cmapss-damage-propagation-modeling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.033273,
     "end_time": "2021-01-08T16:09:55.341592",
     "exception": false,
     "start_time": "2021-01-08T16:09:55.308319",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Damage Propagation Modeling \n",
    "# Aircraft Engine Run-to-Failure Simulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.030585,
     "end_time": "2021-01-08T16:09:55.404059",
     "exception": false,
     "start_time": "2021-01-08T16:09:55.373474",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- Damage propagation was allowed to continue until a failure criterion was reached. A health index was defined as the minimum of several superimposed operational margins at any given time instant and the failure criterion is reached when health index reaches zero. Output of the model was the time series (cycles) of sensed measurements typically available from aircraft gas turbine engines. \n",
    "\n",
    "- Prognostics here exclusively as the estimation of remaining useful component life. The remaining useful life (RUL) estimates are in units of time (e.g., hours or cycles). End-of-life can be subjectively determined as a function of operational thresholds that can be measured. These thresholds depend on user specifications to determine safe operational limits.\n",
    "\n",
    "- An important requirement for the damage modeling process was the availability of a suitable system model that allows input variations of health related parameters and recording of the resulting output sensor measurements. The recently released C-MAPSS (Commercial Modular Aero- Propulsion System Simulation)\n",
    "\n",
    "- C-MAPSS is a tool for simulating a realistic large commercial turbofan engine. The software is coded in the MATLAB® and Simulink® environment, and includes a number of editable input parameters that allow the user to enter specific values of his/her own choice regarding operational profile, closed-loop controllers, environmental conditions, etc\n",
    "\n",
    "Data Set: FD001 ==>\n",
    "Train trjectories: 100,\n",
    "Test trajectories: 100,\n",
    "Conditions: ONE (Sea Level),\n",
    "Fault Modes: ONE (HPC Degradation)\n",
    "\n",
    "Data Set: FD002 ==>\n",
    "Train trjectories: 260,\n",
    "Test trajectories: 259,\n",
    "Conditions: SIX,\n",
    "Fault Modes: ONE (HPC Degradation)\n",
    "\n",
    "###Experimental Scenario###\n",
    "\n",
    "Data sets consists of multiple multivariate time series. Each data set is further divided into training and test subsets. Each time series is from a different engine ñ i.e., the data can be considered to be from a fleet of engines of the same type. Each engine starts with different degrees of initial wear and manufacturing variation which is unknown to the user. This wear and variation is considered normal, i.e., it is not considered a fault condition. There are three operational settings that have a substantial effect on engine performance. These settings are also included in the data. The data is contaminated with sensor noise.\n",
    "\n",
    "The engine is operating normally at the start of each time series, and develops a fault at some point during the series. In the training set, the fault grows in magnitude until system failure. In the test set, the time series ends some time prior to system failure. The objective of the competition is to predict the number of remaining operational cycles before failure in the test set, i.e., the number of operational cycles after the last cycle that the engine will continue to operate. Also provided a vector of true Remaining Useful Life (RUL) values for the test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-08T16:09:55.479293Z",
     "iopub.status.busy": "2021-01-08T16:09:55.478488Z",
     "iopub.status.idle": "2021-01-08T16:10:01.909566Z",
     "shell.execute_reply": "2021-01-08T16:10:01.908451Z"
    },
    "papermill": {
     "duration": 6.474244,
     "end_time": "2021-01-08T16:10:01.909687",
     "exception": false,
     "start_time": "2021-01-08T16:09:55.435443",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import tensorflow as tf\n",
    "import sklearn.metrics\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.inspection import permutation_importance\n",
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import Dense, Dropout\n",
    "import tensorflow as tf\n",
    "import math\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.031414,
     "end_time": "2021-01-08T16:10:01.973872",
     "exception": false,
     "start_time": "2021-01-08T16:10:01.942458",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Each row is a snapshot of data taken during a single operational cycle, each column is a different variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "execution": {
     "iopub.execute_input": "2021-01-08T16:10:02.057999Z",
     "iopub.status.busy": "2021-01-08T16:10:02.057294Z",
     "iopub.status.idle": "2021-01-08T16:10:02.292526Z",
     "shell.execute_reply": "2021-01-08T16:10:02.291525Z"
    },
    "papermill": {
     "duration": 0.284442,
     "end_time": "2021-01-08T16:10:02.292645",
     "exception": false,
     "start_time": "2021-01-08T16:10:02.008203",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "rul_1_gt = pd.read_csv('../input/cmapssdata/RUL_FD001.txt', header=None)\n",
    "\n",
    "rul_1_gt.rename(columns={0: \"RUL_1gt\"}, inplace=True)\n",
    "################   ################   ################   ################   \n",
    "\n",
    "column_name_dict={ 0: \"engine_id\", 1: \"cycle\", 2: \"op_set_1\", 3: \"op_set_2\", 4: \"op_set_3\", 5:\"sensor_1\", 6: \"sensor_2\",\n",
    "                   7: \"sensor_3\", 8: \"sensor_4\", 9: \"sensor_5\", 10: \"sensor_6\", 11: \"sensor_7\", 12: \"sensor_8\", 13: \"sensor_9\",\n",
    "                  14: \"sensor_10\", 15: \"sensor_11\", 16: \"sensor_12\", 17: \"sensor_13\", 18: \"sensor_14\", 19: \"sensor_15\", 20: \"sensor_16\",\n",
    "                  21: \"sensor_17\", 22: \"sensor_18\", 23: \"sensor_19\", 24: \"sensor_20\", 25: \"sensor_21\", 26: \"sensor_22\", 27: \"sensor_23\"}\n",
    "\n",
    "test_1 = pd.read_csv('../input/cmapssdata/test_FD001.txt', header=None, sep=' ')\n",
    "\n",
    "test_1.rename(columns=column_name_dict, inplace=True)\n",
    "\n",
    "################   ################   ################   ################ \n",
    "\n",
    "train_1 = pd.read_csv('../input/cmapssdata/train_FD001.txt', header=None, sep=' ')\n",
    "\n",
    "train_1.rename(columns=column_name_dict, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-08T16:10:02.369145Z",
     "iopub.status.busy": "2021-01-08T16:10:02.368041Z",
     "iopub.status.idle": "2021-01-08T16:10:02.409721Z",
     "shell.execute_reply": "2021-01-08T16:10:02.410219Z"
    },
    "papermill": {
     "duration": 0.085885,
     "end_time": "2021-01-08T16:10:02.410351",
     "exception": false,
     "start_time": "2021-01-08T16:10:02.324466",
     "status": "completed"
    },
    "tags": []
   },
   "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>engine_id</th>\n",
       "      <th>cycle</th>\n",
       "      <th>op_set_1</th>\n",
       "      <th>op_set_2</th>\n",
       "      <th>op_set_3</th>\n",
       "      <th>sensor_1</th>\n",
       "      <th>sensor_2</th>\n",
       "      <th>sensor_3</th>\n",
       "      <th>sensor_4</th>\n",
       "      <th>sensor_5</th>\n",
       "      <th>...</th>\n",
       "      <th>sensor_14</th>\n",
       "      <th>sensor_15</th>\n",
       "      <th>sensor_16</th>\n",
       "      <th>sensor_17</th>\n",
       "      <th>sensor_18</th>\n",
       "      <th>sensor_19</th>\n",
       "      <th>sensor_20</th>\n",
       "      <th>sensor_21</th>\n",
       "      <th>sensor_22</th>\n",
       "      <th>sensor_23</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>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>NaN</td>\n",
       "      <td>NaN</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>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>NaN</td>\n",
       "      <td>NaN</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>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>NaN</td>\n",
       "      <td>NaN</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>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>NaN</td>\n",
       "      <td>NaN</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>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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20626</th>\n",
       "      <td>100</td>\n",
       "      <td>196</td>\n",
       "      <td>-0.0004</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>643.49</td>\n",
       "      <td>1597.98</td>\n",
       "      <td>1428.63</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>8137.60</td>\n",
       "      <td>8.4956</td>\n",
       "      <td>0.03</td>\n",
       "      <td>397</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.49</td>\n",
       "      <td>22.9735</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20627</th>\n",
       "      <td>100</td>\n",
       "      <td>197</td>\n",
       "      <td>-0.0016</td>\n",
       "      <td>-0.0005</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>643.54</td>\n",
       "      <td>1604.50</td>\n",
       "      <td>1433.58</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>8136.50</td>\n",
       "      <td>8.5139</td>\n",
       "      <td>0.03</td>\n",
       "      <td>395</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.30</td>\n",
       "      <td>23.1594</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20628</th>\n",
       "      <td>100</td>\n",
       "      <td>198</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>643.42</td>\n",
       "      <td>1602.46</td>\n",
       "      <td>1428.18</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>8141.05</td>\n",
       "      <td>8.5646</td>\n",
       "      <td>0.03</td>\n",
       "      <td>398</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.44</td>\n",
       "      <td>22.9333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20629</th>\n",
       "      <td>100</td>\n",
       "      <td>199</td>\n",
       "      <td>-0.0011</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>643.23</td>\n",
       "      <td>1605.26</td>\n",
       "      <td>1426.53</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>8139.29</td>\n",
