Source code for botorch.models.transforms.input
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""
Input Transformations.
These classes implement a variety of transformations for
input parameters including: learned input warping functions,
rounding functions, and log transformations. The input transformation
is typically part of a Model and applied within the model.forward()
method.
"""
from __future__ import annotations
from abc import ABC, abstractmethod
from collections import OrderedDict
from typing import List, Optional, Union
import torch
from botorch.exceptions.errors import BotorchTensorDimensionError
from botorch.models.transforms.utils import expand_and_copy_tensor
from botorch.models.utils import fantasize
from botorch.utils.rounding import approximate_round
from gpytorch import Module as GPyTorchModule
from gpytorch.constraints import GreaterThan
from gpytorch.priors import Prior
from torch import Tensor, nn
from torch.distributions import Kumaraswamy
from torch.nn import Module, ModuleDict
[docs]class InputTransform(ABC):
r"""Abstract base class for input transforms.
Note: Input transforms must inherit from `torch.nn.Module`. This
is deferred to the subclasses to avoid any potential conflict
between `gpytorch.module.Module` and `torch.nn.Module` in `Warp`.
Properties:
transform_on_train: A boolean indicating whether to apply the
transform in train() mode.
transform_on_eval: A boolean indicating whether to apply the
transform in eval() mode.
transform_on_fantasize: A boolean indicating whether to apply
the transform when called from within a `fantasize` call.
"""
transform_on_eval: bool
transform_on_train: bool
transform_on_fantasize: bool
[docs] def forward(self, X: Tensor) -> Tensor:
r"""Transform the inputs to a model.
Args:
X: A `batch_shape x n x d`-dim tensor of inputs.
Returns:
A `batch_shape x n' x d`-dim tensor of transformed inputs.
"""
if self.training:
if self.transform_on_train:
return self.transform(X)
elif self.transform_on_eval:
if fantasize.off() or self.transform_on_fantasize:
return self.transform(X)
return X
[docs] @abstractmethod
def transform(self, X: Tensor) -> Tensor:
r"""Transform the inputs to a model.
Args:
X: A `batch_shape x n x d`-dim tensor of inputs.
Returns:
A `batch_shape x n x d`-dim tensor of transformed inputs.
"""
pass # pragma: no cover
[docs] def untransform(self, X: Tensor) -> Tensor:
r"""Un-transform the inputs to a model.
Args:
X: A `batch_shape x n x d`-dim tensor of transformed inputs.
Returns:
A `batch_shape x n x d`-dim tensor of un-transformed inputs.
"""
raise NotImplementedError(
f"{self.__class__.__name__} does not implement the `untransform` method."
)
[docs] def equals(self, other: InputTransform) -> bool:
r"""Check if another input transform is equivalent.
Note: The reason that a custom equals method is defined rather than
defining an __eq__ method is because defining an __eq__ method sets
the __hash__ method to None. Hashing modules is currently used in
pytorch. See https://github.com/pytorch/pytorch/issues/7733.
Args:
other: Another input transform.
Returns:
A boolean indicating if the other transform is equivalent.
"""
other_state_dict = other.state_dict()
return (
type(self) == type(other)
and (self.transform_on_train == other.transform_on_train)
and (self.transform_on_eval == other.transform_on_eval)
and (self.transform_on_fantasize == other.transform_on_fantasize)
and all(
torch.allclose(v, other_state_dict[k].to(v))
for k, v in self.state_dict().items()
)
)
[docs] def preprocess_transform(self, X: Tensor) -> Tensor:
r"""Apply transforms for preprocessing inputs.
The main use cases for this method are 1) to preprocess training data
before calling `set_train_data` and 2) preprocess `X_baseline` for noisy
acquisition functions so that `X_baseline` is "preprocessed" with the
same transformations as the cached training inputs.
Args:
X: A `batch_shape x n x d`-dim tensor of inputs.
Returns:
A `batch_shape x n x d`-dim tensor of (transformed) inputs.
"""
if self.transform_on_train:
return self.transform(X)
return X
[docs]class ChainedInputTransform(InputTransform, ModuleDict):
r"""An input transform representing the chaining of individual transforms."""
def __init__(self, **transforms: InputTransform) -> None:
r"""Chaining of input transforms.
Args:
transforms: The transforms to chain. Internally, the names of the
kwargs are used as the keys for accessing the individual
transforms on the module.
