AutoEncoders-for-Anomaly-Detection_Matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Neural Networks for Annomaly (Outlier) Detection \n",
    "\n",
    "<img src='outlier_pic.jpg', width = 500, height = 600>\n",
    "\n",
    "\n",
    "Agenda:\n",
    "\n",
    "- Data Exploration & Preprocessing\n",
    "- Model Building\n",
    "- Model Evaluation \n",
    "- Model Interpretation\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy\n",
    "import pandas\n",
    "import matplotlib\n",
    "import seaborn\n",
    "import tensorflow\n",
    "import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numpy version      : 1.14.2\n",
      "Pandas version     : 0.22.0\n",
      "matplotlib version : 2.0.2\n",
      "seaborn version    : 0.7.1\n",
      "tensorflow version : 1.8.0\n",
      "keras version      : 2.2.0\n"
     ]
    }
   ],
   "source": [
    "print('Numpy version      :' , numpy.__version__)\n",
    "print('Pandas version     :' ,pandas.__version__)\n",
    "print('matplotlib version :' ,matplotlib.__version__)\n",
    "print('seaborn version    :' , seaborn.__version__)\n",
    "print('tensorflow version :' , tensorflow.__version__)\n",
    "print('keras version      :' , keras.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_row', None)\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcdefaults()\n",
    "from pylab import rcParams\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "####### Deep learning libraries\n",
    "import tensorflow as tf\n",
    "import keras\n",
    "from keras.models import Model, load_model\n",
    "from keras.layers import Input, Dense\n",
    "from keras.callbacks import ModelCheckpoint, TensorBoard\n",
    "from keras import regularizers\n",
    "from ann_visualizer.visualize import ann_viz\n",
    "# \n",
    "from sklearn.preprocessing import  StandardScaler, MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.metrics import (confusion_matrix, classification_report, accuracy_score, roc_auc_score, auc,\n",
    "                             precision_score, recall_score, roc_curve, precision_recall_curve,\n",
    "                             precision_recall_fscore_support, f1_score,\n",
    "                             precision_recall_fscore_support)\n",
    "#\n",
    "from IPython.display import display, Math, Latex\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('JADS_CarrerDay_Data.cvs',index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([   0,    1,    2,    3,    4,    5,    6,    7,    8,    9,\n",
       "            ...\n",
       "            4540, 4541, 4542, 4543, 4544, 4545, 4546, 4547, 4548, 4549],\n",
       "           dtype='int64', length=4550)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.reset_index(inplace=True, drop= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=4550, step=1)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>V_1</th>\n",
       "      <th>V_2</th>\n",
       "      <th>V_3</th>\n",
       "      <th>V_4</th>\n",
       "      <th>V_5</th>\n",
       "      <th>V_6</th>\n",
       "      <th>V_7</th>\n",
       "      <th>V_8</th>\n",
       "      <th>V_9</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.52785</td>\n",
       "      <td>100.0</td>\n",
       "      <td>22.2</td>\n",
       "      <td>47.8</td>\n",
       "      <td>13.0</td>\n",
       "      <td>925</td>\n",
       "      <td>951</td>\n",
       "      <td>938</td>\n",
