutual info features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-16T15:38:51.251936Z",
     "start_time": "2020-01-16T14:33:39.607527Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GradientBoostingRegressor grid search.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'learning_rate': 0.05, 'loss': 'lad', 'max_depth': 6, 'max_features': 0.3, 'min_samples_leaf': 3, 'n_estimators': 2000}\n",
      "Training Score:\t 0.9379608336062498\n",
      "Testing Score:\t 0.8544833146624162\n",
      "GradientBoostingRegressor run at: 2020-01-16 15:38:51.112718 . Saving to models/DRAGON_noVPAS/GradientBoostingRegressor_mutual_info_16_01_2020_15_38_51.joblib\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test = X_train_main[top_15_features_mutual_info], X_test_main[top_15_features_mutual_info]\n",
    "\n",
    "\n",
    "best_hp = grid_search_return_hp_dict(GradientBoostingRegressor(),\n",
    "                                     X_train, y_train, gbr_param_grid)\n",
    "\n",
    "gbr_mu = GradientBoostingRegressor(**best_hp)\n",
    "gbr_mu.fit(X_train, y_train)\n",
    "\n",
    "exp_vs_calc_gbr_mu = produce_exp_vs_pred_df('top_15_features_mutual_info', gbr_mu,\n",
    "                                            \"GradBoost, Mutual Info\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LASSO features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-16T16:37:07.166038Z",
     "start_time": "2020-01-16T15:38:51.256449Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GradientBoostingRegressor grid search.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'learning_rate': 0.02, 'loss': 'ls', 'max_depth': 6, 'max_features': 0.3, 'min_samples_leaf': 3, 'n_estimators': 2000}\n",
      "Training Score:\t 0.996589735091017\n",
      "Testing Score:\t 0.8161460167193553\n",
      "GradientBoostingRegressor run at: 2020-01-16 16:37:06.990069 . Saving to models/DRAGON_noVPAS/GradientBoostingRegressor_lasso_16_01_2020_16_37_06.joblib\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test = X_train_main[top_15_features_lasso], X_test_main[top_15_features_lasso]\n",
    "\n",
    "best_hp = grid_search_return_hp_dict(GradientBoostingRegressor(), \n",
    "                                     X_train, y_train, gbr_param_grid)\n",
    "\n",
    "gbr_lasso = GradientBoostingRegressor(**best_hp)\n",
    "gbr_lasso.fit(X_train, y_train)\n",
    "exp_vs_calc_gbr_lasso = produce_exp_vs_pred_df('top_15_features_lasso', gbr_lasso,\n",
    "                                               \"LASSO-features, GBR model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree\n",
    "### Model-Chosen Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-16T16:37:07.180076Z",
     "start_time": "2020-01-16T16:37:07.172555Z"
    }
   },
   "outputs": [],
   "source": [
    "dtr_param_grid = {\n",
    "    'criterion': ['mse', 'friedman_mse', 'mae'], #function measuring quality of a split\n",
    "    'max_depth': [5, 10, 20] , # max depth of tree\n",
    "    'max_features': [5, 10, 15], # N features to be considered when looking for split\n",
    "    'max_leaf_nodes': [None, 10, 15, 20, 30], #grows tree with N nodes in best-first fashion\n",
    "    'min_impurity_decrease': [0.0, 0.1],\n",
    "    'min_samples_leaf': [1, 2, 3], \n",
    "    'min_samples_split': [1.0, 2, 3],\n",
    "    'min_weight_fraction_leaf': [0.0, 0.1, 0.5] , \n",
    "    'splitter': ['best', 'random']\n",
    "} #hyperparameters for dtr gridsearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-16T16:40:54.361628Z",
     "start_time": "2020-01-16T16:37:07.182580Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeRegressor grid search.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'criterion': 'mse', 'max_depth': 20, 'max_features': 15, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 3, 'min_samples_split': 3, 'min_weight_fraction_leaf': 0.0, 'splitter': 'random'}\n",
      "Training Score:\t 0.9141647290701933\n",
      "Testing Score:\t 0.7956771880980333\n",
      "DecisionTreeRegressor run at: 2020-01-16 16:40:54.355856 . Saving to models/DRAGON_noVPAS/DecisionTreeRegressor_model_16_01_2020_16_40_54.joblib\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test = X_train_main[top_15_features_model], X_test_main[top_15_features_model]\n",
    "\n",
    "best_hp = grid_search_return_hp_dict(DecisionTreeRegressor(), X_train, y_train, dtr_param_grid)\n",
    "\n",
    "dtr_model = DecisionTreeRegressor(**best_hp)\n",
    "\n",
    "dtr_model.fit(X_train, y_train)\n",
    "\n",
    "exp_vs_calc_dtr_model = produce_exp_vs_pred_df('top_15_features_model', dtr_model,\n",
    "                                         \"Decision Tree, model-chosen features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### F-regression features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-16T16:44:48.342240Z",
     "start_time": "2020-01-16T16:40:54.364602Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeRegressor grid search.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'criterion': 'mae', 'max_depth': 10, 'max_features': 10, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 3, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'splitter': 'random'}\n",
