AE WADI.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "import tensorflow as tf\n",
    "import seaborn as sns\n",
    "from pylab import rcParams\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.models import Model, load_model, Sequential\n",
    "from keras.layers import Input, Dense, Dropout\n",
    "from keras.callbacks import ModelCheckpoint, TensorBoard\n",
    "from keras import regularizers\n",
    "\n",
    "%matplotlib inline\n",
    "sns.set(style='whitegrid', palette='muted', font_scale=1.5)\n",
    "rcParams['figure.figsize'] = 14, 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Utility Functions\n",
    "## Plots\n",
    "def tsne_plot(x1, y1, name=\"graph.png\"):\n",
    "    tsne = TSNE(n_components=2, random_state=0)\n",
    "    X_t = tsne.fit_transform(x1)\n",
    "    plt.scatter(X_t[np.where(y1 == 0), 0], X_t[np.where(y1 == 0), 1], marker='o', color='g', linewidth='1', alpha=0.8, label='Non Fraud', s=2)\n",
    "    plt.scatter(X_t[np.where(y1 == 1), 0], X_t[np.where(y1 == 1), 1], marker='o', color='r', linewidth='1', alpha=0.8, label='Fraud', s=2)\n",
    "\n",
    "    plt.legend(loc='best');\n",
    "    plt.savefig(name);\n",
    "    plt.show();\n",
    "    \n",
    "# Plot Keras training history\n",
    "def plot_loss(hist):\n",
    "    plt.plot(hist.history['loss'])\n",
    "    plt.plot(hist.history['val_loss'])\n",
    "    plt.title('model loss')\n",
    "    plt.ylabel('loss')\n",
    "    plt.xlabel('epoch')\n",
    "    plt.legend(['train', 'test'], loc='upper left')\n",
    "    plt.yscale('log',basey=10)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"C:/Users/Ruben/Datasets/WADI/WADI_attackdataLABLE.csv\", low_memory=False)\n",
    "\n",
    "df.columns = [\n",
    "    'ROW','DATE','TIME','1_AIT_001_PV','1_AIT_002_PV','1_AIT_003_PV','1_AIT_004_PV','1_AIT_005_PV','1_FIT_001_PV','1_LS_001_AL',\n",
    "    '1_LS_002_AL','1_LT_001_PV','1_MV_001_STATUS','1_MV_002_STATUS','1_MV_003_STATUS','1_MV_004_STATUS','1_P_001_STATUS',\n",
    "    '1_P_002_STATUS','1_P_003_STATUS','1_P_004_STATUS','1_P_005_STATUS','1_P_006_STATUS','2_DPIT_001_PV','2_FIC_101_CO',\n",
    "    '2_FIC_101_PV','2_FIC_101_SP','2_FIC_201_CO','2_FIC_201_PV','2_FIC_201_SP','2_FIC_301_CO','2_FIC_301_PV','2_FIC_301_SP',\n",
    "    '2_FIC_401_CO','2_FIC_401_PV','2_FIC_401_SP','2_FIC_501_CO','2_FIC_501_PV','2_FIC_501_SP','2_FIC_601_CO','2_FIC_601_PV',\n",
    "    '2_FIC_601_SP','2_FIT_001_PV','2_FIT_002_PV','2_FIT_003_PV','2_FQ_101_PV','2_FQ_201_PV','2_FQ_301_PV','2_FQ_401_PV',\n",
    "    '2_FQ_501_PV','2_FQ_601_PV','2_LS_001_AL','2_LS_002_AL','2_LS_101_AH','2_LS_101_AL','2_LS_201_AH','2_LS_201_AL',\n",
    "    '2_LS_301_AH','2_LS_301_AL','2_LS_401_AH','2_LS_401_AL','2_LS_501_AH','2_LS_501_AL','2_LS_601_AH','2_LS_601_AL',\n",
    "    '2_LT_001_PV','2_LT_002_PV','2_MCV_007_CO','2_MCV_101_CO','2_MCV_201_CO','2_MCV_301_CO','2_MCV_401_CO','2_MCV_501_CO',\n",
    "    '2_MCV_601_CO','2_MV_001_STATUS','2_MV_002_STATUS','2_MV_003_STATUS','2_MV_004_STATUS','2_MV_005_STATUS','2_MV_006_STATUS',\n",
    "    '2_MV_009_STATUS','2_MV_101_STATUS','2_MV_201_STATUS','2_MV_301_STATUS','2_MV_401_STATUS','2_MV_501_STATUS',\n",
    "    '2_MV_601_STATUS','2_P_001_STATUS','2_P_002_STATUS','2_P_003_SPEED','2_P_003_STATUS','2_P_004_SPEED','2_P_004_STATUS',\n",
    "    '2_PIC_003_CO','2_PIC_003_PV','2_PIC_003_SP','2_PIT_001_PV','2_PIT_002_PV','2_PIT_003_PV','2_SV_101_STATUS',\n",
    "    '2_SV_201_STATUS','2_SV_301_STATUS','2_SV_401_STATUS','2_SV_501_STATUS','2_SV_601_STATUS','2A_AIT_001_PV','2A_AIT_002_PV',\n",
    "    '2A_AIT_003_PV','2A_AIT_004_PV','2B_AIT_001_PV','2B_AIT_002_PV','2B_AIT_003_PV','2B_AIT_004_PV','3_AIT_001_PV',\n",
    "    '3_AIT_002_PV','3_AIT_003_PV','3_AIT_004_PV','3_AIT_005_PV','3_FIT_001_PV','3_LS_001_AL','3_LT_001_PV','3_MV_001_STATUS',\n",
    "    '3_MV_002_STATUS','3_MV_003_STATUS','3_P_001_STATUS','3_P_002_STATUS','3_P_003_STATUS','3_P_004_STATUS','LEAK_DIFF_PRESSURE',\n",
    "    'PLANT_START_STOP_LOG','TOTAL_CONS_REQUIRED_FLOW','OUTCOME'\n",
    "]\n",
    "\n",
    "df = df.iloc[1:] # Row only contains labels\n",
    "df = df.drop([172802,172803]) # Rows contain no information, only nan values\n",
    "\n",
    "# 'ROW', 'DATE' and 'TIME' are irrelevant for the thesis.\n",
    "# '2_LS_001_AL', '2_LS_002_AL', '2_P_001_STATUS', '2_P_002_STATUS' contain only nan values\n",
    "# Other dropped columns contain either only 0s, only 1s or only 2s and are therefore irrelevant\n",
    "\n",
    "df = df.drop(['ROW', 'DATE', 'TIME', '1_LS_001_AL', '1_LS_002_AL', '1_P_002_STATUS', '1_P_004_STATUS', '2_LS_001_AL', \n",
    "              '2_LS_002_AL', '2_MV_001_STATUS', '2_MV_002_STATUS', '2_MV_004_STATUS', '2_MV_005_STATUS', '2_MV_009_STATUS',\n",
    "              '2_P_001_STATUS', '2_P_002_STATUS', '2_P_004_STATUS', '2_SV_101_STATUS', '2_SV_201_STATUS', '2_SV_301_STATUS',\n",
    "              '2_SV_401_STATUS', '2_SV_501_STATUS', '2_SV_601_STATUS', '3_AIT_001_PV', '3_LS_001_AL', '3_MV_001_STATUS',\n",
    "              '3_MV_002_STATUS', '3_MV_003_STATUS', '3_P_001_STATUS', '3_P_002_STATUS', '3_P_003_STATUS', '3_P_004_STATUS',\n",
    "              'PLANT_START_STOP_LOG'], axis = 1)\n",
    "\n",
    "# set OUTCOME data type to int and change normal values to 0 / attack values to -1 \n",
    "df['OUTCOME'] = df['OUTCOME'].astype(int)\n",
    "df['OUTCOME'].replace(to_replace = [1], value = 0, inplace = True)\n",
    "df['OUTCOME'].replace(to_replace = [-1], value = 1, inplace = True)\n",
    "\n",
    "# data types need to be numeric to be encoded to z-scores --> convert column object data types to numerics\n",
    "cols = df.columns[df.columns != 'OUTCOME']\n",
    "df[cols] = df[cols].apply(pd.to_numeric, errors='coerce')\n",
    "\n",
    "# Encoding the feature vectors to z-scores\n",
    "cols = list(df.columns[df.columns != 'OUTCOME'])\n",
    "for col in cols:\n",
    "    df[col] = ((df[col] - df[col].mean())/df[col].std(ddof=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1_AIT_001_PV</th>\n",
       "      <th>1_AIT_002_PV</th>\n",
       "      <th>1_AIT_003_PV</th>\n",
       "      <th>1_AIT_004_PV</th>\n",
       "      <th>1_AIT_005_PV</th>\n",
       "      <th>1_FIT_001_PV</th>\n",
       "      <th>1_LT_001_PV</th>\n",
       "      <th>1_MV_001_STATUS</th>\n",
       "      <th>1_MV_002_STATUS</th>\n",
       "      <th>1_MV_003_STATUS</th>\n",
       "      <th>1_MV_004_STATUS</th>\n",
       "      <th>1_P_001_STATUS</th>\n",
       "      <th>1_P_003_STATUS</th>\n",
       "      <th>1_P_005_STATUS</th>\n",
       "      <th>1_P_006_STATUS</th>\n",
       "      <th>2_DPIT_001_PV</th>\n",
       "      <th>2_FIC_101_CO</th>\n",
       "      <th>2_FIC_101_PV</th>\n",
       "      <th>2_FIC_101_SP</th>\n",
       "      <th>2_FIC_201_CO</th>\n",
       "      <th>2_FIC_201_PV</th>\n",
       "      <th>2_FIC_201_SP</th>\n",
       "      <th>2_FIC_301_CO</th>\n",
       "      <th>2_FIC_301_PV</th>\n",
       "      <th>2_FIC_301_SP</th>\n",
       "      <th>2_FIC_401_CO</th>\n",
       "      <th>2_FIC_401_PV</th>\n",
       "      <th>2_FIC_401_SP</th>\n",
       "      <th>2_FIC_501_CO</th>\n",
       "      <th>2_FIC_501_PV</th>\n",
       "      <th>2_FIC_501_SP</th>\n",
       "      <th>2_FIC_601_CO</th>\n",
       "      <th>2_FIC_601_PV</th>\n",
       "      <th>2_FIC_601_SP</th>\n",
       "      <th>2_FIT_001_PV</th>\n",
       "      <th>2_FIT_002_PV</th>\n",
       "      <th>2_FIT_003_PV</th>\n",
       "      <th>2_FQ_101_PV</th>\n",
       "      <th>2_FQ_201_PV</th>\n",
       "      <th>2_FQ_301_PV</th>\n",
       "      <th>2_FQ_401_PV</th>\n",
       "      <th>2_FQ_501_PV</th>\n",
       "      <th>2_FQ_601_PV</th>\n",
       "      <th>2_LS_101_AH</th>\n",
       "      <th>2_LS_101_AL</th>\n",
       "      <th>2_LS_201_AH</th>\n",
       "      <th>2_LS_201_AL</th>\n",
       "      <th>2_LS_301_AH</th>\n",
       "      <th>2_LS_301_AL</th>\n",
       "      <th>2_LS_401_AH</th>\n",
       "      <th>2_LS_401_AL</th>\n",
       "      <th>2_LS_501_AH</th>\n",
       "      <th>2_LS_501_AL</th>\n",
       "      <th>2_LS_601_AH</th>\n",
       "      <th>2_LS_601_AL</th>\n",
       "      <th>2_LT_001_PV</th>\n",
       "      <th>2_LT_002_PV</th>\n",
       "      <th>2_MCV_007_CO</th>\n",
       "      <th>2_MCV_101_CO</th>\n",
       "      <th>2_MCV_201_CO</th>\n",
       "      <th>2_MCV_301_CO</th>\n",
       "      <th>2_MCV_401_CO</th>\n",
       "      <th>2_MCV_501_CO</th>\n",
       "      <th>2_MCV_601_CO</th>\n",
       "      <th>2_MV_003_STATUS</th>\n",
       "      <th>2_MV_006_STATUS</th>\n",
       "      <th>2_MV_101_STATUS</th>\n",
       "      <th>2_MV_201_STATUS</th>\n",
       "      <th>2_MV_301_STATUS</th>\n",
       "      <th>2_MV_401_STATUS</th>\n",
       "      <th>2_MV_501_STATUS</th>\n",
       "      <th>2_MV_601_STATUS</th>\n",
       "      <th>2_P_003_SPEED</th>\n",
       "      <th>2_P_003_STATUS</th>\n",
       "      <th>2_P_004_SPEED</th>\n",
       "      <th>2_PIC_003_CO</th>\n",
       "      <th>2_PIC_003_PV</th>\n",
       "      <th>2_PIC_003_SP</th>\n",
       "      <th>2_PIT_001_PV</th>\n",
       "      <th>2_PIT_002_PV</th>\n",
       "      <th>2_PIT_003_PV</th>\n",
       "      <th>2A_AIT_001_PV</th>\n",
       "      <th>2A_AIT_002_PV</th>\n",
       "      <th>2A_AIT_003_PV</th>\n",
       "      <th>2A_AIT_004_PV</th>\n",
       "      <th>2B_AIT_001_PV</th>\n",
       "      <th>2B_AIT_002_PV</th>\n",
       "      <th>2B_AIT_003_PV</th>\n",
       "      <th>2B_AIT_004_PV</th>\n",
       "      <th>3_AIT_002_PV</th>\n",
       "      <th>3_AIT_003_PV</th>\n",
       "      <th>3_AIT_004_PV</th>\n",
       "      <th>3_AIT_005_PV</th>\n",
       "      <th>3_FIT_001_PV</th>\n",
       "      <th>3_LT_001_PV</th>\n",
       "      <th>LEAK_DIFF_PRESSURE</th>\n",
       "      <th>TOTAL_CONS_REQUIRED_FLOW</th>\n",
       "      <th>OUTCOME</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.642796</td>\n",
       "      <td>-0.339731</td>\n",
       "      <td>0.494158</td>\n",
       "      <td>1.521308</td>\n",
       "      <td>1.495280</td>\n",
       "      <td>-0.627893</td>\n",
       "      <td>-0.810580</td>\n",
       "      <td>-0.605982</td>\n",
       "      <td>-0.08852</td>\n",
       "      <td>-0.087899</td>\n",
       "      <td>-0.572808</td>\n",
       "      <td>-0.623589</td>\n",
       "      <td>-0.623249</td>\n",
       "      <td>-0.535138</td>\n",
       "      <td>-0.022053</td>\n",
       "      <td>0.176691</td>\n",
       "      <td>-0.839160</td>\n",
       "      <td>0.830388</td>\n",
       "      <td>-0.292088</td>\n",
       "      <td>0.126234</td>\n",
       "      <td>1.386156</td>\n",
       "      <td>-0.965812</td>\n",
       "      <td>0.829843</td>\n",
       "      <td>-0.421092</td>\n",
       "      <td>-0.945772</td>\n",
       "      <td>-1.109909</td>\n",
       "      <td>0.431728</td>\n",
       "      <td>-1.030036</td>\n",
       "      <td>0.963487</td>\n",
       "      <td>-0.442924</td>\n",
       "      <td>-0.861834</td>\n",
       "      <td>0.937171</td>\n",
       "      <td>-0.359289</td>\n",
       "      <td>-0.483875</td>\n",
       "      <td>-0.538378</td>\n",
       "      <td>1.123510</td>\n",
       "      <td>-0.362559</td>\n",
       "      <td>0.823785</td>\n",
       "      <td>1.416868</td>\n",
       "      <td>-0.422774</td>\n",
       "      <td>0.397326</td>\n",
       "      <td>-0.444784</td>\n",
       "      <td>-0.358971</td>\n",
       "      <td>-0.044921</td>\n",
       "      <td>-0.059568</td>\n",
       "      <td>-0.037371</td>\n",
       "      <td>-0.056145</td>\n",
       "      <td>-0.048409</td>\n",
       "      <td>-0.053923</td>\n",
       "      <td>-0.037059</td>\n",
       "      <td>-0.055151</td>\n",
       "      <td>-0.044857</td>\n",
       "      <td>-0.058333</td>\n",
       "      <td>-0.083343</td>\n",
       "      <td>-0.056197</td>\n",
       "      <td>0.924761</td>\n",
       "      <td>0.706212</td>\n",
       "      <td>-0.08679</td>\n",
       "      <td>-0.695548</td>\n",
       "      <td>-0.690508</td>\n",
       "      <td>0.190936</td>\n",
       "      <td>-0.652647</td>\n",
       "      <td>2.559985</td>\n",
       "      <td>0.827117</td>\n",
       "      <td>-0.479013</td>\n",
       "      <td>-0.375385</td>\n",
       "      <td>-1.010768</td>\n",
       "      <td>-0.975522</td>\n",
       "      <td>0.847521</td>\n",
       "      <td>-0.938817</td>\n",
       "      <td>1.05419</td>\n",
       "      <td>0.958356</td>\n",
       "      <td>-0.410701</td>\n",
       "      <td>-0.41993</td>\n",
       "      <td>0.129838</td>\n",
       "      <td>0.434857</td>\n",
       "      <td>-0.425299</td>\n",
       "      <td>0.032891</td>\n",
       "      <td>0.423386</td>\n",
       "      <td>0.127837</td>\n",
       "      <td>-0.425037</td>\n",
       "      <td>-1.918165</td>\n",
       "      <td>-0.029486</td>\n",
       "      <td>1.437764</td>\n",
       "      <td>-0.482409</td>\n",
       "      <td>-0.926806</td>\n",
       "      <td>-1.095088</td>\n",
       "      <td>1.530950</td>\n",
       "      <td>-0.636329</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.110155</td>\n",
       "      <td>-0.893221</td>\n",
       "      <td>0.803073</td>\n",
       "      <td>0.421774</td>\n",
       "      <td>-0.729956</td>\n",
       "      <td>-0.013872</td>\n",
       "      <td>-0.353392</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.642796</td>\n",
       "      <td>-0.339731</td>\n",
       "      <td>0.494158</td>\n",
       "      <td>1.521308</td>\n",
       "      <td>1.495280</td>\n",
       "      <td>-0.627893</td>\n",
       "      <td>-0.810580</td>\n",
       "      <td>-0.605982</td>\n",
       "      <td>-0.08852</td>\n",
       "      <td>-0.087899</td>\n",
       "      <td>-0.572808</td>\n",
       "      <td>-0.623589</td>\n",
       "      <td>-0.623249</td>\n",
       "      <td>-0.535138</td>\n",
       "      <td>-0.022053</td>\n",
       "      <td>0.176691</td>\n",
       "      <td>-0.839160</td>\n",
       "      <td>0.830388</td>\n",
       "      <td>-0.292088</td>\n",
       "      <td>0.126234</td>\n",
       "      <td>1.386156</td>\n",
       "      <td>-0.965812</td>\n",
       "      <td>0.829843</td>\n",
       "      <td>-0.421092</td>\n",
       "      <td>-0.945772</td>\n",
       "      <td>-1.109909</td>\n",
       "      <td>0.431728</td>\n",
       "      <td>-1.030036</td>\n",
       "      <td>0.963487</td>\n",
       "      <td>-0.442924</td>\n",
       "      <td>-0.861834</td>\n",
       "      <td>0.937171</td>\n",
       "      <td>-0.359289</td>\n",
       "      <td>-0.483875</td>\n",
       "      <td>-0.538378</td>\n",
       "      <td>1.123510</td>\n",
       "      <td>-0.362559</td>\n",
       "      <td>0.823785</td>\n",
       "      <td>1.416868</td>\n",
       "      <td>-0.422774</td>\n",
       "      <td>0.397326</td>\n",
       "      <td>-0.444784</td>\n",
       "      <td>-0.358971</td>\n",
       "      <td>-0.044921</td>\n",
       "      <td>-0.059568</td>\n",
       "      <td>-0.037371</td>\n",
       "      <td>-0.056145</td>\n",
       "      <td>-0.048409</td>\n",
       "      <td>-0.053923</td>\n",
       "      <td>-0.037059</td>\n",
       "      <td>-0.055151</td>\n",
       "      <td>-0.044857</td>\n",
       "      <td>-0.058333</td>\n",
       "      <td>-0.083343</td>\n",
       "      <td>-0.056197</td>\n",
       "      <td>0.924761</td>\n",
       "      <td>0.706212</td>\n",
       "      <td>-0.08679</td>\n",
       "      <td>-0.695548</td>\n",
       "      <td>-0.690508</td>\n",
       "      <td>0.190936</td>\n",
       "      <td>-0.652647</td>\n",
       "      <td>2.559985</td>\n",
       "      <td>0.827117</td>\n",
       "      <td>-0.479013</td>\n",
       "      <td>-0.375385</td>\n",
       "      <td>-1.010768</td>\n",
       "      <td>-0.975522</td>\n",
       "      <td>0.847521</td>\n",
       "      <td>-0.938817</td>\n",
       "      <td>1.05419</td>\n",
       "      <td>0.958356</td>\n",
       "      <td>-0.410701</td>\n",
       "      <td>-0.41993</td>\n",
       "      <td>0.129838</td>\n",
       "      <td>0.434857</td>\n",
       "      <td>-0.425299</td>\n",
       "      <td>0.032891</td>\n",
       "      <td>0.423386</td>\n",