       "      <td>954</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.43006</td>\n",
       "      <td>100.0</td>\n",
       "      <td>22.2</td>\n",
       "      <td>47.8</td>\n",
       "      <td>12.9</td>\n",
       "      <td>931</td>\n",
       "      <td>953</td>\n",
       "      <td>935</td>\n",
       "      <td>950</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.40669</td>\n",
       "      <td>100.0</td>\n",
       "      <td>22.2</td>\n",
       "      <td>47.8</td>\n",
       "      <td>13.0</td>\n",
       "      <td>932</td>\n",
       "      <td>952</td>\n",
       "      <td>937</td>\n",
       "      <td>950</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.52365</td>\n",
       "      <td>100.0</td>\n",
       "      <td>22.2</td>\n",
       "      <td>47.9</td>\n",
       "      <td>13.0</td>\n",
       "      <td>923</td>\n",
       "      <td>950</td>\n",
       "      <td>942</td>\n",
       "      <td>950</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.43734</td>\n",
       "      <td>100.0</td>\n",
       "      <td>22.3</td>\n",
       "      <td>47.6</td>\n",
       "      <td>13.3</td>\n",
       "      <td>920</td>\n",
       "      <td>952</td>\n",
       "      <td>945</td>\n",
       "      <td>954</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       V_1    V_2   V_3   V_4   V_5  V_6  V_7  V_8  V_9  Label\n",
       "0  7.52785  100.0  22.2  47.8  13.0  925  951  938  954      0\n",
       "1  7.43006  100.0  22.2  47.8  12.9  931  953  935  950      0\n",
       "2  7.40669  100.0  22.2  47.8  13.0  932  952  937  950      0\n",
       "3  7.52365  100.0  22.2  47.9  13.0  923  950  942  950      0\n",
       "4  7.43734  100.0  22.3  47.6  13.3  920  952  945  954      0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4550, 10)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4444\n",
       "1     106\n",
       "Name: Label, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Label.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    97.67033\n",
       "1     2.32967\n",
       "Name: Label, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Label.value_counts(normalize=True)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Highly Imbalanced Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['V_1', 'V_2', 'V_3', 'V_4', 'V_5', 'V_6', 'V_7', 'V_8', 'V_9', 'Label'], dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "numerical_cols = ['V_1', 'V_2', 'V_3', 'V_4', 'V_5', 'V_6', 'V_7', 'V_8', 'V_9']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Label.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = df['Label'].astype(int)\n",
    "labels[labels != 0] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "106"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(labels[labels !=0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4444, 106]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Label.value_counts().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x20ac0b890f0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EEwowGEiStIkZZijhDGA74LeBnwS2mmLZelQFSpKkuTPMUMJK4A1Vdd6oi5EkSeM1zBmD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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20ac0b35390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "objects = tuple(['0', '1']) \n",
    "y_pos = np.arange(len(objects))\n",
    "x   = np.arange(len(objects))\n",
    "y   = df.Label.value_counts().tolist()\n",