Example:
>>> tf1 = Normalize(d=2)
>>> tf2 = Normalize(d=2)
>>> tf = ChainedInputTransform(tf1=tf1, tf2=tf2)
>>> list(tf.keys())
['tf1', 'tf2']
>>> tf["tf1"]
Normalize()
"""
super().__init__(OrderedDict(transforms))
self.transform_on_train = False
self.transform_on_eval = False
self.transform_on_fantasize = False
for tf in transforms.values():
self.transform_on_train |= tf.transform_on_train
self.transform_on_eval |= tf.transform_on_eval
self.transform_on_fantasize |= tf.transform_on_fantasize
[docs] def transform(self, X: Tensor) -> Tensor:
r"""Transform the inputs to a model.
Individual transforms are applied in sequence.
Args:
X: A `batch_shape x n x d`-dim tensor of inputs.
Returns:
A `batch_shape x n x d`-dim tensor of transformed inputs.
"""
for tf in self.values():
X = tf.forward(X)
return X
[docs] def untransform(self, X: Tensor) -> Tensor:
r"""Un-transform the inputs to a model.
Un-transforms of the individual transforms are applied in reverse sequence.
Args:
X: A `batch_shape x n x d`-dim tensor of transformed inputs.
Returns:
A `batch_shape x n x d`-dim tensor of un-transformed inputs.
"""
for tf in reversed(self.values()):
X = tf.untransform(X)
return X
[docs] def equals(self, other: InputTransform) -> bool:
r"""Check if another input transform is equivalent.
Args:
other: Another input transform.
Returns:
A boolean indicating if the other transform is equivalent.
"""
return super().equals(other=other) and all(
t1 == t2 for t1, t2 in zip(self.values(), other.values())
)
[docs] def preprocess_transform(self, X: Tensor) -> Tensor:
r"""Apply transforms for preprocessing inputs.
The main use cases for this method are 1) to preprocess training data
before calling `set_train_data` and 2) preprocess `X_baseline` for noisy
acquisition functions so that `X_baseline` is "preprocessed" with the
same transformations as the cached training inputs.
Args:
X: A `batch_shape x n x d`-dim tensor of inputs.
Returns:
A `batch_shape x n x d`-dim tensor of (transformed) inputs.
"""
for tf in self.values():
X = tf.preprocess_transform(X)
return X
[docs]class ReversibleInputTransform(InputTransform, ABC):
r"""An abstract class for a reversible input transform.
Properties:
reverse: A boolean indicating if the functionality of transform
and untransform methods should be swapped.
"""
reverse: bool
[docs] def transform(self, X: Tensor) -> Tensor:
r"""Transform the inputs.
Args:
X: A `batch_shape x n x d`-dim tensor of inputs.
Returns:
A `batch_shape x n x d`-dim tensor of transformed inputs.
"""
return self._untransform(X) if self.reverse else self._transform(X)
[docs] def untransform(self, X: Tensor) -> Tensor:
r"""Un-transform the inputs.
Args:
X: A `batch_shape x n x d`-dim tensor of inputs.
Returns:
A `batch_shape x n x d`-dim tensor of un-transformed inputs.
"""
return self._transform(X) if self.reverse else self._untransform(X)
@abstractmethod
def _transform(self, X: Tensor) -> Tensor:
r"""Forward transform the inputs.
Args:
X: A `batch_shape x n x d`-dim tensor of inputs.
Returns:
A `batch_shape x n x d`-dim tensor of transformed inputs.
"""
pass # pragma: no cover
@abstractmethod
def _untransform(self, X: Tensor) -> Tensor:
r"""Reverse transform the inputs.
Args:
X: A `batch_shape x n x d`-dim tensor of inputs.
Returns:
A `batch_shape x n x d`-dim tensor of transformed inputs.
"""
pass # pragma: no cover
[docs] def equals(self, other: InputTransform) -> bool:
r"""Check if another input transform is equivalent.
Args:
other: Another input transform.
Returns:
A boolean indicating if the other transform is equivalent.
"""
return super().equals(other=other) and (self.reverse == other.reverse)
[docs]class Normalize(ReversibleInputTransform, Module):
r"""Normalize the inputs to the unit cube.
If no explicit bounds are provided this module is stateful: If in train mode,
calling `forward` updates the module state (i.e. the normalizing bounds). If
in eval mode, calling `forward` simply applies the normalization using the
current module state.
