LSTM based auto-encoder.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-28T01:07:36.094435Z",
     "start_time": "2020-02-28T01:07:36.087411Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pandas_profiling as pp\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from datetime import date\n",
    "import datetime as dt\n",
    "import tensorflow as tf\n",
    "import keras\n",
    "from keras.layers import Input,Dropout,Dense,LSTM,TimeDistributed,RepeatVector\n",
    "from keras.models import Model\n",
    "from keras import regularizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-28T01:30:01.351800Z",
     "start_time": "2020-02-28T01:30:01.348323Z"
    }
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T15:13:41.224444Z",
     "start_time": "2020-02-27T15:10:47.659894Z"
    }
   },
   "outputs": [],
   "source": [
    "notifications=pd.read_excel(r\"...../SAP_Notifications.xlsm\",sheet_name=\"Notifications\")\n",
    "confirmation=pd.read_excel(r\"......../SAP_WO_Confirmations.xlsm\",sheet_name=\"Confirmations\")\n",
    "operations=pd.read_excel(r\"......../SAP_WO_Operations_IW37.xlsm\",sheet_name=\"Operations\")\n",
    "price=pd.read_excel(r\"......../SAP_WO.xlsm\",sheet_name=\"SAPIW38Data\")\n",
    "tanks=pd.read_excel(r\"......./tanks.xlsx\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T19:37:35.328358Z",
     "start_time": "2020-02-27T19:37:35.325817Z"
    }
   },
   "outputs": [],
   "source": [
    "######################## tanks notification ########################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:48:26.639221Z",
     "start_time": "2020-02-27T21:48:26.632188Z"
    }
   },
   "outputs": [],
   "source": [
    "tanks_notification=pd.merge(notifications,tanks,on=['Equipment'],how='outer')\n",
    "tanks_notification=tanks_notification.dropna(subset=['Equipment']).reset_index(drop=True)\n",
    "tanks_notification=tanks_notification.dropna(subset=['Notif.date']).reset_index(drop=True)\n",
    "with_time=pd.merge(operations,tanks,on=['Equipment'],how='left')\n",
    "with_time=with_time.dropna(subset=['Actual finish (date)']).reset_index(drop=True)\n",
    "with_time=with_time.dropna(subset=['Manufacturer of asset']).reset_index(drop=True)\n",
    "tanks_notification['Order']=tanks_notification['Order'].astype('int')\n",
    "with_time['Order']=with_time['Order'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:49:08.793481Z",
     "start_time": "2020-02-27T21:49:08.744996Z"
    }
   },
   "outputs": [],
   "source": [
    "g=pd.merge(tanks_notification,with_time,on=['Order'],how='left')\n",
    "g=g.dropna(subset=['Actual finish (date)']).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:55:34.426224Z",
     "start_time": "2020-02-27T21:55:34.415227Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/soujanyasamineni/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "data_for_anomaly=g[['Notif.date','Equipment_x','Actual finish (date)']]\n",
    "data_for_anomaly['Difference']=\n",
    "    (data_for_anomaly['Actual finish (date)'] - data_for_anomaly['Notif.date']).dt.days\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T21:58:27.290633Z",
     "start_time": "2020-02-27T21:58:27.286384Z"
    }
   },
   "outputs": [],
   "source": [
    "data_for_anomaly=data_for_anomaly[['Notif.date','Equipment_x','Difference']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pivot the table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T22:28:49.488634Z",
     "start_time": "2020-02-27T22:28:49.334427Z"
    }
   },
   "outputs": [],
   "source": [
    "t1=data_for_anomaly.pivot_table(values='Difference', index='Notif.date', \n",
    "                             columns='Equipment_x', aggfunc=[np.mean],fill_value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T22:43:13.987029Z",
     "start_time": "2020-02-27T22:43:13.944178Z"
    }
   },
   "outputs": [],
   "source": [
    "flattened_t1 = pd.DataFrame(t1.to_records())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T23:08:59.156301Z",
     "start_time": "2020-02-27T23:08:59.151980Z"
    }
   },
   "outputs": [],
   "source": [
    "flattened_t1.columns = [i.replace(\"('mean', \", \"\").replace(\")\", \"\") \\\n",
    "                     for i in flattened_t1.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T23:14:04.151257Z",
     "start_time": "2020-02-27T23:14:04.147303Z"
    }
   },
   "outputs": [],
   "source": [
    "f1=flattened_t1.iloc[:,:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T23:14:53.189503Z",
     "start_time": "2020-02-27T23:14:53.184638Z"
    }
   },
   "outputs": [],
   "source": [
    "f1=f1.set_index('Notif.date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T23:15:28.900008Z",
     "start_time": "2020-02-27T23:15:28.272703Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1482ce550>"
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2160x2160 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(30,30))\n",
    "f1.plot(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-27T23:20:08.338281Z",
     "start_time": "2020-02-27T23:20:08.333072Z"
    }
   },
   "outputs": [],
   "source": [
    "train=flattened_t1.iloc[0:500,:]\n",
    "test=flattened_t1.iloc[501:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-28T00:48:04.735494Z",
     "start_time": "2020-02-28T00:48:04.728577Z"
    }
   },
   "outputs": [],
   "source": [
    "train=train.set_index('Notif.date')\n",
