6 - janestreet-ensemble.ipynb

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    "# Jane Street Market Prediction (#6)\n",
    "## Ensemble - Bagging\n",
    "\n",
    "Notebook Navigation<br>\n",
    "[All](https://www.kaggle.com/wendellavila/janestreet-index/) | [#1](https://www.kaggle.com/wendellavila/janestreet-model-selection/) | [#2.1](https://www.kaggle.com/wendellavila/janestreet-preprocessing-selection) | [#2.2](https://www.kaggle.com/wendellavila/janestreet-data-preprocessing) | [#3](https://www.kaggle.com/wendellavila/janestreet-regularization-selection) | [#4.1](https://www.kaggle.com/wendellavila/janestreet-hyperparameter-tuning) | [#4.2](https://www.kaggle.com/wendellavila/janestreet-hyperparameter-evaluation) | [#5.1](https://www.kaggle.com/wendellavila/janestreet-pca) | [#5.2](https://www.kaggle.com/wendellavila/janestreet-autoencoder) | [#5.3](https://www.kaggle.com/wendellavila/janestreet-dimensionality-reduction-evaluation) |[#6](https://www.kaggle.com/wendellavila/janestreet-ensemble)"
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   "source": [
    "## Imports"
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   "source": [
    "#import janestreet\n",
    "import os\n",
    "import glob\n",
    "import IPython\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "pd.set_option('display.max_columns', 150)\n",
    "%matplotlib inline\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from keras import layers\n",
    "from keras.models import load_model\n",
    "from keras.callbacks import Callback, ModelCheckpoint, EarlyStopping, ReduceLROnPlateau"
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    "#data loaded from another notebook\n",
    "#already preprocessed and downsized for faster loading\n",
    "#https://www.kaggle.com/code/wendellavila/janestreet-data-preprocessing/\n",
    "train_data = pd.read_pickle('../input/janestreet-data-preprocessing/train-mean-indicator.pkl')\n",
    "val_data = pd.read_pickle('../input/janestreet-data-preprocessing/val-mean-indicator.pkl')\n",
    "features = [col for col in train_data.columns if 'feature' in col]\n",
    "resp_cols = [col for col in train_data.columns if 'resp' in col]"
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       "      <td>0.678711</td>\n",
       "      <td>1.213867</td>\n",
       "      <td>-1.369141</td>\n",
       "      <td>-2.281250</td>\n",
       "      <td>0.505371</td>\n",
       "      <td>1.325195</td>\n",
       "      <td>-1.443359</td>\n",
       "      <td>1.724609</td>\n",
       "      <td>-1.588867</td>\n",
       "      <td>-0.760254</td>\n",
       "      <td>-1.673828</td>\n",
       "      <td>0.866211</td>\n",
       "      <td>2.693359</td>\n",
       "      <td>5.230469</td>\n",
       "      <td>9.453125</td>\n",
       "      <td>0.458252</td>\n",
       "      <td>1.202148</td>\n",
       "      <td>1.753906</td>\n",
       "      <td>-0.527832</td>\n",
       "      <td>-2.160156</td>\n",
       "      <td>-0.411377</td>\n",
       "      <td>-0.533203</td>\n",
       "      <td>-0.908691</td>\n",
       "      <td>-0.270508</td>\n",
       "      <td>-0.129272</td>\n",
       "      <td>1.299805</td>\n",
       "      <td>1.149414</td>\n",
       "      <td>6.433594</td>\n",
       "      <td>6.621094</td>\n",
       "      <td>5.921875</td>\n",
       "      <td>4.546875</td>\n",
       "      <td>3.875000</td>\n",
       "      <td>-0.049896</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2112173</th>\n",
       "      <td>449</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000196</td>\n",
       "      <td>0.000196</td>\n",
       "      <td>-0.001564</td>\n",
       "      <td>-0.006363</td>\n",
       "      <td>-0.004242</td>\n",
       "      <td>1</td>\n",
       "      <td>1.150391</td>\n",
       "      <td>1.765625</td>\n",
       "      <td>1.018555</td>\n",
       "      <td>1.146484</td>\n",
       "      <td>-0.797852</td>\n",
       "      <td>-0.912109</td>\n",
       "      <td>-0.665527</td>\n",
       "      <td>-0.830566</td>\n",
       "      <td>0.070129</td>\n",
       "      <td>0.395020</td>\n",
       "      <td>-0.707520</td>\n",
       "      <td>-1.249023</td>\n",
       "      <td>0.252441</td>\n",
       "      <td>0.454102</td>\n",
       "      <td>0.035980</td>\n",
       "      <td>0.441406</td>\n",
       "      <td>0.889160</td>\n",
       "      <td>1.526367</td>\n",
       "      <td>0.892578</td>\n",
       "      <td>1.414062</td>\n",
       "      <td>2.214844</td>\n",
       "      <td>1.975586</td>\n",
       "      <td>2.693359</td>\n",
       "      <td>4.437500</td>\n",
       "      <td>0.965820</td>\n",
       "      <td>1.433594</td>\n",
       "      <td>-0.423340</td>\n",
       "      <td>-0.785156</td>\n",
       "      <td>-1.026367</td>\n",
       "      <td>-1.771484</td>\n",
       "      <td>-0.442627</td>\n",
       "      <td>-1.049805</td>\n",
       "      <td>-0.814453</td>\n",
       "      <td>-1.068359</td>\n",
       "      <td>-1.551758</td>\n",
       "      <td>-1.791016</td>\n",
       "      <td>1.630859</td>\n",
       "      <td>2.242188</td>\n",
       "      <td>-0.237549</td>\n",
       "      <td>-0.467529</td>\n",
       "      <td>-0.979492</td>\n",
       "      <td>0.896484</td>\n",
       "      <td>-0.472900</td>\n",
       "      <td>-0.125488</td>\n",
       "      <td>-0.852051</td>\n",
       "      <td>0.674805</td>\n",
       "      <td>1.580078</td>\n",
       "      <td>2.078125</td>\n",
       "      <td>2.677734</td>\n",
       "      <td>0.564941</td>\n",
       "      <td>0.411621</td>\n",
       "      <td>2.154297</td>\n",
       "      <td>1.455078</td>\n",
       "      <td>0.938965</td>\n",
       "      <td>-1.443359</td>\n",
       "      <td>-0.869141</td>\n",
       "      <td>-1.517578</td>\n",
       "      <td>-1.231445</td>\n",
       "      <td>-1.243164</td>\n",
       "      <td>-0.890137</td>\n",
       "      <td>-0.735840</td>\n",
       "      <td>6.492188</td>\n",
       "      <td>6.675781</td>\n",
       "      <td>5.953125</td>\n",
       "      <td>6.183594</td>\n",
       "      <td>5.042969</td>\n",
       "      <td>-0.108215</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2112174</th>\n",
       "      <td>449</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>-0.001841</td>\n",
       "      <td>-0.006153</td>\n",
       "      <td>-0.004051</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.107422</td>\n",
       "      <td>-1.484375</td>\n",
       "      <td>-0.128418</td>\n",
       "      <td>-0.164673</td>\n",
       "      <td>-0.517090</td>\n",
       "      <td>-0.990234</td>\n",
       "      <td>-0.143921</td>\n",
       "      <td>0.069885</td>\n",
       "      <td>-0.197998</td>\n",
       "      <td>1.302734</td>\n",
       "      <td>-0.059143</td>\n",
       "      <td>0.729980</td>\n",
       "      <td>0.084106</td>\n",
       "      <td>1.226562</td>\n",
       "      <td>-0.091797</td>\n",
       "      <td>1.642578</td>\n",
       "      <td>0.281738</td>\n",
       "      <td>0.983398</td>\n",
       "      <td>0.785645</td>\n",
       "      <td>2.423828</td>\n",
       "      <td>0.681641</td>\n",
       "      <td>1.161133</td>\n",
       "      <td>0.885254</td>\n",
       "      <td>2.781250</td>\n",
       "      <td>0.854492</td>\n",
       "      <td>2.521484</td>\n",
       "      <td>-0.070557</td>\n",
       "      <td>-0.353271</td>\n",
       "      <td>-0.495605</td>\n",
       "      <td>-1.457031</td>\n",
       "      <td>-0.161377</td>\n",
       "      <td>-0.847656</td>\n",
       "      <td>-0.328125</td>\n",
       "      <td>-0.867676</td>\n",
       "      <td>-0.686523</td>\n",
       "      <td>-1.465820</td>\n",
       "      <td>0.534668</td>\n",
       "      <td>1.278320</td>\n",
       "      <td>-0.236450</td>\n",
       "      <td>-0.706543</td>\n",
       "      <td>0.912109</td>\n",
       "      <td>-1.977539</td>\n",
       "      <td>-2.783203</td>\n",
       "      <td>-0.823242</td>\n",
       "      <td>-0.326416</td>\n",
       "      <td>0.283203</td>\n",
       "      <td>-0.148438</td>\n",
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       "      <td>4.226562</td>\n",
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       "      <td>1.775391</td>\n",
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       "      <td>4.953125</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2112175</th>\n",
       "      <td>449</td>\n",
       "      <td>1.071289</td>\n",
       "      <td>-0.000363</td>\n",
       "      <td>-0.000363</td>\n",
       "      <td>-0.001492</td>\n",
       "      <td>0.000698</td>\n",
       "      <td>0.001924</td>\n",
       "      <td>-1</td>\n",
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       "      <td>-1.479492</td>\n",
       "      <td>1.538086</td>\n",
       "      <td>1.257812</td>\n",
       "      <td>1.329102</td>\n",
       "      <td>1.121094</td>\n",
       "      <td>-0.214966</td>\n",
       "      <td>-0.302246</td>\n",
       "      <td>0.072571</td>\n",
       "      <td>-0.201172</td>\n",
       "      <td>0.740723</td>\n",
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       "      <td>-0.358887</td>\n",
       "      <td>-0.792480</td>\n",
       "      <td>-1.017578</td>\n",
       "      <td>-0.587402</td>\n",
       "      <td>-0.630859</td>\n",
       "      <td>-0.460938</td>\n",
       "      <td>-0.234253</td>\n",
       "      <td>-0.540039</td>\n",
       "      <td>-0.553223</td>\n",
       "      <td>-0.702637</td>\n",
       "      <td>-0.673828</td>\n",
       "      <td>0.650879</td>\n",
       "      <td>0.807617</td>\n",
       "      <td>1.508789</td>\n",
       "      <td>1.684570</td>\n",
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       "      <td>1.344727</td>\n",
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       "      <td>-1.256836</td>\n",
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       "      <td>-1.259766</td>\n",
       "      <td>1.490234</td>\n",
       "      <td>2.404297</td>\n",
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       "      <td>1.372070</td>\n",
       "      <td>1.628906</td>\n",
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       "      <td>-0.243286</td>\n",
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       "      <td>0.305176</td>\n",
       "      <td>4.234375</td>\n",
       "      <td>3.925781</td>\n",
       "      <td>-1.284180</td>\n",
       "      <td>-1.355469</td>\n",
       "      <td>6.027344</td>\n",
       "      <td>1.771484</td>\n",
       "      <td>1.491211</td>\n",
       "      <td>7.671875</td>\n",
       "      <td>...</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",
       "      <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",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 226 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date    weight    resp_1    resp_2    resp_3    resp_4      resp  \\\n",
       "2112171   449  0.000000  0.000119  0.000119  0.002081  0.003141  0.000895   \n",
       "2112172   449  0.000000  0.000126  0.000126  0.000881 -0.002131 -0.002645   \n",
       "2112173   449  0.000000  0.000196  0.000196 -0.001564 -0.006363 -0.004242   \n",
       "2112174   449  0.000000 -0.000004 -0.000004 -0.001841 -0.006153 -0.004051   \n",
       "2112175   449  1.071289 -0.000363 -0.000363 -0.001492  0.000698  0.001924   \n",
       "\n",
       "         feature_0  feature_1  feature_2  feature_3  feature_4  feature_5  \\\n",
       "2112171         -1  -1.244141  -0.621582   0.965820   1.478516   0.830566   \n",
       "2112172         -1  -0.759766   0.003555  -0.710938  -0.986816   0.446777   \n",
       "2112173          1   1.150391   1.765625   1.018555   1.146484  -0.797852   \n",
       "2112174          1  -2.107422  -1.484375  -0.128418  -0.164673  -0.517090   \n",
       "2112175         -1  -1.113281  -1.479492   1.538086   1.257812   1.329102   \n",
       "\n",
       "         feature_6  feature_7  feature_8  feature_9  feature_10  feature_11  \\\n",
       "2112171   1.325195  -0.286865  -0.300293  -1.616211   -0.738281    0.210449   \n",
       "2112172   0.710449   0.251465   0.659180  -0.863281   -0.005409    0.387451   \n",
       "2112173  -0.912109  -0.665527  -0.830566   0.070129    0.395020   -0.707520   \n",
