class_docstr: Base class for Ranking models (e.g., TextMatcher and Ranker) that
  provides validation methods with different metrics.
class_docstr_tok: ["Base","class","for","Ranking","models","(","e.g.",",","TextMatcher","and","Ranker",")","that","provides","validation","methods","with","different","metrics","."]
docstr: Evaluate using normalized discounted cumulative gain on TextSet.
  
  :param x: TextSet. Each TextFeature should contain Sample with batch features and labels.
            In other words, each Sample should be a batch of records having both positive
            and negative labels.
  :param k: Positive int. Rank position.
  :param threshold: Float. If label > threshold, then it will be considered as
                    a positive record. Default is 0.0.
  
  :return: Float. NDCG result.
docstr_tok: ["Evaluate","using","normalized","discounted","cumulative","gain","on","TextSet",".",":","param","x",":","TextSet",".","Each","TextFeature","should","contain","Sample","with","batch","features","and","labels",".","In","other","words",",","each","Sample","should","be","a","batch","of","records","having","both","positive","and","negative","labels",".",":","param","k",":","Positive","int",".","Rank","position",".",":","param","threshold",":","Float",".","If","label",">","threshold",",","then","it","will","be","considered","as","a","positive","record",".","Default","is","0.0.",":","return",":","Float",".","NDCG","result","."]
code: def evaluate_ndcg(self, x, k, threshold=0.0):
      """
      Evaluate using normalized discounted cumulative gain on TextSet.
  
      :param x: TextSet. Each TextFeature should contain Sample with batch features and labels.
                In other words, each Sample should be a batch of records having both positive
                and negative labels.
      :param k: Positive int. Rank position.
      :param threshold: Float. If label > threshold, then it will be considered as
                        a positive record. Default is 0.0.
  
      :return: Float. NDCG result.
      """
      return callZooFunc(
          self.bigdl_type, "evaluateNDCG", self.value, x, k, threshold
      )
  
imports: import sys
  from bigdl.util.common import JavaValue
  from zoo.common.utils import callZooFunc
label_desc: Use image node_id 1 for calling the Ranker obj's underlying member method code with example usage: obj.evaluate_ndcg(x, k, threshold) and returns: callZooFunc