TurboFanPredictiveMaintenancePreparationAndModelling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LUMEN Data Science 2nd workshop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<body>\n",
    "    <div style=\"float:left;\">\n",
    "        <h2> TurboFan predictive maintenance - simple modelling </h2>\n",
    "    </div>\n",
    "    <div>\n",
    "        <img src=\"http://www.megatrend.com/wp-content/uploads/2015/11/MPR_logo_horizontalni_300px1.png\" alt=\"MPR logo\" style=\"float:left;\"/> \n",
    "    </div> \n",
    "</body>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset:\n",
    "<a href=\"https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/\">Turbofan Engine Degradation Simulation Data Set</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn import linear_model\n",
    "from sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.feature_selection import SelectFromModel, RFECV\n",
    "from sklearn.metrics import max_error\n",
    "from sklearn import preprocessing\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "%matplotlib inline  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_names = ['id','cycle','setting1','setting2','setting3',\n",
    "                's1','s2','s3','s4','s5','s6','s7','s8','s9',\n",
    "                's10','s11','s12','s13','s14','s15','s16','s17',\n",
    "                's18','s19','s20','s21', 's22', 's23']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Loading our data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's check out the readme file:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('CMAPSSData/readme.txt', 'rb') as f:\n",
    "     lines = [l.decode('utf8', 'ignore') for l in f.readlines()]\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Data Set: FD001\\n',\n",
       " 'Train trjectories: 100\\n',\n",
       " 'Test trajectories: 100\\n',\n",
       " 'Conditions: ONE (Sea Level)\\n',\n",
       " 'Fault Modes: ONE (HPC Degradation)\\n',\n",
       " '\\n',\n",
       " 'Data Set: FD002\\n',\n",
       " 'Train trjectories: 260\\n',\n",
       " 'Test trajectories: 259\\n',\n",
       " 'Conditions: SIX \\n',\n",
       " 'Fault Modes: ONE (HPC Degradation)\\n',\n",
       " '\\n',\n",
       " 'Data Set: FD003\\n',\n",
       " 'Train trjectories: 100\\n',\n",
       " 'Test trajectories: 100\\n',\n",
       " 'Conditions: ONE (Sea Level)\\n',\n",
       " 'Fault Modes: TWO (HPC Degradation, Fan Degradation)\\n',\n",
       " '\\n',\n",
       " 'Data Set: FD004\\n',\n",
       " 'Train trjectories: 248\\n',\n",
       " 'Test trajectories: 249\\n',\n",
       " 'Conditions: SIX \\n',\n",
       " 'Fault Modes: TWO (HPC Degradation, Fan Degradation)\\n',\n",
       " '\\n',\n",
       " '\\n',\n",
       " '\\n',\n",
       " 'Experimental Scenario\\n',\n",
       " '\\n',\n",
       " 'Data sets consists of multiple multivariate time series. Each data set is further divided into training and test subsets. Each time series is from a different engine  i.e., the data can be considered to be from a fleet of engines of the same type. Each engine starts with different degrees of initial wear and manufacturing variation which is unknown to the user. This wear and variation is considered normal, i.e., it is not considered a fault condition. There are three operational settings that have a substantial effect on engine performance. These settings are also included in the data. The data is contaminated with sensor noise.\\n',\n",
       " '\\n',\n",
       " 'The engine is operating normally at the start of each time series, and develops a fault at some point during the series. In the training set, the fault grows in magnitude until system failure. In the test set, the time series ends some time prior to system failure. The objective of the competition is to predict the number of remaining operational cycles before failure in the test set, i.e., the number of operational cycles after the last cycle that the engine will continue to operate. Also provided a vector of true Remaining Useful Life (RUL) values for the test data.\\n',\n",
       " '\\n',\n",
       " 'The data are provided as a zip-compressed text file with 26 columns of numbers, separated by spaces. Each row is a snapshot of data taken during a single operational cycle, each column is a different variable. The columns correspond to:\\n',\n",
       " '1)\\tunit number\\n',\n",
       " '2)\\ttime, in cycles\\n',\n",
       " '3)\\toperational setting 1\\n',\n",
       " '4)\\toperational setting 2\\n',\n",
       " '5)\\toperational setting 3\\n',\n",
       " '6)\\tsensor measurement  1\\n',\n",
       " '7)\\tsensor measurement  2\\n',\n",
       " '...\\n',\n",
       " '26)\\tsensor measurement  26\\n',\n",
       " '\\n',\n",
       " '\\n',\n",
       " 'Reference: A. Saxena, K. Goebel, D. Simon, and N. Eklund, Damage Propagation Modeling for Aircraft Engine Run-to-Failure Simulation, in the Proceedings of the Ist International Conference on Prognostics and Health Management (PHM08), Denver CO, Oct 2008.\\n']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# you can use the Linux method as well :)\n",
    "#!cat 'CMAPSSData/readme.txt' "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We will be using only FD001 data, since there are multiple scenarios present in this dataset.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "GT_doc = 'CMAPSSData/RUL_FD001.txt'\n",
    "train_doc = 'CMAPSSData/train_FD001.txt' \n",
    "test_doc = 'CMAPSSData/test_FD001.txt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_csv(train_doc, sep=' ', names = column_names)\n",
    "test_data = pd.read_csv(test_doc, sep=' ', names = column_names)\n",
    "gt_data = pd.read_csv(GT_doc, sep=' ', header=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Basic data exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1403.14</td>\n",
       "      <td>14.62</td>\n",
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       "      <td>1587.99</td>\n",
       "      <td>1404.20</td>\n",
       "      <td>14.62</td>\n",
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       "      <td>8133.23</td>\n",
       "      <td>8.4178</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
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       "      <td>0.0007</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
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       "      <td>1582.79</td>\n",
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       "      <td>14.62</td>\n",
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       "   id  cycle  setting1  setting2  setting3      s1      s2       s3       s4  \\\n",
       "0   1      1   -0.0007   -0.0004     100.0  518.67  641.82  1589.70  1400.60   \n",
       "1   1      2    0.0019   -0.0003     100.0  518.67  642.15  1591.82  1403.14   \n",
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       "3   1      4    0.0007    0.0000     100.0  518.67  642.35  1582.79  1401.87   \n",
       "4   1      5   -0.0019   -0.0002     100.0  518.67  642.37  1582.85  1406.22   \n",
       "\n",
       "      s5 ...       s14     s15   s16  s17   s18    s19    s20      s21  s22  \\\n",
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       "3  14.62 ...   8133.83  8.3682  0.03  392  2388  100.0  38.88  23.3739  NaN   \n",
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       "\n",
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       "\n",
       "[5 rows x 28 columns]"
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()W"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <td>23.4130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  cycle  setting1  setting2  setting3      s1      s2       s3       s4  \\\n",
       "0   1      1    0.0023    0.0003     100.0  518.67  643.02  1585.29  1398.21   \n",
       "1   1      2   -0.0027   -0.0003     100.0  518.67  641.71  1588.45  1395.42   \n",
       "2   1      3    0.0003    0.0001     100.0  518.67  642.46  1586.94  1401.34   \n",
       "3   1      4    0.0042    0.0000     100.0  518.67  642.44  1584.12  1406.42   \n",
       "4   1      5    0.0014    0.0000     100.0  518.67  642.51  1587.19  1401.92   \n",
       "\n",
       "      s5 ...       s14     s15   s16  s17   s18    s19    s20      s21  s22  \\\n",
       "0  14.62 ...   8125.55  8.4052  0.03  392  2388  100.0  38.86  23.3735  NaN   \n",
       "1  14.62 ...   8139.62  8.3803  0.03  393  2388  100.0  39.02  23.3916  NaN   \n",
       "2  14.62 ...   8130.10  8.4441  0.03  393  2388  100.0  39.08  23.4166  NaN   \n",
       "3  14.62 ...   8132.90  8.3917  0.03  391  2388  100.0  39.00  23.3737  NaN   \n",
       "4  14.62 ...   8129.54  8.4031  0.03  390  2388  100.0  38.99  23.4130  NaN   \n",
       "\n",
       "   s23  \n",
       "0  NaN  \n",
       "1  NaN  \n",
       "2  NaN  \n",
       "3  NaN  \n",
       "4  NaN  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>112</td>\n",
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       "      <th>1</th>\n",
       "      <td>98</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>69</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>82</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>91</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0   1\n",
       "0  112 NaN\n",
       "1   98 NaN\n",
       "2   69 NaN\n",
       "3   82 NaN\n",
       "4   91 NaN"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gt_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### No. of unique motors in the dataset FD001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,\n",
       "        27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,\n",
       "        40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,\n",
       "        53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,  65,\n",
       "        66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,  78,\n",
       "        79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,  91,\n",
       "        92,  93,  94,  95,  96,  97,  98,  99, 100])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.id.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop extra columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.drop(columns=['s22', 's23'], inplace=True)\n",
    "test_data.drop(columns=['s22', 's23'],  inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "      <th>id</th>\n",
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       "      <th>0</th>\n",
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       "      <td>100.0</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.15</td>\n",
       "      <td>1591.82</td>\n",
       "      <td>1403.14</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>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",
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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>522.42</td>\n",
       "      <td>2388.03</td>\n",
       "      <td>8133.23</td>\n",
       "      <td>8.4178</td>\n",
       "      <td>0.03</td>\n",
       "      <td>390</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.95</td>\n",
       "      <td>23.3442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.35</td>\n",
       "      <td>1582.79</td>\n",
       "      <td>1401.87</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.86</td>\n",
       "      <td>2388.08</td>\n",
       "      <td>8133.83</td>\n",
       "      <td>8.3682</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.88</td>\n",
       "      <td>23.3739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.0019</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.37</td>\n",
       "      <td>1582.85</td>\n",
       "      <td>1406.22</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.19</td>\n",
       "      <td>2388.04</td>\n",
       "      <td>8133.80</td>\n",
       "      <td>8.4294</td>\n",
       "      <td>0.03</td>\n",
       "      <td>393</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.90</td>\n",
       "      <td>23.4044</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  cycle  setting1  setting2  setting3      s1      s2       s3       s4  \\\n",
       "0   1      1   -0.0007   -0.0004     100.0  518.67  641.82  1589.70  1400.60   \n",
       "1   1      2    0.0019   -0.0003     100.0  518.67  642.15  1591.82  1403.14   \n",
       "2   1      3   -0.0043    0.0003     100.0  518.67  642.35  1587.99  1404.20   \n",
       "3   1      4    0.0007    0.0000     100.0  518.67  642.35  1582.79  1401.87   \n",
       "4   1      5   -0.0019   -0.0002     100.0  518.67  642.37  1582.85  1406.22   \n",
       "\n",
       "      s5   ...        s12      s13      s14     s15   s16  s17   s18    s19  \\\n",
       "0  14.62   ...     521.66  2388.02  8138.62  8.4195  0.03  392  2388  100.0   \n",
       "1  14.62   ...     522.28  2388.07  8131.49  8.4318  0.03  392  2388  100.0   \n",
       "2  14.62   ...     522.42  2388.03  8133.23  8.4178  0.03  390  2388  100.0   \n",
       "3  14.62   ...     522.86  2388.08  8133.83  8.3682  0.03  392  2388  100.0   \n",
       "4  14.62   ...     522.19  2388.04  8133.80  8.4294  0.03  393  2388  100.0   \n",
       "\n",
       "     s20      s21  \n",
       "0  39.06  23.4190  \n",
       "1  39.00  23.4236  \n",
       "2  38.95  23.3442  \n",
       "3  38.88  23.3739  \n",
       "4  38.90  23.4044  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>s21</th>\n",
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       "  </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",
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       "      <td>20631.000000</td>\n",
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       "      <td>20631.000000</td>\n",
       "      <td>20631.000000</td>\n",
       "      <td>20631.000000</td>\n",
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       "      <td>2.063100e+04</td>\n",
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       "      <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>518.67</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>0.00</td>\n",
       "      <td>0.500053</td>\n",
       "      <td>6.131150</td>\n",
       "      <td>9.000605</td>\n",
       "      <td>1.776400e-15</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>1.387812e-17</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>518.67</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>518.67</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>518.67</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>518.67</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>518.67</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": [
       "                 id         cycle      setting1      setting2  setting3  \\\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",
       "             s1            s2            s3            s4            s5  \\\n",
       "count  20631.00  20631.000000  20631.000000  20631.000000  2.063100e+04   \n",
       "mean     518.67    642.680934   1590.523119   1408.933782  1.462000e+01   \n",
       "std        0.00      0.500053      6.131150      9.000605  1.776400e-15   \n",
       "min      518.67    641.210000   1571.040000   1382.250000  1.462000e+01   \n",
       "25%      518.67    642.325000   1586.260000   1402.360000  1.462000e+01   \n",
       "50%      518.67    642.640000   1590.100000   1408.040000  1.462000e+01   \n",
       "75%      518.67    643.000000   1594.380000   1414.555000  1.462000e+01   \n",
       "max      518.67    644.530000   1616.910000   1441.490000  1.462000e+01   \n",
       "\n",
       "           ...                s12           s13           s14           s15  \\\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",
       "                s16           s17      s18      s19           s20  \\\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    1.387812e-17      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",
       "                s21  \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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ! Checking for missing values !\n",
    "\n",
    "<font size=\"3\">In case of missing (NULL / NaN) values, it is generally neccessary to replace these values with those of neighbouring rows/cells in adequate fashion. This primarily depends on your data and the use-case that you are solving. \n",
    "\n",