      "Training Score:\t 0.824735879420048\n",
      "Testing Score:\t 0.7707477970128914\n",
      "DecisionTreeRegressor run at: 2020-01-16 16:44:48.335723 . Saving to models/DRAGON_noVPAS/DecisionTreeRegressor_kbest_16_01_2020_16_44_48.joblib\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test = X_train_main[top_15_features_kbest], X_test_main[top_15_features_kbest]\n",
    "\n",
    "\n",
    "best_hp = grid_search_return_hp_dict(DecisionTreeRegressor(), X_train, y_train, dtr_param_grid)\n",
    "\n",
    "dtr_freg = DecisionTreeRegressor(**best_hp)\n",
    "\n",
    "dtr_freg.fit(X_train, y_train)\n",
    "\n",
    "exp_vs_calc_dtr_freg = produce_exp_vs_pred_df('top_15_features_kbest', dtr_freg,\n",
    "                                         \"Decision Tree, freg features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mutual info features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-16T16:49:12.282181Z",
     "start_time": "2020-01-16T16:44:48.346251Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeRegressor grid search.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'criterion': 'friedman_mse', 'max_depth': 10, 'max_features': 10, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.1, 'min_samples_leaf': 3, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'splitter': 'random'}\n",
      "Training Score:\t 0.8595461536744464\n",
      "Testing Score:\t 0.7715041249308991\n",
      "DecisionTreeRegressor run at: 2020-01-16 16:49:12.276174 . Saving to models/DRAGON_noVPAS/DecisionTreeRegressor_mutual_info_16_01_2020_16_49_12.joblib\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test = X_train_main[top_15_features_mutual_info], X_test_main[top_15_features_mutual_info]\n",
    "\n",
    "best_hp = grid_search_return_hp_dict(DecisionTreeRegressor(), X_train, y_train, dtr_param_grid)\n",
    "\n",
    "dtr_mu = DecisionTreeRegressor(**best_hp)\n",
    "\n",
    "dtr_mu.fit(X_train, y_train)\n",
    "\n",
    "exp_vs_calc_dtr_mu = produce_exp_vs_pred_df('top_15_features_mutual_info', dtr_mu,\n",
    "                                         \"Decision Tree, mutual info features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LASSO features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-16T16:53:05.178607Z",
     "start_time": "2020-01-16T16:49:12.286191Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeRegressor grid search.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'criterion': 'mae', 'max_depth': 10, 'max_features': 10, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 2, 'min_samples_split': 3, 'min_weight_fraction_leaf': 0.0, 'splitter': 'best'}\n",
      "Training Score:\t 0.8840999565633477\n",
      "Testing Score:\t 0.7448102791928264\n",
      "DecisionTreeRegressor run at: 2020-01-16 16:53:05.172089 . Saving to models/DRAGON_noVPAS/DecisionTreeRegressor_lasso_16_01_2020_16_53_05.joblib\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test = X_train_main[top_15_features_lasso], X_test_main[top_15_features_lasso]\n",
    "\n",
    "best_hp = grid_search_return_hp_dict(DecisionTreeRegressor(), X_train, y_train, dtr_param_grid)\n",
    "\n",
    "dtr_lasso = DecisionTreeRegressor(**best_hp)\n",
    "\n",
    "dtr_lasso.fit(X_train, y_train)\n",
    "\n",
    "exp_vs_calc_dtr_lasso = produce_exp_vs_pred_df('top_15_features_lasso', dtr_lasso,\n",
    "                                         \"Decision Tree, lasso features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AdaBoost Regressor\n",
    "### Model Chosen Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-16T16:53:05.190138Z",
     "start_time": "2020-01-16T16:53:05.182116Z"
    }
   },
   "outputs": [],
   "source": [
    "base_models = [ExtraTreesRegressor(n_estimators= 5,\n",
    "                                   criterion= 'mse',\n",
    "                                   max_features = 'log2'),\n",
    "               RandomForestRegressor(n_estimators= 5,\n",
    "                                     criterion= 'mse',\n",
    "                                     max_features = 'sqrt',\n",
    "                                     min_samples_split = 3),\n",
    "              GradientBoostingRegressor(),\n",
    "              DecisionTreeRegressor(),\n",
    "              Lars(),\n",
    "              ElasticNet()]\n",
    "\n",
    "ada_param_grid = {\n",
    "    'base_estimator' : base_models, \n",
    "    'learning_rate' : [0.3, 0.5, 0.8, 1], \n",
    "    'loss' : ['linear', 'square', 'exponential'], \n",
    "    'n_estimators' : [50, 100]\n",
    "} #hyperparameters for adaboost gridsearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-16T17:01:08.672880Z",
     "start_time": "2020-01-16T16:53:05.194650Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoostRegressor grid search.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'base_estimator': ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "                    max_features='log2', max_leaf_nodes=None,\n",
