botorch.acquisition¶
botorch.acquisition.acquisition¶
Abstract base module for all botorch acquisition functions.
AcquisitionFunction¶

class
botorch.acquisition.acquisition.
AcquisitionFunction
(model)[source]¶ Abstract base class for acquisition functions.
Constructor for the AcquisitionFunction base class.
 Parameters
model (
Model
) – A fitted model.
botorch.acquisition.analytic¶
Analytic acquisition functions (not using (q)MC sampling).
Analytic Acquisition Functions that evaluate the posterior without performing MonteCarlo sampling.
AnalyticAcquisitionFunction¶

class
botorch.acquisition.analytic.
AnalyticAcquisitionFunction
(model, objective=None)[source]¶ Base class for analytic acquisition functions.
Base constructor for analytic acquisition functions.
 Parameters
model (
Model
) – A fitted singleoutcome model.objective (
Optional
[ScalarizedObjective
]) – A ScalarizedObjective (optional).
ExpectedImprovement¶

class
botorch.acquisition.analytic.
ExpectedImprovement
(model, best_f, objective=None, maximize=True)[source]¶ Singleoutcome Expected Improvement (analytic).
Computes classic Expected Improvement over the current best observed value, using the analytic formula for a Normal posterior distribution. Unlike the MCbased acquisition functions, this relies on the posterior at single test point being Gaussian (and require the posterior to implement mean and variance properties). Only supports the case of q=1. The model must be singleoutcome.
EI(x) = E(max(y  best_f, 0)), y ~ f(x)
Example
>>> model = SingleTaskGP(train_X, train_Y) >>> EI = ExpectedImprovement(model, best_f=0.2) >>> ei = EI(test_X)
Singleoutcome Expected Improvement (analytic).
 Parameters
model (
Model
) – A fitted singleoutcome model.best_f (
Union
[float
,Tensor
]) – Either a scalar or a bdim Tensor (batch mode) representing the best function value observed so far (assumed noiseless).objective (
Optional
[ScalarizedObjective
]) – A ScalarizedObjective (optional).maximize (
bool
) – If True, consider the problem a maximization problem.

forward
(X)[source]¶ Evaluate Expected Improvement on the candidate set X.
 Parameters
X (
Tensor
) – A b1 x … bk x 1 x ddim batched tensor of ddim design points. Expected Improvement is computed for each point individually, i.e., what is considered are the marginal posteriors, not the joint. Return type
Tensor
 Returns
A b1 x … bkdim tensor of Expected Improvement values at the given design points X.
PosteriorMean¶

class
botorch.acquisition.analytic.
PosteriorMean
(model, objective=None)[source]¶ Singleoutcome Posterior Mean.
Only supports the case of q=1. Requires the model’s posterior to have a mean property. The model must be singleoutcome.
Example
>>> model = SingleTaskGP(train_X, train_Y) >>> PM = PosteriorMean(model) >>> pm = PM(test_X)
Base constructor for analytic acquisition functions.
 Parameters
model (
Model
) – A fitted singleoutcome model.objective (
Optional
[ScalarizedObjective
]) – A ScalarizedObjective (optional).
ProbabilityOfImprovement¶

class
botorch.acquisition.analytic.
ProbabilityOfImprovement
(model, best_f, objective=None, maximize=True)[source]¶ Singleoutcome Probability of Improvement.
Probability of improvment over the current best observed value, computed using the analytic formula under a Normal posterior distribution. Only supports the case of q=1. Requires the posterior to be Gaussian. The model must be singleoutcome.
PI(x) = P(y >= best_f), y ~ f(x)
Example
>>> model = SingleTaskGP(train_X, train_Y) >>> PI = ProbabilityOfImprovement(model, best_f=0.2) >>> pi = PI(test_X)
Singleoutcome analytic Probability of Improvement.
 Parameters
model (
Model
) – A fitted singleoutcome model.best_f (
Union
[float
,Tensor
]) – Either a scalar or a bdim Tensor (batch mode) representing the best function value observed so far (assumed noiseless).objective (
Optional
[ScalarizedObjective
]) – A ScalarizedObjective (optional).maximize (
bool
) – If True, consider the problem a maximization problem.
UpperConfidenceBound¶

