How to use the shap.initjs function in shap

def test_front_page_sklearn():
    import sklearn.ensemble
    import shap

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

    # train model
    X, y = shap.datasets.boston()
    model = sklearn.ensemble.RandomForestRegressor(n_estimators=100)
    model.fit(X, y)

    # 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

def test_front_page_model_agnostic():
    import sklearn
    import shap
    from sklearn.model_selection import train_test_split

    # print the JS visualization code to the notebook
    shap.initjs()

    # train a SVM classifier
    X_train, X_test, Y_train, Y_test = train_test_split(*shap.datasets.iris(), test_size=0.2, random_state=0)
    svm = sklearn.svm.SVC(kernel='rbf', probability=True)
    svm.fit(X_train, Y_train)

    # use Kernel SHAP to explain test set predictions
    explainer = shap.KernelExplainer(svm.predict_proba, X_train, nsamples=100, link="logit")
    shap_values = explainer.shap_values(X_test)

    # plot the SHAP values for the Setosa output of the first instance
    shap.force_plot(shap_values[0][0, :], X_test.iloc[0, :], link="logit")

def test_front_page_sklearn():
    import sklearn.ensemble
    import shap

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

    # train model
    X, y = shap.datasets.boston()
    models = [
        sklearn.ensemble.RandomForestRegressor(n_estimators=100),
        sklearn.ensemble.ExtraTreesRegressor(n_estimators=100),
    ]
    for model in models:
        model.fit(X, y)

        # 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, :])

def test_front_page_sklearn():
    import sklearn.ensemble
    import shap

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

    # train model
    X, y = shap.datasets.boston()
    models = [
        sklearn.ensemble.RandomForestRegressor(n_estimators=100),
        sklearn.ensemble.ExtraTreesRegressor(n_estimators=100),
    ]
    for model in models:
        model.fit(X, y)

        # 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, :])

def test_front_page_model_agnostic():
    from shap import KernelExplainer, DenseData, visualize, initjs
    from sklearn import datasets,neighbors
    from numpy import random, arange

    # print the JS visualization code to the notebook
    initjs()

    # train a k-nearest neighbors classifier on a random subset
    iris = datasets.load_iris()
    random.seed(2)
    inds = arange(len(iris.target))
    random.shuffle(inds)
    knn = neighbors.KNeighborsClassifier()
    knn.fit(iris.data, iris.target == 0)

    # use Shap to explain a single prediction
    background = DenseData(iris.data[inds[:100],:], iris.feature_names) # name the features
    explainer = KernelExplainer(knn.predict, background, nsamples=100)
    x = iris.data[inds[102:103],:]
    visualize(explainer.explain(x))

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

def test_front_page_xgboost():
    import xgboost
    import shap

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

    # train XGBoost model
    X,y,X_display = shap.datasets.boston()
    bst = 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 = bst.predict(xgboost.DMatrix(X), pred_contribs=True)

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

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

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)