RUL Prediction using LSTM Lookback=5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run to Failure degradation simulation of NASA Turbo Jet Engine Fans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a id='index'>Index</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ <a href='#data_analysis'>1. Data Analysis</a>\n",
    "    + <a href='#info_about_data'>1.1 Info about data:</a>\n",
    "+ <a href='#noise_removal'>2. Noise removal and Normalization</a>\n",
    "+ <a href='#training_and_validation'>3. Training and Validation</a>\n",
    "+ <a href='#testing'>4 Testing</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.preprocessing import QuantileTransformer , PowerTransformer\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "from keras import optimizers\n",
    "from keras.models import Sequential\n",
    "from keras.layers import TimeDistributed, Flatten\n",
    "from keras.layers.core import Dense, Dropout, Activation\n",
    "from keras.layers.recurrent import LSTM\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "%matplotlib inline\n",
    "cmap = cm.get_cmap('Spectral') # Colour map (there are many others)\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import explained_variance_score\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "from keras.models import load_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a id='data_analysis'>1. Data analysis</a>\n",
    "<a href='#index'>Go back to Index</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "      <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",
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       "    <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",
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       "    <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",
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       "      <th>4</th>\n",
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       "      <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",
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       "<p>5 rows × 28 columns</p>\n",
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      "text/plain": [
       "   0   1       2       3      4       5       6        7        8      9   \\\n",
       "0   1   1 -0.0007 -0.0004  100.0  518.67  641.82  1589.70  1400.60  14.62   \n",
       "1   1   2  0.0019 -0.0003  100.0  518.67  642.15  1591.82  1403.14  14.62   \n",
       "2   1   3 -0.0043  0.0003  100.0  518.67  642.35  1587.99  1404.20  14.62   \n",
       "3   1   4  0.0007  0.0000  100.0  518.67  642.35  1582.79  1401.87  14.62   \n",
       "4   1   5 -0.0019 -0.0002  100.0  518.67  642.37  1582.85  1406.22  14.62   \n",
       "\n",
       "   ...       18      19    20   21    22     23     24       25  26  27  \n",
       "0  ...  8138.62  8.4195  0.03  392  2388  100.0  39.06  23.4190 NaN NaN  \n",
       "1  ...  8131.49  8.4318  0.03  392  2388  100.0  39.00  23.4236 NaN NaN  \n",
       "2  ...  8133.23  8.4178  0.03  390  2388  100.0  38.95  23.3442 NaN NaN  \n",
       "3  ...  8133.83  8.3682  0.03  392  2388  100.0  38.88  23.3739 NaN NaN  \n",
       "4  ...  8133.80  8.4294  0.03  393  2388  100.0  38.90  23.4044 NaN NaN  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_file = \"train_FD001.txt\" \n",
    "test_file = \"test_FD001.txt\"\n",
    "RUL_file = \"RUL_FD001.txt\"\n",
    "\n",
    "df = pd.read_csv(train_file,sep=\" \",header=None)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Section-0</th>\n",
       "      <th>Section-1</th>\n",
       "      <th>Section-2</th>\n",
       "      <th>Section-3</th>\n",
       "      <th>Section-4</th>\n",
       "      <th>Section-5</th>\n",
       "      <th>Section-6</th>\n",
       "      <th>Section-7</th>\n",
       "      <th>Section-8</th>\n",
       "      <th>Section-9</th>\n",
       "      <th>...</th>\n",
       "      <th>Section-16</th>\n",
       "      <th>Section-17</th>\n",
       "      <th>Section-18</th>\n",
       "      <th>Section-19</th>\n",
       "      <th>Section-20</th>\n",
       "      <th>Section-21</th>\n",
       "      <th>Section-22</th>\n",
       "      <th>Section-23</th>\n",
       "      <th>Section-24</th>\n",
       "      <th>Section-25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.0007</td>\n",
       "      <td>-0.0004</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>641.82</td>\n",
       "      <td>1589.70</td>\n",
       "      <td>1400.60</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>521.66</td>\n",
       "      <td>2388.02</td>\n",
       "      <td>8138.62</td>\n",
       "      <td>8.4195</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.06</td>\n",
       "      <td>23.4190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.15</td>\n",
       "      <td>1591.82</td>\n",
       "      <td>1403.14</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.28</td>\n",
       "      <td>2388.07</td>\n",
       "      <td>8131.49</td>\n",
       "      <td>8.4318</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.00</td>\n",
       "      <td>23.4236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.0043</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.35</td>\n",
       "      <td>1587.99</td>\n",
       "      <td>1404.20</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.42</td>\n",
       "      <td>2388.03</td>\n",
       "      <td>8133.23</td>\n",
       "      <td>8.4178</td>\n",
       "      <td>0.03</td>\n",
       "      <td>390</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.95</td>\n",
       "      <td>23.3442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.35</td>\n",
       "      <td>1582.79</td>\n",
       "      <td>1401.87</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.86</td>\n",
       "      <td>2388.08</td>\n",
       "      <td>8133.83</td>\n",
       "      <td>8.3682</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.88</td>\n",
       "      <td>23.3739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.0019</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.37</td>\n",
       "      <td>1582.85</td>\n",
       "      <td>1406.22</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.19</td>\n",
       "      <td>2388.04</td>\n",
       "      <td>8133.80</td>\n",
       "      <td>8.4294</td>\n",
       "      <td>0.03</td>\n",
       "      <td>393</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.90</td>\n",
       "      <td>23.4044</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Section-0  Section-1  Section-2  Section-3  Section-4  Section-5  \\\n",
       "0          1          1    -0.0007    -0.0004      100.0     518.67   \n",
       "1          1          2     0.0019    -0.0003      100.0     518.67   \n",
       "2          1          3    -0.0043     0.0003      100.0     518.67   \n",
