Copy of Cox_PH_and_RSF-colab.ipynb

copy-of-cox_ph_and_rsf-colab.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/alonsosilvaallende/0371023c5a09eb07b2b8825b3ab99057/copy-of-cox_ph_and_rsf-colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UN3PoUTSb2nT"
      },
      "source": [
        "The objective of this notebook is to compare different models to estimate the survival probability given a set of features/covariables.\n",
        "\n",
        ">[\"Experimental Comparison of Semi-parametric, Parametric, and Machine Learning Models for Time-to-Event Analysis Through the Concordance Index,\"](https://arxiv.org/abs/2003.08820)\n",
        "Camila Fernandez, Chung Shue Chen, Pierre Gaillard, Alonso Silva\n",
        "\n",
        "To perform this analysis we will use [scikit-learn](https://scikit-learn.org/) and [scikit-survival](https://pypi.org/project/scikit-survival/). Finally, we will use [eli5](https://eli5.readthedocs.io/en/latest/index.html) to study feature importances (computed with permutation importance)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pzqGFx_Bb_6A"
      },
      "source": [
        "!pip install -q scikit-survival"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VoQVEI5p_rga"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SnT6e_JPb2ns"
      },
      "source": [
        "We first download a dataset from scikit-survival."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D0xxNWzI-N3j"
      },
      "source": [
        "from sksurv.datasets import load_gbsg2\n",
        "\n",
        "X, y = load_gbsg2()"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IBTo4q_Hb2n0"
      },
      "source": [
        "## An example: German Breast Cancer Study Group 2 (gbcsg)\n",
        "\n",
        "This dataset contains the following 8 features/covariables:\n",
        "\n",
        "- age: age (in years),\n",
        "- estrec: estrogen receptor (in fmol),\n",
        "- horTh: hormonal therapy (yes or no),\n",
        "- menostat: menopausal status (premenopausal or postmenopausal),\n",
        "- pnodes: number of positive nodes,\n",
        "- progrec: progesterone receptor (in fmol),\n",
        "- tgrade: tumor grade (I < II < III),\n",
        "- tsize: tumor size (in mm).\n",
        "\n",
        "and the two outputs:\n",
        "\n",
        "- recurrence free time (in days),\n",
        "- censoring indicator (0 - censored, 1 - event).\n",
        "\n",
        "The dataset has 686 samples and 8 features/covariables.\n",
        "\n",
        "\n",
        "**References**\n",
        "\n",
        "M. Schumacher, G. Basert, H. Bojar, K. Huebner, M. Olschewski, W. Sauerbrei, C. Schmoor, C. Beyerle, R.L.A. Neumann and H.F. Rauschecker for the German Breast Cancer Study Group (1994), [Randomized 2 x 2 trial evaluating hormonal treatment and the duration of chemotherapy in node-positive breast cancer patients](https://www.ncbi.nlm.nih.gov/pubmed/7931478). Journal of Clinical Oncology, 12, 2086–2093."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kAsZ72YYb2n3"
      },
      "source": [
        "Let's take a look at the features/covariates."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_OlmlI6g-X43",
        "outputId": "1eca9943-4e79-4008-ee6e-1b4bbd70e306",
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        }
      },
      "source": [
        "cols = [\"age\", \"estrec\", \"pnodes\", \"progrec\", \"tsize\"]\n",
        "formatdict = {}\n",
        "for col in cols: formatdict[col] = \"{:,.0f}\"\n",
        "X.head(10).style.hide(axis=\"index\").format(formatdict)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<pandas.io.formats.style.Styler at 0x7884921ab910>"
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              "<style type=\"text/css\">\n",
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              "  <thead>\n",
              "    <tr>\n",
              "      <th id=\"T_3e856_level0_col0\" class=\"col_heading level0 col0\" >age</th>\n",
              "      <th id=\"T_3e856_level0_col1\" class=\"col_heading level0 col1\" >estrec</th>\n",
              "      <th id=\"T_3e856_level0_col2\" class=\"col_heading level0 col2\" >horTh</th>\n",
              "      <th id=\"T_3e856_level0_col3\" class=\"col_heading level0 col3\" >menostat</th>\n",
              "      <th id=\"T_3e856_level0_col4\" class=\"col_heading level0 col4\" >pnodes</th>\n",
              "      <th id=\"T_3e856_level0_col5\" class=\"col_heading level0 col5\" >progrec</th>\n",
              "      <th id=\"T_3e856_level0_col6\" class=\"col_heading level0 col6\" >tgrade</th>\n",
              "      <th id=\"T_3e856_level0_col7\" class=\"col_heading level0 col7\" >tsize</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <td id=\"T_3e856_row0_col0\" class=\"data row0 col0\" >70</td>\n",
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              "      <td id=\"T_3e856_row0_col2\" class=\"data row0 col2\" >no</td>\n",
              "      <td id=\"T_3e856_row0_col3\" class=\"data row0 col3\" >Post</td>\n",
              "      <td id=\"T_3e856_row0_col4\" class=\"data row0 col4\" >3</td>\n",
              "      <td id=\"T_3e856_row0_col5\" class=\"data row0 col5\" >48</td>\n",
              "      <td id=\"T_3e856_row0_col6\" class=\"data row0 col6\" >II</td>\n",
              "      <td id=\"T_3e856_row0_col7\" class=\"data row0 col7\" >21</td>\n",
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              "    <tr>\n",
              "      <td id=\"T_3e856_row1_col0\" class=\"data row1 col0\" >56</td>\n",
              "      <td id=\"T_3e856_row1_col1\" class=\"data row1 col1\" >77</td>\n",
              "      <td id=\"T_3e856_row1_col2\" class=\"data row1 col2\" >yes</td>\n",
              "      <td id=\"T_3e856_row1_col3\" class=\"data row1 col3\" >Post</td>\n",
              "      <td id=\"T_3e856_row1_col4\" class=\"data row1 col4\" >7</td>\n",
              "      <td id=\"T_3e856_row1_col5\" class=\"data row1 col5\" >61</td>\n",
              "      <td id=\"T_3e856_row1_col6\" class=\"data row1 col6\" >II</td>\n",
              "      <td id=\"T_3e856_row1_col7\" class=\"data row1 col7\" >12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_3e856_row2_col0\" class=\"data row2 col0\" >58</td>\n",
              "      <td id=\"T_3e856_row2_col1\" class=\"data row2 col1\" >271</td>\n",
              "      <td id=\"T_3e856_row2_col2\" class=\"data row2 col2\" >yes</td>\n",
              "      <td id=\"T_3e856_row2_col3\" class=\"data row2 col3\" >Post</td>\n",
              "      <td id=\"T_3e856_row2_col4\" class=\"data row2 col4\" >9</td>\n",
              "      <td id=\"T_3e856_row2_col5\" class=\"data row2 col5\" >52</td>\n",
              "      <td id=\"T_3e856_row2_col6\" class=\"data row2 col6\" >II</td>\n",
              "      <td id=\"T_3e856_row2_col7\" class=\"data row2 col7\" >35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_3e856_row3_col0\" class=\"data row3 col0\" >59</td>\n",
              "      <td id=\"T_3e856_row3_col1\" class=\"data row3 col1\" >29</td>\n",
              "      <td id=\"T_3e856_row3_col2\" class=\"data row3 col2\" >yes</td>\n",
              "      <td id=\"T_3e856_row3_col3\" class=\"data row3 col3\" >Post</td>\n",
              "      <td id=\"T_3e856_row3_col4\" class=\"data row3 col4\" >4</td>\n",
