summit.multiview_platform.monoview package

Submodules

summit.multiview_platform.monoview.exec_classif_mono_view module

Execution: Script to perform a MonoView classification

exec_monoview(directory, X, Y, database_name, labels_names, classification_indices, k_folds, nb_cores, databaseType, path, random_state, hyper_param_search='Random', metrics={'accuracy_score*': {}}, n_iter=30, view_name='', hps_kwargs={}, feature_ids=[], **args)

exec_monoview_multicore(directory, name, labels_names, classification_indices, k_folds, dataset_file_index, database_type, path, random_state, labels, hyper_param_search='randomized_search', metrics=[['accuracy_score', None]], n_iter=30, **args)

get_hyper_params(classifier_module, search_method, classifier_module_name, classifier_class_name, X_train, y_train, random_state, output_file_name, k_folds, nb_cores, metrics, kwargs, **hps_kwargs)

init_constants(args, X, classification_indices, labels_names, name, directory, view_name)

init_train_test(X, Y, classification_indices)

save_results(string_analysis, output_file_name, full_labels_pred, y_train_pred, y_train, images_analysis, y_test, confusion_matrix)

summit.multiview_platform.monoview.monoview_utils module

class BaseMonoviewClassifier

Bases: BaseClassifier

get_feature_importance(directory, base_file_name, feature_ids, nb_considered_feats=50)

Used to generate a graph and a pickle dictionary representing feature importances

get_interpretation(directory, base_file_name, y_test, feature_ids, multi_class=False)

Base method that returns an empty string if there is not interpretation method in the classifier’s module

get_name_for_fusion()

class MonoviewResult(view_index, classifier_name, view_name, metrics_scores, full_labels_pred, classifier_config, classifier, n_features, hps_duration, fit_duration, pred_duration, class_metric_scores)

Bases: object

get_classifier_name()

class MonoviewResultAnalyzer(view_name, classifier_name, shape, classifier, classification_indices, k_folds, hps_method, metrics_dict, n_iter, class_label_names, pred, directory, base_file_name, labels, database_name, nb_cores, duration, feature_ids)

Bases: ResultAnalyser

get_base_string()

get_view_specific_info()

change_label_to_minus(y)

Change the label 0 to minus one

Parameters:

y

Return type:

label y with -1 instead of 0

change_label_to_zero(y)

Change the label -1 to 0

Parameters:

y

compute_possible_combinations(params_dict)

gen_test_folds_preds(X_train, y_train, KFolds, estimator)

get_accuracy_graph(plotted_data, classifier_name, file_name, name='Accuracies', bounds=None, bound_name=None, boosting_bound=None, set='train', zero_to_one=True)

percent(x, pos)

Used to print percentage of importance on the y axis

Module contents