{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Custom regression models\n",
    "\n",
    "Like for univariate models, it is possible to create your own custom parametric survival models. Why might you want to do this? \n",
    "\n",
    " - Create new / extend AFT models using known probability distributions\n",
    " - Create a piecewise model using domain knowledge about subjects\n",
    " - Iterate and fit a more accurate parametric model\n",
    "\n",
    "*lifelines* has a very simple API to create custom parametric regression models. You only need to define the cumulative hazard function. For example, the cumulative hazard for the constant-hazard regression model looks like:\n",
    "\n",
    "$$ \n",
    "H(t, x) = \\frac{t}{\\lambda(x)}\\\\ \\lambda(x) = \\exp{(\\vec{\\beta} \\cdot \\vec{x}^{\\,T})}\n",
    "$$ \n",
    "\n",
    "where $\\beta$ are the unknowns we will optimize over. \n",
    "\n",
    "\n",
    "Below are some example custom models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>lifelines.ExponentialAFTFitter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration col</th>\n",
       "      <td>'week'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event col</th>\n",
       "      <td>'arrest'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of observations</th>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of events observed</th>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood</th>\n",
       "      <td>-686.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time fit was run</th>\n",
       "      <td>2020-07-26 22:06:42 UTC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>coef</th>\n",
       "      <th>exp(coef)</th>\n",
       "      <th>se(coef)</th>\n",
       "      <th>coef lower 95%</th>\n",
       "      <th>coef upper 95%</th>\n",
       "      <th>exp(coef) lower 95%</th>\n",
       "      <th>exp(coef) upper 95%</th>\n",
       "      <th>z</th>\n",
       "      <th>p</th>\n",
       "      <th>-log2(p)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>param</th>\n",
       "      <th>covariate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">lambda_</th>\n",
       "      <th>Intercept</th>\n",
       "      <td>4.05</td>\n",
       "      <td>57.44</td>\n",
       "      <td>0.59</td>\n",
       "      <td>2.90</td>\n",
       "      <td>5.20</td>\n",
       "      <td>18.21</td>\n",
       "      <td>181.15</td>\n",
       "      <td>6.91</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>37.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.06</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.10</td>\n",
       "      <td>2.55</td>\n",
       "      <td>0.01</td>\n",
       "      <td>6.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fin</th>\n",
       "      <td>0.37</td>\n",
       "      <td>1.44</td>\n",
       "      <td>0.19</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.99</td>\n",
       "      <td>2.10</td>\n",
       "      <td>1.92</td>\n",
       "      <td>0.06</td>\n",
       "      <td>4.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mar</th>\n",
       "      <td>0.43</td>\n",
       "      <td>1.53</td>\n",
       "      <td>0.38</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>1.17</td>\n",
       "      <td>0.73</td>\n",
       "      <td>3.24</td>\n",
       "      <td>1.12</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paro</th>\n",
       "      <td>0.08</td>\n",
       "      <td>1.09</td>\n",
       "      <td>0.20</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prio</th>\n",
       "      <td>-0.09</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.97</td>\n",
       "      <td>-3.03</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>8.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race</th>\n",
       "      <td>-0.30</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.31</td>\n",
       "      <td>-0.91</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1.35</td>\n",
       "      <td>-0.99</td>\n",
       "      <td>0.32</td>\n",
       "      <td>1.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wexp</th>\n",
       "      <td>0.15</td>\n",
       "      <td>1.16</td>\n",
       "      <td>0.21</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.76</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.49</td>\n",
       "      <td>1.03</td>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AIC</th>\n",
       "      <td>1388.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood ratio test</th>\n",
       "      <td>31.22 on 7 df</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-log2(p) of ll-ratio test</th>\n",
       "      <td>14.11</td>\n",
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      "text/latex": [
       "\\begin{tabular}{llrrrrrrrrrr}\n",
       "\\toprule\n",
