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

def test_sklearn_random_forest_multiclass():
    import shap
    from sklearn.ensemble import RandomForestClassifier

    X, y = shap.datasets.iris()
    y[y == 2] = 1
    model = RandomForestClassifier(n_estimators=100, max_depth=None, min_samples_split=2, random_state=0)
    model.fit(X, y)

    explainer = shap.TreeExplainer(model)
    shap_values = explainer.shap_values(X)

    assert np.abs(shap_values[0][0,0] - 0.05) < 1e-3
    assert np.abs(shap_values[1][0,0] + 0.05) < 1e-3

def test_sklearn_interaction():
    import sklearn
    from sklearn.model_selection import train_test_split
    from sklearn.ensemble import RandomForestClassifier

    # train a simple sklean RF model on the iris dataset
    X, y = shap.datasets.iris()
    X_train,X_test,Y_train,Y_test = train_test_split(*shap.datasets.iris(), test_size=0.2, random_state=0)
    rforest = RandomForestClassifier(n_estimators=100, max_depth=None, min_samples_split=2, random_state=0)
    model = rforest.fit(X_train, Y_train)

    # verify symmetry of the interaction values (this typically breaks if anything is wrong)
    interaction_vals = shap.TreeExplainer(model).shap_interaction_values(X)
    for i in range(len(interaction_vals)):
        for j in range(len(interaction_vals[i])):
            for k in range(len(interaction_vals[i][j])):
                for l in range(len(interaction_vals[i][j][k])):
                    assert abs(interaction_vals[i][j][k][l] - interaction_vals[i][j][l][k]) < 1e-6

    # ensure the interaction plot works
    shap.summary_plot(interaction_vals[0], X, show=False)

def test_lightgbm_multiclass():
    try:
        import lightgbm
    except:
        print("Skipping test_lightgbm_multiclass!")
        return
    import shap

    # train lightgbm model
    X, Y = shap.datasets.iris()
    model = lightgbm.sklearn.LGBMClassifier()
    model.fit(X, Y)

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

    # ensure plot works for first class
    shap.dependence_plot(0, shap_values[0], X, show=False)

def test_kernel_sparse_vs_dense_multirow_background():
    import sklearn
    import shap
    from sklearn.model_selection import train_test_split
    from sklearn.linear_model import LogisticRegression

    # train a logistic regression classifier
    X_train, X_test, Y_train, _ = train_test_split(*shap.datasets.iris(), test_size=0.1, random_state=0)
    lr = LogisticRegression(solver='lbfgs')
    lr.fit(X_train, Y_train)

    # use Kernel SHAP to explain test set predictions with dense data
    explainer = shap.KernelExplainer(lr.predict_proba, X_train, nsamples=100, link="logit", l1_reg="rank(3)")
    shap_values = explainer.shap_values(X_test)

    X_sparse_train = sp.sparse.csr_matrix(X_train)
    X_sparse_test = sp.sparse.csr_matrix(X_test)

    lr_sparse = LogisticRegression(solver='lbfgs')
    lr_sparse.fit(X_sparse_train, Y_train)

    # use Kernel SHAP again but with sparse data
    sparse_explainer = shap.KernelExplainer(lr.predict_proba, X_sparse_train, nsamples=100, link="logit", l1_reg="rank(3)")
    sparse_shap_values = sparse_explainer.shap_values(X_sparse_test)

def test_sklearn_decision_tree_multiclass():
    import shap
    from sklearn.tree import DecisionTreeClassifier

    X, y = shap.datasets.iris()
    y[y == 2] = 1
    model = DecisionTreeClassifier(max_depth=None, min_samples_split=2, random_state=0)
    model.fit(X, y)

    explainer = shap.TreeExplainer(model)
    shap_values = explainer.shap_values(X)
    assert np.abs(shap_values[0][0,0] - 0.05) < 1e-1
    assert np.abs(shap_values[1][0,0] + 0.05) < 1e-1

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

    from sklearn.model_selection import train_test_split
    import numpy as np
    import shap
    np.random.seed(10)

    X,y = shap.datasets.iris()
    X = X[:100]
    y = y[:100]
    train_x, test_x, train_y, test_y = train_test_split(X, y, random_state=1)
    feature_names = ["a", "b", "c", "d"]
    dtrain = xgboost.DMatrix(train_x, label=train_y, feature_names=feature_names)
    dtest = xgboost.DMatrix(test_x, feature_names=feature_names)

    params = {
        'booster': 'gbtree',
        'objective': 'binary:logistic',
        'max_depth': 4,
        'eta': 0.1,
        'nthread': -1,
        'silent': 1
    }

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

    # train XGBoost model
    X, Y = shap.datasets.iris()
    model = xgboost.XGBClassifier(objective="binary:logistic", max_depth=4)
    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)

    # ensure plot works for first class
    shap.dependence_plot(0, shap_values[0], X, show=False)

def test_lightgbm_multiclass():
    try:
        import lightgbm
    except:
        print("Skipping test_lightgbm_multiclass!")
        return
    import shap

    # train lightgbm model
    X, Y = shap.datasets.iris()
    model = lightgbm.sklearn.LGBMClassifier()
    model.fit(X, Y)

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

    # ensure plot works for first class
    shap.dependence_plot(0, shap_values[0], X, show=False)