code.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#This function gets the raw data and clean it \n",
    "def data_clean(data):\n",
    "\n",
    "    print(\"Data shape before cleaning:\"  + str(np.shape(data)))\n",
    "    \n",
    "             \n",
    "    #Change the data type of any column if necessary.\n",
    "    print(\"Now it will print only those columns with non-numeric values\")\n",
    "    print(data.select_dtypes(exclude=[np.number]))\n",
    "    \n",
    "\n",
    "    #Now dropping those columns with zero values entirely or which sums to zero\n",
    "    data= data.loc[:, (data != 0).any(axis=0)]\n",
    "\n",
    "    #Now dropping those columns with NAN values entirely \n",
    "    data=data.dropna(axis=1, how='all')\n",
    "    data=data.dropna(axis=0, how='all')\n",
    "\n",
    "    #Keep track of the columns which are exculded after NAN and column zero sum operation above\n",
    "    print(\"Data shape after cleaning:\"  + str(np.shape(data)))\n",
    "\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#This function impute the missing values with features (column mean)\n",
    "def data_impute(data):\n",
    "      \n",
    "    #Seprating out the NAMES of the molecules column and ACTIVITY column because they are not the features to be normalized.\n",
    "    data_input=data.drop(['ACTIVITY', 'NAME'], axis=1)\n",
    "    data_labels= data.ACTIVITY\n",
    "    data_names = data.NAME\n",
    "    \n",
    "\n",
    "    #Imputing the missing values with features mean values\n",
    "    fill_NaN = Imputer(missing_values=np.nan, strategy='mean', axis=1)\n",
    "    Imputed_Data_input = pd.DataFrame(fill_NaN.fit_transform(data_input))\n",
    "    print(np.shape(Imputed_Data_input))\n",
    "    print(\"Data shape after imputation:\"  + str(np.shape(Imputed_Data_input)))\n",
    "    return Imputed_Data_input, data_labels, data_names         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#This function is to normalize features  \n",
    "def data_norm(Imputed_Data_input,data_labels,data_names):   \n",
    "    #Calculatig the mean and STD of the imputed input data set\n",
    "    Imputed_Data_input_mean=Imputed_Data_input.mean()\n",
    "    Imputed_Data_input_std=Imputed_Data_input.std()\n",
    "\n",
    "    #z-score normalizing the whole input data:\n",
    "    Imputed_Data_input_norm = (Imputed_Data_input - Imputed_Data_input_mean)/Imputed_Data_input_std\n",
    "\n",
    "    #Adding names and labels to the data again\n",
    "    frames = [data_names,data_labels, Imputed_Data_input_norm]\n",
    "    full_data_norm = pd.concat(frames,axis=1)\n",
    "    \n",
    "    return full_data_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "#This function gives train-test-split \n",
    "from sklearn.cross_validation import train_test_split as sk_train_test_split\n",
    "def data_split(full_data_norm, test_size):\n",
    "    full_data_norm_input=full_data_norm.drop(['ACTIVITY', 'NAME'], axis=1)\n",
    "    target_attribute = full_data_norm['ACTIVITY']\n",
    "    # We call train set as train_cv as a part of it will be used for cross-validadtion\n",
    "    train_cv_x, test_x, train_cv_y, test_y = sk_train_test_split(full_data_norm_input, target_attribute, test_size=test_size, random_state=55)\n",
    "    return train_cv_x, test_x, train_cv_y, test_y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Optimizing drop_out and threshold  with 3 cross CV validation\n",
    "def hybrid_model_opt():\n",
    "    class fs(TransformerMixin, BaseEstimator):\n",
    "\n",
    "        def __init__(self, n_estimators=1000, threshold='1.7*mean'):\n",
    "            self.ss=None\n",
    "            self.n_estimators = n_estimators\n",
    "            self.x_new = None     \n",
    "            self. threshold= threshold      \n",
    "\n",
    "        def fit(self, X, y):\n",
    "            m = ExtraTreesClassifier(n_estimators=self.n_estimators,  random_state=0)\n",
    "            m.fit(X,y)\n",
    "            self.ss = SelectFromModel(m, threshold=self. threshold , prefit=True)\n",
    "            return self\n",
    "\n",
    "        def transform(self, X):\n",
    "            self.x_new=self.ss.transform(X)\n",
    "            global xx\n",
    "            xx=self.x_new.shape[1]\n",
    "            return self.x_new\n",
    "\n",
    "\n",
    "    def nn_model_opt(dropout_rate=0.5,init_mode='uniform', activation='relu'):\n",
    "\n",
    "        #n_x_new=xx # this is the number of features selected for current iteration\n",
    "        np.random.seed(200000)\n",
    "        model_opt = Sequential()\n",
    "        model_opt.add(Dense(xx,input_dim=xx ,kernel_initializer='he_normal', activation='relu'))\n",
    "        model_opt.add(Dense(10, kernel_initializer='he_normal', activation='relu'))\n",
    "        model_opt.add(Dropout(dropout_rate))\n",
    "        model_opt.add(Dense(1,kernel_initializer='he_normal', activation='sigmoid'))\n",
    "\n",
    "        model_opt.compile(loss='binary_crossentropy',optimizer='adam', metrics=['binary_crossentropy'])\n",
    "\n",
    "        return model_opt\n",
    "\n",
    "\n",
    "    clf=KerasClassifier(build_fn=nn_model_opt, epochs=250, batch_size=3000, verbose=-1)\n",
    "\n",
    "    hybrid_model = Pipeline([('fs', fs()),('clf', clf)])\n",
    "    \n",
    "    return hybrid_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "#Getting fetaures importances of all the features using extra_tree classifier only\n",
    "\n",
    "def feature_imp(train_cv_x,train_cv_y):\n",
    "\n",
    "\n",
    "   \n",
    "    m = ExtraTreesClassifier(n_estimators=1000 )\n",
    "    m.fit(train_cv_x,train_cv_y)\n",
    "    importances = m.feature_importances_\n",
    "\n",
    "    return importances, m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def selected_feature_names(m, thr, train_cv_x):\n",
    "\n",
    "    sel = SelectFromModel(m,threshold=thr ,prefit=True)\n",
    "\n",
    "\n",
    "    feature_idx = sel.get_support()\n",
    "    feature_name = train_cv_x.columns[feature_idx]\n",
    "    feature_name =pd.DataFrame(feature_name )\n",
    "    return feature_name\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def train_test_feature_based_selection(feature_name,train_cv_x,train_cv_y,test_x,test_y ):\n",
    "    \n",
    "    feature_name=feature_name.T\n",
    "    feature_name.columns = feature_name.iloc[0]\n",
    "    feature_name.reindex(feature_name.index.drop(0))\n",
    "    train_selected_x=train_cv_x[train_cv_x.columns.intersection(feature_name.columns)]\n",
    "    test_selected_x=test_x[test_x.columns.intersection(feature_name.columns)]\n",
    "\n",
    "    train_selected_x=train_selected_x.as_matrix()\n",
    "    test_selected_x=test_selected_x.as_matrix()\n",
    "    train_selected_y=train_cv_y.as_matrix()\n",
    "    test_selected_y=test_y.as_matrix()\n",
    "    \n",
    "    return train_selected_x, train_selected_y, test_selected_x, test_selected_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def model_nn_final(train_selected_x, train_selected_y, test_selected_x, test_selected_y, x, drop_out):    \n",
    "    model_final = Sequential()\n",
    "    #n_x_new=train_selected_x.shape[1]\n",
    "    n_x_new=train_selected_x.shape[1]\n",
    "    model_final.add(Dense(n_x_new, input_dim=n_x_new, kernel_initializer ='he_normal', activation='sigmoid'))\n",
    "\n",
    "\n",
    "    model_final.add(Dense(10, kernel_initializer='he_normal', activation='sigmoid'))\n",
    "    model_final.add(Dropout(drop_out))\n",
    "\n",
    "\n",
    "    model_final.add(Dense(1, kernel_initializer='he_normal', activation='sigmoid'))\n",
    "\n",
    "\n",
    "\n",
    "    model_final.compile(loss='binary_crossentropy', optimizer='adam', metrics=['binary_crossentropy'])\n",
    "\n",
    "    seed = 7000\n",
    "    np.random.seed(seed)\n",
    "\n",
    "\n",
    "    model_final.fit(train_selected_x, train_selected_y, epochs=250, batch_size=1064)\n",
    "\n",
    "    pred_test = model_final.predict(test_selected_x)\n",
    "    auc_test = roc_auc_score(test_selected_y, pred_test)\n",
    "    print (\"AUROC_test: \" + str(auc_test))\n",
    "\n",
    "    print(\"  \") \n",
    "    model_json = model_final.to_json()\n",
    "    with open(str(x)+\"_model.json\", \"w\") as json_file:\n",
    "        json_file.write(model_json)\n",
    "    # serialize weights to HDF5\n",
    "    model_final.save_weights(str(x)+\"_model.h5\")\n",
    "    print(\"Saved model to disk\")\n",
    "    print(\"  \")\n",
    "    \n",
    "    return pred_test\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  1) Loading all packages needed "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras import backend as K\n",
    "from keras import optimizers\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Dense, Dropout\n",
    "from keras.models import Sequential\n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "from pandas import ExcelFile\n",
    "from pandas import ExcelWriter\n",
    "from PIL import Image\n",
    "from scipy import ndimage\n",
    "from scipy.stats import randint as sp_randint\n",
    "from sklearn.base import BaseEstimator\n",
    "from sklearn.base import TransformerMixin\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn import datasets\n",
    "from sklearn import metrics\n",
    "from sklearn import pipeline\n",
    "from sklearn.metrics import roc_auc_score, roc_curve\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import PredefinedSplit\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import FunctionTransformer\n",
    "from sklearn.preprocessing import Imputer\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.utils import resample\n",
    "from tensorflow.python.framework import ops\n",
    "import h5py\n",
    "import keras\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import openpyxl\n",
    "import pandas as pd\n",
    "import scipy\n",
    "import tensorflow as tf\n",
    "import xlsxwriter\n",
    "%load_ext autoreload\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2) Loading the data\n",
    "\n",
    "The \"NAME\" Column is for naming the molecule. The \"ACTIVITY\" column is the Activity of molecule. Rest of the columns shows the features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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       "      <td>29.303536</td>\n",
       "      <td>2.093110</td>\n",
       "      <td>5.715301</td>\n",
       "      <td>3.168065</td>\n",
       "      <td>2.547236</td>\n",
       "      <td>274</td>\n",
       "      <td>21</td>\n",
       "      <td>3.033</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AUTOGEN_convert_out (2)_8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2840</td>\n",
       "      <td>1.648656</td>\n",
       "      <td>11.0042</td>\n",
       "      <td>37.495102</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>6.870318</td>\n",
       "      <td>33.215152</td>\n",
       "      <td>2.075947</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>396</td>\n",
       "      <td>26</td>\n",
       "      <td>8.460</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AUTOGEN_convert_out (2)_9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4283</td>\n",
       "      <td>0.183441</td>\n",
       "      <td>32.4598</td>\n",
       "      <td>45.421895</td>\n",
       "      <td>19</td>\n",
       "      <td>22</td>\n",
       "      <td>37</td>\n",
       "      <td>...</td>\n",
       "      <td>7.814399</td>\n",
       "      <td>45.778673</td>\n",
       "      <td>2.080849</td>\n",
       "      <td>15.241572</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.241572</td>\n",
       "      <td>1007</td>\n",
       "      <td>39</td>\n",
       "      <td>3.736</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>AUTOGEN_convert_out (2)_10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5254</td>\n",
       "      <td>0.276045</td>\n",
       "      <td>33.4047</td>\n",
       "      <td>17.413551</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>...</td>\n",
       "      <td>8.532279</td>\n",
       "      <td>18.497693</td>\n",
       "      <td>1.849769</td>\n",
       "      <td>15.925007</td>\n",
       "      <td>7.281943</td>\n",
       "      <td>8.643064</td>\n",
       "      <td>125</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.266</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>AUTOGEN_convert_out (2)_11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.0318</td>\n",
       "      <td>4.128211</td>\n",
       "      <td>17.1682</td>\n",
       "      <td>27.347930</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>24</td>\n",
       "      <td>...</td>\n",
       "      <td>7.920696</td>\n",
       "      <td>28.408782</td>\n",
       "      <td>2.029199</td>\n",
       "      <td>16.737611</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.737611</td>\n",
       "      <td>279</td>\n",
       "      <td>22</td>\n",
       "      <td>0.880</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>AUTOGEN_convert_out (2)_12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.0625</td>\n",
       "      <td>0.003906</td>\n",
       "      <td>39.2611</td>\n",
       "      <td>22.791930</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>...</td>\n",
       "      <td>7.336868</td>\n",
       "      <td>20.482651</td>\n",
       "      <td>1.862059</td>\n",
       "      <td>10.714988</td>\n",
       "      <td>4.893817</td>\n",
       "      <td>5.821171</td>\n",
       "      <td>192</td>\n",
       "      <td>10</td>\n",
       "      <td>-0.069</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>AUTOGEN_convert_out (2)_13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.4487</td>\n",
       "      <td>0.201332</td>\n",
       "      <td>7.2768</td>\n",
       "      <td>38.723516</td>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>7.736294</td>\n",
       "      <td>38.236157</td>\n",
       "      <td>2.124231</td>\n",
       "      <td>2.492862</td>\n",
       "      <td>2.492862</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>517</td>\n",
       "      <td>33</td>\n",
       "      <td>6.683</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>AUTOGEN_convert_out (2)_14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6420</td>\n",
       "      <td>0.412164</td>\n",
       "      <td>5.5021</td>\n",
       "      <td>30.881516</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>6.723756</td>\n",
       "      <td>26.450254</td>\n",
       "      <td>2.034635</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>240</td>\n",
       "      <td>17</td>\n",
       "      <td>7.615</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>AUTOGEN_convert_out (2)_15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.9830</td>\n",
       "      <td>8.898289</td>\n",
       "      <td>90.5646</td>\n",
       "      <td>78.739032</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>64</td>\n",
       "      <td>...</td>\n",
       "      <td>8.377299</td>\n",
       "      <td>84.362567</td>\n",
       "      <td>2.109064</td>\n",
       "      <td>21.005009</td>\n",
       "      <td>21.005009</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3649</td>\n",
       "      <td>122</td>\n",
       "      <td>0.280</td>\n",
       "      <td>272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>AUTOGEN_convert_out (2)_16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.4485</td>\n",
       "      <td>2.098152</td>\n",
       "      <td>14.3429</td>\n",
       "      <td>17.207551</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>7.816884</td>\n",
       "      <td>17.381716</td>\n",
       "      <td>1.931302</td>\n",
       "      <td>13.770789</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.770789</td>\n",
       "      <td>84</td>\n",
       "      <td>9</td>\n",
       "      <td>-0.535</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>AUTOGEN_convert_out (2)_17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.5180</td>\n",
       "      <td>0.268324</td>\n",
       "      <td>23.5047</td>\n",
       "      <td>21.814344</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>...</td>\n",
       "      <td>9.107728</td>\n",
       "      <td>21.406680</td>\n",
       "      <td>1.946062</td>\n",
       "      <td>16.896357</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.381650</td>\n",
       "      <td>158</td>\n",
       "      <td>12</td>\n",
       "      <td>0.262</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>AUTOGEN_convert_out (2)_18</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.5780</td>\n",
       "      <td>2.490084</td>\n",
       "      <td>27.6497</td>\n",
       "      <td>50.302653</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>42</td>\n",
       "      <td>...</td>\n",
       "      <td>6.551605</td>\n",
       "      <td>44.715747</td>\n",
       "      <td>2.129321</td>\n",
       "      <td>3.539349</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.539349</td>\n",
       "      <td>831</td>\n",
       "      <td>37</td>\n",
       "      <td>6.739</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>AUTOGEN_convert_out (2)_19</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.0967</td>\n",
