examples/Lifelines_Example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Use SurvivalEVAL for lifelines Model\n",
    "\n",
    "In this notebook we introduce the use of `LifelinesEvaluator` in the package to evaluate the performance for a lifelines' CoxPH model.\n"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "1791a800c9db5ae2"
  },
  {
   "cell_type": "markdown",
   "source": [
    "The dataset we use in this example is the German Breast Cancer Study Group 2 dataset (GBSG2). The dataset contains 686 samples and 8 features. It has a censoring rate of 43.6\\% with the target event of recurrence-free survival. \n",
    "\n",
    "We first perform a preprocessing on the GBSG2 dataset and split the training and testing datasets."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "78acf4e9bbfac85a"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "!pip install lifelines  # Install the lifelines package if you haven't installed it.",
   "id": "c9f488f8a657ced3"
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2023-09-11T06:08:14.994078684Z",
     "start_time": "2023-09-11T06:08:14.658669543Z"
    }
   },
   "outputs": [],
   "source": [
    "from lifelines.datasets import load_gbsg2\n",
    "# Load the data and split train/test set\n",
    "gbsg2 = load_gbsg2()\n",
    "gbsg2 = gbsg2.replace({\"horTh\": {\"no\": 0, \"yes\": 1}, \"menostat\": {\"Pre\": 0, \"Post\": 1}, \"tgrade\": {\"I\": 1, \"II\": 2, \"III\": 3}})\n",
    "train, test = gbsg2.iloc[:400, :], gbsg2.iloc[400:, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "We then show the example of the first 5 rows of the training dataset: "
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "dea5ac33335b7335"
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "data": {
      "text/plain": "   horTh  age  menostat  tsize  tgrade  pnodes  progrec  estrec  time  cens\n0      0   70         1     21       2       3       48      66  1814     1\n1      1   56         1     12       2       7       61      77  2018     1\n2      1   58         1     35       2       9       52     271   712     1\n3      1   59         1     17       2       4       60      29  1807     1\n4      0   73         1     35       2       1       26      65   772     1",
      "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>horTh</th>\n      <th>age</th>\n      <th>menostat</th>\n      <th>tsize</th>\n      <th>tgrade</th>\n      <th>pnodes</th>\n      <th>progrec</th>\n      <th>estrec</th>\n      <th>time</th>\n      <th>cens</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>70</td>\n      <td>1</td>\n      <td>21</td>\n      <td>2</td>\n      <td>3</td>\n      <td>48</td>\n      <td>66</td>\n      <td>1814</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>56</td>\n      <td>1</td>\n      <td>12</td>\n      <td>2</td>\n      <td>7</td>\n      <td>61</td>\n      <td>77</td>\n      <td>2018</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>58</td>\n      <td>1</td>\n      <td>35</td>\n      <td>2</td>\n      <td>9</td>\n      <td>52</td>\n      <td>271</td>\n      <td>712</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>59</td>\n      <td>1</td>\n      <td>17</td>\n      <td>2</td>\n      <td>4</td>\n      <td>60</td>\n      <td>29</td>\n      <td>1807</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>73</td>\n      <td>1</td>\n      <td>35</td>\n      <td>2</td>\n      <td>1</td>\n      <td>26</td>\n      <td>65</td>\n      <td>772</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-11T06:08:16.511272338Z",
     "start_time": "2023-09-11T06:08:16.506602163Z"
    }
   },
   "id": "dbb76fc1cac815fe"
  },
  {
   "cell_type": "markdown",
   "source": [
    "We can see that the dataset contains 8 features and 2 target columns: time and cens. The time column is the time to the event or censoring, and the cens column is the censoring indicator."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "d269334bb7a246ff"
  },
  {
   "cell_type": "markdown",
   "source": [
    "We then fit the ISD model on the training dataset and predict the survival curves on the testing dataset.\n",
    "\n",
    "To be specific, we choose the most popular Cox proportional hazard (CoxPH) model to check and test the provided functionalities."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "97e78066392cb0b0"
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "from lifelines import CoxPHFitter\n",
    "isd_curves = CoxPHFitter().fit(train, duration_col=\"time\", event_col=\"cens\").predict_survival_function(test)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-11T06:08:18.492577917Z",
     "start_time": "2023-09-11T06:08:18.418093608Z"
    }
   },
   "id": "82b626a2d556b644"
  },
  {
   "cell_type": "markdown",
   "source": [
