def test_hospital_without_init():
db_name = random_database()
try:
# 1. Setup a HoloClean session.
hc = holoclean.HoloClean(db_name='holo',
domain_thresh_1=0.0,
domain_thresh_2=0.0,
weak_label_thresh=0.99,
max_domain=10000,
cor_strength=0.6,
nb_cor_strength=0.8,
epochs=10,
weight_decay=0.01,
learning_rate=0.001,
threads=1,
batch_size=1,
verbose=True,
timeout=3 * 60000,
feature_norm=False,
weight_norm=False,
print_fw=True).session
# 2. Load training data and denial constraints.
hc.load_data('hospital', '../testdata/hospital.csv')
hc.load_dcs('../testdata/hospital_constraints.txt')
hc.ds.set_constraints(hc.get_dcs())
# 3. Detect erroneous cells using these two detectors.
detectors = [NullDetector(), ViolationDetector()]
hc.detect_errors(detectors)
# 4. Repair errors utilizing the defined features.
hc.setup_domain()
featurizers = [
OccurAttrFeaturizer(),
FreqFeaturizer(),
ConstraintFeaturizer(),
]
hc.repair_errors(featurizers)
# 5. Evaluate the correctness of the results.
report = hc.evaluate(fpath='../testdata/hospital_clean.csv',
tid_col='tid',
attr_col='attribute',
val_col='correct_val')
# We assert that our key metrics are exactly as tested for hospital.
# If these assertions ever fail in a new change, the results should
# be comparable if not better than before, unless a clear and correct
# reason can be given.
assert report.correct_repairs == 434
assert report.total_repairs == 456
assert abs(report.precision - 434. / 456) < TOL
assert abs(report.recall - 434. / 509) < TOL
assert abs(report.repair_recall - 434. / 435) < TOL
assert report.total_repairs_grdt_correct == 22
finally:
delete_database(db_name)
def holoclean_detect(hc):
detectors = [NullDetector(), ViolationDetector()]
featurizers = [
InitAttrFeaturizer(),
OccurAttrFeaturizer(),
FreqFeaturizer(),
ConstraintFeaturizer(),
]
hc.detect_errors(detectors)
hc.repair_errors(featurizers)
return hc
def template(featurizers, estimator_type):
db_name = random_database()
try:
# 1. Setup a HoloClean session.
hc = holoclean.HoloClean(
db_name=db_name,
domain_thresh_1=0.0,
domain_thresh_2=0.0,
weak_label_thresh=0.99,
max_domain=10000,
cor_strength=0.6,
nb_cor_strength=0.8,
epochs=10,
weight_decay=0.01,
learning_rate=0.001,
threads=1,
batch_size=1,
verbose=True,
timeout=3 * 60000,
print_fw=True,
estimator_type=estimator_type,
).session
# 2. Load training data and denial constraints.
hc.load_data('hospital', '../testdata/hospital/hospital.csv')
hc.load_dcs('../testdata/hospital/hospital_constraints.txt')
hc.ds.set_constraints(hc.get_dcs())
# 3. Detect erroneous cells using these two detectors.
detectors = [NullDetector(), ViolationDetector()]
hc.detect_errors(detectors)
# 4. Repair errors utilizing the defined features.
hc.generate_domain()
hc.run_estimator()
hc.repair_errors(featurizers)
# 5. Evaluate the correctness of the results.
report = hc.evaluate(fpath='../testdata/hospital/hospital_clean.csv',
tid_col='tid',
attr_col='attribute',
val_col='correct_val')
return report
finally:
delete_database(db_name)
learning_rate=0.001,
threads=1,
batch_size=1,
verbose=True,
timeout=3 * 60000,
feature_norm=False,
weight_norm=False,
print_fw=True).session
# 2. Load training data and denial constraints.
hc.load_data('hospital', '../testdata/hospital.csv')
hc.load_dcs('../testdata/hospital_constraints.txt')
hc.ds.set_constraints(hc.get_dcs())
# 3. Detect erroneous cells using these two detectors.
detectors = [NullDetector(), ViolationDetector()]
hc.detect_errors(detectors)
# 4. Repair errors utilizing the defined features.
hc.setup_domain()
featurizers = [
InitAttrFeaturizer(),
OccurAttrFeaturizer(),
FreqFeaturizer(),
ConstraintFeaturizer(),
]
hc.repair_errors(featurizers)
# 5. Evaluate the correctness of the results.
hc.evaluate(fpath='../testdata/hospital_clean.csv',