Source code for ray.tune.suggest.optuna

import time
import logging
import pickle
import functools
import warnings
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

from ray.tune.result import DEFAULT_METRIC, TRAINING_ITERATION
from ray.tune.sample import Categorical, Domain, Float, Integer, LogUniform, \
    Quantized, Uniform
from ray.tune.suggest.suggestion import UNRESOLVED_SEARCH_SPACE, \
    UNDEFINED_METRIC_MODE, UNDEFINED_SEARCH_SPACE
from ray.tune.suggest.variant_generator import parse_spec_vars
from ray.tune.utils.util import flatten_dict, unflatten_dict, \
    validate_warmstart

try:
    import optuna as ot
    from optuna.distributions import BaseDistribution as OptunaDistribution
    from optuna.samplers import BaseSampler
    from optuna.trial import TrialState as OptunaTrialState
    from optuna.trial import Trial as OptunaTrial
except ImportError:
    ot = None
    OptunaDistribution = None
    BaseSampler = None
    OptunaTrialState = None
    OptunaTrial = None

from ray.tune.suggest import Searcher

logger = logging.getLogger(__name__)

# print a warning if define by run function takes longer than this to execute
DEFINE_BY_RUN_WARN_THRESHOLD_S = 1  # 1 is arbitrary


class _OptunaTrialSuggestCaptor:
    """Utility to capture returned values from Optuna's suggest_ methods.

    This will wrap around the ``optuna.Trial` object and decorate all
    `suggest_` callables with a function capturing the returned value,
    which will be saved in the ``captured_values`` dict.
    """

    def __init__(self, ot_trial: OptunaTrial) -> None:
        self.ot_trial = ot_trial
        self.captured_values: Dict[str, Any] = {}

    def _get_wrapper(self, func: Callable) -> Callable:
        @functools.wraps(func)
        def wrapper(*args, **kwargs):
            # name is always the first arg for suggest_ methods
            name = kwargs.get("name", args[0])
            ret = func(*args, **kwargs)
            self.captured_values[name] = ret
            return ret

        return wrapper

    def __getattr__(self, item_name: str) -> Any:
        item = getattr(self.ot_trial, item_name)
        if item_name.startswith("suggest_") and callable(item):
            return self._get_wrapper(item)
        return item


