Source code for ray.tune.suggest.bayesopt

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import copy
import logging
import pickle
try:  # Python 3 only -- needed for lint test.
    import bayes_opt as byo
except ImportError:
    byo = None

from ray.tune.suggest.suggestion import SuggestionAlgorithm

logger = logging.getLogger(__name__)

[docs]class BayesOptSearch(SuggestionAlgorithm): """A wrapper around BayesOpt to provide trial suggestions. Requires BayesOpt to be installed. You can install BayesOpt with the command: `pip install bayesian-optimization`. Parameters: space (dict): Continuous search space. Parameters will be sampled from this space which will be used to run trials. max_concurrent (int): Number of maximum concurrent trials. Defaults to 10. metric (str): The training result objective value attribute. mode (str): One of {min, max}. Determines whether objective is minimizing or maximizing the metric attribute. utility_kwargs (dict): Parameters to define the utility function. Must provide values for the keys `kind`, `kappa`, and `xi`. random_state (int): Used to initialize BayesOpt. verbose (int): Sets verbosity level for BayesOpt packages. Example: >>> space = { >>> 'width': (0, 20), >>> 'height': (-100, 100), >>> } >>> algo = BayesOptSearch( >>> space, max_concurrent=4, metric="mean_loss", mode="min") """ def __init__(self, space, max_concurrent=10, reward_attr=None, metric="episode_reward_mean", mode="max", utility_kwargs=None, random_state=1, verbose=0, **kwargs): assert byo is not None, ( "BayesOpt must be installed!. You can install BayesOpt with" " the command: `pip install bayesian-optimization`.") assert type(max_concurrent) is int and max_concurrent > 0 assert utility_kwargs is not None, ( "Must define arguments for the utiliy function!") assert mode in ["min", "max"], "`mode` must be 'min' or 'max'!" if reward_attr is not None: mode = "max" metric = reward_attr logger.warning( "`reward_attr` is deprecated and will be removed in a future " "version of Tune. " "Setting `metric={}` and `mode=max`.".format(reward_attr)) self._max_concurrent = max_concurrent self._metric = metric if mode == "max": self._metric_op = 1. elif mode == "min": self._metric_op = -1. self._live_trial_mapping = {} self.optimizer = byo.BayesianOptimization( f=None, pbounds=space, verbose=verbose, random_state=random_state) self.utility = byo.UtilityFunction(**utility_kwargs) super(BayesOptSearch, self).__init__(**kwargs) def _suggest(self, trial_id): if self._num_live_trials() >= self._max_concurrent: return None new_trial = self.optimizer.suggest(self.utility) self._live_trial_mapping[trial_id] = new_trial return copy.deepcopy(new_trial) def on_trial_result(self, trial_id, result): pass def on_trial_complete(self, trial_id, result=None, error=False, early_terminated=False): """Passes the result to BayesOpt unless early terminated or errored""" if result: self.optimizer.register( params=self._live_trial_mapping[trial_id], target=self._metric_op * result[self._metric]) del self._live_trial_mapping[trial_id] def _num_live_trials(self): return len(self._live_trial_mapping) def save(self, checkpoint_dir): trials_object = self.optimizer with open(checkpoint_dir, "wb") as output: pickle.dump(trials_object, output) def restore(self, checkpoint_dir): with open(checkpoint_dir, "rb") as input: trials_object = pickle.load(input) self.optimizer = trials_object