def test_ogbm_regressor(tmp_path: pathlib.Path) -> None:
pytest.importorskip("sklearn", minversion="0.20.0")
# from sklearn.utils.estimator_checks import check_estimator
from sklearn.utils.estimator_checks import check_estimators_pickle
from sklearn.utils.estimator_checks import check_set_params
reg = OGBMRegressor(model_dir=tmp_path)
name = reg.__class__.__name__
# check_estimator(reg)
check_estimators_pickle(name, reg)
check_set_params(name, reg)
def test_ogbm_classifier(tmp_path: pathlib.Path) -> None:
pytest.importorskip("sklearn", minversion="0.20.0")
# from sklearn.utils.estimator_checks import check_estimator
from sklearn.utils.estimator_checks import check_estimators_pickle
from sklearn.utils.estimator_checks import check_set_params
clf = OGBMClassifier(model_dir=tmp_path)
name = clf.__class__.__name__
# check_estimator(clf)
check_estimators_pickle(name, clf)
check_set_params(name, clf)
def test_callbacks():
"""Test estimators with callbacks.
"""
estimator = KerasClassifier(
model=dynamic_classifier,
callbacks=(SentinalCallback(),),
optimizer="adam",
model__hidden_layer_sizes=(100,),
)
# Check for picklign and partial fit
check_estimators_pickle("KerasClassifier", estimator)
check_estimators_partial_fit_n_features("KerasClassifier", estimator)
# Check that callback was called
estimator.fit([[0]], [1]) # quick fit
assert estimator.callbacks[0].called != SentinalCallback.called
serialized_estimator = pickle.dumps(estimator)
deserialized_estimator = pickle.loads(serialized_estimator)
assert deserialized_estimator.callbacks[0].called == estimator.callbacks[0].called
estimator.fit([[0]], [1]) # quick fit
def test_estimators_pickle():
checks.check_estimators_pickle(NAME, CLF)
def test_pickling(name, est):
if hasattr(est, 'fit'):
check_estimators_pickle('b', est)