       "      <td>8.5389</td>\n",
       "      <td>0.03</td>\n",
       "      <td>395</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.29</td>\n",
       "      <td>23.0640</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20630</th>\n",
       "      <td>100</td>\n",
       "      <td>200</td>\n",
       "      <td>-0.0032</td>\n",
       "      <td>-0.0005</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>643.85</td>\n",
       "      <td>1600.38</td>\n",
       "      <td>1432.14</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>8137.33</td>\n",
       "      <td>8.5036</td>\n",
       "      <td>0.03</td>\n",
       "      <td>396</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.37</td>\n",
       "      <td>23.0522</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20631 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       engine_id  cycle  op_set_1  op_set_2  op_set_3  sensor_1  sensor_2  \\\n",
       "0              1      1   -0.0007   -0.0004     100.0    518.67    641.82   \n",
       "1              1      2    0.0019   -0.0003     100.0    518.67    642.15   \n",
       "2              1      3   -0.0043    0.0003     100.0    518.67    642.35   \n",
       "3              1      4    0.0007    0.0000     100.0    518.67    642.35   \n",
       "4              1      5   -0.0019   -0.0002     100.0    518.67    642.37   \n",
       "...          ...    ...       ...       ...       ...       ...       ...   \n",
       "20626        100    196   -0.0004   -0.0003     100.0    518.67    643.49   \n",
       "20627        100    197   -0.0016   -0.0005     100.0    518.67    643.54   \n",
       "20628        100    198    0.0004    0.0000     100.0    518.67    643.42   \n",
       "20629        100    199   -0.0011    0.0003     100.0    518.67    643.23   \n",
       "20630        100    200   -0.0032   -0.0005     100.0    518.67    643.85   \n",
       "\n",
       "       sensor_3  sensor_4  sensor_5  ...  sensor_14  sensor_15  sensor_16  \\\n",
       "0       1589.70   1400.60     14.62  ...    8138.62     8.4195       0.03   \n",
       "1       1591.82   1403.14     14.62  ...    8131.49     8.4318       0.03   \n",
       "2       1587.99   1404.20     14.62  ...    8133.23     8.4178       0.03   \n",
       "3       1582.79   1401.87     14.62  ...    8133.83     8.3682       0.03   \n",
       "4       1582.85   1406.22     14.62  ...    8133.80     8.4294       0.03   \n",
       "...         ...       ...       ...  ...        ...        ...        ...   \n",
       "20626   1597.98   1428.63     14.62  ...    8137.60     8.4956       0.03   \n",
       "20627   1604.50   1433.58     14.62  ...    8136.50     8.5139       0.03   \n",
       "20628   1602.46   1428.18     14.62  ...    8141.05     8.5646       0.03   \n",
       "20629   1605.26   1426.53     14.62  ...    8139.29     8.5389       0.03   \n",
       "20630   1600.38   1432.14     14.62  ...    8137.33     8.5036       0.03   \n",
       "\n",
       "       sensor_17  sensor_18  sensor_19  sensor_20  sensor_21  sensor_22  \\\n",
       "0            392       2388      100.0      39.06    23.4190        NaN   \n",
       "1            392       2388      100.0      39.00    23.4236        NaN   \n",
       "2            390       2388      100.0      38.95    23.3442        NaN   \n",
       "3            392       2388      100.0      38.88    23.3739        NaN   \n",
       "4            393       2388      100.0      38.90    23.4044        NaN   \n",
       "...          ...        ...        ...        ...        ...        ...   \n",
       "20626        397       2388      100.0      38.49    22.9735        NaN   \n",
       "20627        395       2388      100.0      38.30    23.1594        NaN   \n",
       "20628        398       2388      100.0      38.44    22.9333        NaN   \n",
       "20629        395       2388      100.0      38.29    23.0640        NaN   \n",
       "20630        396       2388      100.0      38.37    23.0522        NaN   \n",
       "\n",
       "       sensor_23  \n",
       "0            NaN  \n",
       "1            NaN  \n",
       "2            NaN  \n",
       "3            NaN  \n",
       "4            NaN  \n",
       "...          ...  \n",
       "20626        NaN  \n",
       "20627        NaN  \n",
       "20628        NaN  \n",
       "20629        NaN  \n",
       "20630        NaN  \n",
       "\n",
       "[20631 rows x 28 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-08T16:10:02.528002Z",
     "iopub.status.busy": "2021-01-08T16:10:02.527181Z",
     "iopub.status.idle": "2021-01-08T16:10:02.531581Z",
     "shell.execute_reply": "2021-01-08T16:10:02.531064Z"
    },
    "papermill": {
     "duration": 0.088763,
     "end_time": "2021-01-08T16:10:02.531683",
     "exception": false,
     "start_time": "2021-01-08T16:10:02.442920",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#drop the last 2 columns from train and test sets since NaN\n",
    "train_1.drop(columns=[\"sensor_22\", \"sensor_23\"], inplace=True)\n",
    "test_1.drop(columns=[\"sensor_22\", \"sensor_23\"], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.032763,
     "end_time": "2021-01-08T16:10:02.597520",
     "exception": false,
     "start_time": "2021-01-08T16:10:02.564757",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Compute RUL values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-08T16:10:02.670389Z",
     "iopub.status.busy": "2021-01-08T16:10:02.669688Z",
     "iopub.status.idle": "2021-01-08T16:10:02.719176Z",
     "shell.execute_reply": "2021-01-08T16:10:02.719825Z"
    },
    "papermill": {
     "duration": 0.090423,
     "end_time": "2021-01-08T16:10:02.720003",
     "exception": false,
     "start_time": "2021-01-08T16:10:02.629580",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#RUL for train_1 \n",
    "rul_1 = pd.DataFrame(train_1.groupby('engine_id')['cycle'].max()).reset_index()\n",
    "rul_1.columns = ['engine_id', 'max']\n",
    "\n",
    "rul_train_1 = train_1.merge(rul_1, on=['engine_id'], how='left')\n",
    "rul_train_1['RUL'] = rul_train_1['max'] - rul_train_1['cycle']\n",
    "rul_train_1.drop(['max'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-08T16:10:02.798369Z",
     "iopub.status.busy": "2021-01-08T16:10:02.797511Z",
     "iopub.status.idle": "2021-01-08T16:10:02.819358Z",
     "shell.execute_reply": "2021-01-08T16:10:02.818885Z"
    },
    "papermill": {
     "duration": 0.065479,
     "end_time": "2021-01-08T16:10:02.819485",
     "exception": false,
     "start_time": "2021-01-08T16:10:02.754006",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#RUL for test_1\n",
    "rul_1_gt[\"engine_id\"]=rul_1_gt.index + 1\n",
    "\n",
    "max_1 = pd.DataFrame(test_1.groupby('engine_id')['cycle'].max()).reset_index()\n",
    "max_1.columns = ['engine_id', 'max']\n",
    "max_test_1 = test_1.merge(max_1, on=['engine_id'], how='left')\n",
    "rul_test_1 = max_test_1.merge(rul_1_gt, on=['engine_id'], how='left')\n",
    "\n",
    "rul_test_1['RUL'] = rul_test_1['max'] - rul_test_1['cycle'] + rul_test_1[\"RUL_1gt\"] \n",
    "rul_test_1.drop(['max', 'RUL_1gt'], axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-08T16:10:02.891451Z",
     "iopub.status.busy": "2021-01-08T16:10:02.890837Z",
     "iopub.status.idle": "2021-01-08T16:10:03.117173Z",
     "shell.execute_reply": "2021-01-08T16:10:03.117671Z"
    },
    "papermill": {
     "duration": 0.265136,
     "end_time": "2021-01-08T16:10:03.117819",
     "exception": false,
     "start_time": "2021-01-08T16:10:02.852683",
     "status": "completed"
    },
    "tags": []
   },
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engine_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>192.0</td>\n",
       "      <td>96.5</td>\n",
       "      <td>55.569776</td>\n",
       "      <td>1.0</td>\n",
       "      <td>48.75</td>\n",
       "      <td>96.5</td>\n",
       "      <td>144.25</td>\n",
       "      <td>192.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>287.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>82.993976</td>\n",
       "      <td>1.0</td>\n",
       "      <td>72.50</td>\n",
       "      <td>144.0</td>\n",
       "      <td>215.50</td>\n",
       "      <td>287.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>179.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>51.816986</td>\n",
       "      <td>1.0</td>\n",
       "      <td>45.50</td>\n",
       "      <td>90.0</td>\n",
       "      <td>134.50</td>\n",
       "      <td>179.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>189.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>54.703748</td>\n",
       "      <td>1.0</td>\n",
       "      <td>48.00</td>\n",
       "      <td>95.0</td>\n",
       "      <td>142.00</td>\n",
       "      <td>189.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>269.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>77.797815</td>\n",
       "      <td>1.0</td>\n",
       "      <td>68.00</td>\n",
       "      <td>135.0</td>\n",
       "      <td>202.00</td>\n",
       "      <td>269.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>336.0</td>\n",
       "      <td>168.5</td>\n",
       "      <td>97.139076</td>\n",
       "      <td>1.0</td>\n",
       "      <td>84.75</td>\n",
       "      <td>168.5</td>\n",
       "      <td>252.25</td>\n",
       "      <td>336.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>202.0</td>\n",
       "      <td>101.5</td>\n",
       "      <td>58.456537</td>\n",
       "      <td>1.0</td>\n",
       "      <td>51.25</td>\n",
       "      <td>101.5</td>\n",
       "      <td>151.75</td>\n",
       "      <td>202.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>156.0</td>\n",
       "      <td>78.5</td>\n",
       "      <td>45.177428</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.75</td>\n",
       "      <td>78.5</td>\n",
       "      <td>117.25</td>\n",
       "      <td>156.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>185.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>53.549043</td>\n",
       "      <td>1.0</td>\n",
       "      <td>47.00</td>\n",
       "      <td>93.0</td>\n",
       "      <td>139.00</td>\n",
       "      <td>185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>200.0</td>\n",
       "      <td>100.5</td>\n",
       "      <td>57.879185</td>\n",
       "      <td>1.0</td>\n",
       "      <td>50.75</td>\n",
