diff --git a/multiview_platform/mono_multi_view_classifiers/exec_classif.py b/multiview_platform/mono_multi_view_classifiers/exec_classif.py
index a05a86078dd18383f9b0ae1860346330f17cc82b..67c25a32f9a5d4ee06b862b3aed3017f85020642 100644
--- a/multiview_platform/mono_multi_view_classifiers/exec_classif.py
+++ b/multiview_platform/mono_multi_view_classifiers/exec_classif.py
@@ -875,98 +875,93 @@ 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)
+
+ random_state = execution.init_random_state(args["random_state"],
+ directory)
+ stats_iter_random_states = execution.init_stats_iter_random_states(
+ stats_iter,
+ random_state)
+
+ get_database = execution.get_database_function(dataset_name,
+ args["file_type"])
+
+ dataset_var, labels_dictionary, datasetname = get_database(
+ args["views"],
+ args["pathf"], dataset_name,
+ args["nb_class"],
+ args["classes"],
+ random_state,
+ args["full"],
+ )
+ args["name"] = datasetname
+ splits = execution.gen_splits(dataset_var.get_labels(),
+ args["split"],
+ stats_iter_random_states)
+
+ # multiclass_labels, labels_combinations, indices_multiclass = multiclass.gen_multiclass_labels(
+ # dataset_var.get_labels(), multiclass_method, splits)
+
+ k_folds = execution.gen_k_folds(stats_iter, args["nb_folds"],
+ stats_iter_random_states)
+
+ dataset_files = dataset.init_multiple_datasets(args["pathf"],
+ args["name"],
+ nb_cores)
+
+ views, views_indices, all_views = execution.init_views(dataset_var,
+ args[
+ "views"])
+ views_dictionary = dataset_var.get_view_dict()
+ nb_views = len(views)
+ nb_class = dataset_var.get_nb_class()
+
+ metrics = args["metrics"]
+ if metrics == "all":
+ metrics_names = [name for _, name, isPackage
+ in pkgutil.iter_modules(
+ [os.path.join(os.path.dirname(
+ os.path.dirname(os.path.realpath(__file__))),
+ 'metrics')]) if
+ not isPackage and name not in ["framework",
+ "log_loss",
+ "matthews_corrcoef",
+ "roc_auc_score"]]
+ metrics = dict((metric_name, {})
+ for metric_name in metrics_names)
+ metrics = arange_metrics(metrics, args["metric_princ"])
+
+ benchmark = init_benchmark(cl_type, monoview_algos, multiview_algos,
+ args)
+ init_kwargs = init_kwargs_func(args, benchmark)
+ data_base_time = time.time() - start
+ argument_dictionaries = init_argument_dictionaries(
+ benchmark, views_dictionary,
+ nb_class, init_kwargs, hps_method, hps_kwargs)
+ # argument_dictionaries = initMonoviewExps(benchmark, viewsDictionary,
+ # NB_CLASS, initKWARGS)
+ directories = execution.gen_direcorties_names(directory, stats_iter)
+ benchmark_argument_dictionaries = execution.gen_argument_dictionaries(
+ labels_dictionary, directories,
+ splits,
+ hps_method, args, k_folds,
+ stats_iter_random_states, metrics,
+ argument_dictionaries, benchmark,
+ views, views_indices)
+ results_mean_stds = exec_benchmark(
+ nb_cores, stats_iter,
+ 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)
- 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"],
- directory)
- stats_iter_random_states = execution.init_stats_iter_random_states(
- stats_iter,
- random_state)
-
- get_database = execution.get_database_function(dataset_name,
- args["file_type"])
-
- dataset_var, labels_dictionary, datasetname = get_database(
- args["views"],
- args["pathf"], dataset_name,
- args["nb_class"],
- args["classes"],
- random_state,
- args["full"],
- args["add_noise"],
- noise_std)
- args["name"] = datasetname
-
- splits = execution.gen_splits(dataset_var,
- args["split"],
- stats_iter_random_states)
-
- # multiclass_labels, labels_combinations, indices_multiclass = multiclass.gen_multiclass_labels(
- # dataset_var.get_labels(), multiclass_method, splits)
-
- k_folds = execution.gen_k_folds(stats_iter, args["nb_folds"],
- stats_iter_random_states)
-
- dataset_files = dataset.init_multiple_datasets(args["pathf"],
- args["name"],
- nb_cores)
-
- views, views_indices, all_views = execution.init_views(dataset_var,
- args[
- "views"])
- views_dictionary = dataset_var.get_view_dict()
- nb_views = len(views)
- nb_class = dataset_var.get_nb_class()
-
- metrics = [metric.split(":") for metric in args["metrics"]]
- if metrics == [["all"]]:
- metrics_names = [name for _, name, isPackage
- in pkgutil.iter_modules(
- [os.path.join(os.path.dirname(
- os.path.dirname(os.path.realpath(__file__))),
- 'metrics')]) if
- not isPackage and name not in ["framework",
- "log_loss",
- "matthews_corrcoef",
- "roc_auc_score"]]
- metrics = [[metricName, {}] for metricName 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)
- init_kwargs = init_kwargs_func(args, benchmark)
- data_base_time = time.time() - start
- argument_dictionaries = init_argument_dictionaries(
- benchmark, views_dictionary,
- nb_class, init_kwargs, hps_method, hps_kwargs)
- # argument_dictionaries = initMonoviewExps(benchmark, viewsDictionary,
- # NB_CLASS, initKWARGS)
- directories = execution.gen_direcorties_names(directory, stats_iter)
- benchmark_argument_dictionaries = execution.gen_argument_dictionaries(
- labels_dictionary, directories,
- splits,
- hps_method, args, k_folds,
- stats_iter_random_states, metrics,
- argument_dictionaries, benchmark,
- views, views_indices)
- results_mean_stds = exec_benchmark(
- nb_cores, stats_iter,
- 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)