{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## time-to-event modelling and survival prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Set-up instructions:** On binder, this should run out-of-the-box.\n",
    "\n",
    "To run locally instead, ensure that `skpro` with basic dependency requirements is installed in your python environment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`skpro` provides a unified interface to time-to-event prediction models, also known as survival prediction models.\n",
    "\n",
    "**Time-to-event prediction** is a form of probabilistic regression where **labels can be \"censored\"**, i.e., of the form \"time is t or later\" instead of exact observations.\n",
    "\n",
    "**Section 1** provides an overview of the basic **time-to-event prediction workflows** supported by `skpro`.\n",
    "\n",
    "**Section 2** showcases **performance metrics and benchmarking** for time-to-event prediction with censored data.\n",
    "\n",
    "**Section 3** discusses **advanced composition patterns**, including various ways to leverage `sklearn` regressors for time-to-event prediction with censored data.\n",
    "\n",
    "**Section 4** gives an introduction to how to write **custom estimators** compliant with the `skpro` interface."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hide warnings\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Basic survival prediction interface <a class=\"anchor\" id=\"chapter1\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section:\n",
    "\n",
    "* explanation of censored time-to-event data\n",
    "* `skpro` time-to-event/survival prediction interface\n",
    "* metrics, evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 data representation, censoring"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Survival prediction or time-to-event prediction can be seen a generalization of probabilistic supervised learning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Each sample consists of:\n",
    "\n",
    "* a feature vector, row of a data frame\n",
    "* a label, which can be an exact time of occurrence, or a statement about \"time was t or later\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# simulated toy dataset, lung cancer survival times\n",
    "import numpy as np\n",
    "\n",
    "# demographics - age and smoker yes/no\n",
    "age = np.random.uniform(low=20, high=100, size=50)\n",
    "smoker = np.random.binomial(1, 0.3, size=50)\n",
    "\n",
    "# actual survival time\n",
    "scale = 200 / (0.5 * age + 30 * smoker)\n",
    "survival = scale * np.random.weibull(1, size=50)\n",
    "\n",
    "# patients are observed only for 5 years\n",
    "# if they surviva 5 years, we know they survived 5 years, but not exact time of death\n",
    "censored = survival > 5\n",
    "observation = np.minimum(survival, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`skpro` represents this information in an `sklearn`-like interface:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# features\n",
    "X = pd.DataFrame({\"age\": age, \"smoker\": smoker})\n",
    "\n",
    "# time of survival or censoring\n",
    "y = pd.DataFrame({\"time\": observation})\n",
    "\n",
    "# indicator whether event was observed or censored\n",
    "# censored = 1/True, observed = 0/False\n",
    "# variable names should be the same as for y\n",
    "C = pd.DataFrame({\"time\": censored})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>smoker</th>\n",
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       "      <th>3</th>\n",
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       "      <td>1</td>\n",
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      "text/plain": [
       "         age  smoker\n",
       "0  64.492963       0\n",
       "1  37.484283       0\n",
       "2  98.567493       0\n",
       "3  32.863923       1\n",
       "4  83.944665       0"
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     "execution_count": 53,
     "metadata": {},
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    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1.121047</td>\n",
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       "      <td>5.000000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.000000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       time\n",
       "0  1.121047\n",
       "1  5.000000\n",
       "2  4.435677\n",
       "3  5.000000\n",
       "4  4.049751"
      ]
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     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
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       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>False</td>\n",
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       "      <th>3</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
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       "      <th>4</th>\n",
       "      <td>False</td>\n",
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      "text/plain": [
       "    time\n",
       "0  False\n",
       "1   True\n",
       "2  False\n",
       "3   True\n",
       "4  False"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "C.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 basic survival prediction workflow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "survival prediction is the task:\n",
    "\n",
    "* given censored time-to-event labels and features, `X`, `y`, `C`\n",
    "* learn a model that can predict `y` if it were uncensored, i.e., the true event time\n",
