Merge branch 'private_algos' into cuisine

summit/multiview_platform/monoview/exec_classif_mono_view.py

@@ -215,7 +215,7 @@ def get_hyper_params(classifier_module, search_method, classifier_module_name,
                                    random_state=random_state,
                                    framework="monoview", n_jobs=nb_cores,
                                    **hps_kwargs)
-        hps.fit(X_train, y_train, **kwargs[classifier_module_name])
+        hps.fit(X_train, y_train)
         cl_kwargs = hps.get_best_params()
         hps.gen_report(output_file_name)
         logging.info("Done:\t " + search_method + " best settings")

summit/multiview_platform/multiview/exec_multiview.py

@@ -265,9 +265,9 @@ def exec_multiview(directory, dataset_var, name, classification_indices,
     classifier_module = getattr(multiview_classifiers, cl_type)
     classifier_name = classifier_module.classifier_class_name
     # classifierClass = getattr(classifierModule, CL_type + "Class")
-    logging.debug("Done:\t Getting classifiers modules")
+    logging.info("Done:\t Getting classifiers modules")
 
-    logging.debug("Start:\t Optimizing hyperparameters")
+    logging.info("Start:\t Optimizing hyperparameters")
     hps_beg = time.monotonic()
     if hps_method != "None":
         hps_method_class = getattr(hyper_parameter_search, hps_method)
@@ -298,16 +298,16 @@ def exec_multiview(directory, dataset_var, name, classification_indices,
                                                     **classifier_config),
         random_state, multiview=True,
         y=dataset_var.get_labels())
-    logging.debug("Done:\t Optimizing hyperparameters")
-    logging.debug("Start:\t Fitting classifier")
+    logging.info("Done:\t Optimizing hyperparameters")
+    logging.info("Start:\t Fitting classifier")
     fit_beg = time.monotonic()
     classifier.fit(dataset_var, dataset_var.get_labels(),
                    train_indices=learning_indices,
                    view_indices=views_indices)
     fit_duration = time.monotonic() - fit_beg
-    logging.debug("Done:\t Fitting classifier")
+    logging.info("Done:\t Fitting classifier")
 
-    logging.debug("Start:\t Predicting")
+    logging.info("Start:\t Predicting")
     train_pred = classifier.predict(dataset_var,
                                     sample_indices=learning_indices,
                                     view_indices=views_indices)
@@ -349,10 +349,10 @@ def exec_multiview(directory, dataset_var, name, classification_indices,
         confusion_matrix = result_analyzer.analyze()
     logging.info("Done:\t Result Analysis for " + cl_type)
 
-    logging.debug("Start:\t Saving preds")
+    logging.info("Start:\t Saving preds")
     save_results(string_analysis, images_analysis, output_file_name,
                  confusion_matrix)
-    logging.debug("Start:\t Saving preds")
+    logging.info("Start:\t Saving preds")
 
     return MultiviewResult(cl_type, classifier_config, metrics_scores,
                            full_pred, hps_duration, fit_duration,