def predict_magnification(self, Xnew, kern=None, mean=True, covariance=True, dimensions=None):
        G = self.predict_wishard_embedding(Xnew, kern, mean, covariance)
        if dimensions is None:
            dimensions = self.get_most_significant_input_dimensions()[:2]
        G = G[:, dimensions][:,:,dimensions]
        from ..util.linalg import jitchol
        mag = np.empty(Xnew.shape[0])
        for n in range(Xnew.shape[0]):
            try:
                mag[n] = np.sqrt(np.exp(2*np.sum(np.log(np.diag(jitchol(G[n, :, :]))))))
            except:
                mag[n] = np.sqrt(np.linalg.det(G[n, :, :]))
        return mag
Predict the magnification factor as
dataset/ETHPy150Open SheffieldML/GPy/GPy/core/gp.py/GP.predict_magnification