    "test=test.set_index('Notif.date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-28T00:50:07.807164Z",
     "start_time": "2020-02-28T00:50:07.792092Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['scaler_data']"
      ]
     },
     "execution_count": 384,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import joblib\n",
    "scaler = MinMaxScaler()\n",
    "X_train= scaler.fit_transform(train)\n",
    "X_test=scaler.transform(test)\n",
    "scalet_filename=\"scaler_data\"\n",
    "joblib.dump(scaler,scalet_filename)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-28T00:51:09.857969Z",
     "start_time": "2020-02-28T00:51:09.853918Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train=X_train.reshape(X_train.shape[0],1,X_train.shape[1])\n",
    "X_test=X_test.reshape(X_test.shape[0],1,X_test.shape[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-28T01:01:40.154454Z",
     "start_time": "2020-02-28T01:01:40.147443Z"
    }
   },
   "outputs": [],
   "source": [
    "#reference - https://towardsdatascience.com/anomaly-detection-with-autoencoder-b4cdce4866a6\n",
    "def autoencoder_model(x):\n",
    "    inputs=Input(shape=(x.shape[1],x.shape[2]))\n",
    "    l1=LSTM(16,activation='relu',return_sequences=True,\n",
    "            kernel_regularizer=regularizers.l2(0.00))(inputs)\n",
    "    l2=LSTM(4,activation='relu',return_sequences=False)(l1)\n",
    "    l3=RepeatVector(x.shape[1])(l2)\n",
    "    l4=LSTM(4,activation='relu',return_sequences=True)(l3)\n",
    "    l5=LSTM(16,activation='relu',return_sequences=True)(l4)\n",
    "    output=TimeDistributed(Dense(x.shape[2]))(l5)\n",
    "    model=Model(inputs=inputs,outputs=output)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-28T01:02:23.848156Z",
     "start_time": "2020-02-28T01:02:22.813973Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 1, 341)            0         \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 1, 16)             22912     \n",
      "_________________________________________________________________\n",
      "lstm_2 (LSTM)                (None, 4)                 336       \n",
      "_________________________________________________________________\n",
      "repeat_vector_1 (RepeatVecto (None, 1, 4)              0         \n",
      "_________________________________________________________________\n",
      "lstm_3 (LSTM)                (None, 1, 4)              144       \n",
      "_________________________________________________________________\n",
      "lstm_4 (LSTM)                (None, 1, 16)             1344      \n",
      "_________________________________________________________________\n",
      "time_distributed_1 (TimeDist (None, 1, 341)            5797      \n",
      "=================================================================\n",
      "Total params: 30,533\n",
      "Trainable params: 30,533\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model=autoencoder_model(X_train)\n",
    "model.compile(optimizer='adam',loss='mae')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-28T01:09:23.724905Z",
     "start_time": "2020-02-28T01:08:56.825464Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/soujanyasamineni/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 475 samples, validate on 25 samples\n",
      "Epoch 1/100\n",
      "475/475 [==============================] - 2s 5ms/step - loss: 0.0022 - val_loss: 0.0039\n",
      "Epoch 2/100\n",
      "475/475 [==============================] - 0s 497us/step - loss: 0.0022 - val_loss: 0.0039\n",
      "Epoch 3/100\n",
      "475/475 [==============================] - 0s 483us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 4/100\n",
      "475/475 [==============================] - 0s 443us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 5/100\n",
      "475/475 [==============================] - 0s 495us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 6/100\n",
      "475/475 [==============================] - 0s 467us/step - loss: 0.0022 - val_loss: 0.0039\n",
      "Epoch 7/100\n",
      "475/475 [==============================] - 0s 484us/step - loss: 0.0022 - val_loss: 0.0039\n",
      "Epoch 8/100\n",
      "475/475 [==============================] - 0s 432us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 9/100\n",
      "475/475 [==============================] - 0s 530us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 10/100\n",
      "475/475 [==============================] - 0s 480us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 11/100\n",
      "475/475 [==============================] - 0s 659us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 12/100\n",
      "475/475 [==============================] - 0s 611us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 13/100\n",
      "475/475 [==============================] - 0s 451us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 14/100\n",
      "475/475 [==============================] - 0s 508us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 15/100\n",
      "475/475 [==============================] - 0s 499us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 16/100\n",
      "475/475 [==============================] - 0s 529us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 17/100\n",
      "475/475 [==============================] - 0s 551us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 18/100\n",
