optuna.integration.mlflow 源代码

import optuna
from optuna._experimental import experimental
from optuna._imports import try_import
from optuna import structs
from optuna import type_checking

if type_checking.TYPE_CHECKING:
    from typing import Dict  # NOQA
    from typing import Optional  # NOQA


with try_import() as _imports:
    import mlflow


@experimental("1.4.0")
class MLflowCallback(object):
    """Callback to track Optuna trials with MLflow.

    This callback adds relevant information that is
    tracked by Optuna to MLflow. The MLflow experiment
    will be named after the Optuna study name.

    Example:

        Add MLflow callback to Optuna optimization.

        .. testsetup::

            import pathlib
            import tempfile
            tempdir = tempfile.mkdtemp()
            YOUR_TRACKING_URI = pathlib.Path(tempdir).as_uri()

        .. testcode::

            import optuna
            from optuna.integration.mlflow import MLflowCallback

            def objective(trial):
                x = trial.suggest_uniform('x', -10, 10)
                return (x - 2) ** 2

            mlflc = MLflowCallback(
                tracking_uri=YOUR_TRACKING_URI,
                metric_name='my metric score',
            )

            study = optuna.create_study(study_name='my_study')
            study.optimize(objective, n_trials=10, callbacks=[mlflc])

        .. testcleanup::

            import shutil
            shutil.rmtree(tempdir)

        .. testoutput::
            :hide:
            :options: +NORMALIZE_WHITESPACE

            INFO: 'my_study' does not exist. Creating a new experiment

    Args:
        tracking_uri:
            The URI of the MLflow tracking server.

            Please refer to `mlflow.set_tracking_uri
            <https://www.mlflow.org/docs/latest/python_api/mlflow.html#mlflow.set_tracking_uri>`_
            for more details.
        metric_name:
            Name of the metric. Since the metric itself is just a number,
            `metric_name` can be used to give it a name. So you know later
            if it was roc-auc or accuracy.
    """

    def __init__(self, tracking_uri=None, metric_name="value"):
        # type: (Optional[str], str) -> None

        _imports.check()

        self._tracking_uri = tracking_uri
        self._metric_name = metric_name

    def __call__(self, study, trial):
        # type: (optuna.study.Study, optuna.trial.FrozenTrial) -> None

        # This sets the tracking_uri for MLflow.
        if self._tracking_uri is not None:
            mlflow.set_tracking_uri(self._tracking_uri)

        # This sets the experiment of MLflow.
        mlflow.set_experiment(study.study_name)

        with mlflow.start_run(run_name=str(trial.number)):

            # This sets the metric for MLflow.
            trial_value = trial.value if trial.value is not None else float("nan")
            mlflow.log_metric(self._metric_name, trial_value)

            # This sets the params for MLflow.
            mlflow.log_params(trial.params)

            # This sets the tags for MLflow.
            tags = {}  # type: Dict[str, str]
            tags["number"] = str(trial.number)
            tags["datetime_start"] = str(trial.datetime_start)
            tags["datetime_complete"] = str(trial.datetime_complete)

            # Set state and convert it to str and remove the common prefix.
            trial_state = trial.state
            if isinstance(trial_state, structs.TrialState):
                tags["state"] = str(trial_state).split(".")[-1]

            # Set direction and convert it to str and remove the common prefix.
            study_direction = study.direction
            if isinstance(study_direction, structs.StudyDirection):
                tags["direction"] = str(study_direction).split(".")[-1]

            tags.update(trial.user_attrs)
            distributions = {
                (k + "_distribution"): str(v) for (k, v) in trial.distributions.items()
            }
            tags.update(distributions)
            mlflow.set_tags(tags)