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apply changes after rebase with wrappers

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olasaadi 2022-03-10 13:49:05 +02:00
parent 6afb175d6f
commit b4eddabd37
4 changed files with 32 additions and 36 deletions
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24
tests/test_model.py
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@ -9,28 +9,24 @@ from sklearn.ensemble import RandomForestClassifier
def test_sklearn_classifier():
(x_train, y_train), (x_test, y_test) = dataset_utils.get_iris_dataset()
dataset = dataset_utils.get_iris_dataset()
underlying_model = RandomForestClassifier()
model = SklearnClassifier(underlying_model)
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])
model.fit(dataset.train)
pred = model.predict(dataset.get_test_samples())
assert(pred.shape[0] == dataset.get_test_samples().shape[0])
score = model.score(test)
score = model.score(dataset.test)
assert(0.0 <= score <= 1.0)
def test_sklearn_regressor():
(x_train, y_train), (x_test, y_test) = dataset_utils.get_diabetes_dataset()
dataset = dataset_utils.get_diabetes_dataset()
underlying_model = DecisionTreeRegressor()
model = SklearnRegressor(underlying_model)
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])
model.fit(dataset.train)
pred = model.predict(dataset.get_test_samples())
assert (pred.shape[0] == dataset.get_test_samples().shape[0])
score = model.score(test)
score = model.score(dataset.test)
assert (0 <= score <= 1)