       "2112174  -0.990234  -0.143921   0.069885  -0.197998    1.302734   -0.059143   \n",
       "2112175   1.121094  -0.214966  -0.302246   0.072571   -0.201172    0.740723   \n",
       "\n",
       "         feature_12  feature_13  feature_14  feature_15  feature_16  \\\n",
       "2112171    1.066406   -0.451172    0.047791   -1.174805   -1.286133   \n",
       "2112172    1.487305   -0.015266    0.602539   -0.566406   -0.053131   \n",
       "2112173   -1.249023    0.252441    0.454102    0.035980    0.441406   \n",
       "2112174    0.729980    0.084106    1.226562   -0.091797    1.642578   \n",
       "2112175    0.950684    0.492188    0.171143   -0.008560   -0.358887   \n",
       "\n",
       "         feature_17  feature_18  feature_19  feature_20  feature_21  \\\n",
       "2112171   -0.421387   -0.853027   -0.404297   -0.770508   -0.923340   \n",
       "2112172   -0.167236   -0.405762   -0.575684   -1.169922   -0.447998   \n",
       "2112173    0.889160    1.526367    0.892578    1.414062    2.214844   \n",
       "2112174    0.281738    0.983398    0.785645    2.423828    0.681641   \n",
       "2112175   -0.792480   -1.017578   -0.587402   -0.630859   -0.460938   \n",
       "\n",
       "         feature_22  feature_23  feature_24  feature_25  feature_26  \\\n",
       "2112171   -0.788574   -1.016602   -2.103516   -0.617188   -1.196289   \n",
       "2112172   -0.457275   -0.526367   -1.192383   -0.687012   -1.335938   \n",
       "2112173    1.975586    2.693359    4.437500    0.965820    1.433594   \n",
       "2112174    1.161133    0.885254    2.781250    0.854492    2.521484   \n",
       "2112175   -0.234253   -0.540039   -0.553223   -0.702637   -0.673828   \n",
       "\n",
       "         feature_27  feature_28  feature_29  feature_30  feature_31  \\\n",
       "2112171    0.383057    0.979980    0.479248    1.166992    0.754883   \n",
       "2112172    0.606445    1.525391    0.542969    1.292969    0.872559   \n",
       "2112173   -0.423340   -0.785156   -1.026367   -1.771484   -0.442627   \n",
       "2112174   -0.070557   -0.353271   -0.495605   -1.457031   -0.161377   \n",
       "2112175    0.650879    0.807617    1.508789    1.684570    1.243164   \n",
       "\n",
       "         feature_32  feature_33  feature_34  feature_35  feature_36  \\\n",
       "2112171    2.113281    0.521973    1.006836    0.597168    1.091797   \n",
       "2112172    2.398438    0.748535    1.420898    0.678711    1.213867   \n",
       "2112173   -1.049805   -0.814453   -1.068359   -1.551758   -1.791016   \n",
       "2112174   -0.847656   -0.328125   -0.867676   -0.686523   -1.465820   \n",
       "2112175    1.768555    1.648438    1.575195    1.896484    1.596680   \n",
       "\n",
       "         feature_37  feature_38  feature_39  feature_40  feature_41  \\\n",
       "2112171   -0.742188   -1.187500    0.879883    2.322266    1.257812   \n",
       "2112172   -1.369141   -2.281250    0.505371    1.325195   -1.443359   \n",
       "2112173    1.630859    2.242188   -0.237549   -0.467529   -0.979492   \n",
       "2112174    0.534668    1.278320   -0.236450   -0.706543    0.912109   \n",
       "2112175   -0.821289   -0.775391    1.344727    1.940430   -1.256836   \n",
       "\n",
       "         feature_42  feature_43  feature_44  feature_45  feature_46  \\\n",
       "2112171   -2.091797   -1.640625   -0.419434    0.225830    2.453125   \n",
       "2112172    1.724609   -1.588867   -0.760254   -1.673828    0.866211   \n",
       "2112173    0.896484   -0.472900   -0.125488   -0.852051    0.674805   \n",
       "2112174   -1.977539   -2.783203   -0.823242   -0.326416    0.283203   \n",
       "2112175    1.432617    0.740723   -3.011719   -1.259766    1.490234   \n",
       "\n",
       "         feature_47  feature_48  feature_49  feature_50  feature_51  \\\n",
       "2112171    1.480469    2.640625    4.757812    9.570312   10.085938   \n",
       "2112172    2.693359    5.230469    9.453125    0.458252    1.202148   \n",
       "2112173    1.580078    2.078125    2.677734    0.564941    0.411621   \n",
       "2112174   -0.148438    0.592773    1.741211    2.228516    3.712891   \n",
       "2112175    2.404297    2.425781    2.136719    1.213867   -0.004765   \n",
       "\n",
       "         feature_52  feature_53  feature_54  feature_55  feature_56  \\\n",
       "2112171    3.755859   -1.077148    1.500000   -0.015404    1.159180   \n",
       "2112172    1.753906   -0.527832   -2.160156   -0.411377   -0.533203   \n",
       "2112173    2.154297    1.455078    0.938965   -1.443359   -0.869141   \n",
       "2112174    0.582520   -0.999512    1.517578   -1.285156    0.864258   \n",
       "2112175    1.372070    1.628906    0.585449   -0.593262   -0.076721   \n",
       "\n",
       "         feature_57  feature_58  feature_59  feature_60  feature_61  \\\n",
       "2112171    0.359131    1.323242    1.026367    4.539062    4.199219   \n",
       "2112172   -0.908691   -0.270508   -0.129272    1.299805    1.149414   \n",
       "2112173   -1.517578   -1.231445   -1.243164   -0.890137   -0.735840   \n",
       "2112174   -1.568359   -1.146484    0.221558    4.226562    3.912109   \n",
       "2112175   -0.243286   -0.080627    0.305176    4.234375    3.925781   \n",
       "\n",
       "         feature_62  feature_63  feature_64  feature_65  feature_66  \\\n",
       "2112171   -1.602539   -1.746094    5.867188    1.551758    1.302734   \n",
       "2112172    6.433594    6.621094    5.921875    4.546875    3.875000   \n",
       "2112173    6.492188    6.675781    5.953125    6.183594    5.042969   \n",
       "2112174    1.488281    1.542969    5.953125    1.775391    1.499023   \n",
       "2112175   -1.284180   -1.355469    6.027344    1.771484    1.491211   \n",
       "\n",
       "         feature_67  ...  feature_19_missing  feature_20_missing  \\\n",
       "2112171    8.335938  ...                 0.0                 0.0   \n",
       "2112172   -0.049896  ...                 0.0                 0.0   \n",
       "2112173   -0.108215  ...                 0.0                 0.0   \n",
       "2112174    4.953125  ...                 0.0                 0.0   \n",
       "2112175    7.671875  ...                 0.0                 0.0   \n",
       "\n",
       "         feature_21_missing  feature_22_missing  feature_23_missing  \\\n",
       "2112171                 0.0                 0.0                 0.0   \n",
       "2112172                 0.0                 0.0                 0.0   \n",
       "2112173                 0.0                 0.0                 0.0   \n",
       "2112174                 0.0                 0.0                 0.0   \n",
       "2112175                 0.0                 0.0                 0.0   \n",
       "\n",
       "         feature_24_missing  feature_25_missing  feature_26_missing  \\\n",
       "2112171                 0.0                 0.0                 0.0   \n",
       "2112172                 0.0                 0.0                 0.0   \n",
       "2112173                 0.0                 0.0                 0.0   \n",
       "2112174                 0.0                 0.0                 0.0   \n",
       "2112175                 0.0                 0.0                 0.0   \n",
       "\n",
       "         feature_27_missing  feature_28_missing  feature_29_missing  \\\n",
       "2112171                 0.0                 0.0                 0.0   \n",
       "2112172                 0.0                 0.0                 0.0   \n",
       "2112173                 0.0                 0.0                 0.0   \n",
       "2112174                 0.0                 0.0                 0.0   \n",
       "2112175                 0.0                 0.0                 0.0   \n",
       "\n",
       "         feature_30_missing  feature_31_missing  feature_32_missing  \\\n",
       "2112171                 0.0                 0.0                 0.0   \n",
       "2112172                 0.0                 0.0                 0.0   \n",
       "2112173                 0.0                 0.0                 0.0   \n",
       "2112174                 0.0                 0.0                 0.0   \n",
       "2112175                 0.0                 0.0                 0.0   \n",
       "\n",
       "         feature_33_missing  feature_34_missing  feature_35_missing  \\\n",
       "2112171                 0.0                 0.0                 0.0   \n",
       "2112172                 0.0                 0.0                 0.0   \n",
       "2112173                 0.0                 0.0                 0.0   \n",
       "2112174                 0.0                 0.0                 0.0   \n",
       "2112175                 0.0                 0.0                 0.0   \n",
       "\n",
       "         feature_36_missing  feature_44_missing  feature_45_missing  \\\n",
       "2112171                 0.0                 0.0                 0.0   \n",
       "2112172                 0.0                 0.0                 0.0   \n",
       "2112173                 0.0                 0.0                 0.0   \n",
       "2112174                 0.0                 0.0                 0.0   \n",
       "2112175                 0.0                 0.0                 0.0   \n",
       "\n",
       "         feature_55_missing  feature_56_missing  feature_58_missing  \\\n",
       "2112171                 0.0                 0.0                 0.0   \n",
       "2112172                 0.0                 0.0                 0.0   \n",
       "2112173                 0.0                 0.0                 0.0   \n",
       "2112174                 0.0                 0.0                 0.0   \n",
       "2112175                 0.0                 0.0                 0.0   \n",
       "\n",
       "         feature_59_missing  feature_72_missing  feature_73_missing  \\\n",
       "2112171                 0.0                 0.0                 0.0   \n",
       "2112172                 0.0                 0.0                 0.0   \n",
       "2112173                 0.0                 0.0                 0.0   \n",
       "2112174                 0.0                 0.0                 0.0   \n",
       "2112175                 0.0                 0.0                 0.0   \n",
       "\n",
       "         feature_74_missing  feature_75_missing  feature_76_missing  \\\n",
       "2112171                 0.0                 0.0                 0.0   \n",
       "2112172                 0.0                 0.0                 0.0   \n",
       "2112173                 0.0                 0.0                 0.0   \n",
       "2112174                 0.0                 0.0                 0.0   \n",
       "2112175                 0.0                 0.0                 0.0   \n",
       "\n",
       "         feature_78_missing  feature_79_missing  feature_80_missing  \\\n",
       "2112171                 0.0                 0.0                 0.0   \n",
       "2112172                 0.0                 0.0                 0.0   \n",
       "2112173                 0.0                 0.0                 0.0   \n",
       "2112174                 0.0                 0.0                 0.0   \n",
       "2112175                 0.0                 0.0                 0.0   \n",
       "\n",
       "         feature_81_missing  feature_82_missing  feature_84_missing  \\\n",
       "2112171                 0.0                 0.0                 0.0   \n",
       "2112172                 0.0                 0.0                 0.0   \n",
       "2112173                 0.0                 0.0                 0.0   \n",
       "2112174                 0.0                 0.0                 0.0   \n",
       "2112175                 0.0                 0.0                 0.0   \n",
       "\n",
       "         feature_85_missing  feature_86_missing  feature_87_missing  \\\n",
       "2112171                 0.0                 0.0                 0.0   \n",
       "2112172                 0.0                 0.0                 0.0   \n",
       "2112173                 0.0                 0.0                 0.0   \n",
       "2112174                 0.0                 0.0                 0.0   \n",
       "2112175                 0.0                 0.0                 0.0   \n",
       "\n",
       "         feature_88_missing  feature_90_missing  feature_91_missing  \\\n",
