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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import paths as pt\n",
    "from tools import file_reader, data_loader\n",
    "from utility.settings import load_settings\n",
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "\n",
    "# Load data\n",
    "target_settings = load_settings(pt.CONFIGS_DIR, \"alarm.yaml\")\n",
    "dl = data_loader.AlarmDataLoader(pt.PROCESSED_DATA_DIR,\n",
    "                                 \"alarm_emb.csv\",\n",
    "                                 target_settings).load_data()\n",
    "X, y = dl.get_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>BirthYear</th>\n",
       "      <th>Gender</th>\n",
       "      <th>LoanPeriod</th>\n",
       "      <th>NumberAts</th>\n",
       "      <th>1Ats</th>\n",
       "      <th>2Ats</th>\n",
       "      <th>3Ats</th>\n",
       "      <th>4Ats</th>\n",
       "      <th>5Ats</th>\n",
       "      <th>6Ats</th>\n",
       "      <th>7Ats</th>\n",
       "      <th>8Ats</th>\n",
       "      <th>9Ats</th>\n",
       "      <th>10Ats</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1356</td>\n",
       "      <td>1</td>\n",
       "      <td>0.127259</td>\n",
       "      <td>0.072884</td>\n",
       "      <td>0.075570</td>\n",
       "      <td>-0.108418</td>\n",
       "      <td>-0.104761</td>\n",
       "      <td>0.117267</td>\n",
       "      <td>-0.145492</td>\n",
       "      <td>-0.082936</td>\n",
       "      <td>-0.152768</td>\n",
       "      <td>-0.180756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>941</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.492148</td>\n",
       "      <td>-0.393624</td>\n",
       "      <td>0.075570</td>\n",
       "      <td>-0.108418</td>\n",
       "      <td>-0.104761</td>\n",
       "      <td>0.117267</td>\n",
       "      <td>-0.145492</td>\n",
       "      <td>-0.082936</td>\n",
       "      <td>-0.152768</td>\n",
       "      <td>-0.180756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>756</td>\n",
       "      <td>19</td>\n",
       "      <td>-0.248044</td>\n",
       "      <td>-0.005034</td>\n",
       "      <td>0.055204</td>\n",
       "      <td>-0.137931</td>\n",
       "      <td>-0.429392</td>\n",
       "      <td>-0.063547</td>\n",
       "      <td>-0.045989</td>\n",
       "      <td>0.216916</td>\n",
       "      <td>0.006597</td>\n",
       "      <td>-0.154118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>1374</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.138043</td>\n",
       "      <td>-0.108415</td>\n",
       "      <td>0.075570</td>\n",
       "      <td>-0.108418</td>\n",
       "      <td>-0.104761</td>\n",
       "      <td>0.117267</td>\n",
       "      <td>-0.145492</td>\n",
       "      <td>-0.082936</td>\n",
       "      <td>-0.152768</td>\n",
       "      <td>-0.180756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>1129</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.138043</td>\n",
       "      <td>-0.108415</td>\n",
       "      <td>0.075570</td>\n",
       "      <td>-0.108418</td>\n",
       "      <td>-0.104761</td>\n",
       "      <td>0.117267</td>\n",
       "      <td>-0.145492</td>\n",
       "      <td>-0.082936</td>\n",
       "      <td>-0.152768</td>\n",
       "      <td>-0.180756</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12390</th>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>792</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.172863</td>\n",
       "      <td>-0.152233</td>\n",
       "      <td>0.791598</td>\n",
       "      <td>-0.108418</td>\n",
       "      <td>-0.104761</td>\n",
       "      <td>0.117267</td>\n",
       "      <td>-0.145492</td>\n",
       "      <td>-0.082936</td>\n",
       "      <td>-0.152768</td>\n",
       "      <td>-0.180756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12391</th>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>1412</td>\n",
       "      <td>17</td>\n",
       "      <td>0.126841</td>\n",
       "      <td>0.056413</td>\n",
       "      <td>0.055204</td>\n",
       "      <td>0.507260</td>\n",
       "      <td>0.444011</td>\n",
       "      <td>0.154733</td>\n",
       "      <td>-0.060687</td>\n",
       "      <td>-0.086729</td>\n",
       "      <td>-0.253628</td>\n",
       "      <td>-0.154118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12392</th>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>1406</td>\n",
       "      <td>7</td>\n",
       "      <td>-0.274823</td>\n",
       "      <td>-0.230284</td>\n",
       "      <td>0.024911</td>\n",
       "      <td>-0.177859</td>\n",
       "      <td>0.211263</td>\n",
       "      <td>0.361291</td>\n",
       "      <td>-0.078357</td>\n",
       "      <td>-0.082936</td>\n",
       "      <td>-0.152768</td>\n",
       "      <td>-0.180756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12393</th>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>1423</td>\n",
       "      <td>1</td>\n",
       "      <td>1.082245</td>\n",
       "      <td>0.072884</td>\n",
       "      <td>0.075570</td>\n",
