Source code for ray.tune.suggest.skopt

import logging
import pickle
    import skopt as sko
except ImportError:
    sko = None

from ray.tune.suggest import Searcher

logger = logging.getLogger(__name__)

def _validate_warmstart(parameter_names, points_to_evaluate,
    if points_to_evaluate:
        if not isinstance(points_to_evaluate, list):
            raise TypeError(
                "points_to_evaluate expected to be a list, got {}.".format(
        for point in points_to_evaluate:
            if not isinstance(point, list):
                raise TypeError(
                    "points_to_evaluate expected to include list, got {}.".

            if not len(point) == len(parameter_names):
                raise ValueError("Dim of point {}".format(point) +
                                 " and parameter_names {}".format(
                                     parameter_names) + " do not match.")

    if points_to_evaluate and evaluated_rewards:
        if not isinstance(evaluated_rewards, list):
            raise TypeError(
                "evaluated_rewards expected to be a list, got {}.".format(
        if not len(evaluated_rewards) == len(points_to_evaluate):
            raise ValueError(
                "Dim of evaluated_rewards {}".format(evaluated_rewards) +
                " and points_to_evaluate {}".format(points_to_evaluate) +
                " do not match.")

[docs]class SkOptSearch(Searcher): """Uses Scikit Optimize (skopt) to optimize hyperparameters. Scikit-optimize is a black-box optimization library. Read more here: You will need to install Scikit-Optimize to use this module. .. code-block:: bash pip install scikit-optimize This Search Algorithm requires you to pass in a `skopt Optimizer object`_. Parameters: optimizer (skopt.optimizer.Optimizer): Optimizer provided from skopt. parameter_names (list): List of parameter names. Should match the dimension of the optimizer output. 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. (See tune/examples/ max_concurrent: Deprecated. use_early_stopped_trials: Deprecated. Example: .. code-block:: python from skopt import Optimizer optimizer = Optimizer([(0,20),(-100,100)]) current_best_params = [[10, 0], [15, -20]] algo = SkOptSearch(optimizer, ["width", "height"], metric="mean_loss", mode="min", points_to_evaluate=current_best_params) """ def __init__(self, optimizer, parameter_names, metric="episode_reward_mean", mode="max", points_to_evaluate=None, evaluated_rewards=None, max_concurrent=None, use_early_stopped_trials=None): assert sko is not None, """skopt must be installed! You can install Skopt with the command: `pip install scikit-optimize`.""" _validate_warmstart(parameter_names, points_to_evaluate, evaluated_rewards) assert mode in ["min", "max"], "`mode` must be 'min' or 'max'!" self.max_concurrent = max_concurrent super(SkOptSearch, self).__init__( metric=metric, mode=mode, max_concurrent=max_concurrent, use_early_stopped_trials=use_early_stopped_trials) self._initial_points = [] if points_to_evaluate and evaluated_rewards: optimizer.tell(points_to_evaluate, evaluated_rewards) elif points_to_evaluate: self._initial_points = points_to_evaluate self._parameters = parameter_names # Skopt internally minimizes, so "max" => -1 if mode == "max": self._metric_op = -1. elif mode == "min": self._metric_op = 1. self._skopt_opt = optimizer self._live_trial_mapping = {} def suggest(self, trial_id): if self.max_concurrent: if len(self._live_trial_mapping) >= self.max_concurrent: return None if self._initial_points: suggested_config = self._initial_points[0] del self._initial_points[0] else: suggested_config = self._skopt_opt.ask() self._live_trial_mapping[trial_id] = suggested_config return dict(zip(self._parameters, suggested_config)) def on_trial_complete(self, trial_id, result=None, error=False): """Notification for the completion of trial. The result is internally negated when interacting with Skopt so that Skopt 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, result): skopt_trial_info = self._live_trial_mapping[trial_id] self._skopt_opt.tell(skopt_trial_info, self._metric_op * result[self._metric]) def save(self, checkpoint_dir): trials_object = (self._initial_points, self._skopt_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._skopt_opt = trials_object[1]