Source code for ax.modelbridge.random

#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

# pyre-strict

from typing import Dict, List, Optional

from ax.core.experiment import Experiment
from ax.core.observation import Observation, ObservationData, ObservationFeatures
from ax.core.optimization_config import OptimizationConfig
from ax.core.search_space import SearchSpace
from ax.modelbridge.base import GenResults, ModelBridge
from ax.modelbridge.modelbridge_utils import (
from ax.models.random.base import RandomModel
from ax.models.types import TConfig

FIT_MODEL_ERROR = "Model must be fit before {action}."

# pyre-fixme[13]: Attribute `model` is never initialized.
# pyre-fixme[13]: Attribute `parameters` is never initialized.
[docs]class RandomModelBridge(ModelBridge): """A model bridge for using purely random 'models'. Data and optimization configs are not required. This model bridge interfaces with RandomModel. Attributes: model: A RandomModel used to generate candidates (note: this an awkward use of the word 'model'). parameters: Params found in search space on modelbridge init. """ model: RandomModel parameters: List[str] def _fit( self, model: RandomModel, search_space: SearchSpace, observations: Optional[List[Observation]] = None, ) -> None: self.model = model # Extract and fix parameters from initial search space. self.parameters = list(search_space.parameters.keys()) def _gen( self, n: int, search_space: SearchSpace, pending_observations: Dict[str, List[ObservationFeatures]], fixed_features: Optional[ObservationFeatures], optimization_config: Optional[OptimizationConfig], model_gen_options: Optional[TConfig], ) -> GenResults: """Generate new candidates according to a search_space.""" # Extract parameter values search_space_digest = extract_search_space_digest(search_space, self.parameters) # Get fixed features fixed_features_dict = get_fixed_features(fixed_features, self.parameters) # Extract param constraints linear_constraints = extract_parameter_constraints( search_space.parameter_constraints, self.parameters ) # Generate the candidates X, w = self.model.gen( n=n, bounds=search_space_digest.bounds, linear_constraints=linear_constraints, fixed_features=fixed_features_dict, model_gen_options=model_gen_options, rounding_func=transform_callback(self.parameters, self.transforms), ) observation_features = parse_observation_features(X, self.parameters) return GenResults( observation_features=observation_features, weights=w.tolist(), ) def _predict( self, observation_features: List[ObservationFeatures] ) -> List[ObservationData]: """Apply terminal transform, predict, and reverse terminal transform on output. """ raise NotImplementedError("RandomModelBridge does not support prediction.") def _cross_validate( self, search_space: SearchSpace, cv_training_data: List[Observation], cv_test_points: List[ObservationFeatures], ) -> List[ObservationData]: raise NotImplementedError def _set_status_quo( self, experiment: Optional[Experiment], status_quo_name: Optional[str], status_quo_features: Optional[ObservationFeatures], ) -> None: pass