       "      <td>4.396151</td>\n",
       "      <td>49.8003</td>\n",
       "      <td>42.153411</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>...</td>\n",
       "      <td>4.555515</td>\n",
       "      <td>24.678355</td>\n",
       "      <td>1.898335</td>\n",
       "      <td>3.359810</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>300</td>\n",
       "      <td>12</td>\n",
       "      <td>5.439</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>AUTOGEN_convert_out (2)_20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5236</td>\n",
       "      <td>2.321357</td>\n",
       "      <td>60.7228</td>\n",
       "      <td>30.600344</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>12.649089</td>\n",
       "      <td>27.994112</td>\n",
       "      <td>1.866274</td>\n",
       "      <td>20.262852</td>\n",
       "      <td>7.200016</td>\n",
       "      <td>2.909216</td>\n",
       "      <td>410</td>\n",
       "      <td>17</td>\n",
       "      <td>1.929</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>AUTOGEN_convert_out (2)_21</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0062</td>\n",
       "      <td>1.012438</td>\n",
       "      <td>62.4194</td>\n",
       "      <td>45.070204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>...</td>\n",
       "      <td>4.842305</td>\n",
       "      <td>30.067208</td>\n",
       "      <td>2.004481</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>332</td>\n",
       "      <td>24</td>\n",
       "      <td>7.829</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>AUTOGEN_convert_out (2)_22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3907</td>\n",
       "      <td>0.152646</td>\n",
       "      <td>27.0084</td>\n",
       "      <td>17.674723</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>5.616405</td>\n",
       "      <td>12.833789</td>\n",
       "      <td>1.833398</td>\n",
       "      <td>8.487437</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.487437</td>\n",
       "      <td>56</td>\n",
       "      <td>4</td>\n",
       "      <td>0.879</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>AUTOGEN_convert_out (2)_23</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1998</td>\n",
       "      <td>0.039920</td>\n",
       "      <td>6.4546</td>\n",
       "      <td>17.399965</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>9.268463</td>\n",
       "      <td>19.385511</td>\n",
       "      <td>1.938551</td>\n",
       "      <td>10.345181</td>\n",
       "      <td>7.328021</td>\n",
       "      <td>3.017161</td>\n",
       "      <td>117</td>\n",
       "      <td>11</td>\n",
       "      <td>1.720</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>AUTOGEN_convert_out (2)_24</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6601</td>\n",
       "      <td>0.435732</td>\n",
       "      <td>3.8900</td>\n",
       "      <td>36.865137</td>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>8.823690</td>\n",
       "      <td>40.079207</td>\n",
       "      <td>2.109432</td>\n",
       "      <td>7.911887</td>\n",
       "      <td>4.858770</td>\n",
       "      <td>3.053117</td>\n",
       "      <td>619</td>\n",
       "      <td>35</td>\n",
       "      <td>7.168</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>AUTOGEN_convert_out (2)_25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.6028</td>\n",
       "      <td>0.363368</td>\n",
       "      <td>8.8959</td>\n",
       "      <td>14.404758</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>7.541291</td>\n",
       "      <td>13.585236</td>\n",
       "      <td>1.940748</td>\n",
       "      <td>5.383752</td>\n",
       "      <td>5.383752</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>43</td>\n",
       "      <td>4</td>\n",
       "      <td>0.383</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>AUTOGEN_convert_out (2)_26</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.4185</td>\n",
       "      <td>0.175142</td>\n",
       "      <td>17.5062</td>\n",
       "      <td>39.630688</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>33</td>\n",
       "      <td>...</td>\n",
       "      <td>6.791519</td>\n",
       "      <td>34.787636</td>\n",
       "      <td>2.046332</td>\n",
       "      <td>2.788604</td>\n",
       "      <td>2.788604</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>593</td>\n",
       "      <td>21</td>\n",
       "      <td>7.334</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>AUTOGEN_convert_out (2)_27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.4603</td>\n",
       "      <td>0.211876</td>\n",
       "      <td>2.5646</td>\n",
       "      <td>40.483516</td>\n",
       "      <td>18</td>\n",
       "      <td>21</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>7.873833</td>\n",
       "      <td>40.209031</td>\n",
       "      <td>2.116265</td>\n",
       "      <td>2.593236</td>\n",
       "      <td>2.593236</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>620</td>\n",
       "      <td>35</td>\n",
       "      <td>7.461</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>AUTOGEN_convert_out (2)_28</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.4603</td>\n",
       "      <td>0.211876</td>\n",
       "      <td>2.5646</td>\n",
       "      <td>39.163516</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>8.003063</td>\n",
       "      <td>39.289336</td>\n",
       "      <td>2.067860</td>\n",
       "      <td>8.792897</td>\n",
       "      <td>2.573506</td>\n",
       "      <td>6.219391</td>\n",
       "      <td>722</td>\n",
       "      <td>28</td>\n",
       "      <td>6.591</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>AUTOGEN_convert_out (2)_29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.2800</td>\n",
       "      <td>5.198400</td>\n",
       "      <td>55.1646</td>\n",
       "      <td>42.416618</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>...</td>\n",
       "      <td>5.127063</td>\n",
       "      <td>30.307569</td>\n",
       "      <td>2.020505</td>\n",
       "      <td>5.040847</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.040847</td>\n",
       "      <td>404</td>\n",
       "      <td>18</td>\n",
       "      <td>2.631</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>AUTOGEN_convert_out (2)_30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8314</td>\n",
       "      <td>0.691226</td>\n",
       "      <td>73.0263</td>\n",
       "      <td>44.703860</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>...</td>\n",
       "      <td>6.903404</td>\n",
       "      <td>39.923520</td>\n",
       "      <td>1.996176</td>\n",
       "      <td>10.165631</td>\n",
       "      <td>10.165631</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>678</td>\n",
       "      <td>39</td>\n",
       "      <td>2.681</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6470</th>\n",
       "      <td>AUTOGEN_convert_out_12_484</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.2799</td>\n",
       "      <td>0.078344</td>\n",
       "      <td>21.1640</td>\n",
       "      <td>26.474723</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>7.004143</td>\n",
       "      <td>24.476851</td>\n",
       "      <td>2.039738</td>\n",
       "      <td>9.360206</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.360206</td>\n",
       "      <td>209</td>\n",
       "      <td>11</td>\n",
       "      <td>2.365</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6471</th>\n",
       "      <td>AUTOGEN_convert_out_12_485</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.7581</td>\n",
       "      <td>0.574716</td>\n",
       "      <td>57.0820</td>\n",
       "      <td>32.687895</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>7.769447</td>\n",
       "      <td>28.979898</td>\n",
       "      <td>1.931993</td>\n",
       "      <td>19.776617</td>\n",
       "      <td>7.159101</td>\n",
       "      <td>9.397996</td>\n",
       "      <td>388</td>\n",
       "      <td>17</td>\n",
       "      <td>1.520</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6472</th>\n",
       "      <td>AUTOGEN_convert_out_12_486</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.9436</td>\n",
       "      <td>8.664781</td>\n",
       "      <td>50.6896</td>\n",
       "      <td>30.491516</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>...</td>\n",
       "      <td>8.416578</td>\n",
       "      <td>33.817925</td>\n",
       "      <td>1.989290</td>\n",
       "      <td>25.100440</td>\n",
       "      <td>13.159296</td>\n",
       "      <td>11.941145</td>\n",
       "      <td>496</td>\n",
       "      <td>27</td>\n",
       "      <td>-2.892</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6473</th>\n",
       "      <td>AUTOGEN_convert_out_12_487</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6627</td>\n",
       "      <td>2.764571</td>\n",
       "      <td>27.2320</td>\n",
       "      <td>11.386793</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>32.807929</td>\n",
       "      <td>8.696802</td>\n",
       "      <td>1.739360</td>\n",
       "      <td>6.955220</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.329115</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>1.358</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6474</th>\n",
       "      <td>AUTOGEN_convert_out_12_488</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0082</td>\n",
       "      <td>4.032867</td>\n",
       "      <td>20.1971</td>\n",
       "      <td>33.344758</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "      <td>24</td>\n",
       "      <td>...</td>\n",
       "      <td>11.624455</td>\n",
       "      <td>36.863080</td>\n",
       "      <td>2.047949</td>\n",
       "      <td>20.222271</td>\n",
       "      <td>4.880681</td>\n",
       "      <td>9.731567</td>\n",
       "      <td>571</td>\n",
       "      <td>27</td>\n",
       "      <td>3.397</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6475</th>\n",
       "      <td>AUTOGEN_convert_out_12_489</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.7473</td>\n",
       "      <td>0.558457</td>\n",
       "      <td>58.8408</td>\n",
       "      <td>43.881481</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>39</td>\n",
       "      <td>...</td>\n",
       "      <td>7.772645</td>\n",
       "      <td>44.871559</td>\n",
       "      <td>2.039616</td>\n",
       "      <td>23.671295</td>\n",
       "      <td>7.431073</td>\n",
       "      <td>16.240221</td>\n",
       "      <td>1016</td>\n",
       "      <td>38</td>\n",
       "      <td>0.577</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6476</th>\n",
       "      <td>AUTOGEN_convert_out_12_490</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.2426</td>\n",
       "      <td>1.544055</td>\n",
       "      <td>29.7279</td>\n",
       "      <td>21.563516</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>...</td>\n",
       "      <td>6.099760</td>\n",
       "      <td>17.756797</td>\n",
       "      <td>1.972977</td>\n",
       "      <td>8.535827</td>\n",
       "      <td>2.459821</td>\n",
       "      <td>6.076006</td>\n",
       "      <td>88</td>\n",
       "      <td>11</td>\n",
       "      <td>1.176</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6477</th>\n",
       "      <td>AUTOGEN_convert_out_12_491</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.0898</td>\n",
       "      <td>0.008064</td>\n",
       "      <td>8.1121</td>\n",
       "      <td>38.631930</td>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>8.735808</td>\n",
       "      <td>41.993558</td>\n",
       "      <td>2.099678</td>\n",
       "      <td>10.530168</td>\n",
       "      <td>4.876820</td>\n",
       "      <td>5.653347</td>\n",
       "      <td>685</td>\n",
       "      <td>40</td>\n",
       "      <td>4.661</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6478</th>\n",
       "      <td>AUTOGEN_convert_out_12_492</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.8424</td>\n",
       "      <td>3.394438</td>\n",
       "      <td>12.0502</td>\n",
       "      <td>18.838172</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>11.999691</td>\n",
       "      <td>21.156620</td>\n",
       "      <td>1.923329</td>\n",
       "      <td>9.630425</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>152</td>\n",
       "      <td>13</td>\n",
       "      <td>4.006</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6479</th>\n",
       "      <td>AUTOGEN_convert_out_12_493</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1142</td>\n",
       "      <td>0.013042</td>\n",
       "      <td>17.0511</td>\n",
       "      <td>9.551172</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>8.002348</td>\n",
       "      <td>8.696802</td>\n",
       "      <td>1.739360</td>\n",
       "      <td>4.642168</td>\n",
       "      <td>4.642168</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.345</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6480</th>\n",
       "      <td>AUTOGEN_convert_out_12_494</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.1487</td>\n",
       "      <td>4.616912</td>\n",
       "      <td>15.1212</td>\n",
       "      <td>19.624379</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>13.639577</td>\n",
       "      <td>21.233990</td>\n",
       "      <td>1.930363</td>\n",
       "      <td>12.857014</td>\n",
       "      <td>4.815666</td>\n",
       "      <td>3.015789</td>\n",
       "      <td>150</td>\n",
       "      <td>14</td>\n",
       "      <td>2.875</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6481</th>\n",
       "      <td>AUTOGEN_convert_out_12_495</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-2.6791</td>\n",
       "      <td>7.177577</td>\n",
       "      <td>81.7963</td>\n",
       "      <td>79.137411</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>65</td>\n",
       "      <td>...</td>\n",
       "      <td>8.540400</td>\n",
       "      <td>76.470814</td>\n",
       "      <td>2.012390</td>\n",
       "      <td>29.562275</td>\n",
       "      <td>16.936597</td>\n",
       "      <td>6.074331</td>\n",
       "      <td>4220</td>\n",
       "      <td>69</td>\n",
       "      <td>3.290</td>\n",
       "      <td>208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6482</th>\n",
       "      <td>AUTOGEN_convert_out_12_496</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4977</td>\n",
       "      <td>0.247705</td>\n",
       "      <td>27.3222</td>\n",
       "      <td>58.153895</td>\n",
       "      <td>20</td>\n",
       "      <td>24</td>\n",
       "      <td>46</td>\n",
       "      <td>...</td>\n",
       "      <td>8.980216</td>\n",
       "      <td>64.467177</td>\n",
       "      <td>2.079586</td>\n",
       "      <td>18.856617</td>\n",
       "      <td>15.777402</td>\n",
       "      <td>3.079214</td>\n",
       "      <td>2158</td>\n",
       "      <td>63</td>\n",
       "      <td>7.543</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6483</th>\n",
       "      <td>AUTOGEN_convert_out_12_497</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.7663</td>\n",
       "      <td>7.652416</td>\n",
       "      <td>39.8801</td>\n",
       "      <td>51.683790</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>48</td>\n",
       "      <td>...</td>\n",
       "      <td>5.129891</td>\n",
       "      <td>35.708490</td>\n",
       "      <td>1.983805</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>889</td>\n",
       "      <td>17</td>\n",
       "      <td>10.644</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6484</th>\n",
       "      <td>AUTOGEN_convert_out_12_498</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.7294</td>\n",
       "      <td>0.532024</td>\n",
       "      <td>46.8188</td>\n",
       "      <td>42.148653</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>39</td>\n",
       "      <td>...</td>\n",
       "      <td>6.439799</td>\n",
       "      <td>35.142049</td>\n",
       "      <td>1.952336</td>\n",
       "      <td>11.051046</td>\n",
       "      <td>7.651341</td>\n",
       "      <td>3.399704</td>\n",
       "      <td>569</td>\n",
       "      <td>28</td>\n",
       "      <td>2.667</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6485</th>\n",
       "      <td>AUTOGEN_convert_out_12_499</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.9929</td>\n",
       "      <td>8.957450</td>\n",
       "      <td>90.8233</td>\n",
       "      <td>52.807032</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>...</td>\n",
       "      <td>6.438644</td>\n",
       "      <td>44.392584</td>\n",
       "      <td>2.017845</td>\n",
       "      <td>5.117567</td>\n",
       "      <td>5.117567</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>868</td>\n",
       "      <td>45</td>\n",
       "      <td>2.896</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6486</th>\n",
       "      <td>AUTOGEN_convert_out_12_500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4044</td>\n",
       "      <td>1.972339</td>\n",
       "      <td>9.5056</td>\n",
       "      <td>18.111172</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>11.213790</td>\n",