[docs]class OptunaSearch(Searcher): """A wrapper around Optuna to provide trial suggestions. `Optuna <https://optuna.org/>`_ is a hyperparameter optimization library. In contrast to other libraries, it employs define-by-run style hyperparameter definitions. This Searcher is a thin wrapper around Optuna's search algorithms. You can pass any Optuna sampler, which will be used to generate hyperparameter suggestions. Args: space (dict|Callable): Hyperparameter search space definition for Optuna's sampler. This can be either a :class:`dict` with parameter names as keys and ``optuna.distributions`` as values, or a Callable - in which case, it should be a define-by-run function using ``optuna.trial`` to obtain the hyperparameter values. The function should return either a :class:`dict` of constant values with names as keys, or None. For more information, see https://optuna.readthedocs.io\ /en/stable/tutorial/10_key_features/002_configurations.html. .. warning:: No actual computation should take place in the define-by-run function. Instead, put the training logic inside the function or class trainable passed to ``tune.run``. 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. sampler (optuna.samplers.BaseSampler): Optuna sampler used to draw hyperparameter configurations. Defaults to ``TPESampler``. seed (int): Seed to initialize sampler with. This parameter is only used when ``sampler=None``. In all other cases, the sampler you pass should be initialized with the seed already. evaluated_rewards (list): If you have previously evaluated the parameters passed in as points_to_evaluate you can avoid re-running those trials by passing in the reward attributes as a list so the optimiser can be told the results without needing to re-compute the trial. Must be the same length as points_to_evaluate. ..warning:: When using ``evaluated_rewards``, the search space ``space`` must be provided as a :class:`dict` with parameter names as keys and ``optuna.distributions`` instances as values. The define-by-run search space definition is not yet supported with this functionality. Tune automatically converts search spaces to Optuna's format: .. code-block:: python from ray.tune.suggest.optuna import OptunaSearch config = { "a": tune.uniform(6, 8) "b": tune.loguniform(1e-4, 1e-2) } optuna_search = OptunaSearch( metric="loss", mode="min") tune.run(trainable, config=config, search_alg=optuna_search) If you would like to pass the search space manually, the code would look like this: .. code-block:: python from ray.tune.suggest.optuna import OptunaSearch import optuna space = { "a": optuna.distributions.UniformDistribution(6, 8), "b": optuna.distributions.LogUniformDistribution(1e-4, 1e-2), } optuna_search = OptunaSearch( space, metric="loss", mode="min") tune.run(trainable, search_alg=optuna_search) # Equivalent Optuna define-by-run function approach: def define_search_space(trial: optuna.Trial): trial.suggest_float("a", 6, 8) trial.suggest_float("b", 1e-4, 1e-2, log=True) # training logic goes into trainable, this is just # for search space definition optuna_search = OptunaSearch( define_search_space, metric="loss", mode="min") tune.run(trainable, search_alg=optuna_search) You can pass configs that will be evaluated first using ``points_to_evaluate``: .. code-block:: python from ray.tune.suggest.optuna import OptunaSearch import optuna space = { "a": optuna.distributions.UniformDistribution(6, 8), "b": optuna.distributions.LogUniformDistribution(1e-4, 1e-2), } optuna_search = OptunaSearch( space, points_to_evaluate=[{"a": 6.5, "b": 5e-4}, {"a": 7.5, "b": 1e-3}] metric="loss", mode="min") tune.run(trainable, search_alg=optuna_search) Avoid re-running evaluated trials by passing the rewards together with `points_to_evaluate`: .. code-block:: python from ray.tune.suggest.optuna import OptunaSearch import optuna space = { "a": optuna.distributions.UniformDistribution(6, 8), "b": optuna.distributions.LogUniformDistribution(1e-4, 1e-2), } optuna_search = OptunaSearch( space, points_to_evaluate=[{"a": 6.5, "b": 5e-4}, {"a": 7.5, "b": 1e-3}] evaluated_rewards=[0.89, 0.42] metric="loss", mode="min") tune.run(trainable, search_alg=optuna_search) .. versionadded:: 0.8.8 """ def __init__(self, space: Optional[Union[Dict[str, "OptunaDistribution"], List[ Tuple], Callable[["OptunaTrial"], Optional[Dict[ str, Any]]]]] = None, metric: Optional[str] = None, mode: Optional[str] = None, points_to_evaluate: Optional[List[Dict]] = None, sampler: Optional["BaseSampler"] = None, seed: Optional[int] = None, evaluated_rewards: Optional[List] = None): assert ot is not None, ( "Optuna must be installed! Run `pip install optuna`.") super(OptunaSearch, self).__init__( metric=metric, mode=mode, max_concurrent=None, use_early_stopped_trials=None) 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).__name__)) space = self.convert_search_space(space) else: # Flatten to support nested dicts space = flatten_dict(space, "/") self._space = space self._points_to_evaluate = points_to_evaluate or [] self._evaluated_rewards = evaluated_rewards self._study_name = "optuna" # Fixed study name for in-memory storage if sampler and seed: logger.warning( "You passed an initialized sampler to `OptunaSearch`. The " "`seed` parameter has to be passed to the sampler directly " "and will be ignored.") self._sampler = sampler or ot.samplers.TPESampler(seed=seed) assert isinstance(self._sampler, BaseSampler), \ "You can only pass an instance of `optuna.samplers.BaseSampler` " \ "as a sampler to `OptunaSearcher`." self._ot_trials = {} self._ot_study = None if self._space: self._setup_study(mode) def _setup_study(self, mode: str): if self._metric is None and self._mode: # If only a mode was passed, use anonymous metric self._metric = DEFAULT_METRIC pruner = ot.pruners.NopPruner() storage = ot.storages.InMemoryStorage() self._ot_study = ot.study.create_study( storage=storage, sampler=self._sampler, pruner=pruner, study_name=self._study_name, direction="minimize" if mode == "min" else "maximize", load_if_exists=True) if self._points_to_evaluate: validate_warmstart( self._space, self._points_to_evaluate, self._evaluated_rewards, validate_point_name_lengths=not