Source code for ray.tune.suggest.bayesopt

from collections import defaultdict
import logging
import pickle
import json
from typing import Dict, List, Optional, Tuple

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

try:  # Python 3 only -- needed for lint test.
    import bayes_opt as byo
except ImportError:
    byo = None

from ray.tune.suggest import Searcher
from ray.tune.utils import flatten_dict

logger = logging.getLogger(__name__)

def _dict_hash(config, precision):
    flatconfig = flatten_dict(config)
    for param, value in flatconfig.items():
        if isinstance(value, float):
            flatconfig[param] = "{:.{digits}f}".format(value, digits=precision)

    hashed = json.dumps(flatconfig, sort_keys=True, default=str)
    return hashed

[docs]class BayesOptSearch(Searcher): """Uses fmfn/BayesianOptimization to optimize hyperparameters. fmfn/BayesianOptimization is a library for Bayesian Optimization. More info can be found here: This searcher will automatically filter out any NaN, inf or -inf results. You will need to install fmfn/BayesianOptimization via the following: .. code-block:: bash pip install bayesian-optimization This algorithm requires setting a search space using the `BayesianOptimization search space specification`_. Args: space (dict): Continuous search space. Parameters will be sampled from this space which will be used to run trials. 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. utility_kwargs (dict): Parameters to define the utility function. The default value is a dictionary with three keys: - kind: ucb (Upper Confidence Bound) - kappa: 2.576 - xi: 0.0 random_state (int): Used to initialize BayesOpt. random_search_steps (int): Number of initial random searches. This is necessary to avoid initial local overfitting of the Bayesian process. analysis (ExperimentAnalysis): Optionally, the previous analysis to integrate. verbose (int): Sets verbosity level for BayesOpt packages. max_concurrent: Deprecated. use_early_stopped_trials: Deprecated. Tune automatically converts search spaces to BayesOptSearch's format: .. code-block:: python from ray import tune from ray.tune.suggest.bayesopt import BayesOptSearch config = { "width": tune.uniform(0, 20), "height": tune.uniform(-100, 100) } bayesopt = BayesOptSearch(metric="mean_loss", mode="min"), config=config, search_alg=bayesopt) If you would like to pass the search space manually, the code would look like this: .. code-block:: python from ray import tune from ray.tune.suggest.bayesopt import BayesOptSearch space = { 'width': (0, 20), 'height': (-100, 100), } bayesopt = BayesOptSearch(space, metric="mean_loss", mode="min"), search_alg=bayesopt) """ # bayes_opt.BayesianOptimization: Optimization object optimizer = None def __init__(self, space: Optional[Dict] = None, metric: Optional[str] = None, mode: Optional[str] = None, points_to_evaluate: Optional[List[Dict]] = None, utility_kwargs: Optional[Dict] = None, random_state: int = 42, random_search_steps: int = 10, verbose: int = 0, patience: int = 5, skip_duplicate: bool = True, analysis: Optional[ExperimentAnalysis] = None, max_concurrent: Optional[int] = None, use_early_stopped_trials: Optional[bool] = None): assert byo is not None, ( "BayesOpt must be installed!. You can install BayesOpt with" " the command: `pip install bayesian-optimization`.") if mode: assert mode in ["min", "max"], "`mode` must be 'min' or 'max'." self.max_concurrent = max_concurrent self._config_counter = defaultdict(int) self._patience = patience # int: Precision at which to hash values. self.repeat_float_precision = 5 if self._patience <= 0: raise ValueError("patience must be set to a value greater than 0!") self._skip_duplicate = skip_duplicate super(BayesOptSearch, self).__init__( metric=metric, mode=mode, max_concurrent=max_concurrent, use_early_stopped_trials=use_early_stopped_trials) if utility_kwargs is None: # The defaults arguments are the same # as in the package BayesianOptimization utility_kwargs = dict( kind="ucb", kappa=2.576, xi=0.0, ) if mode == "max": self._metric_op = 1. elif mode == "min": self._metric_op = -1. self._points_to_evaluate = points_to_evaluate self._live_trial_mapping = {} self._buffered_trial_results = [] self.random_search_trials = random_search_steps self._total_random_search_trials = 0 self.utility = byo.UtilityFunction(**utility_kwargs) self._analysis = analysis 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 self._verbose = verbose self._random_state = random_state self.optimizer = None if space: self._setup_optimizer() def _setup_optimizer(self): if self._metric is None and self._mode: # If only a mode was passed, use anonymous metric self._metric = DEFAULT_METRIC self.optimizer = byo.BayesianOptimization( f=None, pbounds=self._space, verbose=self._verbose, random_state=self._random_state) # Registering the provided analysis, if given if self._analysis is not None: self.register_analysis(self._analysis) def set_search_properties(self, metric: Optional[str], mode: Optional[str], config: Dict) -> bool: if self.optimizer: return False space = self.convert_search_space(config) self._space = space if metric: self._metric = metric if mode: self._mode = mode if self._mode == "max": self._metric_op = 1. elif self._mode == "min": self._metric_op = -1. self._setup_optimizer() return True def suggest(self, trial_id: str) -> Optional[Dict]: """Return new point to be explored by black box function. Args: trial_id (str): Id of the trial. This is a short alphanumerical string. Returns: Either a dictionary describing the new point to explore or None, when no new point is to be explored for the time being. """ if not self.optimizer: 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 we have more active trials than the allowed maximum total_live_trials = len(self._live_trial_mapping) if self.max_concurrent and self.max_concurrent <= total_live_trials: # we stop the suggestion and return None. return None if self._points_to_evaluate: config = self._points_to_evaluate.pop(0) else: # We compute the new point to explore config = self.optimizer.suggest(self.utility) config_hash = _dict_hash(config, self.repeat_float_precision) # Check if already computed already_seen = config_hash in self._config_counter self._config_counter[config_hash] += 1 top_repeats = max(self._config_counter.values()) # If patience is set and we've repeated a trial numerous times, # we terminate the experiment. if self._patience is not None and top_repeats > self._patience: return Searcher.FINISHED # If we have seen a value before, we'll skip it. if already_seen and self._skip_duplicate:"Skipping duplicated config: {}.".format(config)) return None # If we are still in the random search part and we are waiting for # trials to complete if len(self._buffered_trial_results) < self.random_search_trials: # We check if we have already maxed out the number of requested # random search trials if self._total_random_search_trials == self.random_search_trials: # If so we stop the suggestion and return None return None # Otherwise we increase the total number of rndom search trials if config: self._total_random_search_trials += 1 # Save the new trial to the trial mapping self._live_trial_mapping[trial_id] = config # Return a deep copy of the mapping return unflatten_dict(config) def register_analysis(self, analysis: ExperimentAnalysis): """Integrate the given analysis into the gaussian process. Args: analysis (ExperimentAnalysis): Optionally, the previous analysis to integrate. """ for (_, report), params in zip( analysis.dataframe(metric=self._metric, mode=self._mode).iterrows(), analysis.get_all_configs().values()): # We add the obtained results to the # gaussian process optimizer self._register_result(params, report) def on_trial_complete(self, trial_id: str, result: Optional[Dict] = None, error: bool = False): """Notification for the completion of trial. Args: trial_id (str): Id of the trial. This is a short alphanumerical string. result (dict): Dictionary of result. May be none when some error occurs. error (bool): Boolean representing a previous error state. The result should be None when error is True. """ # We try to get the parameters used for this trial params = self._live_trial_mapping.pop(trial_id, None) # The results may be None if some exception is raised during the trial. # Also, if the parameters are None (were already processed) # we interrupt the following procedure. # Additionally, if somehow the error is True but # the remaining values are not we also block the method if result is None or params is None or error: return # If we don't have to execute some random search steps if len(self._buffered_trial_results) >= self.random_search_trials: # we simply register the obtained result self._register_result(params, result) return # We store the results into a temporary cache self._buffered_trial_results.append((params, result)) # If the random search finished, # we update the BO with all the computer points. if len(self._buffered_trial_results) == self.random_search_trials: for params, result in self._buffered_trial_results: self._register_result(params, result) def _register_result(self, params: Tuple[str], result: Dict): """Register given tuple of params and results.""" if is_nan_or_inf(result[self.metric]): return self.optimizer.register(params, self._metric_op * result[self.metric])
[docs] def save(self, checkpoint_path: str): """Storing current optimizer state.""" with open(checkpoint_path, "wb") as f: pickle.dump((self.optimizer, self._buffered_trial_results, self._total_random_search_trials, self._config_counter, self._points_to_evaluate), f)
[docs] def restore(self, checkpoint_path: str): """Restoring current optimizer state.""" with open(checkpoint_path, "rb") as f: (self.optimizer, self._buffered_trial_results, self._total_random_search_trials, self._config_counter, self._points_to_evaluate) = pickle.load(f)
@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 BayesOpt 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) -> Tuple[float, float]: sampler = domain.get_sampler() if isinstance(sampler, Quantized): logger.warning( "BayesOpt search does not support quantization. " "Dropped quantization.") sampler = sampler.get_sampler() if isinstance(domain, Float): if domain.sampler is not None: logger.warning( "BayesOpt does not support specific sampling methods. " "The {} sampler will be dropped.".format(sampler)) return (domain.lower, domain.upper) raise ValueError("BayesOpt does not support parameters of type " "`{}`".format(type(domain).__name__)) # Parameter name is e.g. "a/b/c" for nested dicts bounds = { "/".join(path): resolve_value(domain) for path, domain in domain_vars } if join: spec.update(bounds) bounds = spec return bounds