# botorch.utils¶

## Constraints¶

Helpers for handling outcome constraints.

botorch.utils.constraints.get_outcome_constraint_transforms(outcome_constraints)[source]

Create outcome constraint callables from outcome constraint tensors.

Parameters

outcome_constraints (Optional[Tuple[Tensor, Tensor]]) – A tuple of (A, b). For k outcome constraints and m outputs at f(x), A is k x m and b is k x 1 such that A f(x) <= b.

Return type

Optional[List[Callable[[Tensor], Tensor]]]

Returns

A list of callables, each mapping a Tensor of size b x q x m to a tensor of size b x q, where m is the number of outputs of the model. Negative values imply feasibility. The callables support broadcasting (e.g. for calling on a tensor of shape mc_samples x b x q x m).

Example

>>> # constrain f(x)[0] <= 0
>>> A = torch.tensor([[1., 0.]])
>>> b = torch.tensor([[0.]])
>>> outcome_constraints = get_outcome_constraint_transforms((A, b))


## Objective¶

Helpers for handling objectives.

botorch.utils.objective.get_objective_weights_transform(weights)[source]

Create a linear objective callable from a set of weights.

Create a callable mapping a Tensor of size b x q x m to a Tensor of size b x q, where m is the number of outputs of the model using scalarization via the objective weights. This callable supports broadcasting (e.g. for calling on a tensor of shape mc_samples x b x q x m). For m = 1, the objective weight is used to determine the optimization direction.

Parameters

weights (Optional[Tensor]) – a 1-dimensional Tensor containing a weight for each task. If not provided, the identity mapping is used.

Return type

Callable[[Tensor], Tensor]

Returns

Transform function using the objective weights.

Example

>>> weights = torch.tensor([0.75, 0.25])
>>> transform = get_objective_weights_transform(weights)

botorch.utils.objective.apply_constraints_nonnegative_soft(obj, constraints, samples, eta)[source]

Applies constraints to a non-negative objective.

This function uses a sigmoid approximation to an indicator function for each constraint.

Parameters
• obj (Tensor) – A n_samples x b x q Tensor of objective values.

• constraints (List[Callable[[Tensor], Tensor]]) – A list of callables, each mapping a Tensor of size b x q x m to a Tensor of size b x q, where negative values imply feasibility. This callable must support broadcasting. Only relevant for multi- output models (m > 1).

• samples (Tensor) – A b x q x m Tensor of samples drawn from the posterior.

• eta (float) – The temperature parameter for the sigmoid function.

Return type

Tensor

Returns

A n_samples x b x q-dim tensor of feasibility-weighted objectives.

botorch.utils.objective.soft_eval_constraint(lhs, eta=0.001)[source]

Element-wise evaluation of a constraint in a ‘soft’ fashion

value(x) = 1 / (1 + exp(x / eta))

Parameters
• lhs (Tensor) – The left hand side of the constraint lhs <= 0.

• eta (float) – The temperature parameter of the softmax function. As eta grows larger, this approximates the Heaviside step function.

Return type

Tensor

Returns

Element-wise ‘soft’ feasibility indicator of the same shape as lhs. For each element x, value(x) -> 0 as x becomes positive, and value(x) -> 1 as x becomes negative.

botorch.utils.objective.apply_constraints(obj, constraints, samples, infeasible_cost, eta=0.001)[source]

Apply constraints using an infeasible_cost M for negative objectives.

This allows feasibility-weighting an objective for the case where the objective can be negative by usingthe following strategy: (1) add M to make obj nonnegative (2) apply constraints using the sigmoid approximation (3) shift by -M

Parameters
• obj (Tensor) – A n_samples x b x q Tensor of objective values.

• constraints (List[Callable[[Tensor], Tensor]]) – A list of callables, each mapping a Tensor of size b x q x m to a Tensor of size b x q, where negative values imply feasibility. This callable must support broadcasting. Only relevant for multi- output models (m > 1).

• samples (Tensor) – A b x q x m Tensor of samples drawn from the posterior.

• infeasible_cost (float) – The infeasible value.

• eta (float) – The temperature parameter of the sigmoid function.

Return type

Tensor

Returns

A n_samples x b x q-dim tensor of feasibility-weighted objectives.

## Sampling¶

Utilities for MC and qMC sampling.

botorch.utils.sampling.manual_seed(seed=None)[source]

Contextmanager for manual setting the torch.random seed.

Parameters

seed (Optional[int]) – The seed to set the random number generator to.

