User Attributes

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

Adding User Attributes to Studies

A Study object provides 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 sklearn.datasets
import sklearn.model_selection
import sklearn.svm

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 user_attr property.

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

Out:

{'contributors': ['Akiba', 'Sano'], 'dataset': 'MNIST'}

StudySummary object, which can be retrieved by 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'}

Out:

{'contributors': ['Akiba', 'Sano'], 'dataset': 'MNIST'}

See also

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

Adding User Attributes to Trials

As with Study, a Trial object provides 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


study.optimize(objective, n_trials=1)

We can access annotated attributes as:

study.trials[0].user_attrs

Out:

{'accuracy': 0.9266666666666667}

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

Total running time of the script: ( 0 minutes 1.441 seconds)

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