吴裕雄--天生自然 人工智能机器学习实战代码：回归决策树模型

import numpy as np
import matplotlib.pyplot as plt

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier,DecisionTreeRegressor

def load_data():
    '''
    加载用于分类问题的数据集。数据集采用 scikit-learn 自带的 iris 数据集
    '''
    # scikit-learn 自带的 iris 数据集
    iris=datasets.load_iris()
    X_train=iris.data
    y_train=iris.target
    return train_test_split(X_train, y_train,test_size=0.25,random_state=0,stratify=y_train)

#分类决策树DecisionTreeClassifier模型
def test_DecisionTreeClassifier(*data):
    X_train,X_test,y_train,y_test=data
    clf = DecisionTreeClassifier()
    clf.fit(X_train, y_train)
    print("Training score:%f"%(clf.score(X_train,y_train)))
    print("Testing score:%f"%(clf.score(X_test,y_test)))
    
# 产生用于分类问题的数据集
X_train,X_test,y_train,y_test=load_data()
# 调用 test_DecisionTreeClassifier
test_DecisionTreeClassifier(X_train,X_test,y_train,y_test)

def test_DecisionTreeClassifier_criterion(*data):
    '''
    测试 DecisionTreeClassifier 的预测性能随 criterion 参数的影响
    '''
    X_train,X_test,y_train,y_test=data
    criterions=['gini','entropy']
    for criterion in criterions:
        clf = DecisionTreeClassifier(criterion=criterion)
        clf.fit(X_train, y_train)
        print("criterion:%s"%criterion)
        print("Training score:%f"%(clf.score(X_train,y_train)))
        print("Testing score:%f"%(clf.score(X_test,y_test)))
        
# 调用 test_DecisionTreeClassifier_criterion
test_DecisionTreeClassifier_criterion(X_train,X_test,y_train,y_test)

def test_DecisionTreeClassifier_splitter(*data):
    '''
    测试 DecisionTreeClassifier 的预测性能随划分类型的影响
    '''
    X_train,X_test,y_train,y_test=data
    splitters=['best','random']
    for splitter in splitters:
        clf = DecisionTreeClassifier(splitter=splitter)
        clf.fit(X_train, y_train)
        print("splitter:%s"%splitter)
        print("Training score:%f"%(clf.score(X_train,y_train)))
        print("Testing score:%f"%(clf.score(X_test,y_test)))
        
# 调用 test_DecisionTreeClassifier_splitter
test_DecisionTreeClassifier_splitter(X_train,X_test,y_train,y_test)

def test_DecisionTreeClassifier_depth(*data,maxdepth):
    '''
    测试 DecisionTreeClassifier 的预测性能随 max_depth 参数的影响
    '''
    X_train,X_test,y_train,y_test=data
    depths=np.arange(1,maxdepth)
    training_scores=[]
    testing_scores=[]
    for depth in depths:
        clf = DecisionTreeClassifier(max_depth=depth)
        clf.fit(X_train, y_train)
        training_scores.append(clf.score(X_train,y_train))
        testing_scores.append(clf.score(X_test,y_test))

    ## 绘图
    fig=plt.figure()
    ax=fig.add_subplot(1,1,1)
    ax.plot(depths,training_scores,label="traing score",marker='o')
    ax.plot(depths,testing_scores,label="testing score",marker='*')
    ax.set_xlabel("maxdepth")
    ax.set_ylabel("score")
    ax.set_title("Decision Tree Classification")
    ax.legend(framealpha=0.5,loc='best')
    plt.show()
    
# 调用 test_DecisionTreeClassifier_depth
test_DecisionTreeClassifier_depth(X_train,X_test,y_train,y_test,maxdepth=100)

import os
import pydotplus

from io import StringIO
from sklearn.tree import export_graphviz
from sklearn.tree import DecisionTreeClassifier,DecisionTreeRegressor

X_train,X_test,y_train,y_test=load_data()
clf = DecisionTreeClassifier()
clf.fit(X_train,y_train)
export_graphviz(clf,"F://out")