examples/Model_BecCox.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## BecCox for Survival Analysis\n",
    "\n",
    "### Introduction\n",
    "`BecCox` is a method of survival analysis that predicts hazard ratio of coxph using `xgboost` by optimizing the loss function of *Efron Approximation* + *Convex function approximated Concordance Index*.\n",
    "\n",
    "Compared with `EfnBoost`, `BecCox` is more capable of risk ranking. Moreover, its training phase takes less time.\n",
    "\n",
    "Here we show you how to use BecCox model to predict hazard ratio from scratch with the help of `libsurv`.\n",
    "\n",
    "### Preparation\n",
    "\n",
    "For all things going well, you would be better to get acquainted with **Survival Analysis**. Otherwise, I suggest you read the [reference](https://lifelines.readthedocs.io/en/latest/Survival%20Analysis%20intro.html).\n",
    "\n",
    "And it may help a lot if you know a little of the Efron Approximation and Concordance Index.\n",
    "\n",
    "### Get it started\n",
    "\n",
    "#### Load datasets\n",
    "\n",
    "We provide `WHAS` and `METABRIC` datasets for further studies. It can be found in module `libsurv.datasets`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from libsurv.datasets import load_whas_train\n",
    "from libsurv.datasets import load_whas_test\n",
    "\n",
    "data_train = load_whas_train()\n",
    "data_test = load_whas_test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['age', 'gender', 'bmi', 'chf', 'miord', 'e', 't'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(data_train.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Statistics of datasets\n",
    "Print statistics of survival data. The function `survival_stats` in `libsurv.datasets` provides the tool."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------- Survival Data Statistics ---------------\n",
      "# Rows: 1310\n",
      "# Columns: 5 + e + t\n",
      "# Events Ratio: 42.14%\n",
      "# Min Time: 1\n",
      "# Max Time: 67\n",
      "\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from libsurv.datasets import survival_stats\n",
    "\n",
    "survival_stats(data_train, t_col=\"t\", e_col=\"e\", plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------- Survival Data Statistics ---------------\n",
      "# Rows: 328\n",
      "# Columns: 5 + e + t\n",
      "# Events Ratio: 42.07%\n",
      "# Min Time: 1\n",
      "# Max Time: 67\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "survival_stats(data_test, t_col=\"t\", e_col=\"e\", plot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Survival data transfrom\n",
    "\n",
    "The column of time and status in survival data will be packed into an new column as label in the transformed data.\n",
    "\n",
    "The transformed survival data contains an new label, where negtive values are considered as right censored, and positive values are considered as event occurrence.\n",
    "\n",
    "**NOTE**: In `libsurv`, survival data must be transformed via `libsurv.datasets.survival_df`. But for `xgboost` model, the type of data must be `DMatrix`, so we **have two ways to achive this as listed below**.\n",
    "\n",
    "```python\n",
    "##############################################################################\n",
    "# method 1:\n",
    "from libsurv.datasets import survival_dmat\n",
    "\n",
    "surv_train = survival_dmat(data_train, t_col=\"t\", e_col=\"e\", label_col=\"Y\")\n",
    "surv_test = survival_dmat(data_test, t_col=\"t\", e_col=\"e\", label_col=\"Y\")\n",
    "\n",
    "##############################################################################\n",
    "\n",
    "##############################################################################\n",
    "# method 2\n",
    "from libsurv.datasets import survival_df\n",
