def test_feature_fn_valid_nosim_tok(self):
A = read_csv_metadata(path_a)
B = read_csv_metadata(path_b, key='ID')
feature_table = get_features_for_matching(A, B)
len1 = len(feature_table)
feature_string = "exact_match(ltuple['zipcode'], rtuple['zipcode'])"
f_dict = get_feature_fn(feature_string, dict(), dict())
def test_vis_debug_matcher_rf_ex_attrs_notin_test(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')
rf = RFMatcher()
train_test = mu.split_train_test(feature_vectors)
train = train_test['train']
test = train_test['test']
test.drop('_id', inplace=True, axis=1)
_vis_debug_rf(
rf,
train,
test,
exclude_attrs=['_id', 'ltable_ID', 'rtable_ID', 'labels'],
target_attr='labels',
show_window=False)
def test_feature_fn_valid_nosim_tok(self):
A = read_csv_metadata(path_a)
B = read_csv_metadata(path_b, key='ID')
feature_table = get_features_for_matching(A, B, validate_inferred_attr_types=False)
len1 = len(feature_table)
feature_string = "exact_match(ltuple['zipcode'], rtuple['zipcode'])"
f_dict = get_feature_fn(feature_string, dict(), dict())
def test_vis_debug_matcher_dt_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()
train_test = mu.split_train_test(feature_vectors)
train = train_test['train']
test = train_test['test']
_vis_debug_dt(
dt,
train,
test,
exclude_attrs=['_id', 'ltable_ID', 'rtable_ID', 'labels'],
target_attr='labels',
show_window=False)
def test_get_features_for_matching_valid(self):
A = read_csv_metadata(path_a)
B = read_csv_metadata(path_b, key='ID')
feat_table = afg.get_features_for_matching(A, B)
self.assertEqual(isinstance(feat_table, pd.DataFrame), True)
functions = feat_table['function']
for f in functions:
x = f(A.ix[1], B.ix[2])
self.assertEqual(x >= 0, True)
def test_get_features_for_matching_validate_types_valid_no(self):
A = read_csv_metadata(path_a)
B = read_csv_metadata(path_b, key='ID')
with mockInput('n'):
feat_table = afg.get_features_for_matching(
A, B, validate_inferred_attr_types=True)
self.assertEqual(feat_table, None)
def test_get_features_for_matching_valid(self):
A = read_csv_metadata(path_a)
B = read_csv_metadata(path_b, key='ID')
feat_table = afg.get_features_for_matching(A, B, validate_inferred_attr_types=False)
self.assertEqual(isinstance(feat_table, pd.DataFrame), True)
functions = feat_table['function']
for f in functions:
x = f(A.ix[1], B.ix[2])
self.assertEqual(x >= 0, True)
def test_extract_feature_vecs_invalid_feature_table(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)
col_pos = len(C.columns)
C.insert(col_pos, 'label', [0] * len(C))
feature_table = get_features_for_matching(A, B, validate_inferred_attr_types=False)
F = extract_feature_vecs(C, attrs_before='ltable_name',
feature_table=None,
attrs_after=['label', '_id'])
def test_add_features_valid_1(self):
A = read_csv_metadata(path_a)
B = read_csv_metadata(path_b, key='ID')
feature_table = get_features_for_matching(A, B, validate_inferred_attr_types=False)
len1 = len(feature_table)
feature_string = "exact_match(ltuple['zipcode'], rtuple['zipcode'])"
f_dict = get_feature_fn(feature_string, get_tokenizers_for_matching(), get_sim_funs_for_matching())
add_feature(feature_table, 'test', f_dict)
len2 = len(feature_table)
self.assertEqual(len1+1, len2)
self.assertEqual(feature_table.ix[len(feature_table)-1, 'function'](A.ix[1], B.ix[2]), 1.0)
def test_add_bb_feature_valid_1(self):
A = read_csv_metadata(path_a)
B = read_csv_metadata(path_b, key='ID')
feature_table = get_features_for_matching(A, B, validate_inferred_attr_types=False)
def bb_fn(ltuple, rtuple):
return 1.0
len1 = len(feature_table)
add_blackbox_feature(feature_table, 'test', bb_fn)
len2 = len(feature_table)
self.assertEqual(len1+1, len2)
self.assertEqual(feature_table.ix[len(feature_table)-1, 'function'](A.ix[1], B.ix[2]), 1.0)