    "#plt.ylim([0, 1.2])\n",
    "plt.bar(x, y, align='center', alpha=0.5)\n",
    "plt.xticks(y_pos, objects,fontsize=18)\n",
    "plt.xlabel('Label', fontsize=18)\n",
    "plt.ylabel('Frequency', fontsize=18)\n",
    "plt.title('Data per Label',fontsize=18)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preprocessing\n",
    "\n",
    "- Let's leverage the fact we know the labels of our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data size   : (3558, 9)\n",
      "Validation data size : (910, 9)\n"
     ]
    }
   ],
   "source": [
    "RANDOM_SEED = 101\n",
    "\n",
    "X_train, X_test = train_test_split(df, test_size=0.2, random_state = RANDOM_SEED)\n",
    "\n",
    "X_train = X_train[X_train['Label'] == 0]\n",
    "X_train = X_train.drop(['Label'], axis=1)\n",
    "y_test  = X_test['Label']\n",
    "X_test  = X_test.drop(['Label'], axis=1)\n",
    "X_train = X_train.values\n",
    "X_test  = X_test.values\n",
    "print('Training data size   :', X_train.shape)\n",
    "print('Validation data size :', X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()\n",
    "#scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled  = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AutoEncoders\n",
    "\n",
    "- Feed Forward Neural Network model for unsupervised tasks (No Lables)\n",
    "- Model the identity function f(x) ≈  x (It encodes itself)\n",
    "- Simple to undersatnd!\n",
    "- Compress data and learn some features \n",
    "\n",
    "Ref:\n",
    "https://probablydance.com/2016/04/30/neural-networks-are-impressively-good-at-compression/\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "<img src='AutoEncoders.png',>\n",
    "\n",
    "\n",
    "# Reconstruction error\n",
    "\n",
    "\n",
    "min $$L(x, x')  ≈ ||x - x'||^{2}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 9)                 0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 6)                 60        \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 3)                 21        \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 4         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 3)                 6         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 6)                 24        \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 9)                 63        \n",
      "=================================================================\n",
      "Total params: 178\n",
      "Trainable params: 178\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "input_dim = X_train.shape[1]\n",
    "encoding_dim = 6\n",
    "\n",
    "input_layer = Input(shape=(input_dim, ))\n",
    "encoder = Dense(encoding_dim, activation=\"tanh\",activity_regularizer=regularizers.l1(10e-5))(input_layer)\n",
    "encoder = Dense(int(encoding_dim / 2), activation=\"tanh\")(encoder)\n",
    "encoder = Dense(int(encoding_dim / 4), activation=\"tanh\")(encoder)\n",
    "decoder = Dense(int(encoding_dim/ 2), activation='tanh')(encoder)\n",
    "decoder = Dense(int(encoding_dim), activation='tanh')(decoder)\n",
    "decoder = Dense(input_dim, activation='tanh')(decoder)\n",
    "autoencoder = Model(inputs=input_layer, outputs=decoder)\n",
    "autoencoder.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "nb_epoch = 100\n",
    "batch_size = 50\n",
    "autoencoder.compile(optimizer='adam', loss='mse' )\n",
    "\n",
    "history = autoencoder.fit(X_train_scaled, X_train_scaled,\n",
