def nll(self, theta):
        """
        Returns the negative marginal log likelihood (+ the prior) for
        a hyperparameter configuration theta.
        (negative because we use scipy minimize for optimization)

        Parameters
        ----------
        theta : np.ndarray(H)
            Hyperparameter vector. Note that all hyperparameter are
            on a log scale.

        Returns
        ----------
        float
            lnlikelihood + prior
        """

        # Specify bounds to keep things sane
        if np.any((theta > -10) + (theta > 10)):
            return 1e25

        self.model.kernel[:] = theta
        ll = self.model.lnlikelihood(self.Y[:, 0], quiet=True)

        # Add prior
        if self.prior is not None:
            ll += self.prior.lnprob(theta)

        # We add a minus here because scipy is minimizing
        return -ll if np.isfinite(ll) else 1e25
dataset/ETHPy150Open automl/RoBO/robo/models/gaussian_process.py/GaussianProcess.nll/swapped operands of `>`