    "Most of the time a mean or median values would be used (considering the column or row), as well as zeros in case of numerical data. There is also an option to drop the missing values(rows), but there is a possibility that by doing that, you would be losing valuable information. Furthermore, there are a number of algorithms that support data with missing values, but most of the time you should not rely on it, but instead try your best to replace them.\n",
    "\n",
    "**Luckily enough, this data comes without missing values, but that might not be the case for the data you were provided with.** \n",
    "\n",
    "\n",
    "helpful links:\n",
    "\n",
    "* https://lmgtfy.com/?q=how+to+replace+missing+values+in+python\n",
    "\n",
    "* https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html\n",
    "\n",
    "* https://jakevdp.github.io/PythonDataScienceHandbook/03.04-missing-values.html\n",
    "\n",
    "* https://towardsdatascience.com/6-different-ways-to-compensate-for-missing-values-data-imputation-with-examples-6022d9ca0779\n",
    "\n",
    "* https://machinelearningmastery.com/handle-missing-data-python/\n",
    "</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id          0\n",
       "cycle       0\n",
       "setting1    0\n",
       "setting2    0\n",
       "setting3    0\n",
       "s1          0\n",
       "s2          0\n",
       "s3          0\n",
       "s4          0\n",
       "s5          0\n",
       "s6          0\n",
       "s7          0\n",
       "s8          0\n",
       "s9          0\n",
       "s10         0\n",
       "s11         0\n",
       "s12         0\n",
       "s13         0\n",
       "s14         0\n",
       "s15         0\n",
       "s16         0\n",
       "s17         0\n",
       "s18         0\n",
       "s19         0\n",
       "s20         0\n",
       "s21         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id          0\n",
       "cycle       0\n",
       "setting1    0\n",
       "setting2    0\n",
       "setting3    0\n",
       "s1          0\n",
       "s2          0\n",
       "s3          0\n",
       "s4          0\n",
       "s5          0\n",
       "s6          0\n",
       "s7          0\n",
       "s8          0\n",
       "s9          0\n",
       "s10         0\n",
       "s11         0\n",
       "s12         0\n",
       "s13         0\n",
       "s14         0\n",
       "s15         0\n",
       "s16         0\n",
       "s17         0\n",
       "s18         0\n",
       "s19         0\n",
       "s20         0\n",
       "s21         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " test_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id            int64\n",
       "cycle         int64\n",
       "setting1    float64\n",
       "setting2    float64\n",
       "setting3    float64\n",
       "s1          float64\n",
       "s2          float64\n",
       "s3          float64\n",
       "s4          float64\n",
       "s5          float64\n",
       "s6          float64\n",
       "s7          float64\n",
       "s8          float64\n",
       "s9          float64\n",
       "s10         float64\n",
       "s11         float64\n",
       "s12         float64\n",
       "s13         float64\n",
       "s14         float64\n",
       "s15         float64\n",
       "s16         float64\n",
       "s17           int64\n",
       "s18           int64\n",
       "s19         float64\n",
       "s20         float64\n",
       "s21         float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Max no. of cycles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "362"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['cycle'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "303"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data['cycle'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preview last cycles in training data - neccessary for TTF (time to failure) regression variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>id</th>\n",
       "      <th>last_cycle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>50.500000</td>\n",
       "      <td>206.310000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>29.011492</td>\n",
       "      <td>46.342749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>128.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>25.750000</td>\n",
       "      <td>177.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>50.500000</td>\n",
       "      <td>199.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>75.250000</td>\n",
       "      <td>229.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>362.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               id  last_cycle\n",
       "count  100.000000  100.000000\n",
       "mean    50.500000  206.310000\n",
       "std     29.011492   46.342749\n",
       "min      1.000000  128.000000\n",
       "25%     25.750000  177.000000\n",
       "50%     50.500000  199.000000\n",
       "75%     75.250000  229.250000\n",
       "max    100.000000  362.000000"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_last_cycle = pd.DataFrame(train_data.groupby('id')['cycle'].max())\n",
    "df_last_cycle.reset_index(level=0 , inplace=True)\n",
    "df_last_cycle.columns = ['id', 'last_cycle']\n",
    "df_last_cycle.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>id</th>\n",
       "      <th>last_cycle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>269</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  last_cycle\n",
       "0   1         192\n",
       "1   2         287\n",
       "2   3         179\n",
       "3   4         189\n",
       "4   5         269"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_last_cycle.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Formatting the ground truth data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>ttf</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": [
       "   ttf\n",
       "0  112\n",
       "1   98\n",
       "2   69\n",
       "3   82\n",
       "4   91"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gt_data.columns = ['ttf','1']\n",
    "gt_data=gt_data.drop(columns=['1'])\n",
    "gt_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>ttf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>75.52000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>41.76497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>32.75000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>86.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>112.25000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>145.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ttf\n",
       "count  100.00000\n",
       "mean    75.52000\n",
       "std     41.76497\n",
       "min      7.00000\n",
       "25%     32.75000\n",
       "50%     86.00000\n",
       "75%    112.25000\n",
       "max    145.00000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gt_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting basic data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,\n",
       "        27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,\n",
       "        40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,\n",
       "        53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,  65,\n",
       "        66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,  78,\n",
       "        79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,  91,\n",
       "        92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103, 104,\n",
       "       105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n",
       "       118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n",
       "       131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n",
       "       144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n",
       "       157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169,\n",
       "       170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,\n",
       "       183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n",
       "       196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208,\n",
       "       209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221,\n",
       "       222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234,\n",
       "       235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,\n",
       "       248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260,\n",
       "       261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273,\n",
       "       274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286,\n",
       "       287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,\n",
       "       300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,\n",
       "       313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325,\n",
       "       326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,\n",
       "       339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351,\n",
       "       352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.cycle.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>cycle</th>\n",
       "      <th>setting1</th>\n",
       "      <th>setting2</th>\n",
       "      <th>setting3</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>...</th>\n",
       "      <th>s12</th>\n",
       "      <th>s13</th>\n",
       "      <th>s14</th>\n",
       "      <th>s15</th>\n",
       "      <th>s16</th>\n",
       "      <th>s17</th>\n",
       "      <th>s18</th>\n",
       "      <th>s19</th>\n",
       "      <th>s20</th>\n",
       "      <th>s21</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.0007</td>\n",
       "      <td>-0.0004</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>641.82</td>\n",
       "      <td>1589.70</td>\n",
       "      <td>1400.60</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>521.66</td>\n",
       "      <td>2388.02</td>\n",
       "      <td>8138.62</td>\n",
       "      <td>8.4195</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.06</td>\n",
       "      <td>23.4190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.15</td>\n",
       "      <td>1591.82</td>\n",
       "      <td>1403.14</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.28</td>\n",
       "      <td>2388.07</td>\n",
       "      <td>8131.49</td>\n",
       "      <td>8.4318</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.00</td>\n",
       "      <td>23.4236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.0043</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.35</td>\n",
       "      <td>1587.99</td>\n",
       "      <td>1404.20</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.42</td>\n",
       "      <td>2388.03</td>\n",
       "      <td>8133.23</td>\n",
       "      <td>8.4178</td>\n",
       "      <td>0.03</td>\n",
       "      <td>390</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.95</td>\n",
       "      <td>23.3442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.35</td>\n",
       "      <td>1582.79</td>\n",
       "      <td>1401.87</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.86</td>\n",
       "      <td>2388.08</td>\n",
       "      <td>8133.83</td>\n",
       "      <td>8.3682</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.88</td>\n",
       "      <td>23.3739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.0019</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.37</td>\n",
       "      <td>1582.85</td>\n",
       "      <td>1406.22</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.19</td>\n",
       "      <td>2388.04</td>\n",
       "      <td>8133.80</td>\n",
       "      <td>8.4294</td>\n",
       "      <td>0.03</td>\n",
       "      <td>393</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.90</td>\n",
       "      <td>23.4044</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  cycle  setting1  setting2  setting3      s1      s2       s3       s4  \\\n",
       "0   1      1   -0.0007   -0.0004     100.0  518.67  641.82  1589.70  1400.60   \n",
       "1   1      2    0.0019   -0.0003     100.0  518.67  642.15  1591.82  1403.14   \n",
       "2   1      3   -0.0043    0.0003     100.0  518.67  642.35  1587.99  1404.20   \n",
       "3   1      4    0.0007    0.0000     100.0  518.67  642.35  1582.79  1401.87   \n",
       "4   1      5   -0.0019   -0.0002     100.0  518.67  642.37  1582.85  1406.22   \n",
       "\n",
       "      s5   ...        s12      s13      s14     s15   s16  s17   s18    s19  \\\n",
       "0  14.62   ...     521.66  2388.02  8138.62  8.4195  0.03  392  2388  100.0   \n",
       "1  14.62   ...     522.28  2388.07  8131.49  8.4318  0.03  392  2388  100.0   \n",
       "2  14.62   ...     522.42  2388.03  8133.23  8.4178  0.03  390  2388  100.0   \n",
       "3  14.62   ...     522.86  2388.08  8133.83  8.3682  0.03  392  2388  100.0   \n",
       "4  14.62   ...     522.19  2388.04  8133.80  8.4294  0.03  393  2388  100.0   \n",
       "\n",
       "     s20      s21  \n",
       "0  39.06  23.4190  \n",
       "1  39.00  23.4236  \n",
       "2  38.95  23.3442  \n",
       "3  38.88  23.3739  \n",
       "4  38.90  23.4044  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "idxs = [10, 30, 50]\n",
    "colors = ['#3B9AB2', '#EBCC2A', '#F21A00']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(figsize=(15,7))\n",
    "plt.xlabel('No. cycles')\n",
    "plt.ylabel('Sensor 12')\n",
    "\n",
    "for i in range(len(idxs)):\n",
    "    temp = train_data[train_data['id'] == idxs[i]]['s12'].rolling(30, min_periods = 1).mean().reset_index(0,drop=True)\n",
    "    plt.plot(temp, label = 'rolling mean ' + str(idxs[i]), color='black')\n",
    "    plt.plot(train_data[train_data['id'] == idxs[i]]['cycle'], \n",
    "             train_data[train_data['id'] == idxs[i]]['s12'], label = 'motor '+ str(idxs[i]),\n",
    "            color=colors[i])\n",
    "    \n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Constructing features: Feature Engineering for Time Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>id</th>\n",
       "      <th>cycle</th>\n",
       "      <th>setting1</th>\n",
       "      <th>setting2</th>\n",
       "      <th>setting3</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>...</th>\n",
       "      <th>s12</th>\n",
       "      <th>s13</th>\n",
       "      <th>s14</th>\n",
       "      <th>s15</th>\n",
       "      <th>s16</th>\n",
       "      <th>s17</th>\n",
       "      <th>s18</th>\n",
       "      <th>s19</th>\n",
       "      <th>s20</th>\n",
       "      <th>s21</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.0007</td>\n",
       "      <td>-0.0004</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>641.82</td>\n",
       "      <td>1589.70</td>\n",
       "      <td>1400.60</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>521.66</td>\n",
       "      <td>2388.02</td>\n",
       "      <td>8138.62</td>\n",
       "      <td>8.4195</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.06</td>\n",
       "      <td>23.4190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.15</td>\n",
       "      <td>1591.82</td>\n",
       "      <td>1403.14</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.28</td>\n",
       "      <td>2388.07</td>\n",
       "      <td>8131.49</td>\n",
       "      <td>8.4318</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.00</td>\n",
       "      <td>23.4236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.0043</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.35</td>\n",
       "      <td>1587.99</td>\n",
       "      <td>1404.20</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.42</td>\n",
       "      <td>2388.03</td>\n",
       "      <td>8133.23</td>\n",
       "      <td>8.4178</td>\n",
       "      <td>0.03</td>\n",
       "      <td>390</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.95</td>\n",
       "      <td>23.3442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.35</td>\n",
       "      <td>1582.79</td>\n",
       "      <td>1401.87</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.86</td>\n",
       "      <td>2388.08</td>\n",
       "      <td>8133.83</td>\n",
       "      <td>8.3682</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.88</td>\n",
       "      <td>23.3739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.0019</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.37</td>\n",