       "        &      &  coef &  exp(coef) &  se(coef) &  coef lower 95\\% &  coef upper 95\\% &  exp(coef) lower 95\\% &  exp(coef) upper 95\\% &     z &    p &  -log2(p) \\\\\n",
       "param & covariate &       &            &           &                 &                 &                      &                      &       &      &           \\\\\n",
       "\\midrule\n",
       "lambda\\_ & Intercept &  4.05 &      57.44 &      0.59 &            2.90 &            5.20 &                18.21 &               181.15 &  6.91 & 0.00 &     37.61 \\\\\n",
       "        & age &  0.06 &       1.06 &      0.02 &            0.01 &            0.10 &                 1.01 &                 1.10 &  2.55 & 0.01 &      6.52 \\\\\n",
       "        & fin &  0.37 &       1.44 &      0.19 &           -0.01 &            0.74 &                 0.99 &                 2.10 &  1.92 & 0.06 &      4.18 \\\\\n",
       "        & mar &  0.43 &       1.53 &      0.38 &           -0.32 &            1.17 &                 0.73 &                 3.24 &  1.12 & 0.26 &      1.93 \\\\\n",
       "        & paro &  0.08 &       1.09 &      0.20 &           -0.30 &            0.47 &                 0.74 &                 1.59 &  0.42 & 0.67 &      0.57 \\\\\n",
       "        & prio & -0.09 &       0.92 &      0.03 &           -0.14 &           -0.03 &                 0.87 &                 0.97 & -3.03 & 0.00 &      8.65 \\\\\n",
       "        & race & -0.30 &       0.74 &      0.31 &           -0.91 &            0.30 &                 0.40 &                 1.35 & -0.99 & 0.32 &      1.63 \\\\\n",
       "        & wexp &  0.15 &       1.16 &      0.21 &           -0.27 &            0.56 &                 0.76 &                 1.75 &  0.69 & 0.49 &      1.03 \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.ExponentialAFTFitter: fitted with 432 total observations, 318 right-censored observations>\n",
       "             duration col = 'week'\n",
       "                event col = 'arrest'\n",
       "   number of observations = 432\n",
       "number of events observed = 114\n",
       "           log-likelihood = -686.37\n",
       "         time fit was run = 2020-07-26 22:06:42 UTC\n",
       "\n",
       "---\n",
       "                    coef  exp(coef)   se(coef)   coef lower 95%   coef upper 95%  exp(coef) lower 95%  exp(coef) upper 95%\n",
       "param   covariate                                                                                                         \n",
       "lambda_ Intercept   4.05      57.44       0.59             2.90             5.20                18.21               181.15\n",
       "        age         0.06       1.06       0.02             0.01             0.10                 1.01                 1.10\n",
       "        fin         0.37       1.44       0.19            -0.01             0.74                 0.99                 2.10\n",
       "        mar         0.43       1.53       0.38            -0.32             1.17                 0.73                 3.24\n",
       "        paro        0.08       1.09       0.20            -0.30             0.47                 0.74                 1.59\n",
       "        prio       -0.09       0.92       0.03            -0.14            -0.03                 0.87                 0.97\n",
       "        race       -0.30       0.74       0.31            -0.91             0.30                 0.40                 1.35\n",
       "        wexp        0.15       1.16       0.21            -0.27             0.56                 0.76                 1.75\n",
       "                      z      p   -log2(p)\n",
       "param   covariate                        \n",
       "lambda_ Intercept  6.91 <0.005      37.61\n",
       "        age        2.55   0.01       6.52\n",
       "        fin        1.92   0.06       4.18\n",
       "        mar        1.12   0.26       1.93\n",
       "        paro       0.42   0.67       0.57\n",
       "        prio      -3.03 <0.005       8.65\n",
       "        race      -0.99   0.32       1.63\n",
       "        wexp       0.69   0.49       1.03\n",
       "---\n",
       "AIC = 1388.73\n",
       "log-likelihood ratio test = 31.22 on 7 df\n",
       "-log2(p) of ll-ratio test = 14.11"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "from lifelines.fitters import ParametricRegressionFitter\n",
    "from autograd import numpy as np\n",
    "from lifelines.datasets import load_rossi\n",
    "\n",
    "\n",
    "class ExponentialAFTFitter(ParametricRegressionFitter):\n",
    "    \n",
    "    # this class property is necessary, and should always be a non-empty list of strings. \n",
    "    _fitted_parameter_names = ['lambda_']\n",
    "    \n",
    "    def _cumulative_hazard(self, params, t, Xs):\n",
    "        # params is a dictionary that maps unknown parameters to a numpy vector. \n",
    "        # Xs is a dictionary that maps unknown parameters to a numpy 2d array \n",
