original_docstring: 
      calculate the similarity score of the clusters
          -davies_bouldin_score
          -calinski_harabasz
          -silhouette_score
      :param embedding_model_name:
      :return:
      
docstring: calculate the similarity score of the clusters
  davies_bouldin_score
  calinski_harabasz
  silhouette_score
docstring_tokens: calculate the similarity score of the clusters davies_bouldin_score calinski_harabasz silhouette_score
code: def calculate_similarity(embedding_model_name):
      """
      calculate the similarity score of the clusters
          -davies_bouldin_score
          -calinski_harabasz
          -silhouette_score
      :param embedding_model_name:
      :return:
      """
      x = ut.load_numpy_file(ut.embedding_path + embedding_model_name + "_embedding.npy")
      y = ut.node_labels
  
      davies_bouldin = sk.davies_bouldin_score(x, y)
      calinski_harabasz = sk.calinski_harabasz_score(x, y)
      silhouette_score = sk.silhouette_score(x, y)
  
      add_score(embedding_model_name, 'davies_bouldin', davies_bouldin)
      add_score(embedding_model_name, 'calinski_harabasz', calinski_harabasz)
      add_score(embedding_model_name, 'silhouette_score', silhouette_score)
code_tokens: def calculate_similarity ( embedding_model_name ) : x = ut . load_numpy_file ( ut . embedding_path + embedding_model_name + "_embedding.npy" ) y = ut . node_labels davies_bouldin = sk . davies_bouldin_score ( x , y ) calinski_harabasz = sk . calinski_harabasz_score ( x , y ) silhouette_score = sk . silhouette_score ( x , y ) add_score ( embedding_model_name , 'davies_bouldin' , davies_bouldin ) add_score ( embedding_model_name , 'calinski_harabasz' , calinski_harabasz ) add_score ( embedding_model_name , 'silhouette_score' , silhouette_score )
short_docstring: calculate the similarity score of the clusters
  davies_bouldin_score
  calinski_harabasz
  silhouette_score
short_docstring_tokens: calculate the similarity score of the clusters davies_bouldin_score calinski_harabasz silhouette_score
comment: """
      calculate the similarity score of the clusters
          -davies_bouldin_score
          -calinski_harabasz
          -silhouette_score
      :param embedding_model_name:
      :return:
      """