def test_extract_feature_vecs_invalid_feature_table(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)
col_pos = len(C.columns)
C.insert(col_pos, 'label', [0] * len(C))
feature_table = get_features_for_matching(A, B)
F = extract_feature_vecs(C,
attrs_before='ltable_name',
feature_table=None,
attrs_after=['label', '_id'])
def test_extract_feature_vecs_invalid_attrs_after(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)
col_pos = len(C.columns)
C.insert(col_pos, 'label', [0] * len(C))
feature_table = get_features_for_matching(
A, B, validate_inferred_attr_types=False)
F = extract_feature_vecs(
C,
attrs_before='ltable_name',
feature_table=pd.DataFrame(columns=feature_table.columns),
attrs_after=['label1', '_id'])
def test_get_features_for_matching_validate_types_valid_yes(self):
A = read_csv_metadata(path_a)
B = read_csv_metadata(path_b, key='ID')
with mockInput('y'):
feat_table = afg.get_features_for_matching(
A, B, validate_inferred_attr_types=True)
self.assertEqual(isinstance(feat_table, pd.DataFrame), True)
functions = feat_table['function']
for f in functions:
x = f(A.ix[1], B.ix[2])
self.assertEqual(x >= 0, True)
def test_add_features_valid_1(self):
A = read_csv_metadata(path_a)
B = read_csv_metadata(path_b, key='ID')
feature_table = get_features_for_matching(A, B)
len1 = len(feature_table)
feature_string = "exact_match(ltuple['zipcode'], rtuple['zipcode'])"
f_dict = get_feature_fn(feature_string, get_tokenizers_for_matching(),
get_sim_funs_for_matching())
add_feature(feature_table, 'test', f_dict)
len2 = len(feature_table)
self.assertEqual(len1 + 1, len2)
self.assertEqual(
feature_table.ix[len(feature_table) - 1, 'function'](A.ix[1],
B.ix[2]), 1.0)
def test_extract_feature_vecs_with_parralel_job_count_less_than_zero(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)
col_pos = len(C.columns)
C.insert(col_pos, 'label', [0] * len(C))
feature_table = get_features_for_matching(A, B, validate_inferred_attr_types=False)
F = extract_feature_vecs(C, attrs_before=['ltable_name', 'rtable_name'], feature_table=feature_table, n_jobs=-1)
self.assertEqual(isinstance(F, pd.DataFrame), True)
self.assertEqual(F.columns[0], '_id')
self.assertEqual(F.columns[1], cm.get_fk_ltable(C))
self.assertEqual(F.columns[2], cm.get_fk_rtable(C))
self.assertEqual(F.columns[4], 'rtable_name')
self.assertEqual(F.columns[len(F.columns) - 1] == 'label', False)
self.assertEqual(cm.get_all_properties(C) == cm.get_all_properties(F), True)
def test_add_bb_feature_valid_1(self):
A = read_csv_metadata(path_a)
B = read_csv_metadata(path_b, key='ID')
feature_table = get_features_for_matching(A, B)
def bb_fn(ltuple, rtuple):
return 1.0
len1 = len(feature_table)
add_blackbox_feature(feature_table, 'test', bb_fn)
len2 = len(feature_table)
self.assertEqual(len1 + 1, len2)
self.assertEqual(
feature_table.ix[len(feature_table) - 1, 'function'](A.ix[1],
B.ix[2]), 1.0)
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_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], feature_table=feature_table,
table_columns=feature_vectors.columns,
exclude_attrs=['ltable_ID', 'rtable_ID', '_id', 'labels'])
def test_extract_feature_vecs_valid_8(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)
col_pos = len(C.columns)
C.insert(col_pos, 'label', [0] * len(C))
feature_table = get_features_for_matching(A, B, validate_inferred_attr_types=False)
F = extract_feature_vecs(C,
feature_table=pd.DataFrame(columns=feature_table.columns),
attrs_after=['label', '_id'])
self.assertEqual(isinstance(F, pd.DataFrame), True)
self.assertEqual(F.columns[0], '_id')
self.assertEqual(F.columns[1], cm.get_fk_ltable(C))
self.assertEqual(F.columns[2], cm.get_fk_rtable(C))
# self.assertEqual(F.columns[3], 'ltable_name')
self.assertEqual(F.columns[len(F.columns) - 1] == 'label', True)
self.assertEqual(cm.get_all_properties(C) == cm.get_all_properties(F), True)
