def test_imputation_conditional_spaces(self):
bohb = BOHB(self.configspace, random_fraction=0)
raw_array = []
for i in range(128):
config = self.configspace.sample_configuration()
raw_array.append(config.get_array())
imputed_array = bohb.impute_conditional_data(np.array(raw_array))
self.assertFalse(np.any(np.isnan(imputed_array)))
job = Job(i, budget=1, config=config)
job.result = {'loss': np.random.rand(), 'info': {}}
bohb.new_result(job)
for j in range(64):
conf, info = bohb.get_config(1)
self.assertTrue(info['model_based_pick'])
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): Deprecated. Use
``tune.suggest.ConcurrencyLimiter()``.
metric (str): The training result objective value attribute. If None
but a mode was passed, the anonymous metric `_metric` will be used
per default.
mode (str): One of {min, max}. Determines whether objective is
minimizing or maximizing the metric attribute.
points_to_evaluate (list): Initial parameter suggestions to be run
first. This is for when you already have some good parameters
you want to run first to help the algorithm make better suggestions
for future parameters. Needs to be a list of dicts containing the
configurations.
seed (int): Optional random seed to initialize the random number
generator. Setting this should lead to identical initial
configurations at each run.
Tune automatically converts search spaces to TuneBOHB's format:
.. code-block:: python
config = {
"width": tune.uniform(0, 20),
"height": tune.uniform(-100, 100),
"activation": tune.choice(["relu", "tanh"])
}
algo = TuneBOHB(metric="mean_loss", mode="min")
bohb = HyperBandForBOHB(
time_attr="training_iteration",
metric="mean_loss",
mode="min",
max_t=100)
run(my_trainable, config=config, scheduler=bohb, search_alg=algo)
If you would like to pass the search space manually, the code would
look like this:
.. 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, metric="mean_loss", mode="min")
bohb = HyperBandForBOHB(
time_attr="training_iteration",
metric="mean_loss",
mode="min",
max_t=100)
run(my_trainable, scheduler=bohb, search_alg=algo)
"""
def __init__(
self,
space: Optional[Union[Dict,
"ConfigSpace.ConfigurationSpace"]] = None,
bohb_config: Optional[Dict] = None,
max_concurrent: Optional[int] = None,
metric: Optional[str] = None,
mode: Optional[str] = None,
points_to_evaluate: Optional[List[Dict]] = None,
seed: Optional[int] = None):
assert BOHB is not None, """HpBandSter must be installed!
You can install HpBandSter with the command:
`pip install hpbandster ConfigSpace`."""
if mode:
assert mode in ["min", "max"], "`mode` must be 'min' or 'max'."
self._max_concurrent = max_concurrent
self.trial_to_params = {}
self._metric = metric
self._bohb_config = bohb_config
if isinstance(space, dict) and space:
resolved_vars, domain_vars, grid_vars = parse_spec_vars(space)
if domain_vars or grid_vars:
logger.warning(
UNRESOLVED_SEARCH_SPACE.format(par="space",
cls=type(self)))
space = self.convert_search_space(space)
self._space = space
self._seed = seed
self._points_to_evaluate = points_to_evaluate
super(TuneBOHB, self).__init__(metric=self._metric,
mode=mode,
max_concurrent=max_concurrent)
if self._space:
self._setup_bohb()
def _setup_bohb(self):
from hpbandster.optimizers.config_generators.bohb import BOHB
if self._metric is None and self._mode:
# If only a mode was passed, use anonymous metric
self._metric = DEFAULT_METRIC
if self._mode == "max":
self._metric_op = -1.
elif self._mode == "min":
self._metric_op = 1.
if self._seed is not None:
self._space.seed(self._seed)
bohb_config = self._bohb_config or {}
self.bohber = BOHB(self._space, **bohb_config)
def set_search_properties(self, metric: Optional[str], mode: Optional[str],
config: Dict, **spec) -> bool:
if self._space:
return False
space = self.convert_search_space(config)
self._space = space
if metric:
self._metric = metric
if mode:
self._mode = mode
self._setup_bohb()
return True
def suggest(self, trial_id: str) -> Optional[Dict]:
if not self._space:
raise RuntimeError(
UNDEFINED_SEARCH_SPACE.format(cls=self.__class__.__name__,
space="space"))
if not self._metric or not self._mode:
raise RuntimeError(
UNDEFINED_METRIC_MODE.format(cls=self.__class__.__name__,
metric=self._metric,
mode=self._mode))
if self._points_to_evaluate:
config = self._points_to_evaluate.pop(0)
else:
