def forward()

in botorch/acquisition/preference.py [0:0]

• 30 lines of code
• 7 McCabe index (conditional complexity)

    def forward(self, X: Tensor) -> Tensor:
        r"""Evaluate analytical EUBO on the candidate set X.

        Args:
            X: A `batch_shape x q x d`-dim Tensor, where `q = 2` if `previous_winner`
                is not `None`, and `q = 1` otherwise.

        Returns:
            The acquisition value for each batch as a tensor of shape `batch_shape`.
        """
        if not (
            (X.shape[-2] == 2)
            or ((X.shape[-2] == 1) and (self.previous_winner is not None))
        ):
            raise UnsupportedError(
                f"{self.__class__.__name__} only support q=2 or q=1"
                "with a previous winner specified"
            )

        Y = X if self.outcome_model is None else self.outcome_model(X)

        if self.previous_winner is not None:
            Y = torch.cat([Y, match_batch_shape(self.previous_winner, Y)], dim=-2)

        # Calling forward directly instead of posterior here to
        # obtain the full covariance matrix
        pref_posterior = self.model(Y)
        pref_mean = pref_posterior.mean
        pref_cov = pref_posterior.covariance_matrix
        delta = pref_mean[..., 0] - pref_mean[..., 1]
        sigma = torch.sqrt(
            pref_cov[..., 0, 0]
            + pref_cov[..., 1, 1]
            - pref_cov[..., 0, 1]
            - pref_cov[..., 1, 0]
        )
        u = delta / sigma

        ucdf = self.std_norm.cdf(u)
        updf = torch.exp(self.std_norm.log_prob(u))
        acqf_val = sigma * (updf + u * ucdf)
        if self.previous_winner is None:
            acqf_val = acqf_val + pref_mean[..., 1]
        return acqf_val