apunkt/ai-privacy-toolkit
1
0
Fork 0
mirror of https://github.com/IBM/ai-privacy-toolkit.git synced 2026-06-02 14:45:13 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Fix computing generalizations from transformed data + add some tests

Browse source 
Signed-off-by: abigailt <abigailt@il.ibm.com>
This commit is contained in:
abigailt 2023-05-29 21:27:01 +03:00
parent 26adcf3528
commit aa38a1d716
2 changed files with 213 additions and 126 deletions
Download patch file Download diff file Expand all files Collapse all files

136
tests/test_minimizer.py
View file

@ -164,6 +164,138 @@ def test_minimizer_fit(data):
assert ((rel_accuracy >= target_accuracy) or (target_accuracy - rel_accuracy) <= 0.05)
def test_minimizer_ncp(data):
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])
X1 = np.array([[33, 165],
[43, 150],
[71, 143],
[92, 194],
[13, 125],
[22, 169]])
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)
ad1 = ArrayDataset(X1, features_names=features)
predictions = model.predict(ad)
if predictions.shape[1] > 1:
predictions = np.argmax(predictions, axis=1)
target_accuracy = 0.4
train_dataset = ArrayDataset(X, predictions, features_names=features)
gen1 = GeneralizeToRepresentative(model, target_accuracy=target_accuracy, generalize_using_transform=False)
gen1.fit(dataset=train_dataset)
ncp1 = gen1.ncp
gen1.calculate_ncp(ad1)
ncp2 = gen1.ncp
gen2 = GeneralizeToRepresentative(model, target_accuracy=target_accuracy)
gen2.fit(dataset=train_dataset)
ncp3 = gen2.ncp
gen2.transform(dataset=ad1)
ncp4 = gen2.ncp
gen2.transform(dataset=ad)
ncp5 = gen2.ncp
gen2.transform(dataset=ad1)
ncp6 = gen2.ncp
assert(ncp1 <= ncp3)
assert(ncp2 != ncp3)
assert(ncp3 != ncp4)
assert(ncp4 != ncp5)
assert(ncp6 == ncp4)
def test_minimizer_ncp_categorical(data):
features = ['age', 'height', 'sex', 'ola']
X = [[23, 165, 'f', 'aa'],
[45, 158, 'f', 'aa'],
[56, 123, 'f', 'bb'],
[67, 154, 'm', 'aa'],
[45, 149, 'f', 'bb'],
[42, 166, 'm', 'bb'],
[73, 172, 'm', 'bb'],
[94, 168, 'f', 'aa'],
[69, 175, 'm', 'aa'],
[24, 181, 'm', 'bb'],
[18, 190, 'm', 'bb']]
X = pd.DataFrame(X, columns=features)
y = np.array([1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0])
X1 = [[33, 165, 'f', 'aa'],
[43, 150, 'm', 'aa'],
[71, 143, 'f', 'aa'],
[92, 194, 'm', 'aa'],
[13, 125, 'f', 'aa'],
[22, 169, 'f', 'bb']]
X1 = pd.DataFrame(X1, columns=features)
numeric_features = ["age", "height"]
numeric_transformer = Pipeline(
steps=[('imputer', SimpleImputer(strategy='constant', fill_value=0))]
)
categorical_features = ["sex", "ola"]
categorical_transformer = OneHotEncoder(handle_unknown="ignore")
preprocessor = ColumnTransformer(
transformers=[
("num", numeric_transformer, numeric_features),
("cat", categorical_transformer, categorical_features),
]
)
encoded = preprocessor.fit_transform(X)
encoded = pd.DataFrame(encoded)
base_est = DecisionTreeClassifier(random_state=0, min_samples_split=2,
min_samples_leaf=1)
model = SklearnClassifier(base_est, ModelOutputType.CLASSIFIER_PROBABILITIES)
model.fit(ArrayDataset(encoded, y))
ad = ArrayDataset(X)
ad1 = ArrayDataset(X1)
predictions = model.predict(ArrayDataset(encoded))
if predictions.shape[1] > 1:
predictions = np.argmax(predictions, axis=1)
target_accuracy = 0.4
train_dataset = ArrayDataset(X, predictions, features_names=features)
gen1 = GeneralizeToRepresentative(model, target_accuracy=target_accuracy, generalize_using_transform=False,
categorical_features=categorical_features)
gen1.fit(dataset=train_dataset)
ncp1 = gen1.ncp
gen1.calculate_ncp(ad1)
ncp2 = gen1.ncp
gen2 = GeneralizeToRepresentative(model, target_accuracy=target_accuracy, categorical_features=categorical_features)
gen2.fit(dataset=train_dataset)
ncp3 = gen2.ncp
gen2.transform(dataset=ad1)
ncp4 = gen2.ncp
gen2.transform(dataset=ad)
ncp5 = gen2.ncp
gen2.transform(dataset=ad1)
ncp6 = gen2.ncp
assert(ncp1 <= ncp3)
assert(ncp2 != ncp3)
assert(ncp3 != ncp4)
assert(ncp4 != ncp5)
assert(ncp6 == ncp4)
def test_minimizer_fit_not_transform(data):
features = ['age', 'height']
X = np.array([[23, 165],
@ -1099,5 +1231,9 @@ def test_errors():
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)
with pytest.raises(ValueError):
gen.calculate_ncp(ad)