trieste.space#

This module contains implementations of various types of search space.

Module Contents#

SearchSpaceType[source]#

A type variable bound to SearchSpace.

DEFAULT_DTYPE: tensorflow.DType[source]#

Default dtype to use when none is provided.

EncoderFunction[source]#

Type alias for point encoders. These transform points from one search space to another.

exception SampleTimeoutError[source]#

Bases: Exception

Raised when sampling from a search space has timed out.

Initialize self. See help(type(self)) for accurate signature.

class NonlinearConstraint(fun: Callable[[trieste.types.TensorType], trieste.types.TensorType], lb: Sequence[float] | trieste.types.TensorType, ub: Sequence[float] | trieste.types.TensorType, keep_feasible: bool = False)[source]#

Bases: scipy.optimize.NonlinearConstraint

A wrapper class for nonlinear constraints on variables. The constraints expression is of the form:

lb <= fun(x) <= ub
Parameters:

  • fun – The function defining the nonlinear constraints; with input shape […, D] and output shape […, 1], returning a scalar value for each input point.

  • lb – The lower bound of the constraint. Should be a scalar or of shape [1].

  • ub – The upper bound of the constraint. Should be a scalar or of shape [1].

  • keep_feasible – Keep the constraints feasible throughout optimization iterations if this is True.

residual(points: trieste.types.TensorType) → trieste.types.TensorType[source]#

Calculate the residuals between the constraint function and its lower/upper limits.

Parameters:

points – The points to calculate the residuals for, with shape […, D].

Returns:

A tensor containing the lower and upper residual values with shape […, 2].

__repr__() → str[source]#

Return repr(self).

__eq__(other: object) → bool[source]#
Parameters:

other – A constraint.

Returns:

Whether the constraint is identical to this one.

class LinearConstraint(A: trieste.types.TensorType, lb: Sequence[float] | trieste.types.TensorType, ub: Sequence[float] | trieste.types.TensorType, keep_feasible: bool = False)[source]#

Bases: scipy.optimize.LinearConstraint

A wrapper class for linear constraints on variables. The constraints expression is of the form:

lb <= A @ x <= ub
Parameters:

  • A – The matrix defining the linear constraints with shape [M, D], where M is the number of constraints.

  • lb – The lower bound of the constraint. Should be a scalar or of shape [M].

  • ub – The upper bound of the constraint. Should be a scalar or of shape [M].

  • keep_feasible – Keep the constraints feasible throughout optimization iterations if this is True.

residual(points: trieste.types.TensorType) → trieste.types.TensorType[source]#

Calculate the residuals between the constraint function and its lower/upper limits.

Parameters:

points – The points to calculate the residuals for, with shape […, D].

Returns:

A tensor containing the lower and upper residual values with shape […, M*2].

__repr__() → str[source]#

Return repr(self).

__eq__(other: object) → bool[source]#
Parameters:

other – A constraint.

Returns:

Whether the constraint is identical to this one.

Constraint[source]#

Type alias for constraints.

class SearchSpace[source]#

Bases: abc.ABC

A SearchSpace represents the domain over which an objective function is optimized.

abstract sample(num_samples: int, seed: int | None = None) → trieste.types.TensorType[source]#
Parameters:

  • num_samples – The number of points to sample from this search space.

  • seed – Random seed for reproducibility.

Returns:

num_samples i.i.d. random points, sampled uniformly from this search space.

contains(value: trieste.types.TensorType) → trieste.types.TensorType[source]#

Method for checking membership.

Parameters:

value – A point or points to check for membership of this SearchSpace.

Returns:

A boolean array showing membership for each point in value.

Raises:

ValueError (or tf.errors.InvalidArgumentError) – If value has a different dimensionality points from this SearchSpace.

abstract _contains(value: trieste.types.TensorType) → trieste.types.TensorType[source]#

Space-specific implementation of membership. Can assume valid input shape.