       "2112171                 0.0                 0.0                 0.0   \n",
       "2112172                 0.0                 0.0                 0.0   \n",
       "2112173                 0.0                 0.0                 0.0   \n",
       "2112174                 0.0                 0.0                 0.0   \n",
       "2112175                 0.0                 0.0                 0.0   \n",
       "\n",
       "         feature_92_missing  feature_93_missing  feature_94_missing  \\\n",
       "2112171                 0.0                 0.0                 0.0   \n",
       "2112172                 0.0                 0.0                 0.0   \n",
       "2112173                 0.0                 0.0                 0.0   \n",
       "2112174                 0.0                 0.0                 0.0   \n",
       "2112175                 0.0                 0.0                 0.0   \n",
       "\n",
       "         feature_96_missing  feature_97_missing  feature_98_missing  \\\n",
       "2112171                 0.0                 0.0                 0.0   \n",
       "2112172                 0.0                 0.0                 0.0   \n",
       "2112173                 0.0                 0.0                 0.0   \n",
       "2112174                 0.0                 0.0                 0.0   \n",
       "2112175                 0.0                 0.0                 0.0   \n",
       "\n",
       "         feature_99_missing  feature_100_missing  feature_102_missing  \\\n",
       "2112171                 0.0                  0.0                  0.0   \n",
       "2112172                 0.0                  0.0                  0.0   \n",
       "2112173                 0.0                  0.0                  0.0   \n",
       "2112174                 0.0                  0.0                  0.0   \n",
       "2112175                 0.0                  0.0                  0.0   \n",
       "\n",
       "         feature_103_missing  feature_104_missing  feature_105_missing  \\\n",
       "2112171                  0.0                  0.0                  0.0   \n",
       "2112172                  0.0                  0.0                  0.0   \n",
       "2112173                  0.0                  0.0                  0.0   \n",
       "2112174                  0.0                  0.0                  0.0   \n",
       "2112175                  0.0                  0.0                  0.0   \n",
       "\n",
       "         feature_106_missing  feature_108_missing  feature_109_missing  \\\n",
       "2112171                  0.0                  0.0                  0.0   \n",
       "2112172                  0.0                  0.0                  0.0   \n",
       "2112173                  0.0                  0.0                  0.0   \n",
       "2112174                  0.0                  0.0                  0.0   \n",
       "2112175                  0.0                  0.0                  0.0   \n",
       "\n",
       "         feature_110_missing  feature_111_missing  feature_112_missing  \\\n",
       "2112171                  0.0                  0.0                  0.0   \n",
       "2112172                  0.0                  0.0                  0.0   \n",
       "2112173                  0.0                  0.0                  0.0   \n",
       "2112174                  0.0                  0.0                  0.0   \n",
       "2112175                  0.0                  0.0                  0.0   \n",
       "\n",
       "         feature_114_missing  feature_115_missing  feature_116_missing  \\\n",
       "2112171                  0.0                  0.0                  0.0   \n",
       "2112172                  0.0                  0.0                  0.0   \n",
       "2112173                  0.0                  0.0                  0.0   \n",
       "2112174                  0.0                  0.0                  0.0   \n",
       "2112175                  0.0                  0.0                  0.0   \n",
       "\n",
       "         feature_117_missing  feature_118_missing  feature_120_missing  \\\n",
       "2112171                  0.0                  0.0                  0.0   \n",
       "2112172                  0.0                  0.0                  0.0   \n",
       "2112173                  0.0                  0.0                  0.0   \n",
       "2112174                  0.0                  0.0                  0.0   \n",
       "2112175                  0.0                  0.0                  0.0   \n",
       "\n",
       "         feature_121_missing  feature_122_missing  feature_123_missing  \\\n",
       "2112171                  0.0                  0.0                  0.0   \n",
       "2112172                  0.0                  0.0                  0.0   \n",
       "2112173                  0.0                  0.0                  0.0   \n",
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       "\n",
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       "2112171                  0.0                  0.0                  0.0       1  \n",
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       "\n",
       "[5 rows x 226 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c822df70",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T18:58:51.800876Z",
     "iopub.status.busy": "2022-08-01T18:58:51.800572Z",
     "iopub.status.idle": "2022-08-01T18:58:51.957418Z",
     "shell.execute_reply": "2022-08-01T18:58:51.956346Z"
    },
    "papermill": {
     "duration": 0.173558,
     "end_time": "2022-08-01T18:58:51.961092",
     "exception": false,
     "start_time": "2022-08-01T18:58:51.787534",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2112178</th>\n",
       "      <td>450</td>\n",
       "      <td>0.651855</td>\n",
       "      <td>-0.001053</td>\n",
       "      <td>-0.001618</td>\n",
       "      <td>-0.003260</td>\n",
       "      <td>-0.005676</td>\n",
       "      <td>-0.003983</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.681641</td>\n",
       "      <td>-0.964844</td>\n",
       "      <td>-0.045197</td>\n",
       "      <td>-0.031647</td>\n",
       "      <td>-0.723145</td>\n",
       "      <td>-1.272461</td>\n",
       "      <td>0.05307</td>\n",
       "      <td>0.026321</td>\n",
       "      <td>-2.964844</td>\n",
       "      <td>-2.164062</td>\n",
       "      <td>0.09198</td>\n",
       "      <td>0.049011</td>\n",
       "      <td>-2.578125</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-1.966797</td>\n",
       "      <td>-2.994141</td>\n",
       "      <td>0.122131</td>\n",
       "      <td>0.114197</td>\n",
       "      <td>-0.551758</td>\n",
       "      <td>-1.301758</td>\n",
       "      <td>0.190308</td>\n",
       "      <td>0.178345</td>\n",
       "      <td>-2.265625</td>\n",
       "      <td>-4.320312</td>\n",
       "      <td>-1.521484</td>\n",
       "      <td>-2.988281</td>\n",
       "      <td>0.137451</td>\n",
       "      <td>0.160889</td>\n",
       "      <td>0.549316</td>\n",
       "      <td>1.609375</td>\n",
       "      <td>0.223389</td>\n",
       "      <td>0.250244</td>\n",
       "      <td>0.384277</td>\n",
       "      <td>0.907227</td>\n",
       "      <td>0.687012</td>\n",
       "      <td>1.523438</td>\n",
       "      <td>-0.587891</td>\n",
       "      <td>-1.042969</td>\n",
       "      <td>0.042816</td>\n",
       "      <td>0.121033</td>\n",
       "      <td>2.902344</td>\n",
       "      <td>-2.810547</td>\n",
       "      <td>-2.326172</td>\n",
       "      <td>-0.329834</td>\n",
       "      <td>0.522461</td>\n",
       "      <td>-1.397461</td>\n",
       "      <td>-2.796875</td>\n",
       "      <td>-2.113281</td>\n",
       "      <td>-1.395508</td>\n",
       "      <td>-1.473633</td>\n",
       "      <td>-0.411133</td>\n",
       "      <td>-0.455811</td>\n",
       "      <td>-0.451172</td>\n",
       "      <td>4.820312</td>\n",
       "      <td>0.703125</td>\n",
       "      <td>2.128906</td>\n",
       "      <td>1.205078</td>\n",
       "      <td>2.429688</td>\n",
       "      <td>2.361328</td>\n",
       "      <td>-1.451172</td>\n",
       "      <td>-1.212891</td>\n",
       "      <td>-1.156250</td>\n",
       "      <td>-1.249023</td>\n",
       "      <td>-4.691406</td>\n",
       "      <td>-1.616211</td>\n",
       "      <td>-1.076172</td>\n",
       "      <td>-2.654297</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>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>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2112179</th>\n",
       "      <td>450</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000168</td>\n",
       "      <td>0.001943</td>\n",
       "      <td>-0.001282</td>\n",
       "      <td>-0.022720</td>\n",
       "      <td>-0.013214</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.752930</td>\n",
       "      <td>-0.037537</td>\n",
       "      <td>-0.101013</td>\n",
       "      <td>-0.098572</td>\n",
       "      <td>0.056732</td>\n",
       "      <td>0.099243</td>\n",
       "      <td>0.05307</td>\n",
       "      <td>0.026321</td>\n",
       "      <td>-0.284912</td>\n",
       "      <td>0.559082</td>\n",
       "      <td>0.09198</td>\n",
       "      <td>0.049011</td>\n",
       "      <td>0.699707</td>\n",
       "      <td>1.487305</td>\n",
       "      <td>-1.174805</td>\n",
       "      <td>-1.320312</td>\n",
       "      <td>0.122131</td>\n",
       "      <td>0.114197</td>\n",
       "      <td>-0.231567</td>\n",
       "      <td>-0.388672</td>\n",
       "      <td>0.190308</td>\n",
       "      <td>0.178345</td>\n",
       "      <td>-1.911133</td>\n",
       "      <td>-3.343750</td>\n",
       "      <td>-0.233887</td>\n",
       "      <td>-0.385254</td>\n",
       "      <td>0.137451</td>\n",
       "      <td>0.160889</td>\n",
       "      <td>0.836426</td>\n",
       "      <td>1.899414</td>\n",
       "      <td>0.223389</td>\n",
       "      <td>0.250244</td>\n",
       "      <td>1.506836</td>\n",
       "      <td>2.736328</td>\n",
       "      <td>1.137695</td>\n",
       "      <td>1.944336</td>\n",
       "      <td>-0.312256</td>\n",
       "      <td>-0.446289</td>\n",
       "      <td>0.179565</td>\n",
       "      <td>0.461426</td>\n",
       "      <td>1.607422</td>\n",
       "      <td>-2.894531</td>\n",
       "      <td>-1.488281</td>\n",
       "      <td>-0.396484</td>\n",
       "      <td>0.847656</td>\n",
       "      <td>-1.141602</td>\n",
       "      <td>-2.345703</td>\n",
       "      <td>-1.802734</td>\n",
       "      <td>-1.257812</td>\n",
       "      <td>-0.927734</td>\n",
       "      <td>-0.592773</td>\n",
       "      <td>0.400146</td>\n",
       "      <td>-0.520020</td>\n",
       "      <td>4.718750</td>\n",
       "      <td>0.703125</td>\n",
       "      <td>-0.128906</td>\n",
       "      <td>-1.547852</td>\n",
       "      <td>-1.126953</td>\n",
       "      <td>-0.343506</td>\n",
       "      <td>-1.534180</td>\n",
       "      <td>-1.284180</td>\n",
       "      <td>-1.392578</td>\n",
       "      <td>-1.506836</td>\n",
       "      <td>-4.683594</td>\n",
       "      <td>-1.603516</td>\n",
       "      <td>-1.068359</td>\n",
       "      <td>-2.218750</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>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>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2112180</th>\n",
       "      <td>450</td>\n",
       "      <td>3.785156</td>\n",
       "      <td>-0.000995</td>\n",
       "      <td>-0.002895</td>\n",
       "      <td>-0.003132</td>\n",
       "      <td>0.000731</td>\n",
       "      <td>-0.000527</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.107422</td>\n",
       "      <td>-0.191040</td>\n",
       "      <td>-0.149048</td>\n",
       "      <td>-0.190308</td>\n",
       "      <td>0.258301</td>\n",
       "      <td>0.489258</td>\n",
       "      <td>0.05307</td>\n",
       "      <td>0.026321</td>\n",
       "      <td>-0.668457</td>\n",
       "      <td>0.460449</td>\n",
       "      <td>0.09198</td>\n",
       "      <td>0.049011</td>\n",
       "      <td>0.128662</td>\n",
       "      <td>1.058594</td>\n",
       "      <td>-1.640625</td>\n",
       "      <td>-2.242188</td>\n",
       "      <td>0.122131</td>\n",
       "      <td>0.114197</td>\n",
       "      <td>0.590332</td>\n",
