How to use the shap.datasets.adult function in shap

def test_sum_match_extra_trees():
    import shap
    import numpy as np
    from sklearn.model_selection import train_test_split
    from sklearn.ensemble import ExtraTreesRegressor
    import sklearn

    X_train,X_test,Y_train,Y_test = train_test_split(*shap.datasets.adult(), test_size=0.2, random_state=0)
    clf = ExtraTreesRegressor(random_state=202, n_estimators=10, max_depth=10)
    clf.fit(X_train, Y_train)
    predicted = clf.predict(X_test)
    ex = shap.TreeExplainer(clf)
    shap_values = ex.shap_values(X_test)
    assert np.abs(shap_values.sum(1) + ex.expected_value - predicted).max() < 1e-6, \
        "SHAP values don't sum to model output!"

def test_sum_match_random_forest():
    import shap
    import numpy as np
    from sklearn.model_selection import train_test_split
    from sklearn.ensemble import RandomForestClassifier
    import sklearn

    X_train,X_test,Y_train,Y_test = train_test_split(*shap.datasets.adult(), test_size=0.2, random_state=0)
    clf = RandomForestClassifier(random_state=202, n_estimators=10, max_depth=10)
    clf.fit(X_train, Y_train)
    predicted = clf.predict_proba(X_test)
    ex = shap.TreeExplainer(clf)
    shap_values = ex.shap_values(X_test)
    assert np.abs(shap_values[0].sum(1) + ex.expected_value[0] - predicted[:,0]).max() < 1e-6, \
        "SHAP values don't sum to model output!"

def test_sum_match_gradient_boosting_regressor():
    import shap
    import numpy as np
    from sklearn.model_selection import train_test_split
    from sklearn.ensemble import GradientBoostingRegressor
    import sklearn

    X_train,X_test,Y_train,Y_test = train_test_split(*shap.datasets.adult(), test_size=0.2, random_state=0)
    clf = GradientBoostingRegressor(random_state=202, n_estimators=10, max_depth=10)
    clf.fit(X_train, Y_train)

    predicted = clf.predict(X_test)
    ex = shap.TreeExplainer(clf)
    shap_values = ex.shap_values(X_test)
    assert np.abs(shap_values.sum(1) + ex.expected_value - predicted).max() < 1e-6, \
        "SHAP values don't sum to model output!"

def test_sum_match_random_forest():
    import shap
    import numpy as np
    from sklearn.model_selection import train_test_split
    from sklearn.ensemble import RandomForestClassifier
    import sklearn

    X_train,X_test,Y_train,Y_test = train_test_split(*shap.datasets.adult(), test_size=0.2, random_state=0)
    clf = RandomForestClassifier(random_state=202, n_estimators=10, max_depth=10)
    clf.fit(X_train, Y_train)
    predicted = clf.predict_proba(X_test)
    ex = shap.TreeExplainer(clf)
    shap_values = ex.shap_values(X_test)
    assert np.abs(shap_values[0].sum(1) + ex.expected_value[0] - predicted[:,0]).max() < 1e-6, \
        "SHAP values don't sum to model output!"

def test_explain_model_keras(self, tabular_explainer):
        X, y = shap.datasets.adult()
        x_train, x_test, y_train, _ = train_test_split(X, y, test_size=0.2, random_state=7)
        # Fit a keras DNN model
        model = create_keras_classifier(x_train.values, y_train)
        test_logger.info('Running explain global for test_explain_model_keras')
        self._explain_model_dnn_common(tabular_explainer, model, x_train, x_test, y_train, X.columns.values)