Feature Importance.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Importance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this file I use following methods to find out important features in data.\n",
    "\n",
    "1. Perturbation : Adding noise in the feature checking how the model performance is deteriorated\n",
    "2. Missing Values : Replacing feature values with 0 and checking how the model performance is deteriorated\n",
    "3. Permutation : Shuffling the feature values and checking how the model performance is deteriorated\n",
    "4. Shap Feature Importance: Using Shap's DeepExplainer to see time-step wise feature importance\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Required Libraries and Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#to plot the data\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams.update(mpl.rcParamsDefault)\n",
    "\n",
    "import time\n",
    "import os\n",
    "os.chdir(\"C:/Data/aircraft/\")\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler #to normalize data\n",
    "from sklearn.metrics import classification_report, confusion_matrix, roc_curve\n",
    "from sklearn.metrics import f1_score, roc_auc_score, accuracy_score, precision_score, recall_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "#for deep learning\n",
    "import keras\n",
    "import keras.backend as k\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, LSTM, Activation, Masking, Dropout\n",
    "from keras.optimizers import RMSprop,adam\n",
    "from keras.callbacks import History\n",
    "from keras import callbacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_data(drop_cols = True):\n",
    "    dependent_var = ['RUL']\n",
    "    index_columns_names =  [\"UnitNumber\",\"Cycle\"]\n",
    "    operational_settings_columns_names = [\"OpSet\"+str(i) for i in range(1,4)]\n",
    "    sensor_measure_columns_names =[\"SensorMeasure\"+str(i) for i in range(1,22)]\n",
    "    input_file_column_names = index_columns_names + operational_settings_columns_names + sensor_measure_columns_names\n",
    "\n",
    "    cols_to_drop = ['OpSet3', 'SensorMeasure1', 'SensorMeasure5', 'SensorMeasure6', 'SensorMeasure10', 'SensorMeasure14',\n",
    "     'SensorMeasure16', 'SensorMeasure18', 'SensorMeasure19']\n",
    "\n",
    "    df_train = pd.read_csv('train_FD001.txt',delim_whitespace=True,names=input_file_column_names)\n",
    "\n",
    "    rul = pd.DataFrame(df_train.groupby('UnitNumber')['Cycle'].max()).reset_index()\n",
    "    rul.columns = ['UnitNumber', 'max']\n",
    "    df_train = df_train.merge(rul, on=['UnitNumber'], how='left')\n",
    "    df_train['RUL'] = df_train['max'] - df_train['Cycle']\n",
    "    df_train.drop('max', axis=1, inplace=True)\n",
    "\n",
    "    df_test = pd.read_csv('test_FD001.txt', delim_whitespace=True, names=input_file_column_names)\n",
    "    \n",
    "    if(drop_cols == True):\n",
    "        df_train = df_train.drop(cols_to_drop, axis = 1)\n",
    "        df_test = df_test.drop(cols_to_drop, axis = 1)\n",
    "\n",
    "    y_true = pd.read_csv('RUL_FD001.txt', delim_whitespace=True,names=[\"RUL\"])\n",
    "    y_true[\"UnitNumber\"] = y_true.index\n",
    "    \n",
    "    return df_train, df_test, y_true"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((20631, 27), (13096, 26), (100, 2))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train, df_test, y_true = prepare_data(drop_cols=False)\n",
    "df_train.shape, df_test.shape, y_true.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "feats = df_train.columns.drop(['UnitNumber', 'Cycle', 'RUL'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_max_scaler = MinMaxScaler(feature_range=(-1,1))\n",
    "\n",
    "df_train[feats] = min_max_scaler.fit_transform(df_train[feats])\n",
    "df_test[feats] = min_max_scaler.transform(df_test[feats])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['failure'] = [1 if i < 50 else 0 for i in df_train.RUL]\n",
    "y_true['failure'] = [1 if i < 50 else 0 for i in y_true.RUL]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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      "text/plain": [
       "   UnitNumber  Cycle    OpSet1    OpSet2  OpSet3  SensorMeasure1  \\\n",
       "0           1      1 -0.080460 -0.666667    -1.0            -1.0   \n",
       "1           1      2  0.218391 -0.500000    -1.0            -1.0   \n",
       "2           1      3 -0.494253  0.500000    -1.0            -1.0   \n",
       "3           1      4  0.080460  0.000000    -1.0            -1.0   \n",
       "4           1      5 -0.218391 -0.333333    -1.0            -1.0   \n",
       "\n",
       "   SensorMeasure2  SensorMeasure3  SensorMeasure4  SensorMeasure5  ...  \\\n",
       "0       -0.632530       -0.186396       -0.380486            -1.0  ...   \n",
       "1       -0.433735       -0.093961       -0.294733            -1.0  ...   \n",
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       "3       -0.313253       -0.487683       -0.337610            -1.0  ...   \n",
       "4       -0.301205       -0.485066       -0.190749            -1.0  ...   \n",
       "\n",
       "   SensorMeasure14  SensorMeasure15  SensorMeasure16  SensorMeasure17  \\\n",
       "0        -0.600784        -0.272028             -1.0        -0.333333   \n",
       "1        -0.674373        -0.177376             -1.0        -0.333333   \n",
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       "4        -0.650532        -0.195845             -1.0        -0.166667   \n",
       "\n",
       "   SensorMeasure18  SensorMeasure19  SensorMeasure20  SensorMeasure21  RUL  \\\n",