      "                    min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                    min_samples_leaf=1, min_samples_split=2,\n",
      "                    min_weight_fraction_leaf=0.0, n_estimators=5, n_jobs=None,\n",
      "                    oob_score=False, random_state=None, verbose=0,\n",
      "                    warm_start=False), 'learning_rate': 0.8, 'loss': 'square', 'n_estimators': 50}\n",
      "Training Score:\t 0.9966830903147815\n",
      "Testing Score:\t 0.8520725299365427\n",
      "AdaBoostRegressor run at: 2020-01-16 17:01:08.526492 . Saving to models/DRAGON_noVPAS/AdaBoostRegressor_model_16_01_2020_17_01_08.joblib\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test = X_train_main[top_15_features_model], X_test_main[top_15_features_model]\n",
    "\n",
    "best_hp = grid_search_return_hp_dict(AdaBoostRegressor(), X_train, y_train, ada_param_grid)\n",
    "\n",
    "ada_model = AdaBoostRegressor(**best_hp)\n",
    "\n",
    "ada_model.fit(X_train, y_train)\n",
    "\n",
    "exp_vs_calc_ada_model = produce_exp_vs_pred_df('top_15_features_model', ada_model,\n",
    "                                               \"AdaBoost, model features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### F-regression features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-16T17:09:18.037626Z",
     "start_time": "2020-01-16T17:01:08.683411Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoostRegressor grid search.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'base_estimator': ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "                    max_features='log2', max_leaf_nodes=None,\n",
      "                    min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                    min_samples_leaf=1, min_samples_split=2,\n",
      "                    min_weight_fraction_leaf=0.0, n_estimators=5, n_jobs=None,\n",
      "                    oob_score=False, random_state=None, verbose=0,\n",
      "                    warm_start=False), 'learning_rate': 0.8, 'loss': 'exponential', 'n_estimators': 50}\n",
      "Training Score:\t 0.9979723730365371\n",
      "Testing Score:\t 0.8433963130222173\n",
      "AdaBoostRegressor run at: 2020-01-16 17:09:17.903303 . Saving to models/DRAGON_noVPAS/AdaBoostRegressor_kbest_16_01_2020_17_09_17.joblib\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test = X_train_main[top_15_features_kbest], X_test_main[top_15_features_kbest]\n",
    "\n",
    "best_hp = grid_search_return_hp_dict(AdaBoostRegressor(), X_train, y_train, ada_param_grid)\n",
    "\n",
    "ada_freg = AdaBoostRegressor(**best_hp)\n",
    "\n",
    "ada_freg.fit(X_train, y_train)\n",
    "\n",
    "exp_vs_calc_ada_freg = produce_exp_vs_pred_df('top_15_features_kbest', ada_freg,\n",
    "                                               \"AdaBoost, freg features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mutual info features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-16T17:18:49.503938Z",
     "start_time": "2020-01-16T17:09:18.044141Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoostRegressor grid search.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'base_estimator': ExtraTreesRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
      "                    max_features='log2', max_leaf_nodes=None,\n",
      "                    min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                    min_samples_leaf=1, min_samples_split=2,\n",
      "                    min_weight_fraction_leaf=0.0, n_estimators=5, n_jobs=None,\n",
      "                    oob_score=False, random_state=None, verbose=0,\n",
      "                    warm_start=False), 'learning_rate': 0.8, 'loss': 'exponential', 'n_estimators': 100}\n",
      "Training Score:\t 0.9997045578005971\n",
      "Testing Score:\t 0.8365182791277487\n",
      "AdaBoostRegressor run at: 2020-01-16 17:18:49.229088 . Saving to models/DRAGON_noVPAS/AdaBoostRegressor_mutual_info_16_01_2020_17_18_49.joblib\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test = X_train_main[top_15_features_mutual_info], X_test_main[top_15_features_mutual_info]\n",
    "\n",
    "best_hp = grid_search_return_hp_dict(AdaBoostRegressor(), X_train, y_train, ada_param_grid)\n",
    "\n",
    "ada_mu = AdaBoostRegressor(**best_hp)\n",
    "\n",
    "ada_mu.fit(X_train, y_train)\n",
    "\n",
    "exp_vs_calc_ada_mu = produce_exp_vs_pred_df('top_15_features_mutual_info', ada_mu,\n",
    "                                               \"AdaBoost, mutual info features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LASSO features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-16T17:26:34.176980Z",
     "start_time": "2020-01-16T17:18:49.506946Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoostRegressor grid search.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'base_estimator': RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
      "                      max_features='sqrt', max_leaf_nodes=None,\n",
      "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                      min_samples_leaf=1, min_samples_split=3,\n",
