def condition_on_observations()

in botorch/models/pairwise_gp.py [0:0]

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

    def condition_on_observations(self, X: Tensor, Y: Tensor, **kwargs: Any) -> Model:
        r"""Condition the model on new observations.

        Note that unlike other BoTorch models, PairwiseGP requires Y to be
        pairwise comparisons

        Args:
            X: A `batch_shape x n x d` dimension tensor X
            Y: A tensor of size `batch_shape x m x 2`. (i, j) means
                f_i is preferred over f_j

        Returns:
            A (deepcopied) `Model` object of the same type, representing the
            original model conditioned on the new observations `(X, Y)`.
        """
        new_model = deepcopy(self)

        if self._has_no_data():
            # If the model previously has no data, set X and Y as the data directly
            new_model.set_train_data(X, Y, update_model=True)
        else:
            # Can only condition on pairwise comparisons instead of the directly
            # observed values. Raise a RuntimeError if Y is not a tensor presenting
            # pairwise comparisons
            if Y.dtype in (float32, float64) or Y.shape[-1] != 2:
                raise RuntimeError(
                    "Conditioning on non-pairwise comparison observations."
                )

            # Reshaping datapoints and comparisons by batches
            Y_new_batch_shape = Y.shape[:-2]
            new_datapoints = self.datapoints.expand(
                Y_new_batch_shape + self.datapoints.shape[-2:]
            )
            new_comparisons = self.comparisons.expand(
                Y_new_batch_shape + self.comparisons.shape[-2:]
            )
            # Reshape X since Y may have additional batch dim. from fantasy models
            X = X.expand(Y_new_batch_shape + X.shape[-2:])

            new_datapoints = torch.cat((new_datapoints, X.to(new_datapoints)), dim=-2)

            shifted_comp = Y.to(new_comparisons) + self.n
            new_comparisons = torch.cat((new_comparisons, shifted_comp), dim=-2)

            # TODO: be smart about how we can update covar matrix here
            new_model.set_train_data(new_datapoints, new_comparisons, update_model=True)
        return new_model