{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lifelines.datasets import load_rossi\n",
    "rossi = load_rossi()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>week</th>\n",
       "      <th>arrest</th>\n",
       "      <th>fin</th>\n",
       "      <th>age</th>\n",
       "      <th>race</th>\n",
       "      <th>wexp</th>\n",
       "      <th>mar</th>\n",
       "      <th>paro</th>\n",
       "      <th>prio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   week  arrest  fin  age  race  wexp  mar  paro  prio\n",
       "0    20       1    0   27     1     0    0     1     3\n",
       "1    17       1    0   18     1     0    0     1     8\n",
       "2    25       1    0   19     0     1    0     1    13\n",
       "3    52       0    1   23     1     1    1     1     1\n",
       "4    52       0    0   19     0     1    0     1     3"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rossi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's b-spline age\n",
    "from patsy import dmatrix\n",
    "from patsy import build_design_matrices \n",
    "\n",
    "\n",
    "design = dmatrix(\"fin + bs(age, df=4) + wexp + mar + paro + prio - 1\", data=rossi, return_type=\"matrix\")\n",
    "transformed_rossi = pd.DataFrame(design, columns=design.design_info.column_names)\n",
    "transformed_rossi['week'] = rossi['week']\n",
    "transformed_rossi['arrest'] = rossi['arrest']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "cph = CoxPHFitter().fit(transformed_rossi, \"week\", \"arrest\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now we need to \"extend\" our data to plot it\n",
    "# we'll plot age over it's observed range\n",
    "age_range = np.linspace(rossi['age'].min(), rossi['age'].max(), 50)\n",
    "\n",
    "# need to create a matrix of variables at their means, _except_ for age. \n",
    "x_bar = rossi.drop([\"week\", \"arrest\"], axis=1).mean(0).to_frame().T\n",
    "new_data = pd.concat([x_bar] * 50).reset_index()\n",
    "new_data['age'] = age_range\n",
    "\n",
    "predict_on = build_design_matrices([design.design_info], new_data, return_type=\"dataframe\")[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x123ccec10>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "cph.predict_log_partial_hazard(predict_on).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}