def run_quora_blocking(sampler = "iterative", lsh_args = None, sequential_args = None):
if (sampler != "iterative") & (sampler != "naive"):
raise ValueError("Sampler should be iterative or naive (completely random).")
# Load Training Set according to sampler
em.del_catalog()
lhs_table = em.read_csv_metadata("../data/processed_quora/quora_" + sampler + "_X_train_lhs.csv").rename(columns = {"Unnamed: 0":"id_lhs"}).sample(n = 1500, random_state = 52)
rhs_table = em.read_csv_metadata("../data/processed_quora/quora_" + sampler + "_X_train_rhs.csv").rename(columns = {"Unnamed: 0":"id_rhs"}).sample(n = 1500, random_state = 52)
y_train = pd.read_csv("../data/processed_quora/quora_" + sampler + "_y_train.csv")
em.del_catalog()
em.set_key(lhs_table, "id_lhs")
em.set_key(rhs_table, "id_rhs")
n_train = lhs_table.shape[0]
# Blocking
blocking_cols = ["question1","question2"]
feature_cols = [["question1"],
['question2']]
id_names = ["qid1","qid2"]
lsh_blocking_col_ids = 1
print("Blocking Train Set of Quora using LSH only.")
candidates = lsh_blocking(lhs_table, rhs_table, lsh_blocking_col_ids, 2, ["qid1","qid2"], char_ngram = lsh_args["char_ngram"], seeds = lsh_args["seeds"], bands = lsh_args["bands"])
print(f"Generated Candidate size has {candidates.shape[0]} rows")
return NotImplementedError
def setup(self):
path_for_A = os.sep.join([datasets_path, 'music', 'A.csv'])
path_for_B = os.sep.join([datasets_path, 'music', 'B.csv'])
try:
A = mg.read_csv_metadata(path_for_A)
mg.set_key(A, 'Sno')
B = mg.read_csv_metadata(path_for_B)
mg.set_key(B, 'Sno')
l_output_attrs = [
'Album_Name', 'Artist_Name', 'CopyRight', 'Released',
'Song_Name', 'Time'
]
r_output_attrs = [
'Album_Name', 'Artist_Name', 'Copyright', 'Released',
'Song_Name', 'Time'
]
C = ob.block_tables(A,
B,
'Album_Name',
'Album_Name',
rem_stop_words=True,
l_output_attrs=l_output_attrs,
r_output_attrs=r_output_attrs)
self.D = ob.block_candset(C,
'Artist_Name',
'Artist_Name',
rem_stop_words=True)
except AssertionError:
print("Dataset \'music\' not found. Please visit the project "
"website to download the dataset.")
raise SystemExit
def setup(self):
p = mg.get_install_path()
path_for_A = os.sep.join([datasets_path, 'bikes', 'A.csv'])
path_for_B = os.sep.join([datasets_path, 'bikes', 'B.csv'])
l_output_attrs = [
'bike_name', 'city_posted', 'km_driven', 'price', 'color',
'model_year'
]
r_output_attrs = [
'bike_name', 'city_posted', 'km_driven', 'price', 'color',
'model_year'
]
try:
A = mg.read_csv_metadata(path_for_A)
mg.set_key(A, 'id')
B = mg.read_csv_metadata(path_for_B)
mg.set_key(B, 'id')
C = ab.block_tables(A, B, 'city_posted', 'city_posted',
l_output_attrs, r_output_attrs)
self.D = ab.block_candset(C, 'model_year', 'model_year')
bb.set_black_box_function(_bikes_function)
except AssertionError:
print("Dataset \'bikes\' not found. Please visit the project "
"website to download the dataset.")
raise SystemExit
def setUp(self):
self.A = em.read_csv_metadata(path_for_A)
em.set_key(self.A, 'ID')
self.B = em.read_csv_metadata(path_for_B)
em.set_key(self.B, 'ID')
self.feature_table = em.get_features_for_blocking(self.A, self.B, validate_inferred_attr_types=False)
self.rb = em.RuleBasedBlocker()
def setup(self):
path_for_A = os.sep.join([datasets_path, 'bikes', 'A.csv'])
path_for_B = os.sep.join([datasets_path, 'bikes', 'B.csv'])
try:
A = mg.read_csv_metadata(path_for_A)
mg.set_key(A, 'id')
B = mg.read_csv_metadata(path_for_B)
mg.set_key(B, 'id')
l_output_attrs = [
'bike_name', 'city_posted', 'km_driven', 'price', 'color',
'model_year'
]
r_output_attrs = [
'bike_name', 'city_posted', 'km_driven', 'price', 'color',
'model_year'
]
self.C = ab.block_tables(A, B, 'city_posted', 'city_posted',
l_output_attrs, r_output_attrs)
except AssertionError:
print("Dataset \'bikes\' not found. Please visit the project"
" website to download the dataset.")
