example-notebooks/Test pem_survival_model with simulated data.ipynb

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      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
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      "/home/jacquelineburos/miniconda3/envs/python3/lib/python3.5/site-packages/Cython/Distutils/old_build_ext.py:30: UserWarning: Cython.Distutils.old_build_ext does not properly handle dependencies and is deprecated.\n",
      "  \"Cython.Distutils.old_build_ext does not properly handle dependencies \"\n"
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      "/home/jacquelineburos/.local/lib/python3.5/site-packages/IPython/html.py:14: ShimWarning: The `IPython.html` package has been deprecated. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.\n",
      "  \"`IPython.html.widgets` has moved to `ipywidgets`.\", ShimWarning)\n",
      "INFO:stancache.seed:Setting seed to 1245502385\n"
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   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "import random\n",
    "random.seed(1100038344)\n",
    "import survivalstan\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from stancache import stancache\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## The model\n",
    "\n",
    "This style of modeling is often called the \"piecewise exponential model\", or PEM. It is the simplest case where we estimate the *hazard* of an event occurring in a time period as the outcome, rather than estimating the *survival* (ie, time to event) as the outcome.\n",
    "\n",
    "Recall that, in the context of survival modeling, we have two models:\n",
    "\n",
    "1. A model for **Survival ($S$)**, ie the probability of surviving to time $t$:\n",
    "\n",
    "  $$ S(t)=Pr(Y > t) $$\n",
    "\n",
    "2. A model for the **instantaneous *hazard* $\\lambda$**, ie the probability of a failure event occuring in the interval [$t$, $t+\\delta t$], given survival to time $t$:\n",
    "\n",
    "  $$ \\lambda(t) =  \\lim_{\\delta t \\rightarrow 0 } \\; \\frac{Pr( t \\le Y \\le t + \\delta t | Y > t)}{\\delta t} $$\n",
    "\n",
    "\n",
    "By definition, these two are related to one another by the following equation:\n",
    "\n",
    "  $$ \\lambda(t) = \\frac{-S'(t)}{S(t)} $$\n",
    "  \n",
    "Solving this, yields the following:\n",
    "\n",
    "  $$ S(t) = \\exp\\left( -\\int_0^t \\lambda(z) dz \\right) $$\n",
    "\n",
    "This model is called the **piecewise exponential model** because of this relationship between the Survival and hazard functions.  It's piecewise because we are not estimating the *instantaneous* hazard; we are instead breaking time periods up into pieces and estimating the hazard for each piece.\n",
    "\n",
    "There are several variations on the PEM model implemented in `survivalstan`. In this notebook, we are exploring just one of them."
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A note about data formatting \n",
    "\n",
    "When we model *Survival*, we typically operate on data in time-to-event form. In this form, we have one record per `Subject` (ie, per patient). Each record contains `[event_status, time_to_event]` as the outcome. This data format is sometimes called *per-subject*.\n",
    "\n",
    "When we model the *hazard* by comparison, we typically operate on data that are transformed to include one record per `Subject` per `time_period`.  This is called *per-timepoint* or *long* form.\n",
    "\n",
    "All other things being equal, a model for *Survival* will typically estimate more efficiently (faster & smaller memory footprint) than one for *hazard* simply because the data are larger in the per-timepoint form than the per-subject form. The benefit of the *hazard* models is increased flexibility in terms of specifying the baseline hazard, time-varying effects, and introducing time-varying covariates.\n",
    "\n",
    "In this example, we are demonstrating use of the standard **PEM survival model**, which uses data in long form. The `stan` code expects to recieve data in this structure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stan code for the model\n",
    "\n",
    "This model is provided in `survivalstan.models.pem_survival_model`. Let's take a look at the stan code. "
   ]
  },
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      "/*  Variable naming:\n",
      " // dimensions\n",
      " N          = total number of observations (length of data)\n",
      " S          = number of sample ids\n",
      " T          = max timepoint (number of timepoint ids)\n",
      " M          = number of covariates\n",
      " \n",
      " // main data matrix (per observed timepoint*record)\n",
      " s          = sample id for each obs\n",
      " t          = timepoint id for each obs\n",
      " event      = integer indicating if there was an event at time t for sample s\n",
      " x          = matrix of real-valued covariates at time t for sample n [N, X]\n",
      " \n",
      " // timepoint-specific data (per timepoint, ordered by timepoint id)\n",
      " t_obs      = observed time since origin for each timepoint id (end of period)\n",
      " t_dur      = duration of each timepoint period (first diff of t_obs)\n",
      " \n",
      "*/\n",
      "// Jacqueline Buros Novik <jackinovik@gmail.com>\n",
      "\n",
      "data {\n",
      "  // dimensions\n",
      "  int<lower=1> N;\n",
      "  int<lower=1> S;\n",
      "  int<lower=1> T;\n",
      "  int<lower=0> M;\n",
      "  \n",
      "  // data matrix\n",
      "  int<lower=1, upper=N> s[N];     // sample id\n",
      "  int<lower=1, upper=T> t[N];     // timepoint id\n",
      "  int<lower=0, upper=1> event[N]; // 1: event, 0:censor\n",
      "  matrix[N, M] x;                 // explanatory vars\n",
      "  \n",
      "  // timepoint data\n",
      "  vector<lower=0>[T] t_obs;\n",
      "  vector<lower=0>[T] t_dur;\n",
      "}\n",
      "transformed data {\n",
      "  vector[T] log_t_dur;  // log-duration for each timepoint\n",
      "  int n_trans[S, T];  \n",
      "  \n",
      "  log_t_dur = log(t_obs);\n",
      "\n",
      "  // n_trans used to map each sample*timepoint to n (used in gen quantities)\n",
      "  // map each patient/timepoint combination to n values\n",
      "  for (n in 1:N) {\n",
      "      n_trans[s[n], t[n]] = n;\n",
      "  }\n",
      "\n",
      "  // fill in missing values with n for max t for that patient\n",
      "  // ie assume \"last observed\" state applies forward (may be problematic for TVC)\n",
      "  // this allows us to predict failure times >= observed survival times\n",
      "  for (samp in 1:S) {\n",
      "      int last_value;\n",
