SurvivalAnalysis-Maintenance.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d9dd96d1-4d39-4681-b95c-f91dfb2e1690",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(rc = {'figure.figsize':(16,9)})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8e8890a",
   "metadata": {},
   "source": [
    "# Import Data and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a9510696-e0ce-4882-a974-48d1fa2a2334",
   "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>lifetime</th>\n",
       "      <th>broken</th>\n",
       "      <th>pressureInd</th>\n",
       "      <th>moistureInd</th>\n",
       "      <th>temperatureInd</th>\n",
       "      <th>team</th>\n",
       "      <th>provider</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>92.178854</td>\n",
       "      <td>104.230204</td>\n",
       "      <td>96.517159</td>\n",
       "      <td>TeamA</td>\n",
       "      <td>Provider4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>72.075938</td>\n",
       "      <td>103.065701</td>\n",
       "      <td>87.271062</td>\n",
       "      <td>TeamC</td>\n",
       "      <td>Provider4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>96.272254</td>\n",
       "      <td>77.801376</td>\n",
       "      <td>112.196170</td>\n",
       "      <td>TeamA</td>\n",
       "      <td>Provider1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>86</td>\n",
       "      <td>1</td>\n",
       "      <td>94.406461</td>\n",
       "      <td>108.493608</td>\n",
       "      <td>72.025374</td>\n",
       "      <td>TeamC</td>\n",
       "      <td>Provider2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>97.752899</td>\n",
       "      <td>99.413492</td>\n",
       "      <td>103.756271</td>\n",
       "      <td>TeamB</td>\n",
       "      <td>Provider1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   lifetime  broken  pressureInd  moistureInd  temperatureInd   team  \\\n",
       "0        56       0    92.178854   104.230204       96.517159  TeamA   \n",
       "1        81       1    72.075938   103.065701       87.271062  TeamC   \n",
       "2        60       0    96.272254    77.801376      112.196170  TeamA   \n",
       "3        86       1    94.406461   108.493608       72.025374  TeamC   \n",
       "4        34       0    97.752899    99.413492      103.756271  TeamB   \n",
       "\n",
       "    provider  \n",
       "0  Provider4  \n",
       "1  Provider4  \n",
       "2  Provider1  \n",
       "3  Provider2  \n",
       "4  Provider1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maintenance_df = pd.read_csv(\"dataset/maintenance.csv\", sep = \";\")\n",
    "maintenance_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e0a3f56c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 7 columns):\n",
      " #   Column          Non-Null Count  Dtype   \n",
      "---  ------          --------------  -----   \n",
      " 0   lifetime        1000 non-null   int64   \n",
      " 1   broken          1000 non-null   bool    \n",
      " 2   pressureInd     1000 non-null   float64 \n",
      " 3   moistureInd     1000 non-null   float64 \n",
      " 4   temperatureInd  1000 non-null   float64 \n",
      " 5   team            1000 non-null   category\n",
      " 6   provider        1000 non-null   category\n",
      "dtypes: bool(1), category(2), float64(3), int64(1)\n",
      "memory usage: 34.6 KB\n"
     ]
    }
   ],
   "source": [
    "maintenance_df[['team', 'provider']] = maintenance_df[['team', 'provider']].astype('category')\n",
    "maintenance_df['broken'] = maintenance_df['broken'].astype('bool')\n",
    "maintenance_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c599052f",
   "metadata": {},
   "source": [
    "# Correlation Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "424b4d90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(maintenance_df.corr(), annot = True,)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33470c55",
   "metadata": {},
   "source": [
    "> Tidak ada korelasi yang tinggi antar variabel"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "402db89a",