    "        beta = params['lambda_']\n",
    "        X = Xs['lambda_']\n",
    "        lambda_ = np.exp(np.dot(X, beta))\n",
    "        return t / lambda_\n",
    "    \n",
    "\n",
    "rossi = load_rossi()\n",
    "\n",
    "# the below variables maps {dataframe columns, formulas} to parameters\n",
    "regressors = {\n",
    "    # could also be: 'lambda_': rossi.columns.difference(['week', 'arrest'])\n",
    "    'lambda_': \"age + fin + mar + paro + prio + race + wexp + 1\"\n",
    "}\n",
    "\n",
    "eaf = ExponentialAFTFitter().fit(rossi, 'week', 'arrest', regressors=regressors)\n",
    "eaf.print_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/camerondavidson-pilon/code/lifelines/lifelines/fitters/__init__.py:1985: StatisticalWarning: The diagonal of the variance_matrix_ has negative values. This could be a problem with DependentCompetingRisksHazard's fit to the data.\n",
      "\n",
      "It's advisable to not trust the variances reported, and to be suspicious of the fitted parameters too.\n",
      "\n",
      "  warnings.warn(warning_text, exceptions.StatisticalWarning)\n"
     ]
    },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>lifelines.DependentCompetingRisksHazard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration col</th>\n",
       "      <td>'week'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event col</th>\n",
       "      <td>'arrest'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>penalizer</th>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of observations</th>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of events observed</th>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood</th>\n",
       "      <td>-239.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time fit was run</th>\n",
       "      <td>2020-07-26 22:06:55 UTC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>coef</th>\n",
       "      <th>exp(coef)</th>\n",
       "      <th>se(coef)</th>\n",
       "      <th>coef lower 95%</th>\n",
       "      <th>coef upper 95%</th>\n",
       "      <th>exp(coef) lower 95%</th>\n",
       "      <th>exp(coef) upper 95%</th>\n",
       "      <th>z</th>\n",
       "      <th>p</th>\n",
       "      <th>-log2(p)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>param</th>\n",
       "      <th>covariate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">lambda1</th>\n",
       "      <th>age</th>\n",
       "      <td>0.05</td>\n",
       "      <td>1.05</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1.09</td>\n",
       "      <td>3.06</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>8.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fin</th>\n",
       "      <td>0.23</td>\n",
       "      <td>1.26</td>\n",
       "      <td>0.19</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.86</td>\n",
       "      <td>1.84</td>\n",
       "      <td>1.18</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mar</th>\n",
       "      <td>0.21</td>\n",
       "      <td>1.23</td>\n",
       "      <td>0.35</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.62</td>\n",
       "      <td>2.46</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paro</th>\n",
       "      <td>0.13</td>\n",
       "      <td>1.14</td>\n",
       "      <td>0.25</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.69</td>\n",
       "      <td>1.88</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prio</th>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.04</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>0.35</td>\n",
       "      <td>1.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race</th>\n",
       "      <td>0.07</td>\n",
       "      <td>1.07</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wexp</th>\n",
       "      <td>0.16</td>\n",
       "      <td>1.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.62</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.35</td>\n",
       "      <td>1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">lambda2</th>\n",
       "      <th>age</th>\n",
       "      <td>0.05</td>\n",
       "      <td>1.05</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.09</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.10</td>\n",
       "      <td>2.34</td>\n",
       "      <td>0.02</td>\n",
       "      <td>5.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fin</th>\n",
       "      <td>0.23</td>\n",
       "      <td>1.26</td>\n",
       "      <td>0.19</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.86</td>\n",
       "      <td>1.84</td>\n",
       "      <td>1.18</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mar</th>\n",
       "      <td>0.21</td>\n",
       "      <td>1.23</td>\n",
       "      <td>0.35</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.62</td>\n",
