Err in exec

multiview_platform/mono_multi_view_classifiers/exec_classif.py

@@ -875,15 +875,14 @@ def exec_classif(arguments):
     # if not args["add_noise"]:
         # args["noise_std"] = [0.0]
     for dataset_name in dataset_list:
-        noise_results = []
-        for noise_std in args["noise_std"]:
+        # noise_results = []
+        # for noise_std in args["noise_std"]:
 
         directory = execution.init_log_file(dataset_name, args["views"],
                                             args["file_type"],
                                             args["log"], args["debug"],
                                             args["label"],
                                             args["res_dir"],
-                                                args["add_noise"], noise_std,
                                             args)
 
         random_state = execution.init_random_state(args["random_state"],
@@ -902,11 +901,9 @@ def exec_classif(arguments):
             args["classes"],
             random_state,
             args["full"],
-                args["add_noise"],
-                noise_std)
+            )
         args["name"] = datasetname
-
-            splits = execution.gen_splits(dataset_var,
+        splits = execution.gen_splits(dataset_var.get_labels(),
                                       args["split"],
                                       stats_iter_random_states)
 
@@ -927,8 +924,8 @@ def exec_classif(arguments):
         nb_views = len(views)
         nb_class = dataset_var.get_nb_class()
 
-            metrics = [metric.split(":") for metric in args["metrics"]]
-            if metrics == [["all"]]:
+        metrics = args["metrics"]
+        if metrics == "all":
             metrics_names = [name for _, name, isPackage
                              in pkgutil.iter_modules(
                     [os.path.join(os.path.dirname(
@@ -938,12 +935,9 @@ def exec_classif(arguments):
                                                             "log_loss",
                                                             "matthews_corrcoef",
                                                             "roc_auc_score"]]
-                metrics = [[metricName, {}] for metricName in metrics_names]
+            metrics = dict((metric_name, {})
+                           for metric_name in metrics_names)
         metrics = arange_metrics(metrics, args["metric_princ"])
-            # TODO : Metric args
-            for metricIndex, metric in enumerate(metrics):
-                if len(metric) == 1:
-                    metrics[metricIndex] = [metric[0], {}]
 
         benchmark = init_benchmark(cl_type, monoview_algos, multiview_algos,
                                    args)
@@ -967,6 +961,7 @@ def exec_classif(arguments):
             benchmark_argument_dictionaries, directory, metrics,
             dataset_var,
             args["track_tracebacks"])
-            noise_results.append([noise_std, results_mean_stds])
-            plot_results_noise(directory, noise_results, metrics[0][0],
-                               dataset_name)
+            # noise_results.append([noise_std, results_mean_stds])
+            # plot_results_noise(directory, noise_results, metrics[0][0],
+            #                    dataset_name)
+