    "                        epochs=nb_epoch,\n",
    "                        batch_size=batch_size,\n",
    "                        shuffle=True,\n",
    "                        validation_split=0.1,\n",
    "                        verbose=0\n",
    "                        ).history\n",
    "\n",
    "# validation_data=(X_test_scaled, X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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E8RS0BmtSmgIAAIAWgngKAgZrAgAAoAtBPAVMXwgAAIBuBPEUBD6zpgAAAGA6gngKAo/B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\nuCRJktQCG3FJkiSpBf8Fo3hZ017IJ+0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20ac3322f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "plt.plot(history['loss'])\n",
    "plt.plot(history['val_loss'])\n",
    "plt.ylabel('Loss',fontsize= 18)\n",
    "plt.xlabel('Epoch',fontsize= 18)\n",
    "plt.legend(['train', 'test'], loc='upper right', fontsize= 18);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predictions & Reconstruction Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Label</th>\n",
       "      <th>reconstruction_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>910.000000</td>\n",
       "      <td>910.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.026374</td>\n",
       "      <td>0.015857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.160332</td>\n",
       "      <td>0.021240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.019788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.154181</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Label  reconstruction_error\n",
       "count  910.000000            910.000000\n",
       "mean     0.026374              0.015857\n",
       "std      0.160332              0.021240\n",
       "min      0.000000              0.000481\n",
       "25%      0.000000              0.005174\n",
       "50%      0.000000              0.009254\n",
       "75%      0.000000              0.019788\n",
       "max      1.000000              0.154181"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = autoencoder.predict(X_test_scaled)\n",
    "\n",
    "mse = np.mean(np.power(X_test_scaled - predictions, 2), axis=1)\n",
    "df_error = pd.DataFrame({'reconstruction_error': mse, 'Label': y_test}, index=y_test.index)\n",
    "df_error.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x20ac4366208>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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73wXg73eN4owT29OxY/1ciMP27MQ2Wy9n7NixDB8+vMj4kiSpiey4445Mmz6NO++8k9ra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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20ac32eef98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "plt.scatter(df_error.index, df_error['reconstruction_error'], c=df_error['Label'], edgecolor='black', s=25)\n",
    "plt.xlabel('Data Index', fontsize = 18)\n",
    "plt.ylabel('Reconstruction Error', fontsize = 18)\n",
    "plt.xlim((-100,4600))\n",
    "plt.title(\"Outlier Score\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Interpretability\n",
    "\n",
    "- Major problem of neural network models is ...?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Label</th>\n",
       "      <th>reconstruction_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4065</th>\n",
       "      <td>0</td>\n",
       "      <td>0.154181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1398</th>\n",