    "We can see that the ISD model returns a pandas dataframe with the survival curves of all the samples in the testing dataset. The index of the dataframe is the time points, and the columns are the sample IDs. \n",
    "\n",
    "Show the first 5 rows of the dataframe:"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "f206f7a51fe9f6fa"
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "            400       401       402       403       404       405       406  \\\n8.0    1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n72.0   0.996027  0.997582  0.997735  0.997859  0.999507  0.997970  0.997636   \n113.0  0.992061  0.995164  0.995470  0.995717  0.999014  0.995938  0.995271   \n148.0  0.992061  0.995164  0.995470  0.995717  0.999014  0.995938  0.995271   \n160.0  0.988080  0.992733  0.993192  0.993563  0.998517  0.993895  0.992894   \n\n            407       408       409  ...       676       677       678  \\\n8.0    1.000000  1.000000  1.000000  ...  1.000000  1.000000  1.000000   \n72.0   0.999192  0.997294  0.998272  ...  0.998179  0.998243  0.998402   \n113.0  0.998383  0.994589  0.996543  ...  0.996356  0.996485  0.996803   \n148.0  0.998383  0.994589  0.996543  ...  0.996356  0.996485  0.996803   \n160.0  0.997569  0.991870  0.994804  ...  0.994523  0.994716  0.995194   \n\n            679       680       681       682       683       684       685  \n8.0    1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  \n72.0   0.998139  0.997620  0.997030  0.995452  0.997326  0.997280  0.997334  \n113.0  0.996277  0.995239  0.994061  0.990913  0.994653  0.994561  0.994669  \n148.0  0.996277  0.995239  0.994061  0.990913  0.994653  0.994561  0.994669  \n160.0  0.994405  0.992846  0.991078  0.986360  0.991966  0.991828  0.991990  \n\n[5 rows x 286 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>400</th>\n      <th>401</th>\n      <th>402</th>\n      <th>403</th>\n      <th>404</th>\n      <th>405</th>\n      <th>406</th>\n      <th>407</th>\n      <th>408</th>\n      <th>409</th>\n      <th>...</th>\n      <th>676</th>\n      <th>677</th>\n      <th>678</th>\n      <th>679</th>\n      <th>680</th>\n      <th>681</th>\n      <th>682</th>\n      <th>683</th>\n      <th>684</th>\n      <th>685</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>8.0</th>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>...</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>72.0</th>\n      <td>0.996027</td>\n      <td>0.997582</td>\n      <td>0.997735</td>\n      <td>0.997859</td>\n      <td>0.999507</td>\n      <td>0.997970</td>\n      <td>0.997636</td>\n      <td>0.999192</td>\n      <td>0.997294</td>\n      <td>0.998272</td>\n      <td>...</td>\n      <td>0.998179</td>\n      <td>0.998243</td>\n      <td>0.998402</td>\n      <td>0.998139</td>\n      <td>0.997620</td>\n      <td>0.997030</td>\n      <td>0.995452</td>\n      <td>0.997326</td>\n      <td>0.997280</td>\n      <td>0.997334</td>\n    </tr>\n    <tr>\n      <th>113.0</th>\n      <td>0.992061</td>\n      <td>0.995164</td>\n      <td>0.995470</td>\n      <td>0.995717</td>\n      <td>0.999014</td>\n      <td>0.995938</td>\n      <td>0.995271</td>\n      <td>0.998383</td>\n      <td>0.994589</td>\n      <td>0.996543</td>\n      <td>...</td>\n      <td>0.996356</td>\n      <td>0.996485</td>\n      <td>0.996803</td>\n      <td>0.996277</td>\n      <td>0.995239</td>\n      <td>0.994061</td>\n      <td>0.990913</td>\n      <td>0.994653</td>\n      <td>0.994561</td>\n      <td>0.994669</td>\n    </tr>\n    <tr>\n      <th>148.0</th>\n      <td>0.992061</td>\n      <td>0.995164</td>\n      <td>0.995470</td>\n      <td>0.995717</td>\n      <td>0.999014</td>\n      <td>0.995938</td>\n      <td>0.995271</td>\n      <td>0.998383</td>\n      <td>0.994589</td>\n      <td>0.996543</td>\n      <td>...</td>\n      <td>0.996356</td>\n      <td>0.996485</td>\n      <td>0.996803</td>\n      <td>0.996277</td>\n      <td>0.995239</td>\n      <td>0.994061</td>\n      <td>0.990913</td>\n      <td>0.994653</td>\n      <td>0.994561</td>\n      <td>0.994669</td>\n    </tr>\n    <tr>\n      <th>160.0</th>\n      <td>0.988080</td>\n      <td>0.992733</td>\n      <td>0.993192</td>\n      <td>0.993563</td>\n      <td>0.998517</td>\n      <td>0.993895</td>\n      <td>0.992894</td>\n      <td>0.997569</td>\n      <td>0.991870</td>\n      <td>0.994804</td>\n      <td>...</td>\n      <td>0.994523</td>\n      <td>0.994716</td>\n      <td>0.995194</td>\n      <td>0.994405</td>\n      <td>0.992846</td>\n      <td>0.991078</td>\n      <td>0.986360</td>\n      <td>0.991966</td>\n      <td>0.991828</td>\n      <td>0.991990</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 286 columns</p>\n</div>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isd_curves.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-11T06:08:19.673649926Z",