       "0             -1.0             -1.0         0.426357         0.449323  191   \n",
       "1             -1.0             -1.0         0.333333         0.462027  190   \n",
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       "4             -1.0             -1.0         0.178295         0.409003  187   \n",
       "\n",
       "   failure  \n",
       "0        0  \n",
       "1        0  \n",
       "2        0  \n",
       "3        0  \n",
       "4        0  \n",
       "\n",
       "[5 rows x 28 columns]"
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "</div>"
      ],
      "text/plain": [
       "   UnitNumber  Cycle    OpSet1    OpSet2  OpSet3  SensorMeasure1  \\\n",
       "0           1      1  0.264368  0.500000    -1.0            -1.0   \n",
       "1           1      2 -0.310345 -0.500000    -1.0            -1.0   \n",
       "2           1      3  0.034483  0.166667    -1.0            -1.0   \n",
       "3           1      4  0.482759  0.000000    -1.0            -1.0   \n",
       "4           1      5  0.160920  0.000000    -1.0            -1.0   \n",
       "\n",
       "   SensorMeasure2  SensorMeasure3  SensorMeasure4  SensorMeasure5  ...  \\\n",
       "0        0.090361       -0.378679       -0.461175            -1.0  ...   \n",
       "1       -0.698795       -0.240898       -0.555368            -1.0  ...   \n",
       "2       -0.246988       -0.306736       -0.355503            -1.0  ...   \n",
       "3       -0.259036       -0.429693       -0.183997            -1.0  ...   \n",
       "4       -0.216867       -0.295836       -0.335922            -1.0  ...   \n",
       "\n",
       "   SensorMeasure12  SensorMeasure13  SensorMeasure14  SensorMeasure15  \\\n",
       "0         0.292111        -0.558824        -0.735680        -0.382070   \n",
       "1         0.479744        -0.470588        -0.590463        -0.573682   \n",
       "2         0.398721        -0.558824        -0.688719        -0.082724   \n",
       "3         0.147122        -0.500000        -0.659820        -0.485956   \n",
       "4         0.475480        -0.558824        -0.694499        -0.398230   \n",
       "\n",
       "   SensorMeasure16  SensorMeasure17  SensorMeasure18  SensorMeasure19  \\\n",
       "0             -1.0        -0.333333             -1.0             -1.0   \n",
       "1             -1.0        -0.166667             -1.0             -1.0   \n",
       "2             -1.0        -0.166667             -1.0             -1.0   \n",
       "3             -1.0        -0.500000             -1.0             -1.0   \n",
       "4             -1.0        -0.666667             -1.0             -1.0   \n",
       "\n",
       "   SensorMeasure20  SensorMeasure21  \n",
       "0         0.116279         0.323667  \n",
       "1         0.364341         0.373654  \n",
       "2         0.457364         0.442695  \n",
       "3         0.333333         0.324220  \n",
       "4         0.317829         0.432753  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head()"
   ]
  },
  {
   "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>RUL</th>\n",
       "      <th>UnitNumber</th>\n",
       "      <th>failure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>112</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>82</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>91</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RUL  UnitNumber  failure\n",
       "0  112           0        0\n",
       "1   98           1        0\n",
       "2   69           2        0\n",
       "3   82           3        0\n",
       "4   91           4        0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_true.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "LSTM expects an input in the shape of a numpy array of 3 dimensions and I will need to convert train and test data accordingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_train(id_df, seq_length, seq_cols):\n",
    "    \"\"\"\n",
    "        function to prepare train data into (samples, time steps, features)\n",
    "        id_df = train dataframe\n",
    "        seq_length = look back period\n",
    "        seq_cols = feature columns\n",
    "    \"\"\"\n",
    "        \n",
    "    data_array = id_df[seq_cols].values\n",
    "    num_elements = data_array.shape[0]\n",
    "    lstm_array=[]\n",
    "    \n",
    "    for start, stop in zip(range(0, num_elements-seq_length+1), range(seq_length, num_elements+1)):\n",
    "        lstm_array.append(data_array[start:stop, :])\n",
    "    \n",
    "    return np.array(lstm_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_target(id_df, seq_length, label):\n",
    "    data_array = id_df[label].values\n",
    "    num_elements = data_array.shape[0]\n",
    "    return data_array[seq_length-1:num_elements+1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_test(id_df, seq_length, seq_cols, mask_value):\n",
    "    \"\"\"\n",
    "        function to prepare test data into (samples, time steps, features)\n",
    "        function only returns last sequence of data for every unit\n",
    "        id_df = test dataframe\n",
    "        seq_length = look back period\n",
    "        seq_cols = feature columns\n",
    "    \"\"\"\n",
    "    df_mask = pd.DataFrame(np.zeros((seq_length-1,id_df.shape[1])),columns=id_df.columns)\n",
    "    df_mask[:] = mask_value\n",
    "    \n",
    "    id_df = df_mask.append(id_df,ignore_index=True)\n",
    "    \n",
    "    data_array = id_df[seq_cols].values\n",
    "    num_elements = data_array.shape[0]\n",
    "    lstm_array=[]\n",
    "    \n",