    "* the prediction should take the form of a survival function or probability distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`skpro` survival predictors extend the interface of probabilistic regressors:\n",
    "\n",
    "* `fit(X, y, C=None)`, with `X`, `y`, `C` as above; if `C=None`, all observations are uncensored\n",
    "* `predict(X_test)` for mean survival time predictions\n",
    "* `predict_proba(X_test)` for distributional predictions\n",
    "\n",
    "Other prediction methods - `predict_interval`, `predict_quantiles`, `predict_var` - also generalize the same way."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because `C` is optional, and means \"uncensored\" if not passed, all survival prediction models can be used as supervised probabilistic regressors.\n",
    "\n",
    "Using probabilistic regressors as survival models is similarly possible, to be revisited later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basic deployment workflow:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from skpro.survival.coxph import CoxPH\n",
    "\n",
    "# step 1: data specification\n",
    "# X, y, C, as above\n",
    "X_train, X_new, y_train, _, C_train, _ = train_test_split(X, y, C)\n",
    "\n",
    "# step 2: specifying the regressor\n",
    "# example - Cox proportional hazards model from statsmodels\n",
    "surv_model_cox = CoxPH()\n",
    "\n",
    "# step 3: fitting the model to training data\n",
    "surv_model_cox.fit(X_train, y_train, C_train)\n",
    "\n",
    "# step 4: predicting labels on new data\n",
    "\n",
    "# full distribution prediction\n",
    "y_pred_proba_cox = surv_model_cox.predict_proba(X_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>3.133901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>3.350896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3.095789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.985662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.079542</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "        time\n",
       "30  3.133901\n",
       "44  3.350896\n",
       "16  3.095789\n",
       "15  2.985662\n",
       "2   2.079542"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean predicted survival time\n",
    "y_pred_proba_cox.mean().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 640x480 with 5 Axes>,\n",
       " array([<Axes: title={'center': 'time'}, ylabel='30'>, <Axes: ylabel='44'>,\n",
       "        <Axes: ylabel='16'>, <Axes: ylabel='15'>, <Axes: ylabel='2'>],\n",
       "       dtype=object))"
      ]
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     "execution_count": 58,
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",
      "text/plain": [
       "<Figure size 640x480 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot of survival functions\n",
    "y_pred_proba_cox.iloc[range(5)].plot(\"surv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting survival functions in one figure, smokers in red\n",
    "from matplotlib.pyplot import subplots\n",
    "\n",
    "_, ax = subplots()\n",
    "\n",
    "for i in range(len(y_pred_proba_cox)):\n",
    "    ax = y_pred_proba_cox.iat[i, 0].plot(\"surv\", ax=ax, color=[\"b\", \"r\"][smoker[i]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 survival prediction with parametric predictive distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "example: using an accelerated failure time model with Weibull hazard\n",
    "\n",
    "same workflow, only using different model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from skpro.survival.aft import AFTWeibull\n",
    "\n",
    "# step 1: data specification\n",
    "# X, y, C, as above\n",
    "X_train, X_new, y_train, _, C_train, _ = train_test_split(X, y, C)\n",
    "\n",
    "# step 2: specifying the regressor\n",
    "# example - Cox proportional hazards model from statsmodels\n",
    "surv_model_aft = AFTWeibull()\n",
    "\n",
    "# step 3: fitting the model to training data\n",
    "surv_model_aft.fit(X_train, y_train, C_train)\n",
    "\n",
    "# step 4: predicting labels on new data\n",
    "\n",
    "# full distribution prediction\n",
    "y_pred_proba_aft = surv_model_aft.predict_proba(X_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting survival functions in one figure, smokers in red\n",
    "from matplotlib.pyplot import subplots\n",
    "\n",
    "_, ax = subplots()\n",
    "\n",
    "for i in range(len(y_pred_proba_cox)):\n",
    "    ax = y_pred_proba_aft.iat[i, 0].plot(\n",
    "        \"surv\", ax=ax, color=[\"b\", \"r\"][smoker[i]], x_bounds=[0, 5]\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "hazard functions can be plotted the same way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 640x480 with 5 Axes>,\n",
       " array([<Axes: title={'center': 'time'}, ylabel='22'>, <Axes: ylabel='1'>,\n",
       "        <Axes: ylabel='42'>, <Axes: ylabel='19'>, <Axes: ylabel='32'>],\n",
       "       dtype=object))"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot of hazard functions\n",
    "y_pred_proba_aft.iloc[range(5)].plot(\"haz\", x_bounds=[0, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting survival functions in one figure, smokers in red\n",
    "from matplotlib.pyplot import subplots\n",
    "\n",
    "_, ax = subplots()\n",
    "\n",
    "for i in range(len(y_pred_proba_aft)):\n",
    "    ax = y_pred_proba_aft.iat[i, 0].plot(\n",
    "        \"haz\", ax=ax, color=[\"b\", \"r\"][smoker[i]], x_bounds=[0, 5]\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>k</th>\n",