       "      <td>19.385511</td>\n",
       "      <td>1.938551</td>\n",
       "      <td>10.345181</td>\n",
       "      <td>4.817250</td>\n",
       "      <td>3.017161</td>\n",
       "      <td>117</td>\n",
       "      <td>11</td>\n",
       "      <td>2.779</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6487</th>\n",
       "      <td>AUTOGEN_convert_out_13_1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.5686</td>\n",
       "      <td>2.460506</td>\n",
       "      <td>34.9789</td>\n",
       "      <td>17.768758</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>8.876663</td>\n",
       "      <td>19.399030</td>\n",
       "      <td>1.939903</td>\n",
       "      <td>10.447522</td>\n",
       "      <td>10.447522</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>117</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.130</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6488</th>\n",
       "      <td>AUTOGEN_convert_out_13_2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2840</td>\n",
       "      <td>0.080656</td>\n",
       "      <td>8.1802</td>\n",
       "      <td>16.875965</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>9.846376</td>\n",
       "      <td>15.532829</td>\n",
       "      <td>1.941604</td>\n",
       "      <td>4.990326</td>\n",
       "      <td>2.495163</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>62</td>\n",
       "      <td>7</td>\n",
       "      <td>1.929</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6489</th>\n",
       "      <td>AUTOGEN_convert_out_13_3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.6254</td>\n",
       "      <td>0.391125</td>\n",
       "      <td>13.6858</td>\n",
       "      <td>36.005516</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>29</td>\n",
       "      <td>...</td>\n",
       "      <td>7.727025</td>\n",
       "      <td>34.666360</td>\n",
       "      <td>2.039198</td>\n",
       "      <td>5.550001</td>\n",
       "      <td>5.550001</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>551</td>\n",
       "      <td>23</td>\n",
       "      <td>5.251</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6490</th>\n",
       "      <td>AUTOGEN_convert_out_13_4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.1364</td>\n",
       "      <td>0.018605</td>\n",
       "      <td>11.4802</td>\n",
       "      <td>18.642758</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>9.534253</td>\n",
       "      <td>17.549594</td>\n",
       "      <td>1.949955</td>\n",
       "      <td>7.725704</td>\n",
       "      <td>2.454688</td>\n",
       "      <td>2.764347</td>\n",
       "      <td>90</td>\n",
       "      <td>9</td>\n",
       "      <td>2.198</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6491</th>\n",
       "      <td>AUTOGEN_convert_out_13_5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.1531</td>\n",
       "      <td>1.329640</td>\n",
       "      <td>35.0624</td>\n",
       "      <td>40.977860</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>...</td>\n",
       "      <td>6.274529</td>\n",
       "      <td>34.262388</td>\n",
       "      <td>2.015435</td>\n",
       "      <td>8.624868</td>\n",
       "      <td>2.545394</td>\n",
       "      <td>6.079474</td>\n",
       "      <td>572</td>\n",
       "      <td>21</td>\n",
       "      <td>4.018</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6492</th>\n",
       "      <td>AUTOGEN_convert_out_13_6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5902</td>\n",
       "      <td>0.348336</td>\n",
       "      <td>7.2347</td>\n",
       "      <td>28.769551</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>21</td>\n",
       "      <td>...</td>\n",
       "      <td>9.619931</td>\n",
       "      <td>29.301674</td>\n",
       "      <td>2.092977</td>\n",
       "      <td>5.731119</td>\n",
       "      <td>3.164557</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>267</td>\n",
       "      <td>22</td>\n",
       "      <td>4.159</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6493</th>\n",
       "      <td>AUTOGEN_convert_out_13_7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.8930</td>\n",
       "      <td>8.369449</td>\n",
       "      <td>20.7368</td>\n",
       "      <td>21.137586</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>16.065365</td>\n",
       "      <td>23.078030</td>\n",
       "      <td>1.923169</td>\n",
       "      <td>15.342106</td>\n",
       "      <td>4.818675</td>\n",
       "      <td>3.018394</td>\n",
       "      <td>188</td>\n",
       "      <td>17</td>\n",
       "      <td>2.971</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6494</th>\n",
       "      <td>AUTOGEN_convert_out_13_8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.6290</td>\n",
       "      <td>0.395641</td>\n",
       "      <td>20.0315</td>\n",
       "      <td>37.772309</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>7.712730</td>\n",
       "      <td>37.032265</td>\n",
       "      <td>2.057348</td>\n",
       "      <td>7.926876</td>\n",
       "      <td>4.953060</td>\n",
       "      <td>2.973816</td>\n",
       "      <td>605</td>\n",
       "      <td>27</td>\n",
       "      <td>3.202</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6495</th>\n",
       "      <td>AUTOGEN_convert_out_13_9</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>-3.0322</td>\n",
       "      <td>9.194237</td>\n",
       "      <td>113.7838</td>\n",
       "      <td>70.418618</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>...</td>\n",
       "      <td>10.122845</td>\n",
       "      <td>76.327939</td>\n",
       "      <td>1.957127</td>\n",
       "      <td>68.394538</td>\n",
       "      <td>55.406185</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5076</td>\n",
       "      <td>58</td>\n",
       "      <td>-8.556</td>\n",
       "      <td>202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6496</th>\n",
       "      <td>AUTOGEN_convert_out_13_10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.9086</td>\n",
       "      <td>0.825554</td>\n",
       "      <td>26.6292</td>\n",
       "      <td>31.435930</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>8.669039</td>\n",
       "      <td>34.032249</td>\n",
       "      <td>2.001897</td>\n",
       "      <td>15.967272</td>\n",
       "      <td>9.737328</td>\n",
       "      <td>6.229944</td>\n",
       "      <td>509</td>\n",
       "      <td>25</td>\n",
       "      <td>1.403</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6497</th>\n",
       "      <td>AUTOGEN_convert_out_13_11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5626</td>\n",
       "      <td>0.316519</td>\n",
       "      <td>30.2516</td>\n",
       "      <td>26.745137</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>8.307369</td>\n",
       "      <td>28.303875</td>\n",
       "      <td>2.021705</td>\n",
       "      <td>14.441400</td>\n",
       "      <td>5.169484</td>\n",
       "      <td>9.271916</td>\n",
       "      <td>314</td>\n",
       "      <td>17</td>\n",
       "      <td>0.522</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6498</th>\n",
       "      <td>AUTOGEN_convert_out_13_12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7559</td>\n",
       "      <td>0.571385</td>\n",
       "      <td>10.3278</td>\n",
       "      <td>39.434723</td>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "      <td>29</td>\n",
       "      <td>...</td>\n",
       "      <td>8.622584</td>\n",
       "      <td>38.243588</td>\n",
       "      <td>2.124644</td>\n",
       "      <td>2.497552</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>507</td>\n",
       "      <td>34</td>\n",
       "      <td>8.157</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6499</th>\n",
       "      <td>AUTOGEN_convert_out_13_13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.5640</td>\n",
       "      <td>0.318096</td>\n",
       "      <td>35.7021</td>\n",
       "      <td>22.897102</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>5.656983</td>\n",
       "      <td>16.666118</td>\n",
       "      <td>1.851791</td>\n",
       "      <td>8.376218</td>\n",
       "      <td>2.432669</td>\n",
       "      <td>5.943550</td>\n",
       "      <td>102</td>\n",
       "      <td>8</td>\n",
       "      <td>1.287</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6500 rows × 1446 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            NAME  ACTIVITY  nAcid   ALogP    ALogp2       AMR  \\\n",
       "0      AUTOGEN_convert_out (2)_1         0      0  0.2537  0.064364  110.2356   \n",
       "1      AUTOGEN_convert_out (2)_2         0      0  0.2108  0.044437   17.2257   \n",
       "2      AUTOGEN_convert_out (2)_3         1      1  0.7472  0.558308   28.9038   \n",
       "3      AUTOGEN_convert_out (2)_4         1      0  0.3352  0.112359   30.8427   \n",
       "4      AUTOGEN_convert_out (2)_5         1      0 -0.0834  0.006956   25.7047   \n",
       "5      AUTOGEN_convert_out (2)_6         0      0 -1.1305  1.278030   26.2871   \n",
       "6      AUTOGEN_convert_out (2)_7         1      0 -0.9040  0.817216    5.8412   \n",
       "7      AUTOGEN_convert_out (2)_8         1      0  1.2840  1.648656   11.0042   \n",
       "8      AUTOGEN_convert_out (2)_9         1      0  0.4283  0.183441   32.4598   \n",
       "9     AUTOGEN_convert_out (2)_10         1      0  0.5254  0.276045   33.4047   \n",
       "10    AUTOGEN_convert_out (2)_11         1      0 -2.0318  4.128211   17.1682   \n",
       "11    AUTOGEN_convert_out (2)_12         1      0 -0.0625  0.003906   39.2611   \n",
       "12    AUTOGEN_convert_out (2)_13         0      0 -0.4487  0.201332    7.2768   \n",
       "13    AUTOGEN_convert_out (2)_14         0      0  0.6420  0.412164    5.5021   \n",
       "14    AUTOGEN_convert_out (2)_15         0      0 -2.9830  8.898289   90.5646   \n",
       "15    AUTOGEN_convert_out (2)_16         0      0 -1.4485  2.098152   14.3429   \n",
       "16    AUTOGEN_convert_out (2)_17         0      0 -0.5180  0.268324   23.5047   \n",
       "17    AUTOGEN_convert_out (2)_18         1      0 -1.5780  2.490084   27.6497   \n",
       "18    AUTOGEN_convert_out (2)_19         1      0 -2.0967  4.396151   49.8003   \n",
       "19    AUTOGEN_convert_out (2)_20         1      1  1.5236  2.321357   60.7228   \n",
       "20    AUTOGEN_convert_out (2)_21         1      0  1.0062  1.012438   62.4194   \n",
       "21    AUTOGEN_convert_out (2)_22         1      0  0.3907  0.152646   27.0084   \n",
       "22    AUTOGEN_convert_out (2)_23         1      0  0.1998  0.039920    6.4546   \n",
       "23    AUTOGEN_convert_out (2)_24         1      0  0.6601  0.435732    3.8900   \n",
       "24    AUTOGEN_convert_out (2)_25         0      0 -0.6028  0.363368    8.8959   \n",
       "25    AUTOGEN_convert_out (2)_26         1      0 -0.4185  0.175142   17.5062   \n",
       "26    AUTOGEN_convert_out (2)_27         1      0 -0.4603  0.211876    2.5646   \n",
       "27    AUTOGEN_convert_out (2)_28         1      0 -0.4603  0.211876    2.5646   \n",
       "28    AUTOGEN_convert_out (2)_29         0      0 -2.2800  5.198400   55.1646   \n",
       "29    AUTOGEN_convert_out (2)_30         1      0  0.8314  0.691226   73.0263   \n",
       "...                          ...       ...    ...     ...       ...       ...   \n",
       "6470  AUTOGEN_convert_out_12_484         1      0 -0.2799  0.078344   21.1640   \n",
       "6471  AUTOGEN_convert_out_12_485         0      0 -0.7581  0.574716   57.0820   \n",
       "6472  AUTOGEN_convert_out_12_486         1      0 -2.9436  8.664781   50.6896   \n",
       "6473  AUTOGEN_convert_out_12_487         1      0  1.6627  2.764571   27.2320   \n",
       "6474  AUTOGEN_convert_out_12_488         1      0  2.0082  4.032867   20.1971   \n",
       "6475  AUTOGEN_convert_out_12_489         1      1 -0.7473  0.558457   58.8408   \n",
       "6476  AUTOGEN_convert_out_12_490         1      0 -1.2426  1.544055   29.7279   \n",
       "6477  AUTOGEN_convert_out_12_491         1      0 -0.0898  0.008064    8.1121   \n",
       "6478  AUTOGEN_convert_out_12_492         0      0  1.8424  3.394438   12.0502   \n",
       "6479  AUTOGEN_convert_out_12_493         1      0  0.1142  0.013042   17.0511   \n",
       "6480  AUTOGEN_convert_out_12_494         1      0  2.1487  4.616912   15.1212   \n",
       "6481  AUTOGEN_convert_out_12_495         0      2 -2.6791  7.177577   81.7963   \n",
       "6482  AUTOGEN_convert_out_12_496         1      0  0.4977  0.247705   27.3222   \n",
       "6483  AUTOGEN_convert_out_12_497         0      0 -2.7663  7.652416   39.8801   \n",
       "6484  AUTOGEN_convert_out_12_498         0      0 -0.7294  0.532024   46.8188   \n",
       "6485  AUTOGEN_convert_out_12_499         0      0  2.9929  8.957450   90.8233   \n",
       "6486  AUTOGEN_convert_out_12_500         0      0  1.4044  1.972339    9.5056   \n",
       "6487    AUTOGEN_convert_out_13_1         1      0 -1.5686  2.460506   34.9789   \n",
       "6488    AUTOGEN_convert_out_13_2         0      0  0.2840  0.080656    8.1802   \n",
       "6489    AUTOGEN_convert_out_13_3         1      0 -0.6254  0.391125   13.6858   \n",
       "6490    AUTOGEN_convert_out_13_4         0      0 -0.1364  0.018605   11.4802   \n",
       "6491    AUTOGEN_convert_out_13_5         0      0 -1.1531  1.329640   35.0624   \n",
       "6492    AUTOGEN_convert_out_13_6         0      0  0.5902  0.348336    7.2347   \n",
       "6493    AUTOGEN_convert_out_13_7         1      0  2.8930  8.369449   20.7368   \n",
       "6494    AUTOGEN_convert_out_13_8         1      0 -0.6290  0.395641   20.0315   \n",
       "6495    AUTOGEN_convert_out_13_9         0      4 -3.0322  9.194237  113.7838   \n",
       "6496   AUTOGEN_convert_out_13_10         1      1 -0.9086  0.825554   26.6292   \n",
       "6497   AUTOGEN_convert_out_13_11         1      0  0.5626  0.316519   30.2516   \n",
       "6498   AUTOGEN_convert_out_13_12         1      0  0.7559  0.571385   10.3278   \n",
       "6499   AUTOGEN_convert_out_13_13         1      0 -0.5640  0.318096   35.7021   \n",
       "\n",
       "           apol  naAromAtom  nAromBond  nAtom   ...          AMW     WTPT-1  \\\n",
       "0     75.264134           0          0     68   ...     6.033560  61.425548   \n",
       "1     12.651551           0          0     13   ...     6.849821  10.679177   \n",
       "2     27.008758          10         11     22   ...     9.910580  32.083784   \n",
       "3     45.002309          14         16     34   ...    10.000291  43.894493   \n",
       "4     24.616344           6          6     21   ...     8.384277  26.306407   \n",
       "5     26.247516           5          5     24   ...     7.003277  23.247109   \n",
       "6     29.023137          13         15     23   ...     7.959496  29.303536   \n",
       "7     37.495102          14         16     30   ...     6.870318  33.215152   \n",
       "8     45.421895          19         22     37   ...     7.814399  45.778673   \n",
       "9     17.413551           0          0     17   ...     8.532279  18.497693   \n",
       "10    27.347930          10         11     24   ...     7.920696  28.408782   \n",
       "11    22.791930           0          0     21   ...     7.336868  20.482651   \n",
       "12    38.723516          16         19     30   ...     7.736294  38.236157   \n",
       "13    30.881516          12         13     25   ...     6.723756  26.450254   \n",
       "14    78.739032          12         12     64   ...     8.377299  84.362567   \n",
       "15    17.207551           6          6     16   ...     7.816884  17.381716   \n",
       "16    21.814344           6          6     19   ...     9.107728  21.406680   \n",
       "17    50.302653          12         13     42   ...     6.551605  44.715747   \n",
       "18    42.153411           0          0     40   ...     4.555515  24.678355   \n",
       "19    30.600344           0          0     23   ...    12.649089  27.994112   \n",
       "20    45.070204           0          0     43   ...     4.842305  30.067208   \n",
       "21    17.674723           0          0     18   ...     5.616405  12.833789   \n",
       "22    17.399965           6          6     15   ...     9.268463  19.385511   \n",
       "23    36.865137          16         19     28   ...     8.823690  40.079207   \n",
       "24    14.404758           5          5     13   ...     7.541291  13.585236   \n",
       "25    39.630688          12         13     33   ...     6.791519  34.787636   \n",
       "26    40.483516          18         21     31   ...     7.873833  40.209031   \n",
       "27    39.163516          16         17     31   ...     8.003063  39.289336   \n",
       "28    42.416618           0          0     41   ...     5.127063  30.307569   \n",
       "29    44.703860           0          0     40   ...     6.903404  39.923520   \n",
       "...         ...         ...        ...    ...   ...          ...        ...   \n",
       "6470  26.474723           6          6     23   ...     7.004143  24.476851   \n",
       "6471  32.687895           0          0     30   ...     7.769447  28.979898   \n",
