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Initial version with first working test

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Signed-off-by: abigailt <abigailt@il.ibm.com>
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abigailt 2023-11-15 08:21:40 -05:00
parent 5dce961092
commit 2a657388af
2 changed files with 150 additions and 11 deletions
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80
tests/test_minimizer.py
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@ -200,9 +200,9 @@ def check_features(features, expected_generalizations, transformed, x, pandas=Fa
if features[i] in modified_features:
indexes.append(i)
if len(indexes) != transformed.shape[1]:
assert ((np.delete(transformed, indexes, axis=1) == np.delete(x, indexes, axis=1)).all())
assert (np.array_equal(np.delete(transformed, indexes, axis=1), np.delete(x, indexes, axis=1)))
if len(expected_generalizations['ranges'].keys()) > 0 or len(expected_generalizations['categories'].keys()) > 0:
assert (((transformed[indexes]) != (x[indexes])).any())
assert (not np.array_equal(transformed[:, indexes], x[:, indexes]))
def check_ncp(ncp, expected_generalizations):
@ -920,6 +920,82 @@ def test_BaseEstimator_regression(diabetes_dataset):
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= ACCURACY_DIFF)
def test_minimizer_ndarray_one_hot():
x_train = np.array([[23, 0, 1, 165],
[45, 0, 1, 158],
[56, 1, 0, 123],
[67, 0, 1, 154],
[45, 1, 0, 149],
[42, 1, 0, 166],
[73, 0, 1, 172],
[94, 0, 1, 168],
[69, 0, 1, 175],
[24, 1, 0, 181],
[18, 1, 0, 190]])
y_train = np.array([1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0])
model = DecisionTreeClassifier()
model.fit(x_train, y_train)
predictions = model.predict(x_train)
features = ['0', '1', '2', '3']
QI = [0, 1, 2]
QI_slices = [[1, 2]]
target_accuracy = 0.7
gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy, feature_slices=QI_slices,
features_to_minimize=QI)
gen.fit(dataset=ArrayDataset(x_train, predictions))
transformed = gen.transform(dataset=ArrayDataset(x_train))
gener = gen.generalizations
expected_generalizations = {'categories': {}, 'category_representatives': {},
'range_representatives': {'0': [34.5]},
'ranges': {'0': [34.5]}, 'untouched': ['3', '1', '2']}
compare_generalizations(gener, expected_generalizations)
check_features(features, expected_generalizations, transformed, x_train)
ncp = gen.ncp.transform_score
check_ncp(ncp, expected_generalizations)
rel_accuracy = model.score(transformed, predictions)
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= ACCURACY_DIFF)
def test_anonymize_pandas_one_hot():
feature_names = ["age", "gender_M", "gender_F", "height"]
x_train = np.array([[23, 0, 1, 165],
[45, 0, 1, 158],
[56, 1, 0, 123],
[67, 0, 1, 154],
[45, 1, 0, 149],
[42, 1, 0, 166],
[73, 0, 1, 172],
[94, 0, 1, 168],
[69, 0, 1, 175],
[24, 1, 0, 181],
[18, 1, 0, 190]])
y_train = np.array([1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0])
x_train = pd.DataFrame(x_train, columns=feature_names)
y_train = pd.Series(y_train)
model = DecisionTreeClassifier()
model.fit(x_train, y_train)
pred = model.predict(x_train)
k = 10
QI = ["age", "gender_M", "gender_F"]
QI_slices = [["gender_M", "gender_F"]]
anonymizer = Anonymize(k, QI, train_only_QI=True, quasi_identifer_slices=QI_slices)
anon = anonymizer.anonymize(ArrayDataset(x_train, pred))
assert (anon.loc[:, QI].drop_duplicates().shape[0] < x_train.loc[:, QI].drop_duplicates().shape[0])
assert (anon.loc[:, QI].value_counts().min() >= k)
np.testing.assert_array_equal(anon.drop(QI, axis=1), x_train.drop(QI, axis=1))
anonymized_slice = anon.loc[:, QI_slices[0]]
assert ((np.sum(anonymized_slice, axis=1) == 1).all())
assert ((np.max(anonymized_slice, axis=1) == 1).all())
assert ((np.min(anonymized_slice, axis=1) == 0).all())
def test_keras_model():
(x, y), (x_test, y_test) = get_iris_dataset_np()