   "metadata": {},
   "source": [
    "# Data Preparation for Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7e944823",
   "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>pressureInd</th>\n",
       "      <th>moistureInd</th>\n",
       "      <th>temperatureInd</th>\n",
       "      <th>team=TeamB</th>\n",
       "      <th>team=TeamC</th>\n",
       "      <th>provider=Provider2</th>\n",
       "      <th>provider=Provider3</th>\n",
       "      <th>provider=Provider4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>92.178854</td>\n",
       "      <td>104.230204</td>\n",
       "      <td>96.517159</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>72.075938</td>\n",
       "      <td>103.065701</td>\n",
       "      <td>87.271062</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>96.272254</td>\n",
       "      <td>77.801376</td>\n",
       "      <td>112.196170</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>94.406461</td>\n",
       "      <td>108.493608</td>\n",
       "      <td>72.025374</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>97.752899</td>\n",
       "      <td>99.413492</td>\n",
       "      <td>103.756271</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pressureInd  moistureInd  temperatureInd  team=TeamB  team=TeamC  \\\n",
       "0    92.178854   104.230204       96.517159         0.0         0.0   \n",
       "1    72.075938   103.065701       87.271062         0.0         1.0   \n",
       "2    96.272254    77.801376      112.196170         0.0         0.0   \n",
       "3    94.406461   108.493608       72.025374         0.0         1.0   \n",
       "4    97.752899    99.413492      103.756271         1.0         0.0   \n",
       "\n",
       "   provider=Provider2  provider=Provider3  provider=Provider4  \n",
       "0                 0.0                 0.0                 1.0  \n",
       "1                 0.0                 0.0                 1.0  \n",
       "2                 0.0                 0.0                 0.0  \n",
       "3                 1.0                 0.0                 0.0  \n",
       "4                 0.0                 0.0                 0.0  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# One Hot Encoding for Categorical Variable\n",
    "from sksurv.preprocessing import OneHotEncoder\n",
    "\n",
    "data_x = OneHotEncoder().fit_transform(maintenance_df.iloc[:, 2:])\n",
    "data_x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "715dc0ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([(False, 56.), ( True, 81.), (False, 60.), ( True, 86.),\n",
       "       (False, 34.), (False, 30.), (False, 68.), ( True, 65.),\n",
       "       (False, 23.), ( True, 81.), (False, 38.), (False, 29.),\n",
       "       ( True, 65.), ( True, 65.), ( True, 82.), ( True, 80.),\n",
       "       (False, 48.), ( True, 80.), ( True, 92.), ( True, 88.),\n",
       "       ( True, 74.), (False, 65.), (False, 61.), (False, 35.),\n",
       "       (False, 26.), (False, 63.), ( True, 88.), ( True, 79.),\n",
       "       (False, 53.), ( True, 73.), ( True, 60.), (False, 13.),\n",
       "       (False, 34.), (False, 36.), ( True, 80.), ( True, 65.),\n",
       "       (False, 31.), ( True, 74.), (False, 25.), (False, 58.),\n",
       "       ( True, 80.), ( True, 65.), (False, 19.), ( True, 60.),\n",
       "       (False, 84.), (False, 13.), (False, 12.), (False, 15.),\n",
       "       (False, 26.), (False, 43.), ( True, 80.), ( True, 65.),\n",
       "       (False,  1.), (False, 34.), ( True, 80.), (False, 12.),\n",
       "       ( True, 92.), ( True, 82.), ( True, 65.), (False, 20.),\n",
       "       (False, 16.), (False,  3.), (False, 18.), (False,  7.),\n",
       "       (False, 47.), (False, 23.), (False, 39.), (False, 48.),\n",
       "       (False, 57.), (False, 68.), (False, 18.), (False,  1.),\n",
       "       ( True, 81.), (False, 65.), (False,  4.), (False, 24.),\n",
       "       ( True, 74.), ( True, 74.), (False, 16.), (False, 88.),\n",
       "       (False, 28.), ( True, 92.), (False, 49.), (False, 39.),\n",
