Source code for ray.tune.suggest.dragonfly

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

import logging
import pickle

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

from ray.tune.suggest.suggestion import Searcher

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. .. code-block:: python from ray import tune from dragonfly.opt.gp_bandit import EuclideanGPBandit from dragonfly.exd.experiment_caller import EuclideanFunctionCaller from dragonfly import load_config domain_vars = [{ "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 }] domain_config = load_config({"domain": domain_vars}) func_caller = EuclideanFunctionCaller(None, domain_config.domain.list_of_domains[0]) optimizer = EuclideanGPBandit(func_caller, ask_tell_mode=True) algo = DragonflySearch(optimizer, metric="objective", mode="max"), algo=algo) Parameters: optimizer (dragonfly.opt.BlackboxOptimiser): Optimizer provided from dragonfly. Choose an optimiser that extends BlackboxOptimiser. metric (str): The training result objective value attribute. mode (str): One of {min, max}. Determines whether objective is minimizing or maximizing the metric attribute. points_to_evaluate (list of lists): A list of points you'd like to run first before sampling from the optimiser, e.g. these could be parameter configurations you already know work well to help the optimiser select good values. Each point is a list of the parameters using the order definition given by parameter_names. 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. """ def __init__(self, optimizer, metric="episode_reward_mean", mode="max", points_to_evaluate=None, evaluated_rewards=None, **kwargs): assert dragonfly is not None, """dragonfly must be installed! You can install Dragonfly with the command: `pip install dragonfly`.""" assert mode in ["min", "max"], "`mode` must be 'min' or 'max'!" self._initial_points = [] self._opt = optimizer self._opt.initialise() if points_to_evaluate and evaluated_rewards: self._opt.tell([(points_to_evaluate, evaluated_rewards)]) elif points_to_evaluate: self._initial_points = points_to_evaluate # Dragonfly internally maximizes, so "min" => -1 if mode == "min": self._metric_op = -1. elif mode == "max": self._metric_op = 1. self._live_trial_mapping = {} super(DragonflySearch, self).__init__( metric=metric, mode=mode, **kwargs) def suggest(self, trial_id): 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 return {"point": suggested_config} def on_trial_complete(self, trial_id, result=None, error=False): """Passes result to Dragonfly unless early terminated or errored.""" trial_info = self._live_trial_mapping.pop(trial_id) if result: self._opt.tell([(trial_info, self._metric_op * result[self._metric])]) def save(self, checkpoint_dir): trials_object = (self._initial_points, self._opt) with open(checkpoint_dir, "wb") as outputFile: pickle.dump(trials_object, outputFile) def restore(self, checkpoint_dir): with open(checkpoint_dir, "rb") as inputFile: trials_object = pickle.load(inputFile) self._initial_points = trials_object[0] self._opt = trials_object[1]