from optuna.pruners import BasePruner
from optuna import type_checking
if type_checking.TYPE_CHECKING:
from optuna.study import Study # NOQA
from optuna.trial import FrozenTrial # NOQA
[docs]class NopPruner(BasePruner):
"""Pruner which never prunes trials.
Example:
.. testcode::
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_uniform('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():
assert False, "should_prune() should always return False with this pruner."
raise optuna.TrialPruned()
return clf.score(X_valid, y_valid)
study = optuna.create_study(direction='maximize',
pruner=optuna.pruners.NopPruner())
study.optimize(objective, n_trials=20)
"""
[docs] def prune(self, study, trial):
# type: (Study, FrozenTrial) -> bool
return False