       "       (False, 18.), (False, 80.), ( True, 81.), ( True, 65.),\n",
       "       ( True, 79.), (False, 19.), (False, 58.), (False, 76.),\n",
       "       ( True, 92.), ( True, 92.), (False, 52.), (False, 29.),\n",
       "       ( True, 65.), (False,  8.), (False, 53.), (False, 40.),\n",
       "       (False, 46.), (False,  5.), (False, 41.), (False, 12.),\n",
       "       ( True, 65.), ( True, 80.), ( True, 93.), (False, 63.),\n",
       "       (False, 77.), ( True, 65.), ( True, 92.), (False, 62.),\n",
       "       (False, 52.), ( True, 85.), ( True, 92.), ( True, 92.),\n",
       "       ( True, 88.), ( True, 80.), (False, 58.), (False, 55.),\n",
       "       (False, 33.), (False, 13.), (False, 34.), ( True, 85.),\n",
       "       ( True, 65.), (False, 80.), (False, 41.), ( True, 65.),\n",
       "       ( True, 65.), (False, 43.), ( True, 80.), ( True, 80.),\n",
       "       (False, 55.), ( True, 88.), (False, 17.), ( True, 86.),\n",
       "       (False,  3.), (False, 81.), ( True, 93.), ( True, 65.),\n",
       "       (False, 55.), (False, 25.), ( True, 93.), ( True, 82.),\n",
       "       (False, 13.), (False, 19.), ( True, 86.), ( True, 60.),\n",
       "       ( True, 81.), ( True, 80.), ( True, 93.), (False, 43.),\n",
       "       (False, 63.), (False, 45.), ( True, 93.), (False, 77.),\n",
       "       (False,  9.), ( True, 60.), (False, 62.), ( True, 82.),\n",
       "       (False, 29.), ( True, 65.), (False, 49.), (False, 72.),\n",
       "       (False, 16.), (False, 56.), ( True, 80.), (False, 40.),\n",
       "       (False, 57.), ( True, 88.), ( True, 93.), (False, 20.),\n",
       "       (False, 12.), (False, 57.), ( True, 65.), (False, 50.),\n",
       "       (False, 42.), (False, 49.), (False, 65.), ( True, 65.),\n",
       "       (False, 44.), ( True, 92.), (False,  9.), (False, 48.),\n",
       "       (False, 49.), ( True, 65.), (False, 36.), ( True, 60.),\n",
       "       (False, 81.), (False, 24.), ( True, 60.), ( True, 65.),\n",
       "       (False, 63.), ( True, 93.), ( True, 92.), ( True, 65.),\n",
       "       (False, 28.), (False, 25.), ( True, 74.), (False, 20.),\n",
       "       ( True, 93.), (False, 57.), (False, 46.), ( True, 60.),\n",
       "       (False, 56.), (False,  1.), (False, 76.), (False, 31.),\n",
       "       (False, 54.), (False,  1.), (False, 64.), (False, 53.),\n",
       "       (False, 62.), (False, 12.), (False, 76.), (False, 53.),\n",
       "       ( True, 86.), (False, 29.), ( True, 80.), ( True, 65.),\n",
       "       (False, 60.), ( True, 65.), (False, 88.), (False, 47.),\n",
       "       (False,  3.), ( True, 65.), (False, 19.), ( True, 65.),\n",
       "       (False, 50.), (False, 30.), (False, 58.), ( True, 82.),\n",
       "       (False, 26.), ( True, 93.), ( True, 80.), (False, 64.),\n",
       "       (False, 46.), ( True, 80.), (False, 58.), (False, 25.),\n",
       "       ( True, 80.), (False, 27.), ( True, 93.), (False,  8.),\n",
       "       (False, 24.), ( True, 80.), (False, 48.), (False, 52.),\n",
       "       ( True, 65.), ( True, 74.), (False, 24.), ( True, 85.),\n",
       "       ( True, 93.), (False, 16.), (False, 13.), (False, 22.),\n",
       "       ( True, 86.), (False, 58.), ( True, 60.), ( True, 93.),\n",
       "       ( True, 88.), ( True, 86.), (False, 62.), ( True, 92.),\n",
       "       (False, 65.), ( True, 88.), ( True, 81.), (False, 22.),\n",
       "       (False, 45.), (False, 59.), (False, 35.), (False, 18.),\n",
       "       (False, 38.), ( True, 81.), ( True, 92.), ( True, 80.),\n",
       "       ( True, 61.), (False,  4.), (False, 48.), (False, 63.),\n",
       "       (False, 31.), ( True, 65.), ( True, 65.), ( True, 93.),\n",
       "       ( True, 81.), ( True, 65.), (False, 13.), (False, 35.),\n",
       "       ( True, 92.), ( True, 65.), ( True, 85.), ( True, 73.),\n",
       "       (False, 54.), (False, 56.), (False, 61.), (False, 12.),\n",
       "       (False, 26.), ( True, 60.), (False, 17.), ( True, 65.),\n",