       "      <th>scale</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1.317596</td>\n",
       "      <td>4.537601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.317596</td>\n",
       "      <td>9.504837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1.317596</td>\n",
       "      <td>9.621927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.317596</td>\n",
       "      <td>4.642179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1.317596</td>\n",
       "      <td>6.921167</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        time          \n",
       "           k     scale\n",
       "22  1.317596  4.537601\n",
       "1   1.317596  9.504837\n",
       "42  1.317596  9.621927\n",
       "19  1.317596  4.642179\n",
       "32  1.317596  6.921167"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# estimated scale parameter\n",
    "y_pred_proba_aft.to_df().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6.20222709, 10.67113911,  4.05813303,  4.30737777,  4.76504376])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# actual Weibull scale parameter to compare\n",
    "# unknown in a real scenario, but we know since we simulated the data\n",
    "scale[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4 finding time-to-event prediction models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Time-to-event (aka survival) prediction models are probabilistic regressors.\n",
    "\n",
    "I.e., the type is `regressor_proba`, as for probabilistic regressors.\n",
    "\n",
    "All probabilistic regressors can be used with for survival prediction as above.\n",
    "\n",
    "But, only models with the `\"capability:survival\"` tag set to `True` use the censoring information in `C`.\n",
    "\n",
    "Other models ignore `C`, which will lead to biased predictions (see section 3 on addressing this).\n",
    "\n",
    "The `all_objects` utility can be used to retrieve time-to-event prediction models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>object</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFTFisk</td>\n",
       "      <td>&lt;class 'skpro.survival.aft._aft_lifelines_fisk...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFTLogNormal</td>\n",
       "      <td>&lt;class 'skpro.survival.aft._aft_lifelines_logn...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AFTWeibull</td>\n",
       "      <td>&lt;class 'skpro.survival.aft._aft_lifelines_weib...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AalenAdditive</td>\n",
       "      <td>&lt;class 'skpro.survival.additive._aalen_lifelin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BaggingRegressor</td>\n",
       "      <td>&lt;class 'skpro.regression.ensemble._bagging.Bag...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               name                                             object\n",
       "0           AFTFisk  <class 'skpro.survival.aft._aft_lifelines_fisk...\n",
       "1      AFTLogNormal  <class 'skpro.survival.aft._aft_lifelines_logn...\n",
       "2        AFTWeibull  <class 'skpro.survival.aft._aft_lifelines_weib...\n",
       "3     AalenAdditive  <class 'skpro.survival.additive._aalen_lifelin...\n",
       "4  BaggingRegressor  <class 'skpro.regression.ensemble._bagging.Bag..."
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from skpro.registry import all_objects\n",
    "\n",
    "# time-to-event predictors have \"regressor_proba\" type, i.e., probabilistic regressors\n",
    "# the tag \"capability:survival\" = True indicates models that make use of C\n",
    "all_objects(\n",
    "    \"regressor_proba\", as_dataframe=True, filter_tags={\"capability:survival\": True}\n",
    ").head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.5 simple evaluation workflow for time-to-event predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for simple evaluation:\n",
    "\n",
    "1. split the data into train/test set - including the censoring variable\n",
    "2. make predictions of either type for test features\n",
    "3. compute metric on test set, comparing test predictions to held out test observations,\n",
    "  including censoring indicsator\n",
    "\n",
    "Note:\n",
    "\n",
    "* metrics will compare probabilistic prediction to tabular ground truth and\n",
    "  censoring indicator\n",
    "* the metric needs to be of a compatible type, e.g., for distribution predictions\n",
    "* special survival metrics are available to take into account censoring;\n",
    "  if a non-survival metric is used, the censoring indicator will be ignored"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6491228070175438"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from skpro.metrics import ConcordanceHarrell\n",
    "from skpro.survival.coxph import CoxPH\n",
    "\n",
    "# step 1: data specification\n",
    "X_train, X_test, y_train, y_test, C_train, C_test = train_test_split(X, y, C)\n",
    "\n",
    "# step 2: specifying the regressor\n",
    "# example - Cox proportional hazards model from statsmodels\n",
    "surv_model = CoxPH()\n",
    "\n",
    "# step 3: fitting the model to training data\n",
    "surv_model.fit(X_train, y_train, C_train)\n",
    "\n",
    "# step 4: predicting labels on new data\n",
    "y_pred_proba = surv_model.predict_proba(X_test)\n",
    "\n",
    "# step 5: specifying evaluation metric\n",
    "metric = ConcordanceHarrell()\n",
    "\n",
    "# step 6: evaluate metric, compare predictions to actuals\n",