class
botorch.acquisition.analytic.
UpperConfidenceBound
(model, beta, objective=None, maximize=True)[source]¶ Singleoutcome Upper Confidence Bound (UCB).
Analytic upper confidence bound that comprises of the posterior mean plus an additional term: the posterior standard deviation weighted by a tradeoff parameter, beta. Only supports the case of q=1 (i.e. greedy, nonbatch selection of design points). The model must be singleoutcome.
UCB(x) = mu(x) + sqrt(beta) * sigma(x), where mu and sigma are the posterior mean and standard deviation, respectively.
Example
>>> model = SingleTaskGP(train_X, train_Y) >>> UCB = UpperConfidenceBound(model, beta=0.2) >>> ucb = UCB(test_X)
Singleoutcome Upper Confidence Bound.
 Parameters
model (
Model
) – A fitted singleoutcome GP model (must be in batch mode if candidate sets X will be)beta (
Union
[float
,Tensor
]) – Either a scalar or a onedim tensor with b elements (batch mode) representing the tradeoff parameter between mean and covarianceobjective (
Optional
[ScalarizedObjective
]) – A ScalarizedObjective (optional).maximize (
bool
) – If True, consider the problem a maximization problem.
ConstrainedExpectedImprovement¶

class
botorch.acquisition.analytic.
ConstrainedExpectedImprovement
(model, best_f, objective_index, constraints, maximize=True)[source]¶ Constrained Expected Improvement (feasibilityweighted).
Computes the analytic expected improvement for a Normal posterior distribution, weighted by a probability of feasibility. The objective and constraints are assumed to be independent and have Gaussian posterior distributions. Only supports the case q=1. The model should be multioutcome, with the index of the objective and constraints passed to the constructor.
Constrained_EI(x) = EI(x) * Product_i P(y_i in [lower_i, upper_i]), where y_i ~ constraint_i(x) and lower_i, upper_i are the lower and upper bounds for the ith constraint, respectively.
Example
>>> # example where 0th output has a nonnegativity constraint and ... # 1st output is the objective >>> model = SingleTaskGP(train_X, train_Y) >>> constraints = {0: (0.0, None)} >>> cEI = ConstrainedExpectedImprovement(model, 0.2, 1, constraints) >>> cei = cEI(test_X)
Analytic Constrained Expected Improvement.
 Parameters
model (
Model
) – A fitted singleoutcome model.best_f (
Union
[float
,Tensor
]) – Either a scalar or a bdim Tensor (batch mode) representing the best function value observed so far (assumed noiseless).objective_index (
int
) – The index of the objective.constraints (
Dict
[int
,Tuple
[Optional
[float
],Optional
[float
]]]) – A dictionary of the form {i: [lower, upper]}, where i is the output index, and lower and upper are lower and upper bounds on that output (resp. interpreted as Inf / Inf if None)maximize (
bool
) – If True, consider the problem a maximization problem.
NoisyExpectedImprovement¶

class
botorch.acquisition.analytic.
NoisyExpectedImprovement
(model, X_observed, num_fantasies=20, maximize=True)[source]¶ Singleoutcome Noisy Expected Improvement (via fantasies).
This computes Noisy Expected Improvement by averaging over the Expected Improvemnt values of a number of fantasy models. Only supports the case q=1. Assumes that the posterior distribution of the model is Gaussian. The model must be singleoutcome.
NEI(x) = E(max(y  max Y_baseline), 0)), (y, Y_baseline) ~ f((x, X_baseline)), where X_baseline are previously observed points.
Note: This acquisition function currently relies on using a FixedNoiseGP (required for noiseless fantasies).
Example
>>> model = FixedNoiseGP(train_X, train_Y, train_Yvar=train_Yvar) >>> NEI = NoisyExpectedImprovement(model, train_X) >>> nei = NEI(test_X)
Singleoutcome Noisy Expected Improvement (via fantasies).
 Parameters
model (
GPyTorchModel
) – A fitted singleoutcome model.X_observed (
Tensor
) – A m x d Tensor of observed points that are likely to be the best observed points so far.num_fantasies (
int
) – The number of fantasies to generate. The higher this number the more accurate the model (at the expense of model complexity and performance).maximize (
bool
) – If True, consider the problem a maximization problem.
botorch.acquisition.fixed_feature¶
Derived Acquisition Function for fixing features during optimization.
A wrapper around AquisitionFunctions to fix certain features for optimization. This is useful e.g. for performing contextual optimization.
FixedFeatureAcquisitionFunction¶

