def eval(self, row: Row):
row_dict = row.as_dict()
feed_dict = {}
for feed_tensor_key in self._predictor.feed_tensors.keys():
if feed_tensor_key not in row_dict:
raise RuntimeError(
"input tensor with key {} is not in the row {}".format(
feed_tensor_key, row))
feed_dict[feed_tensor_key] = row_dict[feed_tensor_key]
predict_dict = self._predictor(feed_dict)
output_list = []
for predict_col_name in self._predict_col_names:
if predict_col_name not in predict_dict:
raise RuntimeError(
"predict column name {} is not in the prediction dict {}".
format(predict_col_name, predict_dict))
output_list.append(predict_dict[predict_col_name])
return Row(*row, *output_list)
