class_docstr: Uncertainty Sampling based on Category Vector Inconsistency and Ranking of Scores [RCV18]_
  selects instances based on the inconsistency of predicted labels and per-class label rankings.
class_docstr_tok: ["Uncertainty","Sampling","based","on","Category","Vector","Inconsistency","and","Ranking","of","Scores","[","RCV18","]","_","selects","instances","based","on","the","inconsistency","of","predicted","labels","and","per-class","label","rankings","."]
code: def _compute_ranking(self, indices_unlabeled, proba_unlabeled):
      num_unlabeled, num_classes = (
          proba_unlabeled.shape[0],
          proba_unlabeled.shape[1],
      )
      ranks = self._rank_by_margin(proba_unlabeled)
  
      ranking_denom = num_classes * (num_unlabeled - 1)
  
      ranking_scores = [
          sum([num_unlabeled - ranks[j, i] for j in range(num_classes)])
          / ranking_denom
          for i in range(indices_unlabeled.shape[0])
      ]
      return np.array(ranking_scores)
  
file_path: small-text/small_text/query_strategies/multi_label.py
imports: import numpy
  from small_text.query_strategies.base import constraints
  from small_text.query_strategies.strategies import QueryStrategy
  from small_text.utils.context import build_pbar_context
label_desc: Use image node_id 7 for calling the CategoryVectorInconsistencyAndRanking obj's underlying member method code with example usage: obj._compute_ranking(indices_unlabeled, proba_unlabeled) and returns: np