Source code for

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import inspect
import logging
import numpy as np

# use cloudpickle instead of pickle to make lambda funcs
# in dragonfly pickleable
from ray import cloudpickle
from typing import Dict, List, Optional, Union

from ray.tune.result import DEFAULT_METRIC
from import Domain, Float, Quantized
from import (
from import parse_spec_vars
from ray.tune.utils.util import flatten_dict, is_nan_or_inf, unflatten_dict

try:  # Python 3 only -- needed for lint test.
    import dragonfly
    from dragonfly.opt.blackbox_optimiser import BlackboxOptimiser
except ImportError:
    dragonfly = None
    BlackboxOptimiser = None

logger = logging.getLogger(__name__)

[docs]class DragonflySearch(Searcher): """Uses Dragonfly to optimize hyperparameters. Dragonfly provides an array of tools to scale up Bayesian optimisation to expensive large scale problems, including high dimensional optimisation. parallel evaluations in synchronous or asynchronous settings, multi-fidelity optimisation (using cheap approximations to speed up the optimisation process), and multi-objective optimisation. For more info: * Dragonfly Website: * Dragonfly Documentation: To use this search algorithm, install Dragonfly: .. code-block:: bash $ pip install dragonfly-opt This interface requires using FunctionCallers and optimizers provided by Dragonfly. This searcher will automatically filter out any NaN, inf or -inf results. Parameters: optimizer: Optimizer provided from dragonfly. Choose an optimiser that extends BlackboxOptimiser. If this is a string, `domain` must be set and `optimizer` must be one of [random, bandit, genetic]. domain: Optional domain. Should only be set if you don't pass an optimizer as the `optimizer` argument. Has to be one of [cartesian, euclidean]. space: Search space. Should only be set if you don't pass an optimizer as the `optimizer` argument. Defines the search space and requires a `domain` to be set. Can be automatically converted from the `param_space` dict passed to `tune.Tuner()`. 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. evaluated_rewards: 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. random_state_seed: Seed for reproducible results. Defaults to None. Please note that setting this to a value will change global random state for `numpy` on initalization and loading from checkpoint. Tune automatically converts search spaces to Dragonfly's format: .. code-block:: python from ray import tune config = { "LiNO3_vol": tune.uniform(0, 7), "Li2SO4_vol": tune.uniform(0, 7), "NaClO4_vol": tune.uniform(0, 7) } df_search = DragonflySearch( optimizer="bandit", domain="euclidean", metric="objective", mode="max") tuner = tune.Tuner( my_func, tune_config=tune.TuneConfig( search_alg=df_search ), param_space=config ) If you would like to pass the search space/optimizer manually, the code would look like this: .. code-block:: python from ray import tune space = [{ "name": "LiNO3_vol", "type": "float", "min": 0, "max": 7 }, { "name": "Li2SO4_vol", "type": "float", "min": 0, "max": 7 }, { "name": "NaClO4_vol", "type": "float", "min": 0, "max": 7 }] df_search = DragonflySearch( optimizer="bandit", domain="euclidean", space=space, metric="objective", mode="max") tuner = tune.Tuner( my_func, tune_config=tune.TuneConfig( search_alg=df_search ), ) """ def __init__( self, optimizer: Optional[Union[str, BlackboxOptimiser]] = None, domain: Optional[str] = None, space: Optional[Union[Dict, List[Dict]]] = None, metric: Optional[str] = None, mode: Optional[str] = None, points_to_evaluate: Optional[List[Dict]] = None, evaluated_rewards: Optional[List] = None, random_state_seed: Optional[int] = None, **kwargs ): assert ( dragonfly is not None ), """dragonfly must be installed! You can install Dragonfly with the command: `pip install dragonfly-opt`.""" if mode: assert mode in ["min", "max"], "`mode` must be 'min' or 'max'." if random_state_seed is not None: assert isinstance( random_state_seed, int ), "random_state_seed must be None or int, got '{}'.".format( type(random_state_seed) ) super(DragonflySearch, self).__init__(metric=metric, mode=mode, **kwargs) self._opt_arg = optimizer self._domain = domain 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) self._space = space self._points_to_evaluate = points_to_evaluate self._evaluated_rewards = evaluated_rewards self._initial_points = [] self._live_trial_mapping = {} self._point_parameter_names = [] self._random_state_seed = random_state_seed self._opt = None if isinstance(optimizer, BlackboxOptimiser): if domain or space: raise ValueError( "If you pass an optimizer instance to dragonfly, do not " "pass a `domain` or `space`." ) self._opt = optimizer self.init_dragonfly() elif self._space: self._setup_dragonfly() def _setup_dragonfly(self): """Setup dragonfly when no optimizer has been passed.""" assert not self._opt, "Optimizer already set." from dragonfly import load_config from dragonfly.exd.experiment_caller import ( CPFunctionCaller, EuclideanFunctionCaller, ) from dragonfly.opt.blackbox_optimiser import BlackboxOptimiser from dragonfly.opt.random_optimiser import ( CPRandomOptimiser, EuclideanRandomOptimiser, ) from dragonfly.opt.cp_ga_optimiser import CPGAOptimiser from dragonfly.opt.gp_bandit import CPGPBandit, EuclideanGPBandit if not self._space: raise ValueError( "You have to pass a `space` when initializing dragonfly, or " "pass a search space definition to the `param_space` parameter " "of `tune.Tuner()`." ) if not self._domain: raise ValueError( "You have to set a `domain` when initializing dragonfly. " "Choose one