{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Raport_przezyciowy.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "rulekit",
      "display_name": "rulekit",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.8.6"
    },
    "metadata": {
      "interpreter": {
        "hash": "62266c16fff41e971c13e9cb2ad3d47e4ef45d0678714c255381eb9fdcbd7032"
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Uy-yBGsd9W1"
      },
      "source": [
        "# Survival analysis"
      ]
    },
    {
      "source": [
        "This notebook presents example usage of package for solving survival problem on `bmt` dataset. You can download dataset [here](https://raw.githubusercontent.com/adaa-polsl/RuleKit/master/data/bmt/bmt.arff) \n",
        "\n",
        "This tutorial will cover topics such as:    \n",
        "- training model   \n",
        "- changing model hyperparameters   \n",
        "- hyperparameters tuning   \n",
        "- calculating metrics for model    \n",
        "- getting RuleKit inbuilt   "
      ],
      "cell_type": "markdown",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KjtU7PA8eOTr"
      },
      "source": [
        "## Summary of the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tp1TpfCkd58n"
      },
      "source": [
        "from scipy.io import arff\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "datasets_path = \"\" \n",
        "\n",
        "file_name = 'bmt.arff'\n",
        "\n",
        "data_df = pd.DataFrame(arff.loadarff(open(datasets_path + file_name, 'r', encoding=\"cp1252\"))[0])\n",
        "\n",
        "# code to fix the problem with encoding of the file\n",
        "tmp_df = data_df.select_dtypes([object]) \n",
        "tmp_df = tmp_df.stack().str.decode(\"cp1252\").unstack()\n",
        "for col in tmp_df:\n",
        "    data_df[col] = tmp_df[col]\n",
        "    \n",
        "data_df = data_df.replace({'?': None})"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {},
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Recipientgender Stemcellsource   Donorage Donorage35 IIIV Gendermatch  \\\n",
              "0                 1              1  22.830137          0    1           0   \n",
              "1                 1              0  23.342466          0    1           0   \n",
              "2                 1              0  26.394521          0    1           0   \n",
              "3                 0              0  39.684932          1    1           0   \n",
              "4                 0              1  33.358904          0    0           0   \n",
              "..              ...            ...        ...        ...  ...         ...   \n",
              "182               1              1  37.575342          1    1           0   \n",
              "183               0              1  22.895890          0    0           0   \n",
              "184               0              1  27.347945          0    1           0   \n",
              "185               1              1  27.780822          0    1           0   \n",
              "186               1              1  55.553425          1    1           0   \n",
              "\n",
              "    DonorABO RecipientABO RecipientRh ABOmatch  ... extcGvHD CD34kgx10d6  \\\n",
              "0          1            1           1        0  ...        1        7.20   \n",
              "1         -1           -1           1        0  ...        1        4.50   \n",
              "2         -1           -1           1        0  ...        1        7.94   \n",
              "3          1            2           1        1  ...     None        4.25   \n",
              "4          1            2           0        1  ...        1       51.85   \n",
              "..       ...          ...         ...      ...  ...      ...         ...   \n",
              "182        1            1           0        0  ...        1       11.08   \n",
              "183        1            0           1        1  ...        1        4.64   \n",
              "184        1           -1           1        1  ...        1        7.73   \n",
              "185        1            0           1        1  ...        0       15.41   \n",
              "186        1            2           1        1  ...        1        9.91   \n",
              "\n",
