def tune_by_cv(self, X, all_y, alpha_values, td_splits, n_dev_folds, reuser=None, verbose=1):
y = self.binary_vectors_to_powerset_list(all_y.as_matrix())
alphas = pd.DataFrame(np.zeros([1, 1]), index=['alpha'], columns=['powerset_label'])
valid_f1_summary, best_alpha = SparseModel.tune_by_cv(self, X, y, alpha_values, td_splits, n_dev_folds,
reuser, verbose)
alphas['alpha', 'powerset_label'] = best_alpha
return alphas
