modified: metrics.py

auton_survival/metrics.py

@@ -166,7 +166,8 @@ def treatment_effect(metric, outcomes, treatment_indicator,
                     random_seed=i) for i in range(n_bootstrap)]
 
 def survival_regression_metric(metric, outcomes, predictions,
-                               times, outcomes_train=None):
+                               times, outcomes_train=None, 
+                               n_bootstrap=None, random_seed=0):
   """Compute metrics to assess survival model performance.
 
   Parameters
@@ -189,7 +190,14 @@ def survival_regression_metric(metric, outcomes, predictions,
   outcomes_train : pd.DataFrame
       A pandas dataframe with rows corresponding to individual samples and
       columns 'time' and 'event' for training data.
-
+  n_bootstrap : int, default=None
+      The number of bootstrap samples to use.
+      If None, bootrapping is not performed.
+  size_bootstrap : float, default=1.0
+      The fraction of the population to sample for each bootstrap sample.
+  random_seed: int, default=0
+      Controls the reproducibility random sampling for bootstrapping.
+  
   Returns
   -----------
   float: The metric value for the specified metric.
@@ -213,25 +221,63 @@ def survival_regression_metric(metric, outcomes, predictions,
   survival_test = util.Surv.from_dataframe('event', 'time', outcomes)
 
   if metric == 'brs':
-    return metrics.brier_score(survival_train, survival_test,
-                               predictions, times)[-1]
+    _metric = _brier_score
   elif metric == 'ibs':
-    return metrics.integrated_brier_score(survival_train, survival_test,
-                                          predictions, times)
+    _metric = _integrated_brier_score
   elif metric == 'auc':
-    return metrics.cumulative_dynamic_auc(survival_train, survival_test,
-                                            1-predictions, times)[0]
+    _metric = _cumulative_dynamic_auc
   elif metric == 'ctd':
-    vals = []
-    for i in range(len(times)):
-      vals.append(metrics.concordance_index_ipcw(survival_train, survival_test,
-                                                 1-predictions[:,i],
-                                                 tau=times[i])[0])
-    return vals
-
+    _metric = _concordance_index_ipcw 
   else:
     raise NotImplementedError()
 
+  if n_bootstrap is None:
+    return _metric(survival_train, survival_test, predictions, times)
+  else:
+    return [_metric(survival_train, survival_test, predictions, times, random_seed=i) for i in range(n_bootstrap)]
+
+
+def _brier_score(survival_train, survival_test, predictions, times, random_seed=None):
+
+  idx = np.arange(len(predictions))
+  if random_seed is not None:
+    np.random.seed(random_seed)
+    idx = np.random.choice(idx, len(predictions), replace=True)
+
+  return metrics.brier_score(survival_train, survival_test[idx], predictions[idx], times)[-1]  
+
+def _integrated_brier_score(survival_train, survival_test, predictions, times, random_seed=None):
+
+  idx = np.arange(len(predictions))
+  if random_seed is not None:
+    np.random.seed(random_seed)
+    idx = np.random.choice(idx, len(predictions), replace=True)
+
+  return metrics.integrated_brier_score(survival_train, survival_test[idx], predictions[idx], times)
+
+def _cumulative_dynamic_auc(survival_train, survival_test, predictions, times, random_seed=None):
+
+  idx = np.arange(len(predictions))
+  if random_seed is not None:
+    np.random.seed(random_seed)
+    idx = np.random.choice(idx, len(predictions), replace=True)
+
+  return metrics.cumulative_dynamic_auc(survival_train, survival_test[idx], 1-predictions[idx], times)[0]
+
+def _concordance_index_ipcw(survival_train, survival_test, predictions, times, random_seed=None):
+
+  idx = np.arange(len(predictions))
+  if random_seed is not None:
+    np.random.seed(random_seed)
+    idx = np.random.choice(idx, len(predictions), replace=True)
+
+  vals = []
+  for i in range(len(times)):
+    vals.append(metrics.concordance_index_ipcw(survival_train, survival_test[idx],
+                                                1-predictions[idx][:,i],
+                                                tau=times[i])[0])
+  return vals
+
 def phenotype_purity(phenotypes_train, outcomes_train,
                      phenotypes_test=None, outcomes_test=None,
                      strategy='instantaneous', horizons=None,