       "       (False, 18.), (False, 35.), ( True, 60.), (False, 49.),\n",
       "       ( True, 85.), (False, 30.), ( True, 81.), ( True, 66.),\n",
       "       (False, 83.), (False, 14.), ( True, 80.), ( True, 79.),\n",
       "       ( True, 79.), (False,  7.), (False, 80.), (False, 44.),\n",
       "       (False, 46.), ( True, 85.), ( True, 81.), (False,  5.),\n",
       "       (False, 51.), (False, 49.), (False, 51.), ( True, 80.),\n",
       "       (False,  4.), (False, 55.), (False, 66.), (False,  9.),\n",
       "       (False, 76.), (False, 65.), (False, 71.), (False, 85.),\n",
       "       (False, 21.), (False, 45.), ( True, 74.), (False, 78.),\n",
       "       (False, 56.), ( True, 60.), (False,  6.), (False, 50.),\n",
       "       (False, 28.), ( True, 93.), (False, 69.), (False, 28.),\n",
       "       (False, 46.), (False, 24.), (False, 61.), (False, 77.),\n",
       "       (False, 54.), (False, 22.), (False, 63.), ( True, 81.),\n",
       "       ( True, 65.), ( True, 81.), ( True, 74.), (False, 66.),\n",
       "       (False, 85.), (False, 30.), ( True, 80.), ( True, 80.),\n",
       "       (False, 63.), (False, 19.), (False, 54.), (False, 40.),\n",
       "       (False, 35.), (False, 29.), ( True, 89.), ( True, 80.),\n",
       "       (False, 33.), (False, 81.), ( True, 80.), ( True, 88.),\n",
       "       (False, 77.), (False,  8.), (False,  2.), (False, 14.),\n",
       "       ( True, 80.), ( True, 74.), (False, 38.), ( True, 65.),\n",
       "       ( True, 79.), (False, 41.), (False, 13.), (False, 42.),\n",
       "       ( True, 92.), ( True, 92.), ( True, 65.), (False, 34.),\n",
       "       ( True, 65.), ( True, 65.), ( True, 65.), ( True, 80.),\n",
       "       (False, 61.), (False, 45.), (False, 33.), (False, 55.),\n",
       "       ( True, 86.), (False,  2.), ( True, 80.), (False, 61.),\n",
       "       (False, 62.), (False, 23.), (False, 60.), ( True, 73.),\n",
       "       ( True, 92.), (False, 50.), ( True, 92.), (False, 46.),\n",
       "       ( True, 79.), (False, 67.), (False, 54.), (False, 27.),\n",
       "       ( True, 93.), (False, 81.), ( True, 86.), (False,  6.),\n",
       "       (False, 58.), (False, 41.), (False, 43.), (False, 50.),\n",
       "       (False, 56.), (False, 72.), ( True, 93.), ( True, 86.),\n",
       "       (False,  5.), (False, 67.), ( True, 85.), (False, 83.),\n",
       "       (False, 88.), (False, 33.), (False, 82.), ( True, 93.),\n",
       "       (False, 12.), (False, 20.), ( True, 92.), (False, 69.),\n",
       "       (False, 52.), (False, 55.), (False, 72.), ( True, 65.),\n",
       "       (False, 27.), ( True, 80.), (False, 74.), (False, 66.),\n",
       "       (False,  7.), ( True, 79.), ( True, 92.), ( True, 73.),\n",
       "       (False, 53.), ( True, 88.), ( True, 81.), ( True, 93.),\n",
       "       ( True, 80.), ( True, 60.), (False, 87.), (False, 51.),\n",
       "       (False, 38.), ( True, 88.), ( True, 93.), (False, 49.),\n",
       "       (False, 58.), ( True, 65.), ( True, 88.), (False, 57.),\n",
       "       (False,  1.), (False, 74.), (False, 49.), ( True, 92.),\n",
       "       ( True, 74.), ( True, 65.), (False, 18.), (False,  2.),\n",
       "       (False, 84.), ( True, 93.), (False, 21.), (False, 16.),\n",
       "       (False, 34.), ( True, 88.), ( True, 81.), ( True, 88.),\n",
       "       ( True, 60.), (False, 35.), ( True, 74.), (False, 12.),\n",
       "       ( True, 60.), (False, 11.), (False, 48.), (False, 62.),\n",
       "       (False, 59.), ( True, 93.), (False,  5.), (False, 59.),\n",
       "       (False, 55.), ( True, 79.), (False, 11.), (False, 18.),\n",
       "       (False, 55.), ( True, 93.), ( True, 81.), ( True, 80.),\n",
       "       ( True, 80.), ( True, 74.), ( True, 65.), ( True, 93.),\n",
       "       ( True, 79.), (False, 23.), (False, 34.), ( True, 85.),\n",
       "       (False, 52.), ( True, 65.), (False, 71.), (False, 60.),\n",
       "       (False, 38.), (False, 66.), (False, 39.), (False, 74.),\n",
       "       (False, 55.), (False, 54.), (False, 59.), (False, 27.),\n",
