class_docstr: Basic class of models
  
  Attributes:
      hparams (HParams): A HParams object, holds the entire set of hyperparameters.
      train_iterator (object): An iterator to load the data in training steps.
      test_iterator (object): An iterator to load the data in testing steps.
      graph (object): An optional graph.
      seed (int): Random seed.
class_docstr_tok: ["Basic","class","of","models","Attributes",":","hparams","(","HParams",")",":","A","HParams","object",",","holds","the","entire","set","of","hyperparameters",".","train_iterator","(","object",")",":","An","iterator","to","load","the","data","in","training","steps",".","test_iterator","(","object",")",":","An","iterator","to","load","the","data","in","testing","steps",".","graph","(","object",")",":","An","optional","graph",".","seed","(","int",")",":","Random","seed","."]
docstr: Evaluate the given file and returns some evaluation metrics.
  
  Args:
      filename (str): A file name that will be evaluated.
  
  Returns:
      dict: A dictionary that contains evaluation metrics.
docstr_tok: ["Evaluate","the","given","file","and","returns","some","evaluation","metrics",".","Args",":","filename","(","str",")",":","A","file","name","that","will","be","evaluated",".","Returns",":","dict",":","A","dictionary","that","contains","evaluation","metrics","."]
code: def run_eval(self, news_filename, behaviors_file):
      """Evaluate the given file and returns some evaluation metrics.
  
      Args:
          filename (str): A file name that will be evaluated.
  
      Returns:
          dict: A dictionary that contains evaluation metrics.
      """
  
      if self.support_quick_scoring:
          _, group_labels, group_preds = self.run_fast_eval(
              news_filename, behaviors_file
          )
      else:
          _, group_labels, group_preds = self.run_slow_eval(
              news_filename, behaviors_file
          )
      res = cal_metric(group_labels, group_preds, self.hparams.metrics)
      return res
  
imports: import abc
  import time
  import numpy
  from tqdm import tqdm
  import tensorflow
  from tensorflow.compat.v1 import keras
  from recommenders.models.deeprec.deeprec_utils import cal_metric
label_desc: Use image node_id 12 for calling the BaseModel obj's underlying member method code with example usage: obj.run_eval(news_filename, behaviors_file) and returns: res