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User Attributes

This feature is to annotate experiments with user-defined attributes.

Adding User Attributes to Studies

A ~optuna.study.Study object provides ~optuna.study.Study.set_user_attr method to register a pair of key and value as an user-defined attribute. A key is supposed to be a str, and a value be any object serializable with json.dumps.

import optuna
study = optuna.create_study(storage='sqlite:///example.db')
study.set_user_attr('contributors', ['Akiba', 'Sano'])
study.set_user_attr('dataset', 'MNIST')

We can access annotated attributes with ~optuna.study.Study.user_attr property.

study.user_attrs  # {'contributors': ['Akiba', 'Sano'], 'dataset': 'MNIST'}

~optuna.struct.StudySummary object, which can be retrieved by ~optuna.study.get_all_study_summaries, also contains user-defined attributes.

study_summaries = optuna.get_all_study_summaries('sqlite:///example.db')
study_summaries[0].user_attrs  # {'contributors': ['Akiba', 'Sano'], 'dataset': 'MNIST'}

optuna study set-user-attr command, which sets an attribute via command line interface.

Adding User Attributes to Trials

As with ~optuna.study.Study, a ~optuna.trial.Trial object provides ~optuna.trial.Trial.set_user_attr method. Attributes are set inside an objective function.

def objective(trial):
    iris = sklearn.datasets.load_iris()
    x, y = iris.data, iris.target

    svc_c = trial.suggest_loguniform('svc_c', 1e-10, 1e10)
    clf = sklearn.svm.SVC(C=svc_c)
    accuracy = sklearn.model_selection.cross_val_score(clf, x, y).mean()

    trial.set_user_attr('accuracy', accuracy)

    return 1.0 - accuracy  # return error for minimization

We can access annotated attributes as:

study.trials[0].user_attrs  # {'accuracy': 0.83}

Note that, in this example, the attribute is not annotated to a ~optuna.study.Study but a single ~optuna.trial.Trial.