def test_mleap_module_model_save_with_relative_path_and_valid_sample_input_produces_mleap_flavor(
spark_model_iris):
with TempDir(chdr=True) as tmp:
model_path = os.path.basename(tmp.path("model"))
mlflow_model = Model()
mleap.save_model(spark_model=spark_model_iris.model,
path=model_path,
sample_input=spark_model_iris.spark_df,
mlflow_model=mlflow_model)
assert mleap.FLAVOR_NAME in mlflow_model.flavors
config_path = os.path.join(model_path, "MLmodel")
assert os.path.exists(config_path)
config = Model.load(config_path)
assert mleap.FLAVOR_NAME in config.flavors
def test_mleap_output_json_format(spark_model_iris, model_path):
mlflow_model = Model()
mleap.save_model(spark_model=spark_model_iris.model,
path=model_path,
sample_input=spark_model_iris.spark_df,
mlflow_model=mlflow_model)
mleap_conf = mlflow_model.flavors[mleap.FLAVOR_NAME]
schema_path_sub = mleap_conf["input_schema"]
schema_path_full = os.path.join(model_path, schema_path_sub)
with open(schema_path_full, "r") as f:
json_schema = json.load(f)
assert "fields" in json_schema.keys()
assert len(json_schema["fields"]) > 0
assert type(json_schema["fields"][0]) == dict
assert "name" in json_schema["fields"][0]
def test_mleap_module_model_save_with_absolute_path_and_valid_sample_input_produces_mleap_flavor(
spark_model_iris, model_path):
model_path = os.path.abspath(model_path)
mlflow_model = Model()
mleap.save_model(
spark_model=spark_model_iris.model,
path=model_path,
sample_input=spark_model_iris.spark_df,
mlflow_model=mlflow_model,
)
assert mleap.FLAVOR_NAME in mlflow_model.flavors
config_path = os.path.join(model_path, "MLmodel")
assert os.path.exists(config_path)
config = Model.load(config_path)
assert mleap.FLAVOR_NAME in config.flavors