raise SystemExit
self.l_block_attr = 'model_year'
self.r_block_attr = 'model_year'
def test_ob_block_candset_wi_missing_vals_disallow_missing(self):
path_a = os.sep.join([
p, 'tests', 'test_datasets', 'blocker',
'table_A_wi_missing_vals.csv'
])
path_b = os.sep.join([
p, 'tests', 'test_datasets', 'blocker',
'table_B_wi_missing_vals.csv'
])
A = em.read_csv_metadata(path_a)
em.set_key(A, 'ID')
B = em.read_csv_metadata(path_b)
em.set_key(B, 'ID')
C = self.ob.block_tables(A,
B,
l_overlap_attr_1,
r_overlap_attr_1,
allow_missing=True)
validate_metadata(C)
validate_data(C, expected_ids_4)
D = self.ob.block_candset(C,
l_overlap_attr_2,
r_overlap_attr_2,
rem_stop_words=True,
overlap_size=4)
validate_metadata_two_candsets(C, D)
validate_data(D, expected_ids_2)
def setUp(self):
self.A = em.read_csv_metadata(path_for_A)
em.set_key(self.A, 'ID')
self.B = em.read_csv_metadata(path_for_B)
em.set_key(self.B, 'ID')
self.feature_table = em.get_features_for_blocking(self.A, self.B)
self.rb = em.RuleBasedBlocker()
def test_ab_block_tuples_wi_missing_values_disallow_missing(self):
path_a = os.sep.join([
p, 'tests', 'test_datasets', 'blocker',
'table_A_wi_missing_vals.csv'
])
path_b = os.sep.join([
p, 'tests', 'test_datasets', 'blocker',
'table_B_wi_missing_vals.csv'
])
A = em.read_csv_metadata(path_a)
em.set_key(A, 'ID')
B = em.read_csv_metadata(path_b)
em.set_key(B, 'ID')
assert_equal(
self.ab.block_tuples(A.loc[0], B.loc[0], l_block_attr_1,
r_block_attr_1), True)
assert_equal(
self.ab.block_tuples(A.loc[1], B.loc[2], l_block_attr_1,
r_block_attr_1), False)
assert_equal(
self.ab.block_tuples(A.loc[2], B.loc[1], l_block_attr_1,
r_block_attr_1), True)
assert_equal(
self.ab.block_tuples(A.loc[0], B.loc[1], l_block_attr_1,
r_block_attr_1), True)
assert_equal(
self.ab.block_tuples(A.loc[2], B.loc[2], l_block_attr_1,
r_block_attr_1), True)
def setUp(self):
self.A = em.read_csv_metadata(path_for_A)
em.set_key(self.A, 'ID')
self.B = em.read_csv_metadata(path_for_B)
em.set_key(self.B, 'ID')
self.C = em.read_csv_metadata(path_for_C, ltable=self.A, rtable=self.B)
self.feature_table = em.get_features_for_matching(self.A, self.B, validate_inferred_attr_types=False)
self.brm = em.BooleanRuleMatcher()
def read_tables(params):
global table_A, table_B
basedir = params['basedir']
apath = os.path.join(basedir, params['apath'])
bpath = os.path.join(basedir, params['bpath'])
table_A = em.read_csv_metadata(apath, key='id')
table_B = em.read_csv_metadata(bpath, key='id')
def load_known_dataset(dataset_name):
dataset_dtls = configs.er_dataset_details[dataset_name]
l_file_name = dataset_dtls["dataset_folder_path"] + dataset_dtls["ltable_file_name"]
r_file_name = dataset_dtls["dataset_folder_path"] + dataset_dtls["rtable_file_name"]
#Assumption: key name is always "id"
A = em.read_csv_metadata(l_file_name , key="id", encoding='utf-8')
B = em.read_csv_metadata(r_file_name , key="id", encoding='utf-8')
return A, B
def setUp(self):
self.A = em.read_csv_metadata(path_for_A)
em.set_key(self.A, 'ID')
self.B = em.read_csv_metadata(path_for_B)
em.set_key(self.B, 'ID')
self.C = em.read_csv_metadata(path_for_C, ltable=self.A, rtable=self.B)
self.feature_table = em.get_features_for_matching(self.A, self.B, validate_inferred_attr_types=False)
self.C['neg_trig_labels'] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
self.C['pos_trig_labels'] = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
self.mt = em.MatchTrigger()
def main():
A = em.read_csv_metadata('../Data/A_imdb.csv', key='id')
B = em.read_csv_metadata('../Data/B_tmdb.csv', key='id')
ab = em.AttrEquivalenceBlocker()
shared_attributes = ['title', 'directors', 'release_year', 'languages']
C = ab.block_tables(A,
B,
'directors',
'directors',
l_output_attrs=shared_attributes,
r_output_attrs=shared_attributes)
# Take a sample of 10 pairs
S = em.sample_table(C, 100)
print(S)
G = em.label_table(S, label_column_name='gold_labels')
train_test = em.split_train_test(G, train_proportion=0.5)
train, test = train_test['train'], train_test['test']
# Get feature for matching
match_f = em.get_features_for_matching(A, B)
H = em.extract_feature_vecs(train,
attrs_before=['ltable_title', 'rtable_title'],
feature_table=match_f,
attrs_after=['gold_labels'])
H.fillna(value=0, inplace=True)
print(H)