       "      <td>-0.108418</td>\n",
       "      <td>-0.104761</td>\n",
       "      <td>0.117267</td>\n",
       "      <td>-0.145492</td>\n",
       "      <td>-0.082936</td>\n",
       "      <td>-0.152768</td>\n",
       "      <td>-0.180756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12394</th>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>437</td>\n",
       "      <td>5</td>\n",
       "      <td>0.127259</td>\n",
       "      <td>0.074291</td>\n",
       "      <td>0.055204</td>\n",
       "      <td>0.268637</td>\n",
       "      <td>-0.087402</td>\n",
       "      <td>0.117267</td>\n",
       "      <td>-0.145492</td>\n",
       "      <td>-0.082936</td>\n",
       "      <td>-0.152768</td>\n",
       "      <td>-0.180756</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12395 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       BirthYear  Gender  LoanPeriod  NumberAts      1Ats      2Ats      3Ats  \\\n",
       "0              3       1        1356          1  0.127259  0.072884  0.075570   \n",
       "1             26       0         941          2 -0.492148 -0.393624  0.075570   \n",
       "2             30       0         756         19 -0.248044 -0.005034  0.055204   \n",
       "3             32       0        1374          2 -0.138043 -0.108415  0.075570   \n",
       "4             33       1        1129          2 -0.138043 -0.108415  0.075570   \n",
       "...          ...     ...         ...        ...       ...       ...       ...   \n",
       "12390         59       0         792          3 -0.172863 -0.152233  0.791598   \n",
       "12391         64       0        1412         17  0.126841  0.056413  0.055204   \n",
       "12392         65       0        1406          7 -0.274823 -0.230284  0.024911   \n",
       "12393         75       0        1423          1  1.082245  0.072884  0.075570   \n",
       "12394         98       1         437          5  0.127259  0.074291  0.055204   \n",
       "\n",
       "           4Ats      5Ats      6Ats      7Ats      8Ats      9Ats     10Ats  \n",
       "0     -0.108418 -0.104761  0.117267 -0.145492 -0.082936 -0.152768 -0.180756  \n",
       "1     -0.108418 -0.104761  0.117267 -0.145492 -0.082936 -0.152768 -0.180756  \n",
       "2     -0.137931 -0.429392 -0.063547 -0.045989  0.216916  0.006597 -0.154118  \n",
       "3     -0.108418 -0.104761  0.117267 -0.145492 -0.082936 -0.152768 -0.180756  \n",
       "4     -0.108418 -0.104761  0.117267 -0.145492 -0.082936 -0.152768 -0.180756  \n",
       "...         ...       ...       ...       ...       ...       ...       ...  \n",
       "12390 -0.108418 -0.104761  0.117267 -0.145492 -0.082936 -0.152768 -0.180756  \n",
       "12391  0.507260  0.444011  0.154733 -0.060687 -0.086729 -0.253628 -0.154118  \n",
       "12392 -0.177859  0.211263  0.361291 -0.078357 -0.082936 -0.152768 -0.180756  \n",
       "12393 -0.108418 -0.104761  0.117267 -0.145492 -0.082936 -0.152768 -0.180756  \n",
       "12394  0.268637 -0.087402  0.117267 -0.145492 -0.082936 -0.152768 -0.180756  \n",
       "\n",
       "[12395 rows x 14 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([(False, 1356), (False,  460), (False,  240), ..., (False,  422),\n",
       "       (False, 1423), (False,  371)],\n",
       "      dtype=[('Status', '?'), ('Days_to_alarm', '>i4')])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of samples: 12395\n"
     ]
    }
   ],
   "source": [
    "print(f\"Number of samples: {X.shape[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Status</th>\n",
       "      <th>Days_to_alarm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>1981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>1263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>True</td>\n",
       "      <td>1165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>2420</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Status  Days_to_alarm\n",
       "1   False            155\n",
       "2   False           1981\n",
       "3   False           1263\n",
       "4    True           1165\n",
       "5   False           2420"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame.from_records(y[[11, 6, 32, 23, 31]], index=range(1, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'time $t$')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from sksurv.nonparametric import kaplan_meier_estimator\n",
    "time, survival_prob = kaplan_meier_estimator(y[\"Status\"], y[\"Days_to_alarm\"])\n",
    "plt.step(time, survival_prob, where=\"post\")\n",
    "plt.ylabel(\"est. probability of survival $\\hat{S}(t)$\")\n",
    "plt.xlabel(\"time $t$\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "83.4% of records are censored\n"
     ]
    }
   ],
   "source": [
    "n_censored = y.shape[0] - y[\"Status\"].sum()\n",
    "print(\"%.1f%% of records are censored\" % (n_censored / y.shape[0] * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9, 6))\n",
    "val, bins, patches = plt.hist((y[\"Days_to_alarm\"][y[\"Status\"]],\n",
    "                               y[\"Days_to_alarm\"][~y[\"Status\"]]),\n",
    "                              bins=30, stacked=True)\n",