              "      <td id=\"T_3e856_row3_col5\" class=\"data row3 col5\" >60</td>\n",
              "      <td id=\"T_3e856_row3_col6\" class=\"data row3 col6\" >II</td>\n",
              "      <td id=\"T_3e856_row3_col7\" class=\"data row3 col7\" >17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_3e856_row4_col0\" class=\"data row4 col0\" >73</td>\n",
              "      <td id=\"T_3e856_row4_col1\" class=\"data row4 col1\" >65</td>\n",
              "      <td id=\"T_3e856_row4_col2\" class=\"data row4 col2\" >no</td>\n",
              "      <td id=\"T_3e856_row4_col3\" class=\"data row4 col3\" >Post</td>\n",
              "      <td id=\"T_3e856_row4_col4\" class=\"data row4 col4\" >1</td>\n",
              "      <td id=\"T_3e856_row4_col5\" class=\"data row4 col5\" >26</td>\n",
              "      <td id=\"T_3e856_row4_col6\" class=\"data row4 col6\" >II</td>\n",
              "      <td id=\"T_3e856_row4_col7\" class=\"data row4 col7\" >35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_3e856_row5_col0\" class=\"data row5 col0\" >32</td>\n",
              "      <td id=\"T_3e856_row5_col1\" class=\"data row5 col1\" >13</td>\n",
              "      <td id=\"T_3e856_row5_col2\" class=\"data row5 col2\" >no</td>\n",
              "      <td id=\"T_3e856_row5_col3\" class=\"data row5 col3\" >Pre</td>\n",
              "      <td id=\"T_3e856_row5_col4\" class=\"data row5 col4\" >24</td>\n",
              "      <td id=\"T_3e856_row5_col5\" class=\"data row5 col5\" >0</td>\n",
              "      <td id=\"T_3e856_row5_col6\" class=\"data row5 col6\" >III</td>\n",
              "      <td id=\"T_3e856_row5_col7\" class=\"data row5 col7\" >57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_3e856_row6_col0\" class=\"data row6 col0\" >59</td>\n",
              "      <td id=\"T_3e856_row6_col1\" class=\"data row6 col1\" >0</td>\n",
              "      <td id=\"T_3e856_row6_col2\" class=\"data row6 col2\" >yes</td>\n",
              "      <td id=\"T_3e856_row6_col3\" class=\"data row6 col3\" >Post</td>\n",
              "      <td id=\"T_3e856_row6_col4\" class=\"data row6 col4\" >2</td>\n",
              "      <td id=\"T_3e856_row6_col5\" class=\"data row6 col5\" >181</td>\n",
              "      <td id=\"T_3e856_row6_col6\" class=\"data row6 col6\" >II</td>\n",
              "      <td id=\"T_3e856_row6_col7\" class=\"data row6 col7\" >8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_3e856_row7_col0\" class=\"data row7 col0\" >65</td>\n",
              "      <td id=\"T_3e856_row7_col1\" class=\"data row7 col1\" >25</td>\n",
              "      <td id=\"T_3e856_row7_col2\" class=\"data row7 col2\" >no</td>\n",
              "      <td id=\"T_3e856_row7_col3\" class=\"data row7 col3\" >Post</td>\n",
              "      <td id=\"T_3e856_row7_col4\" class=\"data row7 col4\" >1</td>\n",
              "      <td id=\"T_3e856_row7_col5\" class=\"data row7 col5\" >192</td>\n",
              "      <td id=\"T_3e856_row7_col6\" class=\"data row7 col6\" >II</td>\n",
              "      <td id=\"T_3e856_row7_col7\" class=\"data row7 col7\" >16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_3e856_row8_col0\" class=\"data row8 col0\" >80</td>\n",
              "      <td id=\"T_3e856_row8_col1\" class=\"data row8 col1\" >59</td>\n",
              "      <td id=\"T_3e856_row8_col2\" class=\"data row8 col2\" >no</td>\n",
              "      <td id=\"T_3e856_row8_col3\" class=\"data row8 col3\" >Post</td>\n",
              "      <td id=\"T_3e856_row8_col4\" class=\"data row8 col4\" >30</td>\n",
              "      <td id=\"T_3e856_row8_col5\" class=\"data row8 col5\" >0</td>\n",
              "      <td id=\"T_3e856_row8_col6\" class=\"data row8 col6\" >II</td>\n",
              "      <td id=\"T_3e856_row8_col7\" class=\"data row8 col7\" >39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_3e856_row9_col0\" class=\"data row9 col0\" >66</td>\n",
              "      <td id=\"T_3e856_row9_col1\" class=\"data row9 col1\" >3</td>\n",
              "      <td id=\"T_3e856_row9_col2\" class=\"data row9 col2\" >no</td>\n",
              "      <td id=\"T_3e856_row9_col3\" class=\"data row9 col3\" >Post</td>\n",
              "      <td id=\"T_3e856_row9_col4\" class=\"data row9 col4\" >7</td>\n",
              "      <td id=\"T_3e856_row9_col5\" class=\"data row9 col5\" >0</td>\n",
              "      <td id=\"T_3e856_row9_col6\" class=\"data row9 col6\" >II</td>\n",
              "      <td id=\"T_3e856_row9_col7\" class=\"data row9 col7\" >18</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zcMjHeAEb2n_"
      },
      "source": [
        "Let's take a look at the output."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h8ltRTa4_WOn",
        "outputId": "6cc7e0ec-ff7d-43fd-df6e-78c2d1037257",
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "y[:10]"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([( True, 1814.), ( True, 2018.), ( True,  712.), ( True, 1807.),\n",
              "       ( True,  772.), ( True,  448.), (False, 2172.), (False, 2161.),\n",
              "       ( True,  471.), (False, 2014.)],\n",
              "      dtype=[('cens', '?'), ('time', '<f8')])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A5NXlNYob2oH"
      },
      "source": [
        "For the output, scikit-survival uses a numpy nd array, so to show it we do a dataframe."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_y = pd.DataFrame(data={'time': y['time'].astype(int), 'event': y['cens']})\n",
        "df_y[:10].style.hide(axis=\"index\").highlight_min('event', color='lightgreen')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "AfPvZcjJ-GwQ",
        "outputId": "f5831c4a-7152-4268-ac31-4aeb2c9c71ac"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "<style type=\"text/css\">\n",
              "#T_5ca91_row6_col1, #T_5ca91_row7_col1, #T_5ca91_row9_col1 {\n",
              "  background-color: lightgreen;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_5ca91\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th id=\"T_5ca91_level0_col0\" class=\"col_heading level0 col0\" >time</th>\n",
              "      <th id=\"T_5ca91_level0_col1\" class=\"col_heading level0 col1\" >event</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td id=\"T_5ca91_row0_col0\" class=\"data row0 col0\" >1814</td>\n",
              "      <td id=\"T_5ca91_row0_col1\" class=\"data row0 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_5ca91_row1_col0\" class=\"data row1 col0\" >2018</td>\n",
              "      <td id=\"T_5ca91_row1_col1\" class=\"data row1 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_5ca91_row2_col0\" class=\"data row2 col0\" >712</td>\n",
              "      <td id=\"T_5ca91_row2_col1\" class=\"data row2 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_5ca91_row3_col0\" class=\"data row3 col0\" >1807</td>\n",
              "      <td id=\"T_5ca91_row3_col1\" class=\"data row3 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_5ca91_row4_col0\" class=\"data row4 col0\" >772</td>\n",
              "      <td id=\"T_5ca91_row4_col1\" class=\"data row4 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_5ca91_row5_col0\" class=\"data row5 col0\" >448</td>\n",
              "      <td id=\"T_5ca91_row5_col1\" class=\"data row5 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_5ca91_row6_col0\" class=\"data row6 col0\" >2172</td>\n",
