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Add column distribution comparison, and a third method for dataset asssessment by membership classification (#84)
* Add column distribution comparison, and a third method for dataset assessment by membership classification * Address review comments, add additional distribution comparison tests and make them externally configurable too, in addition to the alpha becoming configurable. Signed-off-by: Maya Anderson <mayaa@il.ibm.com>
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andersonm-ibm
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8 changed files with 676 additions and 205 deletions
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@ -4,6 +4,11 @@ from apt.anonymization import Anonymize
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from apt.risk.data_assessment.dataset_assessment_manager import DatasetAssessmentManager, DatasetAssessmentManagerConfig
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from apt.utils.dataset_utils import get_iris_dataset_np, get_nursery_dataset_pd
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from apt.utils.datasets import ArrayDataset
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from apt.risk.data_assessment.dataset_attack_membership_classification import DatasetAttackScoreMembershipClassification
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from apt.risk.data_assessment.dataset_attack_membership_knn_probabilities import \
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DatasetAttackScoreMembershipKnnProbabilities, DatasetAttackConfigMembershipKnnProbabilities, \
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DatasetAttackMembershipKnnProbabilities
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from apt.risk.data_assessment.dataset_attack_whole_dataset_knn_distance import DatasetAttackScoreWholeDatasetKnnDistance
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from tests.test_data_assessment import kde, preprocess_nursery_x_data
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NUM_SYNTH_SAMPLES = 10
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@ -28,10 +33,10 @@ def teardown_function():
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mgr.dump_all_scores_to_files()
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anon_testdata = [('iris_np', iris_dataset_np, 'np', mgr1)] \
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+ [('nursery_pd', nursery_dataset_pd, 'pd', mgr2)] \
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+ [('iris_np', iris_dataset_np, 'np', mgr3)] \
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+ [('nursery_pd', nursery_dataset_pd, 'pd', mgr4)]
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anon_testdata = ([('iris_np', iris_dataset_np, 'np', mgr1)]
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+ [('nursery_pd', nursery_dataset_pd, 'pd', mgr2)]
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+ [('iris_np', iris_dataset_np, 'np', mgr3)]
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+ [('nursery_pd', nursery_dataset_pd, 'pd', mgr4)])
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@pytest.mark.parametrize("name, data, dataset_type, mgr", anon_testdata)
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@ -44,9 +49,10 @@ def test_risk_anonymization(name, data, dataset_type, mgr):
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preprocessed_x_test = x_test
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QI = [0, 2]
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anonymizer = Anonymize(ANON_K, QI, train_only_QI=True)
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categorical_features = []
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elif "nursery" in name:
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preprocessed_x_train, preprocessed_x_test = preprocess_nursery_x_data(x_train, x_test)
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QI = list(range(15, 27))
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preprocessed_x_train, preprocessed_x_test, categorical_features = preprocess_nursery_x_data(x_train, x_test)
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QI = list(range(15, 20))
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anonymizer = Anonymize(ANON_K, QI, train_only_QI=True)
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else:
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raise ValueError('Pandas dataset missing a preprocessing step')
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@ -57,11 +63,12 @@ def test_risk_anonymization(name, data, dataset_type, mgr):
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dataset_name = f'anon_k{ANON_K}_{name}'
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assess_privacy_and_validate_result(mgr, original_data_members, original_data_non_members, anonymized_data,
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dataset_name)
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dataset_name, categorical_features)
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assess_privacy_and_validate_result(mgr, original_data_members=original_data_members,
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original_data_non_members=original_data_non_members,
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synth_data=anonymized_data, dataset_name=None)
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synth_data=anonymized_data, dataset_name=None,
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categorical_features=categorical_features)
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testdata = [('iris_np', iris_dataset_np, 'np', mgr4),
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@ -72,38 +79,85 @@ testdata = [('iris_np', iris_dataset_np, 'np', mgr4),
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@pytest.mark.parametrize("name, data, dataset_type, mgr", testdata)
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def test_risk_kde(name, data, dataset_type, mgr):
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original_data_members, original_data_non_members, synthetic_data, categorical_features \
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= encode_and_generate_synthetic_data(dataset_type, name, data)
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dataset_name = 'kde' + str(NUM_SYNTH_SAMPLES) + name
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assess_privacy_and_validate_result(mgr, original_data_members, original_data_non_members, synthetic_data,
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dataset_name, categorical_features)
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assess_privacy_and_validate_result(mgr, original_data_members=original_data_members,
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original_data_non_members=original_data_non_members,
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synth_data=synthetic_data, dataset_name=None,
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categorical_features=categorical_features)
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testdata_knn_options = [('iris_np', iris_dataset_np, 'np'),
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('nursery_pd', nursery_dataset_pd, 'pd')]
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@pytest.mark.parametrize("name, data, dataset_type", testdata_knn_options)
