original_docstring: 
      Summarize where the splits have been saved and what their contents are.
      Args:
        csv_folder (str): path to the folder in which we will save oru
          csv files with the SMILES, properties and training splits.
        dataset_type (str): type of problem, e.g. "classification" or 
          "regression".
      Returns:
        None
      
docstring: Summarize where the splits have been saved and what their contents are.
docstring_tokens: Summarize where the splits have been saved and what their contents are .
code: def summarize(csv_folder, dataset_type):
      """
      Summarize where the splits have been saved and what their contents are.
      Args:
        csv_folder (str): path to the folder in which we will save oru
          csv files with the SMILES, properties and training splits.
        dataset_type (str): type of problem, e.g. "classification" or 
          "regression".
      Returns:
        None
      """
      msgs = []
      for name in ['train', 'val', 'test', 'all']:
          if name == 'all':
              path = os.path.join(csv_folder, f"{name}.csv")
          else:
              path = os.path.join(csv_folder, f"{name}_full.csv")
          with open(path, "r") as f:
              lines = f.readlines()[1:]
  
          num_specs = len(lines)
          this_msg = f"{num_specs} species"
          if dataset_type == "classification":
              num_pos = len([line for line in lines
                             if int(line.split(",")[-1]) == 1])
              this_msg += f", {num_pos} positives"
  
          msgs.append(this_msg)
  
      msg = (f"Splits saved in {csv_folder}\n"
             f"Train files: train_smiles.csv and train_full.csv ({msgs[0]})\n"
             f"Validation files: val_smiles.csv and val_full.csv ({msgs[1]}) \n"
             f"Test files: test_smiles.csv and test_full.csv ({msgs[2]})\n"
             f"Combined file: all.csv ({msgs[3]})")
  
      fprint(msg)
code_tokens: def summarize ( csv_folder , dataset_type ) : msgs = [ ] for name in [ 'train' , 'val' , 'test' , 'all' ] : if name == 'all' : path = os . path . join ( csv_folder , f"{name}.csv" ) else : path = os . path . join ( csv_folder , f"{name}_full.csv" ) with open ( path , "r" ) as f : lines = f . readlines ( ) [ 1 : ] num_specs = len ( lines ) this_msg = f"{num_specs} species" if dataset_type == "classification" : num_pos = len ( [ line for line in lines if int ( line . split ( "," ) [ - 1 ] ) == 1 ] ) this_msg += f", {num_pos} positives" msgs . append ( this_msg ) msg = ( f"Splits saved in {csv_folder}\n" f"Train files: train_smiles.csv and train_full.csv ({msgs[0]})\n" f"Validation files: val_smiles.csv and val_full.csv ({msgs[1]}) \n" f"Test files: test_smiles.csv and test_full.csv ({msgs[2]})\n" f"Combined file: all.csv ({msgs[3]})" ) fprint ( msg )
short_docstring: Summarize where the splits have been saved and what their contents are.
short_docstring_tokens: Summarize where the splits have been saved and what their contents are .
comment: """
      Summarize where the splits have been saved and what their contents are.
      Args:
        csv_folder (str): path to the folder in which we will save oru
          csv files with the SMILES, properties and training splits.
        dataset_type (str): type of problem, e.g. "classification" or 
          "regression".
      Returns:
        None
      """