class
botorch.acquisition.fixed_feature.
FixedFeatureAcquisitionFunction
(acq_function, d, columns, values)[source]¶ A wrapper around AquisitionFunctions to fix a subset of features.
Example
>>> model = SingleTaskGP(train_X, train_Y) # d = 5 >>> qEI = qExpectedImprovement(model, best_f=0.0) >>> columns = [2, 4] >>> values = X[..., columns] >>> qEI_FF = FixedFeatureAcquisitionFunction(qEI, 5, columns, values) >>> qei = qEI_FF(test_X) # d' = 3
Derived Acquisition Function by fixing a subset of input features.
 Parameters
acq_function (
AcquisitionFunction
) – The base acquisition function, operating on input tensors X_full of feature dimension d.d (
int
) – The feature dimension expected by acq_function.columns (
List
[int
]) – d_f < d indices of columns in X_full that are to be fixed to the provided values.values (
Union
[Tensor
,List
[float
]]) – The values to which to fix the columns in columns. Either a full batch_shape x q x d_f tensor of values (if values are different for each of the q input points), or an arraylike of values that is broadcastable to the input across tbatch and qbatch dimensions, e.g. a list of length d_f if values are the same across all t and qbatch dimensions.

forward
(X)[source]¶ Evaluate base acquisition function under the fixed features.
 Parameters
X (
Tensor
) – Input tensor of feature dimension d’ < d such that d’ + d_f = d. Returns
Base acquisition function evaluated on tensor X_full constructed by adding values in the appropriate places (see _construct_X_full).
botorch.acquisition.monte_carlo¶
Batch acquisition functions using the reparameterization trick in combination with (quasi) MonteCarlo sampling. See [Rezende2014reparam] and [Wilson2017reparam]
 Rezende2014reparam
D. J. Rezende, S. Mohamed, and D. Wierstra. Stochastic backpropagation and approximate inference in deep generative models. ICML 2014.
 Wilson2017reparam
J. T. Wilson, R. Moriconi, F. Hutter, and M. P. Deisenroth. The reparameterization trick for acquisition functions. ArXiv 2017.
MCAcquisitionFunction¶

class
botorch.acquisition.monte_carlo.
MCAcquisitionFunction
(model, sampler=None, objective=None, X_pending=None)[source]¶ Abstract base class for MonteCarlo based batch acquisition functions.
Constructor for the MCAcquisitionFunction base class.
 Parameters
model (
Model
) – A fitted model.sampler (
Optional
[MCSampler
]) – The sampler used to draw base samples. Defaults to SobolQMCNormalSampler(num_samples=512, collapse_batch_dims=True).objective (
Optional
[MCAcquisitionObjective
]) – The MCAcquisitionObjective under which the samples are evaluated. Defaults to IdentityMCObjective().X_pending (
Optional
[Tensor
]) – A m x ddim Tensor of m design points that have points that have been submitted for function evaluation but have not yet been evaluated.
qExpectedImprovement¶

