Parameters
        ----------

        X: numpy array or DataFrame
            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.
        times: iterable, optional
            an iterable of increasing times to predict the cumulative hazard at. Default
            is the set of all durations (observed and unobserved). Uses a linear interpolation if
            points in time are not in the index.

        Returns
        -------
        cumulative_hazard_ : DataFrame
            the cumulative hazard of individuals over the timeline

def predict_cumulative_hazard(self, X, times=None):
        """
        Parameters
        ----------

        X: numpy array or DataFrame
            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.
        times: iterable, optional
            an iterable of increasing times to predict the cumulative hazard at. Default
            is the set of all durations (observed and unobserved). Uses a linear interpolation if
            points in time are not in the index.

        Returns
        -------
        cumulative_hazard_ : DataFrame
            the cumulative hazard of individuals over the timeline
        """

        if self.strata:
            cumulative_hazard_ = pd.DataFrame()
            for stratum, stratified_X in X.groupby(self.strata):
                try:
                    c_0 = self.baseline_cumulative_hazard_[[stratum]]
                except KeyError:
                    raise StatError(
                        """The stratum %s was not found in the original training data. For example, try
the following on the original dataset, df: `df.groupby(%s).size()`. Expected is that %s is not present in the output.
"""
                        % (stratum, self.strata, stratum)
                    )
                col = _get_index(stratified_X)
                v = self.predict_partial_hazard(stratified_X)
                cumulative_hazard_ = cumulative_hazard_.merge(
                    pd.DataFrame(np.dot(c_0, v.T), index=c_0.index, columns=col),
                    how="outer",
                    right_index=True,
                    left_index=True,
                )
        else:

            c_0 = self.baseline_cumulative_hazard_
            v = self.predict_partial_hazard(X)
            col = _get_index(v)
            cumulative_hazard_ = pd.DataFrame(np.dot(c_0, v.T), columns=col, index=c_0.index)

        if times is not None:
            # non-linear interpolations can push the survival curves above 1 and below 0.
            return dataframe_interpolate_at_times(cumulative_hazard_, times)
        return cumulative_hazard_