added tracebacks tracking in HPS

multiview_platform/mono_multi_view_classifiers/utils/hyper_parameter_search.py

@@ -3,6 +3,7 @@ import sys
 
 import matplotlib.pyplot as plt
 import numpy as np
+import traceback
 from scipy.stats import randint, uniform
 from sklearn.model_selection import RandomizedSearchCV
 
@@ -181,7 +182,8 @@ class MultiviewCompatibleRandomizedSearchCV(RandomizedSearchCV):
         elif self.framework == "multiview":
             return self.fit_multiview(X, y=y, groups=groups,**fit_params)
 
-    def fit_multiview(self, X, y=None, groups=None, **fit_params):
+    def fit_multiview(self, X, y=None, groups=None, track_tracebacks=True,
+                      **fit_params):
         n_splits = self.cv.get_n_splits(self.available_indices, y[self.available_indices])
         folds = list(self.cv.split(self.available_indices, y[self.available_indices]))
         if self.equivalent_draws:
@@ -195,8 +197,11 @@ class MultiviewCompatibleRandomizedSearchCV(RandomizedSearchCV):
         results = {}
         self.cv_results_ = dict(("param_"+param_name, []) for param_name in candidate_params[0].keys())
         self.cv_results_["mean_test_score"] = []
+        n_failed = 0
+        tracebacks = []
         for candidate_param_idx, candidate_param in enumerate(candidate_params):
             test_scores = np.zeros(n_splits)+1000
+            try:
                 for fold_idx, (train_indices, test_indices) in enumerate(folds):
                     current_estimator = clone(base_estimator)
                     current_estimator.set_params(**candidate_param)
@@ -219,6 +224,17 @@ class MultiviewCompatibleRandomizedSearchCV(RandomizedSearchCV):
                 if cross_validation_score <= min(results.values()):
                     self.best_params_ = candidate_params[candidate_param_idx]
                     self.best_score_ = cross_validation_score
+            except:
+                if track_tracebacks:
+                    n_failed += 1
+                    tracebacks.append(traceback.format_exc())
+                else:
+                    raise
+        if n_failed == self.n_iter:
+            raise ValueError(
+                'No fits were performed. All HP combination returned errors \n\n' + '\n'.join(
+                    tracebacks))
+
         if self.refit:
             self.best_estimator_ = clone(base_estimator).set_params(**self.best_params_)
             self.best_estimator_.fit(X, y, **fit_params)