              "      CD3dCD34 CD3dkgx10d8 Rbodymass ANCrecovery PLTrecovery  \\\n",
              "0     1.338760        5.38      35.0        19.0        51.0   \n",
              "1    11.078295        0.41      20.6        16.0        37.0   \n",
              "2    19.013230        0.42      23.4        23.0        20.0   \n",
              "3    29.481647        0.14      50.0        23.0        29.0   \n",
              "4     3.972255       13.05       9.0        14.0        14.0   \n",
              "..         ...         ...       ...         ...         ...   \n",
              "182   2.522750        4.39      44.0        15.0        22.0   \n",
              "183   1.038858        4.47      44.5        12.0        30.0   \n",
              "184   1.635559        4.73      33.0        16.0        16.0   \n",
              "185   8.077770        1.91      24.0        13.0        14.0   \n",
              "186   0.948135       10.45      37.0        18.0        20.0   \n",
              "\n",
              "    time_to_aGvHD_III_IV survival_time survival_status  \n",
              "0                   32.0         999.0             0.0  \n",
              "1              1000000.0         163.0             1.0  \n",
              "2              1000000.0         435.0             1.0  \n",
              "3                   19.0          53.0             1.0  \n",
              "4              1000000.0        2043.0             0.0  \n",
              "..                   ...           ...             ...  \n",
              "182                 16.0         385.0             1.0  \n",
              "183            1000000.0         634.0             1.0  \n",
              "184            1000000.0        1895.0             0.0  \n",
              "185                 54.0         382.0             1.0  \n",
              "186            1000000.0        1109.0             0.0  \n",
              "\n",
              "[187 rows x 37 columns]"
            ],
            "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>Recipientgender</th>\n      <th>Stemcellsource</th>\n      <th>Donorage</th>\n      <th>Donorage35</th>\n      <th>IIIV</th>\n      <th>Gendermatch</th>\n      <th>DonorABO</th>\n      <th>RecipientABO</th>\n      <th>RecipientRh</th>\n      <th>ABOmatch</th>\n      <th>...</th>\n      <th>extcGvHD</th>\n      <th>CD34kgx10d6</th>\n      <th>CD3dCD34</th>\n      <th>CD3dkgx10d8</th>\n      <th>Rbodymass</th>\n      <th>ANCrecovery</th>\n      <th>PLTrecovery</th>\n      <th>time_to_aGvHD_III_IV</th>\n      <th>survival_time</th>\n      <th>survival_status</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>22.830137</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>7.20</td>\n      <td>1.338760</td>\n      <td>5.38</td>\n      <td>35.0</td>\n      <td>19.0</td>\n      <td>51.0</td>\n      <td>32.0</td>\n      <td>999.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>23.342466</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>4.50</td>\n      <td>11.078295</td>\n      <td>0.41</td>\n      <td>20.6</td>\n      <td>16.0</td>\n      <td>37.0</td>\n      <td>1000000.0</td>\n      <td>163.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>0</td>\n      <td>26.394521</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>7.94</td>\n      <td>19.013230</td>\n      <td>0.42</td>\n      <td>23.4</td>\n      <td>23.0</td>\n      <td>20.0</td>\n      <td>1000000.0</td>\n      <td>435.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>39.684932</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>...</td>\n      <td>None</td>\n      <td>4.25</td>\n      <td>29.481647</td>\n      <td>0.14</td>\n      <td>50.0</td>\n      <td>23.0</td>\n      <td>29.0</td>\n      <td>19.0</td>\n      <td>53.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>1</td>\n      <td>33.358904</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>...</td>\n      <td>1</td>\n      <td>51.85</td>\n      <td>3.972255</td>\n      <td>13.05</td>\n      <td>9.0</td>\n      <td>14.0</td>\n      <td>14.0</td>\n      <td>1000000.0</td>\n      <td>2043.0</td>\n      <td>0.0</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>182</th>\n      <td>1</td>\n      <td>1</td>\n      <td>37.575342</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>11.08</td>\n      <td>2.522750</td>\n      <td>4.39</td>\n      <td>44.0</td>\n      <td>15.0</td>\n      <td>22.0</td>\n      <td>16.0</td>\n      <td>385.