    "_ = plt.legend(patches, [\"Time of Alarm\", \"Time of Censoring\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y[\"Status\"],\n",
    "                                                    test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomSurvivalForest(max_depth=3, n_estimators=200, n_jobs=-1, random_state=0)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sksurv.ensemble import RandomSurvivalForest\n",
    "\n",
    "rsf = RandomSurvivalForest(n_estimators=200,\n",
    "                           max_depth=3,\n",
    "                           n_jobs=-1,\n",
    "                           random_state=0)\n",
    "rsf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7053003792196612"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rsf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     46.617733\n",
       "1    108.502148\n",
       "2    123.546918\n",
       "3    131.644403\n",
       "4    110.468181\n",
       "5    210.672085\n",
       "6     95.411071\n",
       "7    121.324315\n",
       "8    110.468181\n",
       "9    200.308833\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_sel = X_test.iloc[50:60]\n",
    "pd.Series(rsf.predict(X_test_sel))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BirthYear</th>\n",
       "      <th>Gender</th>\n",
       "      <th>LoanPeriod</th>\n",
       "      <th>NumberAts</th>\n",
       "      <th>1Ats</th>\n",
       "      <th>2Ats</th>\n",
       "      <th>3Ats</th>\n",
       "      <th>4Ats</th>\n",
       "      <th>5Ats</th>\n",
       "      <th>6Ats</th>\n",
       "      <th>7Ats</th>\n",
       "      <th>8Ats</th>\n",
       "      <th>9Ats</th>\n",
       "      <th>10Ats</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7234</th>\n",
       "      <td>84</td>\n",
       "      <td>1</td>\n",
       "      <td>1045</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>5131</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.172863</td>\n",
       "      <td>0.072884</td>\n",
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       "      <td>-0.152768</td>\n",
       "      <td>-0.180756</td>\n",
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       "    <tr>\n",
       "      <th>11062</th>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.299375</td>\n",
       "      <td>0.072884</td>\n",
       "      <td>0.075570</td>\n",
       "      <td>-0.108418</td>\n",
       "      <td>-0.104761</td>\n",
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       "      <td>-0.082936</td>\n",
       "      <td>-0.152768</td>\n",
       "      <td>-0.180756</td>\n",
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       "    <tr>\n",
       "      <th>11890</th>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>1144</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.276828</td>\n",
       "      <td>0.086263</td>\n",
       "      <td>0.075570</td>\n",
       "      <td>-0.108418</td>\n",
       "      <td>-0.104761</td>\n",
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       "      <td>-0.145492</td>\n",
       "      <td>-0.082936</td>\n",
       "      <td>-0.152768</td>\n",
       "      <td>-0.180756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2610</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>331</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.172863</td>\n",
       "      <td>0.072884</td>\n",
       "      <td>0.075570</td>\n",
       "      <td>-0.108418</td>\n",
       "      <td>-0.104761</td>\n",
       "      <td>0.117267</td>\n",
       "      <td>-0.145492</td>\n",
       "      <td>-0.082936</td>\n",
       "      <td>-0.152768</td>\n",
       "      <td>-0.180756</td>\n",
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       "    <tr>\n",
       "      <th>5489</th>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>735</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.138043</td>\n",
       "      <td>-0.108415</td>\n",
       "      <td>0.140681</td>\n",
       "      <td>-0.146891</td>\n",
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       "      <td>-0.152768</td>\n",
       "      <td>-0.180756</td>\n",
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       "    <tr>\n",
       "      <th>3129</th>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>4565</td>\n",
       "      <td>2</td>\n",
       "      <td>0.721325</td>\n",
       "      <td>-0.108415</td>\n",
       "      <td>0.075570</td>\n",
       "      <td>-0.108418</td>\n",
       "      <td>-0.104761</td>\n",
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       "      <td>-0.145492</td>\n",
       "      <td>-0.082936</td>\n",
       "      <td>-0.152768</td>\n",
       "      <td>-0.180756</td>\n",
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       "    <tr>\n",
       "      <th>12308</th>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>1902</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.172863</td>\n",
       "      <td>0.072884</td>\n",
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       "    <tr>\n",
