def test_sgd_regressor_3(self): reg = SGDRegressor(l1_ratio=0.2, penalty="l1") reg.fit(self.X_train, self.y_train)
def test_sgd_regressor_1(self): reg = SGDRegressor(learning_rate="optimal", eta0=0.2) reg.fit(self.X_train, self.y_train)
def test_sgd_regressor_2(self): reg = SGDRegressor(early_stopping=False, validation_fraction=0.2) reg.fit(self.X_train, self.y_train)
def test_sgd_regressor(self): reg = SGDRegressor(loss="squared_loss", epsilon=0.2) reg.fit(self.X_train, self.y_train)
def test_sgd_regressor_3(self): from sklearn.linear_model import SGDRegressor reg = SGDRegressor(l1_ratio=0.2, penalty='l1') reg.fit(self.X_train, self.y_train)
def test_sgd_regressor_2(self): from lale.lib.sklearn import SGDRegressor reg = SGDRegressor(early_stopping=False, validation_fraction=0.2) reg.fit(self.X_train, self.y_train)
def test_sgd_regressor_1(self): from lale.lib.sklearn import SGDRegressor reg = SGDRegressor(learning_rate='optimal', eta0=0.2) reg.fit(self.X_train, self.y_train)
def test_sgd_regressor(self): from lale.lib.sklearn import SGDRegressor reg = SGDRegressor(loss='squared_loss', epsilon=0.2) reg.fit(self.X_train, self.y_train)