09_semisupervised_TF2_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 09 - Semisupervised Learning "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Main'''\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os, time, re\n",
    "import pickle, gzip\n",
    "\n",
    "'''Data Viz'''\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "color = sns.color_palette()\n",
    "import matplotlib as mpl\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "'''Data Prep and Model Evaluation'''\n",
    "from sklearn import preprocessing as pp\n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.model_selection import StratifiedKFold \n",
    "from sklearn.metrics import log_loss\n",
    "from sklearn.metrics import precision_recall_curve, average_precision_score\n",
    "from sklearn.metrics import roc_curve, auc, roc_auc_score\n",
    "\n",
    "'''Algos'''\n",
    "import lightgbm as lgb\n",
    "\n",
    "'''TensorFlow and Keras'''\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "K = keras.backend\n",
    "\n",
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.keras.layers import Activation, Dense, Dropout\n",
    "from tensorflow.keras.layers import BatchNormalization, Input, Lambda\n",
    "from tensorflow.keras import regularizers\n",
    "from tensorflow.keras.losses import mse, binary_crossentropy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check library versions & set seed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sklearn    0.20.3\n",
      "tensorflow 2.1.0\n",
      "keras      2.2.4-tf\n",
      "numpy      1.17.2\n",
      "lightgbp   2.3.1\n"
     ]
    }
   ],
   "source": [
    "import sys, sklearn\n",
    "print(f'sklearn    {sklearn.__version__}')\n",
    "print(f'tensorflow {tf.__version__}')\n",
    "print(f'keras      {keras.__version__}')\n",
    "print(f'numpy      {np.__version__}')\n",
    "print(f'lightgbp   {lgb.__version__}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To make the output stable across runs\n",
    "tf.random.set_seed(42)\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the data\n",
    "current_path = os.getcwd()\n",
    "file = os.path.sep.join(['', 'datasets', 'credit_card_data', 'credit_card.csv'])\n",
    "data = pd.read_csv(current_path + file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataX = data.copy().drop(['Class','Time'],axis=1)\n",
    "dataY = data['Class'].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scale the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale data\n",
    "featuresToScale = dataX.columns\n",
    "sX = pp.StandardScaler(copy=True, with_mean=True, with_std=True)\n",
    "dataX.loc[:,featuresToScale] = sX.fit_transform(dataX[featuresToScale])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split into train and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(dataX, dataY, test_size=0.33, \n",
    "                                       random_state=2018, stratify=dataY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(190820, 29), (93987, 29), (190820,), (93987,)]\n"
     ]
    }
   ],
   "source": [
    "print([x.shape for x in (X_train, X_test, y_train, y_test)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(190490, 330)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(y_train==0),np.sum(y_train==1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop 90% of the labels from the training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/faisito/anaconda3/envs/tf2/lib/python3.7/site-packages/pandas/core/frame.py:4102: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  errors=errors,\n"
     ]
    }
   ],
   "source": [
    "toDrop = y_train[y_train==1].sample(frac=0.90,random_state=2018)\n",
    "X_train.drop(labels=toDrop.index,inplace=True)\n",
    "y_train.drop(labels=toDrop.index,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the number of fruadulant cases left after dropping 90% of the cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(190490, 33)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(y_train==0),np.sum(y_train==1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define evaluation function and plotting function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def anomalyScores(originalDF, reducedDF):\n",
    "    loss = np.sum((np.array(originalDF) - \n",
    "                   np.array(reducedDF))**2, axis=1)\n",
    "    loss = pd.Series(data=loss,index=originalDF.index)\n",
    "    loss = (loss-np.min(loss))/(np.max(loss)-np.min(loss))\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotResults(trueLabels, anomalyScores, returnPreds = False):\n",
    "    preds = pd.concat([trueLabels, anomalyScores], axis=1)\n",
    "    preds.columns = ['trueLabel', 'anomalyScore']\n",
    "    precision, recall, thresholds = \\\n",
    "        precision_recall_curve(preds['trueLabel'], \n",
    "                               preds['anomalyScore'])\n",
    "    average_precision = average_precision_score( \n",
    "                        preds['trueLabel'], preds['anomalyScore'])\n",
    "    \n",
    "    plt.step(recall, precision, color='k', alpha=0.7, where='post')\n",
    "    plt.fill_between(recall, precision, step='post', alpha=0.3, color='k')\n",
    "\n",
    "    plt.xlabel('Recall')\n",
    "    plt.ylabel('Precision')\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    \n",
    "    plt.title('Precision-Recall curve: Average Precision = \\\n",
    "        {0:0.2f}'.format(average_precision))\n",
    "\n",
    "    fpr, tpr, thresholds = roc_curve(preds['trueLabel'], \n",
    "                                     preds['anomalyScore'])\n",
    "    areaUnderROC = auc(fpr, tpr)\n",
    "\n",
    "    plt.figure()\n",
    "    plt.plot(fpr, tpr, color='r', lw=2, label='ROC curve')\n",
