## How to visualise a tree model - Multiclass Classification
## DataSet: skleran.datasets.load_wine()
def Snippet_180():
    print()
    print(format('How to visualise a tree model - Multiclass Classification','*^82'))

    import warnings
    warnings.filterwarnings("ignore")

    # load libraries 
    from sklearn import datasets
    from sklearn import metrics
    from sklearn.model_selection import train_test_split
    import matplotlib.pyplot as plt

    plt.style.use('ggplot')

    from sklearn import tree
    from sklearn.externals.six import StringIO
    import pydotplus

    # load the wine datasets
    dataset = datasets.load_wine()
    X = dataset.data; y = dataset.target
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)

    # fit a tree.DecisionTreeClassifier() model to the data
    model = tree.DecisionTreeClassifier()
    model.fit(X_train, y_train)
    print(); print(model)

    # make predictions
    expected_y  = y_test
    predicted_y = model.predict(X_test)

    # summarize the fit of the model
    print(); print('tree.DecisionTreeClassifier(): ')
    print(); print(metrics.classification_report(expected_y, predicted_y,
                   target_names=dataset.target_names))
    print(); print(metrics.confusion_matrix(expected_y, predicted_y))

    # Create a dot file
    dotfile = open("tree.dot", 'w')
    tree.export_graphviz(model, out_file = dotfile, feature_names = dataset.feature_names)
    dotfile.close()

    # Create pdf and png from the dot data
    dot_data = StringIO()
    tree.export_graphviz(model, out_file=dot_data,  filled=True, rounded=True,
                         special_characters=True, feature_names = dataset.feature_names)
    graph = pydotplus.graph_from_dot_data(dot_data.getvalue())

    graph.write_png("tree.png")
    graph.write_pdf("tree.pdf")


    import matplotlib.image as mpimg
    img = mpimg.imread('tree.png')
    #img = misc.imread("tree.png")
    plt.figure(figsize=(15,15))
    plt.imshow(img)
    plt.show()
Snippet_180()