Parameters:

value – A point or points to check for membership of this SearchSpace.

Returns:

A boolean array showing membership for each point in value.

__contains__(value: trieste.types.TensorType) → bool[source]#

Method called by in operator. Doesn’t support broadcasting as Python insists on converting the result to a boolean.

Parameters:

value – A single point to check for membership of this SearchSpace.

Returns:

True if value is a member of this search space, else False.

Raises:

ValueError (or tf.errors.InvalidArgumentError) – If value has a different dimensionality from this SearchSpace.

property dimension: trieste.types.TensorType[source]#
Abstractmethod:

The number of inputs in this search space.

property has_bounds: bool[source]#
Abstractmethod:

Whether the search space has meaningful numerical bounds.

property lower: trieste.types.TensorType[source]#
Abstractmethod:

The lowest value taken by each search space dimension.

property upper: trieste.types.TensorType[source]#
Abstractmethod:

The highest value taken by each search space dimension.

abstract product(other: SearchSpaceType) → SearchSpaceType[source]#
Parameters:

other – A search space of the same type as this search space.

Returns:

The Cartesian product of this search space with the other.

__mul__(other: SearchSpaceType) → SearchSpaceType[source]#
__mul__(other: SearchSpace) → SearchSpace
Parameters:

other – A search space.

Returns:

The Cartesian product of this search space with the other. If both spaces are of the same type then this calls the product() method. Otherwise, it generates a TaggedProductSearchSpace.

__pow__(other: int) → SearchSpaceType[source]#

Return the Cartesian product of other instances of this search space. For example, for an exponent of 3, and search space s, this is s ** 3, which is equivalent to s * s * s.

Parameters:

other – The exponent, or number of instances of this search space to multiply together. Must be strictly positive.

Returns:

The Cartesian product of other instances of this search space.

Raises:

tf.errors.InvalidArgumentError – If the exponent other is less than 1.

discretize(num_samples: int) → DiscreteSearchSpace[source]#
Parameters:

num_samples – The number of points in the DiscreteSearchSpace.

Returns:

A discrete search space consisting of num_samples points sampled uniformly from this search space.

Raises:

NotImplementedError – If this SearchSpace has constraints.

abstract __eq__(other: object) → bool[source]#
Parameters:

other – A search space.

Returns:

Whether the search space is identical to this one.

property constraints: Sequence[Constraint][source]#

The sequence of explicit constraints specified in this search space.

abstract constraints_residuals(points: trieste.types.TensorType) → trieste.types.TensorType[source]#

Return residuals for all the constraints in this SearchSpace.

Parameters:

points – The points to get the residuals for, with shape […, D].

Returns:

A tensor of all the residuals with shape […, C], where C is the total number of constraints.

Raises:

NotImplementedError – If this SearchSpace does not support constraints.

is_feasible(points: trieste.types.TensorType) → trieste.types.TensorType[source]#

Checks if points satisfy the explicit constraints of this SearchSpace. Note membership of the search space is not checked.

Parameters:

points – The points to check constraints feasibility for, with shape […, D].

Returns:

A tensor of booleans. Returns True for each point if it is feasible in this search space, else False.

Raises:

NotImplementedError – If this SearchSpace has constraints.

property has_constraints: bool[source]#

Returns True if this search space has any explicit constraints specified.

class GeneralDiscreteSearchSpace(points: trieste.types.TensorType)[source]#

Bases: SearchSpace

An ABC representing different types of discrete search spaces (not just numerical). This contains a default implementation using explicitly provided points which subclasses may ignore.

Parameters:

points – The points that define the discrete space, with shape (‘N’, ‘D’).

Raises:

ValueError (or tf.errors.InvalidArgumentError) – If points has an invalid shape.

property points: trieste.types.TensorType[source]#

All the points in this space.

property dimension: trieste.types.TensorType[source]#

The number of inputs in this search space.