       "      <td>1.649414</td>\n",
       "      <td>0.190308</td>\n",
       "      <td>0.178345</td>\n",
       "      <td>0.833496</td>\n",
       "      <td>2.324219</td>\n",
       "      <td>0.651855</td>\n",
       "      <td>1.736328</td>\n",
       "      <td>0.137451</td>\n",
       "      <td>0.160889</td>\n",
       "      <td>-0.521973</td>\n",
       "      <td>-1.370117</td>\n",
       "      <td>0.223389</td>\n",
       "      <td>0.250244</td>\n",
       "      <td>-0.352295</td>\n",
       "      <td>-0.811035</td>\n",
       "      <td>-0.729492</td>\n",
       "      <td>-1.375000</td>\n",
       "      <td>0.079773</td>\n",
       "      <td>0.216797</td>\n",
       "      <td>-0.145996</td>\n",
       "      <td>-0.397705</td>\n",
       "      <td>1.181641</td>\n",
       "      <td>-1.504883</td>\n",
       "      <td>-2.220703</td>\n",
       "      <td>-1.338867</td>\n",
       "      <td>-0.527832</td>\n",
       "      <td>-0.645020</td>\n",
       "      <td>-1.259766</td>\n",
       "      <td>-0.735352</td>\n",
       "      <td>-0.241455</td>\n",
       "      <td>-0.323486</td>\n",
       "      <td>1.002930</td>\n",
       "      <td>0.542969</td>\n",
       "      <td>-0.454834</td>\n",
       "      <td>2.226562</td>\n",
       "      <td>0.703125</td>\n",
       "      <td>-0.014267</td>\n",
       "      <td>-1.288086</td>\n",
       "      <td>-0.860352</td>\n",
       "      <td>-0.082336</td>\n",
       "      <td>-1.291016</td>\n",
       "      <td>-1.066406</td>\n",
       "      <td>-2.125000</td>\n",
       "      <td>-2.392578</td>\n",
       "      <td>-4.578125</td>\n",
       "      <td>-1.690430</td>\n",
       "      <td>-1.124023</td>\n",
       "      <td>-2.132812</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>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>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 226 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date    weight    resp_1    resp_2    resp_3    resp_4      resp  \\\n",
       "2112176   450  0.097412 -0.000833 -0.000835  0.000108  0.000228 -0.000529   \n",
       "2112177   450  0.000000  0.020889  0.027298  0.022491  0.005302  0.016983   \n",
       "2112178   450  0.651855 -0.001053 -0.001618 -0.003260 -0.005676 -0.003983   \n",
       "2112179   450  0.000000  0.000168  0.001943 -0.001282 -0.022720 -0.013214   \n",
       "2112180   450  3.785156 -0.000995 -0.002895 -0.003132  0.000731 -0.000527   \n",
       "\n",
       "         feature_0  feature_1  feature_2  feature_3  feature_4  feature_5  \\\n",
       "2112176         -1  -3.171875  -3.093750   0.281738   0.138794  -0.790039   \n",
       "2112177          1  -0.743652  -0.171021  -1.235352  -1.635742  -0.556152   \n",
       "2112178         -1  -1.681641  -0.964844  -0.045197  -0.031647  -0.723145   \n",
       "2112179         -1  -0.752930  -0.037537  -0.101013  -0.098572   0.056732   \n",
       "2112180          1  -1.107422  -0.191040  -0.149048  -0.190308   0.258301   \n",
       "\n",
       "         feature_6  feature_7  feature_8  feature_9  feature_10  feature_11  \\\n",
       "2112176  -0.528320    0.05307   0.026321  -5.617188   -3.550781     0.09198   \n",
       "2112177  -0.755371    0.05307   0.026321   1.993164    2.908203     0.09198   \n",
       "2112178  -1.272461    0.05307   0.026321  -2.964844   -2.164062     0.09198   \n",
       "2112179   0.099243    0.05307   0.026321  -0.284912    0.559082     0.09198   \n",
       "2112180   0.489258    0.05307   0.026321  -0.668457    0.460449     0.09198   \n",
       "\n",
       "         feature_12  feature_13  feature_14  feature_15  feature_16  \\\n",
       "2112176    0.049011   -3.115234   -2.291016   -3.880859   -4.265625   \n",
       "2112177    0.049011   -4.371094   -5.128906    1.358398    3.300781   \n",
       "2112178    0.049011   -2.578125   -3.000000   -1.966797   -2.994141   \n",
       "2112179    0.049011    0.699707    1.487305   -1.174805   -1.320312   \n",
       "2112180    0.049011    0.128662    1.058594   -1.640625   -2.242188   \n",
       "\n",
       "         feature_17  feature_18  feature_19  feature_20  feature_21  \\\n",
       "2112176    0.122131    0.114197   -3.255859   -3.400391    0.190308   \n",
       "2112177    0.122131    0.114197    2.072266    4.023438    0.190308   \n",
       "2112178    0.122131    0.114197   -0.551758   -1.301758    0.190308   \n",
       "2112179    0.122131    0.114197   -0.231567   -0.388672    0.190308   \n",
       "2112180    0.122131    0.114197    0.590332    1.649414    0.190308   \n",
       "\n",
       "         feature_22  feature_23  feature_24  feature_25  feature_26  \\\n",
       "2112176    0.178345   -3.373047   -3.753906   -4.378906   -4.339844   \n",
       "2112177    0.178345    1.652344    3.365234    2.234375    4.136719   \n",
       "2112178    0.178345   -2.265625   -4.320312   -1.521484   -2.988281   \n",
       "2112179    0.178345   -1.911133   -3.343750   -0.233887   -0.385254   \n",
       "2112180    0.178345    0.833496    2.324219    0.651855    1.736328   \n",
       "\n",
       "         feature_27  feature_28  feature_29  feature_30  feature_31  \\\n",
       "2112176    0.137451    0.160889    1.413086    1.217773    0.223389   \n",
       "2112177    0.137451    0.160889   -0.574707   -1.184570    0.223389   \n",
       "2112178    0.137451    0.160889    0.549316    1.609375    0.223389   \n",
       "2112179    0.137451    0.160889    0.836426    1.899414    0.223389   \n",
       "2112180    0.137451    0.160889   -0.521973   -1.370117    0.223389   \n",
       "\n",
       "         feature_32  feature_33  feature_34  feature_35  feature_36  \\\n",
       "2112176    0.250244    1.004883    0.676758    1.778320    1.140625   \n",
       "2112177    0.250244   -3.681641   -5.417969   -0.815918   -1.180664   \n",
       "2112178    0.250244    0.384277    0.907227    0.687012    1.523438   \n",
       "2112179    0.250244    1.506836    2.736328    1.137695    1.944336   \n",
       "2112180    0.250244   -0.352295   -0.811035   -0.729492   -1.375000   \n",
       "\n",
       "         feature_37  feature_38  feature_39  feature_40  feature_41  \\\n",
       "2112176   -0.518066   -0.439453    0.268066    0.265625   -0.512695   \n",
       "2112177    0.183472    0.319580   -1.001953   -2.353516    1.073242   \n",
       "2112178   -0.587891   -1.042969    0.042816    0.121033    2.902344   \n",
       "2112179   -0.312256   -0.446289    0.179565    0.461426    1.607422   \n",
       "2112180    0.079773    0.216797   -0.145996   -0.397705    1.181641   \n",
       "\n",
       "         feature_42  feature_43  feature_44  feature_45  feature_46  \\\n",
       "2112176    1.703125    1.589844    0.428467   -0.040222   -0.572266   \n",
       "2112177   -0.181396   -1.191406   -0.199463   -0.330811   -0.721680   \n",
       "2112178   -2.810547   -2.326172   -0.329834    0.522461   -1.397461   \n",
       "2112179   -2.894531   -1.488281   -0.396484    0.847656   -1.141602   \n",
       "2112180   -1.504883   -2.220703   -1.338867   -0.527832   -0.645020   \n",
       "\n",
       "         feature_47  feature_48  feature_49  feature_50  feature_51  \\\n",
       "2112176   -0.777344   -0.917969   -0.922852   -1.410156   -2.005859   \n",
       "2112177   -1.423828   -1.031250   -0.670898   -1.102539    0.242554   \n",
       "2112178   -2.796875   -2.113281   -1.395508   -1.473633   -0.411133   \n",
       "2112179   -2.345703   -1.802734   -1.257812   -0.927734   -0.592773   \n",
       "2112180   -1.259766   -0.735352   -0.241455   -0.323486    1.002930   \n",
       "\n",
       "         feature_52  feature_53  feature_54  feature_55  feature_56  \\\n",
       "2112176    0.770996    0.988281   -0.468994    0.703125    0.572754   \n",
       "2112177   -1.509766    1.751953    3.371094    0.703125    2.048828   \n",
       "2112178   -0.455811   -0.451172    4.820312    0.703125    2.128906   \n",
       "2112179    0.400146   -0.520020    4.718750    0.703125   -0.128906   \n",
       "2112180    0.542969   -0.454834    2.226562    0.703125   -0.014267   \n",
       "\n",
       "         feature_57  feature_58  feature_59  feature_60  feature_61  \\\n",
       "2112176   -0.854980   -0.334473   -0.177002   -1.161133   -0.973145   \n",
       "2112177    6.921875   11.851562   11.773438   -1.180664   -0.982910   \n",
       "2112178    1.205078    2.429688    2.361328   -1.451172   -1.212891   \n",
       "2112179   -1.547852   -1.126953   -0.343506   -1.534180   -1.284180   \n",
       "2112180   -1.288086   -0.860352   -0.082336   -1.291016   -1.066406   \n",
       "\n",
       "         feature_62  feature_63  feature_64  feature_65  feature_66  \\\n",
       "2112176   -2.707031   -3.103516   -5.472656   -1.927734   -1.242188   \n",
       "2112177   -2.562500   -2.041016   -4.992188   -1.850586   -1.215820   \n",
       "2112178   -1.156250   -1.249023   -4.691406   -1.616211   -1.076172   \n",
       "2112179   -1.392578   -1.506836   -4.683594   -1.603516   -1.068359   \n",
       "2112180   -2.125000   -2.392578   -4.578125   -1.690430   -1.124023   \n",
       "\n",
       "         feature_67  ...  feature_19_missing  feature_20_missing  \\\n",
       "2112176   -2.142578  ...                 0.0                 0.0   \n",
       "2112177   -2.140625  ...                 0.0                 0.0   \n",
       "2112178   -2.654297  ...                 0.0                 0.0   \n",
       "2112179   -2.218750  ...                 0.0                 0.0   \n",
       "2112180   -2.132812  ...                 0.0                 0.0   \n",
       "\n",
       "         feature_21_missing  feature_22_missing  feature_23_missing  \\\n",
       "2112176                 1.0                 1.0                 0.0   \n",
       "2112177                 1.0                 1.0                 0.0   \n",
       "2112178                 1.0                 1.0                 0.0   \n",
       "2112179                 1.0                 1.0                 0.0   \n",
       "2112180                 1.0                 1.0                 0.0   \n",
       "\n",
       "         feature_24_missing  feature_25_missing  feature_26_missing  \\\n",
       "2112176                 0.0                 0.0                 0.0   \n",
       "2112177                 0.0                 0.0                 0.0   \n",
       "2112178                 0.0                 0.0                 0.0   \n",
       "2112179                 0.0                 0.0                 0.0   \n",
       "2112180                 0.0                 0.0                 0.0   \n",
       "\n",
       "         feature_27_missing  feature_28_missing  feature_29_missing  \\\n",
       "2112176                 1.0                 1.0                 0.0   \n",
       "2112177                 1.0                 1.0                 0.0   \n",
       "2112178                 1.0                 1.0                 0.0   \n",
       "2112179                 1.0                 1.0                 0.0   \n",
       "2112180                 1.0                 1.0                 0.0   \n",
       "\n",
       "         feature_30_missing  feature_31_missing  feature_32_missing  \\\n",
       "2112176                 0.0                 1.0                 1.0   \n",
       "2112177                 0.0                 1.0                 1.0   \n",
       "2112178                 0.0                 1.0                 1.0   \n",
       "2112179                 0.0                 1.0                 1.0   \n",
       "2112180                 0.0                 1.0                 1.0   \n",
       "\n",
       "         feature_33_missing  feature_34_missing  feature_35_missing  \\\n",
       "2112176                 0.0                 0.0                 0.0   \n",
       "2112177                 0.0                 0.0                 0.0   \n",
       "2112178                 0.0                 0.0                 0.0   \n",
       "2112179                 0.0                 0.0                 0.0   \n",
       "2112180                 0.0                 0.0                 0.0   \n",
       "\n",
       "         feature_36_missing  feature_44_missing  feature_45_missing  \\\n",
       "2112176                 0.0                 0.0                 0.0   \n",
       "2112177                 0.0                 0.0                 0.0   \n",
