def test_ml_matcher_inplace_false_predict(self): A = read_csv_metadata(fpath_a, key='id') B = read_csv_metadata(fpath_b, key='id') feature_vectors = read_csv_metadata(fpath_f, ltable=A, rtable=B) train_test = mu.train_test_split(feature_vectors) train, test = train_test['train'], train_test['test'] dt = DTMatcher(name='DecisionTree') train.drop('ltable.id', axis=1, inplace=True) train.drop('rtable.id', axis=1, inplace=True) test.drop('ltable.id', axis=1, inplace=True) test.drop('rtable.id', axis=1, inplace=True) test.drop('gold', axis=1, inplace=True) dt.fit(table=train, exclude_attrs='_id', target_attr='gold') predictions = dt.predict(table=test, exclude_attrs='_id', target_attr='predicted', inplace=False, append=True) self.assertNotEqual(id(predictions), id(test)) self.assertEqual(len(predictions), len(test)) self.assertEqual( set(list(test.columns)).issubset(list(predictions.columns)), True) p_col = predictions.columns[len(predictions.columns) - 1] self.assertEqual(p_col, 'predicted')
def test_ml_matcher_invalid_input_combn_fit(self): A = read_csv_metadata(fpath_a, key='id') B = read_csv_metadata(fpath_b, key='id') feature_vectors = read_csv_metadata(fpath_f, ltable=A, rtable=B) train_test = mu.train_test_split(feature_vectors) train, test = train_test['train'], train_test['test'] dt = DTMatcher(name='DecisionTree') dt.fit(x=train, table=train)
def test_ml_matcher_invalid_input_combn_fit(self): A = read_csv_metadata(fpath_a, key='id') B = read_csv_metadata(fpath_b, key='id') feature_vectors = read_csv_metadata(fpath_f, ltable=A, rtable=B) train_test = mu.train_test_split(feature_vectors) train, test = train_test['train'], train_test['test'] dt = DTMatcher(name='DecisionTree') dt.fit(x=train, table=train)
def test_ml_matcher_invalid_df_predict(self): A = read_csv_metadata(fpath_a, key='id') B = read_csv_metadata(fpath_b, key='id') feature_vectors = read_csv_metadata(fpath_f, ltable=A, rtable=B) train_test = mu.train_test_split(feature_vectors) train, test = train_test['train'], train_test['test'] dt = DTMatcher(name='DecisionTree') dt.fit(table=train, exclude_attrs=['ltable.id', 'rtable.id', '_id', 'gold'], target_attr='gold') predictions = dt.predict(table="", exclude_attrs=['ltable.id', 'rtable.id', '_id', 'gold'], target_attr='predicted', append=True)
def test_ml_matcher_target_attr_not_present_fit(self): A = read_csv_metadata(fpath_a, key='id') B = read_csv_metadata(fpath_b, key='id') feature_vectors = read_csv_metadata(fpath_f, ltable=A, rtable=B) train_test = mu.train_test_split(feature_vectors) train, test = train_test['train'], train_test['test'] dt = DTMatcher(name='DecisionTree') train.drop('ltable.id', axis=1, inplace=True) train.drop('rtable.id', axis=1, inplace=True) test.drop('ltable.id', axis=1, inplace=True) test.drop('rtable.id', axis=1, inplace=True) dt.fit(table=train, exclude_attrs='_id', target_attr='gold1')
def test_ml_matcher_target_attr_not_present_fit(self): A = read_csv_metadata(fpath_a, key='id') B = read_csv_metadata(fpath_b, key='id') feature_vectors = read_csv_metadata(fpath_f, ltable=A, rtable=B) train_test = mu.train_test_split(feature_vectors) train, test = train_test['train'], train_test['test'] dt = DTMatcher(name='DecisionTree') train.drop('ltable.id', axis=1, inplace=True) train.drop('rtable.id', axis=1, inplace=True) test.drop('ltable.id', axis=1, inplace=True) test.drop('rtable.id', axis=1, inplace=True) dt.fit(table=train, exclude_attrs='_id', target_attr='gold1')
def test_ml_matcher_invalid_df_predict(self): A = read_csv_metadata(fpath_a, key='id') B = read_csv_metadata(fpath_b, key='id') feature_vectors = read_csv_metadata(fpath_f, ltable=A, rtable=B) train_test = mu.train_test_split(feature_vectors) train, test = train_test['train'], train_test['test'] dt = DTMatcher(name='DecisionTree') dt.fit(table=train, exclude_attrs=['ltable.id', 'rtable.id', '_id', 'gold'], target_attr='gold') predictions = dt.predict( table="", exclude_attrs=['ltable.id', 'rtable.id', '_id', 'gold'], target_attr='predicted', append=True)