    "metric(y_test, y_pred_proba, C_true=C_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "how do we know that metric is a genuine survival metric?\n",
    "\n",
    "Via the `capability:survival` tag:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'estimator_type': 'estimator',\n",
       " 'authors': 'fkiraly',\n",
       " 'maintainers': 'skpro developers',\n",
       " 'object_type': ['metric', 'metric_distr'],\n",
       " 'reserved_params': ['multioutput', 'score_average'],\n",
       " 'scitype:y_pred': 'pred_proba',\n",
       " 'lower_is_better': False,\n",
       " 'capability:survival': True}"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metric.get_tags()\n",
    "# capability:survival is True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "how do we find specialized survival metrics?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>object</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ConcordanceHarrell</td>\n",
       "      <td>&lt;class 'skpro.metrics.survival._c_harrell.Conc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SPLL</td>\n",
       "      <td>&lt;class 'skpro.metrics.survival._spll.SPLL'&gt;</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 name                                             object\n",
       "0  ConcordanceHarrell  <class 'skpro.metrics.survival._c_harrell.Conc...\n",
       "1                SPLL        <class 'skpro.metrics.survival._spll.SPLL'>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from skpro.registry import all_objects\n",
    "\n",
    "all_objects(\"metric\", as_dataframe=True, filter_tags={\"capability:survival\": True})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All metrics can be applied to censored data:\n",
    "\n",
    "metrics with the tag being `False` will ignore the censoring variable.\n",
    "\n",
    "This will lead to bias in general, but can be justified if there is a low amount of censoring."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Benchmarking time-to-event models <a class=\"anchor\" id=\"chapter2\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `benchmarking.evaluate` utility can be used for time-to-event models as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_ConcordanceHarrell</th>\n",
       "      <th>fit_time</th>\n",
       "      <th>pred_proba_time</th>\n",
       "      <th>len_y_train</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.620370</td>\n",
       "      <td>0.015508</td>\n",
       "      <td>0.019106</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.608696</td>\n",
       "      <td>0.007775</td>\n",
       "      <td>0.009308</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.608696</td>\n",
       "      <td>0.007695</td>\n",
       "      <td>0.008064</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   test_ConcordanceHarrell  fit_time  pred_proba_time  len_y_train\n",
       "0                 0.620370  0.015508         0.019106           33\n",
       "1                 0.608696  0.007775         0.009308           33\n",
       "2                 0.608696  0.007695         0.008064           34"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "\n",
    "from skpro.benchmarking.evaluate import evaluate\n",
    "from skpro.metrics import ConcordanceHarrell\n",
    "from skpro.survival.coxph import CoxPH\n",
    "\n",
    "# 1. specify dataset\n",
    "# X, y, C are as above\n",
    "\n",
    "# 2. specify estimator\n",
    "estimator = CoxPH()\n",
    "\n",
    "# 3. specify cross-validation schema\n",
    "cv = KFold(n_splits=3)\n",
    "\n",
    "# 4. specify evaluation metric\n",
    "c_index = ConcordanceHarrell()\n",
    "\n",
    "# 5. evaluate - run the benchmark\n",
    "# C needs to be passed here\n",
    "# this will automatically pass it on the test set to the metric\n",
    "results = evaluate(estimator=estimator, X=X, y=y, C=C, cv=cv, scoring=c_index)\n",
    "\n",
    "# results are pd.DataFrame\n",
    "# each row is one repetition of the cross-validation on one fold fit/predict/evaluate\n",
    "# columns report performance, runtime, and other optional information (see docstring)\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Advanced composition patterns <a class=\"anchor\" id=\"chapter3\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we introduce a number of composition patterns available in `skpro`:\n",
    "\n",
    "* reducer-wrappers that turn `sklearn` regressors into survival regressors\n",
    "* pipelines of `sklearn` transformers with `skpro` survival regressors\n",
    "* tuning `skpro` survival regressors via grid/random search, minimizing a probabilistic metric"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "data used in this section:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test, C_train, C_test = train_test_split(X, y, C)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "evaluation metric used in this section:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skpro.metrics import ConcordanceHarrell\n",
    "\n",
    "c_harrell = ConcordanceHarrell()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Reducers to create survival regressors <a class=\"anchor\" id=\"section3_1\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the three main reduction strategies to create survival regressors:\n",
    "\n",
    "1. adding the capability to handle censoring information to a probabilistic supervised regressor\n",
    "2. the above, combined with any strategy to create a probabilistic regressor from an `sklearn` (non-probabilistic) regressors\n",