      "      last_value = 0;\n",
      "      for (tp in 1:T) {\n",
      "          // manual says ints are initialized to neg values\n",
      "          // so <=0 is a shorthand for \"unassigned\"\n",
      "          if (n_trans[samp, tp] <= 0 && last_value != 0) {\n",
      "              n_trans[samp, tp] = last_value;\n",
      "          } else {\n",
      "              last_value = n_trans[samp, tp];\n",
      "          }\n",
      "      }\n",
      "  }  \n",
      "}\n",
      "parameters {\n",
      "  vector[T] log_baseline_raw; // unstructured baseline hazard for each timepoint t\n",
      "  vector[M] beta;         // beta for each covariate\n",
      "  real<lower=0> baseline_sigma;\n",
      "  real log_baseline_mu;\n",
      "}\n",
      "transformed parameters {\n",
      "  vector[N] log_hazard;\n",
      "  vector[T] log_baseline;     // unstructured baseline hazard for each timepoint t\n",
      "  \n",
      "  log_baseline = log_baseline_mu + log_baseline_raw + log_t_dur;\n",
      "  \n",
      "  for (n in 1:N) {\n",
      "    log_hazard[n] = log_baseline[t[n]] + x[n,]*beta;\n",
      "  }\n",
      "}\n",
      "model {\n",
      "  beta ~ cauchy(0, 2);\n",
      "  event ~ poisson_log(log_hazard);\n",
      "  log_baseline_mu ~ normal(0, 1);\n",
      "  baseline_sigma ~ normal(0, 1);\n",
      "  log_baseline_raw ~ normal(0, baseline_sigma);\n",
      "}\n",
      "generated quantities {\n",
      "  real log_lik[N];\n",
      "  vector[T] baseline;\n",
      "  real y_hat_time[S];      // predicted failure time for each sample\n",
      "  int y_hat_event[S];      // predicted event (0:censor, 1:event)\n",
      "  \n",
      "  // compute raw baseline hazard, for summary/plotting\n",
      "  baseline = exp(log_baseline_mu + log_baseline_raw);\n",
      "  \n",
      "  // prepare log_lik for loo-psis\n",
      "  for (n in 1:N) {\n",
      "      log_lik[n] = poisson_log_log(event[n], log_hazard[n]);\n",
      "  }\n",
      "\n",
      "  // posterior predicted values\n",
      "  for (samp in 1:S) {\n",
      "      int sample_alive;\n",
      "      sample_alive = 1;\n",
      "      for (tp in 1:T) {\n",
      "        if (sample_alive == 1) {\n",
      "              int n;\n",
      "              int pred_y;\n",
      "              real log_haz;\n",
      "              \n",
      "              // determine predicted value of this sample's hazard\n",
      "              n = n_trans[samp, tp];\n",
      "              log_haz = log_baseline[tp] + x[n,] * beta;\n",
      "              \n",
      "              // now, make posterior prediction of an event at this tp\n",
      "              if (log_haz < log(pow(2, 30))) \n",
      "                  pred_y = poisson_log_rng(log_haz);\n",
      "              else\n",
      "                  pred_y = 9; \n",
      "              \n",
      "              // summarize survival time (observed) for this pt\n",
      "              if (pred_y >= 1) {\n",
      "                  // mark this patient as ineligible for future tps\n",
      "                  // note: deliberately treat 9s as events \n",
      "                  sample_alive = 0;\n",
      "                  y_hat_time[samp] = t_obs[tp];\n",
      "                  y_hat_event[samp] = 1;\n",
      "              }\n",
      "              \n",
      "          }\n",
      "      } // end per-timepoint loop\n",
      "      \n",
      "      // if patient still alive at max\n",
      "      if (sample_alive == 1) {\n",
      "          y_hat_time[samp] = t_obs[T];\n",
      "          y_hat_event[samp] = 0;\n",
      "      }\n",
      "  } // end per-sample loop  \n",
      "}\n"
     ]
    }
   ],
   "source": [
    "print(survivalstan.models.pem_survival_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulate survival data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to demonstrate the use of this model, we will first simulate some survival data using `survivalstan.sim.sim_data_exp_correlated`. As the name implies, this function simulates data assuming a constant hazard throughout the follow-up time period, which is consistent with the Exponential survival function.\n",
    "\n",
    "This function includes two simulated covariates by default (`age` and `sex`). We also simulate a situation where hazard is a function of the simulated value for `sex`. \n",
    "\n",
    "We also center the `age` variable since this will make it easier to interpret estimates of the baseline hazard.\n"
   ]
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     "text": [
      "INFO:stancache.stancache:sim_data_exp_correlated: cache_filename set to sim_data_exp_correlated.cached.N_100.censor_time_20.rate_coefs_54462717316.rate_form_1 + sex.pkl\n"
     ]
    },
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     "text": [
      "INFO:stancache.stancache:sim_data_exp_correlated: Loading result from cache\n"
     ]
    }
   ],
   "source": [
    "d = stancache.cached(\n",
    "    survivalstan.sim.sim_data_exp_correlated,\n",
    "    N=100,\n",
    "    censor_time=20,\n",
    "    rate_form='1 + sex',\n",
    "    rate_coefs=[-3, 0.5],\n",
    ")\n",
    "d['age_centered'] = d['age'] - d['age'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Aside: In order to make this a more reproducible example, this code is using a file-caching function `stancache.cached` to wrap a function call to `survivalstan.sim.sim_data_exp_correlated`. *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore simulated data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is what these data look like - this is `per-subject` or `time-to-event` form:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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    {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>rate</th>\n",
       "      <th>true_t</th>\n",
       "      <th>t</th>\n",
       "      <th>event</th>\n",
       "      <th>index</th>\n",
       "      <th>age_centered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>20.948771</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>4.18</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61</td>\n",
       "      <td>female</td>\n",
       "      <td>0.049787</td>\n",
       "      <td>27.018886</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>6.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>57</td>\n",
       "      <td>female</td>\n",
       "      <td>0.049787</td>\n",
       "      <td>62.220296</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>2.18</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>10.462045</td>\n",
