Source code for botorch.models.likelihoods.pairwise

#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

r"""
Pairwise likelihood for pairwise preference model (e.g., PairwiseGP).
"""

from __future__ import annotations

import math
from abc import ABC, abstractmethod
from typing import Tuple

import torch
from botorch.utils.probability.utils import (
    log_ndtr,
    log_phi,
    standard_normal_log_hazard,
)
from gpytorch.likelihoods import Likelihood
from torch import Tensor
from torch.distributions import Bernoulli


[docs] class PairwiseLikelihood(Likelihood, ABC): """ Pairwise likelihood base class for pairwise preference GP (e.g., PairwiseGP). """ def __init__(self, max_plate_nesting: int = 1): """ Initialized like a `gpytorch.likelihoods.Likelihood`. Args: max_plate_nesting: Defaults to 1. """ super().__init__(max_plate_nesting)
[docs] def forward(self, utility: Tensor, D: Tensor) -> Bernoulli: """Given the difference in (estimated) utility util_diff = f(v) - f(u), return a Bernoulli distribution object representing the likelihood of the user prefer v over u. Note that this is not used by the `PairwiseGP` model, """ return Bernoulli(probs=self.p(utility=utility, D=D))
[docs] @abstractmethod def p(self, utility: Tensor, D: Tensor) -> Tensor: """Given the difference in (estimated) utility util_diff = f(v) - f(u), return the probability of the user prefer v over u. Args: utility: A Tensor of shape `(batch_size) x n`, the utility at MAP point D: D is `(batch_size x) m x n` matrix with all elements being zero in last dimension except at two positions D[..., i] = 1 and D[..., j] = -1 respectively, representing item i is preferred over item j. log: if true, return log probability """
[docs] def log_p(self, utility: Tensor, D: Tensor) -> Tensor: """return the log of p""" return torch.log(self.p(utility=utility, D=D))
[docs] def negative_log_gradient_sum(self, utility: Tensor, D: Tensor) -> Tensor: """Calculate the sum of negative log gradient with respect to each item's latent utility values. Useful for models using laplace approximation. Args: utility: A Tensor of shape `(batch_size x) n`, the utility at MAP point D: D is `(batch_size x) m x n` matrix with all elements being zero in last dimension except at two positions D[..., i] = 1 and D[..., j] = -1 respectively, representing item i is preferred over item j. Returns: A `(batch_size x) n` Tensor representing the sum of negative log gradient values of the likelihood over all comparisons (i.e., the m dimension) with respect to each item. """ raise NotImplementedError
[docs] def negative_log_hessian_sum(self, utility: Tensor, D: Tensor) -> Tensor: """Calculate the sum of negative log hessian with respect to each item's latent utility values. Useful for models using laplace approximation. Args: utility: A Tensor of shape `(batch_size) x n`, the utility at MAP point D: D is `(batch_size x) m x n` matrix with all elements being zero in last dimension except at two positions D[..., i] = 1 and D[..., j] = -1 respectively, representing item i is preferred over item j. Returns: A `(batch_size x) n x n` Tensor representing the sum of negative log hessian values of the likelihood over all comparisons (i.e., the m dimension) with respect to each item. """ raise NotImplementedError
[docs] class PairwiseProbitLikelihood(PairwiseLikelihood): """Pairwise likelihood using probit function Given two items v and u with utilities f(v) and f(u), the probability that we prefer v over u with probability std_normal_cdf((f(v) - f(u))/sqrt(2)). Note that this formulation implicitly assume the noise term is fixed at 1. """ # Clamping z values for better numerical stability. See self._calc_z for detail # norm_cdf(z=3) ~= 0.999, top 0.1% percent _zlim = 3 def _calc_z(self, utility: Tensor, D: Tensor) -> Tensor: """Calculate the z score given estimated utility values and the comparison matrix D. """ scaled_util = (utility / math.sqrt(2)).unsqueeze(-1) z = D.to(scaled_util) @ scaled_util z = z.clamp(-self._zlim, self._zlim).squeeze(-1) return z def _calc_z_derived(self, z: Tensor) -> Tuple[Tensor, Tensor, Tensor]: """Calculate auxiliary statistics derived from z, including log pdf, log cdf, and the hazard function (pdf divided by cdf) Args: z: A Tensor of arbitrary shape. Returns: Tensors with standard normal logpdf(z), logcdf(z), and hazard function values evaluated at -z. """ return log_phi(z), log_ndtr(z), standard_normal_log_hazard(-z).exp()
