Pseudo-code of Pruning

pruning.py

import ...

def objective(trial):
    ...
    alpha = trial.suggest_loguniform('alpha', 1e-5, 1e-1)
    clf = sklearn.linear_model.SGDClassifier(alpha=alpha)

    for step in range(100):
        clf.partial_fit(train_x, train_y, classes=classes)

        # Report intermediate objective value.
        intermediate_value = 1.0 - clf.score(test_x, test_y)
        trial.report(intermediate_value, step)

        # Handle pruning based on the intermediate value.
        if trial.should_prune():
            raise optuna.structs.TrialPruned()

    return clf.score(test_x, test_y)

  study = optuna.create_study(direction='maximize')
  study.optimize(objective, n_trials=100)