# Specifying Matchers and Performing Matching.
dt = em.DTMatcher(max_depth=5) # A decision tree matcher.
# Train the matcher
dt.fit(table=H,
exclude_attrs=[
'_id', 'ltable_id', 'rtable_id', 'ltable_title', 'rtable_title',
'gold_labels'
],
target_attr='gold_labels')
# Predict
F = em.extract_feature_vecs(test,
attrs_before=['ltable_title', 'rtable_title'],
feature_table=match_f,
attrs_after=['gold_labels'])
F.fillna(value=0, inplace=True)
print(F)
pred_table = dt.predict(table=F,
exclude_attrs=[
'_id', 'ltable_id', 'rtable_id',
'ltable_title', 'rtable_title', 'gold_labels'
],
target_attr='predicted_labels',
return_probs=True,
probs_attr='proba',
append=True,
inplace=True)
print(pred_table)
eval_summary = em.eval_matches(pred_table, 'gold_labels',
'predicted_labels')
em.print_eval_summary(eval_summary)
def setup(self):
path_for_a = os.sep.join([DATASET_PATH, 'books', 'A.csv'])
path_for_b = os.sep.join([DATASET_PATH, 'books', 'B.csv'])
try:
self.A = em.read_csv_metadata(path_for_a)
self.B = em.read_csv_metadata(path_for_b)
self.size = 2000
self.y_param = 2
except AssertionError:
print("Dataset \'books\' not found. Please visit the project website to download the dataset.")
raise SystemExit
def test_ob_block_tables_wi_missing_values_disallow_missing(self):
path_a = os.sep.join([p, 'tests', 'test_datasets', 'blocker',
'table_A_wi_missing_vals.csv'])
path_b = os.sep.join([p, 'tests', 'test_datasets', 'blocker',
'table_B_wi_missing_vals.csv'])
A = em.read_csv_metadata(path_a)
em.set_key(A, 'ID')
B = em.read_csv_metadata(path_b)
em.set_key(B, 'ID')
C = self.ob.block_tables(A, B, l_overlap_attr_1, r_overlap_attr_1)
validate_metadata(C)
validate_data(C, expected_ids_1)
def setup(self):
path_for_A = os.sep.join([datasets_path, 'citations', 'A.csv'])
path_for_B = os.sep.join([datasets_path, 'citations', 'B.csv'])
try:
self.A = mg.read_csv_metadata(path_for_A)
mg.set_key(self.A, 'ID')
self.B = mg.read_csv_metadata(path_for_B)
mg.set_key(self.B, 'ID')
except AssertionError:
print("Dataset \'anime\' not found. Please visit the project"
" website to download the dataset.")
raise SystemExit
def setup(self):
path_for_A = os.sep.join([datasets_path, 'beer', 'A.csv'])
path_for_B = os.sep.join([datasets_path, 'beer', 'B.csv'])
try:
self.A = mg.read_csv_metadata(path_for_A)
mg.set_key(self.A, 'Label')
self.B = mg.read_csv_metadata(path_for_B)
mg.set_key(self.B, 'Label')
bb.set_black_box_function(_beer_function)
except AssertionError:
print("Dataset \'beer\' not found. Please visit the project "
"website to download the dataset.")
raise SystemExit
def setup(self):
path_for_A = os.sep.join([datasets_path, 'electronics', 'A.csv'])
path_for_B = os.sep.join([datasets_path, 'electronics', 'B.csv'])
try:
self.A = mg.read_csv_metadata(path_for_A)
mg.set_key(self.A, 'ID')
self.B = mg.read_csv_metadata(path_for_B)
mg.set_key(self.B, 'ID')
self.feature_table = mg.get_features_for_blocking(self.A, self.B)
except AssertionError:
print("Dataset \'electronics\' not found. Please visit the project "
"website to download the dataset.")
