def top_N(self, query_item, labels, N, distance_metric='jaccard', threshold = 0.1):

            # Run the similarity query, get the match list, sort it, look
            # up the labels for those indices and return that data + sim score
            matches = self.similarity_query(query_item, distance_metric, threshold)
            matches.sort(reverse=True)
            matches_data = []
            for sim, index in matches[:N]:
                matches_data.append({'sim':sim, 'label':labels[index]})

            # Return the matches labels
            return matches_data
This is a convenience methods that shows the label data for the
dataset/ETHPy150Open ClickSecurity/data_hacking/data_hacking/lsh_sims/lsh_sims.py/LSHSimilarities.top_N