       "6472  30.491516           0          0     29   ...     8.416578  33.817925   \n",
       "6473  11.386793           0          0      6   ...    32.807929   8.696802   \n",
       "6474  33.344758          14         16     24   ...    11.624455  36.863080   \n",
       "6475  43.881481           6          6     39   ...     7.772645  44.871559   \n",
       "6476  21.563516           0          0     21   ...     6.099760  17.756797   \n",
       "6477  38.631930          16         19     30   ...     8.735808  41.993558   \n",
       "6478  18.838172           6          6     15   ...    11.999691  21.156620   \n",
       "6479   9.551172           0          0      9   ...     8.002348   8.696802   \n",
       "6480  19.624379           6          6     14   ...    13.639577  21.233990   \n",
       "6481  79.137411          16         17     65   ...     8.540400  76.470814   \n",
       "6482  58.153895          20         24     46   ...     8.980216  64.467177   \n",
       "6483  51.683790           6          6     48   ...     5.129891  35.708490   \n",
       "6484  42.148653           6          6     39   ...     6.439799  35.142049   \n",
       "6485  52.807032           0          0     46   ...     6.438644  44.392584   \n",
       "6486  18.111172           6          6     14   ...    11.213790  19.385511   \n",
       "6487  17.768758           0          0     16   ...     8.876663  19.399030   \n",
       "6488  16.875965           6          6     13   ...     9.846376  15.532829   \n",
       "6489  36.005516          12         12     29   ...     7.727025  34.666360   \n",
       "6490  18.642758           6          6     15   ...     9.534253  17.549594   \n",
       "6491  40.977860           6          6     37   ...     6.274529  34.262388   \n",
       "6492  28.769551          13         15     21   ...     9.619931  29.301674   \n",
       "6493  21.137586           6          6     14   ...    16.065365  23.078030   \n",
       "6494  37.772309          12         13     31   ...     7.712730  37.032265   \n",
       "6495  70.418618           0          0     65   ...    10.122845  76.327939   \n",
       "6496  31.435930           9         10     27   ...     8.669039  34.032249   \n",
       "6497  26.745137           6          6     23   ...     8.307369  28.303875   \n",
       "6498  39.434723          16         19     29   ...     8.622584  38.243588   \n",
       "6499  22.897102           0          0     23   ...     5.656983  16.666118   \n",
       "\n",
       "        WTPT-2     WTPT-3     WTPT-4     WTPT-5  WPATH  WPOL   XLogP  Zagreb  \n",
       "0     2.047518   8.138095   8.138095   0.000000   2493    57   6.513     164  \n",
       "1     1.779863   7.606551   4.695141   2.911411     32     3   0.963      20  \n",
       "2     2.005237  15.860677   9.717629   6.143049    403    25   1.756      82  \n",
       "3     2.090214   7.707102   5.093812   0.000000    828    41   5.223     120  \n",
       "4     2.023570  14.009842   2.401987  11.607855    249    15  -0.527      66  \n",
       "5     1.937259   7.867033   7.867033   0.000000    219    11   0.459      54  \n",
       "6     2.093110   5.715301   3.168065   2.547236    274    21   3.033      78  \n",
       "7     2.075947   0.000000   0.000000   0.000000    396    26   8.460      88  \n",
       "8     2.080849  15.241572   0.000000  15.241572   1007    39   3.736     124  \n",
       "9     1.849769  15.925007   7.281943   8.643064    125    12  -0.266      38  \n",
       "10    2.029199  16.737611   0.000000  16.737611    279    22   0.880      70  \n",
       "11    1.862059  10.714988   4.893817   5.821171    192    10  -0.069      42  \n",
       "12    2.124231   2.492862   2.492862   0.000000    517    33   6.683     104  \n",
       "13    2.034635   0.000000   0.000000   0.000000    240    17   7.615      64  \n",
       "14    2.109064  21.005009  21.005009   0.000000   3649   122   0.280     272  \n",
       "15    1.931302  13.770789   0.000000  13.770789     84     9  -0.535      42  \n",
       "16    1.946062  16.896357   0.000000  14.381650    158    12   0.262      50  \n",
       "17    2.129321   3.539349   0.000000   3.539349    831    37   6.739     124  \n",
       "18    1.898335   3.359810   0.000000   0.000000    300    12   5.439      48  \n",
       "19    1.866274  20.262852   7.200016   2.909216    410    17   1.929      64  \n",
       "20    2.004481   0.000000   0.000000   0.000000    332    24   7.829      78  \n",
       "21    1.833398   8.487437   0.000000   8.487437     56     4   0.879      22  \n",
       "22    1.938551  10.345181   7.328021   3.017161    117    11   1.720      46  \n",
       "23    2.109432   7.911887   4.858770   3.053117    619    35   7.168     110  \n",
       "24    1.940748   5.383752   5.383752   0.000000     43     4   0.383      30  \n",
       "25    2.046332   2.788604   2.788604   0.000000    593    21   7.334      88  \n",
       "26    2.116265   2.593236   2.593236   0.000000    620    35   7.461     108  \n",
       "27    2.067860   8.792897   2.573506   6.219391    722    28   6.591      98  \n",
       "28    2.020505   5.040847   0.000000   5.040847    404    18   2.631      74  \n",
       "29    1.996176  10.165631  10.165631   0.000000    678    39   2.681     118  \n",
       "...        ...        ...        ...        ...    ...   ...     ...     ...  \n",
       "6470  2.039738   9.360206   0.000000   9.360206    209    11   2.365      58  \n",
       "6471  1.931993  19.776617   7.159101   9.397996    388    17   1.520      72  \n",
       "6472  1.989290  25.100440  13.159296  11.941145    496    27  -2.892      88  \n",
       "6473  1.739360   6.955220   0.000000   2.329115     18     2   1.358      16  \n",
       "6474  2.047949  20.222271   4.880681   9.731567    571    27   3.397      98  \n",
       "6475  2.039616  23.671295   7.431073  16.240221   1016    38   0.577     116  \n",
       "6476  1.972977   8.535827   2.459821   6.076006     88    11   1.176      38  \n",
       "6477  2.099678  10.530168   4.876820   5.653347    685    40   4.661     116  \n",
       "6478  1.923329   9.630425   0.000000   0.000000    152    13   4.006      54  \n",
       "6479  1.739360   4.642168   4.642168   0.000000     18     2  -0.345      16  \n",
       "6480  1.930363  12.857014   4.815666   3.015789    150    14   2.875      52  \n",
       "6481  2.012390  29.562275  16.936597   6.074331   4220    69   3.290     208  \n",
       "6482  2.079586  18.856617  15.777402   3.079214   2158    63   7.543     176  \n",
       "6483  1.983805   0.000000   0.000000   0.000000    889    17  10.644      74  \n",
       "6484  1.952336  11.051046   7.651341   3.399704    569    28   2.667      82  \n",
       "6485  2.017845   5.117567   5.117567   0.000000    868    45   2.896     122  \n",
       "6486  1.938551  10.345181   4.817250   3.017161    117    11   2.779      46  \n",
       "6487  1.939903  10.447522  10.447522   0.000000    117    12  -0.130      46  \n",
       "6488  1.941604   4.990326   2.495163   0.000000     62     7   1.929      36  \n",
       "6489  2.039198   5.550001   5.550001   0.000000    551    23   5.251      90  \n",
       "6490  1.949955   7.725704   2.454688   2.764347     90     9   2.198      40  \n",
       "6491  2.015435   8.624868   2.545394   6.079474    572    21   4.018      82  \n",
       "6492  2.092977   5.731119   3.164557   0.000000    267    22   4.159      78  \n",
       "6493  1.923169  15.342106   4.818675   3.018394    188    17   2.971      58  \n",
       "6494  2.057348   7.926876   4.953060   2.973816    605    27   3.202      98  \n",
       "6495  1.957127  68.394538  55.406185   0.000000   5076    58  -8.556     202  \n",
       "6496  2.001897  15.967272   9.737328   6.229944    509    25   1.403      86  \n",
       "6497  2.021705  14.441400   5.169484   9.271916    314    17   0.522      70  \n",
       "6498  2.124644   2.497552   0.000000   0.000000    507    34   8.157     104  \n",
       "6499  1.851791   8.376218   2.432669   5.943550    102     8   1.287      32  \n",
       "\n",
       "[6500 rows x 1446 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_excel(r'full_data.xlsx')\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3) Cleaning the data\n",
    "Removing NAN values from the data. Other attributes can also be added here to clean the data as per requirement. After executing this function, only those columns will be displayed which have non-numeric values in it. If these non-numeric values appear in numeric features columns, then these should be treated before going further. It will also print the data shape before and after cleaning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data shape before cleaning:(6500, 1446)\n",
      "Now it will print only those columns with non-numeric values\n",
      "                            NAME\n",
      "0      AUTOGEN_convert_out (2)_1\n",
      "1      AUTOGEN_convert_out (2)_2\n",
      "2      AUTOGEN_convert_out (2)_3\n",
      "3      AUTOGEN_convert_out (2)_4\n",
      "4      AUTOGEN_convert_out (2)_5\n",
      "5      AUTOGEN_convert_out (2)_6\n",
      "6      AUTOGEN_convert_out (2)_7\n",
      "7      AUTOGEN_convert_out (2)_8\n",
      "8      AUTOGEN_convert_out (2)_9\n",
      "9     AUTOGEN_convert_out (2)_10\n",
      "10    AUTOGEN_convert_out (2)_11\n",
      "11    AUTOGEN_convert_out (2)_12\n",
      "12    AUTOGEN_convert_out (2)_13\n",
      "13    AUTOGEN_convert_out (2)_14\n",
      "14    AUTOGEN_convert_out (2)_15\n",
      "15    AUTOGEN_convert_out (2)_16\n",
      "16    AUTOGEN_convert_out (2)_17\n",
      "17    AUTOGEN_convert_out (2)_18\n",
      "18    AUTOGEN_convert_out (2)_19\n",
      "19    AUTOGEN_convert_out (2)_20\n",
      "20    AUTOGEN_convert_out (2)_21\n",
      "21    AUTOGEN_convert_out (2)_22\n",
      "22    AUTOGEN_convert_out (2)_23\n",
      "23    AUTOGEN_convert_out (2)_24\n",
      "24    AUTOGEN_convert_out (2)_25\n",
      "25    AUTOGEN_convert_out (2)_26\n",
      "26    AUTOGEN_convert_out (2)_27\n",
      "27    AUTOGEN_convert_out (2)_28\n",
      "28    AUTOGEN_convert_out (2)_29\n",
      "29    AUTOGEN_convert_out (2)_30\n",
      "...                          ...\n",
      "6470  AUTOGEN_convert_out_12_484\n",
      "6471  AUTOGEN_convert_out_12_485\n",
      "6472  AUTOGEN_convert_out_12_486\n",
      "6473  AUTOGEN_convert_out_12_487\n",
      "6474  AUTOGEN_convert_out_12_488\n",
      "6475  AUTOGEN_convert_out_12_489\n",
      "6476  AUTOGEN_convert_out_12_490\n",
      "6477  AUTOGEN_convert_out_12_491\n",
      "6478  AUTOGEN_convert_out_12_492\n",
      "6479  AUTOGEN_convert_out_12_493\n",
      "6480  AUTOGEN_convert_out_12_494\n",
      "6481  AUTOGEN_convert_out_12_495\n",
      "6482  AUTOGEN_convert_out_12_496\n",
      "6483  AUTOGEN_convert_out_12_497\n",
      "6484  AUTOGEN_convert_out_12_498\n",
      "6485  AUTOGEN_convert_out_12_499\n",
      "6486  AUTOGEN_convert_out_12_500\n",
      "6487    AUTOGEN_convert_out_13_1\n",
      "6488    AUTOGEN_convert_out_13_2\n",
      "6489    AUTOGEN_convert_out_13_3\n",
      "6490    AUTOGEN_convert_out_13_4\n",
      "6491    AUTOGEN_convert_out_13_5\n",
      "6492    AUTOGEN_convert_out_13_6\n",
      "6493    AUTOGEN_convert_out_13_7\n",
      "6494    AUTOGEN_convert_out_13_8\n",
      "6495    AUTOGEN_convert_out_13_9\n",
      "6496   AUTOGEN_convert_out_13_10\n",
      "6497   AUTOGEN_convert_out_13_11\n",
      "6498   AUTOGEN_convert_out_13_12\n",
      "6499   AUTOGEN_convert_out_13_13\n",
      "\n",
      "[6500 rows x 1 columns]\n",
      "Data shape after cleaning:(6500, 1253)\n"
     ]
    }
   ],
   "source": [
    "#Cleaning the data\n",
    "data= data_clean(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4) Imputing the missing data\n",
    "Imputing the missin values in feature columns by means of respective feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6500, 1251)\n",
      "Data shape after imputation:(6500, 1251)\n"
     ]
    }
   ],
   "source": [
    "#imputing the missing values\n",
    "Imputed_Data_input, data_labels, data_names=data_impute(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5) Normalizing the data\n",
    "Imputing the missin values in feature columns by means of respective feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Normalizing the data \n",
    "full_data_norm=data_norm(Imputed_Data_input, data_labels, data_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6) splitting the data the data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Splitting the data into train and test\n",
    "test_size=0.30\n",
    "train_cv_x, test_x, train_cv_y, test_y=data_split(full_data_norm, test_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7) Hybrid Model optimization\n",
    "Currently, only two variables are optimized (drop_out and threshold). This optimization search can be extended as per requiremnet. x-fold cross validation is used in random search setting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\__init__.py:93: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int32 == np.dtype(int).type`.\n",
      "  if np.issubdtype(mask.dtype, np.int):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/250\n",
      "Epoch 2/250\n",
      "Epoch 3/250\n",
      "Epoch 4/250\n",
      "Epoch 5/250\n",
      "Epoch 6/250\n",
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      "Epoch 8/250\n",
      "Epoch 9/250\n",
      "Epoch 10/250\n",
      "Epoch 11/250\n",
      "Epoch 12/250\n",
      "Epoch 13/250\n",
      "Epoch 14/250\n",
      "Epoch 15/250\n",
      "Epoch 16/250\n",
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      "  if np.issubdtype(mask.dtype, np.int):\n"
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      "  if np.issubdtype(mask.dtype, np.int):\n"
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      "Epoch 88/250\n",
      "Epoch 89/250\n",
      "Epoch 90/250\n",
      "Epoch 91/250\n",
      "Epoch 92/250\n",
      "Epoch 93/250\n",
      "Epoch 94/250\n",
      "Epoch 95/250\n",
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      "Epoch 100/250\n",
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      "Epoch 144/250\n",
      "Epoch 145/250\n",
      "Epoch 146/250\n",
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      "Epoch 157/250\n",
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      "Epoch 159/250\n",
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      "Epoch 163/250\n",
      "Epoch 164/250\n",
      "Epoch 165/250\n",
      "Epoch 166/250\n",
      "Epoch 167/250\n",
      "Epoch 168/250\n",
      "Epoch 169/250\n",
      "Epoch 170/250\n",
      "Epoch 171/250\n",
      "Epoch 172/250\n",
      "Epoch 173/250\n",
      "Epoch 174/250\n",
      "Epoch 175/250\n",
      "Epoch 176/250\n",
      "Epoch 177/250\n",
      "Epoch 178/250\n",
      "Epoch 179/250\n",
      "Epoch 180/250\n",
      "Epoch 181/250\n",
      "Epoch 182/250\n",
      "Epoch 183/250\n",
      "Epoch 184/250\n",
      "Epoch 185/250\n",
      "Epoch 186/250\n",
      "Epoch 187/250\n",
      "Epoch 188/250\n",
      "Epoch 189/250\n",
      "Epoch 190/250\n",
      "Epoch 191/250\n",
      "Epoch 192/250\n",
      "Epoch 193/250\n",
      "Epoch 194/250\n",
      "Epoch 195/250\n",
      "Epoch 196/250\n",
      "Epoch 197/250\n",
      "Epoch 198/250\n",
      "Epoch 199/250\n",
      "Epoch 200/250\n",
      "Epoch 201/250\n",
      "Epoch 202/250\n",
      "Epoch 203/250\n",
      "Epoch 204/250\n",
      "Epoch 205/250\n",
      "Epoch 206/250\n",
      "Epoch 207/250\n",
      "Epoch 208/250\n",
      "Epoch 209/250\n",
      "Epoch 210/250\n",
      "Epoch 211/250\n",
      "Epoch 212/250\n",
      "Epoch 213/250\n",
      "Epoch 214/250\n",
      "Epoch 215/250\n",
      "Epoch 216/250\n",
      "Epoch 217/250\n",
      "Epoch 218/250\n",
      "Epoch 219/250\n",
      "Epoch 220/250\n",
      "Epoch 221/250\n",
      "Epoch 222/250\n",
      "Epoch 223/250\n",
      "Epoch 224/250\n",
      "Epoch 225/250\n",
      "Epoch 226/250\n",
      "Epoch 227/250\n",
      "Epoch 228/250\n",
      "Epoch 229/250\n",
      "Epoch 230/250\n",
      "Epoch 231/250\n",
      "Epoch 232/250\n",
      "Epoch 233/250\n",
      "Epoch 234/250\n",
      "Epoch 235/250\n",
      "Epoch 236/250\n",
      "Epoch 237/250\n",
      "Epoch 238/250\n",
      "Epoch 239/250\n",
      "Epoch 240/250\n",
      "Epoch 241/250\n",
      "Epoch 242/250\n",
      "Epoch 243/250\n",
      "Epoch 244/250\n",
      "Epoch 245/250\n",
      "Epoch 246/250\n",
      "Epoch 247/250\n",
      "Epoch 248/250\n",
      "Epoch 249/250\n",
      "Epoch 250/250\n",
      "Best: 0.832360 using {'fs__threshold': '0.10*mean', 'clf__dropout_rate': 0.4}\n",
      "0.832360 (0.002874) with: {'fs__threshold': '0.10*mean', 'clf__dropout_rate': 0.4}\n"
     ]
    }
   ],
   "source": [
    "xx=0  #This variable stores the number of features selected\n",
    "hybrid_model=hybrid_model_opt() #calling the hybrid model for optimizattion\n",
    "\n",
    "#Defining two important paramters of hybrid model to be optimized using random cv search\n",
