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))


Containers¶

Containers to standardize inputs into models and acquisition functions.

class botorch.utils.containers.TrainingData(X: torch.Tensor, Y: torch.Tensor, Yvar: Optional[torch.Tensor] = None)[source]

Bases: tuple

Standardized struct of model training data for a single outcome.

Create new instance of TrainingData(X, Y, Yvar)

X: torch.Tensor

Alias for field number 0

Y: torch.Tensor

Alias for field number 1

Yvar: Optional[torch.Tensor]

Alias for field number 2

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 and an (optional) Tensor of size b x q x d 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, Optional[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.

Rounding¶

botorch.utils.rounding.approximate_round(X, tau=0.001)[source]

Diffentiable approximate rounding function.

This method is a piecewise approximation of a rounding function where each piece is a hyperbolic tangent function.

Parameters
• X (Tensor) – The tensor to round to the nearest integer (element-wise).

• tau (float) – A temperature hyperparameter.

Return type

Tensor

Returns

The approximately rounded input tensor.

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, batch_shape=None, seed=None)[source]

Draw qMC samples from the box defined by bounds.

Parameters
• bounds – 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 – The number of (q-batch) samples.

• q – The size of each q-batch.

• batch_shape – The batch shape of the samples. If given, returns samples of shape n x batch_shape x q x d, where each batch is an n x q x d-dim tensor of qMC samples.

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

Returns

A n x batch_shape 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

class botorch.utils.sampling.PolytopeSampler(inequality_constraints, n_burnin=0, equality_constraints=None, initial_point=None)[source]

Bases: object

Sampling points from a polytope described via a set of inequality and equality constraints.

Parameters
• inequality_constraints (Tuple[Tensor, Tensor]) – Tensors (A, b) describing inequality constraints: A*x<=b, where A is (n_ineq_con, d_sample)-dim Tensor and b is (n_ineq_con, 1)-dim Tensor with n_ineq_con being the number of inequalities and d_sample the dimension of the sample space.

• n_burnin (int) – The number of burn in samples.

• equality_constraints (Optional[Tuple[Tensor, Tensor]]) – Tensors (C, d) describing the equality constraints: C*x=d, where C is (n_eq_con, d_sample)-dim Tensor and d is (n_eq_con-dim, 1) Tensor with n_eq_con being the number of equalities.

• initial_point (Optional[Tensor]) – An (d_sample, 1)-dim Tensor presenting an inital point of the chain satisfying all the conditions. Determined automatically (by solving an LP) if not provided.

feasible(x)[source]
Return type

bool

find_initial_point()[source]

Finds a feasible point of the original problem. Details: Solves the following LP: min -s such that Ax<=b-2*s, s>=0, plus equality constraints. The number of inequality constrains in LP: nrow(A) + 1. LP solution dimension: dim(x) + dim(s) = dim(x) + 1.

draw(n=1, seed=None)[source]
Return type

Tensor

botorch.utils.sampling.sample_polytope(A, b, x0, n=10000, n0=100, seed=None)[source]

Hit and run sampler from uniform sampling points from a polytope, described via inequality constraints A*x<=b.

Parameters
• A (Tensor) – A Tensor describing inequality constraints so that all samples satisfy Ax<=b.

• b (Tensor) – A Tensor describing the inequality constraints so that all samples satisfy Ax<=b.

• x0 (Tensor) – d dim Tensor representing a starting point of the chain satisfying the constraints.

• n (int) – The number of resulting samples kept in the output.

• n0 (int) – The number of burn-in samples. The chain will produce n+n0 samples but the first n0 samples are not saved.

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

Return type

Tensor

Returns

(n, d) dim Tensor containing the resulting samples.

Sampling from GP priors¶

class botorch.utils.gp_sampling.GPDraw(model, seed=None)[source]

Bases: torch.nn.modules.module.Module

Convenience wrapper for sampling a function from a GP prior.

This wrapper implicitly defines the GP sample as a self-updating function by keeping track of the evaluated points and respective base samples used during the evaluation.

This does not yet support multi-output models.

Construct a GP function sampler.

Parameters

model (Model) – The Model defining the GP prior.

property Xs

A (batch_shape) x n_eval x d-dim tensor of locations at which the GP was evaluated (or None if the sample has never been evaluated).

Return type

Tensor

property Ys

A (batch_shape) x n_eval x d-dim tensor of associated function values (or None if the sample has never been evaluated).

