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https://github.com/IBM/ai-privacy-toolkit.git
synced 2026-06-23 15:48:06 +02:00
Remove unused code, renaming and additional review comments
Signed-off-by: abigailt <abigailt@il.ibm.com>
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abigailt
parent
69e45d99e5
commit
256dfbbc71
2 changed files with 74 additions and 94 deletions
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@ -30,7 +30,7 @@ def diabetes_dataset():
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@pytest.fixture
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def get_cells():
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def cells():
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cells = [{"id": 1, "ranges": {"age": {"start": None, "end": 38}, "height": {"start": None, "end": 170}}, "label": 0,
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'categories': {}, "representative": {"age": 26, "height": 149}},
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{"id": 2, "ranges": {"age": {"start": 39, "end": None}, "height": {"start": None, "end": 170}}, "label": 1,
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@ -49,7 +49,7 @@ def get_cells():
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@pytest.fixture
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def get_cells_categorical():
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def cells_categorical():
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cells = [{'id': 1, 'label': 0, 'ranges': {'age': {'start': None, 'end': None}},
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'categories': {'sex': ['f', 'm']}, 'hist': [2, 0],
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'representative': {'age': 45, 'height': 149, 'sex': 'f'},
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@ -80,7 +80,7 @@ def get_cells_categorical():
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@pytest.fixture
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def get_data_two_features():
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def data_two_features():
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x = np.array([[23, 165],
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[45, 158],
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[56, 123],
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@ -104,7 +104,7 @@ def get_data_two_features():
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@pytest.fixture
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def get_data_three_features():
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def data_three_features():
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features = ['age', 'height', 'weight']
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x = np.array([[23, 165, 70],
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[45, 158, 67],
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@ -122,7 +122,7 @@ def get_data_three_features():
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@pytest.fixture
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def get_data_four_features():
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def data_four_features():
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features = ['age', 'height', 'sex', 'ola']
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x = [[23, 165, 'f', 'aa'],
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[45, 158, 'f', 'aa'],
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@ -146,7 +146,7 @@ def get_data_four_features():
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@pytest.fixture
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def get_data_five_features():
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def data_five_features():
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features = ['age', 'height', 'weight', 'sex', 'ola']
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x = [[23, 165, 65, 'f', 'aa'],
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[45, 158, 76, 'f', 'aa'],
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@ -205,9 +205,9 @@ def check_ncp(ncp, expected_generalizations):
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assert (ncp > 0.0)
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def test_minimizer_params(get_cells):
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def test_minimizer_params(cells):
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# Assume two features, age and height, and boolean label
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cells, features, x, y = get_cells
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cells, features, x, y = cells
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base_est = DecisionTreeClassifier(random_state=0, min_samples_split=2,
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min_samples_leaf=1)
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@ -247,9 +247,9 @@ def create_encoder(numeric_features, categorical_features, x):
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return preprocessor, encoded
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def test_minimizer_params_not_transform(get_cells):
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def test_minimizer_params_not_transform(cells):
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# Assume two features, age and height, and boolean label
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cells, features, x, y = get_cells
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cells, features, x, y = cells
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samples = ArrayDataset(x, y, features)
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base_est = DecisionTreeClassifier(random_state=0, min_samples_split=2,
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min_samples_leaf=1)
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@ -261,8 +261,8 @@ def test_minimizer_params_not_transform(get_cells):
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assert (ncp > 0.0)
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def test_minimizer_fit(get_data_two_features):
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x, y, features, _ = get_data_two_features
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def test_minimizer_fit(data_two_features):
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x, y, features, _ = data_two_features
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base_est = DecisionTreeClassifier(random_state=0, min_samples_split=2,
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min_samples_leaf=1)
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model = SklearnClassifier(base_est, ModelOutputType.CLASSIFIER_PROBABILITIES)
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@ -289,8 +289,8 @@ def test_minimizer_fit(get_data_two_features):
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assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
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def test_minimizer_ncp(get_data_two_features):
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x, y, features, x1 = get_data_two_features
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def test_minimizer_ncp(data_two_features):
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x, y, features, x1 = data_two_features
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base_est = DecisionTreeClassifier(random_state=0, min_samples_split=2,
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min_samples_leaf=1)
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@ -326,8 +326,8 @@ def test_minimizer_ncp(get_data_two_features):
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assert (ncp6 == ncp4)
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def test_minimizer_ncp_categorical(get_data_four_features):
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x, y, features, x1 = get_data_four_features
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def test_minimizer_ncp_categorical(data_four_features):
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x, y, features, x1 = data_four_features
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x = pd.DataFrame(x, columns=features)
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x1 = pd.DataFrame(x1, columns=features)
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@ -370,8 +370,8 @@ def test_minimizer_ncp_categorical(get_data_four_features):
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assert (ncp6 == ncp4)
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def test_minimizer_fit_not_transform(get_data_two_features):
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x, y, features, x1 = get_data_two_features
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def test_minimizer_fit_not_transform(data_two_features):
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x, y, features, x1 = data_two_features
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base_est = DecisionTreeClassifier(random_state=0, min_samples_split=2,
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min_samples_leaf=1)
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model = SklearnClassifier(base_est, ModelOutputType.CLASSIFIER_PROBABILITIES)
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@ -394,8 +394,8 @@ def test_minimizer_fit_not_transform(get_data_two_features):
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check_ncp(ncp, expected_generalizations)
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def test_minimizer_fit_pandas(get_data_four_features):
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x, y, features, _ = get_data_four_features
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def test_minimizer_fit_pandas(data_four_features):
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x, y, features, _ = data_four_features
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x = pd.DataFrame(x, columns=features)
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numeric_features = ["age", "height"]
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@ -431,9 +431,9 @@ def test_minimizer_fit_pandas(get_data_four_features):
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assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
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def test_minimizer_params_categorical(get_cells_categorical):
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def test_minimizer_params_categorical(cells_categorical):
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# Assume three features, age, sex and height, and boolean label
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cells, features, x, y = get_cells_categorical
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cells, features, x, y = cells_categorical
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x = pd.DataFrame(x, columns=features)
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numeric_features = ["age", "height"]
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@ -459,8 +459,8 @@ def test_minimizer_params_categorical(get_cells_categorical):
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assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
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def test_minimizer_fit_qi(get_data_three_features):
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x, y, features = get_data_three_features
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def test_minimizer_fit_qi(data_three_features):
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x, y, features = data_three_features
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qi = ['age', 'weight']
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base_est = DecisionTreeClassifier(random_state=0, min_samples_split=2,
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min_samples_leaf=1)
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@ -487,8 +487,8 @@ def test_minimizer_fit_qi(get_data_three_features):
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assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
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def test_minimizer_fit_pandas_qi(get_data_five_features):
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x, y, features = get_data_five_features
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def test_minimizer_fit_pandas_qi(data_five_features):
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x, y, features = data_five_features
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x = pd.DataFrame(x, columns=features)
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qi = ['age', 'weight', 'ola']
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@ -809,8 +809,8 @@ def test_x_y_features_names():
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assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
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def test_BaseEstimator_classification(get_data_five_features):
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x, y, features = get_data_five_features
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def test_BaseEstimator_classification(data_five_features):
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x, y, features = data_five_features
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x = pd.DataFrame(x, columns=features)
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QI = ['age', 'weight', 'ola']
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