Source code for optuna.integration.mlflow

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

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

try:
    import mlflow

    _available = True
except ImportError as e:
    _import_error = e
    _available = False
    mlflow = object


def _check_mlflow_availability():
    # type: () -> None

    if not _available:
        raise ImportError(
            "MLflow is not available. Please install MLflow to use this "
            "feature. It can be installed by executing `$ pip install "
            "mlflow`. For further information, please refer to the installation guide "
            "of MLflow. (The actual import error is as follows: " + str(_import_error) + ")"
        )


[docs]@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 _check_mlflow_availability() 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)