       "3          1          4     0.0007     0.0000      100.0     518.67   \n",
       "4          1          5    -0.0019    -0.0002      100.0     518.67   \n",
       "\n",
       "   Section-6  Section-7  Section-8  Section-9  ...  Section-16  Section-17  \\\n",
       "0     641.82    1589.70    1400.60      14.62  ...      521.66     2388.02   \n",
       "1     642.15    1591.82    1403.14      14.62  ...      522.28     2388.07   \n",
       "2     642.35    1587.99    1404.20      14.62  ...      522.42     2388.03   \n",
       "3     642.35    1582.79    1401.87      14.62  ...      522.86     2388.08   \n",
       "4     642.37    1582.85    1406.22      14.62  ...      522.19     2388.04   \n",
       "\n",
       "   Section-18  Section-19  Section-20  Section-21  Section-22  Section-23  \\\n",
       "0     8138.62      8.4195        0.03         392        2388       100.0   \n",
       "1     8131.49      8.4318        0.03         392        2388       100.0   \n",
       "2     8133.23      8.4178        0.03         390        2388       100.0   \n",
       "3     8133.83      8.3682        0.03         392        2388       100.0   \n",
       "4     8133.80      8.4294        0.03         393        2388       100.0   \n",
       "\n",
       "   Section-24  Section-25  \n",
       "0       39.06     23.4190  \n",
       "1       39.00     23.4236  \n",
       "2       38.95     23.3442  \n",
       "3       38.88     23.3739  \n",
       "4       38.90     23.4044  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#columns = ['unit_number','time_in_cycles','setting_1','setting_2','TRA','T2','T24','T30','T50','P2','P15','P30','Nf',\n",
    "#           'Nc','epr','Ps3 0','phi','NRf','NRc','BPR','farB','htBleed','Nf_dmd','PCNfR_dmd','W31','W32' ]\n",
    "#delete NaN values\n",
    "df.drop(columns=[26,27],inplace=True)\n",
    "columns = [\"Section-{}\".format(i)  for i in range(26)]\n",
    "df.columns = columns\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dataset statistics  for each parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <th>Section-0</th>\n",
       "      <th>Section-1</th>\n",
       "      <th>Section-2</th>\n",
       "      <th>Section-3</th>\n",
       "      <th>Section-4</th>\n",
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       "      <th>Section-9</th>\n",
       "      <th>...</th>\n",
       "      <th>Section-16</th>\n",
       "      <th>Section-17</th>\n",
       "      <th>Section-18</th>\n",
       "      <th>Section-19</th>\n",
       "      <th>Section-20</th>\n",
       "      <th>Section-21</th>\n",
       "      <th>Section-22</th>\n",
       "      <th>Section-23</th>\n",
       "      <th>Section-24</th>\n",
       "      <th>Section-25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20631.000000</td>\n",
       "      <td>20631.000000</td>\n",
       "      <td>20631.000000</td>\n",
       "      <td>20631.000000</td>\n",
       "      <td>20631.0</td>\n",
       "      <td>2.063100e+04</td>\n",
       "      <td>20631.000000</td>\n",
       "      <td>20631.000000</td>\n",
       "      <td>20631.000000</td>\n",
       "      <td>2.063100e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>20631.000000</td>\n",
       "      <td>20631.000000</td>\n",
       "      <td>20631.000000</td>\n",
       "      <td>20631.000000</td>\n",
       "      <td>2.063100e+04</td>\n",
       "      <td>20631.000000</td>\n",
       "      <td>20631.0</td>\n",
       "      <td>20631.0</td>\n",
       "      <td>20631.000000</td>\n",
       "      <td>20631.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>51.506568</td>\n",
       "      <td>108.807862</td>\n",
       "      <td>-0.000009</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.186700e+02</td>\n",
       "      <td>642.680934</td>\n",
       "      <td>1590.523119</td>\n",
       "      <td>1408.933782</td>\n",
       "      <td>1.462000e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>521.413470</td>\n",
       "      <td>2388.096152</td>\n",
       "      <td>8143.752722</td>\n",
       "      <td>8.442146</td>\n",
       "      <td>3.000000e-02</td>\n",
       "      <td>393.210654</td>\n",
       "      <td>2388.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.816271</td>\n",
       "      <td>23.289705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>29.227633</td>\n",
       "      <td>68.880990</td>\n",
       "      <td>0.002187</td>\n",
       "      <td>0.000293</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.537152e-11</td>\n",
       "      <td>0.500053</td>\n",
       "      <td>6.131150</td>\n",
       "      <td>9.000605</td>\n",
       "      <td>3.394700e-12</td>\n",
       "      <td>...</td>\n",
       "      <td>0.737553</td>\n",
       "      <td>0.071919</td>\n",
       "      <td>19.076176</td>\n",
       "      <td>0.037505</td>\n",
       "      <td>1.556432e-14</td>\n",
       "      <td>1.548763</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.180746</td>\n",
       "      <td>0.108251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.008700</td>\n",
       "      <td>-0.000600</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.186700e+02</td>\n",
       "      <td>641.210000</td>\n",
       "      <td>1571.040000</td>\n",
       "      <td>1382.250000</td>\n",
       "      <td>1.462000e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>518.690000</td>\n",
       "      <td>2387.880000</td>\n",
       "      <td>8099.940000</td>\n",
       "      <td>8.324900</td>\n",
       "      <td>3.000000e-02</td>\n",
       "      <td>388.000000</td>\n",
       "      <td>2388.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.140000</td>\n",
       "      <td>22.894200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>-0.001500</td>\n",
       "      <td>-0.000200</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.186700e+02</td>\n",
       "      <td>642.325000</td>\n",
       "      <td>1586.260000</td>\n",
       "      <td>1402.360000</td>\n",
       "      <td>1.462000e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>520.960000</td>\n",
       "      <td>2388.040000</td>\n",
       "      <td>8133.245000</td>\n",
       "      <td>8.414900</td>\n",
       "      <td>3.000000e-02</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>2388.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.700000</td>\n",
       "      <td>23.221800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>52.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.186700e+02</td>\n",
       "      <td>642.640000</td>\n",
       "      <td>1590.100000</td>\n",
       "      <td>1408.040000</td>\n",
       "      <td>1.462000e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>521.480000</td>\n",
       "      <td>2388.090000</td>\n",
       "      <td>8140.540000</td>\n",
       "      <td>8.438900</td>\n",
       "      <td>3.000000e-02</td>\n",