    "    start = num_elements-seq_length\n",
    "    stop = num_elements\n",
    "    \n",
    "    lstm_array.append(data_array[start:stop, :])\n",
    "    \n",
    "    return np.array(lstm_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Let's define look back period and mask_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "sequence_length = 50 #predicting using last 30 cycle values\n",
    "mask_value = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Let's prepare data using above functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(15731, 50, 24)\n"
     ]
    }
   ],
   "source": [
    "#generate train\n",
    "x_train=np.concatenate(list(list(gen_train(df_train[df_train['UnitNumber']==unit], sequence_length, feats)) for unit in df_train['UnitNumber'].unique()))\n",
    "print(x_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15731,)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#generate target of train\n",
    "y_train = np.concatenate(list(list(gen_target(df_train[df_train['UnitNumber']==unit], sequence_length, \"failure\")) for unit in df_train['UnitNumber'].unique()))\n",
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 50, 24)\n"
     ]
    }
   ],
   "source": [
    "#generate test\n",
    "x_test=np.concatenate(list(list(gen_test(df_test[df_test['UnitNumber']==unit], sequence_length, feats, mask_value)) for unit in df_test['UnitNumber'].unique()))\n",
    "print(x_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100,)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#true target of test \n",
    "y_test = y_true.RUL.values\n",
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "nb_features = x_train.shape[2]\n",
    "nb_out = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_0 = pd.Series(y_train).value_counts()[0]\n",
    "class_1 = pd.Series(y_train).value_counts()[1]\n",
    "total = class_0 + class_1\n",
    "\n",
    "cls_wt = {0: class_1/total, 1: class_0/total}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.3178437480134766, 1: 0.6821562519865234}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cls_wt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\archd\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_1 (LSTM)                (None, 8)                 1056      \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 9         \n",
      "=================================================================\n",
      "Total params: 1,065\n",
      "Trainable params: 1,065\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "history = History()\n",
    "\n",
    "model = Sequential()\n",
    "model.add(LSTM(\n",
    "         units=8,\n",
    "         return_sequences=False,\n",
    "         input_shape=(sequence_length, nb_features)))\n",
    "model.add(Dense(units=1, activation='sigmoid'))\n",
    "model.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\archd\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Train on 14157 samples, validate on 1574 samples\n",
      "Epoch 1/100\n",
      "14157/14157 [==============================] - 19s 1ms/step - loss: 0.1605 - acc: 0.8996 - val_loss: 0.0956 - val_acc: 0.9136\n",
      "Epoch 2/100\n",
      "14157/14157 [==============================] - 15s 1ms/step - loss: 0.0791 - acc: 0.9399 - val_loss: 0.0711 - val_acc: 0.9295\n",
      "Epoch 3/100\n",
      "14157/14157 [==============================] - 15s 1ms/step - loss: 0.0634 - acc: 0.9486 - val_loss: 0.0738 - val_acc: 0.9212\n",
      "Epoch 4/100\n",
      "14157/14157 [==============================] - 15s 1ms/step - loss: 0.0548 - acc: 0.9530 - val_loss: 0.0540 - val_acc: 0.9473\n",
      "Epoch 5/100\n",
      "14157/14157 [==============================] - 16s 1ms/step - loss: 0.0526 - acc: 0.9535 - val_loss: 0.0792 - val_acc: 0.9276\n",
      "Epoch 6/100\n",
      "14157/14157 [==============================] - 16s 1ms/step - loss: 0.0483 - acc: 0.9573 - val_loss: 0.0746 - val_acc: 0.9199\n",
      "Epoch 7/100\n",
      "14157/14157 [==============================] - 15s 1ms/step - loss: 0.0443 - acc: 0.9593 - val_loss: 0.0720 - val_acc: 0.9193\n",
      "Epoch 8/100\n",
      "14157/14157 [==============================] - 15s 1ms/step - loss: 0.0419 - acc: 0.9618 - val_loss: 0.0540 - val_acc: 0.9307\n",
      "Epoch 9/100\n",
      "14157/14157 [==============================] - 15s 1ms/step - loss: 0.0400 - acc: 0.9611 - val_loss: 0.0691 - val_acc: 0.9219 ETA: 0s - loss: 0.0408 \n",
      "Epoch 10/100\n",
      "14157/14157 [==============================] - 15s 1ms/step - loss: 0.0366 - acc: 0.9664 - val_loss: 0.0746 - val_acc: 0.9212\n",
      "Epoch 11/100\n",
      "14157/14157 [==============================] - 15s 1ms/step - loss: 0.0358 - acc: 0.9658 - val_loss: 0.0552 - val_acc: 0.9403\n",
      "Epoch 12/100\n",
      "14157/14157 [==============================] - 15s 1ms/step - loss: 0.0348 - acc: 0.9675 - val_loss: 0.0551 - val_acc: 0.9358\n",
      "Epoch 13/100\n",
      "14157/14157 [==============================] - 17s 1ms/step - loss: 0.0329 - acc: 0.9688 - val_loss: 0.0660 - val_acc: 0.9263\n",
      "Epoch 14/100\n",
      "14157/14157 [==============================] - 17s 1ms/step - loss: 0.0321 - acc: 0.9693 - val_loss: 0.0557 - val_acc: 0.9365\n",
      "Epoch 15/100\n",
      "14157/14157 [==============================] - 16s 1ms/step - loss: 0.0313 - acc: 0.9701 - val_loss: 0.0616 - val_acc: 0.9416\n",
      "Epoch 16/100\n",
      "14157/14157 [==============================] - 16s 1ms/step - loss: 0.0323 - acc: 0.9704 - val_loss: 0.0579 - val_acc: 0.9371\n",
      "Epoch 17/100\n",
