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_scores(self, indices_unlabeled, y, proba):
      y_pred = (proba > self.prediction_threshold).astype(int)
      vector_inconsistency_scores = self._compute_vector_inconsistency(
          y, y_pred, proba.shape[1]
      )
      ranking_scores = self._compute_ranking(indices_unlabeled, proba)
      return vector_inconsistency_scores * 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 3 for calling the CategoryVectorInconsistencyAndRanking obj's underlying member method code with example usage: obj._compute_scores(indices_unlabeled, y, proba) and returns: unknown