       "      <td>0.127837</td>\n",
       "      <td>-0.425037</td>\n",
       "      <td>-1.918165</td>\n",
       "      <td>-0.029486</td>\n",
       "      <td>1.437764</td>\n",
       "      <td>-0.482409</td>\n",
       "      <td>-0.926806</td>\n",
       "      <td>-1.095088</td>\n",
       "      <td>1.530950</td>\n",
       "      <td>-0.636329</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.110155</td>\n",
       "      <td>-0.893221</td>\n",
       "      <td>0.803073</td>\n",
       "      <td>0.421774</td>\n",
       "      <td>-0.729956</td>\n",
       "      <td>-0.013872</td>\n",
       "      <td>-0.353392</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.642796</td>\n",
       "      <td>-0.339731</td>\n",
       "      <td>0.494158</td>\n",
       "      <td>1.521308</td>\n",
       "      <td>1.495280</td>\n",
       "      <td>-0.627893</td>\n",
       "      <td>-0.810580</td>\n",
       "      <td>-0.605982</td>\n",
       "      <td>-0.08852</td>\n",
       "      <td>-0.087899</td>\n",
       "      <td>-0.572808</td>\n",
       "      <td>-0.623589</td>\n",
       "      <td>-0.623249</td>\n",
       "      <td>-0.535138</td>\n",
       "      <td>-0.022053</td>\n",
       "      <td>0.176691</td>\n",
       "      <td>-0.839160</td>\n",
       "      <td>0.830388</td>\n",
       "      <td>-0.292088</td>\n",
       "      <td>0.126234</td>\n",
       "      <td>1.386156</td>\n",
       "      <td>-0.965812</td>\n",
       "      <td>0.829843</td>\n",
       "      <td>-0.421092</td>\n",
       "      <td>-0.945772</td>\n",
       "      <td>-1.109909</td>\n",
       "      <td>0.431728</td>\n",
       "      <td>-1.030036</td>\n",
       "      <td>0.963487</td>\n",
       "      <td>-0.442924</td>\n",
       "      <td>-0.861834</td>\n",
       "      <td>0.937171</td>\n",
       "      <td>-0.359289</td>\n",
       "      <td>-0.483875</td>\n",
       "      <td>-0.538378</td>\n",
       "      <td>1.123510</td>\n",
       "      <td>-0.362559</td>\n",
       "      <td>0.823785</td>\n",
       "      <td>1.416868</td>\n",
       "      <td>-0.422774</td>\n",
       "      <td>0.397326</td>\n",
       "      <td>-0.444784</td>\n",
       "      <td>-0.358971</td>\n",
       "      <td>-0.044921</td>\n",
       "      <td>-0.059568</td>\n",
       "      <td>-0.037371</td>\n",
       "      <td>-0.056145</td>\n",
       "      <td>-0.048409</td>\n",
       "      <td>-0.053923</td>\n",
       "      <td>-0.037059</td>\n",
       "      <td>-0.055151</td>\n",
       "      <td>-0.044857</td>\n",
       "      <td>-0.058333</td>\n",
       "      <td>-0.083343</td>\n",
       "      <td>-0.056197</td>\n",
       "      <td>0.924761</td>\n",
       "      <td>0.706212</td>\n",
       "      <td>-0.08679</td>\n",
       "      <td>-0.695548</td>\n",
       "      <td>-0.690508</td>\n",
       "      <td>0.190936</td>\n",
       "      <td>-0.652647</td>\n",
       "      <td>2.559985</td>\n",
       "      <td>0.827117</td>\n",
       "      <td>-0.479013</td>\n",
       "      <td>-0.375385</td>\n",
       "      <td>-1.010768</td>\n",
       "      <td>-0.975522</td>\n",
       "      <td>0.847521</td>\n",
       "      <td>-0.938817</td>\n",
       "      <td>1.05419</td>\n",
       "      <td>0.958356</td>\n",
       "      <td>-0.410701</td>\n",
       "      <td>-0.41993</td>\n",
       "      <td>0.129838</td>\n",
       "      <td>0.434857</td>\n",
       "      <td>-0.425299</td>\n",
       "      <td>0.032891</td>\n",
       "      <td>0.423386</td>\n",
       "      <td>0.127837</td>\n",
       "      <td>-0.425037</td>\n",
       "      <td>-1.918165</td>\n",
       "      <td>-0.029486</td>\n",
       "      <td>1.437764</td>\n",
       "      <td>-0.482409</td>\n",
       "      <td>-0.926806</td>\n",
       "      <td>-1.095088</td>\n",
       "      <td>1.530950</td>\n",
       "      <td>-0.636329</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.110155</td>\n",
       "      <td>-0.893221</td>\n",
       "      <td>0.803073</td>\n",
       "      <td>0.421774</td>\n",
       "      <td>-0.729956</td>\n",
       "      <td>-0.013872</td>\n",
       "      <td>-0.353392</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.642796</td>\n",
       "      <td>-0.339731</td>\n",
       "      <td>0.494158</td>\n",
       "      <td>1.521308</td>\n",
       "      <td>1.495280</td>\n",
       "      <td>-0.627893</td>\n",
       "      <td>-0.810580</td>\n",
       "      <td>-0.605982</td>\n",
       "      <td>-0.08852</td>\n",
       "      <td>-0.087899</td>\n",
       "      <td>-0.572808</td>\n",
       "      <td>-0.623589</td>\n",
       "      <td>-0.623249</td>\n",
       "      <td>-0.535138</td>\n",
       "      <td>-0.022053</td>\n",
       "      <td>0.176691</td>\n",
       "      <td>-0.839160</td>\n",
       "      <td>0.830388</td>\n",
       "      <td>-0.292088</td>\n",
       "      <td>0.126234</td>\n",
       "      <td>1.386156</td>\n",
       "      <td>-0.965812</td>\n",
       "      <td>0.829843</td>\n",
       "      <td>-0.421092</td>\n",
       "      <td>-0.945772</td>\n",
       "      <td>-1.109909</td>\n",
       "      <td>0.431728</td>\n",
       "      <td>-1.030036</td>\n",
       "      <td>0.963487</td>\n",
       "      <td>-0.442924</td>\n",
       "      <td>-0.861834</td>\n",
       "      <td>0.937171</td>\n",
       "      <td>-0.359289</td>\n",
       "      <td>-0.483875</td>\n",
       "      <td>-0.538378</td>\n",
       "      <td>1.123510</td>\n",
       "      <td>-0.362559</td>\n",
       "      <td>0.823785</td>\n",
       "      <td>1.416868</td>\n",
       "      <td>-0.422774</td>\n",
       "      <td>0.397326</td>\n",
       "      <td>-0.444784</td>\n",
       "      <td>-0.358971</td>\n",
       "      <td>-0.044921</td>\n",
       "      <td>-0.059568</td>\n",
       "      <td>-0.037371</td>\n",
       "      <td>-0.056145</td>\n",
       "      <td>-0.048409</td>\n",
       "      <td>-0.053923</td>\n",
       "      <td>-0.037059</td>\n",
       "      <td>-0.055151</td>\n",
       "      <td>-0.044857</td>\n",
       "      <td>-0.058333</td>\n",
       "      <td>-0.083343</td>\n",
       "      <td>-0.056197</td>\n",
       "      <td>0.924761</td>\n",
       "      <td>0.706212</td>\n",
       "      <td>-0.08679</td>\n",
       "      <td>-0.695548</td>\n",
       "      <td>-0.690508</td>\n",
       "      <td>0.190936</td>\n",
       "      <td>-0.652647</td>\n",
       "      <td>2.559985</td>\n",
       "      <td>0.827117</td>\n",
       "      <td>-0.479013</td>\n",
       "      <td>-0.375385</td>\n",
       "      <td>-1.010768</td>\n",
       "      <td>-0.975522</td>\n",
       "      <td>0.847521</td>\n",
       "      <td>-0.938817</td>\n",
       "      <td>1.05419</td>\n",
       "      <td>0.958356</td>\n",
       "      <td>-0.410701</td>\n",
       "      <td>-0.41993</td>\n",
       "      <td>0.129838</td>\n",
       "      <td>0.434857</td>\n",
       "      <td>-0.425299</td>\n",
       "      <td>0.032891</td>\n",
       "      <td>0.423386</td>\n",
       "      <td>0.127837</td>\n",
       "      <td>-0.425037</td>\n",
       "      <td>-1.918165</td>\n",
       "      <td>-0.029486</td>\n",
       "      <td>1.437764</td>\n",
       "      <td>-0.493730</td>\n",
       "      <td>-0.926806</td>\n",
       "      <td>-1.095072</td>\n",
       "      <td>2.226073</td>\n",
       "      <td>-0.659509</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.110155</td>\n",
       "      <td>-0.893221</td>\n",
       "      <td>0.803073</td>\n",
       "      <td>0.421774</td>\n",
       "      <td>-0.729956</td>\n",
       "      <td>-0.013872</td>\n",
       "      <td>-0.353392</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.642796</td>\n",
       "      <td>-0.339731</td>\n",
       "      <td>0.494158</td>\n",
       "      <td>1.521308</td>\n",
       "      <td>1.495280</td>\n",
       "      <td>-0.627893</td>\n",
       "      <td>-0.810580</td>\n",
       "      <td>-0.605982</td>\n",
       "      <td>-0.08852</td>\n",
       "      <td>-0.087899</td>\n",
       "      <td>-0.572808</td>\n",
       "      <td>-0.623589</td>\n",
       "      <td>-0.623249</td>\n",
       "      <td>-0.535138</td>\n",
       "      <td>-0.022053</td>\n",
       "      <td>0.176691</td>\n",
       "      <td>-0.839160</td>\n",
       "      <td>0.830388</td>\n",
       "      <td>-0.292088</td>\n",
       "      <td>0.126234</td>\n",
       "      <td>1.386156</td>\n",
       "      <td>-0.965812</td>\n",
       "      <td>0.829843</td>\n",
       "      <td>-0.421092</td>\n",
       "      <td>-0.945772</td>\n",
       "      <td>-1.109909</td>\n",
       "      <td>0.431728</td>\n",
       "      <td>-1.030036</td>\n",
       "      <td>0.963487</td>\n",
       "      <td>-0.442924</td>\n",
       "      <td>-0.861834</td>\n",
       "      <td>0.937171</td>\n",
       "      <td>-0.359289</td>\n",
       "      <td>-0.483875</td>\n",
       "      <td>-0.538378</td>\n",
       "      <td>1.123510</td>\n",
       "      <td>-0.362559</td>\n",
       "      <td>0.823785</td>\n",
       "      <td>1.416868</td>\n",
       "      <td>-0.422774</td>\n",
       "      <td>0.397326</td>\n",
       "      <td>-0.444784</td>\n",
       "      <td>-0.358971</td>\n",
       "      <td>-0.044921</td>\n",
       "      <td>-0.059568</td>\n",
       "      <td>-0.037371</td>\n",
       "      <td>-0.056145</td>\n",
       "      <td>-0.048409</td>\n",
       "      <td>-0.053923</td>\n",
       "      <td>-0.037059</td>\n",
       "      <td>-0.055151</td>\n",
       "      <td>-0.044857</td>\n",
       "      <td>-0.058333</td>\n",
       "      <td>-0.083343</td>\n",
       "      <td>-0.056197</td>\n",
       "      <td>0.924761</td>\n",
       "      <td>0.706212</td>\n",
       "      <td>-0.08679</td>\n",
       "      <td>-0.695548</td>\n",
       "      <td>-0.690508</td>\n",
       "      <td>0.190936</td>\n",
       "      <td>-0.652647</td>\n",
       "      <td>2.559985</td>\n",
       "      <td>0.827117</td>\n",
       "      <td>-0.479013</td>\n",
       "      <td>-0.375385</td>\n",
       "      <td>-1.010768</td>\n",
       "      <td>-0.975522</td>\n",
       "      <td>0.847521</td>\n",
       "      <td>-0.938817</td>\n",
       "      <td>1.05419</td>\n",
       "      <td>0.958356</td>\n",
       "      <td>-0.410701</td>\n",
       "      <td>-0.41993</td>\n",
       "      <td>0.129838</td>\n",
       "      <td>0.434857</td>\n",
       "      <td>-0.425299</td>\n",
       "      <td>0.032891</td>\n",
       "      <td>0.423386</td>\n",
       "      <td>0.127837</td>\n",
       "      <td>-0.425037</td>\n",
       "      <td>-1.918165</td>\n",
       "      <td>-0.029486</td>\n",
       "      <td>1.437764</td>\n",
       "      <td>-0.493730</td>\n",
       "      <td>-0.926806</td>\n",
       "      <td>-1.095072</td>\n",
       "      <td>2.226073</td>\n",
       "      <td>-0.659509</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.110155</td>\n",
       "      <td>-0.893221</td>\n",
       "      <td>0.803073</td>\n",
       "      <td>0.421774</td>\n",
       "      <td>-0.729956</td>\n",
       "      <td>-0.013872</td>\n",
       "      <td>-0.353392</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172797</th>\n",
       "      <td>-0.174161</td>\n",
       "      <td>-0.288534</td>\n",
       "      <td>-0.071880</td>\n",
       "      <td>0.649421</td>\n",
       "      <td>1.153118</td>\n",
       "      <td>-0.627952</td>\n",
       "      <td>0.002189</td>\n",
       "      <td>-0.605982</td>\n",
       "      <td>-0.08852</td>\n",
       "      <td>-0.087899</td>\n",
       "      <td>-0.572808</td>\n",
       "      <td>-0.623589</td>\n",
       "      <td>-0.623249</td>\n",
       "      <td>-0.535138</td>\n",
       "      <td>-0.022053</td>\n",
       "      <td>0.243474</td>\n",
       "      <td>0.887867</td>\n",
       "      <td>-0.405890</td>\n",
       "      <td>-0.873199</td>\n",
       "      <td>0.976027</td>\n",
       "      <td>-0.567383</td>\n",
       "      <td>-0.965812</td>\n",
       "      <td>0.820536</td>\n",
       "      <td>-0.339814</td>\n",
       "      <td>-0.785912</td>\n",
       "      <td>0.999352</td>\n",
       "      <td>-0.409679</td>\n",
       "      <td>-0.783570</td>\n",
       "      <td>0.940042</td>\n",
       "      <td>-0.443868</td>\n",
       "      <td>-0.937742</td>\n",
       "      <td>0.928048</td>\n",
       "      <td>-0.646071</td>\n",
       "      <td>-0.873484</td>\n",
       "      <td>-0.538378</td>\n",
       "      <td>-1.057105</td>\n",
       "      <td>-0.362559</td>\n",
       "      <td>-0.406610</td>\n",
       "      <td>-0.567744</td>\n",
       "      <td>-0.341258</td>\n",
       "      <td>-0.410656</td>\n",
       "      <td>-0.445733</td>\n",
       "      <td>-0.645019</td>\n",
       "      <td>-0.044921</td>\n",
       "      <td>-0.059568</td>\n",
       "      <td>-0.037371</td>\n",
       "      <td>-0.056145</td>\n",
       "      <td>-0.048409</td>\n",
       "      <td>-0.053923</td>\n",
       "      <td>-0.037059</td>\n",
       "      <td>-0.055151</td>\n",
       "      <td>-0.044857</td>\n",
       "      <td>-0.058333</td>\n",
       "      <td>-0.083343</td>\n",
       "      <td>-0.056197</td>\n",
       "      <td>1.039966</td>\n",
       "      <td>-0.261952</td>\n",
       "      <td>-0.08679</td>\n",
       "      <td>-0.695548</td>\n",
       "      <td>-0.690508</td>\n",
       "      <td>-0.741944</td>\n",
       "      <td>-0.652647</td>\n",
       "      <td>-0.757642</td>\n",
       "      <td>-0.706346</td>\n",
       "      <td>-0.479013</td>\n",
       "      <td>-0.375385</td>\n",
       "      <td>0.983155</td>\n",
       "      <td>1.019191</td>\n",
       "      <td>0.847521</td>\n",
       "      <td>1.058677</td>\n",
       "      <td>1.05419</td>\n",
       "      <td>0.958356</td>\n",
       "      <td>-0.410349</td>\n",
       "      <td>-0.41993</td>\n",
       "      <td>0.214881</td>\n",
       "      <td>0.434857</td>\n",
       "      <td>-0.388217</td>\n",
       "      <td>0.032891</td>\n",
       "      <td>-0.294806</td>\n",
       "      <td>0.326778</td>\n",
       "      <td>-0.387998</td>\n",
       "      <td>0.249258</td>\n",
       "      <td>-0.029486</td>\n",
       "      <td>0.175800</td>\n",
       "      <td>-0.444474</td>\n",
       "      <td>0.228799</td>\n",
       "      <td>0.909737</td>\n",
       "      <td>-0.701943</td>\n",
       "      <td>-0.157926</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.782327</td>\n",
       "      <td>-1.011287</td>\n",
       "      <td>0.648958</td>\n",
       "      <td>0.669969</td>\n",
       "      <td>1.506010</td>\n",
       "      <td>-0.553200</td>\n",
       "      <td>-1.201093</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172798</th>\n",
       "      <td>-0.174161</td>\n",
       "      <td>-0.288534</td>\n",
       "      <td>-0.071880</td>\n",
       "      <td>0.649421</td>\n",
       "      <td>1.153118</td>\n",
       "      <td>-0.627952</td>\n",
       "      <td>0.002189</td>\n",
       "      <td>-0.605982</td>\n",
       "      <td>-0.08852</td>\n",
       "      <td>-0.087899</td>\n",
       "      <td>-0.572808</td>\n",
       "      <td>-0.623589</td>\n",
       "      <td>-0.623249</td>\n",
       "      <td>-0.535138</td>\n",
       "      <td>-0.022053</td>\n",
       "      <td>0.243474</td>\n",
       "      <td>0.887867</td>\n",
       "      <td>-0.405890</td>\n",
       "      <td>-0.873199</td>\n",
       "      <td>0.976027</td>\n",
       "      <td>-0.567383</td>\n",
       "      <td>-0.965812</td>\n",
       "      <td>0.820536</td>\n",
       "      <td>-0.339814</td>\n",
       "      <td>-0.785912</td>\n",
       "      <td>0.999352</td>\n",
       "      <td>-0.409679</td>\n",
       "      <td>-0.783570</td>\n",
       "      <td>0.940042</td>\n",
       "      <td>-0.443868</td>\n",
       "      <td>-0.937742</td>\n",
       "      <td>0.928048</td>\n",
       "      <td>-0.646071</td>\n",
       "      <td>-0.873484</td>\n",
       "      <td>-0.538378</td>\n",
       "      <td>-1.057105</td>\n",
       "      <td>-0.362559</td>\n",
       "      <td>-0.406610</td>\n",
       "      <td>-0.567744</td>\n",
       "      <td>-0.341258</td>\n",
