optuna.pruners.PatientPruner

class optuna.pruners.PatientPruner(wrapped_pruner, patience, min_delta=0.0)[source]

Pruner which wraps another pruner with tolerance.

Example

import numpy as np
from sklearn.datasets import load_iris
from sklearn.linear_model import SGDClassifier
from sklearn.model_selection import train_test_split

import optuna

X, y = load_iris(return_X_y=True)
X_train, X_valid, y_train, y_valid = train_test_split(X, y)
classes = np.unique(y)


def objective(trial):
    alpha = trial.suggest_float("alpha", 0.0, 1.0)
    clf = SGDClassifier(alpha=alpha)
    n_train_iter = 100

    for step in range(n_train_iter):
        clf.partial_fit(X_train, y_train, classes=classes)

        intermediate_value = clf.score(X_valid, y_valid)
        trial.report(intermediate_value, step)

        if trial.should_prune():
            raise optuna.TrialPruned()

    return clf.score(X_valid, y_valid)


study = optuna.create_study(
    direction="maximize",
    pruner=optuna.pruners.PatientPruner(optuna.pruners.MedianPruner(), patience=1),
)
study.optimize(objective, n_trials=20)
Parameters
  • wrapped_pruner – Wrapped pruner to perform pruning when PatientPruner allows a trial to be pruned. If it is None, this pruner is equivalent to early-stopping taken the intermediate values in the individual trial.

  • patience – Pruning is disabled until the objective doesn’t improve for patience consecutive steps.

  • min_delta – Tolerance value to check whether or not the objective improves. This value should be non-negative.

Note

Added in v2.8.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v2.8.0.

Methods

prune(study, trial)

Judge whether the trial should be pruned based on the reported values.

prune(study, trial)[source]

Judge whether the trial should be pruned based on the reported values.

Note that this method is not supposed to be called by library users. Instead, optuna.trial.Trial.report() and optuna.trial.Trial.should_prune() provide user interfaces to implement pruning mechanism in an objective function.

Parameters
  • study (optuna.study.Study) – Study object of the target study.

  • trial (optuna.trial._frozen.FrozenTrial) – FrozenTrial object of the target trial. Take a copy before modifying this object.

Returns

A boolean value representing whether the trial should be pruned.

Return type

bool