    "    plt.plot([0, 1], [0, 1], color='k', lw=2, linestyle='--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver operating characteristic: Area under the \\\n",
    "        curve = {0:0.2f}'.format(areaUnderROC))\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.show()\n",
    "    \n",
    "    if returnPreds==True:\n",
    "        return preds, average_precision"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a new function to assess precision at a given recall threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def precisionAnalysis(df, column, threshold):\n",
    "    df.sort_values(by=column, ascending=False, inplace=True)\n",
    "    threshold_value = threshold*df.trueLabel.sum()\n",
    "    i = 0\n",
    "    j = 0\n",
    "    while i < threshold_value+1:\n",
    "        if df.iloc[j][\"trueLabel\"]==1:\n",
    "            i += 1\n",
    "        j += 1\n",
    "    return df, i/j"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Supervised Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "k_fold = StratifiedKFold(n_splits=5,shuffle=True,random_state=2018)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "params_lightGB = {\n",
    "    'task': 'train',\n",
    "    'application':'binary',\n",
    "    'num_class':1,\n",
    "    'boosting': 'gbdt',\n",
    "    'objective': 'binary',\n",
    "    'metric': 'binary_logloss',\n",
    "    'metric_freq':50,\n",
    "    'is_training_metric':False,\n",
    "    'max_depth':4,\n",
    "    'num_leaves': 31,\n",
    "    'learning_rate': 0.01,\n",
    "    'feature_fraction': 1.0,\n",
    "    'bagging_fraction': 1.0,\n",
    "    'bagging_freq': 0,\n",
    "    'bagging_seed': 2018,\n",
    "    'verbose': 0,\n",
    "    'num_threads':16\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\tvalid_0's binary_logloss: 0.00516281\n",
      "Training until validation scores don't improve for 200 rounds\n",
      "[2]\tvalid_0's binary_logloss: 0.00199983\n",
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      "Early stopping, best iteration is:\n",
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      "Training Log Loss: 1.1415121057817208e-05\n",
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      "Training Log Loss: 0.0031222480947869303\n",
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      "Training until validation scores don't improve for 200 rounds\n",
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      "Training Log Loss: 7.892448577578828e-05\n",
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      "Training Log Loss: 4.1986897135584226e-05\n",
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      "[1169]\tvalid_0's binary_logloss: 0.00158932\n",
      "[1170]\tvalid_0's binary_logloss: 0.00158955\n",
      "[1171]\tvalid_0's binary_logloss: 0.00158893\n",
      "[1172]\tvalid_0's binary_logloss: 0.00159027\n",
      "[1173]\tvalid_0's binary_logloss: 0.00159051\n",
      "[1174]\tvalid_0's binary_logloss: 0.00159189\n",
      "[1175]\tvalid_0's binary_logloss: 0.00159167\n",
      "[1176]\tvalid_0's binary_logloss: 0.00159265\n",
      "[1177]\tvalid_0's binary_logloss: 0.00159298\n",
      "[1178]\tvalid_0's binary_logloss: 0.00159236\n",
      "[1179]\tvalid_0's binary_logloss: 0.00159334\n",
      "[1180]\tvalid_0's binary_logloss: 0.00159314\n",
      "[1181]\tvalid_0's binary_logloss: 0.00159451\n",
      "[1182]\tvalid_0's binary_logloss: 0.00159447\n",
      "[1183]\tvalid_0's binary_logloss: 0.00159432\n",
      "[1184]\tvalid_0's binary_logloss: 0.00159541\n",
      "[1185]\tvalid_0's binary_logloss: 0.00159557\n",
      "[1186]\tvalid_0's binary_logloss: 0.00159659\n",
      "[1187]\tvalid_0's binary_logloss: 0.00159685\n",
      "[1188]\tvalid_0's binary_logloss: 0.00159681\n",
      "[1189]\tvalid_0's binary_logloss: 0.00159666\n",
      "[1190]\tvalid_0's binary_logloss: 0.00159802\n",
      "[1191]\tvalid_0's binary_logloss: 0.00159818\n",
      "[1192]\tvalid_0's binary_logloss: 0.00159955\n",
      "[1193]\tvalid_0's binary_logloss: 0.00159981\n",
      "[1194]\tvalid_0's binary_logloss: 0.00160117\n",
      "[1195]\tvalid_0's binary_logloss: 0.00160219\n",
      "[1196]\tvalid_0's binary_logloss: 0.00160244\n",
      "[1197]\tvalid_0's binary_logloss: 0.00160229\n",
      "[1198]\tvalid_0's binary_logloss: 0.00160362\n",
      "[1199]\tvalid_0's binary_logloss: 0.00160358\n",
      "[1200]\tvalid_0's binary_logloss: 0.00160343\n",
      "[1201]\tvalid_0's binary_logloss: 0.00160479\n",
      "[1202]\tvalid_0's binary_logloss: 0.00160581\n",
      "[1203]\tvalid_0's binary_logloss: 0.00160598\n",
      "[1204]\tvalid_0's binary_logloss: 0.00160614\n",
      "[1205]\tvalid_0's binary_logloss: 0.00160599\n",
      "[1206]\tvalid_0's binary_logloss: 0.00160735\n",
      "[1207]\tvalid_0's binary_logloss: 0.00160761\n",
      "[1208]\tvalid_0's binary_logloss: 0.00160896\n",
      "[1209]\tvalid_0's binary_logloss: 0.00160877\n",
      "[1210]\tvalid_0's binary_logloss: 0.00160978\n",
      "[1211]\tvalid_0's binary_logloss: 0.00160974\n",
      "[1212]\tvalid_0's binary_logloss: 0.0016096\n",
      "[1213]\tvalid_0's binary_logloss: 0.00160977\n",
      "[1214]\tvalid_0's binary_logloss: 0.00161112\n",
      "[1215]\tvalid_0's binary_logloss: 0.0016113\n",
      "[1216]\tvalid_0's binary_logloss: 0.00161241\n",
      "[1217]\tvalid_0's binary_logloss: 0.00161226\n",
      "[1218]\tvalid_0's binary_logloss: 0.00161359\n",
      "[1219]\tvalid_0's binary_logloss: 0.00161385\n",
      "[1220]\tvalid_0's binary_logloss: 0.0016152\n",
      "[1221]\tvalid_0's binary_logloss: 0.00161501\n",
      "[1222]\tvalid_0's binary_logloss: 0.00161636\n",
      "[1223]\tvalid_0's binary_logloss: 0.00161653\n",
      "[1224]\tvalid_0's binary_logloss: 0.00161753\n",
      "[1225]\tvalid_0's binary_logloss: 0.00161698\n",