      "475/475 [==============================] - 0s 462us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 19/100\n",
      "475/475 [==============================] - 0s 464us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 20/100\n",
      "475/475 [==============================] - 0s 431us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 21/100\n",
      "475/475 [==============================] - 0s 462us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 22/100\n",
      "475/475 [==============================] - 0s 462us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 23/100\n",
      "475/475 [==============================] - 0s 438us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 24/100\n",
      "475/475 [==============================] - 0s 468us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 25/100\n",
      "475/475 [==============================] - 0s 444us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 26/100\n",
      "475/475 [==============================] - 0s 484us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 27/100\n",
      "475/475 [==============================] - 0s 507us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 28/100\n",
      "475/475 [==============================] - 0s 494us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 29/100\n",
      "475/475 [==============================] - 0s 478us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 30/100\n",
      "475/475 [==============================] - 0s 490us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 31/100\n",
      "475/475 [==============================] - 0s 483us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 32/100\n",
      "475/475 [==============================] - 0s 524us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 33/100\n",
      "475/475 [==============================] - 0s 632us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 34/100\n",
      "475/475 [==============================] - 0s 556us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 35/100\n",
      "475/475 [==============================] - 0s 466us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 36/100\n",
      "475/475 [==============================] - 0s 491us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 37/100\n",
      "475/475 [==============================] - 0s 464us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 38/100\n",
      "475/475 [==============================] - 0s 447us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 39/100\n",
      "475/475 [==============================] - 0s 536us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 40/100\n",
      "475/475 [==============================] - 0s 586us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 41/100\n",
      "475/475 [==============================] - 0s 447us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 42/100\n",
      "475/475 [==============================] - 0s 470us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 43/100\n",
      "475/475 [==============================] - 0s 511us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 44/100\n",
      "475/475 [==============================] - 0s 519us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 45/100\n",
      "475/475 [==============================] - 0s 450us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 46/100\n",
      "475/475 [==============================] - 0s 428us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 47/100\n",
      "475/475 [==============================] - 0s 515us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 48/100\n",
      "475/475 [==============================] - 0s 500us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 49/100\n",
      "475/475 [==============================] - 0s 465us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 50/100\n",
      "475/475 [==============================] - 0s 521us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 51/100\n",
      "475/475 [==============================] - 0s 517us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 52/100\n",
      "475/475 [==============================] - 0s 500us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 53/100\n",
      "475/475 [==============================] - 0s 467us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 54/100\n",
      "475/475 [==============================] - 0s 498us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 55/100\n",
      "475/475 [==============================] - 0s 519us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 56/100\n",
      "475/475 [==============================] - 0s 505us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 57/100\n",
      "475/475 [==============================] - 0s 522us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 58/100\n",
      "475/475 [==============================] - 0s 521us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 59/100\n",
      "475/475 [==============================] - 0s 483us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 60/100\n",
      "475/475 [==============================] - 0s 486us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 61/100\n",
      "475/475 [==============================] - 0s 443us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 62/100\n",
