code: def test_StratifiedShuffleSplit(self):
      iris = datasets.load_iris()
      df = pdml.ModelFrame(iris)
      sf1 = df.model_selection.StratifiedShuffleSplit(
          random_state=self.random_state
      )
      sf2 = ms.StratifiedShuffleSplit(random_state=self.random_state)
  
      # consume generator
      ind1 = [x for x in sf1.split(df.data.values, df.target.values)]
      ind2 = [x for x in sf2.split(iris.data, iris.target)]
  
      for i1, i2 in zip(ind1, ind2):
          self.assertIsInstance(i1, tuple)
          self.assertEqual(len(i1), 2)
          self.assertIsInstance(i2, tuple)
          self.assertEqual(len(i2), 2)
          tm.assert_numpy_array_equal(i1[0], i1[0])
          tm.assert_numpy_array_equal(i1[1], i2[1])
  
      sf1 = df.model_selection.StratifiedShuffleSplit(
          random_state=self.random_state
      )
      with tm.assert_produces_warning(FutureWarning):
          gen = df.model_selection.iterate(sf1)
  
      # StratifiedShuffleSplit is not a subclass of BaseCrossValidator
      for train_df, test_df in gen:
          self.assertIsInstance(train_df, pdml.ModelFrame)
          self.assertIsInstance(test_df, pdml.ModelFrame)
          tm.assert_index_equal(df.columns, train_df.columns)
          tm.assert_index_equal(df.columns, test_df.columns)
  
          self.assertEqual(len(df), len(train_df) + len(test_df))
  
imports: import sklearn.datasets
  import sklearn.svm
  import sklearn.naive_bayes
  import numpy
  import pandas
  import pandas_ml
  import pandas_ml.util.testing
label_desc: Use image node_id 10 for calling the TestSplitter obj's underlying member method code with example usage: obj.test_StratifiedShuffleSplit() without return types