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externalbo.py
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externalbo.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
from . import base
from .base import IntOrParameter
import nevergrad.common.typing as tp
from nevergrad.parametrization import transforms
from nevergrad.parametrization import parameter as p
import hyperopt # type: ignore
from hyperopt import hp, Trials, Domain, tpe # type: ignore
def _hp_parametrization_to_dict(x, **kwargs):
if isinstance(x, p.Instrumentation):
x_dict = kwargs.get("default", {})
for idx_param in range(len(x[0].value)):
x_dict.update(_hp_parametrization_to_dict(x[0][idx_param], name=str(idx_param)))
for name in x[1].value.keys():
x_dict.update(_hp_parametrization_to_dict(x[1][name], name=name))
return x_dict
elif isinstance(x, (p.Log, p.Scalar)):
return {kwargs["name"]: [x.value]}
elif isinstance(x, p.Choice):
x_dict = {}
for i in range(len(x.choices)):
if x.value == x.choices[i].value:
x_dict[kwargs["name"]] = [i]
if isinstance(x.choices[i], (p.Log, p.Scalar, p.Choice)):
x_dict[kwargs["name"] + f"__{i}"] = [x.choices[i].value]
elif isinstance(x.choices[i], p.Instrumentation):
x_dict.update(_hp_parametrization_to_dict(x.choices[i]))
return x_dict
def _hp_dict_to_parametrization(x):
if isinstance(x, dict) and ("args" in x) and ("kwargs" in x):
x["args"] = tuple([_hp_dict_to_parametrization(x["args"][str(i)]) for i in range(len(x["args"]))])
x["kwargs"] = {k: _hp_dict_to_parametrization(v) for k, v in x["kwargs"].items()}
return (x["args"], x["kwargs"])
return x
def _get_search_space(param_name, param):
if isinstance(param, p.Instrumentation):
space = {}
space["args"] = {
str(idx_param): _get_search_space(str(idx_param), param[0][idx_param]) # type: ignore
for idx_param in range(len(param[0].value))
}
space["kwargs"] = {
param_name: _get_search_space(param_name, param[1][param_name]) # type: ignore
for param_name in param[1].value.keys()
}
return space
elif isinstance(param, (p.Log, p.Scalar)):
if (param.bounds[0][0] is None) or (param.bounds[1][0] is None):
if isinstance(param, p.Scalar) and not param.integer:
return hp.lognormal(label=param_name, mu=0, sigma=1)
raise ValueError(f"Scalar {param_name} not bounded.")
elif isinstance(param, p.Log):
return hp.loguniform(
label=param_name, low=np.log(param.bounds[0][0]), high=np.log(param.bounds[1][0])
)
elif isinstance(param, p.Scalar):
if param.integer:
return hp.randint(label=param_name, low=int(param.bounds[0][0]), high=int(param.bounds[1][0]))
else:
return hp.uniform(label=param_name, low=param.bounds[0][0], high=param.bounds[1][0])
elif isinstance(param, p.Choice):
list_types = [
type(param.choices[i])
for i in range(len(param.choices))
if not isinstance(param.choices[i], (p.Instrumentation, p.Constant))
]
if len(list_types) != len(set(list_types)):
raise NotImplementedError
return hp.choice(
param_name,
[
_get_search_space(param_name + "__" + str(i), param.choices[i])
for i in range(len(param.choices))
],
)
elif isinstance(param, p.Constant):
return param.value
# Hyperopt do not support array
raise NotImplementedError
class _HyperOpt(base.Optimizer):
# pylint: disable=too-many-instance-attributes
def __init__(
self,
parametrization: IntOrParameter,
budget: tp.Optional[int] = None,
num_workers: int = 1,
*,
prior_weight: float = 1.0,
n_startup_jobs: int = 20,
n_EI_candidates: int = 24,
gamma: float = 0.25,
verbose: bool = False,
) -> None:
super().__init__(parametrization, budget=budget, num_workers=num_workers)
try:
# try to convert parametrization to hyperopt search space
if not isinstance(self.parametrization, p.Instrumentation):
raise NotImplementedError
self.space = _get_search_space(self.parametrization.name, self.parametrization)
self._transform = None
except NotImplementedError:
self._transform = transforms.ArctanBound(0, 1)
self.space = {f"x_{i}": hp.uniform(f"x_{i}", 0, 1) for i in range(self.dimension)}
self.trials = Trials()
self.domain = Domain(fn=None, expr=self.space, pass_expr_memo_ctrl=False)
self.tpe_args = {
"prior_weight": prior_weight,
