Simplified the config file by allowing to no config for a classifier

config_files/config_test.yml

@@ -20,12 +20,12 @@ Classification:
   multiclass_method: "oneVersusOne"
   split: 0.4
   nb_folds: 2
-  nb_class: 3
+  nb_class: 2
   classes:
-  type: ["monoview"]
-  algos_monoview: ["decision_tree",]
+  type: ["multiview"]
+  algos_monoview: ["decision_tree","bayesian_inference_fusion"]
   algos_multiview: ["svm_jumbo_fusion"]
-  stats_iter: 2
+  stats_iter: 1
   metrics: ["accuracy_score", "f1_score"]
   metric_princ: "f1_score"
   hps_type: "randomized_search-equiv"
@@ -132,7 +132,7 @@ weighted_linear_early_fusion:
       splitter: ["best"]
 
 entropy_fusion:
-  classifier_names: [["decision_tree"]]
+  classifiers_names: [["decision_tree"]]
   classifier_configs:
     decision_tree:
       max_depth: [1]
@@ -140,7 +140,7 @@ entropy_fusion:
       splitter: ["best"]
 
 disagree_fusion:
-  classifier_names: [["decision_tree"]]
+  classifiers_names: [["decision_tree"]]
   classifier_configs:
     decision_tree:
       max_depth: [1]
@@ -149,7 +149,7 @@ disagree_fusion:
 
 
 double_fault_fusion:
-  classifier_names: [["decision_tree"]]
+  classifiers_names: [["decision_tree"]]
   classifier_configs:
     decision_tree:
       max_depth: [1]
@@ -157,7 +157,7 @@ double_fault_fusion:
       splitter: ["best"]
 
 difficulty_fusion:
-  classifier_names: [["decision_tree"]]
+  classifiers_names: [["decision_tree"]]
   classifier_configs:
     decision_tree:
       max_depth: [1]
@@ -165,7 +165,7 @@ difficulty_fusion:
       splitter: ["best"]
 
 scm_late_fusion:
-  classifier_names: [["decision_tree"]]
+  classifiers_names: [["decision_tree"]]
   p: 0.1
   max_rules: 10
   model_type: 'conjunction'
@@ -176,7 +176,7 @@ scm_late_fusion:
       splitter: ["best"]
 
 majority_voting_fusion:
-  classifier_names: [["decision_tree", "decision_tree", "decision_tree", ]]
+  classifiers_names: [["decision_tree", "decision_tree", "decision_tree", ]]
   classifier_configs:
     decision_tree:
       max_depth: [1]
@@ -184,7 +184,7 @@ majority_voting_fusion:
       splitter: ["best"]
 
 bayesian_inference_fusion:
-  classifier_names: [["decision_tree", "decision_tree", "decision_tree", ]]
+  classifiers_names: [["decision_tree", "decision_tree", "decision_tree", ]]
   classifier_configs:
     decision_tree:
       max_depth: [1]
@@ -192,7 +192,7 @@ bayesian_inference_fusion:
       splitter: ["best"]
 
 weighted_linear_late_fusion:
-  classifier_names: [["decision_tree", "decision_tree", "decision_tree", ]]
+  classifiers_names: [["decision_tree", "decision_tree", "decision_tree", ]]
   classifier_configs:
     decision_tree:
       max_depth: [1]
@@ -222,5 +222,4 @@ mvml:
   kernel_configs:
     gamma: [0.1]
 
-svm_jumbo_fusion:
-  degree: [1]
+

multiview_platform/mono_multi_view_classifiers/exec_classif.py

@@ -107,6 +107,7 @@ def init_multiview_exps(classifier_names, views_dictionary, nb_class, kwargs_ini
                                                                   views_dictionary=views_dictionary,
                                                                   framework="multiview")
         else:
+            print(classifier_name)
             arguments = get_path_dict(kwargs_init[classifier_name])
             multiview_arguments += [gen_single_multiview_arg_dictionary(classifier_name,
                                                                         arguments,
@@ -161,8 +162,12 @@ def init_monoview_exps(classifier_names,
 
 def gen_single_monoview_arg_dictionary(classifier_name, arguments, nb_class,
                                        view_index, view_name):
-    return {classifier_name: dict((key, value[0]) for key, value in arguments[
-                                                 classifier_name].items()),
+    if classifier_name in arguments:
+        classifier_config = dict((key, value[0]) for key, value in arguments[
+                            classifier_name].items())
+    else:
+        classifier_config = {}
+    return {classifier_name: classifier_config,
             "view_name": view_name,
             "view_index": view_index,
             "classifier_name": classifier_name,
@@ -375,7 +380,10 @@ def init_kwargs(args, classifiers_names, framework="monoview"):
             raise AttributeError(
                 classifiers_name + " is not implemented in monoview_classifiers, "
                                   "please specify the name of the file in monoview_classifiers")
+        if classifiers_name in args:
             kwargs[classifiers_name] = args[classifiers_name]
+        else:
+            kwargs[classifiers_name] = {}
     logging.debug("Done:\t Initializing monoview classifiers arguments")
 
     return kwargs

multiview_platform/mono_multi_view_classifiers/result_analysis.py

@@ -685,7 +685,7 @@ def publish_tracebacks(directory, database_name, labels_names, tracebacks, flag)
         with open(os.path.join(directory, time.strftime(
                 "%Y_%m_%d-%H_%M_%S") + "-" + database_name + "-" + "_vs_".join(
                 labels_names) + "-iter"+str(flag[0])+"-"+str(flag[1][0])+"vs"+
-                str(flag[1][1])+"tacebacks.txt"), "w") as traceback_file:
+                str(flag[1][1])+"-tacebacks.txt"), "w") as traceback_file:
             failed_list = save_dict_to_text(tracebacks, traceback_file)
         flagged_list = [_ + "-iter"+str(flag[0])+"-"+str(flag[1][0])+"vs"+
                         str(flag[1][1]) for _ in failed_list]