old_contents: import unittest
  from sklearn.naive_bayes import GaussianNB
  from sklearn.svm import SVC
  
  from skmultilearn.ensemble import FixedLabelPartitionClassifier
  from skmultilearn.problem_transform import LabelPowerset
  from skmultilearn.tests.classifier_basetest import ClassifierBaseTest
  
  
  class LabelSpacePartitioningClassifierTest(ClassifierBaseTest):
  
      def get_labelpowerset_with_svc(self):
          return LabelPowerset(classifier=SVC(), require_dense=[False, True])
  
      def get_labelpowerset_with_nb(self):
          return LabelPowerset(classifier=GaussianNB(), require_dense=[True, True])
  
      def get_classifier(self, base_classifier):
          partition = [[0, 1, 2], [3, 4]]
          return FixedLabelPartitionClassifier(partition=partition, classifier=base_classifier)
  
      def test_if_sparse_classification_works_on_non_dense_base_classifier(self):
          classifier = self.get_classifier(self.get_labelpowerset_with_svc())
  
          self.assertClassifierWorksWithSparsity(classifier, 'sparse')
  
      def test_if_dense_classification_works_on_non_dense_base_classifier(self):
          classifier = self.get_classifier(self.get_labelpowerset_with_svc())
  
          self.assertClassifierWorksWithSparsity(classifier, 'dense')
  
      def test_if_sparse_classification_works_on_dense_base_classifier(self):
          classifier = self.get_classifier(self.get_labelpowerset_with_nb())
  
          self.assertClassifierWorksWithSparsity(classifier, 'sparse')
  
      def test_if_dense_classification_works_on_dense_base_classifier(self):
          classifier = self.get_classifier(self.get_labelpowerset_with_nb())
  
          self.assertClassifierWorksWithSparsity(classifier, 'dense')
  
      def test_if_works_with_cross_validation(self):
          classifier = self.get_classifier(self.get_labelpowerset_with_nb())
  
          self.assertClassifierWorksWithCV(classifier)
  
  if __name__ == '__main__':
      unittest.main()
  
new_contents: import unittest
  from sklearn.naive_bayes import GaussianNB
  from sklearn.svm import SVC
  
  from skmultilearn.ensemble import FixedLabelPartitionClassifier
  from skmultilearn.problem_transform import LabelPowerset
  from skmultilearn.tests.classifier_basetest import ClassifierBaseTest
  
  
  class LabelSpacePartitioningClassifierTest(ClassifierBaseTest):
  
      def get_labelpowerset_with_svc(self):
          return LabelPowerset(classifier=SVC(), require_dense=[False, True])
  
      def get_labelpowerset_with_nb(self):
          return LabelPowerset(classifier=GaussianNB(), require_dense=[True, True])
  
      def get_classifier(self, base_classifier):
          partition = [[0, 1], [2]]
          return FixedLabelPartitionClassifier(partition=partition, classifier=base_classifier)
  
      def test_if_sparse_classification_works_on_non_dense_base_classifier(self):
          classifier = self.get_classifier(self.get_labelpowerset_with_svc())
  
          self.assertClassifierWorksWithSparsity(classifier, 'sparse')
  
      def test_if_dense_classification_works_on_non_dense_base_classifier(self):
          classifier = self.get_classifier(self.get_labelpowerset_with_svc())
  
          self.assertClassifierWorksWithSparsity(classifier, 'dense')
  
      def test_if_sparse_classification_works_on_dense_base_classifier(self):
          classifier = self.get_classifier(self.get_labelpowerset_with_nb())
  
          self.assertClassifierWorksWithSparsity(classifier, 'sparse')
  
      def test_if_dense_classification_works_on_dense_base_classifier(self):
          classifier = self.get_classifier(self.get_labelpowerset_with_nb())
  
          self.assertClassifierWorksWithSparsity(classifier, 'dense')
  
      def test_if_works_with_cross_validation(self):
          classifier = self.get_classifier(self.get_labelpowerset_with_nb())
  
          self.assertClassifierWorksWithCV(classifier)
  
  if __name__ == '__main__':
      unittest.main()
  
subject: adjust partitions to new test data