def mlobject_from_dict(schema_type, schema_version, dict_value):
ml_object = MLObject()
ml_object.set_type(
schema_version=schema_version,
schema_type=schema_type,
)
dict_value['schema_type'] = schema_type
dict_value['schema_version'] = schema_version
MLObject.update_tree(ml_object, dict_value)
errors = ml_object.validate()
if errors:
return None, errors
else:
return ml_object, None
def execute(self, result_object_schema_type, result_object_schema_version):
# Create Result object
results_object = MLObject()
results_object.set_type(
schema_type=result_object_schema_type,
schema_version=result_object_schema_version,
)
# Mocked up results
return_dict = {
"data_output_path": str(Path("tests/data/data_output.csv")),
"data_statistics_path": str(Path("tests/data/data_stats.csv")),
"data_schemas_path": str(Path("tests/data/data_schemas.yaml")),
"feature_file_path": str(Path("tests/data/feature_file.yaml")),
}
results_object.data_output_path = return_dict["data_output_path"]
results_object.data_statistics_path = return_dict[
"data_statistics_path"]
results_object.data_schemas_path = return_dict["data_schemas_path"]
results_object.feature_file_path = return_dict["feature_file_path"]
_ = results_object.validate() # noqa
return results_object
def execute(self, result_object_schema_type, result_object_schema_version):
# Create Result object
results_object = MLObject()
results_object.set_type(
schema_type=result_object_schema_type,
schema_version=result_object_schema_version,
)
# Mocked up results
return_dict = {
"training_execution_id": str(uuid.uuid4()),
"accuracy": float(random.randrange(0, 100) / 100),
"global_step": 10**random.randrange(2, 4),
"loss": float(random.randrange(1000, 9999) / 1000),
}
results_object.training_execution_id = return_dict[
"training_execution_id"]
results_object.accuracy = return_dict["accuracy"]
results_object.global_step = return_dict["global_step"]
results_object.loss = return_dict["loss"]
errors = results_object.validate() # noqa
return results_object