       "      <td>2.46</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paro</th>\n",
       "      <td>0.13</td>\n",
       "      <td>1.14</td>\n",
       "      <td>0.25</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.69</td>\n",
       "      <td>1.88</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prio</th>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.04</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>0.35</td>\n",
       "      <td>1.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race</th>\n",
       "      <td>0.07</td>\n",
       "      <td>1.07</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wexp</th>\n",
       "      <td>0.16</td>\n",
       "      <td>1.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.62</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.35</td>\n",
       "      <td>1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rho1</th>\n",
       "      <th>Intercept</th>\n",
       "      <td>0.15</td>\n",
       "      <td>1.16</td>\n",
       "      <td>0.13</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.14</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rho2</th>\n",
       "      <th>Intercept</th>\n",
       "      <td>0.15</td>\n",
       "      <td>1.16</td>\n",
       "      <td>0.15</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.86</td>\n",
       "      <td>1.56</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.32</td>\n",
       "      <td>1.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <th>Intercept</th>\n",
       "      <td>0.18</td>\n",
       "      <td>1.19</td>\n",
       "      <td>0.15</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.61</td>\n",
       "      <td>1.18</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><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",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AIC</th>\n",
       "      <td>512.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood ratio test</th>\n",
       "      <td>127.04 on 12 df</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-log2(p) of ll-ratio test</th>\n",
       "      <td>68.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "\\begin{tabular}{llrrrrrrrrrr}\n",
       "\\toprule\n",
       "      &           &  coef &  exp(coef) &  se(coef) &  coef lower 95\\% &  coef upper 95\\% &  exp(coef) lower 95\\% &  exp(coef) upper 95\\% &     z &    p &  -log2(p) \\\\\n",
       "param & covariate &       &            &           &                 &                 &                      &                      &       &      &           \\\\\n",
       "\\midrule\n",
       "lambda1 & age &  0.05 &       1.05 &      0.02 &            0.02 &            0.08 &                 1.02 &                 1.09 &  3.06 & 0.00 &      8.80 \\\\\n",
       "      & fin &  0.23 &       1.26 &      0.19 &           -0.15 &            0.61 &                 0.86 &                 1.84 &  1.18 & 0.24 &      2.06 \\\\\n",
       "      & mar &  0.21 &       1.23 &      0.35 &           -0.48 &            0.90 &                 0.62 &                 2.46 &  0.59 & 0.56 &      0.84 \\\\\n",
       "      & paro &  0.13 &       1.14 &      0.25 &           -0.36 &            0.63 &                 0.69 &                 1.88 &  0.52 & 0.60 &      0.74 \\\\\n",
       "      & prio & -0.04 &       0.96 &      0.04 &           -0.11 &            0.04 &                 0.89 &                 1.04 & -0.93 & 0.35 &      1.50 \\\\\n",
       "      & race &  0.07 &       1.07 &       NaN &             NaN &             NaN &                  NaN &                  NaN &   NaN &  NaN &       NaN \\\\\n",
       "      & wexp &  0.16 &       1.17 &      0.17 &           -0.17 &            0.48 &                 0.85 &                 1.62 &  0.94 & 0.35 &      1.53 \\\\\n",
       "lambda2 & age &  0.05 &       1.05 &      0.02 &            0.01 &            0.09 &                 1.01 &                 1.10 &  2.34 & 0.02 &      5.70 \\\\\n",
       "      & fin &  0.23 &       1.26 &      0.19 &           -0.15 &            0.61 &                 0.86 &                 1.84 &  1.18 & 0.24 &      2.06 \\\\\n",
       "      & mar &  0.21 &       1.23 &      0.35 &           -0.48 &            0.90 &                 0.62 &                 2.46 &  0.59 & 0.56 &      0.84 \\\\\n",
       "      & paro &  0.13 &       1.14 &      0.25 &           -0.36 &            0.63 &                 0.69 &                 1.88 &  0.52 & 0.60 &      0.74 \\\\\n",
       "      & prio & -0.04 &       0.96 &      0.04 &           -0.11 &            0.04 &                 0.89 &                 1.04 & -0.93 & 0.35 &      1.50 \\\\\n",
       "      & race &  0.07 &       1.07 &       NaN &             NaN &             NaN &                  NaN &                  NaN &   NaN &  NaN &       NaN \\\\\n",
       "      & wexp &  0.16 &       1.17 &      0.17 &           -0.17 &            0.48 &                 0.85 &                 1.62 &  0.94 & 0.35 &      1.53 \\\\\n",