def test_visualize_tree_invalid_df(self): A = read_csv_metadata(path_a) B = read_csv_metadata(path_b, key='ID') C = read_csv_metadata(path_c, ltable=A, rtable=B) labels = [0] * 7 labels.extend([1] * 8) C['labels'] = labels feature_table = get_features_for_matching(A, B) feature_vectors = extract_feature_vecs(C, feature_table=feature_table, attrs_after='labels') dt = DTMatcher() dt.fit(table=feature_vectors, exclude_attrs=['_id', 'ltable_ID', 'rtable_ID', 'labels'], target_attr='labels') visualize_tree(dt.clf, feature_vectors.columns, exclude_attrs=['_id', 'ltable_ID', 'rtable_ID', 'labels'])
def test_ml_matcher_valid_1(self): A = read_csv_metadata(fpath_a, key='id') B = read_csv_metadata(fpath_b, key='id') feature_vectors = read_csv_metadata(fpath_f, ltable=A, rtable=B) train_test = mu.train_test_split(feature_vectors) train, test = train_test['train'], train_test['test'] dt = DTMatcher(name='DecisionTree') dt.fit(table=train, exclude_attrs=['ltable.id', 'rtable.id', '_id'], target_attr='gold') predictions = dt.predict(table=test, exclude_attrs=['ltable.id', 'rtable.id', '_id', 'gold'], target_attr='predicted', append=True) self.assertEqual(len(predictions), len(test)) self.assertEqual(set(list(predictions.columns)).issubset(list(test.columns)), True) p_col = predictions.columns[len(predictions.columns)-1] self.assertEqual(p_col, 'predicted')
def test_debug_dt_matcher_valid(self): A = read_csv_metadata(path_a) B = read_csv_metadata(path_b, key='ID') C = read_csv_metadata(path_c, ltable=A, rtable=B) labels = [0] * 7 labels.extend([1] * 8) C['labels'] = labels feature_table = get_features_for_matching(A, B) feature_vectors = extract_feature_vecs(C, feature_table=feature_table, attrs_after='labels') dt = DTMatcher() dt.fit(table=feature_vectors, exclude_attrs=['_id', 'ltable_ID', 'rtable_ID', 'labels'], target_attr='labels') debug_decisiontree_matcher(dt, A.ix[1], B.ix[2], feat_table=feature_table, fv_columns=feature_vectors.columns, exclude_attrs=['ltable_ID', 'rtable_ID', '_id', 'labels'])
def test_ml_matcher_append_false_predict(self): A = read_csv_metadata(fpath_a, key='id') B = read_csv_metadata(fpath_b, key='id') feature_vectors = read_csv_metadata(fpath_f, ltable=A, rtable=B) train_test = mu.train_test_split(feature_vectors) train, test = train_test['train'], train_test['test'] dt = DTMatcher(name='DecisionTree') train.drop('ltable.id', axis=1, inplace=True) train.drop('rtable.id', axis=1, inplace=True) test.drop('ltable.id', axis=1, inplace=True) test.drop('rtable.id', axis=1, inplace=True) test.drop('gold', axis=1, inplace=True) dt.fit(table=train, exclude_attrs='_id', target_attr='gold') predictions = dt.predict(table=test, exclude_attrs='_id', target_attr='predicted', append=False) self.assertEqual(len(predictions), len(test))
def test_ml_matcher_valid_2(self): A = read_csv_metadata(fpath_a, key='id') B = read_csv_metadata(fpath_b, key='id') feature_vectors = read_csv_metadata(fpath_f, ltable=A, rtable=B) train_test = mu.train_test_split(feature_vectors) train, test = train_test['train'], train_test['test'] dt = DTMatcher(name='DecisionTree') col_list = list(feature_vectors.columns) l = list_diff(col_list, [cm.get_key(feature_vectors), cm.get_fk_ltable(feature_vectors), cm.get_fk_rtable(feature_vectors), 'gold']) X = train[l] Y = train['gold'] dt.fit(x=X, y=Y) predictions = dt.predict(test[l]) self.assertEqual(len(predictions), len(test))