       "      <td>100.5</td>\n",
       "      <td>150.25</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           count   mean        std  min    25%    50%     75%    max\n",
       "engine_id                                                           \n",
       "1          192.0   96.5  55.569776  1.0  48.75   96.5  144.25  192.0\n",
       "2          287.0  144.0  82.993976  1.0  72.50  144.0  215.50  287.0\n",
       "3          179.0   90.0  51.816986  1.0  45.50   90.0  134.50  179.0\n",
       "4          189.0   95.0  54.703748  1.0  48.00   95.0  142.00  189.0\n",
       "5          269.0  135.0  77.797815  1.0  68.00  135.0  202.00  269.0\n",
       "...          ...    ...        ...  ...    ...    ...     ...    ...\n",
       "96         336.0  168.5  97.139076  1.0  84.75  168.5  252.25  336.0\n",
       "97         202.0  101.5  58.456537  1.0  51.25  101.5  151.75  202.0\n",
       "98         156.0   78.5  45.177428  1.0  39.75   78.5  117.25  156.0\n",
       "99         185.0   93.0  53.549043  1.0  47.00   93.0  139.00  185.0\n",
       "100        200.0  100.5  57.879185  1.0  50.75  100.5  150.25  200.0\n",
       "\n",
       "[100 rows x 8 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rul_train_1.groupby('engine_id')['cycle'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-08T16:10:03.198373Z",
     "iopub.status.busy": "2021-01-08T16:10:03.197445Z",
     "iopub.status.idle": "2021-01-08T16:10:03.288961Z",
     "shell.execute_reply": "2021-01-08T16:10:03.289462Z"
    },
    "papermill": {
     "duration": 0.137482,
     "end_time": "2021-01-08T16:10:03.289630",
     "exception": false,
     "start_time": "2021-01-08T16:10:03.152148",
     "status": "completed"
    },
    "tags": []
   },
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>engine_id</th>\n",
       "      <td>20631.0</td>\n",
       "      <td>51.506568</td>\n",
       "      <td>2.922763e+01</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>77.0000</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cycle</th>\n",
       "      <td>20631.0</td>\n",
       "      <td>108.807862</td>\n",
       "      <td>6.888099e+01</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>104.0000</td>\n",
       "      <td>156.0000</td>\n",
       "      <td>362.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>op_set_1</th>\n",
       "      <td>20631.0</td>\n",
       "      <td>-0.000009</td>\n",
       "      <td>2.187313e-03</td>\n",
       "      <td>-0.0087</td>\n",
       "      <td>-0.0015</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>0.0087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>op_set_2</th>\n",
       "      <td>20631.0</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>2.930621e-04</td>\n",
       "      <td>-0.0006</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>op_set_3</th>\n",
       "      <td>20631.0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sensor_1</th>\n",
       "      <td>20631.0</td>\n",
       "      <td>518.670000</td>\n",
       "      <td>6.537152e-11</td>\n",
       "      <td>518.6700</td>\n",
       "      <td>518.6700</td>\n",
       "      <td>518.6700</td>\n",
       "      <td>518.6700</td>\n",
       "      <td>518.6700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sensor_2</th>\n",
       "      <td>20631.0</td>\n",
       "      <td>642.680934</td>\n",
       "      <td>5.000533e-01</td>\n",
       "      <td>641.2100</td>\n",
       "      <td>642.3250</td>\n",
       "      <td>642.6400</td>\n",
       "      <td>643.0000</td>\n",
       "      <td>644.5300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sensor_3</th>\n",
       "      <td>20631.0</td>\n",
       "      <td>1590.523119</td>\n",
       "      <td>6.131150e+00</td>\n",
       "      <td>1571.0400</td>\n",
       "      <td>1586.2600</td>\n",
       "      <td>1590.1000</td>\n",
       "      <td>1594.3800</td>\n",
       "      <td>1616.9100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sensor_4</th>\n",
       "      <td>20631.0</td>\n",
       "      <td>1408.933782</td>\n",
       "      <td>9.000605e+00</td>\n",
       "      <td>1382.2500</td>\n",
       "      <td>1402.3600</td>\n",
       "      <td>1408.0400</td>\n",
       "      <td>1414.5550</td>\n",
       "      <td>1441.4900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sensor_5</th>\n",
       "      <td>20631.0</td>\n",
       "      <td>14.620000</td>\n",
       "      <td>3.394700e-12</td>\n",
       "      <td>14.6200</td>\n",
       "      <td>14.6200</td>\n",
       "      <td>14.6200</td>\n",
       "      <td>14.6200</td>\n",
       "      <td>14.6200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sensor_6</th>\n",
       "      <td>20631.0</td>\n",
       "      <td>21.609803</td>\n",
       "      <td>1.388985e-03</td>\n",
       "      <td>21.6000</td>\n",
       "      <td>21.6100</td>\n",
       "      <td>21.6100</td>\n",
       "      <td>21.6100</td>\n",
       "      <td>21.6100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sensor_7</th>\n",
       "      <td>20631.0</td>\n",
       "      <td>553.367711</td>\n",
       "      <td>8.850923e-01</td>\n",
       "      <td>549.8500</td>\n",
       "      <td>552.8100</td>\n",
       "      <td>553.4400</td>\n",
       "      <td>554.0100</td>\n",
       "      <td>556.0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sensor_8</th>\n",
       "      <td>20631.0</td>\n",
       "      <td>2388.096652</td>\n",
       "      <td>7.098548e-02</td>\n",
       "      <td>2387.9000</td>\n",
       "      <td>2388.0500</td>\n",
       "      <td>2388.0900</td>\n",
       "      <td>2388.1400</td>\n",
       "      <td>2388.5600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sensor_9</th>\n",
       "      <td>20631.0</td>\n",
       "      <td>9065.242941</td>\n",
       "      <td>2.208288e+01</td>\n",
       "      <td>9021.7300</td>\n",
       "      <td>9053.1000</td>\n",
       "      <td>9060.6600</td>\n",
       "      <td>9069.4200</td>\n",
       "      <td>9244.5900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sensor_10</th>\n",
       "      <td>20631.0</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>4.660829e-13</td>\n",
       "      <td>1.3000</td>\n",
       "      <td>1.3000</td>\n",
       "      <td>1.3000</td>\n",
       "      <td>1.3000</td>\n",
       "      <td>1.3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sensor_11</th>\n",
       "      <td>20631.0</td>\n",
       "      <td>47.541168</td>\n",
       "      <td>2.670874e-01</td>\n",
       "      <td>46.8500</td>\n",
       "      <td>47.3500</td>\n",
       "      <td>47.5100</td>\n",
       "      <td>47.7000</td>\n",
       "      <td>48.5300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sensor_12</th>\n",
       "      <td>20631.0</td>\n",
       "      <td>521.413470</td>\n",
       "      <td>7.375534e-01</td>\n",
       "      <td>518.6900</td>\n",
       "      <td>520.9600</td>\n",
       "      <td>521.4800</td>\n",
       "      <td>521.9500</td>\n",
       "      <td>523.3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sensor_13</th>\n",
       "      <td>20631.0</td>\n",
       "      <td>2388.096152</td>\n",
       "      <td>7.191892e-02</td>\n",
       "      <td>2387.8800</td>\n",
       "      <td>2388.0400</td>\n",
       "      <td>2388.0900</td>\n",
       "      <td>2388.1400</td>\n",
       "      <td>2388.5600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sensor_14</th>\n",
       "      <td>20631.0</td>\n",
       "      <td>8143.752722</td>\n",
       "      <td>1.907618e+01</td>\n",
       "      <td>8099.9400</td>\n",
       "      <td>8133.2450</td>\n",
       "      <td>8140.5400</td>\n",
       "      <td>8148.3100</td>\n",
       "      <td>8293.7200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sensor_15</th>\n",
       "      <td>20631.0</td>\n",
       "      <td>8.442146</td>\n",
       "      <td>3.750504e-02</td>\n",
       "      <td>8.3249</td>\n",
       "      <td>8.4149</td>\n",
       "      <td>8.4389</td>\n",
       "      <td>8.4656</td>\n",
       "      <td>8.5848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sensor_16</th>\n",
       "      <td>20631.0</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>1.556432e-14</td>\n",
       "      <td>0.0300</td>\n",
       "      <td>0.0300</td>\n",
       "      <td>0.0300</td>\n",
       "      <td>0.0300</td>\n",
       "      <td>0.0300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sensor_17</th>\n",
       "      <td>20631.0</td>\n",
       "      <td>393.210654</td>\n",
       "      <td>1.548763e+00</td>\n",
       "      <td>388.0000</td>\n",
       "      <td>392.0000</td>\n",
       "      <td>393.0000</td>\n",
       "      <td>394.0000</td>\n",
       "      <td>400.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sensor_18</th>\n",
       "      <td>20631.0</td>\n",
       "      <td>2388.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2388.0000</td>\n",
       "      <td>2388.0000</td>\n",
       "      <td>2388.0000</td>\n",
       "      <td>2388.0000</td>\n",
       "      <td>2388.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sensor_19</th>\n",
       "      <td>20631.0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sensor_20</th>\n",
       "      <td>20631.0</td>\n",
       "      <td>38.816271</td>\n",
       "      <td>1.807464e-01</td>\n",
       "      <td>38.1400</td>\n",
       "      <td>38.7000</td>\n",
       "      <td>38.8300</td>\n",
       "      <td>38.9500</td>\n",
       "      <td>39.4300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sensor_21</th>\n",
       "      <td>20631.0</td>\n",
       "      <td>23.289705</td>\n",
       "      <td>1.082509e-01</td>\n",
       "      <td>22.8942</td>\n",
       "      <td>23.2218</td>\n",
       "      <td>23.2979</td>\n",
       "      <td>23.3668</td>\n",
       "      <td>23.6184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RUL</th>\n",
       "      <td>20631.0</td>\n",
       "      <td>107.807862</td>\n",
       "      <td>6.888099e+01</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>51.0000</td>\n",
       "      <td>103.0000</td>\n",
       "      <td>155.0000</td>\n",
       "      <td>361.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             count         mean           std        min        25%  \\\n",
       "engine_id  20631.0    51.506568  2.922763e+01     1.0000    26.0000   \n",