raise SystemExit
def setup(self):
path_for_A = os.sep.join([datasets_path, 'anime', 'A.csv'])
path_for_B = os.sep.join([datasets_path, 'anime', 'B.csv'])
try:
self.A = mg.read_csv_metadata(path_for_A)
mg.set_key(self.A, 'ID')
self.B = mg.read_csv_metadata(path_for_B)
mg.set_key(self.B, 'ID')
self.feature_table = mg.get_features_for_blocking(self.A, self.B)
except AssertionError:
print("Dataset \'anime\' not found. Please visit the project "
"website to download the dataset.")
raise SystemExit
def setup(self):
path_for_A = os.sep.join([datasets_path, 'restaurants', 'A.csv'])
path_for_B = os.sep.join([datasets_path, 'restaurants', 'B.csv'])
self.l_output_attrs = ['NAME', 'PHONENUMBER', 'ADDRESS']
self.r_output_attrs = ['NAME', 'PHONENUMBER', 'ADDRESS']
try:
self.A = mg.read_csv_metadata(path_for_A)
mg.set_key(self.A, 'ID')
self.B = mg.read_csv_metadata(path_for_B)
mg.set_key(self.B, 'ID')
except AssertionError:
print("Dataset \'restaurants\' not found. Please visit the project"
" website to download the dataset.")
raise SystemExit
def test_ob_block_tuples_wi_missing_vals_disallow_missing(self):
path_a = os.sep.join([p, 'tests', 'test_datasets', 'blocker',
'table_A_wi_missing_vals.csv'])
path_b = os.sep.join([p, 'tests', 'test_datasets', 'blocker',
'table_B_wi_missing_vals.csv'])
A = em.read_csv_metadata(path_a)
em.set_key(A, 'ID')
B = em.read_csv_metadata(path_b)
em.set_key(B, 'ID')
assert_equal(self.ob.block_tuples(A.ix[1], B.ix[3], l_overlap_attr_1,
r_overlap_attr_1), True)
assert_equal(self.ob.block_tuples(A.ix[3], B.ix[2], l_overlap_attr_1,
r_overlap_attr_1), True)
assert_equal(self.ob.block_tuples(A.ix[3], B.ix[3], l_overlap_attr_1,
r_overlap_attr_1), True)
def test_ab_block_tables_wi_missing_values_allow_missing(self):
path_a = os.sep.join([p, 'tests', 'test_datasets', 'blocker',
'table_A_wi_missing_vals.csv'])
path_b = os.sep.join([p, 'tests', 'test_datasets', 'blocker',
'table_B_wi_missing_vals.csv'])
A = em.read_csv_metadata(path_a)
em.set_key(A, 'ID')
B = em.read_csv_metadata(path_b)
em.set_key(B, 'ID')
C = self.ab.block_tables(A, B, l_block_attr_1, r_block_attr_1,
l_output_attrs, r_output_attrs,
l_output_prefix, r_output_prefix, True)
validate_metadata(C, l_output_attrs, r_output_attrs,
l_output_prefix, r_output_prefix)
validate_data(C, expected_ids_3)
def setUp(self):
self.A = em.read_csv_metadata(path_for_A)
em.set_key(self.A, 'ID')
self.B = em.read_csv_metadata(path_for_B)
em.set_key(self.B, 'ID')
self.C = em.read_csv_metadata(path_for_C, ltable=self.A, rtable=self.B)
self.feature_table = em.get_features_for_matching(
self.A, self.B, validate_inferred_attr_types=False)
self.C['neg_trig_labels'] = [
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
]
self.C['pos_trig_labels'] = [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
]
self.mt = em.MatchTrigger()
def test_ab_block_candset_wi_missing_values_disallow_missing(self):
path_a = os.sep.join([p, 'tests', 'test_datasets', 'blocker',
'table_A_wi_missing_vals.csv'])
path_b = os.sep.join([p, 'tests', 'test_datasets', 'blocker',
'table_B_wi_missing_vals.csv'])
A = em.read_csv_metadata(path_a)
em.set_key(A, 'ID')
B = em.read_csv_metadata(path_b)
em.set_key(B, 'ID')
C = self.ab.block_tables(A, B, l_block_attr_1, r_block_attr_1)
validate_metadata(C)
validate_data(C, expected_ids_4)
D = self.ab.block_candset(C, l_block_attr_2, r_block_attr_2)
validate_metadata_two_candsets(C, D)
validate_data(D, [('a5','b5')])
def setup(self):
path_for_A = os.sep.join([datasets_path, 'restaurants', 'A.csv'])
path_for_B = os.sep.join([datasets_path, 'restaurants', 'B.csv'])
self.l_block_attr = 'PHONENUMBER'
self.r_block_attr = 'PHONENUMBER'
self.l_output_attrs = ['NAME', 'PHONENUMBER', 'ADDRESS']
self.r_output_attrs = ['NAME', 'PHONENUMBER', 'ADDRESS']
try:
self.A = mg.read_csv_metadata(path_for_A)
mg.set_key(self.A, 'ID')
self.B = mg.read_csv_metadata(path_for_B)
mg.set_key(self.B, 'ID')
except AssertionError:
print("Dataset \'restaurants\' not found. Please visit the project"
" website to download the dataset.")
