Get split value histogram of a feature

        Parameters
        ----------
        feature: str
            The name of the feature.
        fmap: str (optional)
            The name of feature map file.
        bin: int, default None
            The maximum number of bins.
            Number of bins equals number of unique split values n_unique,
            if bins == None or bins > n_unique.
        as_pandas: bool, default True
            Return pd.DataFrame when pandas is installed.
            If False or pandas is not installed, return numpy ndarray.

        Returns
        -------
        a histogram of used splitting values for the specified feature
        either as numpy array or pandas DataFrame.

def get_split_value_histogram(self, feature, fmap='', bins=None, as_pandas=True):
        """Get split value histogram of a feature

        Parameters
        ----------
        feature: str
            The name of the feature.
        fmap: str (optional)
            The name of feature map file.
        bin: int, default None
            The maximum number of bins.
            Number of bins equals number of unique split values n_unique,
            if bins == None or bins > n_unique.
        as_pandas: bool, default True
            Return pd.DataFrame when pandas is installed.
            If False or pandas is not installed, return numpy ndarray.

        Returns
        -------
        a histogram of used splitting values for the specified feature
        either as numpy array or pandas DataFrame.
        """
        xgdump = self.get_dump(fmap=fmap)
        values = []
        regexp = re.compile(r"\[{0}<([\d.Ee+-]+)\]".format(feature))
        for i, _ in enumerate(xgdump):
            m = re.findall(regexp, xgdump[i])
            values.extend([float(x) for x in m])

        n_unique = len(np.unique(values))
        bins = max(min(n_unique, bins) if bins is not None else n_unique, 1)

        nph = np.histogram(values, bins=bins)
        nph = np.column_stack((nph[1][1:], nph[0]))
        nph = nph[nph[:, 1] > 0]

        if as_pandas and PANDAS_INSTALLED:
            return DataFrame(nph, columns=['SplitValue', 'Count'])
        if as_pandas and not PANDAS_INSTALLED:
            sys.stderr.write(
                "Returning histogram as ndarray (as_pandas == True, but pandas is not installed).")
        return nph