def test_can_load_with_model(self, classifier: Model,
tmp_path: pathlib.Path):
storage = FileStorage(tmp_path)
expected_file = classifier.save_estimator(storage)
assert expected_file.exists()
loaded_file = classifier.load_estimator(expected_file, storage=storage)
assert isinstance(loaded_file, Model)
storage_context = FileStorage(tmp_path)
context_loaded_file = classifier.load_estimator(
expected_file, storage=storage_context)
assert isinstance(context_loaded_file, Model)
def test_save_estimator_uses_default_storage_if_no_storage_is_passed(
self, tmp_path: pathlib.Path, classifier: Model):
classifier.config.ESTIMATOR_DIR = tmp_path
classifier.save_estimator()
models = classifier.config.default_storage.get_list()
assert len(models) == 1
new_classifier = Model.load_estimator(models[0])
assert (classifier.estimator.get_params() ==
new_classifier.estimator.get_params())
def test_regression_model_can_be_saved(self, classifier: Model,
tmp_path: pathlib.Path,
train_iris_dataset):
classifier.score_estimator(train_iris_dataset)
load_storage = FileStorage(tmp_path)
storage = FileStorage(tmp_path)
saved_model_path = classifier.save_estimator(storage)
assert saved_model_path.exists()
loaded_model = classifier.load_estimator(saved_model_path,
storage=load_storage)
assert loaded_model.estimator.get_params(
) == classifier.estimator.get_params()