       "      <td>10.462045</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "   age     sex      rate     true_t          t  event  index  age_centered\n",
       "0   59    male  0.082085  20.948771  20.000000  False      0          4.18\n",
       "1   58    male  0.082085  12.827519  12.827519   True      1          3.18\n",
       "2   61  female  0.049787  27.018886  20.000000  False      2          6.18\n",
       "3   57  female  0.049787  62.220296  20.000000  False      3          2.18\n",
       "4   55    male  0.082085  10.462045  10.462045   True      4          0.18"
      ]
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   "source": [
    "d.head()"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*It's not that obvious from the field names, but in this example \"subjects\" are indexed by the field `index`.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot these data using `lifelines`, or the rudimentary plotting functions provided by `survivalstan`."
   ]
  },
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JUdRrYhRvBlQ/r7yLmOaU+HFdAdXRVjcgA6rFYmFEYhgjEsO4YnoqG3ftY8Wm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc064221f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survivalstan.utils.plot_observed_survival(df=d[d['sex']=='female'], event_col='event', time_col='t', label='female')\n",
    "survivalstan.utils.plot_observed_survival(df=d[d['sex']=='male'], event_col='event', time_col='t', label='male')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transform to `long` or `per-timepoint` form"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, since this is a PEM model, we transform our data to `long` or `per-timepoint` form."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:stancache.stancache:prep_data_long_surv: cache_filename set to prep_data_long_surv.cached.df_33772694934.event_col_event.time_col_t.pkl\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:stancache.stancache:prep_data_long_surv: Loading result from cache\n"
     ]
    }
   ],
   "source": [
    "dlong = stancache.cached(\n",
    "    survivalstan.prep_data_long_surv,\n",
    "    df=d, event_col='event', time_col='t'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have one record per timepoint (distinct values of `end_time`) per subject (`index`, in the original data frame)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>rate</th>\n",
       "      <th>true_t</th>\n",
       "      <th>t</th>\n",
       "      <th>event</th>\n",
       "      <th>index</th>\n",
       "      <th>age_centered</th>\n",
       "      <th>key</th>\n",
       "      <th>end_time</th>\n",
       "      <th>end_failure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>0.118611</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196923</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>0.262114</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>0.641174</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>0.944220</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>1.105340</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>1.397562</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>1.476557</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>1.530035</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>2.111333</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>2.330953</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>2.357800</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>2.639054</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>2.724832</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>2.743388</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>3.015604</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>3.095814</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>3.471401</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>3.637968</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>3.792521</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>4.090998</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>4.613828</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>4.829138</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>4.856847</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>5.008202</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>5.084885</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>5.359748</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>6.434233</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>6.512257</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>6.688216</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>7.001683</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>7.157144</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>7.329006</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>7.351628</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>7.405822</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>7.417478</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>7.442196</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>7.561702</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>7.679609</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>8.228047</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>8.263575</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>8.456715</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>8.817222</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>9.244121</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>9.336164</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>9.344597</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>9.590623</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>9.731395</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>9.984362</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>10.159427</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>10.462045</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>10.787069</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>11.371130</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>11.540905</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>11.751679</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>12.145235</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>12.156584</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>12.157394</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>12.559011</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>58</td>\n",
       "      <td>male</td>\n",
       "      <td>0.082085</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>3.18</td>\n",
       "      <td>1</td>\n",
