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

def test_lightgbm_constant_prediction():
    # note: this test used to fail with lightgbm 2.2.1 with error:
    # ValueError: zero-size array to reduction operation maximum which has no identity
    # on TreeExplainer when trying to compute max nodes:
    # max_nodes = np.max([len(t.values) for t in self.trees])
    # The test does not fail with latest lightgbm 2.2.3 however
    try:
        import lightgbm
    except:
        print("Skipping test_lightgbm_constant_prediction!")
        return
    import shap

    # train lightgbm model with a constant value for y
    X, y = shap.datasets.boston()
    # use the mean for all values
    mean = np.mean(y)
    y.fill(mean)
    model = lightgbm.sklearn.LGBMRegressor(n_estimators=1)
    model.fit(X, y)

    # explain the model's predictions using SHAP values
    shap_values = shap.TreeExplainer(model).shap_values(X)

def test_front_page_xgboost_global_path_dependent():
    try:
        import xgboost
    except:
        print("Skipping test_front_page_xgboost!")
        return

    # train XGBoost model
    X, y = shap.datasets.boston()
    model = xgboost.XGBRegressor()
    model.fit(X, y)

    # explain the model's predictions using SHAP values
    explainer = shap.TreeExplainer(model, X, feature_perturbation="global_path_dependent")
    shap_values = explainer.shap_values(X)

    assert np.allclose(shap_values.sum(1) + explainer.expected_value, model.predict(X))

def test_catboost():
    try:
        import catboost
    except:
        print("Skipping test_catboost!")
        return
    import shap

    # train catboost model
    X, y = shap.datasets.boston()
    X["RAD"] = X["RAD"].astype(np.int)
    model = catboost.CatBoostRegressor(iterations=300, learning_rate=0.1, random_seed=123)
    p = catboost.Pool(X, y, cat_features=["RAD"])
    model.fit(p, verbose=False, plot=False)

    # explain the model's predictions using SHAP values
    ex = shap.TreeExplainer(model)
    shap_values = ex.shap_values(p)

    predicted = model.predict(X)

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

def test_front_page_xgboost():
    import xgboost
    import shap

    # load JS visualization code to notebook
    shap.initjs()

    # train XGBoost model
    X, y = shap.datasets.boston()
    model = xgboost.train({"learning_rate": 0.01}, xgboost.DMatrix(X, label=y), 100)

    # explain the model's predictions using SHAP values (use pred_contrib in LightGBM)
    shap_values = shap.TreeExplainer(model).shap_values(X)

    # visualize the first prediction's explaination
    shap.force_plot(shap_values[0, :], X.iloc[0, :])

    # visualize the training set predictions
    shap.force_plot(shap_values, X)

    # create a SHAP dependence plot to show the effect of a single feature across the whole dataset
    shap.dependence_plot(5, shap_values, X, show=False)
    shap.dependence_plot("RM", shap_values, X, show=False)

    # summarize the effects of all the features

def test_front_page_xgboost():
    try:
        import xgboost
    except Exception as e:
        print("Skipping test_front_page_xgboost!")
        return
    import shap

    # load JS visualization code to notebook
    shap.initjs()

    # train XGBoost model
    X, y = shap.datasets.boston()
    model = xgboost.train({"learning_rate": 0.01, "silent": 1}, xgboost.DMatrix(X, label=y), 100)

    # explain the model's predictions using SHAP values
    explainer = shap.TreeExplainer(model)
    shap_values = explainer.shap_values(X)

    # visualize the first prediction's explaination
    shap.force_plot(explainer.expected_value, shap_values[0, :], X.iloc[0, :])

    # visualize the training set predictions
    shap.force_plot(explainer.expected_value, shap_values, X)

    # create a SHAP dependence plot to show the effect of a single feature across the whole dataset
    shap.dependence_plot(5, shap_values, X, show=False)
    shap.dependence_plot("RM", shap_values, X, show=False)

def test_xgboost_mixed_types():
    try:
        import xgboost
    except Exception as e:
        print("Skipping test_xgboost_mixed_types!")
        return
    import shap
    import numpy as np

    X,y = shap.datasets.boston()
    X["LSTAT"] = X["LSTAT"].astype(np.int64)
    X["B"] = X["B"].astype(np.bool)
    bst = xgboost.train({"learning_rate": 0.01, "silent": 1}, xgboost.DMatrix(X, label=y), 1000)
    shap_values = shap.TreeExplainer(bst).shap_values(X)
    shap.dependence_plot(0, shap_values, X, show=False)

def test_front_page_xgboost():
    try:
        import xgboost
    except Exception as e:
        print("Skipping test_front_page_xgboost!")
        return
    import shap

    # load JS visualization code to notebook
    shap.initjs()

    # train XGBoost model
    X, y = shap.datasets.boston()
    model = xgboost.train({"learning_rate": 0.01, "silent": 1}, xgboost.DMatrix(X, label=y), 100)

    # explain the model's predictions using SHAP values
    explainer = shap.TreeExplainer(model)
    shap_values = explainer.shap_values(X)

    # visualize the first prediction's explaination
    shap.force_plot(explainer.expected_value, shap_values[0, :], X.iloc[0, :])

    # visualize the training set predictions
    shap.force_plot(explainer.expected_value, shap_values, X)

    # create a SHAP dependence plot to show the effect of a single feature across the whole dataset
    shap.dependence_plot(5, shap_values, X, show=False)
    shap.dependence_plot("RM", shap_values, X, show=False)

def test_xgboost_mixed_types():
    try:
        import xgboost
    except Exception as e:
        print("Skipping test_xgboost_mixed_types!")
        return
    import shap
    import numpy as np

    X,y = shap.datasets.boston()
    X["LSTAT"] = X["LSTAT"].astype(np.int64)
    X["B"] = X["B"].astype(np.bool)
    bst = xgboost.train({"learning_rate": 0.01, "silent": 1}, xgboost.DMatrix(X, label=y), 1000)
    shap_values = shap.TreeExplainer(bst).shap_values(X)
    shap.dependence_plot(0, shap_values, X, show=False)