    "param_grid= {'fs__threshold': ['0.08*mean','0.09*mean','0.10*mean','0.2*mean','0.3*mean','0.4*mean','0.5*mean','0.6*mean','0.7*mean','0.8*mean','0.9*mean','1*mean','1.1*mean','1.2*mean','1.3*mean','1.4*mean','1.5*mean','1.6*mean','1.7*mean','1.8*mean','1.9*mean','2.0*mean','2.1*mean','2.2*mean','2.3*mean'],\n",
    "'clf__dropout_rate': [0.1, 0.2, 0.3, 0.4, 0.5,0.6,0.7,0.8,0.9]}\n",
    "\n",
    "#Random CV search\n",
    "grid = RandomizedSearchCV(estimator=hybrid_model, param_distributions=param_grid,n_iter = 1,scoring='roc_auc',cv = 3 , n_jobs=1)\n",
    "opt_result = grid.fit(train_cv_x, train_cv_y)\n",
    "\n",
    "#Printing the optimization results\n",
    "print(\"Best: %f using %s\" % (opt_result.best_score_, opt_result.best_params_))\n",
    "means = opt_result.cv_results_['mean_test_score']\n",
    "stds = opt_result.cv_results_['std_test_score']\n",
    "params = opt_result.cv_results_['params']\n",
    "for mean, stdev, param in zip(means, stds, params):\n",
    "    print(\"%f (%f) with: %r\" % (mean, stdev, param))\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8) Gini_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#getting the importances of all the features    \n",
    "importances, m =feature_imp(train_cv_x,train_cv_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9) Features names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#getting the features names of the selected features based on optimized threshold\n",
    "feature_name=selected_feature_names(m, opt_result.best_params_[\"fs__threshold\"], train_cv_x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10) Saving the gini-importance and selected features names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#saving gini-importance of all the featues\n",
    "writer = pd.ExcelWriter('importances.xlsx',engine='xlsxwriter')\n",
    "pd.DataFrame(importances).to_excel(writer,sheet_name='importances')\n",
    "writer.save()\n",
    "\n",
    "\n",
    "\n",
    "#Saving features names which are selected on the basis of optimized threshold\n",
    "writer = pd.ExcelWriter('feature_name.xlsx',engine='xlsxwriter')\n",
    "pd.DataFrame(feature_name).to_excel(writer,sheet_name='feature_name')\n",
    "writer.save()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 11) Features selection in train and test "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  if __name__ == '__main__':\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:10: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:11: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  # This is added back by InteractiveShellApp.init_path()\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:12: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  if sys.path[0] == '':\n"
     ]
    }
   ],
   "source": [
    "#Selection of train and test features based on optimized value of threshold\n",
    "train_selected_x, train_selected_y, test_selected_x, test_selected_y=train_test_feature_based_selection(feature_name,train_cv_x,train_cv_y,test_x,test_y )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 12) Saving the test on the basis of selected features columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Saving the selected test set\n",
    "writer = pd.ExcelWriter('test_selected.xlsx',engine='xlsxwriter')\n",
    "pd.DataFrame(test_selected_x).to_excel(writer,sheet_name='test_selected_x')\n",
    "pd.DataFrame(test_selected_y).to_excel(writer,sheet_name='test_selected_y')\n",
    "writer.save()\n",
    "  \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 13) Final prediction based on ensembling.\n",
    "\n",
    "This will also save all the ensembled average models and weight matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/250\n",
      "4550/4550 [==============================] - 1s 217us/step - loss: 0.6833 - binary_crossentropy: 0.6833\n",
      "Epoch 2/250\n",
      "4550/4550 [==============================] - 1s 124us/step - loss: 0.6133 - binary_crossentropy: 0.6133\n",
      "Epoch 3/250\n",
      "4550/4550 [==============================] - 1s 125us/step - loss: 0.6006 - binary_crossentropy: 0.6006\n",
      "Epoch 4/250\n",
      "4550/4550 [==============================] - 1s 127us/step - loss: 0.5873 - binary_crossentropy: 0.5873\n",
      "Epoch 5/250\n",
      "4550/4550 [==============================] - 1s 124us/step - loss: 0.5732 - binary_crossentropy: 0.5732\n",
      "Epoch 6/250\n",
      "4550/4550 [==============================] - 1s 123us/step - loss: 0.5619 - binary_crossentropy: 0.5619\n",
      "Epoch 7/250\n",
      "4550/4550 [==============================] - 1s 129us/step - loss: 0.5525 - binary_crossentropy: 0.5525\n",
      "Epoch 8/250\n",
      "4550/4550 [==============================] - 1s 132us/step - loss: 0.5505 - binary_crossentropy: 0.5505\n",
      "Epoch 9/250\n",
      "4550/4550 [==============================] - 1s 126us/step - loss: 0.5369 - binary_crossentropy: 0.5369\n",
      "Epoch 10/250\n",
      "4550/4550 [==============================] - 1s 125us/step - loss: 0.5325 - binary_crossentropy: 0.5325\n",
      "Epoch 11/250\n",
      "4550/4550 [==============================] - 1s 139us/step - loss: 0.5249 - binary_crossentropy: 0.5249\n",
      "Epoch 12/250\n",
      "4550/4550 [==============================] - 1s 126us/step - loss: 0.5233 - binary_crossentropy: 0.5233\n",
      "Epoch 13/250\n",
      "4550/4550 [==============================] - 1s 124us/step - loss: 0.5173 - binary_crossentropy: 0.5173\n",
      "Epoch 14/250\n",
      "4550/4550 [==============================] - 1s 137us/step - loss: 0.5204 - binary_crossentropy: 0.5204\n",
      "Epoch 15/250\n",
      "4550/4550 [==============================] - 1s 122us/step - loss: 0.5111 - binary_crossentropy: 0.5111\n",
      "Epoch 16/250\n",
      "4550/4550 [==============================] - 1s 124us/step - loss: 0.5055 - binary_crossentropy: 0.5055\n",
      "Epoch 17/250\n",
      "4550/4550 [==============================] - 1s 126us/step - loss: 0.4991 - binary_crossentropy: 0.4991\n",
      "Epoch 18/250\n",
      "4550/4550 [==============================] - 1s 135us/step - loss: 0.4974 - binary_crossentropy: 0.4974\n",
      "Epoch 19/250\n",
      "4550/4550 [==============================] - 1s 124us/step - loss: 0.4921 - binary_crossentropy: 0.4921\n",
      "Epoch 20/250\n",
      "4550/4550 [==============================] - 1s 126us/step - loss: 0.4892 - binary_crossentropy: 0.4892\n",
      "Epoch 21/250\n",
      "4550/4550 [==============================] - 1s 138us/step - loss: 0.4864 - binary_crossentropy: 0.4864\n",
      "Epoch 22/250\n",
      "4550/4550 [==============================] - 1s 123us/step - loss: 0.4797 - binary_crossentropy: 0.4797\n",
      "Epoch 23/250\n",
      "4550/4550 [==============================] - 1s 123us/step - loss: 0.4786 - binary_crossentropy: 0.4786\n",
      "Epoch 24/250\n",
      "4550/4550 [==============================] - 1s 129us/step - loss: 0.4776 - binary_crossentropy: 0.4776\n",
      "Epoch 25/250\n",
      "4550/4550 [==============================] - 1s 127us/step - loss: 0.4683 - binary_crossentropy: 0.4683\n",
      "Epoch 26/250\n",
      "4550/4550 [==============================] - 1s 129us/step - loss: 0.4651 - binary_crossentropy: 0.4651\n",
      "Epoch 27/250\n",
      "4550/4550 [==============================] - 1s 138us/step - loss: 0.4625 - binary_crossentropy: 0.4625\n",
      "Epoch 28/250\n",
      "4550/4550 [==============================] - 1s 141us/step - loss: 0.4602 - binary_crossentropy: 0.4602\n",
      "Epoch 29/250\n",
      "4550/4550 [==============================] - 1s 123us/step - loss: 0.4548 - binary_crossentropy: 0.4548\n",
      "Epoch 30/250\n",
      "4550/4550 [==============================] - 1s 124us/step - loss: 0.4495 - binary_crossentropy: 0.4495\n",
      "Epoch 31/250\n",
      "4550/4550 [==============================] - 1s 124us/step - loss: 0.4492 - binary_crossentropy: 0.4492\n",
      "Epoch 32/250\n",
      "4550/4550 [==============================] - 1s 125us/step - loss: 0.4499 - binary_crossentropy: 0.4499\n",
      "Epoch 33/250\n",
      "4550/4550 [==============================] - 1s 124us/step - loss: 0.4443 - binary_crossentropy: 0.4443\n",
      "Epoch 34/250\n",
      "4550/4550 [==============================] - 1s 124us/step - loss: 0.4398 - binary_crossentropy: 0.4398\n",
      "Epoch 35/250\n",
      "4550/4550 [==============================] - 1s 125us/step - loss: 0.4374 - binary_crossentropy: 0.4374\n",
      "Epoch 36/250\n",
      "4550/4550 [==============================] - 1s 130us/step - loss: 0.4296 - binary_crossentropy: 0.4296\n",
      "Epoch 37/250\n",
      "4550/4550 [==============================] - 1s 131us/step - loss: 0.4239 - binary_crossentropy: 0.4239\n",
      "Epoch 38/250\n",
      "4550/4550 [==============================] - 1s 128us/step - loss: 0.4194 - binary_crossentropy: 0.4194\n",
      "Epoch 39/250\n",
      "4550/4550 [==============================] - 1s 125us/step - loss: 0.4141 - binary_crossentropy: 0.4141\n",
      "Epoch 40/250\n",
      "4550/4550 [==============================] - 1s 125us/step - loss: 0.4057 - binary_crossentropy: 0.4057\n",
      "Epoch 41/250\n",
      "4550/4550 [==============================] - 1s 126us/step - loss: 0.4065 - binary_crossentropy: 0.4065\n",
      "Epoch 42/250\n",
      "4550/4550 [==============================] - 1s 126us/step - loss: 0.4104 - binary_crossentropy: 0.4104\n",
      "Epoch 43/250\n",
      "4550/4550 [==============================] - 1s 135us/step - loss: 0.4030 - binary_crossentropy: 0.4030\n",
      "Epoch 44/250\n",
      "4550/4550 [==============================] - 1s 136us/step - loss: 0.3905 - binary_crossentropy: 0.3905\n",
      "Epoch 45/250\n",
      "4550/4550 [==============================] - 1s 137us/step - loss: 0.3880 - binary_crossentropy: 0.3880\n",
      "Epoch 46/250\n",
      "4550/4550 [==============================] - 1s 122us/step - loss: 0.3802 - binary_crossentropy: 0.3802\n",
      "Epoch 47/250\n",
      "4550/4550 [==============================] - 1s 125us/step - loss: 0.3802 - binary_crossentropy: 0.3802\n",
      "Epoch 48/250\n",
      "4550/4550 [==============================] - 1s 125us/step - loss: 0.3765 - binary_crossentropy: 0.3765\n",
      "Epoch 49/250\n",
      "4550/4550 [==============================] - 1s 126us/step - loss: 0.3786 - binary_crossentropy: 0.3786\n",
      "Epoch 50/250\n",
      "4550/4550 [==============================] - 1s 126us/step - loss: 0.3640 - binary_crossentropy: 0.3640\n",
      "Epoch 51/250\n",
      "4550/4550 [==============================] - 1s 128us/step - loss: 0.3600 - binary_crossentropy: 0.3600\n",
      "Epoch 52/250\n",
      "4550/4550 [==============================] - 1s 150us/step - loss: 0.3509 - binary_crossentropy: 0.3509\n",
      "Epoch 53/250\n",
      "4550/4550 [==============================] - 1s 167us/step - loss: 0.3460 - binary_crossentropy: 0.3460\n",
      "Epoch 54/250\n",
      "4550/4550 [==============================] - 1s 125us/step - loss: 0.3418 - binary_crossentropy: 0.3418\n",
      "Epoch 55/250\n",
      "4550/4550 [==============================] - 1s 119us/step - loss: 0.3383 - binary_crossentropy: 0.3383\n",
      "Epoch 56/250\n",
      "4550/4550 [==============================] - 1s 118us/step - loss: 0.3252 - binary_crossentropy: 0.3252\n",
      "Epoch 57/250\n",
      "4550/4550 [==============================] - 1s 118us/step - loss: 0.3207 - binary_crossentropy: 0.3207\n",
      "Epoch 58/250\n",
      "4550/4550 [==============================] - 1s 124us/step - loss: 0.3125 - binary_crossentropy: 0.3125\n",
      "Epoch 59/250\n",
      "4550/4550 [==============================] - 1s 122us/step - loss: 0.3090 - binary_crossentropy: 0.3090\n",
      "Epoch 60/250\n",
      "4550/4550 [==============================] - 1s 118us/step - loss: 0.3021 - binary_crossentropy: 0.3021\n",
      "Epoch 61/250\n",
      "4550/4550 [==============================] - 1s 118us/step - loss: 0.3031 - binary_crossentropy: 0.3031\n",
      "Epoch 62/250\n",
      "4550/4550 [==============================] - 1s 119us/step - loss: 0.2938 - binary_crossentropy: 0.2938\n",
      "Epoch 63/250\n",
      "4550/4550 [==============================] - 1s 118us/step - loss: 0.2854 - binary_crossentropy: 0.2854\n",
      "Epoch 64/250\n",
      "4550/4550 [==============================] - 1s 121us/step - loss: 0.2883 - binary_crossentropy: 0.2883\n",
      "Epoch 65/250\n",
      "4550/4550 [==============================] - 1s 118us/step - loss: 0.2776 - binary_crossentropy: 0.2776\n",
      "Epoch 66/250\n",
      "4550/4550 [==============================] - 1s 117us/step - loss: 0.2720 - binary_crossentropy: 0.2720\n",
      "Epoch 67/250\n",
      "4550/4550 [==============================] - 1s 118us/step - loss: 0.2646 - binary_crossentropy: 0.2646\n",
      "Epoch 68/250\n",
      "4550/4550 [==============================] - 1s 117us/step - loss: 0.2577 - binary_crossentropy: 0.2577\n",
      "Epoch 69/250\n",
      "4550/4550 [==============================] - 1s 118us/step - loss: 0.2543 - binary_crossentropy: 0.2543\n",
      "Epoch 70/250\n",
      "4550/4550 [==============================] - 1s 118us/step - loss: 0.2423 - binary_crossentropy: 0.2423\n",
      "Epoch 71/250\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4550/4550 [==============================] - 1s 119us/step - loss: 0.2445 - binary_crossentropy: 0.2445\n",
      "Epoch 72/250\n",
      "4550/4550 [==============================] - 1s 116us/step - loss: 0.2337 - binary_crossentropy: 0.2337\n",
      "Epoch 73/250\n",
      "4550/4550 [==============================] - 1s 115us/step - loss: 0.2316 - binary_crossentropy: 0.2316\n",
      "Epoch 74/250\n",
      "4550/4550 [==============================] - 1s 115us/step - loss: 0.2260 - binary_crossentropy: 0.2260\n",
      "Epoch 75/250\n",
      "4550/4550 [==============================] - 1s 116us/step - loss: 0.2164 - binary_crossentropy: 0.2164\n",
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      "Epoch 140/250\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4550/4550 [==============================] - 1s 117us/step - loss: 0.0781 - binary_crossentropy: 0.0781\n",
      "Epoch 141/250\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "Epoch 230/250\n",
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      "Epoch 231/250\n",
      "4550/4550 [==============================] - 1s 116us/step - loss: 0.0471 - binary_crossentropy: 0.0471\n",
      "Epoch 232/250\n",
      "4550/4550 [==============================] - 1s 116us/step - loss: 0.0442 - binary_crossentropy: 0.0442\n",
      "Epoch 233/250\n",
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      "Epoch 234/250\n",
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      "Epoch 235/250\n",
      "4550/4550 [==============================] - 1s 116us/step - loss: 0.0440 - binary_crossentropy: 0.0440\n",
      "Epoch 236/250\n",
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      "Epoch 237/250\n",
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      "Epoch 238/250\n",
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      "Epoch 239/250\n",
      "4550/4550 [==============================] - 1s 117us/step - loss: 0.0485 - binary_crossentropy: 0.0485\n",
      "Epoch 240/250\n",
      "4550/4550 [==============================] - 1s 120us/step - loss: 0.0422 - binary_crossentropy: 0.0422\n",
      "Epoch 241/250\n",
      "4550/4550 [==============================] - 1s 116us/step - loss: 0.0457 - binary_crossentropy: 0.0457\n",
      "Epoch 242/250\n",
      "4550/4550 [==============================] - 1s 116us/step - loss: 0.0450 - binary_crossentropy: 0.0450\n",
      "Epoch 243/250\n",
      "4550/4550 [==============================] - 1s 116us/step - loss: 0.0466 - binary_crossentropy: 0.0466\n",
      "Epoch 244/250\n",
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      "Epoch 245/250\n",
      "4550/4550 [==============================] - 1s 116us/step - loss: 0.0430 - binary_crossentropy: 0.0430\n",
      "Epoch 246/250\n",
      "4550/4550 [==============================] - 1s 115us/step - loss: 0.0404 - binary_crossentropy: 0.0404\n",
      "Epoch 247/250\n",
      "4550/4550 [==============================] - 1s 116us/step - loss: 0.0427 - binary_crossentropy: 0.0427\n",
      "Epoch 248/250\n",
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      "Epoch 249/250\n",
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      "Epoch 250/250\n",
      "4550/4550 [==============================] - 1s 116us/step - loss: 0.0438 - binary_crossentropy: 0.0438\n",
      "AUROC_test: 0.8663519151673127\n",
      "  \n",
      "Saved model to disk\n",
      "  \n",
      "Epoch 1/250\n",
      "4550/4550 [==============================] - 1s 209us/step - loss: 0.7090 - binary_crossentropy: 0.7090\n",
      "Epoch 2/250\n",
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      "Epoch 4/250\n",
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      "Epoch 5/250\n",
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      "Epoch 6/250\n",
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      "Epoch 7/250\n",
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      "Epoch 8/250\n",
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      "Epoch 9/250\n",
