find the expirical quantile of the model

        Parameters
        ----------
        X : array-like, shape (n_samples, m_features)
            Training vectors, where n_samples is the number of samples
            and m_features is the number of features.
        y : array-like, shape (n_samples,)
            Target values (integers in classification, real numbers in
            regression)
            For classification, labels must correspond to classes.

        Returns
        -------
        ratio : float on [0, 1]

def _get_quantile_ratio(self, X, y):
        """find the expirical quantile of the model

        Parameters
        ----------
        X : array-like, shape (n_samples, m_features)
            Training vectors, where n_samples is the number of samples
            and m_features is the number of features.
        y : array-like, shape (n_samples,)
            Target values (integers in classification, real numbers in
            regression)
            For classification, labels must correspond to classes.

        Returns
        -------
        ratio : float on [0, 1]
        """
        y_pred = self.predict(X)
        return (y_pred > y).mean()