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def test_risk_kde_knn_options(name, data, dataset_type):
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original_data_members, original_data_non_members, synthetic_data, categorical_features \
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= encode_and_generate_synthetic_data(dataset_type, name, data)
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dataset_name = 'kde' + str(NUM_SYNTH_SAMPLES) + name
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config_g = DatasetAttackConfigMembershipKnnProbabilities(use_batches=True, generate_plot=False,
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distribution_comparison_alpha=0.1)
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numeric_tests = ['KS', 'CVM', 'AD', 'ES']
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categorical_tests = ['CHI', 'AD', 'ES']
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for numeric_test in numeric_tests:
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for categorical_test in categorical_tests:
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attack_g = DatasetAttackMembershipKnnProbabilities(original_data_members,
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original_data_non_members,
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synthetic_data,
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config_g,
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dataset_name,
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categorical_features,
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distribution_comparison_numeric_test=numeric_test,
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distribution_comparison_categorical_test=categorical_test
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)
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score_g = attack_g.assess_privacy()
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assert score_g.roc_auc_score > MIN_ROC_AUC
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assert score_g.average_precision_score > MIN_PRECISION
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def encode_and_generate_synthetic_data(dataset_type, name, data):
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(x_train, y_train), (x_test, y_test) = data
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if dataset_type == 'np':
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encoded = x_train
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encoded_test = x_test
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num_synth_components = NUM_SYNTH_COMPONENTS
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categorical_features = []
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elif "nursery" in name:
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encoded, encoded_test = preprocess_nursery_x_data(x_train, x_test)
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encoded, encoded_test, categorical_features = preprocess_nursery_x_data(x_train, x_test)
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num_synth_components = 10
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else:
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raise ValueError('Pandas dataset missing a preprocessing step')
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synth_data = ArrayDataset(
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synthetic_data = ArrayDataset(
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kde(NUM_SYNTH_SAMPLES, n_components=num_synth_components, original_data=encoded))
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original_data_members = ArrayDataset(encoded, y_train)
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original_data_non_members = ArrayDataset(encoded_test, y_test)
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dataset_name = 'kde' + str(NUM_SYNTH_SAMPLES) + name
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assess_privacy_and_validate_result(mgr, original_data_members, original_data_non_members, synth_data, dataset_name)
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assess_privacy_and_validate_result(mgr, original_data_members=original_data_members,
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original_data_non_members=original_data_non_members,
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synth_data=synth_data, dataset_name=None)
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return original_data_members, original_data_non_members, synthetic_data, categorical_features
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def assess_privacy_and_validate_result(mgr, original_data_members, original_data_non_members, synth_data,
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dataset_name):
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if dataset_name:
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[score_g, score_h] = mgr.assess(original_data_members, original_data_non_members, synth_data,
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dataset_name)
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else:
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[score_g, score_h] = mgr.assess(original_data_members, original_data_non_members, synth_data)
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assert (score_g.roc_auc_score > MIN_ROC_AUC)
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assert (score_g.average_precision_score > MIN_PRECISION)
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assert (score_h.share > MIN_SHARE)
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def assess_privacy_and_validate_result(mgr, original_data_members, original_data_non_members, synth_data, dataset_name,
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categorical_features):
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attack_scores = mgr.assess(original_data_members, original_data_non_members, synth_data, dataset_name,
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categorical_features)
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for i, (assessment_type, scores) in enumerate(attack_scores.items()):
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if assessment_type == 'MembershipKnnProbabilities':
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score_g: DatasetAttackScoreMembershipKnnProbabilities = scores[0]
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assert score_g.roc_auc_score > MIN_ROC_AUC
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assert score_g.average_precision_score > MIN_PRECISION
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elif assessment_type == 'WholeDatasetKnnDistance':
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score_h: DatasetAttackScoreWholeDatasetKnnDistance = scores[0]
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assert score_h.share > MIN_SHARE
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if assessment_type == 'MembershipClassification':
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score_mc: DatasetAttackScoreMembershipClassification = scores[0]
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assert score_mc.synthetic_data_quality_warning is False
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assert 0 <= score_mc.normalized_ratio <= 1
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