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)