       "      <td>-0.410656</td>\n",
       "      <td>-0.445733</td>\n",
       "      <td>-0.645019</td>\n",
       "      <td>-0.044921</td>\n",
       "      <td>-0.059568</td>\n",
       "      <td>-0.037371</td>\n",
       "      <td>-0.056145</td>\n",
       "      <td>-0.048409</td>\n",
       "      <td>-0.053923</td>\n",
       "      <td>-0.037059</td>\n",
       "      <td>-0.055151</td>\n",
       "      <td>-0.044857</td>\n",
       "      <td>-0.058333</td>\n",
       "      <td>-0.083343</td>\n",
       "      <td>-0.056197</td>\n",
       "      <td>1.039966</td>\n",
       "      <td>-0.261952</td>\n",
       "      <td>-0.08679</td>\n",
       "      <td>-0.695548</td>\n",
       "      <td>-0.690508</td>\n",
       "      <td>-0.741944</td>\n",
       "      <td>-0.652647</td>\n",
       "      <td>-0.757642</td>\n",
       "      <td>-0.706346</td>\n",
       "      <td>-0.479013</td>\n",
       "      <td>-0.375385</td>\n",
       "      <td>0.983155</td>\n",
       "      <td>1.019191</td>\n",
       "      <td>0.847521</td>\n",
       "      <td>1.058677</td>\n",
       "      <td>1.05419</td>\n",
       "      <td>0.958356</td>\n",
       "      <td>-0.410349</td>\n",
       "      <td>-0.41993</td>\n",
       "      <td>0.214881</td>\n",
       "      <td>0.434857</td>\n",
       "      <td>-0.388217</td>\n",
       "      <td>0.032891</td>\n",
       "      <td>-0.294806</td>\n",
       "      <td>0.326778</td>\n",
       "      <td>-0.387998</td>\n",
       "      <td>0.249258</td>\n",
       "      <td>-0.029486</td>\n",
       "      <td>0.175800</td>\n",
       "      <td>-0.444474</td>\n",
       "      <td>0.228799</td>\n",
       "      <td>0.909737</td>\n",
       "      <td>-0.701943</td>\n",
       "      <td>-0.157926</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.782327</td>\n",
       "      <td>-1.011287</td>\n",
       "      <td>0.648958</td>\n",
       "      <td>0.669969</td>\n",
       "      <td>1.506010</td>\n",
       "      <td>-0.553200</td>\n",
       "      <td>-1.201093</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172799</th>\n",
       "      <td>-0.176517</td>\n",
       "      <td>-0.186134</td>\n",
       "      <td>-0.052486</td>\n",
       "      <td>0.650322</td>\n",
       "      <td>1.155760</td>\n",
       "      <td>-0.627908</td>\n",
       "      <td>0.021404</td>\n",
       "      <td>-0.605982</td>\n",
       "      <td>-0.08852</td>\n",
       "      <td>-0.087899</td>\n",
       "      <td>-0.572808</td>\n",
       "      <td>-0.623589</td>\n",
       "      <td>-0.623249</td>\n",
       "      <td>-0.535138</td>\n",
       "      <td>-0.022053</td>\n",
       "      <td>0.246579</td>\n",
       "      <td>0.903004</td>\n",
       "      <td>-0.405890</td>\n",
       "      <td>-0.873199</td>\n",
       "      <td>0.976027</td>\n",
       "      <td>-0.567383</td>\n",
       "      <td>-0.965812</td>\n",
       "      <td>0.829843</td>\n",
       "      <td>-0.339814</td>\n",
       "      <td>-0.785912</td>\n",
       "      <td>0.999352</td>\n",
       "      <td>-0.409679</td>\n",
       "      <td>-0.783570</td>\n",
       "      <td>0.951955</td>\n",
       "      <td>-0.443868</td>\n",
       "      <td>-0.937742</td>\n",
       "      <td>0.937171</td>\n",
       "      <td>-0.646071</td>\n",
       "      <td>-0.873484</td>\n",
       "      <td>-0.538378</td>\n",
       "      <td>-1.057105</td>\n",
       "      <td>-0.362559</td>\n",
       "      <td>-0.406610</td>\n",
       "      <td>-0.567744</td>\n",
       "      <td>-0.341258</td>\n",
       "      <td>-0.410656</td>\n",
       "      <td>-0.445733</td>\n",
       "      <td>-0.645019</td>\n",
       "      <td>-0.044921</td>\n",
       "      <td>-0.059568</td>\n",
       "      <td>-0.037371</td>\n",
       "      <td>-0.056145</td>\n",
       "      <td>-0.048409</td>\n",
       "      <td>-0.053923</td>\n",
       "      <td>-0.037059</td>\n",
       "      <td>-0.055151</td>\n",
       "      <td>-0.044857</td>\n",
       "      <td>-0.058333</td>\n",
       "      <td>-0.083343</td>\n",
       "      <td>-0.056197</td>\n",
       "      <td>1.049412</td>\n",
       "      <td>-0.260409</td>\n",
       "      <td>-0.08679</td>\n",
       "      <td>-0.695548</td>\n",
       "      <td>-0.690508</td>\n",
       "      <td>-0.741944</td>\n",
       "      <td>-0.652647</td>\n",
       "      <td>-0.757642</td>\n",
       "      <td>-0.706346</td>\n",
       "      <td>-0.479013</td>\n",
       "      <td>-0.375385</td>\n",
       "      <td>0.983155</td>\n",
       "      <td>1.019191</td>\n",
       "      <td>0.847521</td>\n",
       "      <td>1.058677</td>\n",
       "      <td>1.05419</td>\n",
       "      <td>0.958356</td>\n",
       "      <td>-0.410512</td>\n",
       "      <td>-0.41993</td>\n",
       "      <td>0.094939</td>\n",
       "      <td>0.434857</td>\n",
       "      <td>-0.388205</td>\n",
       "      <td>0.032891</td>\n",
       "      <td>-0.233257</td>\n",
       "      <td>0.359912</td>\n",
       "      <td>-0.387753</td>\n",
       "      <td>0.249258</td>\n",
       "      <td>-0.029486</td>\n",
       "      <td>0.175800</td>\n",
       "      <td>-0.444474</td>\n",
       "      <td>0.228799</td>\n",
       "      <td>0.909737</td>\n",
       "      <td>-0.701943</td>\n",
       "      <td>-0.157926</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.070183</td>\n",
       "      <td>1.683305</td>\n",
       "      <td>0.649108</td>\n",
       "      <td>0.670181</td>\n",
       "      <td>1.507815</td>\n",
       "      <td>-0.643043</td>\n",
       "      <td>-1.201093</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172800</th>\n",
       "      <td>-0.176517</td>\n",
       "      <td>-0.186134</td>\n",
       "      <td>-0.052486</td>\n",
       "      <td>0.650322</td>\n",
       "      <td>1.155760</td>\n",
       "      <td>-0.627908</td>\n",
       "      <td>0.021404</td>\n",
       "      <td>-0.605982</td>\n",
       "      <td>-0.08852</td>\n",
       "      <td>-0.087899</td>\n",
       "      <td>-0.572808</td>\n",
       "      <td>-0.623589</td>\n",
       "      <td>-0.623249</td>\n",
       "      <td>-0.535138</td>\n",
       "      <td>-0.022053</td>\n",
       "      <td>0.246579</td>\n",
       "      <td>0.903004</td>\n",
       "      <td>-0.405890</td>\n",
       "      <td>-0.873199</td>\n",
       "      <td>0.976027</td>\n",
       "      <td>-0.567383</td>\n",
       "      <td>-0.965812</td>\n",
       "      <td>0.829843</td>\n",
       "      <td>-0.339814</td>\n",
       "      <td>-0.785912</td>\n",
       "      <td>0.999352</td>\n",
       "      <td>-0.409679</td>\n",
       "      <td>-0.783570</td>\n",
       "      <td>0.951955</td>\n",
       "      <td>-0.443868</td>\n",
       "      <td>-0.937742</td>\n",
       "      <td>0.937171</td>\n",
       "      <td>-0.646071</td>\n",
       "      <td>-0.873484</td>\n",
       "      <td>-0.538378</td>\n",
       "      <td>-1.057105</td>\n",
       "      <td>-0.362559</td>\n",
       "      <td>-0.406610</td>\n",
       "      <td>-0.567744</td>\n",
       "      <td>-0.341258</td>\n",
       "      <td>-0.410656</td>\n",
       "      <td>-0.445733</td>\n",
       "      <td>-0.645019</td>\n",
       "      <td>-0.044921</td>\n",
       "      <td>-0.059568</td>\n",
       "      <td>-0.037371</td>\n",
       "      <td>-0.056145</td>\n",
       "      <td>-0.048409</td>\n",
       "      <td>-0.053923</td>\n",
       "      <td>-0.037059</td>\n",
       "      <td>-0.055151</td>\n",
       "      <td>-0.044857</td>\n",
       "      <td>-0.058333</td>\n",
       "      <td>-0.083343</td>\n",
       "      <td>-0.056197</td>\n",
       "      <td>1.049412</td>\n",
       "      <td>-0.260409</td>\n",
       "      <td>-0.08679</td>\n",
       "      <td>-0.695548</td>\n",
       "      <td>-0.690508</td>\n",
       "      <td>-0.741944</td>\n",
       "      <td>-0.652647</td>\n",
       "      <td>-0.757642</td>\n",
       "      <td>-0.706346</td>\n",
       "      <td>-0.479013</td>\n",
       "      <td>-0.375385</td>\n",
       "      <td>0.983155</td>\n",
       "      <td>1.019191</td>\n",
       "      <td>0.847521</td>\n",
       "      <td>1.058677</td>\n",
       "      <td>1.05419</td>\n",
       "      <td>0.958356</td>\n",
       "      <td>-0.410512</td>\n",
       "      <td>-0.41993</td>\n",
       "      <td>0.094939</td>\n",
       "      <td>0.434857</td>\n",
       "      <td>-0.388205</td>\n",
       "      <td>0.032891</td>\n",
       "      <td>-0.233257</td>\n",
       "      <td>0.359912</td>\n",
       "      <td>-0.387753</td>\n",
       "      <td>0.249258</td>\n",
       "      <td>-0.029486</td>\n",
       "      <td>0.175800</td>\n",
       "      <td>-0.439310</td>\n",
       "      <td>0.228799</td>\n",
       "      <td>0.909851</td>\n",
       "      <td>-0.701943</td>\n",
       "      <td>-0.134006</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.070183</td>\n",
       "      <td>1.683305</td>\n",
       "      <td>0.649108</td>\n",
       "      <td>0.670181</td>\n",
       "      <td>1.507815</td>\n",
       "      <td>-0.643043</td>\n",
       "      <td>-1.201093</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172801</th>\n",
       "      <td>-0.176517</td>\n",
       "      <td>-0.186134</td>\n",
       "      <td>-0.052486</td>\n",
       "      <td>0.650322</td>\n",
       "      <td>1.155760</td>\n",
       "      <td>-0.627908</td>\n",
       "      <td>0.021404</td>\n",
       "      <td>-0.605982</td>\n",
       "      <td>-0.08852</td>\n",
       "      <td>-0.087899</td>\n",
       "      <td>-0.572808</td>\n",
       "      <td>-0.623589</td>\n",
       "      <td>-0.623249</td>\n",
       "      <td>-0.535138</td>\n",
       "      <td>-0.022053</td>\n",
       "      <td>0.246579</td>\n",
       "      <td>0.903004</td>\n",
       "      <td>-0.405890</td>\n",
       "      <td>-0.873199</td>\n",
       "      <td>0.976027</td>\n",
       "      <td>-0.567383</td>\n",
       "      <td>-0.965812</td>\n",
       "      <td>0.829843</td>\n",
       "      <td>-0.339814</td>\n",
       "      <td>-0.785912</td>\n",
       "      <td>0.999352</td>\n",
       "      <td>-0.409679</td>\n",
       "      <td>-0.783570</td>\n",
       "      <td>0.951955</td>\n",
       "      <td>-0.443868</td>\n",
       "      <td>-0.937742</td>\n",
       "      <td>0.937171</td>\n",
       "      <td>-0.646071</td>\n",
       "      <td>-0.873484</td>\n",
       "      <td>-0.538378</td>\n",
       "      <td>-1.057105</td>\n",
       "      <td>-0.362559</td>\n",
       "      <td>-0.406610</td>\n",
       "      <td>-0.567744</td>\n",
       "      <td>-0.341258</td>\n",
       "      <td>-0.410656</td>\n",
       "      <td>-0.445733</td>\n",
       "      <td>-0.645019</td>\n",
       "      <td>-0.044921</td>\n",
       "      <td>-0.059568</td>\n",
       "      <td>-0.037371</td>\n",
       "      <td>-0.056145</td>\n",
       "      <td>-0.048409</td>\n",
       "      <td>-0.053923</td>\n",
       "      <td>-0.037059</td>\n",
       "      <td>-0.055151</td>\n",
       "      <td>-0.044857</td>\n",
       "      <td>-0.058333</td>\n",
       "      <td>-0.083343</td>\n",
       "      <td>-0.056197</td>\n",
       "      <td>1.049412</td>\n",
       "      <td>-0.260409</td>\n",
       "      <td>-0.08679</td>\n",
       "      <td>-0.695548</td>\n",
       "      <td>-0.690508</td>\n",
       "      <td>-0.741944</td>\n",
       "      <td>-0.652647</td>\n",
       "      <td>-0.757642</td>\n",
       "      <td>-0.706346</td>\n",
       "      <td>-0.479013</td>\n",
       "      <td>-0.375385</td>\n",
       "      <td>0.983155</td>\n",
       "      <td>1.019191</td>\n",
       "      <td>0.847521</td>\n",
       "      <td>1.058677</td>\n",
       "      <td>1.05419</td>\n",
       "      <td>0.958356</td>\n",
       "      <td>-0.410512</td>\n",
       "      <td>-0.41993</td>\n",
       "      <td>0.094939</td>\n",
       "      <td>0.434857</td>\n",
       "      <td>-0.388205</td>\n",
       "      <td>0.032891</td>\n",
       "      <td>-0.233257</td>\n",
       "      <td>0.359912</td>\n",
       "      <td>-0.387753</td>\n",
       "      <td>0.249258</td>\n",
       "      <td>-0.029486</td>\n",
       "      <td>0.175800</td>\n",
       "      <td>-0.439310</td>\n",
       "      <td>0.228799</td>\n",
       "      <td>0.909851</td>\n",
       "      <td>-0.701943</td>\n",
       "      <td>-0.134006</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.070183</td>\n",
       "      <td>1.683305</td>\n",
       "      <td>0.649108</td>\n",
       "      <td>0.670181</td>\n",
       "      <td>1.507815</td>\n",
       "      <td>-0.643043</td>\n",
       "      <td>-1.201093</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>172801 rows × 98 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        1_AIT_001_PV  1_AIT_002_PV  1_AIT_003_PV  1_AIT_004_PV  1_AIT_005_PV  \\\n",
       "1          -0.642796     -0.339731      0.494158      1.521308      1.495280   \n",
       "2          -0.642796     -0.339731      0.494158      1.521308      1.495280   \n",
       "3          -0.642796     -0.339731      0.494158      1.521308      1.495280   \n",
       "4          -0.642796     -0.339731      0.494158      1.521308      1.495280   \n",
       "5          -0.642796     -0.339731      0.494158      1.521308      1.495280   \n",
       "...              ...           ...           ...           ...           ...   \n",
       "172797     -0.174161     -0.288534     -0.071880      0.649421      1.153118   \n",
       "172798     -0.174161     -0.288534     -0.071880      0.649421      1.153118   \n",
       "172799     -0.176517     -0.186134     -0.052486      0.650322      1.155760   \n",
       "172800     -0.176517     -0.186134     -0.052486      0.650322      1.155760   \n",
       "172801     -0.176517     -0.186134     -0.052486      0.650322      1.155760   \n",
       "\n",
       "        1_FIT_001_PV  1_LT_001_PV  1_MV_001_STATUS  1_MV_002_STATUS  \\\n",
       "1          -0.627893    -0.810580        -0.605982         -0.08852   \n",
       "2          -0.627893    -0.810580        -0.605982         -0.08852   \n",
       "3          -0.627893    -0.810580        -0.605982         -0.08852   \n",
       "4          -0.627893    -0.810580        -0.605982         -0.08852   \n",
       "5          -0.627893    -0.810580        -0.605982         -0.08852   \n",
       "...              ...          ...              ...              ...   \n",
       "172797     -0.627952     0.002189        -0.605982         -0.08852   \n",
       "172798     -0.627952     0.002189        -0.605982         -0.08852   \n",
       "172799     -0.627908     0.021404        -0.605982         -0.08852   \n",
       "172800     -0.627908     0.021404        -0.605982         -0.08852   \n",
       "172801     -0.627908     0.021404        -0.605982         -0.08852   \n",
       "\n",
       "        1_MV_003_STATUS  1_MV_004_STATUS  1_P_001_STATUS  1_P_003_STATUS  \\\n",
       "1             -0.087899        -0.572808       -0.623589       -0.623249   \n",
       "2             -0.087899        -0.572808       -0.623589       -0.623249   \n",
       "3             -0.087899        -0.572808       -0.623589       -0.623249   \n",
       "4             -0.087899        -0.572808       -0.623589       -0.623249   \n",
       "5             -0.087899        -0.572808       -0.623589       -0.623249   \n",
       "...                 ...              ...             ...             ...   \n",
       "172797        -0.087899        -0.572808       -0.623589       -0.623249   \n",
       "172798        -0.087899        -0.572808       -0.623589       -0.623249   \n",
       "172799        -0.087899        -0.572808       -0.623589       -0.623249   \n",
       "172800        -0.087899        -0.572808       -0.623589       -0.623249   \n",
       "172801        -0.087899        -0.572808       -0.623589       -0.623249   \n",
       "\n",
       "        1_P_005_STATUS  1_P_006_STATUS  2_DPIT_001_PV  2_FIC_101_CO  \\\n",
       "1            -0.535138       -0.022053       0.176691     -0.839160   \n",
       "2            -0.535138       -0.022053       0.176691     -0.839160   \n",