       "cycle      20631.0   108.807862  6.888099e+01     1.0000    52.0000   \n",
       "op_set_1   20631.0    -0.000009  2.187313e-03    -0.0087    -0.0015   \n",
       "op_set_2   20631.0     0.000002  2.930621e-04    -0.0006    -0.0002   \n",
       "op_set_3   20631.0   100.000000  0.000000e+00   100.0000   100.0000   \n",
       "sensor_1   20631.0   518.670000  6.537152e-11   518.6700   518.6700   \n",
       "sensor_2   20631.0   642.680934  5.000533e-01   641.2100   642.3250   \n",
       "sensor_3   20631.0  1590.523119  6.131150e+00  1571.0400  1586.2600   \n",
       "sensor_4   20631.0  1408.933782  9.000605e+00  1382.2500  1402.3600   \n",
       "sensor_5   20631.0    14.620000  3.394700e-12    14.6200    14.6200   \n",
       "sensor_6   20631.0    21.609803  1.388985e-03    21.6000    21.6100   \n",
       "sensor_7   20631.0   553.367711  8.850923e-01   549.8500   552.8100   \n",
       "sensor_8   20631.0  2388.096652  7.098548e-02  2387.9000  2388.0500   \n",
       "sensor_9   20631.0  9065.242941  2.208288e+01  9021.7300  9053.1000   \n",
       "sensor_10  20631.0     1.300000  4.660829e-13     1.3000     1.3000   \n",
       "sensor_11  20631.0    47.541168  2.670874e-01    46.8500    47.3500   \n",
       "sensor_12  20631.0   521.413470  7.375534e-01   518.6900   520.9600   \n",
       "sensor_13  20631.0  2388.096152  7.191892e-02  2387.8800  2388.0400   \n",
       "sensor_14  20631.0  8143.752722  1.907618e+01  8099.9400  8133.2450   \n",
       "sensor_15  20631.0     8.442146  3.750504e-02     8.3249     8.4149   \n",
       "sensor_16  20631.0     0.030000  1.556432e-14     0.0300     0.0300   \n",
       "sensor_17  20631.0   393.210654  1.548763e+00   388.0000   392.0000   \n",
       "sensor_18  20631.0  2388.000000  0.000000e+00  2388.0000  2388.0000   \n",
       "sensor_19  20631.0   100.000000  0.000000e+00   100.0000   100.0000   \n",
       "sensor_20  20631.0    38.816271  1.807464e-01    38.1400    38.7000   \n",
       "sensor_21  20631.0    23.289705  1.082509e-01    22.8942    23.2218   \n",
       "RUL        20631.0   107.807862  6.888099e+01     0.0000    51.0000   \n",
       "\n",
       "                 50%        75%        max  \n",
       "engine_id    52.0000    77.0000   100.0000  \n",
       "cycle       104.0000   156.0000   362.0000  \n",
       "op_set_1      0.0000     0.0015     0.0087  \n",
       "op_set_2      0.0000     0.0003     0.0006  \n",
       "op_set_3    100.0000   100.0000   100.0000  \n",
       "sensor_1    518.6700   518.6700   518.6700  \n",
       "sensor_2    642.6400   643.0000   644.5300  \n",
       "sensor_3   1590.1000  1594.3800  1616.9100  \n",
       "sensor_4   1408.0400  1414.5550  1441.4900  \n",
       "sensor_5     14.6200    14.6200    14.6200  \n",
       "sensor_6     21.6100    21.6100    21.6100  \n",
       "sensor_7    553.4400   554.0100   556.0600  \n",
       "sensor_8   2388.0900  2388.1400  2388.5600  \n",
       "sensor_9   9060.6600  9069.4200  9244.5900  \n",
       "sensor_10     1.3000     1.3000     1.3000  \n",
       "sensor_11    47.5100    47.7000    48.5300  \n",
       "sensor_12   521.4800   521.9500   523.3800  \n",
       "sensor_13  2388.0900  2388.1400  2388.5600  \n",
       "sensor_14  8140.5400  8148.3100  8293.7200  \n",
       "sensor_15     8.4389     8.4656     8.5848  \n",
       "sensor_16     0.0300     0.0300     0.0300  \n",
       "sensor_17   393.0000   394.0000   400.0000  \n",
       "sensor_18  2388.0000  2388.0000  2388.0000  \n",
       "sensor_19   100.0000   100.0000   100.0000  \n",
       "sensor_20    38.8300    38.9500    39.4300  \n",
       "sensor_21    23.2979    23.3668    23.6184  \n",
       "RUL         103.0000   155.0000   361.0000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rul_train_1.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-08T16:10:03.371070Z",
     "iopub.status.busy": "2021-01-08T16:10:03.370224Z",
     "iopub.status.idle": "2021-01-08T16:10:03.373726Z",
     "shell.execute_reply": "2021-01-08T16:10:03.374152Z"
    },
    "papermill": {
     "duration": 0.048844,
     "end_time": "2021-01-08T16:10:03.374283",
     "exception": false,
     "start_time": "2021-01-08T16:10:03.325439",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#drop columns which has std=0 because they don't carry info about the state of the unit\n",
    "deleted_1=[\"op_set_3\", \"sensor_18\", \"sensor_19\"]\n",
    "\n",
    "rul_train_1.drop(columns=deleted_1, inplace=True)\n",
    "rul_test_1.drop(columns=deleted_1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.034914,
     "end_time": "2021-01-08T16:10:03.444957",
     "exception": false,
     "start_time": "2021-01-08T16:10:03.410043",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Corr Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-08T16:10:03.529799Z",
     "iopub.status.busy": "2021-01-08T16:10:03.526918Z",
     "iopub.status.idle": "2021-01-08T16:10:06.060623Z",
     "shell.execute_reply": "2021-01-08T16:10:06.061150Z"
    },
    "papermill": {
     "duration": 2.579912,
     "end_time": "2021-01-08T16:10:06.061290",
     "exception": false,
     "start_time": "2021-01-08T16:10:03.481378",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1728x1152 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#heatmap for correlation coefficient\n",
    "\n",
    "# calculate correlation\n",
    "df_corr = rul_train_1.drop(columns=[\"engine_id\"]).corr()\n",
    "\n",
    "# correlation matrix\n",
    "sns.set(font_scale=0.8)\n",
    "plt.figure(figsize=(24,16))\n",
    "sns.heatmap(df_corr, annot=True, fmt=\".4f\",vmin=-1, vmax=1, linewidths=.5, cmap = sns.color_palette(\"coolwarm\", 200))\n",
    "\n",
    "plt.figtext(.45, 0.9,'correlation matrix of train_1', fontsize=16, ha='center')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-08T16:10:06.158479Z",
     "iopub.status.busy": "2021-01-08T16:10:06.157508Z",
     "iopub.status.idle": "2021-01-08T16:10:06.160810Z",
     "shell.execute_reply": "2021-01-08T16:10:06.160271Z"
    },
    "papermill": {
     "duration": 0.054622,
     "end_time": "2021-01-08T16:10:06.160910",
     "exception": false,
     "start_time": "2021-01-08T16:10:06.106288",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#corr(RUL&feature )==0 ==> will be deleted since it has no corr between target(RUL) \n",
    "corr_del=[\"sensor_1\", \"sensor_5\", \"sensor_10\", \"sensor_16\"]\n",
    "rul_train_1.drop(columns=corr_del, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.041977,
     "end_time": "2021-01-08T16:10:06.246852",
     "exception": false,
     "start_time": "2021-01-08T16:10:06.204875",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# FEATURE SELECTION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.043058,
     "end_time": "2021-01-08T16:10:06.333817",
     "exception": false,
     "start_time": "2021-01-08T16:10:06.290759",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "1. Random Forest importances:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-08T16:10:06.432326Z",
     "iopub.status.busy": "2021-01-08T16:10:06.431065Z",
     "iopub.status.idle": "2021-01-08T16:10:36.766043Z",
     "shell.execute_reply": "2021-01-08T16:10:36.766698Z"
    },
    "papermill": {
     "duration": 30.389542,
     "end_time": "2021-01-08T16:10:36.766846",
     "exception": false,
     "start_time": "2021-01-08T16:10:06.377304",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2a95503f90>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#X, y splitting\n",
    "X_imp=rul_train_1.drop(columns=[\"RUL\", \"engine_id\", \"cycle\"])\n",
    "y_imp=rul_train_1[\"RUL\"]\n",
    "\n",
    "#feature importances\n",
    "rf_clf = RandomForestClassifier(n_estimators = 500, max_depth=5)\n",
    "rf_clf.fit(X_imp, y_imp)\n",
    "\n",
    "pd.Series(rf_clf.feature_importances_, index = X_imp.columns).nlargest(30).plot(kind = 'pie',\n",
    "                                                                                figsize = (8, 8),\n",
    "                                                                                title = 'Feature importance from RandomForest for train set', colormap='twilight', fontsize=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.045831,
     "end_time": "2021-01-08T16:10:36.858444",
     "exception": false,
     "start_time": "2021-01-08T16:10:36.812613",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "2. w Permutation importance:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.044091,
     "end_time": "2021-01-08T16:10:36.947671",
     "exception": false,
     "start_time": "2021-01-08T16:10:36.903580",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- [permutation importances](http://scikit-learn.org/stable/modules/permutation_importance.html#permutation-importance)\n",
    "\n",
    "- This procedure breaks the relationship between the feature and the target, thus the drop in the model score is indicative of how much the model depends on the feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-08T16:10:37.049006Z",
     "iopub.status.busy": "2021-01-08T16:10:37.047878Z",
     "iopub.status.idle": "2021-01-08T17:05:58.843989Z",
     "shell.execute_reply": "2021-01-08T17:05:58.843080Z"
    },
    "papermill": {
     "duration": 3321.85207,
     "end_time": "2021-01-08T17:05:58.844151",
     "exception": false,
     "start_time": "2021-01-08T16:10:36.992081",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sensor_8 0.041 +/- 0.001\n",
      "sensor_2 0.034 +/- 0.001\n",
      "sensor_9 0.024 +/- 0.001\n",
      "sensor_4 0.022 +/- 0.001\n",
      "sensor_7 0.020 +/- 0.001\n",
      "sensor_13 0.020 +/- 0.001\n",
      "sensor_17 0.016 +/- 0.001\n",
      "sensor_12 0.016 +/- 0.001\n",
      "sensor_15 0.015 +/- 0.001\n",
      "op_set_1 0.010 +/- 0.001\n",
      "op_set_2 0.009 +/- 0.001\n",
      "sensor_11 0.007 +/- 0.001\n",
      "sensor_14 0.007 +/- 0.001\n",
      "sensor_6 0.006 +/- 0.001\n",
      "engine_id 0.003 +/- 0.000\n",
      "cycle    0.001 +/- 0.000\n",
      "sensor_3 0.000 +/- 0.000\n"
     ]
    }
   ],
   "source": [
    "# permutation importance with RF classifier for deciding to elimination bet. 9&14 since they are highly correlated\n",
    "atts=rul_train_1.columns\n",
    "atts_arr = np.array(atts)\n",
    "result = permutation_importance(rf_clf, X_imp, y_imp, n_repeats=10, random_state=42, n_jobs=-1)\n",
