modified: estimators.py

	modified:   experiments.py
	modified:   reporting.py

auton_survival/estimators.py

@@ -102,9 +102,9 @@ def _fit_dcm(features, outcomes, random_seed, **hyperparams):
   np.array : A float or list of the times at which to compute the survival probability.
 
   """
+  raise NotImplementedError()
 
-  from .models.dcm import DeepCoxMixture
-  from sdcm.dcm_utils import train
+  from .models.dcm import DeepCoxMixtures
 
   import torch
   torch.manual_seed(random_seed)
@@ -162,6 +162,8 @@ def _predict_dcm(model, features, times):
 
   """
 
+  raise NotImplementedError()
+
   from sdcm.dcm_utils import predict_scores
 
   import torch
@@ -352,7 +354,9 @@ def _fit_cph(features, outcomes, random_seed, **hyperparams):
   data = outcomes.join(features)
   penalizer = hyperparams.get('l2', 1e-3)
 
-  return CoxPHFitter(penalizer=penalizer).fit(data, duration_col='time', event_col='event')
+  return CoxPHFitter(penalizer=penalizer).fit(data, 
+                                              duration_col='time', 
+                                              event_col='event')
 
 def _fit_rsf(features, outcomes, random_seed, **hyperparams):
 
@@ -708,9 +712,8 @@ def predict_risk(self, features, times):
 
 class CounterfactualSurvivalModel:
 
-  """Universal interface to train multiple differenct counterfactual survival models."""
-
-  _VALID_MODELS = ['rsf', 'cph', 'dsm']
+  """Universal interface to train multiple different counterfactual 
+     survival models."""
 
   def __init__(self, treated_model, control_model):
 
@@ -727,4 +730,5 @@ def predict_counterfactuals(self, features, times):
     control_outcomes = self.control_model.predict(features, times)
     treated_outcomes = self.treated_model.predict(features, times)
 
-    return treated_outcomes, control_outcomes
+    return treated_outcomes, control_outcomes
+

auton_survival/experiments.py

@@ -179,7 +179,6 @@ def evaluate(self, features, outcomes, metrics=['auc', 'ctd'], horizons=[]):
         for metric in metrics:
           raise NotImplementedError()
 
-
 class CounterfactualSurvivalRegressionCV:
 
   r"""Universal interface to train Counterfactual Survival Analysis models in a
@@ -227,7 +226,7 @@ class CounterfactualSurvivalRegressionCV:
   In Machine Learning for Healthcare Conference, pages 674–708. PMLR
 
   """
-  
+
   def __init__(self, model, cv_folds=5, random_seed=0, hyperparam_grid={}):
 
     self.model = model

auton_survival/reporting.py

@@ -14,8 +14,6 @@
 
 from sklearn.metrics import roc_curve, auc
 
-import os
-
 
 def plot_kaplanmeier(outcomes, groups=None, plot_counts=False, **kwargs):