def log_params_flatten(params: dict,
parent_key: str = "",
sep: str = ".") -> None:
"""
Log a batch of params after flattening.
Parameters
----------
params : dict
Dictionary of parameters to log.
parent_key : str, default ""
Parent key.
sep : str, default "."
Key separator.
Returns
-------
None
None
Examples
--------
>>> with mlflow.start_run() as run:
... params = {"a": {"b": 0}}
... mlflow.log_params_flatten(params)
... mlflow.log_params_flatten(params, parent_key="d")
... mlflow.log_params_flatten(params, sep="_")
>>> r = mlflow.get_run(run.info.run_id)
>>> sorted(r.data.params.items())
[('a.b', '0'), ('a_b', '0'), ('d.a.b', '0')]
"""
mlflow.log_params(flatten_dict(params, parent_key, sep))
def log_metrics_flatten(
metrics: dict,
step: Optional[int] = None,
parent_key: str = "",
sep: str = ".",
) -> None:
"""
Log a batch of metrics after flattening.
Parameters
----------
metrics : dict
Dictionary of metrics to log.
step : int, default None
Metric step. Defaults to zero if unspecified.
parent_key : str, default ""
Parent key.
sep : str, default "."
Key separator.
Examples
--------
>>> with mlflow.start_run() as run:
... metrics = {"a": {"b": 0.0}}
... mlflow.log_metrics_flatten(metrics)
... mlflow.log_metrics_flatten(metrics, parent_key="d")
... mlflow.log_metrics_flatten(metrics, sep="_")
>>> r = mlflow.get_run(run.info.run_id)
>>> sorted(r.data.metrics.items())
[('a.b', 0.0), ('a_b', 0.0), ('d.a.b', 0.0)]
"""
mlflow.log_metrics(flatten_dict(metrics, parent_key, sep), step)
def test_flatten_dict():
dct = {"a": {"b": "c"}}
assert utils.flatten_dict(dct) == {"a.b": "c"}
assert utils.flatten_dict(dct, parent_key="d") == {"d.a.b": "c"}
assert utils.flatten_dict(dct, sep="_") == {"a_b": "c"}