       "      <td>393.000000</td>\n",
       "      <td>2388.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.830000</td>\n",
       "      <td>23.297900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>0.001500</td>\n",
       "      <td>0.000300</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.186700e+02</td>\n",
       "      <td>643.000000</td>\n",
       "      <td>1594.380000</td>\n",
       "      <td>1414.555000</td>\n",
       "      <td>1.462000e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>521.950000</td>\n",
       "      <td>2388.140000</td>\n",
       "      <td>8148.310000</td>\n",
       "      <td>8.465600</td>\n",
       "      <td>3.000000e-02</td>\n",
       "      <td>394.000000</td>\n",
       "      <td>2388.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.950000</td>\n",
       "      <td>23.366800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>362.000000</td>\n",
       "      <td>0.008700</td>\n",
       "      <td>0.000600</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.186700e+02</td>\n",
       "      <td>644.530000</td>\n",
       "      <td>1616.910000</td>\n",
       "      <td>1441.490000</td>\n",
       "      <td>1.462000e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>523.380000</td>\n",
       "      <td>2388.560000</td>\n",
       "      <td>8293.720000</td>\n",
       "      <td>8.584800</td>\n",
       "      <td>3.000000e-02</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>2388.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.430000</td>\n",
       "      <td>23.618400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Section-0     Section-1     Section-2     Section-3  Section-4  \\\n",
       "count  20631.000000  20631.000000  20631.000000  20631.000000    20631.0   \n",
       "mean      51.506568    108.807862     -0.000009      0.000002      100.0   \n",
       "std       29.227633     68.880990      0.002187      0.000293        0.0   \n",
       "min        1.000000      1.000000     -0.008700     -0.000600      100.0   \n",
       "25%       26.000000     52.000000     -0.001500     -0.000200      100.0   \n",
       "50%       52.000000    104.000000      0.000000      0.000000      100.0   \n",
       "75%       77.000000    156.000000      0.001500      0.000300      100.0   \n",
       "max      100.000000    362.000000      0.008700      0.000600      100.0   \n",
       "\n",
       "          Section-5     Section-6     Section-7     Section-8     Section-9  \\\n",
       "count  2.063100e+04  20631.000000  20631.000000  20631.000000  2.063100e+04   \n",
       "mean   5.186700e+02    642.680934   1590.523119   1408.933782  1.462000e+01   \n",
       "std    6.537152e-11      0.500053      6.131150      9.000605  3.394700e-12   \n",
       "min    5.186700e+02    641.210000   1571.040000   1382.250000  1.462000e+01   \n",
       "25%    5.186700e+02    642.325000   1586.260000   1402.360000  1.462000e+01   \n",
       "50%    5.186700e+02    642.640000   1590.100000   1408.040000  1.462000e+01   \n",
       "75%    5.186700e+02    643.000000   1594.380000   1414.555000  1.462000e+01   \n",
       "max    5.186700e+02    644.530000   1616.910000   1441.490000  1.462000e+01   \n",
       "\n",
       "       ...    Section-16    Section-17    Section-18    Section-19  \\\n",
       "count  ...  20631.000000  20631.000000  20631.000000  20631.000000   \n",
       "mean   ...    521.413470   2388.096152   8143.752722      8.442146   \n",
       "std    ...      0.737553      0.071919     19.076176      0.037505   \n",
       "min    ...    518.690000   2387.880000   8099.940000      8.324900   \n",
       "25%    ...    520.960000   2388.040000   8133.245000      8.414900   \n",
       "50%    ...    521.480000   2388.090000   8140.540000      8.438900   \n",
       "75%    ...    521.950000   2388.140000   8148.310000      8.465600   \n",
       "max    ...    523.380000   2388.560000   8293.720000      8.584800   \n",
       "\n",
       "         Section-20    Section-21  Section-22  Section-23    Section-24  \\\n",
       "count  2.063100e+04  20631.000000     20631.0     20631.0  20631.000000   \n",
       "mean   3.000000e-02    393.210654      2388.0       100.0     38.816271   \n",
       "std    1.556432e-14      1.548763         0.0         0.0      0.180746   \n",
       "min    3.000000e-02    388.000000      2388.0       100.0     38.140000   \n",
       "25%    3.000000e-02    392.000000      2388.0       100.0     38.700000   \n",
       "50%    3.000000e-02    393.000000      2388.0       100.0     38.830000   \n",
       "75%    3.000000e-02    394.000000      2388.0       100.0     38.950000   \n",
       "max    3.000000e-02    400.000000      2388.0       100.0     39.430000   \n",
       "\n",
       "         Section-25  \n",
       "count  20631.000000  \n",
       "mean      23.289705  \n",
       "std        0.108251  \n",
       "min       22.894200  \n",
       "25%       23.221800  \n",
       "50%       23.297900  \n",
       "75%       23.366800  \n",
       "max       23.618400  \n",
       "\n",
       "[8 rows x 26 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='info_about_data'>1.1 Info about data:</a>\n",
    "- Section-0 is MachineID\n",
    "- Section-1 is time in, Cycles\n",
    "- Section-2...4 is Opertional Settings\n",
    "- Section-5...25 is sensor's data \n",
    "\n",
    "\n",
    "- Data Set: FD001\n",
    "- Train trjectories: 100\n",
    "- Test trajectories: 100\n",
    "- Conditions: ONE (Sea Level)\n",
    "- Fault Modes: ONE (HPC Degradation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Names \n",
    "MachineID_name = [\"Section-0\"]\n",
    "RUL_name = [\"Section-1\"]\n",
    "OS_name = [\"Section-{}\".format(i) for i in range(2,5)]\n",
    "Sensor_name = [\"Section-{}\".format(i) for i in range(5,26)]\n",
    "\n",
    "# Data in pandas DataFrame\n",
    "MachineID_data = df[MachineID_name]\n",
    "RUL_data = df[RUL_name]\n",
    "OS_data = df[OS_name]\n",
    "Sensor_data = df[Sensor_name]\n",
    "\n",
    "# Data in pandas Series\n",
    "MachineID_series = df[\"Section-0\"]\n",
    "RUL_series = df[\"Section-1\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max Life >>  362\n",
      "Mean Life >>  206.31\n",
      "Min Life >>  128\n"
     ]
    }
   ],
   "source": [
    "grp = RUL_data.groupby(MachineID_series)\n",
    "max_cycles = np.array([max(grp.get_group(i)[\"Section-1\"]) for i in MachineID_series.unique()])\n",
    "print(\"Max Life >> \",max(max_cycles))\n",
    "print(\"Mean Life >> \",np.mean(max_cycles))\n",
    "print(\"Min Life >> \",min(max_cycles))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above vizulization its clear that \n",
    "- Section-4 (Oprational Setting-3)\n",
    "- Section-5 (Sensor-1)\n",
    "- Section-9  (Sensor-5)\n",
    "- Section-14 (Sensor-10)\n",
    "- Section-20 (Sensor-16)\n",