       "      <td>12.827519</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>63 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age   sex      rate     true_t          t event  index  age_centered  \\\n",
       "140   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "81    58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "139   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "149   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "104   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "136   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "146   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "86    58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "135   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "103   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "147   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "83    58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "138   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "113   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "125   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "142   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "118   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "108   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "143   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "126   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "133   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "128   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "119   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "117   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "96    58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "95    58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "141   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "144   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "116   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "90    58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "..   ...   ...       ...        ...        ...   ...    ...           ...   \n",
       "88    58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "127   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "130   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "153   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "148   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "105   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "101   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "131   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "91    58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "112   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "151   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "106   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "114   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "82    58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "98    58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "109   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "123   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "92    58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "124   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "121   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "80    58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "93    58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "102   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "85    58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "155   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "97    58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "122   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "115   58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "89    58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "79    58  male  0.082085  12.827519  12.827519  True      1          3.18   \n",
       "\n",
       "     key   end_time end_failure  \n",
       "140    1   0.118611       False  \n",
       "81     1   0.196923       False  \n",
       "139    1   0.262114       False  \n",
       "149    1   0.641174       False  \n",
       "104    1   0.944220       False  \n",
       "136    1   1.105340       False  \n",
       "146    1   1.397562       False  \n",
       "86     1   1.476557       False  \n",
       "135    1   1.530035       False  \n",
       "103    1   2.111333       False  \n",
       "147    1   2.330953       False  \n",
       "83     1   2.357800       False  \n",
       "138    1   2.639054       False  \n",
       "113    1   2.724832       False  \n",
       "125    1   2.743388       False  \n",
       "142    1   3.015604       False  \n",
       "118    1   3.095814       False  \n",
       "108    1   3.471401       False  \n",
       "143    1   3.637968       False  \n",
       "126    1   3.792521       False  \n",
       "133    1   4.090998       False  \n",
       "128    1   4.613828       False  \n",
       "119    1   4.829138       False  \n",
       "117    1   4.856847       False  \n",
       "96     1   5.008202       False  \n",
       "95     1   5.084885       False  \n",
       "141    1   5.359748       False  \n",
       "144    1   6.434233       False  \n",
       "116    1   6.512257       False  \n",
       "90     1   6.688216       False  \n",
       "..   ...        ...         ...  \n",
       "88     1   7.001683       False  \n",
       "127    1   7.157144       False  \n",
       "130    1   7.329006       False  \n",
       "153    1   7.351628       False  \n",
       "148    1   7.405822       False  \n",
       "105    1   7.417478       False  \n",
       "101    1   7.442196       False  \n",
       "131    1   7.561702       False  \n",
       "91     1   7.679609       False  \n",
       "112    1   8.228047       False  \n",
       "151    1   8.263575       False  \n",
       "106    1   8.456715       False  \n",
       "114    1   8.817222       False  \n",
       "82     1   9.244121       False  \n",
       "98     1   9.336164       False  \n",
       "109    1   9.344597       False  \n",
       "123    1   9.590623       False  \n",
       "92     1   9.731395       False  \n",
       "124    1   9.984362       False  \n",
       "121    1  10.159427       False  \n",
       "80     1  10.462045       False  \n",
       "93     1  10.787069       False  \n",
       "102    1  11.371130       False  \n",
       "85     1  11.540905       False  \n",
       "155    1  11.751679       False  \n",
       "97     1  12.145235       False  \n",
       "122    1  12.156584       False  \n",
       "115    1  12.157394       False  \n",
       "89     1  12.559011       False  \n",
