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Commit 3b59eabe authored by Baptiste Bauvin's avatar Baptiste Bauvin
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Err in exec

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Stage: test
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multiview_platform/mono_multi_view_classifiers/exec_classif.py
+ 89
− 94
View file @ 3b59eabe
......@@ -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)
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