RUL Prediction using LSTM Lookback=10.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",
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       "      <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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       "      <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": 6,
   "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(11):\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=10):\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[11:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20620, 10, 17) (20620, 1)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape,y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[181]\n",
      " [180]\n",
      " [179]\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",
    "        #d = 0.2\n",
    "        model = Sequential()\n",
    "        model.add(LSTM(128, input_shape=(layers[1], layers[0]), return_sequences=True))\n",
    "        #model.add(Dropout(d))\n",
    "        model.add(LSTM(64, input_shape=(layers[1], layers[0]), return_sequences=False))\n",
    "        #model.add(Dropout(d))\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, 10, 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([17,10])\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 [==============================] - 8s 236ms/step - loss: 13745.6045 - val_loss: 16783.5469\n",
      "Epoch 2/75\n",
      "35/35 [==============================] - 6s 186ms/step - loss: 13180.2676 - val_loss: 15810.9551 ETA: 4s - loss: 13 - ETA:\n",
      "Epoch 3/75\n",
      "35/35 [==============================] - 6s 173ms/step - loss: 12035.3965 - val_loss: 14272.7705\n",
      "Epoch 4/75\n",
      "35/35 [==============================] - 6s 173ms/step - loss: 10458.2529 - val_loss: 12362.3252\n",
      "Epoch 5/75\n",
      "35/35 [==============================] - 6s 175ms/step - loss: 8698.9580 - val_loss: 10404.8213\n",
      "Epoch 6/75\n",
      "35/35 [==============================] - 6s 180ms/step - loss: 7059.8643 - val_loss: 8702.7285\n",
      "Epoch 7/75\n",
      "35/35 [==============================] - 6s 183ms/step - loss: 5703.8940 - val_loss: 6953.0112\n",
      "Epoch 8/75\n",
      "35/35 [==============================] - 7s 193ms/step - loss: 4090.2271 - val_loss: 5375.2939\n",
      "Epoch 9/75\n",
      "35/35 [==============================] - 6s 175ms/step - loss: 2985.7681 - val_loss: 4243.5898\n",
      "Epoch 10/75\n",
      "35/35 [==============================] - 7s 192ms/step - loss: 2257.2803 - val_loss: 3495.8582\n",
      "Epoch 11/75\n",
      "35/35 [==============================] - 7s 200ms/step - loss: 1791.6670 - val_loss: 2981.0999\n",
      "Epoch 12/75\n",
      "35/35 [==============================] - 7s 191ms/step - loss: 1494.2345 - val_loss: 2703.4299\n",
      "Epoch 13/75\n",
      "35/35 [==============================] - 6s 174ms/step - loss: 1306.1266 - val_loss: 2530.5500\n",
      "Epoch 14/75\n",
      "35/35 [==============================] - 6s 172ms/step - loss: 1178.0892 - val_loss: 2478.3562\n",
      "Epoch 15/75\n",
      "35/35 [==============================] - 6s 184ms/step - loss: 1088.6219 - val_loss: 2343.2041\n",
      "Epoch 16/75\n",
      "35/35 [==============================] - 6s 181ms/step - loss: 1002.4315 - val_loss: 2288.3169\n",
      "Epoch 17/75\n",
      "35/35 [==============================] - 7s 202ms/step - loss: 950.8064 - val_loss: 2193.4299\n",
      "Epoch 18/75\n",
      "35/35 [==============================] - 6s 176ms/step - loss: 913.2183 - val_loss: 2357.9958\n",
      "Epoch 19/75\n",
      "35/35 [==============================] - 6s 182ms/step - loss: 871.4974 - val_loss: 2208.2561\n",
      "Epoch 20/75\n",
      "35/35 [==============================] - 6s 175ms/step - loss: 823.3252 - val_loss: 2188.5432\n",
      "Epoch 21/75\n",
      "35/35 [==============================] - 6s 172ms/step - loss: 791.1013 - val_loss: 2130.9482\n",
      "Epoch 22/75\n",
      "35/35 [==============================] - 6s 182ms/step - loss: 759.2065 - val_loss: 2354.2673\n",
      "Epoch 23/75\n",
      "35/35 [==============================] - 6s 170ms/step - loss: 716.4920 - val_loss: 2423.0059\n",
      "Epoch 24/75\n",
      "35/35 [==============================] - 6s 172ms/step - loss: 674.2611 - val_loss: 2625.6199\n",
      "Epoch 25/75\n",
      "35/35 [==============================] - 7s 189ms/step - loss: 636.6786 - val_loss: 2284.3232\n",
      "Epoch 26/75\n",
      "35/35 [==============================] - 6s 182ms/step - loss: 614.6468 - val_loss: 2746.1555\n",
      "Epoch 27/75\n",