def test_vis_tuple_debug_dt_matcher_valid_2(self): A = read_csv_metadata(path_a) B = read_csv_metadata(path_b, key='ID') C = read_csv_metadata(path_c, ltable=A, rtable=B) labels = [0] * 7 labels.extend([1] * 8) C['labels'] = labels feature_table = get_features_for_matching(A, B) feature_vectors = extract_feature_vecs(C, feature_table=feature_table, attrs_after='labels') dt = DTMatcher() dt.fit(table=feature_vectors, exclude_attrs=['_id', 'ltable_ID', 'rtable_ID', 'labels'], target_attr='labels') s = pd.DataFrame(feature_vectors.ix[0]) s1 = s.T vis_tuple_debug_dt_matcher(dt.clf, s1, exclude_attrs=['_id', 'ltable_ID', 'rtable_ID', 'labels'])
def test_ml_matcher_valid_with_id_in_y(self): A = read_csv_metadata(fpath_a, key='id') B = read_csv_metadata(fpath_b, key='id') feature_vectors = read_csv_metadata(fpath_f, ltable=A, rtable=B) train_test = mu.train_test_split(feature_vectors) train, test = train_test['train'], train_test['test'] dt = DTMatcher(name='DecisionTree') col_list = list(feature_vectors.columns) l = list_diff(col_list, [ cm.get_fk_ltable(feature_vectors), cm.get_fk_rtable(feature_vectors), 'gold' ]) X = train[l] Y = train[['_id', 'gold']] dt.fit(x=X, y=Y) predictions = dt.predict(test[l]) self.assertEqual(len(predictions), len(test))
def test_visualize_tree_invalid_df(self): A = read_csv_metadata(path_a) B = read_csv_metadata(path_b, key='ID') C = read_csv_metadata(path_c, ltable=A, rtable=B) labels = [0] * 7 labels.extend([1] * 8) C['labels'] = labels feature_table = get_features_for_matching(A, B) feature_vectors = extract_feature_vecs(C, feature_table=feature_table, attrs_after='labels') dt = DTMatcher() dt.fit(table=feature_vectors, exclude_attrs=['_id', 'ltable_ID', 'rtable_ID', 'labels'], target_attr='labels') visualize_tree( dt.clf, feature_vectors.columns, exclude_attrs=['_id', 'ltable_ID', 'rtable_ID', 'labels'])
def test_ml_matcher_target_attr_present_in_ex_attrs(self): A = read_csv_metadata(fpath_a, key='id') B = read_csv_metadata(fpath_b, key='id') feature_vectors = read_csv_metadata(fpath_f, ltable=A, rtable=B) train_test = mu.train_test_split(feature_vectors) train, test = train_test['train'], train_test['test'] dt = DTMatcher(name='DecisionTree') dt.fit(table=train, exclude_attrs=['ltable.id', 'rtable.id', '_id', 'gold'], target_attr='gold') predictions = dt.predict( table=test, exclude_attrs=['ltable.id', 'rtable.id', '_id', 'gold'], target_attr='predicted', append=True) self.assertEqual(len(predictions), len(test)) l = len(set(list(predictions.columns)).difference(list(test.columns))) self.assertEqual(l, 0) p_col = predictions.columns[len(predictions.columns) - 1] self.assertEqual(p_col, 'predicted')
def test_ml_matcher_ex_attrs_not_list(self): A = read_csv_metadata(fpath_a, key='id') B = read_csv_metadata(fpath_b, key='id') feature_vectors = read_csv_metadata(fpath_f, ltable=A, rtable=B) train_test = mu.train_test_split(feature_vectors) train, test = train_test['train'], train_test['test'] dt = DTMatcher(name='DecisionTree') train.drop('ltable.id', axis=1, inplace=True) train.drop('rtable.id', axis=1, inplace=True) test.drop('ltable.id', axis=1, inplace=True) test.drop('rtable.id', axis=1, inplace=True) dt.fit(table=train, exclude_attrs='_id', target_attr='gold') predictions = dt.predict(table=test, exclude_attrs=['_id', 'gold'], target_attr='predicted', append=True) self.assertEqual(len(predictions), len(test)) l = len(set(list(predictions.columns)).difference(list(test.columns))) self.assertEqual(l, 0) p_col = predictions.columns[len(predictions.columns)-1] self.assertEqual(p_col, 'predicted')
