old_contents: import unittest
  from sklearn.naive_bayes import GaussianNB
  from sklearn.svm import SVC
  
  from skmultilearn.ensemble import RakelO
  from skmultilearn.problem_transform import LabelPowerset
  from skmultilearn.tests.classifier_basetest import ClassifierBaseTest
  
  
  class RakelOTest(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 test_if_sparse_classification_works_on_non_dense_base_classifier(self):
          classifier = RakelO(
              classifier=self.get_labelpowerset_with_svc(), model_count=20, labelset_size=5)
  
          self.assertClassifierWorksWithSparsity(classifier, 'sparse')
  
      def test_if_dense_classification_works_on_non_dense_base_classifier(self):
          classifier = RakelO(
              classifier=self.get_labelpowerset_with_svc(), model_count=20, labelset_size=5)
  
          self.assertClassifierWorksWithSparsity(classifier, 'dense')
  
      def test_if_sparse_classification_works_on_dense_base_classifier(self):
          classifier = RakelO(
              classifier=self.get_labelpowerset_with_nb(), model_count=20, labelset_size=5)
  
          self.assertClassifierWorksWithSparsity(classifier, 'sparse')
  
      def test_if_dense_classification_works_on_dense_base_classifier(self):
          classifier = RakelO(
              classifier=self.get_labelpowerset_with_nb(), model_count=20, labelset_size=5)
  
          self.assertClassifierWorksWithSparsity(classifier, 'dense')
  
      def test_if_works_with_cross_validation(self):
          classifier = RakelO(
              classifier=self.get_labelpowerset_with_nb(), model_count=20, labelset_size=5)
  
          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 RakelO
  from skmultilearn.problem_transform import LabelPowerset
  from skmultilearn.tests.classifier_basetest import ClassifierBaseTest
  
  
  class RakelOTest(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 test_if_sparse_classification_works_on_non_dense_base_classifier(self):
          classifier = RakelO(
              classifier=self.get_labelpowerset_with_svc(), model_count=3, labelset_size=2)
  
          self.assertClassifierWorksWithSparsity(classifier, 'sparse')
  
      def test_if_dense_classification_works_on_non_dense_base_classifier(self):
          classifier = RakelO(
              classifier=self.get_labelpowerset_with_svc(), model_count=3, labelset_size=2)
  
          self.assertClassifierWorksWithSparsity(classifier, 'dense')
  
      def test_if_sparse_classification_works_on_dense_base_classifier(self):
          classifier = RakelO(
              classifier=self.get_labelpowerset_with_nb(), model_count=3, labelset_size=2)
  
          self.assertClassifierWorksWithSparsity(classifier, 'sparse')
  
      def test_if_dense_classification_works_on_dense_base_classifier(self):
          classifier = RakelO(
              classifier=self.get_labelpowerset_with_nb(), model_count=3, labelset_size=2)
  
          self.assertClassifierWorksWithSparsity(classifier, 'dense')
  
      def test_if_works_with_cross_validation(self):
          #TODO: fix this for labelset_size > 1 per issue #42
          classifier = RakelO(classifier=self.get_labelpowerset_with_nb(), model_count=3, labelset_size=1)
  
          self.assertClassifierWorksWithCV(classifier)
  
  
  if __name__ == '__main__':
      unittest.main()
  
subject: adjust labelset sizes to new test data