raise SystemExit
def setup(self):
path_for_A = os.sep.join([datasets_path, 'anime', 'A.csv'])
path_for_B = os.sep.join([datasets_path, 'anime', 'B.csv'])
self.l_block_attr = 'Year'
self.r_block_attr = 'Year'
self.l_output_attrs = ['Title', 'Year', 'Episodes']
self.r_output_attrs = ['Title', 'Year', 'Episodes']
try:
self.A = mg.read_csv_metadata(path_for_A)
mg.set_key(self.A, 'ID')
self.B = mg.read_csv_metadata(path_for_B)
mg.set_key(self.B, 'ID')
except AssertionError:
print("Dataset \'anime\' not found. Please visit the project"
" website to download the dataset.")
raise SystemExit
def setup(self):
path_for_A = os.sep.join([datasets_path, 'anime', 'A.csv'])
path_for_B = os.sep.join([datasets_path, 'anime', 'B.csv'])
self.l_block_attr = 'Year'
self.r_block_attr = 'Year'
self.l_output_attrs = ['Title', 'Year', 'Episodes']
self.r_output_attrs = ['Title', 'Year', 'Episodes']
try:
self.A = mg.read_csv_metadata(path_for_A)
mg.set_key(self.A, 'ID')
self.B = mg.read_csv_metadata(path_for_B)
mg.set_key(self.B, 'ID')
except AssertionError:
print("Dataset \'anime\' not found. Please visit the project"
" website to download the dataset.")
raise SystemExit
def setup(self):
path_for_A = os.sep.join([datasets_path, 'electronics', 'A.csv'])
path_for_B = os.sep.join([datasets_path, 'electronics', 'B.csv'])
self.l_block_attr = 'Brand'
self.r_block_attr = 'Brand'
self.l_output_attrs = ['Brand', 'Amazon_Price']
self.r_output_attrs = ['Brand', 'Price']
try:
self.A = mg.read_csv_metadata(path_for_A)
mg.set_key(self.A, 'ID')
self.B = mg.read_csv_metadata(path_for_B)
mg.set_key(self.B, 'ID')
except AssertionError:
print("Dataset \'electronics\' not found. Please visit the project"
" website to download the dataset.")
raise SystemExit
def test_ob_block_tables_wi_missing_values_disallow_missing(self):
path_a = os.sep.join([
p, 'tests', 'test_datasets', 'blocker',
'table_A_wi_missing_vals.csv'
])
path_b = os.sep.join([
p, 'tests', 'test_datasets', 'blocker',
'table_B_wi_missing_vals.csv'
])
A = em.read_csv_metadata(path_a)
em.set_key(A, 'ID')
B = em.read_csv_metadata(path_b)
em.set_key(B, 'ID')
C = self.ob.block_tables(A, B, l_overlap_attr_1, r_overlap_attr_1)
validate_metadata(C)
validate_data(C, expected_ids_1)
def setup(self):
path_for_A = os.sep.join([datasets_path, 'electronics', 'A.csv'])
path_for_B = os.sep.join([datasets_path, 'electronics', 'B.csv'])
self.l_block_attr = 'Brand'
self.r_block_attr = 'Brand'
self.l_output_attrs = ['Brand', 'Amazon_Price']
self.r_output_attrs = ['Brand', 'Price']
try:
self.A = mg.read_csv_metadata(path_for_A)
mg.set_key(self.A, 'ID')
self.B = mg.read_csv_metadata(path_for_B)
mg.set_key(self.B, 'ID')
except AssertionError:
print("Dataset \'electronics\' not found. Please visit the project"
" website to download the dataset.")