       "      <td>0</td>\n",
       "      <td>0.153616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1387</th>\n",
       "      <td>0</td>\n",
       "      <td>0.152783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4063</th>\n",
       "      <td>0</td>\n",
       "      <td>0.151417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1391</th>\n",
       "      <td>0</td>\n",
       "      <td>0.150628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1329</th>\n",
       "      <td>1</td>\n",
       "      <td>0.150120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1330</th>\n",
       "      <td>0</td>\n",
       "      <td>0.149963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1383</th>\n",
       "      <td>0</td>\n",
       "      <td>0.149212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4073</th>\n",
       "      <td>0</td>\n",
       "      <td>0.149013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328</th>\n",
       "      <td>1</td>\n",
       "      <td>0.148200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Label  reconstruction_error\n",
       "4065      0              0.154181\n",
       "1398      0              0.153616\n",
       "1387      0              0.152783\n",
       "4063      0              0.151417\n",
       "1391      0              0.150628\n",
       "1329      1              0.150120\n",
       "1330      0              0.149963\n",
       "1383      0              0.149212\n",
       "4073      0              0.149013\n",
       "1328      1              0.148200"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_error.sort_values(ascending=False, by = 'reconstruction_error').head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>Label</th>\n",
       "      <th>reconstruction_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>910.000000</td>\n",
       "      <td>910.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.026374</td>\n",
       "      <td>0.015857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.160332</td>\n",
       "      <td>0.021240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.019788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.154181</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Label  reconstruction_error\n",
       "count  910.000000            910.000000\n",
       "mean     0.026374              0.015857\n",
       "std      0.160332              0.021240\n",
       "min      0.000000              0.000481\n",
       "25%      0.000000              0.005174\n",
       "50%      0.000000              0.009254\n",
       "75%      0.000000              0.019788\n",
       "max      1.000000              0.154181"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_error.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "outliers = df_error.index[df_error.reconstruction_error > 0.1].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_n = pd.DataFrame(X_test_scaled, index= y_test.index, columns=numerical_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V_1</th>\n",
       "      <th>V_2</th>\n",
       "      <th>V_3</th>\n",
       "      <th>V_4</th>\n",
       "      <th>V_5</th>\n",
       "      <th>V_6</th>\n",
       "      <th>V_7</th>\n",
       "      <th>V_8</th>\n",
       "      <th>V_9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1192</th>\n",
       "      <td>0.623761</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.538462</td>\n",