# This parameter is not used in hpbandster implementation.
config, _ = self.bohber.get_config(None)
self.trial_to_params[trial_id] = copy.deepcopy(config)
return unflatten_list_dict(config)
def on_trial_result(self, trial_id: str, result: Dict):
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: str,
result: Optional[Dict] = None,
error: bool = False):
del self.trial_to_params[trial_id]
def to_wrapper(self, trial_id: str, result: Dict) -> _BOHBJobWrapper:
return _BOHBJobWrapper(self._metric_op * result[self.metric],
result["hyperband_info"]["budget"],
self.trial_to_params[trial_id])
@staticmethod
def convert_search_space(spec: Dict) -> "ConfigSpace.ConfigurationSpace":
resolved_vars, domain_vars, grid_vars = parse_spec_vars(spec)
if grid_vars:
raise ValueError(
"Grid search parameters cannot be automatically converted "
"to a TuneBOHB search space.")
# Flatten and resolve again after checking for grid search.
spec = flatten_dict(spec, prevent_delimiter=True)
resolved_vars, domain_vars, grid_vars = parse_spec_vars(spec)
def resolve_value(
par: str,
domain: Domain) -> ConfigSpace.hyperparameters.Hyperparameter:
quantize = None
sampler = domain.get_sampler()
if isinstance(sampler, Quantized):
quantize = sampler.q
sampler = sampler.sampler
if isinstance(domain, Float):
if isinstance(sampler, LogUniform):
lower = domain.lower
upper = domain.upper
if quantize:
lower = math.ceil(domain.lower / quantize) * quantize
upper = math.floor(domain.upper / quantize) * quantize
return ConfigSpace.UniformFloatHyperparameter(par,
lower=lower,
upper=upper,
q=quantize,
log=True)
elif isinstance(sampler, Uniform):
lower = domain.lower
upper = domain.upper
if quantize:
lower = math.ceil(domain.lower / quantize) * quantize
upper = math.floor(domain.upper / quantize) * quantize
return ConfigSpace.UniformFloatHyperparameter(par,
lower=lower,
upper=upper,
q=quantize,
log=False)
elif isinstance(sampler, Normal):
return ConfigSpace.hyperparameters.\
NormalFloatHyperparameter(
par,
mu=sampler.mean,
sigma=sampler.sd,
q=quantize,
log=False)
elif isinstance(domain, Integer):
if isinstance(sampler, LogUniform):
lower = domain.lower
upper = domain.upper
if quantize:
lower = math.ceil(domain.lower / quantize) * quantize
upper = math.floor(domain.upper / quantize) * quantize
else:
# Tune search space integers are exclusive
upper -= 1
return ConfigSpace.UniformIntegerHyperparameter(
par, lower=lower, upper=upper, q=quantize, log=True)
elif isinstance(sampler, Uniform):
lower = domain.lower
upper = domain.upper
if quantize:
lower = math.ceil(domain.lower / quantize) * quantize
upper = math.floor(domain.upper / quantize) * quantize
else:
# Tune search space integers are exclusive
upper -= 1
return ConfigSpace.UniformIntegerHyperparameter(
par, lower=lower, upper=upper, q=quantize, log=False)
elif isinstance(domain, Categorical):
if isinstance(sampler, Uniform):
return ConfigSpace.CategoricalHyperparameter(
par, choices=domain.categories)
raise ValueError("TuneBOHB does not support parameters of type "
"`{}` with samplers of type `{}`".format(
type(domain).__name__,
type(domain.sampler).__name__))
cs = ConfigSpace.ConfigurationSpace()
for path, domain in domain_vars:
par = "/".join(str(p) for p in path)
value = resolve_value(par, domain)
cs.add_hyperparameter(value)
return cs
def save(self, checkpoint_path: str):
save_object = self.__dict__
with open(checkpoint_path, "wb") as outputFile:
cloudpickle.dump(save_object, outputFile)
def restore(self, checkpoint_path: str):
with open(checkpoint_path, "rb") as inputFile:
save_object = cloudpickle.load(inputFile)
self.__dict__.update(save_object)
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.