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>183</th>\n      <td>0</td>\n      <td>1</td>\n      <td>22.895890</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>...</td>\n      <td>1</td>\n      <td>4.64</td>\n      <td>1.038858</td>\n      <td>4.47</td>\n      <td>44.5</td>\n      <td>12.0</td>\n      <td>30.0</td>\n      <td>1000000.0</td>\n      <td>634.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>184</th>\n      <td>0</td>\n      <td>1</td>\n      <td>27.347945</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>...</td>\n      <td>1</td>\n      <td>7.73</td>\n      <td>1.635559</td>\n      <td>4.73</td>\n      <td>33.0</td>\n      <td>16.0</td>\n      <td>16.0</td>\n      <td>1000000.0</td>\n      <td>1895.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>185</th>\n      <td>1</td>\n      <td>1</td>\n      <td>27.780822</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>...</td>\n      <td>0</td>\n      <td>15.41</td>\n      <td>8.077770</td>\n      <td>1.91</td>\n      <td>24.0</td>\n      <td>13.0</td>\n      <td>14.0</td>\n      <td>54.0</td>\n      <td>382.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>186</th>\n      <td>1</td>\n      <td>1</td>\n      <td>55.553425</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>...</td>\n      <td>1</td>\n      <td>9.91</td>\n      <td>0.948135</td>\n      <td>10.45</td>\n      <td>37.0</td>\n      <td>18.0</td>\n      <td>20.0</td>\n      <td>1000000.0</td>\n      <td>1109.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>187 rows × 37 columns</p>\n</div>"
          },
          "metadata": {},
          "execution_count": 97
        }
      ],
      "source": [
        "data_df"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 378
        },
        "id": "y9uVi9SFeZSa",
        "outputId": "6809c06d-5d8c-48a0-9b6d-3c433574f7f7"
      },
      "source": [
        "print(\"Dataset overview:\")\n",
        "print(f\"Name: {file_name}\")\n",
        "print(f\"Objects number: {data_df.shape[0]}; Attributes number: {data_df.shape[1]}\")\n",
        "print(\"Basic attribute statistics:\")\n",
        "data_df.describe()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset overview:\nName: bmt.arff\nObjects number: 187; Attributes number: 37\nBasic attribute statistics:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Donorage  Recipientage  CD34kgx10d6    CD3dCD34  CD3dkgx10d8  \\\n",
              "count  187.000000    187.000000   187.000000  182.000000   182.000000   \n",
              "mean    33.472068      9.931551    11.891781    5.385096     4.745714   \n",
              "std      8.271826      5.305639     9.914386    9.598716     3.859128   \n",
              "min     18.646575      0.600000     0.790000    0.204132     0.040000   \n",
              "25%     27.039726      5.050000     5.350000    1.786683     1.687500   \n",
              "50%     33.550685      9.600000     9.720000    2.734462     4.325000   \n",
              "75%     40.117809     14.050000    15.415000    5.823565     6.785000   \n",
              "max     55.553425     20.200000    57.780000   99.560970    20.020000   \n",
              "\n",
              "        Rbodymass     ANCrecovery     PLTrecovery  time_to_aGvHD_III_IV  \\\n",
              "count  185.000000      187.000000      187.000000            187.000000   \n",
              "mean    35.801081    26752.866310    90937.919786         775408.042781   \n",
              "std     19.650922   161747.200525   288242.407688         418425.252689   \n",
              "min      6.000000        9.000000        9.000000             10.000000   \n",
              "25%     19.000000       13.000000       16.000000        1000000.000000   \n",
              "50%     33.000000       15.000000       21.000000        1000000.000000   \n",
              "75%     50.600000       17.000000       37.000000        1000000.000000   \n",
              "max    103.400000  1000000.000000  1000000.000000        1000000.000000   \n",
              "\n",
              "       survival_time  survival_status  \n",
              "count     187.000000       187.000000  \n",
              "mean      938.743316         0.454545  \n",