    "from xgboost import DMatrix\n",
    "\n",
    "\n",
    "data_train = survival_df(data_train, t_col=\"t\", e_col=\"e\", label_col=\"Y\")\n",
    "data_test = survival_df(data_test, t_col=\"t\", e_col=\"e\", label_col=\"Y\")\n",
    "\n",
    "x_cols = list(data_train.columns)[:-1]\n",
    "\n",
    "surv_train = DMatrix(data_train[x_cols], label=data_train['Y'].values)\n",
    "surv_test = DMatrix(data_test[x_cols], label=data_test['Y'].values)\n",
    "################################################################################\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from libsurv.datasets import survival_dmat\n",
    "\n",
    "# via survival_dmat function\n",
    "surv_train = survival_dmat(data_train, t_col=\"t\", e_col=\"e\", label_col=\"Y\")\n",
    "surv_test = survival_dmat(data_test, t_col=\"t\", e_col=\"e\", label_col=\"Y\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model initialization\n",
    "\n",
    "Here we start to initialize a BecCox model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from libsurv import BecCox\n",
    "\n",
    "params = {\n",
    "    'eta':0.3,\n",
    "    'max_depth':6, \n",
    "    'min_child_weight': 8, \n",
    "    'subsample': 0.9,\n",
    "    'colsample_bytree': 0.5,\n",
    "    'gamma': 0.20,\n",
    "    'lambda': 0,\n",
    "}\n",
    "\n",
    "model = BecCox(params, loss_alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model training\n",
    "\n",
    "Training model and plotting the learning curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-rmse:41.3776\ttest-rmse:41.5729\ttrain-ce_evals:0.669067\ttest-ce_evals:0.641569\n",
      "[10]\ttrain-rmse:40.8777\ttest-rmse:41.0733\ttrain-ce_evals:0.825932\ttest-ce_evals:0.779014\n",
      "[20]\ttrain-rmse:40.6771\ttest-rmse:40.8913\ttrain-ce_evals:0.846761\ttest-ce_evals:0.790753\n",
      "[30]\ttrain-rmse:40.5887\ttest-rmse:40.8106\ttrain-ce_evals:0.857653\ttest-ce_evals:0.797563\n",
      "[40]\ttrain-rmse:40.5022\ttest-rmse:40.7209\ttrain-ce_evals:0.867127\ttest-ce_evals:0.802423\n",
      "[50]\ttrain-rmse:40.3883\ttest-rmse:40.608\ttrain-ce_evals:0.8744\ttest-ce_evals:0.812793\n",
      "[60]\ttrain-rmse:40.3215\ttest-rmse:40.5512\ttrain-ce_evals:0.878573\ttest-ce_evals:0.814402\n",
      "[70]\ttrain-rmse:40.2704\ttest-rmse:40.5148\ttrain-ce_evals:0.882365\ttest-ce_evals:0.817174\n",
      "[80]\ttrain-rmse:40.2027\ttest-rmse:40.4461\ttrain-ce_evals:0.884169\ttest-ce_evals:0.817276\n",
      "[90]\ttrain-rmse:40.1496\ttest-rmse:40.3874\ttrain-ce_evals:0.886236\ttest-ce_evals:0.819056\n",
      "[99]\ttrain-rmse:40.106\ttest-rmse:40.3487\ttrain-ce_evals:0.888398\ttest-ce_evals:0.820322\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "eval_result = model.train(\n",
    "    surv_train,\n",
    "    num_rounds=100,\n",
    "    skip_rounds=10,\n",
    "    evals=[(surv_train, 'train'), (surv_test, 'test')],\n",
    "    plot=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prediction and Evaluation\n",
    "\n",
    "Now we use the trained model to make prediction or evaluate model performance.\n",
    "\n",
    "Give the prediction of BecCox model with:\n",
    "- `model.predict`: Hazard Ratio of coxph\n",
    "- `model.predict_survival_function`: survival function transformed from hazard ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction of hazard ratio: [0.1296317  8.277655   0.33950415 0.43014628 3.3611019 ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# select the first 5 samples\n",
    "surv_data_now = surv_test.slice([i for i in range(5)])\n",
    "\n",
    "# Predict Hazard Ratio\n",
    "print(\"Prediction of hazard ratio:\", model.predict(surv_data_now, output_margin=False))\n",
    "\n",
    "# Predict Survival Function\n",