"""
def __init__(
self,
d: int,
bounds: Optional[Tensor] = None,
batch_shape: torch.Size = torch.Size(), # noqa: B008
transform_on_train: bool = True,
transform_on_eval: bool = True,
transform_on_fantasize: bool = True,
reverse: bool = False,
min_range: float = 1e-8,
) -> None:
r"""Normalize the inputs to the unit cube.
Args:
d: The dimension of the input space.
bounds: If provided, use these bounds to normalize the inputs. If
omitted, learn the bounds in train mode.
batch_shape: The batch shape of the inputs (asssuming input tensors
of shape `batch_shape x n x d`). If provided, perform individual
normalization per batch, otherwise uses a single normalization.
transform_on_train: A boolean indicating whether to apply the
transforms in train() mode. Default: True.
transform_on_eval: A boolean indicating whether to apply the
transform in eval() mode. Default: True.
transform_on_fantasize: A boolean indicating whether to apply the
transform when called from within a `fantasize` call. Default: True.
reverse: A boolean indicating whether the forward pass should untransform
the inputs.
min_range: Amount of noise to add to the range to ensure no division by
zero errors.
"""
super().__init__()
if bounds is not None:
if bounds.size(-1) != d:
raise BotorchTensorDimensionError(
"Dimensions of provided `bounds` are incompatible with `d`!"
)
mins = bounds[..., 0:1, :]
ranges = bounds[..., 1:2, :] - mins
self.learn_bounds = False
else:
mins = torch.zeros(*batch_shape, 1, d)
ranges = torch.zeros(*batch_shape, 1, d)
self.learn_bounds = True
self.register_buffer("mins", mins)
self.register_buffer("ranges", ranges)
self._d = d
self.transform_on_train = transform_on_train
self.transform_on_eval = transform_on_eval
self.transform_on_fantasize = transform_on_fantasize
self.reverse = reverse
self.batch_shape = batch_shape
self.min_range = min_range
def _transform(self, X: Tensor) -> Tensor:
r"""Normalize the inputs.
If no explicit bounds are provided, this is stateful: In train mode,
calling `forward` updates the module state (i.e. the normalizing bounds).
In eval mode, calling `forward` simply applies the normalization using
the current module state.
Args:
X: A `batch_shape x n x d`-dim tensor of inputs.
Returns:
A `batch_shape x n x d`-dim tensor of inputs normalized to the
module's bounds.
"""
if self.learn_bounds and self.training:
if X.size(-1) != self.mins.size(-1):
raise BotorchTensorDimensionError(
f"Wrong input dimension. Received {X.size(-1)}, "
f"expected {self.mins.size(-1)}."
)
self.mins = X.min(dim=-2, keepdim=True)[0]
ranges = X.max(dim=-2, keepdim=True)[0] - self.mins
ranges[torch.where(ranges <= self.min_range)] = self.min_range
self.ranges = ranges
return (X - self.mins) / self.ranges
def _untransform(self, X: Tensor) -> Tensor:
r"""Un-normalize the inputs.
Args:
X: A `batch_shape x n x d`-dim tensor of normalized inputs.
Returns:
A `batch_shape x n x d`-dim tensor of un-normalized inputs.
"""
return self.mins + X * self.ranges
@property
def bounds(self) -> Tensor:
r"""The bounds used for normalizing the inputs."""
return torch.cat([self.mins, self.mins + self.ranges], dim=-2)
[docs] def equals(self, other: InputTransform) -> bool:
r"""Check if another input transform is equivalent.
Args:
other: Another input transform.
Returns:
A boolean indicating if the other transform is equivalent.
"""
return (
super().equals(other=other)
and (self._d == other._d)
and (self.learn_bounds == other.learn_bounds)
)
[docs]class Round(InputTransform, Module):
r"""A rounding transformation for integer inputs.
This will typically be used in conjunction with normalization as
follows:
In eval() mode (i.e. after training), the inputs pass
would typically be normalized to the unit cube (e.g. during candidate
optimization). 1. These are unnormalized back to the raw input space.
2. The integers are rounded. 3. All values are normalized to the unit
cube.
In train() mode, the inputs can either (a) be normalized to the unit
cube or (b) provided using their raw values. In the case of (a)
transform_on_train should be set to True, so that the normalized inputs
are unnormalized before rounding. In the case of (b) transform_on_train
should be set to False, so that the raw inputs are rounded and then
normalized to the unit cube.
