code: import unittest
  
  from sklearn.ensemble import RandomForestClassifier
  from autosklearn.pipeline.components.feature_preprocessing.feature_agglomeration import FeatureAgglomeration
  from autosklearn.pipeline.util import _test_preprocessing, PreprocessingTestCase, \
      get_dataset
  import sklearn.metrics
  
  
  class FeatureAgglomerationComponentTest(PreprocessingTestCase):
      def test_default_configuration(self):
          transformation, original = _test_preprocessing(FeatureAgglomeration)
          self.assertEqual(transformation.shape[0], original.shape[0])
          self.assertFalse((transformation == 0).all())
  
      def test_default_configuration_classify(self):
          for i in range(3):
              X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits',
                                                             make_sparse=False)
              configuration_space = FeatureAgglomeration.get_hyperparameter_search_space()
              default = configuration_space.get_default_configuration()
              preprocessor = FeatureAgglomeration(random_state=1,
                                                  **{hp_name: default[hp_name] for
                                                     hp_name in default})
              preprocessor.fit(X_train, Y_train)
              X_train_trans = preprocessor.transform(X_train)
              X_test_trans = preprocessor.transform(X_test)
  
              # fit a classifier on top
              classifier = RandomForestClassifier(random_state=1)
              predictor = classifier.fit(X_train_trans, Y_train)
              predictions = predictor.predict(X_test_trans)
              accuracy = sklearn.metrics.accuracy_score(predictions, Y_test)
              self.assertAlmostEqual(accuracy, 0.8099574984820886)
  
      def test_preprocessing_dtype(self):
          super(FeatureAgglomerationComponentTest,
                self)._test_preprocessing_dtype(FeatureAgglomeration,
                                                test_sparse=False)
  
apis: sklearn.ensemble.RandomForestClassifier