old_contents: import unittest
  
  import matplotlib
  import matplotlib.axes
  from sklearn.exceptions import NotFittedError
  from sklearn.preprocessing import StandardScaler
  
  from kenchi.datasets import make_blobs
  from kenchi.outlier_detection import SparseStructureLearning
  from kenchi.pipeline import Pipeline
  
  matplotlib.use('Agg')
  
  import matplotlib.pyplot as plt
  
  
  class PipelineTest(unittest.TestCase):
      def setUp(self):
          self.X_train, _          = make_blobs(centers=1, random_state=1)
          self.X_test, self.y_test = make_blobs(random_state=2)
          self.sut                 = Pipeline([
              ('standardize', StandardScaler()),
              ('detect',      SparseStructureLearning(
                  glasso_params    = {'assume_centered': True}
              ))
          ])
          _, self.ax               = plt.subplots()
  
      def tearDown(self):
          plt.close()
  
      def test_anomaly_score_notfitted(self):
          with self.assertRaises(NotFittedError):
              self.sut.anomaly_score(self.X_train)
  
      def test_featurewise_anomaly_score_notfitted(self):
          with self.assertRaises(NotFittedError):
              self.sut.featurewise_anomaly_score(self.X_train)
  
      def test_plot_anomaly_score(self):
          self.assertIsInstance(
              self.sut.fit(self.X_train).plot_anomaly_score(),
              matplotlib.axes.Axes
          )
  
      def test_plot_roc_curve(self):
          self.assertIsInstance(
              self.sut.fit(
                  self.X_train
              ).plot_roc_curve(self.X_test, self.y_test),
              matplotlib.axes.Axes
          )
  
      def test_plot_graphical_model(self):
          self.assertIsInstance(
              self.sut.fit(self.X_train).plot_graphical_model(),
              matplotlib.axes.Axes
          )
  
      def test_plot_partial_corrcoef(self):
          self.assertIsInstance(
              self.sut.fit(self.X_train).plot_partial_corrcoef(),
              matplotlib.axes.Axes
          )
  
new_contents: import unittest
  
  import matplotlib
  import matplotlib.axes
  from sklearn.exceptions import NotFittedError
  from sklearn.preprocessing import StandardScaler
  
  from kenchi.datasets import make_blobs
  from kenchi.outlier_detection import SparseStructureLearning
  from kenchi.pipeline import Pipeline
  
  matplotlib.use('Agg')
  
  import matplotlib.pyplot as plt
  
  
  class PipelineTest(unittest.TestCase):
      def setUp(self):
          self.X_train, _          = make_blobs(centers=1, random_state=1)
          self.X_test, self.y_test = make_blobs(random_state=2)
          self.sut                 = Pipeline([
              ('standardize', StandardScaler()),
              ('detect',      SparseStructureLearning(
                  glasso_params    = {'assume_centered': True}
              ))
          ])
          _, self.ax               = plt.subplots()
  
      def tearDown(self):
          plt.close()
  
      def test_anomaly_score_notfitted(self):
          with self.assertRaises(NotFittedError):
              self.sut.anomaly_score(self.X_train)
  
      def test_featurewise_anomaly_score_notfitted(self):
          with self.assertRaises(NotFittedError):
              self.sut.featurewise_anomaly_score(self.X_train)
  
      def test_plot_anomaly_score(self):
          self.assertIsInstance(
              self.sut.fit(self.X_train).plot_anomaly_score(ax=self.ax),
              matplotlib.axes.Axes
          )
  
      def test_plot_roc_curve(self):
          self.assertIsInstance(
              self.sut.fit(
                  self.X_train
              ).plot_roc_curve(self.X_test, self.y_test, ax=self.ax),
              matplotlib.axes.Axes
          )
  
      def test_plot_graphical_model(self):
          self.assertIsInstance(
              self.sut.fit(self.X_train).plot_graphical_model(ax=self.ax),
              matplotlib.axes.Axes
          )
  
      def test_plot_partial_corrcoef(self):
          self.assertIsInstance(
              self.sut.fit(self.X_train).plot_partial_corrcoef(ax=self.ax),
              matplotlib.axes.Axes
          )
  
subject: fix test_pipeline.py