_contains(value: trieste.types.TensorType) → trieste.types.TensorType[source]#

Space-specific implementation of membership. Can assume valid input shape.

Parameters:

value – A point or points to check for membership of this SearchSpace.

Returns:

A boolean array showing membership for each point in value.

sample(num_samples: int, seed: int | None = None) → trieste.types.TensorType[source]#
Parameters:

  • num_samples – The number of points to sample from this search space.

  • seed – Random seed for reproducibility.

Returns:

num_samples i.i.d. random points, sampled uniformly, from this search space.

class DiscreteSearchSpace(points: trieste.types.TensorType)[source]#

Bases: GeneralDiscreteSearchSpace

A discrete SearchSpace representing a finite set of \(D\)-dimensional points in \(\mathbb{R}^D\).

For example:

>>> points = tf.constant([[-1.0, 0.4], [-1.0, 0.6], [0.0, 0.4]])
>>> search_space = DiscreteSearchSpace(points)
>>> assert tf.constant([0.0, 0.4]) in search_space
>>> assert tf.constant([1.0, 0.5]) not in search_space
Parameters:

points – The points that define the discrete space, with shape (‘N’, ‘D’).

Raises:

ValueError (or tf.errors.InvalidArgumentError) – If points has an invalid shape.

__repr__() → str[source]#

Return repr(self).

property has_bounds: bool[source]#

Whether the search space has meaningful numerical bounds.

property lower: trieste.types.TensorType[source]#

The lowest value taken across all points by each search space dimension.

property upper: trieste.types.TensorType[source]#

The highest value taken across all points by each search space dimension.

product(other: DiscreteSearchSpace) → DiscreteSearchSpace[source]#

Return the Cartesian product of the two DiscreteSearchSpaces. For example:

>>> sa = DiscreteSearchSpace(tf.constant([[0, 1], [2, 3]]))
>>> sb = DiscreteSearchSpace(tf.constant([[4, 5, 6], [7, 8, 9]]))
>>> (sa * sb).points.numpy()
array([[0, 1, 4, 5, 6],
       [0, 1, 7, 8, 9],
       [2, 3, 4, 5, 6],
       [2, 3, 7, 8, 9]], dtype=int32)
Parameters:

other – A DiscreteSearchSpace with points of the same dtype as this search space.

Returns:

The Cartesian product of the two DiscreteSearchSpaces.

Raises:

TypeError – If one DiscreteSearchSpace has points of a different dtype to the other.

__eq__(other: object) → bool[source]#
Parameters:

other – A search space.

Returns:

Whether the search space is identical to this one.

class HasOneHotEncoder[source]#

Bases: typing_extensions.Protocol

A categorical search space that contains default logic for one-hot encoding.

property one_hot_encoder: EncoderFunction[source]#
Abstractmethod:

A one-hot encoder for points in the search space.

one_hot_encoder(space: SearchSpace) → EncoderFunction[source]#

A utility function for one-hot encoding a search space when it supports it.

cast_encoder(encoder: EncoderFunction, input_dtype: tensorflow.DType | None = None, output_dtype: tensorflow.DType | None = None) → EncoderFunction[source]#

A utility function for casting the input and/or output of an encoder.

one_hot_encoded_space(space: SearchSpace) → SearchSpace[source]#

A bounded search space corresponding to the one-hot encoding of the given space.

class CategoricalSearchSpace(categories: int | Sequence[int] | Sequence[str] | Sequence[Sequence[str]], dtype: tensorflow.DType = DEFAULT_DTYPE)[source]#

Bases: GeneralDiscreteSearchSpace, HasOneHotEncoder

A categorical SearchSpace representing a finite set \(\mathcal{C}\) of categories, or a finite Cartesian product \(\mathcal{C}_1 \times \cdots \times \mathcal{C}_n\) of such sets.