       "      <td>1582.85</td>\n",
       "      <td>1406.22</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.19</td>\n",
       "      <td>2388.04</td>\n",
       "      <td>8133.80</td>\n",
       "      <td>8.4294</td>\n",
       "      <td>0.03</td>\n",
       "      <td>393</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.90</td>\n",
       "      <td>23.4044</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  cycle  setting1  setting2  setting3      s1      s2       s3       s4  \\\n",
       "0   1      1   -0.0007   -0.0004     100.0  518.67  641.82  1589.70  1400.60   \n",
       "1   1      2    0.0019   -0.0003     100.0  518.67  642.15  1591.82  1403.14   \n",
       "2   1      3   -0.0043    0.0003     100.0  518.67  642.35  1587.99  1404.20   \n",
       "3   1      4    0.0007    0.0000     100.0  518.67  642.35  1582.79  1401.87   \n",
       "4   1      5   -0.0019   -0.0002     100.0  518.67  642.37  1582.85  1406.22   \n",
       "\n",
       "      s5   ...        s12      s13      s14     s15   s16  s17   s18    s19  \\\n",
       "0  14.62   ...     521.66  2388.02  8138.62  8.4195  0.03  392  2388  100.0   \n",
       "1  14.62   ...     522.28  2388.07  8131.49  8.4318  0.03  392  2388  100.0   \n",
       "2  14.62   ...     522.42  2388.03  8133.23  8.4178  0.03  390  2388  100.0   \n",
       "3  14.62   ...     522.86  2388.08  8133.83  8.3682  0.03  392  2388  100.0   \n",
       "4  14.62   ...     522.19  2388.04  8133.80  8.4294  0.03  393  2388  100.0   \n",
       "\n",
       "     s20      s21  \n",
       "0  39.06  23.4190  \n",
       "1  39.00  23.4236  \n",
       "2  38.95  23.3442  \n",
       "3  38.88  23.3739  \n",
       "4  38.90  23.4044  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding simple lag features for sensor data -  train & test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_lag_features(df_in, num_lags):\n",
    "    \n",
    "    df_temp = df_in.copy()\n",
    "    #define our sensor cols\n",
    "    sensor_cols = [col for col in df_in.iloc[:, 2:]] \n",
    "    \n",
    "    for sensor in sensor_cols:\n",
    "        for lag in range(1,num_lags+1):\n",
    "            df_temp[str(sensor) + '_lag_' + str(lag)] = df_temp.groupby('id').shift(lag)[sensor].bfill()\n",
    "\n",
    "    return df_temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can verify that we have added quite a lot of columns to our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train_data_features.shape, test_data_features.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding extra features utilizing rolling window \n",
    "\n",
    "*Based on smoothing the time series for sensors by adding rolling mean and rolling standard deviation by using* **pd.DataFrame.rolling**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "??pd.DataFrame.rolling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_rolling_features(df_in, rolling_win_size, wanted_features):\n",
    "    \n",
    "    #sensor_cols = [col for col in train_data.iloc[:, 5:]]    \n",
    "    sensor_cols = [col for col in wanted_features[2:]] \n",
    "\n",
    "    sensor_av_cols = [s.replace('s', 'av') for s in sensor_cols]\n",
    "    sensor_sd_cols = [s.replace('s', 'sd') for s in sensor_cols]\n",
    "    \n",
    "    df_out = pd.DataFrame()\n",
    "    \n",
    "    ws = rolling_win_size\n",
    "    \n",
    "    # calculate rolling stats for each engine id\n",
    "    for eng_id in pd.unique(df_in.id):\n",
    "        \n",
    "        df_engine = df_in[df_in['id'] == eng_id]\n",
    "        df_sub = df_engine[sensor_cols]\n",
    "        \n",
    "        av = df_sub.rolling(ws, min_periods = 1).mean()\n",
    "        av.columns = sensor_av_cols\n",
    "        \n",
    "        sd = df_sub.rolling(ws, min_periods = 1).std().fillna(0)\n",
    "        sd.columns = sensor_sd_cols\n",
    "        \n",
    "        new_features = pd.concat([df_engine, av, sd], axis = 1)\n",
    "        \n",
    "        df_out = pd.concat([df_out, new_features])\n",
    "    \n",
    "    return df_out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate TTF feature for training data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are preparing datasets by adding TTF variable and a classification variable dependant on a window that we want to predict. We will not be modelling classification in this workshop, but you can still play around with the data yourself afterwards."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_training_data(df_in, period):\n",
    "    \n",
    "    # make a dataframe to hold the last cycle for each enginge in the dataset\n",
    "    df_max_cycle = pd.DataFrame(df_in.groupby('id')['cycle'].max())\n",
    "    \n",
    "    df_max_cycle.reset_index(level = 0, inplace = True)\n",
    "    df_max_cycle.columns = ['id', 'last_cycle']\n",
    "    \n",
    "    # add time-to-failure column as a regression label   \n",
    "    df_in = pd.merge(df_in, df_max_cycle, on='id')\n",
    "    df_in['ttf'] = df_in['last_cycle'] - df_in['cycle']\n",
    "    df_in.drop(['last_cycle'], axis = 1, inplace = True)\n",
    "    \n",
    "    # create binary classification label\n",
    "    df_in['label_bnc'] = df_in['ttf'].apply(lambda x: 1 if x <= period else 0)\n",
    "    \n",
    "    return df_in"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our test data is prepared in a different fashion, since we are merging ground truth data in order to add the TTF variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_test_data(df_test_in, df_truth_in, period):\n",
    "        \n",
    "    df_tst_last_cycle = pd.DataFrame(df_test_in.groupby('id')['cycle'].max())\n",
    "    \n",
    "    df_tst_last_cycle.reset_index(level=0, inplace=True)\n",
    "    df_tst_last_cycle.columns = ['id', 'last_cycle']\n",
    "    df_test_in = pd.merge(df_test_in, df_tst_last_cycle, on='id')\n",
    "    df_test_in = df_test_in[df_test_in['cycle'] == df_test_in['last_cycle']]\n",
    "\n",
    "    # remove the last cycle column\n",
    "    df_test_in.drop(['last_cycle'], axis=1, inplace=True)    \n",
    "    df_test_in.reset_index(drop=True, inplace=True)\n",
    "    \n",
    "    df_test_in = pd.concat([df_test_in, gt_data], axis=1)\n",
    "    \n",
    "    # create binary classification label\n",
    "    df_test_in['label_bnc'] = df_test_in['ttf'].apply(lambda x: 1 if x <= period else 0)\n",
    "    return df_test_in"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Original data preparation\n",
    "\n",
    "*We are preparing original data in order to be able to model it and **compare** it using the same approach that we use with data with extra features.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>id</th>\n",
       "      <th>cycle</th>\n",
       "      <th>setting1</th>\n",
       "      <th>setting2</th>\n",
       "      <th>setting3</th>\n",
       "      <th>s1</th>\n",
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       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>...</th>\n",
       "      <th>s14</th>\n",
       "      <th>s15</th>\n",
       "      <th>s16</th>\n",
       "      <th>s17</th>\n",
       "      <th>s18</th>\n",
       "      <th>s19</th>\n",
       "      <th>s20</th>\n",
       "      <th>s21</th>\n",
       "      <th>ttf</th>\n",
       "      <th>label_bnc</th>\n",
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       "  </thead>\n",
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       "      <td>641.82</td>\n",
       "      <td>1589.70</td>\n",
       "      <td>1400.60</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>8138.62</td>\n",
       "      <td>8.4195</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.06</td>\n",
       "      <td>23.4190</td>\n",
       "      <td>191</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.15</td>\n",
       "      <td>1591.82</td>\n",
       "      <td>1403.14</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>8131.49</td>\n",
       "      <td>8.4318</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.00</td>\n",
       "      <td>23.4236</td>\n",
       "      <td>190</td>\n",
       "      <td>0</td>\n",
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       "      <th>2</th>\n",
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       "      <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>189</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.35</td>\n",
       "      <td>1582.79</td>\n",
       "      <td>1401.87</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>8133.83</td>\n",
       "      <td>8.3682</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.88</td>\n",
       "      <td>23.3739</td>\n",
       "      <td>188</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.0019</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.37</td>\n",
       "      <td>1582.85</td>\n",
       "      <td>1406.22</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>8133.80</td>\n",
       "      <td>8.4294</td>\n",
       "      <td>0.03</td>\n",
       "      <td>393</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.90</td>\n",
       "      <td>23.4044</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  cycle  setting1  setting2  setting3      s1      s2       s3       s4  \\\n",
       "0   1      1   -0.0007   -0.0004     100.0  518.67  641.82  1589.70  1400.60   \n",
       "1   1      2    0.0019   -0.0003     100.0  518.67  642.15  1591.82  1403.14   \n",
       "2   1      3   -0.0043    0.0003     100.0  518.67  642.35  1587.99  1404.20   \n",
       "3   1      4    0.0007    0.0000     100.0  518.67  642.35  1582.79  1401.87   \n",
       "4   1      5   -0.0019   -0.0002     100.0  518.67  642.37  1582.85  1406.22   \n",
       "\n",
       "      s5    ...          s14     s15   s16  s17   s18    s19    s20      s21  \\\n",
       "0  14.62    ...      8138.62  8.4195  0.03  392  2388  100.0  39.06  23.4190   \n",
       "1  14.62    ...      8131.49  8.4318  0.03  392  2388  100.0  39.00  23.4236   \n",
       "2  14.62    ...      8133.23  8.4178  0.03  390  2388  100.0  38.95  23.3442   \n",
       "3  14.62    ...      8133.83  8.3682  0.03  392  2388  100.0  38.88  23.3739   \n",
       "4  14.62    ...      8133.80  8.4294  0.03  393  2388  100.0  38.90  23.4044   \n",
       "\n",
       "   ttf  label_bnc  \n",
       "0  191          0  \n",
       "1  190          0  \n",
       "2  189          0  \n",
       "3  188          0  \n",
       "4  187          0  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_original_prepared = prepare_training_data(train_data, 5)\n",
    "train_data_original_prepared.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1581.22</td>\n",
       "      <td>1398.91</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>8130.11</td>\n",
       "      <td>8.4024</td>\n",
       "      <td>0.03</td>\n",
       "      <td>393</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.81</td>\n",
       "      <td>23.3552</td>\n",
       "      <td>112</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>49</td>\n",
       "      <td>0.0018</td>\n",
       "      <td>-0.0001</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.55</td>\n",
       "      <td>1586.59</td>\n",
       "      <td>1410.83</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>8126.90</td>\n",
       "      <td>8.4505</td>\n",
       "      <td>0.03</td>\n",
       "      <td>391</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.81</td>\n",
       "      <td>23.2618</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>126</td>\n",
       "      <td>-0.0016</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.88</td>\n",
       "      <td>1589.75</td>\n",
       "      <td>1418.89</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>8131.46</td>\n",
       "      <td>8.4119</td>\n",
       "      <td>0.03</td>\n",
       "      <td>395</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.93</td>\n",
       "      <td>23.2740</td>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>106</td>\n",
       "      <td>0.0012</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.78</td>\n",
       "      <td>1594.53</td>\n",
       "      <td>1406.88</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>8133.64</td>\n",
       "      <td>8.4634</td>\n",
       "      <td>0.03</td>\n",
       "      <td>395</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.58</td>\n",
       "      <td>23.2581</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>98</td>\n",
       "      <td>-0.0013</td>\n",
       "      <td>-0.0004</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.27</td>\n",
       "      <td>1589.94</td>\n",
       "      <td>1419.36</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>8125.74</td>\n",
       "      <td>8.4362</td>\n",
       "      <td>0.03</td>\n",
       "      <td>394</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.75</td>\n",
       "      <td>23.4117</td>\n",
       "      <td>91</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  cycle  setting1  setting2  setting3      s1      s2       s3       s4  \\\n",
       "0   1     31   -0.0006    0.0004     100.0  518.67  642.58  1581.22  1398.91   \n",
       "1   2     49    0.0018   -0.0001     100.0  518.67  642.55  1586.59  1410.83   \n",
       "2   3    126   -0.0016    0.0004     100.0  518.67  642.88  1589.75  1418.89   \n",
       "3   4    106    0.0012    0.0004     100.0  518.67  642.78  1594.53  1406.88   \n",
       "4   5     98   -0.0013   -0.0004     100.0  518.67  642.27  1589.94  1419.36   \n",
       "\n",
       "      s5    ...          s14     s15   s16  s17   s18    s19    s20      s21  \\\n",
       "0  14.62    ...      8130.11  8.4024  0.03  393  2388  100.0  38.81  23.3552   \n",
       "1  14.62    ...      8126.90  8.4505  0.03  391  2388  100.0  38.81  23.2618   \n",
       "2  14.62    ...      8131.46  8.4119  0.03  395  2388  100.0  38.93  23.2740   \n",
       "3  14.62    ...      8133.64  8.4634  0.03  395  2388  100.0  38.58  23.2581   \n",
       "4  14.62    ...      8125.74  8.4362  0.03  394  2388  100.0  38.75  23.4117   \n",
       "\n",
       "   ttf  label_bnc  \n",
       "0  112          0  \n",
       "1   98          0  \n",
       "2   69          0  \n",
       "3   82          0  \n",
       "4   91          0  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data_original_prepared = prepare_test_data(test_data, gt_data, 30)\n",
    "test_data_original_prepared.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((20631, 28), (100, 28))"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_original_prepared.shape, test_data_original_prepared.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_original_prepared.to_csv('train_data_original.csv', index = False)\n",
    "test_data_original_prepared.to_csv('test_data_original.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's plot and compare the standard deviation of original features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = [col for col in train_data.iloc[:,2:]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3534f99898>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data[features].std().plot(kind='bar', figsize=(20,10), title=\"Standard Deviation of features\", color='grey')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For features **setting1, setting2, setting3, s1, s5, s6, s10, s16, s18 and s19** we can see that standard deviation on these features is 0. We do not have enough information on the sensors in question, and the depicted graph here is used to visualize the amount of variation in our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>id</th>\n",
       "      <th>cycle</th>\n",
       "      <th>setting1</th>\n",
       "      <th>setting2</th>\n",
       "      <th>setting3</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>...</th>\n",
       "      <th>s12</th>\n",
       "      <th>s13</th>\n",
       "      <th>s14</th>\n",
       "      <th>s15</th>\n",
       "      <th>s16</th>\n",
       "      <th>s17</th>\n",
       "      <th>s18</th>\n",
       "      <th>s19</th>\n",
       "      <th>s20</th>\n",
       "      <th>s21</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.0007</td>\n",
       "      <td>-0.0004</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>641.82</td>\n",
       "      <td>1589.70</td>\n",
