Deep Learning Basics for Predictive Maintenance.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Deep Learning for Predictive Maintenance\n",
    "\n",
    "Deep learning has proven to show superior performance in certain domains such as object recognition and image classification. It has also gained popularity in  domains such as finance where time-series data plays an important role. Predictive Maintenance is also a domain where data is collected over time to monitor the state of an asset with the goal of finding patterns to predict failures which can also benefit from certain deep learning algorithms. Among the deep learning methods, Long Short Term Memory [(LSTM)](http://colah.github.io/posts/2015-08-Understanding-LSTMs/) networks are especially appealing to the predictive maintenance domain due to the fact that they are very good at learning from sequences. This fact lends itself to their applications using time series data by making it possible to look back for longer periods of time to detect failure patterns. In this notebook, we build an LSTM network for the data set and scenerio described at [Predictive Maintenance Template](https://gallery.cortanaintelligence.com/Collection/Predictive-Maintenance-Template-3) to predict remaining useful life of aircraft engines. In summary, the template uses simulated aircraft sensor values to predict when an aircraft engine will fail in the future so that maintenance can be planned in advance. \n",
    "\n",
    "This notebook serves as a tutorial for beginners looking to apply deep learning in predictive maintenance domain and uses a simple scenario where only one data source (sensor values) is used to make predictions. In more advanced predictive maintenance scenarios such as in [Predictive Maintenance Modelling Guide](https://gallery.cortanaintelligence.com/Notebook/Predictive-Maintenance-Modelling-Guide-R-Notebook-1), there are many other data sources (i.e. historical maintenance records, error logs, machine and operator features etc.) which may require different types of treatments to be used in the deep learning networks. Since predictive maintenance is not a typical domain for deep learning, its application is an open area of research. \n",
    "\n",
    "This notebook uses [keras](https://keras.io/) deep learning library with Microsoft Cognitive Toolkit [CNTK](https://docs.microsoft.com/en-us/cognitive-toolkit/Using-CNTK-with-Keras) as backend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using CNTK backend\n"
     ]
    }
   ],
   "source": [
    "import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Setting seed for reproducability\n",
    "np.random.seed(1234)  \n",
    "PYTHONHASHSEED = 0\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import confusion_matrix, recall_score, precision_score\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, LSTM, Activation\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Data Ingestion\n",
    "In the following section, we ingest the training, test and ground truth datasets from azure storage. The training data consists of multiple multivariate time series with \"cycle\" as the time unit, together with 21 sensor readings for each cycle. Each time series can be assumed as being generated from a different engine of the same type. The testing data has the same data schema as the training data. The only difference is that the data does not indicate when the failure occurs. Finally, the ground truth data provides the number of remaining working cycles for the engines in the testing data. You can find more details about the type of data used for this notebook at [Predictive Maintenance Template](https://gallery.cortanaintelligence.com/Collection/Predictive-Maintenance-Template-3)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2017-06-14 13:33:11--  http://azuremlsamples.azureml.net/templatedata/PM_train.txt\n",
      "Resolving azuremlsamples.azureml.net (azuremlsamples.azureml.net)... 72.21.81.200, 2606:2800:11f:17a5:191a:18d5:537:22f9\n",
      "Connecting to azuremlsamples.azureml.net (azuremlsamples.azureml.net)|72.21.81.200|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 3515356 (3.4M) [text/plain]\n",
      "Saving to: ‘PM_train.txt.2’\n",
      "\n",
      "PM_train.txt.2      100%[===================>]   3.35M  20.6MB/s    in 0.2s    \n",
      "\n",
      "2017-06-14 13:33:12 (20.6 MB/s) - ‘PM_train.txt.2’ saved [3515356/3515356]\n",
      "\n",
      "--2017-06-14 13:33:12--  http://azuremlsamples.azureml.net/templatedata/PM_test.txt\n",
      "Resolving azuremlsamples.azureml.net (azuremlsamples.azureml.net)... 72.21.81.200, 2606:2800:11f:17a5:191a:18d5:537:22f9\n",
      "Connecting to azuremlsamples.azureml.net (azuremlsamples.azureml.net)|72.21.81.200|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 2228855 (2.1M) [text/plain]\n",
      "Saving to: ‘PM_test.txt.2’\n",
      "\n",
      "PM_test.txt.2       100%[===================>]   2.12M  --.-KB/s    in 0.1s    \n",
      "\n",
      "2017-06-14 13:33:12 (17.5 MB/s) - ‘PM_test.txt.2’ saved [2228855/2228855]\n",
      "\n",
      "--2017-06-14 13:33:12--  http://azuremlsamples.azureml.net/templatedata/PM_truth.txt\n",
      "Resolving azuremlsamples.azureml.net (azuremlsamples.azureml.net)... 72.21.81.200, 2606:2800:11f:17a5:191a:18d5:537:22f9\n",
      "Connecting to azuremlsamples.azureml.net (azuremlsamples.azureml.net)|72.21.81.200|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 429 [text/plain]\n",
      "Saving to: ‘PM_truth.txt.2’\n",
      "\n",
      "PM_truth.txt.2      100%[===================>]     429  --.-KB/s    in 0s      \n",
      "\n",
      "2017-06-14 13:33:12 (128 MB/s) - ‘PM_truth.txt.2’ saved [429/429]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Data ingestion - reading the datasets from Azure blob \n",
    "!wget http://azuremlsamples.azureml.net/templatedata/PM_train.txt \n",
    "!wget http://azuremlsamples.azureml.net/templatedata/PM_test.txt\n",
    "!wget http://azuremlsamples.azureml.net/templatedata/PM_truth.txt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# read training data \n",
    "train_df = pd.read_csv('PM_train.txt', sep=\" \", header=None)\n",
    "train_df.drop(train_df.columns[[26, 27]], axis=1, inplace=True)\n",
    "train_df.columns = ['id', 'cycle', 'setting1', 'setting2', 'setting3', 's1', 's2', 's3',\n",
    "                     's4', 's5', 's6', 's7', 's8', 's9', 's10', 's11', 's12', 's13', 's14',\n",
    "                     's15', 's16', 's17', 's18', 's19', 's20', 's21']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# read test data\n",
    "test_df = pd.read_csv('PM_test.txt', sep=\" \", header=None)\n",
    "test_df.drop(test_df.columns[[26, 27]], axis=1, inplace=True)\n",
    "test_df.columns = ['id', 'cycle', 'setting1', 'setting2', 'setting3', 's1', 's2', 's3',\n",