       "2112178                 0.0                 0.0                 0.0   \n",
       "2112179                 0.0                 0.0                 0.0   \n",
       "2112180                 0.0                 0.0                 0.0   \n",
       "\n",
       "         feature_55_missing  feature_56_missing  feature_58_missing  \\\n",
       "2112176                 1.0                 0.0                 0.0   \n",
       "2112177                 1.0                 0.0                 0.0   \n",
       "2112178                 1.0                 0.0                 0.0   \n",
       "2112179                 1.0                 0.0                 0.0   \n",
       "2112180                 1.0                 0.0                 0.0   \n",
       "\n",
       "         feature_59_missing  feature_72_missing  feature_73_missing  \\\n",
       "2112176                 0.0                 1.0                 0.0   \n",
       "2112177                 0.0                 1.0                 0.0   \n",
       "2112178                 0.0                 1.0                 0.0   \n",
       "2112179                 0.0                 1.0                 0.0   \n",
       "2112180                 0.0                 1.0                 0.0   \n",
       "\n",
       "         feature_74_missing  feature_75_missing  feature_76_missing  \\\n",
       "2112176                 1.0                 0.0                 0.0   \n",
       "2112177                 1.0                 0.0                 0.0   \n",
       "2112178                 1.0                 0.0                 0.0   \n",
       "2112179                 1.0                 0.0                 0.0   \n",
       "2112180                 1.0                 0.0                 0.0   \n",
       "\n",
       "         feature_78_missing  feature_79_missing  feature_80_missing  \\\n",
       "2112176                 1.0                 0.0                 1.0   \n",
       "2112177                 1.0                 0.0                 1.0   \n",
       "2112178                 1.0                 0.0                 1.0   \n",
       "2112179                 1.0                 0.0                 1.0   \n",
       "2112180                 1.0                 0.0                 1.0   \n",
       "\n",
       "         feature_81_missing  feature_82_missing  feature_84_missing  \\\n",
       "2112176                 0.0                 0.0                 1.0   \n",
       "2112177                 0.0                 0.0                 1.0   \n",
       "2112178                 0.0                 0.0                 1.0   \n",
       "2112179                 0.0                 0.0                 1.0   \n",
       "2112180                 0.0                 0.0                 1.0   \n",
       "\n",
       "         feature_85_missing  feature_86_missing  feature_87_missing  \\\n",
       "2112176                 0.0                 1.0                 0.0   \n",
       "2112177                 0.0                 1.0                 0.0   \n",
       "2112178                 0.0                 1.0                 0.0   \n",
       "2112179                 0.0                 1.0                 0.0   \n",
       "2112180                 0.0                 1.0                 0.0   \n",
       "\n",
       "         feature_88_missing  feature_90_missing  feature_91_missing  \\\n",
       "2112176                 0.0                 1.0                 0.0   \n",
       "2112177                 0.0                 1.0                 0.0   \n",
       "2112178                 0.0                 1.0                 0.0   \n",
       "2112179                 0.0                 1.0                 0.0   \n",
       "2112180                 0.0                 1.0                 0.0   \n",
       "\n",
       "         feature_92_missing  feature_93_missing  feature_94_missing  \\\n",
       "2112176                 1.0                 0.0                 0.0   \n",
       "2112177                 1.0                 0.0                 0.0   \n",
       "2112178                 1.0                 0.0                 0.0   \n",
       "2112179                 1.0                 0.0                 0.0   \n",
       "2112180                 1.0                 0.0                 0.0   \n",
       "\n",
       "         feature_96_missing  feature_97_missing  feature_98_missing  \\\n",
       "2112176                 1.0                 0.0                 1.0   \n",
       "2112177                 1.0                 0.0                 1.0   \n",
       "2112178                 1.0                 0.0                 1.0   \n",
       "2112179                 1.0                 0.0                 1.0   \n",
       "2112180                 1.0                 0.0                 1.0   \n",
       "\n",
       "         feature_99_missing  feature_100_missing  feature_102_missing  \\\n",
       "2112176                 0.0                  0.0                  1.0   \n",
       "2112177                 0.0                  0.0                  1.0   \n",
       "2112178                 0.0                  0.0                  1.0   \n",
       "2112179                 0.0                  0.0                  1.0   \n",
       "2112180                 0.0                  0.0                  1.0   \n",
       "\n",
       "         feature_103_missing  feature_104_missing  feature_105_missing  \\\n",
       "2112176                  0.0                  1.0                  0.0   \n",
       "2112177                  0.0                  1.0                  0.0   \n",
       "2112178                  0.0                  1.0                  0.0   \n",
       "2112179                  0.0                  1.0                  0.0   \n",
       "2112180                  0.0                  1.0                  0.0   \n",
       "\n",
       "         feature_106_missing  feature_108_missing  feature_109_missing  \\\n",
       "2112176                  0.0                  1.0                  0.0   \n",
       "2112177                  0.0                  1.0                  0.0   \n",
       "2112178                  0.0                  1.0                  0.0   \n",
       "2112179                  0.0                  1.0                  0.0   \n",
       "2112180                  0.0                  1.0                  0.0   \n",
       "\n",
       "         feature_110_missing  feature_111_missing  feature_112_missing  \\\n",
       "2112176                  1.0                  0.0                  0.0   \n",
       "2112177                  1.0                  0.0                  0.0   \n",
       "2112178                  1.0                  0.0                  0.0   \n",
       "2112179                  1.0                  0.0                  0.0   \n",
       "2112180                  1.0                  0.0                  0.0   \n",
       "\n",
       "         feature_114_missing  feature_115_missing  feature_116_missing  \\\n",
       "2112176                  1.0                  0.0                  1.0   \n",
       "2112177                  1.0                  0.0                  1.0   \n",
       "2112178                  1.0                  0.0                  1.0   \n",
       "2112179                  1.0                  0.0                  1.0   \n",
       "2112180                  1.0                  0.0                  1.0   \n",
       "\n",
       "         feature_117_missing  feature_118_missing  feature_120_missing  \\\n",
       "2112176                  0.0                  0.0                  1.0   \n",
       "2112177                  0.0                  0.0                  1.0   \n",
       "2112178                  0.0                  0.0                  1.0   \n",
       "2112179                  0.0                  0.0                  1.0   \n",
       "2112180                  0.0                  0.0                  1.0   \n",
       "\n",
       "         feature_121_missing  feature_122_missing  feature_123_missing  \\\n",
       "2112176                  1.0                  0.0                  0.0   \n",
       "2112177                  1.0                  0.0                  0.0   \n",
       "2112178                  1.0                  0.0                  0.0   \n",
       "2112179                  1.0                  0.0                  0.0   \n",
       "2112180                  1.0                  0.0                  0.0   \n",
       "\n",
       "         feature_124_missing  feature_125_missing  feature_126_missing  \\\n",
       "2112176                  0.0                  0.0                  0.0   \n",
       "2112177                  0.0                  0.0                  0.0   \n",
       "2112178                  0.0                  0.0                  0.0   \n",
       "2112179                  0.0                  0.0                  0.0   \n",
       "2112180                  0.0                  0.0                  0.0   \n",
       "\n",
       "         feature_127_missing  feature_128_missing  feature_129_missing  action  \n",
       "2112176                  0.0                  0.0                  0.0       0  \n",
       "2112177                  0.0                  0.0                  0.0       1  \n",
       "2112178                  0.0                  0.0                  0.0       0  \n",
       "2112179                  0.0                  0.0                  0.0       0  \n",
       "2112180                  0.0                  0.0                  0.0       0  \n",
       "\n",
       "[5 rows x 226 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8250ee11",
   "metadata": {
    "papermill": {
     "duration": 0.013978,
     "end_time": "2022-08-01T18:58:51.989262",
     "exception": false,
     "start_time": "2022-08-01T18:58:51.975284",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c43f8bab",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T18:58:52.018331Z",
     "iopub.status.busy": "2022-08-01T18:58:52.017738Z",
     "iopub.status.idle": "2022-08-01T18:58:52.041121Z",
     "shell.execute_reply": "2022-08-01T18:58:52.040248Z"
    },
    "papermill": {
     "duration": 0.040565,
     "end_time": "2022-08-01T18:58:52.043520",
     "exception": false,
     "start_time": "2022-08-01T18:58:52.002955",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "%config InlineBackend.figure_format = 'svg'\n",
    "def plot_metric(history, metric):\n",
    "    train_metrics = history.history[metric]\n",
    "    val_metrics = history.history['val_'+metric]\n",
    "    epochs = range(1, len(train_metrics) + 1)\n",
    "    plt.plot(epochs, train_metrics, 'b')\n",
    "    plt.plot(epochs, val_metrics, 'g')\n",
    "    if metric == 'AUC':\n",
    "        plt.axhline(y=0.5,ls=\":\",c=\"gray\")\n",
    "        plt.ylim(ymin=0.48)\n",
    "    elif metric == 'MSE':\n",
    "        plt.axhline(y=0.0,ls=\":\",c=\"gray\")\n",
    "        plt.ylim(ymin=-0.05, ymax=0.25)\n",
    "    plt.title('Training and validation '+ metric)\n",
    "    plt.xlabel(\"Epochs\")\n",
    "    plt.ylabel(metric)\n",
    "    plt.legend([\"train_\"+metric, 'val_'+metric])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81d3017d",
   "metadata": {
    "papermill": {
     "duration": 0.013169,
     "end_time": "2022-08-01T18:58:52.071051",
     "exception": false,
     "start_time": "2022-08-01T18:58:52.057882",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Misc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f1a2c9be",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T18:58:52.100236Z",
     "iopub.status.busy": "2022-08-01T18:58:52.099921Z",
     "iopub.status.idle": "2022-08-01T18:58:52.104998Z",
     "shell.execute_reply": "2022-08-01T18:58:52.104004Z"
    },
    "papermill": {
     "duration": 0.021975,
     "end_time": "2022-08-01T18:58:52.107051",
     "exception": false,
     "start_time": "2022-08-01T18:58:52.085076",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def set_seed(seed : int):    \n",
    "    random.seed(seed)\n",
    "    np.random.seed(seed)\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "    tf.random.set_seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "63b16e84",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T18:58:52.135087Z",
     "iopub.status.busy": "2022-08-01T18:58:52.134820Z",
