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"