    "                     's4', 's5', 's6', 's7', 's8', 's9', 's10', 's11', 's12', 's13', 's14',\n",
    "                     's15', 's16', 's17', 's18', 's19', 's20', 's21']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# read ground truth data\n",
    "truth_df = pd.read_csv('PM_truth.txt', sep=\" \", header=None)\n",
    "truth_df.drop(truth_df.columns[[1]], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>cycle</th>\n",
       "      <th>setting1</th>\n",
       "      <th>setting2</th>\n",
       "      <th>setting3</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>...</th>\n",
       "      <th>s12</th>\n",
       "      <th>s13</th>\n",
       "      <th>s14</th>\n",
       "      <th>s15</th>\n",
       "      <th>s16</th>\n",
       "      <th>s17</th>\n",
       "      <th>s18</th>\n",
       "      <th>s19</th>\n",
       "      <th>s20</th>\n",
       "      <th>s21</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.0007</td>\n",
       "      <td>-0.0004</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>641.82</td>\n",
       "      <td>1589.70</td>\n",
       "      <td>1400.60</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>521.66</td>\n",
       "      <td>2388.02</td>\n",
       "      <td>8138.62</td>\n",
       "      <td>8.4195</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.06</td>\n",
       "      <td>23.4190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.15</td>\n",
       "      <td>1591.82</td>\n",
       "      <td>1403.14</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.28</td>\n",
       "      <td>2388.07</td>\n",
       "      <td>8131.49</td>\n",
       "      <td>8.4318</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.00</td>\n",
       "      <td>23.4236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.0043</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.35</td>\n",
       "      <td>1587.99</td>\n",
       "      <td>1404.20</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.42</td>\n",
       "      <td>2388.03</td>\n",
       "      <td>8133.23</td>\n",
       "      <td>8.4178</td>\n",
       "      <td>0.03</td>\n",
       "      <td>390</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.95</td>\n",
       "      <td>23.3442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.35</td>\n",
       "      <td>1582.79</td>\n",
       "      <td>1401.87</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.86</td>\n",
       "      <td>2388.08</td>\n",
       "      <td>8133.83</td>\n",
       "      <td>8.3682</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.88</td>\n",
       "      <td>23.3739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.0019</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.37</td>\n",
       "      <td>1582.85</td>\n",
       "      <td>1406.22</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.19</td>\n",
       "      <td>2388.04</td>\n",
       "      <td>8133.80</td>\n",
       "      <td>8.4294</td>\n",
       "      <td>0.03</td>\n",
       "      <td>393</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.90</td>\n",
       "      <td>23.4044</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  cycle  setting1  setting2  setting3      s1      s2       s3       s4  \\\n",
       "0   1      1   -0.0007   -0.0004     100.0  518.67  641.82  1589.70  1400.60   \n",
       "1   1      2    0.0019   -0.0003     100.0  518.67  642.15  1591.82  1403.14   \n",
       "2   1      3   -0.0043    0.0003     100.0  518.67  642.35  1587.99  1404.20   \n",
       "3   1      4    0.0007    0.0000     100.0  518.67  642.35  1582.79  1401.87   \n",
       "4   1      5   -0.0019   -0.0002     100.0  518.67  642.37  1582.85  1406.22   \n",
       "\n",
       "      s5   ...        s12      s13      s14     s15   s16  s17   s18    s19  \\\n",
       "0  14.62   ...     521.66  2388.02  8138.62  8.4195  0.03  392  2388  100.0   \n",
       "1  14.62   ...     522.28  2388.07  8131.49  8.4318  0.03  392  2388  100.0   \n",
       "2  14.62   ...     522.42  2388.03  8133.23  8.4178  0.03  390  2388  100.0   \n",
       "3  14.62   ...     522.86  2388.08  8133.83  8.3682  0.03  392  2388  100.0   \n",
       "4  14.62   ...     522.19  2388.04  8133.80  8.4294  0.03  393  2388  100.0   \n",
       "\n",
       "     s20      s21  \n",
       "0  39.06  23.4190  \n",
       "1  39.00  23.4236  \n",
       "2  38.95  23.3442  \n",
       "3  38.88  23.3739  \n",
       "4  38.90  23.4044  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.sort_values(['id','cycle'])\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing\n",
    "First step is to generate labels for the training data which are Remaining Useful Life (RUL), label1 and label2 as was done in the [Predictive Maintenance Template](https://gallery.cortanaintelligence.com/Collection/Predictive-Maintenance-Template-3). Here, we will only make use of \"label1\" for binary clasification, while trying to answer the question: is a specific engine going to fail within w1 cycles?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>cycle</th>\n",
       "      <th>setting1</th>\n",
       "      <th>setting2</th>\n",
       "      <th>setting3</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>...</th>\n",
       "      <th>s13</th>\n",
       "      <th>s14</th>\n",
       "      <th>s15</th>\n",
       "      <th>s16</th>\n",
       "      <th>s17</th>\n",
       "      <th>s18</th>\n",
       "      <th>s19</th>\n",
       "      <th>s20</th>\n",
       "      <th>s21</th>\n",
       "      <th>RUL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.0007</td>\n",
       "      <td>-0.0004</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>641.82</td>\n",
       "      <td>1589.70</td>\n",
       "      <td>1400.60</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>2388.02</td>\n",
       "      <td>8138.62</td>\n",
       "      <td>8.4195</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.06</td>\n",
       "      <td>23.4190</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.15</td>\n",
       "      <td>1591.82</td>\n",
       "      <td>1403.14</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>2388.07</td>\n",
       "      <td>8131.49</td>\n",
       "      <td>8.4318</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.00</td>\n",
       "      <td>23.4236</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.0043</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.35</td>\n",
       "      <td>1587.99</td>\n",
       "      <td>1404.20</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>2388.03</td>\n",
       "      <td>8133.23</td>\n",
       "      <td>8.4178</td>\n",
       "      <td>0.03</td>\n",
       "      <td>390</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.95</td>\n",
       "      <td>23.3442</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.35</td>\n",