     "iopub.status.idle": "2022-08-01T18:58:52.140710Z",
     "shell.execute_reply": "2022-08-01T18:58:52.139628Z"
    },
    "papermill": {
     "duration": 0.022697,
     "end_time": "2022-08-01T18:58:52.143240",
     "exception": false,
     "start_time": "2022-08-01T18:58:52.120543",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# weighted average as per Donate et al.'s formula\n",
    "# https://doi.org/10.1016/j.neucom.2012.02.053\n",
    "def weighted_average(a):\n",
    "    w = []\n",
    "    n = len(a)\n",
    "    for i in range(1, n + 1):\n",
    "        i = 2 if i == 1 else i\n",
    "        w.append(1 / (2**(n + 1 - i)))\n",
    "    return np.average(a, weights = w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fe4e5809",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T18:58:52.172178Z",
     "iopub.status.busy": "2022-08-01T18:58:52.171829Z",
     "iopub.status.idle": "2022-08-01T18:58:52.564347Z",
     "shell.execute_reply": "2022-08-01T18:58:52.561371Z"
    },
    "papermill": {
     "duration": 0.416114,
     "end_time": "2022-08-01T18:58:52.573207",
     "exception": false,
     "start_time": "2022-08-01T18:58:52.157093",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class ClearTrainingOutput(tf.keras.callbacks.Callback):\n",
    "      def on_train_end(*args, **kwargs):\n",
    "        IPython.display.clear_output(wait = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a48facbf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T18:58:52.650372Z",
     "iopub.status.busy": "2022-08-01T18:58:52.649919Z",
     "iopub.status.idle": "2022-08-01T18:58:52.676032Z",
     "shell.execute_reply": "2022-08-01T18:58:52.672378Z"
    },
    "papermill": {
     "duration": 0.063328,
     "end_time": "2022-08-01T18:58:52.683958",
     "exception": false,
     "start_time": "2022-08-01T18:58:52.620630",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class Hyperparameters:\n",
    "  def __init__(self, topology, dropout_rates, learning_rate, activation, num_input,\n",
    "               num_batch, num_epochs, label_smoothing, use_batchnorm, use_encoder):\n",
    "    self.topology = topology\n",
    "    self.dropout_rates = dropout_rates\n",
    "    self.learning_rate = learning_rate\n",
    "    self.activation = activation\n",
    "    self.num_input = num_input\n",
    "    self.num_batch = num_batch\n",
    "    self.num_epochs = num_epochs\n",
    "    self.label_smoothing = label_smoothing\n",
    "    self.use_batchnorm = use_batchnorm\n",
    "    self.use_encoder = use_encoder"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b82fabeb",
   "metadata": {
    "papermill": {
     "duration": 0.037302,
     "end_time": "2022-08-01T18:58:52.777780",
     "exception": false,
     "start_time": "2022-08-01T18:58:52.740478",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Utility Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9d120cf8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T18:58:52.843935Z",
     "iopub.status.busy": "2022-08-01T18:58:52.843523Z",
     "iopub.status.idle": "2022-08-01T18:58:52.853099Z",
     "shell.execute_reply": "2022-08-01T18:58:52.852195Z"
    },
    "papermill": {
     "duration": 0.046531,
     "end_time": "2022-08-01T18:58:52.862664",
     "exception": false,
     "start_time": "2022-08-01T18:58:52.816133",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def utility_score(d, w, r, a):\n",
    "    Pi = np.bincount(d, w * r * a)\n",
    "    t = np.sum(Pi) / np.sqrt(np.sum(Pi ** 2)) * np.sqrt(250 / len(Pi))\n",
    "    u = min(max(t, 0), 6) * np.sum(Pi)\n",
    "    return u"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ebe93b8",
   "metadata": {
    "papermill": {
     "duration": 0.038406,
     "end_time": "2022-08-01T18:58:52.936678",
     "exception": false,
     "start_time": "2022-08-01T18:58:52.898272",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## CV - Purged Group Time Series Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "68842c4b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T18:58:53.016738Z",
     "iopub.status.busy": "2022-08-01T18:58:53.016355Z",
     "iopub.status.idle": "2022-08-01T18:58:53.925979Z",
     "shell.execute_reply": "2022-08-01T18:58:53.924986Z"
    },
    "jupyter": {
     "source_hidden": true
    },
    "papermill": {
     "duration": 0.954308,
     "end_time": "2022-08-01T18:58:53.928528",
     "exception": false,
     "start_time": "2022-08-01T18:58:52.974220",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection._split import _BaseKFold, indexable, _num_samples\n",
    "from sklearn.utils.validation import _deprecate_positional_args\n",
    "\n",
    "# modified code for group gaps; source\n",
    "# https://github.com/getgaurav2/scikit-learn/blob/d4a3af5cc9da3a76f0266932644b884c99724c57/sklearn/model_selection/_split.py#L2243\n",
    "class PurgedGroupTimeSeriesSplit(_BaseKFold):\n",
    "    \"\"\"Time Series cross-validator variant with non-overlapping groups.\n",
    "    Allows for a gap in groups to avoid potentially leaking info from\n",
    "    train into test if the model has windowed or lag features.\n",
    "    Provides train/test indices to split time series data samples\n",
    "    that are observed at fixed time intervals according to a\n",
    "    third-party provided group.\n",
    "    In each split, test indices must be higher than before, and thus shuffling\n",
    "    in cross validator is inappropriate.\n",
    "    This cross-validation object is a variation of :class:`KFold`.\n",
    "    In the kth split, it returns first k folds as train set and the\n",
    "    (k+1)th fold as test set.\n",
    "    The same group will not appear in two different folds (the number of\n",
    "    distinct groups has to be at least equal to the number of folds).\n",
    "    Note that unlike standard cross-validation methods, successive\n",
    "    training sets are supersets of those that come before them.\n",
    "    Read more in the :ref:`User Guide <cross_validation>`.\n",
    "    Parameters\n",
    "    ----------\n",
    "    n_splits : int, default=5\n",
    "        Number of splits. Must be at least 2.\n",
    "    max_train_group_size : int, default=Inf\n",
    "        Maximum group size for a single training set.\n",
    "    group_gap : int, default=None\n",
    "        Gap between train and test\n",
    "    max_test_group_size : int, default=Inf\n",
    "        We discard this number of groups from the end of each train split\n",
    "    \"\"\"\n",
    "\n",
    "    @_deprecate_positional_args\n",
    "    def __init__(self,\n",
    "                 n_splits=5,\n",
    "                 *,\n",
    "                 max_train_group_size=np.inf,\n",
    "                 max_test_group_size=np.inf,\n",
    "                 group_gap=None,\n",
    "                 verbose=False\n",
    "                 ):\n",
    "        super().__init__(n_splits, shuffle=False, random_state=None)\n",
    "        self.max_train_group_size = max_train_group_size\n",
    "        self.group_gap = group_gap\n",
    "        self.max_test_group_size = max_test_group_size\n",
    "        self.verbose = verbose\n",
    "\n",
    "    def split(self, X, y=None, groups=None):\n",
    "        \"\"\"Generate indices to split data into training and test set.\n",
    "        Parameters\n",
    "        ----------\n",
    "        X : array-like of shape (n_samples, n_features)\n",
    "            Training data, where n_samples is the number of samples\n",
    "            and n_features is the number of features.\n",
    "        y : array-like of shape (n_samples,)\n",
    "            Always ignored, exists for compatibility.\n",
    "        groups : array-like of shape (n_samples,)\n",
    "            Group labels for the samples used while splitting the dataset into\n",
    "            train/test set.\n",
    "        Yields\n",
    "        ------\n",
    "        train : ndarray\n",
    "            The training set indices for that split.\n",
    "        test : ndarray\n",
    "            The testing set indices for that split.\n",
    "        \"\"\"\n",
    "        if groups is None:\n",
    "            raise ValueError(\n",
    "                \"The 'groups' parameter should not be None\")\n",
    "        X, y, groups = indexable(X, y, groups)\n",
    "        n_samples = _num_samples(X)\n",
    "        n_splits = self.n_splits\n",
    "        group_gap = self.group_gap\n",
    "        max_test_group_size = self.max_test_group_size\n",
    "        max_train_group_size = self.max_train_group_size\n",
    "        n_folds = n_splits + 1\n",
    "        group_dict = {}\n",
    "        u, ind = np.unique(groups, return_index=True)\n",
    "        unique_groups = u[np.argsort(ind)]\n",
    "        n_samples = _num_samples(X)\n",
    "        n_groups = _num_samples(unique_groups)\n",
    "        for idx in np.arange(n_samples):\n",
    "            if (groups[idx] in group_dict):\n",
    "                group_dict[groups[idx]].append(idx)\n",
    "            else:\n",
    "                group_dict[groups[idx]] = [idx]\n",
    "        if n_folds > n_groups:\n",
    "            raise ValueError(\n",
    "                (\"Cannot have number of folds={0} greater than\"\n",
    "                 \" the number of groups={1}\").format(n_folds,\n",
    "                                                     n_groups))\n",
    "\n",
    "        group_test_size = min(n_groups // n_folds, max_test_group_size)\n",
    "        group_test_starts = range(n_groups - n_splits * group_test_size,\n",
    "                                  n_groups, group_test_size)\n",
    "        for group_test_start in group_test_starts:\n",
    "            train_array = []\n",
    "            test_array = []\n",
    "\n",
    "            group_st = max(0, group_test_start - group_gap - max_train_group_size)\n",
    "            for train_group_idx in unique_groups[group_st:(group_test_start - group_gap)]:\n",
    "                train_array_tmp = group_dict[train_group_idx]\n",
    "                \n",
    "                train_array = np.sort(np.unique(\n",
    "                                      np.concatenate((train_array,\n",
    "                                                      train_array_tmp)),\n",
    "                                      axis=None), axis=None)\n",
    "\n",
    "            train_end = train_array.size\n",
    " \n",
    "            for test_group_idx in unique_groups[group_test_start:\n",
    "                                                group_test_start +\n",
    "                                                group_test_size]:\n",
    "                test_array_tmp = group_dict[test_group_idx]\n",
    "                test_array = np.sort(np.unique(\n",
    "                                              np.concatenate((test_array,\n",
    "                                                              test_array_tmp)),\n",
    "                                     axis=None), axis=None)\n",
    "\n",
    "            test_array  = test_array[group_gap:]\n",
    "            \n",
    "            \n",
    "            if self.verbose > 0:\n",
    "                    pass\n",
    "                    \n",
    "            yield [int(i) for i in train_array], [int(i) for i in test_array]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1eb71b61",
   "metadata": {
    "papermill": {
     "duration": 0.013443,
     "end_time": "2022-08-01T18:58:53.955899",
     "exception": false,
     "start_time": "2022-08-01T18:58:53.942456",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Building and Training Models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ece48b40",
   "metadata": {
    "papermill": {
     "duration": 0.013143,
     "end_time": "2022-08-01T18:58:53.982445",
     "exception": false,
     "start_time": "2022-08-01T18:58:53.969302",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Defining model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "11bc4472",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T18:58:54.011519Z",
     "iopub.status.busy": "2022-08-01T18:58:54.011196Z",
     "iopub.status.idle": "2022-08-01T18:58:54.020968Z",
     "shell.execute_reply": "2022-08-01T18:58:54.020102Z"
    },
    "papermill": {
     "duration": 0.026865,
     "end_time": "2022-08-01T18:58:54.023378",
     "exception": false,