      "35/35 [==============================] - 6s 178ms/step - loss: 561.1602 - val_loss: 2744.8992\n",
      "Epoch 28/75\n",
      "35/35 [==============================] - 6s 183ms/step - loss: 526.9392 - val_loss: 2587.4221\n",
      "Epoch 29/75\n",
      "35/35 [==============================] - 6s 185ms/step - loss: 497.2709 - val_loss: 2932.8806\n",
      "Epoch 30/75\n",
      "35/35 [==============================] - 7s 194ms/step - loss: 468.6881 - val_loss: 2787.8035\n",
      "Epoch 31/75\n",
      "35/35 [==============================] - 6s 184ms/step - loss: 432.8174 - val_loss: 2724.7441\n",
      "Epoch 32/75\n",
      "35/35 [==============================] - 7s 193ms/step - loss: 415.5409 - val_loss: 2830.2192\n",
      "Epoch 33/75\n",
      "35/35 [==============================] - 7s 190ms/step - loss: 385.0613 - val_loss: 2918.1682\n",
      "Epoch 34/75\n",
      "35/35 [==============================] - 7s 189ms/step - loss: 366.7937 - val_loss: 2754.1260\n",
      "Epoch 35/75\n",
      "35/35 [==============================] - 6s 180ms/step - loss: 345.7479 - val_loss: 2904.0078\n",
      "Epoch 36/75\n",
      "35/35 [==============================] - 6s 186ms/step - loss: 334.4514 - val_loss: 2709.6267\n",
      "Epoch 37/75\n",
      "35/35 [==============================] - 7s 191ms/step - loss: 318.9210 - val_loss: 2721.7825\n",
      "Epoch 38/75\n",
      "35/35 [==============================] - 7s 191ms/step - loss: 287.6789 - val_loss: 2855.9463\n",
      "Epoch 39/75\n",
      "35/35 [==============================] - 7s 190ms/step - loss: 275.2870 - val_loss: 2922.2522\n",
      "Epoch 40/75\n",
      "35/35 [==============================] - 6s 184ms/step - loss: 260.5267 - val_loss: 2920.5208\n",
      "Epoch 41/75\n",
      "35/35 [==============================] - 7s 193ms/step - loss: 246.3361 - val_loss: 2832.8970\n",
      "Epoch 42/75\n",
      "35/35 [==============================] - 7s 196ms/step - loss: 230.7391 - val_loss: 2928.9033\n",
      "Epoch 43/75\n",
      "35/35 [==============================] - 7s 200ms/step - loss: 218.2791 - val_loss: 2934.5471\n",
      "Epoch 44/75\n",
      "35/35 [==============================] - 6s 183ms/step - loss: 206.6009 - val_loss: 2913.8823\n",
      "Epoch 45/75\n",
      "35/35 [==============================] - 6s 185ms/step - loss: 193.9807 - val_loss: 2946.5686\n",
      "Epoch 46/75\n",
      "35/35 [==============================] - 7s 188ms/step - loss: 186.5633 - val_loss: 2916.8037\n",
      "Epoch 47/75\n",
      "35/35 [==============================] - 7s 205ms/step - loss: 177.0092 - val_loss: 2955.5396\n",
      "Epoch 48/75\n",
      "35/35 [==============================] - 7s 188ms/step - loss: 166.3742 - val_loss: 2904.9094\n",
      "Epoch 49/75\n",
      "35/35 [==============================] - 6s 175ms/step - loss: 150.4158 - val_loss: 3004.0791\n",
      "Epoch 50/75\n",
      "35/35 [==============================] - 6s 181ms/step - loss: 141.4907 - val_loss: 2965.8503\n",
      "Epoch 51/75\n",
      "35/35 [==============================] - 7s 186ms/step - loss: 141.2274 - val_loss: 3042.3401\n",
      "Epoch 52/75\n",
      "35/35 [==============================] - 6s 173ms/step - loss: 127.3700 - val_loss: 3098.8911\n",
      "Epoch 53/75\n",
      "35/35 [==============================] - 6s 170ms/step - loss: 124.2065 - val_loss: 2983.0667\n",
      "Epoch 54/75\n",
      "35/35 [==============================] - 7s 214ms/step - loss: 122.1288 - val_loss: 2897.3616\n",
      "Epoch 55/75\n",
      "35/35 [==============================] - 7s 211ms/step - loss: 105.7123 - val_loss: 3031.8667\n",
      "Epoch 56/75\n",
      "35/35 [==============================] - 6s 170ms/step - loss: 105.8145 - val_loss: 3039.7351\n",
      "Epoch 57/75\n",
      "35/35 [==============================] - 6s 173ms/step - loss: 100.8785 - val_loss: 2981.1833\n",
      "Epoch 58/75\n",
      "35/35 [==============================] - 7s 192ms/step - loss: 94.2808 - val_loss: 2988.0994\n",
      "Epoch 59/75\n",
      "35/35 [==============================] - 6s 172ms/step - loss: 86.6358 - val_loss: 3053.9275\n",
      "Epoch 60/75\n",
      "35/35 [==============================] - 7s 196ms/step - loss: 84.3037 - val_loss: 3006.6846\n",
      "Epoch 61/75\n",
      "35/35 [==============================] - 7s 192ms/step - loss: 77.9950 - val_loss: 3023.7415\n",
      "Epoch 62/75\n",
      "35/35 [==============================] - 7s 197ms/step - loss: 72.8783 - val_loss: 3089.7415\n",
      "Epoch 63/75\n",
      "35/35 [==============================] - 7s 187ms/step - loss: 71.4342 - val_loss: 2925.1084\n",
      "Epoch 64/75\n",
