text: Title: Add Sklearn pipeline test for more complicated Visualizers
  Body: There are some visualizers that require additional work in order to write sklearn pipeline test.  It is likely that the underlying visualizer needs to expose learned attributes needed to generate the visualizers.  The following is an example using sklearn pipeline for the InterClusterDistanceMetric visualizer:
  
  ```
  AttributeError: 'Pipeline' object has no attribute 'cluster_centers_'
  ```
  See issues and PR
  https://github.com/DistrictDataLabs/yellowbrick/issues/1253
  https://github.com/DistrictDataLabs/yellowbrick/issues/1248
  https://github.com/DistrictDataLabs/yellowbrick/pull/1249
  
  Issue:
  https://github.com/DistrictDataLabs/yellowbrick/issues/1257
  PR:
  https://github.com/DistrictDataLabs/yellowbrick/pull/1259
  
  Issue:
  https://github.com/DistrictDataLabs/yellowbrick/issues/1256
  PR:
  https://github.com/DistrictDataLabs/yellowbrick/pull/1262
  
  - [ ] Decision Boundaries
  - [ ] RFECV
  - [ ] ValidationCurve
  - [ ] Add a pipeline model input test and quick method test for feature importances
  - [ ] Add a pipeline model input test and quick method test for alpha selection
  - [ ] Add a pipeline model input test and quick method test for InterClusterDistanceMetric
  - [ ] KElbowVisualizer
  - [ ] SilhouetteVisualizer
  - [ ] GridSearchColorPlot
  
  Example
  ```
       def test_within_pipeline(self):
          """
          Test that visualizer can be accessed within a sklearn pipeline
          """
          X, y = load_mushroom(return_dataset=True).to_numpy()
          X = OneHotEncoder().fit_transform(X).toarray()
  
          cv = StratifiedKFold(n_splits=2, shuffle=True, random_state=11)
          model = Pipeline([
              ('minmax', MinMaxScaler()), 
              ('cvscores', CVScores(BernoulliNB(), cv=cv))
          ])
  
          model.fit(X, y)
          model['cvscores'].finalize()
          self.assert_images_similar(model['cvscores'], tol=2.0)
  
      def test_within_pipeline_quickmethod(self):
          """
          Test that visualizer quickmethod can be accessed within a
          sklearn pipeline
          """
          X, y = load_mushroom(return_dataset=True).to_numpy()
          X = OneHotEncoder().fit_transform(X).toarray()
          
          cv = StratifiedKFold(n_splits=2, shuffle=True, random_state=11)
          model = Pipeline([
              ('minmax', MinMaxScaler()), 
              ('cvscores', cv_scores(BernoulliNB(), X, y, cv=cv, show=False,
                                        random_state=42))
              ])
          self.assert_images_similar(model['cvscores'], tol=2.0)
  
      def test_pipeline_as_model_input(self):
          """
          Test that visualizer can handle sklearn pipeline as model input
          """
          X, y = load_mushroom(return_dataset=True).to_numpy()
          X = OneHotEncoder().fit_transform(X).toarray()
  
          cv = StratifiedKFold(n_splits=2, shuffle=True, random_state=11)
          model = Pipeline([
              ('minmax', MinMaxScaler()), 
              ('nb', BernoulliNB())
          ])
  
          oz = CVScores(model, cv=cv)
          oz.fit(X, y)
          oz.finalize()
          self.assert_images_similar(oz, tol=2.0)
  
      def test_pipeline_as_model_input_quickmethod(self):
          """
          Test that visualizer can handle sklearn pipeline as model input
          within a quickmethod
          """
          X, y = load_mushroom(return_dataset=True).to_numpy()
          X = OneHotEncoder().fit_transform(X).toarray()
  
          cv = StratifiedKFold(n_splits=2, shuffle=True, random_state=11)
          model = Pipeline([
              ('minmax', MinMaxScaler()), 
              ('nb', BernoulliNB())
          ])
  
          oz = cv_scores(model, X, y, show=False, cv=cv)
          self.assert_images_similar(oz, tol=2.0)
  
  ```
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