       "      <td>1582.79</td>\n",
       "      <td>1401.87</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>2388.08</td>\n",
       "      <td>8133.83</td>\n",
       "      <td>8.3682</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.88</td>\n",
       "      <td>23.3739</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.0019</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.37</td>\n",
       "      <td>1582.85</td>\n",
       "      <td>1406.22</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>2388.04</td>\n",
       "      <td>8133.80</td>\n",
       "      <td>8.4294</td>\n",
       "      <td>0.03</td>\n",
       "      <td>393</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.90</td>\n",
       "      <td>23.4044</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  cycle  setting1  setting2  setting3      s1      s2       s3       s4  \\\n",
       "0   1      1   -0.0007   -0.0004     100.0  518.67  641.82  1589.70  1400.60   \n",
       "1   1      2    0.0019   -0.0003     100.0  518.67  642.15  1591.82  1403.14   \n",
       "2   1      3   -0.0043    0.0003     100.0  518.67  642.35  1587.99  1404.20   \n",
       "3   1      4    0.0007    0.0000     100.0  518.67  642.35  1582.79  1401.87   \n",
       "4   1      5   -0.0019   -0.0002     100.0  518.67  642.37  1582.85  1406.22   \n",
       "\n",
       "      s5 ...       s13      s14     s15   s16  s17   s18    s19    s20  \\\n",
       "0  14.62 ...   2388.02  8138.62  8.4195  0.03  392  2388  100.0  39.06   \n",
       "1  14.62 ...   2388.07  8131.49  8.4318  0.03  392  2388  100.0  39.00   \n",
       "2  14.62 ...   2388.03  8133.23  8.4178  0.03  390  2388  100.0  38.95   \n",
       "3  14.62 ...   2388.08  8133.83  8.3682  0.03  392  2388  100.0  38.88   \n",
       "4  14.62 ...   2388.04  8133.80  8.4294  0.03  393  2388  100.0  38.90   \n",
       "\n",
       "       s21  RUL  \n",
       "0  23.4190  191  \n",
       "1  23.4236  190  \n",
       "2  23.3442  189  \n",
       "3  23.3739  188  \n",
       "4  23.4044  187  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data Labeling - generate column RUL\n",
    "rul = pd.DataFrame(train_df.groupby('id')['cycle'].max()).reset_index()\n",
    "rul.columns = ['id', 'max']\n",
    "train_df = train_df.merge(rul, on=['id'], how='left')\n",
    "train_df['RUL'] = train_df['max'] - train_df['cycle']\n",
    "train_df.drop('max', axis=1, inplace=True)\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>cycle</th>\n",
       "      <th>setting1</th>\n",
       "      <th>setting2</th>\n",
       "      <th>setting3</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>...</th>\n",
       "      <th>s15</th>\n",
       "      <th>s16</th>\n",
       "      <th>s17</th>\n",
       "      <th>s18</th>\n",
       "      <th>s19</th>\n",
       "      <th>s20</th>\n",
       "      <th>s21</th>\n",
       "      <th>RUL</th>\n",
       "      <th>label1</th>\n",
       "      <th>label2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.0007</td>\n",
       "      <td>-0.0004</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>641.82</td>\n",
       "      <td>1589.70</td>\n",
       "      <td>1400.60</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>8.4195</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.06</td>\n",
       "      <td>23.4190</td>\n",
       "      <td>191</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.15</td>\n",
       "      <td>1591.82</td>\n",
       "      <td>1403.14</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>8.4318</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.00</td>\n",
       "      <td>23.4236</td>\n",
       "      <td>190</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.0043</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.35</td>\n",
       "      <td>1587.99</td>\n",
       "      <td>1404.20</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>8.4178</td>\n",
       "      <td>0.03</td>\n",
       "      <td>390</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.95</td>\n",
       "      <td>23.3442</td>\n",
       "      <td>189</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.35</td>\n",
       "      <td>1582.79</td>\n",
       "      <td>1401.87</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>8.3682</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.88</td>\n",
       "      <td>23.3739</td>\n",
       "      <td>188</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.0019</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.37</td>\n",
       "      <td>1582.85</td>\n",
       "      <td>1406.22</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>8.4294</td>\n",
       "      <td>0.03</td>\n",
       "      <td>393</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.90</td>\n",
       "      <td>23.4044</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  cycle  setting1  setting2  setting3      s1      s2       s3       s4  \\\n",
       "0   1      1   -0.0007   -0.0004     100.0  518.67  641.82  1589.70  1400.60   \n",
       "1   1      2    0.0019   -0.0003     100.0  518.67  642.15  1591.82  1403.14   \n",
       "2   1      3   -0.0043    0.0003     100.0  518.67  642.35  1587.99  1404.20   \n",
       "3   1      4    0.0007    0.0000     100.0  518.67  642.35  1582.79  1401.87   \n",
       "4   1      5   -0.0019   -0.0002     100.0  518.67  642.37  1582.85  1406.22   \n",
       "\n",
       "      s5   ...       s15   s16  s17   s18    s19    s20      s21  RUL  label1  \\\n",
       "0  14.62   ...    8.4195  0.03  392  2388  100.0  39.06  23.4190  191       0   \n",
       "1  14.62   ...    8.4318  0.03  392  2388  100.0  39.00  23.4236  190       0   \n",
       "2  14.62   ...    8.4178  0.03  390  2388  100.0  38.95  23.3442  189       0   \n",
       "3  14.62   ...    8.3682  0.03  392  2388  100.0  38.88  23.3739  188       0   \n",
       "4  14.62   ...    8.4294  0.03  393  2388  100.0  38.90  23.4044  187       0   \n",
       "\n",
       "   label2  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# generate label columns for training data\n",
    "w1 = 30\n",
    "w0 = 15\n",
    "train_df['label1'] = np.where(train_df['RUL'] <= w1, 1, 0 )\n",
    "train_df['label2'] = train_df['label1']\n",
    "train_df.loc[train_df['RUL'] <= w0, 'label2'] = 2\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the [Predictive Maintenance Template](https://gallery.cortanaintelligence.com/Collection/Predictive-Maintenance-Template-3) , cycle column is also used for training so we will also include the cycle column. Here, we normalize the columns in the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>cycle</th>\n",
       "      <th>setting1</th>\n",
       "      <th>setting2</th>\n",
       "      <th>setting3</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>...</th>\n",