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      "Epoch 10/250\n",
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      "Epoch 11/250\n",
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      "Epoch 12/250\n",
      "4550/4550 [==============================] - 1s 115us/step - loss: 0.5165 - binary_crossentropy: 0.5165\n",
      "Epoch 13/250\n",
      "4550/4550 [==============================] - 1s 116us/step - loss: 0.5159 - binary_crossentropy: 0.5159\n",
      "Epoch 14/250\n",
      "4550/4550 [==============================] - 1s 115us/step - loss: 0.5094 - binary_crossentropy: 0.5094\n",
      "Epoch 15/250\n",
      "4550/4550 [==============================] - 1s 117us/step - loss: 0.5076 - binary_crossentropy: 0.5076\n",
      "Epoch 16/250\n",
      "4550/4550 [==============================] - 1s 116us/step - loss: 0.4969 - binary_crossentropy: 0.4969\n",
      "Epoch 17/250\n",
      "4550/4550 [==============================] - 1s 116us/step - loss: 0.4926 - binary_crossentropy: 0.4926\n",
      "Epoch 18/250\n",
      "4550/4550 [==============================] - 1s 117us/step - loss: 0.4859 - binary_crossentropy: 0.4859\n",
      "Epoch 19/250\n",
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      "Epoch 20/250\n",
      "4550/4550 [==============================] - 1s 116us/step - loss: 0.4827 - binary_crossentropy: 0.4827\n",
      "Epoch 21/250\n",
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      "Epoch 22/250\n",
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      "Epoch 23/250\n",
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      "Epoch 24/250\n",
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      "Epoch 25/250\n",
      "4550/4550 [==============================] - 1s 129us/step - loss: 0.4692 - binary_crossentropy: 0.4692\n",
      "Epoch 26/250\n",
      "4550/4550 [==============================] - 1s 135us/step - loss: 0.4698 - binary_crossentropy: 0.4698\n",
      "Epoch 27/250\n",
      "4550/4550 [==============================] - 1s 141us/step - loss: 0.4598 - binary_crossentropy: 0.4598\n",
      "Epoch 28/250\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4550/4550 [==============================] - 1s 138us/step - loss: 0.4543 - binary_crossentropy: 0.4543\n",
      "Epoch 29/250\n",
      "4550/4550 [==============================] - 1s 139us/step - loss: 0.4540 - binary_crossentropy: 0.4540\n",
      "Epoch 30/250\n",
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      "Epoch 31/250\n",
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      "Epoch 32/250\n",
      "4550/4550 [==============================] - 1s 129us/step - loss: 0.4434 - binary_crossentropy: 0.4434\n",
      "Epoch 33/250\n",
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      "Epoch 34/250\n",
      "4550/4550 [==============================] - 1s 132us/step - loss: 0.4405 - binary_crossentropy: 0.4405\n",
      "Epoch 35/250\n",
      "4550/4550 [==============================] - 1s 126us/step - loss: 0.4310 - binary_crossentropy: 0.4310\n",
      "Epoch 36/250\n",
      "4550/4550 [==============================] - 1s 134us/step - loss: 0.4302 - binary_crossentropy: 0.4302\n",
      "Epoch 37/250\n",
      "4550/4550 [==============================] - 1s 126us/step - loss: 0.4293 - binary_crossentropy: 0.4293\n",
      "Epoch 38/250\n",
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      "Epoch 39/250\n",
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      "Epoch 40/250\n",
      "4550/4550 [==============================] - 1s 139us/step - loss: 0.4132 - binary_crossentropy: 0.4132\n",
      "Epoch 41/250\n",
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      "Epoch 42/250\n",
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      "Epoch 43/250\n",
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      "Epoch 44/250\n",
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      "Epoch 45/250\n",
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      "Epoch 46/250\n",
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      "Epoch 47/250\n",
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      "Epoch 48/250\n",
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      "Epoch 49/250\n",
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      "Epoch 50/250\n",
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      "Epoch 51/250\n",
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      "Epoch 52/250\n",
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      "Epoch 53/250\n",
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      "Epoch 54/250\n",
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      "Epoch 55/250\n",
      "4550/4550 [==============================] - 1s 122us/step - loss: 0.3504 - binary_crossentropy: 0.3504\n",
      "Epoch 56/250\n",
      "4550/4550 [==============================] - 1s 122us/step - loss: 0.3419 - binary_crossentropy: 0.3419\n",
      "Epoch 57/250\n",
      "4550/4550 [==============================] - 1s 120us/step - loss: 0.3344 - binary_crossentropy: 0.3344\n",
      "Epoch 58/250\n",
      "4550/4550 [==============================] - 1s 122us/step - loss: 0.3292 - binary_crossentropy: 0.3292\n",
      "Epoch 59/250\n",
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      "Epoch 60/250\n",
      "4550/4550 [==============================] - 1s 123us/step - loss: 0.3140 - binary_crossentropy: 0.3140\n",
      "Epoch 61/250\n",
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      "Epoch 62/250\n",
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      "Epoch 63/250\n",
      "4550/4550 [==============================] - 1s 121us/step - loss: 0.2984 - binary_crossentropy: 0.2984\n",
      "Epoch 64/250\n",
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      "Epoch 65/250\n",
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      "Epoch 66/250\n",
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      "Epoch 67/250\n",
      "4550/4550 [==============================] - 1s 136us/step - loss: 0.2720 - binary_crossentropy: 0.2720\n",
      "Epoch 68/250\n",
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      "Epoch 69/250\n",
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      "Epoch 70/250\n",
      "4550/4550 [==============================] - 1s 131us/step - loss: 0.2483 - binary_crossentropy: 0.2483\n",
      "Epoch 71/250\n",
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      "Epoch 72/250\n",
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      "Epoch 73/250\n",
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      "Epoch 74/250\n",
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      "Epoch 75/250\n",
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      "Epoch 78/250\n",
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      "Epoch 79/250\n",
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      "Epoch 80/250\n",
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      "Epoch 81/250\n",
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      "Epoch 82/250\n",
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      "Epoch 83/250\n",
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      "Epoch 84/250\n",
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      "Epoch 85/250\n",
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      "Epoch 86/250\n",
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      "Epoch 87/250\n",
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      "Epoch 89/250\n",
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      "Epoch 90/250\n",
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      "Epoch 93/250\n",
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      "Epoch 97/250\n",
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      "Epoch 98/250\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4550/4550 [==============================] - 1s 131us/step - loss: 0.1303 - binary_crossentropy: 0.1303\n",
      "Epoch 99/250\n",
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      "Epoch 100/250\n",
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      "Epoch 101/250\n",
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      "Epoch 102/250\n",
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      "Epoch 103/250\n",
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      "Epoch 106/250\n",
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      "Epoch 107/250\n",
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      "Epoch 108/250\n",
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      "Epoch 109/250\n",
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      "Epoch 110/250\n",
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      "Epoch 111/250\n",
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      "Epoch 112/250\n",
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      "Epoch 113/250\n",
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      "Epoch 114/250\n",
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      "Epoch 116/250\n",
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      "Epoch 117/250\n",
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      "Epoch 119/250\n",
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      "Epoch 120/250\n",
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      "Epoch 121/250\n",
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      "Epoch 123/250\n",
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      "Epoch 124/250\n",
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      "Epoch 125/250\n",
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      "Epoch 126/250\n",
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      "Epoch 127/250\n",
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      "Epoch 128/250\n",
      "4550/4550 [==============================] - 1s 142us/step - loss: 0.0756 - binary_crossentropy: 0.0756\n",
      "Epoch 129/250\n",
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      "Epoch 130/250\n",
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      "Epoch 131/250\n",
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      "Epoch 132/250\n",
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      "Epoch 133/250\n",
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      "Epoch 134/250\n",
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      "Epoch 154/250\n",
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      "Epoch 155/250\n",
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      "Epoch 156/250\n",
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      "Epoch 157/250\n",
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      "Epoch 158/250\n",
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      "Epoch 159/250\n",
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      "Epoch 160/250\n",
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      "Epoch 161/250\n",
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      "Epoch 162/250\n",
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      "Epoch 163/250\n",
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      "Epoch 164/250\n",
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      "Epoch 165/250\n",
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      "Epoch 166/250\n",
      "4550/4550 [==============================] - 1s 171us/step - loss: 0.0526 - binary_crossentropy: 0.0526\n",
      "Epoch 167/250\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4550/4550 [==============================] - 1s 149us/step - loss: 0.0536 - binary_crossentropy: 0.0536\n",
      "Epoch 168/250\n",
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      "Epoch 169/250\n",
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      "Epoch 170/250\n",
      "4550/4550 [==============================] - 1s 163us/step - loss: 0.0511 - binary_crossentropy: 0.0511\n",
      "Epoch 171/250\n",
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      "Epoch 172/250\n",
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      "Epoch 173/250\n",
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      "Epoch 174/250\n",
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      "Epoch 175/250\n",
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      "Epoch 176/250\n",
      "4550/4550 [==============================] - 1s 120us/step - loss: 0.0490 - binary_crossentropy: 0.0490\n",
      "Epoch 177/250\n",
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      "Epoch 178/250\n",
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      "Epoch 179/250\n",
      "4550/4550 [==============================] - 1s 133us/step - loss: 0.0497 - binary_crossentropy: 0.0497\n",
      "Epoch 180/250\n",
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      "Epoch 181/250\n",
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      "Epoch 182/250\n",
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      "Epoch 183/250\n",
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      "Epoch 184/250\n",
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      "Epoch 185/250\n",
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      "Epoch 186/250\n",
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      "Epoch 187/250\n",
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      "Epoch 188/250\n",
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      "Epoch 189/250\n",
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      "Epoch 190/250\n",
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      "Epoch 191/250\n",
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      "Epoch 192/250\n",
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      "Epoch 193/250\n",
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      "Epoch 194/250\n",
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      "Epoch 195/250\n",
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      "Epoch 196/250\n",
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      "Epoch 197/250\n",
      "4550/4550 [==============================] - 1s 141us/step - loss: 0.0426 - binary_crossentropy: 0.0426\n",
      "Epoch 198/250\n",
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      "Epoch 199/250\n",
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      "Epoch 200/250\n",
      "4550/4550 [==============================] - 1s 119us/step - loss: 0.0444 - binary_crossentropy: 0.0444\n",
      "Epoch 201/250\n",
      "4550/4550 [==============================] - 1s 116us/step - loss: 0.0428 - binary_crossentropy: 0.0428\n",
      "Epoch 202/250\n",
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      "Epoch 203/250\n",
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      "Epoch 204/250\n",
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      "Epoch 205/250\n",
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      "Epoch 206/250\n",
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      "Epoch 207/250\n",
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      "Epoch 208/250\n",
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      "Epoch 209/250\n",
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      "Epoch 210/250\n",
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      "Epoch 211/250\n",
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      "Epoch 212/250\n",
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      "Epoch 213/250\n",
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      "Epoch 214/250\n",
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      "Epoch 217/250\n",
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      "Epoch 218/250\n",
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      "Epoch 219/250\n",
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      "Epoch 220/250\n",
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      "Epoch 221/250\n",
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      "Epoch 222/250\n",
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      "Epoch 223/250\n",
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      "Epoch 224/250\n",
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      "Epoch 225/250\n",
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      "Epoch 226/250\n",
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      "Epoch 227/250\n",
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      "Epoch 228/250\n",
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      "Epoch 229/250\n",
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      "Epoch 230/250\n",
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      "Epoch 231/250\n",
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      "Epoch 232/250\n",
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      "Epoch 233/250\n",
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      "Epoch 234/250\n",
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      "Epoch 235/250\n",
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      "Epoch 236/250\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4550/4550 [==============================] - 1s 117us/step - loss: 0.0369 - binary_crossentropy: 0.0369\n",
