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Revert to having generalize_using_transform as an instance param (passed at init) and throwing an exception when used incorrectly.

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Signed-off-by: abigailt <abigailt@il.ibm.com>
This commit is contained in:
abigailt 2023-08-21 18:09:06 +03:00
parent 256dfbbc71
commit 5e84f3fac4
2 changed files with 64 additions and 32 deletions
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49
tests/test_minimizer.py
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@ -256,8 +256,8 @@ def test_minimizer_params_not_transform(cells):
model = SklearnClassifier(base_est, ModelOutputType.CLASSIFIER_PROBABILITIES)
model.fit(ArrayDataset(x, y))
gen = GeneralizeToRepresentative(model, cells=cells)
ncp = gen.calculate_ncp(samples, generalize_using_transform=False)
gen = GeneralizeToRepresentative(model, cells=cells, generalize_using_transform=False)
ncp = gen.calculate_ncp(samples)
assert (ncp > 0.0)
@ -304,10 +304,10 @@ def test_minimizer_ncp(data_two_features):
target_accuracy = 0.4
train_dataset = ArrayDataset(x, predictions, features_names=features)
gen1 = GeneralizeToRepresentative(model, target_accuracy=target_accuracy)
gen1.fit(dataset=train_dataset, generalize_using_transform=False)
gen1 = GeneralizeToRepresentative(model, target_accuracy=target_accuracy, generalize_using_transform=False)
gen1.fit(dataset=train_dataset)
ncp1 = gen1.ncp.fit_score
ncp2 = gen1.calculate_ncp(ad1, generalize_using_transform=False)
ncp2 = gen1.calculate_ncp(ad1)
gen2 = GeneralizeToRepresentative(model, target_accuracy=target_accuracy)
gen2.fit(dataset=train_dataset)
@ -348,10 +348,10 @@ def test_minimizer_ncp_categorical(data_four_features):
train_dataset = ArrayDataset(x, predictions, features_names=features)
gen1 = GeneralizeToRepresentative(model, target_accuracy=target_accuracy,
categorical_features=categorical_features)
gen1.fit(dataset=train_dataset, generalize_using_transform=False)
categorical_features=categorical_features, generalize_using_transform=False)
gen1.fit(dataset=train_dataset)
ncp1 = gen1.ncp.fit_score
ncp2 = gen1.calculate_ncp(ad1, generalize_using_transform=False)
ncp2 = gen1.calculate_ncp(ad1)
gen2 = GeneralizeToRepresentative(model, target_accuracy=target_accuracy, categorical_features=categorical_features)
gen2.fit(dataset=train_dataset)
@ -381,10 +381,10 @@ def test_minimizer_fit_not_transform(data_two_features):
if predictions.shape[1] > 1:
predictions = np.argmax(predictions, axis=1)
target_accuracy = 0.5
gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy)
gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy, generalize_using_transform=False)
train_dataset = ArrayDataset(x, predictions, features_names=features)
gen.fit(dataset=train_dataset, generalize_using_transform=False)
gen.fit(dataset=train_dataset)
gener = gen.generalizations
expected_generalizations = {'ranges': {'age': [], 'height': [157.0]}, 'categories': {}, 'untouched': []}
@ -954,3 +954,32 @@ def test_untouched():
gener = gen.generalizations
expected_generalizations = {'ranges': {'age': [38, 39]}, 'categories': {}, 'untouched': ['gender']}
compare_generalizations(gener, expected_generalizations)
def test_errors():
features = ['age', 'height']
X = np.array([[23, 165],
[45, 158],
[56, 123],
[67, 154],
[45, 149],
[42, 166],
[73, 172],
[94, 168],
[69, 175],
[24, 181],
[18, 190]])
y = np.array([1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0])
base_est = DecisionTreeClassifier(random_state=0, min_samples_split=2,
min_samples_leaf=1)
model = SklearnClassifier(base_est, ModelOutputType.CLASSIFIER_PROBABILITIES)
model.fit(ArrayDataset(X, y))
ad = ArrayDataset(X)
predictions = model.predict(ad)
if predictions.shape[1] > 1:
predictions = np.argmax(predictions, axis=1)
gen = GeneralizeToRepresentative(model, generalize_using_transform=False)
train_dataset = ArrayDataset(X, predictions, features_names=features)
gen.fit(dataset=train_dataset)
with pytest.raises(ValueError):
gen.transform(X)