class
botorch.acquisition.monte_carlo.
qExpectedImprovement
(model, best_f, sampler=None, objective=None, X_pending=None)[source]¶ MCbased batch Expected Improvement.
This computes qEI by (1) sampling the joint posterior over q points (2) evaluating the improvement over the current best for each sample (3) maximizing over q (4) averaging over the samples
qEI(X) = E(max(max Y  best_f, 0)), Y ~ f(X), where X = (x_1,…,x_q)
Example
>>> model = SingleTaskGP(train_X, train_Y) >>> best_f = train_Y.max()[0] >>> sampler = SobolQMCNormalSampler(1000) >>> qEI = qExpectedImprovement(model, best_f, sampler) >>> qei = qEI(test_X)
qExpected Improvement.
 Parameters
model (
Model
) – A fitted model.best_f (
Union
[float
,Tensor
]) – The best (feasible) function value observed so far (assumed noiseless).sampler (
Optional
[MCSampler
]) – The sampler used to draw base samples. Defaults to SobolQMCNormalSampler(num_samples=500, collapse_batch_dims=True)objective (
Optional
[MCAcquisitionObjective
]) – The MCAcquisitionObjective under which the samples are evaluated. Defaults to IdentityMCObjective().X_pending (
Optional
[Tensor
]) – A m x ddim Tensor of m design points that have points that have been submitted for function evaluation but have not yet been evaluated. Concatenated into X upon forward call. Copied and set to have no gradient.
qNoisyExpectedImprovement¶

class
botorch.acquisition.monte_carlo.
qNoisyExpectedImprovement
(model, X_baseline, sampler=None, objective=None, X_pending=None)[source]¶ MCbased batch Noisy Expected Improvement.
This function does not assume a best_f is known (which would require noiseless observations). Instead, it uses samples from the joint posterior over the q test points and previously observed points. The improvement over previously observed points is computed for each sample and averaged.
qNEI(X) = E(max(max Y  max Y_baseline, 0)), where (Y, Y_baseline) ~ f((X, X_baseline)), X = (x_1,…,x_q)
Example
>>> model = SingleTaskGP(train_X, train_Y) >>> sampler = SobolQMCNormalSampler(1000) >>> qNEI = qNoisyExpectedImprovement(model, train_X, sampler) >>> qnei = qNEI(test_X)
qNoisy Expected Improvement.
 Parameters
model (
Model
) – A fitted model.X_baseline (
Tensor
) – A r x ddim Tensor of r design points that have already been observed. These points are considered as the potential best design point.sampler (
Optional
[MCSampler
]) – The sampler used to draw base samples. Defaults to SobolQMCNormalSampler(num_samples=500, collapse_batch_dims=True).objective (
Optional
[MCAcquisitionObjective
]) – The MCAcquisitionObjective under which the samples are evaluated. Defaults to IdentityMCObjective().X_pending (
Optional
[Tensor
]) – A m x ddim Tensor of m design points that have points that have been submitted for function evaluation but have not yet been evaluated. Concatenated into X upon forward call. Copied and set to have no gradient.
qProbabilityOfImprovement¶

class
botorch.acquisition.monte_carlo.
qProbabilityOfImprovement
(model, best_f, sampler=None, objective=None, X_pending=None, tau=0.001)[source]¶ MCbased batch Probability of Improvement.
Estimates the probability of improvement over the current best observed value by sampling from the joint posterior distribution of the qbatch. MCbased estimates of a probability involves taking expectation of an indicator function; to support autodifferntiation, the indicator is replaced with a sigmoid function with temperature parameter tau.
qPI(X) = P(max Y >= best_f), Y ~ f(X), X = (x_1,…,x_q)
Example
>>> model = SingleTaskGP(train_X, train_Y) >>> best_f = train_Y.max()[0] >>> sampler = SobolQMCNormalSampler(1000) >>> qPI = qProbabilityOfImprovement(model, best_f, sampler) >>> qpi = qPI(test_X)
qProbability of Improvement.
 Parameters
model (
Model
) – A fitted model.best_f (
Union
[float
,Tensor
]) – The best (feasible) function value observed so far (assumed noiseless).sampler (
Optional
[MCSampler
]) – The sampler used to draw base samples. Defaults to SobolQMCNormalSampler(num_samples=500, collapse_batch_dims=True)objective (
Optional
[MCAcquisitionObjective
]) – The MCAcquisitionObjective under which the samples are evaluated. Defaults to IdentityMCObjective().X_pending (
Optional
[Tensor
]) – A m x ddim Tensor of m design points that have points that have been submitted for function evaluation but have not yet been evaluated. Concatenated into X upon forward call. Copied and set to have no gradient.tau (
float
) – The temperature parameter used in the sigmoid approximation of the step function. Smaller values yield more accurate approximations of the function, but result in gradients estimates with higher variance.
qSimpleRegret¶

