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 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 (
    extract_parameter_constraints,
    extract_search_space_digest,
    get_fixed_features,
    parse_observation_features,
    transform_callback,
)
from ax.models.random.base import RandomModel
from ax.models.types import TConfig


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



[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.
    """

    # pyre-fixme[13]: Attribute `model` is never initialized.
    model: RandomModel
    # pyre-fixme[13]: Attribute `parameters` is never initialized.
    parameters: list[str]

    def _fit(
        self,
        model: RandomModel,
        search_space: SearchSpace,
        observations: list[Observation] | None = 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: ObservationFeatures | None,
        optimization_config: OptimizationConfig | None,
        model_gen_options: TConfig | None,
    ) -> 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],
        use_posterior_predictive: bool = False,
    ) -> list[ObservationData]:
        raise NotImplementedError

    def _set_status_quo(
        self,
        experiment: Experiment | None,
        status_quo_name: str | None,
        status_quo_features: ObservationFeatures | None,
    ) -> None:
        pass