print

summit/__init__.py

-from . import exec_classif, result_analysis, metrics, monoview_classifiers, \
-    monoview, multiview, utils, multiview_classifiers
+__version__ = "0.0.0.0"
+__url__ = "https://gitlab.lis-lab.fr/baptiste.bauvin/summit"
 
-__all__ = ['metrics', 'monoview', 'monoview_classifiers', 'multiview', 'utils']
+from . import multiview_platform, execute

summit/multiview_platform/monoview_classifiers/additions/CBBoostUtils.py

@@ -22,8 +22,8 @@ class CBBoostClassifier(BaseEstimator, ClassifierMixin, BaseBoost):
     def __init__(self, n_max_iterations=100, estimators_generator="Stumps",
                  random_state=42, self_complemented=True, twice_the_same=True,
                  random_start=False, n_stumps=1, c_bound_sol=True,
-                 plotted_metric=metrics.zero_one_loss, save_train_data=True,
-                 test_graph=True, mincq_tracking=False):
+                 plotted_metric=metrics.zero_one_loss, save_train_data=False,
+                 test_graph=False, mincq_tracking=False):
         super(CBBoostClassifier, self).__init__()
         r"""
 
@@ -457,7 +457,6 @@ class CBBoostClassifier(BaseEstimator, ClassifierMixin, BaseBoost):
         np.savetxt(os.path.join(directory, "y_test_step.csv"), self.step_decisions,
                    delimiter=',')
         step_metrics = []
-        print(y_test.shape)
         for step_index in range(self.step_decisions.shape[1] - 1):
             step_metrics.append(self.plotted_metric.score(y_test,
                                                           self.step_decisions[:,

summit/multiview_platform/result_analysis/feature_importances.py

@@ -30,7 +30,7 @@ def get_feature_importances(result, feature_ids=None, view_names=None,):
                 feature_importances[classifier_result.view_name] = pd.DataFrame(
                     index=feature_ids[classifier_result.view_index])
             if hasattr(classifier_result.clf, 'feature_importances_'):
-                # print(classifier_result.classifier_name, classifier_result.view_name)
+                print(classifier_result.classifier_name, classifier_result.view_name)
                 feature_importances[classifier_result.view_name][
                     classifier_result.classifier_name] = classifier_result.clf.feature_importances_
             else: