09_semisupervised.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "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",
    "import keras\n",
    "from keras import backend as K\n",
    "from keras.models import Sequential, Model\n",
    "from keras.layers import Activation, Dense, Dropout\n",
    "from keras.layers import BatchNormalization, Input, Lambda\n",
    "from keras import regularizers\n",
    "from keras.losses import mse, binary_crossentropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "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)\n",
    "\n",
    "dataX = data.copy().drop(['Class','Time'],axis=1)\n",
    "dataY = data['Class'].copy()\n",
    "\n",
    "# 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])\n",
    "\n",
    "# Split into train and test\n",
    "X_train, X_test, y_train, y_test = \\\n",
    "    train_test_split(dataX, dataY, test_size=0.33, \\\n",
    "                     random_state=2018, stratify=dataY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ankur_\\Anaconda3\\envs\\unsupervisedLearning\\lib\\site-packages\\pandas\\core\\frame.py:3697: 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/indexing.html#indexing-view-versus-copy\n",
      "  errors=errors)\n"
     ]
    }
   ],
   "source": [
    "# Drop 95% of the labels from the training set\n",
    "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": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Define evaluation function and plotting function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "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": 42,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "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": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Define new function to assess precision at a given recall threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "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": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "k_fold = StratifiedKFold(n_splits=5,shuffle=True,random_state=2018)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Supervised"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "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",
    "}"
   ]
  },
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   "cell_type": "code",
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      "Training until validation scores don't improve for 200 rounds.\n",
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      "Early stopping, best iteration is:\n",
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      "Training Log Loss:  1.1415121057817204e-05\n",
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      "Early stopping, best iteration is:\n",
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      "Training Log Loss:  7.892448577578827e-05\n",
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      "Training until validation scores don't improve for 200 rounds.\n",
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      "Early stopping, best iteration is:\n",
      "[440]\tvalid_0's binary_logloss: 0.0022428\n",
      "Training Log Loss:  4.198689713558419e-05\n",
      "CV Log Loss:  0.002242798619429291\n",
      "[1]\tvalid_0's binary_logloss: 0.00295068\n",
      "Training until validation scores don't improve for 200 rounds.\n",
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     "output_type": "stream",
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      "[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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[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:  1.909783012101701e-06\n",
      "CV Log Loss:  0.0015564059221184856\n",
      "LightGBM Gradient Boosting Log Loss:  0.0018326986342952088\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,:], \\\n",
    "        X_train.iloc[cv_index,:]\n",
    "    y_train_fold, y_cv_fold = y_train.iloc[train_index], \\\n",
    "        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'] = \\\n",
    "        gbm.predict(X_cv_fold, num_iteration=gbm.best_iteration) \n",
    "    loglossCV = log_loss(y_cv_fold, \\\n",
    "        predictionsBasedOnKFolds.loc[X_cv_fold.index,'prediction'])\n",
    "    cvScores.append(loglossCV)\n",
    "    \n",
    "    print('Training Log Loss: ', loglossTraining)\n",
    "    print('CV Log Loss: ', loglossCV)\n",
    "    \n",
    "loglossLightGBMGradientBoosting = log_loss(y_train, \\\n",
    "        predictionsBasedOnKFolds.loc[:,'prediction'])\n",
    "print('LightGBM Gradient Boosting Log Loss: ', \\\n",
    "        loglossLightGBMGradientBoosting)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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": 50,
   "metadata": {
    "scrolled": false
   },