       "3            -0.535138       -0.022053       0.176691     -0.839160   \n",
       "4            -0.535138       -0.022053       0.176691     -0.839160   \n",
       "5            -0.535138       -0.022053       0.176691     -0.839160   \n",
       "...                ...             ...            ...           ...   \n",
       "172797       -0.535138       -0.022053       0.243474      0.887867   \n",
       "172798       -0.535138       -0.022053       0.243474      0.887867   \n",
       "172799       -0.535138       -0.022053       0.246579      0.903004   \n",
       "172800       -0.535138       -0.022053       0.246579      0.903004   \n",
       "172801       -0.535138       -0.022053       0.246579      0.903004   \n",
       "\n",
       "        2_FIC_101_PV  2_FIC_101_SP  2_FIC_201_CO  2_FIC_201_PV  2_FIC_201_SP  \\\n",
       "1           0.830388     -0.292088      0.126234      1.386156     -0.965812   \n",
       "2           0.830388     -0.292088      0.126234      1.386156     -0.965812   \n",
       "3           0.830388     -0.292088      0.126234      1.386156     -0.965812   \n",
       "4           0.830388     -0.292088      0.126234      1.386156     -0.965812   \n",
       "5           0.830388     -0.292088      0.126234      1.386156     -0.965812   \n",
       "...              ...           ...           ...           ...           ...   \n",
       "172797     -0.405890     -0.873199      0.976027     -0.567383     -0.965812   \n",
       "172798     -0.405890     -0.873199      0.976027     -0.567383     -0.965812   \n",
       "172799     -0.405890     -0.873199      0.976027     -0.567383     -0.965812   \n",
       "172800     -0.405890     -0.873199      0.976027     -0.567383     -0.965812   \n",
       "172801     -0.405890     -0.873199      0.976027     -0.567383     -0.965812   \n",
       "\n",
       "        2_FIC_301_CO  2_FIC_301_PV  2_FIC_301_SP  2_FIC_401_CO  2_FIC_401_PV  \\\n",
       "1           0.829843     -0.421092     -0.945772     -1.109909      0.431728   \n",
       "2           0.829843     -0.421092     -0.945772     -1.109909      0.431728   \n",
       "3           0.829843     -0.421092     -0.945772     -1.109909      0.431728   \n",
       "4           0.829843     -0.421092     -0.945772     -1.109909      0.431728   \n",
       "5           0.829843     -0.421092     -0.945772     -1.109909      0.431728   \n",
       "...              ...           ...           ...           ...           ...   \n",
       "172797      0.820536     -0.339814     -0.785912      0.999352     -0.409679   \n",
       "172798      0.820536     -0.339814     -0.785912      0.999352     -0.409679   \n",
       "172799      0.829843     -0.339814     -0.785912      0.999352     -0.409679   \n",
       "172800      0.829843     -0.339814     -0.785912      0.999352     -0.409679   \n",
       "172801      0.829843     -0.339814     -0.785912      0.999352     -0.409679   \n",
       "\n",
       "        2_FIC_401_SP  2_FIC_501_CO  2_FIC_501_PV  2_FIC_501_SP  2_FIC_601_CO  \\\n",
       "1          -1.030036      0.963487     -0.442924     -0.861834      0.937171   \n",
       "2          -1.030036      0.963487     -0.442924     -0.861834      0.937171   \n",
       "3          -1.030036      0.963487     -0.442924     -0.861834      0.937171   \n",
       "4          -1.030036      0.963487     -0.442924     -0.861834      0.937171   \n",
       "5          -1.030036      0.963487     -0.442924     -0.861834      0.937171   \n",
       "...              ...           ...           ...           ...           ...   \n",
       "172797     -0.783570      0.940042     -0.443868     -0.937742      0.928048   \n",
       "172798     -0.783570      0.940042     -0.443868     -0.937742      0.928048   \n",
       "172799     -0.783570      0.951955     -0.443868     -0.937742      0.937171   \n",
       "172800     -0.783570      0.951955     -0.443868     -0.937742      0.937171   \n",
       "172801     -0.783570      0.951955     -0.443868     -0.937742      0.937171   \n",
       "\n",
       "        2_FIC_601_PV  2_FIC_601_SP  2_FIT_001_PV  2_FIT_002_PV  2_FIT_003_PV  \\\n",
       "1          -0.359289     -0.483875     -0.538378      1.123510     -0.362559   \n",
       "2          -0.359289     -0.483875     -0.538378      1.123510     -0.362559   \n",
       "3          -0.359289     -0.483875     -0.538378      1.123510     -0.362559   \n",
       "4          -0.359289     -0.483875     -0.538378      1.123510     -0.362559   \n",
       "5          -0.359289     -0.483875     -0.538378      1.123510     -0.362559   \n",
       "...              ...           ...           ...           ...           ...   \n",
       "172797     -0.646071     -0.873484     -0.538378     -1.057105     -0.362559   \n",
       "172798     -0.646071     -0.873484     -0.538378     -1.057105     -0.362559   \n",
       "172799     -0.646071     -0.873484     -0.538378     -1.057105     -0.362559   \n",
       "172800     -0.646071     -0.873484     -0.538378     -1.057105     -0.362559   \n",
       "172801     -0.646071     -0.873484     -0.538378     -1.057105     -0.362559   \n",
       "\n",
       "        2_FQ_101_PV  2_FQ_201_PV  2_FQ_301_PV  2_FQ_401_PV  2_FQ_501_PV  \\\n",
       "1          0.823785     1.416868    -0.422774     0.397326    -0.444784   \n",
       "2          0.823785     1.416868    -0.422774     0.397326    -0.444784   \n",
       "3          0.823785     1.416868    -0.422774     0.397326    -0.444784   \n",
       "4          0.823785     1.416868    -0.422774     0.397326    -0.444784   \n",
       "5          0.823785     1.416868    -0.422774     0.397326    -0.444784   \n",
       "...             ...          ...          ...          ...          ...   \n",
       "172797    -0.406610    -0.567744    -0.341258    -0.410656    -0.445733   \n",
       "172798    -0.406610    -0.567744    -0.341258    -0.410656    -0.445733   \n",
       "172799    -0.406610    -0.567744    -0.341258    -0.410656    -0.445733   \n",
       "172800    -0.406610    -0.567744    -0.341258    -0.410656    -0.445733   \n",
       "172801    -0.406610    -0.567744    -0.341258    -0.410656    -0.445733   \n",
       "\n",
       "        2_FQ_601_PV  2_LS_101_AH  2_LS_101_AL  2_LS_201_AH  2_LS_201_AL  \\\n",
       "1         -0.358971    -0.044921    -0.059568    -0.037371    -0.056145   \n",
       "2         -0.358971    -0.044921    -0.059568    -0.037371    -0.056145   \n",
       "3         -0.358971    -0.044921    -0.059568    -0.037371    -0.056145   \n",
       "4         -0.358971    -0.044921    -0.059568    -0.037371    -0.056145   \n",
       "5         -0.358971    -0.044921    -0.059568    -0.037371    -0.056145   \n",
       "...             ...          ...          ...          ...          ...   \n",
       "172797    -0.645019    -0.044921    -0.059568    -0.037371    -0.056145   \n",
       "172798    -0.645019    -0.044921    -0.059568    -0.037371    -0.056145   \n",
       "172799    -0.645019    -0.044921    -0.059568    -0.037371    -0.056145   \n",
       "172800    -0.645019    -0.044921    -0.059568    -0.037371    -0.056145   \n",
       "172801    -0.645019    -0.044921    -0.059568    -0.037371    -0.056145   \n",
       "\n",
       "        2_LS_301_AH  2_LS_301_AL  2_LS_401_AH  2_LS_401_AL  2_LS_501_AH  \\\n",
       "1         -0.048409    -0.053923    -0.037059    -0.055151    -0.044857   \n",
       "2         -0.048409    -0.053923    -0.037059    -0.055151    -0.044857   \n",
       "3         -0.048409    -0.053923    -0.037059    -0.055151    -0.044857   \n",
       "4         -0.048409    -0.053923    -0.037059    -0.055151    -0.044857   \n",
       "5         -0.048409    -0.053923    -0.037059    -0.055151    -0.044857   \n",
       "...             ...          ...          ...          ...          ...   \n",
       "172797    -0.048409    -0.053923    -0.037059    -0.055151    -0.044857   \n",
       "172798    -0.048409    -0.053923    -0.037059    -0.055151    -0.044857   \n",
       "172799    -0.048409    -0.053923    -0.037059    -0.055151    -0.044857   \n",
       "172800    -0.048409    -0.053923    -0.037059    -0.055151    -0.044857   \n",
       "172801    -0.048409    -0.053923    -0.037059    -0.055151    -0.044857   \n",
       "\n",
       "        2_LS_501_AL  2_LS_601_AH  2_LS_601_AL  2_LT_001_PV  2_LT_002_PV  \\\n",
       "1         -0.058333    -0.083343    -0.056197     0.924761     0.706212   \n",
       "2         -0.058333    -0.083343    -0.056197     0.924761     0.706212   \n",
       "3         -0.058333    -0.083343    -0.056197     0.924761     0.706212   \n",
       "4         -0.058333    -0.083343    -0.056197     0.924761     0.706212   \n",
       "5         -0.058333    -0.083343    -0.056197     0.924761     0.706212   \n",
       "...             ...          ...          ...          ...          ...   \n",
       "172797    -0.058333    -0.083343    -0.056197     1.039966    -0.261952   \n",
       "172798    -0.058333    -0.083343    -0.056197     1.039966    -0.261952   \n",
       "172799    -0.058333    -0.083343    -0.056197     1.049412    -0.260409   \n",
       "172800    -0.058333    -0.083343    -0.056197     1.049412    -0.260409   \n",
       "172801    -0.058333    -0.083343    -0.056197     1.049412    -0.260409   \n",
       "\n",
       "        2_MCV_007_CO  2_MCV_101_CO  2_MCV_201_CO  2_MCV_301_CO  2_MCV_401_CO  \\\n",
       "1           -0.08679     -0.695548     -0.690508      0.190936     -0.652647   \n",
       "2           -0.08679     -0.695548     -0.690508      0.190936     -0.652647   \n",
       "3           -0.08679     -0.695548     -0.690508      0.190936     -0.652647   \n",
       "4           -0.08679     -0.695548     -0.690508      0.190936     -0.652647   \n",
       "5           -0.08679     -0.695548     -0.690508      0.190936     -0.652647   \n",
       "...              ...           ...           ...           ...           ...   \n",
       "172797      -0.08679     -0.695548     -0.690508     -0.741944     -0.652647   \n",
       "172798      -0.08679     -0.695548     -0.690508     -0.741944     -0.652647   \n",
       "172799      -0.08679     -0.695548     -0.690508     -0.741944     -0.652647   \n",
       "172800      -0.08679     -0.695548     -0.690508     -0.741944     -0.652647   \n",
       "172801      -0.08679     -0.695548     -0.690508     -0.741944     -0.652647   \n",
       "\n",
       "        2_MCV_501_CO  2_MCV_601_CO  2_MV_003_STATUS  2_MV_006_STATUS  \\\n",
       "1           2.559985      0.827117        -0.479013        -0.375385   \n",
       "2           2.559985      0.827117        -0.479013        -0.375385   \n",
       "3           2.559985      0.827117        -0.479013        -0.375385   \n",
       "4           2.559985      0.827117        -0.479013        -0.375385   \n",
       "5           2.559985      0.827117        -0.479013        -0.375385   \n",
       "...              ...           ...              ...              ...   \n",
       "172797     -0.757642     -0.706346        -0.479013        -0.375385   \n",
       "172798     -0.757642     -0.706346        -0.479013        -0.375385   \n",
       "172799     -0.757642     -0.706346        -0.479013        -0.375385   \n",
       "172800     -0.757642     -0.706346        -0.479013        -0.375385   \n",
       "172801     -0.757642     -0.706346        -0.479013        -0.375385   \n",
       "\n",
       "        2_MV_101_STATUS  2_MV_201_STATUS  2_MV_301_STATUS  2_MV_401_STATUS  \\\n",
       "1             -1.010768        -0.975522         0.847521        -0.938817   \n",
       "2             -1.010768        -0.975522         0.847521        -0.938817   \n",
       "3             -1.010768        -0.975522         0.847521        -0.938817   \n",
       "4             -1.010768        -0.975522         0.847521        -0.938817   \n",
       "5             -1.010768        -0.975522         0.847521        -0.938817   \n",
       "...                 ...              ...              ...              ...   \n",
       "172797         0.983155         1.019191         0.847521         1.058677   \n",
       "172798         0.983155         1.019191         0.847521         1.058677   \n",
       "172799         0.983155         1.019191         0.847521         1.058677   \n",
       "172800         0.983155         1.019191         0.847521         1.058677   \n",
       "172801         0.983155         1.019191         0.847521         1.058677   \n",
       "\n",
       "        2_MV_501_STATUS  2_MV_601_STATUS  2_P_003_SPEED  2_P_003_STATUS  \\\n",
       "1               1.05419         0.958356      -0.410701        -0.41993   \n",
       "2               1.05419         0.958356      -0.410701        -0.41993   \n",
       "3               1.05419         0.958356      -0.410701        -0.41993   \n",
       "4               1.05419         0.958356      -0.410701        -0.41993   \n",
       "5               1.05419         0.958356      -0.410701        -0.41993   \n",
       "...                 ...              ...            ...             ...   \n",
       "172797          1.05419         0.958356      -0.410349        -0.41993   \n",
       "172798          1.05419         0.958356      -0.410349        -0.41993   \n",
       "172799          1.05419         0.958356      -0.410512        -0.41993   \n",
       "172800          1.05419         0.958356      -0.410512        -0.41993   \n",
       "172801          1.05419         0.958356      -0.410512        -0.41993   \n",
       "\n",
       "        2_P_004_SPEED  2_PIC_003_CO  2_PIC_003_PV  2_PIC_003_SP  2_PIT_001_PV  \\\n",
       "1            0.129838      0.434857     -0.425299      0.032891      0.423386   \n",
       "2            0.129838      0.434857     -0.425299      0.032891      0.423386   \n",
       "3            0.129838      0.434857     -0.425299      0.032891      0.423386   \n",
       "4            0.129838      0.434857     -0.425299      0.032891      0.423386   \n",
       "5            0.129838      0.434857     -0.425299      0.032891      0.423386   \n",
       "...               ...           ...           ...           ...           ...   \n",
       "172797       0.214881      0.434857     -0.388217      0.032891     -0.294806   \n",
       "172798       0.214881      0.434857     -0.388217      0.032891     -0.294806   \n",
       "172799       0.094939      0.434857     -0.388205      0.032891     -0.233257   \n",
       "172800       0.094939      0.434857     -0.388205      0.032891     -0.233257   \n",
       "172801       0.094939      0.434857     -0.388205      0.032891     -0.233257   \n",
       "\n",
       "        2_PIT_002_PV  2_PIT_003_PV  2A_AIT_001_PV  2A_AIT_002_PV  \\\n",
       "1           0.127837     -0.425037      -1.918165      -0.029486   \n",
       "2           0.127837     -0.425037      -1.918165      -0.029486   \n",
       "3           0.127837     -0.425037      -1.918165      -0.029486   \n",
       "4           0.127837     -0.425037      -1.918165      -0.029486   \n",
       "5           0.127837     -0.425037      -1.918165      -0.029486   \n",
       "...              ...           ...            ...            ...   \n",
       "172797      0.326778     -0.387998       0.249258      -0.029486   \n",
       "172798      0.326778     -0.387998       0.249258      -0.029486   \n",
       "172799      0.359912     -0.387753       0.249258      -0.029486   \n",
       "172800      0.359912     -0.387753       0.249258      -0.029486   \n",
       "172801      0.359912     -0.387753       0.249258      -0.029486   \n",
       "\n",
       "        2A_AIT_003_PV  2A_AIT_004_PV  2B_AIT_001_PV  2B_AIT_002_PV  \\\n",
       "1            1.437764      -0.482409      -0.926806      -1.095088   \n",
       "2            1.437764      -0.482409      -0.926806      -1.095088   \n",
       "3            1.437764      -0.482409      -0.926806      -1.095088   \n",
       "4            1.437764      -0.493730      -0.926806      -1.095072   \n",
       "5            1.437764      -0.493730      -0.926806      -1.095072   \n",
       "...               ...            ...            ...            ...   \n",
       "172797       0.175800      -0.444474       0.228799       0.909737   \n",
