def test_log_metric_none(tmpdir: py.path.local) -> None:
tracking_file_name = "file:{}".format(tmpdir)
metric_name = "my_metric_name"
study_name = "my_study"
metric_value = None
mlflc = MLflowCallback(tracking_uri=tracking_file_name,
metric_name=metric_name)
study = optuna.create_study(study_name=study_name)
mlflc._initialize_experiment(study)
with mlflow.start_run():
mlflc._log_metrics(metric_value)
mlfl_client = MlflowClient(tracking_file_name)
experiments = mlfl_client.list_experiments()
experiment = experiments[0]
experiment_id = experiment.experiment_id
run_infos = mlfl_client.list_run_infos(experiment_id)
assert len(run_infos) == 1
first_run_id = run_infos[0].run_id
first_run = mlfl_client.get_run(first_run_id)
first_run_dict = first_run.to_dictionary()
# when `values` is `None`, do not save values with metric names
assert metric_name not in first_run_dict["data"]["metrics"]
def test_log_metric(tmpdir: py.path.local, names: List[str],
values: List[float]) -> None:
tracking_file_name = "file:{}".format(tmpdir)
study_name = "my_study"
mlflc = MLflowCallback(tracking_uri=tracking_file_name, metric_name=names)
study = optuna.create_study(study_name=study_name)
mlflc._initialize_experiment(study)
with mlflow.start_run():
mlflc._log_metrics(values)
mlfl_client = MlflowClient(tracking_file_name)
experiments = mlfl_client.list_experiments()
experiment = experiments[0]
experiment_id = experiment.experiment_id
run_infos = mlfl_client.list_run_infos(experiment_id)
assert len(run_infos) == 1
first_run_id = run_infos[0].run_id
first_run = mlfl_client.get_run(first_run_id)
first_run_dict = first_run.to_dictionary()
assert all(name in first_run_dict["data"]["metrics"] for name in names)
assert all([
first_run_dict["data"]["metrics"][name] == val
for name, val in zip(names, values)
])