Build more Sklearn Pipeline Test - Part 2

[lwgray]

• - PredictionError
• - SilhouetteVisualizer
• - KElbowVisualizer
• - InterclusterDistance
• - GridSearchColorPlot

example below

    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)

@DistrictDataLabs/team-oz-maintainers