       "172798       0.175800      -0.444474       0.228799       0.909737   \n",
       "172799       0.175800      -0.444474       0.228799       0.909737   \n",
       "172800       0.175800      -0.439310       0.228799       0.909851   \n",
       "172801       0.175800      -0.439310       0.228799       0.909851   \n",
       "\n",
       "        2B_AIT_003_PV  2B_AIT_004_PV  3_AIT_002_PV  3_AIT_003_PV  \\\n",
       "1            1.530950      -0.636329          -1.0      0.110155   \n",
       "2            1.530950      -0.636329          -1.0      0.110155   \n",
       "3            1.530950      -0.636329          -1.0      0.110155   \n",
       "4            2.226073      -0.659509          -1.0      0.110155   \n",
       "5            2.226073      -0.659509          -1.0      0.110155   \n",
       "...               ...            ...           ...           ...   \n",
       "172797      -0.701943      -0.157926          -1.0      0.782327   \n",
       "172798      -0.701943      -0.157926          -1.0      0.782327   \n",
       "172799      -0.701943      -0.157926          -1.0     -1.070183   \n",
       "172800      -0.701943      -0.134006          -1.0     -1.070183   \n",
       "172801      -0.701943      -0.134006          -1.0     -1.070183   \n",
       "\n",
       "        3_AIT_004_PV  3_AIT_005_PV  3_FIT_001_PV  3_LT_001_PV  \\\n",
       "1          -0.893221      0.803073      0.421774    -0.729956   \n",
       "2          -0.893221      0.803073      0.421774    -0.729956   \n",
       "3          -0.893221      0.803073      0.421774    -0.729956   \n",
       "4          -0.893221      0.803073      0.421774    -0.729956   \n",
       "5          -0.893221      0.803073      0.421774    -0.729956   \n",
       "...              ...           ...           ...          ...   \n",
       "172797     -1.011287      0.648958      0.669969     1.506010   \n",
       "172798     -1.011287      0.648958      0.669969     1.506010   \n",
       "172799      1.683305      0.649108      0.670181     1.507815   \n",
       "172800      1.683305      0.649108      0.670181     1.507815   \n",
       "172801      1.683305      0.649108      0.670181     1.507815   \n",
       "\n",
       "        LEAK_DIFF_PRESSURE  TOTAL_CONS_REQUIRED_FLOW  OUTCOME  \n",
       "1                -0.013872                 -0.353392        0  \n",
       "2                -0.013872                 -0.353392        0  \n",
       "3                -0.013872                 -0.353392        0  \n",
       "4                -0.013872                 -0.353392        0  \n",
       "5                -0.013872                 -0.353392        0  \n",
       "...                    ...                       ...      ...  \n",
       "172797           -0.553200                 -1.201093        0  \n",
       "172798           -0.553200                 -1.201093        0  \n",
       "172799           -0.643043                 -1.201093        0  \n",
       "172800           -0.643043                 -1.201093        0  \n",
       "172801           -0.643043                 -1.201093        0  \n",
       "\n",
       "[172801 rows x 98 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with pd.option_context('display.max_rows', 10, 'display.max_columns', None):\n",
    "    display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "normal = df[df['OUTCOME'] == 0].sample(1000)\n",
    "attack = df[df['OUTCOME'] == 1]\n",
    "\n",
    "df1 = normal.append(attack).sample(frac=1).reset_index(drop=True)\n",
    "\n",
    "X = df1.drop(['OUTCOME'], axis = 1).values\n",
    "Y = df1[\"OUTCOME\"].values\n",
    "\n",
    "# tsne_plot(X, Y, \"original.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "normal_mask = df[df.OUTCOME == 0]\n",
    "attack_mask = df[df.OUTCOME == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(130242, 97)\n",
      "(34561, 97)\n",
      "(34561,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test = train_test_split(df, test_size = 0.2, random_state = 42)\n",
    "\n",
    "X_train = X_train[X_train.OUTCOME == 0]\n",
    "X_train = X_train.drop(['OUTCOME'], axis=1)\n",
    "\n",
    "y_test = X_test['OUTCOME']\n",
    "X_test = X_test.drop(['OUTCOME'], axis=1)\n",
    "\n",
    "X_train = X_train.values\n",
    "X_test = X_test.values\n",
    "\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)\n",
    "\n",
    "# X_train contains all but labels of 80% of normal data \n",
    "# X_test contains all but labels of 20% of total data \n",
    "# y_test contains the labels of 20% of the total data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 130242 samples, validate on 34561 samples\n",
      "Epoch 1/50\n",
      "130242/130242 [==============================] - 11s 84us/step - loss: 0.1665 - accuracy: 0.4651 - val_loss: 0.1623 - val_accuracy: 0.5021\n",
      "Epoch 2/50\n",
      "130242/130242 [==============================] - 10s 79us/step - loss: 0.0633 - accuracy: 0.5779 - val_loss: 0.1269 - val_accuracy: 0.5627\n",
      "Epoch 3/50\n",
      "130242/130242 [==============================] - 10s 78us/step - loss: 0.0455 - accuracy: 0.6187 - val_loss: 0.1045 - val_accuracy: 0.6728\n",
      "Epoch 4/50\n",
      "130242/130242 [==============================] - 10s 78us/step - loss: 0.0393 - accuracy: 0.6383 - val_loss: 0.1230 - val_accuracy: 0.6090\n",
      "Epoch 5/50\n",
      "130242/130242 [==============================] - 10s 80us/step - loss: 0.0354 - accuracy: 0.6535 - val_loss: 0.0927 - val_accuracy: 0.6487\n",
      "Epoch 6/50\n",
      "130242/130242 [==============================] - 10s 78us/step - loss: 0.0316 - accuracy: 0.6664 - val_loss: 0.0985 - val_accuracy: 0.6120\n",
      "Epoch 7/50\n",
      "130242/130242 [==============================] - 10s 76us/step - loss: 0.0306 - accuracy: 0.6730 - val_loss: 0.0876 - val_accuracy: 0.6660\n",
      "Epoch 8/50\n",
      "130242/130242 [==============================] - 10s 80us/step - loss: 0.0266 - accuracy: 0.6868 - val_loss: 0.0869 - val_accuracy: 0.6485\n",
      "Epoch 9/50\n",
      "130242/130242 [==============================] - 10s 77us/step - loss: 0.0269 - accuracy: 0.6858 - val_loss: 0.0855 - val_accuracy: 0.6864\n",
      "Epoch 10/50\n",
      "130242/130242 [==============================] - 10s 77us/step - loss: 0.0245 - accuracy: 0.6933 - val_loss: 0.0829 - val_accuracy: 0.6514\n",
      "Epoch 11/50\n",
      "130242/130242 [==============================] - 10s 77us/step - loss: 0.0239 - accuracy: 0.6958 - val_loss: 0.1080 - val_accuracy: 0.5899\n",
      "Epoch 12/50\n",
      "130242/130242 [==============================] - 10s 77us/step - loss: 0.0246 - accuracy: 0.6957 - val_loss: 0.0820 - val_accuracy: 0.6522\n",
      "Epoch 13/50\n",
      "130242/130242 [==============================] - 11s 81us/step - loss: 0.0215 - accuracy: 0.7034 - val_loss: 0.0813 - val_accuracy: 0.6989\n",
      "Epoch 14/50\n",
      "130242/130242 [==============================] - 11s 82us/step - loss: 0.0218 - accuracy: 0.7014 - val_loss: 0.0747 - val_accuracy: 0.7162\n",
      "Epoch 15/50\n",
      "130242/130242 [==============================] - 10s 77us/step - loss: 0.0209 - accuracy: 0.7024 - val_loss: 0.0731 - val_accuracy: 0.6652\n",
      "Epoch 16/50\n",
      "130242/130242 [==============================] - 10s 80us/step - loss: 0.0201 - accuracy: 0.7027 - val_loss: 0.0852 - val_accuracy: 0.6654\n",
      "Epoch 17/50\n",
      "130242/130242 [==============================] - 10s 80us/step - loss: 0.0199 - accuracy: 0.7058 - val_loss: 0.0951 - val_accuracy: 0.6330\n",
      "Epoch 18/50\n",
      "130242/130242 [==============================] - 10s 77us/step - loss: 0.0189 - accuracy: 0.7084 - val_loss: 0.0674 - val_accuracy: 0.6929\n",
      "Epoch 19/50\n",
      "130242/130242 [==============================] - 10s 78us/step - loss: 0.0182 - accuracy: 0.7145 - val_loss: 0.0671 - val_accuracy: 0.6956\n",
      "Epoch 20/50\n",
      "130242/130242 [==============================] - 10s 79us/step - loss: 0.0182 - accuracy: 0.7136 - val_loss: 0.0574 - val_accuracy: 0.6977\n",
      "Epoch 21/50\n",
      "130242/130242 [==============================] - 10s 80us/step - loss: 0.0171 - accuracy: 0.7177 - val_loss: 0.0619 - val_accuracy: 0.7177\n",
      "Epoch 22/50\n",
      "130242/130242 [==============================] - 10s 76us/step - loss: 0.0170 - accuracy: 0.7200 - val_loss: 0.0667 - val_accuracy: 0.6983\n",
      "Epoch 23/50\n",
      "130242/130242 [==============================] - 11s 83us/step - loss: 0.0169 - accuracy: 0.7188 - val_loss: 0.0561 - val_accuracy: 0.7061\n",
      "Epoch 24/50\n",
      "130242/130242 [==============================] - 11s 81us/step - loss: 0.0156 - accuracy: 0.7241 - val_loss: 0.0567 - val_accuracy: 0.7652\n",
      "Epoch 25/50\n",
      "130242/130242 [==============================] - 10s 79us/step - loss: 0.0178 - accuracy: 0.7241 - val_loss: 0.0566 - val_accuracy: 0.7023\n",
      "Epoch 26/50\n",
      "130242/130242 [==============================] - 10s 79us/step - loss: 0.0152 - accuracy: 0.7335 - val_loss: 0.0597 - val_accuracy: 0.7437\n",
      "Epoch 27/50\n",
      "130242/130242 [==============================] - 10s 77us/step - loss: 0.0134 - accuracy: 0.7414 - val_loss: 0.0635 - val_accuracy: 0.6912\n",
      "Epoch 28/50\n",
      "130242/130242 [==============================] - 10s 79us/step - loss: 0.0156 - accuracy: 0.7373 - val_loss: 0.0821 - val_accuracy: 0.6182\n",
      "Epoch 29/50\n",
      "130242/130242 [==============================] - 11s 84us/step - loss: 0.0136 - accuracy: 0.7429 - val_loss: 0.0578 - val_accuracy: 0.7578\n",
      "Epoch 30/50\n",
      "130242/130242 [==============================] - 11s 81us/step - loss: 0.0134 - accuracy: 0.7463 - val_loss: 0.0596 - val_accuracy: 0.7414\n",
      "Epoch 31/50\n",
      "130242/130242 [==============================] - 10s 81us/step - loss: 0.0140 - accuracy: 0.7441 - val_loss: 0.0536 - val_accuracy: 0.7507\n",
      "Epoch 32/50\n",
      "130242/130242 [==============================] - 10s 79us/step - loss: 0.0126 - accuracy: 0.7490 - val_loss: 0.0597 - val_accuracy: 0.7329\n",
      "Epoch 33/50\n",
      "130242/130242 [==============================] - 10s 75us/step - loss: 0.0138 - accuracy: 0.7465 - val_loss: 0.0576 - val_accuracy: 0.7480\n",
      "Epoch 34/50\n",
      "130242/130242 [==============================] - 10s 77us/step - loss: 0.0129 - accuracy: 0.7532 - val_loss: 0.0529 - val_accuracy: 0.7548\n",
      "Epoch 35/50\n",
      "130242/130242 [==============================] - 10s 76us/step - loss: 0.0126 - accuracy: 0.7542 - val_loss: 0.0529 - val_accuracy: 0.7595\n",
      "Epoch 36/50\n",
      "130242/130242 [==============================] - 10s 75us/step - loss: 0.0118 - accuracy: 0.7549 - val_loss: 0.0542 - val_accuracy: 0.7468\n",
      "Epoch 37/50\n",
      "130242/130242 [==============================] - 10s 76us/step - loss: 0.0125 - accuracy: 0.7581 - val_loss: 0.0569 - val_accuracy: 0.7282\n",
      "Epoch 38/50\n",
      "130242/130242 [==============================] - 10s 75us/step - loss: 0.0115 - accuracy: 0.7626 - val_loss: 0.0609 - val_accuracy: 0.7180\n",
      "Epoch 39/50\n",
      "130242/130242 [==============================] - 10s 76us/step - loss: 0.0117 - accuracy: 0.7630 - val_loss: 0.0549 - val_accuracy: 0.7797\n",
      "Epoch 40/50\n",
      "130242/130242 [==============================] - 10s 77us/step - loss: 0.0110 - accuracy: 0.7686 - val_loss: 0.0538 - val_accuracy: 0.7707\n",
      "Epoch 41/50\n",
      "130242/130242 [==============================] - 10s 75us/step - loss: 0.0123 - accuracy: 0.7667 - val_loss: 0.0566 - val_accuracy: 0.6842\n",
      "Epoch 42/50\n",
      "130242/130242 [==============================] - 10s 75us/step - loss: 0.0108 - accuracy: 0.7748 - val_loss: 0.0513 - val_accuracy: 0.7784\n",
      "Epoch 43/50\n",
      "130242/130242 [==============================] - 10s 77us/step - loss: 0.0107 - accuracy: 0.7760 - val_loss: 0.0511 - val_accuracy: 0.7396\n",
      "Epoch 44/50\n",
      "130242/130242 [==============================] - 10s 77us/step - loss: 0.0104 - accuracy: 0.7743 - val_loss: 0.0698 - val_accuracy: 0.5978\n",
      "Epoch 45/50\n",
      "130242/130242 [==============================] - 10s 75us/step - loss: 0.0125 - accuracy: 0.7715 - val_loss: 0.0509 - val_accuracy: 0.7660\n",
      "Epoch 46/50\n",
      "130242/130242 [==============================] - 10s 77us/step - loss: 0.0103 - accuracy: 0.7770 - val_loss: 0.0485 - val_accuracy: 0.7629\n",
      "Epoch 47/50\n",
      "130242/130242 [==============================] - 10s 76us/step - loss: 0.0107 - accuracy: 0.7764 - val_loss: 0.0468 - val_accuracy: 0.7936\n",
      "Epoch 48/50\n",
      "130242/130242 [==============================] - 10s 76us/step - loss: 0.0096 - accuracy: 0.7813 - val_loss: 0.0521 - val_accuracy: 0.7948\n",
      "Epoch 49/50\n",
      "130242/130242 [==============================] - 10s 76us/step - loss: 0.0100 - accuracy: 0.7803 - val_loss: 0.0565 - val_accuracy: 0.6931\n",
      "Epoch 50/50\n",
      "130242/130242 [==============================] - 10s 76us/step - loss: 0.0100 - accuracy: 0.7787 - val_loss: 0.0559 - val_accuracy: 0.7012\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Dropout\n",
    "nb_epoch = 50\n",
    "batch_size = 64\n",
    "\n",
    "ae = model = Sequential()\n",
    "model.add(Dense(85, input_dim=X_train.shape[1], activation='relu'))\n",
    "model.add(Dense(75, activation='relu'))\n",
    "model.add(Dense(65, activation='relu'))\n",
    "model.add(Dense(60, activation='relu'))\n",
    "model.add(Dense(65, activation='relu'))\n",
    "model.add(Dense(75, activation='relu'))\n",
    "model.add(Dense(85, activation='relu'))\n",
    "model.add(Dense(X_train.shape[1]))\n",
    "\n",
    "ae.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])\n",
    "\n",
    "checkpointer = ModelCheckpoint(filepath=\"model.ae\", verbose=0, save_best_only=True)\n",
    "\n",
    "tensorboard = TensorBoard(log_dir='./logs', histogram_freq=0, write_graph=True, write_images=True)\n",
    "\n",
    "history = ae.fit(X_train, X_train, epochs=nb_epoch, batch_size=batch_size, shuffle=True,\n",
    "                 validation_data=(X_test, X_test), verbose=1, callbacks=[checkpointer, tensorboard]).history\n",
    "\n",
    "# from keras.utils.vis_utils import plot_model\n",
    "# plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpointer = ModelCheckpoint(filepath=\"model.ae\", verbose=0, save_best_only=True)\n",
    "\n",
    "tensorboard = TensorBoard(log_dir='./logs', histogram_freq=0, write_graph=True, write_images=True)\n",
    "\n",
    "history = ae.fit(X_train, X_train, epochs=nb_epoch, batch_size=batch_size, shuffle=True,\n",
    "                 validation_data=(X_test, X_test), verbose=1, callbacks=[checkpointer, tensorboard]).history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history['accuracy'])\n",
    "plt.plot(history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history['loss'])\n",
    "plt.plot(history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#autoencoder = load_model('model.test1000')\n",
    "ae = load_model('model.ae')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history['loss'])\n",
    "plt.plot(history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper right');\n",
    "# plt.savefig('Loss.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#predictions = autoencoder1.predict(X_test)\n",
    "predictions = ae.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "mse = np.mean(np.power(X_test - predictions, 2), axis=1)\n",
    "error_df = pd.DataFrame({'reconstruction_error': mse,\n",
    "                        'true_class': y_test})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>reconstruction_error</th>\n",
       "      <th>true_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>34561.000000</td>\n",
       "      <td>34561.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.046799</td>\n",
       "      <td>0.057261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.371243</td>\n",
