def f_np_wrapper()

in botorch/generation/gen.py [0:0]

• 26 lines of code
• 4 McCabe index (conditional complexity)

    def f_np_wrapper(x: np.ndarray, f: Callable):
        """Given a torch callable, compute value + grad given a numpy array."""
        if np.isnan(x).any():
            raise RuntimeError(
                f"{np.isnan(x).sum()} elements of the {x.size} element array "
                f"`x` are NaN."
            )
        X = (
            torch.from_numpy(x)
            .to(initial_conditions)
            .view(shapeX)
            .contiguous()
            .requires_grad_(True)
        )
        X_fix = fix_features(X, fixed_features=fixed_features)
        loss = f(X_fix).sum()
        # compute gradient w.r.t. the inputs (does not accumulate in leaves)
        gradf = _arrayify(torch.autograd.grad(loss, X)[0].contiguous().view(-1))
        if np.isnan(gradf).any():
            msg = (
                f"{np.isnan(gradf).sum()} elements of the {x.size} element "
                "gradient array `gradf` are NaN. This often indicates numerical issues."
            )
            if initial_conditions.dtype != torch.double:
                msg += " Consider using `dtype=torch.double`."
            raise RuntimeError(msg)
        fval = loss.item()
        return fval, gradf