class
botorch.acquisition.monte_carlo.
qSimpleRegret
(model, sampler=None, objective=None, X_pending=None)[source]¶ MCbased batch Simple Regret.
Samples from the joint posterior over the qbatch and computes the simple regret.
qSR(X) = E(max Y), Y ~ f(X), X = (x_1,…,x_q)
Example
>>> model = SingleTaskGP(train_X, train_Y) >>> sampler = SobolQMCNormalSampler(1000) >>> qSR = qSimpleRegret(model, sampler) >>> qsr = qSR(test_X)
Constructor for the MCAcquisitionFunction base class.
 Parameters
model (
Model
) – A fitted model.sampler (
Optional
[MCSampler
]) – The sampler used to draw base samples. Defaults to SobolQMCNormalSampler(num_samples=512, collapse_batch_dims=True).objective (
Optional
[MCAcquisitionObjective
]) – The MCAcquisitionObjective under which the samples are evaluated. Defaults to IdentityMCObjective().X_pending (
Optional
[Tensor
]) – A m x ddim Tensor of m design points that have points that have been submitted for function evaluation but have not yet been evaluated.
qUpperConfidenceBound¶

class
botorch.acquisition.monte_carlo.
qUpperConfidenceBound
(model, beta, sampler=None, objective=None, X_pending=None)[source]¶ MCbased batch Upper Confidence Bound.
Uses a reparameterization to extend UCB to qUCB for q > 1 (See Appendix A of [Wilson2017reparam].)
qUCB = E(max(mu + Y_tilde  mu)), where Y_tilde ~ N(mu, beta pi/2 Sigma) and f(X) has distribution N(mu, Sigma).
Example
>>> model = SingleTaskGP(train_X, train_Y) >>> sampler = SobolQMCNormalSampler(1000) >>> qUCB = qUpperConfidenceBound(model, 0.1, sampler) >>> qucb = qUCB(test_X)
qUpper Confidence Bound.
 Parameters
model (
Model
) – A fitted model.beta (
float
) – Controls tradeoff between mean and standard deviation in UCB.sampler (
Optional
[MCSampler
]) – The sampler used to draw base samples. Defaults to SobolQMCNormalSampler(num_samples=500, collapse_batch_dims=True)objective (
Optional
[MCAcquisitionObjective
]) – The MCAcquisitionObjective under which the samples are evaluated. Defaults to IdentityMCObjective().X_pending (
Optional
[Tensor
]) – A m x ddim Tensor of m design points that have points that have been submitted for function evaluation but have not yet been evaluated. Concatenated into X upon forward call. Copied and set to have no gradient.
botorch.acquisition.objective¶
Objective Modules to be used with acquisition functions.
ScalarizedObjective¶

class
botorch.acquisition.objective.
ScalarizedObjective
(weights, offset=0.0)[source]¶ Affine objective to be used with analytic acquisition functions.
For a Gaussian posterior at a single point (q=1) with mean mu and covariance matrix Sigma, this yields a singleoutput posterior with mean weights^T * mu and variance weights^T Sigma w.
Example
Example for a model with two outcomes:
>>> weights = torch.tensor([0.5, 0.25]) >>> objective = ScalarizedObjective(weights) >>> EI = ExpectedImprovement(model, best_f=0.1, objective=objective)
Affine objective.
 Parameters
weights (
Tensor
) – A onedimensional tensor with o elements representing the linear weights on the outputs.offset (
float
) – An offset to be added to posterior mean.