              "      <td id=\"T_5ca91_row6_col1\" class=\"data row6 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_5ca91_row7_col0\" class=\"data row7 col0\" >2161</td>\n",
              "      <td id=\"T_5ca91_row7_col1\" class=\"data row7 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_5ca91_row8_col0\" class=\"data row8 col0\" >471</td>\n",
              "      <td id=\"T_5ca91_row8_col1\" class=\"data row8 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_5ca91_row9_col0\" class=\"data row9 col0\" >2014</td>\n",
              "      <td id=\"T_5ca91_row9_col1\" class=\"data row9 col1\" >False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xzW5x7ljb2oP"
      },
      "source": [
        "One of the main challenges of survival analysis is **right censoring**, i.e., by the end of the study, the event of interest (for example, in medicine 'death of a patient' or in this dataset 'recurrence of cancer') has only occurred for a subset of the observations.\n",
        "\n",
        "The **right censoring** in this dataset is given by the column named 'event' and it's a variable which can take value 'True' if the patient had a recurrence of cancer or 'False' if the patient is recurrence free at the indicated time (right-censored samples)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VmfAR7igb2oW"
      },
      "source": [
        "Let's see how many right-censored samples do we have."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rzS8h1GG_o_A",
        "outputId": "7b9af53e-91f8-48f0-93c1-7ee71dea4bb3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(f'Number of samples: {len(df_y)}')\n",
        "print(f'Number of right censored samples: {len(df_y.query(\"event == False\"))}')\n",
        "print(f'Percentage of right censored samples: {100*len(df_y.query(\"event == False\"))/len(df_y):.1f}%')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of samples: 686\n",
            "Number of right censored samples: 387\n",
            "Percentage of right censored samples: 56.4%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VtsENFsnQhZx"
      },
      "source": [
        "There are 387 patients (56.4%) who were right censored (recurrence free) at the end of the study.\n",
        "\n",
        "Let's divide our dataset in training and test sets."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.preprocessing import OrdinalEncoder"
      ],
      "metadata": {
        "id": "PV9SQ8LZ20BL"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X[\"horTh\"] = [1 if X[\"horTh\"].iloc[i] == 'yes' else 0 for i in range(X.shape[0])]"
      ],
      "metadata": {
        "id": "MxHGiw0E4-hP"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X[\"menostat\"] = [1 if X[\"menostat\"].iloc[i] == 'Post' else 0 for i in range(X.shape[0])]"
      ],
      "metadata": {
        "id": "P_R8Fr4a5JUt"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X[\"tgrade\"] = OrdinalEncoder(categories=[['I', 'II', 'III']]).fit_transform(X[[\"tgrade\"]])"
      ],
      "metadata": {
        "id": "b6ABtXNK3Dd9"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "duYhddUr_1nH",
        "outputId": "8e91b087-2c84-41e8-bedd-80fc729a574f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_trn, X_test, y_trn, y_test = train_test_split(X, y, random_state=20)\n",
        "\n",
        "print(f'Number of training samples: {len(y_trn)}')\n",
        "print(f'Number of test samples: {len(y_test)}')"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of training samples: 514\n",
            "Number of test samples: 172\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3VEOV-vWb2ow"
      },
      "source": [
        "We divide the features/covariates into continuous and categorical."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4OqMuC40mXDb",
        "outputId": "ec0c6469-75e7-4262-e618-d327768bb17f"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age         float64\n",
              "estrec      float64\n",
              "horTh         int64\n",
              "menostat      int64\n",
              "pnodes      float64\n",
              "progrec     float64\n",
              "tgrade      float64\n",
              "tsize       float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jDjgk9PWb2o3"
      },
      "source": [
        "We use ordinal encoding for categorical features/covariates and standard scaling for continuous features/covariates."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N_nodxlEb2o-"
      },
      "source": [
        "# Baseline: Cox Proportional Hazards model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KqdChwYJb2o_"
      },
      "source": [
        "Cox Proportional Hazards model assumes that the log-hazard of a subject is a linear function of their $m$ static covariates/features $h_i, i\\in\\{1,\\ldots,m\\}$, and a population-level baseline hazard function $h_0(t)$ that changes over time:\n",
        "\\begin{equation}\n",
        "h(t|x)=h_0(t)\\exp\\left(\\sum_{i=1}^mh_i(x_i-\\bar{x_i})\\right).\n",
        "\\end{equation}\n",
        "\n",
        "The term *proportional hazards* refers to the assumption of a constant relationship between the dependent variable and the regression coefficients."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "77YbwMKvAFHQ",
        "outputId": "dbeb5e0a-6759-4792-91a9-a3a536a59699",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "from sklearn.pipeline import make_pipeline\n",
        "from sksurv.linear_model import CoxPHSurvivalAnalysis\n",
        "from sksurv.metrics import concordance_index_censored\n",
        "\n",
        "cox = CoxPHSurvivalAnalysis()\n",
        "cox.fit(X_trn, y_trn)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CoxPHSurvivalAnalysis()"
            ],
            "text/html": [
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            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's now attempt to quantify how a survival curve estimated on a training set performs on a test set.\n",
        "\n",
        "## Survival model evaluation using the Integrated Brier Score (IBS) and the Concordance Index (C-index)"
      ],
      "metadata": {
        "id": "I2wAzNXar551"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Brier score and the C-index are measures that **assess the quality of a predicted survival curve** on a finite data sample.\n",
        "\n",
        "- **The Brier score is a proper scoring rule**, meaning that an estimate of the survival curve has minimal Brier score if and only if it matches the true survival probabilities induced by the underlying data generating process. In that respect the **Brier score** assesses both the **calibration** and the **ranking power** of a survival probability estimator.\n",
        "\n",
        "- On the other hand, the **C-index** only assesses the **ranking power**: it is invariant to a monotonic transform of the survival probabilities. It only focus on the ability of a predictive survival model to identify which individual is likely to fail first out of any pair of two individuals.\n",
        "\n",
        "\n",
        "\n",
        "It is comprised between 0 and 1 (lower is better).\n",
        "It answers the question \"how close to the real probabilities are our estimates?\"."
