Source code for ray.tune.suggest.bohb

"""BOHB (Bayesian Optimization with HyperBand)"""

import copy
import logging

from ray.tune.suggest import Searcher

logger = logging.getLogger(__name__)

class _BOHBJobWrapper():
    """Mock object for HpBandSter to process."""

    def __init__(self, loss, budget, config):
        self.result = {"loss": loss}
        self.kwargs = {"budget": budget, "config": config.copy()}
        self.exception = None

[docs]class TuneBOHB(Searcher): """BOHB suggestion component. Requires HpBandSter and ConfigSpace to be installed. You can install HpBandSter and ConfigSpace with: ``pip install hpbandster ConfigSpace``. This should be used in conjunction with HyperBandForBOHB. Args: space (ConfigurationSpace): Continuous ConfigSpace search space. Parameters will be sampled from this space which will be used to run trials. bohb_config (dict): configuration for HpBandSter BOHB algorithm 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. Example: .. code-block:: python import ConfigSpace as CS config_space = CS.ConfigurationSpace() config_space.add_hyperparameter( CS.UniformFloatHyperparameter('width', lower=0, upper=20)) config_space.add_hyperparameter( CS.UniformFloatHyperparameter('height', lower=-100, upper=100)) config_space.add_hyperparameter( CS.CategoricalHyperparameter( name='activation', choices=['relu', 'tanh'])) algo = TuneBOHB( config_space, max_concurrent=4, metric='mean_loss', mode='min') bohb = HyperBandForBOHB( time_attr='training_iteration', metric='mean_loss', mode='min', max_t=100) run(MyTrainableClass, scheduler=bohb, search_alg=algo) """ def __init__(self, space, bohb_config=None, max_concurrent=10, metric="neg_mean_loss", mode="max"): from hpbandster.optimizers.config_generators.bohb import BOHB assert BOHB is not None, "HpBandSter must be installed!" assert mode in ["min", "max"], "`mode` must be 'min' or 'max'!" self._max_concurrent = max_concurrent self.trial_to_params = {} self.running = set() self.paused = set() self._metric = metric if mode == "max": self._metric_op = -1. elif mode == "min": self._metric_op = 1. bohb_config = bohb_config or {} self.bohber = BOHB(space, **bohb_config) super(TuneBOHB, self).__init__(metric=self._metric, mode=mode) def suggest(self, trial_id): if len(self.running) < self._max_concurrent: # This parameter is not used in hpbandster implementation. config, info = self.bohber.get_config(None) self.trial_to_params[trial_id] = copy.deepcopy(config) self.running.add(trial_id) return config return None def on_trial_result(self, trial_id, result): if trial_id not in self.paused: self.running.add(trial_id) if "hyperband_info" not in result: logger.warning("BOHB Info not detected in result. Are you using " "HyperBandForBOHB as a scheduler?") elif "budget" in result.get("hyperband_info", {}): hbs_wrapper = self.to_wrapper(trial_id, result) self.bohber.new_result(hbs_wrapper) def on_trial_complete(self, trial_id, result=None, error=False): del self.trial_to_params[trial_id] if trial_id in self.paused: self.paused.remove(trial_id) if trial_id in self.running: self.running.remove(trial_id) def to_wrapper(self, trial_id, result): return _BOHBJobWrapper(self._metric_op * result[self.metric], result["hyperband_info"]["budget"], self.trial_to_params[trial_id]) def on_pause(self, trial_id): self.paused.add(trial_id) self.running.remove(trial_id) def on_unpause(self, trial_id): self.paused.remove(trial_id) self.running.add(trial_id)