callable(self._space)) if self._evaluated_rewards: for point, reward in zip(self._points_to_evaluate, self._evaluated_rewards): self.add_evaluated_point(point, reward) else: for point in self._points_to_evaluate: self._ot_study.enqueue_trial(point) 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_study(mode) return True def _suggest_from_define_by_run_func( self, func: Callable[["OptunaTrial"], Optional[Dict[str, Any]]], ot_trial: "OptunaTrial") -> Dict: captor = _OptunaTrialSuggestCaptor(ot_trial) time_start = time.time() ret = func(captor) time_taken = time.time() - time_start if time_taken > DEFINE_BY_RUN_WARN_THRESHOLD_S: warnings.warn( "Define-by-run function passed in the `space` argument " f"took {time_taken} seconds to " "run. Ensure that actual computation, training takes " "place inside Tune's train functions or Trainables " "passed to `tune.run`.") if ret is not None: if not isinstance(ret, dict): raise TypeError( "The return value of the define-by-run function " "passed in the `space` argument should be " "either None or a `dict` with `str` keys. " f"Got {type(ret)}.") if not all(isinstance(k, str) for k in ret.keys()): raise TypeError( "At least one of the keys in the dict returned by the " "define-by-run function passed in the `space` argument " "was not a `str`.") return { **captor.captured_values, **ret } if ret else captor.captured_values 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 callable(self._space): if trial_id not in self._ot_trials: self._ot_trials[trial_id] = self._ot_study.ask() ot_trial = self._ot_trials[trial_id] params = self._suggest_from_define_by_run_func( self._space, ot_trial) else: # Use Optuna ask interface (since version 2.6.0) if trial_id not in self._ot_trials: self._ot_trials[trial_id] = self._ot_study.ask( fixed_distributions=self._space) ot_trial = self._ot_trials[trial_id] params = ot_trial.params return unflatten_dict(params) def on_trial_result(self, trial_id: str, result: Dict): metric = result[self.metric] step = result[TRAINING_ITERATION] ot_trial = self._ot_trials[trial_id] ot_trial.report(metric, step) def on_trial_complete(self, trial_id: str, result: Optional[Dict] = None, error: bool = False): ot_trial = self._ot_trials[trial_id] val = result.get(self.metric, None) if result else None ot_trial_state = OptunaTrialState.COMPLETE if val is None: if error: ot_trial_state = OptunaTrialState.FAIL else: ot_trial_state = OptunaTrialState.PRUNED try: self._ot_study.tell(ot_trial, val, state=ot_trial_state) except ValueError as exc: logger.warning(exc) # E.g. if NaN was reported def add_evaluated_point(self, parameters: Dict, value: float, error: bool = False, pruned: bool = False, intermediate_values: Optional[List[float]] = None): 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 callable(self._space): raise TypeError( "Define-by-run function passed in `space` argument is not " "yet supported when using `evaluated_rewards`. Please provide " "an `OptunaDistribution` dict or pass a Ray Tune " "search space to `tune.run()`.") ot_trial_state = OptunaTrialState.COMPLETE if error: ot_trial_state = OptunaTrialState.FAIL elif pruned: ot_trial_state = OptunaTrialState.PRUNED if intermediate_values: intermediate_values_dict = { i: value for i, value in enumerate(intermediate_values) } else: intermediate_values_dict = None trial = ot.trial.create_trial( state=ot_trial_state, value=value, params=parameters, distributions=self._space, intermediate_values=intermediate_values_dict) self._ot_study.add_trial(trial) def save(self, checkpoint_path: str): save_object = (self._sampler, self._ot_trials, self._ot_study, self._points_to_evaluate, self._evaluated_rewards) with open(checkpoint_path, "wb") as outputFile: pickle.dump(save_object, outputFile) def restore(self, checkpoint_path: str): with open(checkpoint_path, "rb") as inputFile: save_object = pickle.load(inputFile) if len(save_object) == 5: self._sampler, self._ot_trials, self._ot_study, \ self._points_to_evaluate, self._evaluated_rewards = save_object else: # Backwards compatibility self._sampler, self._ot_trials, self._ot_study, \ self._points_to_evaluate = save_object @staticmethod def convert_search_space(spec: Dict) -> Dict[str, Any]: resolved_vars, domain_vars, grid_vars = parse_spec_vars(spec) if not domain_vars and not grid_vars: return {} if grid_vars: raise ValueError( "Grid search parameters cannot be automatically converted " "to an Optuna 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(domain: Domain) -> ot.distributions.BaseDistribution: quantize = None sampler = domain.get_sampler() if isinstance(sampler, Quantized): quantize = sampler.q sampler = sampler.sampler if isinstance(sampler, LogUniform): logger.warning( "Optuna does not handle quantization in loguniform " "sampling. The parameter will be passed but it will " "probably be ignored.") if isinstance(domain, Float): if isinstance(sampler, LogUniform): if quantize: logger.warning( "Optuna does not support both quantization and " "sampling from LogUniform. Dropped quantization.") return ot.distributions.LogUniformDistribution( domain.lower, domain.upper) elif isinstance(sampler, Uniform): if quantize: return ot.distributions.DiscreteUniformDistribution( domain.lower, domain.upper, quantize) return ot.distributions.UniformDistribution( domain.lower, domain.upper) elif isinstance(domain, Integer): if isinstance(sampler, LogUniform): return ot.distributions.IntLogUniformDistribution( domain.lower, domain.upper - 1, step=quantize or 1) elif isinstance(sampler, Uniform): # Upper bound should be inclusive for quantization and # exclusive otherwise return ot.distributions.IntUniformDistribution( domain.lower, domain.upper - int(bool(not quantize)), step=quantize or 1) elif isinstance(domain, Categorical): if isinstance(sampler, Uniform): return ot.distributions.CategoricalDistribution( domain.categories) raise ValueError( "Optuna search does not support parameters of type " "`{}` with samplers of type `{}`".format( type(domain).__name__, type(domain.sampler).__name__)) # Parameter name is e.g. "a/b/c" for nested dicts values = { "/".join(path): resolve_value(domain) for path, domain in domain_vars } return values