       "      <th>s16</th>\n",
       "      <th>s17</th>\n",
       "      <th>s18</th>\n",
       "      <th>s19</th>\n",
       "      <th>s20</th>\n",
       "      <th>s21</th>\n",
       "      <th>RUL</th>\n",
       "      <th>label1</th>\n",
       "      <th>label2</th>\n",
       "      <th>cycle_norm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.459770</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.183735</td>\n",
       "      <td>0.406802</td>\n",
       "      <td>0.309757</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.713178</td>\n",
       "      <td>0.724662</td>\n",
       "      <td>191</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.609195</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.283133</td>\n",
       "      <td>0.453019</td>\n",
       "      <td>0.352633</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.731014</td>\n",
       "      <td>190</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.252874</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.343373</td>\n",
       "      <td>0.369523</td>\n",
       "      <td>0.370527</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.627907</td>\n",
       "      <td>0.621375</td>\n",
       "      <td>189</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.540230</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.343373</td>\n",
       "      <td>0.256159</td>\n",
       "      <td>0.331195</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573643</td>\n",
       "      <td>0.662386</td>\n",
       "      <td>188</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.390805</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.349398</td>\n",
       "      <td>0.257467</td>\n",
       "      <td>0.404625</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.589147</td>\n",
       "      <td>0.704502</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.01108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  cycle  setting1  setting2  setting3   s1        s2        s3        s4  \\\n",
       "0   1      1  0.459770  0.166667       0.0  0.0  0.183735  0.406802  0.309757   \n",
       "1   1      2  0.609195  0.250000       0.0  0.0  0.283133  0.453019  0.352633   \n",
       "2   1      3  0.252874  0.750000       0.0  0.0  0.343373  0.369523  0.370527   \n",
       "3   1      4  0.540230  0.500000       0.0  0.0  0.343373  0.256159  0.331195   \n",
       "4   1      5  0.390805  0.333333       0.0  0.0  0.349398  0.257467  0.404625   \n",
       "\n",
       "    s5     ...      s16       s17  s18  s19       s20       s21  RUL  label1  \\\n",
       "0  0.0     ...      0.0  0.333333  0.0  0.0  0.713178  0.724662  191       0   \n",
       "1  0.0     ...      0.0  0.333333  0.0  0.0  0.666667  0.731014  190       0   \n",
       "2  0.0     ...      0.0  0.166667  0.0  0.0  0.627907  0.621375  189       0   \n",
       "3  0.0     ...      0.0  0.333333  0.0  0.0  0.573643  0.662386  188       0   \n",
       "4  0.0     ...      0.0  0.416667  0.0  0.0  0.589147  0.704502  187       0   \n",
       "\n",
       "   label2  cycle_norm  \n",
       "0       0     0.00000  \n",
       "1       0     0.00277  \n",
       "2       0     0.00554  \n",
       "3       0     0.00831  \n",
       "4       0     0.01108  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MinMax normalization\n",
    "train_df['cycle_norm'] = train_df['cycle']\n",
    "cols_normalize = train_df.columns.difference(['id','cycle','RUL','label1','label2'])\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "norm_train_df = pd.DataFrame(min_max_scaler.fit_transform(train_df[cols_normalize]), \n",
    "                             columns=cols_normalize, \n",
    "                             index=train_df.index)\n",
    "join_df = train_df[train_df.columns.difference(cols_normalize)].join(norm_train_df)\n",
    "train_df = join_df.reindex(columns = train_df.columns)\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we prepare the test data. We first normalize the test data using the parameters from the MinMax normalization applied on the training data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>cycle</th>\n",
       "      <th>setting1</th>\n",
       "      <th>setting2</th>\n",
       "      <th>setting3</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>...</th>\n",
       "      <th>s13</th>\n",
       "      <th>s14</th>\n",
       "      <th>s15</th>\n",
       "      <th>s16</th>\n",
       "      <th>s17</th>\n",
       "      <th>s18</th>\n",
       "      <th>s19</th>\n",
       "      <th>s20</th>\n",
       "      <th>s21</th>\n",
       "      <th>cycle_norm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.632184</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.545181</td>\n",
       "      <td>0.310661</td>\n",
       "      <td>0.269413</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.220588</td>\n",
       "      <td>0.132160</td>\n",
       "      <td>0.308965</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.558140</td>\n",
       "      <td>0.661834</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344828</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.150602</td>\n",
       "      <td>0.379551</td>\n",
       "      <td>0.222316</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.264706</td>\n",
       "      <td>0.204768</td>\n",
       "      <td>0.213159</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.682171</td>\n",
       "      <td>0.686827</td>\n",
       "      <td>0.00277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.517241</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.376506</td>\n",
       "      <td>0.346632</td>\n",
       "      <td>0.322248</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.220588</td>\n",
       "      <td>0.155640</td>\n",
       "      <td>0.458638</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.728682</td>\n",
       "      <td>0.721348</td>\n",
       "      <td>0.00554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.741379</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.370482</td>\n",
       "      <td>0.285154</td>\n",
       "      <td>0.408001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.170090</td>\n",
       "      <td>0.257022</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.662110</td>\n",
       "      <td>0.00831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.580460</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.391566</td>\n",
       "      <td>0.352082</td>\n",
       "      <td>0.332039</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.220588</td>\n",
       "      <td>0.152751</td>\n",
       "      <td>0.300885</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.658915</td>\n",
       "      <td>0.716377</td>\n",