       "      <td>0.232344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000846</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.002472</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.003746</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.007523</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>11.407603</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       reconstruction_error    true_class\n",
       "count          34561.000000  34561.000000\n",
       "mean               0.046799      0.057261\n",
       "std                0.371243      0.232344\n",
       "min                0.000846      0.000000\n",
       "25%                0.002472      0.000000\n",
       "50%                0.003746      0.000000\n",
       "75%                0.007523      0.000000\n",
       "max               11.407603      1.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+uWbNmqU///nPmjt3rvbt26etW7dq2rRpGjhwoO6//35J0sSJE+VwOJSamqp33nlHW7du1VNPPaXExETdddddkqSUlBSFhoYqPT1d+fn52rFjhzIyMtS/f38lJydLkmJjYxUXF6esrCxt3bpV77zzjlJTUxUYGKjHHnvMi60CAAAAgEatvmzigw8+0BdffKG//OUvmjRpksfynJwc3X///Xr55Ze1Zs0a/fCHP1S3bt00duxYZWVl2a8pDw4O1qZNm/TCCy9o7ty5cjqdSkpK0rx58+y5HA6HNmzYoEWLFmn+/PlyOBxKSEjQM888o86dv9zU1atX68UXX1ROTo4uXbqk6OhorVixQt27d2+PngAAAABAi1oNUg888IAeeOCBVicaM2aMxowZ02JNRESENm7c2GJNz549tXr16hZrunfvrsWLF2vx4sWtbhcAAAAAtDez94UDAAAAAAhSAAAAAGCKIAUAAAAAhghSAAAAAGCIIAUAAAAAhghSAAAAAGCIIAUAAAAAhghSAAAAAGCIIAUAAAAAhghSAAAAAGCIIAUAAAAAhghSAAAAAGCIIAUAAAAAhghSAAAAAGCIIAUAAAAAhghSAAAAAGCIIAUAAAAAhghSAAAAAGCIIAUAAAAAhghSAAAAAGCIIAUAAAAAhghSAAAAAGCIIAUAAAAAhghSAAAAAGCIIAUAAAAAhghSAAAAAGCIIAUAAAAAhghSAAAAAGCIIAUAAAAAhghSAAAAAGCIIAUAAAAAhghSAAAAAGCIIAUAAAAAhghSAAAAAGCIIAUAAAAAhghSAAAAAGCIIAUAAAAAhghSAAAAAGCIIAUAAAAAhghSAAAAAGCIIAUAAAAAhghSAAAAAGCIIAUAAAAAhghSAAAAAGCIIAUAAAAAhghSAAAAAGCIIAUAAAAAhghSAAAAAGCIIAUAAAAAhghSAAAAAGCIIAUAAAAAhghSAAAAAGCIIAUAAAAAhghSAAAAAGCIIAUAAAAAhghSAAAAAGCoTUHq0qVLeuONN3TfffcpKipKY8aM0eLFi+VyueyaI0eOaPLkyYqKitKIESO0bNky1dfXu81TWlqq6dOnKyYmRvHx8Xruuefc5pCkzz77TNnZ2YqPj1d0dLSysrJ05swZt5qamhr99Kc/VUJCgqKiopSRkaHS0tJrbAEAAAAAmOnclqLXXntNK1asUFpamoYNG6aSkhKtXLlSx48f1/r161VWVqbU1FRFRUVpxYoVOnHihJYvXy6Xy6UFCxZIks6dO6cpU6aoR48eWrJkiSorK5Wbm6vTp09r7dq1kqSGhgalpaWptrZWzz//vBoaGrR06VKlp6dr27Zt6ty5cXPnzJmjI0eOaN68eXI6nVq9erUef/xx5efnKzAw0EutAgAAAIBGrQYpy7L02muv6dFHH1V2drYkafjw4QoKCtKcOXN07Ngxbd68WYGBgXrllVfkcDg0cuRIdevWTQsXLtS0adMUEhKiLVu2qKqqStu3b1dQUJAkKSQkRJmZmTp8+LAiIyOVn5+voqIi7dy5U+Hh4ZKkgQMHavz48dq9e7dSUlJ08OBB7d27V6+++qoSExMlSTExMbr77rv1xhtvKDMz01u9AgAAAABJbbi1r6amRhMmTND48ePdxvv27StJKi8v1759+zR69Gg5HA57eVJSki5evKiCggJJ0r59+xQbG2uHKEkaMWKEnE6n9u7da9f069fPDlGS7O8vr3E6nUpISLBrgoODFRsbq/fff9+4AQAAAABgqtUgFRAQoPnz5ys6Otpt/N1335UkhYeH69SpUwoLC3NbHhwcrICAAJWUlEiSiouLPWr8/f3Vu3fvFmskqU+fPm41oaGh8vf3v2oNAAAAAHhTm56RutLhw4e1bt06jRkzRl/72tckNQauKzmdTvtlEtXV1W2q6devX7M1ZWVlkiSXy9XqPG119OhRo3pvKCwslCSPoAp3TX3q6DnQOvrsO/Tad+i1b9Bn36HXvkOvfcfXvTYOUoWFhZo+fbp69+6thQsX6sKFC5KkTp06edRaliU/vy8verVHjWVZV922y+dpi8GDB6tr165G67SnwsJCAlQbXW+f6LVv0Gffode+Q699gz77Dr32HXrtO97qdV1d3VUvvhglj507d2rq1Knq2bOnNm7cqKCgIPvqUHNXg2pra+236AUEBDRbU1NTY8/R1pqampoWawAAAADAm9ocpDZs2KCsrCwNHTpUW7Zs0Te/+U1JjbfUhYSE2LfeNamsrJTL5bKfeQoLC/OouXjxok6ePNlijdT4QovLayoqKjyuTJWVlTX7fBUAAAAAtLc2BamtW7fqxRdfVHJysl577TWPv9WUkJCgPXv22Lf5SdLbb78tf39/xcXF2TUHDhzQ2bNn7ZqCggLV1tZq+PDhkhrf4vfpp5+quLjYrjl+/LiKi4vdaqqqqrR//3675vPPP9fBgwftGgAAAADwplafkaqsrNSiRYt02223adKkSfroo4/clvfp00fp6enKz89XZmampkyZotLSUi1btkyPPPKIevXqJUmaOHGiNm/erNTUVM2cOVNnz55Vbm6uEhMTddddd0mSUlJStGbNGqWnpys7O1uWZWnp0qXq37+/kpOTJUmxsbGKi4tTVlaW5s6dq1tvvVWrVq1SYGCgHnvssfbuDwAAAAB4aDVIffDBB/riiy/0l7/8RZMmTfJYnpOTo/vvv195eXnKycnR7NmzFRQUpKlTp2rWrFl2XXBwsDZt2qQXXnhBc+fOldPpVFJSkubNm2fXOBwObdiwQYsWLdL8+fPlcDiUkJCgZ555Rp07f7mpq1ev1osvvqicnBxdunRJ0dHRWrFihbp37369/QAAAACAVrUapB544AE98MADrU4UExOjt956q8WaiIgIbdy4scWanj17avXq1S3WdO/eXYsXL9bixYtb3S4AAAAAaG9m7wsHAAAAABCkAAAAAMAUQQoAAAAADBGkAAAAAMAQQQoAAAAADBGkAAAAAMAQQQoAAAAADBGkAAAAAMAQQQoAAAAADBGkAAAAAMAQQQoAAAAADBGkAAAAAMAQQQoAAAAADBGkAAAAAMAQQQoAAAAADBGkAAAAAMAQQQoAAAAADBGkAAAAAMAQQQoAAAAADBGkAAAAAMAQQQoAAAAADBGkAAAAAMAQQQoAAAAADBGkAAAAAMAQQQoAAAAADBGkAAAAAMAQQQoAAAAADBGkAAAAAMAQQQoAAAAADBGkAAAAAMAQQQoAAAAADBGkAAAAAMAQQQoAAAAADBGkAAAAAMAQQQoAAAAADBGkAAAAAMAQQQoAAAAADBGkAAAAAMAQQQoAAAAADBGkAAAAAMAQQQoAAAAADBGkAAAAAMAQQQoAAAAADBGkAAAAAMAQQQoAAAAADBGkAAAAAMAQQQoAAAAADBGkAAAAAMAQQQoAAAAADBGkAAAAAMAQQQoAAAAADBGkAAAAAMAQQQoAAAAADBGkAAAAAMAQQQoAAAAADBkHqWPHjunb3/62Tp8+7TY+duxYDRgwwOPr888/t2uOHDmiyZMnKyoqSiNGjNCyZctUX1/vNk9paammT5+umJgYxcfH67nnnpPL5XKr+eyzz5Sdna34+HhFR0crKytLZ86cMd0VAAAAALgmnU2Ki4uLNW3aNDU0NLiN19TUqKKiQtnZ2YqLi3Nb9rWvfU2SVFZWptTUVEVFRWnFihU6ceKEli9fLpfLpQULFkiSzp07pylTpqhHjx5asmSJKisrlZubq9OnT2vt2rWSpIaGBqWlpam2tlbPP/+8GhoatHTpUqWnp2vbtm3q3NlolwAAAADAWJtSR0NDg958800tXbpUXbp08Vj+8ccfy7Is3X333QoPD292jnXr1ikwMFCvvPKKHA6HRo4cqW7dumnhwoWaNm2aQkJCtGXLFlVVVWn79u0KCgqSJIWEhCgzM1OHDx9WZGSk8vPzVVRUpJ07d9qfNXDgQI0fP167d+9WSkrKtfYCAAAAANqkTbf2FRYW6qWXXtITTzyhuXPneiw/duyYunbtqjvuuOOqc+zbt0+jR4+Ww+Gwx5KSknTx4kUVFBTYNbGxsXaIkqQRI0bI6XRq7969dk2/fv3cAlvT9001AAAAAOBNbQpS4eHhevfdd/XDH/5Q/v7+Hss//vhj3XrrrcrKylJMTIyioqI0Z84c+7mlL774QqdOnVJYWJjbesHBwQoICFBJSYmkxlsHr6zx9/dX7969W6yRpD59+tg1AAAAAOBNbbq17xvf+EaLy4uKivTZZ5+pf//+mjx5soqLi7Vy5Uo9/vjj+s1vfqPq6mpJUkBAgMe6TqfTfplEdXV1m2r69evXbE1ZWVlbdsd29OhRo3pvKCwslCRFR0d38Jbc2Jr61NFzoHX02Xfote/Qa9+gz75Dr32HXvuOr3vdLm9mmD9/vizLUmRkpCQpJiZG4eHhmjhxon77299q5MiRkqROnTp5rGtZlvz8vrww1l41bTF48GB17drVaJ32VFhYSIBqo+vtE732DfrsO/Tad+i1b9Bn36HXvkOvfcdbva6rq7vqxZd2+TtSQ4YMsUNUk+joaAUGBqqoqMi+ynTla8wlqba2VoGBgZIar1g1V1NTU2PP0ZYaAAAAAPCm6w5StbW12rZtm4qKitzGLctSfX29goKC5HQ6FRIS4nHrXWVlpVwul/3MU1hYmEfNxYsXdfLkyRZrJKm8vLzZZ6cAAAAAoL1dd5Dq2rWrlixZotWrV7uNv/feezp//rz9d6USEhK0Z88eXbhwwa55++235e/v71Zz4MABnT171q4pKChQbW2thg8fLqnxLX6ffvqpiouL7Zrjx4+ruLjYrgEAAAAAb7ruIOXv768ZM2bonXfe0cKFC7V//35t3LhRTz/9tO6++27Fx8dLktLT03XmzBllZmZqz5492rBhgxYvXqxHHnlEvXr1kiRNnDhRDodDqampeuedd7R161Y99dRTSkxM1F133SVJSklJUWhoqNLT05Wfn68dO3YoIyND/fv3V3Jy8vXuDgAAAAC0ql1eNjF16lQFBARo06ZN2rp1q7p3766///u/16xZs+ya8PBw5eXlKScnR7Nnz1ZQUJCmTp3qVhMcHKxNmzbphRde0Ny5c+V0OpWUlKR58+bZNQ6HQxs2bNCiRYs0f/58ORwOJSQk6JlnnlHnzu2yOwAAAADQIuPk8dBDD+mhhx7yGH/44Yf18MMPt7huTEyM3nrrrRZrIiIitHHjxhZrevbs6XErIQAAAAD4Sru8tQ8AAAAAvkoIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgiCAFAAAAAIYIUgAAAABgyDhIHTt2TN/+9rd1+vRpt/GCggJ9//vfV2RkpL73ve8pLy/PY90jR45o8uTJioqK0ogRI7Rs2TLV19e71ZSWlmr69OmKiYlRfHy8nnvuOblcLreazz77TNnZ2YqPj1d0dLSysrJ05swZ010BAAAAgGvS2aS4uLhY06ZNU0NDg9v4oUOHNH36dCUnJ+vJJ59UYWGhcnJyZFmW0tLSJEllZWVKTU1VVFSUVqxYoRMnTmj58uVyuVxasGCBJOncuXOaMmWKevTooSVLlqiyslK5ubk6ffq01q5dK0lqaGhQWiprZd0AAByFSURBVFqaamtr9fzzz6uhoUFLly5Venq6tm3bps6djXYJAAAAAIy1KXU0NDTozTff1NKlS9WlSxeP5StXrtSgQYOUm5srSUpMTFRDQ4PWrFmjyZMny+FwaN26dQoMDNQrr7wih8OhkSNHqlu3blq4cKGmTZumkJAQbdmyRVVVVdq+fbuCgoIkSSEhIcrMzNThw4cVGRmp/Px8FRUVaefOnQoPD5ckDRw4UOPHj9fu3buVkpLSXr0BAAAAgGa16da+wsJCvfTSS3riiSc0d+5ct2V1dXU6ePCg7rnnHrfxcePGqaqqSocOHZIk7du3T6NHj5bD4bBrkpKSdPHiRRUUFNg1sbGxdoiSpBEjRsjpdGrv3r12Tb9+/ewQJcn+vqkGAAAAALypTUEqPDxc7777rn74wx/K39/fbVlFRYXq6+sVFhbmNh4aGipJKikp0RdffKFTp0551AQHBysgIEAlJSWSGm8dvLLG399fvXv3brFGkvr06WPXAAAAAIA3tenWvm984xtXXVZdXS1JCggIcBt3Op2SJJfLddWaprqml0lUV1e3qaZfv37N1pSVlbVld2xHjx41qveGwsJCSVJ0dHQHb8mNralPHT0HWkeffYde+w699g367Dv02nfote/4utfX/WYGy7IkSZ06dWp2uZ+fX4s1lmXJz+/LC2PtVdMWgwcPVteuXY3WaU+FhYUEqDa63j7Ra9+gz75Dr32HXvsGffYdeu079Np3vNXrurq6q158ue6/IxUYGChJHq8ob/o+MDDQvsp0ZY0k1dbW2nMEBAQ0W1NTU2PP0ZYaAAAAAPCm6w5Sffr0kb+/v8rLy93Gm74PCwuT0+lUSEiIx613lZWVcrlc9jNPYWFhHjUXL17UyZMnW6xp+rzmnp0CAAAAgPZ23UGqa9euiomJ0e7du+1b+CTp7bffVmBgoAYPHixJSkhI0J49e3ThwgW3Gn9/f8XFxdk1Bw4c0NmzZ+2agoIC1dbWavjw4ZIa3+L36aefqri42K45fvy4iouL7RoAAAAA8KbrDlKSNGPGDB06dEhz5szR3r17tWLFCq1fv17Tpk3TLbfcIklKT0/XmTNnlJmZqT179mjDhg1avHixHnnkEfXq1UuSNHHiRDkcDqWmpuqdd97R1q1b9dRTTykxMVF33XWXJCklJUWhoaFKT09Xfn6+duzYoYyMDPXv31/JycntsTsAAAAA0KJ2CVLDhg3TqlWrdOLECc2cOVO/+93vNG/ePGVkZNg14eHhysvLU21trWbPnq0NGzZo6tSp+sd//Ee7Jjg4WJs2bdKtt96quXPnavny5UpKStLy5cvtGofDoQ0bNmjQoEGaP3++fv7znysqKkrr169X587X/e4MAAAAAGiVcfJ46KGH9NBDD3mMjx07VmPHjm1x3ZiYGL311lst1kRERGjjxo0t1vTs2VOrV69udVsBAAAAwBva5YoUAAAAAHyVEKQAAAAAwBBBCgAAAAAMEaQAAAAAwBBBCgAAAAAMEaQAAAAAwBBBCgAAAAAMEaQAAAAAwBBBCgAAAAAMEaQAAAAAwBBBCgAAAAAMEaQAAAAAwBBBCgAAAAAMEaQAAAAAwBBBCgAAAAAMEaQAAAAAwBBBCgAAAAAMEaQAAAAAwBBBCgAAAAAMEaQAAAAAwBBBCgAAAAAMEaQAAAAAwBBBCgAAAAAMEaQAAAAAwBBBCgAAAAAMEaQAAAAAwBBBCgAAAAAMEaQAAAAAwBBBCgAAAAAMEaQAAAAAwBBBCgAAAAAMEaQAAAAAwBBBCgAAAAAMEaQAAAAAwBBBCgAAAAAMEaQAAAAAwBBBCgAAAAAMEaQAAAAAwBBBCgAAAAAMEaQAAAAAwBBBCgAAAAAMEaQAAAAAwBBBCgAAAAAMEaQAAAAAwBBBCgAAAAAMEaQAAAAAwBBBCgAAAAAMEaQAAAAAwBBBCgAAAAAMEaQAAAAAwBBBCgAAAAAMEaQAAAAAwBBBCgAAAAAMEaQAAAAAwBBBCgAAAAAMtVuQamho0JAhQzRgwAC3r6ioKLumoKBA3//+9xUZGanvfe97ysvL85jnyJEjmjx5sqKiojRixAgtW7ZM9fX1bjWlpaWaPn26YmJiFB8fr+eee04ul6u9dgUAAAAAWtS5vSYqKSlRXV2dlixZojvuuMMe9/NrzGqHDh3S9OnTlZycrCeffFKFhYXKycmRZVlKS0uTJJWVlSk1NVVRUVFasWKFTpw4oeXLl8vlcmnBggWSpHPnzmnKlCnq0aOHlixZosrKSuXm5ur06dNau3Zte+0OAAAAAFxVuwWpoqIi+fn5ady4cbrllls8lq9cuVKDBg1Sbm6uJCkxMVENDQ1as2aNJk+eLIfDoXXr1ikwMFCvvPKKHA6HRo4cqW7dumnhwoWaNm2aQkJCtGXLFlVVVWn79u0KCgqSJIWEhCgzM1OHDx9WZGRke+0SAAAAADSr3W7tO3bsmPr06dNsiKqrq9PBgwd1zz33uI2PGzdOVVVVOnTokCRp3759Gj16tBwOh12TlJSkixcvqqCgwK6JjY21Q5QkjRgxQk6nU3v37m2v3QEAAACAq2q3IPXxxx/L4XAoLS1NUVFRio2N1YIFC+RyuVRRUaH6+nqFhYW5rRMaGiqp8bbAL774QqdOnfKoCQ4OVkBAgEpKSiRJxcXFHjX+/v7q3bu3XQMAAAAA3tRuQaqoqEjl5eUaOXKk1q1bpx/84AfasWOHZsyYoerqaklSQECA2zpOp1OS5HK5rlrTVNf0Monq6upWawAAAADAm9rtGanly5ere/fuGjBggCQpNjZWX//61/XUU09p3759kqROnTo1u66fn58sy7pqjWVZ9ksr2lrTFkePHjWq94bCwkJJUnR0dAdvyY2tqU8dPQdaR599h177Dr32DfrsO/Tad+i17/i61+0WpOLi4jzGRo0a5fb9lVeMmr4PDAy0rzI1d1WptrZWgYGBkhqvWDVXU1NTo9tuu81omwcPHqyuXbsardOeCgsLCVBtdL19ote+QZ99h177Dr32DfrsO/Tad+i173ir13V1dVe9+NIut/ZVVlZq69atqqiocBs/f/68JOnrX/+6/P39VV5e7ra86fuwsDA5nU6FhISorKzMY26Xy2U/FxUWFuZRc/HiRZ08edLj2SkAAAAA8IZ2CVKdOnXSggULtHnzZrfxnTt3yt/fX8OHD1dMTIx2795t38InSW+//bYCAwM1ePBgSVJCQoL27NmjCxcuuNX4+/vbV7wSEhJ04MABnT171q4pKChQbW2thg8f3h67AwAAAAAtapdb+4KDgzVp0iS9/vrrCggIUExMjAoLC7VmzRpNmjRJoaGhmjFjhqZOnao5c+bowQcf1Icffqj169crOzvbfmV6enq68vPzlZmZqSlTpqi0tFTLli3TI488ol69ekmSJk6cqM2bNys1NVUzZ87U2bNnlZubq8TERN11113tsTsAAAAA0KJ2e0bq6aefVkhIiLZt26Z169YpJCREs2fPVnp6uiRp2LBhWrVqlVauXKmZM2cqJCRE8+bN0xNPPGHPER4erry8POXk5Gj27NkKCgrS1KlTNWvWLLsmODhYmzZt0gsvvKC5c+fK6XQqKSlJ8+bNa69dAQAAAIAWtVuQ6tKlizIyMpSRkXHVmrFjx2rs2LEtzhMTE6O33nqrxZqIiAht3LjxWjYTAAAAAK5bu/0dKQAAAAD4qiBIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghQAAAAAGCJIAQAAAIAhghRueBfqL133HNHR0e2wJTee9ugNAAAAzHXu6A0AWuPo4qfkZw939GbckHYtjuzoTQAAAPhK4ooUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUAAAAABgiSAEAAACAIYIUcBO70f6O1I3097putN4AAIC/LfwdKeAmxt/Yujr+xhYAAPCmm/aK1I4dO3TvvfdqyJAhSk5O1vbt2zt6kwAAAAB8RdyUQWrXrl2aO3euEhIS9PLLLysuLk5PP/20fv/733f0pgEAAAD4Crgpb+1btmyZkpOT9eMf/1iS9N3vflfnzp3TL37xCyUlJXXw1gG4EVyovyRHl5vyd0Vtcj3Po/2t9wYAAF+46YJURUWFysvLlZWV5TY+btw47dq1SxUVFbr99ts7aOsA3Ch4fuzq/vVnd3b0JtywCJkAgLa66YJUcXGxJCksLMxtPDQ0VJJUUlLSapCyLEuSdOHCBS9soZm6ujr7f9/6/6wO3JIbV11dHb25CnpzdfTm6qxL9Xrs58c6ejNuSK9mDZB1yT1IDR482O1c/VV1oeGSHJ29FzJv5j7XN1xSFy/2xhtu1l7fjOi173ij1015oSk/XK6T1dzoDWzHjh3Kzs7We++9p969e9vjZWVluueee7R8+XKlpKS0OEd1dbU++eQTb28qAAAAgL8BERERCgwMdBu76a5INeW+Tp06NTvu59f6b4ScTqciIiLUpUsXj3kAAAAAQGrMGPX19XI6nR7Lbrog1ZQEXS6X23hNTY3b8pb4+fm1qQ4AAADAV1u3bt2aHb+5bujVl89GlZeXu42XlZW5LQcAAAAAb7npglRoaKh69+7t8Tejdu/erTvuuEO9evXqoC0DAAAA8FVx093aJ0kzZ87Us88+q+7du2vUqFH6wx/+oF27dmn58uUdvWkAAAAAvgJuurf2Nfnnf/5n5eXl6dSpU7r99tuVmZmpBx54oKM3CwAAAMBXwE0bpAAAAACgo9x0z0gBAAAAQEcjSAEAAACAIYIUAAAAABgiSF2DHTt26N5779WQIUOUnJys7du3t1hfU1Ojn/70p0pISFBUVJQyMjJUWlrqVtPQ0KAVK1Zo5MiRioyM1MSJE/Vf//VfHnP98pe/1NixYzVkyBA9+OCD2rt3b3vu2g3HG712uVxasmSJxowZo6FDh+q+++7Tr371K13+uGBDQ4OGDBmiAQMGuH1FRUV5Yzc7nDf6fPDgQY/+DRgwQNOmTXOr45i+vl6vWrWq2T43ff3lL3+RJJ0+fbrZ5ePHj/fm7nYo015fbsmSJUpNTfUY51zdPG/0mnO1J2/0mXN189q715yrr86012fOnNH8+fM1evRoRUVF6aGHHtKuXbs86tpyzF7Pz1mSZMHIzp07rQEDBliLFi2y3n//fWvBggVWRESEtWvXrquuk5GRYX3nO9+xfv3rX1tvv/22dd9991nf/e53raqqKrvm+eeftyIjI63XX3/deu+996x/+Id/sKKioqzy8nK75tVXX7UGDhxovfzyy9Yf//hHa9asWdagQYOsQ4cOeXWfO4q3ep2enm7FxcVZmzdvtvbv32+99NJL1re+9S1rzZo1ds0nn3xiRUREWL/5zW+sDz/80P46fPiwV/e5I3irz5s3b7aGDh3q1r8PP/zQOnHihF3DMX39vT516pRHj/fs2WMNGTLEysjIsC5evGhZlmXt2bPHioiIsD744AO32mPHjvlk333tWnrd5PXXX7ciIiKsKVOmeCzjXO3JW73mXO3OW33mXO3JG73mXN08017X1dVZEyZMsEaPHm39+te/tgoKCqyf/OQnVkREhPW73/3OrmvLMXs9P+cmBClDY8aMsX70ox+5jT355JNWUlJSs/X/8R//YUVERFh79+61xyorK62hQ4daa9eutSzLsioqKqyBAwdav/rVr+yauro6a9SoUdaCBQssy7KsmpoaKzo62srNzbVrLl26ZD3yyCNWWlpau+3fjcQbvf7oo4+siIgIa+fOnW7rLliwwIqOjra//+1vf2t961vfsmpra9trd25Y3uizZVnW/PnzrYcffviqn8sx3ag9en2lH/zgB1ZiYqJ17tw5e+yf/umfrOHDh1/nHtw8THttWZZ1+vRpKysry/rWt75lRUdHe/wfIc7VzfNGrzlXe/JGny2Lc3VzvNXrK3GuNu/1O++8Y0VERHj8siQtLc2aMGGCZVltP2av5ed8JW7tM1BRUaHy8nLdc889buPjxo1TcXGxKioqPNbZt2+fnE6nEhIS7LHg4GDFxsbq/ffflyT96U9/0sWLFzVu3Di7xuFwaNSoUXbN4cOHVV1d7fbZnTp10tixY/Vv//ZvunDhQrvua0fzVq8ty9Kjjz6qYcOGua3bt29fVVdX669//ask6dixY+rTp49uueWW9t61G4q3+iw19nDAgAFX/WyO6Ubt0evL/fGPf9S7776rZ599Vl/72tfs8dZ+Hn9LrqXXkrR8+XJ99NFH2rBhgwYOHOixnHO1J2/1mnO1O2/1WeJcfSVv9vpynKuvrddOp1OPPvqo7rzzTrfxvn37qry8XFLbjtlr/TlfiSBloLi4WJIUFhbmNh4aGipJKikpaXad0NBQ+fv7u4336dPHri8uLlb37t0VHBzsMe9///d/6/z58/Zn9+3b16OmoaGhzT/wm4W3ej1o0CD97Gc/06233upW8+6776pHjx72+McffyyHw6G0tDRFRUUpNjZWCxYskMvlap8dvEF4q8+XLl3Sp59+qtOnT+vBBx/U4MGDNWrUKOXl5dnPN3BMN7reXl/Osizl5OQoLi5OSUlJbsuKiop0/vx5PfbYY7rzzjs1fPhwLV26VPX19de1Xzeia+m1JKWnpys/P1/f+c53rjov52p33uo152p33uoz52pP3ur15ThXN7qWXg8bNkw/+9nP1KlTJ3usvr5ee/fuVf/+/d3mbemYvdaf85U6t6kKkqTq6mpJUkBAgNu40+mUpGZP3C6Xy6O+aZ2m+pZqpMaHzZtqm8aaq/lb4q1eN+eXv/yl/v3f/10//vGP7X+YRUVFcrlcevjhhzV9+nQdPXpUq1atUklJiTZt2uT2D/hm5q0+l5SU6Pz58yopKVFWVpaCgoL03nvvKScnRy6XS7Nnz+aY/j/teUz/4Q9/0IkTJ/STn/zEbfyLL75QeXm5zp07p6eeekpz5szRn/70J61bt07/+7//qyVLllzzft2IrqXXktSvX78W5+Vc7clbvW4O5+r27zPnak++OKY5Vze61l5f6aWXXlJpaalefvllt/VaOmbb67MJUgaafjtz5Ym5adzPz/MCn3XZ24Wu1FR/tZrLP8+yrGb/g3C1bbrZeavXV9q8ebMWL16s5ORkPf744/b48uXL1b17d/vyemxsrL7+9a/rqaee0v79+91utbqZeavPISEhevXVVzVw4ED16NFDUuNvkc6fP69XX31VTzzxBMf0FePtcUxv2bJFgwYN8rgdyt/fX3l5ebrtttvUp08fSVJcXJy6dOmiFStWaMaMGbrjjjuM9udGdi29Npm3pc/juHYfv9ZeX4lztXf6zLnaky+Oac7Vja6315ZlKTc3Vxs3blRaWprGjBljj7d2zLbXz5lb+wwEBgZK8kypTb+NaVp+uYCAgGZ/W1NTU2On4JZqmpYHBgbKsiyPupY++2bmrV43uXTpkpYsWaKf//znuvfee/XSSy+5/WOKi4vzuEd51KhRkhp/A/q3wpvHdGJiov0f5iajRo3ShQsXVFJSwjH9f9rrmD579qwOHDig+++/36Pe4XBo2LBh9n+Ym/wtHtPStfW6LThXe/JWr5twrm7kzWOac7U7bx/TnKu/dD29vnDhgrKzs7V+/XqlpaVp3rx5bvO2dsy218+ZIGWg6T7KpofZmpSVlbktv3KdiooKj99klpWV2fV9+/bV2bNnde7cOY+a3r17y+FwtPjZDodDvXr1uo49u/F4q9dS4720P/rRj5SXl6cnnnhCL730kjp3/vLibGVlpbZu3epx3/f58+clSUFBQdexZzcWb/X5448/1q9+9SuPe7ov7yHHdKP2OKYl6YMPPlBDQ4OSk5M95qmoqNCbb76pzz//3G38b/GYlq6t123BudqTt3otca6+nLf6zLnakzePaYlz9eWutdcul0tTp07Vrl279OMf/9gtRLU2b9Mx214/Z4KUgdDQUPXu3Vu///3v3cZ3796tO+64o9mTyYgRI1RVVaX9+/fbY/+/vXsJSW0NwwD8uiOxEIkGFVijKCiM6E4NCoyIoiAHTaJo1KCxgyzoChkUOQikG9GgG0QhRUiXidGkokFNgoYR3QaZBEEY+e1BZ0sebR/XPq69Y/M+sBCWvy7+148PfsX1e71enJycoKKiAgCCjzs7O8Exfr8f+/v7wecKCgqQmJgYMkZEsLe3h5KSEmi12thN9AtQK2sA6Onpwe7uLrq7u9HV1RX2s65Go0FfXx8WFxdDzrvdbsTFxaGoqCgWU/wS1Mr58vISg4ODYXeWc7vdSE9Ph9FoZE3/IxY1DbzfpchoNCI1NTXsfZ6entDX14etra2Q8263G3q9Hrm5uf9nal/Or2QdDfbqcGplDbBXf6RWzuzV4dSsaYC9+qNfyfrt7Q2dnZ04OzuDw+FAe3t72JhoajZWn3PcwMDAQFQjCcD7T32Tk5N4fHyERqPB/Pw8XC4X+vv7kZWVBa/Xi4uLC+j1emi1WhiNRhwfH2N5eRlJSUm4ublBT08PRAR2ux06nQ4GgwHX19eYm5tDQkICHh8fMTQ0hKurK4yOjiIpKQnx8fF4fX3F1NQUAoEAXl5e4HA4cHR0hJGRkb/uGyFAnaw9Hg/Gx8dhNpvR0NCAu7u7kCM5ORl6vR4+nw9LS0sIBAIIBALY2NjAxMQEWlpa0NjY+KejiSk1cs7IyMDBwQE2NzdhMBjg9XrhdDqxvb0Nu92OzMxM1nSMsv5hdnYWqampEeszJSUF5+fnWFtbg06nw/PzMxYXF7GwsACr1YqysrLfGcNvoTTrf3O5XPj27RuampqC59irI1Mja/bqcGrkzF4dmRpZ/8BeHUpp1isrK1heXkZzczNKS0tD+sL9/T3S0tKirtn/unZUot5xioJWVlakpqZGTCaT1NXVicvlCj63vr4u2dnZcnh4GDzn8/nEZrNJcXGxFBYWSkdHR8iO4SLvmzoODw9LeXm55OfnS0tLi5yenoaMCQQC4nQ6paqqSvLy8sRisYjH41F3sn9YrLO22WySnZ396XF7eysiIn6/X2ZmZqS2tlZMJpNUV1fL9PR0cOfxv40aNf3w8CC9vb1SWVkpJpNJLBaL7O3thYxhTccmaxGRuro6sVqtn173+flZxsbGxGw2i8lkkvr6elldXY3t5L4YpVl/1NraGnFDTfbqyGKdNXt1ZGrUNHt1ZGpkLcJeHYmSrNva2j7tCzk5OcHXRVuzP7t2NDQiP7ktFBEREREREYXhf6SIiIiIiIgU4kKKiIiIiIhIIS6kiIiIiIiIFOJCioiIiIiISCEupIiIiIiIiBTiQoqIiIiIiEghLqSIiIiIiIgU4kKKiIiIiIhIoe+CZEXBIWACFQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "normal_error_df = error_df[(error_df['true_class'] == 0) & (error_df['reconstruction_error'] < 0.2)]\n",
    "_ = ax.hist(normal_error_df.reconstruction_error.values, bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "fraud_error_df = error_df[error_df['true_class'] == 1]\n",
    "_ = ax.hist(fraud_error_df.reconstruction_error.values, bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import (confusion_matrix, precision_recall_curve, auc,\n",
    "                             roc_curve, recall_score, classification_report, f1_score,\n",
