Source code for ray.tune.suggest.hyperopt

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

import numpy as np
import copy
import logging
    hyperopt_logger = logging.getLogger("hyperopt")
    import hyperopt as hpo
except ImportError:
    hpo = None

from ray.tune.error import TuneError
from ray.tune.suggest.suggestion import SuggestionAlgorithm

[docs]class HyperOptSearch(SuggestionAlgorithm): """A wrapper around HyperOpt to provide trial suggestions. Requires HyperOpt to be installed from source. Uses the Tree-structured Parzen Estimators algorithm, although can be trivially extended to support any algorithm HyperOpt uses. Externally added trials will not be tracked by HyperOpt. Parameters: space (dict): HyperOpt configuration. Parameters will be sampled from this configuration and will be used to override parameters generated in the variant generation process. max_concurrent (int): Number of maximum concurrent trials. Defaults to 10. reward_attr (str): The training result objective value attribute. This refers to an increasing value. points_to_evaluate (list): Initial parameter suggestions to be run first. This is for when you already have some good parameters you want hyperopt to run first to help the TPE algorithm make better suggestions for future parameters. Needs to be a list of dict of hyperopt-named variables. Choice variables should be indicated by their index in the list (see example) Example: >>> space = { >>> 'width': hp.uniform('width', 0, 20), >>> 'height': hp.uniform('height', -100, 100), >>> 'activation': hp.choice("activation", ["relu", "tanh"]) >>> } >>> current_best_params = [{ >>> 'width': 10, >>> 'height': 0, >>> 'activation': 0, # The index of "relu" >>> }] >>> algo = HyperOptSearch( >>> space, max_concurrent=4, reward_attr="neg_mean_loss", >>> points_to_evaluate=current_best_params) """ def __init__(self, space, max_concurrent=10, reward_attr="episode_reward_mean", points_to_evaluate=None, **kwargs): assert hpo is not None, "HyperOpt must be installed!" from hyperopt.fmin import generate_trials_to_calculate assert type(max_concurrent) is int and max_concurrent > 0 self._max_concurrent = max_concurrent self._reward_attr = reward_attr self.algo = hpo.tpe.suggest self.domain = hpo.Domain(lambda spc: spc, space) if points_to_evaluate is None: self._hpopt_trials = hpo.Trials() self._points_to_evaluate = 0 else: assert type(points_to_evaluate) == list self._hpopt_trials = generate_trials_to_calculate( points_to_evaluate) self._hpopt_trials.refresh() self._points_to_evaluate = len(points_to_evaluate) self._live_trial_mapping = {} self.rstate = np.random.RandomState() super(HyperOptSearch, self).__init__(**kwargs) def _suggest(self, trial_id): if self._num_live_trials() >= self._max_concurrent: return None if self._points_to_evaluate > 0: new_trial = self._hpopt_trials.trials[self._points_to_evaluate - 1] self._points_to_evaluate -= 1 else: new_ids = self._hpopt_trials.new_trial_ids(1) self._hpopt_trials.refresh() # Get new suggestion from Hyperopt new_trials = self.algo(new_ids, self.domain, self._hpopt_trials, self.rstate.randint(2**31 - 1)) self._hpopt_trials.insert_trial_docs(new_trials) self._hpopt_trials.refresh() new_trial = new_trials[0] self._live_trial_mapping[trial_id] = (new_trial["tid"], new_trial) # Taken from HyperOpt.base.evaluate config = hpo.base.spec_from_misc(new_trial["misc"]) ctrl = hpo.base.Ctrl(self._hpopt_trials, current_trial=new_trial) memo = self.domain.memo_from_config(config) hpo.utils.use_obj_for_literal_in_memo(self.domain.expr, ctrl, hpo.base.Ctrl, memo) suggested_config = hpo.pyll.rec_eval( self.domain.expr, memo=memo, print_node_on_error=self.domain.rec_eval_print_node_on_error) return copy.deepcopy(suggested_config) def on_trial_result(self, trial_id, result): ho_trial = self._get_hyperopt_trial(trial_id) if ho_trial is None: return now = hpo.utils.coarse_utcnow() ho_trial["book_time"] = now ho_trial["refresh_time"] = now def on_trial_complete(self, trial_id, result=None, error=False, early_terminated=False): """Passes the result to HyperOpt unless early terminated or errored. The result is internally negated when interacting with HyperOpt so that HyperOpt can "maximize" this value, as it minimizes on default. """ ho_trial = self._get_hyperopt_trial(trial_id) if ho_trial is None: return ho_trial["refresh_time"] = hpo.utils.coarse_utcnow() if error: ho_trial["state"] = hpo.base.JOB_STATE_ERROR ho_trial["misc"]["error"] = (str(TuneError), "Tune Error") elif early_terminated: ho_trial["state"] = hpo.base.JOB_STATE_ERROR ho_trial["misc"]["error"] = (str(TuneError), "Tune Removed") else: ho_trial["state"] = hpo.base.JOB_STATE_DONE hp_result = self._to_hyperopt_result(result) ho_trial["result"] = hp_result self._hpopt_trials.refresh() del self._live_trial_mapping[trial_id] def _to_hyperopt_result(self, result): return {"loss": -result[self._reward_attr], "status": "ok"} def _get_hyperopt_trial(self, trial_id): if trial_id not in self._live_trial_mapping: return hyperopt_tid = self._live_trial_mapping[trial_id][0] return [ t for t in self._hpopt_trials.trials if t["tid"] == hyperopt_tid ][0] def _num_live_trials(self): return len(self._live_trial_mapping)