docstr: Method to run recommender algorithm in k folds
code: def execute_algorithm(self):
      """
      Method to run recommender algorithm in k folds
  
      """
  
      for k in range(self.k_folds):
          train_file = self.dir_folds + "folds/%d/train.dat" % k
          test_file = self.dir_folds + "folds/%d/test.dat" % k
  
          self.recommender.train_file = train_file
          self.recommender.test_file = test_file
  
          if self.write_predictions:
              output_file = self.dir_folds + "folds/%d/output.dat" % k
              self.recommender.output_file = output_file
  
          self.recommender.compute(
              verbose=self.recommender_verbose,
              verbose_evaluation=self.evaluation_in_fold_verbose,
              metrics=self.metrics,
          )
  
          if self.metrics is None:
              self.metrics = self.recommender.evaluation_results.keys()
  
          for metric in self.metrics:
              self.folds_results[metric.upper()].append(
                  self.recommender.evaluation_results[metric.upper()]
              )
  
file_path: CaseRecommender/caserec/utils/cross_validation.py
imports: from collections import defaultdict
  import numpy
  import shutil
  from caserec.utils.split_database import SplitDatabase
label_desc: Use image node_id 3 for calling the CrossValidation obj's underlying member method code with example usage: obj.execute_algorithm() without return types