def test_pipeline_with_classification(classification_dataset, model_key):
X_train, X_test, y_train, y_test = classification_dataset
model_const = models[model_key]
if model_key == 'RandomForestClassifier':
model = model_const(n_bins=2)
else:
model = model_const()
pipe = Pipeline(steps=[('scaler', StandardScaler()), ('model', model)])
pipe.fit(X_train, y_train)
prediction = pipe.predict(X_test)
assert isinstance(prediction, cupy.ndarray)
def test_pipeline_with_regression(regression_dataset, model_key,
instantiation):
X_train, X_test, y_train, y_test = regression_dataset
model_const = models[model_key]
if model_key == 'RandomForestRegressor':
model = model_const(n_bins=2)
else:
model = model_const()
if instantiation == 'Pipeline':
pipe = Pipeline(steps=[('scaler', StandardScaler()), ('model', model)])
elif instantiation == 'make_pipeline':
pipe = make_pipeline(StandardScaler(), model)
pipe.fit(X_train, y_train)
prediction = pipe.predict(X_test)
assert isinstance(prediction, cupy.ndarray)
_ = pipe.score(X_test, y_test)
def test_pipeline_with_classification(classification_dataset, model_key,
instantiation):
X_train, X_test, y_train, y_test = classification_dataset
model_const = models[model_key]
if model_key == 'RandomForestClassifier':
model = model_const(n_bins=2)
else:
model = model_const()
if instantiation == 'Pipeline':
pipe = Pipeline(steps=[('scaler', StandardScaler()), ('model', model)])
elif instantiation == 'make_pipeline':
pipe = make_pipeline(StandardScaler(), model)
pipe.fit(X_train, y_train)
prediction = pipe.predict(X_test)
assert isinstance(prediction, cupy.ndarray)
if model_key == 'RandomForestClassifier':
pytest.skip("RandomForestClassifier is not yet supported"
"by the Pipeline utility")
_ = pipe.score(X_test, y_test)