      "475/475 [==============================] - 0s 443us/step - loss: 0.0022 - val_loss: 0.0039\n",
      "Epoch 63/100\n",
      "475/475 [==============================] - 0s 458us/step - loss: 0.0022 - val_loss: 0.0039\n",
      "Epoch 64/100\n",
      "475/475 [==============================] - 0s 471us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 65/100\n",
      "475/475 [==============================] - 0s 459us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 66/100\n",
      "475/475 [==============================] - 0s 456us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 67/100\n",
      "475/475 [==============================] - 0s 477us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 68/100\n",
      "475/475 [==============================] - 0s 440us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 69/100\n",
      "475/475 [==============================] - 0s 474us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 70/100\n",
      "475/475 [==============================] - 0s 475us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 71/100\n",
      "475/475 [==============================] - 0s 450us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 72/100\n",
      "475/475 [==============================] - 0s 529us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 73/100\n",
      "475/475 [==============================] - 0s 544us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 74/100\n",
      "475/475 [==============================] - 0s 471us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 75/100\n",
      "475/475 [==============================] - 0s 497us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 76/100\n",
      "475/475 [==============================] - 0s 578us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 77/100\n",
      "475/475 [==============================] - 0s 491us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 78/100\n",
      "475/475 [==============================] - 0s 443us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 79/100\n",
      "475/475 [==============================] - 0s 430us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 80/100\n",
      "475/475 [==============================] - 0s 442us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 81/100\n",
      "475/475 [==============================] - 0s 403us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 82/100\n",
      "475/475 [==============================] - 0s 482us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 83/100\n",
      "475/475 [==============================] - 0s 457us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 84/100\n",
      "475/475 [==============================] - 0s 454us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 85/100\n",
      "475/475 [==============================] - 0s 452us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 86/100\n",
      "475/475 [==============================] - 0s 427us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 87/100\n",
      "475/475 [==============================] - 0s 434us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 88/100\n",
      "475/475 [==============================] - 0s 413us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 89/100\n",
      "475/475 [==============================] - 0s 416us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 90/100\n",
      "475/475 [==============================] - 0s 454us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 91/100\n",
      "475/475 [==============================] - 0s 511us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 92/100\n",
      "475/475 [==============================] - 0s 437us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 93/100\n",
      "475/475 [==============================] - 0s 433us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 94/100\n",
      "475/475 [==============================] - 0s 419us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 95/100\n",
      "475/475 [==============================] - 0s 441us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 96/100\n",
      "475/475 [==============================] - 0s 450us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 97/100\n",
      "475/475 [==============================] - 0s 456us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 98/100\n",
      "475/475 [==============================] - 0s 438us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 99/100\n",
      "475/475 [==============================] - 0s 468us/step - loss: 0.0021 - val_loss: 0.0039\n",
      "Epoch 100/100\n",
      "475/475 [==============================] - 0s 453us/step - loss: 0.0021 - val_loss: 0.0039\n"
     ]
    }
   ],
   "source": [
    "batch_size=10\n",
    "history=model.fit(X_train,X_train,nb_epoch=100, \n",
    "                  batch_size=batch_size,validation_split=0.05).history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-08T01:31:46.023843Z",
     "start_time": "2020-05-08T01:31:46.003607Z"
    }
   },
   "outputs": [],
   "source": [
    "# fig,ax=plt.subplots(figsize=(14,6), dpi=80)\n",
    "# ax.plot(history['loss'],'b',label='Train',linewidth=2)\n",
    "# ax.plot(history['val_loss'],'r',label='Validation',linewidth=2)\n",
    "# ax.set_title(\"Loss\",fontsize=12)\n",
    "# ax.set_ylabel(\"MAE\")\n",
    "# ax.set_xlabel(\"Epoch\")\n",