       "       (False, 34.), (False,  7.), (False, 20.), ( True, 88.),\n",
       "       (False, 41.), (False,  8.), (False, 31.), (False, 36.),\n",
       "       ( True, 92.), ( True, 81.), (False, 23.), (False, 86.),\n",
       "       ( True, 88.), (False, 34.), (False, 25.), (False, 60.),\n",
       "       (False, 13.), ( True, 65.), (False, 42.), ( True, 74.),\n",
       "       (False, 16.), (False, 56.), (False,  2.), (False, 40.),\n",
       "       (False,  8.), ( True, 93.), ( True, 74.), (False, 53.),\n",
       "       (False, 12.), ( True, 65.), ( True, 92.), (False, 34.),\n",
       "       ( True, 92.), ( True, 93.), ( True, 80.), (False, 44.),\n",
       "       ( True, 60.), (False, 35.), (False, 22.), (False, 46.),\n",
       "       (False,  8.), (False, 71.), (False, 85.), (False, 40.),\n",
       "       ( True, 92.), (False,  9.), (False, 47.), (False, 53.),\n",
       "       (False, 16.), (False, 17.), ( True, 65.), ( True, 88.),\n",
       "       (False, 19.), (False, 77.), ( True, 65.), (False, 63.),\n",
       "       (False, 47.), (False, 10.), ( True, 80.), ( True, 74.),\n",
       "       (False, 53.), ( True, 92.), (False, 55.), (False, 48.),\n",
       "       ( True, 85.), ( True, 65.), (False, 61.), (False, 32.),\n",
       "       ( True, 86.), ( True, 60.), (False, 85.), (False, 18.),\n",
       "       ( True, 81.), ( True, 81.), ( True, 80.), (False, 78.),\n",
       "       (False, 39.), ( True, 88.), ( True, 79.), (False, 28.),\n",
       "       ( True, 86.), ( True, 74.), ( True, 81.), (False, 34.),\n",
       "       ( True, 80.), (False,  2.), ( True, 80.), (False, 49.),\n",
       "       (False, 73.), (False, 12.), (False, 18.), ( True, 88.),\n",
       "       (False, 60.), (False, 15.), (False, 44.), (False, 30.),\n",
       "       (False, 12.), (False, 80.), ( True, 60.), (False, 77.),\n",
       "       (False,  9.), (False, 17.), ( True, 74.), ( True, 92.),\n",
       "       ( True, 85.), ( True, 80.), (False, 61.), (False, 38.),\n",
       "       (False, 48.), ( True, 65.), (False, 55.), ( True, 88.),\n",
       "       (False, 80.), ( True, 86.), (False, 61.), (False, 77.),\n",
       "       (False, 61.), ( True, 80.), ( True, 85.), (False, 71.),\n",
       "       (False, 35.), (False,  7.), (False, 10.), ( True, 80.),\n",
       "       ( True, 60.), ( True, 80.), ( True, 60.), ( True, 65.),\n",
       "       (False, 46.), ( True, 93.), (False, 50.), (False, 28.),\n",
       "       ( True, 92.), (False, 51.), (False, 12.), (False,  4.),\n",
       "       ( True, 65.), (False, 53.), (False, 35.), (False, 20.),\n",
       "       (False, 60.), (False, 56.), (False, 72.), ( True, 60.),\n",
       "       (False, 37.), ( True, 80.), (False, 10.), ( True, 60.),\n",
       "       (False,  9.), (False,  9.), (False, 32.), (False, 31.),\n",
       "       ( True, 65.), ( True, 65.), ( True, 86.), ( True, 73.),\n",
       "       ( True, 60.), ( True, 80.), (False, 58.), (False, 32.),\n",
       "       ( True, 79.), (False, 24.), ( True, 65.), (False, 71.),\n",
       "       (False, 56.), ( True, 79.), (False, 56.), ( True, 65.),\n",
       "       (False, 46.), ( True, 80.), (False, 49.), (False,  2.),\n",
       "       ( True, 85.), ( True, 73.), (False, 24.), (False, 67.),\n",
       "       ( True, 88.), ( True, 81.), (False, 42.), (False,  5.),\n",
       "       ( True, 88.), (False, 31.), ( True, 73.), ( True, 88.),\n",
       "       ( True, 80.), (False, 20.), (False, 16.), (False,  4.),\n",
       "       (False,  7.), (False, 56.), (False, 41.), (False, 45.),\n",
       "       (False, 62.), (False, 26.), (False,  8.), (False, 19.),\n",
       "       ( True, 88.), (False, 79.), (False,  2.), (False, 90.),\n",
       "       (False,  5.), ( True, 88.), (False, 18.), (False, 57.),\n",
       "       (False, 72.), ( True, 80.), ( True, 60.), ( True, 80.),\n",
       "       ( True, 65.), (False, 42.), ( True, 93.), (False, 38.),\n",
       "       ( True, 60.), (False, 62.), ( True, 65.), (False, 42.),\n",
       "       ( True, 65.), (False, 26.), (False, 35.), (False, 17.),\n",
