def fit(self, multilabel_dataset):
features = get_instance_features_train(multilabel_dataset)
labels = get_instance_labels_train(multilabel_dataset)
self.classifier.fit(features, labels)
return self
def fit(self, multilabel_dataset):
features = get_instance_features_train(multilabel_dataset)
labels = get_instance_labels_train(multilabel_dataset)
features_matrix = csr_matrix(features)
self.selector_train.fit(features_matrix, labels)
self.selector_test.fit(features_matrix, labels)
return self.selector_train
def transform(self, multilabel_dataset):
features_train = get_instance_features_train(multilabel_dataset)
selected_train = self.selector_train.transform(features_train)
set_instance_features_train(multilabel_dataset, selected_train)
self.filter_total_features_and_update_train(multilabel_dataset)
features_test = get_instance_features_test(multilabel_dataset)
selected_test = self.selector_test.transform(features_test)
set_instance_features_test(multilabel_dataset, selected_test)
self.filter_total_features_and_update_test(multilabel_dataset)
return multilabel_dataset
def test_get_instance_features_train(self):
features = get_instance_features_train(self.multilabel_dataset)
assert type(features) == type(
[]), "instance_features_train should be a list"