      ],
      "metadata": {
        "id": "Gap1YWH5sAA1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<summary>Mathematical formulation</summary>\n",
        "    \n",
        "$$\\mathrm{BS}^c(t) = \\frac{1}{n} \\sum_{i=1}^n I(d_i \\leq t \\land \\delta_i = 1)\n",
        "        \\frac{(0 - \\hat{S}(t | \\mathbf{x}_i))^2}{\\hat{G}(d_i)} + I(d_i > t)\n",
        "        \\frac{(1 - \\hat{S}(t | \\mathbf{x}_i))^2}{\\hat{G}(t)}$$\n",
        "    \n",
        "In the survival analysis context, the Brier Score can be seen as the Mean Squared Error (MSE) between our probability $\\hat{S}(t)$ and our target label $\\delta_i \\in {0, 1}$, weighted by the inverse probability of censoring $\\frac{1}{\\hat{G}(t)}$. In practice we estimate $\\hat{G}(t)$ using a variant of the Kaplan-Estimator with swapped event indicator.\n",
        "\n",
        "- When no event or censoring has happened at $t$ yet, i.e. $I(d_i > t)$, we penalize a low probability of survival with $(1 - \\hat{S}(t|\\mathbf{x}_i))^2$.\n",
        "- Conversely, when an individual has experienced an event before $t$, i.e. $I(d_i \\leq t \\land \\delta_i = 1)$, we penalize a high probability of survival with $(0 - \\hat{S}(t|\\mathbf{x}_i))^2$."
      ],
      "metadata": {
        "id": "3bmwqNQisHup"
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    },
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      "cell_type": "markdown",
      "source": [
        "![BrierScore.svg](data:image/svg+xml;base64,<svg width="456" height="278" viewBox="0 0 456 278" fill="none" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
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"/>
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"/>
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"/>
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xYRGltZW5zaW9uPjE3MjwvZXhpZjpQaXhlbFhEaW1lbnNpb24+CiAgICAgICAgIDxleGlmOlVzZXJDb21tZW50PlNjcmVlbnNob3Q8L2V4aWY6VXNlckNvbW1lbnQ+CiAgICAgIDwvcmRmOkRlc2NyaXB0aW9uPgogICA8L3JkZjpSREY+CjwveDp4bXBtZXRhPgrGKWyiAAAAHGlET1QAAAACAAAAAAAAAEQAAAAoAAAARAAAAEQAAAyBDUSlFgAADE1JREFUeAHsXQWs1EwQHtw9eAIBQoDgENz94e7uBJfg7u4Ed3f3B4EQ5BHcEiR4grv7//9f85f0etu+9trta+92kkuv6zv9up2dnZ2N9uDBg39IkOCARzgQ7Z//yCNtFc0UHCABWAECT3FAANZTj0s0VgBWYMBTHBCA9dTjEo0VgBUY8BQHBGA99bhEYwVgBQY8xYGgAeyGDRvo/PnzTOZnyJCBevXqxYwTgd7iQFAANiIigpo1a6bL+QkTJlCTJk1004hI93PA84B9/fo1hYWFEa6R0f79+yl79uyRJRPxLuaApwH7588f6tChAx0/ftwQizNlykS7d++mBAkSGEovErmPA54G7KJFi2jy5MmmuFqnTh2aMWOGqTwisXs44FnAXrx4kRo0aBAQJydOnEiNGzcOKK/IFLUc8CRg3717R9WqVaNnz54FzD0hzwbMuijN6DnAwhqyS5cuFB4ebolxQp61xL4oy+w5wK5atYpGjx6tyTBMqJAmRYoU1K5dO7p//75m2rp169L06dM140WE+zjgKcBeu3aNateurclFjJorVqwgLBSA3r9/Tz169KCTJ09q5sGkrWHDhprxIsJdHPAMYD98+EC1atWiR48eMTlYsmRJmjt3LiVJksQn/tevX9KIvG7dOp9w5c2BAwcoW7ZsyiDx36Uc8AxgMVLu27ePycamTZtKoIwZMyYzHoHLly+ncePGMeMxMu/Zs4fix4/PjBeB7uGAJwALO4GhQ4cyuTZ48GDq2LEjM04deOzYMWrfvr06WLqvX78+TZ06lRknAt3DAdcD9ubNm5IKi8WyhQsXUuXKlVlRmmEoD5MxlkpsypQpAet2NSsUEbZywNWA/fLlC9WsWdNvpg8NACZXuXLlCogZL168oE6dOtHVq1f98h86dIiyZs3qFy4C3MEBVwO2f//+tG3bNh9OwXhl2bJllDZtWp9wszdfv36lAQMG+MnFQp41y0ln07sWsFu3bpUApWRHhQoVaNasWbYZr8B4BpqF2bNnK6uRxAKIB4LcxwFXAvbjx4+UN29eH25B7sQEK0aMGD7hdtxAQ6A28EZYzpw57ShelGEjB1wJWCy/fv/+3aebcePG9bm3++bHjx+EEVcm3vXJ9YirOQ64ErDmuiBShxIHBGBD6WkHQV8FYIPgIYZSFwRgQ+lpB0FfBWCD4CGGUhcEYEPpaQdBXwVgg+AhhlIXBGBD6WkHQV8FYIPgIYZSFwRgQ+lpB0FfBWCD4CGGUhc8B1iYBcaOHZuLEQzsb0Fx4sThUr4RYMGwfOTIkVSiRAlq1aqVkSyOp9m7d6+0M3nYsGF+Rkq8G+M6wAI0ly5donPnztF/Z4jRmzdv6NWrV/Ty5Usfh28w4k6fPr1kF5s6dWrCD7ayBQsWpMSJE5vm261bt6hq1apSvqiy1EK9Q4YMoc+fP0vtgHebpEmTmu4LzwzYBFq2bNm/VXTu3Jl69uxJ8eLF+xvG848rAHvv3j1pEyC2Y1+4cMFyf/Pnz0+lS5emwoULU4ECBaQRM7JCBw4cSFu2bJGSYRsNRnGn6O3btzRixAgfY/I0adJI98mSJXOqGYbqwSCCXSDyS4VMMHpfsmQJZc6c2VAZVhJFKWCvX79OcOimtRvWSseUeatXry7t/cJWcBYAdu7cSX379pWyFCtWjPS2hCvLteM/NkZi54PSXSgAgDYAtG6khw8fUuvWrf223AO0MLLnSVECWHR4+PDhug4ueHU6T548kuiAfVsA79mzZ/+OrKizT58+kvMNXvXL5UL0wbbzjRs3ykHSFe3DfjXWi+WTMIpvIKphtzLENyX17t2bunfvTtGjR1cG2/bfccDCCRs6ZJbwILNkyUIZM2akdOnSSQbeGJUg3+IzpefdxUxdGNkwyvIkiABt27b12wSJLwB2AnvFPwImwPgyYeOmkjDKwgVUIHMJZTms/44B9ufPnzR+/HhavXo1qx3MMEysWrZsKbnGxKRKjz59+kRnzpyhI0eO+IyYenlYcRBTeALm6dOn1KJFC7+dwPDGCL+1TsrOrP6bDfv9+7f0pYA/MyVhArx+/XrbJ42OABZbXjCpwcZCIwTZDbNl+BwI5AFC5IBTDIzmZgijOORZXnT37l3pnAWlvIq6MEFcunQp6Xmu4dUmO8rVer7Yhr9mzRo/91FW6nQEsNiZOnPmTEPtxOdk2rRptnTy1KlTkmtO5YxWrxFw44kJEA+CIzscHKJuCx4qPNt43Y09vqCQaU+cOOHDPgwCAG2iRIl8wgO94Q5YOFrr1q2bofaVK1eOFi9ebKvSHioqiBXqUY3VIPg7QBvsptOnT0tigLpcfEl27dpFKVOmVEd58h5iWaNGjQg8VxJ04ytXrrTlpeQKWMyEy5QpYwgscJEJxbldb6KSYRARatSo4Te6KdPgP3TAds/OMYuG3y41YUQFWJ3QXarr5nn//Plzgt9dtSuookWLSqtjsWLFslQ9V8DOnz9f+rwbaSFmlegoL8LLoPY9oKwLai71bFcZH8h/uESCDpg1ujuhjTDSZogogwYNkrQv/fr1o2jRohnJppsGIywmkWqCbwks51ohboDFOQRYZTJC0AZA3gxkgmWkfDkNAAvgsgiKcKzh20WQ6SCzslbu4EFRyxujXfUbLUeeX+AZYDncLpLLVZc3b948JpjV6bTuuQFWuXqkVbkcXqlSJWnFS77ndcUScMWKFZnFW2WkutBRo0YxVXhYxYLxiFNr7+p2Ke+hw8byNchu9/l4YSGG3blzR1ml9B+qx0BFIW6A7dq1Kx08eNCvsawAJ0cc1IXlUDVhhLfqYE4uk+UXTI7DwXaBel2Uy7DritVGeRmax1FQWi7+IX7t2LEjIH03F8DCzVCOHDkM8xUWP9DTOkE4NRGylJIwW8dM3g6CC08cXsciiCR6cjQrD68wNR94uRmFUz2s3qkJ5/7i/F+zxAWwmJWbUQ9hYgKZxwnCJCN37tw+VeFQDrMnKvoU8P8NPoMQb1jnMEArgJW4hAkTsrI6GoalYSzKyJNBtO3KlStc1v9xNkW+fPmY/YMrVVjWmSEugMUx8NDHGSXIdUePHjWa3HI6jIBKZ8Z2ed6Gglxr4oYZuFF9tOUO6hSArx++MHh5ZKpSpQotWLBAvrX9isEAVnlqgmgE0cCMR0ougDWzWCB3wsmTCWElhUM6ZLIyCZDLwBFLMF5Rr2QhHiMYZGQexiBy/UauWPeHNZXanBMvGbQkvOjJkycSb1jlw74Eh6oYJS6AjUznyWqck3Ks8lBlgAmjrVX9o9Yogr4CJLDKj0qCuAIdqGykrmwLFjDUYpIy3o7/+MJgNFUT+I/lXKMLNlwAi89N8+bN1W2L9B4TAflQuEgTW0iwefNmSVmOIuyQn9XbRtRNg4iUPHlydbBj9xj9obVRigHKym/fvs3d8ObGjRvSTgVlvfJ/WK+NGTNGvtW9cgHs48ePpSVZ3ZoZkbBDhRzIy/hXrlJpkwtGgWFWCPa9WpZhEBPMmFRaaYc6L6yoDh8+LJ1hpl4qldPCUgzr/E4QDJu0jlLFyxSZCSnayAWwKFivcYjXIrMyjVY5euH4BGJnAQjiixXX8JcvX6Z69eppVgfVDVQ4ThNsGFA3a6VN2RZYp8FKzQnSUnGhbohMEJ0iI26AnTNnjnSARmQNYMXzXmfftGmTdF4C6sZKjJlZqrq9rJNulGkiIiIoVapUyiCu/7GaB0NwrRFfXTl4UahQIXUwl3vwAsvVLIIsi+1KkRnPcwMsbAlKlSrFnDWzGqwO2759u6b+Tp3W7D3ahm3jsF3AlptACbKhnh4RQAAgeBLOZsB2cExcoBpkLYXq1Y9RTQskbdq0sVW2xcRP70xfI19XboAFk2BfikYEQnjjsEHPyuc6kHrN5NE7UhTlYPUO2g8eBH0qwGa3hZncVvCfx2KC3pnBmHDjpdP74nEF7Ldv36h8+fJ+tpEyU4xcsYMUNrVuJJwujj1gWoR9TvjK8CAtEz676oLvAfX5ZXaUDXEPNgxatHbtWipevLhWNL9Jl1xjIDpZOa98haJfS/aR0zh9hSooLCxMt1qjM1/dQjQiYfHFU7c7adIkU6uVGs30C4acqrdQADEETkW0iOsIK1eKmajRDYhyHvUVZ8NigqP3uVDn4XmPkX/s2LGaVeCTCmslXhQeHs5N3ECbYdFmRb7X6jfmDkWKFNGKJtjlAtRaqs1/AQAA//8VrMOMAAAPFUlEQVTtnQfsFNUTxx9WFKVYohgLoGLFir0BKipFRewKdgJWVCxYkC5gBxEFBQUFrLFExdiwVxBpFrAEQqxYCIqd/+9zyf6yv91XZu/e7q3+d5LL3e5rs+9m386b+c5svZU1pFKmFStWqKOPPlotWLCgopEOP/xwddNNN6m11lqron58ND7rrLPUyy+/bOyqbdu26p577jGW+yjgr9P9fZzbe++91dKlS43DwN+4ceOM5aussoqxrNKCVq1aqV9++cXYzRNPPKGoo6N6NReXusAy8Oeff66OOuooK6M6BqPndtppp9JEb7jhhtGizI7/+OMPte2221rHO//889Ull1xirZNWIXN9yCGHWLvv27evOuecc6x10io8+eST1dtvv23s/qKLLlJ8dJSZwDL4Bx98oLp27arjI9G5jTfeWE2ePFk1a9YsUTtfld9//311/PHHW7sbMmSIOumkk6x10ip86KGH1JVXXmnt/tFHH1W77rqrtU5ahQMGDFD33XefsfvmzZurF198UVueqcDCwWuvvaZOO+00LTNJTjZo0EA98MADihU3a7rjjjvUjTfeaB32zjvvVO3bt7fWSauwd+/e6sknn7R2/8knn6jVV1/dWietQlSR66+/3tr9O++8o3RP0cwFFi7feOMN1a1bNyvD0sIJEyaogw46SFrdSz0eV0899ZS1r0ceeUTttttu1jppFKLhcRPbdMT9999fTZw4MY3hRX0yN5dffrm17v3336/23XffWJ2qCCxczJ07t/TItE1sjFvDCTZiXbp0MZT6P92hQwf18ccfWztmQ7bFFltY66RRuHDhQufK3qdPH3XuueemMbyoTx73Lv35mmuuUWeeeWasv6oJLJwsWrRIde/evfQd4yzhieHDh6vjjjsuYavk1f/++2+19dZbOxvOmjVLNWzY0FnPd4UpU6aoq6++2trt1KlT1Z577mmtk2bhzJkz1bHHHmsdgr3ODTfcEKtTVYGFG0wvmIhmz54dYy7pifHjx6s2bdokbZao/pIlS9QBBxzgbPPZZ5+pevXqOev5rnDBBReop59+2trtvHnzqmoalFgxWBSee+652HVUXWDh6Ndff1UXX3yxev7552MMJj1hs+El7UtX/80331Snnnqqrqj2HBvCOXPm1B5n9eOff/5RO++8s1V/3WOPPdSDDz6YFUvacX744QfVunVrbVn4pO7GyoXAwiSPWnaOrJKVEMLywgsvqI022qiSboxt2WyZbIRBo/XXX1+99957wWFm359++qnCuWIjm43T1s5n2W+//aa23357Z5e6fUBuBDbgftKkSeq6664LDsv6Puyww9SYMWPKautqhP531VVXWathJ2Ylzpow81177bXWYZnf/fbbz1on7ULpPgDT3I477liHndwJLNxxZ6HXVkLYSl2rTTn9427FKWAjm+Hb1q7SMrxrzzzzjLUbVBWeQtWmFi1aOFngBtxnn33q1MulwMIhXjEsCOWavXgsoxM3bty4zgVXejBy5Eh16623WrvBbesSHGsHZRSyau2yyy7W+cI++/jjj5fRu/8mEoHVOV9yK7BMEUr3iSeeaP0TbFN54YUXKrw+PmnYsGFq7Nix1i6rIRjYhbEP26hnz55Og72tvc8yicCOGDEiZv7KtcAyQSC8jjnmmLKEllUWkMWqq67qba7REXlU2QgfPb76LEmi+6POgNLKA7kQW/DIXJ9xxhl12M29wMKtBGxS56pCB75ts/3793e6Nauxwvbq1UtrtwxNRUnNatSoUfhU1X5jXrPBH2EM9y1PhTD9KwQWhtG9yoHr8Zi8/fbbw9dc0e9bbrlFjRo1ytpH1jqsRH/NmifrBNUUSlZYrEVRoJRYYC+99FL13Xfflfz/RxxxhIufVMpdoGnToKgVvtSCu+++Ww0dOtQ0VOl81lYCdP3OnTtbecIvj38+LyQRWJ27XSSw33//fa3vWbdMZzUJixcvLguZ9frrr6tNNtnEC5sSrOnmm2+upk+f7mU8SScg1gYNGmStqttxWxukXCjZdGGR6dSpUx1ORAKLTxcdCaq2p6Rfv34K6FkSevjhh9Xuu++epImx7rPPPqvOO+88YzkFWXu6QD6ZAM8Bo++++67aYIMNgsOqf0sEVrdJFAlsGCHeo0cPJ5o9zdkoJ2rhtttucz4ypTxLsLxZYgkkXqOsVRTXXEp4pg+iSohNC5NIYA8++GD1xRdflNqBOwV/Wi0CoIxr8euvvxazcNlll9U+IcSNDBXxFBGb5iIQSVmQhB8cMFg38kK///672m677ZzsTJs2TbVs2bJOPafAfvPNN3XcY7jKXHbIOiOkcJB08+VTjVm+fLkoLOfDDz9U6667bgpXX7dLiatYpwvW7SXbo2XLlpW8cq5Ry0Jr4WLERx1Q1huKYNzwt8QWGq4/ePBgRaSmLwJzC/jcRmy6mKu0SXLzAsQBkJMXYu5cuGXTPsC5wuo8OzNmzFBNmjSp2vVjVsK8JCWd8i5tq6vHiu2K6coiKvXPP/9U22yzjY7F2nPVQo7VMqD5wdPHFdJ04IEHqnvvvTfW2imwutXEt/coxpXjBPA+YH5SQrh22GEHaXVnvbvuukthI7QRN1S7du1sVSouIwwHt7WNCEcH/5AnkqDxTj/9dIVFKEpWgf3qq6+02MlqgyjYRGBblZJvSN0rr7wS83FHedEBN6J1Kj0GhOMSxptvvrmUdafSsXy2l0TNAuY/4YQTYsNaBdaErke/eOutt9Rqq60W6zDtE1KTSMBHx44dna7UoK70GwuFLgQ53B6UGGixNIlV6NVXX7UOkZUubWUiUijJSwA0dMstt4y0VMoqsLZHb7U8J5IwkPBVEnlABIJvCpv6dH0DHgdEnhZJ0iWZNi5p8STt17UHgW8cHbogTqvAsoqY7J14jghmSzNpmG4CeNRys0hJZxqRtrXVwxnBx0Rpb3YkodImmzn2TVZmsAVrr7226RJSO+96Mpj4hiGjwEpMDyY9I60rJXgNWJo0CsGUjMEHfyQCOfLII61dpWmL5abl5rUR+m00B1gY6O0byWbjJVzmAr7Y9H+jwD722GOKDCE2wgWJrpGVjU8C7Qv4Ja6d+Py09GyJxy1N05Zk4xnVA8mpQNrTAIcK7NIVpRDMp6/vb7/9NuZujfZtsxsbBRZUFrs5FxHVSCa6tO2ykkdgmNcsclu5dLG0nkDkH9hqq63Clxv7zWJCcpJADySF0dlnn13r8KiWu9aV1wEzqi3U3yiwEkR4MEusZrhr00ID8fglVCJYGYJxTd9ZQSBdkRCsZpiVfFPUXa7rn4UkyGD40ksvlVB2gSpFRAQwyTXWWEPXNNVzCCOeRxO53Mhagf3yyy8TG71xQxL+7DvmnaQYIMSklGUiYUxsWAtMblpWOdBlvtUSKWINUDdYZkyQAbEDx1yZlRoXjBt820J5mC+sA7YM61qBBT96xRVXBGMk+j700ENLiSYqzdyHsZ9warwiUqoGqt41VzqInPR6TPVM9nFT/fB5sCGu7OHh+j5/k5Iqmhgj3P8pp5ziBKJrBZbYqUrj18ExkrYcnUQC1kUvw8ZKih/AyC6DePhC+Q3iHoBLoLNFy9M6BipHvlWTuoLe6MoUk5Q31C9Xhhddn+w1JInsdG19nHM9LSVOjpjAsvslFaPpDyiHcR4/m222WSmjMnrueuutpxBQkoL9+OOPpW82CIGOlWQM9DH0RMlNkaTfJHVtnps07LFRBJ2EV5upSNLeRx3bRl76dIwJrC4hLqYP8nU2bdq0xDeYUHAGpB1nQ5R0NfRx8eg7pJbkQn3riEn5c+E7MRGSlcUXgZZLkgt39OjRqlqBo8E1//XXX6V3KpgWJZ6s6NcuiglsNCGuBHUEEyj27EbRr0xMuZiRlKN/EcPETbTmmmtKmmRSx+aB822gZ7Pneq0RF83qjq21Gqnro5MejguMliXJCB4T2HBC3HIMy0zm/PnzS+8xQBdlNaiUeOzjJuZdBqgrWeupEv5/+umnkn5oull1qSMl/ZrquBBP2FkRhHXWWcfURWbnUTMBIenS7LOqgn6TuohjAhvYX23+3CRX+vPPP5dMFR999JEiKzU5AshvgJs1/OdiFiNxGxfAZ9NNNy1FupKgNw+TLrlm2+6dJMgDBw6UdCOuwyaGMXmcYgoi/on5Qn3TvYFF3LHnijx52XzqCOcL+dOkFBNYVkjuCEAtaQNbGIfNl68kF9KLTrMeOjW7XR3lLdRax6Pvc/zHLH66VwLgcGIDmeT/jwmsb4b/3/qzJfsg7U6lyZr/bfNpA7tHsQ6SaysEVjJLCevY0hllgXFIyG5q1VH5cCTpIKrl4pQLgU3h70KtAtaHCzVKJLXgMZgnC0eUR1/HvH4Jq1OUKgmxKgQ2OpuejoHRsTPWOWCwxPDWnP8ymQINMceRyzaJ3hqep0Jgw7Ph+bctqyBYXUn2E88sZdIdHkxMkGErEANj/SE3WSWovkJgU/4LTQZzzHh4wHBT/5eIWDNMWLqoZh+A9kJgM5AW09u/iYvj8Vi/fv0MuEh/CHR38ggHONzwiOiye+21V/hUWb8LgS1r2pI1whbZt2/fEmg62hI9l2DGtG3e0XHTOAaYHY0WAPMBxJI4Lh9UCKyPWRT0gYOEl/2SNSZKvFnbFT8XbZO3Y11QJDorwoqDwBcVAutrJoX9mLK1kIO3W7duwl7yVQ2hjKajR0dH3QFW6pMKgfU5m8K+TMAV9D9Xdm/hEJlV071ojxV14sSJqbzvtxDYzP7augOZACFgEYhQyLtOywaLtKfRXMFk2SFa2PcbKIPZKwQ2mIkqfBMShO4KCD5MoK3406sNTA/zFP69YsWK0hsmwQKEKYuohkJgwzNehd/keMXsFU17RDQu7wPLm8kLuCh21jDOmTc/EqZUaeCpZPoLgZXMUgZ1SGtE8GfwLgmG5AUgQVhSBiyIhoiG58AzodvlulpFg4Yr1dgIC8rJDNSEQa+sif5dWQOQWVmzackJV3E2aqKTV9a4XlfWJFSOF6Z8plhhw3dvTn6T/AIbZh5DgZgiMAKsqNVQVwqBzYmQFmzIZqAQWNk8FbVyMgOFwObkjyjYkM1AIbCyeSpq5WQGCoHNyR9RsCGbgUJgZfNU1MrJDBQCm5M/omBDNgOFwMrmqaiVkxn4Hx2jjEQhv3JiAAAAAElFTkSuQmCC"/>