     "start_time": "2022-08-01T18:58:53.996513",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def create_autoencoder(num_input,num_output,noise=0.05):\n",
    "    i = layers.Input(num_input)\n",
    "    encoded = layers.BatchNormalization()(i)\n",
    "    encoded = layers.GaussianNoise(noise)(encoded)\n",
    "    encoded = layers.Dense(64,activation='relu')(encoded)\n",
    "    decoded = layers.Dropout(0.2)(encoded)\n",
    "    decoded = layers.Dense(num_input,name='decoded')(decoded)\n",
    "    x = layers.Dense(32,activation='relu')(decoded)\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    x = layers.Dropout(0.2)(x)\n",
    "    x = layers.Dense(num_output,activation='sigmoid',name='label_output')(x)\n",
    "    \n",
    "    encoder = tf.keras.models.Model(inputs=i,outputs=decoded)\n",
    "    autoencoder = tf.keras.models.Model(inputs=i,outputs=[decoded,x])\n",
    "\n",
    "    autoencoder.compile(optimizer=tf.keras.optimizers.Adam(0.001),\n",
    "                        loss={'decoded':'mse','label_output':'binary_crossentropy'})\n",
    "    return autoencoder, encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f63fc282",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T18:58:54.051693Z",
     "iopub.status.busy": "2022-08-01T18:58:54.051414Z",
     "iopub.status.idle": "2022-08-01T18:58:54.062891Z",
     "shell.execute_reply": "2022-08-01T18:58:54.062042Z"
    },
    "papermill": {
     "duration": 0.028036,
     "end_time": "2022-08-01T18:58:54.064905",
     "exception": false,
     "start_time": "2022-08-01T18:58:54.036869",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def create_model(topology, dropout_rates, num_input, num_output, lrate, activation,\n",
    "                 label_smoothing, use_batchnorm, use_encoder):\n",
    "    \n",
    "    inp = layers.Input(shape = (num_input,), name='X_train')\n",
    "    \n",
    "    x = inp\n",
    "    if use_encoder == True:\n",
    "        autoencoder, encoder = create_autoencoder(num_input,num_output,noise=0.05)\n",
    "        encoder.load_weights('../input/janestreet-autoencoder/encoder.h5')\n",
    "        x = encoder(x)\n",
    "    if use_batchnorm == True:\n",
    "        x = layers.BatchNormalization()(x)\n",
    "    x = layers.Dropout(dropout_rates[0])(x)\n",
    "        \n",
    "    for i in range(len(topology)): \n",
    "        x = layers.Dense(topology[i])(x)\n",
    "        if use_batchnorm == True:\n",
    "            x = layers.BatchNormalization()(x)\n",
    "        x = layers.Activation(activation)(x)\n",
    "        x = layers.Dropout(dropout_rates[i+1])(x)\n",
    "    \n",
    "    x = layers.Dense(num_output)(x)\n",
    "    out = layers.Activation('sigmoid')(x)\n",
    "    \n",
    "    model = tf.keras.models.Model(inputs=inp, outputs=out)\n",
    "    \n",
    "    opt = tf.keras.optimizers.Adam(learning_rate = lrate)\n",
    "    model.compile(optimizer = opt,\n",
    "                  loss = tf.keras.losses.BinaryCrossentropy(label_smoothing = label_smoothing),\n",
    "                  metrics = tf.keras.metrics.AUC(name='AUC'))\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "350a5f5b",
   "metadata": {
    "papermill": {
     "duration": 0.013954,
     "end_time": "2022-08-01T18:58:54.092611",
     "exception": false,
     "start_time": "2022-08-01T18:58:54.078657",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Defining Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "90fac956",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T18:58:54.121344Z",
     "iopub.status.busy": "2022-08-01T18:58:54.120975Z",
     "iopub.status.idle": "2022-08-01T18:58:54.133640Z",
     "shell.execute_reply": "2022-08-01T18:58:54.132783Z"
    },
    "papermill": {
     "duration": 0.029605,
     "end_time": "2022-08-01T18:58:54.135722",
     "exception": false,
     "start_time": "2022-08-01T18:58:54.106117",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def train_model(name, hp, n_folds):\n",
    "    \n",
    "    print(f'{name}: Training...')\n",
    "    cv = PurgedGroupTimeSeriesSplit(\n",
    "        n_splits=n_folds,\n",
    "        max_train_group_size=150,\n",
    "        group_gap=20,\n",
    "        max_test_group_size=60\n",
    "    )\n",
    "    \n",
    "    for fold, (train_index, test_index) in enumerate(cv.split(train_data, groups=train_data['date'].values)):\n",
    "        \n",
    "        print(f'{name} - Fold {fold+1} of {n_folds}')\n",
    "        train_set = train_data.iloc[train_index]\n",
    "        test_set = train_data.iloc[test_index]\n",
    "        \n",
    "        X_train = train_set.loc[:, features].values\n",
    "        X_test = test_set.loc[:, features].values\n",
    "        \n",
    "        y_train = np.stack([(train_set[col] > 0).astype('int') for col in resp_cols]).T #Multitarget\n",
    "        y_test = np.stack([(test_set[col] > 0).astype('int') for col in resp_cols]).T #Multitarget\n",
    "        model = create_model(hp.topology, hp.dropout_rates, hp.num_input, len(resp_cols), hp.learning_rate,\n",
    "                             hp.activation, hp.label_smoothing, hp. use_batchnorm, hp.use_encoder)\n",
    "        metric = 'AUC'\n",
    "        metric_mode = 'max'\n",
    "        \n",
    "        early_stopping = EarlyStopping(monitor=f'val_{metric}',patience=15,min_delta=0.0001,mode=metric_mode,\n",
    "                                       restore_best_weights=True)\n",
    "        #rlr = ReduceLROnPlateau(monitor=f'val_{metric}', factor=0.1, patience=5, verbose=0, min_delta = 0.0001, mode=metric_mode)\n",
    "        \n",
    "        callbacks = [early_stopping, [ClearTrainingOutput()]]\n",
    "\n",
    "        history = model.fit(X_train, y_train,\n",
    "                            batch_size = hp.num_batch,\n",
    "                            epochs = hp.num_epochs,\n",
    "                            validation_data = (X_test, y_test),\n",
    "                            callbacks=callbacks,\n",
    "                            verbose=2)\n",
    "        \n",
    "        plot_metric(history, metric)\n",
    "        model.save(f'./{name}-fold{str(fold+1)}-seed{str(SEED)}.h5')"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "### Defining Utility Score Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "72fed04f",
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   "source": [
    "# def validate_model(name, n_folds, n_seeds):\n",
    "#     print(f'{name}: Validating...')\n",
    "    \n",
    "#     #validation data\n",
    "#     X_val = val_data.loc[:, features].values\n",
    "#     y_val = val_data.loc[:, 'action'].values \n",
    "    \n",
    "#     scores = []\n",
    "    \n",
    "#     for SEED in range(n_seeds):\n",
    "#         set_seed(SEED)\n",
    "#         #loading pretrained models\n",
    "#         models = []\n",
    "#         for i in range(n_folds):\n",
    "#             models.append(tf.keras.models.load_model(f'./{name}-fold{str(i+1)}-seed{str(SEED)}.h5'))\n",
    "#         print(f'SEED {SEED} - All models loaded.')\n",
    "    \n",
    "#         curr_val_preds = []\n",
    "#         for model in models:\n",
    "#             curr_val_pred = model.predict(X_val)\n",
    "#             curr_val_pred = np.array([np.mean(preds) for preds in curr_val_pred])\n",
    "#             curr_val_preds.append(curr_val_pred)\n",
    "#         curr_val_preds_avg = np.asarray([weighted_average(idx) for idx in zip(*curr_val_preds)])\n",
    "#         curr_val_preds_avg = ((curr_val_preds_avg > 0.501)*1)\n",
    "            \n",
    "#         curr_uscore = utility_score(val_data['date'].values, val_data['weight'].values,\n",
    "#                                         val_data['resp'].values, curr_val_preds_avg)\n",
    "#         print(f'SEED {SEED} {name} {n_folds}-fold Utility Score: {curr_uscore}\\n')\n",
    "#         scores.append(curr_uscore)\n",
    "        \n",
    "#     print(f'Average {n_folds}-fold Utility Score: {np.mean(scores)}\\n')"
   ]
  },
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   "execution_count": 16,
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   "source": [
    "def validate_models_ensemble(names, n_folds, n_seeds):\n",
    "    print('Ensemble: Validating...\\n')\n",
    "    \n",
    "    #validation data\n",
    "    X_val = val_data.loc[:, features].values\n",
    "    y_val = val_data.loc[:, 'action'].values\n",
    "    \n",
    "    \n",
    "    val_preds = []\n",
    "    scores = []\n",
    "    \n",
    "    for SEED in range(n_seeds):\n",
    "        print(f'SEED {SEED}: Validating...')\n",
    "        seed_val_preds = []\n",
    "        for name in names:\n",
    "            set_seed(SEED)\n",
    "            #loading pretrained models\n",
    "            models = []\n",
    "            for i in range(n_folds):\n",
    "                models.append(tf.keras.models.load_model(f'./{name}-fold{str(i+1)}-seed{str(SEED)}.h5'))\n",
    "            print(f'SEED {SEED} {name} - All models loaded.')\n",
    "\n",
    "            curr_val_preds = []\n",
    "            for model in models:\n",
    "                curr_val_pred = model.predict(X_val)\n",
    "                curr_val_pred = np.array([np.mean(preds) for preds in curr_val_pred])\n",
    "                curr_val_preds.append(curr_val_pred)\n",
    "            curr_val_preds_avg = np.asarray([weighted_average(idx) for idx in zip(*curr_val_preds)])\n",
    "            val_preds.append(curr_val_preds_avg)\n",
    "            seed_val_preds.append(curr_val_preds_avg)\n",
    "            \n",
    "        seed_val_preds_avg = np.asarray([np.mean(idx) for idx in zip(*seed_val_preds)])\n",
    "        seed_val_preds_avg = ((seed_val_preds_avg > 0.501)*1)\n",
    "        seed_uscore = utility_score(val_data['date'].values, val_data['weight'].values,\n",
    "                                            val_data['resp'].values, seed_val_preds_avg)\n",
    "        print(f'SEED {SEED} {len(names)}-model 5-fold Utility Score: {seed_uscore}\\n')\n",
    "        scores.append(seed_uscore)\n",
    "\n",
    "    val_preds_avg = np.asarray([np.mean(idx) for idx in zip(*val_preds)])\n",
    "    val_preds_avg = ((val_preds_avg > 0.501)*1)\n",
    "    ensemble_uscore = utility_score(val_data['date'].values, val_data['weight'].values,\n",
    "                                            val_data['resp'].values, val_preds_avg)\n",
    "    \n",
    "    print(f'SEED scores Average: {np.mean(scores)}')\n",
    "    print(f'{n_seeds} SEEDS x {len(names)} Models x {n_folds} Fold Ensemble (Total {n_seeds * len(names) * n_folds} models): Utility Score: {ensemble_uscore}\\n')"
   ]
  },
  {
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   "id": "43755fb2",
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    "tags": []
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   "source": [
    "### Training and validating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7571d49a",
   "metadata": {
    "execution": {
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   "source": [
    "n_seeds = 5\n",
    "n_folds = 5\n",
    "num_batch = 4096\n",
    "num_epochs = 150\n",
    "num_input = len(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "77c76a11",
   "metadata": {
    "execution": {
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   "source": [
    "topology = [208, 48, 112, 848, 624]\n",
    "dropout_rates = [0.15543385946124283,0.4769936942603337,0.2004355862256995,\n",
    "                 0.4212563172737503,0.46071906728999595,0.43182424636157835]\n",
    "learning_rate = 0.001\n",
    "activation = 'swish'\n",
    "\n",
    "label_smoothing = 0.47213403813669336\n",
    "use_batchnorm = True\n",
    "use_encoder = True\n",
    "model1_hyperparameters = Hyperparameters(topology, dropout_rates, learning_rate,\n",
    "                                  activation, num_input, num_batch, num_epochs,\n",
    "                                  label_smoothing, use_batchnorm, use_encoder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f9179dd2",
   "metadata": {
    "execution": {
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   "source": [
    "topology = [240,352,976,176,448]\n",
    "dropout_rates = [0.13021918620948872,0.2612506117600616,0.43285170482208624,\n",
    "                 0.42634106968809327,0.449529191355194,0.30937895483115463]\n",
    "learning_rate = 0.001\n",
    "activation = 'swish'\n",