    "3. strategies that directly create a survival regressor from a tabular `sklearn` regressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section shows examples for all the above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1.1 adding survival capability to a probabilistic regressor <a class=\"anchor\" id=\"section3_1_1\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "this type of reduction strategy takes a probabilistic regressor and adds treatment for censored data.\n",
    "\n",
    "Examples:\n",
    "\n",
    "* native use - every probabilistic regressor can be used as a survival regressor, ignoring `C`\n",
    "* `FitUncensored` - fits the model on the uncensored data only\n",
    "* `ConditionUncensored` - uses `C` as a feature in fitting, and sets it to `C=0` in `Predict`\n",
    "\n",
    "The first two strategies introduce bias into the model, and should only be applied when the censoring fraction is low.\n",
    "\n",
    "Example with `ConditionUncensored`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3492063492063492"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from skpro.regression.linear import GLMRegressor\n",
    "from skpro.survival.compose import ConditionUncensored\n",
    "\n",
    "# estimator specification - use any skpro regressor, including composites\n",
    "reg_proba = GLMRegressor()\n",
    "\n",
    "# turning the regressor into a survival predictor\n",
    "surv_model = ConditionUncensored(reg_proba)\n",
    "\n",
    "# fit and predict\n",
    "surv_model.fit(X_train, y_train, C_train)\n",
    "y_pred_proba = surv_model.predict_proba(X_test)\n",
    "\n",
    "# evaluate\n",
    "c_harrell(y_test, y_pred_proba, C_true=C_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1.2 adding probabilistic then survival capability to `sklearn` regressor <a class=\"anchor\" id=\"section3_1_2\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5158730158730159"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "from skpro.regression.residual import ResidualDouble\n",
    "from skpro.survival.compose import ConditionUncensored\n",
    "\n",
    "# estimator specification - use any skpro regressor, including composites\n",
    "reg_mean = RandomForestRegressor()\n",
    "reg_resid = RandomForestRegressor()\n",
    "reg_proba = ResidualDouble(reg_mean, reg_resid)\n",
    "\n",
    "# turning the regressor into a survival predictor\n",
    "surv_model = ConditionUncensored(reg_proba)\n",
    "\n",
    "# fit and predict\n",
    "surv_model.fit(X_train, y_train, C_train)\n",
    "y_pred_proba = surv_model.predict_proba(X_test)\n",
    "\n",
    "# evaluate\n",
    "c_harrell(y_test, y_pred_proba, C_true=C_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1.3 bootstrap prediction intervals <a class=\"anchor\" id=\"section3_1_3\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5384615384615384"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "from skpro.regression.bootstrap import BootstrapRegressor\n",
    "\n",
    "# estimator specification - use any sklearn regressor for reg_mean\n",
    "reg_mean = LinearRegression()\n",
    "reg_proba = BootstrapRegressor(reg_mean, n_bootstrap_samples=100)\n",
    "\n",
    "# fit and predict\n",
    "reg_proba.fit(X_train, y_train)\n",
    "y_pred_proba = reg_proba.predict_proba(X_test)\n",
    "\n",
    "# evaluate\n",
    "c_harrell(y_test, y_pred_proba)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Pipelines of `skpro` survival regressors and `sklearn` transformers <a class=\"anchor\" id=\"section3_2\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`skpro` survival regressors can be pipelined with `sklearn` transformers, using the `skpro` pipeline.\n",
    "\n",
    "This ensure presence of `predict_proba` etc in the pipeline object.\n",
    "\n",
    "The syntax is exactly the same as for `sklearn`'s pipeline,\n",
    "or the `skpro` pipeline with non-survival regressors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer as Imputer\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "from skpro.regression.compose import Pipeline\n",
    "from skpro.survival.coxph import CoxPH\n",
    "\n",
    "# estimator specification\n",
    "surv_model = CoxPH()\n",
    "\n",
    "# pipeline is specified as a list of tuples (name, estimator)\n",
    "pipe = Pipeline(\n",
    "    steps=[\n",
    "        (\"imputer\", Imputer()),  # an sklearn transformer\n",
    "        (\"scaler\", MinMaxScaler()),  # an sklearn transformer\n",
    "        (\"regressor\", surv_model),  # an skpro regressor\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-ebaff2c7-b0a0-4df7-b5ec-7956dd0a307f {color: black;background-color: white;}#sk-ebaff2c7-b0a0-4df7-b5ec-7956dd0a307f pre{padding: 0;}#sk-ebaff2c7-b0a0-4df7-b5ec-7956dd0a307f div.sk-toggleable {background-color: white;}#sk-ebaff2c7-b0a0-4df7-b5ec-7956dd0a307f label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-ebaff2c7-b0a0-4df7-b5ec-7956dd0a307f label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-ebaff2c7-b0a0-4df7-b5ec-7956dd0a307f label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-ebaff2c7-b0a0-4df7-b5ec-7956dd0a307f div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-ebaff2c7-b0a0-4df7-b5ec-7956dd0a307f div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-ebaff2c7-b0a0-4df7-b5ec-7956dd0a307f div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-ebaff2c7-b0a0-4df7-b5ec-7956dd0a307f input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-ebaff2c7-b0a0-4df7-b5ec-7956dd0a307f