    "sorted_idx = result.importances_mean.argsort()\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(result.importances[sorted_idx].T, vert=False, labels=atts_arr[sorted_idx])\n",
    "ax.set_title(\"Permutation Importances (train set)\")\n",
    "fig.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "for i in result.importances_mean.argsort()[::-1]:\n",
    "     print(f\"{atts_arr[i]:<8} \"\n",
    "           f\"{result.importances_mean[i]:.3f}\"\n",
    "           f\" +/- {result.importances_std[i]:.3f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-08T17:05:58.943529Z",
     "iopub.status.busy": "2021-01-08T17:05:58.942851Z",
     "iopub.status.idle": "2021-01-08T17:05:58.946859Z",
     "shell.execute_reply": "2021-01-08T17:05:58.946353Z"
    },
    "papermill": {
     "duration": 0.056773,
     "end_time": "2021-01-08T17:05:58.946959",
     "exception": false,
     "start_time": "2021-01-08T17:05:58.890186",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#corr matrix results to be deleted 14-9\n",
    "train_1_del=[\"sensor_14\"]\n",
    "train_1.drop(columns=train_1_del, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-08T17:05:59.048372Z",
     "iopub.status.busy": "2021-01-08T17:05:59.047790Z",
     "iopub.status.idle": "2021-01-08T17:05:59.300018Z",
     "shell.execute_reply": "2021-01-08T17:05:59.298902Z"
    },
    "papermill": {
     "duration": 0.30597,
     "end_time": "2021-01-08T17:05:59.300160",
     "exception": false,
     "start_time": "2021-01-08T17:05:58.994190",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dd_1=train_1.groupby('engine_id')['cycle'].max().reset_index().sort_values(\"cycle\", ascending=False)\n",
    "\n",
    "dd_1.reset_index(drop=True, inplace=True)\n",
    "train_1_top=dd_1.head(10)\n",
    "\n",
    "plt.figure(figsize=(7,7))\n",
    "sns.barplot(x=\"engine_id\", y=\"cycle\", data=train_1_top, palette=\"rocket\", order=train_1_top['engine_id'])\n",
    "plt.title(\"Train_1-->Top 10 engines\")\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.046001,
     "end_time": "2021-01-08T17:05:59.395372",
     "exception": false,
     "start_time": "2021-01-08T17:05:59.349371",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#  NORMALIZATION: MinMax Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-08T17:05:59.501565Z",
     "iopub.status.busy": "2021-01-08T17:05:59.500490Z",
     "iopub.status.idle": "2021-01-08T17:05:59.518469Z",
     "shell.execute_reply": "2021-01-08T17:05:59.517923Z"
    },
    "papermill": {
     "duration": 0.077632,
     "end_time": "2021-01-08T17:05:59.518577",
     "exception": false,
     "start_time": "2021-01-08T17:05:59.440945",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#train_1 minmax scaling\n",
    "cols_normalize_1 = rul_train_1.columns.difference(['engine_id','cycle'])\n",
    "\n",
    "scaler_1 = MinMaxScaler()\n",
    "norm_rul_train_1 = pd.DataFrame(scaler_1.fit_transform(rul_train_1[cols_normalize_1]), \n",
    "                                columns=cols_normalize_1, \n",
    "                                index=rul_train_1.index)\n",
    "\n",
    "norm_rul_train_1=pd.concat([norm_rul_train_1, rul_train_1[[\"engine_id\", \"cycle\"]]], axis=1)\n",
    "\n",
    "################   ################   ################   ################   ################\n",
    "\n",
    "#test_1 minmax scaling\n",
    "norm_rul_test_1 = pd.DataFrame(scaler_1.transform(rul_test_1[cols_normalize_1]), \n",
    "                                columns=cols_normalize_1, \n",
    "                                index=rul_test_1.index)\n",
    "\n",
    "norm_rul_test_1=pd.concat([norm_rul_test_1, rul_test_1[[\"engine_id\", \"cycle\"]]], axis=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-08T17:05:59.620000Z",
     "iopub.status.busy": "2021-01-08T17:05:59.619149Z",
     "iopub.status.idle": "2021-01-08T17:05:59.667345Z",
     "shell.execute_reply": "2021-01-08T17:05:59.667810Z"
    },
    "papermill": {
     "duration": 0.103236,
     "end_time": "2021-01-08T17:05:59.667946",
     "exception": false,
     "start_time": "2021-01-08T17:05:59.564710",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#split engines for set_1\n",
    "g_1=norm_rul_train_1.groupby('engine_id')\n",
    "g_test1=norm_rul_test_1.groupby('engine_id')\n",
    "\n",
    "#list of dfs(engines)\n",
    "train_list = []\n",
    "test_list = []  \n",
    "\n",
    "for engineid in g_1.groups.keys():\n",
    "    train_list.append(g_1.get_group(engineid)) \n",
    "\n",
    "for engineid in g_test1.groups.keys():\n",
    "    test_list.append(g_test1.get_group(engineid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-08T17:05:59.764759Z",
     "iopub.status.busy": "2021-01-08T17:05:59.763879Z",
     "iopub.status.idle": "2021-01-08T17:05:59.767324Z",
     "shell.execute_reply": "2021-01-08T17:05:59.767877Z"
    },
    "papermill": {
     "duration": 0.054726,
     "end_time": "2021-01-08T17:05:59.768012",
     "exception": false,
     "start_time": "2021-01-08T17:05:59.713286",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-08T17:05:59.867778Z",
     "iopub.status.busy": "2021-01-08T17:05:59.867135Z",
     "iopub.status.idle": "2021-01-08T17:05:59.872644Z",
     "shell.execute_reply": "2021-01-08T17:05:59.873113Z"
    },
    "papermill": {
     "duration": 0.057305,
     "end_time": "2021-01-08T17:05:59.873258",
     "exception": false,
     "start_time": "2021-01-08T17:05:59.815953",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-08T17:05:59.982333Z",
     "iopub.status.busy": "2021-01-08T17:05:59.981392Z",
     "iopub.status.idle": "2021-01-08T17:06:00.140723Z",
     "shell.execute_reply": "2021-01-08T17:06:00.140153Z"
    },
    "papermill": {
     "duration": 0.220193,
     "end_time": "2021-01-08T17:06:00.140829",
     "exception": false,
     "start_time": "2021-01-08T17:05:59.920636",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#generating sequences for each engine --> multivariate one step problem\n",
    "from numpy import array\n",
    "\n",
    "#df: df extracted from train_list, n_steps: window size\n",
    "def split_sequences(df, n_steps):\n",
    "    X, y = list(), list()\n",
    "    for i in range(len(df)):\n",
    "        end_ix = i + n_steps\n",
    "        if end_ix > len(df):\n",
    "            break\n",
    "        seq_x, seq_y = df[i:end_ix, 1:], df[end_ix-1, 0]\n",
    "        X.append(seq_x)\n",
    "        y.append(seq_y)\n",
    "    return array(X), array(y)\n",
    "\n",
    "\n",
    "list_x=[]\n",
    "list_y=[]\n",
    "for engine_df in train_list:\n",
    "    #convert df to arr \n",
    "    engine_arr=engine_df.drop(columns=[\"engine_id\", \"cycle\"]).to_numpy()\n",
    "    X, y = split_sequences(engine_arr, 21)#since smallest df has 21 rows\n",
    "    list_x.append(X)\n",
    "    list_y.append(y)\n",
    "\n",
    "#concat alt alta  \n",
    "X_arr=np.concatenate(list_x)\n",
    "y_arr=np.concatenate(list_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-08T17:06:00.246580Z",
     "iopub.status.busy": "2021-01-08T17:06:00.245297Z",
     "iopub.status.idle": "2021-01-08T17:06:00.249173Z",
     "shell.execute_reply": "2021-01-08T17:06:00.249662Z"
    },
    "papermill": {
     "duration": 0.059887,
     "end_time": "2021-01-08T17:06:00.249788",
     "exception": false,
     "start_time": "2021-01-08T17:06:00.189901",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 18)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "engine_arr.shape #input of split_seq function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-08T17:06:00.367312Z",
     "iopub.status.busy": "2021-01-08T17:06:00.366506Z",
     "iopub.status.idle": "2021-01-08T17:06:00.370444Z",
     "shell.execute_reply": "2021-01-08T17:06:00.369898Z"
    },
    "papermill": {
     "duration": 0.06586,
     "end_time": "2021-01-08T17:06:00.370542",
     "exception": false,
     "start_time": "2021-01-08T17:06:00.304682",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(180, 21, 17)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape #output of split_seq function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-08T17:06:00.475389Z",
     "iopub.status.busy": "2021-01-08T17:06:00.474569Z",
     "iopub.status.idle": "2021-01-08T17:06:00.478485Z",
     "shell.execute_reply": "2021-01-08T17:06:00.477972Z"
    },
    "papermill": {
     "duration": 0.059683,
     "end_time": "2021-01-08T17:06:00.478583",
     "exception": false,
     "start_time": "2021-01-08T17:06:00.418900",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(180,)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape #output of split_seq function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-08T17:06:00.583365Z",
     "iopub.status.busy": "2021-01-08T17:06:00.582468Z",
     "iopub.status.idle": "2021-01-08T17:06:00.586759Z",
     "shell.execute_reply": "2021-01-08T17:06:00.586208Z"
    },
    "papermill": {
     "duration": 0.05865,
     "end_time": "2021-01-08T17:06:00.586873",
     "exception": false,
     "start_time": "2021-01-08T17:06:00.528223",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18631, 21, 17)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_arr.shape #concat all engines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-08T17:06:00.694496Z",
     "iopub.status.busy": "2021-01-08T17:06:00.693712Z",
     "iopub.status.idle": "2021-01-08T17:06:00.696818Z",
     "shell.execute_reply": "2021-01-08T17:06:00.697383Z"
    },
    "papermill": {
     "duration": 0.058018,
     "end_time": "2021-01-08T17:06:00.697522",
     "exception": false,
     "start_time": "2021-01-08T17:06:00.639504",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18631,)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_arr.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.049799,
     "end_time": "2021-01-08T17:06:00.797616",
     "exception": false,
     "start_time": "2021-01-08T17:06:00.747817",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Define LSTM Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-08T17:06:00.910716Z",
     "iopub.status.busy": "2021-01-08T17:06:00.910039Z",
     "iopub.status.idle": "2021-01-08T17:21:02.976355Z",
     "shell.execute_reply": "2021-01-08T17:21:02.975804Z"
    },
    "papermill": {
     "duration": 902.127732,
     "end_time": "2021-01-08T17:21:02.976516",
     "exception": false,
     "start_time": "2021-01-08T17:06:00.848784",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0160\n",
      "Epoch 2/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0123\n",