   "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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xxoTVjBkzWLRo0YnhES1akGU3yhEqEhJVCnChz3AFnP6UjDHGBOmPP/6gWbNmNG/enAcffJDU1FQASgLnehtatkVCopoJtHNr/zUA9tjzKWOMCU5qaiqDBw8mISGBmTNnUrRoUTp16kS+fO5lPoofl4S8MoWITAauAUqJSArwLBALoKrjgM+Am4F1wEGgQ6hjMsaY3GTevHl07tyZ5cuXA9CqVSuGDRtGuXLlTs5UooRH0Z29cNT6a5vFdAW6hToOY4zJjQ4cOMCtt97Kzp07qVSpEmPGjKFp0wx+EXT8ePiDyyFR2x+VMcbkVapKWloaMTExFC5cmCFDhrBu3Tr69+9PoUKFMl7IEpUxxphwWLNmDV26dKFp06b07dsXgA4dAnhiEsWJKhIqUxhjjMnCoUOHeOaZZ0hMTGTOnDmMHj2ao0ePBr4CS1TGGGNC5csvv6RWrVoMGDCAo0eP8sADD/DTTz9RoECBwFeyeHHoAgwxS1TGGBOh9u/fT9u2bbnxxhtZv349NWrU4Pvvv+eNN96gZMmSwa2sUqXQBBkGlqiMMSZCFSpUiE2bNhEXF8egQYP46aefaNSoUfZWFsVFf1aZwhhjIsjSpUs577zziI+PJ1++fLz11lvExsYSHx9/ditOS8uR+Lxgd1TGGBMB9u3bR8+ePUlKSqJr1644PzGFKlWqnH2SArujMsYYkz2qyvTp0+nRowebN28mX758VK1alWPHjhEbG5tzG4riOypLVMYY45Hff/+d7t278+mnnwJQr149xo8fT926dXN+Y3ZHZYwxJhh79+6lTp067Nmzh3PPPZeXXnqJhx56iJiYmNBscPPm0Kw3DCxRGWOMB84991y6devGhg0bGDp0KGXLlg3tBjdtynqeCGWJyhhjwmDHjh08/vjjNGnShFatWgEwYMCAk91whFqJEpCSEp5t5TBLVMYYE0Kqyttvv03v3r3ZsWMHs2fPpkWLFuTPnz90SerXX6F9e9i9+9RxUcoSlTHGhMjq1avp0qUL3333HQDXXnstY8aMIX/+HLj0pqbCa6/Blgz6mR0xIuPKEyVKwM6dZ7/tMLNEZYwxOezw4cMMHDiQV155hdTUVM4//3yGDh3K3XffjYgEviJVWLgQ9uw5fdrXX8PgwWde/o474PnnTw5XqADFiwe+/QhhicoYY3KYqjJlyhRSU1Pp1KkTL730EiWy08PupElwzz1Zz/fqq6ePi4uDtm2jMjH5s0RljDE54M8//6RQoUIUL16cuLg43nrrLWJiYrjiiisCW8Htt8PHH2c+vUmT08flzw99+sA112Qr5mhhicoYY87C8ePHGTNmDP379+fee+9l9OjRAPzzn/8MfCV79mSepOLiYOpU+Ne/ciDa6GSJyhiTu/zyi/P8JgyWbNrEQ1OnssSt9v3n3Lkcf/VVYoKtzffeeydfHzsG/ssH81wrF7JEZYzx1q5dGddcy67atXNuXZnYAzwNjAbSgAuBkUCzFSugd+/sr/i++yBULVNEMUtUxhjv7NgBF10EBw7k/LrvuQdKlcrx1e48fJha777LnwcOECPCY3Xr8lyDBhQJprfdjBQqBA8/nDNB5jKWqIwxoTdkCIwc6VS39uXbrE/16jm3vQYN4D//CUmRWQngmr172bBhA+PGjaN2GO7g8jpLVMbkZfPmwdtvh74LiDfeOPP09u3hrbdCG0M2HT16lCFDhnDdddfRoEEDAMaPH0+hQoXC1/xRHmeJyphod+wYfPMN7N0b/LItW+Z8PGcyfz6UK3fqOBHnh6gR6LvvvqNLly6sXr2axMREli5dSr58+ShSpIjXoeUplqiMiXbvvgv333926+jcGS67LGfiycyllzpFclHg77//pk+fPkycOBFwetkdOnSo3UF5xBKVMdFu3LiTr++4I/jlL74YBg06vUp0HpSWlsbEiRPp06cPO3fupECBAvTr14/HH3+cggULeh1enmWJyphIsmePc3f011+BL7NokfO/Tx945ZXQxJVH7Ny5k969e7Nr1y4aN27MmDFjuPTSS70OK8+zRGWMl778EhYsODn8f/8HycnZW1ebNjkTUx5z8OBBYmNjiY2NpVSpUowaNQqAtm3bBteArAkZS1TG5LS//4b167Oe7+hRuPHGjKfFx5/aWkFWypSBypUDn98A8Omnn9K9e3e6dOlC3759Abjrrrs8jsr4s0RlTE46fNipNLBrV3DLPf30ydexsc6PVStVytnYzAkpKSk88sgjfPTRRwDMmDGD3r17W2WJCGWJypicsGQJtGgBGzeeHFe/fmDLNm8OTz4ZmrjMKY4dO8aoUaN4+umn2b9/P4ULF2bAgAE8/PDDlqQiWFgSlYg0BYYDMcAbqjrIb3pF4G2guDvPE6r6WThiMyYon38O77xzegsL779/6nCLFvDhh+GLy2Rp27ZtNG3alKVLlwLQvHlzRowYwYUXXuhxZCYrIU9UIhKD03bjDUAKsFhEZqrqKp/ZngI+UNWxIpIAfAbEhzo2k0f99BP8/HP2ls3q90pPPgl9+0KxYtlbvwmZUqVKUahQISpWrMjIkSO57bbbvA7JBCgcd1T1gXWqugFARKYAzQDfRKXAue7rYsCfYYjL5BYHD8Lu3YHNe+gQXH752W/zhRegSpVTxxUu7HRud845Z79+c9ZUlffff5/69etz8cUXky9fPiZPnkyJEiUoXLiw1+GZIIQjUZUHfFqeJAX4h988zwFfisjDQGHg+oxWJCKdgE4AFStWzPFATRTautWpvJCd5oPat8/eNqtVc+6arOpyxFq3bh3dunXjyy+/5KabbuLTTz9FRKyYL0qFI1Fl9Gn2K+CnLTBRVV8VkYbAuyJSU1VPaSlTVScAEwCSkpL812HykuHDnYZOV6w4Oa5s2cCXv/tuGDw45+Mynjpy5AiDBw9m4MCBHDlyhPPOO4/bb7/d67DMWQpHokrB6VcsXQVOL9p7AGgKoKrzRaQgUArYFob4TCRbsMCplOBfeeHVV08d7tAB3nwzfHGZiDNnzhy6dOnCmjVrAGjXrh2DBw+mdOnSHkdmzlY4EtVioIqIVAI2A20A/1/UbQQaAxNFpDpQENgehthMOKjCsmVOJ3nBuuGGM0+fPRsuuCBn+zIyUWfLli00bdqUo0ePUrVqVcaOHcu1117rdVgmh4Q8UanqMRHpDszCqXr+pqquFJEXgGRVnQk8BrwuIj1xigXbq/p/hTZR64sv4Oabz24dDz10essLCQnQuPHZrddErbS0NEQEEaFs2bI89dRTiAh9+vThHKvQkqtItOaDpKQkTc5um2gmfI4fh/w+34eyk1hq1IDXXrPKC+aE5cuX07lzZx555BFatWrldThRRUSWqGqS13EEw1qmMKExaBDMnQvbfB4zvveeU4nBmGw6cOAAzz//PEOHDuX48eMcPnyYli1bWuOxuZwlKhOYGTPg998Dm/fQodObBKpVy5KUOSv/93//R/fu3dm4cSMiQvfu3Rk4cKAlqTzAElVel5bmtPSdlpb5PKtWOU0CZcennzr/A233zhg/f//9Nx07duTjjz8GoG7duowfP5569ep5HJkJF0tUeV2XLjBhQuDz9+gR+Lw33nj2lShMnle4cGGWL19OkSJFGDhwIN26dSN/frt05SX2bkeDOXOga1enSC2n/fHHyddn6sk0Xz6nNYYOHXI+BmP8LFy4kKpVq1K8eHHi4uJ4//33KVOmDBUqVPA6NOMBS1SRas4cp0Vu1eDueLKjcGH49dfgWnYwJgR27dpFv379GD9+PF26dGH06NEAXJ4T7TOaqGWJKtxWrgT3l/NndMcdp4978UVo2zbnYypZEooWzfn1GhMgVWXy5Mn07NmTbdu2kT9/fooXL46qWmUJE1yiEpECQEVVXReieHKXw4dPraSwaxfUrBncOvr2hYsvhvPOczrYK1AgZ2M0xmNr166la9eufP311wA0atSIcePGUaNGDY8jM5Ei4EQlIv8ChgIFgEoiUgd4VlWtxceMDB0KvXuf3kZdukAayqxRw+lOwr5Rmlzqjz/+IDExkSNHjlCyZEkGDx7MfffdZ73tmlMEc0f1Ak73HHMAVHWZiFQ+8yK52JAhzm+LMvPDDydfx8WdOq1bN2u52xjgoosu4s4776RAgQK88sorlCpVyuuQTAQKJlGlqupuv/Li6Gx/KVh//QXvvgtHjpwc9/TTgS27ZAlcdllo4jImymzdupXHHnuMbt260bBhQwDefvttYmJiPI7MRLJgEtVqEWkF5HNbQn8EWBCasCLA7t1OTTiARx6B+fMznm/27MyfG11wwem9wBqTB6WlpTFhwgSeeOIJ9uzZw7p165g/fz4iYknKZCmYRNUdeAZIAz7CaQ39yTMuEa2OHXNa5t6y5dTx5cuf2itsw4bWercxWfj555956KGHWLhwIQBNmzZl9OjRVpvPBCyYRHWjqj4OPJ4+QkRa4CSt3GX//pNJKsltZLhUKfjPf6BcOe/iMiaK7N+/n2effZbhw4dz/PhxypUrx/Dhw7njjjssSZmgBJOonuL0pNQ/g3HR7//+z/lfsiQsXuxtLMZEqYMHD/Lmm2+iqvTo0YMBAwZw7rnneh2WiUJZJioRuRGnm/jyIjLUZ9K5OMWAkevIEfjkE+cOKRjpxXv2rc+YoGzcuJGyZcsSGxtL6dKleeutt7jwwgutZQlzVgK5o9oGrAAOAyt9xu8DnghFUEFThe3bT28BfORIpzWH7Jo69eziMiaPSE1N5bXXXuO5557j2WefpW/fvgA0b97c48hMbpBlolLVpcBSEfmvqh4OQ0zB69IFxo8/8zzt2gW3zoQEuPrq7MdkTB4xb948OnfuzPLlywH43//+53FEJrcJ5hlVeRH5N5AAFEwfqapnaHI7THyTVJkyp04rWhT++1/rD8mYHLZz506eeOIJXn/9dQAuvvhiRo8eTdOmTT2OzOQ2wSSqicBAYAhwE9ABL59Rbd0Kjz9+6rj165128YwxIbVu3TquuOIKtm/fTmxsLH379qV///7E+bfCYkwOEM2sLTr/GUWWqOrlIrJcVWu5475X1X+GNMJMJIlosv/IgwdPb67IGJPj0tLSuOqqq8ifPz9jx46levXqXodkAuRey5O8jiMYwdxRHRHnxw/rRaQzsBkoHZqwApSYCHfd5bxOSrIkZUyIHDp0iEGDBnHfffdx8cUXky9fPmbOnMl5551nv4kyIRdMouoJFAF6AP8GigH3hyKogCUknF78Z4zJUV9++SVdu3Zl/fr1LFmyhE8++QSAEiVKeByZySsCTlSqutB9uQ+4F0BEvO0Xes8eTzdvTG62ZcsWevXqxZQpUwCoUaMGTzwRGb9IMXlLQJ2+iEg9EWkuIqXc4Roi8g5eN0rbrJmnmzcmNzp+/DhjxoyhWrVqTJkyhbi4OF5++WWWLl1Ko0aNvA7P5EFZJioReQn4L3A38IWI9Mfpk+pnwNuq6ampnm7emNxow4YNPProo+zdu5d//etfrFq1ir59+xIbG+t1aCaPCqTorxlQW1UPiUgJ4E93eE1oQwtA3bpeR2BMrnDgwAEKFSqEiFClShVefvll4uPjad68uVWWMJ4LpOjvsKoeAlDVncD/IiJJAZT2ttKhMdFOVfnwww+pWrUqU32aDOvZsye33367JSkTEQK5o7pYRNJbSBcg3mcYVW0RksgCYdXRjcm23377je7du/PZZ58BMGnSJFq1auVxVMacLpBEdYff8KhQBJItVmZuTNCOHj3K0KFDeeGFFzh06BDFihXjpZdeolOnTl6HZkyGAmmU9utwBJItlqiMCcr69etp1qwZK1c6HSG0bduWoUOHcsEFF3gcmTGZC+YHv5HHEpUxQSlXrhyHDx+mcuXKjBkzhhtuuMHrkIzJUkC/ozpbItJURNaIyDoRyfAXgyLSSkRWichKEZkU0IoLFMjROI3JbVSVd999l927dwMQFxfHp59+yvLlyy1JmagRdKISkXOCnD8GGI3T4noC0FZEEvzmqQI8CVypqjWARwNaud1RGZOpVatWcc0119CuXTv69+9/YnzVqlUpWLDgGZY0JrIEnKhEpL6ILAd+dYdri8jIABatD6xT1Q2qehSYgvPbLF8dgdGqugtCwZJMAAAgAElEQVRAVbcFFn1YbgiNiSoHDx6kf//+1KlTh7lz53L++edzxRVXeB2WMdkWzJV+BHALsANAVX8Grg1gufLAJp/hFHecr0uBS0XkRxFZICJZ97xmv+8w5jRffPEFNWvW5MUXXyQ1NZWHHnqINWvWcPfdd3sdmjHZFkxlinyq+offDwCPB7BcRhnFvxOs/EAV4BqgAvC9iNRU1d2nrEikE9AJ4DJLVMac4pdffuGmm24CoFatWowfP56GDRt6HJUxZy+YRLVJROoD6j53ehhYG8ByKcCFPsMVcJph8p9ngaqmAr+JyBqcxLXYdyZVnQBMAEiKiQmsx0djcjFVPdF6RGJiIp06deLSSy+lR48e1jafyTWCKfrrAvQCKgJbgQbuuKwsBqqISCURKQC0AWb6zfMxbjGi20L7pcCGM641LS2I0I3JfZKTk6lfvz7z588/MW78+PE89thjlqRMrhJMojqmqm1UtZT710ZV/85qIVU9BnQHZgGrgQ9UdaWIvCAit7mzzQJ2iMgqnJbZ+6jqjiD3xZg8Yc+ePTz88MPUr1+f5ORkBg4c6HVIxoSUqAZWgiYi64E1wPvAR6q6L5SBZSWpQAFNPnrUyxCMCStVZerUqTz66KNs2bKFmJgYevbsybPPPkuRIkW8Ds9ECRFZoqpJXscRjGB6+L1ERK7AKbp7XkSWAVNUdUrIojPGAJCSkkLHjh354osvAGjQoAHjxo2jdu3aHkdmTOgF9UMkVZ2nqj2Ay4C9OB0qGmNCrECBAixYsIDixYszfvx4fvzxR0tSJs8I+I5KRIrg/FC3DVAdmAHYrwiNCZF58+ZRr149YmNjKV26NB9++CE1atSgTJkyXodmTFgFc0e1Aqem3yuqWllVH1PVhSGKy5g8a/v27bRv354rr7ySYcOGnRh/3XXXWZIyeVIwv6O6WFWtTrgxIZKWlsZbb71F37592blzJwUKFCDQyk7G5GZZJioReVVVHwM+FJHTPjWe9vBrTC6xYsUKunTpwg8//ADA9ddfz5gxY6hSpYrHkRnjvUDuqN53/0dOz77G5CLJyck0bNiQY8eOUaZMGYYNG0abNm0QaybMGCCwHn4XuS+rq+opyUpEugOR2wOwMVHgsssu44orrqBGjRq8+OKLFC9e3OuQjIkowVSmuD+DcQ/kVCDG5BUpKSncddddbNjgtBKWL18+Zs+ezZgxYyxJGZOBQJ5Rtcapkl5JRD7ymVQU2J3xUsYYf8eOHWPUqFE8/fTT7N+/n6NHjzJt2jQAa5vPmDMI5BnVIpw+qCrg9NSbbh+wNBRBGZPbLFq0iIceeohly5YB0KJFi1OqnhtjMhfIM6rfgN+A2aEPx5jcZffu3fTr149x48ahqlSsWJHRo0dzyy23eB2aMVEjy2dUIvKd+3+XiOz0+dslIjtDH2KmgXm2aWMC9eeff/L6668TExND3759WbVqlSUpY4IUSNFfenfzpUIZiDG5RUpKCuXLl0dESEhIYNy4cdSvX59atWp5HZoxUSnLOyqf1iguBGJU9TjQEHgIKBzC2IyJKkeOHGHAgAFUrlyZqVOnnhj/wAMPWJIy5iwEUz39Y5xu6C8B3sFpmHZSSKIyJsrMmTOH2rVr88wzz3DkyBEWL17sdUjG5BrBJKo0VU0FWgCvqerDQPnQhGVMdNi2bRvt2rXjuuuuY82aNVStWpU5c+YwePBgr0MzJtcIplHaYyLSErgXaO6Osx9/mDwrOTmZJk2asGvXLgoWLMhTTz1F7969Oeecc7wOzZhcJZhEdT/QFaebjw0iUgmYHJqwjIl8NWrUoESJEtSrV48xY8ZwySWXeB2SMbmSBNONgIjkByq7g+tU9VhIogpA0jnnaPKRI15t3uRBBw4c4JVXXqFnz54nmjraunUrpUuXtgZkTdQQkSWqmuR1HMEIpofffwLvApsBAS4QkXtV9cdQBWdMpJg5cyYPP/wwGzdu5O+//2b0aKeRFuvI0JjQC6bobxhws6quAhCR6jiJK6oyszHB2LhxIz169GDGjBmA09J5hw4dPI7KmLwlmFp/BdKTFICqrgYK5HxIxngvNTWVV199lYSEBGbMmEHRokUZPnw4ixYtIinJvpsZE07B3FH9JCLjce6iAO7GGqU1udSCBQvo3bs3AHfeeSevvfYa5cvbrzGM8ULAlSlEpCDQA2iE84xqLjBSVQ+HLrzMWWUKk9MOHz5MwYIFTwz37duXa665hptvvtnDqIzJWdFYmSKgRCUitYBLgJWq+mvIowqAJSqTU1SVSZMm0bt3bz766CMaNmzodUjGhEw0JqpAWk/vh9N80t3AVyKSUU+/xkSltWvXcsMNN3DPPffw119/MXHiRK9DMsb4CaQyxd1Aoqq2BOoBXUIbkjGhd/jwYZ577jlq1arF119/TcmSJXnzzTcZO3as16EZY/wEUpniiKoeAFDV7SISTE1BYyLOzz//TMuWLfn1V6cU+/777+fll1+mVCnrycaYSBRIorpYRD5yXwtwic8wqtoiJJEZEyJly5Zl+/btJCQkMHbsWK666iqvQzLGnEEgieoOv+FRoQjEmFBJS0tj8uTJtGrVitjYWEqXLs0333xDjRo1KFDAfgpoTKTLMlGp6tfhCMSYUFi2bBmdO3dm4cKFbN68mb59+wJQt25djyMzxgTKnjeZXGnfvn306tWLyy+/nIULF1KuXDmqVKnidVjGmGwIS6ISkaYiskZE1onIE2eY704RURGJqjr+JnKoKtOnTychIYFhw4YB0KNHD1avXs3tt9/ucXTGmOwIpgklAETkHFUN+Je2IhIDjAZuAFKAxSIy07fdQHe+ojgtXywMNiZj0s2aNYsWLZz6PUlJSYwbN47LL7/c46iMMWcj4DsqEakvIsuBX93h2iIyMoBF6+P0XbVBVY8CU4BmGcw3AHgFCKxJJuv/x7h8W1dp0qQJN998MyNHjmTBggWWpIzJBYIp+hsB3ALsAFDVn4FrA1iuPLDJZzjFHXeCiNQFLlTVT860IhHpJCLJIpJ8/PjxIEI3udWPP/5IgwYN2LBhAwD58uXjk08+oXv37sTExHgcnTEmJwSTqPKp6h9+4wLJFhnd+pz4Cuz+gHgY8FhWK1LVCaqapKpJdhHK23bu3EnHjh1p1KgRixYt4qWXXjoxzXrbNSZ3CSZRbRKR+oCKSIyIPAqsDWC5FOBCn+EKwJ8+w0WBmsC3IvI70ACYaRUqTEZUlXfeeYeqVavyxhtvEBsby1NPPcWIESO8Ds0YEyLBVKboglP8VxHYCswmsHb/FgNVRKQSTjf2bYC70ieq6h7gRNs1IvIt0FtVk4OIzeQB69ato2PHjnz77bcAXH311YwdO5bq1at7G5gxJqQCTlSqug0nyQRFVY+JSHdgFhADvKmqK0XkBSBZVWcGu06TNx09epQffviBUqVK8eqrr3LvvfdaMZ8xeUAwHSe+js+zpXSq2imngwpEUsGCmnzYkz4bTRgtXryYpKSkEwlp+vTpXHXVVZQsWdLjyIyJTrmyPyofs4Gv3b8fgdKA9VxoQmLLli20bduW+vXrM3Xq1BPjb7/9dktSxuQxwRT9ve87LCLvAl/leEQmTzt+/Djjxo2jX79+7N27l7i4OHbt2uV1WMYYDwXdMoWPSsBFORWIMT/99BOdO3dm8eLFANxyyy2MHDmS+Ph4bwMzxngq4EQlIrs4+YwqH7ATyLTdPmOC8fnnn3PLLbeQlpZG+fLlGTlyJM2bN7fKEsaYwBKVOFeL2jjVywHSNNBaGMYE4JprrqFKlSrcfPPNPP/88xQtWtTrkIwxESKgyhRuUpquqsfdP0tS5qz89ttvtGvXjt27dwMQFxfHsmXLGDp0qCUpY8wpgqn1t0hELgtZJCZPOHr0KIMGDaJGjRq8++67PP/88yemFSxY0MPIjDGRKsuiPxHJr6rHgEZARxFZDxzAacNPVdWSlwnI999/T5cuXVi5ciUAd911F48//rjHURljIl0gz6gWAZcBzUMci8mlduzYQd++fXnzzTcBqFy5MmPHjuX666/3ODJjTDQIJFEJgKquD3EsJpdatmwZb775JgUKFODJJ5/kiSeesGI+Y0zAAklU54tIr8wmqurQHIzH5BJbt26lTJkyADRu3JiXX36ZZs2aUbVqVY8jM8ZEm0AqU8QARXC648joz5gTDh48SL9+/ahYsSLz588/Mb5v376WpIwx2RLIHdUWVX0h5JGYqPf555/TrVs3fvvtNwDmzp1Lw4YNPY7KGBPtAn5GZUxmNm/ezKOPPsq0adMASExMZNy4cZakjDE5IpBE1TjkUZio9eWXX3LnnXeyb98+ChcuzPPPP88jjzxC/vxn04ykMcaclOXVRFV3hiMQE53q1KlDTEwMzZs3Z/jw4VSsWNHrkIwxuUwwLVMYw549exg4cCCpqakAlC5dmuXLlzN9+nRLUsaYkIje8hlrVTusVJUPPviARx99lL/++osCBQrQt29fACpUqOBxdMaY3Cx6E5UJm/Xr19OtWzdmzZoFQMOGDWnatKnHURlj8gor+jOZOnLkCAMHDqRmzZrMmjWL4sWLM378eH744QcSExO9Ds8Yk0fYHZXJ1EcffcTTTz8NwD333MOQIUNOtDZhjDHhYonKnCI1NZXY2FgAWrduzaxZs2jXrh3XXXedx5EZY/IqK/ozAKSlpfHGG29QqVIlNmzYAEC+fPmYOHGiJSljjKcsURlWrFjBVVddRceOHdm8eTMTJ070OiRjjDnBElUeduDAAR5//HHq1q3Ljz/+SJkyZZg0adIpve4aY4zX7BlVHvXDDz9wzz