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Richard Liaw
115468de2c
* easyrepeat * done * suggest * doc * ok * commit * Apply suggestions from code review Co-Authored-By: Ujval Misra <misraujval@gmail.com> * Apply suggestions from code review Co-Authored-By: Ujval Misra <misraujval@gmail.com> * Apply suggestions from code review * ok * docs Co-authored-by: Ujval Misra <misraujval@gmail.com>
125 lines
4.6 KiB
Python
125 lines
4.6 KiB
Python
"""BOHB (Bayesian Optimization with HyperBand)"""
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import copy
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import logging
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from ray.tune.suggest import SuggestionAlgorithm
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logger = logging.getLogger(__name__)
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class _BOHBJobWrapper():
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"""Mock object for HpBandSter to process."""
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def __init__(self, loss, budget, config):
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self.result = {"loss": loss}
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self.kwargs = {"budget": budget, "config": config.copy()}
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self.exception = None
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class TuneBOHB(SuggestionAlgorithm):
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"""BOHB suggestion component.
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Requires HpBandSter and ConfigSpace to be installed. You can install
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HpBandSter and ConfigSpace with: `pip install hpbandster ConfigSpace`.
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This should be used in conjunction with HyperBandForBOHB.
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Args:
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space (ConfigurationSpace): Continuous ConfigSpace search space.
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Parameters will be sampled from this space which will be used
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to run trials.
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bohb_config (dict): configuration for HpBandSter BOHB algorithm
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max_concurrent (int): Number of maximum concurrent trials. Defaults
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to 10.
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metric (str): The training result objective value attribute.
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mode (str): One of {min, max}. Determines whether objective is
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minimizing or maximizing the metric attribute.
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Example:
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>>> import ConfigSpace as CS
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>>> config_space = CS.ConfigurationSpace()
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>>> config_space.add_hyperparameter(
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CS.UniformFloatHyperparameter('width', lower=0, upper=20))
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>>> config_space.add_hyperparameter(
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CS.UniformFloatHyperparameter('height', lower=-100, upper=100))
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>>> config_space.add_hyperparameter(
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CS.CategoricalHyperparameter(
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name='activation', choices=['relu', 'tanh']))
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>>> algo = TuneBOHB(
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config_space, max_concurrent=4, metric='mean_loss', mode='min')
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>>> bohb = HyperBandForBOHB(
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time_attr='training_iteration',
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metric='mean_loss',
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mode='min',
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max_t=100)
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>>> run(MyTrainableClass, scheduler=bohb, search_alg=algo)
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"""
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def __init__(self,
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space,
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bohb_config=None,
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max_concurrent=10,
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metric="neg_mean_loss",
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mode="max"):
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from hpbandster.optimizers.config_generators.bohb import BOHB
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assert BOHB is not None, "HpBandSter must be installed!"
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assert mode in ["min", "max"], "`mode` must be 'min' or 'max'!"
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self._max_concurrent = max_concurrent
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self.trial_to_params = {}
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self.running = set()
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self.paused = set()
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self._metric = metric
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if mode == "max":
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self._metric_op = -1.
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elif mode == "min":
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self._metric_op = 1.
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bohb_config = bohb_config or {}
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self.bohber = BOHB(space, **bohb_config)
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super(TuneBOHB, self).__init__(metric=self._metric, mode=mode)
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def suggest(self, trial_id):
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if len(self.running) < self._max_concurrent:
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# This parameter is not used in hpbandster implementation.
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config, info = self.bohber.get_config(None)
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self.trial_to_params[trial_id] = copy.deepcopy(config)
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self.running.add(trial_id)
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return config
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return None
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def on_trial_result(self, trial_id, result):
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if trial_id not in self.paused:
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self.running.add(trial_id)
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if "hyperband_info" not in result:
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logger.warning("BOHB Info not detected in result. Are you using "
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"HyperBandForBOHB as a scheduler?")
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elif "budget" in result.get("hyperband_info", {}):
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hbs_wrapper = self.to_wrapper(trial_id, result)
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self.bohber.new_result(hbs_wrapper)
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def on_trial_complete(self,
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trial_id,
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result=None,
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error=False,
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early_terminated=False):
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del self.trial_to_params[trial_id]
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if trial_id in self.paused:
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self.paused.remove(trial_id)
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if trial_id in self.running:
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self.running.remove(trial_id)
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def to_wrapper(self, trial_id, result):
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return _BOHBJobWrapper(self._metric_op * result[self.metric],
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result["hyperband_info"]["budget"],
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self.trial_to_params[trial_id])
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def on_pause(self, trial_id):
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self.paused.add(trial_id)
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self.running.remove(trial_id)
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def on_unpause(self, trial_id):
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self.paused.remove(trial_id)
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self.running.add(trial_id)
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