       "      <td>0.01108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  cycle  setting1  setting2  setting3   s1        s2        s3        s4  \\\n",
       "0   1      1  0.632184  0.750000       0.0  0.0  0.545181  0.310661  0.269413   \n",
       "1   1      2  0.344828  0.250000       0.0  0.0  0.150602  0.379551  0.222316   \n",
       "2   1      3  0.517241  0.583333       0.0  0.0  0.376506  0.346632  0.322248   \n",
       "3   1      4  0.741379  0.500000       0.0  0.0  0.370482  0.285154  0.408001   \n",
       "4   1      5  0.580460  0.500000       0.0  0.0  0.391566  0.352082  0.332039   \n",
       "\n",
       "    s5     ...           s13       s14       s15  s16       s17  s18  s19  \\\n",
       "0  0.0     ...      0.220588  0.132160  0.308965  0.0  0.333333  0.0  0.0   \n",
       "1  0.0     ...      0.264706  0.204768  0.213159  0.0  0.416667  0.0  0.0   \n",
       "2  0.0     ...      0.220588  0.155640  0.458638  0.0  0.416667  0.0  0.0   \n",
       "3  0.0     ...      0.250000  0.170090  0.257022  0.0  0.250000  0.0  0.0   \n",
       "4  0.0     ...      0.220588  0.152751  0.300885  0.0  0.166667  0.0  0.0   \n",
       "\n",
       "        s20       s21  cycle_norm  \n",
       "0  0.558140  0.661834     0.00000  \n",
       "1  0.682171  0.686827     0.00277  \n",
       "2  0.728682  0.721348     0.00554  \n",
       "3  0.666667  0.662110     0.00831  \n",
       "4  0.658915  0.716377     0.01108  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df['cycle_norm'] = test_df['cycle']\n",
    "norm_test_df = pd.DataFrame(min_max_scaler.transform(test_df[cols_normalize]), \n",
    "                            columns=cols_normalize, \n",
    "                            index=test_df.index)\n",
    "test_join_df = test_df[test_df.columns.difference(cols_normalize)].join(norm_test_df)\n",
    "test_df = test_join_df.reindex(columns = test_df.columns)\n",
    "test_df = test_df.reset_index(drop=True)\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we use the ground truth dataset to generate labels for the test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# generate column max for test data\n",
    "rul = pd.DataFrame(test_df.groupby('id')['cycle'].max()).reset_index()\n",
    "rul.columns = ['id', 'max']\n",
    "truth_df.columns = ['more']\n",
    "truth_df['id'] = truth_df.index + 1\n",
    "truth_df['max'] = rul['max'] + truth_df['more']\n",
    "truth_df.drop('more', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>cycle</th>\n",
       "      <th>setting1</th>\n",
       "      <th>setting2</th>\n",
       "      <th>setting3</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>...</th>\n",
       "      <th>s14</th>\n",
       "      <th>s15</th>\n",
       "      <th>s16</th>\n",
       "      <th>s17</th>\n",
       "      <th>s18</th>\n",
       "      <th>s19</th>\n",
       "      <th>s20</th>\n",
       "      <th>s21</th>\n",
       "      <th>cycle_norm</th>\n",
       "      <th>RUL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.632184</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.545181</td>\n",
       "      <td>0.310661</td>\n",
       "      <td>0.269413</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.132160</td>\n",
       "      <td>0.308965</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.558140</td>\n",
       "      <td>0.661834</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344828</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.150602</td>\n",
       "      <td>0.379551</td>\n",
       "      <td>0.222316</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.204768</td>\n",
       "      <td>0.213159</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.682171</td>\n",
       "      <td>0.686827</td>\n",
       "      <td>0.00277</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.517241</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.376506</td>\n",
       "      <td>0.346632</td>\n",
       "      <td>0.322248</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.155640</td>\n",
       "      <td>0.458638</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.728682</td>\n",
       "      <td>0.721348</td>\n",
       "      <td>0.00554</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.741379</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.370482</td>\n",
       "      <td>0.285154</td>\n",
       "      <td>0.408001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.170090</td>\n",
       "      <td>0.257022</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.662110</td>\n",
       "      <td>0.00831</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.580460</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.391566</td>\n",
       "      <td>0.352082</td>\n",
       "      <td>0.332039</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.152751</td>\n",
       "      <td>0.300885</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.658915</td>\n",
       "      <td>0.716377</td>\n",
       "      <td>0.01108</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  cycle  setting1  setting2  setting3   s1        s2        s3        s4  \\\n",
       "0   1      1  0.632184  0.750000       0.0  0.0  0.545181  0.310661  0.269413   \n",
       "1   1      2  0.344828  0.250000       0.0  0.0  0.150602  0.379551  0.222316   \n",
       "2   1      3  0.517241  0.583333       0.0  0.0  0.376506  0.346632  0.322248   \n",
       "3   1      4  0.741379  0.500000       0.0  0.0  0.370482  0.285154  0.408001   \n",
       "4   1      5  0.580460  0.500000       0.0  0.0  0.391566  0.352082  0.332039   \n",
       "\n",
       "    s5 ...        s14       s15  s16       s17  s18  s19       s20       s21  \\\n",
       "0  0.0 ...   0.132160  0.308965  0.0  0.333333  0.0  0.0  0.558140  0.661834   \n",
       "1  0.0 ...   0.204768  0.213159  0.0  0.416667  0.0  0.0  0.682171  0.686827   \n",
       "2  0.0 ...   0.155640  0.458638  0.0  0.416667  0.0  0.0  0.728682  0.721348   \n",
       "3  0.0 ...   0.170090  0.257022  0.0  0.250000  0.0  0.0  0.666667  0.662110   \n",
       "4  0.0 ...   0.152751  0.300885  0.0  0.166667  0.0  0.0  0.658915  0.716377   \n",
       "\n",
       "   cycle_norm  RUL  \n",
       "0     0.00000  142  \n",
       "1     0.00277  141  \n",
       "2     0.00554  140  \n",
       "3     0.00831  139  \n",
       "4     0.01108  138  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# generate RUL for test data\n",
    "test_df = test_df.merge(truth_df, on=['id'], how='left')\n",
    "test_df['RUL'] = test_df['max'] - test_df['cycle']\n",
    "test_df.drop('max', axis=1, inplace=True)\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>cycle</th>\n",
       "      <th>setting1</th>\n",
       "      <th>setting2</th>\n",
       "      <th>setting3</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>...</th>\n",