Return type

Generator[None, None, None]

Returns

Generator

Example

>>> with manual_seed(1234):
>>>     X = torch.rand(3)

botorch.utils.sampling.construct_base_samples(batch_shape, output_shape, sample_shape, qmc=True, seed=None, device=None, dtype=None)[source]

Construct base samples from a multi-variate standard normal N(0, I_qo).

Parameters
• batch_shape (Size) – The batch shape of the base samples to generate. Typically, this is used with each dimension of size 1, so as to eliminate sampling variance across batches.

• output_shape (Size) – The output shape (q x m) of the base samples to generate.

• sample_shape (Size) – The sample shape of the samples to draw.

• qmc (bool) – If True, use quasi-MC sampling (instead of iid draws).

• seed (Optional[int]) – If provided, use as a seed for the RNG.

Return type

Tensor

Returns

A sample_shape x batch_shape x mutput_shape dimensional tensor of base samples, drawn from a N(0, I_qm) distribution (using QMC if qmc=True). Here output_shape = q x m.

Example

>>> batch_shape = torch.Size([2])
>>> output_shape = torch.Size([3])
>>> sample_shape = torch.Size([10])
>>> samples = construct_base_samples(batch_shape, output_shape, sample_shape)

botorch.utils.sampling.construct_base_samples_from_posterior(posterior, sample_shape, qmc=True, collapse_batch_dims=True, seed=None)[source]

Construct a tensor of normally distributed base samples.

Parameters
• posterior (Posterior) – A Posterior object.

• sample_shape (Size) – The sample shape of the samples to draw.

• qmc (bool) – If True, use quasi-MC sampling (instead of iid draws).

• seed (Optional[int]) – If provided, use as a seed for the RNG.

Return type

Tensor

Returns

A num_samples x 1 x q x m dimensional Tensor of base samples, drawn from a N(0, I_qm) distribution (using QMC if qmc=True). Here q and m are the same as in the posterior’s event_shape b x q x m. Importantly, this only obtain a single t-batch of samples, so as to not introduce any sampling variance across t-batches.

Example

>>> sample_shape = torch.Size([10])
>>> samples = construct_base_samples_from_posterior(posterior, sample_shape)

botorch.utils.sampling.draw_sobol_samples(bounds, n, q, seed=None)[source]

Draw qMC samples from the box defined by bounds.

Parameters
• bounds (Tensor) – A 2 x d dimensional tensor specifying box constraints on a d-dimensional space, where bounds[0, :] and bounds[1, :] correspond to lower and upper bounds, respectively.

• n (int) – The number of (q-batch) samples.

• q (int) – The size of each q-batch.

• seed (Optional[int]) – The seed used for initializing Owen scrambling. If None (default), use a random seed.

Return type

Tensor

Returns

A n x q x d-dim tensor of qMC samples from the box defined by bounds.

Example

>>> bounds = torch.stack([torch.zeros(3), torch.ones(3)])
>>> samples = draw_sobol_samples(bounds, 10, 2)

botorch.utils.sampling.draw_sobol_normal_samples(d, n, device=None, dtype=None, seed=None)[source]

Draw qMC samples from a multi-variate standard normal N(0, I_d)

A primary use-case for this functionality is to compute an QMC average of f(X) over X where each element of X is drawn N(0, 1).

Parameters
• d (int) – The dimension of the normal distribution.

• n (int) – The number of samples to return.

• device (Optional[device]) – The torch device.

• dtype (Optional[dtype]) – The torch dtype.

• seed (Optional[int]) – The seed used for initializing Owen scrambling. If None (default), use a random seed.

Return type

Tensor

Returns

A tensor of qMC standard normal samples with dimension n x d with device and dtype specified by the input.

Example

>>> samples = draw_sobol_normal_samples(2, 10)

botorch.utils.sampling.sample_hypersphere(d, n=1, qmc=False, seed=None, device=None, dtype=None)[source]

Sample uniformly from a unit d-sphere.

Parameters
• d (int) – The dimension of the hypersphere.

• n (int) – The number of samples to return.

• qmc (bool) – If True, use QMC Sobol sampling (instead of i.i.d. uniform).

• seed (Optional[int]) – If provided, use as a seed for the RNG.

• device (Optional[device]) – The torch device.

• dtype (Optional[dtype]) – The torch dtype.

Return type

Tensor

Returns

An n x d tensor of uniform samples from from the d-hypersphere.

Example

>>> sample_hypersphere(d=5, n=10)

botorch.utils.sampling.sample_simplex(d, n=1, qmc=False, seed=None, device=None, dtype=None)[source]

Sample uniformly from a d-simplex.

Parameters
• d (int) – The dimension of the simplex.

• n (int) – The number of samples to return.

