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ray/python/ray/tune/suggest/__init__.py
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Python

from ray.utils import get_function_args
from ray.tune.suggest.search import SearchAlgorithm
from ray.tune.suggest.basic_variant import BasicVariantGenerator
from ray.tune.suggest.suggestion import Searcher, ConcurrencyLimiter
from ray.tune.suggest.search_generator import SearchGenerator
from ray.tune.suggest.variant_generator import grid_search
from ray.tune.suggest.repeater import Repeater
def create_searcher(
search_alg,
**kwargs,
):
"""Instantiate a search algorithm based on the given string.
This is useful for swapping between different search algorithms.
Args:
search_alg (str): The search algorithm to use.
metric (str): The training result objective value attribute. Stopping
procedures will use this attribute.
mode (str): One of {min, max}. Determines whether objective is
minimizing or maximizing the metric attribute.
**kwargs: Additional parameters.
These keyword arguments will be passed to the initialization
function of the chosen class.
Returns:
ray.tune.suggest.Searcher: The search algorithm.
Example:
>>> search_alg = tune.create_searcher('ax')
"""
def _import_variant_generator():
return BasicVariantGenerator
def _import_ax_search():
from ray.tune.suggest.ax import AxSearch
return AxSearch
def _import_dragonfly_search():
from ray.tune.suggest.dragonfly import DragonflySearch
return DragonflySearch
def _import_skopt_search():
from ray.tune.suggest.skopt import SkOptSearch
return SkOptSearch
def _import_hyperopt_search():
from ray.tune.suggest.hyperopt import HyperOptSearch
return HyperOptSearch
def _import_bayesopt_search():
from ray.tune.suggest.bayesopt import BayesOptSearch
return BayesOptSearch
def _import_bohb_search():
from ray.tune.suggest.bohb import TuneBOHB
return TuneBOHB
def _import_nevergrad_search():
from ray.tune.suggest.nevergrad import NevergradSearch
return NevergradSearch
def _import_optuna_search():
from ray.tune.suggest.optuna import OptunaSearch
return OptunaSearch
def _import_zoopt_search():
from ray.tune.suggest.zoopt import ZOOptSearch
return ZOOptSearch
def _import_sigopt_search():
from ray.tune.suggest.sigopt import SigOptSearch
return SigOptSearch
SEARCH_ALG_IMPORT = {
"variant_generator": _import_variant_generator,
"random": _import_variant_generator,
"ax": _import_ax_search,
"dragonfly": _import_dragonfly_search,
"skopt": _import_skopt_search,
"hyperopt": _import_hyperopt_search,
"bayesopt": _import_bayesopt_search,
"bohb": _import_bohb_search,
"nevergrad": _import_nevergrad_search,
"optuna": _import_optuna_search,
"zoopt": _import_zoopt_search,
"sigopt": _import_sigopt_search,
}
search_alg = search_alg.lower()
if search_alg not in SEARCH_ALG_IMPORT:
raise ValueError(
f"Search alg must be one of {list(SEARCH_ALG_IMPORT)}. "
f"Got: {search_alg}")
SearcherClass = SEARCH_ALG_IMPORT[search_alg]()
search_alg_args = get_function_args(SearcherClass)
trimmed_kwargs = {k: v for k, v in kwargs.items() if k in search_alg_args}
return SearcherClass(**trimmed_kwargs)
__all__ = [
"SearchAlgorithm", "Searcher", "BasicVariantGenerator", "SearchGenerator",
"grid_search", "Repeater", "ConcurrencyLimiter"
]
def BayesOptSearch(*args, **kwargs):
raise DeprecationWarning("""This class has been moved. Please import via
`from ray.tune.suggest.bayesopt import BayesOptSearch`""")
def HyperOptSearch(*args, **kwargs):
raise DeprecationWarning("""This class has been moved. Please import via
`from ray.tune.suggest.hyperopt import HyperOptSearch`""")
def NevergradSearch(*args, **kwargs):
raise DeprecationWarning("""This class has been moved. Please import via
`from ray.tune.suggest.nevergrad import NevergradSearch`""")
def SkOptSearch(*args, **kwargs):
raise DeprecationWarning("""This class has been moved. Please import via
`from ray.tune.suggest.skopt import SkOptSearch`""")
def SigOptSearch(*args, **kwargs):
raise DeprecationWarning("""This class has been moved. Please import via
`from ray.tune.suggest.sigopt import SigOptSearch`""")