     "start_time": "2023-09-11T06:08:19.647419739Z"
    }
   },
   "id": "8f63eaab3ad4f896"
  },
  {
   "cell_type": "markdown",
   "source": [
    "Once we get the predicted ISD curves for the test data from the model, we can then use the APIs in `SurvivalEVAL` to seamlessly plug in these ISD outputs. \n",
    "\n",
    "We first import the `LifelinesEvaluator` class from the package. The `LifelinesEvaluator` class takes the ISD curves, the time and censoring status of the test data, and the time and censoring status of the training data as inputs.\n"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "147718f7ffa7bebd"
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "from Evaluator import LifelinesEvaluator\n",
    "evl = LifelinesEvaluator(isd_curves, test.time, test.cens, train.time, train.cens)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-11T06:08:22.176710875Z",
     "start_time": "2023-09-11T06:08:20.969555667Z"
    }
   },
   "id": "9c86ef73c9b5d33b"
  },
  {
   "cell_type": "markdown",
   "source": [
    "We can plot the survival curves of the first 5 samples after the initialization of the `LifelinesEvaluator` class."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "d919203d29d1b51"
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "(<Figure size 640x480 with 1 Axes>,\n <Axes: xlabel='Time', ylabel='Survival probability'>)"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evl.plot_survival_curves(curve_indices=[0, 1, 2, 3, 4])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-11T06:08:22.329967055Z",
     "start_time": "2023-09-11T06:08:22.258126385Z"
    }
   },
   "id": "1af0936c2e77b80d"
  },
  {
   "cell_type": "markdown",
   "source": [
    "We can see that the five samples have the same shape of survival curves, but the survival probabilities are different. This is the nature of semi-parametric models.\n",
    "\n",
    "Once the user gets everything ready, s/he can perform the evaluation, starting from the C-index.\n",
    "The `concordance` method take two arguments: `ties` and `pair_method`, which specify the method to handle ties and the method to generate pairs, respectively. The default values are `None` and `Comparable`.\n",
    "\n",
    "The method returns the C-index, the number of correctly ordered pairs, and the number of total comparable pairs."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "e6043968c577b764"
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C-index: 0.6877; \n",
      "Number of correctly ordered pairs: 13630.0; \n",
      "Number of total comparable pairs: 19821.0.\n"
     ]
    }
   ],
   "source": [
    "cindex, correct_pairs, total_pairs = evl.concordance()\n",
    "print(\"C-index: {:.4f}; \\nNumber of correctly ordered pairs: {}; \\nNumber of total comparable pairs: {}.\".format(cindex, correct_pairs, total_pairs))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-11T06:08:23.643641145Z",
     "start_time": "2023-09-11T06:08:23.402823550Z"
    }
   },
   "id": "f413ca61cc70971"
  },
  {
   "cell_type": "markdown",
   "source": [
    "To calculate MAE, one can use the `mae` method. The method takes three arguments: `method`, `weighted` and `log_scale`, which specify the method to calculate the pseudo observations,  whether to use the weighted MAE, and whether to use the logarithmic scale respectively. The default values are `Hinge`, `True`, and `False`."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "c0335420092baf13"
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE-PO: 1770.7339.\n"
     ]
    }
   ],
   "source": [
    "mae = evl.mae(method=\"Pseudo_obs\", weighted=True, log_scale=False)\n",
    "print(\"MAE-PO: {:.4f}.\".format(mae))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-11T06:08:24.863947302Z",
     "start_time": "2023-09-11T06:08:24.785423193Z"
    }
   },
   "id": "9e3eddce4af6f817"
  },
  {
   "cell_type": "markdown",
   "source": [
    "One can also calculate the MSE using the `mse` method. The method takes the three arguments: `method`, `weighted` and `log_scale` as the `mae` method."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "1207674ae4c88970"
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Logarithmic Squared Error (MLSE) -Hinge: 0.4478.\n"
     ]
    }
   ],
   "source": [
    "mse = evl.mse(method=\"Hinge\", weighted=False, log_scale=True)\n",
    "print(\"Mean Logarithmic Squared Error (MLSE) -Hinge: {:.4f}.\".format(mse))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-11T06:08:26.164850848Z",
     "start_time": "2023-09-11T06:08:26.145071630Z"
    }
   },
   "id": "d4cd8592770321fe"
  },
  {
   "cell_type": "markdown",
   "source": [
    "To calculate the integrated Brier score, one can use the `integrated_brier_score` method. The method takes three argument: \n",