input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-ebaff2c7-b0a0-4df7-b5ec-7956dd0a307f div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-ebaff2c7-b0a0-4df7-b5ec-7956dd0a307f div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-ebaff2c7-b0a0-4df7-b5ec-7956dd0a307f 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-ebaff2c7-b0a0-4df7-b5ec-7956dd0a307f 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-ebaff2c7-b0a0-4df7-b5ec-7956dd0a307f div.sk-estimator:hover {background-color: #d4ebff;}#sk-ebaff2c7-b0a0-4df7-b5ec-7956dd0a307f div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-ebaff2c7-b0a0-4df7-b5ec-7956dd0a307f div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-ebaff2c7-b0a0-4df7-b5ec-7956dd0a307f div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-ebaff2c7-b0a0-4df7-b5ec-7956dd0a307f div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-ebaff2c7-b0a0-4df7-b5ec-7956dd0a307f div.sk-item {z-index: 1;}#sk-ebaff2c7-b0a0-4df7-b5ec-7956dd0a307f div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-ebaff2c7-b0a0-4df7-b5ec-7956dd0a307f div.sk-parallel::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-ebaff2c7-b0a0-4df7-b5ec-7956dd0a307f div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-ebaff2c7-b0a0-4df7-b5ec-7956dd0a307f div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-ebaff2c7-b0a0-4df7-b5ec-7956dd0a307f div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-ebaff2c7-b0a0-4df7-b5ec-7956dd0a307f div.sk-parallel-item:only-child::after {width: 0;}#sk-ebaff2c7-b0a0-4df7-b5ec-7956dd0a307f 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;position: relative;}#sk-ebaff2c7-b0a0-4df7-b5ec-7956dd0a307f div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-ebaff2c7-b0a0-4df7-b5ec-7956dd0a307f div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-ebaff2c7-b0a0-4df7-b5ec-7956dd0a307f 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-ebaff2c7-b0a0-4df7-b5ec-7956dd0a307f div.sk-text-repr-fallback {display: none;}</style><div id='sk-ebaff2c7-b0a0-4df7-b5ec-7956dd0a307f' class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;imputer&#x27;, SimpleImputer()), (&#x27;scaler&#x27;, MinMaxScaler()),\n",
       "                (&#x27;regressor&#x27;, CoxPH())])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</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=UUID('836f9903-e576-401a-9ef2-7b5005606ffb') type=\"checkbox\" ><label for=UUID('836f9903-e576-401a-9ef2-7b5005606ffb') class='sk-toggleable__label sk-toggleable__label-arrow'>Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;imputer&#x27;, SimpleImputer()), (&#x27;scaler&#x27;, MinMaxScaler()),\n",
       "                (&#x27;regressor&#x27;, CoxPH())])</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=UUID('62746793-60ad-43af-b665-11937b83d9b8') type=\"checkbox\" ><label for=UUID('62746793-60ad-43af-b665-11937b83d9b8') class='sk-toggleable__label sk-toggleable__label-arrow'>SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class='sk-item'><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('ca256cca-cb25-4178-8396-1939fd74363a') type=\"checkbox\" ><label for=UUID('ca256cca-cb25-4178-8396-1939fd74363a') class='sk-toggleable__label sk-toggleable__label-arrow'>MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div><div class='sk-item'><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('6845a684-1501-4479-ac4a-f9586d72ae5f') type=\"checkbox\" ><label for=UUID('6845a684-1501-4479-ac4a-f9586d72ae5f') class='sk-toggleable__label sk-toggleable__label-arrow'>CoxPH</label><div class=\"sk-toggleable__content\"><pre>CoxPH()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', MinMaxScaler()),\n",
       "                ('regressor', CoxPH())])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the pipeline behaves as any skpro regressor\n",
    "pipe.fit(X_train, y_train, C_train)\n",
    "y_pred = pipe.predict(X=X_test)\n",
    "y_pred_proba = pipe.predict_proba(X=X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the pipeline provides the familiar nested `get_params`, `set_params` interface:\n",
    "\n",
    "nested parameters are keyed `componentname__parametername`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'steps': [('imputer', SimpleImputer()),\n",
       "  ('scaler', MinMaxScaler()),\n",
       "  ('regressor', CoxPH())],\n",
       " 'imputer': SimpleImputer(),\n",
       " 'scaler': MinMaxScaler(),\n",
       " 'regressor': CoxPH(),\n",
       " 'imputer__add_indicator': False,\n",
       " 'imputer__copy': True,\n",
       " 'imputer__fill_value': None,\n",
       " 'imputer__keep_empty_features': False,\n",
       " 'imputer__missing_values': nan,\n",
       " 'imputer__strategy': 'mean',\n",
       " 'scaler__clip': False,\n",
       " 'scaler__copy': True,\n",
       " 'scaler__feature_range': (0, 1),\n",
       " 'regressor__alpha': 0.0,\n",
       " 'regressor__method': 'lpl',\n",
       " 'regressor__missing': 'drop',\n",
       " 'regressor__strata': None,\n",
       " 'regressor__ties': 'breslow'}"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.get_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pipelines can also be created via simple lists of estimators,\n",
    "\n",
    "in this case names are generated automatically:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pipeline is specified as a list of tuples (name, estimator)\n",
    "pipe = Pipeline(\n",
    "    steps=[\n",
    "        Imputer(),  # an sklearn transformer\n",
    "        MinMaxScaler(),  # an sklearn transformer\n",