    "- Section-22 (Sensor-18)\n",
    "- Section-23 (Sensor-19)\n",
    "\n",
    "Does not play a vital role in variation of data and there std is also almost 0 so, these sensor data is useless for us hence, we can drop this coloumn data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#delete columns with constant values that do not carry information about the state of the unit\n",
    "#data = pd.concat([RUL_data,OS_data,Sensor_data], axis=1)\n",
    "df.drop(columns=[\"Section-0\",\n",
    "                \"Section-4\", # Operatinal Setting\n",
    "                \"Section-5\", # Sensor data\n",
    "                \"Section-9\", # Sensor data\n",
    "                \"Section-10\", # Sensor data\n",
    "                \"Section-14\",# Sensor data\n",
    "                \"Section-20\",# Sensor data\n",
    "                \"Section-22\",# Sensor data\n",
    "                \"Section-23\"] , inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Section-1</th>\n",
       "      <th>Section-2</th>\n",
       "      <th>Section-3</th>\n",
       "      <th>Section-6</th>\n",
       "      <th>Section-7</th>\n",
       "      <th>Section-8</th>\n",
       "      <th>Section-11</th>\n",
       "      <th>Section-12</th>\n",
       "      <th>Section-13</th>\n",
       "      <th>Section-15</th>\n",
       "      <th>Section-16</th>\n",
       "      <th>Section-17</th>\n",
       "      <th>Section-18</th>\n",
       "      <th>Section-19</th>\n",
       "      <th>Section-21</th>\n",
       "      <th>Section-24</th>\n",
       "      <th>Section-25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.0007</td>\n",
       "      <td>-0.0004</td>\n",
       "      <td>641.82</td>\n",
       "      <td>1589.70</td>\n",
       "      <td>1400.60</td>\n",
       "      <td>554.36</td>\n",
       "      <td>2388.06</td>\n",
       "      <td>9046.19</td>\n",
       "      <td>47.47</td>\n",
       "      <td>521.66</td>\n",
       "      <td>2388.02</td>\n",
       "      <td>8138.62</td>\n",
       "      <td>8.4195</td>\n",
       "      <td>392</td>\n",
       "      <td>39.06</td>\n",
       "      <td>23.4190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>642.15</td>\n",
       "      <td>1591.82</td>\n",
       "      <td>1403.14</td>\n",
       "      <td>553.75</td>\n",
       "      <td>2388.04</td>\n",
       "      <td>9044.07</td>\n",
       "      <td>47.49</td>\n",
       "      <td>522.28</td>\n",
       "      <td>2388.07</td>\n",
       "      <td>8131.49</td>\n",
       "      <td>8.4318</td>\n",
       "      <td>392</td>\n",
       "      <td>39.00</td>\n",
       "      <td>23.4236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>-0.0043</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>642.35</td>\n",
       "      <td>1587.99</td>\n",
       "      <td>1404.20</td>\n",
       "      <td>554.26</td>\n",
       "      <td>2388.08</td>\n",
       "      <td>9052.94</td>\n",
       "      <td>47.27</td>\n",
       "      <td>522.42</td>\n",
       "      <td>2388.03</td>\n",
       "      <td>8133.23</td>\n",
       "      <td>8.4178</td>\n",
       "      <td>390</td>\n",
       "      <td>38.95</td>\n",
       "      <td>23.3442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>642.35</td>\n",
       "      <td>1582.79</td>\n",
       "      <td>1401.87</td>\n",
       "      <td>554.45</td>\n",
       "      <td>2388.11</td>\n",
       "      <td>9049.48</td>\n",
       "      <td>47.13</td>\n",
       "      <td>522.86</td>\n",
       "      <td>2388.08</td>\n",
       "      <td>8133.83</td>\n",
       "      <td>8.3682</td>\n",
       "      <td>392</td>\n",
       "      <td>38.88</td>\n",
       "      <td>23.3739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>-0.0019</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>642.37</td>\n",
       "      <td>1582.85</td>\n",
       "      <td>1406.22</td>\n",
       "      <td>554.00</td>\n",
       "      <td>2388.06</td>\n",
       "      <td>9055.15</td>\n",
       "      <td>47.28</td>\n",
       "      <td>522.19</td>\n",
       "      <td>2388.04</td>\n",
       "      <td>8133.80</td>\n",
       "      <td>8.4294</td>\n",
       "      <td>393</td>\n",
       "      <td>38.90</td>\n",
       "      <td>23.4044</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Section-1  Section-2  Section-3  Section-6  Section-7  Section-8  \\\n",
       "0          1    -0.0007    -0.0004     641.82    1589.70    1400.60   \n",
       "1          2     0.0019    -0.0003     642.15    1591.82    1403.14   \n",
       "2          3    -0.0043     0.0003     642.35    1587.99    1404.20   \n",
       "3          4     0.0007     0.0000     642.35    1582.79    1401.87   \n",
       "4          5    -0.0019    -0.0002     642.37    1582.85    1406.22   \n",
       "\n",
       "   Section-11  Section-12  Section-13  Section-15  Section-16  Section-17  \\\n",
       "0      554.36     2388.06     9046.19       47.47      521.66     2388.02   \n",
       "1      553.75     2388.04     9044.07       47.49      522.28     2388.07   \n",
       "2      554.26     2388.08     9052.94       47.27      522.42     2388.03   \n",
       "3      554.45     2388.11     9049.48       47.13      522.86     2388.08   \n",
       "4      554.00     2388.06     9055.15       47.28      522.19     2388.04   \n",
       "\n",
       "   Section-18  Section-19  Section-21  Section-24  Section-25  \n",
       "0     8138.62      8.4195         392       39.06     23.4190  \n",
       "1     8131.49      8.4318         392       39.00     23.4236  \n",
       "2     8133.23      8.4178         390       38.95     23.3442  \n",
       "3     8133.83      8.3682         392       38.88     23.3739  \n",
       "4     8133.80      8.4294         393       38.90     23.4044  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a id='noise_removal'>2. Noise removal and Normalization</a>\n",
    "<a href='#index'>Go back to Index</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "print(type(df))\n",
    "gen = MinMaxScaler(feature_range=(0, 1))\n",
    "df = gen.fit_transform(df)\n",
    "df = pd.DataFrame(df)\n",
    "#df = df.rolling(20).mean()\n",
    "pt = PowerTransformer()\n",
    "df = pt.fit_transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=np.nan_to_num(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.84688137, -0.31975365, -1.3916867 , ..., -0.76399955,\n",
       "         1.42665355,  1.24248381],\n",
       "       [-1.82422371,  0.87202066, -1.03174892, ..., -0.76399955,\n",
       "         1.03734696,  1.29226848],\n",
       "       [-1.80166973, -1.9503069 ,  1.01385485, ..., -2.35059786,\n",
       "         0.72414823,  0.465476  ],\n",
       "       ...,\n",
       "       [ 1.25582441,  0.18308976,  0.01311911, ...,  2.66650739,\n",
       "        -1.90974435, -2.75272539],\n",
