old_contents: import unittest
  
  from autosklearn.pipeline.components.regression.gaussian_process import GaussianProcess
  from autosklearn.pipeline.util import _test_regressor
  
  import sklearn.metrics
  
  
  class GaussianProcessComponentTest(unittest.TestCase):
      def test_default_configuration(self):
          # Only twice to reduce the number of warning printed to the command line
          for i in range(2):
              # Float32 leads to numeric instabilities
              predictions, targets = _test_regressor(GaussianProcess,
                                                     dataset='boston')
              # My machine: 0.574913739659292
              # travis-ci: 0.49562471963524557
              self.assertGreaterEqual(
                  sklearn.metrics.r2_score(y_true=targets, y_pred=predictions),
                  0.4)
              self.assertLessEqual(
                  sklearn.metrics.r2_score(y_true=targets, y_pred=predictions),
                  0.4)
  
  
new_contents: import unittest
  
  from autosklearn.pipeline.components.regression.gaussian_process import GaussianProcess
  from autosklearn.pipeline.util import _test_regressor
  
  import sklearn.metrics
  
  
  class GaussianProcessComponentTest(unittest.TestCase):
      def test_default_configuration(self):
          # Only twice to reduce the number of warning printed to the command line
          for i in range(2):
              # Float32 leads to numeric instabilities
              predictions, targets = _test_regressor(GaussianProcess,
                                                     dataset='boston')
              # My machine: 0.574913739659292
              # travis-ci: 0.49562471963524557
              self.assertLessEqual(
                  sklearn.metrics.r2_score(y_true=targets, y_pred=predictions),
                  0.6)
              self.assertGreaterEqual(
                  sklearn.metrics.r2_score(y_true=targets, y_pred=predictions),
                  0.4)
  
  
subject: fix gaussian process unit test