This transformation uses differentiable approximate rounding by default.
The rounding function is approximated with a piece-wise function where
each piece is a hyperbolic tangent function.
Example:
>>> unnormalize_tf = Normalize(
>>> d=d,
>>> bounds=bounds,
>>> transform_on_eval=True,
>>> transform_on_train=True,
>>> reverse=True,
>>> )
>>> round_tf = Round(integer_indices)
>>> normalize_tf = Normalize(d=d, bounds=bounds)
>>> tf = ChainedInputTransform(
>>> tf1=unnormalize_tf, tf2=round_tf, tf3=normalize_tf
>>> )
"""
def __init__(
self,
indices: List[int],
transform_on_train: bool = True,
transform_on_eval: bool = True,
transform_on_fantasize: bool = True,
approximate: bool = True,
tau: float = 1e-3,
) -> None:
r"""Initialize transform.
Args:
indices: The indices of the integer inputs.
transform_on_train: A boolean indicating whether to apply the
transforms in train() mode. Default: True.
transform_on_eval: A boolean indicating whether to apply the
transform in eval() mode. Default: True.
transform_on_fantasize: A boolean indicating whether to apply the
transform when called from within a `fantasize` call. Default: True.
approximate: A boolean indicating whether approximate or exact
rounding should be used. Default: approximate.
tau: The temperature parameter for approximate rounding.
"""
super().__init__()
self.transform_on_train = transform_on_train
self.transform_on_eval = transform_on_eval
self.transform_on_fantasize = transform_on_fantasize
self.register_buffer("indices", torch.tensor(indices, dtype=torch.long))
self.approximate = approximate
self.tau = tau
[docs] def transform(self, X: Tensor) -> Tensor:
r"""Round the inputs.
Args:
X: A `batch_shape x n x d`-dim tensor of inputs.
Returns:
A `batch_shape x n x d`-dim tensor of rounded inputs.
"""
X_rounded = X.clone()
X_int = X_rounded[..., self.indices]
if self.approximate:
X_int = approximate_round(X_int, tau=self.tau)
else:
X_int = X_int.round()
X_rounded[..., self.indices] = X_int
return X_rounded
[docs] def equals(self, other: InputTransform) -> bool:
r"""Check if another input transform is equivalent.
Args:
other: Another input transform.
Returns:
A boolean indicating if the other transform is equivalent.
"""
return (
super().equals(other=other)
and self.approximate == other.approximate
and self.tau == other.tau
)
[docs]class Log10(ReversibleInputTransform, Module):
r"""A base-10 log transformation."""
def __init__(
self,
indices: List[int],
transform_on_train: bool = True,
transform_on_eval: bool = True,
transform_on_fantasize: bool = True,
reverse: bool = False,
) -> None:
r"""Initialize transform.
Args:
indices: The indices of the inputs to log transform.
transform_on_train: A boolean indicating whether to apply the
transforms in train() mode. Default: True.
transform_on_eval: A boolean indicating whether to apply the
transform in eval() mode. Default: True.
transform_on_fantasize: A boolean indicating whether to apply the
transform when called from within a `fantasize` call. Default: True.
reverse: A boolean indicating whether the forward pass should untransform
the inputs.
"""
super().__init__()
self.register_buffer("indices", torch.tensor(indices, dtype=torch.long))
self.transform_on_train = transform_on_train
self.transform_on_eval = transform_on_eval
self.transform_on_fantasize = transform_on_fantasize
self.reverse = reverse
def _transform(self, X: Tensor) -> Tensor:
r"""Log transform the inputs.
Args:
X: A `batch_shape x n x d`-dim tensor of inputs.
Returns:
A `batch_shape x n x d`-dim tensor of transformed inputs.
"""
X_new = X.clone()
X_new[..., self.indices] = X_new[..., self.indices].log10()
return X_new
def _untransform(self, X: Tensor) -> Tensor:
r"""Reverse the log transformation.
Args:
X: A `batch_shape x n x d`-dim tensor of normalized inputs.
Returns:
A `batch_shape x n x d`-dim tensor of un-normalized inputs.
"""
X_new = X.clone()
X_new[..., self.indices] = 10.0 ** X_new[..., self.indices]
return X_new
[docs]class Warp(ReversibleInputTransform, GPyTorchModule):
r"""A transform that uses learned input warping functions.