       "      <td>1400.60</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>521.66</td>\n",
       "      <td>2388.02</td>\n",
       "      <td>8138.62</td>\n",
       "      <td>8.4195</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.06</td>\n",
       "      <td>23.4190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.15</td>\n",
       "      <td>1591.82</td>\n",
       "      <td>1403.14</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.28</td>\n",
       "      <td>2388.07</td>\n",
       "      <td>8131.49</td>\n",
       "      <td>8.4318</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.00</td>\n",
       "      <td>23.4236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.0043</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.35</td>\n",
       "      <td>1587.99</td>\n",
       "      <td>1404.20</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.42</td>\n",
       "      <td>2388.03</td>\n",
       "      <td>8133.23</td>\n",
       "      <td>8.4178</td>\n",
       "      <td>0.03</td>\n",
       "      <td>390</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.95</td>\n",
       "      <td>23.3442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.35</td>\n",
       "      <td>1582.79</td>\n",
       "      <td>1401.87</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.86</td>\n",
       "      <td>2388.08</td>\n",
       "      <td>8133.83</td>\n",
       "      <td>8.3682</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.88</td>\n",
       "      <td>23.3739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.0019</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.37</td>\n",
       "      <td>1582.85</td>\n",
       "      <td>1406.22</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.19</td>\n",
       "      <td>2388.04</td>\n",
       "      <td>8133.80</td>\n",
       "      <td>8.4294</td>\n",
       "      <td>0.03</td>\n",
       "      <td>393</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.90</td>\n",
       "      <td>23.4044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>-0.0043</td>\n",
       "      <td>-0.0001</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.10</td>\n",
       "      <td>1584.47</td>\n",
       "      <td>1398.37</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>521.68</td>\n",
       "      <td>2388.03</td>\n",
       "      <td>8132.85</td>\n",
       "      <td>8.4108</td>\n",
       "      <td>0.03</td>\n",
       "      <td>391</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.98</td>\n",
       "      <td>23.3669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.48</td>\n",
       "      <td>1592.32</td>\n",
       "      <td>1397.77</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.32</td>\n",
       "      <td>2388.03</td>\n",
       "      <td>8132.32</td>\n",
       "      <td>8.3974</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.10</td>\n",
       "      <td>23.3774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>-0.0034</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.56</td>\n",
       "      <td>1582.96</td>\n",
       "      <td>1400.97</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.47</td>\n",
       "      <td>2388.03</td>\n",
       "      <td>8131.07</td>\n",
       "      <td>8.4076</td>\n",
       "      <td>0.03</td>\n",
       "      <td>391</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.97</td>\n",
       "      <td>23.3106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0008</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.12</td>\n",
       "      <td>1590.98</td>\n",
       "      <td>1394.80</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>521.79</td>\n",
       "      <td>2388.05</td>\n",
       "      <td>8125.69</td>\n",
       "      <td>8.3728</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.05</td>\n",
       "      <td>23.4066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>-0.0033</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>641.71</td>\n",
       "      <td>1591.24</td>\n",
       "      <td>1400.46</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>521.79</td>\n",
       "      <td>2388.06</td>\n",
       "      <td>8129.38</td>\n",
       "      <td>8.4286</td>\n",
       "      <td>0.03</td>\n",
       "      <td>393</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.95</td>\n",
       "      <td>23.4694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0018</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.28</td>\n",
       "      <td>1581.75</td>\n",
       "      <td>1400.64</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>521.40</td>\n",
       "      <td>2388.01</td>\n",
       "      <td>8140.58</td>\n",
       "      <td>8.4340</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.94</td>\n",
       "      <td>23.4787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0016</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.06</td>\n",
       "      <td>1583.41</td>\n",
       "      <td>1400.15</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>521.80</td>\n",
       "      <td>2388.02</td>\n",
       "      <td>8134.25</td>\n",
       "      <td>8.3938</td>\n",
       "      <td>0.03</td>\n",
       "      <td>391</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.06</td>\n",
       "      <td>23.3660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>-0.0019</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>643.07</td>\n",
       "      <td>1582.19</td>\n",
       "      <td>1400.83</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>521.85</td>\n",
       "      <td>2388.08</td>\n",
       "      <td>8128.10</td>\n",
       "      <td>8.4152</td>\n",
       "      <td>0.03</td>\n",
       "      <td>393</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.93</td>\n",
       "      <td>23.2757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.35</td>\n",
       "      <td>1592.95</td>\n",
       "      <td>1399.16</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>521.67</td>\n",
       "      <td>2388.00</td>\n",
       "      <td>8134.43</td>\n",
       "      <td>8.3964</td>\n",
       "      <td>0.03</td>\n",
       "      <td>393</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.18</td>\n",
       "      <td>23.3826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>-0.0018</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.43</td>\n",
       "      <td>1583.82</td>\n",
       "      <td>1402.13</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.50</td>\n",
       "      <td>2388.08</td>\n",
       "      <td>8127.56</td>\n",
       "      <td>8.4199</td>\n",
       "      <td>0.03</td>\n",
       "      <td>391</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.99</td>\n",
       "      <td>23.3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.13</td>\n",
       "      <td>1587.98</td>\n",
       "      <td>1404.50</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>521.49</td>\n",
       "      <td>2388.07</td>\n",
       "      <td>8136.11</td>\n",
       "      <td>8.3936</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.97</td>\n",
       "      <td>23.4550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.58</td>\n",
       "      <td>1584.96</td>\n",
       "      <td>1399.95</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>521.89</td>\n",
       "      <td>2388.04</td>\n",
       "      <td>8137.27</td>\n",
       "      <td>8.4542</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.81</td>\n",
       "      <td>23.3319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>-0.0031</td>\n",
       "      <td>-0.0001</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.62</td>\n",
       "      <td>1591.04</td>\n",
       "      <td>1396.12</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>521.76</td>\n",
       "      <td>2388.07</td>\n",
       "      <td>8132.73</td>\n",
       "      <td>8.4028</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.89</td>\n",
       "      <td>23.3987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>0.0032</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>641.79</td>\n",
       "      <td>1587.56</td>\n",
       "      <td>1400.35</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>521.89</td>\n",
       "      <td>2388.03</td>\n",
       "      <td>8129.13</td>\n",
       "      <td>8.4321</td>\n",
       "      <td>0.03</td>\n",
       "      <td>391</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.80</td>\n",
       "      <td>23.3464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>-0.0037</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>643.04</td>\n",
       "      <td>1581.11</td>\n",
       "      <td>1405.23</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.07</td>\n",
       "      <td>2388.02</td>\n",
       "      <td>8129.71</td>\n",
       "      <td>8.4210</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.03</td>\n",
       "      <td>23.4220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>-0.0012</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.37</td>\n",
       "      <td>1586.07</td>\n",
       "      <td>1398.13</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.42</td>\n",
       "      <td>2388.08</td>\n",
       "      <td>8134.02</td>\n",
       "      <td>8.4049</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.09</td>\n",
       "      <td>23.3101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.77</td>\n",
       "      <td>1592.93</td>\n",
       "      <td>1400.57</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.00</td>\n",
       "      <td>2388.03</td>\n",
       "      <td>8130.41</td>\n",
       "      <td>8.4034</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.92</td>\n",
       "      <td>23.3792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>0.0034</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.14</td>\n",
       "      <td>1588.19</td>\n",
       "      <td>1394.75</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>521.52</td>\n",
       "      <td>2388.05</td>\n",
       "      <td>8127.90</td>\n",
       "      <td>8.4240</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.94</td>\n",
       "      <td>23.4562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>-0.0010</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.38</td>\n",
       "      <td>1590.83</td>\n",
       "      <td>1398.81</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.13</td>\n",
       "      <td>2388.03</td>\n",
       "      <td>8133.88</td>\n",
       "      <td>8.3891</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>23.3696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>0.0023</td>\n",
       "      <td>-0.0004</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.77</td>\n",
       "      <td>1594.10</td>\n",
       "      <td>1399.39</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.56</td>\n",
       "      <td>2388.02</td>\n",
       "      <td>8136.61</td>\n",
       "      <td>8.3917</td>\n",
       "      <td>0.03</td>\n",
       "      <td>393</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.95</td>\n",
       "      <td>23.4288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.16</td>\n",
       "      <td>1589.08</td>\n",
       "      <td>1396.07</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.28</td>\n",
       "      <td>2388.06</td>\n",
       "      <td>8131.15</td>\n",
       "      <td>8.4260</td>\n",
       "      <td>0.03</td>\n",
       "      <td>394</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.86</td>\n",
       "      <td>23.4149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>-0.0012</td>\n",
       "      <td>-0.0004</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.44</td>\n",
       "      <td>1590.47</td>\n",
       "      <td>1401.84</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.00</td>\n",
       "      <td>2388.13</td>\n",
       "      <td>8134.60</td>\n",
       "      <td>8.4046</td>\n",
       "      <td>0.03</td>\n",
       "      <td>393</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.99</td>\n",
       "      <td>23.4472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>-0.0024</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.35</td>\n",
       "      <td>1582.84</td>\n",
       "      <td>1399.13</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.57</td>\n",
       "      <td>2388.08</td>\n",
       "      <td>8127.30</td>\n",
       "      <td>8.4323</td>\n",
       "      <td>0.03</td>\n",
       "      <td>390</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.01</td>\n",
       "      <td>23.2841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>0.0012</td>\n",
       "      <td>-0.0001</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>641.91</td>\n",
       "      <td>1584.83</td>\n",
       "      <td>1400.99</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.41</td>\n",
       "      <td>2388.06</td>\n",
       "      <td>8133.06</td>\n",
       "      <td>8.4189</td>\n",
       "      <td>0.03</td>\n",
       "      <td>393</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.93</td>\n",
       "      <td>23.3597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>-0.0022</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.20</td>\n",
       "      <td>1593.52</td>\n",
       "      <td>1396.08</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.19</td>\n",
       "      <td>2388.00</td>\n",
       "      <td>8137.86</td>\n",
       "      <td>8.4065</td>\n",
       "      <td>0.03</td>\n",
       "      <td>390</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.05</td>\n",
       "      <td>23.4110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20601</th>\n",
       "      <td>100</td>\n",
       "      <td>171</td>\n",
       "      <td>-0.0005</td>\n",
       "      <td>-0.0004</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>643.05</td>\n",
       "      <td>1593.56</td>\n",
       "      <td>1420.48</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>520.50</td>\n",
       "      <td>2388.18</td>\n",
       "      <td>8144.03</td>\n",
       "      <td>8.5085</td>\n",
       "      <td>0.03</td>\n",
       "      <td>396</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.81</td>\n",
       "      <td>23.1513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20602</th>\n",
       "      <td>100</td>\n",
       "      <td>172</td>\n",
       "      <td>-0.0037</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.97</td>\n",
       "      <td>1602.35</td>\n",
       "      <td>1424.93</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>521.04</td>\n",
       "      <td>2388.19</td>\n",
       "      <td>8142.08</td>\n",
       "      <td>8.4970</td>\n",
       "      <td>0.03</td>\n",
       "      <td>394</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.49</td>\n",
       "      <td>23.1922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20603</th>\n",
       "      <td>100</td>\n",
       "      <td>173</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>643.40</td>\n",
       "      <td>1595.53</td>\n",
       "      <td>1418.63</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>520.22</td>\n",
       "      <td>2388.17</td>\n",
       "      <td>8143.13</td>\n",
       "      <td>8.4856</td>\n",
       "      <td>0.03</td>\n",
       "      <td>393</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.42</td>\n",
       "      <td>23.1793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20604</th>\n",
       "      <td>100</td>\n",
       "      <td>174</td>\n",
       "      <td>0.0011</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.91</td>\n",
       "      <td>1602.24</td>\n",
       "      <td>1425.52</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>520.44</td>\n",
       "      <td>2388.15</td>\n",
       "      <td>8143.61</td>\n",
       "      <td>8.4990</td>\n",
       "      <td>0.03</td>\n",
       "      <td>395</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.80</td>\n",
       "      <td>23.1784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20605</th>\n",
       "      <td>100</td>\n",
       "      <td>175</td>\n",
       "      <td>-0.0013</td>\n",
       "      <td>-0.0005</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.85</td>\n",
       "      <td>1602.03</td>\n",
       "      <td>1416.24</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>521.17</td>\n",