      "                      min_weight_fraction_leaf=0.0, n_estimators=5, n_jobs=None,\n",
      "                      oob_score=False, random_state=None, verbose=0,\n",
      "                      warm_start=False), 'learning_rate': 0.5, 'loss': 'exponential', 'n_estimators': 50}\n",
      "Training Score:\t 0.9926354574810684\n",
      "Testing Score:\t 0.8080655222178783\n",
      "AdaBoostRegressor run at: 2020-01-16 17:26:34.046134 . Saving to models/DRAGON_noVPAS/AdaBoostRegressor_lasso_16_01_2020_17_26_34.joblib\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test = X_train_main[top_15_features_lasso], X_test_main[top_15_features_lasso]\n",
    "\n",
    "best_hp = grid_search_return_hp_dict(AdaBoostRegressor(), X_train, y_train, ada_param_grid)\n",
    "\n",
    "ada_lasso = AdaBoostRegressor(**best_hp)\n",
    "\n",
    "ada_lasso.fit(X_train, y_train)\n",
    "\n",
    "exp_vs_calc_ada_lasso = produce_exp_vs_pred_df('top_15_features_lasso', ada_lasso,\n",
    "                                               \"AdaBoost, model features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LASSO\n",
    "### Model Chosen Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-16T17:26:34.188511Z",
     "start_time": "2020-01-16T17:26:34.180996Z"
    }
   },
   "outputs": [],
   "source": [
    "lasso_param_grid = {\n",
    "    'alpha':[0.2, 0.4, 0.6, 0.8],\n",
    "    'max_iter':[1000, 5000, 10000, 50000],\n",
    "    'selection':['cyclic', 'random']\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-16T17:26:40.982758Z",
     "start_time": "2020-01-16T17:26:34.192020Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lasso grid search.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 0.2, 'max_iter': 1000, 'selection': 'cyclic'}\n",
      "Training Score:\t 0.7025515174084993\n",
      "Testing Score:\t 0.7357933019197427\n",
      "Lasso run at: 2020-01-16 17:26:40.950674 . Saving to models/DRAGON_noVPAS/Lasso_model_16_01_2020_17_26_40.joblib\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test = X_train_main[top_15_features_model], X_test_main[top_15_features_model]\n",
    "best_hp = grid_search_return_hp_dict(Lasso(), X_train, y_train, lasso_param_grid)\n",
    "lasso_model = Lasso(**best_hp)\n",
    "lasso_model.fit(X_train, y_train)\n",
    "\n",
    "exp_vs_calc_lasso_model = produce_exp_vs_pred_df('top_15_features_model', lasso_model,\n",
    "                                                 \"LASSO, model features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### F-regression features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-16T17:26:48.532748Z",
     "start_time": "2020-01-16T17:26:40.986267Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lasso grid search.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 62.95777664966954, tolerance: 7.861287863090288\n",
      "  positive)\n",
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 58.1372885694509, tolerance: 7.861287863090288\n",
      "  positive)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 0.2, 'max_iter': 5000, 'selection': 'random'}\n",
      "Training Score:\t 0.7247376293705728\n",
      "Testing Score:\t 0.7171477393839785\n",
      "Lasso run at: 2020-01-16 17:26:48.526732 . Saving to models/DRAGON_noVPAS/Lasso_kbest_16_01_2020_17_26_48.joblib\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test = X_train_main[top_15_features_kbest], X_test_main[top_15_features_kbest]\n",
    "best_hp = grid_search_return_hp_dict(Lasso(), X_train, y_train, lasso_param_grid)\n",
    "lasso_freg = Lasso(**best_hp)\n",
    "lasso_freg.fit(X_train, y_train)\n",
    "\n",
    "exp_vs_calc_lasso_freg = produce_exp_vs_pred_df('top_15_features_kbest', lasso_freg,\n",
    "                                                 \"LASSO, f-reg features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mutual info features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-16T17:26:56.287005Z",
     "start_time": "2020-01-16T17:26:48.536258Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lasso grid search.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 0.2, 'max_iter': 5000, 'selection': 'cyclic'}\n",
      "Training Score:\t 0.6858987473879607\n",
      "Testing Score:\t 0.6714929045146443\n",
      "Lasso run at: 2020-01-16 17:26:56.280489 . Saving to models/DRAGON_noVPAS/Lasso_mutual_info_16_01_2020_17_26_56.joblib\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test = X_train_main[top_15_features_mutual_info], X_test_main[top_15_features_mutual_info]\n",
    "best_hp = grid_search_return_hp_dict(Lasso(), X_train, y_train, lasso_param_grid)\n",
    "lasso_mu = Lasso(**best_hp)\n",
    "lasso_mu.fit(X_train, y_train)\n",
    "\n",
    "exp_vs_calc_lasso_mu = produce_exp_vs_pred_df('top_15_features_mutual_info', lasso_mu,\n",
    "                                                 \"LASSO, mutual info features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LASSO Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-16T17:27:02.310949Z",
     "start_time": "2020-01-16T17:26:56.291518Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lasso grid search.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 0.2, 'max_iter': 1000, 'selection': 'cyclic'}\n",