def test_vis_tuple_debug_dt_matcher_valid_1(self): A = read_csv_metadata(path_a) B = read_csv_metadata(path_b, key='ID') C = read_csv_metadata(path_c, ltable=A, rtable=B) labels = [0] * 7 labels.extend([1] * 8) C['labels'] = labels feature_table = get_features_for_matching(A, B) feature_vectors = extract_feature_vecs(C, feature_table=feature_table, attrs_after='labels') dt = DTMatcher() dt.fit(table=feature_vectors, exclude_attrs=['_id', 'ltable_ID', 'rtable_ID', 'labels'], target_attr='labels') s = pd.DataFrame(feature_vectors.ix[0]) s1 = s.T vis_tuple_debug_dt_matcher( dt, s1, exclude_attrs=['_id', 'ltable_ID', 'rtable_ID', 'labels'])
def test_debug_dt_matcher_valid(self): A = read_csv_metadata(path_a) B = read_csv_metadata(path_b, key='ID') C = read_csv_metadata(path_c, ltable=A, rtable=B) labels = [0] * 7 labels.extend([1] * 8) C['labels'] = labels feature_table = get_features_for_matching(A, B) feature_vectors = extract_feature_vecs(C, feature_table=feature_table, attrs_after='labels') dt = DTMatcher() dt.fit(table=feature_vectors, exclude_attrs=['_id', 'ltable_ID', 'rtable_ID', 'labels'], target_attr='labels') debug_decisiontree_matcher( dt, A.ix[1], B.ix[2], feat_table=feature_table, fv_columns=feature_vectors.columns, exclude_attrs=['ltable_ID', 'rtable_ID', '_id', 'labels'])
def test_ml_matcher_invalid_df(self): dt = DTMatcher(name='DecisionTree') dt.fit(table="", exclude_attrs=['ltable.id', 'rtable.id', '_id'], target_attr='gold')
def test_ml_matcher_invalid_df_1(self): dt = DTMatcher(name='DecisionTree') dt.fit(x="", y="")
import os import magellan.matcher.matcherutils as mu from magellan.io.parsers import read_csv_metadata from magellan.matcher.dtmatcher import DTMatcher from magellan.utils.generic_helper import get_install_path feat_datasets_path = os.sep.join([get_install_path(), 'datasets', 'test_datasets', 'matcherselector']) fpath_a = os.sep.join([feat_datasets_path, 'DBLP_demo.csv']) fpath_b = os.sep.join([feat_datasets_path, 'ACM_demo.csv']) fpath_c = os.sep.join([feat_datasets_path, 'dblp_acm_demo_labels.csv']) fpath_f = os.sep.join([feat_datasets_path, 'feat_vecs.csv']) A = read_csv_metadata(fpath_a, key='id') B = read_csv_metadata(fpath_b, key='id') feature_vectors = read_csv_metadata(fpath_f, ltable=A, rtable=B) train_test = mu.train_test_split(feature_vectors) train, test = train_test['train'], train_test['test'] dt = DTMatcher(name='DecisionTree') dt.fit(table=train, exclude_attrs=['ltable.id', 'rtable.id', '_id', 'gold'], target_attr='gold') predictions = dt.predict(table=test, exclude_attrs=['ltable.id', 'rtable.id', '_id', 'gold'], target_attr='predicted', append=True) print('Done')
def test_ml_matcher_invalid_df_1(self): dt = DTMatcher(name='DecisionTree') dt.fit(x="", y="")
import os import magellan.matcher.matcherutils as mu from magellan.io.parsers import read_csv_metadata from magellan.matcher.dtmatcher import DTMatcher from magellan.utils.generic_helper import get_install_path feat_datasets_path = os.sep.join( [get_install_path(), 'datasets', 'test_datasets', 'matcherselector']) fpath_a = os.sep.join([feat_datasets_path, 'DBLP_demo.csv']) fpath_b = os.sep.join([feat_datasets_path, 'ACM_demo.csv']) fpath_c = os.sep.join([feat_datasets_path, 'dblp_acm_demo_labels.csv']) fpath_f = os.sep.join([feat_datasets_path, 'feat_vecs.csv']) A = read_csv_metadata(fpath_a, key='id') B = read_csv_metadata(fpath_b, key='id') feature_vectors = read_csv_metadata(fpath_f, ltable=A, rtable=B) train_test = mu.train_test_split(feature_vectors) train, test = train_test['train'], train_test['test'] dt = DTMatcher(name='DecisionTree') dt.fit(table=train, exclude_attrs=['ltable.id', 'rtable.id', '_id', 'gold'], target_attr='gold') predictions = dt.predict( table=test, exclude_attrs=['ltable.id', 'rtable.id', '_id', 'gold'], target_attr='predicted', append=True) print('Done')
def test_ml_matcher_invalid_df(self): dt = DTMatcher(name='DecisionTree') dt.fit(table="", exclude_attrs=['ltable.id', 'rtable.id', '_id'], target_attr='gold')