       "rho1 & Intercept &  0.15 &       1.16 &      0.13 &           -0.11 &            0.40 &                 0.90 &                 1.50 &  1.14 & 0.25 &      1.98 \\\\\n",
       "rho2 & Intercept &  0.15 &       1.16 &      0.15 &           -0.15 &            0.44 &                 0.86 &                 1.56 &  0.99 & 0.32 &      1.62 \\\\\n",
       "alpha & Intercept &  0.18 &       1.19 &      0.15 &           -0.12 &            0.47 &                 0.89 &                 1.61 &  1.18 & 0.24 &      2.06 \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.DependentCompetingRisksHazard: fitted with 432 total observations, 318 right-censored observations>\n",
       "             duration col = 'week'\n",
       "                event col = 'arrest'\n",
       "                penalizer = 0.1\n",
       "   number of observations = 432\n",
       "number of events observed = 114\n",
       "           log-likelihood = -239.19\n",
       "         time fit was run = 2020-07-26 22:06:55 UTC\n",
       "\n",
       "---\n",
       "                    coef  exp(coef)   se(coef)   coef lower 95%   coef upper 95%  exp(coef) lower 95%  exp(coef) upper 95%\n",
       "param   covariate                                                                                                         \n",
       "lambda1 age         0.05       1.05       0.02             0.02             0.08                 1.02                 1.09\n",
       "        fin         0.23       1.26       0.19            -0.15             0.61                 0.86                 1.84\n",
       "        mar         0.21       1.23       0.35            -0.48             0.90                 0.62                 2.46\n",
       "        paro        0.13       1.14       0.25            -0.36             0.63                 0.69                 1.88\n",
       "        prio       -0.04       0.96       0.04            -0.11             0.04                 0.89                 1.04\n",
       "        race        0.07       1.07        nan              nan              nan                  nan                  nan\n",
       "        wexp        0.16       1.17       0.17            -0.17             0.48                 0.85                 1.62\n",
       "lambda2 age         0.05       1.05       0.02             0.01             0.09                 1.01                 1.10\n",
       "        fin         0.23       1.26       0.19            -0.15             0.61                 0.86                 1.84\n",
       "        mar         0.21       1.23       0.35            -0.48             0.90                 0.62                 2.46\n",
       "        paro        0.13       1.14       0.25            -0.36             0.63                 0.69                 1.88\n",
       "        prio       -0.04       0.96       0.04            -0.11             0.04                 0.89                 1.04\n",
       "        race        0.07       1.07        nan              nan              nan                  nan                  nan\n",
       "        wexp        0.16       1.17       0.17            -0.17             0.48                 0.85                 1.62\n",
       "rho1    Intercept   0.15       1.16       0.13            -0.11             0.40                 0.90                 1.50\n",
       "rho2    Intercept   0.15       1.16       0.15            -0.15             0.44                 0.86                 1.56\n",
       "alpha   Intercept   0.18       1.19       0.15            -0.12             0.47                 0.89                 1.61\n",
       "                      z      p   -log2(p)\n",
       "param   covariate                        \n",
       "lambda1 age        3.06 <0.005       8.80\n",
       "        fin        1.18   0.24       2.06\n",
       "        mar        0.59   0.56       0.84\n",
       "        paro       0.52   0.60       0.74\n",
       "        prio      -0.93   0.35       1.50\n",
       "        race        nan    nan        nan\n",
       "        wexp       0.94   0.35       1.53\n",
       "lambda2 age        2.34   0.02       5.70\n",
       "        fin        1.18   0.24       2.06\n",
       "        mar        0.59   0.56       0.84\n",
       "        paro       0.52   0.60       0.74\n",
       "        prio      -0.93   0.35       1.50\n",
       "        race        nan    nan        nan\n",
       "        wexp       0.94   0.35       1.53\n",
       "rho1    Intercept  1.14   0.25       1.98\n",
       "rho2    Intercept  0.99   0.32       1.62\n",
       "alpha   Intercept  1.18   0.24       2.06\n",
       "---\n",
       "AIC = 512.38\n",
       "log-likelihood ratio test = 127.04 on 12 df\n",
       "-log2(p) of ll-ratio test = 68.48"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Predicted survival functions for selected subjects')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 261,