      "Epoch 237/250\n",
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      "Epoch 238/250\n",
      "4550/4550 [==============================] - 1s 118us/step - loss: 0.0395 - binary_crossentropy: 0.0395\n",
      "Epoch 239/250\n",
      "4550/4550 [==============================] - 1s 117us/step - loss: 0.0368 - binary_crossentropy: 0.0368\n",
      "Epoch 240/250\n",
      "4550/4550 [==============================] - 1s 119us/step - loss: 0.0388 - binary_crossentropy: 0.0388\n",
      "Epoch 241/250\n",
      "4550/4550 [==============================] - 1s 123us/step - loss: 0.0349 - binary_crossentropy: 0.0349\n",
      "Epoch 242/250\n",
      "4550/4550 [==============================] - 1s 118us/step - loss: 0.0373 - binary_crossentropy: 0.0373\n",
      "Epoch 243/250\n",
      "4550/4550 [==============================] - 1s 117us/step - loss: 0.0388 - binary_crossentropy: 0.0388\n",
      "Epoch 244/250\n",
      "4550/4550 [==============================] - 1s 119us/step - loss: 0.0367 - binary_crossentropy: 0.0367\n",
      "Epoch 245/250\n",
      "4550/4550 [==============================] - 1s 123us/step - loss: 0.0380 - binary_crossentropy: 0.0380\n",
      "Epoch 246/250\n",
      "4550/4550 [==============================] - 1s 119us/step - loss: 0.0388 - binary_crossentropy: 0.0388\n",
      "Epoch 247/250\n",
      "4550/4550 [==============================] - 1s 118us/step - loss: 0.0361 - binary_crossentropy: 0.0361\n",
      "Epoch 248/250\n",
      "4550/4550 [==============================] - 1s 118us/step - loss: 0.0354 - binary_crossentropy: 0.0354\n",
      "Epoch 249/250\n",
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      "Epoch 250/250\n",
      "4550/4550 [==============================] - 1s 118us/step - loss: 0.0348 - binary_crossentropy: 0.0348\n",
      "AUROC_test: 0.8657595081329023\n",
      "  \n",
      "Saved model to disk\n",
      "  \n",
      "Epoch 1/250\n",
      "4550/4550 [==============================] - 1s 218us/step - loss: 0.7091 - binary_crossentropy: 0.7091\n",
      "Epoch 2/250\n",
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      "Epoch 3/250\n",
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      "Epoch 4/250\n",
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      "Epoch 5/250\n",
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      "Epoch 6/250\n",
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      "Epoch 9/250\n",
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      "Epoch 10/250\n",
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      "Epoch 11/250\n",
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      "Epoch 12/250\n",
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      "Epoch 13/250\n",
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      "Epoch 14/250\n",
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      "Epoch 16/250\n",
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      "Epoch 17/250\n",
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      "Epoch 18/250\n",
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      "Epoch 19/250\n",
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      "Epoch 20/250\n",
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      "Epoch 21/250\n",
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      "Epoch 22/250\n",
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      "Epoch 23/250\n",
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      "Epoch 24/250\n",
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      "Epoch 25/250\n",
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      "Epoch 26/250\n",
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      "Epoch 27/250\n",
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      "Epoch 28/250\n",
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      "Epoch 32/250\n",
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      "Epoch 33/250\n",
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      "Epoch 34/250\n",
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      "Epoch 35/250\n",
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      "Epoch 36/250\n",
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      "Epoch 37/250\n",
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      "Epoch 38/250\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "4550/4550 [==============================] - 1s 125us/step - loss: 0.3507 - binary_crossentropy: 0.3507\n",
      "Epoch 56/250\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "4550/4550 [==============================] - 1s 126us/step - loss: 0.0774 - binary_crossentropy: 0.0774\n",
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     ]
    },
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     "output_type": "stream",
     "text": [
      "4550/4550 [==============================] - 1s 132us/step - loss: 0.0475 - binary_crossentropy: 0.0475\n",
      "Epoch 194/250\n",
      "4550/4550 [==============================] - 1s 136us/step - loss: 0.0436 - binary_crossentropy: 0.0436\n",
      "Epoch 195/250\n",
      "4550/4550 [==============================] - 1s 142us/step - loss: 0.0476 - binary_crossentropy: 0.0476\n",
      "Epoch 196/250\n",
      "4550/4550 [==============================] - 1s 144us/step - loss: 0.0476 - binary_crossentropy: 0.0476\n",
      "Epoch 197/250\n",
      "4550/4550 [==============================] - 1s 138us/step - loss: 0.0427 - binary_crossentropy: 0.0427\n",
      "Epoch 198/250\n",
      "4550/4550 [==============================] - 1s 122us/step - loss: 0.0457 - binary_crossentropy: 0.0457\n",
      "Epoch 199/250\n",
      "4550/4550 [==============================] - 1s 127us/step - loss: 0.0423 - binary_crossentropy: 0.0423\n",
      "Epoch 200/250\n",
      "4550/4550 [==============================] - 1s 116us/step - loss: 0.0445 - binary_crossentropy: 0.0445\n",
      "Epoch 201/250\n",
      "4550/4550 [==============================] - 1s 133us/step - loss: 0.0429 - binary_crossentropy: 0.0429\n",
      "Epoch 202/250\n",
      "4550/4550 [==============================] - 1s 134us/step - loss: 0.0443 - binary_crossentropy: 0.0443\n",
      "Epoch 203/250\n",
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      "Epoch 204/250\n",
      "4550/4550 [==============================] - 1s 135us/step - loss: 0.0412 - binary_crossentropy: 0.0412\n",
      "Epoch 205/250\n",
      "4550/4550 [==============================] - 1s 117us/step - loss: 0.0395 - binary_crossentropy: 0.0395\n",
      "Epoch 206/250\n",
      "4550/4550 [==============================] - 1s 118us/step - loss: 0.0437 - binary_crossentropy: 0.0437\n",
      "Epoch 207/250\n",
      "4550/4550 [==============================] - 1s 129us/step - loss: 0.0410 - binary_crossentropy: 0.0410\n",
      "Epoch 208/250\n",
      "4550/4550 [==============================] - 1s 121us/step - loss: 0.0418 - binary_crossentropy: 0.0418\n",
      "Epoch 209/250\n",
      "4550/4550 [==============================] - 1s 118us/step - loss: 0.0461 - binary_crossentropy: 0.0461\n",
      "Epoch 210/250\n",
      "4550/4550 [==============================] - 1s 118us/step - loss: 0.0467 - binary_crossentropy: 0.0467\n",
      "Epoch 211/250\n",
      "4550/4550 [==============================] - 1s 117us/step - loss: 0.0416 - binary_crossentropy: 0.0416\n",
      "Epoch 212/250\n",
      "4550/4550 [==============================] - 1s 126us/step - loss: 0.0424 - binary_crossentropy: 0.0424\n",
      "Epoch 213/250\n",
      "4550/4550 [==============================] - 1s 120us/step - loss: 0.0437 - binary_crossentropy: 0.0437\n",
      "Epoch 214/250\n",
      "4550/4550 [==============================] - 1s 119us/step - loss: 0.0441 - binary_crossentropy: 0.0441\n",
      "Epoch 215/250\n",
      "4550/4550 [==============================] - 1s 119us/step - loss: 0.0430 - binary_crossentropy: 0.0430\n",
      "Epoch 216/250\n",
      "4550/4550 [==============================] - 1s 118us/step - loss: 0.0408 - binary_crossentropy: 0.0408\n",
      "Epoch 217/250\n",
      "4550/4550 [==============================] - 1s 120us/step - loss: 0.0418 - binary_crossentropy: 0.0418\n",
      "Epoch 218/250\n",
      "4550/4550 [==============================] - 1s 119us/step - loss: 0.0414 - binary_crossentropy: 0.0414\n",
      "Epoch 219/250\n",
      "4550/4550 [==============================] - 1s 120us/step - loss: 0.0405 - binary_crossentropy: 0.0405\n",
      "Epoch 220/250\n",
      "4550/4550 [==============================] - 1s 120us/step - loss: 0.0386 - binary_crossentropy: 0.0386\n",
      "Epoch 221/250\n",
      "4550/4550 [==============================] - 1s 119us/step - loss: 0.0390 - binary_crossentropy: 0.0390\n",
      "Epoch 222/250\n",
      "4550/4550 [==============================] - 1s 115us/step - loss: 0.0383 - binary_crossentropy: 0.0383\n",
      "Epoch 223/250\n",
      "4550/4550 [==============================] - 1s 121us/step - loss: 0.0377 - binary_crossentropy: 0.0377\n",
      "Epoch 224/250\n",
      "4550/4550 [==============================] - 1s 117us/step - loss: 0.0417 - binary_crossentropy: 0.0417\n",
      "Epoch 225/250\n",
      "4550/4550 [==============================] - 1s 116us/step - loss: 0.0392 - binary_crossentropy: 0.0392\n",
      "Epoch 226/250\n",
      "4550/4550 [==============================] - 1s 117us/step - loss: 0.0366 - binary_crossentropy: 0.0366\n",
      "Epoch 227/250\n",
      "4550/4550 [==============================] - 1s 121us/step - loss: 0.0381 - binary_crossentropy: 0.0381\n",
      "Epoch 228/250\n",
      "4550/4550 [==============================] - 1s 128us/step - loss: 0.0411 - binary_crossentropy: 0.0411\n",
      "Epoch 229/250\n",
      "4550/4550 [==============================] - 1s 121us/step - loss: 0.0381 - binary_crossentropy: 0.0381\n",
      "Epoch 230/250\n",
      "4550/4550 [==============================] - 1s 118us/step - loss: 0.0353 - binary_crossentropy: 0.0353\n",
      "Epoch 231/250\n",
      "4550/4550 [==============================] - 1s 118us/step - loss: 0.0389 - binary_crossentropy: 0.0389\n",
      "Epoch 232/250\n",
      "4550/4550 [==============================] - 1s 118us/step - loss: 0.0388 - binary_crossentropy: 0.0388\n",
      "Epoch 233/250\n",
      "4550/4550 [==============================] - 1s 119us/step - loss: 0.0352 - binary_crossentropy: 0.0352\n",
      "Epoch 234/250\n",
      "4550/4550 [==============================] - 1s 126us/step - loss: 0.0384 - binary_crossentropy: 0.0384\n",
      "Epoch 235/250\n",
      "4550/4550 [==============================] - 1s 118us/step - loss: 0.0349 - binary_crossentropy: 0.0349\n",
      "Epoch 236/250\n",
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      "Epoch 237/250\n",
      "4550/4550 [==============================] - 1s 120us/step - loss: 0.0372 - binary_crossentropy: 0.0372\n",
      "Epoch 238/250\n",
      "4550/4550 [==============================] - 1s 119us/step - loss: 0.0395 - binary_crossentropy: 0.0395\n",
      "Epoch 239/250\n",
      "4550/4550 [==============================] - 1s 119us/step - loss: 0.0368 - binary_crossentropy: 0.0368\n",
      "Epoch 240/250\n",
      "4550/4550 [==============================] - 1s 118us/step - loss: 0.0387 - binary_crossentropy: 0.0387\n",
      "Epoch 241/250\n",
      "4550/4550 [==============================] - 1s 125us/step - loss: 0.0350 - binary_crossentropy: 0.0350\n",
      "Epoch 242/250\n",
      "4550/4550 [==============================] - 1s 118us/step - loss: 0.0373 - binary_crossentropy: 0.0373\n",
      "Epoch 243/250\n",
      "4550/4550 [==============================] - 1s 122us/step - loss: 0.0389 - binary_crossentropy: 0.0389\n",
      "Epoch 244/250\n",
      "4550/4550 [==============================] - 1s 122us/step - loss: 0.0368 - binary_crossentropy: 0.0368\n",
      "Epoch 245/250\n",
      "4550/4550 [==============================] - 1s 120us/step - loss: 0.0383 - binary_crossentropy: 0.0383\n",
      "Epoch 246/250\n",
      "4550/4550 [==============================] - 1s 120us/step - loss: 0.0392 - binary_crossentropy: 0.0392\n",
      "Epoch 247/250\n",
      "4550/4550 [==============================] - 1s 119us/step - loss: 0.0373 - binary_crossentropy: 0.0373\n",
      "Epoch 248/250\n",
      "4550/4550 [==============================] - 1s 118us/step - loss: 0.0358 - binary_crossentropy: 0.0358\n",
      "Epoch 249/250\n",
      "4550/4550 [==============================] - 1s 134us/step - loss: 0.0386 - binary_crossentropy: 0.0386\n",
      "Epoch 250/250\n",
      "4550/4550 [==============================] - 1s 138us/step - loss: 0.0357 - binary_crossentropy: 0.0357\n",
      "AUROC_test: 0.8651967214502124\n",
      "  \n",
      "Saved model to disk\n",
      "  \n",
      "Epoch 1/250\n",
      "4550/4550 [==============================] - 1s 263us/step - loss: 0.7090 - binary_crossentropy: 0.7090\n",
      "Epoch 2/250\n",
      "4550/4550 [==============================] - 1s 122us/step - loss: 0.6441 - binary_crossentropy: 0.6441\n",
      "Epoch 3/250\n",
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      "Epoch 4/250\n",
      "4550/4550 [==============================] - 1s 121us/step - loss: 0.5853 - binary_crossentropy: 0.5853\n",
      "Epoch 5/250\n",
      "4550/4550 [==============================] - 1s 119us/step - loss: 0.5695 - binary_crossentropy: 0.5695\n",
      "Epoch 6/250\n",
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      "Epoch 7/250\n",
      "4550/4550 [==============================] - 1s 118us/step - loss: 0.5512 - binary_crossentropy: 0.5512\n",
      "Epoch 8/250\n",
      "4550/4550 [==============================] - 1s 131us/step - loss: 0.5447 - binary_crossentropy: 0.5447\n",
      "Epoch 9/250\n",
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      "Epoch 10/250\n",
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      "Epoch 11/250\n",
      "4550/4550 [==============================] - 1s 124us/step - loss: 0.5205 - binary_crossentropy: 0.5205\n",
      "Epoch 12/250\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4550/4550 [==============================] - 1s 128us/step - loss: 0.5165 - binary_crossentropy: 0.5165\n",
      "Epoch 13/250\n",
      "4550/4550 [==============================] - 1s 127us/step - loss: 0.5159 - binary_crossentropy: 0.5159\n",
      "Epoch 14/250\n",
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      "Epoch 15/250\n",
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      "Epoch 16/250\n",
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      "Epoch 17/250\n",
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      "Epoch 18/250\n",
      "4550/4550 [==============================] - 1s 121us/step - loss: 0.4859 - binary_crossentropy: 0.4859\n",
      "Epoch 19/250\n",
      "4550/4550 [==============================] - 1s 124us/step - loss: 0.4900 - binary_crossentropy: 0.4900\n",
      "Epoch 20/250\n",
      "4550/4550 [==============================] - 1s 136us/step - loss: 0.4827 - binary_crossentropy: 0.4827\n",
      "Epoch 21/250\n",
      "4550/4550 [==============================] - 1s 128us/step - loss: 0.4818 - binary_crossentropy: 0.4818\n",
      "Epoch 22/250\n",
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      "Epoch 23/250\n",
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      "Epoch 24/250\n",
      "4550/4550 [==============================] - 1s 118us/step - loss: 0.4680 - binary_crossentropy: 0.4680\n",
      "Epoch 25/250\n",
      "4550/4550 [==============================] - 1s 124us/step - loss: 0.4691 - binary_crossentropy: 0.4691\n",
      "Epoch 26/250\n",
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      "Epoch 27/250\n",
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      "Epoch 28/250\n",
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      "Epoch 29/250\n",
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      "Epoch 30/250\n",
      "4550/4550 [==============================] - 1s 135us/step - loss: 0.4534 - binary_crossentropy: 0.4534\n",
      "Epoch 31/250\n",
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      "Epoch 32/250\n",
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      "Epoch 33/250\n",
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      "Epoch 34/250\n",
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      "Epoch 35/250\n",
      "4550/4550 [==============================] - 1s 119us/step - loss: 0.4310 - binary_crossentropy: 0.4310\n",
      "Epoch 36/250\n",
      "4550/4550 [==============================] - 1s 121us/step - loss: 0.4302 - binary_crossentropy: 0.4302\n",
      "Epoch 37/250\n",
      "4550/4550 [==============================] - 1s 124us/step - loss: 0.4293 - binary_crossentropy: 0.4293\n",
      "Epoch 38/250\n",
      "4550/4550 [==============================] - 1s 122us/step - loss: 0.4231 - binary_crossentropy: 0.4231\n",
      "Epoch 39/250\n",
      "4550/4550 [==============================] - 1s 135us/step - loss: 0.4226 - binary_crossentropy: 0.4226\n",
      "Epoch 40/250\n",
      "4550/4550 [==============================] - 1s 123us/step - loss: 0.4132 - binary_crossentropy: 0.4132\n",
