class_docstr: Class for evaluating the performance of mapping W
class_docstr_tok: ["Class","for","evaluating","the","performance","of","mapping","W"]
docstr: Builds various graphs needed for CSLS calculation
  
  Returns:
      Returns a dictionary with various graphs constructed
docstr_tok: ["Builds","various","graphs","needed","for","CSLS","calculation","Returns",":","Returns","a","dictionary","with","various","graphs","constructed"]
code: def build_csls_subgraphs(self):
      """
      Builds various graphs needed for CSLS calculation
  
      Returns:
          Returns a dictionary with various graphs constructed
  
      """
      # Graph for calculating only score
      scores_s2t = self.build_eval_graph(
          "ScoreS2T",
          self.src_ten,
          self.tgt_ten,
          en_knn=False,
          en_mean=False,
      )
      scores_t2s = self.build_eval_graph(
          "ScoreT2S",
          self.tgt_ten,
          self.src_ten,
          en_knn=False,
          en_mean=False,
      )
      # Graphs for calculating average between source and target
      avg1_s2t = self.build_eval_graph(
          "Avg1", self.src_ten, self.tgt_ten, knn=10
      )
      avg2_s2t = self.build_eval_graph(
          "Avg2", self.tgt_ten, self.src_ten, knn=10
      )
      # Graph for selecting top 100 elements
      top100_matches = tf.nn.top_k(self.score_ph, 100)
      # Graph for selecting top 2 elements
      top2_matches = tf.nn.top_k(self.score_ph, 2)
      # Graph for calculating similarity
      csls_mean_score = self.build_sim_graph("SimGraph")
  
      # Dictionary
      csls_graphs = {
          "ScoreGraph": scores_s2t,
          "ScoreG_T2S": scores_t2s,
          "Avg1S2T": avg1_s2t,
          "Avg2S2T": avg2_s2t,
          "Top100": top100_matches,
          "Top2": top2_matches,
          "CSLS_Criteria": csls_mean_score,
      }
      return csls_graphs
  
file_path: nlp-architect/examples/crosslingembs/evaluate.py
imports: from __future__ import print_function, division
  import numpy
  import tensorflow
  from nlp_architect.data.fasttext_emb import get_eval_data
label_desc: Use image node_id 6 for calling the Evaluate obj's underlying member method code with example usage: obj.build_csls_subgraphs() and returns: csls_graphs