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.distributions.distributions import Kumaraswamy
from botorch.exceptions.errors import BotorchTensorDimensionError
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.nn import Module, ModuleDict


[docs]class InputTransform(ABC): r"""Abstract base class for input transforms. 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_preprocess: A boolean indicating whether to apply the transform when preprocessing inputs. """ transform_on_eval: bool transform_on_train: bool transform_on_preprocess: 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 and self.transform_on_train) or ( not self.training and self.transform_on_eval ): 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 definde 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 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_preprocess: 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_preprocess = 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_preprocess |= tf.transform_on_preprocess
[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_preprocess: bool = False, reverse: bool = False, ) -> 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_preprocess: A boolean indicating whether to apply the transform when preprocessing inputs. Default: False reverse: A boolean indicating whether the forward pass should untransform the inputs. """ super().__init__() if bounds is not None: if bounds.size(-1) != d: raise BotorchTensorDimensionError( "Incompatible dimensions of provided bounds" ) 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_preprocess = transform_on_preprocess self.reverse = reverse self.batch_shape = batch_shape 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] self.ranges = X.max(dim=-2, keepdim=True)[0] - self.mins 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_preprocess: bool = False, 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_preprocess: A boolean indicating whether to apply the transform when preprocessing inputs. Default: False 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_preprocess = transform_on_preprocess 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_preprocess: bool = False, 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_preprocess: A boolean indicating whether to apply the transform when preprocessing inputs. Default: False 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_preprocess = transform_on_preprocess 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_preprocess: bool = False, reverse: bool = False, eps: float = 1e-7, concentration1_prior: Optional[Prior] = None, concentration0_prior: Optional[Prior] = 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_preprocess: A boolean indicating whether to apply the transform when preprocessing. Default: False. 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. """ super().__init__() self.eps = eps 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_preprocess = transform_on_preprocess self.reverse = reverse for i in (0, 1): p_name = f"concentration{i}" self.register_parameter( p_name, nn.Parameter(torch.full(self.indices.shape, 1.0)) ) if concentration0_prior is not None: self.register_prior( "concentration0_prior", concentration0_prior, lambda: self.concentration0, lambda v: self._set_concentration(i=0, value=v), ) if concentration1_prior is not None: self.register_prior( "concentration1_prior", concentration1_prior, lambda: self.concentration1, lambda v: self._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 `batch_shape x n x d`-dim tensor of inputs. Returns: A `batch_shape x n x d`-dim tensor of transformed inputs. """ X_tf = X.clone() k = Kumaraswamy( concentration1=self.concentration1, concentration0=self.concentration0 ) X_tf[..., self.indices] = k.cdf( X[..., self.indices].clamp(self.eps, 1 - self.eps) ) return X_tf def _untransform(self, X: Tensor) -> Tensor: X_tf = X.clone() k = Kumaraswamy( concentration1=self.concentration1, concentration0=self.concentration0 ) X_tf[..., self.indices] = k.icdf(X[..., self.indices]) return X_tf