import numpy as np
import matplotlib.pyplot as plt

from sklearn import datasets
iris = datasets.load_iris()
X = iris.data[:,2:]
y = iris.target

from sklearn.tree import DecisionTreeClassifier
dt_clf = DecisionTreeClassifier(max_depth = 2,criterion = 'entropy')#熵
dt_clf.fit(X,y)

def plot_decision_boundary(model, axis):

    x0, x1 = np.meshgrid(
        np.linspace(axis[0], axis[1], int((axis[1]-axis[0])*100)).reshape(-1, 1),
        np.linspace(axis[2], axis[3], int((axis[3]-axis[2])*100)).reshape(-1, 1),
    )
    X_new = np.c_[x0.ravel(), x1.ravel()]

    y_predict = model.predict(X_new)
    zz = y_predict.reshape(x0.shape)

    from matplotlib.colors import ListedColormap
    custom_cmap = ListedColormap(['#EF9A9A','#FFF59D','#90CAF9'])

    plt.contourf(x0, x1, zz, linewidth=5, cmap=custom_cmap)

plot_decision_boundary(dt_clf,axis=[0.5,7.5,0,3])
plt.scatter(X[y == 0,0],X[y == 0,1])
plt.scatter(X[y == 1,0],X[y == 1,1])
plt.scatter(X[y == 2,0],X[y == 2,1])