       "      <td>0.643411</td>\n",
       "      <td>0.000678</td>\n",
       "      <td>0.991848</td>\n",
       "      <td>0.837209</td>\n",
       "      <td>0.937269</td>\n",
       "      <td>0.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2689</th>\n",
       "      <td>0.654662</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.705426</td>\n",
       "      <td>0.000169</td>\n",
       "      <td>0.940217</td>\n",
       "      <td>0.869767</td>\n",
       "      <td>0.852399</td>\n",
       "      <td>0.653061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2351</th>\n",
       "      <td>0.604390</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.820513</td>\n",
       "      <td>0.643411</td>\n",
       "      <td>0.000508</td>\n",
       "      <td>0.839674</td>\n",
       "      <td>0.548837</td>\n",
       "      <td>0.778598</td>\n",
       "      <td>0.496599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3121</th>\n",
       "      <td>0.667710</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.743590</td>\n",
       "      <td>0.395349</td>\n",
       "      <td>0.000847</td>\n",
       "      <td>0.836957</td>\n",
       "      <td>0.897674</td>\n",
       "      <td>0.789668</td>\n",
       "      <td>0.789116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4513</th>\n",
       "      <td>0.662489</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.820513</td>\n",
       "      <td>0.310078</td>\n",
       "      <td>0.001863</td>\n",
       "      <td>0.880435</td>\n",
       "      <td>0.586047</td>\n",
       "      <td>0.826568</td>\n",
       "      <td>0.741497</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           V_1  V_2       V_3       V_4       V_5       V_6       V_7  \\\n",
       "1192  0.623761  1.0  0.538462  0.643411  0.000678  0.991848  0.837209   \n",
       "2689  0.654662  1.0  0.615385  0.705426  0.000169  0.940217  0.869767   \n",
       "2351  0.604390  1.0  0.820513  0.643411  0.000508  0.839674  0.548837   \n",
       "3121  0.667710  1.0  0.743590  0.395349  0.000847  0.836957  0.897674   \n",
       "4513  0.662489  1.0  0.820513  0.310078  0.001863  0.880435  0.586047   \n",
       "\n",
       "           V_8       V_9  \n",
       "1192  0.937269  0.857143  \n",
       "2689  0.852399  0.653061  \n",
       "2351  0.778598  0.496599  \n",
       "3121  0.789668  0.789116  \n",
       "4513  0.826568  0.741497  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_n.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_error_per_dim(point):\n",
    "    \n",
    "    initial_pt = np.array(data_n.loc[point,:]).reshape(1,9)\n",
    "    reconstrcuted_pt = autoencoder.predict(initial_pt)\n",
    "    \n",
    "    return abs(np.array(initial_pt  - reconstrcuted_pt)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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K6BLYvg4cmGS50JZkR+BFbRtJkiSNUZfLehxOM5PB6UmOBs5sl+8M/A3we+BN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\nSZJ67v8HcNFxZKNRONcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20ac43a62e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HOkmSJC2HLoHtu8A+SW4X2pJsA7yobSNJkqQx6nJaj7fRXMngxCSHAKe087cD\nXgjcBLx1vOVJkiSpy5UOzk2yC3Ao8JKhxacDe1fVOWOsTZIkSXS/0sFJwP3b861t2c4+u6q86Lsk\nSdKEdL7SAUBVncKtm0QlSZI0QV0OOgAgyaOSvC3JJ5Ns3c5bq52/7vhLlCRJWty6XJpqSZL/Bn4A\nvJbmQION2sU3A1/l9vu2SZIkaQV1GWF7DfB04FXAfYHMLKiqG4GvAE8Ya3WSJEnqFNheABxWVR8G\nfj/L8jOB+4ylKkmSJP1Fl8C2GfCTeZZfBay3QtVIkiTpdroEtmuAu82zfAvgshUrR5IkScO6BLb/\nD3hekgwvSLIezUEIPxhXYZIkSWp0CWxvpzlZ7veBv2/nbZvkn4GTgTWBd423PEmSJHW5NNVJSZ4O\nfAr4dDv7fTRHi14KPLWqzhh/iZIkSYtb10tTfTPJZsCu3Hpqj3OAo6vq+rFXJ0mSpO6XpqqqPwLf\naG+SJEmasM6XpppLkr9N8r1xrU+SJEmNkUbYkqxPc1LcK6pq6dCyHYG3ALsAt4y9QkmSpEVu3hG2\n9vqhBwKX0Jw096wkP07yV0nWSXI48CNgZ+Bw4AETr1iSJGmRWdYI28uAfYELgRNoTo67I/AxYBNg\nB+CzwFur6twJ1ilJkrRoLSuwPR84DXj4zFGgST4GvBi4HHhkVc13uSpJkiStoGUddLAVzQXfB0/Z\n8Yn257sNa5IkSZO3rMC2JnDx0LyZ6dPGX44kSZKGjXJaj5pj+k9jrkWSJEmzGOW0Hk9I8tcD03eh\nCW3PSLLdUNuqqg+OrTpJkiSNFNie096G/fMs8wowsEmSJI3RsgLbzgtShSRJkuY0b2CrquMWqhBJ\nkiTNbmzXEl0eSXZLclaSpUkOmGX5c5P8PMlp7RUWtp1GnZIkSdM0tcCWZAnNFRN2B7YBnp1km6Fm\nvwIeXVUPAN4KHLSwVUqSJE3fNEfYdgCWVtV5VXUT8AVgj8EGVfXjqrqynTyB5nJYkiRJi8o0A9vG\nwAUD0xe28+byIuBbE61IkiSph0Y5rcfUJdmZJrA9co7l+9JcpJ5NN910ASuTJEmavGmOsP0WuOfA\n9CbtvNtI8kDgU8AeVXX5bCuqqoOqavuq2n7DDTecSLGSJEnTMs3AdiKwZZLNk9wR2BM4crBBkk2B\nI4DnV9XZU6hRkiRp6qa2SbSqbk6yP3A0sAQ4pKpOT7Jfu/xA4A3A+sDHkwDcXFXbT6tmSZKkaZjq\nPmxVdRT96sKZAAANlUlEQVRw1NC8Awfu7wPss9B1SZIk9clUT5wrSZKkZTOwSZIk9ZyBTZIkqecM\nbJIkST1nYJMkSeo5A5skSVLPGdgkSZJ6zsAmSZLUcwY2SZKknjOwSZIk9ZyBTZIkqecMbJIkST1n\nYJMkSeo5A5skSVLPGdgkSZJ6zsAmSZLUcwY2SZKknjOwSZIk9ZyBTZIkqecMbJIkST1nYJMkSeo5\nA5skSVLPGdgkSZJ6zsAmSZLUcwY2SZKknjOwSZIk9ZyBTZIkqecMbJIkST1nYJMkSeo5A5skSVLP\nGdgkSZJ6zsAmSZLUcwY2SZKknjOwSZIk9ZyBTZIkqecMbJIkST1nYJMkSeo5A5skSVLPrTbtAqRp\n++AxZ0+7hOXyyl23mnYJkqQF4gibJElSzxnYJEmSes7AJkmS1HMGNkmSpJ4zsEmSJPWcgU2SJKnn\nDGySJEk9Z2CTJEnqOQObJElSzxnYJEmSes7AJkmS1HMGNkmSpJ7z4u+StJL54DFnT7uE5fLKXbca\nue3K2kfo1k9pVI6wSZIk9ZwjbJIkTcnKOpLoKOLCc4RNkiSp5wxskiRJPTfVTaJJdgM+DCwBPlVV\n7xpannb5E4Drgb2q6uQFL1SSJC03N/2uuKmNsCVZAnwM2B3YBnh2km2Gmu0ObNne9gU+saBFSpIk\n9cA0N4nuACytqvOq6ibgC8AeQ232AA6rxgnAuknusdCFSpIkTdM0N4luDFwwMH0h8LAR2mwMXDTZ\n0qRVy8q6OQIWx7m7+rTZRVI/rRKn9UiyL80mU4Brk5w1zXrGYAPg95NY8asmsdLlsxj6CPZzhfWo\nn4uhj7A4+jmxPsLi6GeP+ggrfz/vNUqjaQa23wL3HJjepJ3XtQ1VdRBw0LgLnJYkJ1XV9tOuY5IW\nQx/Bfq5KFkMfYXH0czH0Eeznqmaa+7CdCGyZZPMkdwT2BI4canMk8II0dgSurio3h0qSpEVlaiNs\nVXVzkv2Bo2lO63FIVZ2eZL92+YHAUTSn9FhKc1qPvadVryRJ0rRMdR+2qjqKJpQNzjtw4H4BL13o\nunpgldm8O4/F0Eewn6uSxdBHWBz9XAx9BPu5SkmTiSRJktRXXppKkiSp5wxskiRJPWdgW2BJfpDk\n8UPzXpFk1stuJfl2kquSfGNhKlxxXfqYZLskP0lyepKfJ3nWwlW6Yjr2815JTk5yStvX/Rau0hXT\n9TPbLl8nyYVJPjr5Clfccvxe/rl9L09JMnx0e28tRz83TfKdJGcmOSPJZgtR54ro+Hu588D7eEqS\nG5M8ZeGqXX7L8V6+p/3bc2aSj7TX6u695ejnu5P8or2tNN8nozCwLbzP05zCZNCe7fzZvBd4/kQr\nGr8ufbweeEFV3Q/YDfhQknUnXN+4dOnnRcDDq2o7mit6HJBkownXNy5dP7MAbwWOn1hF49e1jzdU\n1Xbt7cmTLW2suvbzMOC9VXVfmssJXjrB2sZl5D5W1Q9m3kfgMTR/j74z+RLHYuR+JnkE8LfAA4H7\nAw8FHj3pAsekSz+fCDwYmPk7++ok60y8wgViYFt4Xwae2J57jvY/1o2AH87WuKq+B1yzUMWNych9\nrKqzq+qc9v7vaL4QNlywSldMl37eVFV/bCfvxMr1u9fpM5vkIcDdWXm++KBjH1diI/czyTbAalV1\nDEBVXVtV1y9cqctted/LfwC+tZL0Ebr1s4A1gDvS/P1ZHbhkQapccV36uQ1wfFXdXFXXAT+nGQhY\nJaxMXxqrhKq6AvhfYPd21p7AF2sVOlx3efuYZAeaPyjnTrbC8ejazyT3TPJzmuvjvrsNqL3XpZ9J\n7gC8H3j1wlW44pbjM7tGu4n7hJVlExp07udWwFVJjkjy/5K8N8mShap1ea3A39hljRr3Spd+VtVP\ngB/QjPRfBBxdVWcuVK0rouP7eSqwW5K7JNkA2JnbXi1ppWZgm47BId6V6o9EB536mOQewGeBvavq\nlgnXNk4j97OqLqiqBwJbAP+Y5O4LUN+4jNrPlwBHVdWFC1LVeHX5zN6rqh4MPIdmM/59Jl3cGI3a\nz9WAv6MJ3w8F7g3sNenixmR5/v48gOZE7iuTkfqZZAvgvjSXd9wYeEySv1uQCsdjpH5W1Xdozu36\n47bNT4A/L0SBC8HANh1fA3ZJ8mDgLlX1s2kXNAEj97Hdx+CbwL9X1QkLVeCYdH4v25G1X9B8Ga4s\nRu3nw4H9k/waeB/NpeXetUA1rqiR38uq+m378zzgWOBBC1LheIzazwuBU6rqvKq6Gfgqzf5BK4Ou\nv5fPBL5SVX+afGljNWo/nwqc0G7Wvhb4Fs3v6sqiy+/m29v9EncFApy9UEVOmoFtCtpfmB8Ah7Bq\njq6N3Md2v