       "79     1  12.827519        True  \n",
       "\n",
       "[63 rows x 11 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dlong.query('index == 1').sort_values('end_time')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit stan model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we are ready to fit our model using `survivalstan.fit_stan_survival_model`. \n",
    "\n",
    "We pass a few parameters to the fit function, many of which are required. See ?survivalstan.fit_stan_survival_model for details. \n",
    "\n",
    "Similar to what we did above, we are asking `survivalstan` to cache this model fit object. See [stancache](http://github.com/jburos/stancache) for more details on how this works. Also, if you didn't want to use the cache, you could omit the parameter `FIT_FUN` and `survivalstan` would use the standard pystan functionality.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:stancache.stancache:Step 1: Get compiled model code, possibly from cache\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:stancache.stancache:StanModel: cache_filename set to anon_model.cython_0_25_1.model_code_49777972005.pystan_2_12_0_0.stanmodel.pkl\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:stancache.stancache:StanModel: Loading result from cache\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:stancache.stancache:Step 2: Get posterior draws from model, possibly from cache\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:stancache.stancache:sampling: cache_filename set to anon_model.cython_0_25_1.model_code_49777972005.pystan_2_12_0_0.stanfit.chains_4.data_31278094506.iter_5000.seed_9001.pkl\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:stancache.stancache:sampling: Starting execution\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:stancache.stancache:sampling: Execution completed (0:03:04.556861 elapsed)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:stancache.stancache:sampling: Saving results to cache\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jacquelineburos/miniconda3/envs/python3/lib/python3.5/site-packages/stancache/stancache.py:251: UserWarning: Pickling fit objects is an experimental feature!\n",
      "The relevant StanModel instance must be pickled along with this fit object.\n",
      "When unpickling the StanModel must be unpickled first.\n",
      "  pickle.dump(res, open(cache_filepath, 'wb'), pickle.HIGHEST_PROTOCOL)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jacquelineburos/miniconda3/envs/python3/lib/python3.5/site-packages/stanity/psis.py:228: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  elif sort == 'in-place':\n",
      "/home/jacquelineburos/miniconda3/envs/python3/lib/python3.5/site-packages/stanity/psis.py:246: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  bs /= 3 * x[sort[np.floor(n/4 + 0.5) - 1]]\n",
      "/home/jacquelineburos/miniconda3/envs/python3/lib/python3.5/site-packages/stanity/psis.py:262: RuntimeWarning: overflow encountered in exp\n",
      "  np.exp(temp, out=temp)\n"
     ]
    }
   ],
   "source": [
    "testfit = survivalstan.fit_stan_survival_model(\n",
    "    model_cohort = 'test model',\n",
    "    model_code = survivalstan.models.pem_survival_model,\n",
    "    df = dlong,\n",
    "    sample_col = 'index',\n",
    "    timepoint_end_col = 'end_time',\n",
    "    event_col = 'end_failure',\n",
    "    formula = '~ age_centered + sex',\n",
    "    iter = 5000,\n",
    "    chains = 4,\n",
    "    seed = 9001,\n",
    "    FIT_FUN = stancache.cached_stan_fit,\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Superficial review of convergence\n",
    "\n",
    "We will note here some top-level summaries of posterior draws -- this is a minimal example so it's unlikely that this model converged very well. \n",
    "\n",
    "In practice, you would want to do a lot more investigation of convergence issues, etc. For now the goal is to demonstrate the functionalities available here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can summarize posterior estimates for a single parameter, (e.g. the built-in Stan parameter `lp__`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            mean   se_mean         sd        2.5%         50%       97.5%      Rhat\n",
      "lp__ -278.136728  5.000714  50.256551 -360.824357 -284.525363 -177.241899  1.023704\n"
     ]
    }
   ],
   "source": [
    "survivalstan.utils.print_stan_summary([testfit], pars='lp__')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or, for sets of parameters with the same name:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                          mean   se_mean        sd      2.5%       50%     97.5%      Rhat\n",
      "log_baseline_raw[0]   0.018405  0.001410  0.141042 -0.266412  0.008231  0.349270  0.999859\n",
      "log_baseline_raw[1]   0.018517  0.001413  0.141319 -0.263341  0.008995  0.342834  1.000190\n",
      "log_baseline_raw[2]   0.018302  0.001414  0.141387 -0.257858  0.009377  0.347234  0.999925\n",
      "log_baseline_raw[3]   0.016333  0.001371  0.137110 -0.253927  0.008481  0.330025  1.000333\n",
      "log_baseline_raw[4]   0.011990  0.001368  0.136849 -0.259225  0.005920  0.325580  1.000145\n",
      "log_baseline_raw[5]   0.013532  0.001370  0.137043 -0.259307  0.007282  0.324367  1.000157\n",
      "log_baseline_raw[6]   0.011470  0.001354  0.135406 -0.266722  0.004746  0.319663  0.999911\n",
      "log_baseline_raw[7]   0.012491  0.001347  0.134695 -0.260132  0.006712  0.316462  1.000008\n",
      "log_baseline_raw[8]   0.011526  0.001393  0.139255 -0.270375  0.004809  0.331765  1.000029\n",
      "log_baseline_raw[9]   0.007375  0.001363  0.136255 -0.279145  0.003548  0.308510  0.999796\n",
      "log_baseline_raw[10]  0.005951  0.001356  0.135572 -0.269977  0.004103  0.304337  0.999750\n",
      "log_baseline_raw[11]  0.005699  0.001387  0.138687 -0.286487  0.002771  0.312076  0.999869\n",
      "log_baseline_raw[12]  0.004665  0.001376  0.137565 -0.283921  0.000912  0.311435  0.999783\n",
      "log_baseline_raw[13]  0.007392  0.001347  0.134663 -0.270574  0.002433  0.308283  1.000123\n",
      "log_baseline_raw[14]  0.004534  0.001342  0.134165 -0.285146  0.001556  0.292968  0.999952\n",
      "log_baseline_raw[15]  0.005103  0.001340  0.134046 -0.277456  0.002392  0.301938  0.999905\n",
      "log_baseline_raw[16]  0.003518  0.001378  0.137844 -0.277368  0.002277  0.307551  0.999839\n",
      "log_baseline_raw[17]  0.002659  0.001332  0.133164 -0.275810  0.001411  0.295238  0.999671\n",
      "log_baseline_raw[18]  0.003533  0.001351  0.135064 -0.277553  0.001838  0.292063  0.999870\n",
