"""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)