    "        surv_model,  # an skpro regressor\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Tuning of `skpro` regressors via grid and random search <a class=\"anchor\" id=\"section3_3\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`skpro` provides grid and random search tuners to tune arbitrary probabilistic regressors,\n",
    "\n",
    "using probabilistic metrics.\n",
    "\n",
    "Survival metrics can be used to tune survival regressors, same as non-survival regressors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "\n",
    "from skpro.metrics import ConcordanceHarrell\n",
    "from skpro.model_selection import GridSearchCV\n",
    "from skpro.survival.coxph import CoxPH\n",
    "\n",
    "# cross-validation specification for tuner\n",
    "cv = KFold(n_splits=3)\n",
    "\n",
    "# estimator to be tuned\n",
    "estimator = CoxPH()\n",
    "\n",
    "# tuning grid - partial likelihood or elastic net penalty?\n",
    "param_grid = {\"method\": [\"lpl\", \"elastic_net\"]}\n",
    "\n",
    "# metric to be optimized\n",
    "c_harrell = ConcordanceHarrell()\n",
    "\n",
    "# specification of the grid search tuner\n",
    "gscv = GridSearchCV(\n",
    "    estimator=estimator,\n",
    "    param_grid=param_grid,\n",
    "    cv=cv,\n",
    "    scoring=c_harrell,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-412566cd-360e-4a14-8f8c-0f1e249ba6f7 {color: black;background-color: white;}#sk-412566cd-360e-4a14-8f8c-0f1e249ba6f7 pre{padding: 0;}#sk-412566cd-360e-4a14-8f8c-0f1e249ba6f7 div.sk-toggleable {background-color: white;}#sk-412566cd-360e-4a14-8f8c-0f1e249ba6f7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-412566cd-360e-4a14-8f8c-0f1e249ba6f7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-412566cd-360e-4a14-8f8c-0f1e249ba6f7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-412566cd-360e-4a14-8f8c-0f1e249ba6f7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-412566cd-360e-4a14-8f8c-0f1e249ba6f7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-412566cd-360e-4a14-8f8c-0f1e249ba6f7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-412566cd-360e-4a14-8f8c-0f1e249ba6f7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-412566cd-360e-4a14-8f8c-0f1e249ba6f7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-412566cd-360e-4a14-8f8c-0f1e249ba6f7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-412566cd-360e-4a14-8f8c-0f1e249ba6f7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-412566cd-360e-4a14-8f8c-0f1e249ba6f7 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-412566cd-360e-4a14-8f8c-0f1e249ba6f7 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-412566cd-360e-4a14-8f8c-0f1e249ba6f7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-412566cd-360e-4a14-8f8c-0f1e249ba6f7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-412566cd-360e-4a14-8f8c-0f1e249ba6f7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-412566cd-360e-4a14-8f8c-0f1e249ba6f7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-412566cd-360e-4a14-8f8c-0f1e249ba6f7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-412566cd-360e-4a14-8f8c-0f1e249ba6f7 div.sk-item {z-index: 1;}#sk-412566cd-360e-4a14-8f8c-0f1e249ba6f7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-412566cd-360e-4a14-8f8c-0f1e249ba6f7 div.sk-parallel::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-412566cd-360e-4a14-8f8c-0f1e249ba6f7 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-412566cd-360e-4a14-8f8c-0f1e249ba6f7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-412566cd-360e-4a14-8f8c-0f1e249ba6f7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-412566cd-360e-4a14-8f8c-0f1e249ba6f7 div.sk-parallel-item:only-child::after {width: 0;}#sk-412566cd-360e-4a14-8f8c-0f1e249ba6f7 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;position: relative;}#sk-412566cd-360e-4a14-8f8c-0f1e249ba6f7 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-412566cd-360e-4a14-8f8c-0f1e249ba6f7 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-412566cd-360e-4a14-8f8c-0f1e249ba6f7 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-412566cd-360e-4a14-8f8c-0f1e249ba6f7 div.sk-text-repr-fallback {display: none;}</style><div id='sk-412566cd-360e-4a14-8f8c-0f1e249ba6f7' class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=KFold(n_splits=3, random_state=None, shuffle=False),\n",
       "             estimator=CoxPH(), param_grid={&#x27;method&#x27;: [&#x27;lpl&#x27;, &#x27;elastic_net&#x27;]},\n",
       "             scoring=ConcordanceHarrell())</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</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=UUID('77deac80-408b-4ae8-85e9-f390da84dd4b') type=\"checkbox\" ><label for=UUID('77deac80-408b-4ae8-85e9-f390da84dd4b') class='sk-toggleable__label sk-toggleable__label-arrow'>GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=KFold(n_splits=3, random_state=None, shuffle=False),\n",
       "             estimator=CoxPH(), param_grid={&#x27;method&#x27;: [&#x27;lpl&#x27;, &#x27;elastic_net&#x27;]},\n",
       "             scoring=ConcordanceHarrell())</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class='sk-item'><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('20540a25-0699-4c1c-8f46-b68630cdb085') type=\"checkbox\" ><label for=UUID('20540a25-0699-4c1c-8f46-b68630cdb085') class='sk-toggleable__label sk-toggleable__label-arrow'>CoxPH</label><div class=\"sk-toggleable__content\"><pre>CoxPH()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class='sk-item'><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=UUID('8a9b4c45-cd6c-414e-bddc-b67ada63bd7f') type=\"checkbox\" ><label for=UUID('8a9b4c45-cd6c-414e-bddc-b67ada63bd7f') class='sk-toggleable__label sk-toggleable__label-arrow'>ConcordanceHarrell</label><div class=\"sk-toggleable__content\"><pre>ConcordanceHarrell()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=KFold(n_splits=3, random_state=None, shuffle=False),\n",