       "       [ 1.26682846, -0.50209065,  1.01385485, ...,  1.14636389,\n",
       "        -2.49938469, -1.91568423],\n",
       "       [ 1.27779988, -1.45465135, -1.75831429, ...,  1.68933126,\n",
       "        -2.19492018, -1.99819889]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# grouping w.r.t MID (Machine ID)\n",
    "# col_names = df.columns\n",
    "# def grouping(datafile, mid_series):\n",
    "#     data = [x for x in datafile.groupby(mid_series)]\n",
    "#     return data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a id='training_and_validation'>3. Training and Validation using LSTM</a>\n",
    "<a href='#index'>Go back to Index</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rul</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rul\n",
       "0  192\n",
       "1  191\n",
       "2  190\n",
       "3  189\n",
       "4  188"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def RUL_df():\n",
    "    rul_lst = [j  for i in MachineID_series.unique() for j in np.array(grp.get_group(i)[::-1][\"Section-1\"])]\n",
    "    rul_col = pd.DataFrame({\"rul\":rul_lst})\n",
    "    return rul_col\n",
    "\n",
    "RUL_df().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20631, 17) (20631, 1)\n"
     ]
    }
   ],
   "source": [
    "X_train = np.array(df)\n",
    "\n",
    "y_train = np.array(RUL_df()).reshape(-1,1)\n",
    "\n",
    "#X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20) \n",
    "\n",
    "\n",
    "print(X_train.shape,y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[192 287 179 189 269 188 259 150 201 222 240 170 163 180 207 209 276 195\n",
      " 158 234 195 202 168 147 230 199 156 165 163 194 234 191 200 195 181 158\n",
      " 170 194 128 188 216 196 207 192 158 256 214 231 215 198 213 213 195 257\n",
      " 193 275 137 147 231 172 185 180 174 283 153 202 313 199 362 137 208 213\n",
      " 213 166 229 210 154 231 199 185 240 214 293 267 188 278 178 213 217 154\n",
      " 135 341 155 258 283 336 202 156 185 200]\n",
      "20631\n",
      "20631\n"
     ]
    }
   ],
   "source": [
    "print(max_cycles)\n",
    "print(sum(max_cycles))\n",
    "count = 0\n",
    "for cycle_len in max_cycles:\n",
    "    for i in range(6):\n",
    "        y_train[count+i] = 0\n",
    "    count = count + cycle_len\n",
    "print(count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0]\n",
      " [0]\n",
      " [0]\n",
      " ...\n",
      " [3]\n",
      " [2]\n",
      " [1]]\n"
     ]
    }
   ],
   "source": [
    "print(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dataset(X, look_back=5):\n",
    "    data = []\n",
    "    for i in range(len(X)-look_back-1):\n",
    "        data.append(X[i:(i+look_back)])\n",
    "    return np.array(data)\n",
    "\n",
    "\n",
    "X_train = create_dataset(X_train)\n",
    "y_train = y_train[6:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20625, 5, 17) (20625, 1)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape,y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[186]\n",
      " [185]\n",
      " [184]\n",
      " ...\n",
      " [  3]\n",
      " [  2]\n",
      " [  1]]\n"
     ]
    }
   ],
   "source": [
    "print(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model(layers):\n",
    "        model = Sequential()\n",
    "        model.add(LSTM(128, input_shape=(layers[1], layers[0]), return_sequences=True))\n",
    "        model.add(LSTM(64, input_shape=(layers[1], layers[0]), return_sequences=False))\n",
    "        model.add(Dense(16,kernel_initializer='uniform',activation='relu'))        \n",
    "        model.add(Dense(1,kernel_initializer='uniform',activation='relu'))\n",
    "        model.compile(loss='mean_squared_error',optimizer='adam')\n",
    "        return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm (LSTM)                  (None, 5, 128)            74752     \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 64)                49408     \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 16)                1040      \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 125,217\n",
      "Trainable params: 125,217\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model = build_model([17,5])\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/75\n",
      "35/35 [==============================] - 10s 292ms/step - loss: 14788.4990 - val_loss: 18003.5293\n",
      "Epoch 2/75\n",
      "35/35 [==============================] - 3s 96ms/step - loss: 14503.9561 - val_loss: 17449.7383\n",
      "Epoch 3/75\n",
      "35/35 [==============================] - 3s 91ms/step - loss: 13792.3887 - val_loss: 16481.7812\n",
      "Epoch 4/75\n",
      "35/35 [==============================] - 3s 98ms/step - loss: 12747.5654 - val_loss: 15182.2549\n",
      "Epoch 5/75\n",
      "35/35 [==============================] - 3s 92ms/step - loss: 11454.1592 - val_loss: 13668.8936\n",
      "Epoch 6/75\n",
      "35/35 [==============================] - 4s 108ms/step - loss: 10036.0625 - val_loss: 12077.8691\n",
      "Epoch 7/75\n",
      "35/35 [==============================] - 4s 123ms/step - loss: 8631.2920 - val_loss: 10554.0078\n",
      "Epoch 8/75\n",
      "35/35 [==============================] - 4s 106ms/step - loss: 7335.3965 - val_loss: 9041.4023\n",
      "Epoch 9/75\n",
      "35/35 [==============================] - 3s 96ms/step - loss: 5941.3389 - val_loss: 7548.8804\n",
      "Epoch 10/75\n",
      "35/35 [==============================] - 3s 99ms/step - loss: 4744.9888 - val_loss: 6289.1572\n",
      "Epoch 11/75\n",
      "35/35 [==============================] - 4s 116ms/step - loss: 3793.9622 - val_loss: 5280.7339\n",
      "Epoch 12/75\n",
      "35/35 [==============================] - 4s 107ms/step - loss: 3056.5701 - val_loss: 4500.0459\n",
      "Epoch 13/75\n",
      "35/35 [==============================] - 3s 93ms/step - loss: 2511.0471 - val_loss: 3882.0112\n",
      "Epoch 14/75\n",
      "35/35 [==============================] - 3s 90ms/step - loss: 2103.2009 - val_loss: 3460.3706\n",
      "Epoch 15/75\n",
      "35/35 [==============================] - 3s 98ms/step - loss: 1805.6189 - val_loss: 3307.5630\n",
      "Epoch 16/75\n",
      "35/35 [==============================] - 4s 105ms/step - loss: 1593.9640 - val_loss: 2999.1829\n",
      "Epoch 17/75\n",
      "35/35 [==============================] - 3s 98ms/step - loss: 1433.9491 - val_loss: 3161.8494\n",
      "Epoch 18/75\n",
      "35/35 [==============================] - 4s 121ms/step - loss: 1323.8196 - val_loss: 2775.9998\n",
      "Epoch 19/75\n",
      "35/35 [==============================] - 4s 105ms/step - loss: 1230.1484 - val_loss: 2785.0750\n",
      "Epoch 20/75\n",
      "35/35 [==============================] - 3s 94ms/step - loss: 1157.2069 - val_loss: 2688.3760\n",
      "Epoch 21/75\n",
      "35/35 [==============================] - 3s 91ms/step - loss: 1095.2949 - val_loss: 2715.3384\n",