of [Cartesian, Euclidean]." ) self._point_parameter_names = [param["name"] for param in self._space] if self._random_state_seed is not None: np.random.seed(self._random_state_seed) if self._domain.lower().startswith("cartesian"): function_caller_cls = CPFunctionCaller elif self._domain.lower().startswith("euclidean"): function_caller_cls = EuclideanFunctionCaller else: raise ValueError( "Dragonfly's `domain` argument must be one of " "[Cartesian, Euclidean]." ) optimizer_cls = None if inspect.isclass(self._opt_arg) and issubclass( self._opt_arg, BlackboxOptimiser ): optimizer_cls = self._opt_arg elif isinstance(self._opt_arg, str): if self._opt_arg.lower().startswith("random"): if function_caller_cls == CPFunctionCaller: optimizer_cls = CPRandomOptimiser else: optimizer_cls = EuclideanRandomOptimiser elif self._opt_arg.lower().startswith("bandit"): if function_caller_cls == CPFunctionCaller: optimizer_cls = CPGPBandit else: optimizer_cls = EuclideanGPBandit elif self._opt_arg.lower().startswith("genetic"): if function_caller_cls == CPFunctionCaller: optimizer_cls = CPGAOptimiser else: raise ValueError( "Currently only the `cartesian` domain works with " "the `genetic` optimizer." ) else: raise ValueError( "Invalid optimizer specification. Either pass a full " "dragonfly optimizer, or a string " "in [random, bandit, genetic]." ) assert optimizer_cls, "No optimizer could be determined." domain_config = load_config({"domain": self._space}) function_caller = function_caller_cls( None, domain_config.domain.list_of_domains[0] ) self._opt = optimizer_cls(function_caller, ask_tell_mode=True) self.init_dragonfly() def init_dragonfly(self): if self._points_to_evaluate: points_to_evaluate = [ [config[par] for par in self._point_parameter_names] for config in self._points_to_evaluate ] else: points_to_evaluate = None self._opt.initialise() if points_to_evaluate and self._evaluated_rewards: self._opt.tell([(points_to_evaluate, self._evaluated_rewards)]) elif points_to_evaluate: self._initial_points = points_to_evaluate # Dragonfly internally maximizes, so "min" => -1 if self._mode == "min": self._metric_op = -1.0 elif self._mode == "max": 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 def add_evaluated_point( self, parameters: Dict, value: float, error: bool = False, pruned: bool = False, intermediate_values: Optional[List[float]] = None, ): assert self._opt, "Optimizer must be set." if intermediate_values: logger.warning("dragonfly doesn't use intermediate_values. Ignoring.") if not error and not pruned: self._opt.tell( [([parameters[par] for par in self._point_parameter_names], value)] ) else: logger.warning( "Only non errored and non pruned points can be added to dragonfly." ) def set_search_properties( self, metric: Optional[str], mode: Optional[str], config: Dict, **spec ) -> bool: if self._opt: return False space = self.convert_search_space(config) self._space = space if metric: self._metric = metric if mode: self._mode = mode self._setup_dragonfly() return True def suggest(self, trial_id: str) -> Optional[Dict]: if not self._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._initial_points: suggested_config = self._initial_points[0] del self._initial_points[0] else: try: suggested_config = self._opt.ask() except Exception as exc: logger.warning( "Dragonfly errored when querying. This may be due to a " "higher level of parallelism than supported. Try reducing " "parallelism in the experiment: %s", str(exc), ) return None self._live_trial_mapping[trial_id] = suggested_config config = dict(zip(self._point_parameter_names, suggested_config)) # Keep backwards compatibility config.update(point=suggested_config) return unflatten_dict(config)
[docs] def on_trial_complete( self, trial_id: str, result: Optional[Dict] = None, error: bool = False ): """Passes result to Dragonfly unless early terminated or errored.""" trial_info = self._live_trial_mapping.pop(trial_id) if result and not is_nan_or_inf(result[self._metric]): self._opt.tell([(trial_info, self._metric_op * result[self._metric])])
@staticmethod def convert_search_space(spec: Dict) -> List[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 Dragonfly 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(par: str, domain: Domain) -> Dict: sampler = domain.get_sampler() if isinstance(sampler, Quantized): logger.warning( "Dragonfly search does not support quantization. " "Dropped quantization." ) sampler = sampler.get_sampler() if isinstance(domain, Float): if domain.sampler is not None: logger.warning( "Dragonfly does not support specific sampling methods." " The {} sampler will be dropped.".format(sampler) ) return { "name": par, "type": "float", "min": domain.lower, "max": domain.upper, } raise ValueError( "Dragonfly does not support parameters of type " "`{}`".format(type(domain).__name__) ) # Parameter name is e.g. "a/b/c" for nested dicts space = [resolve_value("/".join(path), domain) for path, domain in domain_vars] return space def save(self, checkpoint_path: str): if self._random_state_seed is not None: numpy_random_state = np.random.get_state() else: numpy_random_state = None save_object = self.__dict__ save_object["_random_state_seed_to_set"] = numpy_random_state with open(checkpoint_path, "wb") as outputFile: cloudpickle.dump(save_object, outputFile) def restore(self, checkpoint_path: str): with open(checkpoint_path, "rb") as inputFile: save_object = cloudpickle.load(inputFile) numpy_random_state = save_object.pop("_random_state_seed_to_set", None) self.__dict__.update(save_object) if numpy_random_state is not None: np.random.set_state(numpy_random_state)