Merge branch 'develop' into private_algos

multiview_platform/mono_multi_view_classifiers/monoview/exec_classif_mono_view.py

@@ -150,12 +150,13 @@ def exec_monoview(directory, X, Y, database_name, labels_names, classification_i
                                              database_name=database_name,
                                              nb_cores=nb_cores,
                                              duration=whole_duration)
-    string_analysis, images_analysis, metrics_scores, class_metrics_scores = result_analyzer.analyze()
+    string_analysis, images_analysis, metrics_scores, class_metrics_scores, \
+    confusion_matrix = result_analyzer.analyze()
     logging.debug("Done:\t Getting results")
 
     logging.debug("Start:\t Saving preds")
     save_results(string_analysis, output_file_name, full_pred, train_pred,
-                 y_train, images_analysis, y_test)
+                 y_train, images_analysis, y_test, confusion_matrix)
     logging.info("Done:\t Saving results")
 
     view_index = args["view_index"]
@@ -222,11 +223,13 @@ def get_hyper_params(classifier_module, search_method, classifier_module_name,
 
 def save_results(string_analysis, output_file_name, full_labels_pred,
                  y_train_pred,
-                 y_train, images_analysis, y_test):
+                 y_train, images_analysis, y_test, confusion_matrix):
     logging.info(string_analysis)
     output_text_file = open(output_file_name + 'summary.txt', 'w')
     output_text_file.write(string_analysis)
     output_text_file.close()
+    np.savetxt(output_file_name+"confusion_matrix.csv", confusion_matrix,
+               delimiter=', ')
     np.savetxt(output_file_name + "full_pred.csv",
                full_labels_pred.astype(np.int16), delimiter=",")
     np.savetxt(output_file_name + "train_pred.csv",

multiview_platform/mono_multi_view_classifiers/multiview/exec_multiview.py

@@ -71,7 +71,8 @@ def init_constants(kwargs, classification_indices, metrics,
            directory, base_file_name
 
 
-def save_results(string_analysis, images_analysis, output_file_name):
+def save_results(string_analysis, images_analysis, output_file_name,
+                 confusion_matrix):
     """
     Save results in derectory
 
@@ -104,6 +105,8 @@ def save_results(string_analysis, images_analysis, output_file_name):
     output_text_file = open(output_file_name + 'summary.txt', 'w')
     output_text_file.write(string_analysis)
     output_text_file.close()
+    np.savetxt(output_file_name+"confusion_matrix.csv", confusion_matrix,
+               delimiter=',')
 
     if images_analysis is not None:
         for image_name in images_analysis.keys():
@@ -339,11 +342,12 @@ def exec_multiview(directory, dataset_var, name, classification_indices,
                                               database_name=dataset_var.get_name(),
                                               nb_cores=nb_cores,
                                               duration=whole_duration)
-    string_analysis, images_analysis, metrics_scores, class_metrics_scores = result_analyzer.analyze()
+    string_analysis, images_analysis, metrics_scores, class_metrics_scores, \
+    confusion_matrix = result_analyzer.analyze()
     logging.info("Done:\t Result Analysis for " + cl_type)
 
     logging.debug("Start:\t Saving preds")
-    save_results(string_analysis, images_analysis, output_file_name)
+    save_results(string_analysis, images_analysis, output_file_name, confusion_matrix)
     logging.debug("Start:\t Saving preds")
 
     return MultiviewResult(cl_type, classifier_config, metrics_scores,

multiview_platform/mono_multi_view_classifiers/utils/base.py

@@ -285,7 +285,7 @@ class ResultAnalyser():
             metric_score_string += "\n\t\t- Score on test : {}".format(self.metric_scores[metric][1])
             metric_score_string += "\n\n"
         metric_score_string += "Test set confusion matrix : \n\n"
-        confusion_matrix = confusion(y_true=self.labels[self.test_indices], y_pred=self.pred[self.test_indices])
+        self.confusion_matrix = confusion(y_true=self.labels[self.test_indices], y_pred=self.pred[self.test_indices])
         formatted_conf = [[label_name]+list(row) for label_name, row in zip(self.class_label_names, confusion_matrix)]
         metric_score_string+=tabulate(formatted_conf, headers= ['']+self.class_label_names, tablefmt='fancy_grid')
         metric_score_string += "\n\n"
@@ -361,7 +361,8 @@ class ResultAnalyser():
             self.directory, self.base_file_name,
             self.labels[self.test_indices])
         image_analysis = {}
-        return string_analysis, image_analysis, self.metric_scores, self.class_metric_scores
+        return string_analysis, image_analysis, self.metric_scores, \
+               self.class_metric_scores, self.confusion_matrix
 
 
 base_boosting_estimators = [DecisionTreeClassifier(max_depth=1),