raise SystemExit
def setup(self):
path_for_A = os.sep.join([datasets_path, 'books', 'A.csv'])
path_for_B = os.sep.join([datasets_path, 'books', 'B.csv'])
path_for_C = os.sep.join([datasets_path, 'books', 'C.csv'])
try:
self.A = em.read_csv_metadata(path_for_A)
em.set_key(self.A, 'ID')
self.B = em.read_csv_metadata(path_for_B)
em.set_key(self.B, 'ID')
self.C = em.read_csv_metadata(path_for_C)
cm.set_candset_properties(self.C, '_id', 'ltable_ID', 'rtable_ID',
self.A, self.B)
except AssertionError:
print("Dataset \'books\' not found. Please visit the project"
" website to download the dataset.")
def test_ob_block_candset_wi_missing_vals_disallow_missing(self):
path_a = os.sep.join([p, 'tests', 'test_datasets', 'blocker',
'table_A_wi_missing_vals.csv'])
path_b = os.sep.join([p, 'tests', 'test_datasets', 'blocker',
'table_B_wi_missing_vals.csv'])
A = em.read_csv_metadata(path_a)
em.set_key(A, 'ID')
B = em.read_csv_metadata(path_b)
em.set_key(B, 'ID')
C = self.ob.block_tables(A, B, l_overlap_attr_1,
r_overlap_attr_1, allow_missing=True)
validate_metadata(C)
validate_data(C, expected_ids_4)
D = self.ob.block_candset(C, l_overlap_attr_2, r_overlap_attr_2,
rem_stop_words=True, overlap_size=4)
validate_metadata_two_candsets(C, D)
validate_data(D, expected_ids_2)
def setup(self):
path_for_A = os.sep.join([datasets_path, 'books', 'A.csv'])
path_for_B = os.sep.join([datasets_path, 'books', 'B.csv'])
try:
A = mg.read_csv_metadata(path_for_A)
mg.set_key(A, 'ID')
B = mg.read_csv_metadata(path_for_B)
mg.set_key(B, 'ID')
self.C = snb.block_tables(A, B, 'Author', 'Author',
['Title', 'Author', 'ISBN13', 'Publisher'],
['Title', 'Author', 'ISBN13', 'Publisher'])
except AssertionError:
print("Dataset \'books\' not found. Please visit the project"
" website to download the dataset.")
raise SystemExit
self.l_block_attr = 'ISBN13'
self.r_block_attr = 'ISBN13'
def load_data(left_file_name,
right_file_name,
label_file_name,
blocking_fn,
include_self_join=False):
A = em.read_csv_metadata(left_file_name, key="id", encoding='iso-8859-1')
B = em.read_csv_metadata(right_file_name, key="id", encoding='iso-8859-1')
try:
G = pd.read_csv(label_file_name)
except:
G = None
C = blocking_fn(A, B)
if include_self_join:
C_A = blocking_fn(A, A)
C_B = blocking_fn(B, B)
return A, B, G, C, C_A, C_B
else:
return A, B, G, C
def setup(self):
p = mg.get_install_path()
path_for_A = os.sep.join([datasets_path, 'electronics', 'A.csv'])
path_for_B = os.sep.join([datasets_path, 'electronics', 'B.csv'])
try:
A = mg.read_csv_metadata(path_for_A)
mg.set_key(A, 'ID')
B = mg.read_csv_metadata(path_for_B)
mg.set_key(B, 'ID')
self.C = ab.block_tables(A, B, 'Brand', 'Brand',
['Brand', 'Amazon_Price'],
['Brand', 'Price'])
bb.set_black_box_function(_electronics_function)
except AssertionError:
print(
"Dataset \'electronics\' not found. Please visit the project "
"website to download the dataset.")
raise SystemExit
def setup(self):
path_for_A = os.sep.join([datasets_path, 'anime', 'A.csv'])
path_for_B = os.sep.join([datasets_path, 'anime', 'B.csv'])
try:
A = mg.read_csv_metadata(path_for_A)
mg.set_key(A, 'ID')
B = mg.read_csv_metadata(path_for_B)
mg.set_key(B, 'ID')
self.C = ab.block_tables(A, B, 'Year', 'Year',
['Title', 'Year', 'Episodes'],
['Title', 'Year', 'Episodes'])
except AssertionError:
print("Dataset \'anime\' not found. Please visit the project"
" website to download the dataset.")
raise SystemExit
self.l_block_attr = 'Episodes'
self.r_block_attr = 'Episodes'
def setup(self):
path_for_A = os.sep.join([datasets_path, 'books', 'A.csv'])
path_for_B = os.sep.join([datasets_path, 'books', 'B.csv'])
self.l_block_attr = 'Author'
self.r_block_attr = 'Author'
self.l_output_attrs = ['Title', 'Author', 'ISBN13', 'Publisher',
'Publication_Date']
self.r_output_attrs = ['Title', 'Author', 'ISBN13', 'Publisher',
'Publication_Date']
try:
self.A = mg.read_csv_metadata(path_for_A)
mg.set_key(self.A, 'ID')
self.B = mg.read_csv_metadata(path_for_B)
mg.set_key(self.B, 'ID')
except AssertionError:
print("Dataset \'books\' not found. Please visit the project"
" website to download the dataset.")