def test_debug_rf_matcher_invalid_feat_table(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')
rf = RFMatcher()
rf.fit(table=feature_vectors, exclude_attrs=['_id', 'ltable_ID', 'rtable_ID', 'labels'],
target_attr='labels')
debug_randomforest_matcher(rf, A.ix[1], B.ix[2], feature_table=None,
table_columns=feature_vectors.columns,
exclude_attrs=['ltable_ID', 'rtable_ID', '_id', 'labels'])
def test_extract_feature_vecs_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)
col_pos = len(C.columns)
C.insert(col_pos, 'label', [0] * len(C))
feature_table = get_features_for_matching(A, B)
F = extract_feature_vecs(C,
attrs_before=['ltable_name', 'rtable_name'],
feature_table=feature_table)
self.assertEqual(isinstance(F, pd.DataFrame), True)
self.assertEqual(F.columns[0], '_id')
self.assertEqual(F.columns[1], cm.get_fk_ltable(C))
self.assertEqual(F.columns[2], cm.get_fk_rtable(C))
self.assertEqual(F.columns[3], 'ltable_name')
self.assertEqual(F.columns[4], 'rtable_name')
self.assertEqual(F.columns[len(F.columns) - 1] == 'label', False)
self.assertEqual(
cm.get_all_properties(C) == cm.get_all_properties(F), True)
def test_add_bb_feature_with_attrs(self):
A = read_csv_metadata(path_a)
B = read_csv_metadata(path_b, key='ID')
feature_table = get_features_for_matching(
A, B, validate_inferred_attr_types=False)
def bb_fn(ltuple, rtuple):
return min(len(ltuple['name']), len(rtuple['name']))
len1 = len(feature_table)
attrs = {'left_attribute': 'name', 'right_attribute': 'name'}
add_blackbox_feature(feature_table, 'bb_attr_test', bb_fn, **attrs)
len2 = len(feature_table)
self.assertEqual(len1 + 1, len2)
added_feature = feature_table.iloc[len(feature_table) - 1]
self.assertEqual(added_feature.feature_name, 'bb_attr_test')
self.assertEqual(added_feature.left_attribute, 'name')
self.assertEqual(added_feature.right_attribute, 'name')
self.assertEqual(added_feature.simfunction, None)
def test_vis_tuple_debug_rf_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')
rf = RFMatcher()
rf.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_rf_matcher(rf, s1,
exclude_attrs=['_id', 'ltable_ID', 'rtable_ID', 'labels'])
def test_extract_feature_vecs_valid_8(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)
col_pos = len(C.columns)
C.insert(col_pos, 'label', [0] * len(C))
feature_table = get_features_for_matching(
A, B, validate_inferred_attr_types=False)
F = extract_feature_vecs(
C,
feature_table=pd.DataFrame(columns=feature_table.columns),
attrs_after=['label', '_id'])
self.assertEqual(isinstance(F, pd.DataFrame), True)
self.assertEqual(F.columns[0], '_id')
self.assertEqual(F.columns[1], cm.get_fk_ltable(C))
self.assertEqual(F.columns[2], cm.get_fk_rtable(C))
# self.assertEqual(F.columns[3], 'ltable_name')
self.assertEqual(F.columns[len(F.columns) - 1] == 'label', True)
self.assertEqual(
cm.get_all_properties(C) == cm.get_all_properties(F), True)
def test_vis_debug_matcher_rf_label_col_wi_sp_name(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['_predicted'] = labels
feature_table = get_features_for_matching(A, B)
feature_vectors = extract_feature_vecs(C, feature_table=feature_table,
attrs_after='_predicted')
rf = RFMatcher()
train_test = mu.split_train_test(feature_vectors)
train = train_test['train']
test = train_test['test']
_vis_debug_rf(rf, train, test,
exclude_attrs=['_id', 'ltable_ID', 'rtable_ID'],
target_attr='_predicted', show_window=False)
def test_extract_feature_vecs_with_parralel_job_count_more_than_one(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)
col_pos = len(C.columns)
C.insert(col_pos, 'label', [0] * len(C))
feature_table = get_features_for_matching(
A, B, validate_inferred_attr_types=False)
F = extract_feature_vecs(C,
attrs_before=['ltable_name', 'rtable_name'],
