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79 lines
3.3 KiB
Python
79 lines
3.3 KiB
Python
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try:
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from ray.tune import sample
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except ImportError:
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from . import sample
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from typing import Dict, Optional, Any
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import logging
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logger = logging.getLogger(__name__)
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def define_by_run_func(
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trial, space: Dict, path: str = ""
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) -> Optional[Dict[str, Any]]:
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"""Define-by-run function to create the search space.
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Returns:
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None or a dict with constant values.
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"""
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config = {}
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for key, domain in space.items():
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if path:
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key = path + '/' + key
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if not isinstance(domain, sample.Domain):
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config[key] = domain
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continue
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sampler = domain.get_sampler()
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quantize = None
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if isinstance(sampler, sample.Quantized):
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quantize = sampler.q
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sampler = sampler.sampler
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if isinstance(sampler, sample.LogUniform):
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logger.warning(
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"Optuna does not handle quantization in loguniform "
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"sampling. The parameter will be passed but it will "
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"probably be ignored.")
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if isinstance(domain, sample.Float):
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if isinstance(sampler, sample.LogUniform):
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if quantize:
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logger.warning(
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"Optuna does not support both quantization and "
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"sampling from LogUniform. Dropped quantization.")
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trial.suggest_float(
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key, domain.lower, domain.upper, log=True)
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elif isinstance(sampler, sample.Uniform):
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if quantize:
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trial.suggest_float(
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key, domain.lower, domain.upper, step=quantize)
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trial.suggest_float(key, domain.lower, domain.upper)
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elif isinstance(domain, sample.Integer):
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if isinstance(sampler, sample.LogUniform):
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trial.suggest_int(
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key, domain.lower, domain.upper, step=quantize or 1, log=True)
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elif isinstance(sampler, sample.Uniform):
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# Upper bound should be inclusive for quantization and
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# exclusive otherwise
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trial.suggest_int(
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key, domain.lower, domain.upper, step=quantize or 1)
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elif isinstance(domain, sample.Categorical):
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if isinstance(sampler, sample.Uniform):
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if not hasattr(domain, 'choices'):
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domain.choices = list(range(len(domain.categories)))
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choices = domain.choices
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# This choice needs to be removed from the final config
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index = trial.suggest_categorical(key + '_choice_', choices)
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choice = domain.categories[index]
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if isinstance(choice, dict):
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key += f":{index}"
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# the suffix needs to be removed from the final config
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config[key] = define_by_run_func(trial, choice, key)
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else:
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raise ValueError(
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"Optuna search does not support parameters of type "
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"`{}` with samplers of type `{}`".format(
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type(domain).__name__,
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type(domain.sampler).__name__))
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# Return all constants in a dictionary.
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return config
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