      "14157/14157 [==============================] - 14s 1ms/step - loss: 0.0296 - acc: 0.9723 - val_loss: 0.0610 - val_acc: 0.9371\n",
      "Epoch 18/100\n",
      "14157/14157 [==============================] - 15s 1ms/step - loss: 0.0292 - acc: 0.9732 - val_loss: 0.0732 - val_acc: 0.9396\n",
      "Wall time: 4min 44s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1a82b1fb0b8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# fit the model\n",
    "model.fit(x_train, y_train, epochs=100, batch_size=64, validation_split=0.1, verbose=1, class_weight = cls_wt,\n",
    "          callbacks = [history,\n",
    "                       keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=0, mode='auto')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Loss')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows = 1, ncols = 2, figsize = (10, 4))\n",
    "\n",
    "# Accuracy\n",
    "ax[0].plot(history.history['acc'])\n",
    "ax[0].plot(history.history['val_acc'])\n",
    "ax[0].set_ylabel('Accuracy')\n",
    "ax[0].set_xlabel('# Epoch')\n",
    "ax[0].legend(['train', 'validation'], loc='upper left')\n",
    "ax[0].set_title('Accuracy')\n",
    "\n",
    "# Loss\n",
    "ax[1].plot(history.history['loss'])\n",
    "ax[1].plot(history.history['val_loss'])\n",
    "ax[1].set_ylabel('Loss')\n",
    "ax[1].set_xlabel('# Epoch')\n",
    "ax[1].legend(['train', 'validation'], loc='upper left')\n",
    "ax[1].set_title('Loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_results(y_test, y_pred):\n",
    "    \n",
    "    #f1-score\n",
    "    f1 = f1_score(y_test, y_pred)\n",
    "    print(\"F1 Score: \", f1)\n",
    "    print(classification_report(y_true.failure, y_pred))\n",
    "    \n",
    "    conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "    plt.figure(figsize=(12,12))\n",
    "    plt.subplot(221)\n",
    "    sns.heatmap(conf_matrix, fmt = \"d\",annot=True, cmap='Blues')\n",
    "    b, t = plt.ylim()\n",
    "    plt.ylim(b + 0.5, t - 0.5)\n",
    "    plt.title('Confuion Matrix')\n",
    "    plt.ylabel('True Values')\n",
    "    plt.xlabel('Predicted Values')\n",
    "\n",
    "    #roc_auc_score\n",
    "    model_roc_auc = roc_auc_score(y_test, y_pred) \n",
    "    print (\"Area under curve : \",model_roc_auc,\"\\n\")\n",
    "    fpr,tpr,thresholds = roc_curve(y_test, y_pred)\n",
    "    gmeans = np.sqrt(tpr * (1-fpr))\n",
    "    ix = np.argmax(gmeans)\n",
    "    threshold = np.round(thresholds[ix],3)\n",
    "\n",
    "    plt.subplot(222)\n",
    "    plt.plot(fpr, tpr, color='darkorange', lw=1, label = \"Auc : %.3f\" %model_roc_auc)\n",
    "    plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "    plt.scatter(fpr[ix], tpr[ix], marker='o', color='black', label='Best Threshold:' + str(threshold))\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver operating characteristic')\n",
    "    plt.legend(loc=\"lower right\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score:  0.9523809523809523\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.96      0.98        70\n",
      "           1       0.91      1.00      0.95        30\n",
      "\n",
      "    accuracy                           0.97       100\n",
      "   macro avg       0.95      0.98      0.97       100\n",
      "weighted avg       0.97      0.97      0.97       100\n",
      "\n",
      "Area under curve :  0.9785714285714286 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = model.predict_classes(x_test)\n",
    "\n",
    "print_results(y_true.failure, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10350,   381],\n",
       "       [  110,  4890]], dtype=int64)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_train, model.predict_classes(x_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_probs = model.predict_proba(x_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perturbation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>RUL</th>\n",
       "      <th>UnitNumber</th>\n",
       "      <th>failure</th>\n",
       "      <th>failure_probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>112</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.373701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>82</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.008113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>91</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.006418</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RUL  UnitNumber  failure  failure_probability\n",
       "0  112           0        0             0.000598\n",
       "1   98           1        0             0.001048\n",
       "2   69           2        0             0.373701\n",
       "3   82           3        0             0.008113\n",
       "4   91           4        0             0.006418"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_true['failure_probability'] = model.predict_proba(x_test)\n",
    "y_true.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_importance = pd.DataFrame(columns=[\"feature\", \"Importance\"])\n",
    "\n",
    "for i, feat in enumerate(feats):\n",
    "    \n",
    "    new_x = x_test.copy()\n",
    "    \n",
    "    np.random.seed(42)\n",
    "    perturbation = np.random.normal(0.0, 1, size = new_x.shape[:2])\n",
    "    new_x[:, :, i] = new_x[:, :, i] + perturbation\n",
    "    perturbed_out = model.predict_proba(new_x)\n",
    "    \n",
    "    effect = mean_squared_error(y_true.failure_probability, perturbed_out)\n",
    "    feature_importance = feature_importance.append({\"feature\" : feat, \"Importance\" : effect}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a82f2093c8>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (8,10))\n",
