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