def test_regressor(self):
X_train, y_train = self.X_train, self.y_train
import importlib
module_name = ".".join(clf_name.split(".")[0:-1])
class_name = clf_name.split(".")[-1]
module = importlib.import_module(module_name)
class_ = getattr(module, class_name)
regr = None
if class_name in ["StackingRegressor", "VotingRegressor"]:
regr = class_(estimators=[("base", SGDRegressor())])
else:
regr = class_()
# test_schemas_are_schemas
lale.type_checking.validate_is_schema(regr.input_schema_fit())
lale.type_checking.validate_is_schema(regr.input_schema_predict())
lale.type_checking.validate_is_schema(regr.output_schema_predict())
lale.type_checking.validate_is_schema(regr.hyperparam_schema())
# test_init_fit_predict
trained = regr.fit(self.X_train, self.y_train)
_ = trained.predict(self.X_test)
# test score
_ = trained.score(self.X_test, self.y_test)
# test_predict_on_trainable
trained = regr.fit(X_train, y_train)
regr.predict(X_train)
# test_to_json
regr.to_json()
# test_in_a_pipeline
pipeline = NoOp() >> regr
trained = pipeline.fit(self.X_train, self.y_train)
_ = trained.predict(self.X_test)
# test_with_hyperopt
from lale.lib.sklearn.ridge import Ridge
if isinstance(regr, Ridge): # type: ignore
from lale.lib.lale import Hyperopt
hyperopt = Hyperopt(estimator=pipeline, max_evals=1)
trained = hyperopt.fit(self.X_train, self.y_train)
_ = trained.predict(self.X_test)
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)