Each specified input dimension is warped using the CDF of a
Kumaraswamy distribution. Typically, MAP estimates of the
parameters of the Kumaraswamy distribution, for each input
dimension, are learned jointly with the GP hyperparameters.
TODO: implement support using independent warping functions
for each output in batched multi-output and multi-task models.
For now, ModelListGPs should be used to learn independent warping
functions for each output.
"""
# TODO: make minimum value dtype-dependent
_min_concentration_level = 1e-4
def __init__(
self,
indices: List[int],
transform_on_train: bool = True,
transform_on_eval: bool = True,
transform_on_fantasize: bool = True,
reverse: bool = False,
eps: float = 1e-7,
concentration1_prior: Optional[Prior] = None,
concentration0_prior: Optional[Prior] = None,
batch_shape: Optional[torch.Size] = None,
) -> None:
r"""Initialize transform.
Args:
indices: The indices of the inputs to warp.
transform_on_train: A boolean indicating whether to apply the
transforms in train() mode. Default: True.
transform_on_eval: A boolean indicating whether to apply the
transform in eval() mode. Default: True.
transform_on_fantasize: A boolean indicating whether to apply the
transform when called from within a `fantasize` call. Default: True.
reverse: A boolean indicating whether the forward pass should untransform
the inputs.
eps: A small value used to clip values to be in the interval (0, 1).
concentration1_prior: A prior distribution on the concentration1 parameter
of the Kumaraswamy distribution.
concentration0_prior: A prior distribution on the concentration0 parameter
of the Kumaraswamy distribution.
batch_shape: The batch shape.
"""
super().__init__()
self.register_buffer("indices", torch.tensor(indices, dtype=torch.long))
self.transform_on_train = transform_on_train
self.transform_on_eval = transform_on_eval
self.transform_on_fantasize = transform_on_fantasize
self.reverse = reverse
self.batch_shape = batch_shape or torch.Size([])
self._X_min = eps
self._X_range = 1 - 2 * eps
if len(self.batch_shape) > 0:
# Note: this follows the gpytorch shape convention for lengthscales
# There is ongoing discussion about the extra `1`.
# TODO: update to follow new gpytorch convention resulting from
# https://github.com/cornellius-gp/gpytorch/issues/1317
batch_shape = self.batch_shape + torch.Size([1])
else:
batch_shape = self.batch_shape
for i in (0, 1):
p_name = f"concentration{i}"
self.register_parameter(
p_name,
nn.Parameter(torch.full(batch_shape + self.indices.shape, 1.0)),
)
if concentration0_prior is not None:
self.register_prior(
"concentration0_prior",
concentration0_prior,
lambda m: m.concentration0,
lambda m, v: m._set_concentration(i=0, value=v),
)
if concentration1_prior is not None:
self.register_prior(
"concentration1_prior",
concentration1_prior,
lambda m: m.concentration1,
lambda m, v: m._set_concentration(i=1, value=v),
)
for i in (0, 1):
p_name = f"concentration{i}"
constraint = GreaterThan(
self._min_concentration_level,
transform=None,
# set the initial value to be the identity transformation
initial_value=1.0,
)
self.register_constraint(param_name=p_name, constraint=constraint)
def _set_concentration(self, i: int, value: Union[float, Tensor]) -> None:
if not torch.is_tensor(value):
value = torch.as_tensor(value).to(self.concentration0)
self.initialize(**{f"concentration{i}": value})
def _transform(self, X: Tensor) -> Tensor:
r"""Warp the inputs through the Kumaraswamy CDF.
Args:
X: A `input_batch_shape x (batch_shape) x n x d`-dim tensor of inputs.
batch_shape here can either be self.batch_shape or 1's such that
it is broadcastable with self.batch_shape if self.batch_shape is set.
Returns:
A `input_batch_shape x (batch_shape) x n x d`-dim tensor of transformed
inputs.
"""
X_tf = expand_and_copy_tensor(X=X, batch_shape=self.batch_shape)
k = Kumaraswamy(
concentration1=self.concentration1, concentration0=self.concentration0
)
# normalize to [eps, 1-eps]
X_tf[..., self.indices] = k.cdf(
torch.clamp(
X_tf[..., self.indices] * self._X_range + self._X_min,
self._X_min,
1.0 - self._X_min,
)
)
return X_tf
def _untransform(self, X: Tensor) -> Tensor:
r"""Warp the inputs through the Kumaraswamy inverse CDF.