      "[1226]\tvalid_0's binary_logloss: 0.00161715\n",
      "[1227]\tvalid_0's binary_logloss: 0.00161697\n",
      "[1228]\tvalid_0's binary_logloss: 0.00161832\n",
      "[1229]\tvalid_0's binary_logloss: 0.00161818\n",
      "[1230]\tvalid_0's binary_logloss: 0.0016195\n",
      "[1231]\tvalid_0's binary_logloss: 0.00161946\n",
      "[1232]\tvalid_0's binary_logloss: 0.00162056\n",
      "[1233]\tvalid_0's binary_logloss: 0.00162043\n",
      "[1234]\tvalid_0's binary_logloss: 0.00162069\n",
      "[1235]\tvalid_0's binary_logloss: 0.00162203\n",
      "[1236]\tvalid_0's binary_logloss: 0.00162221\n",
      "[1237]\tvalid_0's binary_logloss: 0.00162352\n",
      "[1238]\tvalid_0's binary_logloss: 0.0016237\n",
      "[1239]\tvalid_0's binary_logloss: 0.0016247\n",
      "[1240]\tvalid_0's binary_logloss: 0.00162467\n",
      "[1241]\tvalid_0's binary_logloss: 0.00162448\n",
      "[1242]\tvalid_0's binary_logloss: 0.00162435\n",
      "Early stopping, best iteration is:\n",
      "[1042]\tvalid_0's binary_logloss: 0.00155641\n",
      "Training Log Loss: 1.9097830121016974e-06\n",
      "CV Log Loss: 0.0015564059221184858\n"
     ]
    }
   ],
   "source": [
    "trainingScores = []\n",
    "cvScores = []\n",
    "predictionsBasedOnKFolds = pd.DataFrame(data=[], index=y_train.index, \n",
    "                                        columns=['prediction'])\n",
    "\n",
    "for train_index, cv_index in k_fold.split(np.zeros(len(X_train)), \n",
    "                                          y_train.ravel()):\n",
    "    X_train_fold, X_cv_fold = X_train.iloc[train_index,:], X_train.iloc[cv_index,:]\n",
    "    y_train_fold, y_cv_fold = y_train.iloc[train_index],   y_train.iloc[cv_index]\n",
    "    \n",
    "    lgb_train = lgb.Dataset(X_train_fold, y_train_fold)\n",
    "    lgb_eval = lgb.Dataset(X_cv_fold, y_cv_fold, reference=lgb_train)\n",
    "    gbm = lgb.train(params_lightGB, lgb_train, num_boost_round=2000,\n",
    "                   valid_sets=lgb_eval, early_stopping_rounds=200)\n",
    "    \n",
    "    loglossTraining = log_loss(y_train_fold, gbm.predict(X_train_fold, \n",
    "                                num_iteration=gbm.best_iteration))\n",
    "    trainingScores.append(loglossTraining)\n",
    "    \n",
    "    predictionsBasedOnKFolds.loc[X_cv_fold.index,'prediction'] = gbm.predict(\n",
    "        X_cv_fold, num_iteration=gbm.best_iteration) \n",
    "    loglossCV = log_loss(y_cv_fold, predictionsBasedOnKFolds.loc[X_cv_fold.index,'prediction'])\n",
    "    cvScores.append(loglossCV)\n",
    "    \n",
    "    print(f'Training Log Loss: {loglossTraining}')\n",
    "    print(f'CV Log Loss: {loglossCV}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LightGBM Gradient Boosting Log Loss: 0.0018\n"
     ]
    }
   ],
   "source": [
    "loglossLightGBMGradientBoosting = log_loss(y_train, \n",
    "        predictionsBasedOnKFolds.loc[:,'prediction'])\n",
    "print(f'LightGBM Gradient Boosting Log Loss: {round(loglossLightGBMGradientBoosting,4)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds, average_precision = plotResults(y_train, \n",
    "                        predictionsBasedOnKFolds.loc[:,'prediction'], True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions = pd.Series(data=gbm.predict(X_test, \n",
    "                num_iteration=gbm.best_iteration), index=X_test.index)\n",
    "preds, average_precision = plotResults(y_test, predictions, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision at 75% recall 0.0047\n"
     ]
    }
   ],
   "source": [
    "preds, precision = precisionAnalysis(preds, \"anomalyScore\", 0.75)\n",
    "print(f'Precision at 75% recall {round(precision,4)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unsupervised Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take the 33 fraudulent cases, duplicate these 100 times and then append them to the training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "oversample_multiplier = 100\n",
    "\n",
    "X_train_original = X_train.copy()\n",
    "y_train_original = y_train.copy()\n",
    "X_test_original = X_test.copy()\n",
    "y_test_original = y_test.copy()\n",
    "\n",
    "X_train_oversampled = X_train.copy()\n",
    "y_train_oversampled = y_train.copy()\n",
    "X_train_oversampled = X_train_oversampled.append( \\\n",
    "        [X_train_oversampled[y_train==1]]*oversample_multiplier, \\\n",
    "        ignore_index=False)\n",
    "y_train_oversampled = y_train_oversampled.append( \\\n",
    "        [y_train_oversampled[y_train==1]]*oversample_multiplier, \\\n",
    "        ignore_index=False)\n",
    "\n",
    "X_train = X_train_oversampled.copy()\n",
    "y_train = y_train_oversampled.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((193823, 29), (193823,))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A sparse two-layer overcomplete autoencoder with a linear a linear activation function.  \n",
    "Forty nodes in the hidden layer and a dropout of 2%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(units=40, activation='linear', activity_regularizer=regularizers.l1(1e-4),\n",
    "                input_dim=29, name='hidden_layer'))\n",
    "model.add(Dropout(0.02))\n",
    "model.add(Dense(units=29, activation='linear'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 155058 samples, validate on 38765 samples\n",
      "Epoch 1/5\n",
      "155058/155058 [==============================] - 4s 27us/sample - loss: 0.0729 - accuracy: 0.8671 - val_loss: 0.0698 - val_accuracy: 0.9660\n",
      "Epoch 2/5\n",
      "155058/155058 [==============================] - 4s 25us/sample - loss: 0.0182 - accuracy: 0.9263 - val_loss: 0.0262 - val_accuracy: 0.9795\n",
      "Epoch 3/5\n",