    "feature_importance = feature_importance.sort_values(by = \"Importance\")\n",
    "feature_importance.plot.barh(x = 'feature', y = 'Importance', ax = ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is not right because OpSet3 is having on of the highest importance. Since we have only 8 nodes in hidden layer, network is not sparse and the weight of different features are shared. \n",
    "\n",
    "To remedy this, I would make sparse network with nodes in a hidden layer more than input features. This also helps for feature learning and to enrich feature represenatation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(feats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_3 (LSTM)                (None, 50)                15000     \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 51        \n",
      "=================================================================\n",
      "Total params: 15,051\n",
      "Trainable params: 15,051\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "history = History()\n",
    "\n",
    "model2 = Sequential()\n",
    "model2.add(LSTM(\n",
    "         units=50,\n",
    "         return_sequences=False,\n",
    "         input_shape=(sequence_length, nb_features)))\n",
    "model2.add(Dense(units=1, activation='sigmoid'))\n",
    "model2.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=['accuracy'])\n",
    "\n",
    "model2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 14157 samples, validate on 1574 samples\n",
      "Epoch 1/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13696/14157 [============================>.] - ETA: 7:26 - loss: 0.3056 - acc: 0.312 - ETA: 3:50 - loss: 0.2974 - acc: 0.335 - ETA: 2:38 - loss: 0.2930 - acc: 0.515 - ETA: 2:01 - loss: 0.2811 - acc: 0.628 - ETA: 1:39 - loss: 0.2824 - acc: 0.659 - ETA: 1:24 - loss: 0.2790 - acc: 0.695 - ETA: 1:14 - loss: 0.2729 - acc: 0.727 - ETA: 1:06 - loss: 0.2678 - acc: 0.748 - ETA: 59s - loss: 0.2634 - acc: 0.765 - ETA: 54s - loss: 0.2581 - acc: 0.78 - ETA: 50s - loss: 0.2550 - acc: 0.79 - ETA: 47s - loss: 0.2509 - acc: 0.80 - ETA: 44s - loss: 0.2456 - acc: 0.81 - ETA: 42s - loss: 0.2418 - acc: 0.82 - ETA: 39s - loss: 0.2375 - acc: 0.82 - ETA: 37s - loss: 0.2341 - acc: 0.83 - ETA: 36s - loss: 0.2301 - acc: 0.83 - ETA: 34s - loss: 0.2261 - acc: 0.83 - ETA: 33s - loss: 0.2228 - acc: 0.83 - ETA: 32s - loss: 0.2205 - acc: 0.83 - ETA: 31s - loss: 0.2169 - acc: 0.83 - ETA: 30s - loss: 0.2134 - acc: 0.84 - ETA: 29s - loss: 0.2097 - acc: 0.84 - ETA: 28s - loss: 0.2057 - acc: 0.84 - ETA: 27s - loss: 0.2067 - acc: 0.83 - ETA: 26s - loss: 0.2037 - acc: 0.83 - ETA: 26s - loss: 0.2000 - acc: 0.84 - ETA: 25s - loss: 0.1993 - acc: 0.84 - ETA: 24s - loss: 0.1991 - acc: 0.84 - ETA: 24s - loss: 0.1962 - acc: 0.84 - ETA: 23s - loss: 0.1940 - acc: 0.84 - ETA: 23s - loss: 0.1915 - acc: 0.84 - ETA: 22s - loss: 0.1886 - acc: 0.84 - ETA: 22s - loss: 0.1860 - acc: 0.85 - ETA: 21s - loss: 0.1846 - acc: 0.85 - ETA: 21s - loss: 0.1827 - acc: 0.85 - ETA: 21s - loss: 0.1804 - acc: 0.85 - ETA: 20s - loss: 0.1781 - acc: 0.85 - ETA: 20s - loss: 0.1770 - acc: 0.85 - ETA: 20s - loss: 0.1757 - acc: 0.85 - ETA: 19s - loss: 0.1738 - acc: 0.85 - ETA: 19s - loss: 0.1729 - acc: 0.85 - ETA: 19s - loss: 0.1725 - acc: 0.85 - ETA: 18s - loss: 0.1709 - acc: 0.86 - ETA: 18s - loss: 0.1694 - acc: 0.86 - ETA: 18s - loss: 0.1676 - acc: 0.86 - ETA: 17s - loss: 0.1666 - acc: 0.86 - ETA: 17s - loss: 0.1652 - acc: 0.86 - ETA: 17s - loss: 0.1646 - acc: 0.86 - ETA: 17s - loss: 0.1634 - acc: 0.86 - ETA: 16s - loss: 0.1623 - acc: 0.86 - 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ETA: 0s - loss: 0.0955 - acc: 0.918 - ETA: 0s - loss: 0.0954 - acc: 0.918 - ETA: 0s - loss: 0.0951 - acc: 0.9190\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
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     "text": [
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      "Epoch 2/100\n"
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      "Epoch 3/100\n"
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      "Epoch 4/100\n"
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      "14157/14157 [==============================] - ETA: 0s - loss: 0.0469 - acc: 0.951 - ETA: 0s - loss: 0.0470 - acc: 0.951 - ETA: 0s - loss: 0.0469 - acc: 0.951 - ETA: 0s - loss: 0.0468 - acc: 0.951 - ETA: 0s - loss: 0.0470 - acc: 0.951 - ETA: 0s - loss: 0.0472 - acc: 0.951 - 15s 1ms/step - loss: 0.0471 - acc: 0.9513 - val_loss: 0.0505 - val_acc: 0.9422\n",
      "Epoch 5/100\n"
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      "Epoch 6/100\n"
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      "Epoch 8/100\n"
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      "Epoch 9/100\n"
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     "text": [
      "14157/14157 [==============================] - ETA: 0s - loss: 0.0353 - acc: 0.964 - ETA: 0s - loss: 0.0353 - acc: 0.964 - ETA: 0s - loss: 0.0352 - acc: 0.964 - ETA: 0s - loss: 0.0353 - acc: 0.964 - ETA: 0s - loss: 0.0354 - acc: 0.964 - ETA: 0s - loss: 0.0354 - acc: 0.964 - 15s 1ms/step - loss: 0.0354 - acc: 0.9641 - val_loss: 0.0464 - val_acc: 0.9435\n",
      "Epoch 10/100\n"
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      "14157/14157 [==============================] - ETA: 0s - loss: 0.0339 - acc: 0.966 - ETA: 0s - loss: 0.0340 - acc: 0.966 - ETA: 0s - loss: 0.0340 - acc: 0.966 - ETA: 0s - loss: 0.0339 - acc: 0.966 - ETA: 0s - loss: 0.0341 - acc: 0.966 - ETA: 0s - loss: 0.0341 - acc: 0.966 - 15s 1ms/step - loss: 0.0341 - acc: 0.9664 - val_loss: 0.0636 - val_acc: 0.9295\n",