Args:
X: A `input_batch_shape x batch_shape x n x d`-dim tensor of inputs.
Returns:
A `input_batch_shape x batch_shape x n x d`-dim tensor of transformed
inputs.
"""
if len(self.batch_shape) > 0:
if self.batch_shape != X.shape[-2 - len(self.batch_shape) : -2]:
raise BotorchTensorDimensionError(
"The right most batch dims of X must match self.batch_shape: "
f"({self.batch_shape})."
)
X_tf = X.clone()
k = Kumaraswamy(
concentration1=self.concentration1, concentration0=self.concentration0
)
# unnormalize from [eps, 1-eps] to [0,1]
X_tf[..., self.indices] = (
(k.icdf(X_tf[..., self.indices]) - self._X_min) / self._X_range
).clamp(0.0, 1.0)
return X_tf
[docs]class AppendFeatures(InputTransform, Module):
r"""A transform that appends the input with a given set of features.
As an example, this can be used with `RiskMeasureMCObjective` to optimize risk
measures as described in [Cakmak2020risk]_. A tutorial notebook implementing the
rhoKG acqusition function introduced in [Cakmak2020risk]_ can be found at
https://botorch.org/tutorials/risk_averse_bo_with_environmental_variables.
The steps for using this to obtain samples of a risk measure are as follows:
- Train a model on `(x, w)` inputs and the corresponding observations;
- Pass in an instance of `AppendFeatures` with the `feature_set` denoting the
samples of `W` as the `input_transform` to the trained model;
- Call `posterior(...).rsample(...)` on the model with `x` inputs only to
get the joint posterior samples over `(x, w)`s, where the `w`s come
from the `feature_set`;
- Pass these posterior samples through the `RiskMeasureMCObjective` of choice to
get the samples of the risk measure.
Note: The samples of the risk measure obtained this way are in general biased
since the `feature_set` does not fully represent the distribution of the
environmental variable.
Example:
>>> # We consider 1D `x` and 1D `w`, with `W` having a
>>> # uniform distribution over [0, 1]
>>> model = SingleTaskGP(
... train_X=torch.rand(10, 2),
... train_Y=torch.randn(10, 1),
... input_transform=AppendFeatures(feature_set=torch.rand(10, 1))
... )
>>> mll = ExactMarginalLogLikelihood(model.likelihood, model)
>>> fit_gpytorch_model(mll)
>>> test_x = torch.rand(3, 1)
>>> # `posterior_samples` is a `10 x 30 x 1`-dim tensor
>>> posterior_samples = model.posterior(test_x).rsamples(torch.size([10]))
>>> risk_measure = VaR(alpha=0.8, n_w=10)
>>> # `risk_measure_samples` is a `10 x 3`-dim tensor of samples of the
>>> # risk measure VaR
>>> risk_measure_samples = risk_measure(posterior_samples)
"""
def __init__(
self,
feature_set: Tensor,
transform_on_train: bool = False,
transform_on_eval: bool = True,
transform_on_fantasize: bool = False,
) -> None:
r"""Append `feature_set` to each input.
Args:
feature_set: An `n_f x d_f`-dim tensor denoting the features to be
appended to the inputs.
transform_on_train: A boolean indicating whether to apply the
transforms in train() mode. Default: False.
transform_on_eval: A boolean indicating whether to apply the
transform in eval() mode. Default: True.
transform_on_fantasize: A boolean indicating whether to apply the
transform when called from within a `fantasize` call. Default: False.
"""
super().__init__()
if feature_set.dim() != 2:
raise ValueError("`feature_set` must be an `n_f x d_f`-dim tensor!")
self.feature_set = feature_set
self.transform_on_train = transform_on_train
self.transform_on_eval = transform_on_eval
self.transform_on_fantasize = transform_on_fantasize
[docs] def transform(self, X: Tensor) -> Tensor:
r"""Transform the inputs by appending `feature_set` to each input.
For each `1 x d`-dim element in the input tensor, this will produce
an `n_f x (d + d_f)`-dim tensor with `feature_set` appended as the last `d_f`
dimensions. For a generic `batch_shape x q x d`-dim `X`, this translates to a
`batch_shape x (q * n_f) x (d + d_f)`-dim output, where the values corresponding
to `X[..., i, :]` are found in `output[..., i * n_f: (i + 1) * n_f, :]`.