    "# ax.legend(loc=\"upper right\")\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-28T01:31:58.083778Z",
     "start_time": "2020-02-28T01:31:57.784442Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 0.02)"
      ]
     },
     "execution_count": 413,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_pred=model.predict(X_train)\n",
    "X_pred=X_pred.reshape(X_pred.shape[0],X_pred.shape[2])\n",
    "X_pred=pd.DataFrame(X_pred,columns=train.columns)\n",
    "X_pred.index=train.index\n",
    "scored=pd.DataFrame(index=train.index)\n",
    "Xtrain=X_train.reshape(X_train.shape[0],X_train.shape[2])\n",
    "scored['Loss_mae']=np.mean(np.abs(X_pred-Xtrain),axis=1)\n",
    "plt.figure(figsize=(10,9),dpi=80)\n",
    "sns.distplot(scored['Loss_mae'],bins=20,kde=True,color='b');\n",
    "plt.xlim([0.0,.02])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test and plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-05T01:44:11.540181Z",
     "start_time": "2020-03-05T01:44:11.496864Z"
    }
   },
   "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>Loss_mae</th>\n",
       "      <th>Threshold</th>\n",
       "      <th>Anomaly</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Notif.date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-07-17</th>\n",
       "      <td>0.001477</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-18</th>\n",
       "      <td>0.000109</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-19</th>\n",
       "      <td>0.000258</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-20</th>\n",
       "      <td>0.325622</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-23</th>\n",
       "      <td>0.000109</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Loss_mae  Threshold  Anomaly\n",
       "Notif.date                              \n",
       "2018-07-17  0.001477     0.0003     True\n",
       "2018-07-18  0.000109     0.0003    False\n",
       "2018-07-19  0.000258     0.0003    False\n",
       "2018-07-20  0.325622     0.0003     True\n",
       "2018-07-23  0.000109     0.0003    False"
      ]
     },
     "execution_count": 465,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#threshold of anomaly 0.01; loss on test set\n",
    "X_pred=model.predict(X_test)\n",
    "X_pred\n",
    "X_pred=X_pred.reshape(X_pred.shape[0],-1)\n",
    "X_pred=pd.DataFrame(X_pred,columns=test.columns)\n",
    "X_pred.index=test.index\n",
    "\n",
    "scored=pd.DataFrame(index=test.index)\n",
    "Xtest=X_test.reshape(X_test.shape[0],X_test.shape[2])\n",
    "scored['Loss_mae']=np.mean(np.abs(X_pred-Xtest),axis=1)\n",
    "scored['Threshold']=0.0003 # can be altered\n",
    "scored['Anomaly']=scored['Loss_mae']>scored['Threshold']\n",
    "scored.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-05T01:44:27.891279Z",
     "start_time": "2020-03-05T01:44:27.883281Z"
    }
   },
   "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>Loss_mae</th>\n",
       "      <th>Threshold</th>\n",
       "      <th>Anomaly</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Notif.date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-07-17</th>\n",
       "      <td>0.001477</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-18</th>\n",
       "      <td>0.000109</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-19</th>\n",
       "      <td>0.000258</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-20</th>\n",
       "      <td>0.325622</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-23</th>\n",
       "      <td>0.000109</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Loss_mae  Threshold  Anomaly\n",
       "Notif.date                              \n",
       "2018-07-17  0.001477     0.0003     True\n",
       "2018-07-18  0.000109     0.0003    False\n",
       "2018-07-19  0.000258     0.0003    False\n",
       "2018-07-20  0.325622     0.0003     True\n",
       "2018-07-23  0.000109     0.0003    False"
      ]
     },
     "execution_count": 466,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scored.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-05T01:44:44.493165Z",
     "start_time": "2020-03-05T01:44:44.458587Z"
    }
   },
   "outputs": [],
   "source": [
    "#calculate for entire dataset\n",
    "X_pred_train=model.predict(X_train)\n",
    "X_pred_train=X_pred_train.reshape(X_pred_train.shape[0],X_pred_train.shape[2])\n",
    "X_pred_train=pd.DataFrame(X_pred_train,columns=train.columns)\n",
    "X_pred_train.index=train.index\n",
    "scored_train=pd.DataFrame(index=train.index)\n",
    "scored_train['Loss_mae']=np.mean(np.abs(X_pred_train-Xtrain),axis=1)\n",
    "scored_train['Threshold']=0.0003 # can be altered\n",
    "scored_train['Anomaly']=scored_train['Loss_mae']>scored_train['Threshold']\n",
    "scored=pd.concat([scored_train,scored])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-05T01:45:01.838249Z",
     "start_time": "2020-03-05T01:45:01.309386Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b039ea90>"
      ]
     },
     "execution_count": 468,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scored.plot(logy=True, figsize=(16,9),ylim=[1e-5,1e1],color=['blue','red'])"
   ]
  },
  {
   "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"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
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   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}