How to use 'syntax to import decision tree classifier in sklearn' in Python

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def get_fast_classifiers(n_classes):
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    """Get a list of very fast classifiers.
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    Parameters
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    ----------
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    n_classes : int
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        Number of classes in the dataset. Used to decide on the complexity
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        of some of the classifiers.
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    Returns
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    -------
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    fast_classifiers : list of sklearn estimators
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        List of classification models that can be fitted and evaluated very
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        quickly.
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    """
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    return [
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        # These are sorted by approximate speed
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        DummyClassifier(strategy="prior"),
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        GaussianNB(),
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        make_pipeline(MinMaxScaler(), MultinomialNB()),
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        DecisionTreeClassifier(max_depth=1, class_weight="balanced"),
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        DecisionTreeClassifier(max_depth=max(5, n_classes),
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                               class_weight="balanced"),
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        DecisionTreeClassifier(class_weight="balanced",
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                               min_impurity_decrease=.01),
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        LogisticRegression(C=.1, solver='lbfgs', multi_class='auto',
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                           class_weight='balanced', max_iter=1000),
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        # FIXME Add warm starting here?
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        LogisticRegression(C=1, solver='lbfgs', multi_class='auto',
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                           class_weight='balanced', max_iter=1000)
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    ]

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def test_export_to_sklearn_pipeline(self):
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    from lale.lib.sklearn import PCA
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    from lale.lib.sklearn import KNeighborsClassifier
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    from sklearn.pipeline import make_pipeline
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    lale_pipeline = PCA(n_components=3) >>  KNeighborsClassifier()
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    trained_lale_pipeline = lale_pipeline.fit(self.X_train, self.y_train)
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    sklearn_pipeline = trained_lale_pipeline.export_to_sklearn_pipeline()
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    for i, pipeline_step in enumerate(sklearn_pipeline.named_steps):
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        sklearn_step_params = sklearn_pipeline.named_steps[pipeline_step].get_params()
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        lale_sklearn_params = trained_lale_pipeline.steps()[i]._impl._wrapped_model.get_params()
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        self.assertEqual(sklearn_step_params, lale_sklearn_params)
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    self.assert_equal_predictions(sklearn_pipeline, trained_lale_pipeline)