Source code for

import inspect
import logging
import pickle
from typing import Dict, List, Optional, Sequence, Type, Union

from ray.tune.result import DEFAULT_METRIC
from import (
from import (
from import parse_spec_vars
from ray.tune.utils.util import flatten_dict, unflatten_dict

    import nevergrad as ng
    from nevergrad.optimization import Optimizer
    from nevergrad.optimization.base import ConfiguredOptimizer

    Parameter = ng.p.Parameter
except ImportError:
    ng = None
    Optimizer = None
    ConfiguredOptimizer = None
    Parameter = None

logger = logging.getLogger(__name__)

[docs]class NevergradSearch(Searcher): """Uses Nevergrad to optimize hyperparameters. Nevergrad is an open source tool from Facebook for derivative free optimization. More info can be found at: You will need to install Nevergrad via the following command: .. code-block:: bash $ pip install nevergrad Parameters: optimizer: Optimizer class provided from Nevergrad. See here for available optimizers: This can also be an instance of a `ConfiguredOptimizer`. See the section on configured optimizers in the above link. optimizer_kwargs: Kwargs passed in when instantiating the `optimizer` space: Nevergrad parametrization to be passed to optimizer on instantiation, or list of parameter names if you passed an optimizer object. metric: The training result objective value attribute. If None but a mode was passed, the anonymous metric `_metric` will be used per default. mode: One of {min, max}. Determines whether objective is minimizing or maximizing the metric attribute. points_to_evaluate: 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. Tune automatically converts search spaces to Nevergrad's format: .. code-block:: python import nevergrad as ng config = { "width": tune.uniform(0, 20), "height": tune.uniform(-100, 100), "activation": tune.choice(["relu", "tanh"]) } current_best_params = [{ "width": 10, "height": 0, "activation": relu", }] ng_search = NevergradSearch( optimizer=ng.optimizers.OnePlusOne, metric="mean_loss", mode="min", points_to_evaluate=current_best_params) run(my_trainable, config=config, search_alg=ng_search) If you would like to pass the search space manually, the code would look like this: .. code-block:: python import nevergrad as ng space = ng.p.Dict( width=ng.p.Scalar(lower=0, upper=20), height=ng.p.Scalar(lower=-100, upper=100), activation=ng.p.Choice(choices=["relu", "tanh"]) ) ng_search = NevergradSearch( optimizer=ng.optimizers.OnePlusOne, space=space, metric="mean_loss", mode="min") run(my_trainable, search_alg=ng_search) """ def __init__( self, optimizer: Optional[ Union[Optimizer, Type[Optimizer], ConfiguredOptimizer] ] = None, optimizer_kwargs: Optional[Dict] = None, space: Optional[Union[Dict, Parameter]] = None, metric: Optional[str] = None, mode: Optional[str] = None, points_to_evaluate: Optional[List[Dict]] = None, ): assert ( ng is not None ), """Nevergrad must be installed! You can install Nevergrad with the command: `pip install nevergrad`.""" if mode: assert mode in ["min", "max"], "`mode` must be 'min' or 'max'." super(NevergradSearch, self).__init__(metric=metric, mode=mode) self._space = None self._opt_factory = None self._nevergrad_opt = None self._optimizer_kwargs = optimizer_kwargs or {} if points_to_evaluate is None: self._points_to_evaluate = None elif not isinstance(points_to_evaluate, Sequence): raise ValueError( "Invalid object type passed for `points_to_evaluate`: " f"{type(points_to_evaluate)}. " "Please pass a list of points (dictionaries) instead." ) else: self._points_to_evaluate = list(points_to_evaluate) 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) if isinstance(optimizer, Optimizer): if space is not None and not isinstance(space, list): raise ValueError( "If you pass a configured optimizer to Nevergrad, either " "pass a list of parameter names or None as the `space` " "parameter." ) if self._optimizer_kwargs: raise ValueError( "If you pass in optimizer kwargs, either pass " "an `Optimizer` subclass or an instance of " "`ConfiguredOptimizer`." ) self._parameters = space self._nevergrad_opt = optimizer elif ( inspect.isclass(optimizer) and