brier_score.py 文件源码

def __call__(self, y_true_proba, y_proba):
        """
        See Murphy (1973) A vector partition of the probability score
        """
        np.seterr(divide="ignore")
        pos_obs_freq = np.histogram(
            y_proba[y_true_proba == 1], bins=self.bins)[0]
        fore_freq = np.histogram(y_proba, bins=self.bins)[0]
        climo = y_true_proba.mean()
        unc = climo * (1 - climo)
        pos_obs_rel_freq = np.zeros(pos_obs_freq.size)
        for p in range(pos_obs_rel_freq.size):
            if fore_freq[p] > 0:
                pos_obs_rel_freq[p] = pos_obs_freq[p] / fore_freq[p]
            else:
                pos_obs_rel_freq[p] = np.nan
        score = np.nansum(fore_freq * (pos_obs_rel_freq - climo) ** 2)
        score /= float(y_proba.size)
        return score / unc