      "Epoch 3/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0110\n",
      "Epoch 4/200\n",
      "583/583 [==============================] - 4s 8ms/step - loss: 0.0106\n",
      "Epoch 5/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0103\n",
      "Epoch 6/200\n",
      "583/583 [==============================] - 5s 9ms/step - loss: 0.0102\n",
      "Epoch 7/200\n",
      "583/583 [==============================] - 5s 8ms/step - loss: 0.0101\n",
      "Epoch 8/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0098\n",
      "Epoch 9/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0097\n",
      "Epoch 10/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0095\n",
      "Epoch 11/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0094\n",
      "Epoch 12/200\n",
      "583/583 [==============================] - 5s 8ms/step - loss: 0.0093\n",
      "Epoch 13/200\n",
      "583/583 [==============================] - 4s 8ms/step - loss: 0.0092\n",
      "Epoch 14/200\n",
      "583/583 [==============================] - 5s 8ms/step - loss: 0.0092\n",
      "Epoch 15/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0091\n",
      "Epoch 16/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0089\n",
      "Epoch 17/200\n",
      "583/583 [==============================] - 4s 8ms/step - loss: 0.0089\n",
      "Epoch 18/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0090\n",
      "Epoch 19/200\n",
      "583/583 [==============================] - 5s 9ms/step - loss: 0.0089\n",
      "Epoch 20/200\n",
      "583/583 [==============================] - 5s 8ms/step - loss: 0.0087\n",
      "Epoch 21/200\n",
      "583/583 [==============================] - 5s 8ms/step - loss: 0.0086\n",
      "Epoch 22/200\n",
      "583/583 [==============================] - 4s 8ms/step - loss: 0.0086\n",
      "Epoch 23/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0087\n",
      "Epoch 24/200\n",
      "583/583 [==============================] - 5s 8ms/step - loss: 0.0084\n",
      "Epoch 25/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0084\n",
      "Epoch 26/200\n",
      "583/583 [==============================] - 5s 8ms/step - loss: 0.0083\n",
      "Epoch 27/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0082\n",
      "Epoch 28/200\n",
      "583/583 [==============================] - 4s 8ms/step - loss: 0.0081\n",
      "Epoch 29/200\n",
      "583/583 [==============================] - 5s 8ms/step - loss: 0.0081\n",
      "Epoch 30/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0081\n",
      "Epoch 31/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0079\n",
      "Epoch 32/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0079\n",
      "Epoch 33/200\n",
      "583/583 [==============================] - 5s 9ms/step - loss: 0.0077\n",
      "Epoch 34/200\n",
      "583/583 [==============================] - 5s 8ms/step - loss: 0.0075\n",
      "Epoch 35/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0073\n",
      "Epoch 36/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0072\n",
      "Epoch 37/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0073\n",
      "Epoch 38/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0070\n",
      "Epoch 39/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0068\n",
      "Epoch 40/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0067\n",
      "Epoch 41/200\n",
      "583/583 [==============================] - 4s 8ms/step - loss: 0.0066\n",
      "Epoch 42/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0065\n",
      "Epoch 43/200\n",
      "583/583 [==============================] - 4s 8ms/step - loss: 0.0062\n",
      "Epoch 44/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0063\n",
      "Epoch 45/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0059\n",
      "Epoch 46/200\n",
      "583/583 [==============================] - 5s 8ms/step - loss: 0.0061\n",
      "Epoch 47/200\n",
      "583/583 [==============================] - 5s 8ms/step - loss: 0.0058\n",
      "Epoch 48/200\n",
      "583/583 [==============================] - 5s 9ms/step - loss: 0.0059\n",
      "Epoch 49/200\n",
      "583/583 [==============================] - 4s 8ms/step - loss: 0.0054\n",
      "Epoch 50/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0052\n",
      "Epoch 51/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0051\n",
      "Epoch 52/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0050\n",
      "Epoch 53/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0048\n",
      "Epoch 54/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0047\n",
      "Epoch 55/200\n",
      "583/583 [==============================] - 5s 8ms/step - loss: 0.0046\n",
      "Epoch 56/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0046\n",
      "Epoch 57/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0042\n",
      "Epoch 58/200\n",
      "583/583 [==============================] - 4s 8ms/step - loss: 0.0042\n",
      "Epoch 59/200\n",
      "583/583 [==============================] - 4s 8ms/step - loss: 0.0042\n",
      "Epoch 60/200\n",
      "583/583 [==============================] - 5s 8ms/step - loss: 0.0041\n",
      "Epoch 61/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0038\n",
      "Epoch 62/200\n",
      "583/583 [==============================] - 5s 8ms/step - loss: 0.0037\n",
      "Epoch 63/200\n",
      "583/583 [==============================] - 5s 9ms/step - loss: 0.0036\n",
      "Epoch 64/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0034\n",
      "Epoch 65/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0033\n",
      "Epoch 66/200\n",
      "583/583 [==============================] - 4s 8ms/step - loss: 0.0032\n",
      "Epoch 67/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0031\n",
      "Epoch 68/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0032\n",
      "Epoch 69/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0030\n",
      "Epoch 70/200\n",
      "583/583 [==============================] - 5s 8ms/step - loss: 0.0027\n",
      "Epoch 71/200\n",
      "583/583 [==============================] - 4s 8ms/step - loss: 0.0026\n",
      "Epoch 72/200\n",
      "583/583 [==============================] - 4s 8ms/step - loss: 0.0026\n",
      "Epoch 73/200\n",
      "583/583 [==============================] - 5s 8ms/step - loss: 0.0025\n",
      "Epoch 74/200\n",
      "583/583 [==============================] - 5s 8ms/step - loss: 0.0024\n",
      "Epoch 75/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0023\n",
      "Epoch 76/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0023\n",
      "Epoch 77/200\n",
      "583/583 [==============================] - 5s 8ms/step - loss: 0.0024\n",
      "Epoch 78/200\n",
      "583/583 [==============================] - 5s 9ms/step - loss: 0.0021\n",
      "Epoch 79/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0021\n",
      "Epoch 80/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0020\n",
      "Epoch 81/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0021\n",
      "Epoch 82/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0020\n",
      "Epoch 83/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0018\n",
      "Epoch 84/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0019\n",
      "Epoch 85/200\n",
      "583/583 [==============================] - 4s 8ms/step - loss: 0.0020\n",
      "Epoch 86/200\n",
      "583/583 [==============================] - 5s 9ms/step - loss: 0.0019\n",
      "Epoch 87/200\n",
      "583/583 [==============================] - 5s 8ms/step - loss: 0.0017\n",
      "Epoch 88/200\n",
      "583/583 [==============================] - 5s 8ms/step - loss: 0.0016\n",
      "Epoch 89/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0016\n",
      "Epoch 90/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0016\n",
      "Epoch 91/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0017\n",
      "Epoch 92/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0017\n",
      "Epoch 93/200\n",
      "583/583 [==============================] - 5s 8ms/step - loss: 0.0015\n",
      "Epoch 94/200\n",
      "583/583 [==============================] - 5s 8ms/step - loss: 0.0015\n",
      "Epoch 95/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0015\n",
      "Epoch 96/200\n",
      "583/583 [==============================] - 4s 8ms/step - loss: 0.0014\n",
      "Epoch 97/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0015\n",
      "Epoch 98/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0014\n",
      "Epoch 99/200\n",
      "583/583 [==============================] - 4s 8ms/step - loss: 0.0015\n",
      "Epoch 100/200\n",
      "583/583 [==============================] - 4s 8ms/step - loss: 0.0014\n",
      "Epoch 101/200\n",
      "583/583 [==============================] - 6s 11ms/step - loss: 0.0016\n",
      "Epoch 102/200\n",
      "583/583 [==============================] - 5s 8ms/step - loss: 0.0015\n",
      "Epoch 103/200\n",
      "583/583 [==============================] - 4s 8ms/step - loss: 0.0013\n",
      "Epoch 104/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0013\n",
      "Epoch 105/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0013\n",
      "Epoch 106/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0013\n",
      "Epoch 107/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0017\n",
      "Epoch 108/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0014\n",
      "Epoch 109/200\n",
      "583/583 [==============================] - 5s 9ms/step - loss: 0.0012\n",
      "Epoch 110/200\n",
      "583/583 [==============================] - 5s 9ms/step - loss: 0.0012\n",
      "Epoch 111/200\n",
      "583/583 [==============================] - 4s 8ms/step - loss: 0.0012\n",
      "Epoch 112/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0012\n",
      "Epoch 113/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0014\n",
      "Epoch 114/200\n",
      "583/583 [==============================] - 5s 8ms/step - loss: 0.0012\n",
      "Epoch 115/200\n",
      "583/583 [==============================] - 5s 8ms/step - loss: 0.0012\n",
      "Epoch 116/200\n",
      "583/583 [==============================] - 4s 8ms/step - loss: 0.0017\n",
      "Epoch 117/200\n",
      "583/583 [==============================] - 6s 10ms/step - loss: 0.0012\n",
      "Epoch 118/200\n",