For example:

>>> CategoricalSearchSpace(5)
CategoricalSearchSpace([('0', '1', '2', '3', '4')])
>>> CategoricalSearchSpace(["Red", "Green", "Blue"])
CategoricalSearchSpace([('Red', 'Green', 'Blue')])
>>> CategoricalSearchSpace([2,3])
CategoricalSearchSpace([('0', '1'), ('0', '1', '2')])
>>> CategoricalSearchSpace([["R", "G", "B"], ["Y", "N"]])
CategoricalSearchSpace([('R', 'G', 'B'), ('Y', 'N')])

Note that internally categories are represented by numeric indices:

>>> rgb = CategoricalSearchSpace(["Red", "Green", "Blue"])
>>> assert tf.constant([1], dtype=tf.float64) in rgb
>>> assert tf.constant([3], dtype=tf.float64) not in rgb
>>> rgb.to_tags(tf.constant([[1], [0], [2]]))
<tf.Tensor: shape=(3, 1), dtype=string, numpy=
array([[b'Green'],
       [b'Red'],
       [b'Blue']], dtype=object)>
Parameters:

  • categories – Number of categories or category names. Can be an array for multidimensional spaces.

  • dtype – The dtype of the returned indices, either tf.float32 or tf.float64.

__repr__() → str[source]#

Return repr(self).

property has_bounds: bool[source]#

Whether the search space has meaningful numerical bounds.

property lower: trieste.types.TensorType[source]#

The lowest value taken by each search space dimension.

property upper: trieste.types.TensorType[source]#

The highest value taken by each search space dimension.

property tags: Sequence[Sequence[str]][source]#

The tags of the categories.

property one_hot_encoder: EncoderFunction[source]#

A one-hot encoder for the numerical indices. Note that binary categories are left unchanged instead of adding an unnecessary second feature.

to_tags(indices: trieste.types.TensorType) → trieste.types.TensorType[source]#

Convert a tensor of indices (such as one returned by sample()) to one of category tags.

Parameters:

indices – A tensor of integer indices.

Returns:

A tensor of string tags.

product(other: CategoricalSearchSpace) → CategoricalSearchSpace[source]#

Return the Cartesian product of the two CategoricalSearchSpaces. For example:

>>> rgb = CategoricalSearchSpace(["Red", "Green", "Blue"])
>>> yn = CategoricalSearchSpace(["Yes", "No"])
>>> rgb * yn
CategoricalSearchSpace([('Red', 'Green', 'Blue'), ('Yes', 'No')])
Parameters:

other – A CategoricalSearchSpace.

Returns:

The Cartesian product of the two CategoricalSearchSpaces.

__eq__(other: object) → bool[source]#
Parameters:

other – A search space.

Returns:

Whether the search space is identical to this one.

class Box(lower: Sequence[float], upper: Sequence[float], constraints: Sequence[Constraint] | None = None, ctol: float | trieste.types.TensorType = 1e-07)[source]#
class Box(lower: trieste.types.TensorType, upper: trieste.types.TensorType, constraints: Sequence[Constraint] | None = None, ctol: float | trieste.types.TensorType = 1e-07)

Bases: SearchSpace

Continuous SearchSpace representing a \(D\)-dimensional box in \(\mathbb{R}^D\). Mathematically it is equivalent to the Cartesian product of \(D\) closed bounded intervals in \(\mathbb{R}\).

If lower and upper are Sequences of floats (such as lists or tuples), they will be converted to tensors of dtype DEFAULT_DTYPE.

Parameters:

  • lower – The lower (inclusive) bounds of the box. Must have shape [D] for positive D, and if a tensor, must have float type.

  • upper – The upper (inclusive) bounds of the box. Must have shape [D] for positive D, and if a tensor, must have float type.

  • constraints – Sequence of explicit input constraints for this search space.

  • ctol – Tolerance to use to check constraints satisfaction.