       "      <td>2388.17</td>\n",
       "      <td>8140.88</td>\n",
       "      <td>8.4851</td>\n",
       "      <td>0.03</td>\n",
       "      <td>394</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.54</td>\n",
       "      <td>23.0713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20606</th>\n",
       "      <td>100</td>\n",
       "      <td>176</td>\n",
       "      <td>-0.0017</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>643.33</td>\n",
       "      <td>1601.44</td>\n",
       "      <td>1421.40</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>520.76</td>\n",
       "      <td>2388.19</td>\n",
       "      <td>8139.27</td>\n",
       "      <td>8.4405</td>\n",
       "      <td>0.03</td>\n",
       "      <td>396</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.46</td>\n",
       "      <td>23.1020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20607</th>\n",
       "      <td>100</td>\n",
       "      <td>177</td>\n",
       "      <td>-0.0011</td>\n",
       "      <td>-0.0005</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>643.34</td>\n",
       "      <td>1593.22</td>\n",
       "      <td>1418.91</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>520.28</td>\n",
       "      <td>2388.18</td>\n",
       "      <td>8142.95</td>\n",
       "      <td>8.5133</td>\n",
       "      <td>0.03</td>\n",
       "      <td>395</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.60</td>\n",
       "      <td>23.0352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20608</th>\n",
       "      <td>100</td>\n",
       "      <td>178</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.98</td>\n",
       "      <td>1594.80</td>\n",
       "      <td>1422.69</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>519.81</td>\n",
       "      <td>2388.16</td>\n",
       "      <td>8141.85</td>\n",
       "      <td>8.4876</td>\n",
       "      <td>0.03</td>\n",
       "      <td>395</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.55</td>\n",
       "      <td>23.2252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20609</th>\n",
       "      <td>100</td>\n",
       "      <td>179</td>\n",
       "      <td>0.0020</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>643.22</td>\n",
       "      <td>1599.36</td>\n",
       "      <td>1423.94</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>520.36</td>\n",
       "      <td>2388.19</td>\n",
       "      <td>8138.58</td>\n",
       "      <td>8.5218</td>\n",
       "      <td>0.03</td>\n",
       "      <td>396</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.62</td>\n",
       "      <td>23.1685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20610</th>\n",
       "      <td>100</td>\n",
       "      <td>180</td>\n",
       "      <td>-0.0010</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>643.64</td>\n",
       "      <td>1595.98</td>\n",
       "      <td>1416.45</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>520.80</td>\n",
       "      <td>2388.22</td>\n",
       "      <td>8138.98</td>\n",
       "      <td>8.5150</td>\n",
       "      <td>0.03</td>\n",
       "      <td>395</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.54</td>\n",
       "      <td>23.2345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20611</th>\n",
       "      <td>100</td>\n",
       "      <td>181</td>\n",
       "      <td>0.0024</td>\n",
       "      <td>-0.0005</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>643.25</td>\n",
       "      <td>1597.83</td>\n",
       "      <td>1414.63</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>520.42</td>\n",
       "      <td>2388.22</td>\n",
       "      <td>8139.30</td>\n",
       "      <td>8.5518</td>\n",
       "      <td>0.03</td>\n",
       "      <td>396</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.52</td>\n",
       "      <td>23.1774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20612</th>\n",
       "      <td>100</td>\n",
       "      <td>182</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>-0.0001</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>643.52</td>\n",
       "      <td>1604.31</td>\n",
       "      <td>1417.73</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>520.05</td>\n",
       "      <td>2388.22</td>\n",
       "      <td>8140.87</td>\n",
       "      <td>8.4855</td>\n",
       "      <td>0.03</td>\n",
       "      <td>396</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.41</td>\n",
       "      <td>23.1289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20613</th>\n",
       "      <td>100</td>\n",
       "      <td>183</td>\n",
       "      <td>-0.0011</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>643.34</td>\n",
       "      <td>1594.60</td>\n",
       "      <td>1427.27</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>520.64</td>\n",
       "      <td>2388.23</td>\n",
       "      <td>8144.21</td>\n",
       "      <td>8.5006</td>\n",
       "      <td>0.03</td>\n",
       "      <td>395</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.49</td>\n",
       "      <td>23.0709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20614</th>\n",
       "      <td>100</td>\n",
       "      <td>184</td>\n",
       "      <td>0.0027</td>\n",
       "      <td>-0.0004</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.91</td>\n",
       "      <td>1598.88</td>\n",
       "      <td>1420.89</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>520.05</td>\n",
       "      <td>2388.20</td>\n",
       "      <td>8142.28</td>\n",
       "      <td>8.4989</td>\n",
       "      <td>0.03</td>\n",
       "      <td>396</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.44</td>\n",
       "      <td>23.1229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20615</th>\n",
       "      <td>100</td>\n",
       "      <td>185</td>\n",
       "      <td>-0.0014</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>643.95</td>\n",
       "      <td>1600.81</td>\n",
       "      <td>1420.34</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>520.71</td>\n",
       "      <td>2388.20</td>\n",
       "      <td>8142.32</td>\n",
       "      <td>8.4804</td>\n",
       "      <td>0.03</td>\n",
       "      <td>395</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.60</td>\n",
       "      <td>23.2127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20616</th>\n",
       "      <td>100</td>\n",
       "      <td>186</td>\n",
       "      <td>0.0026</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>643.61</td>\n",
       "      <td>1593.55</td>\n",
       "      <td>1425.32</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>520.66</td>\n",
       "      <td>2388.27</td>\n",
       "      <td>8138.08</td>\n",
       "      <td>8.4735</td>\n",
       "      <td>0.03</td>\n",
       "      <td>394</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.51</td>\n",
       "      <td>23.1173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20617</th>\n",
       "      <td>100</td>\n",
       "      <td>187</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>643.63</td>\n",
       "      <td>1596.96</td>\n",
       "      <td>1421.49</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>520.15</td>\n",
       "      <td>2388.22</td>\n",
       "      <td>8140.49</td>\n",
       "      <td>8.5087</td>\n",
       "      <td>0.03</td>\n",
       "      <td>396</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.67</td>\n",
       "      <td>23.2308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20618</th>\n",
       "      <td>100</td>\n",
       "      <td>188</td>\n",
       "      <td>-0.0008</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>643.19</td>\n",
       "      <td>1597.77</td>\n",
       "      <td>1426.57</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>519.98</td>\n",
       "      <td>2388.21</td>\n",
       "      <td>8139.94</td>\n",
       "      <td>8.4814</td>\n",
       "      <td>0.03</td>\n",
       "      <td>395</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.36</td>\n",
       "      <td>23.0552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20619</th>\n",
       "      <td>100</td>\n",
       "      <td>189</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>643.69</td>\n",
       "      <td>1599.85</td>\n",
       "      <td>1423.15</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>519.50</td>\n",
       "      <td>2388.24</td>\n",
       "      <td>8139.78</td>\n",
       "      <td>8.4870</td>\n",
       "      <td>0.03</td>\n",
       "      <td>397</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.65</td>\n",
       "      <td>23.0591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20620</th>\n",
       "      <td>100</td>\n",
       "      <td>190</td>\n",
       "      <td>-0.0001</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>643.12</td>\n",
       "      <td>1594.45</td>\n",
       "      <td>1426.04</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>519.52</td>\n",
       "      <td>2388.26</td>\n",
       "      <td>8142.28</td>\n",
       "      <td>8.5162</td>\n",
       "      <td>0.03</td>\n",
       "      <td>395</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.42</td>\n",
       "      <td>23.0603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20621</th>\n",
       "      <td>100</td>\n",
       "      <td>191</td>\n",
       "      <td>-0.0005</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>643.69</td>\n",
       "      <td>1610.87</td>\n",
       "      <td>1427.19</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>519.80</td>\n",
       "      <td>2388.28</td>\n",
       "      <td>8143.56</td>\n",
       "      <td>8.5092</td>\n",
       "      <td>0.03</td>\n",
       "      <td>398</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.39</td>\n",
       "      <td>23.1218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20622</th>\n",
       "      <td>100</td>\n",
       "      <td>192</td>\n",
       "      <td>-0.0009</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>643.53</td>\n",
       "      <td>1601.23</td>\n",
       "      <td>1419.48</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>520.59</td>\n",
       "      <td>2388.21</td>\n",
       "      <td>8143.46</td>\n",
       "      <td>8.4892</td>\n",
       "      <td>0.03</td>\n",
       "      <td>397</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.56</td>\n",
       "      <td>23.0770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20623</th>\n",
       "      <td>100</td>\n",
       "      <td>193</td>\n",
       "      <td>-0.0001</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>643.09</td>\n",
       "      <td>1599.81</td>\n",
       "      <td>1428.93</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>520.11</td>\n",
       "      <td>2388.19</td>\n",
       "      <td>8142.02</td>\n",
       "      <td>8.5424</td>\n",
       "      <td>0.03</td>\n",
       "      <td>397</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.47</td>\n",
       "      <td>23.0230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20624</th>\n",
       "      <td>100</td>\n",
       "      <td>194</td>\n",
       "      <td>-0.0011</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>643.72</td>\n",
       "      <td>1597.29</td>\n",
       "      <td>1427.41</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>519.55</td>\n",
       "      <td>2388.22</td>\n",
       "      <td>8139.67</td>\n",
       "      <td>8.5215</td>\n",
       "      <td>0.03</td>\n",
       "      <td>394</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.38</td>\n",
       "      <td>23.1324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20625</th>\n",
       "      <td>100</td>\n",
       "      <td>195</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>-0.0001</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>643.41</td>\n",
       "      <td>1600.04</td>\n",
       "      <td>1431.90</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>519.71</td>\n",
       "      <td>2388.28</td>\n",
       "      <td>8142.90</td>\n",
       "      <td>8.5519</td>\n",
       "      <td>0.03</td>\n",
       "      <td>394</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.14</td>\n",
       "      <td>23.1923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20626</th>\n",
       "      <td>100</td>\n",
       "      <td>196</td>\n",
       "      <td>-0.0004</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>643.49</td>\n",
       "      <td>1597.98</td>\n",
       "      <td>1428.63</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>519.49</td>\n",
       "      <td>2388.26</td>\n",
       "      <td>8137.60</td>\n",
       "      <td>8.4956</td>\n",
       "      <td>0.03</td>\n",
       "      <td>397</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.49</td>\n",
       "      <td>22.9735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20627</th>\n",
       "      <td>100</td>\n",
       "      <td>197</td>\n",
       "      <td>-0.0016</td>\n",
       "      <td>-0.0005</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>643.54</td>\n",
       "      <td>1604.50</td>\n",
       "      <td>1433.58</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>519.68</td>\n",
       "      <td>2388.22</td>\n",
       "      <td>8136.50</td>\n",
       "      <td>8.5139</td>\n",
       "      <td>0.03</td>\n",
       "      <td>395</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.30</td>\n",
       "      <td>23.1594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20628</th>\n",
       "      <td>100</td>\n",
       "      <td>198</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>643.42</td>\n",
       "      <td>1602.46</td>\n",
       "      <td>1428.18</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>520.01</td>\n",
       "      <td>2388.24</td>\n",
       "      <td>8141.05</td>\n",
       "      <td>8.5646</td>\n",
       "      <td>0.03</td>\n",
       "      <td>398</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.44</td>\n",
       "      <td>22.9333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20629</th>\n",
       "      <td>100</td>\n",
       "      <td>199</td>\n",
       "      <td>-0.0011</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>643.23</td>\n",
       "      <td>1605.26</td>\n",
       "      <td>1426.53</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>519.67</td>\n",
       "      <td>2388.23</td>\n",
       "      <td>8139.29</td>\n",
       "      <td>8.5389</td>\n",
       "      <td>0.03</td>\n",
       "      <td>395</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.29</td>\n",
       "      <td>23.0640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20630</th>\n",
       "      <td>100</td>\n",
       "      <td>200</td>\n",
       "      <td>-0.0032</td>\n",
       "      <td>-0.0005</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>643.85</td>\n",
       "      <td>1600.38</td>\n",
       "      <td>1432.14</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>519.30</td>\n",
       "      <td>2388.26</td>\n",
       "      <td>8137.33</td>\n",
       "      <td>8.5036</td>\n",
       "      <td>0.03</td>\n",
       "      <td>396</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.37</td>\n",
       "      <td>23.0522</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20631 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  cycle  setting1  setting2  setting3      s1      s2       s3  \\\n",
       "0        1      1   -0.0007   -0.0004     100.0  518.67  641.82  1589.70   \n",
       "1        1      2    0.0019   -0.0003     100.0  518.67  642.15  1591.82   \n",
       "2        1      3   -0.0043    0.0003     100.0  518.67  642.35  1587.99   \n",
       "3        1      4    0.0007    0.0000     100.0  518.67  642.35  1582.79   \n",
       "4        1      5   -0.0019   -0.0002     100.0  518.67  642.37  1582.85   \n",
       "5        1      6   -0.0043   -0.0001     100.0  518.67  642.10  1584.47   \n",
       "6        1      7    0.0010    0.0001     100.0  518.67  642.48  1592.32   \n",
       "7        1      8   -0.0034    0.0003     100.0  518.67  642.56  1582.96   \n",
       "8        1      9    0.0008    0.0001     100.0  518.67  642.12  1590.98   \n",
