Source code for optuna.pruners._patient

from typing import Optional

import numpy as np

import optuna
from optuna._experimental import experimental
from optuna.pruners import BasePruner
from import StudyDirection

[docs]@experimental("2.8.0") class PatientPruner(BasePruner): """Pruner which wraps another pruner with tolerance. 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_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), 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) Args: wrapped_pruner: Wrapped pruner to perform pruning when :class:`~optuna.pruners.PatientPruner` allows a trial to be pruned. If it is :obj:`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. """ def __init__( self, wrapped_pruner: Optional[BasePruner], patience: int, min_delta: float = 0.0 ) -> None: if patience < 0: raise ValueError(f"patience cannot be negative but got {patience}.") if min_delta < 0: raise ValueError(f"min_delta cannot be negative but got {min_delta}.") self._wrapped_pruner = wrapped_pruner self._patience = patience self._min_delta = min_delta
[docs] def prune(self, study: "", trial: "optuna.trial.FrozenTrial") -> bool: step = trial.last_step if step is None: return False intermediate_values = trial.intermediate_values steps = np.asarray(list(intermediate_values.keys())) # Do not prune if number of step to determine are insufficient. if steps.size <= self._patience + 1: return False steps.sort() # This is the score patience steps ago steps_before_patience = steps[: -self._patience - 1] scores_before_patience = np.asarray( list(intermediate_values[step] for step in steps_before_patience) ) # And these are the scores after that steps_after_patience = steps[-self._patience - 1 :] scores_after_patience = np.asarray( list(intermediate_values[step] for step in steps_after_patience) ) direction = study.direction if direction == StudyDirection.MINIMIZE: maybe_prune = np.nanmin(scores_before_patience) + self._min_delta < np.nanmin( scores_after_patience ) else: maybe_prune = np.nanmax(scores_before_patience) - self._min_delta > np.nanmax( scores_after_patience ) if maybe_prune: if self._wrapped_pruner is not None: return self._wrapped_pruner.prune(study, trial) else: return True else: return False