Source code for optuna.pruners._successive_halving

from __future__ import annotations

import math

import optuna
from optuna.pruners._base import BasePruner
from import StudyDirection
from optuna.trial._state import TrialState

[docs] class SuccessiveHalvingPruner(BasePruner): """Pruner using Asynchronous Successive Halving Algorithm. `Successive Halving <>`_ is a bandit-based algorithm to identify the best one among multiple configurations. This class implements an asynchronous version of Successive Halving. Please refer to the paper of `Asynchronous Successive Halving < a06f20b349c6cf09a6b171c71b88bbfc-Paper.pdf>`_ for detailed descriptions. Note that, this class does not take care of the parameter for the maximum resource, referred to as :math:`R` in the paper. The maximum resource allocated to a trial is typically limited inside the objective function (e.g., ``step`` number in ` <>`_, ``EPOCH`` number in ` <>`_). .. seealso:: Please refer to :meth:``. Example: We minimize an objective function with ``SuccessiveHalvingPruner``. .. 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.SuccessiveHalvingPruner() ) study.optimize(objective, n_trials=20) Args: min_resource: A parameter for specifying the minimum resource allocated to a trial (in the `paper < a06f20b349c6cf09a6b171c71b88bbfc-Paper.pdf>`_ this parameter is referred to as :math:`r`). This parameter defaults to 'auto' where the value is determined based on a heuristic that looks at the number of required steps for the first trial to complete. A trial is never pruned until it executes :math:`\\mathsf{min}\\_\\mathsf{resource} \\times \\mathsf{reduction}\\_\\mathsf{factor}^{ \\mathsf{min}\\_\\mathsf{early}\\_\\mathsf{stopping}\\_\\mathsf{rate}}` steps (i.e., the completion point of the first rung). When the trial completes the first rung, it will be promoted to the next rung only if the value of the trial is placed in the top :math:`{1 \\over \\mathsf{reduction}\\_\\mathsf{factor}}` fraction of the all trials that already have reached the point (otherwise it will be pruned there). If the trial won the competition, it runs until the next completion point (i.e., :math:`\\mathsf{min}\\_\\mathsf{resource} \\times \\mathsf{reduction}\\_\\mathsf{factor}^{ (\\mathsf{min}\\_\\mathsf{early}\\_\\mathsf{stopping}\\_\\mathsf{rate} + \\mathsf{rung})}` steps) and repeats the same procedure. .. note:: If the step of the last intermediate value may change with each trial, please manually specify the minimum possible step to ``min_resource``. reduction_factor: A parameter for specifying reduction factor of promotable trials (in the `paper < a06f20b349c6cf09a6b171c71b88bbfc-Paper.pdf>`_ this parameter is referred to as :math:`\\eta`). At the completion point of each rung, about :math:`{1 \\over \\mathsf{reduction}\\_\\mathsf{factor}}` trials will be promoted. min_early_stopping_rate: A parameter for specifying the minimum early-stopping rate (in the `paper < a06f20b349c6cf09a6b171c71b88bbfc-Paper.pdf>`_ this parameter is referred to as :math:`s`). bootstrap_count: Minimum number of trials that need to complete a rung before any trial is considered for promotion into the next rung. """ def __init__( self, min_resource: str | int = "auto", reduction_factor: int = 4, min_early_stopping_rate: int = 0, bootstrap_count: int = 0, ) -> None: if isinstance(min_resource, str) and min_resource != "auto": raise ValueError( "The value of `min_resource` is {}, " "but must be either `min_resource` >= 1 or 'auto'".format(min_resource) ) if isinstance(min_resource, int) and min_resource < 1: raise ValueError( "The value of `min_resource` is {}, " "but must be either `min_resource >= 1` or 'auto'".format(min_resource) ) if reduction_factor < 2: raise ValueError( "The value of `reduction_factor` is {}, " "but must be `reduction_factor >= 2`".format(reduction_factor) ) if min_early_stopping_rate < 0: raise ValueError( "The value of `min_early_stopping_rate` is {}, " "but must be `min_early_stopping_rate >= 0`".format(min_early_stopping_rate) ) if bootstrap_count < 0: raise ValueError( "The value of `bootstrap_count` is {}, " "but must be `bootstrap_count >= 0`".format(bootstrap_count) ) if bootstrap_count > 0 and min_resource == "auto": raise ValueError( "bootstrap_count > 0 and min_resource == 'auto' " "are mutually incompatible, bootstrap_count is {}".format(bootstrap_count) ) self._min_resource: int | None = None if isinstance(min_resource, int): self._min_resource = min_resource self._reduction_factor = reduction_factor self._min_early_stopping_rate = min_early_stopping_rate self._bootstrap_count = bootstrap_count
[docs] def prune(self, study: "", trial: "optuna.trial.FrozenTrial") -> bool: step = trial.last_step if step is None: return False rung = _get_current_rung(trial) value = trial.intermediate_values[step] trials: list["optuna.trial.FrozenTrial"] | None = None while True: if self._min_resource is None: if trials is None: trials = study.get_trials(deepcopy=False) self._min_resource = _estimate_min_resource(trials) if self._min_resource is None: return False assert self._min_resource is not None rung_promotion_step = self._min_resource * ( self._reduction_factor ** (self._min_early_stopping_rate + rung) ) if step < rung_promotion_step: return False if math.isnan(value): return True if trials is None: trials = study.get_trials(deepcopy=False) rung_key = _completed_rung_key(rung) study._storage.set_trial_system_attr(trial._trial_id, rung_key, value) competing = _get_competing_values(trials, value, rung_key) # 'competing' already includes the current trial # Therefore, we need to use the '<=' operator here if len(competing) <= self._bootstrap_count: return True if not _is_trial_promotable_to_next_rung( value, competing, self._reduction_factor, study.direction, ): return True rung += 1
def _estimate_min_resource(trials: list["optuna.trial.FrozenTrial"]) -> int | None: n_steps = [ t.last_step for t in trials if t.state == TrialState.COMPLETE and t.last_step is not None ] if not n_steps: return None # Get the maximum number of steps and divide it by 100. last_step = max(n_steps) return max(last_step // 100, 1) def _get_current_rung(trial: "optuna.trial.FrozenTrial") -> int: # The following loop takes `O(log step)` iterations. rung = 0 while _completed_rung_key(rung) in trial.system_attrs: rung += 1 return rung def _completed_rung_key(rung: int) -> str: return "completed_rung_{}".format(rung) def _get_competing_values( trials: list["optuna.trial.FrozenTrial"], value: float, rung_key: str ) -> list[float]: competing_values = [t.system_attrs[rung_key] for t in trials if rung_key in t.system_attrs] competing_values.append(value) return competing_values def _is_trial_promotable_to_next_rung( value: float, competing_values: list[float], reduction_factor: int, study_direction: StudyDirection, ) -> bool: promotable_idx = (len(competing_values) // reduction_factor) - 1 if promotable_idx == -1: # Optuna does not support suspending or resuming ongoing trials. Therefore, for the first # `eta - 1` trials, this implementation instead promotes the trial if its value is the # smallest one among the competing values. promotable_idx = 0 competing_values.sort() if study_direction == StudyDirection.MAXIMIZE: return value >= competing_values[-(promotable_idx + 1)] return value <= competing_values[promotable_idx]