def test_pipeline_clone_fit_score(self):
X, y = load_boston(return_X_y=True)
X, y = pd.DataFrame(X), pd.Series(y)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
random_state=42)
transformer_list = [('pca', PCA(n_components=1)),
('svd', TruncatedSVD(n_components=2))]
union = FeatureUnion(transformer_list=transformer_list, n_jobs=-1)
params = dict(n_estimators=10,
objective='reg:squarederror',
booster='gbtree')
reg = Regressor(regressor_choice='xgbregressor',
pipeline_transform=('tr', union),
params=params)
reg.get_pipeline(y=y_train)
_class_before_clone = reg.pipe.__class__
reg.pipe = clone(reg.pipe)
y_pred = reg.fit(X_train, y_train).predict(X_test)
score = reg.score(y_test, y_pred)
self.assertEqual(_class_before_clone, reg.pipe.__class__)
self.assertCountEqual(y_pred.index, y_test.index)
self.assertGreaterEqual(score['mae'].values, 0.0)
self.assertGreaterEqual(score['mse'].values, 0.0)
self.assertLess(score['mae'].values, 11.0)
self.assertLess(score['mse'].values, 232.0)
def test_with_cv_pipeline_clone_fit_score(self):
X, y = load_boston(return_X_y=True)
X, y = pd.DataFrame(X), pd.Series(y)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
random_state=42)
transformer_list = [('pca', PCA(n_components=1)),
('svd', TruncatedSVD(n_components=2))]
union = FeatureUnion(transformer_list=transformer_list, n_jobs=-1)
stack = dict(regressors=['kneighborsregressor', 'bayesianridge'],
final_regressor='lasso')
reg = Regressor(regressor_choice='mlxtendstackingcvregressor',
pipeline_transform=('tr', union),
stacking_options=dict(layers=stack))
reg.get_pipeline(y=y_train)
_class_before_clone = reg.pipe.__class__
reg.pipe = clone(reg.pipe)
y_pred = reg.fit(X_train, y_train).predict(X_test)
score = reg.score(y_test, y_pred)
self.assertEqual(_class_before_clone, reg.pipe.__class__)
self.assertCountEqual(y_pred.index, y_test.index)
self.assertGreaterEqual(score['mae'].values, 0.0)
self.assertGreaterEqual(score['mse'].values, 0.0)
self.assertLess(score['mae'].values, 11.0)
self.assertLess(score['mse'].values, 232.0)
