code: def test_model_triplot_class(self):
      mt = ModelTriplot()
      mt2 = ModelTriplot(type="difference")
  
      mt.fit(self.aspect)
      mt2.fit(self.aspect2)
  
      self.assertEqual(mt.loss_function, loss_one_minus_auc)
      self.assertEqual(mt2.loss_function, loss_one_minus_auc)
  
      self.assertIsInstance(mt, dx.aspect.ModelTriplot)
      self.assertIsInstance(mt2, dx.aspect.ModelTriplot)
  
      self.assertIsInstance(mt.result, pd.DataFrame)
      self.assertIsInstance(mt2.result, pd.DataFrame)
  
      self.assertEqual(
          set(mt.result.columns),
          set(
              [
                  "variable_names",
                  "dropout_loss",
                  "dropout_loss_change",
                  "label",
                  "min_depend",
                  "vars_min_depend",
              ]
          ),
      )
      self.assertEqual(
          set(mt2.result.columns),
          set(
              [
                  "variable_names",
                  "dropout_loss",
                  "dropout_loss_change",
                  "label",
                  "min_depend",
                  "vars_min_depend",
              ]
          ),
      )
  
      self.assertGreaterEqual(1, mt.result.min_depend.max())
      self.assertGreaterEqual(1, mt2.result.min_depend.max())
  
      self.assertGreaterEqual(mt.result.min_depend.min(), 0)
      self.assertGreaterEqual(mt2.result.min_depend.min(), 0)
  
      self.assertEqual(
          set(mt.result["variable_names"].iloc[-1]),
          set(self.X.columns),
      )
      self.assertEqual(
          set(mt2.result["variable_names"].iloc[-1]),
          set(self.X.columns),
      )
  
      fig = mt.plot(show=False)
      self.assertIsInstance(fig, Figure)
      fig2 = mt2.plot(show=False, width=1200)
      self.assertIsInstance(fig2, Figure)
      fig3 = mt.plot(
          show=False, digits=2, bar_width=22, width=1200, title="Test"
      )
      self.assertIsInstance(fig3, Figure)
      fig4 = mt.plot(show=False, width=1200, show_change=False)
      self.assertIsInstance(fig4, Figure)
      fig5 = mt.plot(
          show=False, width=1200, show_change=False, widget=True
      )
      self.assertIsInstance(fig5, HBox)
      fig6 = mt.plot(show=False, digits=9, bar_width=22)
      self.assertIsInstance(fig6, Figure)
      fig7 = mt.plot(show=False, digits=9, bar_width=22, widget=True)
      self.assertIsInstance(fig7, HBox)
      fig8 = mt.plot(
          show=False,
          digits=2,
          bar_width=22,
          width=1200,
          title="Test",
          widget=True,
      )
      self.assertIsInstance(fig8, HBox)
  
imports: import unittest
  import numpy
  import pandas
  from plotly.graph_objs import Figure
  from ipywidgets.widgets.widget_box import HBox
  from sklearn.compose import ColumnTransformer
  from sklearn.impute import SimpleImputer
  from sklearn.neural_network import MLPClassifier
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import StandardScaler, LabelEncoder, OneHotEncoder
  from sklearn.ensemble import RandomForestRegressor
  from traitlets.traitlets import Dict
  import dalex
  from dalex.model_explanations._variable_importance.loss_functions import *
  from dalex.aspect._predict_aspect_importance.object import PredictAspectImportance
  from dalex.aspect._model_triplot.object import ModelTriplot
  from dalex.aspect._predict_triplot.object import PredictTriplot
  from dalex.aspect._model_aspect_importance.object import ModelAspectImportance
label_desc: Use image node_id 10 for calling the AspectTestTitanic obj's underlying member method code with example usage: obj.test_model_triplot_class() without return types