      "583/583 [==============================] - 5s 8ms/step - loss: 0.0012\n",
      "Epoch 119/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0012\n",
      "Epoch 120/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0012\n",
      "Epoch 121/200\n",
      "583/583 [==============================] - 4s 8ms/step - loss: 0.0012\n",
      "Epoch 122/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0012\n",
      "Epoch 123/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0012\n",
      "Epoch 124/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0012\n",
      "Epoch 125/200\n",
      "583/583 [==============================] - 5s 9ms/step - loss: 0.0013\n",
      "Epoch 126/200\n",
      "583/583 [==============================] - 5s 9ms/step - loss: 0.0011\n",
      "Epoch 127/200\n",
      "583/583 [==============================] - 5s 8ms/step - loss: 0.0011\n",
      "Epoch 128/200\n",
      "583/583 [==============================] - 4s 8ms/step - loss: 0.0012\n",
      "Epoch 129/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0018\n",
      "Epoch 130/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0011\n",
      "Epoch 131/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0011\n",
      "Epoch 132/200\n",
      "583/583 [==============================] - 4s 8ms/step - loss: 0.0010\n",
      "Epoch 133/200\n",
      "583/583 [==============================] - 5s 8ms/step - loss: 0.0011\n",
      "Epoch 134/200\n",
      "583/583 [==============================] - 5s 9ms/step - loss: 0.0011\n",
      "Epoch 135/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0011\n",
      "Epoch 136/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0012\n",
      "Epoch 137/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0011\n",
      "Epoch 138/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0011\n",
      "Epoch 139/200\n",
      "583/583 [==============================] - 4s 8ms/step - loss: 0.0011\n",
      "Epoch 140/200\n",
      "583/583 [==============================] - 5s 8ms/step - loss: 0.0011\n",
      "Epoch 141/200\n",
      "583/583 [==============================] - 5s 8ms/step - loss: 0.0010\n",
      "Epoch 142/200\n",
      "583/583 [==============================] - 6s 10ms/step - loss: 0.0016\n",
      "Epoch 143/200\n",
      "583/583 [==============================] - 4s 8ms/step - loss: 0.0011\n",
      "Epoch 144/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0010\n",
      "Epoch 145/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0010\n",
      "Epoch 146/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0011\n",
      "Epoch 147/200\n",
      "583/583 [==============================] - 4s 8ms/step - loss: 0.0011\n",
      "Epoch 148/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0010\n",
      "Epoch 149/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0011\n",
      "Epoch 150/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0011\n",
      "Epoch 151/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0010\n",
      "Epoch 152/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0011\n",
      "Epoch 153/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0010\n",
      "Epoch 154/200\n",
      "583/583 [==============================] - 5s 9ms/step - loss: 0.0011\n",
      "Epoch 155/200\n",
      "583/583 [==============================] - 5s 8ms/step - loss: 0.0016\n",
      "Epoch 156/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0010\n",
      "Epoch 157/200\n",
      "583/583 [==============================] - 5s 9ms/step - loss: 9.9672e-04\n",
      "Epoch 158/200\n",
      "583/583 [==============================] - 6s 10ms/step - loss: 9.9139e-04\n",
      "Epoch 159/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0010\n",
      "Epoch 160/200\n",
      "583/583 [==============================] - 4s 8ms/step - loss: 0.0010\n",
      "Epoch 161/200\n",
      "583/583 [==============================] - 4s 8ms/step - loss: 0.0014\n",
      "Epoch 162/200\n",
      "583/583 [==============================] - 5s 8ms/step - loss: 0.0010\n",
      "Epoch 163/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0010\n",
      "Epoch 164/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0010\n",
      "Epoch 165/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 9.9388e-04\n",
      "Epoch 166/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0010\n",
      "Epoch 167/200\n",
      "583/583 [==============================] - 4s 8ms/step - loss: 0.0011\n",
      "Epoch 168/200\n",
      "583/583 [==============================] - 5s 9ms/step - loss: 0.0011\n",
      "Epoch 169/200\n",
      "583/583 [==============================] - 5s 9ms/step - loss: 0.0010\n",
      "Epoch 170/200\n",
      "583/583 [==============================] - 5s 9ms/step - loss: 9.9259e-04\n",
      "Epoch 171/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0010\n",
      "Epoch 172/200\n",
      "583/583 [==============================] - 5s 9ms/step - loss: 0.0010\n",
      "Epoch 173/200\n",
      "583/583 [==============================] - 6s 9ms/step - loss: 0.0012\n",
      "Epoch 174/200\n",
      "583/583 [==============================] - 4s 8ms/step - loss: 0.0010\n",
      "Epoch 175/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0014\n",
      "Epoch 176/200\n",
      "583/583 [==============================] - 4s 8ms/step - loss: 9.7063e-04\n",
      "Epoch 177/200\n",
      "583/583 [==============================] - 4s 8ms/step - loss: 0.0010\n",
      "Epoch 178/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0012\n",
      "Epoch 179/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0011\n",
      "Epoch 180/200\n",
      "583/583 [==============================] - 4s 8ms/step - loss: 9.6718e-04\n",
      "Epoch 181/200\n",
      "583/583 [==============================] - 4s 8ms/step - loss: 9.6387e-04\n",
      "Epoch 182/200\n",
      "583/583 [==============================] - 5s 8ms/step - loss: 9.9242e-04\n",
      "Epoch 183/200\n",
      "583/583 [==============================] - 5s 8ms/step - loss: 0.0010\n",
      "Epoch 184/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0010\n",
      "Epoch 185/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0012\n",
      "Epoch 186/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0010\n",
      "Epoch 187/200\n",
      "583/583 [==============================] - 5s 8ms/step - loss: 9.7607e-04\n",
      "Epoch 188/200\n",
      "583/583 [==============================] - 5s 9ms/step - loss: 9.5747e-04\n",
      "Epoch 189/200\n",
      "583/583 [==============================] - 5s 9ms/step - loss: 9.8810e-04\n",
      "Epoch 190/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 9.4253e-04\n",
      "Epoch 191/200\n",
      "583/583 [==============================] - 5s 8ms/step - loss: 0.0010\n",
      "Epoch 192/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0010\n",
      "Epoch 193/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 9.4640e-04\n",
      "Epoch 194/200\n",
      "583/583 [==============================] - 5s 8ms/step - loss: 9.9614e-04\n",
      "Epoch 195/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0010\n",
      "Epoch 196/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0010\n",
      "Epoch 197/200\n",
      "583/583 [==============================] - 4s 8ms/step - loss: 9.3693e-04\n",
      "Epoch 198/200\n",
      "583/583 [==============================] - 4s 8ms/step - loss: 9.7389e-04\n",
      "Epoch 199/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0010\n",
      "Epoch 200/200\n",
      "583/583 [==============================] - 4s 7ms/step - loss: 0.0014\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seq_len=X_arr.shape[1]\n",
    "num_features=X_arr.shape[2]\n",
    "\n",
    "model = Sequential()\n",
    "model.add(LSTM(input_shape=(seq_len, num_features), units=100, return_sequences=True))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(LSTM(units=50, return_sequences=True))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(LSTM(units=10, return_sequences=False))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Dense(units=1))\n",
    "model.compile(optimizer='adam', loss='mse')\n",
    "\n",
    "#This callback will stop the training when there is no improvement in the validation loss for 20 consecutive epochs. \n",
    "#regularization technique as preventing overfitting, model runtime gets shorter\n",
    "callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=20)\n",
    "\n",
    "# fit model\n",
    "history=model.fit(X_arr, y_arr, epochs=200, callbacks=[callback])\n",
    "len(history.history['loss']) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 5.976094,
     "end_time": "2021-01-08T17:21:15.147436",
     "exception": false,
     "start_time": "2021-01-08T17:21:09.171342",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# PREDICTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-08T17:21:27.715320Z",
     "iopub.status.busy": "2021-01-08T17:21:27.714342Z",
     "iopub.status.idle": "2021-01-08T17:21:27.820613Z",
     "shell.execute_reply": "2021-01-08T17:21:27.819857Z"
    },
    "papermill": {
     "duration": 6.445617,
     "end_time": "2021-01-08T17:21:27.820729",
     "exception": false,
     "start_time": "2021-01-08T17:21:21.375112",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#prepare test set to make predictions\n",
    "list_x_test=[]\n",
    "list_y_test=[]\n",
    "\n",
    "for engine_df in test_list:\n",
    "    #convert df to arr \n",
    "    engine_arr=engine_df.drop(columns=[\"engine_id\", \"cycle\"]).to_numpy()\n",
    "    X, y = split_sequences(engine_arr, seq_len)\n",
    "    \n",
    "    #use only last seq for each engine\n",
    "    list_x_test.append(X[-1].reshape((1, seq_len, num_features)))\n",
    "    list_y_test.append(y[-1].reshape((1, )))\n",
    "\n",
    "#concat alt alta  \n",
    "X_arr_test=np.concatenate(list_x_test)\n",
    "y_arr_test=np.concatenate(list_y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-08T17:21:40.734203Z",
     "iopub.status.busy": "2021-01-08T17:21:40.733261Z",
     "iopub.status.idle": "2021-01-08T17:21:40.736426Z",
     "shell.execute_reply": "2021-01-08T17:21:40.736955Z"
    },
    "papermill": {
     "duration": 6.528131,
     "end_time": "2021-01-08T17:21:40.737105",
     "exception": false,
