def _check_compatibility()

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

• 44 lines of code
• 28 McCabe index (conditional complexity)

def _check_compatibility(models: ModelListGP) -> None:
    """Check if a ModelListGP can be converted."""
    # Check that all submodules are of the same type.
    for modn, mod in models[0].named_modules():
        mcls = mod.__class__
        if not all(isinstance(_get_module(m, modn), mcls) for m in models[1:]):
            raise UnsupportedError(
                "Sub-modules must be of the same type across models."
            )

    # Check that each model is a BatchedMultiOutputGPyTorchModel.
    if not all(isinstance(m, BatchedMultiOutputGPyTorchModel) for m in models):
        raise UnsupportedError(
            "All models must be of type BatchedMultiOutputGPyTorchModel."
        )

    # TODO: Add support for HeteroskedasticSingleTaskGP.
    if any(isinstance(m, HeteroskedasticSingleTaskGP) for m in models):
        raise NotImplementedError(
            "Conversion of HeteroskedasticSingleTaskGP is currently unsupported."
        )

    # TODO: Add support for custom likelihoods.
    if any(getattr(m, "_is_custom_likelihood", False) for m in models):
        raise NotImplementedError(
            "Conversion of models with custom likelihoods is currently unsupported."
        )

    # TODO: Add support for outcome transforms.
    if any(getattr(m, "outcome_transform", None) is not None for m in models):
        raise UnsupportedError(
            "Conversion of models with outcome transforms is currently unsupported."
        )

    # check that each model is single-output
    if not all(m._num_outputs == 1 for m in models):
        raise UnsupportedError("All models must be single-output.")

    # check that training inputs are the same
    if not all(
        torch.equal(ti, tj)
        for m in models[1:]
        for ti, tj in zip(models[0].train_inputs, m.train_inputs)
    ):
        raise UnsupportedError("training inputs must agree for all sub-models.")

    # check that there are no batched input transforms
    default_size = torch.Size([])
    for m in models:
        if hasattr(m, "input_transform"):
            if (
                m.input_transform is not None
                and len(getattr(m.input_transform, "batch_shape", default_size)) != 0
            ):
                raise UnsupportedError("Batched input_transforms are not supported.")

    # check that all models have the same input transforms
    if any(hasattr(m, "input_transform") for m in models):
        if not all(
            m.input_transform.equals(models[0].input_transform) for m in models[1:]
        ):
            raise UnsupportedError("All models must have the same input_transforms.")