Raises:

ValueError (or tf.errors.InvalidArgumentError) –

If any of the following are true:

  • lower and upper have invalid shapes.

  • lower and upper do not have the same floating point type.

  • upper is not greater or equal to lower across all dimensions.

__repr__() → str[source]#

Return repr(self).

property has_bounds: bool[source]#

Whether the search space has meaningful numerical bounds.

property lower: tensorflow.Tensor[source]#

The lower bounds of the box.

property upper: tensorflow.Tensor[source]#

The upper bounds of the box.

property dimension: trieste.types.TensorType[source]#

The number of inputs in this search space.

property constraints: Sequence[Constraint][source]#

The sequence of explicit constraints specified in this search space.

_contains(value: trieste.types.TensorType) → trieste.types.TensorType[source]#

For each point in value, return True if the point is a member of this search space, else False. A point is a member if all of its coordinates lie in the closed intervals bounded by the lower and upper bounds.

Parameters:

value – A point or points to check for membership of this SearchSpace.

Returns:

A boolean array showing membership for each point in value.

sample(num_samples: int, seed: int | None = None) → trieste.types.TensorType[source]#

Sample randomly from the space.

Parameters:

  • num_samples – The number of points to sample from this search space.

  • seed – Random seed for reproducibility.

Returns:

num_samples i.i.d. random points, sampled uniformly, from this search space with shape ‘[num_samples, D]’ , where D is the search space dimension.

sample_halton(num_samples: int, seed: int | None = None) → trieste.types.TensorType[source]#

Sample from the space using a Halton sequence. The resulting samples are guaranteed to be diverse and are reproducible by using the same choice of seed.

Parameters:

  • num_samples – The number of points to sample from this search space.

  • seed – Random seed for the halton sequence

Returns:

num_samples of points, using halton sequence with shape ‘[num_samples, D]’ , where D is the search space dimension.

sample_sobol(num_samples: int, skip: int | None = None) → trieste.types.TensorType[source]#

Sample a diverse set from the space using a Sobol sequence. If skip is specified, then the resulting samples are reproducible.

Parameters:

  • num_samples – The number of points to sample from this search space.

  • skip – The number of initial points of the Sobol sequence to skip

Returns:

num_samples of points, using sobol sequence with shape ‘[num_samples, D]’ , where D is the search space dimension.

_sample_feasible_loop(num_samples: int, sampler: Callable[[], trieste.types.TensorType], max_tries: int = 100) → trieste.types.TensorType[source]#

Rejection sampling using provided callable. Try max_tries number of times to find num_samples feasible points.

Parameters:

  • num_samples – The number of feasible points to sample from this search space.

  • sampler – Callable to return samples. Called potentially multiple times.

  • max_tries – Maximum attempts to sample the requested number of points.

Returns:

num_samples feasible points sampled using sampler.

Raises:

SampleTimeoutError – If max_tries are exhausted before num_samples are sampled.

sample_feasible(num_samples: int, seed: int | None = None, max_tries: int = 100) → trieste.types.TensorType[source]#

Sample feasible points randomly from the space.

Parameters:

  • num_samples – The number of feasible points to sample from this search space.

  • seed – Random seed for reproducibility.

  • max_tries – Maximum attempts to sample the requested number of points.

Returns:

num_samples i.i.d. random points, sampled uniformly, from this search space with shape ‘[num_samples, D]’ , where D is the search space dimension.

Raises:

SampleTimeoutError – If max_tries are exhausted before num_samples are sampled.

sample_halton_feasible(num_samples: int, seed: int | None = None, max_tries: int = 100) → trieste.types.TensorType[source]#

Sample feasible points from the space using a Halton sequence. The resulting samples are guaranteed to be diverse and are reproducible by using the same choice of seed.

Parameters:

  • num_samples – The number of feasible points to sample from this search space.

  • seed – Random seed for the halton sequence

  • max_tries – Maximum attempts to sample the requested number of points.

