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, cast

import numpy as np
import torch
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 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[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, 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] 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, **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.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], bounds: List[Tuple[float, float]], task_features: List[int], feature_names: List[str], metric_names: List[str], fidelity_features: List[int], 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 # ensure indices are non-negative self.task_features = normalize_indices(task_features, d=Xs[0].size(-1)) self.fidelity_features = normalize_indices(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, **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): model, objective_weights, outcome_constraints, _ = subset_model( model=model, # pyre-ignore [6] objective_weights=objective_weights, outcome_constraints=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( self, Xs_train: List[Tensor], Ys_train: List[Tensor], Yvars_train: List[Tensor], X_test: Tensor, ) -> 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()) # pyre-ignore: [16] 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, **self._kwargs, ) return self.model_predictor(model=model, X=X_test) # pyre-ignore: [28]
[docs] @copy_doc(TorchModel.update) def update( self, Xs: List[Tensor], Ys: List[Tensor], Yvars: List[Tensor], candidate_metadata: Optional[List[List[TCandidateMetadata]]] = None, ) -> 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()) # pyre-ignore: [16] 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, **self._kwargs, )
[docs] def feature_importances(self) -> np.ndarray: if self.model is None: raise RuntimeError( "Cannot calculate feature_importances without a fitted model" ) else: ls = self.model.covar_module.base_kernel.lengthscale # pyre-ignore: [16] return cast(Tensor, (1 / ls)).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