338McffyAidO3alX//+98UL17c69CMMeYUlqjyqBIlSvDnn39Sp04dxo0bxz/+8Q+vQzLGmAxZ0V8ecezYMaZNm4aqApCQkMCcOXNYvHixJSljTESzRJUHLFq0iHr16tGyZUumTp16YvyVV15prZwbYyKeJapcbPfu3XTt2pUGDRqwbNkyLrroInsGZYyJOpaociFVZfLkyVSrVo2xY8cSExPD448/zsqVK2nSpInX4RljTFCs3CcXmjhxIvfffz/gFO+NHTuWWrVqeRyVMcZkT1juqESkqYisEZF1IvJEBtN7icgqEflFRL4WkYvCEVdu1aZNG+rXr88bb7zB3LlzLUkZY6JayBOViMQAo4GbgASgrYgk+M22FEhS1URgGvBKqOPKTb755huuvvpqdu/eDUBcXBwLFizggQceIF8+K901xkS3cFzF6gPrVHWDqh4FpgDNfGdQ1TmqetAdXABYT3wB2LZtG/feey+NGzdm7ty5vPbaayemiXUsaYzJJcKRqMoDm3yGU9xxmXkA+DyjCSLSSUSSRST52PHjORhidElLS2PChAlUrVqV9957j4IFCzJw4ED69evndWjGGJPjwlGZIqOv9prhjCL3AEnA1RlNV9UJwASApLi4DNeR261YsYJOnToxf/58AG688UZGjx7NJZdc4nFkxhgTGuFIVCnAhT7DFYA//WcSkeuB/sDVqnokDHFFpb/++ov58+dTtmxZXnvtNVq2bGnFfMaYXC0cRX+LgSoiUklECgBtgJm+M4hIXWA8cJuqbgtDTFHll19+OfH6+uuvZ+LEiaxevZpWrVpZkjLG5HohT1SqegzoDswCVgMfqOpKEXlBRG5zZxsMFAGmisgyEZmZyerylI0bN9K8eXNq1659oqgP4L777qNYsWIeRmaMMeETlh/8qupnwGd+457xeX190CvNxXcSqampDB8+nGeffZaDBw9StGhRNm7cSMOGDb0OzRhjws5apogw8+fPp3PnzieK+1q2bMmwYcMoX/5MFSWNMSb3skQVQd5++23at28PQKVKlRg1ahQ333yzt0EZY4zHLFFFkJtuuonSpUvz4IMP0r9/fwoVKuR1SMYY4zlLVB5as2YNw4YNY+TIkcTGxlK6dGk2bNhA4cKFvQ7NGGMihjUE54HDhw/z7LPPkpiYyPjx4xk5cuSJaZakjDHmVHZHFWZfffUVXbt2Zd26dQDcf//9tGvXzuOojDEmclmiCpO//vqLXr16MXnyZAASEhIYO3YsV111lceRGWNMZLOivzD57LPPmDx5MnFxcbz00kssXbrUkpQxxgTA7qhCaOfOnZQoUQKA9u3bs3btWh566CEqVarkcWTGGBM97I4qBPbt20evXr2Ij4/nt99+AyBfvnwMGjTIkpQxxgTJElUOUlWmT59OQkICw4YN48CBA3zzzTdeh2WMMVHNiv5yyO+//87DDz/MJ598AkBSUhLjx4/nsssu8zgyY4yJbnZHlQPef/99EhIS+OSTTzj33HMZNWoUCxYssCRljDE5wO6ockCtWrU4duwYrVu3ZujQoZQrV87rkIyJeKmpqaSkpHD48GGvQ8mVChYsSIUKFYiNjfU6lLNmiSobdu7cyTvvvMMjjzyCiJCQkMDq1autO3hjgpCSkkLRokWJj4+3DkBzmKqyY8cOUlJSckUFLiv6C4Kq8s4771C1alV69uzJ1KlTT0yzJGVMcA4fPkzJkiUtSYWAiFCyZMlcc7dqd1QB+t///keXLl349ttvAbjmmmtITEz0NihjopwlqdDJTcfW7qiycOjQIZ5++mkSExP59ttvKVWqFG+//TbffPMN1apV8zo8Y4zJ9SxRZWH06NEMHDiQ1NRUOnbsyJo1a2jXrl2u+rZiTF4VExNDnTp1qFmzJrfeeiu7d+8+MW3lypVcd911XHrppVSpUoUBAwagqiemf/755yQlJVG9enWqVatG7969vdiFPMESVQaOHz9+4nW3bt1o1qwZP/zwAxMmTDjRJJIxJvrFxcWxbNkyVqxYQYkSJRg9ejTglKTcdtttPPHEE6xdu5aff/6ZefPmMWbMGABWrFhB9+7dee+991i9ejUrVqzg4osvztHYjh07lqPri2aWqHwcP36c0aNHU6NGjRPfrOLi4vj444+58sorPY7OmFxMJDR/QWjYsCGbN28GYNKkSVx55ZU0adIEgEKFCjFq1CgGDRoEwCuvvEL//v1PFP/nz5+frl27nrbO/fv306FDB2rVqkViYiIffvghAEWKFDkxz7Rp02jfvj3gtAnaq1cvrr32Wvr06UN8fPwpd3mVK1dm69atbN++nTvuuIN69epRr149fvzxx6D2NdpYZQrXTz/9ROfOnVm8eDEAkydPpkuXLh5HZYwJh+PHj/P111/zwAMPAE6x3+WXX37KPJdccgn79+9n7969rFixgsceeyzL9Q4YMIBixYqxfPlyAHbt2pXlMmvXrmX27NnExMSQlpbG9OnT6dChAwsXLiQ+Pp4yZcpw11130bNnTxo1asTGjRu58cYbWb16dTb2PDpEb6LKoWdEe/fu5ZlnnmHkyJGkpaVRoUIFRo4cSbNmzXJk/caYAPg8+wmnQ4cOUadOHX7//Xcuv/xybrjhBjcczfQ5dDDPp2fPns2UKVNODJ933nlZLtOyZUtiYmIAaN26NS+88AIdOnRgypQptG7d+sR6V61adWKZvXv3sm/fPooWLRpwbNEkTxf9ffHFF1SvXp3hw4cjIvTq1YtVq1bRvHlzqyxhTB6Q/ozqjz/+4OjRoyeeUdWoUYPk5ORT5t2wYQNFihShaNGi1KhRgyVLlmS5/swSnu84/986FS5c+MTrhg0bsm7dOrZv387HH39MixYtAEhLS2P+/PksW7aMZcuWsXnz5lybpCCPJ6r8+fPz559/Ur9+fZKTk3n11Vdz9ZttjMlYsWLFGDFiBEOGDCE1NZW7776bH374gdmzZwPOnVePHj3o27cvAH369OHFF19k7dq1gJM4hg4detp6mzRpwqhRo04Mpxf9lSlThtWrV58o2suMiHD77bfTq1cvqlevTsmSJTNc77Jly87yCES2PJWojh49yhdffHFi+Prrr2fWrFnMmzePOnXqeBiZMcZrdevWpXbt2kyZMoW4uDhmzJjBwIEDqVq1KrVq1aJevXp0794dgMTERF577TXatm1L9erVqVmzJlu2bDltnU899RS7du2iZs2a1K5dmzlz5gAwaNAgbrnlFq677jrKli17xrhat27Ne++9d6LYD2DEiBEkJyeTmJhIQkIC48aNy8EjEXlEPSobPltJFSpockpKwPN///33dO7cmVWrVjFv3jwaNmwYwuiMMVlZvXo11atX9zqMXC2jYywiS1Q1yaOQsiV676guuCCg2f7++2/uv/9+rrrqKlatWkWVKlVIS0sLcXDGGGNySvQmqiyoKm+99RbVqlXjrbfeokCBAjz77LP88ssv9psoY4yJItFbPT0LL774Ik899RQAjRs3ZsyYMVx66aUeR2WM8XWmauDm7ETrY52M5No7qgcffJBq1arx3nvv8dVXX1mSMibCFCxYkB07duSqC2qkSO+PqmDBgl6HkiNyzR3V559/zrhx45g2bRqxsbGUKVOGlStXki9frs3FxkS1ChUqkJKSwvbt270OJVdK7+E3N4j6RLV582YeffRRpk2bBsDEiRPp2LEjgCUpYyJYbGxsruh91oReWK7kItJURNaIyDoReSKD6eeIyPvu9IUiEh/IekeMGEH16tWZNm0ahQsXZsiQIXTo0CGnwzfGGOOhkP+OSkRigLXADUAKsBhoq6qrfObpCiSqamcRaQPcrqqtM1yhq3Dhwnrw4EEAmjdvzvDhw6lYsWKodsMYY3IF+x1VxuoD61R1g6oeBaYA/i2+NgPedl9PAxpLFlWBDh48yIUXXsiMGTOYPn26JSljjMmlwvGMqjywyWc4BfhHZvOo6jER2QOUBP72nUlEOgGd3MEjmzZtWmGtnANQCr9jlYfZsTjJjsVJdixOqup1AMEKR6LK6M7Iv7wxkHlQ1QnABAARSY6229dQsWNxkh2Lk+xYnGTH4iQRSc56rsgSjqK/FOBCn+EKwJ+ZzSMi+YFiwM4wxGaMMSbChSNRLQaqiEglESkAtAFm+s0zE7jPfX0n8I3arwCNMcYQhqI/95lTd2AWEAO8qaorReQFIFlVZwL/Ad4VkXU4d1JtAlj1hJAFHX3sWJxkx+IkOxYn2bE4KeqORdR282GMMSZvsKYbjDHGRDRLVMYYYyJaxCeqUDW/FG0COA69RGSViPwiIl+LyEVexBkOWR0Ln/nuFBEVkVxbLTmQYyEirdxzY6WITAp3jOESwGekoojMEZGl7ufkZi/iDAcReVNEtonIikymi4iMcI/VLyJyWbhjDIqqRuwfTuWL9cDFQAHgZyDBb56uwDj3dRvgfa/j9ug4XAsUcl93yY3HIdBj4c5XFJgLLACSvI7bw/OiCrAUOM8dLu113B4eiwlAF/d1AvC713GH8HhcBVwGrMhk+s3A5zi/YW0ALPQ65jP9RfodVUiaX4pCWR4HVZ2jqgfdwQU4v1fLjQI5JwAGAK8Ah8MZXJgFciw6AqNVdReAqm4Lc4zhEsixUOBc93UxTv89Z66hqnM5829RmwHvqGMBUFxEyoYnuuBFeqLKqPml8pnNo6rHgPTml3KTQI6Drwdwvi3lRlkeCxGpC1yoqp+EMzAPBHJeXApcKiI/isgCEWkatujCK5Bj8Rxwj4ikAJ8BD4cntIgU7DXFU5HeH1WONb8U5QLeRxG5B0gCrg5pRN4547EQkXzAMKB9uALyUCDnRX6c4r9rcO6yvxeRmqq6O8SxhVsgx6ItMFFVXxWRhji/3aypqmmhDy/iRNV1M9LvqKz5JUcgxwERuR7oD9ymqkfCFFu4ZXUsigI1gW9F5Hec8veZubRCRaCfjxmqmqqqvwFrcBJXbhPIsXgA+ABAVecDBXEaq82LArqmRIpIT1TW/JIjy+PgFneNx0lSufU5BGRxLFR1j6qWUtV4VY3HeV53m6pGXUOcAQjk8/ExTkUbRKQUTlHghrBGGR6BHIuNQGMAEamOk6i2hzXKyDETaOfW/msA7FHVLV4HlZmILvrT0DW/FFUCPA6DgSLAVLcuyUZVvc2zoEMkwGORJwR4LGYBTURkFXAc6KOqO7yLOjQCPBaPAa+LSE+cYq72ufBLLQAiMhmnuLeU+0zuWSAWQFXH4TyjuxlYBxwEIrprdGtCyRhjTESL9KI/Y4wxeZwlKmOMMRHNEpUxxpiIZonKGGNMRLNEZYwxJqJZojIRS0SOi8gyn7/4M8wbn1lL0UFu81u3Be6f3WaHqmZjHZ1FpJ37ur2IlPOZ9oaIJORwnItFpE4AyzwqIoXOdtvGhJslKhPJDqlqHZ+/38O03btVtTZOY8eDg11YVcep6jvuYHugnM+0B1V1VY5EeTLOMQQW56OAJSoTdSxRmaji3jl9LyI/uX9XZDBPDRFZ5N6F/SIiVdzx9/iMHy8iMVlsbi5Q2V22sduP0XK3r59z3PGD5GQ/YEPccc+JSG8RuROn3cX/utuMc++EkkSki4i84hNzexEZmc045+PToKiIjBWRZHH6n3reHdcDJ2HOEZE57rgmIjLfPY5TRaRIFtsxxhOWqEwki/Mp9pvujtsG3KCqlwGtgREZLNcZGK6qdXASRYrbZE5r4Ep3/HHg7iy2fyuwXEQKAhOB1qpaC6dFly4iUgK4HaihqonAQN+FVXUakIxz51NHVQ/5TJ4GtPAZbg28n804m+I0lZSuv6omAYnA1SKSqKojcNpyu1ZVr3WbU3oKuN49lslAryy2Y4wnIroJJZPnHXIv1r5igVHuM5njOG3X+ZsP9BeRCsBHqvqriDQGLgcWu01MxeEkvYz8V0QOAb/jdAVRFfhNVde6098GugGjcPq7ekNEPgUC7lZEVbeLyAa3nbVf3W386K43mDgL4zQZ5NtDaysR6YTz+S6L00ngL37LNnDH/+hupwDOcTMm4liiMtGmJ7AVqI1TInBax4iqOklEFgL/AmaJyIM43Rq8rapPBrCNu30bsRWRDPs3c9uXq4/T0GkboDtwXRD78j7QCiNv/1UAAAFkSURBVPgfMF1VVZysEXCcOD3ZDgJGAy1EpBLQG6inqrtEZCJO46v+BPhKVdsGEa8xnrCiPxNtigFb3D6E7sW5mziFiFwMbHCLu2biFIF9DdwpIqXdeUqIyEUBbvN/QLyIVHaH7wW+c5/pFFPVz3AqKmRU824fTtcjGfkIaI7TT9L77rig4lTVVJwivAZuseG5wAFgj4iUAW7KJJYFwJXp+yQihUQko7tTYzxnicpEmzHAfSKyAKfY70AG87QGVojIMqAaTpfbq3Au6F+KyC/AVzjFYllS1cM4rUtPFZHlQBowDuei/4m7vu9w7vb8TQTGpVem8FvvLmAVcJGqLnLHBR2n++zrVaC3qv4MLAVWAm/iFCemmwB8LiJzVHU7To3Eye52FuAcK2MijrWebowxJqLZHZUxxpiIZonKGGNMRLNEZYwxJqJZojLGGBPRLFEZY4yJaJaojDHGRDRLVMYYYyLa/wPDQmY5jnbhRwAAAABJRU5ErkJggg==\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": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision at 75% recall 0.004698601879440752\n"
     ]
    }
   ],
   "source": [
    "preds, precision = precisionAnalysis(preds, \"anomalyScore\", 0.75)\n",
    "print(\"Precision at 75% recall\", precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Unsupervised"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "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": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(units=40, activation='linear', \\\n",
    "                activity_regularizer=regularizers.l1(10e-5), \\\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": 18,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss='mean_squared_error',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 155058 samples, validate on 38765 samples\n",
      "Epoch 1/5\n",
      "155058/155058 [==============================] - 16s 104us/step - loss: 0.1347 - acc: 0.8660 - val_loss: 0.1847 - val_acc: 0.9719\n",
      "Epoch 2/5\n",
      "155058/155058 [==============================] - 14s 91us/step - loss: 0.0515 - acc: 0.9246 - val_loss: 0.3361 - val_acc: 0.9587\n",
      "Epoch 3/5\n",
      "155058/155058 [==============================] - 15s 99us/step - loss: 0.0445 - acc: 0.9255 - val_loss: 0.1167 - val_acc: 0.9779\n",
      "Epoch 4/5\n",
      "155058/155058 [==============================] - 15s 100us/step - loss: 0.0408 - acc: 0.9249 - val_loss: 0.1459 - val_acc: 0.9531\n",
      "Epoch 5/5\n",
      "155058/155058 [==============================] - 14s 93us/step - loss: 0.0381 - acc: 0.9242 - val_loss: 0.0670 - val_acc: 0.9663\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 5\n",
    "batch_size = 32\n",
    "\n",
    "history = model.fit(x=X_train, y=X_train,\n",
    "                    epochs=num_epochs,\n",
    "                    batch_size=batch_size,\n",
    "                    shuffle=True,\n",
    "                    validation_split=0.20,\n",
    "                    verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "190523/190523 [==============================] - 3s 18us/step\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, \\\n",
    "                                      anomalyScoresAETrain, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "93987/93987 [==============================] - 1s 13us/step\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": [
    "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": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision at 75% recall 0.45387453874538747\n"
     ]
    }
   ],
   "source": [
    "preds, precision = precisionAnalysis(preds, \"anomalyScore\", 0.75)\n",
    "print(\"Precision at 75% recall\", precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Semi-supervised"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "layer_name = 'hidden_layer'\n",
    "\n",
    "intermediate_layer_model = Model(inputs=model.input, \\\n",
    "                                 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": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "intermediate_output_trainDF = \\\n",
    "    pd.DataFrame(data=intermediate_output_train,index=X_train_original.index)\n",
    "intermediate_output_testDF = \\\n",
    "    pd.DataFrame(data=intermediate_output_test,index=X_test_original.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train = X_train_original.merge(intermediate_output_trainDF, \\\n",
    "                                 left_index=True,right_index=True)\n",
    "X_test = X_test_original.merge(intermediate_output_testDF, \\\n",
    "                               left_index=True,right_index=True)\n",
    "y_train = y_train_original.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\tvalid_0's binary_logloss: 0.0026001\n",
      "Training until validation scores don't improve for 200 rounds.\n",
      "[2]\tvalid_0's binary_logloss: 0.00195274\n",
