apunkt/ai-privacy-toolkit
1
0
Fork 0
mirror of https://github.com/IBM/ai-privacy-toolkit.git synced 2026-05-06 10:32:38 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Add more to wrappers

Browse source
This commit is contained in:
abigailt 2022-03-15 11:42:57 +02:00 • committed by olasaadi
parent a2b560920f
commit a9162fbc43
6 changed files with 86 additions and 38 deletions
Download patch file Download diff file Expand all files Collapse all files

24
tests/test_model.py
View file

@ -9,24 +9,28 @@ from sklearn.ensemble import RandomForestClassifier
def test_sklearn_classifier():
dataset = dataset_utils.get_iris_dataset()
(x_train, y_train), (x_test, y_test) = dataset_utils.get_iris_dataset()
underlying_model = RandomForestClassifier()
model = SklearnClassifier(underlying_model)
model.fit(dataset.train)
pred = model.predict(dataset.get_test_samples())
assert(pred.shape[0] == dataset.get_test_samples().shape[0])
train = ArrayDataset(x_train, y_train)
test = ArrayDataset(x_test, y_test)
model.fit(train)
pred = model.predict(x_test)
assert(pred.shape[0] == x_test.shape[0])
score = model.score(dataset.test)
score = model.score(test)
assert(0.0 <= score <= 1.0)
def test_sklearn_regressor():
dataset = dataset_utils.get_diabetes_dataset()
(x_train, y_train), (x_test, y_test) = dataset_utils.get_diabetes_dataset()
underlying_model = DecisionTreeRegressor()
model = SklearnRegressor(underlying_model)
model.fit(dataset.train)
pred = model.predict(dataset.get_test_samples())
assert (pred.shape[0] == dataset.get_test_samples().shape[0])
train = ArrayDataset(x_train, y_train)
test = ArrayDataset(x_test, y_test)
model.fit(train)
pred = model.predict(x_test)
assert (pred.shape[0] == x_test.shape[0])
score = model.score(dataset.test)
score = model.score(test)
assert (0 <= score <= 1)