def prewarp()

in bayesmark/builtin_opt/nevergrad_optimizer.py [0:0]

• 14 lines of code
• 3 McCabe index (conditional complexity)

    def prewarp(self, xx):
        """Extra work needed to get variables into the Gaussian space
        representation."""
        xxw = {}
        for arg_name, vv in xx.items():
            assert np.isscalar(vv)
            space = self.space[arg_name]

            if space is not None:
                # Warp so we think it is apriori uniform in [a, b]
                vv = space.warp(vv)
                assert vv.size == 1

                # Now make uniform on [0, 1], also unpack warped to scalar
                (lb, ub), = space.get_bounds()
                vv = linear_rescale(vv.item(), lb, ub, 0, 1)

                # Now make std Gaussian apriori
                vv = norm.ppf(vv)
            assert np.isscalar(vv)
            xxw[arg_name] = vv
        return xxw