Source code for ray.tune.suggest.skopt

import copy
import logging
import pickle
from typing import Dict, List, Optional, Tuple, Union, Any

from ray.tune.result import DEFAULT_METRIC
from ray.tune.sample import Categorical, Domain, Float, Integer, Quantized, \
from ray.tune.suggest.suggestion import UNRESOLVED_SEARCH_SPACE, \
from ray.tune.suggest.variant_generator import parse_spec_vars
from ray.tune.utils import flatten_dict
from ray.tune.utils.util import is_nan_or_inf, unflatten_dict, \

    import skopt as sko
except ImportError:
    sko = None

from ray.tune.suggest import Searcher

logger = logging.getLogger(__name__)

[docs]class SkOptSearch(Searcher): """Uses Scikit Optimize (skopt) to optimize hyperparameters. Scikit-optimize is a black-box optimization library. Read more here: You will need to install Scikit-Optimize to use this module. .. code-block:: bash pip install scikit-optimize This Search Algorithm requires you to pass in a `skopt Optimizer object`_. This searcher will automatically filter out any NaN, inf or -inf results. Parameters: optimizer (skopt.optimizer.Optimizer): Optimizer provided from skopt. space (dict|list): A dict mapping parameter names to valid parameters, i.e. tuples for numerical parameters and lists for categorical parameters. If you passed an optimizer instance as the `optimizer` argument, this should be a list of parameter names instead. 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. 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. (See tune/examples/ max_concurrent: Deprecated. use_early_stopped_trials: Deprecated. Tune automatically converts search spaces to SkOpt's format: .. code-block:: python config = { "width": tune.uniform(0, 20), "height": tune.uniform(-100, 100) } current_best_params = [ { "width": 10, "height": 0, }, { "width": 15, "height": -20, } ] skopt_search = SkOptSearch( metric="mean_loss", mode="min", points_to_evaluate=current_best_params), config=config, search_alg=skopt_search) If you would like to pass the search space/optimizer manually, the code would look like this: .. code-block:: python parameter_names = ["width", "height"] parameter_ranges = [(0,20),(-100,100)] current_best_params = [[10, 0], [15, -20]] skopt_search = SkOptSearch( parameter_names=parameter_names, parameter_ranges=parameter_ranges, metric="mean_loss", mode="min", points_to_evaluate=current_best_params), search_alg=skopt_search) """ def __init__(self, optimizer: Optional["sko.optimizer.Optimizer"] = None, space: Union[List[str], Dict[str, Union[Tuple, List]]] = None, metric: Optional[str] = None, mode: Optional[str] = None, points_to_evaluate: Optional[List[Dict]] = None, evaluated_rewards: Optional[List] = None, max_concurrent: Optional[int] = None, use_early_stopped_trials: Optional[bool] = None): assert sko is not None, ("skopt must be installed! " "You can install Skopt with the command: " "`pip install scikit-optimize`.") if mode: assert mode in ["min", "max"], "`mode` must be 'min' or 'max'." self.max_concurrent = max_concurrent super(SkOptSearch, self).__init__( metric=metric, mode=mode, max_concurrent=max_concurrent, use_early_stopped_trials=use_early_stopped_trials) self._initial_points = [] self._parameters = None self._parameter_names = None self._parameter_ranges = 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))) space = self.convert_search_space(space, join=True) self._space = space if self._space: if isinstance(optimizer, sko.Optimizer): if not isinstance(space, list): raise ValueError( "You passed an optimizer instance to SkOpt. Your " "`space` parameter should be a list of parameter" "names.") self._parameter_names = space else: self._parameter_names = list(space.keys()) self._parameter_ranges = list(space.values()) self._points_to_evaluate = copy.deepcopy(points_to_evaluate) self._evaluated_rewards = evaluated_rewards self._skopt_opt = optimizer if self._skopt_opt or self._space: self._setup_skopt() self._live_trial_mapping = {} def _setup_skopt(self): if self._points_to_evaluate and isinstance(self._points_to_evaluate, list): if isinstance(self._points_to_evaluate[0], list): # Keep backwards compatibility self._points_to_evaluate = [ dict(zip(self._parameter_names, point)) for point in self._points_to_evaluate ] # Else: self._points_to_evaluate is already in correct format validate_warmstart(self._parameter_names, self._points_to_evaluate, self._evaluated_rewards) if not self._skopt_opt: if not self._space: raise ValueError( "If you don't pass an optimizer instance to SkOptSearch, " "pass a valid `space` parameter.") self._skopt_opt = sko.Optimizer(self._parameter_ranges) if self._points_to_evaluate and self._evaluated_rewards: skopt_points = [[point[par] for par in self._parameter_names] for point in self._points_to_evaluate] self._skopt_opt.tell(skopt_points, self._evaluated_rewards) elif self._points_to_evaluate: self._initial_points = self._points_to_evaluate self._parameters = self._parameter_names # Skopt internally minimizes, so "max" => -1 if self._mode == "max": self._metric_op = -1. elif self._mode == "min": self._metric_op = 1. if self._metric is None and self._mode: # If only a mode was passed, use anonymous metric self._metric = DEFAULT_METRIC def set_search_properties(self, metric: Optional[str], mode: Optional[str], config: Dict) -> bool: if self._skopt_opt: return False space = self.convert_search_space(config) self._space = space self._parameter_names = list(space.keys()) self._parameter_ranges = list(space.values()) if metric: self._metric = metric if mode: self._mode = mode self._setup_skopt() return True def suggest(self, trial_id: str) -> Optional[Dict]: if not self._skopt_opt: 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 self.max_concurrent: if len(self._live_trial_mapping) >= self.max_concurrent: return None if self._initial_points: suggested_config = self._initial_points.pop(0) skopt_config = [suggested_config[par] for par in self._parameters] else: skopt_config = self._skopt_opt.ask() suggested_config = dict(zip(self._parameters, skopt_config)) self._live_trial_mapping[trial_id] = skopt_config return unflatten_dict(suggested_config) def on_trial_complete(self, trial_id: str, result: Optional[Dict] = None, error: bool = False): """Notification for the completion of trial. The result is internally negated when interacting with Skopt so that Skopt Optimizers can "maximize" this value, as it minimizes on default. """ if result: self._process_result(trial_id, result) self._live_trial_mapping.pop(trial_id) def _process_result(self, trial_id: str, result: Dict): skopt_trial_info = self._live_trial_mapping[trial_id] if result and not is_nan_or_inf(result[self._metric]): self._skopt_opt.tell(skopt_trial_info, self._metric_op * result[self._metric]) def get_state(self) -> Dict[str, Any]: state = self.__dict__.copy() return state def set_state(self, state: Dict[str, Any]): self.__dict__.update(state)
[docs] def save(self, checkpoint_path: str): with open(checkpoint_path, "wb") as f: pickle.dump((self._initial_points, self._skopt_opt), f)
[docs] def restore(self, checkpoint_path: str): with open(checkpoint_path, "rb") as f: state = pickle.load(f) self._initial_points, self._skopt_opt = state
@staticmethod def convert_search_space(spec: Dict, join: bool = False) -> Dict: resolved_vars, domain_vars, grid_vars = parse_spec_vars(spec) if grid_vars: raise ValueError( "Grid search parameters cannot be automatically converted " "to a SkOpt 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) -> Union[Tuple, List]: sampler = domain.get_sampler() if isinstance(sampler, Quantized): logger.warning("SkOpt search does not support quantization. " "Dropped quantization.") sampler = sampler.get_sampler() if isinstance(domain, Float): if isinstance(domain.sampler, LogUniform): return domain.lower, domain.upper, prior="log-uniform") return domain.lower, domain.upper, prior="uniform") elif isinstance(domain, Integer): if isinstance(domain.sampler, LogUniform): return domain.lower, domain.upper, prior="log-uniform") return domain.lower, domain.upper, prior="uniform") elif isinstance(domain, Categorical): return domain.categories raise ValueError("SkOpt 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 space = { "/".join(path): resolve_value(domain) for path, domain in domain_vars } if join: spec.update(space) space = spec return space