      "Epoch 22/75\n",
      "35/35 [==============================] - 3s 92ms/step - loss: 1049.9283 - val_loss: 2465.5396\n",
      "Epoch 23/75\n",
      "35/35 [==============================] - 4s 109ms/step - loss: 1006.8624 - val_loss: 2740.1328\n",
      "Epoch 24/75\n",
      "35/35 [==============================] - 3s 98ms/step - loss: 974.1634 - val_loss: 2684.0010\n",
      "Epoch 25/75\n",
      "35/35 [==============================] - 3s 90ms/step - loss: 942.6711 - val_loss: 2628.6824\n",
      "Epoch 26/75\n",
      "35/35 [==============================] - 3s 89ms/step - loss: 907.2866 - val_loss: 2626.5466\n",
      "Epoch 27/75\n",
      "35/35 [==============================] - 3s 90ms/step - loss: 882.7153 - val_loss: 2789.1685\n",
      "Epoch 28/75\n",
      "35/35 [==============================] - 3s 89ms/step - loss: 855.8068 - val_loss: 2661.0342\n",
      "Epoch 29/75\n",
      "35/35 [==============================] - 3s 92ms/step - loss: 828.2498 - val_loss: 2571.2583\n",
      "Epoch 30/75\n",
      "35/35 [==============================] - 3s 92ms/step - loss: 804.9408 - val_loss: 2928.2473\n",
      "Epoch 31/75\n",
      "35/35 [==============================] - 3s 89ms/step - loss: 784.9645 - val_loss: 2774.5442\n",
      "Epoch 32/75\n",
      "35/35 [==============================] - 3s 93ms/step - loss: 764.7063 - val_loss: 2886.6387\n",
      "Epoch 33/75\n",
      "35/35 [==============================] - 4s 110ms/step - loss: 737.8899 - val_loss: 2875.3220\n",
      "Epoch 34/75\n",
      "35/35 [==============================] - 3s 97ms/step - loss: 711.9347 - val_loss: 3152.3381\n",
      "Epoch 35/75\n",
      "35/35 [==============================] - 3s 97ms/step - loss: 694.8361 - val_loss: 2989.9578\n",
      "Epoch 36/75\n",
      "35/35 [==============================] - 4s 104ms/step - loss: 669.1603 - val_loss: 2926.1309\n",
      "Epoch 37/75\n",
      "35/35 [==============================] - 4s 102ms/step - loss: 655.5927 - val_loss: 2815.7163\n",
      "Epoch 38/75\n",
      "35/35 [==============================] - 3s 98ms/step - loss: 634.8848 - val_loss: 2908.6655\n",
      "Epoch 39/75\n",
      "35/35 [==============================] - 3s 99ms/step - loss: 613.9764 - val_loss: 2967.6328\n",
      "Epoch 40/75\n",
      "35/35 [==============================] - 3s 98ms/step - loss: 602.3044 - val_loss: 2785.2903\n",
      "Epoch 41/75\n",
      "35/35 [==============================] - 3s 97ms/step - loss: 587.5095 - val_loss: 2864.6648\n",
      "Epoch 42/75\n",
      "35/35 [==============================] - 3s 97ms/step - loss: 575.9238 - val_loss: 2962.9949\n",
      "Epoch 43/75\n",
      "35/35 [==============================] - 3s 96ms/step - loss: 552.2709 - val_loss: 2972.0061\n",
      "Epoch 44/75\n",
      "35/35 [==============================] - 3s 98ms/step - loss: 543.0718 - val_loss: 3022.6829\n",
      "Epoch 45/75\n",
      "35/35 [==============================] - 4s 103ms/step - loss: 528.8755 - val_loss: 2994.7676\n",
      "Epoch 46/75\n",
      "35/35 [==============================] - 4s 106ms/step - loss: 509.4252 - val_loss: 2903.9851\n",
      "Epoch 47/75\n",
      "35/35 [==============================] - 3s 98ms/step - loss: 499.7947 - val_loss: 3028.5981\n",
      "Epoch 48/75\n",
      "35/35 [==============================] - 3s 97ms/step - loss: 484.3274 - val_loss: 3025.5879\n",
      "Epoch 49/75\n",
      "35/35 [==============================] - 3s 98ms/step - loss: 468.4702 - val_loss: 2984.3943\n",
      "Epoch 50/75\n",
      "35/35 [==============================] - 3s 99ms/step - loss: 455.1407 - val_loss: 3032.3425\n",
      "Epoch 51/75\n",
      "35/35 [==============================] - 3s 98ms/step - loss: 450.9351 - val_loss: 3077.9561\n",
      "Epoch 52/75\n",
      "35/35 [==============================] - 3s 97ms/step - loss: 442.2509 - val_loss: 2962.0669\n",
      "Epoch 53/75\n",
      "35/35 [==============================] - 4s 110ms/step - loss: 434.0155 - val_loss: 2925.6436\n",
      "Epoch 54/75\n",
      "35/35 [==============================] - 4s 100ms/step - loss: 414.5179 - val_loss: 3022.4211\n",
      "Epoch 55/75\n",
      "35/35 [==============================] - 3s 98ms/step - loss: 399.7417 - val_loss: 2995.8232\n",
      "Epoch 56/75\n",
      "35/35 [==============================] - 3s 99ms/step - loss: 389.4770 - val_loss: 3161.9192\n",
      "Epoch 57/75\n",
      "35/35 [==============================] - 3s 99ms/step - loss: 384.3790 - val_loss: 3024.4602\n",
      "Epoch 58/75\n",
      "35/35 [==============================] - 4s 102ms/step - loss: 373.0992 - val_loss: 3141.5417\n",
      "Epoch 59/75\n",
      "35/35 [==============================] - 3s 98ms/step - loss: 362.4028 - val_loss: 3160.6604\n",
      "Epoch 60/75\n",
      "35/35 [==============================] - 3s 94ms/step - loss: 346.1940 - val_loss: 3122.0737\n",
      "Epoch 61/75\n",
      "35/35 [==============================] - 3s 91ms/step - loss: 339.7000 - val_loss: 3122.7712\n",
      "Epoch 62/75\n",
      "35/35 [==============================] - 4s 106ms/step - loss: 328.0911 - val_loss: 3054.2126\n",
      "Epoch 63/75\n",
      "35/35 [==============================] - 4s 101ms/step - loss: 328.5106 - val_loss: 3031.3521\n",
      "Epoch 64/75\n",
      "35/35 [==============================] - 3s 96ms/step - loss: 313.8663 - val_loss: 3255.9453\n",
      "Epoch 65/75\n",
      "35/35 [==============================] - 3s 96ms/step - loss: 306.3543 - val_loss: 3179.7236\n",
      "Epoch 66/75\n",
      "35/35 [==============================] - 3s 96ms/step - loss: 298.7932 - val_loss: 3200.0098\n",
      "Epoch 67/75\n",
      "35/35 [==============================] - 3s 97ms/step - loss: 286.3541 - val_loss: 3195.5657\n",
      "Epoch 68/75\n",
      "35/35 [==============================] - 3s 97ms/step - loss: 275.4257 - val_loss: 3159.5518\n",
      "Epoch 69/75\n",
      "35/35 [==============================] - 3s 96ms/step - loss: 272.8557 - val_loss: 3131.0876\n",
      "Epoch 70/75\n",
      "35/35 [==============================] - 3s 97ms/step - loss: 264.2657 - val_loss: 3172.4321\n",
      "Epoch 71/75\n",
      "35/35 [==============================] - 3s 99ms/step - loss: 252.6499 - val_loss: 3117.9531\n",
      "Epoch 72/75\n",
      "35/35 [==============================] - 3s 97ms/step - loss: 247.8868 - val_loss: 3278.2522 - los - ETA: 0s - loss: \n",
      "Epoch 73/75\n",
      "35/35 [==============================] - 3s 97ms/step - loss: 248.7711 - val_loss: 3203.7434\n",
      "Epoch 74/75\n",
      "35/35 [==============================] - 3s 97ms/step - loss: 237.2292 - val_loss: 3221.1665\n",
      "Epoch 75/75\n",
      "35/35 [==============================] - 3s 97ms/step - loss: 230.2315 - val_loss: 3285.0505\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    X_train,\n",