              "std       849.589495         0.499266  \n",
              "min         6.000000         0.000000  \n",
              "25%       168.500000         0.000000  \n",
              "50%       676.000000         0.000000  \n",
              "75%      1604.000000         1.000000  \n",
              "max      3364.000000         1.000000  "
            ],
            "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>Donorage</th>\n      <th>Recipientage</th>\n      <th>CD34kgx10d6</th>\n      <th>CD3dCD34</th>\n      <th>CD3dkgx10d8</th>\n      <th>Rbodymass</th>\n      <th>ANCrecovery</th>\n      <th>PLTrecovery</th>\n      <th>time_to_aGvHD_III_IV</th>\n      <th>survival_time</th>\n      <th>survival_status</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>187.000000</td>\n      <td>187.000000</td>\n      <td>187.000000</td>\n      <td>182.000000</td>\n      <td>182.000000</td>\n      <td>185.000000</td>\n      <td>187.000000</td>\n      <td>187.000000</td>\n      <td>187.000000</td>\n      <td>187.000000</td>\n      <td>187.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>33.472068</td>\n      <td>9.931551</td>\n      <td>11.891781</td>\n      <td>5.385096</td>\n      <td>4.745714</td>\n      <td>35.801081</td>\n      <td>26752.866310</td>\n      <td>90937.919786</td>\n      <td>775408.042781</td>\n      <td>938.743316</td>\n      <td>0.454545</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>8.271826</td>\n      <td>5.305639</td>\n      <td>9.914386</td>\n      <td>9.598716</td>\n      <td>3.859128</td>\n      <td>19.650922</td>\n      <td>161747.200525</td>\n      <td>288242.407688</td>\n      <td>418425.252689</td>\n      <td>849.589495</td>\n      <td>0.499266</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>18.646575</td>\n      <td>0.600000</td>\n      <td>0.790000</td>\n      <td>0.204132</td>\n      <td>0.040000</td>\n      <td>6.000000</td>\n      <td>9.000000</td>\n      <td>9.000000</td>\n      <td>10.000000</td>\n      <td>6.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>27.039726</td>\n      <td>5.050000</td>\n      <td>5.350000</td>\n      <td>1.786683</td>\n      <td>1.687500</td>\n      <td>19.000000</td>\n      <td>13.000000</td>\n      <td>16.000000</td>\n      <td>1000000.000000</td>\n      <td>168.500000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>33.550685</td>\n      <td>9.600000</td>\n      <td>9.720000</td>\n      <td>2.734462</td>\n      <td>4.325000</td>\n      <td>33.000000</td>\n      <td>15.000000</td>\n      <td>21.000000</td>\n      <td>1000000.000000</td>\n      <td>676.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>40.117809</td>\n      <td>14.050000</td>\n      <td>15.415000</td>\n      <td>5.823565</td>\n      <td>6.785000</td>\n      <td>50.600000</td>\n      <td>17.000000</td>\n      <td>37.000000</td>\n      <td>1000000.000000</td>\n      <td>1604.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>55.553425</td>\n      <td>20.200000</td>\n      <td>57.780000</td>\n      <td>99.560970</td>\n      <td>20.020000</td>\n      <td>103.400000</td>\n      <td>1000000.000000</td>\n      <td>1000000.000000</td>\n      <td>1000000.000000</td>\n      <td>3364.000000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "al2J-WKIesF7"
      },
      "source": [
        "### Survival curve for the entire set (Kaplan Meier curve)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "vQSEvAcRfES0",
        "outputId": "acd03cb0-a7a8-4d7c-dee9-7f0bb0bddd6f"
      },
      "source": [
        "from lifelines import KaplanMeierFitter\n",
        "\n",
        "# create a kmf object\n",
        "kmf = KaplanMeierFitter() \n",
        "\n",
        "# Fit the data into the model\n",
        "kmf.fit(data_df['survival_time'], data_df['survival_status'],label='Kaplan Meier Estimate')\n",
        "\n",
        "# Create an estimate\n",
        "kmf.plot(ci_show=False) "
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='timeline'>"
            ]
          },
          "metadata": {},
          "execution_count": 3
        },
        {
          "output_type": "display_data",
          "data": {
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r8TgXKGmmSJf"
      },
      "source": [
        "## Import and init RuleKit"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w0hYM-8Ele2j"
      },
      "source": [