       "             estimator=CoxPH(), param_grid={'method': ['lpl', 'elastic_net']},\n",
       "             scoring=ConcordanceHarrell())"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gscv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the grid search tuner behaves like any `skpro` survival regressor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "gscv.fit(X_train, y_train, C_train)\n",
    "y_pred = gscv.predict(X_test)\n",
    "y_pred_proba = gscv.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "random search is similar, except that instead of a grid a parameter sampler should be specified:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skpro.model_selection import RandomizedSearchCV\n",
    "\n",
    "# only difference to GridSearchCV is the param_distributions argument\n",
    "\n",
    "# specification of the random search parameter sampler\n",
    "param_distributions = {\"method\": [\"lpl\", \"elastic_net\"]}\n",
    "\n",
    "# specification of the random search tuner\n",
    "rscv = RandomizedSearchCV(\n",
    "    estimator=estimator,\n",
    "    param_distributions=param_distributions,\n",
    "    cv=cv,\n",
    "    scoring=c_harrell,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Extension guide - implementing your own time-to-event regressor <a class=\"anchor\" id=\"chapter4\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "`skpro` is meant to be easily extensible, for direct contribution to `skpro` as well as for local/private extension with custom methods.\n",
    "\n",
    "To get started:\n",
    "\n",
    "* Follow the [\"implementing estimator\" developer guide](https://skpro.readthedocs.io/en/stable/developer_guide/add_estimators.html)\n",
    "* Use the [survival regressor template](https://github.com/sktime/skpro/blob/main/extension_templates/survival.py) to get started\n",
    "\n",
    "1. Read through the [survival regression extension template](https://github.com/sktime/skpro/blob/main/extension_templates/survival.py) - this is a `python` file with `todo` blocks that mark the places in which changes need to be added.\n",
    "2. Copy the proba regressor extension template to a local folder in your own repository (local/private extension), or to a suitable location in your clone of the `skpro` or affiliated repository (if contributed extension), inside `skpro.survival`; rename the file and update the file docstring appropriately.\n",
    "3. Address the \"todo\" parts. Usually, this means: changing the name of the class, setting the tag values, specifying hyper-parameters, filling in `__init__`, `_fit`, and at least one of the probabilistic prediction methods, preferably `_predict_proba` (for details see the extension template). You can add private methods as long as they do not override the default public interface. For more details, see the extension template.\n",
    "4. To test your estimator manually: import your estimator and run it in the workflows in Section 1; then use it in the compositors in Section 3.\n",
    "5. To test your estimator automatically: call `skpro.utils.check_estimator` on your estimator. You can call this on a class or object instance. Ensure you have specified test parameters in the `get_test_params` method, according to the extension template.\n",
    "\n",
    "In case of direct contribution to `skpro` or one of its affiliated packages, additionally:\n",
    "\n",
    "* Add yourself as an author and/or a maintainer for the new estimator file(s), via `\"authors\"` and `\"maintainers\"` tag.\n",
    "* Create a pull request that contains only the new estimators (and their inheritance tree, if it's not just one class), as well as the automated tests as described above.\n",
    "* In the pull request, describe the estimator and optimally provide a publication or other technical reference for the strategy it implements.\n",
    "* Before making the pull request, ensure that you have all necessary permissions to contribute the code to a permissive license (BSD-3) open source project."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Summary<a class=\"anchor\" id=\"chapter5\"></a>\n",
    "\n",
    "* `skpro` is a unified interface toolbox for probabilistic supervised regression, that is, for prediction intervals, quantiles, fully distributional predictions, in a tabular regression setting. The interface is fully interoperable with `scikit-learn` and `scikit-base` interface specifications.\n",
    "\n",
    "* `skpro` comes with rich composition functionality that allows to build complex pipelines easily, and connect easily with other parts of the open source ecosystem, such as `scikit-learn` and individual algorithm libraries.\n",
    "\n",
    "* `skpro` is easy to extend, and comes with user friendly tools to facilitate implementing and testing your own probabilistic regressors and composition principles."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "### Credits:\n",
    "\n",
    "noteook creation: fkiraly\n",
    "\n",
    "skpro: https://github.com/sktime/skpro/blob/main/CONTRIBUTORS.md"
   ]
  }
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