Return type

Tensor

forward(X)[source]

Evaluate the GP sample function at a set of points X.

Parameters

X (Tensor) – A batch_shape x n x d-dim tensor of points

Return type

Tensor

Returns

The value of the GP sample at the n points.

training: bool

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[botorch.test_functions.base.BaseTestProblem]
test_forward()[source]
class botorch.utils.testing.SyntheticTestFunctionBaseTestCase[source]
test_optimal_value()[source]
test_optimizer()[source]
functions: List[botorch.test_functions.base.BaseTestProblem]
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

property batch_shape

The batch shape of the model.

This is a batch shape from an I/O perspective, independent of the internal representation of the model (as e.g. in BatchedMultiOutputGPyTorchModel). For a model with m outputs, a test_batch_shape x q x d-shaped input X to the posterior method returns a Posterior object over an output of shape broadcast(test_batch_shape, model.batch_shape) x q x m.

Return type

Size

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

training: bool
class botorch.utils.testing.MockAcquisitionFunction[source]

Bases: object

Mock acquisition function object that implements dummy methods.

set_X_pending(X_pending=None)[source]
class botorch.utils.testing.MultiObjectiveTestProblemBaseTestCase[source]
test_attributes()[source]
test_max_hv()[source]
test_ref_point()[source]
functions: List[botorch.test_functions.base.BaseTestProblem]
class botorch.utils.testing.ConstrainedMultiObjectiveTestProblemBaseTestCase[source]
test_num_constraints()[source]
test_evaluate_slack_true()[source]
functions: List[botorch.test_functions.base.BaseTestProblem]

Torch¶

class botorch.utils.torch.BufferDict(buffers=None)[source]

Bases: torch.nn.modules.module.Module

Holds buffers in a dictionary.

BufferDict can be indexed like a regular Python dictionary, but buffers it contains are properly registered, and will be visible by all Module methods.

BufferDict is an ordered dictionary that respects

• the order of insertion, and

• in update(), the order of the merged OrderedDict or another BufferDict (the argument to update()).

Note that update() with other unordered mapping types (e.g., Python’s plain dict) does not preserve the order of the merged mapping.

Parameters

buffers (iterable, optional) – a mapping (dictionary) of (string : Tensor) or an iterable of key-value pairs of type (string, Tensor)

Example:

class MyModule(nn.Module):
def __init__(self):
super(MyModule, self).__init__()
self.buffers = nn.BufferDict({
'left': torch.randn(5, 10),
'right': torch.randn(5, 10)
})

def forward(self, x, choice):
x = self.buffers[choice].mm(x)
return x


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

clear()[source]

Remove all items from the BufferDict.

pop(key)[source]

Remove key from the BufferDict and return its buffer.

Parameters

key (string) – key to pop from the BufferDict

keys()[source]

Return an iterable of the BufferDict keys.

items()[source]

Return an iterable of the BufferDict key/value pairs.

values()[source]

Return an iterable of the BufferDict values.

update(buffers)[source]

Update the BufferDict with the key-value pairs from a mapping or an iterable, overwriting existing keys.

Note

If buffers is an OrderedDict, a BufferDict, or an iterable of key-value pairs, the order of new elements in it is preserved.

Parameters

buffers (iterable) – a mapping (dictionary) from string to Tensor, or an iterable of key-value pairs of type (string, Tensor)

extra_repr()[source]

Set the extra representation of the module

To print customized extra information, you should re-implement this method in your own modules. Both single-line and multi-line strings are acceptable.

training: bool

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 (or 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, assert_output_shape=True)[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, and the output shape if assert_output_shape is True.

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

• assert_output_shape (bool) – If True, this will raise an AssertionError if the output shape does not match either the t-batch shape of X, or the acqf.model.batch_shape for acquisition functions using batched models.

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

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

Abstract Box Decompositions¶

Box decomposition algorithms.

class botorch.utils.multi_objective.box_decompositions.box_decomposition.BoxDecomposition(ref_point, sort, Y=None)[source]

Bases: torch.nn.modules.module.Module, abc.ABC

An abstract class for box decompositions.

Note: Internally, we store the negative reference point (minimization).

Initialize BoxDecomposition.

Parameters
• ref_point (Tensor) – A m-dim tensor containing the reference point.

• sort (bool) – A boolean indicating whether to sort the Pareto frontier.

• Y (Optional[Tensor]) – A (batch_shape) x n x m-dim tensor of outcomes.

property pareto_Y

This returns the non-dominated set.