    "                             precision_recall_fscore_support)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr, tpr, thresholds = roc_curve(error_df.true_class, error_df.reconstruction_error)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr, tpr, label='AUC = %0.4f'% roc_auc)\n",
    "plt.legend(loc='lower right')\n",
    "plt.plot([-1,1],[-1,1],'r--')\n",
    "plt.xlim([-0.001, 1])\n",
    "plt.ylim([0, 1.001])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "# plt.savefig('ROC.png')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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huEFxcNygODhuUBwcNyiO8nbcnOvyHyaWAAAAAAA3UKIAAAAAwA2UKAAAAABwAyUKAAAAANxAiQIAAAAAN1CiAAAAAMANlCgAAAAAcAMlCgAAAADcQIkCAAAAADdQogAAAADADZQoAAAAAHADJQoAAAAA3FBuStT27dvVrFkzHT169ILbZWRkaOLEiercubNat26te+65RwkJCWUTEgAAAECVVy5KVHx8vO677z7l5eVddNuxY8fq66+/VmxsrKZMmaJjx47pjjvuUFpaWhkkBQAAAFDVGVqi8vLyNG/ePN10003Kycm56PYbN27U6tWrNWXKFA0ePFh9+/bVnDlzlJaWpk8//bQMEgMAAACo6gwtUZs2bdK0adN09913KzY29qLbr1u3Tr6+vurcubNrWXBwsNq1a6cffvihNKMCAAAAgCTJw8gXj4qK0ooVK1S9enUtXrz4otvHx8crIiJCFoul0PJ69eopLi6utGKWqt2HMrXsN7N+PnzI6ChlolawTTd0DjU6BgAAAFBshpaokJAQt7ZPT0+Xn59fkeW+vr5KT093+/W3bNni9j4l7ae9Jv263yztP250lFKXly/lOUyq6ZEom6FHXuWxadMmoyOgAuK4QXFw3KA4OG5QHBXhuKlQv8o6nc7zrjOb3T8zsXnz5vL09LycSJetbVupQ9QmtW3b1tAcZWHRD3/qn3FH1KpVK3l7Wi6+Ay5o06aqcdygZHHcoDg4blAcHDcojvJ03OTk5Jx30KVClSg/Pz8dPHiwyPKMjIxzjlChanI4nMrNcyjH7lSO3aFcu0PZdodyTz/O+dv3Zx7b8xzq0yZYYSHGFmsAAACUbxWqREVGRmr9+vVyOp0ymUyu5fv371dkZKSByXC5nE6nMnMcSsvMU1pWvjKzHcrMyVdWjkOZ2fnKzC34mpVTsDwzx6GsnNPb5RZ8zcl1KCfPIXve+UcsL+bPFLtG9qut7FyHsu35studigrzls1aLu4GUCbyHQWFMtfulFNSoG+F+msCAACg1FWo3466dOmi9957Tz/++KNrhr6kpCRt3LhR9913n8HpcKneXnJImTn5OnW6MKVl5istM0/5jgvvZzZLPjaLfLzM8va0yMfTLF9vs0KrWeXtaZa3zSKb1SRPq1meVrNsVpO8rGbZTv/xsppkO72u4E/hx5PmJWjlr8la+Wtyode9vU8t3da7Zil+IufmdDqVY3cqO7dgtCw711Ho++0HTUoxJSk3r2DkzZ7nVK79rO/P+pqb55Td7ii8bd7psnRm2el9//7f4fqrQ9TmCn/Z8wu2t+c5izy/Pc+pfIdTfdsGq24Nrwu+p7z8v1777ByBvh4K8reW8qcKAABw+cp1iUpKSlJiYqIaNmwoPz8/tWvXTu3bt9djjz2m2NhYVatWTW+99Zb8/f116623Gh0XF1Gvhpd8vczavC9d/t4WBfh4KKKGVf4+Bd/7+1jk713w1deroCSdKUvenhZ5Wk2FRiBL2ugBYWrZwE+eNrO8rGZ52sx65dP9yszJv+i+BYXHocxshzKyC0bKMnPyC0bRTo+mZWQ7lJWbr5zcgtMLs3Mdhb4vKEjOQmXpwizS/w4UWWo2SzYPs2weJllPf7VZzzw2yeZhlp+XSVbr37bxMLvW26wF227bn6kvfjyhL348ccEkZrPkcEiLfjiuRuHehcqR/W/F7ULu7Fur0Pb2PKfs+adPt8x3KtfuUEigVaMHhv1VAO0F2+TlO//aJ+/04/y/Hhc8l7NQGXTtk/+3x3mOs7b963FeXtFlzev76uVRURc7RAAAQCVSrkvU999/r/Hjx2vu3Lnq0KGDJOntt9/WK6+8oqlTp8rhcKht27aaPn26AgMDDU6Li2nfJECLnos2OsZ5hYd6Kjy08PTrFrNJOw5k6qOvjyg9K1/p2WeK0V/l6Myph45LOIvQ6lEwOuZlM7vKmpfNLH9vD4UGFl7uaTv92GqSl+v7gq9eVrP27tmh1i2buwqTzWqS1WKWxVJyRTOmo1NDuoTK4XSeVbL+Kl5Wj79e872vDmnf0SzXdmdvc6FlNg+zVvySpF/3pOvjb466Pier5azyZykofQlHsyVJX/+cdNnvzWxWwfNazmQreB0PD3PB8tPLfL3Msnp4uJZ5WAreh9Vi0ub4dP0en66H39ole55D98fUUf7p8pV7jlE7e37BiGDTCF9d1Tjgst8DAAAwRrkpUUOGDNGQIUMuuiwwMFCTJ0/W5MmTyzIeqqiQQKu2JmRoR2KG/Lw95Odtka+XWT6eFlXzs8rHs+B7H6+/vvp6WuRzeiTN9dXTIm9Ps6weJXdtVcZxKax66U6CYTab1LiuzyVte39MnWK/Tq/WQcrKyZfFUlBkzjfimHTKrpW/JstiPl3mrGeVIFfxMZ8uQ2eWmc8qSH9tbzFfftnctOuUFq89rl92F9xiYcI/493af/TA2kVOtTTJpFt61lAwpzYCAFBulZsSBZRH7zzaSPn5TnnZzKV6KiF0SdPeBwdYNbR7jTJIc2naNgpQ20YBys1zaPv+TJlMOmtUy+waHbSeNfq251Cmxn2wV7l5Tn24/IgkyWQqOAXTYpYycxw6mpyr8BBP9WxdTfVresujBEcXAQDA5aNEARdg8zDzfwkuyuZhVsuoS7vNQpN6vlryQrROZea7ipXFLJlMJmVk5+vuadv1y+40bdhRMMpVt4an3n64kfIcTvlwfzUAAMoFfj0EgDJmMpnOOXW8r5dF/366mUwmk9ZuSdGXP57QH/sydP3//SFJCvb30FWNA5Rr/2uWxoJ7nTmVnZMvfx8PXdM2uGCdvWDikly7Q91bBinQ1yI/b/7KBwCgJPATFQDKkTOnjXZpXk0Nanlr1W/JslhMmvvNUWXmOLRpV5q8rCZ52v6ant/fx6yftmdJJ3O140Bmkef8z7q/Zle8v5dU48/sC05FDwAALowSBQDlVFiIp0b0qSVJurXnhe9VlmN36GSq3TXDo6fVLA+LST9uTVXSKbs+X3tcR5Ny9d4qD2nVTjUO99FLoxrI14tTBAEAcBclCgAqAU+rWWEhRWdrvLpZwe0fureqpt/2pOvXLfGK22zRzoOZumniFi2d1KJEp8UHAKAqKLn5lgEA5Za/t4e6RldT50ZO/Wdic9fyrfszDEwFAEDFRIkCgCrGy2bRa/c3lCR9u+nyb1wMAEBVQ4kCgCqoYZi3JOnnnWkGJwEAoOKhRAFAFWQ7PfFEakae0VEAAKhwKFEAUEUN7RZqdAQAACokShQAVFFn7km153Cm8vOdBqcBAKDioEQBQBVlsxaUqEfe2q1VvyUbnAYAgIqDEgUAVdSgDiEac2O4JCkjO9/gNAAAVByUKACoovy8La6b8c5aepgiBQDAJaJEAUAV5udlUURNL0lScprd4DQAAFQMlCgAqMJMJpOG9ahhdAwAACoUShQAAAAAuIESBQAAAABuoEQBAAAAgBsoUQAAAADgBkoUAECS9OZ/DsrpdBodAwCAco8SBQBVXP1aBVOc/7EvQwlHsw1OAwBA+UeJAoAqrn4tbz09PEKS9M6Xh3T4ZI7BiQAAKN8oUQAA1T99w90tCRkaNW2H5q08anAiAADKL0oUAEDhoV5a8EwzDe9dU5L0yYpj2pGYYXAqAADKJ0oUAECSFODroRF9aqlphI8kaey7e3QkiVP7AAD4O0oUAKCQyaOjNLBDdUnSL7vTlJaZZ3AiAADKF0oUAKAQm4dZHZoESJLeXnJIn6w4ZnAiAADKF0oUAKCINlf464WRkfK2mfXl+hM6ecpudCQAAMoNShQAoAgPi0ntGgcoKsxbkrRtP5NMAABwBiUKAHBeD11fx+gIAACUO5QoAMBFcQNeAAD+QokCAJxXgI+HJOnX3ekGJwEAoPygRAEAzis4wKpaQTZ5e/LjAgCAM/ipCAC4IB8vs3YezNSoadu1NYEJJgAAoEQBAC6oSV1fJafl6fDJXE37LNHoOAAAGI4SBQC4oEcGh2v5yy3UrL6vLPzUAACAEgUAuDiTyaTq/h4ym01GRwEAwHCUKAAAAABwAyUKAAAAANxAiQIAAAAAN1CiAAAAAMANlCgAAAAAcAMlCgBwSZySDhzPkdPpNDoKAACGokQBAC5Jela+JCluQ5LBSQAAMBYlCgBwSW7rVVOS9M6XBw1OAgCAsShRAIBL0qy+r9pe4S+blR8dAICqjZ+EAIBLYjKZVLu6TVYPk9FRAAAwFCUKAAAAANxAiQIAAAAAN1CiAABuOZWRr6X/O2F0DAAADEOJAgBcsq7R1SRJ2/dnGpwEAADjUKIAAJesRQM/1QqyycTcEgCAKowSBQAAAABuoEQBAAAAgBsoUQAAAADgBkoUAMAt+Q6ncvMcRscAAMAwlCgAgFvSs/O15o9UZeXkGx0FAABDUKIAAG5pUtdHkvTzzjSDkwAAYAxKFADALQ9eFy6p4LQ+AACqIkoUAAAAALiBEgUAAAAAbqBEAQAAAIAbKFEAAAAA4AZKFACgWGYtPSynk8klAABVDyUKAOCW4AAPSVJqRp7Ss7hXFACg6qFEAQDc4uNp0X3XhkmStidmMhoFAKhyKFEAALc1qO0tSXru4316ena8wWkAAChblCgAgNtaNPDT4M4hkqSEY9nKyM6Xg5vvAgCqCEoUAKBY7r22jnq1ClJyWp5umrhFD7+1S/Y8h9GxAAAodYaXqKVLl2rQoEFq0aKFBgwYoCVLllxw+6SkJI0fP15dunRR+/btdd999ykhIaFswgIACukaHaiu0YGSpH1Hs/XErL0GJwIAoPQZWqLi4uIUGxurzp07a+bMmWrfvr3GjRunr7/++pzbO51OPfTQQ/rhhx8UGxurqVOn6vjx47rjjjuUmppaxukBAB2bBmrCbfX14eNNJEk7D2YqL5/T+gAAlZuHkS/++uuva8CAAZowYYIkqWvXrkpNTdWMGTPUv3//ItsnJCTol19+0ZQpU3TDDTdIkqKiotSnTx+tWrVKgwcPLtP8AIACdUI8FR3pqz/2ZWjtlhRlZOerdZS/wkI8jY4GAECJM2wk6sCBA0pMTFTfvn0LLe/Xr5/i4+N14MCBIvvk5ORIknx9fV3LAgMLTiNJSUkpxbQAgIvpd1WwJGnKvxP19pJDGvXaDk2cu48JJwAAlY5hI1Hx8QVT4kZGRhZaHhERIUnat2+f6tatW2hdkyZN1KFDB82cOVMNGjRQUFCQXnnlFfn4+KhPnz5lExwAcE49WgbpZFqewqrbtHjNcW1PzNT/tp/Sp6uOqXZ1T/VsVU0mk8nomAAAXDbDSlRaWpokyc/Pr9DyM6NM6enp59zv+eef1+jRozVw4EBJks1m08yZM4sUrkuxZcsWt/cpLZs2bTI6AiogjhsUR2keN1F+knKk4e2ljaEmLdlk0Scrj0mSspLjVSuw1F4apYy/b1AcHDcojopw3BhWos7c4f7v/yp5ZrnZXPRMw71792rYsGGqV6+eJkyYIC8vL3322Wf6xz/+oQ8//FBXXXWVWxmaN28uT0/jz9fftGmT2rZta3QMVDAcNyiOsjxuWrV2qmPrDP2+N02ffvenjubU1qC2YWXy2ihZ/H2D4uC4QXGUp+MmJyfnvIMuhl0T5e/vL6noiFNGRkah9WebM2eOJGn27Nnq06ePunTpohkzZujKK6/Uyy+/XLqBAQBusZhNahnlpxu71ZAkLfrhuDJz8g1OBQDA5TOsRJ25FioxMbHQ8v379xdaf7bDhw8rKirKNZmEVDCS1bZtW+3Zs6cU0wIAisvXyyJPa8FZBzsSMw1OAwDA5TOsREVERCg8PLzIPaG++eYb1a9fX2FhRU/5iIyM1O7du4vcE+r3339XnTp1SjUvAKD4Xro7SpL09Ox4g5MAAHD5DL1P1EMPPaTx48crMDBQPXr00KpVqxQXF6c33nhDkpSUlKTExEQ1bNhQfn5+GjlypL788kuNGjVK9957r7y8vPTFF19ow4YNrn0AAOVPVJi36/ujSTmqFWz89agAABSXYSNRkjRkyBBNnDhRa9eu1UMPPaQNGzZoypQprpn3vv/+e91yyy3aunWrJCk8PFyffvqpQkJC9NRTT+mxxx7TkSNH9NFHH7n2AQCUP142s+7uX1uSdNerOzuAuNwAACAASURBVJSfz72jAAAVl6EjUZI0bNgwDRs27JzrhgwZoiFDhhRaFhUVpffee68sogEAStB1V4do7rdHlZfv1PTFBzTmxrqymLlvFACg4jF0JAoAUHV4Ws166taCG6qv+CVZx5JyDU4EAEDxUKIAAGWmc7NAPXaT+zdHBwCgPKFEAQDKFKfwAQAqOkoUAAAAALiBEgUAMMTUBYkX3wgAgHKIEgUAKFMtGvhJknYezNSSdccNTgMAgPsoUQCAMhUSaNWr90ZJkg6fyDE4DQAA7qNEAQDKXPNIP5nN0lf/O6l/xh2W08nNdwEAFQclCgBgiMbhPpKkRT8c18870wxOAwDApaNEAQAM8foDV2jinZGSpG82JhmcBgCAS0eJAgAYptHp0ah1W1O1/1i2wWkAALg0lCgAgGGq+XloWM8akqT7p+/UDf+3Wf/bnmpwKgAALowSBQAw1M3da6hOdZskKcfu1MS5CZry7/1Ky8wzOBkAAOdGiQIAGMrb06IPY6/U8pdbqOOVAZKk739P0c0vblV6Vr7B6QAAKIoSBQAoF0wmk567I1IfPdFEQf4ekqShL2yhSAEAyh1KFACgXKkV7Km3Hm7kerx4zZ8GpgEAoCgPowMAAPB31QOs+uzZZrr5xa369Ls/9el3fyrY30NJaX9dJ+VhMalL80D5+1hUzdeqYT1ryGw2GZgaAFBVUKIAAOWSv4+HBravruUbTspskpLTC080kZfv1Pe/p7geZ9vzdXf/sLKOCQCogihRAIBy65HB4Xr4hjpyOlVklOl4aq6STuXJYpEeeWu3Fq4+riA/q2I6hcjDwogUAKD0cE0UAKBcM5lM5zxNLzTQpsZ1fdQwzEf3DCwYgXp/2WHFPLNZc7854touP9+pnQcytXT9CR08nq2Dx3OUa3do2/4Mff9bshwOZ5m9FwBA5cBIFACgwhvSNVRh1W2a+lmisnIc+mr9SXlYzNqSkK7tiZnKznWcd9/a1T3VKNxbknTilF0Ws0lBfh5Kz8qXn7dFJlPhAudwOJX4Z7a2JWbqp+2n5O9t0S+703RDl1Dd3L1Gqb5PAED5QIkCAFQKHZsGavHz0Zq6YL+++y1Fn6w8qvo1vXRNmyA1j/TTtv0Zcjic2rgrTZ2aBsrqYdKC7//UmHd2n/c5h3QN1YjeNbXzYKa2JWRqe2KGtidmKCP7r1IW6Ouh1Iw8ffT1EX27KUnP3xGpOiGeZfGWAQAGoUQBACqVu/rVVq9WQWpcz0f+3n/9mOvWolqh7TKy87VxV5pS0vPkaTUpJNAqe55T2xMz1b5JgDbsOKXFa45r8ZrjkiSTSYqo4aVuLarpynq+urKer3y8zAry89A/447o8zXHdfB4jka/tkP92wXr7v615etlYcZAAKiEKFEAgEoltJpNodVsF93O18uitx9pdN71P2xO0Vv/OaiYq0PUtJ6PmtTzlZ+35Zzbjh4Ypjv61tKcr4/oP+tO6Oufk/T1z0nq3DxQzwyvX9y3AgAopyhRAACcQ7cW1YqMXl2IzcOse6+to8ja3tq4K00/bE7Rui2ppZgQAGAUZucDAKAEXdM2WONvjXAVsP/+fNLgRACAkkaJAgCgFPRuHSRJ+mVPug6dyFE+U6kDQKXB6XwAAJSC9k0CVCvYph82p+iHzSnytpn172ebyebBv18CQEXH3+QAAJSSG7uGKqZTdUlSVq5Dd07ZbnAiAEBJYCQKAIBScm3HEEnSdZ1Cdc/rO5ST61BWTr68Pc89yx8AoGJgJAoAgFIWHuqp3q2DlJXr0ENv7jI6DgDgMlGiAAAoAzGdCkaljiTlyulkkgkAqMgoUQAAlIHGdX00sEPB9VHXPfuHHMzWBwAVFtdEAQBQRq6/OkTLfzqpvHyn7n19h8xmk9o1CdCo/rVlNpuMjgcAuESMRAEAUEbq1fDS+FsjZPMw6dDJXB04nqPFa45r0NOb9dP2UzqRajc6IgDgEjASBQBAGerWopq6RgfK4ZAOnsjR/dN3SpKen7vPtc1n/9dM/t78iAaA8oqRKAAAypjJZJLFYlJETS99NamFYofWVdfoQNf6u6ZuV9IpRqUAoLyiRAEAYCAPi0m92wRrwm319cFjjSVJGdkOPf7eHm2OTzc4HQDgXDhXAACAciI81EtfvBit65/9Q0eTczXug7164uZ6SknPU1ZOvoL8rcrKzZe3zSKTSerbNlgWCxNSAEBZo0QBAFCO2DzMeu3+hlqy7rjW/JGqVz9LPO+2y386qbceaVSG6QAAEiUKAIByp2mEr5pG+GrVr8myWU0KDbQp4ViWnE4pM9uhYH8PTVmQqD2Hs/TErD0a1LG6agbZdGU9X6OjA0CVQIkCAKCc6tU6yPV947o+hdZV8/fQ+A/jtSUhQ1sSMlzLb+gcolt61FQ1P37EA0BpYWIJAAAqoFZR/po3oakeu6muhvWs4Vq+ZN0J3frSViWlMbsfAJQWShQAABVUsL9V17QN1p19a+urSS00+4kmMp+eZ+KFfyUYmg0AKjNKFAAAlYCHxaTawZ768sUWkiR7nkNpWXlKzcjTvqNZWr7hpGYsPqBfdqcZnBQAKj5OmAYAoBKxWEyKjvTVH/sydPMLW4us//rnJPVqFaSxN9WVxSw5nVJGdr7MZpO8rGamTAeAS0CJAgCgkunVOkg5doeqB1jl62VRqyg/XRnhqx+3puqfcUe06rdkrfot+Zz7Lp3UgiIFABdBiQIAoJLp3666+rerXmT5Td1qqHFdH73y6X5l5TrkZTOrVZSfDh7P0e5DWZKkcR/u1bAeNXRV44BSzZib59CxpFzVDLbp0PEcbdyVpgAfi5LT87T3cJa8bGb9uDVVmTkO/fPxJgoL8SzVPADgDkoUAABVSHSkn+ZNaFZk+Y9bU/XiJwnampChZ+fsU0igVVfU8VZIgFXZ6SYt/n2vJMnb06JNu9KUnetQ0wgfDeoQooiaXvL3sWhrQoZqBdvUKNxHFvNfo1kOh1OHTuRo58FM7TqQWfD1YNYlZx712g71vSpYD8TU0f5j2Qr295CPl0V7DmVp96FM/b43XVFh3krNyNMDMXVks3LJN4DSRYkCAAC6ulmgFj/fXDO/OKSVvybrRKpdJ1LPTJNukZQuSapRzarsXIckadv+TG3bn3jB520U7q2Dx3OUmVOwj7fNrEbhPrqpW6hW/JKsrtGBalLXVynpeaoRZFVYdU+FVrPKz8uifId08wtblJXr0Dcbk/TNxqTzvs7GXQUTZvh4WnT7NTXlaTXLZOK0RAClgxIFAAAkFYwyxd5cT2NvrKsTp+xKTrPLnu9UytGdatGipfIdTgX7WyVJOXaHvv8tWcdT7dqw45R6tw7SiVN2/ffnJKVl5bueMzvXoR6tgtSkro8ahfsoPNTTNUo1akDYBfN4WKTFE6N14M9sffztUYWHeCr+SJZy85xq2cBPV9TxVsM6PkpJt8vb06KRU7dr8drjWrz2uCRpzpNXqmaQrZQ+LQBVGSUKAAAUYrGYVDPI5iogm5KkQN/CvzJ4Ws3qd/q6qxF9armWX6wYFUfdGl56Znj9866v5leQ7f6YMH33a4p2HsyUVHCK4uAuoSWeBwA4aRgAAFQK118dqukPXaH5E5pKkt5fdli5dofBqQBURpQoAABQqQT5W+VpLThl8MEZOw1OA6AyokQBAIBK590xjSVJh07mKi/faXAaAJUNJQoAAFQ6tYM9dVUjf0nSoRM5BqcBUNlQogAAQKXU96pgSdL903cqh2ujAJQgShQAAKiUWjf0d32/6/SMfQBQEihRAACgUvLztmjy6AaSpCff36u53x7V52v+lNPJNVIALg8lCgAAVFpn32z301XH9OHyIxo4YbPWb0s1MBWAio6b7QIAgEqrdrCn4ia31M4DmcrOdWjOf49ox4FM/b43XZ2aBhodD0AFxUgUAACo9BrX9VHLKD+98eAVkqQ/9qUbnAhARUaJAgAAVU78kWyjIwCowChRAACgSul7VbD8vS1GxwBQgVGiAABAleJhMSktK18Z2flGRwFQQVGiAABAlWLzMEmSdh7g3lEAiocSBQAAqpQuzatJkp6eHa/Vm5MNTgOgIqJEAQCAKqV2sE3V/Aru8rJ0/UmD0wCoiChRAACgSgkOsOrTp5spoqaXtiRk6IV/7dP/tnPzXQCXjhIFAACqpNBAqyRp/bZTmjg3Qc99vM/gRAAqCg+jAwAAABjh2dvrKyMrX+u2pGrml4e0Yccp5ec7ZbGYjI4GoJwzfCRq6dKlGjRokFq0aKEBAwZoyZIlF9ze4XDo3XffVe/evdWiRQvFxMRo2bJlZZQWAABUFjYPs4L8rbq2U4j6twuWJE1bmGhwKgAVgaElKi4uTrGxsercubNmzpyp9u3ba9y4cfr666/Pu8/LL7+sd955RyNGjNCsWbPUsmVLPf7441q9enUZJgcAAJXJfdeGSZJS0vO04pck/WftcTmdToNTASivin06X05OjlJSUpSff+4b1YWFhV30OV5//XUNGDBAEyZMkCR17dpVqampmjFjhvr3719k+8TERM2bN08vvPCChg4dKknq1KmTEhIStGbNGnXv3r24bwcAAFRhXjaL6tXw1G970/Xb3nRJ0vGUXN17bR2DkwEoj9wuUSkpKZo4caK+/fbb8xYoSdq+ffsFn+fAgQNKTEzUY489Vmh5v379FBcXpwMHDqhu3bqF1q1YsUJeXl664YYbCi3/5JNP3HwXAAAAhT18Q7iOJuXKbJKmLTyguJ+TKFEAzsntEvXKK68oLi5OXbt21ZVXXimbzVasF46Pj5ckRUZGFloeEREhSdq3b1+RErVz505FRkbqxx9/1GuvvaY9e/YoPDxcY8aM0cCBA4uVAwAAQJKiI/0UffrXkt/j0/XtpmSt+SNFXaOrGRsMQLnjdolatWqVhg4dqhdffPGyXjgtLU2S5OfnV2i5r6+vJCk9Pb3IPklJSTpy5IgmTJigRx99VOHh4Vq4cKHGjh2r4OBgdezY0a0MW7ZsKWb6krdp0yajI6AC4rhBcXDcoDiq2nGTecosyayX5+/XpJv2Gh2nwqpqxw1KRkU4btwuUXl5eYqOjr7sFz5zsabJZDrncrO56JwXdrtdSUlJeu+999SzZ09JBddExcfH6+2333a7RDVv3lyenp7FiV+iNm3apLZt2xodAxUMxw2Kg+MGxVEVj5volg5N+yxRa/5I1TOLPBQV5q0ZD17B9OduqIrHDS5feTpucnJyzjvo4vbsfO3atdNPP/102aH8/f0lFR1xysjIKLT+bL6+vrJYLOrcubNrmclk0tVXX62dO3dediYAAACpYPrzmE4hrsd7D2fprle3KzfPYWAqAOWF2yVqwoQJ2rhxo6ZOnarNmzfr4MGDOnz4cJE/F3PmWqjExML3Y9i/f3+h9WeLiIiQw+FQXl5eoeV2u73IiBYAAMDliI70U9zklpr5j0aSpOOpdi347k+DUwEoD9wuUTExMTp58qRmz56tW265Rddcc4169+5d5M/FREREKDw8vMg9ob755hvVr1//nFOkd+3aVU6nU3Fxca5leXl5WrNmTbkZ9gMAAJVLg9reeuOBhpKk+auO6USq3eBEAIzm9jVR99xzT4mN+jz00EMaP368AgMD1aNHD61atUpxcXF64403JBVMJJGYmKiGDRvKz89PnTp1Uvfu3TVp0iRlZmaqfv36mj9/vg4dOqTXXnutRDIBAAD8XZN6vrqlRw0t+P5PHTierZBAq9GRABjI7RL1yCOPlNiLDxkyRLm5uZo9e7YWLlyounXrasqUKa7pyr///nuNHz9ec+fOVYcOHSRJb775pmbMmKH3339fqampatq0qWbPnq3mzZuXWC4AAIC/u6qxvxZ8z+l8AIpRos5Ys2aNVqxYocOHD8tqtSosLEw9evRQly5d3HqeYcOGadiwYedcN2TIEA0ZMqTQMi8vL40bN07jxo0rbnQAAAAAKDa3S5TD4VBsbKzi4uLkdDoVEBAgh8Oh9PR0zZs3T3379tX06dOZ6AEAAABApeT2xBIffvihli9frltvvVVr167Vhg0btHHjRq1du1YjRozQf//7X3388celkRUAAAAADOd2iVq8eLH69Omj//u//1NIyF/3TwgJCdHTTz+ta665RosWLSrRkAAAAABQXrhdog4dOlToZrd/16lTJx04cOCyQgEAAJRX//r2qLbsSzc6BgADuV2igoKClJCQcN71CQkJ8vf3v5xMAAAA5Y6vp0WStD0xU0+8v1dxG04anAiAUdwuUb169dKnn36qVatWFVm3cuVK/fvf/1avXr1KJBwAAEB5EVnbW+8+2kjdogMlSW/+56AWfH/M4FQAjOD27HxjxozR+vXr9dBDDykqKkqRkZGSpPj4eMXHx6tOnToaM2ZMiQcFAAAwWv1a3nrq1gh1bJqiqQsStXjtcd3So6bRsQCUMbdHoqpVq6aFCxdq1KhRcjqd+uGHH7R69Wo5HA7ddddd+vzzzxUcHFwaWQEAAAxnMpnUs1WQurWoplMZ+UrLzDM6EoAyVqyb7QYEBCg2NlaxsbElnQcAAKBCCPQtuEbq8MlcNfYp1q9UACqoi/4ff/jwYQUHB8vLy8v1+FKEhYVdXjIAAIBy7KpGAfpq/UmdSM1V47o+RscBUIYuWqJ69+6tqVOnKiYmRlLBxBImk+miT7x9+/bLTwcAAFBOWT0Kfh+aNG+/3n3UU/VreRucCEBZuWiJeuihh9S4ceNCjy+lRAEAAFRm0ZF+6tQ0QOu3ndKC7//UuGERRkcCUEYuWqIefvjhQo8feeSRUgsDAABQUXhYTBo3LEI3/N8fcjqNTgOgLBX7KsisrCx5excMWycnJ2v58uWyWCzq37+/qlWrVmIBAQAAyitPq1khgVb9tOOU0VEAlCG3S9SpU6c0duxYnTp1SgsXLlR6erqGDBmio0ePyul0aubMmZo/f77q1q1bGnkBAADKlROpdkmSw+GU2cwlD0BV4PZ9oqZPn66ffvpJXbt2lSQtWrRIR44c0RNPPKG5c+fKbDZr+vTpJR4UAACgPBrem5vtAlWN2yVq1apVGjFihP7xj39IklasWKHq1avr7rvvVvv27TV8+HD9+OOPJR4UAACgPDpzPVT80SxjgwAoM26XqJMnT+qKK66QJKWlpem3335T586dXeuDgoKUlcVfIgAAoGpoULvgXpqzvrq0e2kCqPjcLlE1a9bUgQMHJBWMQuXn56tHjx6u9b/88otq165dYgEBAADKs45NAyVJWxIyDE4CoKy4PbFEz5499fHHHys9PV3Lli1TYGCgevXqpWPHjumDDz7QF198oQcffLA0sgIAAJQ7FrNJNapZ9WeKXakZeQr0LfbkxwAqCLdHop544gkNGjRIixYtUkBAgN544w15eXnp2LFjmjdvnmJiYnTvvfeWRlYAAIBy6aZuNSSJ+0UBVYTb/1Ris9k0adIkTZo0qdDyJk2a6IcfflBoaGiJhQMAAACA8sbtkajzsdlsFCgAAAAAld5FR6J69+6tCRMmqHfv3q7HF2MymbRixYrLTwcAAAAA5cxFS1RYWJh8fHwKPQYAAACAquqiJepf//rXBR8DAABUdabTXz9ddUwPXFfH0CwASl+xrok6fPiwpk2bptTUVNeyDz74QFOmTFFSUlKJhQMAAKgIOjQNkCR9uf6EjiTlGJwGQGlzu0Tt2rVLgwcP1kcffaQjR464lqempmrevHm6/vrrXTfjBQAAqApCA226rVdNSdLk+fsNTgOgtLldol577TX5+vpq2bJlatKkiWt5bGysli1bJqvVqmnTppVoSAAAgPJueO+CErX7UJa+Wn/C4DQASpPbJeq3337TnXfeqfr16xdZV7duXY0YMUI///xzSWQDAACoMMxmk166u4EkKeFotsFpAJQmt0uU0+lUTs75z/V1Op3KzuYvDgAAUPW0ucJfQX4XnbcLQAXndolq2bKlFixYoFOnThVZl5GRoYULF6ply5YlEg4AAKAiSk63y57nMDoGgFLi9j+VPPzwwxoxYoSuvfZaxcTEKCIiQiaTSYmJiVq2bJmOHz+uyZMnl0ZWAACAci8716H1207pva8O676YMFnMJlnMpovvCKDCcLtEtWzZUh999JGmTJmi2bNny+l0utY1adJEkydPVuvWrUs0JAAAQEUx9qa6enn+fi3fcFLLN5yUJC14ppkCfDnND6gsivV/81VXXaWFCxcqKSlJhw4dksPhUO3atVWjRo2SzgcAAFChdI2upmE9s7Rtf4Y2x2dIkm6ZtFVP3lJPPVsFGZwOQEm4rH8SsdvtcjgcatCggTw9PeVwOGQ2F+v+vQAAAJXGnX1rS5KOJefqvjd2Ksfu0C+70yhRQCVRrMazadMmDRkyRD169NCwYcO0ZcsWbdiwQT169NDy5ctLOiMAAECFVDPIpiUvRCs00KqzroAAUMG5XaI2b96su+66SxkZGbrzzjtd10QFBgbKw8NDsbGxWr16dYkHBQAAqKhSM/K08tdko2MAKCFul6gZM2YoPDxcX3zxhe69917X8ujoaH355ZeKiorSrFmzSjQkAABARRYe6ilJGjNztxwOhqSAis7tEvXrr79qyJAh8vLykslUeLpOPz8/3Xzzzdq9e3eJBQQAAKjoHr4+XJK082CmsnK5fxRQ0RXrmiibzXbedTk5OXI4+MsBAADgjCsjfHXPwDCjYwAoIW6XqJYtW2rp0qXnXJeZmamFCxcqOjr6soMBAABURpt2pRkdAcBlcrtE/eMf/9C2bds0YsQILVmyRCaTSZs3b9bcuXN1/fXX6+DBg7r//vtLIysAAECF5e1Z8GvX5E/360Sq3eA0AC6H2yWqdevWmjVrlo4ePaopU6bI6XTqjTfe0Msvv6zs7Gy98cYb6tixY2lkBQAAqLD6tAnStR2rS5Ls+Vz6AFRkbt9sNzk5WZ07d9a3336rbdu2KTExUQ6HQ3Xq1FHz5s3l4XFZ9+8FAAColKweZjWu66Ol/zupg8dzVDvY0+hIAIrJ7ZGowYMHa+bMmTKZTGrWrJkGDBigQYMGqVWrVhQoAACAC6gTUlCc3l5y0OAkAC6H2yUqKSlJoaGhpZEFAACgUmtS10eSlJaZb3ASAJfD7RIVExOjBQsW6OBB/gUFAADAHSaTSe0b+ysr16HBz/1hdBwAxeT2+Xdms1nx8fHq16+f6tWrp+rVq8tsLtzFTCaTPv744xILCQAAUFmM7FdbG3amKTvXof3HshVR08voSADc5HaJWrdunYKCgiQV3Fj38OHDJR4KAACgsoqs7a3b+9TSv1Yc1cLVfyr25npGRwLgpksuUXa7XXv27NGMGTPUsGFDeXt7l2YuAACASmtYzxr6bPWfWrc1VbFGhwHgtksqUXPmzNHMmTOVnp4uSbLZbLrtttv0+OOPMyMfAACAm8xmk3LsBfeKys1zyObh9mXqAAx00f9jlyxZoldeeUUBAQEaPny4br/9dtWvX19z5szR1KlTyyIjAABApTOsZw1J0oYdpwxOAsBdFx1Gmj9/vlq1aqWPP/5Ynp4F9zZwOp0aO3asFixYoNjYWNlstlIPCgAAUJn0aBmkf3/3p16at19fTQqUh8VkdCQAl+iiI1F79+5VTEyMq0BJBbPvjRw5Urm5uYqPjy/VgAAAAJVRWPW//hH6tYWJBiYB4K6LlqisrCz5+/sXWR4eHi6n06lTpxiCBgAAcJfVw6x3Hm0kSfr+9xTl5zsNTgTgUl20RDkcDplMRYeXLRaLJCk/nztuAwAAFEdkLW+1bugnSVrw/Z8GpwFwqZgKBgAAwED/GBwuSfrXiqN698tD2nc0y+BEAC