Returns:

num_samples of points, using halton sequence with shape ‘[num_samples, D]’ , where D is the search space dimension.

Raises:

SampleTimeoutError – If max_tries are exhausted before num_samples are sampled.

sample_sobol_feasible(num_samples: int, skip: int | None = None, max_tries: int = 100) → trieste.types.TensorType[source]#

Sample a diverse set of feasible points from the space using a Sobol sequence. If skip is specified, then the resulting samples are reproducible.

Parameters:

  • num_samples – The number of feasible points to sample from this search space.

  • skip – The number of initial points of the Sobol sequence to skip

  • max_tries – Maximum attempts to sample the requested number of points.

Returns:

num_samples of points, using sobol sequence with shape ‘[num_samples, D]’ , where D is the search space dimension.

Raises:

SampleTimeoutError – If max_tries are exhausted before num_samples are sampled.

product(other: Box) → Box[source]#

Return the Cartesian product of the two Boxes (concatenating their respective lower and upper bounds). For example:

>>> unit_interval = Box([0.0], [1.0])
>>> square_at_origin = Box([-2.0, -2.0], [2.0, 2.0])
>>> new_box = unit_interval * square_at_origin
>>> new_box.lower.numpy()
array([ 0., -2., -2.])
>>> new_box.upper.numpy()
array([1., 2., 2.])
Parameters:

other – A Box with bounds of the same type as this Box.

Returns:

The Cartesian product of the two Boxes.

Raises:

TypeError – If the bounds of one Box have different dtypes to those of the other Box.

__eq__(other: object) → bool[source]#
Parameters:

other – A search space.

Returns:

Whether the search space is identical to this one.

constraints_residuals(points: trieste.types.TensorType) → trieste.types.TensorType[source]#

Return residuals for all the constraints in this SearchSpace.

Parameters:

points – The points to get the residuals for, with shape […, D].

Returns:

A tensor of all the residuals with shape […, C], where C is the total number of constraints.

is_feasible(points: trieste.types.TensorType) → trieste.types.TensorType[source]#

Checks if points satisfy the explicit constraints of this SearchSpace. Note membership of the search space is not checked.

Parameters:

points – The points to check constraints feasibility for, with shape […, D].

Returns:

A tensor of booleans. Returns True for each point if it is feasible in this search space, else False.

property has_constraints: bool[source]#

Returns True if this search space has any explicit constraints specified.

class CollectionSearchSpace(spaces: Sequence[SearchSpace], tags: Sequence[str] | None = None)[source]#

Bases: SearchSpace

An abstract SearchSpace consisting of a collection of multiple SearchSpace objects, each with a unique tag. This class provides functionality for accessing each individual space.

Note that the individual spaces are not combined in any way.

Build a CollectionSearchSpace from a list spaces of other spaces. If tags are provided then they form the identifiers of the subspaces, otherwise the subspaces are labelled numerically.

Parameters:

  • spaces – A sequence of SearchSpace objects representing the space’s subspaces

  • tags – An optional list of tags giving the unique identifiers of the space’s subspaces.

Raises:

ValueError (or tf.errors.InvalidArgumentError) – If spaces has a different length to tags when tags is provided or if tags contains duplicates.

__repr__() → str[source]#

Return repr(self).

property has_bounds: bool[source]#

Whether the search space has meaningful numerical bounds.

property subspace_lower: Sequence[trieste.types.TensorType][source]#

The lowest values taken by each space dimension, in the same order as specified when initializing the space.

property subspace_upper: Sequence[trieste.types.TensorType][source]#

The highest values taken by each space dimension, in the same order as specified when initializing the space.

property subspace_tags: tuple[str, Ellipsis][source]#

Return the names of the subspaces contained in this space.

property subspace_dimension: Sequence[trieste.types.TensorType][source]#

The number of inputs in each subspace, in the same order as specified when initializing the space.

get_subspace(tag: str) → SearchSpace[source]#

Return the domain of a particular subspace.