    "1. You can specify the number of points `num_points` to integral. If it is not specify, the default setting will use the censored time points obtained from testing set.\n",
    "2. You can also set the `IPCW_weighted` as True or False to calculate the IPCW-weighted IBS or non-weighted IBS. The default setting is True.\n",
    "3. `draw_figure` is a boolean value to decide whether to draw the IBS curve. The default setting is False."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "c25a9ba7d0dea478"
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": "ibs = evl.integrated_brier_score(num_points=None, IPCW_weighted=True, draw_figure=True)",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-11T06:08:27.739059427Z",
     "start_time": "2023-09-11T06:08:27.392004037Z"
    }
   },
   "id": "2bc14d77825a8af3"
  },
  {
   "cell_type": "markdown",
   "source": [
    "To calculate D-Cal, the user needs to decide how many bins they will use to split the quantile and performs the statistical test. A common choice could be 10 bins:"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "faa8d3a34f9b843"
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model is d-calibrated!\n"
     ]
    }
   ],
   "source": [
    "p_value, bin_hist = evl.d_calibration(num_bins=10)\n",
    "if p_value >= 0.05:\n",
    "    print(\"The model is d-calibrated!\")\n",
    "else:\n",
    "    print(\"The model is not d-calibrated!\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-11T06:08:28.718091462Z",
     "start_time": "2023-09-11T06:08:28.584456781Z"
    }
   },
   "id": "b62832c75c3bf2d8"
  },
  {
   "cell_type": "markdown",
   "source": [
    "We can visualize the D-calibration results using the bin histogram to generate a quantile-quantile (q-q) plot."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "67d1962b72412f97"
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "bin_hist /= bin_hist.sum()\n",
    "\n",
    "default = np.linspace(0, 1, 11)\n",
    "plt.plot(default, default, linestyle='dashed', label=\"x=y\")\n",
    "\n",
    "fraction = np.concatenate((np.array([0]), bin_hist))\n",
    "plt.plot(default, fraction.cumsum(), 'o-', label=\"CoxPH\")\n",
    "\n",
    "plt.xlabel(\"Cumulative Risk\")\n",
    "plt.ylabel(\"Observed Cumulative Fraction\")\n",
    "plt.legend()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-11T06:08:30.022672448Z",
     "start_time": "2023-09-11T06:08:29.953903483Z"
    }
   },
   "id": "75e3286ef0bedaf6"
  },
  {
   "cell_type": "markdown",
   "source": [
    "If the user wants to evaluate the model's performance at a single time point, s/he can calculate the AUC, BS, and 1-Cal with a predefined target time (for example, 10 years)."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "8a3bc7334ec046a1"
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.7200; \n",
      "BS: 0.1751.\n",
      "The model is 1-calibrated!\n"
     ]
    }
   ],
   "source": [
    "target_t = 1000\n",
    "auc = evl.auc(target_t)\n",
    "bs = evl.brier_score(target_t)\n",
    "p, ob, exp = evl.one_calibration(target_t)\n",
    "\n",
    "print(\"AUC: {:.4f}; \\nBS: {:.4f}.\".format(auc, bs))\n",
    "if p >= 0.05:\n",
    "    print(\"The model is 1-calibrated!\")\n",
    "else:\n",
    "    print(\"The model is not 1-calibrated!\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-11T06:08:31.727240902Z",
     "start_time": "2023-09-11T06:08:31.337846677Z"
    }
   },
   "id": "dd628575dd0aa7e3"
  },
  {
   "cell_type": "markdown",
   "source": [
    "We can also visualize the 1-calibration results using the observed and expected probabilities to generate a reliability diagram."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "e497507f01a5eb8a"
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ob.reverse()\n",
    "exp.reverse()\n",
    "\n",
    "bins = np.arange(10)\n",
    "plt.bar(bins - 0.15, ob, width=0.3, label=\"Observed\")\n",
    "plt.bar(bins + 0.15, exp, width=0.3, label=\"Expected\")\n",
    "\n",
    "plt.xlabel('Bin')\n",
    "plt.ylabel('Survival Probability')\n",
    "plt.text(0.5, 0.5, r'$p-value = {:.3f}$'.format(p_value), verticalalignment='top', horizontalalignment='left',\n",
    "         fontsize=12, color='Black')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "## Calculate the distribution calibration (D-Calibration)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-11T06:08:32.990518176Z",
     "start_time": "2023-09-11T06:08:32.886684908Z"
    }
   },
   "id": "48fa427362a618b9"
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-11T06:08:33.772099467Z",
     "start_time": "2023-09-11T06:08:33.729575708Z"
    }
   },
   "id": "bb81b87061685e45"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}