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extract common code, add comments, change ellipsis to pass

Signed-off-by: Maya Anderson <mayaa@il.ibm.com>
This commit is contained in:
Maya Anderson 2023-03-06 13:10:48 +02:00
parent 4a024d8d1e
commit e7e725ea80
6 changed files with 95 additions and 86 deletions
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43
tests/test_data_assessment.py
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@ -22,7 +22,7 @@ diabetes_dataset_np = get_diabetes_dataset_np()
nursery_dataset_pd = get_nursery_dataset_pd()
adult_dataset_pd = get_adult_dataset_pd()
mgr = DatasetAssessmentManager(DatasetAssessmentManagerConfig(persist_reports=True, generate_plots=False))
mgr = DatasetAssessmentManager(DatasetAssessmentManagerConfig(persist_reports=False, generate_plots=False))
def teardown_function():
@ -40,28 +40,26 @@ def test_risk_anonymization(name, data, dataset_type, k, mgr):
(x_train, y_train), (x_test, y_test) = data
if dataset_type == 'np':
original_data_members = ArrayDataset(x_train, y_train)
# no need to preprocess
preprocessed_x_train = x_train
preprocessed_x_test = x_test
QI = [0, 2]
anonymizer = Anonymize(k, QI, train_only_QI=True)
anonymized_data = ArrayDataset(anonymizer.anonymize(original_data_members))
original_data_non_members = ArrayDataset(x_test, y_test)
elif "adult" in name:
encoded, encoded_test = preprocess_adult_x_data(x_train, x_test)
preprocessed_x_train, preprocessed_x_test = preprocess_adult_x_data(x_train, x_test)
QI = list(range(15, 27))
anonymizer = Anonymize(k, QI)
anonymized_data = ArrayDataset(anonymizer.anonymize(ArrayDataset(encoded, y_train)))
original_data_members = ArrayDataset(encoded, y_train)
original_data_non_members = ArrayDataset(encoded_test, y_test)
elif "nursery" in name:
encoded, encoded_test = preprocess_nursery_x_data(x_train, x_test)
preprocessed_x_train, preprocessed_x_test = preprocess_nursery_x_data(x_train, x_test)
QI = list(range(15, 27))
anonymizer = Anonymize(k, QI, train_only_QI=True)
anonymized_data = ArrayDataset(anonymizer.anonymize(ArrayDataset(encoded, y_train)))
original_data_members = ArrayDataset(encoded, y_train)
original_data_non_members = ArrayDataset(encoded_test, y_test)
else:
raise ValueError('Pandas dataset missing a preprocessing step')
anonymized_data = ArrayDataset(anonymizer.anonymize(ArrayDataset(preprocessed_x_train, y_train)))
original_data_members = ArrayDataset(preprocessed_x_train, y_train)
original_data_non_members = ArrayDataset(preprocessed_x_test, y_test)
score_g, score_h = mgr.assess(original_data_members, original_data_non_members, anonymized_data,
f'anon_k{k}_{name}')
assert (score_g.roc_auc_score > 0.5)
@ -80,29 +78,24 @@ testdata = [('iris_np', iris_dataset_np, 'np', mgr),
def test_risk_kde(name, data, dataset_type, mgr):
(x_train, y_train), (x_test, y_test) = data
original_data_members = ArrayDataset(x_train, y_train)
original_data_non_members = ArrayDataset(x_test, y_test)
if dataset_type == 'np':
synth_data = ArrayDataset(kde(NUM_SYNTH_SAMPLES, n_components=NUM_SYNTH_COMPONENTS,
original_data=original_data_members.get_samples()))
encoded = x_train
encoded_test = x_test
num_synth_components = NUM_SYNTH_COMPONENTS
elif "adult" in name:
encoded, encoded_test = preprocess_adult_x_data(x_train, x_test)
num_synth_components = 10
synth_data = ArrayDataset(
kde(NUM_SYNTH_SAMPLES, n_components=num_synth_components, original_data=encoded))
original_data_members = ArrayDataset(encoded, y_train)
original_data_non_members = ArrayDataset(encoded_test, y_test)
elif "nursery" in name:
encoded, encoded_test = preprocess_nursery_x_data(x_train, x_test)
num_synth_components = 10
synth_data = ArrayDataset(
kde(NUM_SYNTH_SAMPLES, n_components=num_synth_components, original_data=encoded))
original_data_members = ArrayDataset(encoded, y_train)
original_data_non_members = ArrayDataset(encoded_test, y_test)
else:
raise ValueError('Pandas dataset missing a preprocessing step')
synth_data = ArrayDataset(
kde(NUM_SYNTH_SAMPLES, n_components=num_synth_components, original_data=encoded))
original_data_members = ArrayDataset(encoded, y_train)
original_data_non_members = ArrayDataset(encoded_test, y_test)
score_g, score_h = mgr.assess(original_data_members, original_data_non_members, synth_data,
'kde' + str(NUM_SYNTH_SAMPLES) + name)