       "       (False,  2.), (False, 16.), (False, 24.), (False, 59.),\n",
       "       ( True, 79.), (False, 37.), ( True, 93.), (False, 26.),\n",
       "       (False, 27.), ( True, 65.), (False, 52.), (False, 62.),\n",
       "       (False, 53.), (False, 67.), (False, 49.), (False, 58.),\n",
       "       (False, 33.), ( True, 88.), ( True, 65.), ( True, 65.),\n",
       "       ( True, 80.), (False, 16.), (False, 10.), (False,  4.),\n",
       "       ( True, 80.), (False, 72.), ( True, 79.), (False, 77.),\n",
       "       (False, 71.), (False, 45.), (False, 81.), ( True, 82.),\n",
       "       (False, 46.), (False, 26.), (False, 21.), (False,  8.),\n",
       "       (False, 31.), ( True, 85.), (False, 19.), ( True, 81.),\n",
       "       (False, 41.), (False, 28.), ( True, 80.), ( True, 88.),\n",
       "       ( True, 66.), (False, 65.), (False, 10.), ( True, 80.),\n",
       "       ( True, 65.), ( True, 65.), (False, 44.), ( True, 80.),\n",
       "       (False, 17.), (False, 22.), (False, 47.), (False,  5.),\n",
       "       (False, 47.), (False, 27.), (False, 42.), ( True, 92.),\n",
       "       (False, 37.), ( True, 92.), (False,  4.), ( True, 88.),\n",
       "       (False, 30.), (False, 78.), (False, 23.), (False,  9.),\n",
       "       (False, 31.), ( True, 79.), (False, 68.), ( True, 80.),\n",
       "       (False, 43.), ( True, 80.), (False, 29.), ( True, 74.),\n",
       "       ( True, 92.), ( True, 80.), (False, 66.), ( True, 88.),\n",
       "       (False, 37.), ( True, 80.), (False, 12.), ( True, 65.),\n",
       "       (False, 23.), (False, 69.), (False, 49.), ( True, 81.),\n",
       "       (False, 40.), (False, 44.), (False, 72.), ( True, 93.),\n",
       "       (False, 69.), (False, 49.), (False,  4.), ( True, 81.),\n",
       "       ( True, 65.), (False,  5.), (False, 22.), ( True, 80.),\n",
       "       ( True, 73.), ( True, 88.), ( True, 88.), (False, 39.),\n",
       "       (False, 58.), (False, 67.), ( True, 60.), (False, 29.),\n",
       "       (False, 44.), (False,  5.), (False, 31.), (False,  5.),\n",
       "       ( True, 79.), (False, 38.), (False,  2.), ( True, 92.),\n",
       "       (False, 34.), ( True, 74.), ( True, 73.), (False, 63.),\n",
       "       ( True, 65.), (False, 31.), (False, 42.), (False, 42.),\n",
       "       (False, 19.), (False, 36.), (False, 36.), (False, 16.),\n",
       "       ( True, 79.), ( True, 80.), ( True, 60.), (False, 51.),\n",
       "       (False, 67.), ( True, 88.), (False, 19.), (False, 35.),\n",
       "       (False,  2.), ( True, 65.), ( True, 65.), (False, 45.),\n",
       "       (False, 17.), ( True, 73.), ( True, 65.), (False, 61.),\n",
       "       ( True, 93.), ( True, 80.), (False, 27.), (False, 29.),\n",
       "       ( True, 60.), (False, 14.), ( True, 60.), (False, 18.),\n",
       "       ( True, 92.), (False, 27.), (False, 44.), (False, 45.),\n",
       "       (False, 11.), (False, 38.), (False, 32.), (False, 56.),\n",
       "       (False,  9.), (False, 78.), ( True, 60.), ( True, 80.),\n",
       "       ( True, 80.), ( True, 93.), ( True, 66.), (False, 41.),\n",
       "       ( True, 88.), (False, 67.), ( True, 80.), (False, 54.),\n",
       "       (False, 55.), ( True, 81.), (False, 40.), ( True, 88.),\n",
       "       (False, 85.), ( True, 60.), (False, 88.), (False, 65.),\n",
       "       ( True, 79.), (False, 41.), ( True, 66.), (False, 10.),\n",
       "       (False, 12.), (False, 25.), (False, 25.), (False, 42.),\n",
       "       (False, 12.), ( True, 80.), (False, 57.), (False, 44.),\n",
       "       (False,  9.), ( True, 81.), (False, 17.), ( True, 65.),\n",
       "       (False, 26.), ( True, 65.), ( True, 65.), (False, 73.),\n",
       "       ( True, 88.), ( True, 73.), ( True, 66.), (False, 53.),\n",
       "       (False, 63.), (False, 77.), ( True, 93.), ( True, 88.),\n",
       "       (False, 68.), ( True, 74.), (False, 27.), (False, 13.),\n",
       "       (False, 49.), (False,  2.), ( True, 80.), (False, 18.),\n",
       "       (False, 23.), (False, 14.), (False, 30.), (False, 28.),\n",
