Select categorical features and transform them using OneHotEncoder.

        Parameters
        ----------
        X: numpy ndarray, {n_samples, n_components}
            New data, where n_samples is the number of samples and n_components is the number of components.

        Returns
        -------
        array-like, {n_samples, n_components}

def transform(self, X):
        """Select categorical features and transform them using OneHotEncoder.

        Parameters
        ----------
        X: numpy ndarray, {n_samples, n_components}
            New data, where n_samples is the number of samples and n_components is the number of components.

        Returns
        -------
        array-like, {n_samples, n_components}
        """
        selected = auto_select_categorical_features(X, threshold=self.threshold)
        X_sel, _, n_selected, _ = _X_selected(X, selected)

        if n_selected == 0:
            # No features selected.
            raise ValueError('No categorical feature was found!')
        else:
            ohe = OneHotEncoder(categorical_features='all', sparse=False, minimum_fraction=self.minimum_fraction)
            return ohe.fit_transform(X_sel)