Note: Adding the `feature_set` on the `q-batch` dimension is necessary to avoid
introducing additional bias by evaluating the inputs on independent GP
sample paths.
Args:
X: A `batch_shape x q x d`-dim tensor of inputs.
Returns:
A `batch_shape x (q * n_f) x (d + d_f)`-dim tensor of appended inputs.
"""
expanded_X = X.unsqueeze(dim=-2).expand(
*X.shape[:-1], self.feature_set.shape[0], -1
)
expanded_features = self.feature_set.expand(*expanded_X.shape[:-1], -1)
appended_X = torch.cat([expanded_X, expanded_features], dim=-1)
return appended_X.view(*X.shape[:-2], -1, appended_X.shape[-1])
[docs]class InputPerturbation(InputTransform, Module):
r"""A transform that adds the set of perturbations to the given input.
Similar to `AppendFeatures`, this can be used with `RiskMeasureMCObjective`
to optimize risk measures. See `AppendFeatures` for additional discussion
on optimizing risk measures.
A tutorial notebook using this with `qNoisyExpectedImprovement` can be found at
https://botorch.org/tutorials/risk_averse_bo_with_input_perturbations.
"""
def __init__(
self,
perturbation_set: Tensor,
bounds: Optional[Tensor] = None,
multiplicative: bool = False,
transform_on_train: bool = False,
transform_on_eval: bool = True,
transform_on_fantasize: bool = False,
) -> None:
r"""Add `perturbation_set` to each input.
Args:
perturbation_set: An `n_p x d`-dim tensor denoting the perturbations
to be added to the inputs.
bounds: A `2 x d`-dim tensor of lower and upper bounds for each
column of the input. If given, the perturbed inputs will be
clamped to these bounds.
multiplicative: A boolean indicating whether the input perturbations
are additive or multiplicative. If True, inputs will be multiplied
with the perturbations.
transform_on_train: A boolean indicating whether to apply the
transforms in train() mode. Default: False.
transform_on_eval: A boolean indicating whether to apply the
transform in eval() mode. Default: True.
transform_on_fantasize: A boolean indicating whether to apply the
transform when called from within a `fantasize` call. Default: False.
"""
super().__init__()
if perturbation_set.dim() != 2:
raise ValueError("`perturbation_set` must be an `n_p x d`-dim tensor!")
self.perturbation_set = perturbation_set
if bounds is not None and bounds.shape[-1] != perturbation_set.shape[-1]:
raise ValueError(
"`bounds` must have the same number of columns (last dimension) as "
f"the `perturbation_set`! Got {bounds.shape[-1]} and "
f"{perturbation_set.shape[-1]}."
)
self.bounds = bounds
self.multiplicative = multiplicative
self.transform_on_train = transform_on_train
self.transform_on_eval = transform_on_eval
self.transform_on_fantasize = transform_on_fantasize
[docs] def transform(self, X: Tensor) -> Tensor:
r"""Transform the inputs by adding `perturbation_set` to each input.
For each `1 x d`-dim element in the input tensor, this will produce
an `n_p x d`-dim tensor with the `perturbation_set` added to the input.
For a generic `batch_shape x q x d`-dim `X`, this translates to a
`batch_shape x (q * n_p) x d`-dim output, where the values corresponding
to `X[..., i, :]` are found in `output[..., i * n_w: (i + 1) * n_w, :]`.
Note: Adding the `perturbation_set` on the `q-batch` dimension is necessary
to avoid introducing additional bias by evaluating the inputs on independent
GP sample paths.
Args:
X: A `batch_shape x q x d`-dim tensor of inputs.
Returns:
A `batch_shape x (q * n_p) x d`-dim tensor of perturbed inputs.
"""
expanded_X = X.unsqueeze(dim=-2).expand(
*X.shape[:-1], self.perturbation_set.shape[0], -1
)
expanded_perturbations = self.perturbation_set.expand(
*expanded_X.shape[:-1], -1
)
if self.multiplicative:
perturbed_inputs = expanded_X * expanded_perturbations
else:
perturbed_inputs = expanded_X + expanded_perturbations
perturbed_inputs = perturbed_inputs.reshape(*X.shape[:-2], -1, X.shape[-1])
if self.bounds is not None:
perturbed_inputs = torch.maximum(
torch.minimum(perturbed_inputs, self.bounds[1]), self.bounds[0]
)
return perturbed_inputs