code: def predict_dataset(self, x, **kwargs):
          
          printout = kwargs.get("printout", None)
          pred = []
          res = []
          x.columns = ["A", "B"]
          for idx, row in x.iterrows():
              a = scale(row['A'].reshape((len(row['A']), 1)))
              b = scale(row['B'].reshape((len(row['B']), 1)))
  
              pred.append(self.predict_proba(a, b, idx=idx))
  
              if printout is not None:
                  res.append([row['SampleID'], pred[-1]])
                  DataFrame(res, columns=['SampleID', 'Predictions']).to_csv(
                      printout, index=False)
          return pred
docs: Generic dataset prediction function.
  
          Runs the score independently on all pairs.
  
          Args:
              x (pandas.DataFrame): a CEPC format Dataframe.
              kwargs (dict): additional arguments for the algorithms
  
          Returns:
              pandas.DataFrame: a Dataframe with the predictions.