def test_load_yaml_file(deeprec_resource_path):
data_path = os.path.join(deeprec_resource_path, "xdeepfm")
yaml_file = os.path.join(data_path, "xDeepFM.yaml")
if not os.path.exists(yaml_file):
download_deeprec_resources(
"https://recodatasets.z20.web.core.windows.net/deeprec/",
data_path,
"xdeepfmresources.zip",
)
config = load_yaml(yaml_file)
assert config is not None
def test_load_yaml_file(resource_path):
data_path = os.path.join(resource_path, "..", "resources", "deeprec", "xdeepfm")
yaml_file = os.path.join(data_path, "xDeepFM.yaml")
if not os.path.exists(yaml_file):
download_deeprec_resources(
"https://recodatasets.blob.core.windows.net/deeprec/",
data_path,
"xdeepfmresources.zip",
)
config = load_yaml(yaml_file)
assert config is not None
def prepare_hparams(yaml_file=None, **kwargs):
"""Prepare the model hyperparameters and check that all have the correct value.
Args:
yaml_file (str): YAML file as configuration.
Returns:
obj: Hyperparameter object in TF (tf.contrib.training.HParams).
"""
if yaml_file is not None:
config = load_yaml(yaml_file)
config = flat_config(config)
else:
config = {}
config.update(kwargs)
check_nn_config(config)
return create_hparams(config)