       "9        1     10   -0.0033    0.0001     100.0  518.67  641.71  1591.24   \n",
       "10       1     11    0.0018   -0.0003     100.0  518.67  642.28  1581.75   \n",
       "11       1     12    0.0016    0.0002     100.0  518.67  642.06  1583.41   \n",
       "12       1     13   -0.0019    0.0004     100.0  518.67  643.07  1582.19   \n",
       "13       1     14    0.0009   -0.0000     100.0  518.67  642.35  1592.95   \n",
       "14       1     15   -0.0018   -0.0003     100.0  518.67  642.43  1583.82   \n",
       "15       1     16    0.0006    0.0005     100.0  518.67  642.13  1587.98   \n",
       "16       1     17    0.0002    0.0002     100.0  518.67  642.58  1584.96   \n",
       "17       1     18   -0.0031   -0.0001     100.0  518.67  642.62  1591.04   \n",
       "18       1     19    0.0032   -0.0003     100.0  518.67  641.79  1587.56   \n",
       "19       1     20   -0.0037    0.0001     100.0  518.67  643.04  1581.11   \n",
       "20       1     21   -0.0012    0.0001     100.0  518.67  642.37  1586.07   \n",
       "21       1     22    0.0002    0.0000     100.0  518.67  642.77  1592.93   \n",
       "22       1     23    0.0034   -0.0003     100.0  518.67  642.14  1588.19   \n",
       "23       1     24   -0.0010    0.0003     100.0  518.67  642.38  1590.83   \n",
       "24       1     25    0.0023   -0.0004     100.0  518.67  642.77  1594.10   \n",
       "25       1     26    0.0000    0.0002     100.0  518.67  642.16  1589.08   \n",
       "26       1     27   -0.0012   -0.0004     100.0  518.67  642.44  1590.47   \n",
       "27       1     28   -0.0024    0.0005     100.0  518.67  642.35  1582.84   \n",
       "28       1     29    0.0012   -0.0001     100.0  518.67  641.91  1584.83   \n",
       "29       1     30   -0.0022    0.0000     100.0  518.67  642.20  1593.52   \n",
       "...    ...    ...       ...       ...       ...     ...     ...      ...   \n",
       "20601  100    171   -0.0005   -0.0004     100.0  518.67  643.05  1593.56   \n",
       "20602  100    172   -0.0037    0.0001     100.0  518.67  642.97  1602.35   \n",
       "20603  100    173    0.0006    0.0004     100.0  518.67  643.40  1595.53   \n",
       "20604  100    174    0.0011    0.0002     100.0  518.67  642.91  1602.24   \n",
       "20605  100    175   -0.0013   -0.0005     100.0  518.67  642.85  1602.03   \n",
       "20606  100    176   -0.0017   -0.0003     100.0  518.67  643.33  1601.44   \n",
       "20607  100    177   -0.0011   -0.0005     100.0  518.67  643.34  1593.22   \n",
       "20608  100    178    0.0005   -0.0003     100.0  518.67  642.98  1594.80   \n",
       "20609  100    179    0.0020    0.0004     100.0  518.67  643.22  1599.36   \n",
       "20610  100    180   -0.0010    0.0001     100.0  518.67  643.64  1595.98   \n",
       "20611  100    181    0.0024   -0.0005     100.0  518.67  643.25  1597.83   \n",
       "20612  100    182    0.0007   -0.0001     100.0  518.67  643.52  1604.31   \n",
       "20613  100    183   -0.0011   -0.0002     100.0  518.67  643.34  1594.60   \n",
       "20614  100    184    0.0027   -0.0004     100.0  518.67  642.91  1598.88   \n",
       "20615  100    185   -0.0014    0.0004     100.0  518.67  643.95  1600.81   \n",
       "20616  100    186    0.0026    0.0004     100.0  518.67  643.61  1593.55   \n",
       "20617  100    187    0.0015    0.0002     100.0  518.67  643.63  1596.96   \n",
       "20618  100    188   -0.0008   -0.0002     100.0  518.67  643.19  1597.77   \n",
       "20619  100    189    0.0015    0.0001     100.0  518.67  643.69  1599.85   \n",
       "20620  100    190   -0.0001    0.0002     100.0  518.67  643.12  1594.45   \n",
       "20621  100    191   -0.0005   -0.0000     100.0  518.67  643.69  1610.87   \n",
       "20622  100    192   -0.0009    0.0001     100.0  518.67  643.53  1601.23   \n",
       "20623  100    193   -0.0001    0.0002     100.0  518.67  643.09  1599.81   \n",
       "20624  100    194   -0.0011    0.0003     100.0  518.67  643.72  1597.29   \n",
       "20625  100    195   -0.0002   -0.0001     100.0  518.67  643.41  1600.04   \n",
       "20626  100    196   -0.0004   -0.0003     100.0  518.67  643.49  1597.98   \n",
       "20627  100    197   -0.0016   -0.0005     100.0  518.67  643.54  1604.50   \n",
       "20628  100    198    0.0004    0.0000     100.0  518.67  643.42  1602.46   \n",
       "20629  100    199   -0.0011    0.0003     100.0  518.67  643.23  1605.26   \n",
       "20630  100    200   -0.0032   -0.0005     100.0  518.67  643.85  1600.38   \n",
       "\n",
       "            s4     s5   ...        s12      s13      s14     s15   s16  s17  \\\n",
       "0      1400.60  14.62   ...     521.66  2388.02  8138.62  8.4195  0.03  392   \n",
       "1      1403.14  14.62   ...     522.28  2388.07  8131.49  8.4318  0.03  392   \n",
       "2      1404.20  14.62   ...     522.42  2388.03  8133.23  8.4178  0.03  390   \n",
       "3      1401.87  14.62   ...     522.86  2388.08  8133.83  8.3682  0.03  392   \n",
       "4      1406.22  14.62   ...     522.19  2388.04  8133.80  8.4294  0.03  393   \n",
       "5      1398.37  14.62   ...     521.68  2388.03  8132.85  8.4108  0.03  391   \n",
       "6      1397.77  14.62   ...     522.32  2388.03  8132.32  8.3974  0.03  392   \n",
       "7      1400.97  14.62   ...     522.47  2388.03  8131.07  8.4076  0.03  391   \n",
       "8      1394.80  14.62   ...     521.79  2388.05  8125.69  8.3728  0.03  392   \n",
       "9      1400.46  14.62   ...     521.79  2388.06  8129.38  8.4286  0.03  393   \n",
       "10     1400.64  14.62   ...     521.40  2388.01  8140.58  8.4340  0.03  392   \n",
       "11     1400.15  14.62   ...     521.80  2388.02  8134.25  8.3938  0.03  391   \n",
       "12     1400.83  14.62   ...     521.85  2388.08  8128.10  8.4152  0.03  393   \n",
       "13     1399.16  14.62   ...     521.67  2388.00  8134.43  8.3964  0.03  393   \n",
       "14     1402.13  14.62   ...     522.50  2388.08  8127.56  8.4199  0.03  391   \n",
       "15     1404.50  14.62   ...     521.49  2388.07  8136.11  8.3936  0.03  392   \n",
       "16     1399.95  14.62   ...     521.89  2388.04  8137.27  8.4542  0.03  392   \n",
       "17     1396.12  14.62   ...     521.76  2388.07  8132.73  8.4028  0.03  392   \n",
       "18     1400.35  14.62   ...     521.89  2388.03  8129.13  8.4321  0.03  391   \n",
       "19     1405.23  14.62   ...     522.07  2388.02  8129.71  8.4210  0.03  392   \n",
       "20     1398.13  14.62   ...     522.42  2388.08  8134.02  8.4049  0.03  392   \n",
       "21     1400.57  14.62   ...     522.00  2388.03  8130.41  8.4034  0.03  392   \n",
       "22     1394.75  14.62   ...     521.52  2388.05  8127.90  8.4240  0.03  392   \n",
       "23     1398.81  14.62   ...     522.13  2388.03  8133.88  8.3891  0.03  392   \n",
       "24     1399.39  14.62   ...     522.56  2388.02  8136.61  8.3917  0.03  393   \n",
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       "20603  1418.63  14.62   ...     520.22  2388.17  8143.13  8.4856  0.03  393   \n",
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       "20610  1416.45  14.62   ...     520.80  2388.22  8138.98  8.5150  0.03  395   \n",
       "20611  1414.63  14.62   ...     520.42  2388.22  8139.30  8.5518  0.03  396   \n",
       "20612  1417.73  14.62   ...     520.05  2388.22  8140.87  8.4855  0.03  396   \n",
       "20613  1427.27  14.62   ...     520.64  2388.23  8144.21  8.5006  0.03  395   \n",
       "20614  1420.89  14.62   ...     520.05  2388.20  8142.28  8.4989  0.03  396   \n",
       "20615  1420.34  14.62   ...     520.71  2388.20  8142.32  8.4804  0.03  395   \n",
       "20616  1425.32  14.62   ...     520.66  2388.27  8138.08  8.4735  0.03  394   \n",
       "20617  1421.49  14.62   ...     520.15  2388.22  8140.49  8.5087  0.03  396   \n",
       "20618  1426.57  14.62   ...     519.98  2388.21  8139.94  8.4814  0.03  395   \n",
       "20619  1423.15  14.62   ...     519.50  2388.24  8139.78  8.4870  0.03  397   \n",
       "20620  1426.04  14.62   ...     519.52  2388.26  8142.28  8.5162  0.03  395   \n",
       "20621  1427.19  14.62   ...     519.80  2388.28  8143.56  8.5092  0.03  398   \n",
       "20622  1419.48  14.62   ...     520.59  2388.21  8143.46  8.4892  0.03  397   \n",
       "20623  1428.93  14.62   ...     520.11  2388.19  8142.02  8.5424  0.03  397   \n",
       "20624  1427.41  14.62   ...     519.55  2388.22  8139.67  8.5215  0.03  394   \n",
       "20625  1431.90  14.62   ...     519.71  2388.28  8142.90  8.5519  0.03  394   \n",
       "20626  1428.63  14.62   ...     519.49  2388.26  8137.60  8.4956  0.03  397   \n",
       "20627  1433.58  14.62   ...     519.68  2388.22  8136.50  8.5139  0.03  395   \n",
       "20628  1428.18  14.62   ...     520.01  2388.24  8141.05  8.5646  0.03  398   \n",
       "20629  1426.53  14.62   ...     519.67  2388.23  8139.29  8.5389  0.03  395   \n",
       "20630  1432.14  14.62   ...     519.30  2388.26  8137.33  8.5036  0.03  396   \n",
       "\n",
       "        s18    s19    s20      s21  \n",
       "0      2388  100.0  39.06  23.4190  \n",
       "1      2388  100.0  39.00  23.4236  \n",
       "2      2388  100.0  38.95  23.3442  \n",
       "3      2388  100.0  38.88  23.3739  \n",
       "4      2388  100.0  38.90  23.4044  \n",
       "5      2388  100.0  38.98  23.3669  \n",
       "6      2388  100.0  39.10  23.3774  \n",
       "7      2388  100.0  38.97  23.3106  \n",
       "8      2388  100.0  39.05  23.4066  \n",
       "9      2388  100.0  38.95  23.4694  \n",
       "10     2388  100.0  38.94  23.4787  \n",
       "11     2388  100.0  39.06  23.3660  \n",
       "12     2388  100.0  38.93  23.2757  \n",
       "13     2388  100.0  39.18  23.3826  \n",
       "14     2388  100.0  38.99  23.3500  \n",
       "15     2388  100.0  38.97  23.4550  \n",
       "16     2388  100.0  38.81  23.3319  \n",
       "17     2388  100.0  38.89  23.3987  \n",
       "18     2388  100.0  38.80  23.3464  \n",
       "19     2388  100.0  39.03  23.4220  \n",
       "20     2388  100.0  39.09  23.3101  \n",
       "21     2388  100.0  38.92  23.3792  \n",
       "22     2388  100.0  38.94  23.4562  \n",
       "23     2388  100.0  39.00  23.3696  \n",
       "24     2388  100.0  38.95  23.4288  \n",
       "25     2388  100.0  38.86  23.4149  \n",
       "26     2388  100.0  38.99  23.4472  \n",
       "27     2388  100.0  39.01  23.2841  \n",
       "28     2388  100.0  38.93  23.3597  \n",
       "29     2388  100.0  39.05  23.4110  \n",
       "...     ...    ...    ...      ...  \n",
       "20601  2388  100.0  38.81  23.1513  \n",
       "20602  2388  100.0  38.49  23.1922  \n",
       "20603  2388  100.0  38.42  23.1793  \n",
       "20604  2388  100.0  38.80  23.1784  \n",
       "20605  2388  100.0  38.54  23.0713  \n",
       "20606  2388  100.0  38.46  23.1020  \n",
       "20607  2388  100.0  38.60  23.0352  \n",
       "20608  2388  100.0  38.55  23.2252  \n",
       "20609  2388  100.0  38.62  23.1685  \n",
       "20610  2388  100.0  38.54  23.2345  \n",
       "20611  2388  100.0  38.52  23.1774  \n",
       "20612  2388  100.0  38.41  23.1289  \n",
       "20613  2388  100.0  38.49  23.0709  \n",
       "20614  2388  100.0  38.44  23.1229  \n",
       "20615  2388  100.0  38.60  23.2127  \n",
       "20616  2388  100.0  38.51  23.1173  \n",
       "20617  2388  100.0  38.67  23.2308  \n",
       "20618  2388  100.0  38.36  23.0552  \n",
       "20619  2388  100.0  38.65  23.0591  \n",
       "20620  2388  100.0  38.42  23.0603  \n",
       "20621  2388  100.0  38.39  23.1218  \n",
       "20622  2388  100.0  38.56  23.0770  \n",
       "20623  2388  100.0  38.47  23.0230  \n",
       "20624  2388  100.0  38.38  23.1324  \n",
       "20625  2388  100.0  38.14  23.1923  \n",
       "20626  2388  100.0  38.49  22.9735  \n",
       "20627  2388  100.0  38.30  23.1594  \n",
       "20628  2388  100.0  38.44  22.9333  \n",
       "20629  2388  100.0  38.29  23.0640  \n",
       "20630  2388  100.0  38.37  23.0522  \n",
       "\n",
       "[20631 rows x 26 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will add a regression variable TTF, or time-to-failure in order to correlate existing features with it and find out which features are more or less important."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_TTF = prepare_training_data(train_data, 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "   ttf  label_bnc  \n",
       "0  191          0  \n",
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     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "train_data_TTF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
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       "      <td>1404.20</td>\n",
       "      <td>14.62</td>\n",
       "      <td>21.61</td>\n",
       "      <td>554.26</td>\n",
       "      <td>2388.08</td>\n",
       "      <td>9052.94</td>\n",
       "      <td>1.3</td>\n",
       "      <td>...</td>\n",
       "      <td>2388.03</td>\n",
       "      <td>8133.23</td>\n",
       "      <td>8.4178</td>\n",
       "      <td>0.03</td>\n",
       "      <td>390</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.95</td>\n",
       "      <td>23.3442</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>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>21.61</td>\n",
       "      <td>554.45</td>\n",
       "      <td>2388.11</td>\n",
       "      <td>9049.48</td>\n",
       "      <td>1.3</td>\n",
       "      <td>...</td>\n",
       "      <td>2388.08</td>\n",
       "      <td>8133.83</td>\n",
       "      <td>8.3682</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.88</td>\n",
       "      <td>23.3739</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>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>21.61</td>\n",
       "      <td>554.00</td>\n",
       "      <td>2388.06</td>\n",
       "      <td>9055.15</td>\n",
       "      <td>1.3</td>\n",
       "      <td>...</td>\n",
       "      <td>2388.04</td>\n",
       "      <td>8133.80</td>\n",
       "      <td>8.4294</td>\n",
       "      <td>0.03</td>\n",
       "      <td>393</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.90</td>\n",
       "      <td>23.4044</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       s1      s2       s3       s4     s5     s6      s7       s8       s9  \\\n",
       "0  518.67  641.82  1589.70  1400.60  14.62  21.61  554.36  2388.06  9046.19   \n",
       "1  518.67  642.15  1591.82  1403.14  14.62  21.61  553.75  2388.04  9044.07   \n",
       "2  518.67  642.35  1587.99  1404.20  14.62  21.61  554.26  2388.08  9052.94   \n",
       "3  518.67  642.35  1582.79  1401.87  14.62  21.61  554.45  2388.11  9049.48   \n",
       "4  518.67  642.37  1582.85  1406.22  14.62  21.61  554.00  2388.06  9055.15   \n",
       "\n",
       "   s10 ...       s13      s14     s15   s16  s17   s18    s19    s20      s21  \\\n",
       "0  1.3 ...   2388.02  8138.62  8.4195  0.03  392  2388  100.0  39.06  23.4190   \n",
       "1  1.3 ...   2388.07  8131.49  8.4318  0.03  392  2388  100.0  39.00  23.4236   \n",
       "2  1.3 ...   2388.03  8133.23  8.4178  0.03  390  2388  100.0  38.95  23.3442   \n",
       "3  1.3 ...   2388.08  8133.83  8.3682  0.03  392  2388  100.0  38.88  23.3739   \n",
       "4  1.3 ...   2388.04  8133.80  8.4294  0.03  393  2388  100.0  38.90  23.4044   \n",
       "\n",
       "   ttf  \n",
       "0  191  \n",
       "1  190  \n",
       "2  189  \n",
       "3  188  \n",
       "4  187  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_TTF.iloc[:, 5:-1].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop columns 's18' and 's19'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_TTF = train_data_TTF.drop(columns = ['s18', 's19'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_corr_TTF = train_data_TTF.iloc[:, 5:-1].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(14, 12))\n",
    "sns.heatmap(train_corr_TTF, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 8}, yticklabels=train_corr_TTF, xticklabels=train_corr_TTF)\n",
    "plt.title('Features Correlation Heatmap')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at our data, we can see that the lowest correlation with 'ttf' is produced with features s1, s5, s10 and s16 (there is no correlation).\n",
    "\n",