• qmc (bool) – If True, use QMC Sobol sampling (instead of i.i.d. uniform).

• seed (Optional[int]) – If provided, use as a seed for the RNG.

• device (Optional[device]) – The torch device.

• dtype (Optional[dtype]) – The torch dtype.

Return type

Tensor

Returns

An n x d tensor of uniform samples from from the d-simplex.

Example

>>> sample_simplex(d=3, n=10)

botorch.utils.sampling.batched_multinomial(weights, num_samples, replacement=False, generator=None, out=None)[source]

Sample from multinomial with an arbitrary number of batch dimensions.

Parameters
• weights (Tensor) – A batch_shape x num_categories tensor of weights. For each batch index i, j, …, this functions samples from a multinomial with input weights[i, j, …, :]. Note that the weights need not sum to one, but must be non-negative, finite and have a non-zero sum.

• num_samples (int) – The number of samples to draw for each batch index. Must be smaller than num_categories if replacement=False.

• replacement (bool) – If True, samples are drawn with replacement.

• generator (Optional[Generator]) – A a pseudorandom number generator for sampling.

• out (Optional[Tensor]) – The output tensor (optional). If provided, must be of size batch_shape x num_samples.

Return type

LongTensor

Returns

A batch_shape x num_samples tensor of samples.

This is a thin wrapper around torch.multinomial that allows weight (input) tensors with an arbitrary number of batch dimensions (torch.multinomial only allows a single batch dimension). The calling signature is the same as for torch.multinomial.

Example

>>> weights = torch.rand(2, 3, 10)
>>> samples = batched_multinomial(weights, 4)  # shape is 2 x 3 x 4


## Testing¶

class botorch.utils.testing.BotorchTestCase(methodName='runTest')[source]

Bases: unittest.case.TestCase

Basic test case for Botorch.

This
1. sets the default device to be torch.device(“cpu”)

2. ensures that no warnings are suppressed by default.

Create an instance of the class that will use the named test method when executed. Raises a ValueError if the instance does not have a method with the specified name.

device = device(type='cpu')
setUp()[source]

Hook method for setting up the test fixture before exercising it.

class botorch.utils.testing.BaseTestProblemBaseTestCase[source]

Bases: object

functions: List[BaseTestProblem] = None
test_forward()[source]
class botorch.utils.testing.SyntheticTestFunctionBaseTestCase[source]
test_optimal_value()[source]
test_optimizer()[source]
functions = None
class botorch.utils.testing.MockPosterior(mean=None, variance=None, samples=None)[source]

Mock object that implements dummy methods and feeds through specified outputs

property device

The torch device of the posterior.

Return type

device

property dtype

The torch dtype of the posterior.

Return type

dtype

property event_shape

The event shape (i.e. the shape of a single sample).

Return type

Size

property mean

The mean of the posterior as a (b) x n x m-dim Tensor.

property variance

The variance of the posterior as a (b) x n x m-dim Tensor.

rsample(sample_shape=None, base_samples=None)[source]

Mock sample by repeating self._samples. If base_samples is provided, do a shape check but return the same mock samples.

Return type

Tensor

class botorch.utils.testing.MockModel(posterior)[source]

Mock object that implements dummy methods and feeds through specified outputs

Initializes internal Module state, shared by both nn.Module and ScriptModule.

posterior(X, output_indices=None, observation_noise=False)[source]

Computes the posterior over model outputs at the provided points.

Parameters
• X (Tensor) – A b x q x d-dim Tensor, where d is the dimension of the feature space, q is the number of points considered jointly, and b is the batch dimension.

• output_indices (Optional[List[int]]) – A list of indices, corresponding to the outputs over which to compute the posterior (if the model is multi-output). Can be used to speed up computation if only a subset of the model’s outputs are required for optimization. If omitted, computes the posterior over all model outputs.

• observation_noise (bool) – If True, add observation noise to the posterior.

Return type

MockPosterior

Returns

A Posterior object, representing a batch of b joint distributions over q points and m outputs each.

property num_outputs

The number of outputs of the model.

Return type

int

state_dict()[source]

Returns a dictionary containing a whole state of the module.

Both parameters and persistent buffers (e.g. running averages) are included. Keys are corresponding parameter and buffer names.

Returns

a dictionary containing a whole state of the module

Return type

dict

Example:

>>> module.state_dict().keys()
['bias', 'weight']

load_state_dict(state_dict=None, strict=False)[source]

Copies parameters and buffers from state_dict into this module and its descendants. If strict is True, then the keys of state_dict must exactly match the keys returned by this module’s state_dict() function.