      "Epoch 41/250\n",
      "4550/4550 [==============================] - 1s 122us/step - loss: 0.4156 - binary_crossentropy: 0.4156\n",
      "Epoch 42/250\n",
      "4550/4550 [==============================] - 1s 123us/step - loss: 0.4100 - binary_crossentropy: 0.4100\n",
      "Epoch 43/250\n",
      "4550/4550 [==============================] - 1s 119us/step - loss: 0.4004 - binary_crossentropy: 0.4004\n",
      "Epoch 44/250\n",
      "4550/4550 [==============================] - 1s 121us/step - loss: 0.3988 - binary_crossentropy: 0.3988\n",
      "Epoch 45/250\n",
      "4550/4550 [==============================] - 1s 121us/step - loss: 0.3906 - binary_crossentropy: 0.3906\n",
      "Epoch 46/250\n",
      "4550/4550 [==============================] - 1s 119us/step - loss: 0.3927 - binary_crossentropy: 0.3927\n",
      "Epoch 47/250\n",
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      "Epoch 48/250\n",
      "4550/4550 [==============================] - 1s 121us/step - loss: 0.3823 - binary_crossentropy: 0.3823\n",
      "Epoch 49/250\n",
      "4550/4550 [==============================] - 1s 120us/step - loss: 0.3778 - binary_crossentropy: 0.3778\n",
      "Epoch 50/250\n",
      "4550/4550 [==============================] - 1s 125us/step - loss: 0.3703 - binary_crossentropy: 0.3703\n",
      "Epoch 51/250\n",
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      "Epoch 52/250\n",
      "4550/4550 [==============================] - 1s 140us/step - loss: 0.3531 - binary_crossentropy: 0.3531\n",
      "Epoch 53/250\n",
      "4550/4550 [==============================] - 1s 135us/step - loss: 0.3588 - binary_crossentropy: 0.3588\n",
      "Epoch 54/250\n",
      "4550/4550 [==============================] - 1s 138us/step - loss: 0.3534 - binary_crossentropy: 0.3534\n",
      "Epoch 55/250\n",
      "4550/4550 [==============================] - 1s 143us/step - loss: 0.3504 - binary_crossentropy: 0.3504\n",
      "Epoch 56/250\n",
      "4550/4550 [==============================] - 1s 124us/step - loss: 0.3419 - binary_crossentropy: 0.3419\n",
      "Epoch 57/250\n",
      "4550/4550 [==============================] - 1s 119us/step - loss: 0.3343 - binary_crossentropy: 0.3343\n",
      "Epoch 58/250\n",
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      "Epoch 59/250\n",
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      "Epoch 60/250\n",
      "4550/4550 [==============================] - 1s 120us/step - loss: 0.3139 - binary_crossentropy: 0.3139\n",
      "Epoch 61/250\n",
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      "Epoch 62/250\n",
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      "Epoch 63/250\n",
      "4550/4550 [==============================] - 1s 139us/step - loss: 0.2984 - binary_crossentropy: 0.2984\n",
      "Epoch 64/250\n",
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      "Epoch 65/250\n",
      "4550/4550 [==============================] - 1s 140us/step - loss: 0.2822 - binary_crossentropy: 0.2822\n",
      "Epoch 66/250\n",
      "4550/4550 [==============================] - 1s 142us/step - loss: 0.2757 - binary_crossentropy: 0.2757\n",
      "Epoch 67/250\n",
      "4550/4550 [==============================] - 1s 124us/step - loss: 0.2719 - binary_crossentropy: 0.2719\n",
      "Epoch 68/250\n",
      "4550/4550 [==============================] - 1s 116us/step - loss: 0.2648 - binary_crossentropy: 0.2648\n",
      "Epoch 69/250\n",
      "4550/4550 [==============================] - 1s 116us/step - loss: 0.2540 - binary_crossentropy: 0.2540\n",
      "Epoch 70/250\n",
      "4550/4550 [==============================] - 1s 119us/step - loss: 0.2483 - binary_crossentropy: 0.2483\n",
      "Epoch 71/250\n",
      "4550/4550 [==============================] - 1s 119us/step - loss: 0.2491 - binary_crossentropy: 0.2491\n",
      "Epoch 72/250\n",
      "4550/4550 [==============================] - 1s 117us/step - loss: 0.2421 - binary_crossentropy: 0.2421\n",
      "Epoch 73/250\n",
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      "Epoch 74/250\n",
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      "Epoch 75/250\n",
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      "Epoch 76/250\n",
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      "Epoch 77/250\n",
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      "Epoch 78/250\n",
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      "Epoch 79/250\n",
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      "Epoch 80/250\n",
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      "Epoch 81/250\n",
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      "Epoch 82/250\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4550/4550 [==============================] - 1s 163us/step - loss: 0.1917 - binary_crossentropy: 0.1917\n",
      "Epoch 83/250\n",
      "4550/4550 [==============================] - 1s 126us/step - loss: 0.1817 - binary_crossentropy: 0.1817\n",
      "Epoch 84/250\n",
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      "Epoch 85/250\n",
      "4550/4550 [==============================] - 1s 145us/step - loss: 0.1754 - binary_crossentropy: 0.1754\n",
      "Epoch 86/250\n",
      "4550/4550 [==============================] - 1s 137us/step - loss: 0.1647 - binary_crossentropy: 0.1647\n",
      "Epoch 87/250\n",
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      "Epoch 88/250\n",
      "4550/4550 [==============================] - 1s 134us/step - loss: 0.1637 - binary_crossentropy: 0.1637\n",
      "Epoch 89/250\n",
      "4550/4550 [==============================] - 1s 140us/step - loss: 0.1561 - binary_crossentropy: 0.1561\n",
      "Epoch 90/250\n",
      "4550/4550 [==============================] - 1s 140us/step - loss: 0.1510 - binary_crossentropy: 0.1510\n",
      "Epoch 91/250\n",
      "4550/4550 [==============================] - 1s 153us/step - loss: 0.1531 - binary_crossentropy: 0.1531\n",
      "Epoch 92/250\n",
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      "Epoch 93/250\n",
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      "Epoch 94/250\n",
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      "Epoch 95/250\n",
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      "Epoch 96/250\n",
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      "Epoch 97/250\n",
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      "Epoch 136/250\n",
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      "Epoch 137/250\n",
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      "Epoch 138/250\n",
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      "Epoch 139/250\n",
      "4550/4550 [==============================] - 1s 131us/step - loss: 0.0697 - binary_crossentropy: 0.0697\n",
      "Epoch 140/250\n",
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      "Epoch 141/250\n",
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      "Epoch 142/250\n",
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      "Epoch 143/250\n",
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      "Epoch 144/250\n",
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      "Epoch 145/250\n",
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      "Epoch 146/250\n",
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      "Epoch 147/250\n",
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      "Epoch 148/250\n",
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      "Epoch 149/250\n",
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      "Epoch 150/250\n",
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      "Epoch 151/250\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4550/4550 [==============================] - 1s 130us/step - loss: 0.0611 - binary_crossentropy: 0.0611\n",
      "Epoch 152/250\n",
      "4550/4550 [==============================] - 1s 132us/step - loss: 0.0596 - binary_crossentropy: 0.0596\n",
      "Epoch 153/250\n",
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      "Epoch 154/250\n",
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      "Epoch 155/250\n",
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      "Epoch 156/250\n",
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      "Epoch 157/250\n",
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      "Epoch 158/250\n",
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      "Epoch 159/250\n",
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      "Epoch 160/250\n",
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      "Epoch 161/250\n",
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      "Epoch 162/250\n",
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      "Epoch 163/250\n",
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      "Epoch 165/250\n",
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      "Epoch 166/250\n",
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      "Epoch 167/250\n",
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      "Epoch 168/250\n",
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      "Epoch 169/250\n",
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      "Epoch 170/250\n",
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      "Epoch 171/250\n",
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      "Epoch 172/250\n",
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      "Epoch 173/250\n",
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      "Epoch 174/250\n",
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      "Epoch 175/250\n",
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      "Epoch 176/250\n",
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      "Epoch 177/250\n",
      "4550/4550 [==============================] - 1s 120us/step - loss: 0.0518 - binary_crossentropy: 0.0518\n",
      "Epoch 178/250\n",
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      "Epoch 179/250\n",
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      "Epoch 180/250\n",
      "4550/4550 [==============================] - 1s 130us/step - loss: 0.0488 - binary_crossentropy: 0.0488\n",
      "Epoch 181/250\n",
      "4550/4550 [==============================] - 1s 128us/step - loss: 0.0467 - binary_crossentropy: 0.0467\n",
      "Epoch 182/250\n",
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      "Epoch 183/250\n",
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      "Epoch 184/250\n",
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      "Epoch 185/250\n",
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      "Epoch 186/250\n",
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      "Epoch 187/250\n",
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      "Epoch 188/250\n",
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      "Epoch 189/250\n",
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      "Epoch 190/250\n",
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      "Epoch 191/250\n",
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      "Epoch 192/250\n",
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      "Epoch 201/250\n",
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      "Epoch 202/250\n",
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      "Epoch 208/250\n",
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      "Epoch 209/250\n",
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      "Epoch 210/250\n",
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      "Epoch 211/250\n",
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      "Epoch 212/250\n",
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      "Epoch 215/250\n",
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      "Epoch 216/250\n",
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      "Epoch 218/250\n",
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      "Epoch 219/250\n",
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      "Epoch 220/250\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4550/4550 [==============================] - 1s 126us/step - loss: 0.0386 - binary_crossentropy: 0.0386\n",
      "Epoch 221/250\n",
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      "Epoch 222/250\n",
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      "Epoch 223/250\n",
      "4550/4550 [==============================] - 1s 128us/step - loss: 0.0374 - binary_crossentropy: 0.0374\n",
      "Epoch 224/250\n",
      "4550/4550 [==============================] - 1s 121us/step - loss: 0.0417 - binary_crossentropy: 0.0417\n",
      "Epoch 225/250\n",
      "4550/4550 [==============================] - 1s 121us/step - loss: 0.0393 - binary_crossentropy: 0.0393\n",
      "Epoch 226/250\n",
      "4550/4550 [==============================] - 1s 122us/step - loss: 0.0366 - binary_crossentropy: 0.0366\n",
      "Epoch 227/250\n",
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      "Epoch 228/250\n",
      "4550/4550 [==============================] - 1s 121us/step - loss: 0.0411 - binary_crossentropy: 0.0411\n",
      "Epoch 229/250\n",
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      "Epoch 230/250\n",
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      "Epoch 231/250\n",
      "4550/4550 [==============================] - 1s 122us/step - loss: 0.0389 - binary_crossentropy: 0.0389\n",
      "Epoch 232/250\n",
      "4550/4550 [==============================] - 1s 128us/step - loss: 0.0389 - binary_crossentropy: 0.0389\n",
      "Epoch 233/250\n",
      "4550/4550 [==============================] - 1s 122us/step - loss: 0.0352 - binary_crossentropy: 0.0352\n",
      "Epoch 234/250\n",
      "4550/4550 [==============================] - 1s 120us/step - loss: 0.0384 - binary_crossentropy: 0.0384\n",
      "Epoch 235/250\n",
      "4550/4550 [==============================] - 1s 122us/step - loss: 0.0349 - binary_crossentropy: 0.0349\n",
      "Epoch 236/250\n",
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      "Epoch 237/250\n",
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      "Epoch 238/250\n",
      "4550/4550 [==============================] - 1s 121us/step - loss: 0.0395 - binary_crossentropy: 0.0395\n",
      "Epoch 239/250\n",
      "4550/4550 [==============================] - 1s 120us/step - loss: 0.0368 - binary_crossentropy: 0.0368\n",
      "Epoch 240/250\n",
      "4550/4550 [==============================] - 1s 122us/step - loss: 0.0388 - binary_crossentropy: 0.0388\n",
      "Epoch 241/250\n",
      "4550/4550 [==============================] - 1s 120us/step - loss: 0.0349 - binary_crossentropy: 0.0349\n",
      "Epoch 242/250\n",
      "4550/4550 [==============================] - 1s 124us/step - loss: 0.0373 - binary_crossentropy: 0.0373\n",
      "Epoch 243/250\n",
      "4550/4550 [==============================] - 1s 124us/step - loss: 0.0388 - binary_crossentropy: 0.0388\n",
      "Epoch 244/250\n",
      "4550/4550 [==============================] - 1s 121us/step - loss: 0.0368 - binary_crossentropy: 0.0368\n",
      "Epoch 245/250\n",
      "4550/4550 [==============================] - 1s 121us/step - loss: 0.0381 - binary_crossentropy: 0.0381\n",
      "Epoch 246/250\n",
      "4550/4550 [==============================] - 1s 122us/step - loss: 0.0388 - binary_crossentropy: 0.0388\n",
      "Epoch 247/250\n",
      "4550/4550 [==============================] - 1s 122us/step - loss: 0.0363 - binary_crossentropy: 0.0363\n",
      "Epoch 248/250\n",
      "4550/4550 [==============================] - 1s 120us/step - loss: 0.0354 - binary_crossentropy: 0.0354\n",
      "Epoch 249/250\n",
      "4550/4550 [==============================] - 1s 120us/step - loss: 0.0381 - binary_crossentropy: 0.0381\n",
      "Epoch 250/250\n",
      "4550/4550 [==============================] - 1s 121us/step - loss: 0.0350 - binary_crossentropy: 0.0350\n",
      "AUROC_test: 0.8657806655269884\n",
      "  \n",
      "Saved model to disk\n",
      "  \n",
      "0.8671162260286726\n"
     ]
    }
   ],
   "source": [
    "    \n",
    "    \n",
    "# At this point, we have obtained the optimized optimized values and selected the features in train and test based on \n",
    "#optimized threshold value of feature selection module of hybrid framework\n",
    "\n",
    "\n",
    "ensemb=4 #Number of ensembling average\n",
    "pred_test=[] #To store the individual model test prediction\n",
    "pred_test_final=np.zeros((test_selected_x.shape[0],1)) # To store the final test prediction after ensembling\n",
    "\n",
    "#As per the above number of ensemble, the models will be saved in the directory\n",
    "for x in range(ensemb):\n",
    "    \n",
    "     pred_test.append(model_nn_final(train_selected_x, train_selected_y, test_selected_x, test_selected_y, x, opt_result.best_params_[\"clf__dropout_rate\"]))\n",
    "     pred_test_final=pred_test[x]+pred_test_final\n",
    "        \n",
    "        \n",
    "#ensemble averaging        \n",
    "pred_test_final=pred_test_final/ensemb        \n",
    "        \n",
    "        \n",
    "#Final Accuracy\n",
    "auc_test_final = roc_auc_score(test_selected_y, pred_test_final)\n",
    "print(auc_test_final)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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