       "       ( True, 65.), (False, 34.), ( True, 88.), (False, 30.),\n",
       "       ( True, 79.), (False, 44.), ( True, 80.), ( True, 88.),\n",
       "       ( True, 88.), (False, 48.), ( True, 81.), ( True, 81.),\n",
       "       (False, 81.), ( True, 60.), (False, 64.), ( True, 88.),\n",
       "       ( True, 86.), ( True, 80.), ( True, 65.), (False, 55.),\n",
       "       (False, 25.), (False, 52.), (False, 29.), (False, 58.),\n",
       "       ( True, 66.), (False, 36.), ( True, 88.), (False, 67.),\n",
       "       (False, 45.), (False, 30.), ( True, 65.), (False, 76.),\n",
       "       ( True, 60.), ( True, 65.), ( True, 80.), ( True, 88.),\n",
       "       ( True, 88.), (False, 22.), (False, 78.), (False, 63.)],\n",
       "      dtype=[('broken', '?'), ('lifetime', '<f8')])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preprocessing for target variable\n",
    "\n",
    "data_y = list(maintenance_df.iloc[:, [1,0]].itertuples(index = None, name = None))\n",
    "data_y = np.array(data_y, dtype=[('broken', bool), ('lifetime', float)])\n",
    "data_y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56b57738",
   "metadata": {},
   "source": [
    "# Kaplan Meier Estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a00d58b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'time $t$')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sksurv.nonparametric import kaplan_meier_estimator\n",
    "\n",
    "time, survival_prob = kaplan_meier_estimator(data_y[\"broken\"], data_y[\"lifetime\"])\n",
    "time, survival_prob = np.append(0, time), np.append(1, survival_prob)\n",
    "plt.step(time, survival_prob, where = \"post\")\n",
    "plt.xlim(left = 0, right = 100)\n",
    "plt.title(\"Kaplan Meier Estimator\")\n",
    "plt.ylabel(\"est. probability of survival $\\hat{S}(t)$\")\n",
    "plt.xlabel(\"time $t$\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fda4abef",
   "metadata": {},
   "source": [
    "## Compare Survival Time for Each Team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "2bb317f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2472b7744f0>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Keplen Meier for every Team\n",
    "for team in maintenance_df['team'].unique():\n",
    "    team_df = maintenance_df[maintenance_df['team'] == team]\n",
    "    time_treatment, survival_prob_treatment = kaplan_meier_estimator(team_df['broken'],\n",
    "                                                                     team_df['lifetime'])\n",
    "    plt.step(time_treatment, survival_prob_treatment, where = \"post\", label = team)\n",
    "    \n",
    "plt.ylabel(\"est. probability of survival $\\hat{S}(t)$\")\n",
    "plt.xlabel(\"time $t$\")\n",
    "plt.legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e8b727a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49.73683801122496,\n",
       " 1.584100941920959e-11,\n",
       "        counts  observed    expected  statistic\n",
       " group                                         \n",
       " TeamA     336       123  146.721543 -23.721543\n",
       " TeamB     356       150  174.910524 -24.910524\n",
       " TeamC     308       124   75.367933  48.632067,\n",
       " array([[ 66.77287697, -44.98880592, -21.78407105],\n",
       "        [-44.98880592,  70.79244675, -25.80364083],\n",
       "        [-21.78407105, -25.80364083,  47.58771187]]))"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sksurv.compare import compare_survival\n",
    "\n",
    "compare_survival(data_y, maintenance_df['team'], return_stats = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b305865",
   "metadata": {},
   "source": [
    "> Kesimpulan: *p-value* $ = 1.58e-11 < 0.05$ maka terdapat distribusi survival yang berbeda diantara ketiga team tersebut"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44f7b54b",
   "metadata": {},
   "source": [
    "## Compare Survival Time for each Provider"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1957532d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x24728d85790>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Keplen Meier for every Provider\n",
