def predict_cumulative_hazard(self, X):
        """
        Returns the hazard rates for the individuals

        Parameters
        ----------
        X: a (n,d) covariate numpy array or DataFrame. If a DataFrame, columns
            can be in any order. If a numpy array, columns must be in the
            same order as the training data.

        """
        n, _ = X.shape

        cols = _get_index(X)
        if isinstance(X, pd.DataFrame):
            order = self.cumulative_hazards_.columns
            order = order.drop("_intercept") if self.fit_intercept else order
            X_ = X[order].values
        else:
            X_ = X

        X_ = X_ if not self.fit_intercept else np.c_[X_, np.ones((n, 1))]

        timeline = self._index
        individual_cumulative_hazards_ = pd.DataFrame(
            np.dot(self.cumulative_hazards_, X_.T), index=timeline, columns=cols
        )

        return individual_cumulative_hazards_
Returns the hazard rates for the individuals

        Parameters
        ----------
        X: a (n,d) covariate numpy array or DataFrame. If a DataFrame, columns
            can be in any order. If a numpy array, columns must be in the
            same order as the training data.