       "width": 436
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 483
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "class DependentCompetingRisksHazard(ParametricRegressionFitter):\n",
    "    \"\"\"\n",
    "\n",
    "    Reference\n",
    "    --------------\n",
    "    Frees and Valdez, UNDERSTANDING RELATIONSHIPS USING COPULAS\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    _fitted_parameter_names = [\"lambda1\", \"rho1\", \"lambda2\", \"rho2\", \"alpha\"]\n",
    "\n",
    "    def _cumulative_hazard(self, params, T, Xs):\n",
    "        lambda1 = np.exp(np.dot(Xs[\"lambda1\"], params[\"lambda1\"]))\n",
    "        lambda2 = np.exp(np.dot(Xs[\"lambda2\"], params[\"lambda2\"]))\n",
    "        rho2 =    np.exp(np.dot(Xs[\"rho2\"],    params[\"rho2\"]))\n",
    "        rho1 =    np.exp(np.dot(Xs[\"rho1\"],    params[\"rho1\"]))\n",
    "        alpha =   np.exp(np.dot(Xs[\"alpha\"],   params[\"alpha\"]))\n",
    "\n",
    "        return ((T / lambda1) ** rho1 + (T / lambda2) ** rho2) ** alpha\n",
    "\n",
    "\n",
    "fitter = DependentCompetingRisksHazard(penalizer=0.1)\n",
    "\n",
    "rossi = load_rossi()\n",
    "rossi[\"week\"] = rossi[\"week\"] / rossi[\"week\"].max() # scaling often helps with convergence\n",
    "\n",
    "covariates = {\n",
    "    \"lambda1\": rossi.columns.difference(['week', 'arrest']),\n",
    "    \"lambda2\": rossi.columns.difference(['week', 'arrest']),\n",
    "    \"rho1\": \"1\",\n",
    "    \"rho2\": \"1\",\n",
    "    \"alpha\": \"1\",\n",
    "}\n",
    "\n",
    "fitter.fit(rossi, \"week\", event_col=\"arrest\", regressors=covariates, timeline=np.linspace(0, 2))\n",
    "fitter.print_summary(2)\n",
    "\n",
    "ax = fitter.plot()\n",
    "\n",
    "ax = fitter.predict_survival_function(rossi.loc[::100]).plot(figsize=(8, 4))\n",
    "ax.set_title(\"Predicted survival functions for selected subjects\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cure models\n",
    "\n",
    "Suppose in our population we have a subpopulation that will never experience the event of interest. Or, for some subjects the event will occur so far in the future that it's essentially at time infinity. In this case, the survival function for an individual should not asymptically approach zero, but _some positive value_. Models that describe this are sometimes called cure models (i.e. the subject is \"cured\" of death and hence no longer susceptible) or time-lagged conversion models. \n",
    "\n",
    "It would be nice to be able to use common survival models _and_ have some \"cure\" component. Let's suppose that for individuals that will experience the event of interest, their survival distribution is a Weibull, denoted $S_W(t)$. For a random selected individual in the population, their survival curve, $S(t)$, is:\n",
    "\n",
    "$$ \n",
    "\\begin{align*}\n",
    "S(t) = P(T > t) &= P(\\text{cured}) P(T > t\\;|\\;\\text{cured}) +  P(\\text{not cured}) P(T > t\\;|\\;\\text{not cured})  \\\\\n",
    "      &= p + (1-p) S_W(t)\n",
    "\\end{align*}\n",
    "$$\n",
    "\n",
    "Even though it's in an unconventional form, we can still determine the cumulative hazard (which is the negative logarithm of the survival function):\n",
    "\n",
    "$$ H(t) =  -\\log{\\left(p + (1-p) S_W(t)\\right)} $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>lifelines.CureModel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration col</th>\n",
       "      <td>'week'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event col</th>\n",
       "      <td>'arrest'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of observations</th>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number of events observed</th>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood</th>\n",
       "      <td>-702.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time fit was run</th>\n",
       "      <td>2020-07-26 22:07:13 UTC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>coef</th>\n",
       "      <th>exp(coef)</th>\n",
       "      <th>se(coef)</th>\n",
       "      <th>coef lower 95%</th>\n",
       "      <th>coef upper 95%</th>\n",
       "      <th>exp(coef) lower 95%</th>\n",
       "      <th>exp(coef) upper 95%</th>\n",
       "      <th>z</th>\n",
       "      <th>p</th>\n",
       "      <th>-log2(p)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>param</th>\n",
       "      <th>covariate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">lambda_</th>\n",
       "      <th>age</th>\n",
       "      <td>0.16</td>\n",
       "      <td>1.17</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1.30</td>\n",
       "      <td>2.96</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>8.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fin</th>\n",