7mkuYnT0lJKXJT3dTUVElSUlLSOW+4GxYWVgLxAAAAKrdawZ5qc4Wfftmdri/Xn1BKRp7G3xphdCwAF3BJJerll1/Wyy+/fM51sbFFbxFnMpm0bdu2y0sGAABQRbw4soGOJufqwRm79MPmFEoUUM5dtEQNHjy4LHIAAABUWWazSWHVPeV0FkwuYc9zyMoNeIFy66IlavLkyWWRAwAAoMq7rXdNzfnvUT3y9m49dH0d+XhalJWbryvr+srCfaSAcuOSTucDAABA6evcLFBz/ntU+49l68n39xZaN298UwUHWA1KBuBsjBMDAACUE+GhXvpnbBPdOyhM/a4K1r2D/pqoa/hkrjcHygtGogAAAMqRsOqeGtwl9P/bu/v4nuvF/+PPz64vjVlzbQxzPWkihkMJQ6odGqcSR1JHVw4HWwqhjosUoTqnC/lJheQrmqQLuaicVHIVMba5WphtNmyf7fP+/bHsWNTxWT57fy4e99ttt9v2/rzf8/zUy7yfe73fr3fZ17d3ilDfJ36QJGXnWZmNApwAM1EAAABOzMvLogdvK52RKiq2mZwGgESJAgAAcHpB/t6SpJHP79OSDSdMTgOAEgUAAODk6kb4S5KKig299UmWcguKTU4EeDZKFAAAgJNrHhWs1GfbaFD3SEnSqi0nZbMZJqcCPBclCgAAwEXcfH01SdI7n/2sJ15PMzkN4LkoUQAAAC6iXmSAxg6sJ0ny4iwOMI3pf/3WrFmjvn37KjY2VgkJCVq1atVVH3v8+HHFxcVp4cKFDkwIAADgPG65IVzRtQL07U/5evKNNBkGl/UBlc3UEpWamqqxY8cqPj5eCxYsUPv27TV+/HitW7fufx5rGIZSUlKUn59fCUkBAACcR7+bIiRJ3+w/qyMnC1VcQpECKpOpD9udM2eOEhISlJKSIknq0qWLcnNzNXfuXPXu3ft3j126dKnS0rgWGAAAeJ6E9tXl7+ulWcsy9MDz+yRJfx9QT7fGhZucDPAMps1EZWZmKiMjQz179iy3vVevXkpLS1NmZubvHjt79mxNnTrV0TEBAACcUofmVdS0bpAiq/pKkuasyFTWmSKTUwGewbQSdXEWqWHDhuW2lQGq2QAAIABJREFUR0VFSZIOHTp0xeNsNpsmTJighIQEde3a1bEhAQAAnFRwgLdeGNVEb45voRuahEiShs7cqyKrzeRkgPszrUSdPXtWkhQSElJue3BwsCT95r1Ob775pjIzM5WcnOzYgAAAAC5iyn3RZZ/f/tROzV2ZqRKeIwU4jGn3RF1cScZisVxxu9cV1u1MS0vTCy+8oHnz5ik0NPQPZ9i1a9cf/h7Xyvbt282OABfEuEFFMG5QEYwb5zclUZq0svTUbt1/slU/+GfVjzA3E+MGFeEK48a0EnWxBP16xqmgoKDc6xeVlJRowoQJ6t27t+Lj41VcXFz2ms1mU3FxsXx87Hs7rVq1kr+/f0XiX1Pbt29XXFyc2THgYhg3qAjGDSqCceM6Um+UvtqbqymLD+u9bwO1JLmFaVkYN6gIZxo3hYWFvznpYtrlfBfvhcrIyCi3PT09vdzrFx0/flw7duzQqlWr1LJly7IPSXrxxRfLPgcAAPBkLaNKb404nWfV/iPnTE4DuCfTZqKioqJUt25drVu3TrfeemvZ9vXr16tBgwaqXbt2uf0jIyO1YsWKy77PgAEDNHjwYP35z392eGYAAABnFxrkoyfvaaCpSw7ro2+yFVM3yOxIgNsx9TlRo0aNUnJyssLCwtStWzd9+umnSk1N1fPPPy9Jys7OVkZGhho3bqyQkBC1bt36it8nMjLyN18DAADwNI3rBEqSPvz6tO67taaqBJt6yge4HdMu55OkxMRETZkyRZs3b9aoUaO0bds2zZgxQ3369JEkff7550pKStLu3bvNjAkAAOBSIqv66babqkuSkqbtVk6+1eREgHsx/dcSgwYN0qBBg674WmJiohITE3/3+H379jkiFgAAgEt7oF8dffDVaUnS4Ol71KphsKYPi5afr6m/QwfcAn+LAAAA3JCPt0Vrp8eqfbMqkqRdhwqU/NpBk1MB7oESBQAA4Ka8vCyacl9D/b8JpUud70k/p0KrzeRUgOujRAEAALi5iDBf3d6p9Mm7dzy1Uz/nFJmcCHBtlCgAAAAPMKx3LXn/cuZ334y9St122txAgAujRAEAAHgAf18vrZjUSj3bhUuS5r1/RLOXZehULiv3AfaiRAEAAHiIAD9vjf5zPbVqECxJ+uS7M7r3n3tkGIbJyQDXQokCAADwMLNGNtaKSa3Kvi4uoUQB9qBEAQAAeKDgAG8N7VVTktT/yZ3asjtX5wtLTE4FuAZKFAAAgIe6uW21ss+nLTmsAVN2mZgGcB2UKAAAAA91XZifUp9to6fubSBJshnSQ3P36cX3j6iomOdJAb/Fx+wAAAAAMFfHFmEa1b+OFqw+qsMnLujwiQv68Jcl0JvWC1L/jhHlZq0AT0eJAgAAgPp1jFC/jhE6kV2ouSuP6PuD+ZKkfZnnNCszQ6u3ntLoAfUUVSPA5KSA+ShRAAAAKFMz3F/P3t9IklRUbNNXe/L07Nvp2nfknB58YZ+GJ9TSgK6RJqcEzMU9UQAAALgiPx8vdY2tqhWTWunuW2pIkl5LPa4LRaziB89GiQIAAMDvCg7w1j09aqpJnUBJ0og5+0xOBJiLEgUAAICrMnlIQ0nSqVyrSnhALzwYJQoAAABXJbyKr+6Ij5Ak9Zv4g3Yeytf5whIZBoUKnoWFJQAAAHDVerYL177Mc9qbcU7j/nWwbHuvduF6/M/1TEwGVB5mogAAAHDVGtYM1HMPNtZtN1VXp5ZhqhfpL0n66JtsvfzBUZ3MKTI5IeB4zEQBAADALhaLRX+7vW7Z11t252raksP6v62n9H9bT2nwzTXUMtzEgICDMRMFAACAPyS+ZZhmPtBIA7peJ0l6+9MsnWdCCm6MmSgAAAD8Ya0bhqh1wxB9fyBfB46d1/TVPuqWnq4gf2/dHh+h+pEBZkcErhlmogAAAHDNzB3VRM3rB0mSPt+Row+3ndbUJYfNDQVcY5QoAAAAXDNeXhbNeaiJRvcu1ouPNJG3l3TkZCHLoMOtUKIAAABwzVUPkRrXDlJ0rUBJUvKraSYnAq4dShQAAAAcZuqwaEnSjrR8JSTv0IvvH5G12GZyKuCPoUQBAADAYcKCfTRtWMOyrz/cdlr9n9ypQitFCq6LEgUAAACHioupotRn22j5U63Kts16N0Pb9581MRVQcZQoAAAAVIqQQG/NebCxpNIH9E5efMjkREDFUKIAAABQaZpHBWvRuOa6vlGIiksMpWddMDsSYDdKFAAAACpVjWp+ujUuXJL03PIM5Z8vMTkRYB9KFAAAACpd4zqlS5//dPS8Bk/fbXIawD6UKAAAAFS6+pEBWj21tSSpuMRg2XO4FEoUAAAATOHr46WkbpGSpN3pBSanAa4eJQoAAACmuaFJqCTp42/OmJwEuHo+ZgcAAACA54qpW3pv1Kffn1Gh1aaxd9VTgJ+3yamA38dMFAAAAEwT4Oetu/5Ueknflt25mr0s0+REwP9GiQIAAICphvWupVfHNJNUWqT+Mn233vrkhN765IQuFNlkGIbJCYHyKFEAAAAwXZ0If026t4Ek6Ux+sZZsyNKSDVm6c9JO3Tlppw6fOG9uQOAS3BMFAAAAp3BTizB9+EyscgtKVGIztHLTSR05VahtP+bpobn7JUlDbq2ppG6R8vKymJwWnoyZKAAAADgNi8WiqiE+ql7FVyP61tb4QfXVuVVY2euLPz6hR+bvNzEhwEwUAAAAnFiQv7eeuLuBJGlf5jk9vvAnpR2/oHOFJQryZxU/mIOZKAAAALiEpvWC1LdDdUlS2nHukYJ5KFEAAABwGfGXXNoHmIUSBQAAAAB2oEQBAADA5WRlF5kdAR6MEgUAAACX4edTevo6e3mmjp4qNDkNPBUlCgAAAC6jef0gxdQNlCTd/9yP+se/DsgwDJNTwdNQogAAAOAyvLwsmnRvQ/3l5hqSpF2HCnQq12pyKngaShQAAABcSngVX917a039fUA9SdKQGXtlLbaZnAqehBIFAAAAl/Sn2Kpln6/YdNLEJPA0lCgAAAC4JD9fLy1JbiFJWrz+hMlp4El8zA4AAAAAVFT1Kr4KDvCSxWIxOwo8CDNRAAAAcGldWldV/vkS/XvtMbOjwENQogAAAODSGtUqXfJ85eaT+v7gWZPTwBNQogAAAODS+nWM0KwHGkmS8gpKTE4DT0CJAgAAgMsLDeJWf1QeShQAAAAA2IESBQAAALdhMwyzI8ADUKIAAADg8ny8S5c4n/FOhnYfLjA5DdwdJQoAAAAur3Z1P7VpFCJJOnTivMlp4O4oUQAAAHB5FotF45Pqmx0DHoISBQAAALeydXeu2RHg5ihRAAAAcAsXlzn/7kC+ioptJqeBO6NEAQAAwC34eFsUGx0sSVqx8aTJaeDOKFEAAABwG1OHRkuSzuRbTU4Cd0aJAgAAgNu4uNT5hm/PmJwE7szH7AAAAADAteLlZVGDmgG6UMg9UXAcZqIAAADgVqIiA+T9y4wU4AiUKAAAAACwAyUKAAAAbsfKEudwINNL1Jo1a9S3b1/FxsYqISFBq1at+t39T548qYkTJ6p79+5q27atEhMTlZqaWklpAQAA4OwuWG36OceqkzlFZkeBmzJ1YYnU1FSNHTtWQ4YMUZcuXbRhwwaNHz9eAQEB6t2792X7FxUV6f7779fZs2f16KOPKjIyUh999JEef/xxlZSUqF+/fia8CwAAADiTVg2C9fXePJ3JL9Z1Vf3MjgM3ZGqJmjNnjhISEpSSkiJJ6tKli3JzczV37twrlqgvvvhCP/74o5YvX67Y2FhJUnx8vI4dO6Z///vflCgAAACo3nUBkqQPvz6tmLpBJqeBOzLtcr7MzExlZGSoZ8+e5bb36tVLaWlpyszMvOyY4OBgJSUlqXXr1uW2R0dHKyMjw6F5AQAA4BpqVS+dffrom2z9kJZvchq4I9NKVFpamiSpYcOG5bZHRUVJkg4dOnTZMR07dtTTTz8ti+W/S1ZarVZt3LhRTZo0cWBaAAAAuIr6kQEan1RfkjR1yWFzw8AtmVaizp49K0kKCQkptz04OFiSlJ9/db81mD17tg4fPqwHHnjg2gYEAACAy+p2fTVJUv75Etlshslp4G5MuyfKMEoH86WzSpdu9/L6/X5nGIZmzZqlRYsWafjw4erRo4fdGXbt2mX3MY6yfft2syPABTFuUBGMG1QE4wYVYfa46Rzjpc37vfT22u/UrDZFylWYPW6uhmklKjQ0VNLlM04FBQXlXr+SoqIiTZgwQWvXrtXw4cM1bty4CmVo1aqV/P39K3TstbR9+3bFxcWZHQMuhnGDimDcoCIYN6gIZxg34bXPa/P+/dp+JFR338atH67AGcbNRYWFhb856WJaibp4L1RGRoaaNm1atj09Pb3c67+Wn5+vkSNH6ttvv1VKSoruu+8+x4cFAACAy2lQs3SVvkB/0x+NCjdj2oiKiopS3bp1tW7dunLb169frwYNGqh27dqXHVNSUqKHHnpIO3bs0Jw5cyhQAAAA+E0Wi0VN6wbpQpHN7ChwM6Y+J2rUqFFKTk5WWFiYunXrpk8//VSpqal6/vnnJUnZ2dnKyMhQ48aNFRISonfeeUfbtm1TUlKSatWqpe+//77se1ksFrVp08astwIAAAAnZDMM7Us/r6cWpenpodFmx4GbMLVEJSYmqqioSK+//rqWL1+uevXqacaMGerTp48k6fPPP1dycrIWL16sDh066KOPPpIkvfvuu3r33XfLfS9vb2/t2bOn0t8DAAAAnNeDt9XRmJcP6MzZYrOjwI2YWqIkadCgQRo0aNAVX0tMTFRiYmLZ14sXL66sWAAAAHADLaKCdWPTUOXkU6Jw7XCXHQAAANyaYUg/HT2via+nKetMkdlx4AYoUQAAAHBrnVuHSZK2/3RWQ2fu1cfbs01OBFdHiQIAAIBb69Wuut5KaaHeN4ZLkr7am2dyIrg6ShQAAADcXniorx5LrKc61f30w8F8FRWz7DkqjhIFAAAAj2Hxsij/Qom27s41OwpcGCUKAAAAHmPswPqSpEIrM1GoOEoUAAAAPEa1UNOf8AM3QIkCAACAx3nhvSMaNW+fLhQxIwX7UaIAAADgMSKq+KpKsLckKe34Bd05aaesLDIBO1GiAAAA4DG8vCx6d2IrfTAttmzbyx8c04nsQhNTwdVwUSgAAAA8jo+3RfMfidHDL+7Xh9tO68Ntp+XtJTWqHagp90WraginyfhtzEQBAADAI0XXCtDzDzVWUrdI+XhbVGKT9h85r8HTd2vAlJ06fOK82RHhpKjYAAAA8EgWi0XN6gerWf1gDe1VS4ZhaPHHJ7Rs488quGDTJ9+d0fCEQLNjwgkxEwUAAACotFTd17OWFo9vIUla8cVJnS8sMTkVnBElCgAAALhE9Sq+ujWumiTp+4P5MgzD5ERwNpQoAAAA4Fc6NKsiSXr6/x1Wn5QfdOTkBZMTwZlQogAAAIBfiW9VVeOS6qtaaOkSAiPm7FPBBS7tQykWlgAAAACuoPv11dStTVX1SflBkjRgyi5FVvVVXEyoRvarI39f5iM8Ff/nAQAAgN9gsVj04TOxats4RK0aBuvnHKtSt2Xrjqd26rsDZ82OB5MwEwUAAAD8DovFomeGN5IkHTtdqJTX0pR1pkgpr6UpPNRHI/rWVrc21UxOicpEiQIAAACuUu3q/lo0rrlWfPGzlmw4oeyzxZrxToYWfXRCYcHealYvWPf1qqkgf2+zo8KBKFEAAACAnQZ0jdSArpH69LszmrUsQ1lnipR1Rtp/5LxWf3lKknRrXDXFxYSqQ7MqCvCjVLkTShQAAABQQTe3raab25ZeypeTb9Wb609o3X+yJUkfbz+jj7efKbd/9Sq+6tI6TP1uilCdCP9Kz4trgxIFAAAAXANVQ3z1WGI9PZZYT0VWmw5nXdDn35+RzZA2/pCjnPxinc6zatWWU1q15ZTaNg5RxxZhyjtXrF7tqsvbS6oW6mv228BVoEQBAAAA15ifr5di6gYppm6QJOnB2+pIks4Vlmjmuxn6em+evjuQr+8O5EuSlmzIKju2b4fqiq4VqPqR/qod4a9qIT6yWCyV/ybwmyhRAAAAQCUJ8vfW5CENZRiGTuZaZbFIn3+fowA/Ly1cfVSStPbr05cdFx7qo5rhfmrZIFhtokMUFuyjxnWCKjs+fkGJAgAAACqZxWJRZFU/SdLAP0VKkm7rGCGbrbRcZf58QXsyzik964IOHjuvrDNFyj5brD3p57R848my79O0XpCeG9lY3t7MVFUmShQAAADgJLy8LKpRzU81qvmpXdMq5V7LOlOkg8fOy2YYWvTRCR09Vah9mefUb+IPigjzla+3RT3bhaukxFDuuWJ1aV1VFovUuHaQAvy8THpH7okSBQAAALiAi+VKkjq3qqoiq01zVx6RtcSmTTtzJUlvrj9Rtv8HX/73ssCoGgFqUidQYwbWr9zQbooSBQAAALggP18v/SOptBQZhqELRTZJkreXRV//mKcgfy/939ZTyj9for2/XBq44dvSJde7tamqZvWC1KZRiBrUDDTtPbgqShQAAADg4iwWiwL9//tA3y6tq0qS4mJKLwk8mVOkhauP6qu9eZKkz3fk6PMdOZKkv/Wvo9s6RlRyYtdGiQIAAADc3HVV/TRpSENJUlGxTWfOFmvr7lz9a+0xLVx9VAtXH1XnVmF6NLGuQgOpCP8Ld5gBAAAAHsTPx0s1qvnpzs7XacaIRoprEipJ2rwrV3c9vVvHswtNTuj8KFEAAACAh4qNDtG0v0Zr0bjmalAjQJL011k/6qXVR5WTbzU5nfOiRAEAAAAerkY1P81/NEbtm5bOSq3+8pQGT9+jhOQdevKNNBmGYXJC50KJAgAAACBvL4umDI3WOxNb6t4eNeXrU/oA32/2n9WgabuVdabI5ITOg7vGAAAAAJQJC/bRX26pob/cUkP550s0aNou5Z0r0dCZeyVJDWoEqEdcNfVsF+6xi1AwEwUAAADgikICvfVWSksN7VWz7EG/h7Mu6NUPj+uup3fr32uPmZzQHJ5ZHQEAAABclbBgHyV1q6GkbjUkSdl5Vr3x0XFt+PaMUv9zWiP61jY5YeWjRAEAAAC4auFVfDVmYH0F+HlpzVenlZC8Q6GB3hqeUEu9bqxudrxKweV8AAAAAOzW44ZwRVb1VfUqPjp7vkQvrDyiO57aqfOFJWZHczhKFAAAAAC7Na0XpDfHt9CS5JZKHhwlSSq02pQ4eZfe33zS5HSORYkCAAAA8Id0ja2qD6bFqvv1VSVJ/1p7TPf+c4+OnS40OZljUKIAAAAA/GE+3haNS4rSA78sNHEq16rhs39UwQX3u7yPEgUAAADgmrmz83VaOz1WbaJDJElvf5plcqJrjxIFAAAA4Jry8rJo6rCGkqSTOUUmp7n2KFEAAAAArjlfn9Kq8eXePJOTXHuUKAAAAAAO0bZxiKzFhqzFNrOjXFOUKAAAAAAOUTPcT5LU/8md+mZfnr796azyz7v+QhM+ZgcAAAAA4J7+1r+uUrdlS5KeXHSobHtkVV/VDPdT/07XKb5lmFnxKowSBQAAAMAhfLwten9Ka6UdP69zF0r0zf6z+uibbP2cY9XPOVb9kFag5vWDVCvcX4YMRYVaFGd26KtAiQIAAADgMAF+XmoRFSxJate0ih68rY4kaevuXE1dclin86zam3Hul729ldTHpKB2oEQBAAAAqHSdWoYp9dk2kqQSm6Hjp4u0Z88uk1NdHRaWAAAAAGAqby+L6l7nr+ohZie5OpQoAAAAALADJQoAAAAA7ECJAgAAAAA7UKIAAAAAwA6UKAAAAACwAyUKAAAAAOxAiQIAAAAAO1CiAAAAAMAOlCgAAAAAsAMlCgAAAADsQIkCAAAAADtQogAAAADADpQoAAAAALADJQoAAAAA7ECJAgAAAAA7UKIAAAAAwA4+Zgcwg2EYkqSioiKTk/xXYWGh2RHgghg3qAjGDSqCcYOKYNygIpxl3FzsChe7w6UsxpW2urmzZ89q//79ZscAAAAA4ORiYmIUGhpabptHliibzaaCggL5+vrKYrGYHQcAAACAkzEMQ1arVcHBwfLyKn8XlEeWKAAAAACoKBaWAAAAAAA7UKIAAAAAwA6UKAAAAACwAyUKAAAAAOxAiQIAAAAAO1CiAAAAAMAOlCgAAAAAsAMlCgAAAADsQIlysDVr1qhv376KjY1VQkKCVq1a9bv7FxQUaMqUKYqPj1fbtm01YsQIHT58uHLCwmnYO25OnjypiRMnqnv37mrbtq0SExOVmppaSWnhLOwdN5c6fvy44uLitHDhQgcmhDOyd9zYbDa99NJLuuWWWxQbG6vbbrtNa9euraS0cBb2jpvs7GwlJyerc+fOat++vUaOHMn5jYfbu3evWrZsqRMnTvzufs56bkyJcqDU1FSNHTtW8fHxWrBggdq3b6/x48dr3bp1v3nM6NGjtW7dOo0dO1YzZsxQVlaWhgwZorNnz1ZicpjJ3nFTVFSk+++/X1u3btWjjz6q+fPnq1WrVnr88ce1Zs2aSk4Ps1Tk581FhmEoJSVF+fn5lZAUzqQi4+aZZ57RwoULdc899+iVV15RmzZtNGbMGG3cuLESk8NM9o4bwzA0atQoffHFFxo7dqxmzpypkydPasiQIcrNza3k9HAGaWlpGjlypIqLi//nvk57bmzAYXr06GE8/vjj5bY99thjRu/eva+4/3/+8x8jJibG2LhxY9m206dPG9dff73xyiuvODQrnIe94+bjjz82YmJijB07dpTbPnz4cKN///4OywnnYu+4udSSJUuMrl27GjExMcaCBQscFRFOyN5xk56ebjRr1sxYtmxZue133323MXXqVIflhHOxd9ykpaUZMTExxvvvv1+2LSMjw4iJiTFWrlzp0KxwLlar1ViyZInRtm1bo3379kZMTIxx/Pjx39zfmc+NmYlykMzMTGVkZKhnz57ltvfq1UtpaWnKzMy87JgtW7YoODhY8fHxZdvCw8N144036osvvnB4ZpivIuMmODhYSUlJat26dbnt0dHRysjIcGheOIeKjJtLj509e7amTp3q6JhwMhUZNxs2bFBAQIDuuOOOctuXLFmiiRMnOjQvnENFxk1hYaGk0n+vLgoLC5Mk5eTkODAtnM327ds1e/Zs/fWvf9XYsWP/5/7OfG5MiXKQtLQ0SVLDhg3LbY+KipIkHTp06IrHREVFydvbu9z2+vXrX3F/uJ+KjJuOHTvq6aeflsViKdtmtVq1ceNGNWnSxIFp4SwqMm6k0ntbJkyYoISEBHXt2tWxIeF0KjJu9u3bp4YNG2rr1q3q37+/WrRooZ49e+rDDz90fGA4hYqMm2bNmqlDhw5asGCBDh48qOzsbE2bNk1BQUHq0aOH40PDaTRq1EgbNmzQww8/fNn57pU487mxj6l/uhu7eJ1mSEhIue0XfwtzpXsP8vPzL9v/4jHcq+AZKjJurmT27Nk6fPiwFixYcG0DwilVdNy8+eabyszM1Msvv+zYgHBKFRk32dnZOn78uFJSUvTYY4+pbt26Wr58uUaPHq3w8HDddNNNjg8OU1X0583kyZN1//33q0+fPpIkPz8/LViwQPXq1XNgWjibiIgIu/Z35nNjSpSDGIYhSeVmBy7d7uV1+STgxdeu5Er7w/1UZNz8er9Zs2Zp0aJFGj58OL/h8xAVGTdpaWl64YUXNG/ePIWGhjo+JJxORcaN1WpVdna2Xn75ZXXv3l1S6Wx4Wlqa5s+fT4nyABUZNwcPHtSgQYNUv359paSkKCAgQMuWLdOjjz6qV199Ve3atXN8cLgkZz435szcQS6elPy6JRcUFJR7/VIhISFlr//6mCu1cLifioybi4qKijRmzBi99tprGj58uMaNG+e4oHAq9o6bkpISTZgwQb1791Z8fLyKi4vLVkiy2WxXtVoSXF9Fft4EBwfL29u73P0JFotFnTp10r59+xyYFs6iIuNm0aJFkqTXX39dPXr0UOfOnTV37lw1b95czzzzjGMDw6U587kxJcpBLl4r/Osb+9PT08u9/utjMjMzL2vd6enpV9wf7qci40Yq/cds2LBhSk1NVUpKCgXKw9g7bo4fP64dO3Zo1apVatmyZdmHJL344otln8O9VeTnTVRU1BWLttVqvWxmAu6pIuPm2LFjatSoUdliElJp+Y6Li9OBAwccmBauzpnPjSlRDhIVFaW6dete9syE9evXq0GDBqpdu/Zlx3Tu3Fl5eXnaunVr2bbs7Gx988036tSpk8Mzw3wVGTclJSV66KGHtGPHDs2ZM0f33XdfZcWFk7B33ERGRmrFihWXfUjS4MGDyz6He6vIz5suXbrIMIxyD/MuLi7Wpk2bFBcX5/DMMF9Fxk3Dhg31008/XfZMqB07dqhOnToOzQvX5sznxt6TJ0+ebGoCNxYaGqqXXnpJZ86ckcVi0RtvvKH3339fkyZNUpMmTZSdna19+/YpJCREfn5+qlOnjrZt26alS5eqatWqOnbsmFJSUmQYhp555hkFBASY/ZZQCewdN2+//baWLl2qgQMHqn379jpx4kTZR1ZWlmrWrGn2W0IlsGfcBAYGqkaNGpd9zJ8/X7fccov69etn9ttBJbH35029evW0c+dOvfPOOwoODlZeXp5mzpypXbt2aebMmYqMjDT7LaES2DtuoqOjtXz5cm3cuFFVq1bV0aNHNW/ePH322Wd66qmnWEnWQ+3du1effPKJhg0bVnZpnkudG1f2g6k8zdtvv23ceuutRqtWrYyEhIRyD5p77733jJiYGOOrr74q25aTk2NMmDDBaNeunXHDDTcYI0aMMA4ePGhGdJjInnFz7733GjExMVf8aN68uVlvASaw9+fNr/GwXc9k77g5f/688c9//tPo3Lmz0bp1ayMpKcn4+uuvzYgOE9k7bg4cOGCMHDnSaNu2rREXF2cMHjzY2LJlixnR4SQujpMHBSnmAAAFcUlEQVRLH7brSufGFsP4nWUvAAAAAADlcE8UAAAAANiBEgUAAAAAdqBEAQAAAIAdKFEAAAAAYAdKFAAAAADYgRIFAAAAAHagRAEAXMaECRPUtGnTyz5iY2PVvXt3paSk6NSpU6ZmbNq0qSZMmPCbXwMAXJ+P2QEAALBXcnKyqlWrVvZ1fn6+vvzyS7333nvatWuXVqxYIT8/PxMTAgDcGSUKAOByevToobp165bbdvfdd2vy5Ml6++23tWHDBvXp08ekdAAAd8flfAAAt3HnnXdKknbs2GFyEgCAO6NEAQDcRmBgoCTJMIyybZ999pkGDRqkNm3a6MYbb9QjjzyiQ4cOXXbsxo0bdc8996ht27aKj4/X6NGjdeTIkbLXrVarXnnlFfXv31/XX3+9YmNj1b9/f61YscLxbwwA4FQoUQAAt7Fp0yZJUosWLSRJK1eu1EMPPaTAwED94x//0NChQ/Xdd9/prrvuKlek1q5dq5EjRyo3N1ePPPKIhgwZoq1bt2ro0KHKy8uTVHof1rx589S+fXs98cQTevjhh3Xu3Dk98cQT2rZtW+W/WQCAabgnCgDgcvLy8pSdnV32dX5+vjZt2qT58+erUaNG6tu3r/Lz8zV9+nT16dNHc+bMKdv3rrvuUt++fTV79mwtWLBANptNzz77rGJiYrRs2TIFBARIklq3bq1hw4bpgw8+UM+ePbVmzRqNGDFCY8aMKftePXr0UEJCgtavX6/27dtX3n8AAICpKFEAAJdz8d6nSwUGBurmm2/Wk08+KV9fX3366afKz89Xjx49yhUub29v3XTTTdq4caOKi4u1Z88enTx5Ug8++GBZgZKkTp06afny5YqOjlZISIi2b98uL6//XsBhGIaKi4slSQUFBQ58twAAZ0OJAgC4nFmzZikiIkJWq1WbNm3SW2+9pYSEBE2ePFn+/v6SpIyMDEnS6NGjf/P7ZGdn6+jRo5KkqKioy16PjY0t+9zPz0+rV6/W5s2bdfjwYaWnp5eVp0vvwQIAuD9KFADA5dxwww1lS5z/6U9/UlRUlKZNm6acnBwtXLhQFotFNptNkjR16tTLlkO/KCwsrGy/S2eZfq2oqEjDhw/X9u3b1aFDB3Xs2FFDhw5V+/bt1a1bt2v75gAATo8SBQBweffee6++/PJLffLJJ3rzzTc1dOhQ1alTR5IUHh6uTp06ldv/66+/ls1mk5+fn2rVqiVJSk9PV3x8fLn9kpOTdcMNN8jX11fbtm3T9OnTNWDAgLLXs7KyHPzOAADOiNX5AABu4emnn1ZYWJheeOEFZWZmqlOnTvL399err74qq9Vatl9WVpb+9re/afbs2bJYLGrVqpXCw8O1cuVKFRUVle337bffauXKlTp37pxycnIkSY0bNy73Zy5evFiSyu6NAgB4BmaiAABuISIiQmPHjtWTTz6pSZMm6fXXX9ff//53Pfvss0pKSlL//v1VXFyspUuXqrCwUOPHj5dUeq/ThAkTNH78eA0ePFj9+/dXQUGBFi9erEaNGmngwIE6cuSIfHx8NG7cON19993y8fHRZ599ps2bN8vX15eFJQDAw1CiAABuY+DAgVq1apW2bNmiVatWaejQoapRo4beeOMNPf/88woICFDLli01a9YsxcXFlR13++23KyQkRK+88oqee+45ValSRd27d9eYMWMUFBSkmJgYzZs3T/Pnz9ecOXMUHBysJk2a6I033tDSpUu1bds2Wa1W+fr6mvjuAQCVxWKwpBAAAAAAXDXuiQIAAAAAO1CiAAAAAMAOlCgAAAAAsAMlCgAAAADsQIkCAAAAADtQogAAAADADpQoAAAAALADJQoAAAAA7ECJAgAAAAA7/H9XlrLjAyslFgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "precision, recall, th = precision_recall_curve(error_df.true_class, error_df.reconstruction_error)\n",
    "plt.plot(recall, precision, 'b', label='Precision-Recall curve')\n",
    "plt.title('Recall vs Precision')\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.show()\n",
    "# high precision = low false positive\n",
    "# high recall = low false negative "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(th, precision[1:], 'b', label='Threshold-Precision curve')\n",
    "plt.title('Precision for different threshold values')\n",
    "plt.xlabel('Threshold')\n",
    "plt.ylabel('Precision')\n",
    "plt.show()\n",
    "# plt.savefig('Precision-Threshold.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(th, recall[1:], 'b', label='Threshold-Recall curve')\n",
    "plt.title('Recall for different threshold values')\n",
    "plt.xlabel('Reconstruction error')\n",
    "plt.ylabel('Recall')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold = 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "groups = error_df.groupby('true_class')\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "for name, group in groups:\n",
    "    ax.plot(group.index, group.reconstruction_error, marker='o', ms=3.5, linestyle='',\n",
    "            label= \"Attack\" if name == 1 else \"Normal\")\n",
    "ax.hlines(threshold, ax.get_xlim()[0], ax.get_xlim()[1], colors=\"r\", zorder=100, label='Threshold')\n",
    "ax.legend()\n",
    "plt.title(\"Reconstruction error for different classes\")\n",
    "plt.ylabel(\"Reconstruction error\")\n",
    "plt.xlabel(\"Data point index\")\n",
    "# plt.savefig('threshold.png')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1008x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# threshold = 0.018\n",
    "threshold = 0.017\n",
    "LABELS = [\"Normal\", \"Attack\"]\n",
    "y_pred = [1 if e > threshold else 0 for e in error_df.reconstruction_error.values]\n",
    "conf_matrix = confusion_matrix(error_df.true_class, y_pred)\n",
    "\n",
    "plt.figure(figsize=(12, 12))\n",
    "sns.heatmap(conf_matrix, xticklabels=LABELS, yticklabels=LABELS, annot=True, fmt=\"d\");\n",
    "plt.title(\"Confusion matrix\")\n",
    "plt.ylabel('True class')\n",
    "plt.xlabel('Predicted class')\n",
    "plt.show()\n",
    "plt.savefig('0018_WADI_CM.png')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7 (tensorflow)",
   "language": "python",
   "name": "tensorflow"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}