       "      <th>s16</th>\n",
       "      <th>s17</th>\n",
       "      <th>s18</th>\n",
       "      <th>s19</th>\n",
       "      <th>s20</th>\n",
       "      <th>s21</th>\n",
       "      <th>cycle_norm</th>\n",
       "      <th>RUL</th>\n",
       "      <th>label1</th>\n",
       "      <th>label2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.632184</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.545181</td>\n",
       "      <td>0.310661</td>\n",
       "      <td>0.269413</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.558140</td>\n",
       "      <td>0.661834</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>142</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344828</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.150602</td>\n",
       "      <td>0.379551</td>\n",
       "      <td>0.222316</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.682171</td>\n",
       "      <td>0.686827</td>\n",
       "      <td>0.00277</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.517241</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.376506</td>\n",
       "      <td>0.346632</td>\n",
       "      <td>0.322248</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.728682</td>\n",
       "      <td>0.721348</td>\n",
       "      <td>0.00554</td>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.741379</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.370482</td>\n",
       "      <td>0.285154</td>\n",
       "      <td>0.408001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.662110</td>\n",
       "      <td>0.00831</td>\n",
       "      <td>139</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.580460</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.391566</td>\n",
       "      <td>0.352082</td>\n",
       "      <td>0.332039</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.658915</td>\n",
       "      <td>0.716377</td>\n",
       "      <td>0.01108</td>\n",
       "      <td>138</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  cycle  setting1  setting2  setting3   s1        s2        s3        s4  \\\n",
       "0   1      1  0.632184  0.750000       0.0  0.0  0.545181  0.310661  0.269413   \n",
       "1   1      2  0.344828  0.250000       0.0  0.0  0.150602  0.379551  0.222316   \n",
       "2   1      3  0.517241  0.583333       0.0  0.0  0.376506  0.346632  0.322248   \n",
       "3   1      4  0.741379  0.500000       0.0  0.0  0.370482  0.285154  0.408001   \n",
       "4   1      5  0.580460  0.500000       0.0  0.0  0.391566  0.352082  0.332039   \n",
       "\n",
       "    s5   ...    s16       s17  s18  s19       s20       s21  cycle_norm  RUL  \\\n",
       "0  0.0   ...    0.0  0.333333  0.0  0.0  0.558140  0.661834     0.00000  142   \n",
       "1  0.0   ...    0.0  0.416667  0.0  0.0  0.682171  0.686827     0.00277  141   \n",
       "2  0.0   ...    0.0  0.416667  0.0  0.0  0.728682  0.721348     0.00554  140   \n",
       "3  0.0   ...    0.0  0.250000  0.0  0.0  0.666667  0.662110     0.00831  139   \n",
       "4  0.0   ...    0.0  0.166667  0.0  0.0  0.658915  0.716377     0.01108  138   \n",
       "\n",
       "   label1  label2  \n",
       "0       0       0  \n",
       "1       0       0  \n",
       "2       0       0  \n",
       "3       0       0  \n",
       "4       0       0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# generate label columns w0 and w1 for test data\n",
    "test_df['label1'] = np.where(test_df['RUL'] <= w1, 1, 0 )\n",
    "test_df['label2'] = test_df['label1']\n",
    "test_df.loc[test_df['RUL'] <= w0, 'label2'] = 2\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the rest of the notebook, we train an LSTM network that we will compare to the results in [Predictive Maintenance Template Step 2B of 3](https://gallery.cortanaintelligence.com/Experiment/Predictive-Maintenance-Step-2B-of-3-train-and-evaluate-binary-classification-models-2) where a series of machine learning models are used to train and evaluate the binary classification model that uses column \"label1\" as the label."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelling\n",
    "\n",
    "The traditional predictive maintenance machine learning models are based on feature engineering which is manual construction of right features using domain expertise and similar methods. This usually makes these models hard to reuse since feature engineering is specific to the problem scenario and the available data which varies from one business to the other. Perhaps the most attractive part of applying deep learning in the predictive maintenance domain is the fact that these networks can automatically extract the right features from the data, eliminating the need for manual feature engineering.\n",
    "\n",
    "When using LSTMs in the time-series domain, one important parameter to pick is the sequence length which is the window for LSTMs to look back. This may be viewed as similar to picking window_size = 5 cycles for calculating the rolling features in the [Predictive Maintenance Template](https://gallery.cortanaintelligence.com/Collection/Predictive-Maintenance-Template-3) which are rolling mean and rolling standard deviation for 21 sensor values. The idea of using LSTMs is to let the model extract abstract features out of the sequence of sensor values in the window rather than engineering those manually. The expectation is that if there is a pattern in these sensor values within the window prior to failure, the pattern should be encoded by the LSTM.\n",
    "\n",
    "One critical advantage of LSTMs is their ability to remember from long-term sequences (window sizes) which is hard to achieve by traditional feature engineering. For example, computing rolling averages over a window size of 50 cycles may lead to loss of information due to smoothing and abstracting of values over such a long period, instead, using all 50 values as input may provide better results. While feature engineering over large window sizes may not make sense, LSTMs are able to use larger window sizes and use all the information in the window as input. Below, we illustrate the approach.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# pick a large window size of 50 cycles\n",
    "sequence_length = 50"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first look at an example of the sensor values 50 cycles prior to the failure for engine id 3. We will be feeding LSTM network this type of data for each time step for each engine id. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# preparing data for visualizations \n",
    "# window of 50 cycles prior to a failure point for engine id 3\n",
    "engine_id3 = test_df[test_df['id'] == 3]\n",
    "engine_id3_50cycleWindow = engine_id3[engine_id3['RUL'] <= engine_id3['RUL'].min() + 50]\n",
    "cols1 = ['s1', 's2', 's3', 's4', 's5', 's6', 's7', 's8', 's9', 's10']\n",