     "start_time": "2021-01-08T17:21:34.208974",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 21, 17)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_arr_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-08T17:21:53.287607Z",
     "iopub.status.busy": "2021-01-08T17:21:53.286425Z",
     "iopub.status.idle": "2021-01-08T17:21:53.290138Z",
     "shell.execute_reply": "2021-01-08T17:21:53.290713Z"
    },
    "papermill": {
     "duration": 6.245115,
     "end_time": "2021-01-08T17:21:53.290846",
     "exception": false,
     "start_time": "2021-01-08T17:21:47.045731",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100,)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_arr_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 6.300737,
     "end_time": "2021-01-08T17:22:05.804581",
     "exception": false,
     "start_time": "2021-01-08T17:21:59.503844",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We have to make inverse scaling for RUL column in order to produce proper prediction results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-08T17:22:17.916282Z",
     "iopub.status.busy": "2021-01-08T17:22:17.914482Z",
     "iopub.status.idle": "2021-01-08T17:22:17.917002Z",
     "shell.execute_reply": "2021-01-08T17:22:17.917490Z"
    },
    "papermill": {
     "duration": 6.24829,
     "end_time": "2021-01-08T17:22:17.917624",
     "exception": false,
     "start_time": "2021-01-08T17:22:11.669334",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#create a dummy df to take inverse only on one col --> \"y_pred\"\n",
    "def invTransform(scaler, y_pred, colNames):\n",
    "    dummy = pd.DataFrame(np.zeros((len(y_pred), len(colNames))), columns=colNames)\n",
    "    dummy[\"RUL\"] = y_pred\n",
    "    dummy = pd.DataFrame(scaler.inverse_transform(dummy), columns=colNames)\n",
    "    return dummy[\"RUL\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-08T17:22:30.192623Z",
     "iopub.status.busy": "2021-01-08T17:22:30.191637Z",
     "iopub.status.idle": "2021-01-08T17:22:31.173646Z",
     "shell.execute_reply": "2021-01-08T17:22:31.172611Z"
    },
    "papermill": {
     "duration": 7.255464,
     "end_time": "2021-01-08T17:22:31.173784",
     "exception": false,
     "start_time": "2021-01-08T17:22:23.918320",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100,)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#prediction\n",
    "y_pred = model.predict(X_arr_test, verbose=0)\n",
    "\n",
    "#inverse scaling for y_pred values\n",
    "y_pred_inv=invTransform(scaler_1, y_pred, cols_normalize_1)\n",
    "\n",
    "y_pred_reshaped=y_pred_inv.reshape((len(y_pred_inv, )))\n",
    "y_pred_reshaped.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-08T17:22:44.761329Z",
     "iopub.status.busy": "2021-01-08T17:22:44.760421Z",
     "iopub.status.idle": "2021-01-08T17:22:44.763488Z",
     "shell.execute_reply": "2021-01-08T17:22:44.763926Z"
    },
    "papermill": {
     "duration": 6.069346,
     "end_time": "2021-01-08T17:22:44.764058",
     "exception": false,
     "start_time": "2021-01-08T17:22:38.694712",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100,)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_truth=rul_1_gt[\"RUL_1gt\"].values\n",
    "y_truth.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 5.914515,
     "end_time": "2021-01-08T17:22:56.931301",
     "exception": false,
     "start_time": "2021-01-08T17:22:51.016786",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# SCORES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-08T17:23:09.876220Z",
     "iopub.status.busy": "2021-01-08T17:23:09.875233Z",
     "iopub.status.idle": "2021-01-08T17:23:09.880448Z",
     "shell.execute_reply": "2021-01-08T17:23:09.880902Z"
    },
    "papermill": {
     "duration": 6.760049,
     "end_time": "2021-01-08T17:23:09.881031",
     "exception": false,
     "start_time": "2021-01-08T17:23:03.120982",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([112,  98,  69,  82,  91,  93,  91,  95, 111,  96,  97, 124,  95,\n",
       "       107,  83,  84,  50,  28,  87,  16,  57, 111, 113,  20, 145, 119,\n",
       "        66,  97,  90, 115,   8,  48, 106,   7,  11,  19,  21,  50, 142,\n",
       "        28,  18,  10,  59, 109, 114,  47, 135,  92,  21,  79, 114,  29,\n",
       "        26,  97, 137,  15, 103,  37, 114, 100,  21,  54,  72,  28, 128,\n",
       "        14,  77,   8, 121,  94, 118,  50, 131, 126, 113,  10,  34, 107,\n",
       "        63,  90,   8,   9, 137,  58, 118,  89, 116, 115, 136,  28,  38,\n",
       "        20,  85,  55, 128, 137,  82,  59, 117,  20])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_truth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-08T17:23:22.008343Z",
     "iopub.status.busy": "2021-01-08T17:23:22.007378Z",
     "iopub.status.idle": "2021-01-08T17:23:22.011253Z",
     "shell.execute_reply": "2021-01-08T17:23:22.011806Z"
    },
    "papermill": {
     "duration": 6.187309,
     "end_time": "2021-01-08T17:23:22.011973",
     "exception": false,
     "start_time": "2021-01-08T17:23:15.824664",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([163.49804845, 150.6297164 ,  43.74402118,  66.79577026,\n",
       "       154.74367985, 168.5031822 , 146.46199203, 156.3521716 ,\n",
       "       144.24781045,  83.85893308,  80.93237902, 109.59312809,\n",
       "       125.75106445, 145.60742262, 159.82878256, 131.50587088,\n",
       "        42.29623121,  33.27971224, 132.77339986,  14.56121457,\n",
       "        67.81602269, 144.41929239, 136.8713975 ,  23.15862001,\n",
       "       123.61828274, 136.27118382, 134.52928495, 129.62922007,\n",
       "        94.09970978,  99.89925063,  12.86784792,  55.67670673,\n",
       "       123.95558757,   7.91279563,  12.52594915,  23.90578593,\n",
       "        39.95096639,  47.84679027, 127.26004955,  32.14457369,\n",
       "        57.80727217,  13.09245063, 110.02765873, 209.79415852,\n",
       "        54.39388973,  38.81556962, 116.32961723, 169.92907834,\n",
       "        16.48404145, 126.73669559, 127.98196495,  31.14876488,\n",
       "        29.87130567, 132.18279365, 130.86696914,  14.98835941,\n",
       "       126.62889403,  47.41069424, 116.93060553, 124.79491198,\n",
       "        26.43226388,  49.87899485,  98.79869571,  21.34399813,\n",
       "       197.39590365,  19.42110132, 126.53697222,  13.54209179,\n",
       "       116.36515301, 149.9781087 , 146.3351692 ,  55.33820231,\n",
       "       105.14879912, 131.78100154, 120.18539488,  12.0635106 ,\n",
       "        40.73304409, 162.00071496, 136.09689388, 138.28168285,\n",
       "         9.37877846,  10.39583558, 151.28357264,  73.1166532 ,\n",
       "       137.83175659,  95.10156494, 146.47625798, 107.79583299,\n",
       "       171.94724828,  38.40000646,  23.42848432,  23.04123251,\n",
       "        38.90825529,  74.21453997, 121.2230548 , 120.28682733,\n",
       "        84.54232717, 109.99097177, 155.59606525,  19.93116298])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_reshaped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-08T17:23:34.437370Z",
     "iopub.status.busy": "2021-01-08T17:23:34.436448Z",
     "iopub.status.idle": "2021-01-08T17:23:34.439577Z",
     "shell.execute_reply": "2021-01-08T17:23:34.439042Z"
    },
    "papermill": {
     "duration": 6.471429,
     "end_time": "2021-01-08T17:23:34.439689",
     "exception": false,
     "start_time": "2021-01-08T17:23:27.968260",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#error cal. for paper\n",
    "def score(y_true, y_pred, a1=10, a2=13):\n",
    "    score = 0\n",
    "    d = y_pred - y_true\n",
    "    for i in d:\n",
    "        if i >= 0 :\n",
    "            score += math.exp(i/a2) - 1   \n",
    "        else:\n",
    "            score += math.exp(- i/a1) - 1\n",
    "    return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-08T17:23:47.252691Z",
     "iopub.status.busy": "2021-01-08T17:23:47.251893Z",
     "iopub.status.idle": "2021-01-08T17:23:47.255043Z",
     "shell.execute_reply": "2021-01-08T17:23:47.255550Z"
    },
    "papermill": {
     "duration": 6.157747,
     "end_time": "2021-01-08T17:23:47.255700",
     "exception": false,
     "start_time": "2021-01-08T17:23:41.097953",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5727.118141201296"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score(y_truth,y_pred_reshaped)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-08T17:23:59.633217Z",
     "iopub.status.busy": "2021-01-08T17:23:59.632333Z",
     "iopub.status.idle": "2021-01-08T17:23:59.635295Z",
     "shell.execute_reply": "2021-01-08T17:23:59.634794Z"
    },
    "papermill": {
     "duration": 6.198397,
     "end_time": "2021-01-08T17:23:59.635422",
     "exception": false,
     "start_time": "2021-01-08T17:23:53.437025",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def score_func(y_true, y_pred):\n",
    "    score_list = [\n",
    "                  round(score(y_true,y_pred), 2), \n",
    "                  round(mean_absolute_error(y_true,y_pred), 2),\n",
    "                  round(mean_squared_error(y_true,y_pred),2) ** 0.5,\n",
    "                  round(r2_score(y_true,y_pred), 2)\n",
    "                  ]\n",
    "    \n",
    "    print(f' R2 score: {score_list[3]}')\n",
    "    print(f' compatitive score: {score_list[0]}')\n",
    "    print(f' mean absolute error: {score_list[1]}')\n",
    "    print(f' root mean squared error: {score_list[2]}')\n",
    "    \n",
    "    return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-08T17:24:12.131286Z",
     "iopub.status.busy": "2021-01-08T17:24:12.130007Z",
     "iopub.status.idle": "2021-01-08T17:24:12.137511Z",
     "shell.execute_reply": "2021-01-08T17:24:12.135312Z"
    },
    "papermill": {
     "duration": 6.30275,
     "end_time": "2021-01-08T17:24:12.137668",
     "exception": false,
     "start_time": "2021-01-08T17:24:05.834918",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " R2 score: 0.36\n",
      " compatitive score: 5727.12\n",
      " mean absolute error: 24.05\n",
      " root mean squared error: 33.24003610106343\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_func(y_truth,y_pred_reshaped)"
   ]
  }
 ],
 "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": "3.7.6"
  },
  "papermill": {
   "duration": 4468.635279,
   "end_time": "2021-01-08T17:24:19.446436",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2021-01-08T16:09:50.811157",
   "version": "2.1.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}