raise SystemExit
def read_files():
# Read in data files
A = em.read_csv_metadata(FOLDER + 'A.csv', key='id') # imdb data
B = em.read_csv_metadata(FOLDER + 'B.csv', key='id') # tmdb data
C = em.read_csv_metadata(
FOLDER + 'C.csv',
key='_id',
ltable=A,
rtable=B,
fk_ltable='l_id',
fk_rtable='r_id') # candidates that survive blocking step
G = em.read_csv_metadata(FOLDER + 'G.csv',
key='_id',
ltable=A,
rtable=B,
fk_ltable='l_id',
fk_rtable='r_id') # labeled data
return A, B, C, G
def test_ab_block_tables_wi_missing_values_allow_missing(self):
path_a = os.sep.join([
p, 'tests', 'test_datasets', 'blocker',
'table_A_wi_missing_vals.csv'
])
path_b = os.sep.join([
p, 'tests', 'test_datasets', 'blocker',
'table_B_wi_missing_vals.csv'
])
A = em.read_csv_metadata(path_a)
em.set_key(A, 'ID')
B = em.read_csv_metadata(path_b)
em.set_key(B, 'ID')
C = self.ab.block_tables(A, B, l_block_attr_1, r_block_attr_1,
l_output_attrs, r_output_attrs,
l_output_prefix, r_output_prefix, True)
validate_metadata(C, l_output_attrs, r_output_attrs, l_output_prefix,
r_output_prefix)
validate_data(C, expected_ids_3)
def test_ab_block_candset_wi_missing_values_disallow_missing(self):
path_a = os.sep.join([
p, 'tests', 'test_datasets', 'blocker',
'table_A_wi_missing_vals.csv'
])
path_b = os.sep.join([
p, 'tests', 'test_datasets', 'blocker',
'table_B_wi_missing_vals.csv'
])
A = em.read_csv_metadata(path_a)
em.set_key(A, 'ID')
B = em.read_csv_metadata(path_b)
em.set_key(B, 'ID')
C = self.ab.block_tables(A, B, l_block_attr_1, r_block_attr_1)
validate_metadata(C)
validate_data(C, expected_ids_4)
D = self.ab.block_candset(C, l_block_attr_2, r_block_attr_2)
validate_metadata_two_candsets(C, D)
validate_data(D, [('a5', 'b5')])
def setup(self):
path_for_A = os.sep.join([datasets_path, 'books', 'A.csv'])
path_for_B = os.sep.join([datasets_path, 'books', 'B.csv'])
try:
A = mg.read_csv_metadata(path_for_A)
mg.set_key(A, 'ID')
B = mg.read_csv_metadata(path_for_B)
mg.set_key(B, 'ID')
self.C = snb.block_tables(
A, B, 'Author', 'Author',
['Title', 'Author', 'ISBN13', 'Publisher'],
['Title', 'Author', 'ISBN13', 'Publisher'])
except AssertionError:
print("Dataset \'books\' not found. Please visit the project"
" website to download the dataset.")
raise SystemExit
self.l_block_attr = 'ISBN13'
self.r_block_attr = 'ISBN13'
def setup(self):
path_for_A = os.sep.join([datasets_path, 'restaurants', 'A.csv'])
path_for_B = os.sep.join([datasets_path, 'restaurants', 'B.csv'])
try:
A = mg.read_csv_metadata(path_for_A)
mg.set_key(A, 'ID')
B = mg.read_csv_metadata(path_for_B)
mg.set_key(B, 'ID')
ob = mg.OverlapBlocker()
self.C = ob.block_tables(A, B, 'ADDRESS', 'ADDRESS', overlap_size=4,
l_output_attrs=['NAME', 'PHONENUMBER', 'ADDRESS'],
r_output_attrs=['NAME', 'PHONENUMBER', 'ADDRESS'])
feature_table = mg.get_features_for_blocking(A,B)
self.rb = mg.RuleBasedBlocker()
self.rb.add_rule(['ADDRESS_ADDRESS_jac_qgm_3_qgm_3(ltuple,rtuple) < 0.44'],
feature_table)
except AssertionError:
print("Dataset \'beer\' not found. Please visit the project "
"website to download the dataset.")