Parameters
• state_dict (dict) – a dict containing parameters and persistent buffers.

• strict (bool, optional) – whether to strictly enforce that the keys in state_dict match the keys returned by this module’s state_dict() function. Default: True

Returns

• missing_keys is a list of str containing the missing keys

• unexpected_keys is a list of str containing the unexpected keys

Return type

NamedTuple with missing_keys and unexpected_keys fields

class botorch.utils.testing.MockAcquisitionFunction[source]

Bases: object

Mock acquisition function object that implements dummy methods.

set_X_pending(X_pending=None)[source]

## Transformations¶

Some basic data transformation helpers.

botorch.utils.transforms.squeeze_last_dim(Y)[source]

Squeeze the last dimension of a Tensor.

Parameters

Y (Tensor) – A … x d-dim Tensor.

Return type

Tensor

Returns

The input tensor with last dimension squeezed.

Example

>>> Y = torch.rand(4, 3)
>>> Y_squeezed = squeeze_last_dim(Y)

botorch.utils.transforms.standardize(Y)[source]

Standardizes (zero mean, unit variance) a tensor by dim=-2.

If the tensor is single-dimensional, simply standardizes the tensor. If for some batch index all elements are equal (of if there is only a single data point), this function will return 0 for that batch index.

Parameters

Y (Tensor) – A batch_shape x n x m-dim tensor.

Return type

Tensor

Returns

The standardized Y.

Example

>>> Y = torch.rand(4, 3)
>>> Y_standardized = standardize(Y)

botorch.utils.transforms.normalize(X, bounds)[source]

Min-max normalize X w.r.t. the provided bounds.

Parameters
• X (Tensor) – … x d tensor of data

• bounds (Tensor) – 2 x d tensor of lower and upper bounds for each of the X’s d columns.

Return type

Tensor

Returns

A … x d-dim tensor of normalized data, given by

(X - bounds[0]) / (bounds[1] - bounds[0]). If all elements of X are contained within bounds, the normalized values will be contained within [0, 1]^d.

Example

>>> X = torch.rand(4, 3)
>>> bounds = torch.stack([torch.zeros(3), 0.5 * torch.ones(3)])
>>> X_normalized = normalize(X, bounds)

botorch.utils.transforms.unnormalize(X, bounds)[source]

Un-normalizes X w.r.t. the provided bounds.

Parameters
• X (Tensor) – … x d tensor of data

• bounds (Tensor) – 2 x d tensor of lower and upper bounds for each of the X’s d columns.

Return type

Tensor

Returns

A … x d-dim tensor of unnormalized data, given by

X * (bounds[1] - bounds[0]) + bounds[0]. If all elements of X are contained in [0, 1]^d, the un-normalized values will be contained within bounds.

Example

>>> X_normalized = torch.rand(4, 3)
>>> bounds = torch.stack([torch.zeros(3), 0.5 * torch.ones(3)])
>>> X = unnormalize(X_normalized, bounds)

botorch.utils.transforms.normalize_indices(indices, d)[source]

Normalize a list of indices to ensure that they are positive.

Parameters
• indices (Optional[List[int]]) – A list of indices (may contain negative indices for indexing “from the back”).

• d (int) – The dimension of the tensor to index.

Return type

Optional[List[int]]

Returns

A normalized list of indices such that each index is between 0 and d-1, or None if indices is None.

botorch.utils.transforms.t_batch_mode_transform(expected_q=None)[source]

Factory for decorators taking a t-batched X tensor.

This method creates decorators for instance methods to transform an input tensor X to t-batch mode (i.e. with at least 3 dimensions). This assumes the tensor has a q-batch dimension. The decorator also checks the q-batch size if expected_q is provided.

Parameters

expected_q (Optional[int]) – The expected q-batch size of X. If specified, this will raise an AssertitionError if X’s q-batch size does not equal expected_q.

Return type

Callable[[Callable[[Any, Tensor], Any]], Callable[[Any, Tensor], Any]]

Returns

The decorated instance method.

Example

>>> class ExampleClass:
>>>     @t_batch_mode_transform(expected_q=1)
>>>     def single_q_method(self, X):
>>>         ...
>>>
>>>     @t_batch_mode_transform()
>>>     def arbitrary_q_method(self, X):
>>>         ...

botorch.utils.transforms.concatenate_pending_points(method)[source]

Decorator concatenating X_pending into an acquisition function’s argument.

This decorator works on the forward method of acquisition functions taking a tensor X as the argument. If the acquisition function has an X_pending attribute (that is not None), this is concatenated into the input X, appropriately expanding the pending points to match the batch shape of X.