      "35/35 [==============================] - 7s 188ms/step - loss: 72.5839 - val_loss: 3052.8525\n",
      "Epoch 65/75\n",
      "35/35 [==============================] - 7s 190ms/step - loss: 66.6863 - val_loss: 3078.0381\n",
      "Epoch 66/75\n",
      "35/35 [==============================] - 6s 183ms/step - loss: 62.8368 - val_loss: 3027.4656\n",
      "Epoch 67/75\n",
      "35/35 [==============================] - 7s 204ms/step - loss: 59.1300 - val_loss: 3045.2930\n",
      "Epoch 68/75\n",
      "35/35 [==============================] - 6s 183ms/step - loss: 54.9165 - val_loss: 3103.9832\n",
      "Epoch 69/75\n",
      "35/35 [==============================] - 6s 184ms/step - loss: 57.8789 - val_loss: 3033.2473\n",
      "Epoch 70/75\n",
      "35/35 [==============================] - 6s 173ms/step - loss: 56.8789 - val_loss: 3022.6624\n",
      "Epoch 71/75\n",
      "35/35 [==============================] - 7s 189ms/step - loss: 53.3056 - val_loss: 3002.1865\n",
      "Epoch 72/75\n",
      "35/35 [==============================] - 7s 203ms/step - loss: 50.1738 - val_loss: 3015.4912\n",
      "Epoch 73/75\n",
      "35/35 [==============================] - 7s 187ms/step - loss: 47.5610 - val_loss: 3040.2749\n",
      "Epoch 74/75\n",
      "35/35 [==============================] - 8s 215ms/step - loss: 47.1762 - val_loss: 3002.9724\n",
      "Epoch 75/75\n",
      "35/35 [==============================] - 7s 205ms/step - loss: 43.6173 - val_loss: 3084.0051\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_10.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 >>  496.2059910109446\n",
      "root_mean_squared_error >>  22.275681605978853\n",
      "mean_absolute_error >> 9.181433875199\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": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = load_model('LSTM_with_lookback_10.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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": 6,
     "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": 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>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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rul = pd.read_csv(RUL_file, names=['rul'])\n",
    "df_rul.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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] - 11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 10,
   "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": 11,
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13096, 17) (100, 1)\n",
      "[ 20  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": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dataset(X, look_back=10):\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": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = create_dataset(X_test)\n",
    "#y_test = y_test[6:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13085, 10, 17) (100, 1)\n"
     ]
    }
   ],
   "source": [
    "print(X_test.shape,y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13085, 1)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[136, 161, 73, 102, 105, 101, 115, 81, 90, 112, 49, 114, 114, 51, 188, 134, 52, 63, 131, 69, 100, 150, 146, 73, 112, 105, 115, 76, 126, 88, 46, 66, 64, 85, 45, 31, 110, 39, 112, 46, 99, 87, 76, 124, 81, 59, 30, 115, 105, 123, 93, 123, 96, 145, 150, 21, 80, 72, 120, 97, 63, 128, 87, 86, 87, 59, 109, 99, 115, 89, 75, 92, 114, 96, 121, 90, 68, 129, 104, 81, 46, 61, 109, 92, 87, 97, 137, 84, 84, 83, 32, 36, 64, 84, 107, 82, 67, 89, 138, 111]\n"
     ]
    }
   ],
   "source": [
    "print(final_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "136"
      ]
     },
     "execution_count": 20,
     "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": [
      "[120, 88, 20, 68, 35, 53, 103, 31, 53, 60, 27, 83, 105, 28, 203, 127, 23, 53, 151, 0, 48, 173, 98, 2, 135, 57, 51, 50, 56, 46, 0, 29, 57, 0, 0, 0, 20, 13, 132, 0, 21, 1, 34, 138, 34, 17, 36, 69, 0, 72, 46, 11, 7, 132, 108, 0, 25, 22, 78, 80, 12, 46, 35, 10, 98, 0, 153, 0, 109, 48, 56, 37, 70, 55, 139, 0, 12, 125, 49, 65, 0, 0, 126, 38, 117, 60, 112, 39, 40, 9, 0, 0, 13, 20, 55, 87, 62, 54, 93, 2]\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 >>  1694.33\n",
      "root_mean_squared_error >>  41.16223997792151\n",
      "mean_absolute_error >> 32.39\n",
      "scoring functon >> 173798.2339902401\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=10', 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 functon >>\",scoring_function(y_test,final_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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
}