    "for provider in maintenance_df['provider'].unique():\n",
    "    provider_df = maintenance_df[maintenance_df['provider'] == provider]\n",
    "    time_treatment, survival_prob_treatment = kaplan_meier_estimator(provider_df['broken'],\n",
    "                                                                     provider_df['lifetime'])\n",
    "    plt.step(time_treatment, survival_prob_treatment, where = \"post\", label = provider)\n",
    "    \n",
    "plt.ylabel(\"est. probability of survival $\\hat{S}(t)$\")\n",
    "plt.xlabel(\"time $t$\")\n",
    "plt.legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "5e3c9e01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(786.4666596767563,\n",
       " 3.726199736391009e-170,\n",
       "            counts  observed    expected   statistic\n",
       " group                                              \n",
       " Provider1     254       116   66.395236   49.604764\n",
       " Provider2     266        91  201.531314 -110.531314\n",
       " Provider3     242       114   21.054720   92.945280\n",
       " Provider4     238        76  108.018730  -32.018730,\n",
       " array([[ 40.3829936 , -18.46634556,  -5.28504725, -16.6316008 ],\n",
       "        [-18.46634556,  61.50950191,  -5.06154476, -37.98161159],\n",
       "        [ -5.28504725,  -5.06154476,  14.81034726,  -4.46375525],\n",
       "        [-16.6316008 , -37.98161159,  -4.46375525,  59.07696764]]))"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_survival(data_y, maintenance_df['provider'], return_stats = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02dabe52",
   "metadata": {},
   "source": [
    "> Kesimpulan: *p-value* $ = 3.72e-170 < 0.05$ maka terdapat distribusi survival yang berbeda diantara mesin yang berasal dari keempat provider tersebut"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc5154b8",
   "metadata": {},
   "source": [
    "# Cox Propotional Hazard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a44b6289",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CoxPHSurvivalAnalysis()"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sksurv.linear_model import CoxPHSurvivalAnalysis\n",
    "\n",
    "estimator = CoxPHSurvivalAnalysis()\n",
    "estimator.fit(data_x, data_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f6258a13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pressureInd            0.001126\n",
       "moistureInd           -0.010328\n",
       "temperatureInd         0.017569\n",
       "team=TeamB             0.073402\n",
       "team=TeamC            40.816495\n",
       "provider=Provider2   -81.462604\n",
       "provider=Provider3    63.070058\n",
       "provider=Provider4   -60.588195\n",
       "dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Coef model\n",
    "pd.Series(estimator.coef_, index = data_x.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6a8d7ba",
   "metadata": {},
   "source": [
    "**Interpretation:**\n",
    "\n",
    "1. Mesin yang dioperasikan TeamC meningkatkan tingkat kerusakan hingga 40x dibanding TeamA\n",
    "2. Mesin yang berasal dari Provider3 memiliki tingkat kerusakan yang paling tinggi dibanding mesin dari provider lainnya"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61c9ab73",
   "metadata": {},
   "source": [
    "# Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "17d7b109",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.999086215561749"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sksurv.metrics import concordance_index_censored\n",
    "\n",
    "prediction = estimator.predict(data_x)\n",
    "result = concordance_index_censored(maintenance_df[\"broken\"], maintenance_df[\"lifetime\"], prediction)\n",
    "result[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "44b7ac09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.999086215561749"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimator.score(data_x, data_y)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}