Return type

Tensor

Returns

A n_pareto x m-dim tensor of outcomes.

property ref_point

Get the reference point.

Return type

Tensor

Returns

A m-dim tensor of outcomes.

property Y

Get the raw outcomes.

Return type

Tensor

Returns

A n x m-dim tensor of outcomes.

partition_space()[source]

Compute box decomposition.

Return type

None

abstract partition_space_2d()[source]

Compute box decomposition for 2 objectives.

Return type

None

abstract get_hypercell_bounds()[source]

Get the bounds of each hypercell in the decomposition.

Return type

Tensor

Returns

A 2 x num_cells x num_outcomes-dim tensor containing the

lower and upper vertices bounding each hypercell.

update(Y)[source]

Update non-dominated front and decomposition.

Parameters

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

Return type

None

training: bool

Box Decomposition Utilities¶

Utilities for box decomposition algorithms.

Box Decompositions [DEPRECATED - use botorch..utils.multi_objective.box_decompositions]¶

DEPRECATED - Box decomposition algorithms. Use the botorch.utils.multi_objective.box_decompositions instead.

Hypervolume¶

Hypervolume Utilities.

References

Fonseca2006(1,2)

C. M. Fonseca, L. Paquete, and M. Lopez-Ibanez. An improved dimension-sweep algorithm for the hypervolume indicator. In IEEE Congress on Evolutionary Computation, pages 1157-1163, Vancouver, Canada, July 2006.

class botorch.utils.multi_objective.hypervolume.Hypervolume(ref_point)[source]

Bases: object

Hypervolume computation dimension sweep algorithm from [Fonseca2006].

Adapted from Simon Wessing’s implementation of the algorithm (Variant 3, Version 1.2) in [Fonseca2006] in PyMOO: https://github.com/msu-coinlab/pymoo/blob/master/pymoo/vendor/hv.py

Maximization is assumed.

TODO: write this in C++ for faster looping.

Initialize hypervolume object.

Parameters

ref_point (Tensor) – m-dim Tensor containing the reference point.

property ref_point

Get reference point (for maximization).

Return type

Tensor

Returns

A m-dim tensor containing the reference point.

compute(pareto_Y)[source]

Compute hypervolume.

Parameters

pareto_Y (Tensor) – A n x m-dim tensor of pareto optimal outcomes

Return type

float

Returns

The hypervolume.

botorch.utils.multi_objective.hypervolume.sort_by_dimension(nodes, i)[source]

Sorts the list of nodes in-place by the specified objective.

Parameters
• nodes (List[Node]) – A list of Nodes

• i (int) – The index of the objective to sort by

Return type

None

class botorch.utils.multi_objective.hypervolume.Node(m, dtype, device, data=None)[source]

Bases: object

Node in the MultiList data structure.

Initialize MultiList.

Parameters
• m (int) – The number of objectives

• dtype (dtype) – The dtype

• device (device) – The device

• data (Optional[Tensor]) – The tensor data to be stored in this Node.

class botorch.utils.multi_objective.hypervolume.MultiList(m, dtype, device)[source]

Bases: object

A special data structure used in hypervolume computation.

It consists of several doubly linked lists that share common nodes. Every node has multiple predecessors and successors, one in every list.

Initialize m doubly linked lists.

Parameters
• m (int) – number of doubly linked lists

• dtype (dtype) – the dtype

• device (device) – the device

append(node, index)[source]

Appends a node to the end of the list at the given index.

Parameters
• node (Node) – the new node

• index (int) – the index where the node should be appended.

Return type

None

extend(nodes, index)[source]

Extends the list at the given index with the nodes.

Parameters
• nodes (List[Node]) – list of nodes to append at the given index.

• index (int) – the index where the nodes should be appended.

Return type

None

remove(node, index, bounds)[source]

Removes and returns ‘node’ from all lists in [0, ‘index’].

Parameters
• node (Node) – The node to remove

• index (int) – The upper bound on the range of indices

• bounds (Tensor) – A 2 x m-dim tensor bounds on the objectives

Return type

Node

reinsert(node, index, bounds)[source]

Re-inserts the node at its original position.

Re-inserts the node at its original position in all lists in [0, ‘index’] before it was removed. This method assumes that the next and previous nodes of the node that is reinserted are in the list.

Parameters
• node (Node) – The node

• index (int) – The upper bound on the range of indices

• bounds (Tensor) – A 2 x m-dim tensor bounds on the objectives

Return type

None

Non-dominated Partitionings¶

Algorithms for partitioning the non-dominated space into rectangles.