raise SystemExit
def setup(self):
path_for_A = os.sep.join([datasets_path, 'books', 'A.csv'])
path_for_B = os.sep.join([datasets_path, 'books', 'B.csv'])
self.l_block_attr = 'Author'
self.r_block_attr = 'Author'
self.l_output_attrs = [
'Title', 'Author', 'ISBN13', 'Publisher', 'Publication_Date'
]
self.r_output_attrs = [
'Title', 'Author', 'ISBN13', 'Publisher', 'Publication_Date'
]
try:
self.A = mg.read_csv_metadata(path_for_A)
mg.set_key(self.A, 'ID')
self.B = mg.read_csv_metadata(path_for_B)
mg.set_key(self.B, 'ID')
except AssertionError:
print("Dataset \'books\' not found. Please visit the project"
" website to download the dataset.")
raise SystemExit
def setup(self):
path_for_A = os.sep.join([datasets_path, 'ebooks', 'A.csv'])
path_for_B = os.sep.join([datasets_path, 'ebooks', 'B.csv'])
try:
A = mg.read_csv_metadata(path_for_A)
mg.set_key(A, 'record_id')
B = mg.read_csv_metadata(path_for_B)
mg.set_key(B, 'record_id')
ob = mg.OverlapBlocker()
self.C = ob.block_tables(A, B, 'title', 'title', overlap_size=2,
rem_stop_words = True,
l_output_attrs=['title', 'author', 'publisher', 'date'],
r_output_attrs=['title', 'author', 'publisher', 'date'])
feature_table = mg.get_features_for_blocking(A,B)
self.rb = mg.RuleBasedBlocker()
self.rb.add_rule(['date_date_lev_sim(ltuple, rtuple) < 0.6'], feature_table)
except AssertionError:
print("Dataset \'beer\' not found. Please visit the project "
"website to download the dataset.")
raise SystemExit
def setup(self):
p = mg.get_install_path()
path_for_A = os.sep.join([datasets_path, 'bikes', 'A.csv'])
path_for_B = os.sep.join([datasets_path, 'bikes', 'B.csv'])
try:
self.A = mg.read_csv_metadata(path_for_A)
mg.set_key(self.A, 'id')
self.B = mg.read_csv_metadata(path_for_B)
mg.set_key(self.B, 'id')
except AssertionError:
print("Dataset \'bikes\' not found. Please visit the project"
" website to download the dataset.")
raise SystemExit
self.l_block_attr = 'city_posted'
self.r_block_attr = 'city_posted'
self.l_output_attrs = ['bike_name', 'city_posted', 'km_driven', 'price',
'color', 'model_year']
self.r_output_attrs = ['bike_name', 'city_posted', 'km_driven', 'price',
'color', 'model_year']
def test_ab_block_tuples_wi_missing_values_allow_missing(self):
path_a = os.sep.join([p, 'tests', 'test_datasets', 'blocker',
'table_A_wi_missing_vals.csv'])
path_b = os.sep.join([p, 'tests', 'test_datasets', 'blocker',
'table_B_wi_missing_vals.csv'])
A = em.read_csv_metadata(path_a)
em.set_key(A, 'ID')
B = em.read_csv_metadata(path_b)
em.set_key(B, 'ID')
assert_equal(self.ab.block_tuples(A.ix[0], B.ix[0], l_block_attr_1,
r_block_attr_1, allow_missing=True),
False)
assert_equal(self.ab.block_tuples(A.ix[1], B.ix[2], l_block_attr_1,
r_block_attr_1, allow_missing=True),
False)
assert_equal(self.ab.block_tuples(A.ix[2], B.ix[1], l_block_attr_1,
r_block_attr_1, allow_missing=True),
False)
assert_equal(self.ab.block_tuples(A.ix[0], B.ix[1], l_block_attr_1,
r_block_attr_1, allow_missing=True),
False)
assert_equal(self.ab.block_tuples(A.ix[2], B.ix[2], l_block_attr_1,
r_block_attr_1, allow_missing=True),
True)
def setUp(self):
self.A = em.read_csv_metadata(path_a)
em.set_key(self.A, 'ID')
self.B = em.read_csv_metadata(path_b)
em.set_key(self.B, 'ID')
self.ob = em.OverlapBlocker()
def setUp(self):
self.A = em.read_csv_metadata(path_a)
em.set_key(self.A, 'ID')
self.B = em.read_csv_metadata(path_b)
em.set_key(self.B, 'ID')
self.ab = em.AttrEquivalenceBlocker()
def setUp(self):
self.A = em.read_csv_metadata(path_a)
em.set_key(self.A, 'ID')
self.B = em.read_csv_metadata(path_b)
em.set_key(self.B, 'ID')
self.bb = em.BlackBoxBlocker()
