Source code for orion.core.worker.primary_algo

# -*- coding: utf-8 -*-
Sanitizing wrapper of main algorithm

Performs checks and organizes required transformations of points.

import orion.core.utils.backward as backward
from orion.core.worker.transformer import build_required_space

# pylint: disable=too-many-public-methods
[docs]class SpaceTransformAlgoWrapper: """Perform checks on points and transformations. Wrap the primary algorithm. 1. Checks requirements on the parameter space from algorithms and create the appropriate transformations. Apply transformations before and after methods of the primary algorithm. 2. Checks whether incoming and outcoming points are compliant with a space. Parameters ---------- algo_constructor: Child class of `BaseAlgorithm` Class constructor to build the algorithm object. space : `` The original definition of a problem's parameters space. algorithm_config : dict Configuration for the algorithm. """ def __init__(self, algo_constructor, space, **algorithm_config): self._space = space requirements = backward.get_algo_requirements(algo_constructor) self.transformed_space = build_required_space(space, **requirements) self.algorithm = algo_constructor(space, **algorithm_config) = self.transformed_space
[docs] def seed_rng(self, seed): """Seed the state of the algorithm's random number generator.""" self.algorithm.seed_rng(seed)
@property def state_dict(self): """Return a state dict that can be used to reset the state of the algorithm.""" return self.algorithm.state_dict
[docs] def set_state(self, state_dict): """Reset the state of the algorithm based on the given state_dict :param state_dict: Dictionary representing state of an algorithm """ self.algorithm.set_state(state_dict)
[docs] def suggest(self, num): """Suggest a `num` of new sets of parameters. Parameters ---------- num: int Number of points to suggest. The algorithm may return less than the number of points requested. Returns ------- list of trials or None A list of trials representing values suggested by the algorithm. The algorithm may opt out if it cannot make a good suggestion at the moment (it may be waiting for other trials to complete), in which case it will return None. Notes ----- New parameters must be compliant with the problem's domain ``. """ transformed_trials = self.algorithm.suggest(num) if transformed_trials is None: return None for trial in transformed_trials: self._verify_trial(trial, space=self.transformed_space) if trial not in self.transformed_space: raise ValueError( f"Trial {} not contained in space:" f"\nParams: {trial.params}\n: Space{self.transformed_space}" ) trials = [] for transformed_trial in transformed_trials: trial = self.transformed_space.reverse(transformed_trial) self._verify_trial(trial, trials.append(trial) return trials
[docs] def observe(self, trials): """Observe evaluated trials. .. seealso:: `orion.algo.base.BaseAlgorithm.observe` """ transformed_trials = [] for trial in trials: self._verify_trial(trial, transformed_trials.append(self.transformed_space.transform(trial)) self.algorithm.observe(transformed_trials)
[docs] def has_suggested(self, trial): """Whether the algorithm has suggested a given trial. .. seealso:: `orion.algo.base.BaseAlgorithm.has_suggested` """ return self.algorithm.has_suggested(self.transformed_space.transform(trial))
[docs] def has_observed(self, trial): """Whether the algorithm has observed a given trial. .. seealso:: `orion.algo.base.BaseAlgorithm.has_observed` """ return self.algorithm.has_observed(self.transformed_space.transform(trial))
@property def n_suggested(self): """Number of trials suggested by the algorithm""" return self.algorithm.n_suggested @property def n_observed(self): """Number of completed trials observed by the algorithm""" return self.algorithm.n_observed @property def is_done(self): """Return True, if an algorithm holds that there can be no further improvement.""" return self.algorithm.is_done
[docs] def score(self, trial): """Allow algorithm to evaluate `point` based on a prediction about this parameter set's performance. Return a subjective measure of expected performance. By default, return the same score any parameter (no preference). """ self._verify_trial(trial) return self.algorithm.score(self.transformed_space.transform(trial))
[docs] def judge(self, trial, measurements): """Inform an algorithm about online `measurements` of a running trial. The algorithm can return a dictionary of data which will be provided as a response to the running environment. Default is None response. """ self._verify_trial(trial) return self.algorithm.judge( self.transformed_space.transform(trial), measurements )
[docs] def should_suspend(self, trial): """Allow algorithm to decide whether a particular running trial is still worth to complete its evaluation, based on information provided by the `judge` method. """ self._verify_trial(trial) return self.algorithm.should_suspend(trial)
@property def configuration(self): """Return tunable elements of this algorithm in a dictionary form appropriate for saving. """ return self.algorithm.configuration @property def space(self): """Domain of problem associated with this algorithm's instance. .. note:: Redefining property here without setter, denies base class' setter. """ return self._space
[docs] def get_id(self, point, ignore_fidelity=False): """Compute a unique hash for a point based on params""" return self.algorithm.get_id( self.transformed_space.transform(point), ignore_fidelity=ignore_fidelity )
@property def fidelity_index(self): """Compute the index of the point where fidelity is. Returns None if there is no fidelity dimension. """ return self.algorithm.fidelity_index def _verify_trial(self, trial, space=None): if space is None: space = if trial not in space: raise ValueError( f"Trial {} not contained in space:" f"\nParams: {trial.params}\nSpace: {space}" )