Parameters:

tag – The tag specifying the target subspace.

Returns:

Target subspace.

subspace_sample(num_samples: int, seed: int | None = None) → Sequence[trieste.types.TensorType][source]#

Sample randomly from the space by sampling from each subspace and returning the resulting samples in the same order as specified when initializing the space.

Parameters:

  • num_samples – The number of points to sample from each subspace.

  • seed – Optional tf.random seed.

Returns:

num_samples i.i.d. random points, sampled uniformly, from each search subspace with shape ‘[num_samples, D]’ , where D is the search space dimension.

__eq__(other: object) → bool[source]#
Parameters:

other – A search space.

Returns:

Whether the search space is identical to this one.

class TaggedProductSearchSpace(spaces: Sequence[SearchSpace], tags: Sequence[str] | None = None)[source]#

Bases: CollectionSearchSpace, HasOneHotEncoder

Product SearchSpace consisting of a product of multiple SearchSpace. This class provides functionality for accessing either the resulting combined search space or each individual space. This class is useful for defining mixed search spaces, for example:

context_space = DiscreteSearchSpace(tf.constant([[-0.5, 0.5]])) decision_space = Box([-1, -2], [2, 3]) mixed_space = TaggedProductSearchSpace(spaces=[context_space, decision_space])

Note: the dtype of all the component search spaces must be the same.

Note that this class assumes that individual points in product spaces are represented with their inputs in the same order as specified when initializing the space.

Build a TaggedProductSearchSpace from a list spaces of other spaces. If tags are provided then they form the identifiers of the subspaces, otherwise the subspaces are labelled numerically.

Parameters:

  • spaces – A sequence of SearchSpace objects representing the space’s subspaces

  • tags – An optional list of tags giving the unique identifiers of the space’s subspaces.

Raises:

ValueError (or tf.errors.InvalidArgumentError) – If spaces has a different length to tags when tags is provided or if tags contains duplicates.

property lower: trieste.types.TensorType[source]#

The lowest values taken by each space dimension, concatenated across subspaces.

property upper: trieste.types.TensorType[source]#

The highest values taken by each space dimension, concatenated across subspaces.

property dimension: trieste.types.TensorType[source]#

The number of inputs in this product search space.

fix_subspace(tag: str, values: trieste.types.TensorType) → TaggedProductSearchSpace[source]#

Return a new TaggedProductSearchSpace with the specified subspace replaced with a DiscreteSearchSpace containing values as its points. This is useful if you wish to restrict subspaces to sets of representative points.

Parameters:

  • tag – The tag specifying the target subspace.

  • values – The values used to populate the new discrete subspace.z

Returns:

New TaggedProductSearchSpace with the specified subspace replaced with a DiscreteSearchSpace containing values as its points.

get_subspace_component(tag: str, values: trieste.types.TensorType) → trieste.types.TensorType[source]#

Returns the components of values lying in a particular subspace.

Parameters:
Returns:

The sub-components of values lying in the specified subspace, of shape [N, Dsub], where Dsub is the dimensionality of the specified subspace.

_contains(value: trieste.types.TensorType) → trieste.types.TensorType[source]#

Return True if value is a member of this search space, else False. A point is a member if each of its subspace components lie in each subspace.

Recall that individual points in product spaces are represented with their inputs in the same order as specified when initializing the space.

Parameters:

value – A point to check for membership of this SearchSpace.

Returns:

True if value is a member of this search space, else False. May return a scalar boolean TensorType instead of the bool itself.

Raises:

ValueError (or tf.errors.InvalidArgumentError) – If value has a different dimensionality from the search space.

sample(num_samples: int, seed: int | None = None) → trieste.types.TensorType[source]#

Sample randomly from the space by sampling from each subspace and concatenating the resulting samples.

Parameters:

  • num_samples – The number of points to sample from this search space.