      "Training Score:\t 0.6833898806906213\n",
      "Testing Score:\t 0.6737594517258707\n",
      "Lasso run at: 2020-01-16 17:27:02.303930 . Saving to models/DRAGON_noVPAS/Lasso_lasso_16_01_2020_17_27_02.joblib\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test = X_train_main[top_15_features_lasso], X_test_main[top_15_features_lasso]\n",
    "best_hp = grid_search_return_hp_dict(Lasso(), X_train, y_train, lasso_param_grid)\n",
    "lasso_lasso = Lasso(**best_hp)\n",
    "lasso_lasso.fit(X_train, y_train)\n",
    "\n",
    "exp_vs_calc_lasso_lasso = produce_exp_vs_pred_df('top_15_features_lasso', lasso_lasso,\n",
    "                                                 \"LASSO, lasso features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Regressor\n",
    "### Model Chosen Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-16T17:27:02.321476Z",
     "start_time": "2020-01-16T17:27:02.314459Z"
    }
   },
   "outputs": [],
   "source": [
    "random_forest_param_grid = {\n",
    "    'max_depth': [10, 20, 30, 40, 50, 60, 70, 80, 90, None],\n",
    "    'max_features': ['auto', 'sqrt'],\n",
    "    'min_samples_leaf': [1, 2, 4],\n",
    "    'min_samples_split': [2, 5, 10],\n",
    "    'n_estimators': [200, 400, 600, 800, 1000, 1200, 1400, 1600]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-16T21:45:19.253824Z",
     "start_time": "2020-01-16T19:42:39.311353Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestRegressor grid search.\n",
      "{'max_depth': 90, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 600}\n",
      "Training Score:\t 0.9774090544159082\n",
      "Testing Score:\t 0.8582042794564964\n",
      "RandomForestRegressor run at: 2020-01-16 21:45:18.928355 . Saving to models/DRAGON_noVPAS/RandomForestRegressor_model_16_01_2020_21_45_18.joblib\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test = X_train_main[top_15_features_model], X_test_main[top_15_features_model]\n",
    "best_hp = grid_search_return_hp_dict(RandomForestRegressor(),\n",
    "                                     X_train, y_train, random_forest_param_grid)\n",
    "random_forest_model = RandomForestRegressor(**best_hp)\n",
    "random_forest_model.fit(X_train, y_train)\n",
    "exp_vs_calc_random_forest_model = produce_exp_vs_pred_df('top_15_features_model', random_forest_model,\n",
    "                                                         \"RandomForest, model features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### F-regression features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-17T01:11:15.484470Z",
     "start_time": "2020-01-16T23:02:41.821348Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestRegressor grid search.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "Training Score:\t 0.9721752491187455\n",
      "Testing Score:\t 0.8401657504277648\n",
      "RandomForestRegressor run at: 2020-01-17 01:11:15.379690 . Saving to models/DRAGON_noVPAS/RandomForestRegressor_kbest_17_01_2020_01_11_15.joblib\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test = X_train_main[top_15_features_kbest], X_test_main[top_15_features_kbest]\n",
    "best_hp = grid_search_return_hp_dict(RandomForestRegressor(),\n",
    "                                     X_train, y_train, random_forest_param_grid)\n",
    "random_forest_freg = RandomForestRegressor(**best_hp)\n",
    "random_forest_freg.fit(X_train, y_train)\n",
    "exp_vs_calc_random_forest_freg = produce_exp_vs_pred_df('top_15_features_kbest', random_forest_freg,\n",
    "                                                         \"RandomForest, freg features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mutual info features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-17T03:47:08.728085Z",
     "start_time": "2020-01-17T01:11:26.589418Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestRegressor grid search.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 600}\n",
      "Training Score:\t 0.9729434173898541\n",
      "Testing Score:\t 0.8415611230823128\n",
      "RandomForestRegressor run at: 2020-01-17 03:47:08.433069 . Saving to models/DRAGON_noVPAS/RandomForestRegressor_mutual_info_17_01_2020_03_47_08.joblib\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test = X_train_main[top_15_features_mutual_info], X_test_main[top_15_features_mutual_info]\n",
    "best_hp = grid_search_return_hp_dict(RandomForestRegressor(),\n",
    "                                     X_train, y_train, random_forest_param_grid)\n",
    "random_forest_mu = RandomForestRegressor(**best_hp)\n",
    "random_forest_mu.fit(X_train, y_train)\n",
    "exp_vs_calc_random_forest_mu = produce_exp_vs_pred_df('top_15_features_mutual_info', random_forest_mu,\n",
    "                                                         \"RandomForest, mutual info features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LASSO features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-17T05:55:00.396379Z",
