Source code for ray.tune.suggest.bayesopt

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

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

from ray.tune.suggest.suggestion import SuggestionAlgorithm


[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. reward_attr (str): The training result objective value attribute. This refers to an increasing value. 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, reward_attr="neg_mean_loss") """ def __init__(self, space, max_concurrent=10, reward_attr="episode_reward_mean", 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!") self._max_concurrent = max_concurrent self._reward_attr = reward_attr 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=result[self._reward_attr]) del self._live_trial_mapping[trial_id] def _num_live_trials(self): return len(self._live_trial_mapping)