       "      <td>1.14</td>\n",
       "      <td>3.13</td>\n",
       "      <td>4320.37</td>\n",
       "      <td>-8466.63</td>\n",
       "      <td>8468.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mar</th>\n",
       "      <td>0.35</td>\n",
       "      <td>1.42</td>\n",
       "      <td>0.32</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.76</td>\n",
       "      <td>2.64</td>\n",
       "      <td>1.11</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paro</th>\n",
       "      <td>0.26</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.24</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.82</td>\n",
       "      <td>2.06</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prio</th>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.91</td>\n",
       "      <td>1.06</td>\n",
       "      <td>-0.51</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race</th>\n",
       "      <td>0.23</td>\n",
       "      <td>1.26</td>\n",
       "      <td>0.28</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.72</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.41</td>\n",
       "      <td>1.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wexp</th>\n",
       "      <td>0.25</td>\n",
       "      <td>1.28</td>\n",
       "      <td>0.17</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.92</td>\n",
       "      <td>1.78</td>\n",
       "      <td>1.49</td>\n",
       "      <td>0.14</td>\n",
       "      <td>2.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rho_</th>\n",
       "      <th>Intercept</th>\n",
       "      <td>0.13</td>\n",
       "      <td>1.14</td>\n",
       "      <td>0.08</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.34</td>\n",
       "      <td>1.57</td>\n",
       "      <td>0.12</td>\n",
       "      <td>3.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">beta_</th>\n",
       "      <th>Intercept</th>\n",
       "      <td>0.18</td>\n",
       "      <td>1.19</td>\n",
       "      <td>0.10</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.08</td>\n",
       "      <td>3.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fin</th>\n",
       "      <td>15.80</td>\n",
       "      <td>7.30e+06</td>\n",
       "      <td>0.17</td>\n",
       "      <td>15.46</td>\n",
       "      <td>16.14</td>\n",
       "      <td>5.20e+06</td>\n",
       "      <td>1.03e+07</td>\n",
       "      <td>90.99</td>\n",
       "      <td>&lt;0.005</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AIC</th>\n",
       "      <td>1425.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log-likelihood ratio test</th>\n",
       "      <td>-12.04 on 7 df</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-log2(p) of ll-ratio test</th>\n",
       "      <td>-0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "\\begin{tabular}{llrrrrrrrrrr}\n",
       "\\toprule\n",
       "      &     &  coef &  exp(coef) &  se(coef) &  coef lower 95\\% &  coef upper 95\\% &  exp(coef) lower 95\\% &  exp(coef) upper 95\\% &     z &    p &  -log2(p) \\\\\n",
       "param & covariate &       &            &           &                 &                 &                      &                      &       &      &           \\\\\n",
       "\\midrule\n",
       "lambda\\_ & age &  0.16 &       1.17 &      0.05 &            0.05 &            0.26 &                 1.06 &                 1.30 &  2.96 & 0.00 &      8.36 \\\\\n",
       "      & fin &  1.14 &       3.13 &   4320.37 &        -8466.63 &         8468.92 &                 0.00 &                  inf &  0.00 & 1.00 &      0.00 \\\\\n",
       "      & mar &  0.35 &       1.42 &      0.32 &           -0.27 &            0.97 &                 0.76 &                 2.64 &  1.11 & 0.27 &      1.90 \\\\\n",
       "      & paro &  0.26 &       1.30 &      0.24 &           -0.20 &            0.72 &                 0.82 &                 2.06 &  1.10 & 0.27 &      1.88 \\\\\n",
       "      & prio & -0.02 &       0.98 &      0.04 &           -0.10 &            0.06 &                 0.91 &                 1.06 & -0.51 & 0.61 &      0.71 \\\\\n",
       "      & race &  0.23 &       1.26 &      0.28 &           -0.32 &            0.79 &                 0.72 &                 2.20 &  0.82 & 0.41 &      1.29 \\\\\n",
       "      & wexp &  0.25 &       1.28 &      0.17 &           -0.08 &            0.57 &                 0.92 &                 1.78 &  1.49 & 0.14 &      2.86 \\\\\n",
       "rho\\_ & Intercept &  0.13 &       1.14 &      0.08 &           -0.03 &            0.29 &                 0.97 &                 1.34 &  1.57 & 0.12 &      3.11 \\\\\n",
       "beta\\_ & Intercept &  0.18 &       1.19 &      0.10 &           -0.02 &            0.37 &                 0.98 &                 1.45 &  1.75 & 0.08 &      3.64 \\\\\n",