     "start_time": "2020-01-17T03:47:19.592342Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestRegressor grid search.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "Training Score:\t 0.9714625413023873\n",
      "Testing Score:\t 0.810773797396149\n",
      "RandomForestRegressor run at: 2020-01-17 05:55:00.292476 . Saving to models/DRAGON_noVPAS/RandomForestRegressor_lasso_17_01_2020_05_55_00.joblib\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test = X_train_main[top_15_features_lasso], X_test_main[top_15_features_lasso]\n",
    "best_hp = grid_search_return_hp_dict(RandomForestRegressor(),\n",
    "                                     X_train, y_train, random_forest_param_grid)\n",
    "random_forest_lasso = RandomForestRegressor(**best_hp)\n",
    "random_forest_lasso.fit(X_train, y_train)\n",
    "exp_vs_calc_random_forest_lasso = produce_exp_vs_pred_df('top_15_features_lasso', random_forest_lasso,\n",
    "                                                         \"RandomForest, lasso features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LARS - Least angle regression\n",
    "### Model chosen features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-17T05:55:14.660635Z",
     "start_time": "2020-01-17T05:55:11.579996Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score:\t -5.118873363102049\n",
      "Testing Score:\t -4.66823156501682\n",
      "Lars run at: 2020-01-17 05:55:14.653620 . Saving to models/DRAGON_noVPAS/Lars_model_17_01_2020_05_55_14.joblib\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test = X_train_main[top_15_features_model], X_test_main[top_15_features_model]\n",
    "\n",
    "lars_model = Lars(fit_intercept=False, n_nonzero_coefs=np.inf)\n",
    "lars_model.fit(X_train, y_train)\n",
    "exp_vs_calc_lars_model = produce_exp_vs_pred_df('top_15_features_model', lars_model, \"LARS model features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### F-regression features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-17T05:55:28.069282Z",
     "start_time": "2020-01-17T05:55:25.653510Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score:\t 0.412098008114794\n",
      "Testing Score:\t 0.36890756929970014\n",
      "Lars run at: 2020-01-17 05:55:28.063264 . Saving to models/DRAGON_noVPAS/Lars_kbest_17_01_2020_05_55_28.joblib\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test = X_train_main[top_15_features_kbest], X_test_main[top_15_features_kbest]\n",
    "\n",
    "lars_freg = Lars(fit_intercept=False, n_nonzero_coefs=np.inf)\n",
    "lars_freg.fit(X_train, y_train)\n",
    "exp_vs_calc_lars_freg = produce_exp_vs_pred_df('top_15_features_kbest', lars_freg, \"LARS freg features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mutual info features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-17T05:55:41.948880Z",
     "start_time": "2020-01-17T05:55:39.134989Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score:\t -4.03397309481404\n",
      "Testing Score:\t -3.6461882169232736\n",
      "Lars run at: 2020-01-17 05:55:41.943866 . Saving to models/DRAGON_noVPAS/Lars_mutual_info_17_01_2020_05_55_41.joblib\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test = X_train_main[top_15_features_mutual_info], X_test_main[top_15_features_mutual_info]\n",
    "\n",
    "lars_mu = Lars(fit_intercept=False, n_nonzero_coefs=np.inf)\n",
    "lars_mu.fit(X_train, y_train)\n",
    "exp_vs_calc_lars_mu = produce_exp_vs_pred_df('top_15_features_mutual_info', lars_mu, \"LARS mutual info features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LASSO features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-17T05:55:56.222599Z",
     "start_time": "2020-01-17T05:55:53.060731Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score:\t 0.6395259500652293\n",
      "Testing Score:\t 0.6194334350344687\n",
      "Lars run at: 2020-01-17 05:55:56.217585 . Saving to models/DRAGON_noVPAS/Lars_lasso_17_01_2020_05_55_56.joblib\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test = X_train_main[top_15_features_lasso], X_test_main[top_15_features_lasso]\n",
    "\n",
    "lars_lasso = Lars(fit_intercept=False, n_nonzero_coefs=np.inf)\n",
    "lars_lasso.fit(X_train, y_train)\n",
    "exp_vs_calc_lars_lasso = produce_exp_vs_pred_df('top_15_features_lasso', lars_lasso, \"LARS lasso features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Elastic Net (linear regression l1 l2 norm regularization)\n",
    "### Model chosen features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-17T05:56:07.757325Z",
     "start_time": "2020-01-17T05:56:07.750305Z"
    }
   },
   "outputs": [],
   "source": [
    "net_param_grid = {\n",
    "    'alpha':[0.1, 0.3, 0.5, 0.8, 1.0],\n",
    "    'l1_ratio':[0.0, 0.2, 0.4, 0.6, 0.8, 1.0],\n",
    "    'fit_intercept':[True, False],\n",
    "    'max_iter': [1000, 3000, 5000],\n",
    "    'tol':[0.0001, 0.001, 0.01],\n",
    "    'selection':['cyclic', 'random']\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-17T05:56:58.966814Z",
     "start_time": "2020-01-17T05:56:19.112124Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ElasticNet grid search.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 0.1, 'fit_intercept': False, 'l1_ratio': 0.8, 'max_iter': 3000, 'selection': 'cyclic', 'tol': 0.0001}\n",