References

Couckuyt2012(1,2)

I. Couckuyt, D. Deschrijver and T. Dhaene, “Towards Efficient Multiobjective Optimization: Multiobjective statistical criterions,” 2012 IEEE Congress on Evolutionary Computation, Brisbane, QLD, 2012, pp. 1-8.

class botorch.utils.multi_objective.box_decompositions.non_dominated.NondominatedPartitioning(ref_point, Y=None, alpha=0.0)[source]

A class for partitioning the non-dominated space into hyper-cells.

Note: this assumes maximization. Internally, it multiplies by -1 and performs the decomposition under minimization. TODO: use maximization internally as well.

Note: it is only feasible to use this algorithm to compute an exact decomposition of the non-dominated space for m<5 objectives (alpha=0.0).

The alpha parameter can be increased to obtain an approximate partitioning faster. The alpha is a fraction of the total hypervolume encapsuling the entire Pareto set. When a hypercell’s volume divided by the total hypervolume is less than alpha, we discard the hypercell. See Figure 2 in [Couckuyt2012] for a visual representation.

This PyTorch implementation of the binary partitioning algorithm ([Couckuyt2012]) is adapted from numpy/tensorflow implementation at: https://github.com/GPflow/GPflowOpt/blob/master/gpflowopt/pareto.py.

TODO: replace this with a more efficient decomposition. E.g. https://link.springer.com/content/pdf/10.1007/s10898-019-00798-7.pdf

Initialize NondominatedPartitioning.

Parameters
• ref_point (Tensor) – A m-dim tensor containing the reference point.

• Y (Optional[Tensor]) – A (batch_shape) x n x m-dim tensor.

• alpha (float) – A thresold fraction of total volume used in an approximate decomposition.

partition_space_2d()[source]

Partition the non-dominated space into disjoint hypercells.

This direct method works for m=2 outcomes.

Return type

None

get_hypercell_bounds()[source]

Get the bounds of each hypercell in the decomposition.

Parameters

ref_point – A (batch_shape) x m-dim tensor containing the reference point.

Return type

Tensor

Returns

A 2 x num_cells x num_outcomes-dim tensor containing the

lower and upper vertices bounding each hypercell.

compute_hypervolume()[source]

Compute the hypervolume for the given reference point.

Note: This assumes minimization.

This method computes the hypervolume of the non-dominated space and computes the difference between the hypervolume between the ideal point and hypervolume of the non-dominated space.

Note there are much more efficient alternatives for computing hypervolume when m > 2 (which do not require partitioning the non-dominated space). Given such a partitioning, this method is quite fast.

Return type

Tensor

Returns

(batch_shape)-dim tensor containing the dominated hypervolume.

training: bool

Pareto¶

botorch.utils.multi_objective.pareto.is_non_dominated(Y, deduplicate=True)[source]

Computes the non-dominated front.

Note: this assumes maximization.

Parameters
• Y (Tensor) – A (batch_shape) x n x m-dim tensor of outcomes.

• deduplicate (bool) – A boolean indicating whether to only return unique points on the pareto frontier.

Return type

Tensor

Returns

A (batch_shape) x n-dim boolean tensor indicating whether each point is non-dominated.

Scalarization¶

Helper utilities for constructing scalarizations.

References

Knowles2005(1,2)

J. Knowles, “ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems,” in IEEE Transactions on Evolutionary Computation, vol. 10, no. 1, pp. 50-66, Feb. 2006.

botorch.utils.multi_objective.scalarization.get_chebyshev_scalarization(weights, Y, alpha=0.05)[source]

Construct an augmented Chebyshev scalarization.

Outcomes are first normalized to [0,1] and then an augmented Chebyshev scalarization is applied.

Augmented Chebyshev scalarization:

objective(y) = min(w * y) + alpha * sum(w * y)

Note: this assumes maximization.

See [Knowles2005] for details.

This scalarization can be used with qExpectedImprovement to implement q-ParEGO as proposed in [Daulton2020qehvi].

Parameters
• weights (Tensor) – A m-dim tensor of weights.

• Y (Tensor) – A n x m-dim tensor of observed outcomes, which are used for scaling the outcomes to [0,1].

• alpha (float) – Parameter governing the influence of the weighted sum term. The default value comes from [Knowles2005].

Return type

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

Returns

Transform function using the objective weights.

Example

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