      "155058/155058 [==============================] - 4s 26us/sample - loss: 0.0177 - accuracy: 0.9266 - val_loss: 0.0165 - val_accuracy: 0.9835\n",
      "Epoch 4/5\n",
      "155058/155058 [==============================] - 4s 26us/sample - loss: 0.0169 - accuracy: 0.9269 - val_loss: 0.0195 - val_accuracy: 0.9834\n",
      "Epoch 5/5\n",
      "155058/155058 [==============================] - 4s 27us/sample - loss: 0.0176 - accuracy: 0.9259 - val_loss: 0.0066 - val_accuracy: 0.9828\n"
     ]
    }
   ],
   "source": [
    "num_epochs =  5\n",
    "batch_size = 32\n",
    "\n",
    "history = model.fit(x=X_train, y=X_train, epochs=num_epochs, batch_size=batch_size, shuffle=True,\n",
    "                    validation_split=0.20, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "190523/190523 [==============================] - 2s 12us/sample\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictionsTrain = model.predict(X_train_original, verbose=1)\n",
    "anomalyScoresAETrain = anomalyScores(X_train_original, predictionsTrain)\n",
    "preds, average_precision = plotResults(y_train_original, anomalyScoresAETrain, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "93987/93987 [==============================] - 1s 11us/sample\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HWuIcbMNRKLRMUYGzH/y21e33l0QlIl2BrgCVK1fOONgYEwlU4YYb4Lvv/J/mmmugSJHgxRTmYoFqy5fz1sGDcOyY1+EELBQSVWaPS8/0r5Sqvgm8CdC4cePwPIc1JlLt3QuPPQa7d5/ffJKT4YcfnM+S2eEhgxYtnKTmz7j5yMyZMylXrhxNmzYF4I29e4mJiaF48UAegB4aQiFRbQUq+XRXxHmekjF5Z/du+OgjOHnS60jC1wcfwNKluTe/KlVgkz2cOlB//PEHvXr14rPPPqNevXosWbKEmJgYSpUq5XVoORYKieozoKeITMWpRHHQrk+ZPHPqFKxbB48/DnPmeB1NZKhWDRISzn8+DRue/zzykeTkZEaNGsVzzz3HsWPHKFasGF27diUqKvzvQgp6ohKRKUALoLSIbAWeBWIAVDUB+BJoC6wHjgGdgx2TMWnatj37WkipUnD//d7FE+4uuAC6dIGqVb2OJF+ZP38+3bp1Y+XKlQDceeedjBw5kvLly3scWe7Ii1p/HbMZrsCjwY7DhKEzZ+CWW2D58uAtY7tbyly8uPMP/oMPoEKF4C3PmFx29OhRbrnlFvbt20fVqlUZP348bdq08TqsXBUKRX/G/NX27dC9O8yaFfxlVaoEv/3mnA0YEwZUlZSUFKKjoylSpAjDhw9n/fr1DBo0iMKFC3sdXq6zRJWfLVoEv//udRSZe+klWLHC+fy3v0EwH5JZurQlKRM21q5dS/fu3WnTpg39+/cHoHPnyL5iYokqEpw5A0eOBDbNhg1wRRg0AFKjBnz9tRXHmXzv+PHjvPLKKwwbNoxTp06xYcMGevfuTcGCBb0OLegsUYW706ehfn1ISsr5PO64I/fiyU3FisFzzzlFc8bkY19//TU9evRgg9sKx0MPPcSwYcPyRZICS1Th6Z13nOq/qrBrV/q9JhdeGNh8oqJg8GDo1y/3YzTGnLcjR47QpUsXpk6dCkB8fDwJCQk0b97c48jyliWqUPfVV7Bgwdn9Xnjhr+M1bQq//JI3MRlj8kThwoXZsmULsbGxPPvss/Tp0yffnEX5skQVLGfOOHfpJyfnfB7JyXDTTVkPnzoVLrnE+VyvXs6XY4wJGUuXLuWiiy6iSpUqREVF8fbbbxMTE0OVKlW8Ds0zlqiC5cknYeTI3Jvfc8+d3V2rFnTokHvzN8Z46vDhwzzzzDO88cYb3HjjjXzxxReICDVq1PA6NM9ZosptJ0/Ctdc6Vb9TXXnl+c2zXTt46qnzm4cxJiSpKjNmzKBXr15s27aNqKgoatWqxenTp4mJifE6vJBgiSo3nTrlJJXUJFW8uHP/T/Xq3sZljAlJv//+Oz179uSLL74AoEmTJkycOJHLLrvM48hCiyWq8/HTT/Drr+ndc+fC7NnO58sug4ULIR9e+DTGZO/QoUM0bNiQgwcPcuGFF/LKK6/wyCOPEB0d7XVoIccSVWYOHMj+Btpdu5yHtWXlq68sSRljsnThhRfy6KOPsnHjRkaMGEG5cuW8DilkWaLK6McfoWVL50Zafz30UPrnAgWc1qPLlMn92IwxYWvv3r0MGDCA1q1bc+eddwLw4osvRsRjOILNEtWJE3D77ek3za5Zkz4su2Z7RKBHD/jXv4IXnzEmrKkq77zzDk888QR79+7l22+/5bbbbqNAgQKWpPxkiWrePHAvZJ5l9Gjo1Svv4zHGRIykpCS6d+/ODz/8AMB1113H+PHjKVDADr2ByN9ba+NGGDo0vXvVKuc9NtZ5SqkxxuTAiRMnGDJkCK+++irJyclcfPHFjBgxgnvuuQcR8Tq8sJN/E9WBAxAX59z3BNC+PcTHexuTMSYiqCpTp04lOTmZrl278sorr1CyZEmvwwpb+TNRqTo34aYmqXbtMm8/zxhj/LR9+3YKFy5MiRIliI2N5e233yY6OpqrrrrK69DCXv68knf6NKxd63x+9FH49FNrK88YkyNnzpxhzJgx1K5dm0GDBqX1v+aaayxJ5ZL8dUa1dy/85z/p90gVLAhjx3obkzEmbC1ZsoRHHnmEJUuWAM5Z1ZkzZ+ym3VyWvxLVyy/DiBHp3YE+v8kYY4CDBw/y9NNPM27cOFJSUqhUqRJjxoyhXbt2XocWkfJXovr3v5332Fh45BG48UZv4zHGhJ19+/ZRr149tm/fTnR0NP369eO5556jaNGiXocWsfJPolqyBA4fdj5PngzuneHGGBOIkiVL0qJFCzZu3EhCQgINGjTwOqSIlz8SVUoKXH11eneLFp6FYowJL6dOnWL48OG0bNmSK91H9kycOJHChQtbyxJ5JH8kqjlz0quiv/SStcNnjPHLDz/8QPfu3UlKSqJ+/fosXbqUqKgoK+bLY/nj74BvU0h9+ngXhzEmLOzZs4fOnTvTokULkpKSqFGjBiNGjLAzKI9E/lbv1g2SkpzPTz/tVKQwxphMpKSkMGnSJGrVqsXkyZMpWLAgzz33HCtWrKBVq1Zeh5dviap6HUOONG7cWBMTE8890tKlcPnlzmcR2LcPSpQIfnDGmLC0Z88eatasyf79+2nVqhXjx4+nZs2aXoeVq0Rkiao29jqOQET2NSqfu8TZs8eSlDHmL44dO0ZMTAwxMTGULl2asW4jAB07drQGZENE5Bb9rV0Ls2Y5n3v2BGsQ0hiTwRdffEF8fDwjR45M63f33Xdz9913W5IKIZGZqD79FGrXTu++6y7vYjHGhJytW7fyz3/+k5tvvpnff/+dmTNnkpKS4nVYJguRmag2bEj/fM89cMUV3sVijAkZp0+fZtSoUdSpU4dPPvmEIkWKMGLECH744Qer0RfCgv7NiEgbEVkrIutF5KlMhhcXkf+KyHIRWS0inc97oW4DkTzxBLz/PtjTNI3J93bt2kXTpk3p06cPR44coX379iQlJdGnTx974m6IC2qiEpFoYBxwExAHdBSRuAyjPQqsUdUGQAvgdREpeF4L3r/feT906LxmY4yJHKVLl6Zw4cJUrlyZmTNnMmPGDCpVquR1WMYPwf4b0RRYr6obAURkKtAOWOMzjgLFxLlyWRTYB5w+r6Wm/ju64Ybzmo0xJnypKh999BFNmzalWrVqREVFMWXKFEqWLEmRIkW8Ds8EINhFfxWALT7dW91+vsYCdYDtwErgcVXN9KqmiHQVkUQRSdy9e3fWS/3mG+e9cOGcxm2MCWPr16+nTZs2dOzYkZ49e5J6v2ilSpUsSYWhYCeqzOp3ZrzD+EZgGVAeaAiMFZFMHxSlqm+qamNVbXzxxRdnvdTUGn+WqIzJV06ePMmQIUOoW7cuX3/9NRdddBG33nqr12GZ8xTsor+tgG8hcEWcMydfnYGh6vzlWS8im4DawKIcL3X5cufdGp81Jt+YO3cu3bt3Z+3atQB06tSJ1157jTJ2HAh7wU5Ui4EaIlIV2AbcBdydYZzNQCvgRxEpC9QCNuZ4iX/8kf75ootyPBtjTPjYsWMHbdq04dSpU9SqVYsJEyZw3XXXeR2WySVBTVSqelpEegKzgWhgkqquFpFu7vAE4EVgsoisxCkqHKCqe3K80IMH0z+XK5fz4I0xIS0lJQURQUQoV64cgwcPRkR48sknueCCC7wOz+SiyGuUdskSaNzYaYw29X4qY0xEWblyJd26dePxxx/nTntad0CsUdpQcOqU8x4T420cxphcd/ToUZ5//nlGjBjBmTNnOHHiBHfccYe1yxfhIq/NkKNHnfeC53fPsDEmtPz3v/8lLi6O1157jZSUFHr27MmcOXMsSeUDkXdGtcitLHjsmLdxGGNyxZ49e+jSpQuffvopAJdddhkTJ06kSZMmHkdm8kpknVEdPpz+DCo7ozImIhQpUoSVK1dStGhRRo0axaJFiyxJ5TORdUa1bVv65xde8C4OY8x5+eWXX6hVqxYlSpQgNjaWjz76iLJly1KxYkWvQzMeiKwzquRk571uXbj+em9jMcYEbP/+/XTv3p1mzZoxyOcJ3Y0aNbIklY9F1hlVaqKyGn/GhBVVZcqUKfTp04ddu3ZRoEABSpQogapaZQkTWKJyH79RWVXXByme87Mn5/cJG2O8sW7dOnr06MF3330HQPPmzUlISCA+Pt7jyEyo8LvoT0T+gdO6+Tdud0MRmRGswHIk9VHSO3d6G4cxxi9//PEH9evX57vvvqNUqVJMmjSJH374wZKUOUsgZ1QvAFcAcwFUdZmIVA9KVDk1caLz3rSpt3EYY/xyySWXcPvtt1OwYEFeffVVSpcu7XVIJgQFUpkiWVUPZOgXWu0vufdZULSot3EYYzK1c+dO7r33XhYsWJDW75133mHSpEmWpEyWAjmjShKRO4EotzX0x4GFwQnrPFnVdGNCSkpKCm+++SZPPfUUBw8eZP369SxYsAARITo62uvwTIgL5IyqJ9AISAE+AU7gJKvQ8Nln6Z+tGqsxIWP58uVcddVVdO/enYMHD9KmTRs+/PBDq81n/BZIorpRVQeo6mXu6yngpmAFFrANG9I/W/V0Yzx35MgR+vXrR6NGjfjll18oX74806ZN48svv6RatWpeh2fCSCCJanAm/QZl0s8bv/3mvPfu7W0cxhgAjh07xqRJk1BVevXqRVJSErfffrudSZmAZXuNSkRuBNoAFURkhM+gC3GKAUNDavNJO3Z4G4cx+djmzZspV64cMTExlClThrfffptKlSrRqFEjr0MzYcyfM6pdwCqca1KrfV5fE0pFf7/+6rzb/RfG5Lnk5GRee+016tSpw8iRI9P6t2/f3pKUOW/ZnlGp6lJgqYh8oKon8iCmwKnCunXO5+qhdWuXMZFu/vz5dOvWjZUrVwLwa+qfRmNySSDXqCqIyFQRWSEi61JfQYssECd88mfr1t7FYUw+sm/fPrp27crVV1/NypUrqVatGrNmzWLSpEleh2YiTCD3UU0GhgDDcYr8OhMq16hSHz9frBiUKuVtLMbkA+vXr+eqq65i9+7dxMTE0L9/fwYNGkRsbKzXoZkIFEiiKqyqs0VkuKpuAAaLyI/BCiwg1mq6MXmqWrVq1KxZk7i4OCZMmECdOnW8DslEsEAS1Ulx6pVuEJFuwDagTHDCClDqGZU91deYoDh+/DhDhw7l/vvvp1q1akRFRfHZZ59x0UUXWXVzE3SBJKo+QFGgF/ASUBx4MBhBBezgQefdzqiMyXVff/01PXr0YMOGDSxZsoTPP/8cgJIlS3ocmckv/E5UqvqL+/EwcB+AiIRGW0VHjjjvW7Z4G4cxEWTHjh307duXqVOnAhAfH89TTz3lcVQmP/Kr1p+INBGR9iJS2u2OF5F3CZVGaU+fdt6vuMLbOIyJAGfOnGH8+PHUrl2bqVOnEhsby7Bhw1i6dCnNmzf3OjyTD2WbqETkFeAD4B7gKxEZhPNMquVAzeCG56czZ5x3a4XZmPO2ceNGevfuzaFDh/jHP/7BmjVr6N+/PzFWtG484k/RXzuggaoeF5GSwHa3e21wQwvA4sXOu13UNSZHjh49SuHChRERatSowbBhw6hSpQrt27e3yhLGc/4U/Z1Q1eMAqroP+DWkkhRAlLsau3d7G4cxYUZV+fjjj6lVqxbTpk1L69+nTx9uvfVWS1ImJPhzRlVNRD5xPwtQxacbVb0tKJEFYvNm571NG2/jMCaMbNq0iZ49e/Lll18C8OGHH3LnnXd6HJUxf+VPovpnhu6xwQjkvOzf77zv3OltHMaEgVOnTjFixAheeOEFjh8/TvHixXnllVfo2rWr16EZkyl/GqX9Li8COS9Fizrvdet6G4cxIW7Dhg20a9eO1atXA9CxY0dGjBjB3/72N48jMyZrgdzwG7rGjHHeixXzNg5jQlz58uU5ceIE1atXZ/z48dxwww1eh2RMtgJpPT1HRKSNiKwVkfUikundgiLSQkSWichqEfkhoAWkFvsBVK16XrEaE2lUlffee48DBw4AEBsbyxdffMHKlSstSZmwEXCiEpELAhg3GhiH09p6HNBRROIyjFMCGA/8n6rGA3cEFNA336R/btkyoEmNiWRr1qyhRYsWdOrUiUGDBqX1r1WrFoUKFfIwMmMC43eiEpGmIrIS+M3tbiAiY7KZrCmwXlU3quopYCrOfVm+7gY+UdXNAKq6y+/oAY4fd95btky/VlZ2M+kAACAASURBVGVMPnbs2DEGDRpEw4YNmTdvHhdffDFXXXWV12EZk2OBnFG9AdwM7AVQ1eXAddlMUwHwbYBvq9vPV03gIhH5XkSWiEinAGJKbzndiv2M4auvvqJu3bq8/PLLJCcn88gjj7B27Vruuecer0MzJscCqUwRpap/ZLgB8Ew202R2t6BmEkMjoBUQCywQkYWq+penB4tIV6ArQOXKlZ2eP7qPxLrA7xJJYyLSihUruOmmmwCoV68eEydOpFmzZh5HZcz5CyRRbRGRpoC6154eA7J7FP1WoJJPd0WcJpgyjrNHVY8CR0VkHtAgs3mr6pvAmwCNGzd2El7hws5A30oVxuQTqprWekT9+vXp2rUrNWvWpFevXtY2n4kYgRT9dQf6ApWBncCVbr9zWQzUEJGqIlIQuAv4LMM4M4FrRKSAiBQGrgCS/I4q9em+VpHC5DOJiYk0bdqUBQsWpPWbOHEi/fr1syRlIkogieq0qt6lqqXd112quudcE6jqaaAnMBsn+fxHVVeLSDf3KcGoahLwFbACWAS8paqr/I4q9RqV/TBNPnHw4EEee+wxmjZtSmJiIkOGDPE6JGOCKpCiv8Uishb4CKeW3mF/JlLVL4EvM/RLyND9GvBaALGke/99590SlYlwqsq0adPo3bs3O3bsIDo6mj59+vDss896HZoxQeX3GZWqXgoMwan4sFJEPhWRu4IWmb8quJUIrdafiWBbt26lbdu2dOjQgR07dnDllVeyZMkSXnvtNYrabRkmwgV0w6+qzlfVXsDlwCGcByp6K/WhiZdc4m0cxgRRwYIFWbhwISVKlGDixIn8/PPPNGjQwOuwjMkTfhf9iUhRnJt17wLq4FSC8P4uwn37nHd7uq+JMPPnz6dJkybExMRQpkwZPv74Y+Lj4ylbtqzXoRmTpwI5o1qFU9PvVVWtrqr9VPWXIMXlH9X0yhQFC3oaijG5Zffu3TzwwANcffXVjBw5Mq1/y5YtLUmZfCmQyhTVVDUlaJHkRGrVdICLLvIuDmNyQUpKCm+//Tb9+/dn3759FCxYENWM98cbk/9km6hE5HVV7Qd8LCJ/+dV4+oTf1Hb+ihTxLARjcsOqVavo3r07P/30EwDXX38948ePp0aNGh5HZoz3/Dmj+sh9D70n+yYmOu9Hj3obhzHnITExkWbNmnH69GnKli3LyJEjueuuu8jQXJkx+ZY/T/hd5H6so6pnJSsR6Ql49wTg6dOd9xIlPAvBmPN1+eWXc9VVVxEfH8/LL79MCdufjTlLIJUpHsyk30O5FUiOrFzpvLdu7WkYxgRi69at3H333WzcuBGAqKgovv32W8aPH29JyphM+HONqgNOlfSqIvKJz6BiwIFgBeaXSy+Fn3+GuLjsxzXGY6dPn2bs2LE8/fTTHDlyhFOnTjHdLRWwtvmMyZo/16gW4TyDqiLO03pTHQaWBiMov6VWTbcLzibELVq0iEceeYRly5YBcNttt51V9dwYkzV/rlFtAjYB3wY/nAClVk+3e6hMiDpw4AADBw4kISEBVaVy5cqMGzeOm2++2evQjAkb2V6jEpEf3Pf9IrLP57VfRPYFP8RzsJbTTYjbvn07//73v4mOjqZ///6sWbPGkpQxAfKn6C/1cfOlgxlIjnz+ufNuicqEkK1bt1KhQgVEhLi4OBISEmjatCn16tXzOjRjwlK2Z1Q+rVFUAqJV9QzQDHgE8PZO2+rVnXdrlcKEgJMnT/Liiy9SvXp1pk2bltb/oYcesiRlzHkIpHr6pziPob8UeBenYdoPgxKVv377zXmvWNHTMIyZO3cuDRo04JlnnuHkyZMsXrzY65CMiRiBJKoUVU0GbgNGqepjQIXghBWg4sW9jsDkU7t27aJTp060bNmStWvXUqtWLebOnctrr+XsOaDGmL8KpFHa0yJyB3Af0N7t593FoRSf9nEvvNCzMEz+lZiYSOvWrdm/fz+FChVi8ODBPPHEE1xwwQVeh2ZMRAkkUT0I9MB5zMdGEakKTAlOWH5IfWBi6dCr42Hyh/j4eEqWLEmTJk0YP348l156qdchGRORJJDHCIhIAcCtwcB6VT0dlKj80LhePU1ctcrpsEchmDxw9OhRXn31Vfr06ZPW1NHOnTspU6aMNSBrwoaILFHVxl7HEYhAnvB7DfAesA0Q4G8icp+q/hys4M4pNTml1vwzJog+++wzHnvsMTZv3syePXsYN85ppMUeZGhM8AVS9DcSaKuqawBEpA5O4vImM6cmKmuVwgTR5s2b6dWrFzNnzgScls47d+7scVTG5C+B1PormJqkAFQ1CfAuS6QmqgKB5Fpj/JOcnMzrr79OXFwcM2fOpFixYowePZpFixbRuHFYlZoYE/YCOcr/T0Qm4pxFAdyD143SgrVKYYJi4cKFPPHEEwDcfvvtjBo1igoVQuNuDGPym0ASVTegF9Af5xrVPGBMMILyS2r1dDujMrnkxIkTFCpUCIBrrrmGJ598khYtWtC2bVuPIzMmf/Or6E9E6gFtgBmq+n+qeouqvqaqJ4Ib3jmcdiscHjzoWQgmMqgqH3zwAVWrVmXBggVp/V999VVLUsaEAH9aTx+I03zSPcA3IpLZk37zXmp14Ohob+MwYW3dunXccMMN3Hvvvfz5559MnjzZ65CMMRn4c0Z1D1BfVe8AmgDdgxuSn6x6ujkPJ06c4LnnnqNevXp89913lCpVikmTJjFhwgSvQzPGZODPBZ6TqnoUQFV3i0ggNQWDx2r9mRxavnw5d9xxB7+5jRo/+OCDDBs2jNLWyokxIcmfo3w1EfnE/SzApT7dqOptQYksO5aoTA6VK1eO3bt3ExcXx4QJE7j22mu9DskYcw7+HOX/maF7bDACyTFLVCYbKSkpTJkyhTvvvJOYmBjKlCnDnDlziI+Pp6DdMG5MyMv2KK+q3+VFIAE7ftx5t8oU5hyWLVtGt27d+OWXX9i2bRv9+/cH4LLLLvM4MmOMv0LjelNOpCaoP//0Ng4Tkg4fPkzfvn1p1KgRv/zyC+XLl6dGjRpeh2WMyYGgJyoRaSMia0VkvYg8dY7xmojIGRG53a8ZnzrlvDdpkitxmsigqsyYMYO4uDhGjhwJQK9evUhKSuLWW2/1ODpjTE4EfIFHRC5Q1ZN+jhsNjANuALYCi0XkM982A33GGwbM9juQ1MoUhw75PYmJfLNnz+a225z6PY0bNyYhIYFGjRp5HJUx5nz4fUYlIk1FZCXwm9vdQESya0KpKc5zqzaq6ilgKtAuk/EeAz4GdvkbT9oNv5Uq+T2JiUy+z1Rr3bo1bdu2ZcyYMSxcuNCSlDERIJCivzeAm4G9AKq6HLgum2kqAFt8ure6/dKISAXgViAhuwBEpKuIJIpI4onUyhTuA+xM/vTzzz9z5ZVXsnHjRgCioqL4/PPP6dmzJ9FW0caYiBBIoopS1T8y9DuTzTSZPfY04+N4RwEDVDW7eaGqb6pqY1VtXOikW/po1YvzpX379tGlSxeaN2/OokWLeOWVV9KG2dN2jYksgVyj2iIiTQF1ryk9BqzLZpqtgG/ZXEVge4ZxGgNT3YNLaaCtiJxW1U/POeczbl6LCt+KiyZwqsp7771Hv3792LNnDzExMQwYMICBAwd6HZoxJkgCSVTdcYr/KgM7gW/Jvt2/xUANEamK8wj7u4C7fUdQ1aqpn0VkMvB5tkkKnASVkgLx8QGsggln69evp0uXLnz//fcA/P3vf2fChAnUqVPH28CMMUHld6JS1V04icZvqnpaRHri1OaLBiap6moR6eYOz/a6VJaio51HfRQunONZmPBy6tQpfvrpJ0qXLs3rr7/OfffdZ8V8xuQD4ltj6pwjivybv15fQlW75nZQ/mh8wQWaeOoUbNgA1ap5EYLJA4sXL6Zx48ZpCWnGjBlce+21lCpVyuPIjAlPIrJEVRt7HUcgArnA8y3wnfv6GSgD+HU/VVCkJlh7FH1E2rFjBx07dqRp06ZMmzYtrf+tt95qScqYfCaQor+PfLtF5D3gm1yPyF/Jyc671fqLKGfOnCEhIYGBAwdy6NAhYmNj2b9/v9dhGWM8dD5Nj1cFLsmtQHLMzqgixv/+9z+6devG4sWLAbj55psZM2YMVapU8TYwY4yn/E5UIrKf9GtUUcA+IMu2+4JOxCn+i431LASTe2bNmsXNN99MSkoKFSpUYMyYMbRv394qSxhj/EtU4hwtGuBUMQdIUX9rYQSLXaOKKC1atKBGjRq0bduW559/nmLFinkdkjEmRPhVmcJNSjNU9Yz78jZJpRKx51GFqU2bNtGpUycOHDgAQGxsLMuWLWPEiBGWpIwxZwmk1t8iEbk8aJHkRExMeuO0JiycOnWKoUOHEh8fz3vvvcfzzz+fNqxQoUIeRmaMCVXZFv2JSAFVPQ00B7qIyAbgKE47fqqq3iUvS1Jh5ccff6R79+6sXr0agLvvvpsBAwZ4HJUxJtT5c41qEXA50D7IsQTupHe3cRn/7d27l/79+zNp0iQAqlevzoQJE7j++us9jswYEw78SVQCoKobghxL4C7xvna8yd6yZcuYNGkSBQsW5F//+hdPPfWUFfMZY/zmT6K6WET6ZjVQVUfkYjyBsYoUIWvnzp2ULVsWgFatWjFs2DDatWtHrVq1PI7MGBNu/KlMEQ0UBYpl8fJOgfO5X9kEw7Fjxxg4cCCVK1dmwYIFaf379+9vScoYkyP+HOl3qOoLQY8kJyxRhZRZs2bx6KOPsmnTJgDmzZtHs2bNPI7KGBPu/L5GFZKs6C8kbNu2jd69ezN9+nQA6tevT0JCgiUpY0yu8CdRtQp6FDllZ1Se+/rrr7n99ts5fPgwRYoU4fnnn+fxxx+ngH03xphcku3RRFX35UUgObIvdEPLLxo2bEh0dDTt27dn9OjRVK5c2euQjDERJpCWKUKPPd03zx08eJAhQ4aQ7D5mpUyZMqxcuZIZM2ZYkjLGBEV4l8/Yk33zjKryn//8h969e/Pnn39SsGBB+vfvD0DFihU9js4YE8nCO1HZdZA8sWHDBh599FFmz54NQLNmzWjTpo3HURlj8ovwLvqzR3wE1cmTJxkyZAh169Zl9uzZlChRgokTJ/LTTz9Rv359r8MzxuQT4X1KcvSo1xFEtE8++YSnn34agHvvvZfhw4entTZhjDF5JbwTVe3aXkcQcZKTk4lxz1Q7dOjA7Nmz6dSpEy1btvQ4MmNMfhXeRX/WJE+uSUlJ4a233qJq1aps3LgRgKioKCZPnmxJyhjjqfBOVNZ6eq5YtWoV1157LV26dGHbtm1MnjzZ65CMMSZNeCeqqPAO32tHjx5lwIABXHbZZfz888+ULVuWDz/88Kyn7hpjjNfC+xqVPeE3x3766Sfuvfde/vjjD0SEHj168NJLL1GiRAmvQzPGmLNYosqnSpYsyfbt22nYsCEJCQlcccUVXodkjDGZCu+ysxMnvI4gbJw+fZrp06ejqgDExcUxd+5cFi9ebEnKGBPSwjtRFfP2uY3hYtGiRTRp0oQ77riDadOmpfW/+uqrrZVzY0zIC+9EZUV/53TgwAF69OjBlVdeybJly7jkkkvsGpQxJuxYoopAqsqUKVOoXbs2EyZMIDo6mgEDBrB69Wpat27tdXjGGBMQK/eJQJMnT+bBBx8EnOK9CRMmUK9ePY+jMsaYnAn6GZWItBGRtSKyXkSeymT4PSKywn3NF5EGAcw8V2ONFHfddRdNmzblrbfeYt68eZakjDFhLaiJSkSigXHATUAc0FFE4jKMtgn4u6rWB14E3gxgAbkUaXibM2cOf//73zlw4AAAsbGxLFy4kIceeogouynaGBPmgn0UawqsV9WNqnoKmAq08x1BVeer6n63cyFgT+Hz065du7jvvvto1aoV8+bNY9SoUWnDxJK4MSZCBDtRVQC2+HRvdftl5SFgVlYDRaSriCSKSKLbIzdiDDspKSm8+eab1KpVi/fff59ChQoxZMgQBg4c6HVoxhiT64JdmSKzTKKZjihyHU6iap7VzFT1TdyiwcYimh8T1apVq+jatSsLFiwA4MYbb2TcuHFceumlHkdmjDHBEewzqq1AJZ/uisD2jCOJSH3gLaCdqu4Nckxh7c8//2TBggWUK1eOjz76iFmzZlmSMsZEtGAnqsVADRGpKiIFgbuAz3xHEJHKwCfAfaq6LqC555MzqhUrVqR9vv7665k8eTJJSUnceeeddi3KGBPxgpqoVPU00BOYDSQB/1HV1SLSTUS6uaM9A5QCxovIsrTrT4bNmzfTvn17GjRokFbUB3D//fdTvHhxDyMzxpi8E/QbflX1S+DLDP0SfD4/DDyco5lH6NlEcnIyo0eP5tlnn+XYsWMUK1aMzZs306xZM69DM8aYPBfeLVNEYKJasGAB3bp1Syvuu+OOOxg5ciQVKpyrsqQxxkSu8E5UEeadd97hgQceAKBq1aqMHTuWtm3behuUMcZ4LLwTVYSdUd10002UKVOGhx9+mEGDBlG4cGGvQzLGGM9ZovLQ2rVrGTlyJGPGjCEmJoYyZcqwceNGihQp4nVoxhgTMqwhOA+cOHGCZ599lvr16zNx4kTGjBmTNsySlDHGnM3OqPLYN998Q48ePVi/fj0ADz74IJ06dfI4KmOMCV2WqPLIn3/+Sd++fZkyZQoAcXFxTJgwgWuvvdbjyIwxJrRZ0V8e+fLLL5kyZQqxsbG88sorLF261JKUMcb4wc6ogmjfvn2ULFkSgAceeIB169bxyCOPULVqVY8jM8aY8BHeZ1QhmqgOHz5M3759qVKlCps2bQIgKiqKoUOHWpIyxpgAhXeiCjGqyowZM4iLi2PkyJEcPXqUOXPmeB2WMcaENSv6yyW///47jz32GJ9//jkAjRs3ZuLEiVx++eUeR2aMMeEtvM+oQiRRffTRR8TFxfH5559z4YUXMnbsWBYuXGhJyhhjckF4n1GFiHr16nH69Gk6dOjAiBEjKF++vNchGRPykpOT2bp1KydOnPA6lIhUqFAhKlasSExMjNehnLfwTlQenVHt27ePd999l8cffxwRIS4ujqSkJHvSrjEB2Lp1K8WKFaNKlSr2ANBcpqrs3buXrVu3RkQFLiv6C4Cq8u6771KrVi369OnDtGnT0oZZkjImMCdOnKBUqVKWpIJARChVqlTEnK2G9xlVHvr111/p3r0733//PQAtWrSgfv363gZlTJizJBU8kbRt7YwqG8ePH+fpp5+mfv36fP/995QuXZp33nmHOXPmULt27aAv3xhj8jtLVNkYN24cQ4YMITk5mS5durB27Vo6deoUUf9WjMmvoqOjadiwIXXr1uWWW27hwIEDacNWr15Ny5YtqVmzJjVq1ODFF19EVdOGz5o1i8aNG1OnTh1q167NE0884cUq5AvhnaiC5MyZM2mfH330Udq1a8dPP/3Em2++mdYkkjEm/MXGxrJs2TJWrVpFyZIlGTduHOCUpPzf//0fTz31FOvWrWP58uXMnz+f8ePHA7Bq1Sp69uzJ+++/T1JSEqtWraJatWq5Gtvp06dzdX7hLLwTVS6f1Zw5c4Zx48YRHx+f9s8qNjaWTz/9lKuvvjpXl2WM8SESnFcAmjVrxrZt2wD48MMPufrqq2ndujUAhQsXZuzYsQwdOhSAV199lUGDBqUV/xcoUIAePXr8ZZ5Hjhyhc+fO1KtXj/r16/Pxxx8DULRo0bRxpk+fzgMPPAA4bYL27duX6667jieffJIqVaqcdZZXvXp1du7cye7du/nnP/9JkyZNaNKkCT///HNA6xpuwrsyRS4mqv/9739069aNxYsXAzBlyhS6d++ea/M3xoSuM2fO8N133/HQQw8BTrFfo0aNzhrn0ksv5ciRIxw6dIhVq1bRr1+/bOf74osvUrx4cVauXAnA/v37s51m3bp1fPvtt0RHR5OSksKMGTPo3Lkzv/zyC1WqVKFs2bLcfffd9OnTh+bNm7N582ZuvPFGkpKScrDm4SG8E1UuOHToEM888wxjxowhJSWFihUrMmbMGNq1a+d1aMbkHz7XfvLS8ePHadiwIb///juNGjXihhtucMPRLK9DB3J9+ttvv2Xq1Klp3RdddFG209xxxx1ER0cD0KFDB1544QU6d+7M1KlT6dChQ9p816xZkzbNoUOHOHz4MMWKFfM7tnCSr4v+vvrqK+rUqcPo0aMREfr27cuaNWto3769VZYwJh9IvUb1xx9/cOrUqbRrVPHx8SQmJp417saNGylatCjFihUjPj6eJUuWZDv/rBKeb7+M9zoVKVIk7XOzZs1Yv349u3fv5tNPP+W2224DICUlhQULFrBs2TKWLVvGtm3bIjZJQT5PVAUKFGD79u00bdqUxMREXn/99Yj+so0xmStevDhvvPEGw4cPJzk5mXvuuYeffvqJb7/9FnDOvHr16kX//v0BePLJJ3n55ZdZt24d4CSOESNG/GW+rVu3ZuzYsWndqUV/ZcuWJSkpKa1oLysiwq233krfvn2pU6cOpUqVynS+y5YtO88tENrCO1EF6NSpU3z11Vdp3ddffz2zZ89m/vz5NGzY0MPIjDFeu+yyy2jQoAFTp04lNjaWmTNnMmTIEGrVqkW9evVo0qQJPXv2BKB+/fqMGjWKjh07UqdOHerWrcuOHTv+Ms/Bgwezf/9+6tatS4MGDZg7dy4AQ4cO5eabb6Zly5aUK1funHF16NCB999/P63YD+CNN94gMTGR+vXrExcXR0JCQi5uidAj6lHZ8PlqLKKJ69ZBjRp+jf/jjz/SrVs31qxZw/z582nWrFmQIzTGnEtSUhJ16tTxOoyIltk2FpElqtrYo5ByJLzPqPwo+tuzZw8PPvgg1157LWvWrKFGjRqkpKTkQXDGGGNyQ3gnqgJZV1pUVd5++21q167N22+/TcGCBXn22WdZsWKF3RNljDFhJLyrpxcunOWgl19+mcGDBwPQqlUrxo8fT82aNfMqMmOMH85VDdycn3C9rJOZ8D6jOscDwR5++GFq167N+++/zzfffGNJypgQU6hQIfbu3RtRB9RQkfo8qkKFCnkdSq4I78oUhw6BW5181qxZJCQkMH369LQnWqakpBAVFd652JhIZU/4Da6snvAbjpUpwrvoLyaGbdu20bt3b6ZPnw7A5MmT6dKlC4AlKWNCWExMTEQ8fdYEX9CP5CLSRkTWish6EXkqk+EiIm+4w1eIyOX+zvuNiROpU6cO06dPp0iRIgwfPpzOnTvn7goYY4zxVFCL/kQkGlgH3ABsBRYDHVV1jc84bYHHgLbAFcBoVb0iu3kXEdFj7uf27dszevRoKleunNurYIwxESUci/6CfUbVFFivqhtV9RQwFcjY2ms74F11LARKiMi5b9UGjgGVKlVi5syZzJgxw5KUMcZEqGBfo6oAbPHp3opz1pTdOBWAv7RHIiJdga5u58ktW7asslbOASgN7PE6iBBh2yKdbYt0ti3S1fI6gEAFO1FldoNExrJGf8Zxeqq+CbwJICKJ4Xb6Giy2LdLZtkhn2yKdbYt0IpKY/VihJdhFf1uBSj7dFYHtORjHGGNMPhXsRLUYqCEiVUWkIHAX8FmGcT4DOrm1/64EDqrqX5shNsYYky8FtehPVU+LSE9gNhANTFLV1SLSzR2eAHyJU+NvPU4dCX/rl78ZhJDDlW2LdLYt0tm2SGfbIl3YbYuwbZnCGGNM/mBNNxhjjAlplqiMMcaEtJBOVMFsfinc+LEt7nG3wQoRmS8iDbyIMy9kty18xmsiImdE5Pa8jC8v+bMtRKSFiCwTkdUi8kNex5hX/PiNFBeR/4rIcndbRGx7ayIySUR2iciqLIaH17FTVUPyhVP5YgNQDSgILAfiMozTFpiFcy/WlcAvXsft4ba4CrjI/XxTft4WPuPNwamsc7vXcXu4X5QA1gCV3e4yXsft4bYYCAxzP18M7AMKeh17kLbHtcDlwKoshofVsTOUz6iC1vxSGMp2W6jqfFXd73YuxLkfLRL5s1+A037kx8CuvAwuj/mzLe4GPlHVzQCqGqnbw59toUAxcZ7UWBQnUZ3O2zDzhqrOw1m/rITVsTOUE1VWTSsFOk4kCHQ9H8L5txSJst0WIlIBuBVIyMO4vODPflETuEhEvheRJSLSKc+iy1v+bIuxQB2cBgVWAo+rakrehBdywurYGcrPo8rV5pfCnN/rKSLX4SSq5kGNyDv+bItRwABVPRPhjzn3Z1sUABoBrYBYYIGILFTVdcEOLo/5sy1uBJYBLYFLgW9E5EdVPRTs4EJQWB07QzlRWfNL6fxaTxGpD7wF3KSqe/Motrzmz7ZoDEx1k1RpoK2InFbVT/MmxDzj729kj6oeBY6KyDygAc7jdyKJP9uiMzBUnYs060VkE1AbWJQ3IYaUsDp2hnLRnzW/lC7bbSEilYFPgPsi8N+yr2y3hapWVdUqqloFmA70iMAkBf79RmYC14hIAREpjPP0gqQ8jjMv+LMtNuOcWSIiZXFaEd+Yp1GGjrA6dobsGZUGt/mlsOLntngGKAWMd88kTmsEthbt57bIF/zZFqqaJCJfASuAFOAtVc20ynI483O/eBGYLCIrcYq+BqhqRD76Q0SmAC2A0iKyFXgWiIHwPHZaE0rGGGNCWigX/RljjDGWqIwxxoQ2S1TGGGNCmiUqY4wxIc0SlTHGmJBmicqELLfl82U+ryrnGLdKVi1FB7jM790WuJeLyM8iUisH8+iW2lSRiDwgIuV9hr0lInG5HOdiEWnoxzS93XupjAkrlqhMKDuuqg19Xr/n0XLvUdUGwDvAa4FO7N6/9K7b+QBQ3mfYw6q6JleiTI9zPP7F2RuwRGXCjiUqE1bcM6cfReR/7uuqTMaJF5FF7lnYChGp4fa/16f/RBGJzmZx84Dq7rStRGSpiKx0n/Vzgdt/qIiscZcz3O33nIg8Ic5zsBoDH7jLjHXPhBqLSHcRedUn5gdEZEwO41yAMfLr6AAAAuFJREFUT4OiIjJBRBLFeebS826/XjgJc66IzHX7tRaRBe52nCYiRbNZjjGesERlQlmsT7HfDLffLuAGVb0c6AC8kcl03YDRqtoQJ1FsFZE67vhXu/3PAPdks/xbgJUiUgiYDHRQ1Xo4Lbp0F5GSOK20x6tqfWCI78SqOh1IxDnzaaiqx30GTwdu8+nuAHyUwzjbAL5NRA1yWyWpD/xdROqr6hs4bbldp6rXiUhpYDBwvbstE4G+2SzHGE+EbBNKxuAW/WXoFwOMda/JnMF5jEVGC4BBIlIR51lMv4lIK5xWxBe7TUzFkvWzqj4QkePA7zjPtaoFbPJpQ/Ed4FGcx0acAN4SkS+Az/1dMVXdLSIb3XbWfnOX8bM730DiLILTZJDvE1rvFJGuOL/vckAcThNKvq50+//sLqcgznYzJuRYojLhpg+wE6cF8CicRHEWVf1QRH4B/gHMFpGHcdp2e0dV/+XHMu5R1cTUDhEpldlIbvtyTXEaOr0L6InzCAl/fQTcCfwKzFBVFSdr+B0nzpNshwLjgNtEpCrwBNBEVfeLyGSgUCbTCvCNqnYMIF5jPGFFfybcFAd2uA+8uw/nbOIsIlIN2OgWd32GUwT2HXC7iJRxxykpIpf4ucxfgSoiUt3tvg/4wb2mU1xVv8SpqJBZzbvDQLEs5vsJ0B7oiJO0CDROVU3GKcK70i02vBA4ChwUp4Xwm7KIZSFwdeo6iUhhEcns7NQYz1miMuFmPHC/iCzEKfY7msk4HYBVIrIM53lD77o17QYDX4vICuAbnGKxbKnqCZzWpae5LW+n4Dw9uBjwuTu/H3DO9jKaDCSkVqbIMN/9wBrgElVd5PYLOE732tfrwBOquhxYCqwGJuEUJ6Z6E5glInNVdTdOjcQp7nIW4mwrY0KOtZ5ujDEmpNkZlTHGmJBmicoYY0xIs0RljDEmpFmiMsYYE9IsURljjAlplqiMMcaENEtUxhhjQtr/B0Jjwpl5f7YSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions = model.predict(X_test, verbose=1)\n",
    "anomalyScoresAE = anomalyScores(X_test, predictions)\n",
    "preds, average_precision = plotResults(y_test, anomalyScoresAE, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision at 75% recall 0.0532\n"
     ]
    }
   ],
   "source": [
    "preds, precision = precisionAnalysis(preds, \"anomalyScore\", 0.75)\n",
    "print(f'Precision at 75% recall {round(precision,4)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Semisupervised Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "layer_name = 'hidden_layer'\n",
    "\n",
    "intermediate_layer_model  = Model(inputs=model.input, outputs=model.get_layer(layer_name).output)\n",
    "intermediate_output_train = intermediate_layer_model.predict(X_train_original)\n",
    "intermediate_output_test  = intermediate_layer_model.predict(X_test_original)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "intermediate_output_trainDF = pd.DataFrame(data=intermediate_output_train,index=X_train_original.index)\n",
    "intermediate_output_testDF  = pd.DataFrame(data=intermediate_output_test,index=X_test_original.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train_original.merge(intermediate_output_trainDF, left_index=True,right_index=True)\n",
    "X_test  = X_test_original.merge(intermediate_output_testDF, left_index=True,right_index=True)\n",
    "y_train = y_train_original.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\tvalid_0's binary_logloss: 0.00651864\n",
      "Training until validation scores don't improve for 200 rounds\n",
      "[2]\tvalid_0's binary_logloss: 0.00613787\n",
      "[3]\tvalid_0's binary_logloss: 0.00568419\n",
      "[4]\tvalid_0's binary_logloss: 0.00563349\n",
      "[5]\tvalid_0's binary_logloss: 0.00557326\n",
      "[6]\tvalid_0's binary_logloss: 0.00555163\n",
      "[7]\tvalid_0's binary_logloss: 0.00553373\n",
      "[8]\tvalid_0's binary_logloss: 0.00553608\n",
      "[9]\tvalid_0's binary_logloss: 0.00552869\n",
      "[10]\tvalid_0's binary_logloss: 0.00551911\n",
      "[11]\tvalid_0's binary_logloss: 0.00550907\n",
      "[12]\tvalid_0's binary_logloss: 0.00549466\n",
      "[13]\tvalid_0's binary_logloss: 0.0054812\n",
      "[14]\tvalid_0's binary_logloss: 0.00547163\n",
      "[15]\tvalid_0's binary_logloss: 0.00545963\n",
      "[16]\tvalid_0's binary_logloss: 0.00544823\n",
      "[17]\tvalid_0's binary_logloss: 0.00543743\n",
      "[18]\tvalid_0's binary_logloss: 0.00542714\n",
      "[19]\tvalid_0's binary_logloss: 0.0054173\n",
      "[20]\tvalid_0's binary_logloss: 0.00540892\n",
      "[21]\tvalid_0's binary_logloss: 0.0053979\n",
      "[22]\tvalid_0's binary_logloss: 0.00538993\n",
      "[23]\tvalid_0's binary_logloss: 0.00538226\n",
      "[24]\tvalid_0's binary_logloss: 0.00537478\n",
      "[25]\tvalid_0's binary_logloss: 0.00536466\n",
      "[26]\tvalid_0's binary_logloss: 0.00535522\n",
      "[27]\tvalid_0's binary_logloss: 0.005346\n",
      "[28]\tvalid_0's binary_logloss: 0.00533704\n",
      "[29]\tvalid_0's binary_logloss: 0.00532829\n",
      "[30]\tvalid_0's binary_logloss: 0.00531973\n",
      "[31]\tvalid_0's binary_logloss: 0.00531134\n",
      "[32]\tvalid_0's binary_logloss: 0.00530319\n",
      "[33]\tvalid_0's binary_logloss: 0.00529517\n",
      "[34]\tvalid_0's binary_logloss: 0.00528737\n",
      "[35]\tvalid_0's binary_logloss: 0.00528114\n",
      "[36]\tvalid_0's binary_logloss: 0.00527501\n",
      "[37]\tvalid_0's binary_logloss: 0.00526899\n",
      "[38]\tvalid_0's binary_logloss: 0.00526308\n",
      "[39]\tvalid_0's binary_logloss: 0.00525731\n",
      "[40]\tvalid_0's binary_logloss: 0.00525102\n",
      "[41]\tvalid_0's binary_logloss: 0.0052455\n",
      "[42]\tvalid_0's binary_logloss: 0.00524035\n",
      "[43]\tvalid_0's binary_logloss: 0.00523641\n",
      "[44]\tvalid_0's binary_logloss: 0.00523112\n",
      "[45]\tvalid_0's binary_logloss: 0.00522653\n",
      "[46]\tvalid_0's binary_logloss: 0.00522141\n",