      "Training Score:\t 0.7134538033619403\n",
      "Testing Score:\t 0.7453632055451128\n",
      "ElasticNet run at: 2020-01-17 05:56:58.961798 . Saving to models/DRAGON_noVPAS/ElasticNet_model_17_01_2020_05_56_58.joblib\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test = X_train_main[top_15_features_model], X_test_main[top_15_features_model]\n",
    "best_hp = grid_search_return_hp_dict(ElasticNet(), X_train, y_train, net_param_grid)\n",
    "\n",
    "net_model = ElasticNet(**best_hp)\n",
    "net_model.fit(X_train, y_train)\n",
    "exp_vs_calc_net_model = produce_exp_vs_pred_df('top_15_features_model', net_model, \"ElasticNet model features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### F regression features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-17T05:58:12.294870Z",
     "start_time": "2020-01-17T05:57:10.374599Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ElasticNet grid search.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10978.14596703574, tolerance: 78.61287863090288\n",
      "  positive)\n",
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10979.363160643945, tolerance: 78.61287863090288\n",
      "  positive)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 0.1, 'fit_intercept': True, 'l1_ratio': 0.0, 'max_iter': 3000, 'selection': 'random', 'tol': 0.001}\n",
      "Training Score:\t 0.7332180807485775\n",
      "Testing Score:\t 0.7301068054649074\n",
      "ElasticNet run at: 2020-01-17 05:58:12.288854 . Saving to models/DRAGON_noVPAS/ElasticNet_kbest_17_01_2020_05_58_12.joblib\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test = X_train_main[top_15_features_kbest], X_test_main[top_15_features_kbest]\n",
    "best_hp = grid_search_return_hp_dict(ElasticNet(), X_train, y_train, net_param_grid)\n",
    "\n",
    "net_freg = ElasticNet(**best_hp)\n",
    "net_freg.fit(X_train, y_train)\n",
    "exp_vs_calc_net_freg = produce_exp_vs_pred_df('top_15_features_kbest', net_freg, \"ElasticNet freg features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mutual info features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-17T05:59:22.498246Z",
     "start_time": "2020-01-17T05:58:23.823817Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ElasticNet grid search.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 113.7454182191832, tolerance: 7.941628996607655\n",
      "  positive)\n",
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 113.7454182191832, tolerance: 7.941628996607655\n",
      "  positive)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 0.1, 'fit_intercept': False, 'l1_ratio': 1.0, 'max_iter': 5000, 'selection': 'cyclic', 'tol': 0.0001}\n",
      "Training Score:\t 0.6970302526384997\n",
      "Testing Score:\t 0.6787535164371025\n",
      "ElasticNet run at: 2020-01-17 05:59:22.492231 . Saving to models/DRAGON_noVPAS/ElasticNet_mutual_info_17_01_2020_05_59_22.joblib\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test = X_train_main[top_15_features_mutual_info], X_test_main[top_15_features_mutual_info]\n",
    "best_hp = grid_search_return_hp_dict(ElasticNet(), X_train, y_train, net_param_grid)\n",
    "\n",
    "net_mu = ElasticNet(**best_hp)\n",
    "net_mu.fit(X_train, y_train)\n",
    "exp_vs_calc_net_mu = produce_exp_vs_pred_df('top_15_features_mutual_info', net_mu, \"ElasticNet mutual info features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LASSO features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-17T06:00:05.016950Z",
     "start_time": "2020-01-17T05:59:34.404713Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ElasticNet grid search.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12487.608190337094, tolerance: 78.61287863090288\n",
      "  positive)\n",
      "C:\\Users\\jtd1g16\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12487.608190337089, tolerance: 78.61287863090288\n",
      "  positive)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 0.1, 'fit_intercept': True, 'l1_ratio': 0.0, 'max_iter': 1000, 'selection': 'random', 'tol': 0.001}\n",
      "Training Score:\t 0.684349499730894\n",
      "Testing Score:\t 0.6739856745936135\n",
      "ElasticNet run at: 2020-01-17 06:00:05.008927 . Saving to models/DRAGON_noVPAS/ElasticNet_lasso_17_01_2020_06_00_05.joblib\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test = X_train_main[top_15_features_lasso], X_test_main[top_15_features_lasso]\n",
    "best_hp = grid_search_return_hp_dict(ElasticNet(), X_train, y_train, net_param_grid)\n",
    "\n",
    "net_lasso = ElasticNet(**best_hp)\n",
    "net_lasso.fit(X_train, y_train)\n",
    "exp_vs_calc_net_lasso = produce_exp_vs_pred_df('top_15_features_lasso', net_lasso, \"ElasticNet lasso features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scores and visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-17T06:00:16.590261Z",
     "start_time": "2020-01-17T06:00:16.554325Z"
    }
   },
   "outputs": [],
   "source": [
    "exp_vs_calc_df_list = []\n",
    "model_list = []\n",
    "for var in dir():\n",