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"/>
</defs>
</svg>
)"
      ],
      "metadata": {
        "id": "CmP1ahaXsy_c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ci_cox = concordance_index_censored(y_test[\"cens\"], y_test[\"time\"], cox.predict(X_test))\n",
        "print(f'The c-index of Cox is given by {ci_cox[0]:.3f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oVdrGM0NtWdR",
        "outputId": "19ea15bc-9e45-4e8e-d0ab-ae3d590fa289"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The c-index of Cox is given by 0.665\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "times = np.arange(365, 1826)"
      ],
      "metadata": {
        "id": "zoUFRn2yqLPF"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "survs = cox.predict_survival_function(X_test)"
      ],
      "metadata": {
        "id": "06i_zOUBqMUt"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "preds = np.asarray([[fn(t) for t in times] for fn in survs])"
      ],
      "metadata": {
        "id": "prIyWOtMqe7s"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sksurv.metrics import integrated_brier_score\n",
        "\n",
        "integrated_brier_score(y_trn, y_test, preds, times)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qyvn1TexqkoO",
        "outputId": "7042880a-70a3-40a6-940f-36a51a0fb246"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.17509710701666106"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q shap\n",
        "import shap\n",
        "explainer = shap.Explainer(cox.predict, X_trn)\n",
        "shap_values = explainer(X_test[:100])\n",
        "shap.plots.waterfall(shap_values[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "id": "J03T1z80N8fg",
        "outputId": "2bc340fd-dd5e-484a-d887-bf48eeed72b0"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X.describe().transpose().round(2).drop(columns=\"count\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "-s0v-Yx5N_3o",
        "outputId": "d872a1e2-5c87-4cfb-803f-a3a5e846d11f"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            mean     std   min   25%   50%     75%     max\n",
              "age        53.05   10.12  21.0  46.0  53.0   61.00    80.0\n",
              "estrec     96.25  153.08   0.0   8.0  36.0  114.00  1144.0\n",
              "horTh       0.36    0.48   0.0   0.0   0.0    1.00     1.0\n",
              "menostat    0.58    0.49   0.0   0.0   1.0    1.00     1.0\n",
              "pnodes      5.01    5.48   1.0   1.0   3.0    7.00    51.0\n",
              "progrec   110.00  202.33   0.0   7.0  32.5  131.75  2380.0\n",
              "tgrade      1.12    0.58   0.0   1.0   1.0    1.00     2.0\n",
              "tsize      29.33   14.30   3.0  20.0  25.0   35.00   120.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-219003f2-8249-4967-887f-11a49065d107\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>53.05</td>\n",
              "      <td>10.12</td>\n",
              "      <td>21.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>61.00</td>\n",
              "      <td>80.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>estrec</th>\n",
              "      <td>96.25</td>\n",
              "      <td>153.08</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>114.00</td>\n",
              "      <td>1144.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>horTh</th>\n",
              "      <td>0.36</td>\n",
              "      <td>0.48</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>menostat</th>\n",
              "      <td>0.58</td>\n",
              "      <td>0.49</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pnodes</th>\n",
              "      <td>5.01</td>\n",
              "      <td>5.48</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>7.00</td>\n",
              "      <td>51.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>progrec</th>\n",
              "      <td>110.00</td>\n",
              "      <td>202.33</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>32.5</td>\n",
              "      <td>131.75</td>\n",
              "      <td>2380.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tgrade</th>\n",
              "      <td>1.12</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tsize</th>\n",
              "      <td>29.33</td>\n",
              "      <td>14.30</td>\n",
              "      <td>3.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>35.00</td>\n",