      "Epoch 11/100\n"
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      "Epoch 12/100\n"
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      "Epoch 16/100\n"
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      "Epoch 17/100\n"
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acc: 0.973 - ETA: 0s - loss: 0.0278 - acc: 0.973 - ETA: 0s - loss: 0.0277 - acc: 0.973 - ETA: 0s - loss: 0.0276 - acc: 0.973 - ETA: 0s - loss: 0.0277 - acc: 0.9737"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14157/14157 [==============================] - ETA: 0s - loss: 0.0276 - acc: 0.973 - ETA: 0s - loss: 0.0276 - acc: 0.973 - ETA: 0s - loss: 0.0276 - acc: 0.973 - ETA: 0s - loss: 0.0277 - acc: 0.973 - ETA: 0s - loss: 0.0276 - acc: 0.973 - ETA: 0s - loss: 0.0276 - acc: 0.973 - 21s 1ms/step - loss: 0.0276 - acc: 0.9737 - val_loss: 0.0477 - val_acc: 0.9511\n",
      "Wall time: 4min 28s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1a83782bb38>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# fit the model\n",
    "model2.fit(x_train, y_train, epochs=100, batch_size=64, validation_split=0.1, verbose=1, class_weight = cls_wt,\n",
    "          callbacks = [history,\n",
    "                       keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=0, mode='auto')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Loss')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows = 1, ncols = 2, figsize = (10, 4))\n",
    "\n",
    "# Accuracy\n",
    "ax[0].plot(history.history['acc'])\n",
    "ax[0].plot(history.history['val_acc'])\n",
    "ax[0].set_ylabel('Accuracy')\n",
    "ax[0].set_xlabel('# Epoch')\n",
    "ax[0].legend(['train', 'validation'], loc='upper left')\n",
    "ax[0].set_title('Accuracy')\n",
    "\n",
    "# Loss\n",
    "ax[1].plot(history.history['loss'])\n",
    "ax[1].plot(history.history['val_loss'])\n",
    "ax[1].set_ylabel('Loss')\n",
    "ax[1].set_xlabel('# Epoch')\n",
    "ax[1].legend(['train', 'validation'], loc='upper left')\n",
    "ax[1].set_title('Loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score:  0.967741935483871\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.97      0.99        70\n",
      "           1       0.94      1.00      0.97        30\n",
      "\n",
      "    accuracy                           0.98       100\n",
      "   macro avg       0.97      0.99      0.98       100\n",
      "weighted avg       0.98      0.98      0.98       100\n",
      "\n",
      "Area under curve :  0.9857142857142857 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = model2.predict_classes(x_test)\n",
    "\n",
    "print_results(y_true.failure, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10488,   243],\n",
       "       [  237,  4763]], dtype=int64)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_train, model2.predict_classes(x_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_probs2 = model2.predict_proba(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perturbation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_importance2 = pd.DataFrame(columns=[\"feature\", \"Importance\"])\n",
    "\n",
    "for i, feat in enumerate(feats):\n",
    "    \n",
    "    new_x = x_test.copy()\n",
    "    \n",
    "    np.random.seed(42)\n",
    "    perturbation = np.random.normal(0.0, 1, size = new_x.shape[:2])\n",
    "    new_x[:, :, i] = new_x[:, :, i] + perturbation\n",
    "    perturbed_out = model2.predict_proba(new_x)\n",
    "    \n",
    "    effect = mean_squared_error(test_probs2, perturbed_out)\n",
    "    feature_importance2 = feature_importance2.append({\"feature\" : feat, \"Importance\" : effect}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Perturbation: Feature Importance')"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (8,10))\n",
    "feature_importance2 = feature_importance2.sort_values(by = \"Importance\")\n",
    "feature_importance2.plot.barh(x = 'feature', y = 'Importance', ax = ax)\n",
    "ax.set_title('Perturbation: Feature Importance')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing Values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_importance2 = pd.DataFrame(columns=[\"feature\", \"Importance\"])\n",
    "\n",
    "for i, feat in enumerate(feats):\n",
    "    \n",
    "    new_x = x_test.copy()\n",
    "    \n",
    "    new_x[:, :, i] = 0\n",
    "    missing_probs = model2.predict_proba(new_x)\n",
    "    \n",
    "    effect = mean_squared_error(test_probs2, missing_probs)\n",
    "    feature_importance2 = feature_importance2.append({\"feature\" : feat, \"Importance\" : effect}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Missing Values: Feature Importance')"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (8,10))\n",
    "feature_importance2 = feature_importance2.sort_values(by = \"Importance\")\n",
    "feature_importance2.plot.barh(x = 'feature', y = 'Importance', ax = ax)\n",
    "ax.set_title('Missing Values: Feature Importance')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Permutation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_importance2 = pd.DataFrame(columns=[\"feature\", \"Importance\"])\n",
    "\n",
    "for i, feat in enumerate(feats):\n",
    "    \n",
    "    new_x = x_test.copy()\n",
    "    \n",
    "    np.random.shuffle(new_x[:, :, i])\n",
    "    permute_probs = model2.predict_proba(new_x)\n",
    "    \n",
    "    effect = mean_squared_error(test_probs2, permute_probs)\n",
    "    feature_importance2 = feature_importance2.append({\"feature\" : feat, \"Importance\" : effect}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Permutation: Feature Importance')"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (8,10))\n",