        "from rulekit import RuleKit\n",
        "from rulekit.survival import SurvivalRules\n",
        "from rulekit.params import Measures\n",
        "\n",
        "\n",
        "RuleKit.init()"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dattexxGmaqJ"
      },
      "source": [
        "## Helper function for creating ruleset characteristics dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aLCZkT_SmU4a"
      },
      "source": [
        "def get_ruleset_stats(model) -> pd.DataFrame:\n",
        "    tmp = model.parameters.__dict__\n",
        "    del tmp['_java_object']\n",
        "    return pd.DataFrame.from_records([{**tmp, **model.stats.__dict__}])"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u4wOfecjme_d"
      },
      "source": [
        "## Rule induction on full dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TrO-LyN2mpiP"
      },
      "source": [
        "x = data_df.drop(['survival_status'], axis=1)\n",
        "y = data_df['survival_status']"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c5tmU4IHnFjw"
      },
      "source": [
        "srv = SurvivalRules(\n",
        "    survival_time_attr = 'survival_time'\n",
        ")\n",
        "srv.fit(x, y)\n",
        "ruleset = srv.model\n",
        "predictions  = srv.predict(x)\n",
        "\n",
        "ruleset_stats = get_ruleset_stats(ruleset)\n",
        "\n",
        "display(ruleset_stats)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x2aab5e8f550>]"
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.plot(predictions[0][\"times\"], predictions[0][\"probabilities\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "abrtDQOtpVoL"
      },
      "source": [
        "### Generated rules"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FskFiB6PpVI_"
      },
      "source": [
        "for rule in ruleset.rules:\n",
        "    print(rule)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "IF Relapse = {0} AND Donorage = (-inf, 45.16) AND Recipientage = (-inf, 17.45) THEN survival_status = {NaN}\nIF HLAmismatch = {0} AND Donorage = <33.34, 42.14) AND Gendermatch = {0} AND RecipientRh = {1} AND Recipientage = <3.30, inf) THEN survival_status = {NaN}\nIF Relapse = {1} AND PLTrecovery = <15.50, inf) THEN survival_status = {NaN}\nIF PLTrecovery = (-inf, 266) THEN survival_status = {NaN}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GkNBc5iBpwmj"
      },
      "source": [
        "### Rules evaluation on full set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9UjrC8r-p59d"
      },
      "source": [
        "integrated_brier_score = srv.score(x, y)\n",
        "print(f'Integrated Brier Score: {integrated_brier_score}')"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Integrated Brier Score: 0.2154545054362314\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FpoSoaKdqAGQ"
      },
      "source": [
        "## Stratified K-Folds cross-validation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0nNv6a84qTsq"
      },
      "source": [
        "from sklearn.model_selection import StratifiedKFold\n",
        "\n",
        "skf = StratifiedKFold(n_splits=10)\n",
        "\n",
        "ruleset_stats = pd.DataFrame()\n",
        "survival_metrics = []\n",
        "\n",
        "\n",
        "for train_index, test_index in skf.split(x, y):\n",
        "    x_train, x_test = x.iloc[train_index], x.iloc[test_index]\n",
        "    y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n",
        "\n",
        "    srv = SurvivalRules(\n",
        "        survival_time_attr = 'survival_time'\n",
        "    )\n",
        "    srv.fit(x_train, y_train)\n",
        "    ruleset = srv.model\n",
        "\n",
        "    ibs = srv.score(x_test, y_test)\n",
        "\n",
        "    survival_metrics.append(ibs)\n",
        "    ruleset_stats = pd.concat([ruleset_stats, get_ruleset_stats(ruleset)])\n"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MfCOH_f3sICm"
      },
      "source": [
        "Ruleset characteristics (average)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xzbazr51sRd3"
      },
      "source": [