    "There are a number of other features that seem to have lower correlation with 'ttf' feature, and there are pairs of features that also highly correlate with each other. This might prove to be an issues when building models that depend on these features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_corr_TTF = train_data_TTF.iloc[:, 5:-1].corr()\n",
    "c = train_corr_TTF.abs()\n",
    "s = c.unstack()\n",
    "so = s.sort_values(kind=\"quicksort\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we take a look at these pairs of correlations, it is evident that some features highly correlate with each other (>|0.8|), and we can use this information to test out our models with and without mentioned features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ttf  ttf    1.000000\n",
       "s20  s20    1.000000\n",
       "s16  s16    1.000000\n",
       "     s10    1.000000\n",
       "     s5     1.000000\n",
       "     s1     1.000000\n",
       "s15  s15    1.000000\n",
       "s14  s14    1.000000\n",
       "s13  s13    1.000000\n",
       "s12  s12    1.000000\n",
       "s11  s11    1.000000\n",
       "s10  s16    1.000000\n",
       "     s10    1.000000\n",
       "     s5     1.000000\n",
       "     s1     1.000000\n",
       "s9   s9     1.000000\n",
       "s8   s8     1.000000\n",
       "s7   s7     1.000000\n",
       "s6   s6     1.000000\n",
       "s5   s16    1.000000\n",
       "     s10    1.000000\n",
       "     s5     1.000000\n",
       "     s1     1.000000\n",
       "s4   s4     1.000000\n",
       "s3   s3     1.000000\n",
       "s2   s2     1.000000\n",
       "s1   s16    1.000000\n",
       "     s10    1.000000\n",
       "     s5     1.000000\n",
       "s17  s17    1.000000\n",
       "s1   s1     1.000000\n",
       "s21  s21    1.000000\n",
       "s14  s9     0.963157\n",
       "s9   s14    0.963157\n",
       "s12  s11    0.846884\n",
       "s11  s12    0.846884\n",
       "s4   s11    0.830136\n",
       "s11  s4     0.830136\n",
       "s13  s8     0.826084\n",
       "s8   s13    0.826084\n",
       "s7   s11    0.822805\n",
       "s11  s7     0.822805\n",
       "s4   s12    0.815591\n",
       "s12  s4     0.815591\n",
       "s7   s12    0.812713\n",
       "s12  s7     0.812713\n",
       "s7   s4     0.793130\n",
       "s4   s7     0.793130\n",
       "s13  s12    0.788441\n",
       "s12  s13    0.788441\n",
       "dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "so[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "removal_corr = ['s1', 's5', 's6', 's10', 's16', 'setting1', 'setting2', 'setting3']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Beacuse of the high correlation between pairs of sensors you could exclude these from your models and observe results in both scenarios. For now, we will leave them in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "mutual_corr = ['s9', 's8', 's7']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_TTF.drop(columns=removal_corr, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['s2', 's3', 's4', 's7', 's8', 's9', 's11', 's12', 's13', 's14', 's15',\n",
       "       's17', 's20', 's21'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_TTF.iloc[:, 2:-2].columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will be saving **selected features** in a separate list for further manipulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_features = train_data_TTF.iloc[:, :-2].columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'cycle', 's2', 's3', 's4', 's7', 's8', 's9', 's11', 's12', 's13',\n",
       "       's14', 's15', 's17', 's20', 's21'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4b. (optional) Scaling data and selecting features\n",
    "\n",
    "**[This section is meant to be used for a variety of data preparation approaches, but can still be helpful with your data]**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling data (standardizing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler(copy = True)\n",
    "scaler.fit(train_data_TTF.iloc[:, 2:-2])\n",
    "train_data_TTF_scaled = scaler.transform(train_data_TTF.iloc[:, 2:-2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.72172539, -0.13425518, -0.92593596, ..., -0.78170979,\n",
       "         1.34849274,  1.19442705],\n",
       "       [-1.06177971,  0.21152849, -0.64372587, ..., -0.78170979,\n",
       "         1.01652793,  1.23692196],\n",
       "       [-0.66181262, -0.41316559, -0.52595315, ..., -2.07309423,\n",
       "         0.73989059,  0.50342281],\n",
       "       ...,\n",
       "       [ 1.47801126,  1.94697106,  2.13837684, ...,  3.09244354,\n",
       "        -2.08181033, -3.29248147],\n",
       "       [ 1.09804254,  2.40366648,  1.95505138, ...,  1.15536688,\n",
       "        -2.91172236, -2.08507166],\n",
       "       [ 2.33794049,  1.60771161,  2.57835793, ...,  1.8010591 ,\n",
       "        -2.46910261, -2.19408035]])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_TTF_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Tree Based Feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesClassifier(bootstrap=False, ccp_alpha=0.0, class_weight=None,\n",
       "                     criterion='gini', max_depth=None, max_features='auto',\n",
       "                     max_leaf_nodes=None, max_samples=None,\n",
       "                     min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                     min_samples_leaf=1, min_samples_split=2,\n",
       "                     min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                     n_jobs=None, oob_score=False, random_state=None, verbose=0,\n",
       "                     warm_start=False)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "\n",
    "# fit an Extra Trees model to the data\n",
    "model = ExtraTreesClassifier()\n",
    "model.fit(train_data_TTF_scaled, train_data_TTF['ttf'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s2 :  0.07389661873437273\n",
      "s3 :  0.0741128346873858\n",
      "s4 :  0.07495012112675388\n",
      "s7 :  0.0741694070652344\n",
      "s8 :  0.06869296693030175\n",
      "s9 :  0.07493964026607838\n",
      "s11 :  0.07416545834300803\n",
      "s12 :  0.0737285394933295\n",
      "s13 :  0.06869149863686651\n",
      "s14 :  0.07422639997638329\n",
      "s15 :  0.07422742218020983\n",
      "s17 :  0.04787994930300891\n",
      "s20 :  0.07222025506304164\n",
      "s21 :  0.0740988881940255\n"
     ]
    }
   ],
   "source": [
    "# display the relative importance of each attribute\n",
    "for feature, importance in zip(train_data_TTF.iloc[:, 2:-2].columns, model.feature_importances_):\n",
    "    print(feature, \": \", importance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Recursive Feature Elimination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "model = LogisticRegression(max_iter = 100, n_jobs=-1)\n",
    "\n",
    "# create the RFE model and select [n] attributes\n",
    "rfe = RFE(model, 10)\n",
    "rfe = rfe.fit(train_data_TTF_scaled, train_data_TTF['ttf'])\n",
    "\n",
    "# summarize the selection of the attributes\n",
    "print(rfe.support_)\n",
    "print(rfe.ranking_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.1 Adding created features - expanding the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Index(['id', 'cycle', 'setting1', 'setting2', 'setting3', 's1', 's2', 's3',\n",
       "        's4', 's5', 's6', 's7', 's8', 's9', 's10', 's11', 's12', 's13', 's14',\n",
       "        's15', 's16', 's17', 's18', 's19', 's20', 's21'],\n",
       "       dtype='object'), (20631, 26))"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.columns, train_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting certain features (correlation graph) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will select a subset of our data by taking only a portion of features for modelling and feature extraction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_features = train_data[selected_features]\n",
    "test_data_features = test_data[selected_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20631, 16)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_features.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will add rolling windows for STD and mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_features = add_rolling_features(train_data_features, 100, selected_features)\n",
    "test_data_features = add_rolling_features(test_data_features, 100, selected_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "3   1      4  642.44  1584.12  1406.42  554.07  2388.03  9045.29  47.28   \n",
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       "[5 rows x 44 columns]"
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     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data_features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add lag features with a defined period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_features = add_lag_features(train_data_features, 10)\n",
    "test_data_features = add_lag_features(test_data_features, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
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       "4   1      5  642.37  1582.85  1406.22  554.00  2388.06  9055.15  47.28   \n",
       "\n",
       "      s12     ...       sd21_lag_1  sd21_lag_2  sd21_lag_3  sd21_lag_4  \\\n",
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       "   sd21_lag_5  sd21_lag_6  sd21_lag_7  sd21_lag_8  sd21_lag_9  sd21_lag_10  \n",
       "0         0.0         0.0         0.0         0.0         0.0          0.0  \n",
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       "[5 rows x 464 columns]"
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     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding final touches (ttf variable with classification variable) to both datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_prepared = prepare_training_data(train_data_features, 30)\n",
    "test_data_prepared = prepare_test_data(test_data_features, gt_data, 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
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     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "train_data_prepared.head()"
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  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
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     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data_prepared.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exporting data - ease of use for later on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_prepared.to_csv('train_prepared.csv', index=False)\n",
    "test_data_prepared.to_csv('test_prepared.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Creating simple regression models for our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('train_prepared.csv')\n",
    "df_test = pd.read_csv('test_prepared.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = df_train.iloc[:, 2:-2]\n",
    "y_train = df_train['ttf']\n",
    "\n",
    "X_test = df_test.iloc[:, 2:-2]\n",
    "y_test = df_test['ttf']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>s2</th>\n",
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       "      <th>s7</th>\n",
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       "      <th>s12</th>\n",
       "      <th>s13</th>\n",
       "      <th>s14</th>\n",
       "      <th>...</th>\n",
       "      <th>sd21_lag_1</th>\n",
       "      <th>sd21_lag_2</th>\n",
       "      <th>sd21_lag_3</th>\n",
       "      <th>sd21_lag_4</th>\n",
       "      <th>sd21_lag_5</th>\n",
       "      <th>sd21_lag_6</th>\n",
       "      <th>sd21_lag_7</th>\n",
       "      <th>sd21_lag_8</th>\n",
       "      <th>sd21_lag_9</th>\n",
       "      <th>sd21_lag_10</th>\n",
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       "      <th>0</th>\n",
       "      <td>642.58</td>\n",
       "      <td>1581.22</td>\n",
       "      <td>1398.91</td>\n",
       "      <td>554.42</td>\n",
       "      <td>2388.08</td>\n",
       "      <td>9056.40</td>\n",
       "      <td>47.23</td>\n",
       "      <td>521.79</td>\n",
       "      <td>2388.06</td>\n",
       "      <td>8130.11</td>\n",
       "      <td>...</td>\n",
       "      <td>0.035165</td>\n",
       "      <td>0.035409</td>\n",
       "      <td>0.035907</td>\n",
       "      <td>0.036118</td>\n",
       "      <td>0.036746</td>\n",
       "      <td>0.037156</td>\n",
       "      <td>0.037951</td>\n",
       "      <td>0.038650</td>\n",
       "      <td>0.037065</td>\n",
       "      <td>0.037952</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>642.55</td>\n",
       "      <td>1586.59</td>\n",
       "      <td>1410.83</td>\n",
       "      <td>553.52</td>\n",
       "      <td>2388.10</td>\n",
       "      <td>9044.77</td>\n",
       "      <td>47.67</td>\n",
       "      <td>521.74</td>\n",
       "      <td>2388.09</td>\n",
       "      <td>8126.90</td>\n",
       "      <td>...</td>\n",
       "      <td>0.063373</td>\n",
       "      <td>0.063964</td>\n",
       "      <td>0.064608</td>\n",
       "      <td>0.064043</td>\n",
       "      <td>0.064648</td>\n",
       "      <td>0.065186</td>\n",
       "      <td>0.065671</td>\n",
       "      <td>0.064573</td>\n",
       "      <td>0.065314</td>\n",
       "      <td>0.065680</td>\n",
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       "      <th>2</th>\n",
       "      <td>642.88</td>\n",
       "      <td>1589.75</td>\n",
       "      <td>1418.89</td>\n",
       "      <td>552.59</td>\n",
       "      <td>2388.16</td>\n",
       "      <td>9049.26</td>\n",
       "      <td>47.88</td>\n",
       "      <td>520.83</td>\n",
       "      <td>2388.14</td>\n",
       "      <td>8131.46</td>\n",
       "      <td>...</td>\n",
       "      <td>0.063087</td>\n",
       "      <td>0.062950</td>\n",
       "      <td>0.063286</td>\n",
       "      <td>0.063195</td>\n",
       "      <td>0.063229</td>\n",
       "      <td>0.062656</td>\n",
       "      <td>0.061910</td>\n",
       "      <td>0.061236</td>\n",
       "      <td>0.062448</td>\n",
       "      <td>0.063018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>642.78</td>\n",
       "      <td>1594.53</td>\n",
       "      <td>1406.88</td>\n",
       "      <td>552.64</td>\n",
       "      <td>2388.13</td>\n",
       "      <td>9051.30</td>\n",
       "      <td>47.65</td>\n",
       "      <td>521.88</td>\n",
       "      <td>2388.11</td>\n",
       "      <td>8133.64</td>\n",
       "      <td>...</td>\n",
       "      <td>0.059976</td>\n",
       "      <td>0.059927</td>\n",
       "      <td>0.059806</td>\n",
       "      <td>0.060246</td>\n",
       "      <td>0.059663</td>\n",
       "      <td>0.058224</td>\n",
       "      <td>0.058126</td>\n",
       "      <td>0.058364</td>\n",
       "      <td>0.058230</td>\n",
       "      <td>0.058497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>642.27</td>\n",
       "      <td>1589.94</td>\n",
       "      <td>1419.36</td>\n",
       "      <td>553.29</td>\n",
       "      <td>2388.10</td>\n",
       "      <td>9053.99</td>\n",
       "      <td>47.46</td>\n",
       "      <td>521.00</td>\n",
       "      <td>2388.15</td>\n",
       "      <td>8125.74</td>\n",
       "      <td>...</td>\n",
       "      <td>0.059774</td>\n",
       "      <td>0.060083</td>\n",
       "      <td>0.059992</td>\n",
       "      <td>0.059954</td>\n",
       "      <td>0.058583</td>\n",
       "      <td>0.058623</td>\n",
       "      <td>0.058615</td>\n",
       "      <td>0.058692</td>\n",
       "      <td>0.058510</td>\n",
       "      <td>0.057270</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 462 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       s2       s3       s4      s7       s8       s9    s11     s12      s13  \\\n",
       "0  642.58  1581.22  1398.91  554.42  2388.08  9056.40  47.23  521.79  2388.06   \n",
       "1  642.55  1586.59  1410.83  553.52  2388.10  9044.77  47.67  521.74  2388.09   \n",
       "2  642.88  1589.75  1418.89  552.59  2388.16  9049.26  47.88  520.83  2388.14   \n",
       "3  642.78  1594.53  1406.88  552.64  2388.13  9051.30  47.65  521.88  2388.11   \n",
       "4  642.27  1589.94  1419.36  553.29  2388.10  9053.99  47.46  521.00  2388.15   \n",
       "\n",
       "       s14     ...       sd21_lag_1  sd21_lag_2  sd21_lag_3  sd21_lag_4  \\\n",
       "0  8130.11     ...         0.035165    0.035409    0.035907    0.036118   \n",