      "[3]\tvalid_0's binary_logloss: 0.00194893\n",
      "[4]\tvalid_0's binary_logloss: 0.00194533\n",
      "[5]\tvalid_0's binary_logloss: 0.0019298\n",
      "[6]\tvalid_0's binary_logloss: 0.00192764\n",
      "[7]\tvalid_0's binary_logloss: 0.00192773\n",
      "[8]\tvalid_0's binary_logloss: 0.00192766\n",
      "[9]\tvalid_0's binary_logloss: 0.0019276\n",
      "[10]\tvalid_0's binary_logloss: 0.0019278\n",
      "[11]\tvalid_0's binary_logloss: 0.00192776\n",
      "[12]\tvalid_0's binary_logloss: 0.00192775\n",
      "[13]\tvalid_0's binary_logloss: 0.00192792\n",
      "[14]\tvalid_0's binary_logloss: 0.00192791\n",
      "[15]\tvalid_0's binary_logloss: 0.00192679\n",
      "[16]\tvalid_0's binary_logloss: 0.00194954\n",
      "[17]\tvalid_0's binary_logloss: 0.00194901\n",
      "[18]\tvalid_0's binary_logloss: 0.00194781\n",
      "[19]\tvalid_0's binary_logloss: 0.00194705\n",
      "[20]\tvalid_0's binary_logloss: 0.00194916\n",
      "[21]\tvalid_0's binary_logloss: 0.00194777\n",
      "[22]\tvalid_0's binary_logloss: 0.00194645\n",
      "[23]\tvalid_0's binary_logloss: 0.00194512\n",
      "[24]\tvalid_0's binary_logloss: 0.00196772\n",
      "[25]\tvalid_0's binary_logloss: 0.00197017\n",
      "[26]\tvalid_0's binary_logloss: 0.00197267\n",
      "[27]\tvalid_0's binary_logloss: 0.00197538\n",
      "[28]\tvalid_0's binary_logloss: 0.001978\n",
      "[29]\tvalid_0's binary_logloss: 0.00197714\n",
      "[30]\tvalid_0's binary_logloss: 0.00197749\n",
      "[31]\tvalid_0's binary_logloss: 0.00197712\n",
      "[32]\tvalid_0's binary_logloss: 0.00197473\n",
      "[33]\tvalid_0's binary_logloss: 0.00197187\n",
      "[34]\tvalid_0's binary_logloss: 0.00196958\n",
      "[35]\tvalid_0's binary_logloss: 0.00197131\n",
      "[36]\tvalid_0's binary_logloss: 0.00197288\n",
      "[37]\tvalid_0's binary_logloss: 0.00197033\n",
      "[38]\tvalid_0's binary_logloss: 0.00197144\n",
      "[39]\tvalid_0's binary_logloss: 0.0019719\n",
      "[40]\tvalid_0's binary_logloss: 0.00197221\n",
      "[41]\tvalid_0's binary_logloss: 0.00196983\n",
      "[42]\tvalid_0's binary_logloss: 0.00196854\n",
      "[43]\tvalid_0's binary_logloss: 0.00196755\n",
      "[44]\tvalid_0's binary_logloss: 0.00196602\n",
      "[45]\tvalid_0's binary_logloss: 0.00196509\n",
      "[46]\tvalid_0's binary_logloss: 0.00196279\n",
      "[47]\tvalid_0's binary_logloss: 0.00196052\n",
      "[48]\tvalid_0's binary_logloss: 0.00196081\n",
      "[49]\tvalid_0's binary_logloss: 0.00195853\n",
      "[50]\tvalid_0's binary_logloss: 0.00195578\n",
      "[51]\tvalid_0's binary_logloss: 0.00195357\n",
      "[52]\tvalid_0's binary_logloss: 0.00195083\n",
      "[53]\tvalid_0's binary_logloss: 0.00194817\n",
      "[54]\tvalid_0's binary_logloss: 0.00194603\n",
      "[55]\tvalid_0's binary_logloss: 0.00194341\n",
      "[56]\tvalid_0's binary_logloss: 0.00194082\n",
      "[57]\tvalid_0's binary_logloss: 0.00193825\n",
      "[58]\tvalid_0's binary_logloss: 0.00193657\n",
      "[59]\tvalid_0's binary_logloss: 0.00193446\n",
      "[60]\tvalid_0's binary_logloss: 0.00193257\n",
      "[61]\tvalid_0's binary_logloss: 0.00193095\n",
      "[62]\tvalid_0's binary_logloss: 0.0019291\n",
      "[63]\tvalid_0's binary_logloss: 0.00192728\n",
      "[64]\tvalid_0's binary_logloss: 0.0019256\n",
      "[65]\tvalid_0's binary_logloss: 0.00192451\n",
      "[66]\tvalid_0's binary_logloss: 0.00192274\n",
      "[67]\tvalid_0's binary_logloss: 0.0019211\n",
      "[68]\tvalid_0's binary_logloss: 0.00191948\n",
      "[69]\tvalid_0's binary_logloss: 0.00191789\n",
      "[70]\tvalid_0's binary_logloss: 0.00191563\n",
      "[71]\tvalid_0's binary_logloss: 0.00191408\n",
      "[72]\tvalid_0's binary_logloss: 0.00191254\n",
      "[73]\tvalid_0's binary_logloss: 0.00191102\n",
      "[74]\tvalid_0's binary_logloss: 0.00190885\n",
      "[75]\tvalid_0's binary_logloss: 0.00190717\n",
      "[76]\tvalid_0's binary_logloss: 0.0019055\n",
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      "Early stopping, best iteration is:\n",
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      "Training Log Loss:  9.721267927111629e-05\n",
      "CV Log Loss:  0.0018688329238841733\n",
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      "Training until validation scores don't improve for 200 rounds.\n",
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      "Early stopping, best iteration is:\n",
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      "Training Log Loss:  0.00020349604975396007\n",
      "CV Log Loss:  0.0004984434368852255\n",
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      "Training until validation scores don't improve for 200 rounds.\n",
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      "Early stopping, best iteration is:\n",
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      "Training until validation scores don't improve for 200 rounds.\n",
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      "Early stopping, best iteration is:\n",
      "[77]\tvalid_0's binary_logloss: 0.00077721\n",
      "Training Log Loss:  0.00024230954642378113\n",
      "CV Log Loss:  0.0007772098153701629\n",
      "[1]\tvalid_0's binary_logloss: 0.00340922\n",