              "      <td>120.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-219003f2-8249-4967-887f-11a49065d107')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-219003f2-8249-4967-887f-11a49065d107 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-219003f2-8249-4967-887f-11a49065d107');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e3611107-f6c5-45e6-ad1c-b101e98fa6aa\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e3611107-f6c5-45e6-ad1c-b101e98fa6aa')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e3611107-f6c5-45e6-ad1c-b101e98fa6aa button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shap.plots.beeswarm(shap_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "0kbYd1wAODT9",
        "outputId": "1c9aa6ea-f435-4c32-c143-ad0f6f71b61f"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x470 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZNHQ-bkWALNy",
        "outputId": "3a63b8c0-5ce3-4c82-f22b-dfd4325c5fdd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from scipy.stats import reciprocal\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "\n",
        "param_distributions = {\n",
        "    'alpha': reciprocal(0.1, 100),\n",
        "}\n",
        "\n",
        "model_random_search = RandomizedSearchCV(\n",
        "    cox, param_distributions=param_distributions, n_iter=50, n_jobs=-1, cv=3, random_state=42)\n",
        "model_random_search.fit(X_trn, y_trn)\n",
        "\n",
        "print(\n",
        "    f\"The c-index of Cox using a {model_random_search.__class__.__name__} is \"\n",
        "    f\"{model_random_search.score(X_test, y_test):.3f}\")\n",
        "print(\n",
        "    f\"The best set of parameters is: {model_random_search.best_params_}\"\n",
        ")"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The c-index of Cox using a RandomizedSearchCV is 0.660\n",
            "The best set of parameters is: {'alpha': 39.67605077052987}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MrhAveCQAdxF",
        "outputId": "75475cd8-1ec8-4df1-ec0a-2ff2d00038d4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "alpha = model_random_search.best_params_['alpha']\n",
        "cox_best = make_pipeline(CoxPHSurvivalAnalysis(alpha=alpha))\n",
        "cox_best.fit(X_trn, y_trn)\n",
        "\n",
        "ci_cox = concordance_index_censored(y_test[\"cens\"], y_test[\"time\"], cox_best.predict(X_test))\n",
        "print(f'The c-index of Cox is given by {ci_cox[0]:.3f}')"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The c-index of Cox is given by 0.660\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sksurv.ensemble import RandomSurvivalForest"
      ],
      "metadata": {
        "id": "s34_kkDKKzw5"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rsf = RandomSurvivalForest(\n",
        "    n_estimators=100, min_samples_leaf=15, n_jobs=-1, random_state=20\n",
        ")\n",
        "rsf.fit(X_trn, y_trn)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 91
        },
        "id": "vU06jwE7Kuec",
        "outputId": "e3cc5cf0-0655-46f6-91ea-d74ed12d6c5b"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomSurvivalForest(min_samples_leaf=15, min_samples_split=10, n_jobs=-1,\n",
              "                     random_state=20)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomSurvivalForest(min_samples_leaf=15, min_samples_split=10, n_jobs=-1,\n",
              "                     random_state=20)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomSurvivalForest</label><div class=\"sk-toggleable__content\"><pre>RandomSurvivalForest(min_samples_leaf=15, min_samples_split=10, n_jobs=-1,\n",
              "                     random_state=20)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rsf.score(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LJ0Tp63pKvMJ",
        "outputId": "908b6916-2562-4533-ab15-853054aab6c8"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6716457023060797"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ci_cox = concordance_index_censored(y_test[\"cens\"], y_test[\"time\"], rsf.predict(X_test))\n",
        "print(f'The c-index of Cox is given by {ci_cox[0]:.3f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y1hNGO_QO2pW",
        "outputId": "93786c36-160e-4b80-fd59-c779879fff39"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The c-index of Cox is given by 0.672\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "times = np.arange(365, 1826)"
      ],
      "metadata": {
        "id": "XHsIhuW7PHik"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "survs = rsf.predict_survival_function(X_test)"
      ],
      "metadata": {
        "id": "iSZ0pa17PSJS"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "preds = np.asarray([[fn(t) for t in times] for fn in survs])"
      ],
      "metadata": {
        "id": "np-god8mPW5W"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "integrated_brier_score(y_trn, y_test, preds, times)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U1avp5nfPaOS",
        "outputId": "b1e50567-5ccd-40a5-9f19-b3aeb70881ee"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.17751179194838101"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "param_distributions = {\n",
        "    'min_samples_leaf': [3, 7, 15],\n",
        "    'max_depth': [3, 7, None]\n",
        "}\n",
        "\n",
        "model_random_search = RandomizedSearchCV(\n",
        "   rsf, param_distributions=param_distributions, n_iter=50, n_jobs=-1, cv=3, random_state=42)\n",