Example

>>> class ExampleAcquisitionFunction:
>>>     @concatenate_pending_points
>>>     @t_batch_mode_transform()
>>>     def forward(self, X):
>>>         ...

Return type

Callable[[Any, Tensor], Any]

botorch.utils.transforms.match_batch_shape(X, Y)[source]

Matches the batch dimension of a tensor to that of another tensor.

Parameters
• X (Tensor) – A batch_shape_X x q x d tensor, whose batch dimensions that correspond to batch dimensions of Y are to be matched to those (if compatible).

• Y (Tensor) – A batch_shape_Y x q’ x d tensor.

Return type

Tensor

Returns

A batch_shape_Y x q x d tensor containing the data of X expanded to the batch dimensions of Y (if compatible). For instance, if X is b’’ x b’ x q x d and Y is b x q x d, then the returned tensor is b’’ x b x q x d.

Example

>>> X = torch.rand(2, 1, 5, 3)
>>> Y = torch.rand(2, 6, 4, 3)
>>> X_matched = match_batch_shape(X, Y)
>>> X_matched.shape
torch.Size([2, 6, 5, 3])

botorch.utils.transforms.convert_to_target_pre_hook(module, *args)[source]

Pre-hook for automatically calling .to(X) on module prior to forward

botorch.utils.transforms.gpt_posterior_settings()[source]

Context manager for settings used for computing model posteriors.

## Feasible Volume¶

botorch.utils.feasible_volume.get_feasible_samples(samples, inequality_constraints=None)[source]

Checks which of the samples satisfy all of the inequality constraints.

Parameters
• samples (Tensor) – A sample size x d size tensor of feature samples, where d is a feature dimension.

• constraints (inequality) – A list of tuples (indices, coefficients, rhs), with each tuple encoding an inequality constraint of the form sum_i (X[indices[i]] * coefficients[i]) >= rhs.

Return type

Tuple[Tensor, float]

Returns

2-element tuple containing

• Samples satisfying the linear constraints.

• Estimated proportion of samples satisfying the linear constraints.

botorch.utils.feasible_volume.get_outcome_feasibility_probability(model, X, outcome_constraints, threshold=0.1, nsample_outcome=1000, seed=None)[source]

Monte Carlo estimate of the feasible volume with respect to the outcome constraints.

Parameters
• model (Model) – The model used for sampling the posterior.

• X (Tensor) – A tensor of dimension batch-shape x 1 x d, where d is feature dimension.

• outcome_constraints (List[Callable[[Tensor], Tensor]]) – A list of callables, each mapping a Tensor of dimension sample_shape x batch-shape x q x m to a Tensor of dimension sample_shape x batch-shape x q, where negative values imply feasibility.

• threshold (float) – A lower limit for the probability of posterior samples feasibility.

• nsample_outcome (int) – The number of samples from the model posterior.

• seed (Optional[int]) – The seed for the posterior sampler. If omitted, use a random seed.

Return type

float

Returns

Estimated proportion of features for which posterior samples satisfy given outcome constraints with probability above or equal to the given threshold.

botorch.utils.feasible_volume.estimate_feasible_volume(bounds, model, outcome_constraints, inequality_constraints=None, nsample_feature=1000, nsample_outcome=1000, threshold=0.1, verbose=False, seed=None, device=None, dtype=None)[source]

Monte Carlo estimate of the feasible volume with respect to feature constraints and outcome constraints.

Parameters
• bounds (Tensor) – A 2 x d tensor of lower and upper bounds for each column of X.

• model (Model) – The model used for sampling the outcomes.

• outcome_constraints (List[Callable[[Tensor], Tensor]]) – A list of callables, each mapping a Tensor of dimension sample_shape x batch-shape x q x m to a Tensor of dimension sample_shape x batch-shape x q, where negative values imply feasibility.

• constraints (inequality) – A list of tuples (indices, coefficients, rhs), with each tuple encoding an inequality constraint of the form sum_i (X[indices[i]] * coefficients[i]) >= rhs.

• nsample_feature (int) – The number of feature samples satisfying the bounds.

• nsample_outcome (int) – The number of outcome samples from the model posterior.

• threshold (float) – A lower limit for the probability of outcome feasibility

• seed (Optional[int]) – The seed for both feature and outcome samplers. If omitted, use a random seed.

• verbose (bool`) – An indicator for whether to log the results.

Returns

• Estimated proportion of volume in feature space that is

feasible wrt the bounds and the inequality constraints (linear).

• Estimated proportion of feasible features for which

posterior samples (outcome) satisfies the outcome constraints with probability above the given threshold.

Return type

2-element tuple containing