    "    if isinstance(eval(var), pd.core.frame.DataFrame):\n",
    "        if var[0:3] == 'exp':\n",
    "            exp_vs_calc_df_list.append(var)\n",
    "            model_list.append(var[12:])\n",
    "exp_vs_calc_model_dict = dict(zip(model_list, exp_vs_calc_df_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-17T06:00:27.956342Z",
     "start_time": "2020-01-17T06:00:27.942319Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['test_scores.joblib']"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(train_scores, \"train_scores.joblib\")\n",
    "joblib.dump(test_scores, \"test_scores.joblib\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-17T10:47:04.876735Z",
     "start_time": "2020-01-17T10:47:04.264566Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Training Scores'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'GradBoost, model-chosen features': 0.9136506916481263,\n",
       " 'GradBoost, f_reg features': 0.9317395172085381,\n",
       " 'GradBoost, Mutual Info': 0.9379608336062498,\n",
       " 'LASSO-features, GBR model': 0.996589735091017,\n",
       " 'Decision Tree, model-chosen features': 0.9141647290701933,\n",
       " 'Decision Tree, freg features': 0.824735879420048,\n",
       " 'Decision Tree, mutual info features': 0.8595461536744464,\n",
       " 'Decision Tree, lasso features': 0.8840999565633477,\n",
       " 'AdaBoost, model features': 0.9926354574810684,\n",
       " 'AdaBoost, freg features': 0.9979723730365371,\n",
       " 'AdaBoost, mutual info features': 0.9997045578005971,\n",
       " 'LASSO, model features': 0.7025515174084993,\n",
       " 'LASSO, f-reg features': 0.7247376293705728,\n",
       " 'LASSO, mutual info features': 0.6858987473879607,\n",
       " 'LASSO, lasso features': 0.6833898806906213,\n",
       " 'RandomForest, model features': 0.9774090544159082,\n",
       " 'RandomForest, freg features': 0.9721752491187455,\n",
       " 'RandomForest, mutual info features': 0.9729434173898541,\n",
       " 'RandomForest, lasso features': 0.9714625413023873,\n",
       " 'LARS model features': -5.118873363102049,\n",
       " 'LARS freg features': 0.412098008114794,\n",
       " 'LARS mutual info features': -4.03397309481404,\n",
       " 'LARS lasso features': 0.6395259500652293,\n",
       " 'ElasticNet model features': 0.7134538033619403,\n",
       " 'ElasticNet freg features': 0.7332180807485775,\n",
       " 'ElasticNet mutual info features': 0.6970302526384997,\n",
       " 'ElasticNet lasso features': 0.684349499730894}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Testing Scores'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'GradBoost, model-chosen features': 0.8532764923340206,\n",
       " 'GradBoost, f_reg features': 0.8411981283092483,\n",
       " 'GradBoost, Mutual Info': 0.8544833146624162,\n",
       " 'LASSO-features, GBR model': 0.8161460167193553,\n",
       " 'Decision Tree, model-chosen features': 0.7956771880980333,\n",
       " 'Decision Tree, freg features': 0.7707477970128914,\n",
       " 'Decision Tree, mutual info features': 0.7715041249308991,\n",
       " 'Decision Tree, lasso features': 0.7448102791928264,\n",
       " 'AdaBoost, model features': 0.8080655222178783,\n",
       " 'AdaBoost, freg features': 0.8433963130222173,\n",
       " 'AdaBoost, mutual info features': 0.8365182791277487,\n",
       " 'LASSO, model features': 0.7357933019197427,\n",
       " 'LASSO, f-reg features': 0.7171477393839785,\n",
       " 'LASSO, mutual info features': 0.6714929045146443,\n",
       " 'LASSO, lasso features': 0.6737594517258707,\n",
       " 'RandomForest, model features': 0.8582042794564964,\n",
       " 'RandomForest, freg features': 0.8401657504277648,\n",
       " 'RandomForest, mutual info features': 0.8415611230823128,\n",
       " 'RandomForest, lasso features': 0.810773797396149,\n",
       " 'LARS model features': -4.66823156501682,\n",
       " 'LARS freg features': 0.36890756929970014,\n",
       " 'LARS mutual info features': -3.6461882169232736,\n",
       " 'LARS lasso features': 0.6194334350344687,\n",
       " 'ElasticNet model features': 0.7453632055451128,\n",
       " 'ElasticNet freg features': 0.7301068054649074,\n",
       " 'ElasticNet mutual info features': 0.6787535164371025,\n",
       " 'ElasticNet lasso features': 0.6739856745936135}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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       "model_id": "66605d5a09e04ff3823de762bb6900af",
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      "text/plain": [
       "interactive(children=(Dropdown(description='Model', options=('ada_freg', 'ada_lasso', 'ada_model', 'ada_mu', '…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(\"Training Scores\", train_scores, \"Testing Scores\", test_scores)\n",
    "mpld3.enable_notebook() #must enable\n",
    "@widgets.interact()\n",
    "\n",
    "def show_pred_vs_actual(Model = model_list):\n",
    "    pred_df = eval(exp_vs_calc_model_dict[Model])\n",
    "    model = eval(Model)\n",
    "    prediction_plot_scores(model, pred_df)"
   ]
  }
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