  • seed – Optional tf.random seed.

Returns:

num_samples i.i.d. random points, sampled uniformly, from this search space with shape ‘[num_samples, D]’ , where D is the search space dimension.

product(other: TaggedProductSearchSpace) → TaggedProductSearchSpace[source]#

Return the Cartesian product of the two TaggedProductSearchSpaces, building a tree of TaggedProductSearchSpaces.

Parameters:

other – A search space of the same type as this search space.

Returns:

The Cartesian product of this search space with the other.

property one_hot_encoder: EncoderFunction[source]#

An encoder that one-hot-encodes all subpsaces that support it (and leaves the other subspaces unchanged).

class TaggedMultiSearchSpace(spaces: Sequence[SearchSpace], tags: Sequence[str] | None = None)[source]#

Bases: CollectionSearchSpace

A SearchSpace made up of a collection of multiple SearchSpace subspaces, each with a unique tag. All subspaces must have the same dimensionality.

Each subspace is treated as an independent space and not combined in any way. This class provides functionality for accessing all the subspaces at once by using the usual search space methods, as well as for accessing individual subspaces.

When accessing all subspaces at once from this class (e.g. lower(), upper(), sample()), the returned tensors have an extra dimension corresponding to the subspaces.

This class can be useful to represent a collection of search spaces that do not interact with each other. For example, it is used to implement batch trust region rules in the BatchTrustRegion class.

Build a TaggedMultiSearchSpace from a list spaces of other spaces. If tags are provided then they form the identifiers of the subspaces, otherwise the subspaces are labelled numerically.

Parameters:

  • spaces – A sequence of SearchSpace objects representing the space’s subspaces

  • tags – An optional list of tags giving the unique identifiers of the space’s subspaces.

Raises:

  • ValueError (or tf.errors.InvalidArgumentError) – If spaces has a different length to tags when tags is provided or if tags contains duplicates.

  • ValueError (or tf.errors.InvalidArgumentError) – If spaces has a different dimension to each other.

property lower: trieste.types.TensorType[source]#

Returns the stacked lower bounds of all the subspaces.

Returns:

The lower bounds of shape [V, D], where V is the number of subspaces and D is the dimensionality of each subspace.

property upper: trieste.types.TensorType[source]#

Returns the stacked upper bounds of all the subspaces.

Returns:

The upper bounds of shape [V, D], where V is the number of subspaces and D is the dimensionality of each subspace.

property dimension: trieste.types.TensorType[source]#

The number of inputs in this search space.

sample(num_samples: int, seed: int | None = None) → trieste.types.TensorType[source]#

Sample randomly from the space by sampling from each subspace and returning the resulting samples stacked along the second axis in the same order as specified when initializing the space.

Parameters:

  • num_samples – The number of points to sample from each subspace.

  • seed – Optional tf.random seed.

Returns:

num_samples i.i.d. random points, sampled uniformly, from each search subspace with shape ‘[num_samples, V, D]’ , where V is the number of subspaces and D is the search space dimension.

_contains(value: trieste.types.TensorType) → trieste.types.TensorType[source]#

Return True if value is a member of this search space, else False. A point is a member if it is a member of any of the subspaces.

Parameters:

value – A point or points to check for membership of this SearchSpace.

Returns:

A boolean array showing membership for each point in value.

product(other: TaggedMultiSearchSpace) → TaggedMultiSearchSpace[source]#

Return a bigger collection of two TaggedMultiSearchSpaces, regenerating the tags.

Parameters:

other – A search space of the same type as this search space.

Returns:

The product of this search space with the other.

discretize(num_samples: int) → DiscreteSearchSpace[source]#
Parameters:

num_samples – The number of points in the DiscreteSearchSpace.

Returns:

A discrete search space consisting of num_samples points sampled uniformly from this search space.

Raises:

NotImplementedError – If this SearchSpace has constraints.

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