forward
(posterior)[source]¶ Compute the posterior of the affine transformation.
 Parameters
posterior (
GPyTorchPosterior
) – A posterior with the same number of outputs as the elements in self.weights. Return type
 Returns
A singleoutput posterior.
MCAcquisitionObjective¶

class
botorch.acquisition.objective.
MCAcquisitionObjective
[source]¶ Abstract base class for MCbased objectives.

abstract
forward
(samples)[source]¶ Evaluate the objective on the samples.
 Parameters
samples (
Tensor
) – A sample_shape x batch_shape x q x odim Tensors of samples from a model posterior. Returns
A sample_shape x batch_shape x qdim Tensor of objective values (assuming maximization).
 Return type
Tensor
This method is usually not called directly, but via the objectives
Example
>>> # `__call__` method: >>> samples = sampler(posterior) >>> outcome = mc_obj(samples)

abstract
IdentityMCObjective¶

class
botorch.acquisition.objective.
IdentityMCObjective
[source]¶ Trivial objective extracting the last dimension.
Example
>>> identity_objective = IdentityMCObjective() >>> samples = sampler(posterior) >>> objective = identity_objective(samples)

forward
(samples)[source]¶ Evaluate the objective on the samples.
 Parameters
samples (
Tensor
) – A sample_shape x batch_shape x q x odim Tensors of samples from a model posterior. Returns
A sample_shape x batch_shape x qdim Tensor of objective values (assuming maximization).
 Return type
Tensor
This method is usually not called directly, but via the objectives
Example
>>> # `__call__` method: >>> samples = sampler(posterior) >>> outcome = mc_obj(samples)

LinearMCObjective¶

class
botorch.acquisition.objective.
LinearMCObjective
(weights)[source]¶ Linear objective constructed from a weight tensor.
For input samples and mc_obj = LinearMCObjective(weights), this produces mc_obj(samples) = sum_{i} weights[i] * samples[…, i]
Example
Example for a model with two outcomes:
>>> weights = torch.tensor([0.75, 0.25]) >>> linear_objective = LinearMCObjective(weights) >>> samples = sampler(posterior) >>> objective = linear_objective(samples)
Linear Objective.
 Parameters
weights (
Tensor
) – A onedimensional tensor with o elements representing the linear weights on the outputs.
GenericMCObjective¶

class
botorch.acquisition.objective.
GenericMCObjective
(objective)[source]¶ Objective generated from a generic callable.
Allows to construct arbitrary MCobjective functions from a generic callable. In order to be able to use gradientbased acquisition function optimization it should be possible to backpropagate through the callable.
Example
>>> generic_objective = GenericMCObjective(lambda Y: torch.sqrt(Y).sum(dim=1)) >>> samples = sampler(posterior) >>> objective = generic_objective(samples)
Objective generated from a generic callable.
 Parameters
objective (
Callable
[[Tensor
],Tensor
]) – A callable mapping a sample_shape x batchshape x q x o dim Tensor to a sample_shape x batchshape x qdim Tensor of objective values.

forward
(samples)[source]¶ Evaluate the feasibilityweigthed objective on the samples.
 Parameters
samples (
Tensor
) – A sample_shape x batch_shape x q x odim Tensors of samples from a model posterior. Return type
Tensor
 Returns
A sample_shape x batch_shape x qdim Tensor of objective values weighted by feasibility (assuming maximization).
ConstrainedMCObjective¶