    "result_survf = model.predict_survival_function(surv_data_now, plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\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>1.0</th>\n",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
       "      <th>4.0</th>\n",
       "      <th>5.0</th>\n",
       "      <th>6.0</th>\n",
       "      <th>7.0</th>\n",
       "      <th>8.0</th>\n",
       "      <th>9.0</th>\n",
       "      <th>10.0</th>\n",
       "      <th>...</th>\n",
       "      <th>58.0</th>\n",
       "      <th>59.0</th>\n",
       "      <th>60.0</th>\n",
       "      <th>61.0</th>\n",
       "      <th>62.0</th>\n",
       "      <th>63.0</th>\n",
       "      <th>64.0</th>\n",
       "      <th>65.0</th>\n",
       "      <th>66.0</th>\n",
       "      <th>67.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.998411</td>\n",
       "      <td>0.997559</td>\n",
       "      <td>0.997313</td>\n",
       "      <td>0.996672</td>\n",
       "      <td>0.996365</td>\n",
       "      <td>0.995901</td>\n",
       "      <td>0.995374</td>\n",
       "      <td>0.995216</td>\n",
       "      <td>0.994936</td>\n",
       "      <td>0.994482</td>\n",
       "      <td>...</td>\n",
       "      <td>0.923224</td>\n",
       "      <td>0.921167</td>\n",
       "      <td>0.911332</td>\n",
       "      <td>0.906372</td>\n",
       "      <td>0.906372</td>\n",
       "      <td>0.906372</td>\n",
       "      <td>0.892401</td>\n",
       "      <td>0.878117</td>\n",
       "      <td>0.815759</td>\n",
       "      <td>0.815759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.903451</td>\n",
       "      <td>0.855530</td>\n",
       "      <td>0.842141</td>\n",
       "      <td>0.808288</td>\n",
       "      <td>0.792525</td>\n",
       "      <td>0.769271</td>\n",
       "      <td>0.743715</td>\n",
       "      <td>0.736204</td>\n",
       "      <td>0.723113</td>\n",
       "      <td>0.702353</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006091</td>\n",
       "      <td>0.005282</td>\n",
       "      <td>0.002662</td>\n",
       "      <td>0.001878</td>\n",
       "      <td>0.001878</td>\n",
       "      <td>0.001878</td>\n",
       "      <td>0.000697</td>\n",
       "      <td>0.000249</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.995844</td>\n",
       "      <td>0.993621</td>\n",
       "      <td>0.992978</td>\n",
       "      <td>0.991309</td>\n",
       "      <td>0.990508</td>\n",
       "      <td>0.989299</td>\n",
       "      <td>0.987929</td>\n",
       "      <td>0.987518</td>\n",
       "      <td>0.986792</td>\n",
       "      <td>0.985613</td>\n",
       "      <td>...</td>\n",
       "      <td>0.811223</td>\n",
       "      <td>0.806497</td>\n",
       "      <td>0.784141</td>\n",
       "      <td>0.773012</td>\n",
       "      <td>0.773012</td>\n",
       "      <td>0.773012</td>\n",
       "      <td>0.742193</td>\n",
       "      <td>0.711482</td>\n",
       "      <td>0.586652</td>\n",
       "      <td>0.586652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.994738</td>\n",
       "      <td>0.991925</td>\n",
       "      <td>0.991112</td>\n",
       "      <td>0.989001</td>\n",
       "      <td>0.987989</td>\n",
       "      <td>0.986462</td>\n",
       "      <td>0.984731</td>\n",
       "      <td>0.984212</td>\n",
       "      <td>0.983295</td>\n",
       "      <td>0.981807</td>\n",
       "      <td>...</td>\n",
       "      <td>0.767153</td>\n",
       "      <td>0.761495</td>\n",
       "      <td>0.734850</td>\n",
       "      <td>0.721662</td>\n",
       "      <td>0.721662</td>\n",
       "      <td>0.721662</td>\n",
       "      <td>0.685405</td>\n",
       "      <td>0.649672</td>\n",
       "      <td>0.508794</td>\n",
       "      <td>0.508794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.959611</td>\n",
       "      <td>0.938609</td>\n",
       "      <td>0.932616</td>\n",