feature_table=feature_table,
n_jobs=2)
self.assertEqual(isinstance(F, pd.DataFrame), True)
self.assertEqual(F.columns[0], '_id')
self.assertEqual(F.columns[1], cm.get_fk_ltable(C))
self.assertEqual(F.columns[2], cm.get_fk_rtable(C))
self.assertEqual(F.columns[4], 'rtable_name')
self.assertEqual(F.columns[len(F.columns) - 1] == 'label', False)
self.assertEqual(
cm.get_all_properties(C) == cm.get_all_properties(F), True)
def test_vis_tuple_debug_dt_matcher_valid_3(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')
feature_vectors.drop(['_id', 'ltable_ID', 'rtable_ID', 'labels'], axis=1, inplace=True)
s = pd.DataFrame(feature_vectors.ix[0])
s1 = s.T
vis_tuple_debug_dt_matcher(dt.clf, s1, exclude_attrs=None)
def test_vis_debug_matcher_dt_tar_attr_notin_train(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()
train_test = mu.split_train_test(feature_vectors)
train = train_test['train']
test = train_test['test']
_vis_debug_dt(dt, train, test,
exclude_attrs=['_id', 'ltable_ID', 'rtable_ID', 'labels'],
target_attr='labels1', show_window=False)
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_vis_debug_matcher_rf_ex_attrs_notin_test(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')
rf = RFMatcher()
train_test = mu.split_train_test(feature_vectors)
train = train_test['train']
test = train_test['test']
test.drop('_id', inplace=True, axis=1)
_vis_debug_rf(rf, train, test,
exclude_attrs=['_id', 'ltable_ID', 'rtable_ID', 'labels'],
target_attr='labels', show_window=False)
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],
feature_table=feature_table,
table_columns=feature_vectors.columns,
exclude_attrs=['ltable_ID', 'rtable_ID', '_id', 'labels'])
def test_vis_tuple_debug_rf_matcher_valid_3(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')
rf = RFMatcher()
rf.fit(table=feature_vectors,
exclude_attrs=['_id', 'ltable_ID', 'rtable_ID', 'labels'],
target_attr='labels')
feature_vectors.drop(['_id', 'ltable_ID', 'rtable_ID', 'labels'],
axis=1,
inplace=True)
s = pd.DataFrame(feature_vectors.ix[0])
s1 = s.T
vis_tuple_debug_rf_matcher(rf.clf, s1, exclude_attrs=None)
def test_vis_debug_matcher_rf_label_col_wi_sp_name(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['_predicted'] = labels
feature_table = get_features_for_matching(A, B)
feature_vectors = extract_feature_vecs(C,
feature_table=feature_table,
attrs_after='_predicted')
rf = RFMatcher()
train_test = mu.split_train_test(feature_vectors)
train = train_test['train']
test = train_test['test']
_vis_debug_rf(rf,
train,
test,
exclude_attrs=['_id', 'ltable_ID', 'rtable_ID'],
target_attr='_predicted',
show_window=False)
def test_debug_rf_matcher_invalid_feat_table(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')
rf = RFMatcher()
rf.fit(table=feature_vectors,
exclude_attrs=['_id', 'ltable_ID', 'rtable_ID', 'labels'],
target_attr='labels')
debug_randomforest_matcher(
rf,
A.ix[1],
B.ix[2],
feature_table=None,
table_columns=feature_vectors.columns,
exclude_attrs=['ltable_ID', 'rtable_ID', '_id', 'labels'])
def test_get_features_for_matching_invalid_df2(self):
A = read_csv_metadata(path_a)
# B = read_csv_metadata(path_b, key='ID')
feat_table = afg.get_features_for_matching(A, None, validate_inferred_attr_types=False)
def test_get_features_for_matching_invalid_df1(self):
# A = read_csv_metadata(path_a)
B = read_csv_metadata(path_b, key='ID')
feat_table = afg.get_features_for_matching(None, B)
def test_get_features_for_matching_invalid_df2(self):
A = read_csv_metadata(path_a)
# B = read_csv_metadata(path_b, key='ID')
feat_table = afg.get_features_for_matching(
A, None, validate_inferred_attr_types=False)
def test_get_features_for_matching_invalid_df2(self):
A = read_csv_metadata(path_a)
# B = read_csv_metadata(path_b, key='ID')
feat_table = afg.get_features_for_matching(A, None)