      "[47]\tvalid_0's binary_logloss: 0.00521698\n",
      "[48]\tvalid_0's binary_logloss: 0.00521158\n",
      "[49]\tvalid_0's binary_logloss: 0.00520684\n",
      "[50]\tvalid_0's binary_logloss: 0.00520208\n",
      "[51]\tvalid_0's binary_logloss: 0.00519748\n",
      "[52]\tvalid_0's binary_logloss: 0.00519313\n",
      "[53]\tvalid_0's binary_logloss: 0.00518864\n",
      "[54]\tvalid_0's binary_logloss: 0.00518509\n",
      "[55]\tvalid_0's binary_logloss: 0.00518171\n",
      "[56]\tvalid_0's binary_logloss: 0.00517744\n",
      "[57]\tvalid_0's binary_logloss: 0.00517235\n",
      "[58]\tvalid_0's binary_logloss: 0.00516723\n",
      "[59]\tvalid_0's binary_logloss: 0.00516219\n",
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     ]
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     "output_type": "stream",
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      "[1204]\tvalid_0's binary_logloss: 0.00414821\n",
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      "Early stopping, best iteration is:\n",
      "[1138]\tvalid_0's binary_logloss: 0.00414423\n",
      "Training Log Loss: 0.0\n",
      "CV Log Loss: 0.0041\n",
      "[1]\tvalid_0's binary_logloss: 0.0026085\n",
      "Training until validation scores don't improve for 200 rounds\n",
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      "Early stopping, best iteration is:\n",
      "[125]\tvalid_0's binary_logloss: 0.000861023\n",
      "Training Log Loss: 0.0001\n",
      "CV Log Loss: 0.0009\n",
      "[1]\tvalid_0's binary_logloss: 0.00248774\n",
      "Training until validation scores don't improve for 200 rounds\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[130]\tvalid_0's binary_logloss: 0.00282763\n",
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      "[200]\tvalid_0's binary_logloss: 0.00252543\n",
      "[201]\tvalid_0's binary_logloss: 0.00252179\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's binary_logloss: 0.00248774\n",
      "Training Log Loss: 0.0027\n",
      "CV Log Loss: 0.0025\n"
     ]
    }
   ],
   "source": [
    "trainingScores = []\n",
    "cvScores = []\n",
    "predictionsBasedOnKFolds = pd.DataFrame(data=[],index=y_train.index,  columns=['prediction'])\n",
    "\n",
    "for train_index, cv_index in k_fold.split(np.zeros(len(X_train)), y_train.ravel()):\n",
    "    X_train_fold, X_cv_fold = X_train.iloc[train_index,:], X_train.iloc[cv_index,:]\n",
    "    y_train_fold, y_cv_fold = y_train.iloc[train_index], y_train.iloc[cv_index]\n",
    "    \n",
    "    lgb_train = lgb.Dataset(X_train_fold, y_train_fold)\n",
    "    lgb_eval = lgb.Dataset(X_cv_fold, y_cv_fold, reference=lgb_train)\n",
    "    gbm = lgb.train(params_lightGB, lgb_train, num_boost_round=5000,\n",
    "                   valid_sets=lgb_eval, early_stopping_rounds=200)\n",
    "    \n",
    "    loglossTraining = log_loss(y_train_fold, gbm.predict(X_train_fold,num_iteration=gbm.best_iteration))\n",
    "    trainingScores.append(loglossTraining)\n",
    "    \n",
    "    predictionsBasedOnKFolds.loc[X_cv_fold.index,'prediction'] = gbm.predict(X_cv_fold, num_iteration=gbm.best_iteration) \n",
    "    loglossCV = log_loss(y_cv_fold, predictionsBasedOnKFolds.loc[X_cv_fold.index,'prediction'])\n",
    "    cvScores.append(loglossCV)\n",
    "    \n",
    "    print(f'Training Log Loss: {round(loglossTraining,4)}')\n",
    "    print(f'CV Log Loss: {round(loglossCV,4)}' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LightGBM Gradient Boosting Log Loss: 0.0022\n"
     ]
    }
   ],
   "source": [
    "loglossLightGBMGradientBoosting = log_loss(y_train, predictionsBasedOnKFolds.loc[:,'prediction'])\n",
    "print(f'LightGBM Gradient Boosting Log Loss: {round(loglossLightGBMGradientBoosting,4)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds, average_precision = plotResults(y_train, predictionsBasedOnKFolds.loc[:,'prediction'], True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions = pd.Series(data=gbm.predict(X_test, num_iteration=gbm.best_iteration),index=X_test.index)\n",
    "preds, average_precision = plotResults(y_test, predictions, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0312\n"
     ]
    }
   ],
   "source": [
    "preds, precision = precisionAnalysis(preds, \"anomalyScore\", 0.75)\n",
    "print(f'{round(precision,4)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>featImportance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>V1</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>V12</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>V7</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>V2</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>69 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     featImportance\n",
       "V1         0.250000\n",
       "V12        0.166667\n",
       "37         0.083333\n",
       "26         0.083333\n",
       "V7         0.083333\n",
       "..              ...\n",
       "2          0.000000\n",
       "4          0.000000\n",
       "V2         0.000000\n",
       "6          0.000000\n",
       "39         0.000000\n",
       "\n",
       "[69 rows x 1 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "featuresImportance = pd.DataFrame(data=list(gbm.feature_importance()), index=X_train.columns,columns=['featImportance'])\n",
    "featuresImportance = featuresImportance/featuresImportance.sum()\n",
    "featuresImportance.sort_values(by='featImportance', ascending=False,inplace=True)\n",
    "featuresImportance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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   "title_cell": "Table of Contents",
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   "toc_window_display": false
  }
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