机器学习之决策树实践：隐形眼镜类型预测

步骤：

收集数据：使用书中提供的小型数据集

准备数据：对文本中的数据进行预处理，如解析数据行

分析数据：快速检查数据，并使用createPlot()函数绘制最终的树形图

训练决策树：使用createTree()函数训练

测试决策树：编写简单的测试函数验证决策树的输出结果&绘图结果

使用决策树：这部分可选择将训练好的决策树进行存储，以便随时使用

1、数据集

young	myope	no	reduced	no lenses
young	myope	no	normal	soft
young	myope	yes	reduced	no lenses
young	myope	yes	normal	hard
young	hyper	no	reduced	no lenses
young	hyper	no	normal	soft
young	hyper	yes	reduced	no lenses
young	hyper	yes	normal	hard
pre	myope	no	reduced	no lenses
pre	myope	no	normal	soft
pre	myope	yes	reduced	no lenses
pre	myope	yes	normal	hard
pre	hyper	no	reduced	no lenses
pre	hyper	no	normal	soft
pre	hyper	yes	reduced	no lenses
pre	hyper	yes	normal	no lenses
presbyopic	myope	no	reduced	no lenses
presbyopic	myope	no	normal	no lenses
presbyopic	myope	yes	reduced	no lenses
presbyopic	myope	yes	normal	hard
presbyopic	hyper	no	reduced	no lenses
presbyopic	hyper	no	normal	soft
presbyopic	hyper	yes	reduced	no lenses
presbyopic	hyper	yes	normal	no lenses

2、代码如下

# -*- coding: UTF-8 -*-
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.externals.six import StringIO
from sklearn import tree
import pandas as pd
import numpy as np
import pydotplus

if __name__ == '__main__':
	with open('lenses.txt', 'r') as fr:										#加载文件
		lenses = [inst.strip().split('t') for inst in fr.readlines()]		#处理文件
	lenses_target = []														#提取每组数据的类别，保存在列表里
	for each in lenses:
		lenses_target.append(each[-1])
	# print(lenses_target)

	lensesLabels = ['age', 'prescript', 'astigmatic', 'tearRate']			#特征标签		
	lenses_list = []														#保存lenses数据的临时列表
	lenses_dict = {}														#保存lenses数据的字典，用于生成pandas
	for each_label in lensesLabels:											#提取信息，生成字典
		for each in lenses:
			lenses_list.append(each[lensesLabels.index(each_label)])
		lenses_dict[each_label] = lenses_list
		lenses_list = []
	# print(lenses_dict)														#打印字典信息
	lenses_pd = pd.DataFrame(lenses_dict)									#生成pandas.DataFrame
	# print(lenses_pd)														#打印pandas.DataFrame
	le = LabelEncoder()														#创建LabelEncoder()对象，用于序列化			
	for col in lenses_pd.columns:											#序列化
		lenses_pd[col] = le.fit_transform(lenses_pd[col])
	# print(lenses_pd)														#打印编码信息

	clf = tree.DecisionTreeClassifier(max_depth = 4)						#创建DecisionTreeClassifier()类
	clf = clf.fit(lenses_pd.values.tolist(), lenses_target)					#使用数据，构建决策树

	dot_data = StringIO()
	tree.export_graphviz(clf, out_file = dot_data,							#绘制决策树
						feature_names = lenses_pd.keys(),
						class_names = clf.classes_,
						filled=True, rounded=True,
						special_characters=True)
	graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
	graph.write_pdf("tree.pdf")												#保存绘制好的决策树，以PDF的形式存储。

	print(clf.predict([[1,1,1,0]]))			

最后

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