"n_startup_jobs": n_startup_jobs,
"n_EI_candidates": n_EI_candidates,
"gamma": gamma,
"verbose": verbose,
}
def _internal_ask_candidate(self) -> p.Parameter:
# Inspired from FMinIter class (hyperopt)
next_id = self.trials.new_trial_ids(1)
new_trial = tpe.suggest(
next_id, self.domain, self.trials, self._rng.randint(2**31 - 1), **self.tpe_args
)[0]
self.trials.insert_trial_doc(new_trial)
self.trials.refresh()
candidate = self.parametrization.spawn_child()
if self._transform:
data = np.array([new_trial["misc"]["vals"][f"x_{i}"][0] for i in range(self.dimension)])
candidate = candidate.set_standardized_data(self._transform.backward(data))
# For consistency, we need to update hyperopt history
# when standardized data is changed
if any(
data
!= self._transform.forward(candidate.get_standardized_data(reference=self.parametrization))
):
for it, val in enumerate(
self._transform.forward(candidate.get_standardized_data(reference=self.parametrization))
):
self.trials._dynamic_trials[next_id[0]]["misc"]["vals"][f"x_{it}"][0] = val
else:
spec = hyperopt.base.spec_from_misc(new_trial["misc"])
config = hyperopt.space_eval(self.space, spec)
candidate.value = _hp_dict_to_parametrization(config)
candidate._meta["trial_id"] = new_trial["tid"]
return candidate
def _internal_tell_candidate(self, candidate: p.Parameter, loss: float) -> None:
result = {"loss": loss, "status": "ok"}
assert "trial_id" in candidate._meta
tid = candidate._meta["trial_id"]
assert self.trials._dynamic_trials[tid]["state"] == hyperopt.JOB_STATE_NEW
now = hyperopt.utils.coarse_utcnow()
self.trials._dynamic_trials[tid]["book_time"] = now
self.trials._dynamic_trials[tid]["refresh_time"] = now
self.trials._dynamic_trials[tid]["state"] = hyperopt.JOB_STATE_DONE
self.trials._dynamic_trials[tid]["result"] = result
self.trials._dynamic_trials[tid]["refresh_time"] = hyperopt.utils.coarse_utcnow()
self.trials.refresh()
def _internal_tell_not_asked(self, candidate: p.Parameter, loss: float) -> None:
next_id = self.trials.new_trial_ids(1)
new_trial = hyperopt.rand.suggest(next_id, self.domain, self.trials, self._rng.randint(2**31 - 1))
self.trials.insert_trial_docs(new_trial)
self.trials.refresh()
tid = next_id[0]
if self._transform:
data = candidate.get_standardized_data(reference=self.parametrization)
data = self._transform.forward(data)
self.trials._dynamic_trials[tid]["misc"]["vals"] = {f"x_{i}": [data[i]] for i in range(len(data))}
else:
null_config: dict = {k: [] for k in self.trials._dynamic_trials[tid]["misc"]["vals"].keys()}
new_vals: dict = _hp_parametrization_to_dict(candidate, default=null_config)
self.trials._dynamic_trials[tid]["misc"]["vals"] = new_vals
self.trials.refresh()
candidate._meta["trial_id"] = tid
self._internal_tell_candidate(candidate, loss)
class ParametrizedHyperOpt(base.ConfiguredOptimizer):
# pylint: disable=too-many-instance-attributes
"""Hyperopt: Distributed Asynchronous Hyper-parameter Optimization.
This class is a wrapper over the `hyperopt <https://github.com/hyperopt/hyperopt>`_ package.
Parameters
----------
parametrization: int or Parameter
Parametrization object
budget: int
Number of iterations
num_workers: int
Number of workers
prior_weight: float (default 1.0)
Smoothing factor to avoid having zero probabilities
n_startup_jobs: int (default 20)
Number of random uniform suggestions at initialization
n_EI_candidates: int (default 24)
Number of generated candidates during EI maximization
gamma: float (default 0.25)
Threshold to split between l(x) and g(x), see eq. 2 in
verbose: bool (default False)
Hyperopt algorithm verbosity
Note
----
HyperOpt is described in Bergstra, James S., et al.
"Algorithms for hyper-parameter optimization."
Advances in neural information processing systems. 2011
"""
no_parallelization = False
# pylint: disable=unused-argument
def __init__(
self,
*,
prior_weight: float = 1.0,
n_startup_jobs: int = 20,
n_EI_candidates: int = 24,
gamma: float = 0.25,
verbose: bool = False,
) -> None:
super().__init__(_HyperOpt, locals())
HyperOpt = ParametrizedHyperOpt().set_name("HyperOpt", register=True)