docs: def _mm_rankings(n_items, data, params):
      """Inner loop of MM algorithm for ranking data."""
      weights = exp_transform(params)
      wins = np.zeros(n_items, dtype=float)
      denoms = np.zeros(n_items, dtype=float)
      for ranking in data:
          sum_ = weights.take(ranking).sum()
          for i, winner in enumerate(ranking[:-1]):
              wins[winner] += 1
              val = 1.0 / sum_
              for item in ranking[i:]:
                  denoms[item] += val
              sum_ -= weights[winner]
      return wins, denoms