       "      & fin & 15.80 & 7302961.31 &      0.17 &           15.46 &           16.14 &           5195804.40 &          10264675.08 & 90.99 & 0.00 &       inf \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "<lifelines.CureModel: fitted with 432 total observations, 318 right-censored observations>\n",
       "             duration col = 'week'\n",
       "                event col = 'arrest'\n",
       "   number of observations = 432\n",
       "number of events observed = 114\n",
       "           log-likelihood = -702.64\n",
       "         time fit was run = 2020-07-26 22:07:13 UTC\n",
       "\n",
       "---\n",
       "                    coef  exp(coef)   se(coef)   coef lower 95%   coef upper 95%  exp(coef) lower 95%  exp(coef) upper 95%\n",
       "param   covariate                                                                                                         \n",
       "lambda_ age         0.16       1.17       0.05             0.05             0.26                 1.06                 1.30\n",
       "        fin         1.14       3.13    4320.37         -8466.63          8468.92                 0.00                  inf\n",
       "        mar         0.35       1.42       0.32            -0.27             0.97                 0.76                 2.64\n",
       "        paro        0.26       1.30       0.24            -0.20             0.72                 0.82                 2.06\n",
       "        prio       -0.02       0.98       0.04            -0.10             0.06                 0.91                 1.06\n",
       "        race        0.23       1.26       0.28            -0.32             0.79                 0.72                 2.20\n",
       "        wexp        0.25       1.28       0.17            -0.08             0.57                 0.92                 1.78\n",
       "rho_    Intercept   0.13       1.14       0.08            -0.03             0.29                 0.97                 1.34\n",
       "beta_   Intercept   0.18       1.19       0.10            -0.02             0.37                 0.98                 1.45\n",
       "        fin        15.80   7.30e+06       0.17            15.46            16.14             5.20e+06             1.03e+07\n",
       "                      z      p   -log2(p)\n",
       "param   covariate                        \n",
       "lambda_ age        2.96 <0.005       8.36\n",
       "        fin        0.00   1.00       0.00\n",
       "        mar        1.11   0.27       1.90\n",
       "        paro       1.10   0.27       1.88\n",
       "        prio      -0.51   0.61       0.71\n",
       "        race       0.82   0.41       1.29\n",
       "        wexp       1.49   0.14       2.86\n",
       "rho_    Intercept  1.57   0.12       3.11\n",
       "beta_   Intercept  1.75   0.08       3.64\n",
       "        fin       90.99 <0.005        inf\n",
       "---\n",
       "AIC = 1425.29\n",
       "log-likelihood ratio test = -12.04 on 7 df\n",
       "-log2(p) of ll-ratio test = -0.00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from autograd.scipy.special import expit\n",
    "\n",
    "class CureModel(ParametricRegressionFitter):\n",
    "    _scipy_fit_method = \"SLSQP\"\n",
    "    _scipy_fit_options = {\"ftol\": 1e-10, \"maxiter\": 200}\n",
    "\n",
    "    _fitted_parameter_names = [\"lambda_\", \"beta_\", \"rho_\"]\n",
    "\n",
    "    def _cumulative_hazard(self, params, T, Xs):\n",
    "        c = expit(np.dot(Xs[\"beta_\"], params[\"beta_\"]))\n",
    "\n",
    "        lambda_ = np.exp(np.dot(Xs[\"lambda_\"], params[\"lambda_\"]))\n",
    "        rho_ = np.exp(np.dot(Xs[\"rho_\"], params[\"rho_\"]))\n",
    "        sf = np.exp(-(T / lambda_) ** rho_)\n",
    "\n",
    "        return -np.log((1 - c) + c * sf)\n",
    "\n",
    "\n",
    "cm = CureModel(penalizer=0.0)\n",
    "\n",
    "rossi = load_rossi()\n",
    "\n",
    "covariates = {\"lambda_\": rossi.columns.difference(['week', 'arrest']), \"rho_\": \"1\", \"beta_\": 'fin + 1'}\n",
    "\n",
    "cm.fit(rossi, \"week\", event_col=\"arrest\", regressors=covariates, timeline=np.arange(250)) \n",
    "cm.print_summary(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 357,
       "width": 706
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm.predict_survival_function(rossi.loc[::100]).plot(figsize=(12,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 357,
       "width": 707
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# what's the effect on the survival curve if I vary \"age\"\n",
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "\n",
    "cm.plot_covariate_groups(['age'], values=np.arange(20, 50, 5), cmap='coolwarm', ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spline models\n",
    "\n",
    "See `royston_parmar_splines.py` in the examples folder: https://github.com/CamDavidsonPilon/lifelines/tree/master/examples"
   ]
  }
 ],
 "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
}