Source code for ax.models.torch.botorch

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

from __future__ import annotations

from copy import deepcopy
from typing import Any, Callable, Dict, List, Optional, Tuple

import numpy as np
import torch
from ax.core.search_space import SearchSpaceDigest
from ax.core.types import TCandidateMetadata, TConfig, TGenMetadata
from ax.models.torch.botorch_defaults import (
    get_and_fit_model,
    get_NEI,
    recommend_best_observed_point,
    scipy_optimizer,
)
from ax.models.torch.utils import (
    _get_X_pending_and_observed,
    _to_inequality_constraints,
    normalize_indices,
    predict_from_model,
    subset_model,
)
from ax.models.torch_base import TorchModel
from ax.utils.common.constants import Keys
from ax.utils.common.docutils import copy_doc
from ax.utils.common.logger import get_logger
from ax.utils.common.typeutils import checked_cast
from botorch.acquisition.acquisition import AcquisitionFunction
from botorch.models.model import Model
from botorch.models.model_list_gp_regression import ModelListGP
from torch import Tensor

logger = get_logger(__name__)


TModelConstructor = Callable[
    [
        List[Tensor],
        List[Tensor],
        List[Tensor],
        List[int],
        List[int],
        List[str],
        Optional[Dict[str, Tensor]],
        Any,
    ],
    Model,
]
TModelPredictor = Callable[[Model, Tensor], Tuple[Tensor, Tensor]]
TAcqfConstructor = Callable[
    [
        Model,
        Tensor,
        Optional[Tuple[Tensor, Tensor]],
        Optional[Tensor],
        Optional[Tensor],
        Any,
    ],
    AcquisitionFunction,
]
TOptimizer = Callable[
    [
        AcquisitionFunction,
        Tensor,
        int,
        Optional[List[Tuple[Tensor, Tensor, float]]],
        Optional[List[Tuple[Tensor, Tensor, float]]],
        Optional[Dict[int, float]],
        Optional[Callable[[Tensor], Tensor]],
        Any,
    ],
    Tuple[Tensor, Tensor],
]
TBestPointRecommender = Callable[
    [
        TorchModel,
        List[Tuple[float, float]],
        Tensor,
        Optional[Tuple[Tensor, Tensor]],
        Optional[Tuple[Tensor, Tensor]],
        Optional[Dict[int, float]],
        Optional[TConfig],
        Optional[Dict[int, float]],
    ],
    Optional[Tensor],
]


