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 _entropy(self, numerator, denominator):
      ratio = numerator / (denominator + self.epsilon)
      result = -ratio * np.log2(ratio + self.epsilon)
      return result
  
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 6 for calling the CategoryVectorInconsistencyAndRanking obj's underlying member method code with example usage: obj._entropy(numerator, denominator) and returns: result