issubclass(optimizer, Optimizer) ) or isinstance(optimizer, ConfiguredOptimizer): self._opt_factory = optimizer self._parameters = None self._space = space else: raise ValueError( "The `optimizer` argument passed to NevergradSearch must be " "either an `Optimizer` or a `ConfiguredOptimizer`." ) self._live_trial_mapping = {} if self._nevergrad_opt or self._space: self._setup_nevergrad() def _setup_nevergrad(self): if self._opt_factory: self._nevergrad_opt = self._opt_factory( self._space, **self._optimizer_kwargs ) # nevergrad.tell internally minimizes, so "max" => -1 if self._mode == "max": self._metric_op = -1.0 elif self._mode == "min": self._metric_op = 1.0 if self._metric is None and self._mode: # If only a mode was passed, use anonymous metric self._metric = DEFAULT_METRIC if hasattr(self._nevergrad_opt, "instrumentation"): # added in v0.2.0 if self._nevergrad_opt.instrumentation.kwargs: if self._nevergrad_opt.instrumentation.args: raise ValueError("Instrumented optimizers should use kwargs only") if self._parameters is not None: raise ValueError( "Instrumented optimizers should provide " "None as parameter_names" ) else: if self._parameters is None: raise ValueError( "Non-instrumented optimizers should have " "a list of parameter_names" ) if len(self._nevergrad_opt.instrumentation.args) != 1: raise ValueError("Instrumented optimizers should use kwargs only") if self._parameters is not None and self._nevergrad_opt.dimension != len( self._parameters ): raise ValueError( "len(parameters_names) must match optimizer " "dimension for non-instrumented optimizers" ) if self._points_to_evaluate: # Nevergrad is LIFO, so we add the points to evaluate in reverse # order. for i in range(len(self._points_to_evaluate) - 1, -1, -1): self._nevergrad_opt.suggest(self._points_to_evaluate[i]) def set_search_properties( self, metric: Optional[str], mode: Optional[str], config: Dict, **spec ) -> bool: if self._nevergrad_opt or 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_nevergrad() return True def suggest(self, trial_id: str) -> Optional[Dict]: if not self._nevergrad_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 ) ) suggested_config = self._nevergrad_opt.ask() self._live_trial_mapping[trial_id] = suggested_config # in v0.2.0+, output of ask() is a Candidate, # with fields args and kwargs if not suggested_config.kwargs: if self._parameters: return unflatten_dict( dict(zip(self._parameters, suggested_config.args[0])) ) return unflatten_dict(suggested_config.value) else: return unflatten_dict(suggested_config.kwargs)
[docs] 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 Nevergrad so that Nevergrad 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): ng_trial_info = self._live_trial_mapping[trial_id] self._nevergrad_opt.tell(ng_trial_info, self._metric_op * result[self._metric]) def save(self, checkpoint_path: str): save_object = self.__dict__ 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) self.__dict__.update(save_object) @staticmethod def convert_search_space(spec: Dict) -> Parameter: resolved_vars, domain_vars, grid_vars = parse_spec_vars(spec) if grid_vars: raise ValueError( "Grid search parameters cannot be automatically converted " "to a Nevergrad 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) -> Parameter: sampler = domain.get_sampler() if isinstance(sampler, Quantized): logger.warning( "Nevergrad does not support quantization. Dropped quantization." ) sampler = sampler.get_sampler() if isinstance(domain, Float): if isinstance(sampler, LogUniform): return ng.p.Log( lower=domain.lower, upper=domain.upper, exponent=sampler.base ) return ng.p.Scalar(lower=domain.lower, upper=domain.upper) elif isinstance(domain, Integer): if isinstance(sampler, LogUniform): return ng.p.Log( lower=domain.lower, upper=domain.upper - 1, # Upper bound exclusive exponent=sampler.base, ).set_integer_casting() return ng.p.Scalar( lower=domain.lower, upper=domain.upper - 1, # Upper bound exclusive ).set_integer_casting() elif isinstance(domain, Categorical): return ng.p.Choice(choices=domain.categories) raise ValueError( "Nevergrad 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} return ng.p.Dict(**space)