    "feature_importance2 = feature_importance2.sort_values(by = \"Importance\")\n",
    "feature_importance2.plot.barh(x = 'feature', y = 'Importance', ax = ax)\n",
    "ax.set_title('Permutation: Feature Importance')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shap Values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50, 24)\n"
     ]
    }
   ],
   "source": [
    "import shap\n",
    "\n",
    "random_ind = np.random.choice(x_train.shape[0], 1000, replace = False)\n",
    "data = x_train[random_ind[0:1000]]\n",
    "\n",
    "DE = shap.DeepExplainer(model2, data)\n",
    "shap_val = DE.shap_values(x_test)\n",
    "shap_val = np.array(shap_val)\n",
    "shap_val = np.reshape(shap_val, (shap_val.shape[1], shap_val.shape[2], shap_val.shape[3]))\n",
    "shap_abs = np.abs(shap_val)\n",
    "sum_0 = np.sum(shap_abs, axis = 0)\n",
    "\n",
    "print(sum_0.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Last day's important features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Shap Feature Importance of last cycle')"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap_feat_importance = pd.DataFrame(columns=[\"features\", \"Importance\"])\n",
    "shap_feat_importance.features = feats\n",
    "shap_feat_importance.Importance = sum_0[49]\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (8,10))\n",
    "shap_feat_importance = shap_feat_importance.sort_values(by = \"Importance\")\n",
    "shap_feat_importance.plot.barh(x = 'features', y = 'Importance', ax = ax)\n",
    "ax.set_title('Shap Feature Importance of last cycle')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### First day's important features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Shap Feature Importance of first cycle')"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap_feat_importance = pd.DataFrame(columns=[\"features\", \"Importance\"])\n",
    "shap_feat_importance.features = feats\n",
    "shap_feat_importance.Importance = sum_0[0]\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (8,10))\n",
    "shap_feat_importance = shap_feat_importance.sort_values(by = \"Importance\")\n",
    "shap_feat_importance.plot.barh(x = 'features', y = 'Importance', ax = ax)\n",
    "ax.set_title('Shap Feature Importance of first cycle')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Over all feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Shap Feature Importance')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap_feat_importance = pd.DataFrame(columns=[\"features\", \"Importance\"])\n",
    "shap_feat_importance.features = feats\n",
    "shap_feat_importance.Importance = sum_0.sum(axis = 0)\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (8,10))\n",
    "shap_feat_importance = shap_feat_importance.sort_values(by = \"Importance\")\n",
    "shap_feat_importance.plot.barh(x = 'features', y = 'Importance', ax = ax)\n",
    "ax.set_title('Shap Feature Importance')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importance Visualization over time steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>OpSet1</th>\n",
       "      <th>OpSet2</th>\n",
       "      <th>OpSet3</th>\n",
       "      <th>SensorMeasure1</th>\n",
       "      <th>SensorMeasure2</th>\n",
       "      <th>SensorMeasure3</th>\n",
       "      <th>SensorMeasure4</th>\n",
       "      <th>SensorMeasure5</th>\n",
       "      <th>SensorMeasure6</th>\n",
       "      <th>SensorMeasure7</th>\n",
       "      <th>...</th>\n",
       "      <th>SensorMeasure12</th>\n",
       "      <th>SensorMeasure13</th>\n",
       "      <th>SensorMeasure14</th>\n",
       "      <th>SensorMeasure15</th>\n",
       "      <th>SensorMeasure16</th>\n",
       "      <th>SensorMeasure17</th>\n",
       "      <th>SensorMeasure18</th>\n",
       "      <th>SensorMeasure19</th>\n",
       "      <th>SensorMeasure20</th>\n",
       "      <th>SensorMeasure21</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.177128</td>\n",
       "      <td>0.153225</td>\n",
       "      <td>0.017702</td>\n",
       "      <td>0.020642</td>\n",
       "      <td>0.043494</td>\n",
       "      <td>0.133174</td>\n",
       "      <td>0.171820</td>\n",
       "      <td>0.011259</td>\n",
       "      <td>0.033200</td>\n",
       "      <td>0.339510</td>\n",
       "      <td>...</td>\n",
       "      <td>0.117782</td>\n",
       "      <td>0.100679</td>\n",
       "      <td>0.051401</td>\n",
       "      <td>0.129179</td>\n",
       "      <td>0.015316</td>\n",
       "      <td>0.036476</td>\n",
       "      <td>0.023623</td>\n",
       "      <td>0.004098</td>\n",
       "      <td>0.168036</td>\n",
       "      <td>0.104745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.170273</td>\n",
       "      <td>0.088665</td>\n",
       "      <td>0.014327</td>\n",
       "      <td>0.020354</td>\n",
       "      <td>0.036693</td>\n",
       "      <td>0.148077</td>\n",
       "      <td>0.154789</td>\n",
       "      <td>0.008803</td>\n",
       "      <td>0.026685</td>\n",
       "      <td>0.229829</td>\n",
       "      <td>...</td>\n",
       "      <td>0.070765</td>\n",
       "      <td>0.099017</td>\n",
       "      <td>0.054793</td>\n",
       "      <td>0.132376</td>\n",
       "      <td>0.014042</td>\n",
       "      <td>0.034819</td>\n",
       "      <td>0.018682</td>\n",
       "      <td>0.002637</td>\n",
       "      <td>0.106628</td>\n",
       "      <td>0.068968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.112825</td>\n",
       "      <td>0.055524</td>\n",