[docs]class BotorchModel(TorchModel): r""" Customizable botorch model. By default, this uses a noisy Expected Improvement acquisition function on top of a model made up of separate GPs, one for each outcome. This behavior can be modified by providing custom implementations of the following components: - a `model_constructor` that instantiates and fits a model on data - a `model_predictor` that predicts outcomes using the fitted model - a `acqf_constructor` that creates an acquisition function from a fitted model - a `acqf_optimizer` that optimizes the acquisition function - a `best_point_recommender` that recommends a current "best" point (i.e., what the model recommends if the learning process ended now) Args: model_constructor: A callable that instantiates and fits a model on data, with signature as described below. model_predictor: A callable that predicts using the fitted model, with signature as described below. acqf_constructor: A callable that creates an acquisition function from a fitted model, with signature as described below. acqf_optimizer: A callable that optimizes the acquisition function, with signature as described below. best_point_recommender: A callable that recommends the best point, with signature as described below. refit_on_cv: If True, refit the model for each fold when performing cross-validation. refit_on_update: If True, refit the model after updating the training data using the `update` method. warm_start_refitting: If True, start model refitting from previous model parameters in order to speed up the fitting process. Call signatures: :: model_constructor( Xs, Ys, Yvars, task_features, fidelity_features, metric_names, state_dict, **kwargs, ) -> model Here `Xs`, `Ys`, `Yvars` are lists of tensors (one element per outcome), `task_features` identifies columns of Xs that should be modeled as a task, `fidelity_features` is a list of ints that specify the positions of fidelity parameters in 'Xs', `metric_names` provides the names of each `Y` in `Ys`, `state_dict` is a pytorch module state dict, and `model` is a BoTorch `Model`. Optional kwargs are being passed through from the `BotorchModel` constructor. This callable is assumed to return a fitted BoTorch model that has the same dtype and lives on the same device as the input tensors. :: model_predictor(model, X) -> [mean, cov] Here `model` is a fitted botorch model, `X` is a tensor of candidate points, and `mean` and `cov` are the posterior mean and covariance, respectively. :: acqf_constructor( model, objective_weights, outcome_constraints, X_observed, X_pending, **kwargs, ) -> acq_function Here `model` is a botorch `Model`, `objective_weights` is a tensor of weights for the model outputs, `outcome_constraints` is a tuple of tensors describing the (linear) outcome constraints, `X_observed` are previously observed points, and `X_pending` are points whose evaluation is pending. `acq_function` is a BoTorch acquisition function crafted from these inputs. For additional details on the arguments, see `get_NEI`. :: acqf_optimizer( acq_function, bounds, n, inequality_constraints, equality_constraints, fixed_features, rounding_func, **kwargs, ) -> candidates Here `acq_function` is a BoTorch `AcquisitionFunction`, `bounds` is a tensor containing bounds on the parameters, `n` is the number of candidates to be generated, `inequality_constraints` are inequality constraints on parameter values, `fixed_features` specifies features that should be fixed during generation, and `rounding_func` is a callback that rounds an optimization result appropriately. `candidates` is a tensor of generated candidates. For additional details on the arguments, see `scipy_optimizer`. :: best_point_recommender( model, bounds, objective_weights, outcome_constraints, linear_constraints, fixed_features, model_gen_options, target_fidelities, ) -> candidates Here `model` is a TorchModel, `bounds` is a list of tuples containing bounds on the parameters, `objective_weights` is a tensor of weights for the model outputs, `outcome_constraints` is a tuple of tensors describing the (linear) outcome constraints, `linear_constraints` is a tuple of tensors describing constraints on the design, `fixed_features` specifies features that should be fixed during generation, `model_gen_options` is a config dictionary that can contain model-specific options, and `target_fidelities` is a map from fidelity feature column indices to their respective target fidelities, used for multi-fidelity optimization problems. % TODO: refer to an example. """ dtype: Optional[torch.dtype] device: Optional[torch.device] Xs: List[Tensor] Ys: List[Tensor] Yvars: List[Tensor] model: Optional[Model] def __init__( self, model_constructor: TModelConstructor = get_and_fit_model, model_predictor: TModelPredictor = predict_from_model, # pyre-fixme[9]: acqf_constructor has type `Callable[[Model, Tensor, # Optional[Tuple[Tensor, Tensor]], Optional[Tensor], Optional[Tensor], Any], # AcquisitionFunction]`; used as `Callable[[Model, Tensor, # Optional[Tuple[Tensor, Tensor]], Optional[Tensor], Optional[Tensor], # **(Any)], AcquisitionFunction]`. acqf_constructor: TAcqfConstructor = get_NEI, # pyre-fixme[9]: acqf_optimizer declared/used type mismatch acqf_optimizer: TOptimizer = scipy_optimizer, best_point_recommender: TBestPointRecommender = recommend_best_observed_point, refit_on_cv: bool = False, refit_on_update: bool = True, warm_start_refitting: bool = True, use_input_warping: bool = False, use_loocv_pseudo_likelihood: bool = False, **kwargs: Any, ) -> None: self.model_constructor = model_constructor self.model_predictor = model_predictor self.acqf_constructor = acqf_constructor self.acqf_optimizer = acqf_optimizer self.best_point_recommender = best_point_recommender self._kwargs = kwargs self.refit_on_cv = refit_on_cv self.refit_on_update = refit_on_update self.warm_start_refitting = warm_start_refitting self.use_input_warping = use_input_warping self.use_loocv_pseudo_likelihood = use_loocv_pseudo_likelihood self.model: Optional[Model] = None self.Xs = [] self.Ys = [] self.Yvars = [] self.dtype = None self.device = None self.task_features: List[int] = [] self.fidelity_features: List[int] = [] self.metric_names: List[str] = []
[docs] @copy_doc(TorchModel.fit) def fit( self, Xs: List[Tensor], Ys: List[Tensor], Yvars: List[Tensor], search_space_digest: SearchSpaceDigest, metric_names: List[str], candidate_metadata: Optional[List[List[TCandidateMetadata]]] = None, ) -> None: self.dtype = Xs[0].dtype self.device = Xs[0].device self.Xs = Xs self.Ys = Ys self.Yvars = Yvars self.task_features = normalize_indices( search_space_digest.task_features, d=Xs[0].size(-1) ) self.fidelity_features = normalize_indices( search_space_digest.fidelity_features, d=Xs[0].size(-1) ) self.metric_names = metric_names self.model = self.model_constructor( # pyre-ignore [28] Xs=Xs, Ys=Ys, Yvars=Yvars, task_features=self.task_features, fidelity_features=self.fidelity_features, metric_names=self.metric_names, use_input_warping=self.use_input_warping, use_loocv_pseudo_likelihood=self.use_loocv_pseudo_likelihood, **self._kwargs, )
[docs] @copy_doc(TorchModel.predict) def predict(self, X: Tensor) -> Tuple[Tensor, Tensor]: return self.model_predictor(model=self.model, X=X) # pyre-ignore [28]
[docs] @copy_doc(TorchModel.gen) def gen( self, n: int, bounds: List[Tuple[float, float]], objective_weights: Tensor, outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None, linear_constraints: Optional[Tuple[Tensor, Tensor]] = None, fixed_features: Optional[Dict[int, float]] = None, pending_observations: Optional[List[Tensor]] = None, model_gen_options: Optional[TConfig] = None, rounding_func: Optional[Callable[[Tensor], Tensor]] = None, target_fidelities: Optional[Dict[int, float]] = None, ) -> Tuple[Tensor, Tensor, TGenMetadata, Optional[List[TCandidateMetadata]]]: options = model_gen_options or {} acf_options = options.get(Keys.ACQF_KWARGS, {}) optimizer_options = options.get(Keys.OPTIMIZER_KWARGS, {}) if target_fidelities: raise NotImplementedError( "target_fidelities not implemented for base BotorchModel" ) X_pending, X_observed = _get_X_pending_and_observed( Xs=self.Xs, pending_observations=pending_observations, objective_weights=objective_weights, outcome_constraints=outcome_constraints, bounds=bounds, linear_constraints=linear_constraints, fixed_features=fixed_features, ) model = self.model # subset model only to the outcomes we need for the optimization 357 if options.get(Keys.SUBSET_MODEL, True): subset_model_results = subset_model( model=model, # pyre-ignore [6] objective_weights=objective_weights, outcome_constraints=outcome_constraints, ) model = subset_model_results.model objective_weights = subset_model_results.objective_weights outcome_constraints = subset_model_results.outcome_constraints bounds_ = torch.tensor(bounds, dtype=self.dtype, device=self.device) bounds_ = bounds_.transpose(0, 1) botorch_rounding_func = get_rounding_func(rounding_func) # The following logic is to work around the limitation of PyTorch's Sobol # sampler to <1111 dimensions. # TODO: Remove once https://github.com/pytorch/pytorch/issues/41489 is resolved. from botorch.exceptions.errors import UnsupportedError def make_and_optimize_acqf(override_qmc: bool = False) -> Tuple[Tensor, Tensor]: add_kwargs = {"qmc": False} if override_qmc else {} acquisition_function = self.acqf_constructor( # pyre-ignore: [28] model=model, objective_weights=objective_weights, outcome_constraints=outcome_constraints, X_observed=X_observed, X_pending=X_pending, **acf_options, **add_kwargs, ) acquisition_function = checked_cast( AcquisitionFunction, acquisition_function ) # pyre-ignore: [28] candidates, expected_acquisition_value = self.acqf_optimizer( acq_function=checked_cast(AcquisitionFunction, acquisition_function), bounds=bounds_, n=n, inequality_constraints=_to_inequality_constraints( linear_constraints=linear_constraints ), fixed_features=fixed_features, rounding_func=botorch_rounding_func, **optimizer_options, ) return candidates, expected_acquisition_value try: candidates, expected_acquisition_value = make_and_optimize_acqf() except UnsupportedError as e: if "SobolQMCSampler only supports dimensions q * o <= 1111" in str(e): # dimension too large for Sobol, let's use IID candidates, expected_acquisition_value = make_and_optimize_acqf( override_qmc=True ) else: raise e return ( candidates.detach().cpu(), torch.ones(n, dtype=self.dtype), {"expected_acquisition_value": expected_acquisition_value.tolist()}, None, )
[docs] @copy_doc(TorchModel.best_point) def best_point( self, bounds: List[Tuple[float, float]], objective_weights: Tensor, outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None, linear_constraints: Optional[Tuple[Tensor, Tensor]] = None, fixed_features: Optional[Dict[int, float]] = None, model_gen_options: Optional[TConfig] = None, target_fidelities: Optional[Dict[int, float]] = None, ) -> Optional[Tensor]: return self.best_point_recommender( # pyre-ignore [28] model=self, bounds=bounds, objective_weights=objective_weights, outcome_constraints=outcome_constraints, linear_constraints=linear_constraints, fixed_features=fixed_features, model_gen_options=model_gen_options, target_fidelities=target_fidelities, )
[docs] @copy_doc(TorchModel.cross_validate) def cross_validate( # pyre-ignore[14]: Some `TorchModel.cross_validate` kwargs self, # are not needed here and therefore we just use `**kwargs` catchall. Xs_train: List[Tensor], Ys_train: List[Tensor], Yvars_train: List[Tensor], X_test: Tensor, **kwargs: Any, ) -> Tuple[Tensor, Tensor]: if self.model is None: raise RuntimeError("Cannot cross-validate model that has not been fitted") if self.refit_on_cv: state_dict = None else: state_dict = deepcopy(self.model.state_dict()) model = self.model_constructor( # pyre-ignore: [28] Xs=Xs_train, Ys=Ys_train, Yvars=Yvars_train, task_features=self.task_features, state_dict=state_dict, fidelity_features=self.fidelity_features, metric_names=self.metric_names, refit_model=self.refit_on_cv, use_input_warping=self.use_input_warping, use_loocv_pseudo_likelihood=self.use_loocv_pseudo_likelihood, **self._kwargs, ) return self.model_predictor(model=model, X=X_test) # pyre-ignore: [28]
[docs] @copy_doc(TorchModel.update) def update( # pyre-ignore[14]: Some `TorchModel.update` kwargs are not self, # needed here and therefore we just use `**kwargs` catchall. Xs: List[Tensor], Ys: List[Tensor], Yvars: List[Tensor], candidate_metadata: Optional[List[List[TCandidateMetadata]]] = None, **kwargs: Any, ) -> None: if self.model is None: raise RuntimeError("Cannot update model that has not been fitted") self.Xs = Xs self.Ys = Ys self.Yvars = Yvars if self.refit_on_update and not self.warm_start_refitting: state_dict = None # pragma: no cover else: state_dict = deepcopy(self.model.state_dict()) self.model = self.model_constructor( # pyre-ignore: [28] Xs=self.Xs, Ys=self.Ys, Yvars=self.Yvars, task_features=self.task_features, state_dict=state_dict, fidelity_features=self.fidelity_features, metric_names=self.metric_names, refit_model=self.refit_on_update, use_input_warping=self.use_input_warping, use_loocv_pseudo_likelihood=self.use_loocv_pseudo_likelihood, **self._kwargs, )
[docs] def feature_importances(self) -> np.ndarray: if self.model is None: raise RuntimeError( "Cannot calculate feature_importances without a fitted model" ) elif isinstance(self.model, ModelListGP): models = self.model.models else: models = [self.model] lengthscales = [] for m in models: ls = m.covar_module.base_kernel.lengthscale if ls.ndim == 2: ls = ls.unsqueeze(0) lengthscales.append(ls) lengthscales = torch.cat(lengthscales, dim=0) return (1 / lengthscales).detach().cpu().numpy()
[docs]def get_rounding_func( rounding_func: Optional[Callable[[Tensor], Tensor]] ) -> Optional[Callable[[Tensor], Tensor]]: if rounding_func is None: botorch_rounding_func = rounding_func else: # make sure rounding_func is properly applied to q- and t-batches def botorch_rounding_func(X: Tensor) -> Tensor: batch_shape, d = X.shape[:-1], X.shape[-1] X_round = torch.stack( [rounding_func(x) for x in X.view(-1, d)] # pyre-ignore: [16] ) return X_round.view(*batch_shape, d) return botorch_rounding_func