docstr: Method to evaluate the final ranking
  
  :param metrics: List of evaluation metrics
  :type metrics: list, default ('MAE', 'RMSE')
  
  :param verbose: Print the evaluation results
  :type verbose: bool, default True
  
  :param as_table: Print the evaluation results as table
  :type as_table: bool, default False
  
  :param table_sep: Delimiter for print results (only work with verbose=True and as_table=True)
  :type table_sep: str, default ' '
docstr_tok: ["Method","to","evaluate","the","final","ranking",":","param","metrics",":","List","of","evaluation","metrics",":","type","metrics",":","list",",","default","(","'MAE","'",",","'RMSE","'",")",":","param","verbose",":","Print","the","evaluation","results",":","type","verbose",":","bool",",","default","True",":","param","as_table",":","Print","the","evaluation","results","as","table",":","type","as_table",":","bool",",","default","False",":","param","table_sep",":","Delimiter","for","print","results","(","only","work","with","verbose=True","and","as_table=True",")",":","type","table_sep",":","str",",","default","'","'"]
code: def evaluate(
      self, metrics, verbose=True, as_table=False, table_sep="\t"
  ):
      """
      Method to evaluate the final ranking
  
      :param metrics: List of evaluation metrics
      :type metrics: list, default ('MAE', 'RMSE')
  
      :param verbose: Print the evaluation results
      :type verbose: bool, default True
  
      :param as_table: Print the evaluation results as table
      :type as_table: bool, default False
  
      :param table_sep: Delimiter for print results (only work with verbose=True and as_table=True)
      :type table_sep: str, default '\t'
  
      """
  
      self.evaluation_results = {}
  
      if metrics is None:
          metrics = list(["MAE", "RMSE"])
  
      results = RatingPredictionEvaluation(
          verbose=verbose,
          as_table=as_table,
          table_sep=table_sep,
          metrics=metrics,
      ).evaluate_recommender(
          predictions=self.predictions, test_set=self.test_set
      )
  
      for metric in metrics:
          self.evaluation_results[metric.upper()] = results[
              metric.upper()
          ]
  
file_path: CaseRecommender/caserec/recommenders/rating_prediction/base_rating_prediction.py
imports: from scipy.spatial.distance import squareform, pdist
  import numpy
  from caserec.evaluation.rating_prediction import RatingPredictionEvaluation
  from caserec.utils.extra_functions import print_header
  from caserec.utils.process_data import ReadFile, WriteFile
label_desc: Use image node_id 5 for calling the BaseRatingPrediction obj's underlying member method code with example usage: obj.evaluate(metrics, verbose, as_table, table_sep) without return types