    "\n",
    "label_smoothing = 0.47457896527611876\n",
    "use_batchnorm = True\n",
    "use_encoder = True\n",
    "model2_hyperparameters = Hyperparameters(topology, dropout_rates, learning_rate,\n",
    "                                  activation, num_input, num_batch, num_epochs,\n",
    "                                  label_smoothing, use_batchnorm, use_encoder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "36bae655",
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   "source": [
    "topology = [416,504,256,568,368]\n",
    "dropout_rates = [0.1747245172397526,0.46609158038970466,0.42073578763921193,\n",
    "                 0.23349072903009116,0.413193305598212,0.4562599801134036]\n",
    "learning_rate = 0.001\n",
    "activation = 'swish'\n",
    "\n",
    "label_smoothing = 0.39645093182491814\n",
    "use_batchnorm = True\n",
    "use_encoder = True\n",
    "model3_hyperparameters = Hyperparameters(topology, dropout_rates, learning_rate,\n",
    "                                  activation, num_input, num_batch, num_epochs,\n",
    "                                  label_smoothing, use_batchnorm, use_encoder)"
   ]
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   "source": [
    "#training model\n",
    "for SEED in range(n_seeds):\n",
    "    set_seed(SEED)\n",
    "    train_model('Model1', model1_hyperparameters, n_folds)"
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  {
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   "source": [
    "#training model\n",
    "for SEED in range(n_seeds):\n",
    "    set_seed(SEED)\n",
    "    train_model('Model2', model2_hyperparameters, n_folds)"
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  {
   "cell_type": "code",
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   "id": "eeb55317",
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     "start_time": "2022-08-01T20:23:22.504395",
     "status": "completed"
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   "source": [
    "#training model\n",
    "for SEED in range(n_seeds):\n",
    "    set_seed(SEED)\n",
    "    train_model('Model3', model3_hyperparameters, n_folds)"
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     "text": [
      "Ensemble: Validating...\n",
      "\n",
      "SEED 0: Validating...\n",
      "SEED 0 Model1 - All models loaded.\n",
      "SEED 0 1-model 5-fold Utility Score: 798.9094884894906\n",
      "\n",
      "SEED 1: Validating...\n",
      "SEED 1 Model1 - All models loaded.\n",
      "SEED 1 1-model 5-fold Utility Score: 944.1402155633461\n",
      "\n",
      "SEED 2: Validating...\n",
      "SEED 2 Model1 - All models loaded.\n",
      "SEED 2 1-model 5-fold Utility Score: 805.3486913049086\n",
      "\n",
      "SEED 3: Validating...\n",
      "SEED 3 Model1 - All models loaded.\n",
      "SEED 3 1-model 5-fold Utility Score: 851.9457126049226\n",
      "\n",
      "SEED 4: Validating...\n",
      "SEED 4 Model1 - All models loaded.\n",
      "SEED 4 1-model 5-fold Utility Score: 893.893687693111\n",
      "\n",
      "SEED scores Average: 858.8475591311559\n",
      "5 SEEDS x 1 Models x 5 Fold Ensemble (Total 25 models): Utility Score: 856.8154014457946\n",
      "\n"
     ]
    }
   ],
   "source": [
    "validate_models_ensemble(['Model1'], n_folds, n_seeds)"
   ]
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  {
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   "execution_count": 25,
   "id": "dc94a2e4",
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    {
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     "text": [
      "Ensemble: Validating...\n",
      "\n",
      "SEED 0: Validating...\n",
      "SEED 0 Model2 - All models loaded.\n",
      "SEED 0 1-model 5-fold Utility Score: 981.6727929881409\n",
      "\n",
      "SEED 1: Validating...\n",
      "SEED 1 Model2 - All models loaded.\n",
      "SEED 1 1-model 5-fold Utility Score: 1006.6897795053286\n",
      "\n",
      "SEED 2: Validating...\n",
      "SEED 2 Model2 - All models loaded.\n",
      "SEED 2 1-model 5-fold Utility Score: 720.212460781769\n",
      "\n",
      "SEED 3: Validating...\n",
      "SEED 3 Model2 - All models loaded.\n",
      "SEED 3 1-model 5-fold Utility Score: 667.5287626037463\n",
      "\n",
      "SEED 4: Validating...\n",
      "SEED 4 Model2 - All models loaded.\n",
      "SEED 4 1-model 5-fold Utility Score: 895.1789522406388\n",
      "\n",
      "SEED scores Average: 854.2565496239247\n",
      "5 SEEDS x 1 Models x 5 Fold Ensemble (Total 25 models): Utility Score: 879.3841889123593\n",
      "\n"
     ]
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   ],
   "source": [
    "validate_models_ensemble(['Model2'], n_folds, n_seeds)"
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   "execution_count": 26,
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     "text": [
      "Ensemble: Validating...\n",
      "\n",
      "SEED 0: Validating...\n",
      "SEED 0 Model3 - All models loaded.\n",
      "SEED 0 1-model 5-fold Utility Score: 878.570980199967\n",
      "\n",
      "SEED 1: Validating...\n",
      "SEED 1 Model3 - All models loaded.\n",
      "SEED 1 1-model 5-fold Utility Score: 807.7255955552691\n",
      "\n",
      "SEED 2: Validating...\n",
      "SEED 2 Model3 - All models loaded.\n",
      "SEED 2 1-model 5-fold Utility Score: 812.6969941154254\n",
      "\n",
      "SEED 3: Validating...\n",
      "SEED 3 Model3 - All models loaded.\n",
      "SEED 3 1-model 5-fold Utility Score: 809.023330655536\n",
      "\n",
      "SEED 4: Validating...\n",
      "SEED 4 Model3 - All models loaded.\n",
      "SEED 4 1-model 5-fold Utility Score: 670.0604256771497\n",
      "\n",
      "SEED scores Average: 795.6154652406694\n",
      "5 SEEDS x 1 Models x 5 Fold Ensemble (Total 25 models): Utility Score: 790.9755087215033\n",
      "\n"
     ]
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   ],
   "source": [
    "validate_models_ensemble(['Model3'], n_folds, n_seeds)"
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     "text": [
      "Ensemble: Validating...\n",
      "\n",
      "SEED 0: Validating...\n",
      "SEED 0 Model1 - All models loaded.\n",
      "SEED 0 Model2 - All models loaded.\n",
      "SEED 0 2-model 5-fold Utility Score: 935.4887317118834\n",
      "\n",
      "SEED 1: Validating...\n",
      "SEED 1 Model1 - All models loaded.\n",
      "SEED 1 Model2 - All models loaded.\n",
      "SEED 1 2-model 5-fold Utility Score: 967.3908561802273\n",
      "\n",
      "SEED 2: Validating...\n",
      "SEED 2 Model1 - All models loaded.\n",
      "SEED 2 Model2 - All models loaded.\n",
      "SEED 2 2-model 5-fold Utility Score: 901.6169270361515\n",
      "\n",
      "SEED 3: Validating...\n",
      "SEED 3 Model1 - All models loaded.\n",
      "SEED 3 Model2 - All models loaded.\n",
      "SEED 3 2-model 5-fold Utility Score: 760.4148365345109\n",
      "\n",
      "SEED 4: Validating...\n",
      "SEED 4 Model1 - All models loaded.\n",
      "SEED 4 Model2 - All models loaded.\n",
      "SEED 4 2-model 5-fold Utility Score: 942.4764015315071\n",
      "\n",
      "SEED scores Average: 901.477550598856\n",
      "5 SEEDS x 2 Models x 5 Fold Ensemble (Total 50 models): Utility Score: 896.8312680422695\n",
      "\n"
     ]
    }
   ],
   "source": [
    "validate_models_ensemble(['Model1', 'Model2'], n_folds, n_seeds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9e97701f",
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     "text": [
      "Ensemble: Validating...\n",
      "\n",
      "SEED 0: Validating...\n",
      "SEED 0 Model1 - All models loaded.\n",
      "SEED 0 Model3 - All models loaded.\n",
      "SEED 0 2-model 5-fold Utility Score: 828.2896986816537\n",
      "\n",
      "SEED 1: Validating...\n",
      "SEED 1 Model1 - All models loaded.\n",
      "SEED 1 Model3 - All models loaded.\n",
      "SEED 1 2-model 5-fold Utility Score: 851.8375585395013\n",
      "\n",
      "SEED 2: Validating...\n",
      "SEED 2 Model1 - All models loaded.\n",
      "SEED 2 Model3 - All models loaded.\n",
      "SEED 2 2-model 5-fold Utility Score: 825.447277980851\n",
      "\n",
      "SEED 3: Validating...\n",
      "SEED 3 Model1 - All models loaded.\n",
      "SEED 3 Model3 - All models loaded.\n",
      "SEED 3 2-model 5-fold Utility Score: 871.8551998622431\n",
      "\n",
      "SEED 4: Validating...\n",
      "SEED 4 Model1 - All models loaded.\n",
      "SEED 4 Model3 - All models loaded.\n",
      "SEED 4 2-model 5-fold Utility Score: 802.1461418277952\n",
      "\n",
      "SEED scores Average: 835.9151753784088\n",
      "5 SEEDS x 2 Models x 5 Fold Ensemble (Total 50 models): Utility Score: 827.9430078827919\n",
      "\n"
     ]
    }
   ],
   "source": [
    "validate_models_ensemble(['Model1', 'Model3'], n_folds, n_seeds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7e0de001",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T22:13:22.942908Z",
     "iopub.status.busy": "2022-08-01T22:13:22.942594Z",
     "iopub.status.idle": "2022-08-01T22:34:01.831559Z",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ensemble: Validating...\n",
      "\n",
      "SEED 0: Validating...\n",
      "SEED 0 Model2 - All models loaded.\n",
      "SEED 0 Model3 - All models loaded.\n",
      "SEED 0 2-model 5-fold Utility Score: 979.1557437689792\n",
      "\n",
      "SEED 1: Validating...\n",
      "SEED 1 Model2 - All models loaded.\n",
      "SEED 1 Model3 - All models loaded.\n",
      "SEED 1 2-model 5-fold Utility Score: 911.8296727987017\n",
      "\n",
      "SEED 2: Validating...\n",
      "SEED 2 Model2 - All models loaded.\n",
      "SEED 2 Model3 - All models loaded.\n",
      "SEED 2 2-model 5-fold Utility Score: 706.125590811856\n",
      "\n",
      "SEED 3: Validating...\n",
      "SEED 3 Model2 - All models loaded.\n",
      "SEED 3 Model3 - All models loaded.\n",
      "SEED 3 2-model 5-fold Utility Score: 709.3604839368143\n",
      "\n",
      "SEED 4: Validating...\n",
      "SEED 4 Model2 - All models loaded.\n",
      "SEED 4 Model3 - All models loaded.\n",
      "SEED 4 2-model 5-fold Utility Score: 813.5846208897012\n",
      "\n",
      "SEED scores Average: 824.0112224412105\n",
      "5 SEEDS x 2 Models x 5 Fold Ensemble (Total 50 models): Utility Score: 855.4715807692072\n",
      "\n"
     ]
    }
   ],
   "source": [
    "validate_models_ensemble(['Model2', 'Model3'], n_folds, n_seeds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "895c1274",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T22:34:01.884601Z",
     "iopub.status.busy": "2022-08-01T22:34:01.884286Z",
     "iopub.status.idle": "2022-08-01T23:03:05.005813Z",
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     "start_time": "2022-08-01T22:34:01.859008",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ensemble: Validating...\n",
      "\n",
      "SEED 0: Validating...\n",
      "SEED 0 Model1 - All models loaded.\n",
      "SEED 0 Model2 - All models loaded.\n",
      "SEED 0 Model3 - All models loaded.\n",
      "SEED 0 3-model 5-fold Utility Score: 883.0071122126501\n",
      "\n",
      "SEED 1: Validating...\n",
      "SEED 1 Model1 - All models loaded.\n",
      "SEED 1 Model2 - All models loaded.\n",
      "SEED 1 Model3 - All models loaded.\n",
      "SEED 1 3-model 5-fold Utility Score: 902.7807779863917\n",
      "\n",
      "SEED 2: Validating...\n",
      "SEED 2 Model1 - All models loaded.\n",
      "SEED 2 Model2 - All models loaded.\n",
      "SEED 2 Model3 - All models loaded.\n",
      "SEED 2 3-model 5-fold Utility Score: 787.3821816117941\n",
      "\n",
      "SEED 3: Validating...\n",
      "SEED 3 Model1 - All models loaded.\n",
      "SEED 3 Model2 - All models loaded.\n",
      "SEED 3 Model3 - All models loaded.\n",
      "SEED 3 3-model 5-fold Utility Score: 748.5955868045047\n",
      "\n",
      "SEED 4: Validating...\n",
      "SEED 4 Model1 - All models loaded.\n",
      "SEED 4 Model2 - All models loaded.\n",
      "SEED 4 Model3 - All models loaded.\n",
      "SEED 4 3-model 5-fold Utility Score: 876.4227938840686\n",
      "\n",
      "SEED scores Average: 839.6376904998818\n",
      "5 SEEDS x 3 Models x 5 Fold Ensemble (Total 75 models): Utility Score: 858.661831183327\n",
      "\n"
     ]
    }
   ],
   "source": [
    "validate_models_ensemble(['Model1','Model2', 'Model3'], n_folds, n_seeds)"
   ]
  }
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