      "Training until validation scores don't improve for 200 rounds.\n",
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     ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "[3802]\tvalid_0's binary_logloss: 0.00122089\n",
      "[3803]\tvalid_0's binary_logloss: 0.00122084\n",
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      "[3805]\tvalid_0's binary_logloss: 0.00122071\n",
      "[3806]\tvalid_0's binary_logloss: 0.00122077\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3807]\tvalid_0's binary_logloss: 0.00122079\n",
      "[3808]\tvalid_0's binary_logloss: 0.0012207\n",
      "[3809]\tvalid_0's binary_logloss: 0.00122083\n",
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      "[3820]\tvalid_0's binary_logloss: 0.00122087\n",
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      "[3975]\tvalid_0's binary_logloss: 0.00122156\n",
      "[3976]\tvalid_0's binary_logloss: 0.00122151\n",
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      "[3979]\tvalid_0's binary_logloss: 0.00122158\n",
      "[3980]\tvalid_0's binary_logloss: 0.00122166\n",
      "[3981]\tvalid_0's binary_logloss: 0.00122166\n",
      "[3982]\tvalid_0's binary_logloss: 0.00122166\n",
      "[3983]\tvalid_0's binary_logloss: 0.00122168\n",
      "[3984]\tvalid_0's binary_logloss: 0.00122166\n",
      "[3985]\tvalid_0's binary_logloss: 0.00122175\n",
      "[3986]\tvalid_0's binary_logloss: 0.00122181\n",
      "[3987]\tvalid_0's binary_logloss: 0.00122172\n",
      "Early stopping, best iteration is:\n",
      "[3787]\tvalid_0's binary_logloss: 0.00122067\n",
      "Training Log Loss:  1.4822545241612539e-08\n",
      "CV Log Loss:  0.0012206658982393806\n",
      "LightGBM Gradient Boosting Log Loss:  0.0016476266882114693\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,:], \\\n",
    "        X_train.iloc[cv_index,:]\n",
    "    y_train_fold, y_cv_fold = y_train.iloc[train_index], \\\n",
    "        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, \n",
    "                                gbm.predict(X_train_fold, \\\n",
    "                                num_iteration=gbm.best_iteration))\n",
    "    trainingScores.append(loglossTraining)\n",
    "    \n",
    "    predictionsBasedOnKFolds.loc[X_cv_fold.index,'prediction'] = \\\n",
    "        gbm.predict(X_cv_fold, num_iteration=gbm.best_iteration) \n",
    "    loglossCV = log_loss(y_cv_fold, \\\n",
    "            predictionsBasedOnKFolds.loc[X_cv_fold.index,'prediction'])\n",
    "    cvScores.append(loglossCV)\n",
    "    \n",
    "    print('Training Log Loss: ', loglossTraining)\n",
    "    print('CV Log Loss: ', loglossCV)\n",
    "    \n",
    "loglossLightGBMGradientBoosting = log_loss(y_train, \\\n",
    "                        predictionsBasedOnKFolds.loc[:,'prediction'])\n",
    "print('LightGBM Gradient Boosting Log Loss: ', \\\n",
    "                        loglossLightGBMGradientBoosting)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": 28,
   "metadata": {
    "scrolled": false
   },
   "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": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.917910447761194\n"
     ]
    }
   ],
   "source": [
    "preds, precision = precisionAnalysis(preds, \"anomalyScore\", 0.75)\n",
    "print(precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "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",
       "      <th>V28</th>\n",
       "      <td>0.047843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amount</th>\n",
       "      <td>0.037263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.030244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V21</th>\n",
       "      <td>0.029624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V26</th>\n",
       "      <td>0.029469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V12</th>\n",
       "      <td>0.028334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V27</th>\n",
       "      <td>0.028024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.027405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.026941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.024050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.022347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V19</th>\n",
       "      <td>0.021212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.020851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.020386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V23</th>\n",
       "      <td>0.020283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V25</th>\n",
       "      <td>0.020231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.020231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.020076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.019612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V13</th>\n",
       "      <td>0.019509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.018115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V7</th>\n",
       "      <td>0.017960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.017444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V5</th>\n",
       "      <td>0.017135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V3</th>\n",
