def predict(self, X, index=None, codes=None):
n, p = X.shape
powerset_predictions = SparseModel.predict(self, X)
example_str = self.powerset_labels[0]
example = [int(i) for i in example_str[1:-1].split(',')]
n_codes = len(example)
predictions = np.zeros([n, n_codes])
for i in range(n):
predictions[i, :] = self.powerset_index_to_binary_vec(powerset_predictions[i])
if index is not None:
predictions = pd.DataFrame(predictions, index=index, columns=codes)
return predictions
