RUL Prediction using LSTM Lookback=20.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>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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       "    <tr>\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",
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       "      <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": 6,
   "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": 7,
   "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": 8,
   "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": 8,
     "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": 9,
   "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": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=np.nan_to_num(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 13,
   "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": 13,
     "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": 14,
   "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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[192],\n",
       "       [191],\n",
       "       [190],\n",
       "       ...,\n",
       "       [  3],\n",
       "       [  2],\n",
       "       [  1]], dtype=int64)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "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(21):\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=20):\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[21:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20610, 20, 17) (20610, 1)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape,y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[171]\n",
      " [170]\n",
      " [169]\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, 20, 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([17,20])\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 [==============================] - 14s 393ms/step - loss: 11910.3164 - val_loss: 14817.2275\n",
      "Epoch 2/75\n",
      "35/35 [==============================] - 13s 381ms/step - loss: 11420.8916 - val_loss: 13980.8340\n",
      "Epoch 3/75\n",
      "35/35 [==============================] - 14s 404ms/step - loss: 10429.8779 - val_loss: 12619.2314\n",
      "Epoch 4/75\n",
      "35/35 [==============================] - 14s 394ms/step - loss: 9049.8584 - val_loss: 10932.0000\n",
      "Epoch 5/75\n",
      "35/35 [==============================] - 13s 385ms/step - loss: 7539.7832 - val_loss: 9240.2617\n",
      "Epoch 6/75\n",
      "35/35 [==============================] - 14s 387ms/step - loss: 6182.1201 - val_loss: 7826.4707\n",
      "Epoch 7/75\n",
      "35/35 [==============================] - 14s 396ms/step - loss: 5196.2007 - val_loss: 6859.4458\n",
      "Epoch 8/75\n",
      "35/35 [==============================] - 12s 350ms/step - loss: 4641.6357 - val_loss: 6327.7896\n",
      "Epoch 9/75\n",
      "35/35 [==============================] - 12s 344ms/step - loss: 3824.5132 - val_loss: 4603.2979\n",
      "Epoch 10/75\n",
      "35/35 [==============================] - 12s 347ms/step - loss: 2427.2188 - val_loss: 3578.3623\n",
      "Epoch 11/75\n",
      "35/35 [==============================] - 12s 345ms/step - loss: 1779.3516 - val_loss: 2868.7019\n",
      "Epoch 12/75\n",
      "35/35 [==============================] - 12s 345ms/step - loss: 1381.2778 - val_loss: 2439.7188\n",
      "Epoch 13/75\n",
      "35/35 [==============================] - 12s 347ms/step - loss: 1136.3750 - val_loss: 2180.7085\n",
      "Epoch 14/75\n",
      "35/35 [==============================] - 12s 355ms/step - loss: 965.2902 - val_loss: 2014.0045\n",
      "Epoch 15/75\n",
      "35/35 [==============================] - 12s 351ms/step - loss: 845.6010 - val_loss: 2058.5381\n",
      "Epoch 16/75\n",
      "35/35 [==============================] - 12s 349ms/step - loss: 769.9328 - val_loss: 2166.0203\n",
      "Epoch 17/75\n",
      "35/35 [==============================] - 12s 351ms/step - loss: 714.5649 - val_loss: 1874.8591\n",
      "Epoch 18/75\n",
      "35/35 [==============================] - 12s 350ms/step - loss: 650.1425 - val_loss: 2210.0808\n",
      "Epoch 19/75\n",
      "35/35 [==============================] - 12s 351ms/step - loss: 599.6448 - val_loss: 2301.6431\n",
      "Epoch 20/75\n",
      "35/35 [==============================] - 12s 353ms/step - loss: 550.9728 - val_loss: 2346.5068\n",
      "Epoch 21/75\n",
      "35/35 [==============================] - 12s 351ms/step - loss: 506.0330 - val_loss: 2201.9238\n",
