#%%demo
##  基礎(chǔ)函數(shù)庫導(dǎo)入
import numpy as np 

## 導(dǎo)入畫圖庫
import matplotlib.pyplot as plt
import seaborn as sns

## 導(dǎo)入決策樹模型函數(shù)
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree
import pydotplus 
from IPython.display import Image
##Demo演示DecisionTree分類
## 構(gòu)造數(shù)據(jù)集
x_fearures = np.array([[-1, -2], [-2, -1], [-3, -2], [1, 3], [2, 1], [3, 2]])
y_label = np.array([0, 1, 0, 1, 0, 1])
## 調(diào)用決策樹回歸模型
tree_clf = DecisionTreeClassifier()
## 調(diào)用決策樹模型擬合構(gòu)造的數(shù)據(jù)集
tree_clf = tree_clf.fit(x_fearures, y_label)
## 可視化構(gòu)造的數(shù)據(jù)樣本點
plt.figure()
plt.scatter(x_fearures[:,0],x_fearures[:,1], c=y_label, s=50, cmap='viridis')
plt.title('Dataset')
plt.show()
## 可視化決策樹
import graphviz
dot_data = tree.export_graphviz(tree_clf, out_file=None)
graph = pydotplus.graph_from_dot_data(dot_data)
graph.write_pdf("D:\Python\ML\DecisionTree.pdf") 
# 模型預(yù)測
## 創(chuàng)建新樣本
x_fearures_new1 = np.array([[0, -1]])
x_fearures_new2 = np.array([[2, 1]])

## 在訓(xùn)練集和測試集上分布利用訓(xùn)練好的模型進行預(yù)測
y_label_new1_predict = tree_clf.predict(x_fearures_new1)
y_label_new2_predict = tree_clf.predict(x_fearures_new2)

print('The New point 1 predict class:\n',y_label_new1_predict)
print('The New point 2 predict class:\n',y_label_new2_predict)