      "log_baseline_raw[19]  0.002062  0.001383  0.138273 -0.287297 -0.000670  0.307131  1.000221\n",
      "log_baseline_raw[20]  0.003071  0.001356  0.135590 -0.292247  0.000741  0.302829  0.999768\n",
      "log_baseline_raw[21] -0.001834  0.001394  0.139369 -0.296805 -0.000915  0.287677  0.999744\n",
      "log_baseline_raw[22] -0.002089  0.001341  0.134124 -0.286115 -0.000353  0.282904  0.999881\n",
      "log_baseline_raw[23] -0.002234  0.001374  0.137426 -0.304963 -0.001630  0.291352  1.000080\n",
      "log_baseline_raw[24] -0.000190  0.001343  0.134303 -0.283250 -0.000253  0.294825  0.999723\n",
      "log_baseline_raw[25] -0.001287  0.001369  0.136858 -0.295716 -0.000231  0.287421  0.999792\n",
      "log_baseline_raw[26] -0.002714  0.001372  0.137223 -0.308940 -0.001566  0.291775  0.999712\n",
      "log_baseline_raw[27] -0.006300  0.001364  0.136440 -0.302099 -0.003365  0.270088  0.999816\n",
      "log_baseline_raw[28] -0.006234  0.001344  0.134426 -0.304960 -0.002756  0.279475  1.000183\n",
      "log_baseline_raw[29] -0.005409  0.001320  0.131976 -0.292328 -0.002348  0.274455  0.999847\n",
      "log_baseline_raw[30] -0.007161  0.001320  0.132008 -0.300255 -0.003781  0.268071  0.999809\n",
      "log_baseline_raw[31] -0.006813  0.001362  0.136225 -0.312780 -0.002422  0.278778  0.999783\n",
      "log_baseline_raw[32] -0.005802  0.001366  0.136638 -0.308640 -0.002019  0.280386  1.000023\n",
      "log_baseline_raw[33] -0.008136  0.001322  0.132165 -0.294911 -0.005311  0.267101  0.999791\n",
      "log_baseline_raw[34] -0.005414  0.001347  0.134679 -0.301907 -0.001826  0.271617  0.999739\n",
      "log_baseline_raw[35] -0.005833  0.001332  0.133184 -0.294655 -0.003908  0.264071  0.999886\n",
      "log_baseline_raw[36] -0.005597  0.001333  0.133337 -0.292467 -0.002439  0.264261  0.999732\n",
      "log_baseline_raw[37] -0.006969  0.001347  0.134747 -0.301223 -0.002385  0.271992  0.999948\n",
      "log_baseline_raw[38] -0.004569  0.001330  0.133014 -0.291487 -0.003056  0.271717  1.000080\n",
      "log_baseline_raw[39] -0.005369  0.001377  0.137687 -0.311480 -0.001002  0.278568  0.999943\n",
      "log_baseline_raw[40] -0.004928  0.001316  0.131619 -0.293840 -0.002247  0.271888  0.999953\n",
      "log_baseline_raw[41] -0.006589  0.001354  0.135430 -0.310065 -0.002537  0.275856  0.999951\n",
      "log_baseline_raw[42] -0.005687  0.001369  0.136899 -0.308345 -0.002883  0.281115  0.999867\n",
      "log_baseline_raw[43] -0.007082  0.001337  0.133696 -0.307673 -0.002852  0.269971  1.000021\n",
      "log_baseline_raw[44] -0.006966  0.001380  0.138005 -0.305034 -0.004202  0.277748  0.999742\n",
      "log_baseline_raw[45] -0.006925  0.001344  0.134400 -0.302803 -0.003980  0.271892  0.999753\n",
      "log_baseline_raw[46] -0.007264  0.001343  0.134262 -0.300234 -0.005482  0.274120  0.999905\n",
      "log_baseline_raw[47] -0.008346  0.001373  0.137330 -0.312680 -0.003998  0.273031  1.000193\n",
      "log_baseline_raw[48] -0.006099  0.001325  0.132452 -0.296824 -0.002946  0.271214  0.999884\n",
      "log_baseline_raw[49] -0.007845  0.001339  0.133894 -0.307824 -0.002242  0.268274  0.999954\n",
      "log_baseline_raw[50] -0.006161  0.001353  0.135278 -0.310913 -0.002115  0.270951  1.000001\n",
      "log_baseline_raw[51] -0.004106  0.001367  0.136722 -0.305186 -0.000375  0.280699  0.999947\n",
      "log_baseline_raw[52] -0.006734  0.001337  0.133681 -0.307962 -0.003439  0.277566  0.999966\n",
      "log_baseline_raw[53] -0.004135  0.001350  0.134955 -0.294451 -0.000209  0.275918  0.999780\n",
      "log_baseline_raw[54] -0.006873  0.001353  0.135333 -0.303199 -0.002728  0.273141  1.000029\n",
      "log_baseline_raw[55] -0.007751  0.001396  0.139585 -0.318518 -0.003133  0.285527  0.999913\n",
      "log_baseline_raw[56] -0.007375  0.001338  0.133818 -0.307137 -0.002825  0.275709  0.999920\n",
      "log_baseline_raw[57] -0.008380  0.001351  0.135141 -0.311733 -0.004041  0.267765  0.999744\n",
      "log_baseline_raw[58] -0.003854  0.001357  0.135702 -0.297579 -0.001496  0.288015  0.999748\n",
      "log_baseline_raw[59] -0.005154  0.001355  0.135456 -0.299541 -0.001827  0.272893  0.999782\n",
      "log_baseline_raw[60] -0.005536  0.001351  0.135127 -0.309023 -0.002620  0.279765  0.999889\n",
      "log_baseline_raw[61] -0.005031  0.001368  0.136847 -0.310338 -0.002176  0.283778  1.000248\n",
      "log_baseline_raw[62] -0.004126  0.001335  0.133517 -0.292910 -0.001112  0.273674  0.999906\n",
      "log_baseline_raw[63] -0.006565  0.001341  0.134096 -0.299221 -0.003177  0.267685  1.000108\n",
      "log_baseline_raw[64] -0.005644  0.001361  0.136146 -0.307535 -0.002344  0.276061  0.999890\n",
      "log_baseline_raw[65] -0.005163  0.001322  0.132204 -0.288991 -0.002578  0.277272  1.000024\n",
      "log_baseline_raw[66] -0.003780  0.001344  0.134406 -0.298615 -0.002005  0.282805  0.999966\n",
      "log_baseline_raw[67] -0.003824  0.001327  0.132728 -0.292247 -0.003482  0.279543  0.999709\n",
      "log_baseline_raw[68] -0.007090  0.001373  0.137329 -0.304829 -0.004162  0.273670  0.999759\n",
      "log_baseline_raw[69] -0.006359  0.001352  0.135188 -0.300134 -0.003754  0.273260  0.999711\n",
      "log_baseline_raw[70] -0.004139  0.001339  0.133909 -0.299168 -0.001363  0.278754  0.999813\n",
      "log_baseline_raw[71] -0.005135  0.001337  0.133750 -0.302788 -0.002349  0.277571  0.999863\n",
      "log_baseline_raw[72] -0.004251  0.001329  0.132932 -0.295977 -0.003475  0.273927  0.999838\n",
      "log_baseline_raw[73] -0.004634  0.001385  0.138541 -0.305831 -0.001851  0.290301  0.999982\n",
      "log_baseline_raw[74] -0.004999  0.001328  0.132767 -0.298910 -0.002887  0.269649  0.999897\n",
      "log_baseline_raw[75] -0.004575  0.001344  0.134395 -0.304516 -0.001487  0.278224  0.999981\n",
      "log_baseline_raw[76] -0.003373  0.001372  0.137236 -0.301230 -0.001954  0.288494  0.999889\n",
      "log_baseline_raw[77] -0.021396  0.001397  0.139665 -0.349297 -0.009878  0.254990  1.000517\n"
     ]
    }
   ],
   "source": [
    "survivalstan.utils.print_stan_summary([testfit], pars='log_baseline_raw')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's also not uncommon to graphically summarize the `Rhat` values, to get a sense of similarity among the chains for particular parameters.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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IDJ0xoUBJTvvJKhcAgCERVSj+3e9+N+C/Fy9erNmzZ2vLli0688wzIx93uVzKzc393Gus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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc05cfc8320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survivalstan.utils.plot_stan_summary([testfit], pars='log_baseline_raw')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot posterior estimates of parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use `plot_coefs` to summarize posterior estimates of parameters. \n",