       "      <td>0.013298</td>\n",
       "      <td>0.020747</td>\n",
       "      <td>0.027213</td>\n",
       "      <td>0.158710</td>\n",
       "      <td>0.176549</td>\n",
       "      <td>0.007364</td>\n",
       "      <td>0.023955</td>\n",
       "      <td>0.265730</td>\n",
       "      <td>...</td>\n",
       "      <td>0.061481</td>\n",
       "      <td>0.084197</td>\n",
       "      <td>0.059605</td>\n",
       "      <td>0.159666</td>\n",
       "      <td>0.013694</td>\n",
       "      <td>0.035956</td>\n",
       "      <td>0.015727</td>\n",
       "      <td>0.001511</td>\n",
       "      <td>0.092378</td>\n",
       "      <td>0.065964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.104163</td>\n",
       "      <td>0.055922</td>\n",
       "      <td>0.012496</td>\n",
       "      <td>0.020455</td>\n",
       "      <td>0.020965</td>\n",
       "      <td>0.153333</td>\n",
       "      <td>0.223608</td>\n",
       "      <td>0.005855</td>\n",
       "      <td>0.028843</td>\n",
       "      <td>0.165332</td>\n",
       "      <td>...</td>\n",
       "      <td>0.049015</td>\n",
       "      <td>0.074528</td>\n",
       "      <td>0.069548</td>\n",
       "      <td>0.147011</td>\n",
       "      <td>0.012782</td>\n",
       "      <td>0.045271</td>\n",
       "      <td>0.013496</td>\n",
       "      <td>0.000761</td>\n",
       "      <td>0.072804</td>\n",
       "      <td>0.099781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.078619</td>\n",
       "      <td>0.059778</td>\n",
       "      <td>0.007774</td>\n",
       "      <td>0.008041</td>\n",
       "      <td>0.016626</td>\n",
       "      <td>0.151519</td>\n",
       "      <td>0.211282</td>\n",
       "      <td>0.003982</td>\n",
       "      <td>0.009674</td>\n",
       "      <td>0.145528</td>\n",
       "      <td>...</td>\n",
       "      <td>0.052937</td>\n",
       "      <td>0.063619</td>\n",
       "      <td>0.068165</td>\n",
       "      <td>0.114973</td>\n",
       "      <td>0.002798</td>\n",
       "      <td>0.054150</td>\n",
       "      <td>0.009466</td>\n",
       "      <td>0.000765</td>\n",
       "      <td>0.061762</td>\n",
       "      <td>0.129174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     OpSet1    OpSet2    OpSet3  SensorMeasure1  SensorMeasure2  \\\n",
       "0  0.177128  0.153225  0.017702        0.020642        0.043494   \n",
       "1  0.170273  0.088665  0.014327        0.020354        0.036693   \n",
       "2  0.112825  0.055524  0.013298        0.020747        0.027213   \n",
       "3  0.104163  0.055922  0.012496        0.020455        0.020965   \n",
       "4  0.078619  0.059778  0.007774        0.008041        0.016626   \n",
       "\n",
       "   SensorMeasure3  SensorMeasure4  SensorMeasure5  SensorMeasure6  \\\n",
       "0        0.133174        0.171820        0.011259        0.033200   \n",
       "1        0.148077        0.154789        0.008803        0.026685   \n",
       "2        0.158710        0.176549        0.007364        0.023955   \n",
       "3        0.153333        0.223608        0.005855        0.028843   \n",
       "4        0.151519        0.211282        0.003982        0.009674   \n",
       "\n",
       "   SensorMeasure7  ...  SensorMeasure12  SensorMeasure13  SensorMeasure14  \\\n",
       "0        0.339510  ...         0.117782         0.100679         0.051401   \n",
       "1        0.229829  ...         0.070765         0.099017         0.054793   \n",
       "2        0.265730  ...         0.061481         0.084197         0.059605   \n",
       "3        0.165332  ...         0.049015         0.074528         0.069548   \n",
       "4        0.145528  ...         0.052937         0.063619         0.068165   \n",
       "\n",
       "   SensorMeasure15  SensorMeasure16  SensorMeasure17  SensorMeasure18  \\\n",
       "0         0.129179         0.015316         0.036476         0.023623   \n",
       "1         0.132376         0.014042         0.034819         0.018682   \n",
       "2         0.159666         0.013694         0.035956         0.015727   \n",
       "3         0.147011         0.012782         0.045271         0.013496   \n",
       "4         0.114973         0.002798         0.054150         0.009466   \n",
       "\n",
       "   SensorMeasure19  SensorMeasure20  SensorMeasure21  \n",
       "0         0.004098         0.168036         0.104745  \n",
       "1         0.002637         0.106628         0.068968  \n",
       "2         0.001511         0.092378         0.065964  \n",
       "3         0.000761         0.072804         0.099781  \n",
       "4         0.000765         0.061762         0.129174  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_imp_df = pd.DataFrame(sum_0, columns=feats)\n",
    "feat_imp_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Operational Setting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Feature Importance')"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (7,5))\n",
    "feat_imp_df[[\"OpSet1\", \"OpSet2\", \"OpSet3\"]].plot(ax = ax)\n",
    "ax.set_xlabel(\"Cycle\")\n",
    "ax.set_ylabel(\"Feature Importance\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top 5 Sensor Measures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Feature Importance')"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (7,5))\n",
    "feat_imp_df[[\"SensorMeasure4\", \"SensorMeasure21\", \"SensorMeasure7\", \"SensorMeasure12\", \"SensorMeasure11\"]].plot(ax = ax)\n",
    "ax.set_xlabel(\"Cycle\")\n",
    "ax.set_ylabel(\"Feature Importance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}