python实现决策树算法

from sklearn.feature_extraction import DictVectorizer

import csv

from sklearn import tree

from sklearn import preprocessing

from sklearn.externals.six import StringIO

# 读取csv数据，并将数据和特征值存入字典和类标签列表

allElectronicsData = open(r'AllElectronics.csv', 'rt')

reader = csv.reader(allElectronicsData)

headers = next(reader)

# 原代码中用的是：

# headers = reader.next()

# 这句代码应该是之前的版本用的，现在已经更新了没有next这个函数

# print(headers)

featureList = []

labelList = []

for row in reader:

    labelList.append(row[len(row) - 1])

    rowDict = {}

    for i in range(1, len(row) - 1):

        rowDict[headers[i]] = row[i]

    featureList.append(rowDict)

# print(featureList)

# 将特征值矢量化,代表将各种参数进行矢量化

vec = DictVectorizer()

dummyX = vec.fit_transform(featureList).toarray()

# print("dummyX: " + str(dummyX))

# print(vec.get_feature_names())

# print("labelList: " + str(labelList))

# 将类标签列表矢量化，就是最后的结果

lb = preprocessing.LabelBinarizer()

dummyY = lb.fit_transform(labelList)

# print("dummyY: " + str(dummyY))

# 使用决策树进行分类

clf = tree.DecisionTreeClassifier()

# clf = tree.DecisionTreeClassifier(criterion = 'entropy')

clf = clf.fit(dummyX, dummyY)

# print("clf: " + str(clf))

# 将模型进行可视化

with open("allElectrionicInformationOri.dot", 'w') as f:

    f = tree.export_graphviz(clf, feature_names = vec.get_feature_names(), out_file = f)

oneRowX = dummyX[0, :]

# print("oneRowX: " + str(oneRowX))

# 接下来改变一些数据进行预测

newRowX = oneRowX

newRowX[0] = 0

newRowX[1] = 1

print("newRowX: " + str(newRowX))

predictedY = clf.predict(newRowX.reshape(1, -1))  # 预测的结果需要加上后面的reshape(1, -1),不然会

# 报错：

# ValueError: Expected 2D array, got 1D array instead:

# array=[0. 1. 1. 0. 1. 1. 0. 0. 1. 0.].

# Reshape your data either using array.reshape(-1, 1)

# if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

print("预测的结果为： " + str(predictedY))