        "model_random_search.fit(X_trn, y_trn)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "id": "QjiQ8TfBPfZg",
        "outputId": "5438c05e-b20d-4cb7-f05d-b93740c08857"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The total space of parameters 9 is smaller than n_iter=50. Running 9 iterations. For exhaustive searches, use GridSearchCV.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=3,\n",
              "                   estimator=RandomSurvivalForest(min_samples_leaf=15,\n",
              "                                                  min_samples_split=10,\n",
              "                                                  n_jobs=-1, random_state=20),\n",
              "                   n_iter=50, n_jobs=-1,\n",
              "                   param_distributions={'max_depth': [3, 7, None],\n",
              "                                        'min_samples_leaf': [3, 7, 15]},\n",
              "                   random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=3,\n",
              "                   estimator=RandomSurvivalForest(min_samples_leaf=15,\n",
              "                                                  min_samples_split=10,\n",
              "                                                  n_jobs=-1, random_state=20),\n",
              "                   n_iter=50, n_jobs=-1,\n",
              "                   param_distributions={&#x27;max_depth&#x27;: [3, 7, None],\n",
              "                                        &#x27;min_samples_leaf&#x27;: [3, 7, 15]},\n",
              "                   random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=3,\n",
              "                   estimator=RandomSurvivalForest(min_samples_leaf=15,\n",
              "                                                  min_samples_split=10,\n",
              "                                                  n_jobs=-1, random_state=20),\n",
              "                   n_iter=50, n_jobs=-1,\n",
              "                   param_distributions={&#x27;max_depth&#x27;: [3, 7, None],\n",
              "                                        &#x27;min_samples_leaf&#x27;: [3, 7, 15]},\n",
              "                   random_state=42)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomSurvivalForest</label><div class=\"sk-toggleable__content\"><pre>RandomSurvivalForest(min_samples_leaf=15, min_samples_split=10, n_jobs=-1,\n",
              "                     random_state=20)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomSurvivalForest</label><div class=\"sk-toggleable__content\"><pre>RandomSurvivalForest(min_samples_leaf=15, min_samples_split=10, n_jobs=-1,\n",
              "                     random_state=20)</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_random_search.score(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_X2klnTmQVXX",
        "outputId": "b15bfb3d-130b-463a-831b-a82dca36fa6a"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6805555555555556"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\n",
        "    f\"The best set of parameters is: {model_random_search.best_params_}\"\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Mam6JN2Qqh3",
        "outputId": "6d5ba74e-d207-4fb6-9d9b-084774b76174"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The best set of parameters is: {'min_samples_leaf': 15, 'max_depth': 3}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rsf_best = RandomSurvivalForest(\n",
        "    n_estimators=100, min_samples_leaf=15, max_depth=3, n_jobs=-1, random_state=20\n",
        ")\n",
        "rsf_best.fit(X_trn, y_trn)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 91
        },
        "id": "PbSNPjgQQruO",
        "outputId": "c0218816-d654-48f5-c9f8-24e7ba37c443"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomSurvivalForest(max_depth=3, min_samples_leaf=15, n_jobs=-1,\n",
              "                     random_state=20)"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomSurvivalForest(max_depth=3, min_samples_leaf=15, n_jobs=-1,\n",
              "                     random_state=20)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomSurvivalForest</label><div class=\"sk-toggleable__content\"><pre>RandomSurvivalForest(max_depth=3, min_samples_leaf=15, n_jobs=-1,\n",
              "                     random_state=20)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "times = np.arange(365, 1826)\n",
        "survs = rsf_best.predict_survival_function(X_test)"
      ],
      "metadata": {
        "id": "iO5hU-exQZup"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "preds = np.asarray([[fn(t) for t in times] for fn in survs])"
      ],
      "metadata": {
        "id": "nVQsQOocQhOI"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "integrated_brier_score(y_trn, y_test, preds, times)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rYRRmruYRCgq",
        "outputId": "a1a4bdf8-27d9-4544-f7af-433429972883"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.17850725604478493"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sksurv.ensemble import GradientBoostingSurvivalAnalysis"
      ],
      "metadata": {
        "id": "wTxXroEmRYMm"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wnfh4hsbRYw6"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}