       "      <td>0.016928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V4</th>\n",
       "      <td>0.016412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V18</th>\n",
       "      <td>0.016257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V14</th>\n",
       "      <td>0.016154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.015173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V16</th>\n",
       "      <td>0.015019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.011096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.010941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.010632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.010632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.010167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.010064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.009341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.009290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V10</th>\n",
       "      <td>0.009187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.008309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.008103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.007845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.007329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.007225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.007071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.006451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V6</th>\n",
       "      <td>0.006193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.005419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.005316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V24</th>\n",
       "      <td>0.005264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.005161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V20</th>\n",
       "      <td>0.004955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.004800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.004284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V2</th>\n",
       "      <td>0.003148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V8</th>\n",
       "      <td>0.003097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.002735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V22</th>\n",
       "      <td>0.002632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V9</th>\n",
       "      <td>0.002374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.001806</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>69 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        featImportance\n",
       "V28           0.047843\n",
       "Amount        0.037263\n",
       "21            0.030244\n",
       "V21           0.029624\n",
       "V26           0.029469\n",
       "V12           0.028334\n",
       "V27           0.028024\n",
       "6             0.027405\n",
       "28            0.026941\n",
       "36            0.024050\n",
       "5             0.022347\n",
       "V19           0.021212\n",
       "11            0.020851\n",
       "15            0.020386\n",
       "V23           0.020283\n",
       "V25           0.020231\n",
       "23            0.020231\n",
       "1             0.020076\n",
       "24            0.019612\n",
       "V13           0.019509\n",
       "10            0.018115\n",
       "V7            0.017960\n",
       "4             0.017444\n",
       "V5            0.017135\n",
       "V3            0.016928\n",
       "V4            0.016412\n",
       "V18           0.016257\n",
       "V14           0.016154\n",
       "18            0.015173\n",
       "V16           0.015019\n",
       "...                ...\n",
       "30            0.011096\n",
       "16            0.010941\n",
       "7             0.010632\n",
       "22            0.010632\n",
       "34            0.010167\n",
       "32            0.010064\n",
       "33            0.009341\n",
       "2             0.009290\n",
       "V10           0.009187\n",
       "9             0.008309\n",
       "35            0.008103\n",
       "31            0.007845\n",
       "29            0.007329\n",
       "27            0.007225\n",
       "39            0.007071\n",
       "37            0.006451\n",
       "V6            0.006193\n",
       "17            0.005419\n",
       "20            0.005316\n",
       "V24           0.005264\n",
       "25            0.005161\n",
       "V20           0.004955\n",
       "0             0.004800\n",
       "26            0.004284\n",
       "V2            0.003148\n",
       "V8            0.003097\n",
       "14            0.002735\n",
       "V22           0.002632\n",
       "V9            0.002374\n",
       "38            0.001806\n",
       "\n",
       "[69 rows x 1 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "featuresImportance = pd.DataFrame(data=list(gbm.feature_importance()), \\\n",
    "                        index=X_train.columns,columns=['featImportance'])\n",
    "featuresImportance = featuresImportance/featuresImportance.sum()\n",
    "featuresImportance.sort_values(by='featImportance', \\\n",
    "                               ascending=False,inplace=True)\n",
    "featuresImportance"
   ]
  }
 ],
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