        "display(ruleset_stats.mean())"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "minimum_covered                 5.000000\nmaximum_uncovered_fraction      0.000000\nignore_missing                  0.000000\npruning_enabled                 1.000000\nmax_growing_condition           0.000000\ntime_total_s                   39.927018\ntime_growing_s                 35.729990\ntime_pruning_s                  4.192022\nrules_count                     5.700000\nconditions_per_rule             3.329405\ninduced_conditions_per_rule    68.789167\navg_rule_coverage               0.389676\navg_rule_precision              1.000000\navg_rule_quality                0.998029\npvalue                          0.001971\nFDR_pvalue                      0.002043\nFWER_pvalue                     0.002328\nfraction_significant            1.000000\nfraction_FDR_significant        1.000000\nfraction_FWER_significant       1.000000\ndtype: float64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_SmDJho4sVEO"
      },
      "source": [
        "Rules evaluation on dataset (average)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Co-fNd9nshWB"
      },
      "source": [
        "print(f'Integrated Brier Score: {np.mean(survival_metrics)}')"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "0.24666778331912664"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d-GdQ-wUtzW9"
      },
      "source": [
        "## Hyperparameters tuning\n",
        "\n",
        "This one gonna take a while..."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [],
      "source": [
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from rulekit.params import Measures"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [],
      "source": [
        "def scorer(estimator, X, y):\n",
        "    return (-1 * estimator.score(X,y))"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xNUji8U7t2wd"
      },
      "source": [
        "# define models and parameters\n",
        "model = SurvivalRules()\n",
        "min_rule_covered = range(3, 15)\n",
        "\n",
        "# define grid search\n",
        "grid = {\n",
        "    'survival_time_attr': ['survival_time'],\n",
        "    'min_rule_covered': min_rule_covered, \n",
        "}\n",
        "\n",
        "cv = StratifiedKFold(n_splits=3)\n",
        "grid_search = GridSearchCV(estimator=model, param_grid=grid, cv=cv, scoring=scorer) \n",
        "grid_result = grid_search.fit(x, y)\n",
        "\n",
        "# summarize results\n",
        "print(\"Best Integrated Brier Score: %f using %s\" % ( (-1)*grid_result.best_score_, grid_result.best_params_))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Integrated Brier Score: 0.228297 using {'min_rule_covered': 5, 'survival_time_attr': 'survival_time'}\n"
          ]
        }
      ]
    },
    {
      "source": [
        "## Building model with tuned hyperparameters\n",
        "\n",
        "### Split dataset to train and test (80%/20%)"
      ],
      "cell_type": "markdown",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "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, test_size=0.2, shuffle=True, stratify=y)\n",
        "\n",
        "srv = SurvivalRules(\n",
        "    survival_time_attr = 'survival_time',\n",
        "    min_rule_covered = 5\n",
        ")\n",
        "srv.fit(x_train, y_train)\n",
        "ruleset = srv.model\n",
        "ruleset_stats = get_ruleset_stats(ruleset)"
      ]
    },
    {
      "source": [
        "Rules evaluation"
      ],
      "cell_type": "markdown",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "display(ruleset_stats.iloc[0])"
      ]
    },
    {
      "source": [
        "### Validate model on test dataset"
      ],
      "cell_type": "markdown",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Integrated Brier Score: 0.19112015424542664\n"
          ]
        }
      ],
      "source": [
        "integrated_brier_score = srv.score(x_test, y_test)\n",
        "print(f'Integrated Brier Score: {integrated_brier_score}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {},
      "outputs": [],
      "source": [
        "predictions = srv.predict(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {},
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x2155f890880>]"
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          "metadata": {},
          "execution_count": 80
        },
        {
          "output_type": "display_data",
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.plot(predictions[0][\"times\"], predictions[0][\"probabilities\"])"
      ]
    }
  ]
}