      "Epoch 22/75\n",
      "35/35 [==============================] - 12s 352ms/step - loss: 459.6111 - val_loss: 2221.0173\n",
      "Epoch 23/75\n",
      "35/35 [==============================] - 12s 354ms/step - loss: 413.3383 - val_loss: 2458.5605\n",
      "Epoch 24/75\n",
      "35/35 [==============================] - 12s 354ms/step - loss: 382.8491 - val_loss: 2481.0481\n",
      "Epoch 25/75\n",
      "35/35 [==============================] - 12s 349ms/step - loss: 346.0735 - val_loss: 2249.9197\n",
      "Epoch 26/75\n",
      "35/35 [==============================] - 12s 353ms/step - loss: 315.5638 - val_loss: 2404.5642\n",
      "Epoch 27/75\n",
      "35/35 [==============================] - 12s 352ms/step - loss: 283.0821 - val_loss: 2663.1243\n",
      "Epoch 28/75\n",
      "35/35 [==============================] - 12s 349ms/step - loss: 264.0901 - val_loss: 2561.8062\n",
      "Epoch 29/75\n",
      "35/35 [==============================] - 12s 350ms/step - loss: 226.0364 - val_loss: 2562.6453\n",
      "Epoch 30/75\n",
      "35/35 [==============================] - 14s 397ms/step - loss: 207.1303 - val_loss: 2603.3120\n",
      "Epoch 31/75\n",
      "35/35 [==============================] - 12s 354ms/step - loss: 189.0724 - val_loss: 2701.1257\n",
      "Epoch 32/75\n",
      "35/35 [==============================] - 12s 353ms/step - loss: 178.4159 - val_loss: 2528.4207\n",
      "Epoch 33/75\n",
      "35/35 [==============================] - 13s 359ms/step - loss: 171.4185 - val_loss: 2567.5410\n",
      "Epoch 34/75\n",
      "35/35 [==============================] - 12s 353ms/step - loss: 150.5004 - val_loss: 2539.0564\n",
      "Epoch 35/75\n",
      "35/35 [==============================] - 12s 352ms/step - loss: 133.0792 - val_loss: 2619.0439\n",
      "Epoch 36/75\n",
      "35/35 [==============================] - 12s 350ms/step - loss: 123.2205 - val_loss: 2577.3796\n",
      "Epoch 37/75\n",
      "35/35 [==============================] - 12s 353ms/step - loss: 107.3994 - val_loss: 2612.3489\n",
      "Epoch 38/75\n",
      "35/35 [==============================] - 12s 351ms/step - loss: 101.2027 - val_loss: 2717.6587\n",
      "Epoch 39/75\n",
      "35/35 [==============================] - 13s 361ms/step - loss: 95.2547 - val_loss: 2778.5645\n",
      "Epoch 40/75\n",
      "35/35 [==============================] - 14s 390ms/step - loss: 82.9480 - val_loss: 2758.9419\n",
      "Epoch 41/75\n",
      "35/35 [==============================] - 13s 360ms/step - loss: 76.2054 - val_loss: 2622.8503\n",
      "Epoch 42/75\n",
      "35/35 [==============================] - 13s 359ms/step - loss: 70.2766 - val_loss: 2622.6387\n",
      "Epoch 43/75\n",
      "35/35 [==============================] - 13s 373ms/step - loss: 63.7392 - val_loss: 2728.8179\n",
      "Epoch 44/75\n",
      "35/35 [==============================] - 12s 355ms/step - loss: 57.0623 - val_loss: 2585.3850\n",
      "Epoch 45/75\n",
      "35/35 [==============================] - 12s 352ms/step - loss: 54.1045 - val_loss: 2679.1511\n",
      "Epoch 46/75\n",
      "35/35 [==============================] - 13s 377ms/step - loss: 51.2956 - val_loss: 2724.9294\n",
      "Epoch 47/75\n",
      "35/35 [==============================] - 15s 419ms/step - loss: 49.9184 - val_loss: 2641.0947\n",
      "Epoch 48/75\n",
      "35/35 [==============================] - 14s 387ms/step - loss: 46.0419 - val_loss: 2678.8157\n",
      "Epoch 49/75\n",
      "35/35 [==============================] - 14s 396ms/step - loss: 41.6894 - val_loss: 2678.0713\n",
      "Epoch 50/75\n",
      "35/35 [==============================] - 12s 353ms/step - loss: 38.8106 - val_loss: 2647.2688\n",
      "Epoch 51/75\n",
      "35/35 [==============================] - 12s 350ms/step - loss: 38.5458 - val_loss: 2806.2192\n",
      "Epoch 52/75\n",
      "35/35 [==============================] - 12s 349ms/step - loss: 36.6801 - val_loss: 2699.2517\n",
      "Epoch 53/75\n",
      "35/35 [==============================] - 12s 357ms/step - loss: 33.5934 - val_loss: 2680.4036\n",
      "Epoch 54/75\n",
      "35/35 [==============================] - 14s 414ms/step - loss: 31.6990 - val_loss: 2640.7981\n",
      "Epoch 55/75\n",
      "35/35 [==============================] - 13s 382ms/step - loss: 28.8457 - val_loss: 2655.3357\n",
      "Epoch 56/75\n",
      "35/35 [==============================] - 13s 377ms/step - loss: 27.3411 - val_loss: 2701.2544\n",
      "Epoch 57/75\n",
      "35/35 [==============================] - 12s 353ms/step - loss: 26.7356 - val_loss: 2702.0022\n",
      "Epoch 58/75\n",
      "35/35 [==============================] - 12s 355ms/step - loss: 24.5325 - val_loss: 2747.2004\n",
      "Epoch 59/75\n",
      "35/35 [==============================] - 12s 348ms/step - loss: 23.7238 - val_loss: 2662.6951\n",
      "Epoch 60/75\n",
      "35/35 [==============================] - 13s 359ms/step - loss: 22.2004 - val_loss: 2712.7812\n",
      "Epoch 61/75\n",
      "35/35 [==============================] - 14s 395ms/step - loss: 20.5948 - val_loss: 2658.3523\n",
      "Epoch 62/75\n",
      "35/35 [==============================] - 12s 356ms/step - loss: 20.0541 - val_loss: 2725.2480\n",
      "Epoch 63/75\n",
      "35/35 [==============================] - 14s 409ms/step - loss: 19.5155 - val_loss: 2730.2786\n",
      "Epoch 64/75\n",
      "35/35 [==============================] - 13s 376ms/step - loss: 18.3288 - val_loss: 2723.7632\n",
      "Epoch 65/75\n",
      "35/35 [==============================] - 13s 369ms/step - loss: 17.3986 - val_loss: 2716.6624\n",
      "Epoch 66/75\n",
      "35/35 [==============================] - 12s 355ms/step - loss: 18.2998 - val_loss: 2684.9114\n",
      "Epoch 67/75\n",
      "35/35 [==============================] - 12s 356ms/step - loss: 17.5459 - val_loss: 2628.5278\n",
      "Epoch 68/75\n",
      "35/35 [==============================] - 13s 363ms/step - loss: 16.9497 - val_loss: 2777.4751\n",
      "Epoch 69/75\n",
      "35/35 [==============================] - 14s 386ms/step - loss: 15.7232 - val_loss: 2724.5933\n",
      "Epoch 70/75\n",
      "35/35 [==============================] - 13s 362ms/step - loss: 15.1306 - val_loss: 2679.8594\n",
      "Epoch 71/75\n",
      "35/35 [==============================] - 14s 393ms/step - loss: 13.8845 - val_loss: 2686.5486\n",
      "Epoch 72/75\n",
      "35/35 [==============================] - 13s 359ms/step - loss: 13.4296 - val_loss: 2721.3118\n",
      "Epoch 73/75\n",
      "35/35 [==============================] - 13s 373ms/step - loss: 12.4829 - val_loss: 2726.3445\n",
      "Epoch 74/75\n",
      "35/35 [==============================] - 13s 383ms/step - loss: 12.5791 - val_loss: 2697.2795\n",
      "Epoch 75/75\n",
      "35/35 [==============================] - 13s 379ms/step - loss: 13.0888 - val_loss: 2681.3357\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": [
    "model.save('LSTM_with_lookback_20.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 >>  412.80822114387706\n",
      "root_mean_squared_error >>  20.31768247472819\n",
      "mean_absolute_error >> 6.679295523591206\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_20.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] - 21"
   ]
  },
  {
   "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",
      "[ 10  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=20):\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": [
      "(13075, 20, 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": [
       "(13075, 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": [
      "[116, 154, 84, 81, 97, 102, 118, 87, 100, 117, 29, 106, 115, 28, 150, 154, 49, 50, 120, 60, 89, 112, 153, 71, 86, 108, 115, 79, 138, 84, 40, 56, 80, 80, 45, 38, 95, 51, 84, 53, 83, 76, 81, 122, 82, 55, 26, 105, 99, 89, 99, 123, 89, 115, 146, 23, 76, 80, 120, 96, 61, 113, 77, 73, 64, 54, 87, 88, 95, 92, 63, 78, 112, 88, 94, 92, 66, 105, 99, 82, 39, 46, 94, 90, 89, 102, 129, 66, 95, 83, 38, 29, 63, 73, 91, 80, 55, 76, 134, 109]\n"
     ]
    }
   ],
   "source": [
    "print(final_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "116"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_pred[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": [
      "[108, 117, 20, 43, 48, 55, 102, 43, 105, 89, 26, 91, 104, 13, 191, 207, 25, 12, 152, 0, 53, 153, 100, 10, 123, 55, 48, 79, 57, 61, 0, 29, 61, 0, 0, 0, 14, 19, 114, 0, 26, 0, 34, 134, 36, 15, 44, 59, 0, 67, 57, 17, 14, 140, 173, 0, 65, 30, 66, 82, 5, 38, 41, 6, 82, 0, 87, 0, 91, 46, 38, 31, 113, 98, 60, 0, 23, 107, 57, 40, 0, 0, 80, 40, 123, 49, 81, 33, 47, 11, 0, 0, 13, 26, 78, 74, 57, 24, 117, 10]\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 >>  1629.12\n",
      "root_mean_squared_error >>  40.362358702137314\n",
      "mean_absolute_error >> 31.48\n",
      "scoring function >> 276120.73887349915\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=20', 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": []
  }
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