    "    y_train,\n",
    "    batch_size=512,\n",
    "    epochs=75,\n",
    "    validation_split=0.15,\n",
    "    verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# saving model\n",
    "model.save('LSTM_with_lookback_5.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'val_loss'])\n"
     ]
    }
   ],
   "source": [
    "print(history.history.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Loss Graph\n",
    "plt.plot(history.epoch, history.history['loss'] , label = \"loss\")\n",
    "plt.plot(history.epoch, history.history['val_loss'] , label = \"val_loss\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_squared_error >>  672.8636213565194\n",
      "root_mean_squared_error >>  25.939614903782196\n",
      "mean_absolute_error >> 14.425796908773798\n"
     ]
    }
   ],
   "source": [
    "y_train_pred = model.predict(X_train)\n",
    "print(\"mean_squared_error >> \", mean_squared_error(y_train,y_train_pred))\n",
    "print(\"root_mean_squared_error >> \", math.sqrt(mean_squared_error(y_train,y_train_pred)))\n",
    "print(\"mean_absolute_error >>\",mean_absolute_error(y_train,y_train_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a id='testing'>4 Testing</a>\n",
    "<a href='#index'>Go back to Index</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = load_model('LSTM_with_lookback_5.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Section-0</th>\n",
       "      <th>Section-1</th>\n",
       "      <th>Section-2</th>\n",
       "      <th>Section-3</th>\n",
       "      <th>Section-4</th>\n",
       "      <th>Section-5</th>\n",
       "      <th>Section-6</th>\n",
       "      <th>Section-7</th>\n",
       "      <th>Section-8</th>\n",
       "      <th>Section-9</th>\n",
       "      <th>...</th>\n",
       "      <th>Section-16</th>\n",
       "      <th>Section-17</th>\n",
       "      <th>Section-18</th>\n",
       "      <th>Section-19</th>\n",
       "      <th>Section-20</th>\n",
       "      <th>Section-21</th>\n",
       "      <th>Section-22</th>\n",
       "      <th>Section-23</th>\n",
       "      <th>Section-24</th>\n",
       "      <th>Section-25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0023</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>643.02</td>\n",
       "      <td>1585.29</td>\n",
       "      <td>1398.21</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>521.72</td>\n",
       "      <td>2388.03</td>\n",
       "      <td>8125.55</td>\n",
       "      <td>8.4052</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.86</td>\n",
       "      <td>23.3735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.0027</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>641.71</td>\n",
       "      <td>1588.45</td>\n",
       "      <td>1395.42</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.16</td>\n",
       "      <td>2388.06</td>\n",
       "      <td>8139.62</td>\n",
       "      <td>8.3803</td>\n",
       "      <td>0.03</td>\n",
       "      <td>393</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.02</td>\n",
       "      <td>23.3916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.46</td>\n",
       "      <td>1586.94</td>\n",
       "      <td>1401.34</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>521.97</td>\n",
       "      <td>2388.03</td>\n",
       "      <td>8130.10</td>\n",
       "      <td>8.4441</td>\n",
       "      <td>0.03</td>\n",
       "      <td>393</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.08</td>\n",
       "      <td>23.4166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0042</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.44</td>\n",
       "      <td>1584.12</td>\n",
       "      <td>1406.42</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>521.38</td>\n",
       "      <td>2388.05</td>\n",
       "      <td>8132.90</td>\n",
       "      <td>8.3917</td>\n",
       "      <td>0.03</td>\n",
       "      <td>391</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.00</td>\n",
       "      <td>23.3737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0014</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.51</td>\n",
       "      <td>1587.19</td>\n",
       "      <td>1401.92</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.15</td>\n",
       "      <td>2388.03</td>\n",
       "      <td>8129.54</td>\n",
       "      <td>8.4031</td>\n",
       "      <td>0.03</td>\n",
       "      <td>390</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.99</td>\n",
       "      <td>23.4130</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Section-0  Section-1  Section-2  Section-3  Section-4  Section-5  \\\n",
       "0          1          1     0.0023     0.0003      100.0     518.67   \n",
       "1          1          2    -0.0027    -0.0003      100.0     518.67   \n",
       "2          1          3     0.0003     0.0001      100.0     518.67   \n",
       "3          1          4     0.0042     0.0000      100.0     518.67   \n",
       "4          1          5     0.0014     0.0000      100.0     518.67   \n",
       "\n",
       "   Section-6  Section-7  Section-8  Section-9  ...  Section-16  Section-17  \\\n",
       "0     643.02    1585.29    1398.21      14.62  ...      521.72     2388.03   \n",
       "1     641.71    1588.45    1395.42      14.62  ...      522.16     2388.06   \n",
       "2     642.46    1586.94    1401.34      14.62  ...      521.97     2388.03   \n",
       "3     642.44    1584.12    1406.42      14.62  ...      521.38     2388.05   \n",
       "4     642.51    1587.19    1401.92      14.62  ...      522.15     2388.03   \n",
       "\n",
       "   Section-18  Section-19  Section-20  Section-21  Section-22  Section-23  \\\n",
       "0     8125.55      8.4052        0.03         392        2388       100.0   \n",
       "1     8139.62      8.3803        0.03         393        2388       100.0   \n",
       "2     8130.10      8.4441        0.03         393        2388       100.0   \n",
       "3     8132.90      8.3917        0.03         391        2388       100.0   \n",
       "4     8129.54      8.4031        0.03         390        2388       100.0   \n",
       "\n",
       "   Section-24  Section-25  \n",
       "0       38.86     23.3735  \n",
       "1       39.02     23.3916  \n",
       "2       39.08     23.4166  \n",
       "3       39.00     23.3737  \n",
       "4       38.99     23.4130  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.read_csv(test_file, sep=\" \",header=None)\n",
    "df_test.drop(columns=[26,27],inplace=True)\n",
    "df_test.columns = columns\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rul</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rul\n",
       "0  112\n",
       "1   98\n",
       "2   69\n",
       "3   82\n",
       "4   91"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rul = pd.read_csv(RUL_file, names=['rul'])\n",