[docs] def p(self, utility: Tensor, D: Tensor, log: bool = False) -> Tensor: z = self._calc_z(utility=utility, D=D) std_norm = torch.distributions.normal.Normal( torch.zeros(1, dtype=z.dtype, device=z.device), torch.ones(1, dtype=z.dtype, device=z.device), ) return std_norm.cdf(z)
[docs] def negative_log_gradient_sum(self, utility: Tensor, D: Tensor) -> Tensor: # Compute the sum over of grad. of negative Log-LH wrt utility f. # Original grad should be of dimension m x n, as in (6) from # [Chu2005preference]_. The sum over the m dimension of grad. of # negative log likelihood with respect to the utility z = self._calc_z(utility, D) _, _, h = self._calc_z_derived(z) h_factor = h / math.sqrt(2) grad = (h_factor.unsqueeze(-2) @ (-D)).squeeze(-2) return grad
[docs] def negative_log_hessian_sum(self, utility: Tensor, D: Tensor) -> Tensor: # Original hess should be of dimension m x n x n, as in (7) from # [Chu2005preference]_ Sum over the first dimension and return a tensor of # shape n x n. # The sum over the m dimension of hessian of negative log likelihood # with respect to the utility DT = D.transpose(-1, -2) z = self._calc_z(utility, D) _, _, h = self._calc_z_derived(z) mul_factor = h * (h + z) / 2 mul_factor = mul_factor.unsqueeze(-2).expand(*DT.size()) # multiply the hessian value by preference signs # (+1 if preferred or -1 otherwise) and sum over the m dimension hess = DT * mul_factor @ D return hess
[docs] class PairwiseLogitLikelihood(PairwiseLikelihood): """Pairwise likelihood using logistic (i.e., sigmoid) function Given two items v and u with utilities f(v) and f(u), the probability that we prefer v over u with probability sigmoid(f(v) - f(u)). Note that this formulation implicitly assume the beta term in logistic function is fixed at 1. """ # Clamping logit values for better numerical stability. # See self._calc_logit for detail logistic(8) ~= 0.9997, top 0.03% percent _logit_lim = 8 def _calc_logit(self, utility: Tensor, D: Tensor) -> Tensor: logit = D.to(utility) @ utility.unsqueeze(-1) logit = logit.clamp(-self._logit_lim, self._logit_lim).squeeze(-1) return logit
[docs] def log_p(self, utility: Tensor, D: Tensor) -> Tensor: logit = self._calc_logit(utility=utility, D=D) return torch.nn.functional.logsigmoid(logit)
[docs] def p(self, utility: Tensor, D: Tensor) -> Tensor: logit = self._calc_logit(utility=utility, D=D) return torch.sigmoid(logit)
[docs] def negative_log_gradient_sum(self, utility: Tensor, D: Tensor) -> Tensor: indices_shape = utility.shape[:-1] + (-1,) winner_indices = (D == 1).nonzero(as_tuple=True)[-1].reshape(indices_shape) loser_indices = (D == -1).nonzero(as_tuple=True)[-1].reshape(indices_shape) ex = torch.exp(torch.gather(utility, -1, winner_indices)) ey = torch.exp(torch.gather(utility, -1, loser_indices)) unsigned_grad = ey / (ex + ey) grad = (unsigned_grad.unsqueeze(-2) @ (-D)).squeeze(-2) return grad
[docs] def negative_log_hessian_sum(self, utility: Tensor, D: Tensor) -> Tensor: DT = D.transpose(-1, -2) # calculating f(v) - f(u) given u > v information in D neg_logit = -(D @ utility.unsqueeze(-1)).squeeze(-1) term = torch.sigmoid(neg_logit) mul_factor = term - (term) ** 2 mul_factor = mul_factor.unsqueeze(-2).expand(*DT.size()) # multiply the hessian value by preference signs # (+1 if preferred or -1 otherwise) and sum over the m dimension hess = DT * mul_factor @ D return hess