    "engine_id3_50cycleWindow1 = engine_id3_50cycleWindow[cols1]\n",
    "cols2 = ['s11', 's12', 's13', 's14', 's15', 's16', 's17', 's18', 's19', 's20', 's21']\n",
    "engine_id3_50cycleWindow2 = engine_id3_50cycleWindow[cols2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABJMAAAP+CAYAAAC/vl6OAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nlgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc2d6dc4c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting sensor data for engine ID 3 prior to a failure point - sensors 1-10 \n",
    "ax1 = engine_id3_50cycleWindow1.plot(subplots=True, sharex=True, figsize=(20,20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABJMAAAP+CAYAAAC/vl6OAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxpRdD+y4nQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc2d6ddaba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting sensor data for engine ID 3 prior to a failure point - sensors 11-21 \n",
    "ax2 = engine_id3_50cycleWindow2.plot(subplots=True, sharex=True, figsize=(20,20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Keras LSTM](https://keras.io/layers/recurrent/) layers expect an input in the shape of a numpy array of 3 dimensions (samples, time steps, features) where samples is the number of training sequences, time steps is the look back window or sequence length and features is the number of features of each sequence at each time step. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# function to reshape features into (samples, time steps, features) \n",
    "def gen_sequence(id_df, seq_length, seq_cols):\n",
    "    \"\"\" Only sequences that meet the window-length are considered, no padding is used. This means for testing\n",
    "    we need to drop those which are below the window-length. An alternative would be to pad sequences so that\n",
    "    we can use shorter ones \"\"\"\n",
    "    data_array = id_df[seq_cols].values\n",
    "    num_elements = data_array.shape[0]\n",
    "    for start, stop in zip(range(0, num_elements-seq_length), range(seq_length, num_elements)):\n",
    "        yield data_array[start:stop, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# pick the feature columns \n",
    "sensor_cols = ['s' + str(i) for i in range(1,22)]\n",
    "sequence_cols = ['setting1', 'setting2', 'setting3', 'cycle_norm']\n",
    "sequence_cols.extend(sensor_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# generator for the sequences\n",
    "seq_gen = (list(gen_sequence(train_df[train_df['id']==id], sequence_length, sequence_cols)) \n",
    "           for id in train_df['id'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15631, 50, 25)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# generate sequences and convert to numpy array\n",
    "seq_array = np.concatenate(list(seq_gen)).astype(np.float32)\n",
    "seq_array.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# function to generate labels\n",
    "def gen_labels(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:num_elements, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15631, 1)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# generate labels\n",
    "label_gen = [gen_labels(train_df[train_df['id']==id], sequence_length, ['label1']) \n",
    "             for id in train_df['id'].unique()]\n",
    "label_array = np.concatenate(label_gen).astype(np.float32)\n",
    "label_array.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LSTM Network\n",
    "Next, we build a deep network. The first layer is an LSTM layer with 100 units followed by another LSTM layer with 50 units. Dropout is also applied after each LSTM layer to control overfitting. Final layer is a Dense output layer with single unit and sigmoid activation since this is a binary classification problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# build the network\n",
    "nb_features = seq_array.shape[2]\n",
    "nb_out = label_array.shape[1]\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "model.add(LSTM(\n",
    "         input_shape=(sequence_length, nb_features),\n",
    "         units=100,\n",
    "         return_sequences=True))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(LSTM(\n",
    "          units=50,\n",
    "          return_sequences=False))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Dense(units=nb_out, activation='sigmoid'))\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_1 (LSTM)                (None, 50, 100)           50400     \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 50, 100)           0         \n",
      "_________________________________________________________________\n",
      "lstm_2 (LSTM)                (None, 50)                30200     \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 50)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 51        \n",
      "=================================================================\n",
      "Total params: 80,651\n",
      "Trainable params: 80,651\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 14849 samples, validate on 782 samples\n",
      "Epoch 1/10\n",
      "14849/14849 [==============================] - 7s - loss: 0.2733 - acc: 0.8819 - val_loss: 0.1307 - val_acc: 0.9412\n",
      "Epoch 2/10\n",
      "14849/14849 [==============================] - 6s - loss: 0.1187 - acc: 0.9525 - val_loss: 0.0906 - val_acc: 0.9578\n",
      "Epoch 3/10\n",
      "14849/14849 [==============================] - 7s - loss: 0.0849 - acc: 0.9648 - val_loss: 0.0750 - val_acc: 0.9629\n",
      "Epoch 4/10\n",
      "14849/14849 [==============================] - 6s - loss: 0.0786 - acc: 0.9675 - val_loss: 0.0538 - val_acc: 0.9872\n",
      "Epoch 5/10\n",
      "14849/14849 [==============================] - 6s - loss: 0.0632 - acc: 0.9748 - val_loss: 0.0626 - val_acc: 0.9744\n",
      "CPU times: user 25.8 s, sys: 9.09 s, total: 34.9 s\n",
      "Wall time: 35.7 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fc2d31ed7f0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# fit the network\n",
    "model.fit(seq_array, label_array, epochs=10, batch_size=200, validation_split=0.05, verbose=1,\n",
    "          callbacks = [keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=0, verbose=0, mode='auto')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15600/15631 [============================>.] - ETA: 0sAccurracy: 0.9700594697969598\n"
     ]
    }
   ],
   "source": [
    "# training metrics\n",
    "scores = model.evaluate(seq_array, label_array, verbose=1, batch_size=200)\n",
    "print('Accurracy: {}'.format(scores[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15631/15631 [==============================] - 2s     \n",
      "Confusion matrix\n",
      "- x-axis is true labels.\n",
      "- y-axis is predicted labels\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[12401,   130],\n",
