class_docstr: Convert evaluate result json to DataFrame for report generator,
  and analysis model robustness according to linguistic classification.
  
  Example::
  
      {
          "model_name": "BERT",
          "dataset_name": "medical data",
          "transformation": {
              "Case": {
                  "ori_precision": 0.70,
                  "trans_precision": 0.65,
                  "ori_f1": 0.63,
                  "trans_f1": 0.60,
                  "size": 5000,
              },
              "Ocr": {
                  "ori_precision": 0.72,
                  "trans_precision": 0.43,
                  "ori_f1": 0.62,
                  "trans_f1": 0.41,
                  "size": 5000,
              }
          },
          "subpopulation": {
              "LengthLengthSubPopulation-0.0-0.1": {
                  "trans_precision": 0.68,
                  "trans_f1": 0.63,
                  "size": 500
              }
          },
          "attack": {
              "Bert-Attack": {
                  "ori_precision": 0.72,
                  "trans_precision": 0.43,
                  "ori_f1": 0.62,
                  "trans_f1": 0.41,
                  "size": 400,
              }
          }
      }
docstr: Parsing and checking evaluation result of specific transformation.
  
  :param dict trans_json: evaluation result.
  :return: dict, dict
docstr_tok: ["Parsing","and","checking","evaluation","result","of","specific","transformation",".",":","param","dict","trans_json",":","evaluation","result",".",":","return",":","dict",",","dict"]
code: def get_metrics(trans_json):
      """
      Parsing and checking evaluation result of specific transformation.
  
      :param dict trans_json: evaluation result.
      :return: dict, dict
  
      """
      original_result = {}
      transform_result = {}
  
      for key in trans_json:
          if "ori_" in key:
              metric = key.split("ori_")[1]
              original_result[metric] = trans_json[key]
          elif "trans_" in key:
              metric = key.split("trans_")[1]
              transform_result[metric] = trans_json[key]
  
      assert set(list(original_result.keys())) == set(
          list(transform_result.keys())
      ), (
          f"Original metric {original_result.keys()} unmatch with "
          f"transform metric {transform_result.keys()}"
      )
  
      return original_result, transform_result
  
imports: import pandas
  import numpy
  from functools import reduce
  from copy import deepcopy
  from collections import OrderedDict
  from ...common.utils import logger
label_desc: Use image node_id 7 for calling the Analyzer obj's underlying member method code with example usage: obj.get_metrics(trans_json) and returns: original_result,  transform_result