plot_terminator_improvement

optuna.visualization.matplotlib.plot_terminator_improvement(study, plot_error=False, improvement_evaluator=None, error_evaluator=None, min_n_trials=20)

将来の目的関数改善の可能性をプロットします。

この関数は improvement_evaluator で評価した目的関数改善の可能性を可視化します。 最適化を継続すべきかどうかを判断するのに役立ちます。 plot_error 引数を True に設定すると、 error_evaluator で評価した誤差も折れ線グラフとしてプロットできます。 なお、改善可能性の計算には時間がかかる場合があります。

See also

optuna.visualization.plot_terminator_improvement() を参照してください。

Parameters:

• study (Study) – 改善状況をプロットする Study オブジェクト

• plot_error (bool) – 誤差を表示するフラグ。True の場合、error_evaluator で評価した誤差も折れ線グラフとしてプロットされます。デフォルトは False です。

• improvement_evaluator (BaseImprovementEvaluator | None) – 目的関数の改善度を評価するオブジェクト。デフォルトは RegretBoundEvaluator です。

• error_evaluator (BaseErrorEvaluator | None) – 目的関数の誤差を評価するオブジェクト。デフォルトは CrossValidationErrorEvaluator です。

• min_n_trials (int) – 終了判定の基準となる最小試行回数。この値未満の試行に対する改善度は薄い色で表示されます。デフォルトは 20 です。

Returns:

matplotlib.axes.Axes オブジェクト

Return type:

Axes

Note

v3.2.0 で実験的機能として追加されました。 今後のバージョンで予告なくインターフェースが変更される可能性があります。 詳細は https://github.com/optuna/optuna/releases/tag/v3.2.0 を参照してください。

The following code snippet shows how to plot improvement potentials, together with cross-validation errors.

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plot_terminator_improvement is experimental (supported from v3.2.0). The interface can change in the future.

/mnt/nfs-mnj-hot-99-home/mshibata/sandbox/optuna-documentation-plamo-ja/optuna-doc-plamo-translation/tmp-optuna/optuna/visualization/_terminator_improvement.py:93: ExperimentalWarning:

RegretBoundEvaluator is experimental (supported from v3.2.0). The interface can change in the future.

/mnt/nfs-mnj-hot-99-home/mshibata/sandbox/optuna-documentation-plamo-ja/optuna-doc-plamo-translation/tmp-optuna/optuna/visualization/_terminator_improvement.py:98: ExperimentalWarning:

CrossValidationErrorEvaluator is experimental (supported from v3.2.0). The interface can change in the future.


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<Axes: title={'center': 'Terminator Improvement Plot'}, xlabel='Trial', ylabel='Terminator Improvement'>

from lightgbm import LGBMClassifier
from sklearn.datasets import load_wine
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
import optuna
from optuna.terminator import report_cross_validation_scores
from optuna.visualization.matplotlib import plot_terminator_improvement


def objective(trial):
    X, y = load_wine(return_X_y=True)
    clf = LGBMClassifier(
        reg_alpha=trial.suggest_float("reg_alpha", 1e-8, 10.0, log=True),
        reg_lambda=trial.suggest_float("reg_lambda", 1e-8, 10.0, log=True),
        num_leaves=trial.suggest_int("num_leaves", 2, 256),
        colsample_bytree=trial.suggest_float("colsample_bytree", 0.4, 1.0),
        subsample=trial.suggest_float("subsample", 0.4, 1.0),
        subsample_freq=trial.suggest_int("subsample_freq", 1, 7),
        min_child_samples=trial.suggest_int("min_child_samples", 5, 100),
    )
    scores = cross_val_score(clf, X, y, cv=KFold(n_splits=5, shuffle=True))
    report_cross_validation_scores(trial, scores)
    return scores.mean()


study = optuna.create_study()
study.optimize(objective, n_trials=30)

plot_terminator_improvement(study, plot_error=True)