class
botorch.acquisition.objective.
ConstrainedMCObjective
(objective, constraints, infeasible_cost=0.0, eta=0.001)[source]¶ Feasibilityweighted objective.
An Objective allowing to maximize some scalable objective on the model outputs subject to a number of constraints. Constraint feasibilty is approximated by a sigmoid function.
mc_acq(X) = objective(X) * prod_i (1  sigmoid(constraint_i(X))) TODO: Document functional form exactly.
See botorch.utils.objective.apply_constraints for details on the constarint handling.
Example
>>> bound = 0.0 >>> objective = lambda Y: Y[..., 0] >>> # apply nonnegativity constraint on f(x)[1] >>> constraint = lambda Y: bound  Y[..., 1] >>> constrained_objective = ConstrainedMCObjective(objective, [constraint]) >>> samples = sampler(posterior) >>> objective = constrained_objective(samples)
Feasibilityweighted objective.
 Parameters
objective (
Callable
[[Tensor
],Tensor
]) – A callable mapping a sample_shape x batchshape x q x o dim Tensor to a sample_shape x batchshape x qdim Tensor of objective values.constraints (
List
[Callable
[[Tensor
],Tensor
]]) – A list of callables, each mapping a Tensor of dimension sample_shape x batchshape x q x o to a Tensor of dimension sample_shape x batchshape x q, where negative values imply feasibility.infeasible_cost (
float
) – The cost of a design if all associated samples are infeasible.eta (
float
) – The temperature parameter of the sigmoid function approximating the constraint.

forward
(samples)[source]¶ Evaluate the feasibilityweighted objective on the samples.
 Parameters
samples (
Tensor
) – A sample_shape x batch_shape x q x odim Tensors of samples from a model posterior. Return type
Tensor
 Returns
A sample_shape x batch_shape x qdim Tensor of objective values weighted by feasibility (assuming maximization).
botorch.acquisition.utils¶
Utilities for acquisition functions.

botorch.acquisition.utils.
get_acquisition_function
(acquisition_function_name, model, objective, X_observed, X_pending=None, mc_samples=500, qmc=True, seed=None, **kwargs)[source]¶ Convenience function for initializing botorch acquisition functions.
 Parameters
acquisition_function_name (
str
) – Name of the acquisition function.model (
Model
) – A fitted model.objective (
MCAcquisitionObjective
) – A MCAcquisitionObjective.X_observed (
Tensor
) – A m1 x ddim Tensor of m1 design points that have already been observed.X_pending (
Optional
[Tensor
]) – A m2 x ddim Tensor of m2 design points whose evaluation is pending.mc_samples (
int
) – The number of samples to use for (q)MC evaluation of the acquisition function.qmc (
bool
) – If True, use quasiMonteCarlo sampling (instead of iid).seed (
Optional
[int
]) – If provided, perform deterministic optimization (i.e. the function to optimize is fixed and not stochastic).
 Return type
 Returns
The requested acquisition function.
Example
>>> model = SingleTaskGP(train_X, train_Y) >>> obj = LinearMCObjective(weights=torch.tensor([1.0, 2.0])) >>> acqf = get_acquisition_function("qEI", model, obj, train_X)

botorch.acquisition.utils.
get_infeasible_cost
(X, model, objective=<function squeeze_last_dim>)[source]¶ Get infeasible cost for a model and objective.
 Computes an infeasible cost M such that M < min_x f(x) almost always,
so that feasible points are preferred.
 Parameters
X (
Tensor
) – A m x d Tensor of m design points to use in evaluating the minimum. These points should cover the design space well. The more points the better the estimate, at the expense of added computation.model (
Model
) – A fitted botorch model.objective (
Callable
[[Tensor
],Tensor
]) – The objective with which to evaluate the model output.
 Return type
float
 Returns
The infeasible cost M value.
Example
>>> model = SingleTaskGP(train_X, train_Y) >>> objective = lambda Y: Y[..., 1] ** 2 >>> M = get_infeasible_cost(train_X, model, obj)

botorch.acquisition.utils.
is_nonnegative
(acq_function)[source]¶ Determine whether a given acquisition function is nonnegative.
 Parameters
acq_function (
AcquisitionFunction
) – The AcquisitionFunction instance. Return type
bool
 Returns
True if acq_function is nonnegative, False if not, or if the behavior is unknown (for custom acquisition functions).
Example
>>> qEI = qExpectedImprovement(model, best_f=0.1) >>> is_nonnegative(qEI) # returns True