       "      <th>4451</th>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.172863</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4757</th>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>889</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.172863</td>\n",
       "      <td>-0.185733</td>\n",
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       "      <td>0.225063</td>\n",
       "      <td>-0.104761</td>\n",
       "      <td>0.117267</td>\n",
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       "      <td>-0.082936</td>\n",
       "      <td>-0.152768</td>\n",
       "      <td>-0.180756</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       BirthYear  Gender  LoanPeriod  NumberAts      1Ats      2Ats      3Ats  \\\n",
       "7234          84       1        1045          1  0.127259  0.072884  0.075570   \n",
       "5131          51       1         163          1 -0.172863  0.072884  0.075570   \n",
       "11062         39       0          12          1 -0.299375  0.072884  0.075570   \n",
       "11890         45       0        1144          2 -0.276828  0.086263  0.075570   \n",
       "2610          41       0         331          1 -0.172863  0.072884  0.075570   \n",
       "5489          29       0         735          4 -0.138043 -0.108415  0.140681   \n",
       "3129          71       0        4565          2  0.721325 -0.108415  0.075570   \n",
       "12308         38       1        1902          1 -0.172863  0.072884  0.075570   \n",
       "4451          43       0          44          1 -0.172863  0.072884  0.075570   \n",
       "4757          34       1         889          4 -0.172863 -0.185733  0.024911   \n",
       "\n",
       "           4Ats      5Ats      6Ats      7Ats      8Ats      9Ats     10Ats  \n",
       "7234  -0.108418 -0.104761  0.117267 -0.145492 -0.082936 -0.152768 -0.180756  \n",
       "5131  -0.108418 -0.104761  0.117267 -0.145492 -0.082936 -0.152768 -0.180756  \n",
       "11062 -0.108418 -0.104761  0.117267 -0.145492 -0.082936 -0.152768 -0.180756  \n",
       "11890 -0.108418 -0.104761  0.117267 -0.145492 -0.082936 -0.152768 -0.180756  \n",
       "2610  -0.108418 -0.104761  0.117267 -0.145492 -0.082936 -0.152768 -0.180756  \n",
       "5489  -0.146891 -0.104761  0.117267 -0.145492 -0.082936 -0.152768 -0.180756  \n",
       "3129  -0.108418 -0.104761  0.117267 -0.145492 -0.082936 -0.152768 -0.180756  \n",
       "12308 -0.108418 -0.104761  0.117267 -0.145492 -0.082936 -0.152768 -0.180756  \n",
       "4451  -0.108418 -0.104761  0.117267 -0.145492 -0.082936 -0.152768 -0.180756  \n",
       "4757   0.225063 -0.104761  0.117267 -0.145492 -0.082936 -0.152768 -0.180756  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_sel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "surv = rsf.predict_survival_function(X_test_sel, return_array=True)\n",
    "\n",
    "for i, s in enumerate(surv):\n",
    "    plt.step(rsf.event_times_, s, where=\"post\", label=str(i))\n",
    "plt.ylabel(\"Survival probability\")\n",
    "plt.xlabel(\"Time in days\")\n",
    "plt.xlim([0, 1000])\n",
    "plt.legend()\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "surv = rsf.predict_cumulative_hazard_function(X_test_sel, return_array=True)\n",
    "\n",
    "for i, s in enumerate(surv):\n",
    "    plt.step(rsf.event_times_, s, where=\"post\", label=str(i))\n",
    "plt.ylabel(\"Cumulative hazard\")\n",
    "plt.xlabel(\"Time in days\")\n",
    "plt.xlim([0, 1000])\n",
    "plt.legend()\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <style>\n",
       "    table.eli5-weights tr:hover {\n",
       "        filter: brightness(85%);\n",
       "    }\n",
       "</style>\n",
       "\n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "        <table class=\"eli5-weights eli5-feature-importances\" style=\"border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto;\">\n",
       "    <thead>\n",
       "    <tr style=\"border: none;\">\n",
       "        <th style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">Weight</th>\n",
       "        <th style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">Feature</th>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 80.00%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0392\n",
       "                \n",
       "                    &plusmn; 0.0000\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                BirthYear\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 83.64%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0295\n",
       "                \n",
       "                    &plusmn; 0.0000\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                1Ats\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 87.74%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0195\n",
       "                \n",
       "                    &plusmn; 0.0000\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                3Ats\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 87.83%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0193\n",
       "                \n",
       "                    &plusmn; 0.0000\n",
       "                \n",
       "            </td>\n",