    "\n",
    "In this basic `pem_survival_model`, we estimate a parameter for baseline hazard for each observed timepoint which is then adjusted for the duration of the timepoint.  For consistency, the baseline values are normalized to the *unit time* given in the input data. This allows us to compare hazard estimates across timepoints without having to know the duration of a timepoint. *(in general, the duration-adjusted hazard paramters are suffixed with `_raw` whereas those which are unit-normalized do not have a suffix).*\n",
    "\n",
    "In this model, the baseline hazard is parameterized by two components -- there is an overall mean across all timepoints (`log_baseline_mu`) and some variance per timepoint (`log_baseline_tp`). The degree of variance is estimated from the data as `log_baseline_sigma`. All components have weak default priors. See the stan code above for details.\n",
    "\n",
    "In this case, the model estimates a minimal degree of variance across timepoints, which is good given that the simulated data assumed a constant hazard over time. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ia9asQY8ePQAAdXV1SExMVM+/7rrrsGTJEixduhRLly7FlVdeiRUrVqB///7q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NAAAg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc05d2d2a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survivalstan.utils.plot_coefs([testfit], element='baseline')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also summarize the posterior estimates for our `beta` coefficients. This is actually the default behavior of `plot_coefs`. Here we hope to see the posterior estimates of beta coefficients include the value we used for our simulation (0.5)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc05cfd2048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survivalstan.utils.plot_coefs([testfit])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Posterior predictive checking\n",
    "\n",
    "Finally, `survivalstan` provides some utilities for posterior predictive checking.\n",
    "\n",
    "The goal of posterior-predictive checking is to compare the uncertainty of model predictions to observed values.\n",
    "\n",
    "We are not doing *true* out-of-sample predictions, but we are able to sanity-check our model's calibration. We expect approximately 5% of observed values to fall outside of their corresponding 95% posterior-predicted intervals.\n",
    "\n",
    "By default, `survivalstan`'s plot_pp_survival method will plot whiskers at the 2.5th and 97.5th percentile values, corresponding to 95% predicted intervals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fbfc340de80>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Zeg26rnPRRedx663f5Zxzzsc0TZ5/fiF///urtLY2c8IJw/jqV7/J6aeflX7OsmUfsmDB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\nLPW4QbdGTXOUeadOoqAkj0BEo3lbA8oABfmOJBjVWL+tnlO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/5kc/+hGzZ89m5cqV3HXX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/Ly8tqdf/r0aZw9exb3338/pLaENndbw/cxY8Zg9erVOO+88yJ67Nzc9JCBeKQyM9O6fd9UxWvu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\nAACAl78A1qaKzswgfwAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc098ad27f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survivalstan.utils.plot_pp_survival([testfit], fill=False)\n",
    "survivalstan.utils.plot_observed_survival(df=d, event_col='event', time_col='t', color='green', label='observed')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also summarize and plot survival by our covariates of interest, provided they are included in the original dataframe provided to `fit_stan_survival_model`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Sn0CcTvHIqdDdxteN7etHDQ1DFGGsIQgCpBQIIZFSrkjV0LUax3bvZuO3voUA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fAAAg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbfc2be18d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survivalstan.utils.plot_pp_survival([testfit], by='sex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot can also be customized by a variety of aesthetic elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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I0hvWr3dYuVLRVjH0330Eq1YJDu8znL5SMZZr7WRRqxnK5ZlyjCd4HiwqjHPa+Bq2eUsZ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\nSaQwhuG77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rcRueor+IEA9sb/xtnvAHXZP5slsOFuPHMn/LE43uAu+KEe5Ewt+xk0yU6p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IQMu/cSAREd1SRkbGfx5rDhewtbZ6AdbcFNZMd4pzkomIzJhSqYRMJmtw5f+ttPTbOra2egHW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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbfc307cd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survivalstan.utils.plot_pp_survival([testfit], by='sex', pal=['red', 'blue'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building up the plot semi-manually, for more customization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also access the utility methods within `survivalstan.utils` to more or less produce the same plot. This sequence is intended to both illustrate how the above-described plot was constructed, and expose some of the \n",
    "functionality in a more concrete fashion.\n",
    "\n",
    "Probably the most useful element is being able to summarize & return posterior-predicted values to begin with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ppsurv = survivalstan.utils.prep_pp_survival_data([testfit], by='sex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are what these data look like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iter</th>\n",
       "      <th>model_cohort</th>\n",
       "      <th>sex</th>\n",
       "      <th>level_3</th>\n",
       "      <th>event_time</th>\n",
       "      <th>survival</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>test model</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>test model</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.397562</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>test model</td>\n",
       "      <td>female</td>\n",
       "      <td>2</td>\n",
       "      <td>2.330953</td>\n",
       "      <td>0.974215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>test model</td>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "      <td>2.357800</td>\n",
       "      <td>0.955702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>test model</td>\n",
       "      <td>female</td>\n",
