code: def create_valid(self, data, label=None, weight=None, group=None,
                       init_score=None, silent=False, params=None):
          
          ret = Dataset(data, label=label, reference=self,
                        weight=weight, group=group, init_score=init_score,
                        silent=silent, params=params, free_raw_data=self.free_raw_data)
          ret._predictor = self._predictor
          ret.pandas_categorical = self.pandas_categorical
          return ret
docs: Create validation data align with current Dataset.
  
          Parameters
          ----------
          data : string, numpy array, pandas DataFrame, H2O DataTable's Frame, scipy.sparse or list of numpy arrays
              Data source of Dataset.
              If string, it represents the path to txt file.
          label : list, numpy 1-D array, pandas Series / one-column DataFrame or None, optional (default=None)
              Label of the data.
          weight : list, numpy 1-D array, pandas Series or None, optional (default=None)
              Weight for each instance.
          group : list, numpy 1-D array, pandas Series or None, optional (default=None)
              Group/query size for Dataset.
          init_score : list, numpy 1-D array, pandas Series or None, optional (default=None)
              Init score for Dataset.
          silent : bool, optional (default=False)
              Whether to print messages during construction.
          params : dict or None, optional (default=None)
              Other parameters for validation Dataset.
  
          Returns
          -------
          valid : Dataset
              Validation Dataset with reference to self.