       "      <td>0.917208</td>\n",
       "      <td>0.909902</td>\n",
       "      <td>0.898966</td>\n",
       "      <td>0.886717</td>\n",
       "      <td>0.883070</td>\n",
       "      <td>0.876660</td>\n",
       "      <td>0.866352</td>\n",
       "      <td>...</td>\n",
       "      <td>0.126033</td>\n",
       "      <td>0.118950</td>\n",
       "      <td>0.090053</td>\n",
       "      <td>0.078170</td>\n",
       "      <td>0.078170</td>\n",
       "      <td>0.078170</td>\n",
       "      <td>0.052253</td>\n",
       "      <td>0.034389</td>\n",
       "      <td>0.005093</td>\n",
       "      <td>0.005093</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       1.0       2.0       3.0       4.0       5.0       6.0       7.0   \\\n",
       "0  0.998411  0.997559  0.997313  0.996672  0.996365  0.995901  0.995374   \n",
       "1  0.903451  0.855530  0.842141  0.808288  0.792525  0.769271  0.743715   \n",
       "2  0.995844  0.993621  0.992978  0.991309  0.990508  0.989299  0.987929   \n",
       "3  0.994738  0.991925  0.991112  0.989001  0.987989  0.986462  0.984731   \n",
       "4  0.959611  0.938609  0.932616  0.917208  0.909902  0.898966  0.886717   \n",
       "\n",
       "       8.0       9.0       10.0  ...      58.0      59.0      60.0      61.0  \\\n",
       "0  0.995216  0.994936  0.994482  ...  0.923224  0.921167  0.911332  0.906372   \n",
       "1  0.736204  0.723113  0.702353  ...  0.006091  0.005282  0.002662  0.001878   \n",
       "2  0.987518  0.986792  0.985613  ...  0.811223  0.806497  0.784141  0.773012   \n",
       "3  0.984212  0.983295  0.981807  ...  0.767153  0.761495  0.734850  0.721662   \n",
       "4  0.883070  0.876660  0.866352  ...  0.126033  0.118950  0.090053  0.078170   \n",
       "\n",
       "       62.0      63.0      64.0      65.0      66.0      67.0  \n",
       "0  0.906372  0.906372  0.892401  0.878117  0.815759  0.815759  \n",
       "1  0.001878  0.001878  0.000697  0.000249  0.000002  0.000002  \n",
       "2  0.773012  0.773012  0.742193  0.711482  0.586652  0.586652  \n",
       "3  0.721662  0.721662  0.685405  0.649672  0.508794  0.508794  \n",
       "4  0.078170  0.078170  0.052253  0.034389  0.005093  0.005093  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_survf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "td-CI on training set: 0.8888220294679342\n",
      "td-CI on test set: 0.8208863983698421\n"
     ]
    }
   ],
   "source": [
    "# evaluate model performance \n",
    "print(\"td-CI on training set:\", model.evals(surv_train))\n",
    "print(\"td-CI on test set:\", model.evals(surv_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Factor improtance\n",
    "\n",
    "Get the factor importance score evaluated by the model.\n",
    "\n",
    "It's suggested that you **repeat obtaining the factor score for multiply times**, such as 20, by specifing a different random seed in `model_params`. \n",
    "\n",
    "Parameter `importance_type`: str. The metrics of importance evaluation. see more in [https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.Booster.get_score].\n",
    "\n",
    "The function returns a dict."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'bmi': 0.4817708333333333, 'miord': 0.052083333333333336, 'age': 0.3802083333333333, 'gender': 0.036458333333333336, 'chf': 0.049479166666666664}\n"
     ]
    }
   ],
   "source": [
    "fscore = model.get_factor_score(importance_type='weight')\n",
    "\n",
    "# normalize the scores\n",
    "sum_score = sum(fscore.values())\n",
    "fscore = {k: v / sum_score for k, v in fscore.items()}\n",
    "print(fscore)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### save model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.save_model(\"BecCox.model\")"
   ]
  }
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