       "      <td>4</td>\n",
       "      <td>2.743388</td>\n",
       "      <td>0.913719</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   iter model_cohort     sex  level_3  event_time  survival\n",
       "0     0   test model  female        0    0.000000  1.000000\n",
       "1     0   test model  female        1    1.397562  1.000000\n",
       "2     0   test model  female        2    2.330953  0.974215\n",
       "3     0   test model  female        3    2.357800  0.955702\n",
       "4     0   test model  female        4    2.743388  0.913719"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ppsurv.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Note that this itself is a summary of the posterior draws returned by `survivalstan.utils.prep_pp_data`. In this case, the survival stats are summarized by values of `['iter', 'model_cohort', by]`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then call out to `survivalstan.utils._plot_pp_survival_data` to construct the plot. In this case, we overlay the posterior predicted intervals with observed values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fbfc304a588>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uvJ3SWef0m5e8PttUzrKq6awoivKl8f3v/5CqqipmzLiL66+/hng8wfDhIzj9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\nltfiNvldnFb6LQ/FzqLb6qCXJv5v8hvsbL/J7b0fZzZrSJGnTyTIpHa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NsGiRz7nnuqxa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wgSaWRrJGGxiHrgOY4a2s7j6Tfxhr51Ot66hgVkgxDhe3hTd91G0NXLbwfP5owzsixIjGPt2I79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cOlKHUEgWH4ZRL5AdqYSqApx7u66tmuRyWb4XgCzgDcO9u9y4fqNkEI1yYzgJZ8CpUwzpag7v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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbfc2ff18d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "subplot = plt.subplots(1, 1)\n",
    "survivalstan.utils._plot_pp_survival_data(ppsurv.query('sex == \"male\"').copy(),\n",
    "                                          subplot=subplot, color='blue', alpha=0.5)\n",
    "survivalstan.utils._plot_pp_survival_data(ppsurv.query('sex == \"female\"').copy(),\n",
    "                                          subplot=subplot, color='red', alpha=0.5)\n",
    "survivalstan.utils.plot_observed_survival(df=d[d['sex']=='female'], event_col='event', time_col='t',\n",
    "                                          color='red', label='female')\n",
    "survivalstan.utils.plot_observed_survival(df=d[d['sex']=='male'], event_col='event', time_col='t',\n",
    "                                          color='blue', label='male')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Use plotly to summarize posterior predicted values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we will precompute 50th and 95th posterior intervals for each observed timepoint, by group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ppsummary = ppsurv.groupby(['sex','event_time'])['survival'].agg({\n",
    "        '95_lower': lambda x: np.percentile(x, 2.5),\n",
    "        '95_upper': lambda x: np.percentile(x, 97.5),\n",
    "        '50_lower': lambda x: np.percentile(x, 25),\n",
    "        '50_upper': lambda x: np.percentile(x, 75),\n",
    "        'median': lambda x: np.percentile(x, 50),\n",
    "    }).reset_index()\n",
    "shade_colors = dict(male='rgba(0, 128, 128, {})', female='rgba(214, 12, 140, {})')\n",
    "line_colors = dict(male='rgb(0, 128, 128)', female='rgb(214, 12, 140)')\n",
    "ppsummary.sort_values(['sex', 'event_time'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we construct our graph \"traces\", consisting of 3 elements (solid line and two shaded areas) per observed group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:requests.packages.urllib3.connectionpool:Starting new HTTPS connection (1): api.plot.ly\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "plotly.offline.init_notebook_mode(connected=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data5 = list()\n",
    "for grp, grp_df in ppsummary.groupby('sex'):\n",
    "    x = list(grp_df['event_time'].values)\n",
    "    x_rev = x[::-1]\n",
    "    y_upper = list(grp_df['50_upper'].values)\n",
    "    y_lower = list(grp_df['50_lower'].values)\n",
    "    y_lower = y_lower[::-1]\n",
    "    y2_upper = list(grp_df['95_upper'].values)\n",
    "    y2_lower = list(grp_df['95_lower'].values)\n",
    "    y2_lower = y2_lower[::-1]\n",
    "    y = list(grp_df['median'].values)\n",
    "    my_shading50 = go.Scatter(\n",
    "        x = x + x_rev,\n",
    "        y = y_upper + y_lower,\n",
    "        fill = 'tozerox',\n",
    "        fillcolor = shade_colors[grp].format(0.3),\n",
    "        line = go.Line(color = 'transparent'),\n",
    "        showlegend = True,\n",
    "        name = '{} - 50% CI'.format(grp),\n",
    "    )\n",
    "    my_shading95 = go.Scatter(\n",
    "        x = x + x_rev,\n",
    "        y = y2_upper + y2_lower,\n",
    "        fill = 'tozerox',\n",
    "        fillcolor = shade_colors[grp].format(0.1),\n",
    "        line = go.Line(color = 'transparent'),\n",
    "        showlegend = True,\n",
    "        name = '{} - 95% CI'.format(grp),\n",
    "    )\n",
    "    my_line = go.Scatter(\n",
    "        x = x,\n",
    "        y = y,\n",
    "        line = go.Line(color=line_colors[grp]),\n",
    "        mode = 'lines',\n",
    "        name = grp,\n",
    "    )\n",
    "    data5.append(my_line)    \n",
    "    data5.append(my_shading50)\n",
    "    data5.append(my_shading95)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we build a minimal layout structure to house our graph:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "layout5 = go.Layout(\n",
    "    yaxis=dict(\n",
    "        title='Survival (%)',\n",
    "        #zeroline=False,\n",
    "        tickformat='.0%',\n",
    "    ),\n",
    "    xaxis=dict(title='Days since enrollment')\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is our plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:requests.packages.urllib3.connectionpool:Starting new HTTPS connection (1): plot.ly\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~jackinovik/146.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "py.iplot(go.Figure(data=data5, layout=layout5), filename='survivalstan/pem_survival_model_ppsummary')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Note: this plot will not render in github, since github disables iframes. You can however view it in nbviewer or on plotly's website directly*"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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