4SvAIdV1ZcXqLyx6dDPTZLcub2/HvBI4KwFKXIMRu1nVT23qjatqs1oRmYOq6oDFqbK\nFdPhvVwvyZ3a+xvQ7Mx9xoIUOQYd/v6cCKybZGaf0sewkvRzOf7GPnvEdr3SoZ/nA49OslqS1WkO\nOFgpNolCp9/NJUnWb+8/kOYgi5VpX9p5Gdim5/PAtix7qP6HwJdo/ru4cPjw5p4bpY/PBB4F7DVw\naP12C1Ld+IzSz/sCP01yKnAc8L6qOm0hihujkT6zK7lR38uT2vfyB8C7qmqlCDIDltnPqvozTej+\nXpLTaEYrPrkw5Y3FqH9jN6PZz+m4yZc0EaP088s0+wafRrOf16lV9fUFqG2cRunn6sAPk5xBc7mq\n57Wjw6sEL00lSZLUc46wSZIk9dxq0y5AkOQBNEdIDvpjVT1sGvVMwmLoI9jPVamfi6GPsDj6uRj6\nCPZzVevnMDeJSpIk9ZybRCVJknrOwCZJktRzBjZJi1aSxyapJM9bzsdv0T7+dSO2f1vbfpPleT5J\ni5eBTdJUJflSG2LmPP9eGr9KctXMCYglaTExsEmatoPbn3vP02ZnYDPgC1V1wxif+/vAnYHDx7hO\nSRo7A5ukafsOcAHw3PZSZbOZCXMHz7G8kyRrA1TVLVV1Y1XdMo71StKkGNgkTVUblg4F1geePLw8\nyTrA04FfVNWJ7bznJPl6kvOT/DHJZUmOSHL/WR5/YZLvJnlIkmOSXA2c3C673T5s7fUWX5fkh0ku\nSXJTkt8k+ViSu83VjyTPTXJakhvb9m9IsmSU1yDJuknek+Tcgf4cnmTzoXZ3TvKWJGcluT7Jle1z\nvmuU55G08vLEuZL64NPA62hG0r48tGxPms2Wg6Nr+wOXAP/Z/twC2Bf4cZIHVdW5Q+vYDPge8N80\n1+Zdc55a1gD+Bfgf4KvAdcAO7fr/NslDq+pPQ495KnBv4GNtPU8B3kxzjcp/mue5SLIe8GNgY5qL\nW58BbAS8BHhskodU1QVt8wOBF9AE3J/QXDtxS5oLs0tahRnYJE1dVf0qyQ+Axye5R1VdNLB4b+Am\n4HMD83atqusG15HkczQjZy8H/s/QU9wH2LuqDh2hnOuBjYb2lTswyU9pAtOTgCOGHrMd8OCqOrWt\n5WM0YW+fJP9ZVSfN83xvA+4F7FBVvxjoz2doLtb9RmCfdvZTga9X1Xz7+0laBblJVFJfHAwsoRlB\nAiDJ1sCOwJFV9fuZ+TNhrT1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      "text/plain": [
       "<matplotlib.figure.Figure at 0x20ac4468908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SJqjLaT0OpbmSwelJjgTObJfvAvwZcBvw5smWJ0mSpC5XOrgoyZ7AUcBLh5rP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\n2vUvazdXbkpzgALzrP8/58L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      "text/plain": [
       "<matplotlib.figure.Figure at 0x20ac450fda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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up/U4hOZKBqcmORI4vV2+I/A84CbgjeMtT5IkSV2udHBBkt2Ao4AXDjWfBexX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\nCsyw/q9OhbWpdQDvaB8+Zab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      "text/plain": [
       "<matplotlib.figure.Figure at 0x20ac4798908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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X1e+nVbMkSdI0TC2wAVTV0cDRQ8sOGrj/W+BRi12XJElSn/T9oANJkqRV3kiBLclaSc5N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\ntwGeDhwH/C7JhjM34Crgx8C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      "text/plain": [
       "<matplotlib.figure.Figure at 0x20ac4844e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in outliers[:5]:\n",
    "    \n",
    "    plt.figure(figsize=(10,6))\n",
    "    objects = tuple(numerical_cols) \n",
    "    y_pos   = np.arange(len(objects))\n",
    "    x       = np.arange(len(objects))\n",
    "    y       = compute_error_per_dim(i).tolist()\n",
    "    plt.ylim([0, 1.0])\n",
    "    plt.bar(x, y, align='center', alpha=0.5)\n",
    "    plt.xticks(y_pos, objects)\n",
    "    plt.xlabel('Variables',fontsize=18)\n",
    "    plt.ylabel('Reconstruction Error', fontsize=18)\n",
    "    plt.title('Reconstruction Error-vs-Variables of {}'.format(i))\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusions\n",
    "\n",
    "- **There is a patten for V_1 & V_2 in contributing mainly to the reconstruction error!**\n",
    "- **AEs can be good choice to reduce dimensions in high dimensional data!**\n",
    "- **AEs are good for outlier detections in complex data!**\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}