class_docstr: Plotting robustness report,
  return radar figure, sunbrust figure, and bar chart figure.
  
  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,
              }
          }
      }
class_docstr_tok: ["Plotting","robustness","report",",","return","radar","figure",",","sunbrust","figure",",","and","bar","chart","figure",".","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: Analysis evaluation result and plot three reports in html.
  
  :param dict evaluate_json: evaluate result of specific model.
docstr_tok: ["Analysis","evaluation","result","and","plot","three","reports","in","html",".",":","param","dict","evaluate_json",":","evaluate","result","of","specific","model","."]
code: def plot(self, evaluate_json):
      r"""
      Analysis evaluation result and plot three reports in html.
  
      :param dict evaluate_json: evaluate result of specific model.
  
      """
      model_name = evaluate_json.get("model_name", None)
      dataset_name = evaluate_json.get("dataset_name", None)
  
      radar_pd = Analyzer.json_to_linguistic_radar(evaluate_json)
      if radar_pd is not None:
          self.get_radar_fig(radar_pd).show()
  
      df, settings = Analyzer.json_to_sunburst(evaluate_json)
      if df is not None:
          self.get_sunburst_fig(df, settings).show()
  
      pd, cols = Analyzer.json_to_bar_chart(evaluate_json)
      if pd is not None:
          self.get_bar_chart(
              pd, cols, model_name=model_name, dataset_name=dataset_name
          ).show()
  
imports: from __future__ import annotations
  import itertools
  from functools import partial
  from typing import Dict, List
  import dill
  import numpy
  import plotly.graph_objects
  from plotly.subplots import make_subplots
  import plotly.express
  from ..analyzer.analyzer import *
label_desc: Use image node_id 1 for calling the ReportGenerator obj's underlying member method code with example usage: obj.plot(evaluate_json) without return types