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 query(
      self, clf, dataset, indices_unlabeled, _indices_labeled, y, n=10
  ):
      self._validate_query_input(indices_unlabeled, n)
  
      y_proba = clf.predict_proba(dataset[indices_unlabeled])
      scores = self._compute_scores(indices_unlabeled, y, y_proba)
  
      if len(indices_unlabeled) == n:
          return np.array(indices_unlabeled)
  
      indices_queried = np.argpartition(-scores, n)[:n]
      return np.array([indices_unlabeled[i] for i in indices_queried])
  
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 2 for calling the CategoryVectorInconsistencyAndRanking obj's underlying member method code with example usage: obj.query(clf, dataset, indices_unlabeled, _indices_labeled, y, n) and returns: np, np