       "       [  338,  2762]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make predictions and compute confusion matrix\n",
    "y_pred = model.predict_classes(seq_array,verbose=1, batch_size=200)\n",
    "y_true = label_array\n",
    "print('Confusion matrix\\n- x-axis is true labels.\\n- y-axis is predicted labels')\n",
    "cm = confusion_matrix(y_true, y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision =  0.955048409405 \n",
      " recall =  0.890967741935\n"
     ]
    }
   ],
   "source": [
    "# compute precision and recall\n",
    "precision = precision_score(y_true, y_pred)\n",
    "recall = recall_score(y_true, y_pred)\n",
    "print( 'precision = ', precision, '\\n', 'recall = ', recall)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we look at the performance on the test data. In the [Predictive Maintenance Template Step 1 of 3](https://gallery.cortanaintelligence.com/Experiment/Predictive-Maintenance-Step-1-of-3-data-preparation-and-feature-engineering-2), only the last cycle data for each engine id in the test data is kept for testing purposes. In order to compare the results to the template, we pick the last sequence for each id in the test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(93, 50, 25)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seq_array_test_last = [test_df[test_df['id']==id][sequence_cols].values[-sequence_length:] \n",
    "                       for id in test_df['id'].unique() if len(test_df[test_df['id']==id]) >= sequence_length]\n",
    "\n",
    "seq_array_test_last = np.asarray(seq_array_test_last).astype(np.float32)\n",
    "seq_array_test_last.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_mask = [len(test_df[test_df['id']==id]) >= sequence_length for id in test_df['id'].unique()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, we pick the labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(93, 1)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_array_test_last = test_df.groupby('id')['label1'].nth(-1)[y_mask].values\n",
    "label_array_test_last = label_array_test_last.reshape(label_array_test_last.shape[0],1).astype(np.float32)\n",
    "label_array_test_last.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(93, 50, 25)\n",
      "(93, 1)\n"
     ]
    }
   ],
   "source": [
    "print(seq_array_test_last.shape)\n",
    "print(label_array_test_last.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accurracy: 0.9784946178877225\n"
     ]
    }
   ],
   "source": [
    "# test metrics\n",
    "scores_test = model.evaluate(seq_array_test_last, label_array_test_last, verbose=2)\n",
    "print('Accurracy: {}'.format(scores_test[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "93/93 [==============================] - 0s     \n",
      "Confusion matrix\n",
      "- x-axis is true labels.\n",
      "- y-axis is predicted labels\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[67,  1],\n",
       "       [ 1, 24]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make predictions and compute confusion matrix\n",
    "y_pred_test = model.predict_classes(seq_array_test_last)\n",
    "y_true_test = label_array_test_last\n",
    "print('Confusion matrix\\n- x-axis is true labels.\\n- y-axis is predicted labels')\n",
    "cm = confusion_matrix(y_true_test, y_pred_test)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision:  0.96 \n",
      " Recall:  0.96 \n",
      " F1-score: 0.96\n"
     ]
    }
   ],
   "source": [
    "# compute precision and recall\n",
    "precision_test = precision_score(y_true_test, y_pred_test)\n",
    "recall_test = recall_score(y_true_test, y_pred_test)\n",
    "f1_test = 2 * (precision_test * recall_test) / (precision_test + recall_test)\n",
    "print( 'Precision: ', precision_test, '\\n', 'Recall: ', recall_test,'\\n', 'F1-score:', f1_test )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LSTM</th>\n",
       "      <td>0.978495</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.960000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Template Best Model</th>\n",
       "      <td>0.940000</td>\n",
       "      <td>0.952381</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.869565</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Accuracy  Precision  Recall  F1-score\n",
       "LSTM                 0.978495   0.960000    0.96  0.960000\n",
       "Template Best Model  0.940000   0.952381    0.80  0.869565"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df = pd.DataFrame([[scores_test[1],precision_test,recall_test,f1_test],\n",
    "                          [0.94, 0.952381, 0.8, 0.869565]],\n",
    "                         columns = ['Accuracy', 'Precision', 'Recall', 'F1-score'],\n",
    "                         index = ['LSTM',\n",
    "                                 'Template Best Model'])\n",
    "results_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing the above test results to the predictive maintenance template, we see that the LSTM results are better than the template. It should be noted that the  data set used here is very small and deep learning models are known to perform superior with large datasets so for a more fair comparison larger datasets should be used."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Future Directions and Improvements\n",
    "This tutorial covers the basics of using deep learning in predictive maintenance and many predictive maintenance problems usually involve a variety of data sources that needs to be taken into account when applying deep learning in this domain. Additionally, it is important to tune the models for the right parameters such as window size. Here are some suggestions on future directions on improvements:\n",
    "- Try different window sizes.\n",
    "- Try different architectures with different number of layers and nodes.\n",
    "- Try tuning hyperparmeters of the network.\n",
    "- Try predicting RUL (regression) such as in  [Predictive Maintenance Template Step 2A of 3](https://gallery.cortanaintelligence.com/Experiment/Predictive-Maintenance-Step-2A-of-3-train-and-evaluate-regression-models-2) and label2 (multi-class classification) such as in [Predictive Maintenance Template Step 2C of 3](https://gallery.cortanaintelligence.com/Experiment/Predictive-Maintenance-Step-2C-of-3-train-and-evaluation-multi-class-classification-models-2).\n",
    "- Try on larger data sets with more records.\n",
    "- Try a different problem scenario such as in [Predictive Maintenance Modelling Guide](https://gallery.cortanaintelligence.com/Notebook/Predictive-Maintenance-Modelling-Guide-R-Notebook-1) where multiple other data sources are involved such as maintenance records.\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:cntk]",
   "language": "python",
   "name": "conda-env-cntk-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}