       "1  8126.90     ...         0.063373    0.063964    0.064608    0.064043   \n",
       "2  8131.46     ...         0.063087    0.062950    0.063286    0.063195   \n",
       "3  8133.64     ...         0.059976    0.059927    0.059806    0.060246   \n",
       "4  8125.74     ...         0.059774    0.060083    0.059992    0.059954   \n",
       "\n",
       "   sd21_lag_5  sd21_lag_6  sd21_lag_7  sd21_lag_8  sd21_lag_9  sd21_lag_10  \n",
       "0    0.036746    0.037156    0.037951    0.038650    0.037065     0.037952  \n",
       "1    0.064648    0.065186    0.065671    0.064573    0.065314     0.065680  \n",
       "2    0.063229    0.062656    0.061910    0.061236    0.062448     0.063018  \n",
       "3    0.059663    0.058224    0.058126    0.058364    0.058230     0.058497  \n",
       "4    0.058583    0.058623    0.058615    0.058692    0.058510     0.057270  \n",
       "\n",
       "[5 rows x 462 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### 6.1 (optional) Normalization - use only one"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### 6.1.1 MinMax normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_train_scaled = pd.DataFrame(X_train_scaled, columns= X_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "X_test_scaled = scaler.fit_transform(X_test)\n",
    "X_test_scaled = pd.DataFrame(X_test_scaled, columns= X_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "X_test_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### 6.1.2. Standard scale normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler(copy = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "X_train_scaled = scaler.fit_transform(df_train.iloc[:, 2:-2])\n",
    "X_train_scaled = pd.DataFrame(X_train_scaled, columns= X_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "X_test_scaled = scaler.fit_transform(df_test.iloc[:, 2:-2])\n",
    "X_test_scaled = pd.DataFrame(X_test_scaled, columns= X_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "X_test_scaled.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2 Load original data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_orig = pd.read_csv('train_data_original.csv')\n",
    "test_data_orig = pd.read_csv('test_data_original.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train_data_orig = prepare_training_data(train_data, 30)\n",
    "#test_data_orig = prepare_test_data(test_data, gt_data, 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_original = train_data_orig.iloc[:, 2:-2]\n",
    "y_train_original = train_data_orig['ttf']\n",
    "\n",
    "X_test_original = test_data_orig.iloc[:, 2:-2]\n",
    "y_test_original = test_data_orig['ttf']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see again that original data has 24 colums, while our newly generated dataset with features has quite a lot more columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24, 462)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_original.shape[1], X_train.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((100, 24), (100, 462))"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_original.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    191\n",
       "1    190\n",
       "2    189\n",
       "3    188\n",
       "4    187\n",
       "Name: ttf, dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.3 Define regression metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to measure how good is the model performance, we will use several known regression metrics such as:\n",
    "RMSE, MAE, R^2, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_regression_metrics(model, actual, predicted):\n",
    "\n",
    "    x= np.mean(np.abs((actual - predicted) / actual)) * 100\n",
    "    regr_metrics = {\n",
    "                        'Root Mean Squared Error' : metrics.mean_squared_error(actual, predicted)**0.5,\n",
    "                        'Mean Absolute Error' : metrics.mean_absolute_error(actual, predicted),\n",
    "                        'R^2' : metrics.r2_score(actual, predicted),\n",
    "                        'Explained Variance' : metrics.explained_variance_score(actual, predicted),\n",
    "                        'Max Error' : metrics.max_error(actual, predicted),\n",
    "                        'Mean absolute percentage error': x\n",
    "               \n",
    "                   }\n",
    "\n",
    "    #return reg_metrics\n",
    "    df_regr_metrics = pd.DataFrame.from_dict(regr_metrics, orient='index')\n",
    "    df_regr_metrics.columns = [model]\n",
    "    return df_regr_metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://www.statisticshowto.datasciencecentral.com/wp-content/uploads/2015/03/residual.png\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Root Mean Square Error (RMSE) is the standard deviation of the residuals (prediction errors). Residuals are a measure of how far from the regression line data points are; RMSE is a measure of how spread out these residuals are. In other words, it tells you how concentrated the data is around the line of best fit. Root mean square error is commonly used in climatology, forecasting, and regression analysis to verify experimental results.*\n",
    "\n",
    "<a href=\"https://www.statisticshowto.datasciencecentral.com/rmse/\">Source</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.4 Linear Regression model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To test the simples model, we will try to fit linear regression on our data and compare the performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "linreg = linear_model.LinearRegression()\n",
    "linereg_orig = linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linreg.fit(X_train, y_train)\n",
    "linereg_orig.fit(X_train_original, y_train_original)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_predict = linreg.predict(X_test)\n",
    "y_train_predict = linreg.predict(X_train)\n",
    "\n",
    "y_test_predict_orig = linereg_orig.predict(X_test_original)\n",
    "y_train_predict_orig = linereg_orig.predict(X_train_original)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "linreg_metrics = get_regression_metrics('Linear Regression with features', y_test, y_test_predict)\n",
    "linreg_metrics_orig = get_regression_metrics('Linear Regression original', y_test_original, y_test_predict_orig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>Linear Regression with features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Root Mean Squared Error</th>\n",
       "      <td>27.127608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean Absolute Error</th>\n",
       "      <td>22.166660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R^2</th>\n",
       "      <td>0.573849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Explained Variance</th>\n",
       "      <td>0.701903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max Error</th>\n",
       "      <td>77.623348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean absolute percentage error</th>\n",
       "      <td>57.922984</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Linear Regression with features\n",
       "Root Mean Squared Error                               27.127608\n",
       "Mean Absolute Error                                   22.166660\n",
       "R^2                                                    0.573849\n",
       "Explained Variance                                     0.701903\n",
       "Max Error                                             77.623348\n",
       "Mean absolute percentage error                        57.922984"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linreg_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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>Linear Regression original</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Root Mean Squared Error</th>\n",
       "      <td>32.041095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean Absolute Error</th>\n",
       "      <td>25.591780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R^2</th>\n",
       "      <td>0.405495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Explained Variance</th>\n",
       "      <td>0.665297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max Error</th>\n",
       "      <td>78.745622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean absolute percentage error</th>\n",
       "      <td>55.357098</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Linear Regression original\n",
       "Root Mean Squared Error                          32.041095\n",
       "Mean Absolute Error                              25.591780\n",
       "R^2                                               0.405495\n",
       "Explained Variance                                0.665297\n",
       "Max Error                                        78.745622\n",
       "Mean absolute percentage error                   55.357098"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linreg_metrics_orig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.5 XGBoost model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try out a different model using decision trees and gradient boosting - decision trees are immune to multi-collinearity, so you could test your models with features that we filtered out beforehand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/redshift/anaconda3/envs/jnb/lib/python3.6/site-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  if getattr(data, 'base', None) is not None and \\\n",
      "/home/redshift/anaconda3/envs/jnb/lib/python3.6/site-packages/xgboost/core.py:588: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  data.base is not None and isinstance(data, np.ndarray) \\\n"
     ]
    }
   ],
   "source": [
    "data_matrix = xgb.DMatrix(data=X_train, label= y_train)\n",
    "data_matrix_orig = xgb.DMatrix(data = X_train_original, label = y_train_original)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold, GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "xg_reg = xgb.XGBRegressor(objective ='reg:squarederror', \n",
    "                          colsample_bytree = 0.3, \n",
    "                          learning_rate = 0.1,\n",
    "                           max_depth = 5, \n",
    "                          alpha = 10, \n",
    "                          n_estimators = 10, \n",
    "                          n_jobs = -1, \n",
    "                          tree_method='gpu_hist')\n",
    "\n",
    "#if you want to run GridSearchCV, you can initialize a \"clean\" XGBRegressor instance\n",
    "#xg_reg = xgb.XGBRegressor(objective ='reg:squarederror')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "xg_reg_orig = xgb.XGBRegressor(objective ='reg:squarederror', \n",
    "                               colsample_bytree = 0.3, \n",
    "                               learning_rate = 0.1,\n",
    "                               max_depth = 5, \n",
    "                               alpha = 10,\n",
    "                               n_estimators = 10,\n",
    "                               n_jobs = -1, \n",
    "                               tree_method='gpu_hist')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### 6.5.1 Parameter grid search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "params = {\n",
    "        'min_child_weight': [1, 5, 10],\n",
    "        'gamma': [0.5, 1, 5],\n",
    "        'subsample': [0.8, 1.0],\n",
    "        'colsample_bytree': [0.3, 0.8, 1.0],\n",
    "        'max_depth': [5, 6, 8],\n",
    "        'n_estimators':[10,15,20],\n",
    "        'learning_rate': [0.1, 0.01, 0.15]\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "folds = 5\n",
    "\n",
    "skf = StratifiedKFold(n_splits=folds, shuffle = True, random_state = 999)\n",
    "\n",
    "random_search = GridSearchCV(xg_reg, \n",
    "                             param_grid=params, \n",
    "                             scoring='neg_root_mean_squared_error', \n",
    "                             cv=skf.split(X_train, y_train), \n",
    "                             verbose=1,\n",
    "                            n_jobs=-1)\n",
    "\n",
    "random_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "print(random_search.best_estimator_)\n",
    "print(random_search.best_score_)\n",
    "print(random_search.best_index_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "random_search.cv_results_['params'][random_search.best_index_]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "pd.DataFrame(random_search.cv_results_).to_csv('grid_search_params.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "xg_reg.fit(X_train, y_train)\n",
    "preds = xg_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "xg_reg_orig.fit(X_train_original,  y_train_original)\n",
    "preds_orig = xg_reg_orig.predict(X_test_original)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "#preds = random_search.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model with extra features RMSE: 23.480178\n",
      "original features RMSE: 24.387860\n"
     ]
    }
   ],
   "source": [
    "rmse = np.sqrt(mean_squared_error(y_test, preds))\n",
    "rmse_orig = np.sqrt(mean_squared_error(y_test_original, preds_orig))\n",
    "\n",
    "print(\"model with extra features RMSE: %f\" % (rmse))\n",
    "print(\"original features RMSE: %f\" % (rmse_orig))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.6 Let's combine the models - ensemble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_pred = (preds + y_test_predict)/2\n",
    "combined_pred_original = (preds_orig + y_test_predict_orig)/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "rmse_combined = np.sqrt(mean_squared_error(y_test, combined_pred))\n",
    "rmse_combined_original = np.sqrt(mean_squared_error(y_test_original, combined_pred_original))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "combined RMSE: 15.531822\n",
      "combined original RMSE: 20.635277\n"
     ]
    }
   ],
   "source": [
    "print(\"combined RMSE: %f\" % (rmse_combined))\n",
    "print(\"combined original RMSE: %f\" % (rmse_combined_original))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "from seaborn import regplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.subplots(figsize=(14,14))\n",
    "axes = plt.gca()\n",
    "axes.set_ylim([0,150])\n",
    "axes.set_xlim([0,150])\n",
    "\n",
    "plt.scatter(y_test, y_test_predict, label='Linear regression', color=colors[0])\n",
    "plt.scatter(y_test, preds, label='XGBoost', color='purple')\n",
    "plt.scatter(y_test, combined_pred, label='combined with features', color=colors[2], s=100, marker='x')\n",
    "\n",
    "sns.regplot(y_test, y_test, color='black', scatter=False)\n",
    "\n",
    "plt.legend()\n",
    "plt.title('Models with added features')\n",
    "plt.xlabel('y_true')\n",
    "plt.ylabel('y_true')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(14,14))\n",
    "axes = plt.gca()\n",
    "axes.set_ylim([0,150])\n",
    "axes.set_xlim([0,150])\n",
    "\n",
    "plt.scatter(y_test_original, y_test_predict_orig, label='Linear regression', color=colors[0])\n",
    "plt.scatter(y_test_original, preds_orig, label='XGBoost', color='purple')\n",
    "plt.scatter(y_test_original, combined_pred_original, label='combined original', color=colors[2], s=100, marker='x')\n",
    "\n",
    "sns.regplot(y_test_original, y_test_original, color='black', scatter=False)\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel('y_true')\n",
    "plt.ylabel('y_true')\n",
    "plt.title('Models without added features')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(14,14))\n",
    "axes = plt.gca()\n",
    "axes.set_ylim([0,150])\n",
    "axes.set_xlim([0,150])\n",
    "\n",
    "plt.scatter(y_test, combined_pred, label='combined with features', color=colors[2], s=100, marker='x')\n",
    "plt.scatter(y_test_original, combined_pred_original, label='combined original', color=colors[0], s=100, marker='x')\n",
    "\n",
    "sns.regplot(y_test_original, y_test_original, color='black', scatter=False)\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel('y_true')\n",
    "plt.ylabel('y_true')\n",
    "plt.title('Combined models')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "file_extension": ".py",
  "hide_input": false,
  "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": "3.6.5"
  },
  "mimetype": "text/x-python",
  "name": "python",
  "npconvert_exporter": "python",
  "pygments_lexer": "ipython3",
  "version": 3
 },
 "nbformat": 4,
 "nbformat_minor": 2
}