Source code for optuna.pruners._median
from optuna.pruners._percentile import PercentilePruner
[docs]class MedianPruner(PercentilePruner):
"""Pruner using the median stopping rule.
Prune if the trial's best intermediate result is worse than median of intermediate results of
previous trials at the same step.
Example:
We minimize an objective function with the median stopping rule.
.. 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_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.MedianPruner(
n_startup_trials=5, n_warmup_steps=30, interval_steps=10
),
)
study.optimize(objective, n_trials=20)
Args:
n_startup_trials:
Pruning is disabled until the given number of trials finish in the same study.
n_warmup_steps:
Pruning is disabled until the trial exceeds the given number of step. Note that
this feature assumes that ``step`` starts at zero.
interval_steps:
Interval in number of steps between the pruning checks, offset by the warmup steps.
If no value has been reported at the time of a pruning check, that particular check
will be postponed until a value is reported.
n_min_trials:
Minimum number of reported trial results at a step to judge whether to prune.
If the number of reported intermediate values from all trials at the current step
is less than ``n_min_trials``, the trial will not be pruned. This can be used to ensure
that a minimum number of trials are run to completion without being pruned.
"""
def __init__(
self,
n_startup_trials: int = 5,
n_warmup_steps: int = 0,
interval_steps: int = 1,
*,
n_min_trials: int = 1,
) -> None:
super().__init__(
50.0, n_startup_trials, n_warmup_steps, interval_steps, n_min_trials=n_min_trials
)