Tune automatically converts search spaces to TuneBOHB's format:
.. code-block:: python
config = {
"width": tune.uniform(0, 20),
"height": tune.uniform(-100, 100),
"activation": tune.choice(["relu", "tanh"])
}
algo = TuneBOHB(max_concurrent=4, metric="mean_loss", mode="min")
bohb = HyperBandForBOHB(
time_attr="training_iteration",
metric="mean_loss",
mode="min",
max_t=100)
run(my_trainable, config=config, scheduler=bohb, search_alg=algo)
If you would like to pass the search space manually, the code would
look like this:
.. 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(my_trainable, scheduler=bohb, search_alg=algo)
"""
def __init__(self,
space=None,
bohb_config=None,
max_concurrent=10,
metric=None,
mode=None):
from hpbandster.optimizers.config_generators.bohb import BOHB
assert BOHB is not None, "HpBandSter must be installed!"
if mode:
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
self._bohb_config = bohb_config
self._space = space
super(TuneBOHB, self).__init__(metric=self._metric, mode=mode)
if self._space:
self.setup_bohb()
def setup_bohb(self):
from hpbandster.optimizers.config_generators.bohb import BOHB
if self._mode == "max":
self._metric_op = -1.
elif self._mode == "min":
self._metric_op = 1.
bohb_config = self._bohb_config or {}
self.bohber = BOHB(self._space, **bohb_config)
def set_search_properties(self, metric, mode, config):
if self._space:
return False
space = self.convert_search_space(config)
self._space = space
if metric:
self._metric = metric
if mode:
self._mode = mode
self.setup_bohb()
return True
def suggest(self, trial_id):
if not self._space:
raise RuntimeError(
"Trying to sample a configuration from {}, but no search "
"space has been defined. Either pass the `{}` argument when "
"instantiating the search algorithm, or pass a `config` to "
"`tune.run()`.".format(self.__class__.__name__, "space"))
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 unflatten_dict(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)
@staticmethod
def convert_search_space(spec: Dict):
spec = flatten_dict(spec, prevent_delimiter=True)
resolved_vars, domain_vars, grid_vars = parse_spec_vars(spec)
if grid_vars:
raise ValueError(
"Grid search parameters cannot be automatically converted "
"to a TuneBOHB search space.")
def resolve_value(par, domain):
quantize = None
sampler = domain.get_sampler()
if isinstance(sampler, Quantized):
quantize = sampler.q
sampler = sampler.sampler
if isinstance(domain, Float):
if isinstance(sampler, LogUniform):
lower = domain.lower
upper = domain.upper
if quantize:
lower = math.ceil(domain.lower / quantize) * quantize
upper = math.floor(domain.upper / quantize) * quantize
return ConfigSpace.UniformFloatHyperparameter(par,
lower=lower,
upper=upper,
q=quantize,
log=True)
elif isinstance(sampler, Uniform):
lower = domain.lower
upper = domain.upper
if quantize:
lower = math.ceil(domain.lower / quantize) * quantize
upper = math.floor(domain.upper / quantize) * quantize
return ConfigSpace.UniformFloatHyperparameter(par,
lower=lower,
upper=upper,
q=quantize,
log=False)
elif isinstance(sampler, Normal):
return ConfigSpace.NormalFloatHyperparameter(
par,
mu=sampler.mean,
sigma=sampler.sd,
q=quantize,
log=False)
elif isinstance(domain, Integer):
if isinstance(sampler, Uniform):
lower = domain.lower
upper = domain.upper
if quantize:
lower = math.ceil(domain.lower / quantize) * quantize
upper = math.floor(domain.upper / quantize) * quantize
return ConfigSpace.UniformIntegerHyperparameter(
par, lower=lower, upper=upper, q=quantize, log=False)
elif isinstance(domain, Categorical):
if isinstance(sampler, Uniform):
return ConfigSpace.CategoricalHyperparameter(
par, choices=domain.categories)
raise ValueError("TuneBOHB does not support parameters of type "
"`{}` with samplers of type `{}`".format(
type(domain).__name__,
type(domain.sampler).__name__))
cs = ConfigSpace.ConfigurationSpace()
for path, domain in domain_vars:
par = "/".join(path)
value = resolve_value(par, domain)
cs.add_hyperparameter(value)
return cs
class TuneBOHB(SuggestionAlgorithm):
"""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,
early_terminated=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)