def get_X_baseline()

in botorch/optim/utils.py [0:0]

• 27 lines of code
• 10 McCabe index (conditional complexity)

def get_X_baseline(acq_function: AcquisitionFunction) -> Optional[Tensor]:
    r"""Extract X_baseline from an acquisition function.

    This tries to find the baseline set of points. First, this checks if the
    acquisition function has an `X_baseline` attribute. If it does not,
    then this method attempts to use the model's `train_inputs` as `X_baseline`.

    Args:
        acq_function: The acquisition function.

    Returns
        An optional `n x d`-dim tensor of baseline points. This is None if no
            baseline points are found.
    """
    try:
        X = acq_function.X_baseline
        # if there are no baseline points, use training points
        if X.shape[0] == 0:
            raise BotorchError
    except (BotorchError, AttributeError):
        try:
            # for entropy MOO methods
            model = acq_function.mo_model
        except AttributeError:
            try:
                # some acquisition functions do not have a model attribute
                # e.g. FixedFeatureAcquisitionFunction
                model = acq_function.model
            except AttributeError:
                warnings.warn("Failed to extract X_baseline.", BotorchWarning)
                return
        try:
            # make sure input transforms are not applied
            model.train()
            if isinstance(model, ModelListGPyTorchModel):
                X = model.models[0].train_inputs[0]
            else:
                X = model.train_inputs[0]
        except (BotorchError, AttributeError):
            warnings.warn("Failed to extract X_baseline.", BotorchWarning)
            return
    # just use one batch
    while X.ndim > 2:
        X = X[0]
    return X