    "df_rul.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "RUL_name = [\"Section-1\"]\n",
    "RUL_data = df_test[RUL_name]\n",
    "MachineID_series = df_test[\"Section-0\"]\n",
    "grp = RUL_data.groupby(MachineID_series)\n",
    "max_cycles = np.array([max(grp.get_group(i)[\"Section-1\"]) for i in MachineID_series.unique()])\n",
    "max_cycles[0] = max_cycles[0] - 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test.drop(df_test[[\"Section-0\",\n",
    "                \"Section-4\", # Operatinal Setting\n",
    "                \"Section-5\", # Sensor data\n",
    "                \"Section-9\", # Sensor data\n",
    "                \"Section-10\", # Sensor data\n",
    "                \"Section-14\",# Sensor data\n",
    "                \"Section-20\",# Sensor data\n",
    "                \"Section-22\",# Sensor data\n",
    "                \"Section-23\"]], axis=1 , inplace=True)\n",
    "#df_test = df_test.groupby([\"Section-0\"])\n",
    "#print(df_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "gen = MinMaxScaler(feature_range=(0, 1))\n",
    "df_test = gen.fit_transform(df_test)\n",
    "df_test = pd.DataFrame(df_test)\n",
    "#df_test = df_test.rolling(20).mean()\n",
    "pt = PowerTransformer()\n",
    "df_test = pt.fit_transform(df_test)\n",
    "df_test=np.nan_to_num(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.7938502 ,  1.04936462,  1.00459956, ..., -0.43585161,\n",
       "        -0.26636316,  0.42059548],\n",
       "       [-1.75983242, -1.22056415, -1.03515135, ...,  0.37849296,\n",
       "         0.89958112,  0.64708667],\n",
       "       [-1.72609272,  0.14086219,  0.34489676, ...,  0.37849296,\n",
       "         1.36106152,  0.96601877],\n",
       "       ...,\n",
       "       [ 1.83603259, -0.26773967, -1.03515135, ...,  1.88097691,\n",
       "        -1.83688403, -1.50936271],\n",
       "       [ 1.84590292, -1.71931856,  0.34489676, ...,  1.88097691,\n",
       "        -1.59013861, -0.80455247],\n",
       "       [ 1.85572407,  0.59502963,  1.00459956, ...,  2.57907106,\n",
       "        -1.33736329, -1.7132478 ]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13096, 17) (100, 1)\n",
      "[ 25  49 126 106  98 105 160 166  55 192  83 217 195  46  76 113 165 133\n",
      " 135 184 148  39 130 186  48  76 140 158 171 143 196 145  50 203 198 126\n",
      " 121 125  37 133 123 156 172  54 152 146  73  78 303  74 144 189 164 121\n",
      " 113 136 160 176  94 147 159 232 155 168  71 147  71 187  54 152  68 131\n",
      " 112 137  88 205 162  72 101 133 213 162  73 172  34 110  56  68 177 146\n",
      " 234 150 244 133  89  97 134 121  97 198]\n"
     ]
    }
   ],
   "source": [
    "X_test = np.array(df_test)\n",
    "\n",
    "y_test = np.array(df_rul)\n",
    "\n",
    "\n",
    "\n",
    "# X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))\n",
    "# X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
    "print(X_test.shape,y_test.shape)\n",
    "print(max_cycles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dataset(X, look_back=5):\n",
    "    data = []\n",
    "    for i in range(len(X)-look_back-1):\n",
    "        data.append(X[i:(i+look_back)])\n",
    "    return np.array(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = create_dataset(X_test)\n",
    "#y_test = y_test[6:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13090, 5, 17) (100, 1)\n"
     ]
    }
   ],
   "source": [
    "print(X_test.shape,y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13090, 1)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_pred = []\n",
    "count = 0\n",
    "for i in range(100):\n",
    "    temp = 0\n",
    "    j = max_cycles[i] \n",
    "    while j>0:\n",
    "        temp = temp + pred[count]\n",
    "        j=j-1\n",
    "        count=count+1\n",
    "    final_pred.append(int(temp/max_cycles[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[159, 146, 85, 116, 111, 119, 117, 88, 120, 116, 53, 112, 111, 52, 159, 142, 57, 80, 137, 76, 107, 151, 137, 69, 132, 123, 106, 90, 127, 87, 55, 65, 101, 92, 60, 48, 118, 60, 148, 63, 105, 87, 87, 146, 86, 62, 34, 128, 107, 132, 98, 124, 105, 144, 146, 28, 87, 78, 137, 107, 69, 121, 94, 97, 95, 67, 133, 105, 134, 94, 101, 104, 130, 107, 125, 93, 80, 143, 114, 91, 52, 85, 123, 94, 152, 104, 166, 87, 85, 93, 39, 42, 70, 96, 117, 88, 78, 93, 141, 110]\n"
     ]
    }
   ],
   "source": [
    "print(final_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "159"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_pred[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using last index as prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scoring_function(actual,predicted):\n",
    "    d = []\n",
    "    for i in range(len(predicted)):\n",
    "        d.append((predicted[i] - actual[i]))\n",
    "    scores = []\n",
    "    for i in range(len(d)):\n",
    "        if d[i] >= 0:\n",
    "            scores.append(math.exp(d[i]/10) - 1)\n",
    "        else :\n",
    "            scores.append(math.exp((-1*d[i])/13) - 1)\n",
    "    return sum(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[142, 43, 20, 59, 46, 85, 72, 33, 89, 117, 30, 70, 76, 18, 114, 89, 24, 55, 157, 0, 38, 118, 73, 2, 136, 61, 43, 79, 57, 51, 0, 20, 91, 0, 0, 0, 24, 15, 118, 1, 21, 0, 45, 130, 34, 19, 48, 76, 1, 81, 30, 11, 5, 102, 50, 0, 30, 25, 176, 65, 14, 36, 23, 11, 104, 0, 214, 0, 160, 44, 56, 58, 245, 69, 71, 0, 12, 93, 65, 54, 0, 0, 113, 37, 135, 45, 100, 44, 32, 5, 0, 0, 11, 19, 69, 63, 51, 44, 107, 3]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_squared_error >>  1854.72\n",
      "root_mean_squared_error >>  43.06646026782327\n",
      "mean_absolute_error >> 33.38\n",
      "scoring function >> 991089.9128373136\n"
     ]
    }
   ],
   "source": [
    "final_pred = []\n",
    "count = 0\n",
    "for i in range(100):\n",
    "    j = max_cycles[i] \n",
    "    temp = pred[count+j-1]\n",
    "    count=count+j\n",
    "    final_pred.append(int(temp))\n",
    "\n",
    "print(final_pred)\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(18,10))\n",
    "plt.plot(final_pred,color='red', label='prediction')\n",
    "plt.plot(y_test,color='blue', label='y_test')\n",
    "\n",
    "fig.suptitle('RUL Prediction using LSTM with lookack=5', fontsize=35)\n",
    "plt.xlabel(\"Engine Number\", fontsize=35)\n",
    "plt.ylabel(\"Remaining Useful Life\", fontsize=35)\n",
    "\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid()\n",
    "plt.show()\n",
    "\n",
    "print(\"mean_squared_error >> \", mean_squared_error(y_test,final_pred))\n",
    "print(\"root_mean_squared_error >> \", math.sqrt(mean_squared_error(y_test,final_pred)))\n",
    "print(\"mean_absolute_error >>\",mean_absolute_error(y_test,final_pred))\n",
    "print(\"scoring function >>\",scoring_function(y_test,final_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}