parameters: (self, y_pred)
docstring: Convert y pred feature -- the predictions of each features for each model into the manifold recognized informat
          :param y_pred: A ndarray/list of data frames. Each data frame is the predict probability of one model,
              which each column being the predicted probability for one class
          :return: A list for the y_pred attribute with the manifold data format
docstring_summary: Convert y pred feature -- the predictions of each features for each model into the manifold recognized informat
          :param y_pred: A ndarray/list of data frames. Each data frame is the predict probability of one model,
              which each column being the predicted probability for one class
          :return: A list for the y_pred attribute with the manifold data format
docstring_tokens: Convert y pred feature -- the predictions of each features for each model into the manifold recognized informat : param y_pred : A ndarray / list of data frames . Each data frame is the predict probability of one model which each column being the predicted probability for one class : return : A list for the y_pred attribute with the manifold data format
function: def process_y_pred(self, y_pred):
          """
          Convert y pred feature -- the predictions of each features for each model into the manifold recognized informat
          :param y_pred: A ndarray/list of data frames. Each data frame is the predict probability of one model,
              which each column being the predicted probability for one class
          :return: A list for the y_pred attribute with the manifold data format
          """
          if isinstance(y_pred, np.ndarray):
              return [y.to_dict('records') if isinstance(y, pd.DataFrame) else y for y in np.nditer(y_pred)]
          else:
              return [y.to_dict('records') if isinstance(y, pd.DataFrame) else y for y in y_pred]
function_tokens: def process_y_pred ( self , y_pred ) : if isinstance ( y_pred , np . ndarray ) : return [ y . to_dict ( 'records' ) if isinstance ( y , pd . DataFrame ) else y for y in np . nditer ( y_pred ) ] else : return [ y . to_dict ( 'records' ) if isinstance ( y , pd . DataFrame ) else y for y in y_pred ]