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colicLogRegres.py
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# -*- coding:UTF-8 -*-
from sklearn.linear_model import LogisticRegression
import numpy as np
import random
"""
函数说明:sigmoid函数
Parameters:
inX - 数据
Returns:
sigmoid函数
Author:
Jack Cui
Blog:
http://blog.csdn.net/c406495762
Zhihu:
https://www.zhihu.com/people/Jack--Cui/
Modify:
2017-09-05
"""
def sigmoid(inX):
return 1.0 / (1 + np.exp(-inX))
"""
函数说明:改进的随机梯度上升算法
Parameters:
dataMatrix - 数据数组
classLabels - 数据标签
numIter - 迭代次数
Returns:
weights - 求得的回归系数数组(最优参数)
Author:
Jack Cui
Blog:
http://blog.csdn.net/c406495762
Zhihu:
https://www.zhihu.com/people/Jack--Cui/
Modify:
2017-09-05
"""
def stocGradAscent1(dataMatrix, classLabels, numIter=150):
m,n = np.shape(dataMatrix) #返回dataMatrix的大小。m为行数,n为列数。
weights = np.ones(n) #参数初始化 #存储每次更新的回归系数
for j in range(numIter):
dataIndex = list(range(m))
for i in range(m):
alpha = 4/(1.0+j+i)+0.01 #降低alpha的大小，每次减小1/(j+i)。
randIndex = int(random.uniform(0,len(dataIndex))) #随机选取样本
h = sigmoid(sum(dataMatrix[randIndex]*weights)) #选择随机选取的一个样本，计算h
error = classLabels[randIndex] - h #计算误差
weights = weights + alpha * error * dataMatrix[randIndex] #更新回归系数
del(dataIndex[randIndex]) #删除已经使用的样本
return weights #返回
"""
函数说明:梯度上升算法
Parameters:
dataMatIn - 数据集
classLabels - 数据标签
Returns:
weights.getA() - 求得的权重数组(最优参数)
Author:
Jack Cui
Blog:
http://blog.csdn.net/c406495762
Zhihu:
https://www.zhihu.com/people/Jack--Cui/
Modify:
2017-08-28
"""
def gradAscent(dataMatIn, classLabels):
dataMatrix = np.mat(dataMatIn) #转换成numpy的mat
labelMat = np.mat(classLabels).transpose() #转换成numpy的mat,并进行转置
m, n = np.shape(dataMatrix) #返回dataMatrix的大小。m为行数,n为列数。
alpha = 0.01 #移动步长,也就是学习速率,控制更新的幅度。
maxCycles = 500 #最大迭代次数
weights = np.ones((n,1))
for k in range(maxCycles):
h = sigmoid(dataMatrix * weights) #梯度上升矢量化公式
error = labelMat - h
weights = weights + alpha * dataMatrix.transpose() * error
return weights.getA() #将矩阵转换为数组，并返回
"""
函数说明:使用Python写的Logistic分类器做预测
Parameters:
无
Returns:
无
Author:
Jack Cui
Blog:
http://blog.csdn.net/c406495762
Zhihu:
https://www.zhihu.com/people/Jack--Cui/
Modify:
2017-09-05
"""
def colicTest():
frTrain = open('horseColicTraining.txt') #打开训练集
frTest = open('horseColicTest.txt') #打开测试集
trainingSet = []; trainingLabels = []
for line in frTrain.readlines():
currLine = line.strip().split('\t')
lineArr = []
for i in range(len(currLine)-1):
lineArr.append(float(currLine[i]))
trainingSet.append(lineArr)
trainingLabels.append(float(currLine[-1]))
trainWeights = stocGradAscent1(np.array(trainingSet), trainingLabels,500) #使用改进的随即上升梯度训练
errorCount = 0; numTestVec = 0.0
for line in frTest.readlines():
numTestVec += 1.0
currLine = line.strip().split('\t')
lineArr =[]
for i in range(len(currLine)-1):
lineArr.append(float(currLine[i]))
if int(classifyVector(np.array(lineArr), trainWeights))!= int(currLine[-1]):
errorCount += 1
errorRate = (float(errorCount)/numTestVec) * 100 #错误率计算
print("测试集错误率为: %.2f%%" % errorRate)
"""
函数说明:分类函数
Parameters:
inX - 特征向量
weights - 回归系数
Returns:
分类结果
Author:
Jack Cui
Blog:
http://blog.csdn.net/c406495762
Zhihu:
https://www.zhihu.com/people/Jack--Cui/
Modify:
2017-09-05
"""
def classifyVector(inX, weights):
prob = sigmoid(sum(inX*weights))
if prob > 0.5: return 1.0
else: return 0.0
"""
函数说明:使用Sklearn构建Logistic回归分类器
Parameters:
无
Returns:
无
Author:
Jack Cui
Blog:
http://blog.csdn.net/c406495762
Zhihu:
https://www.zhihu.com/people/Jack--Cui/
Modify:
2017-09-05
"""
def colicSklearn():
frTrain = open('horseColicTraining.txt') #打开训练集
frTest = open('horseColicTest.txt') #打开测试集
trainingSet = []; trainingLabels = []
testSet = []; testLabels = []
for line in frTrain.readlines():
currLine = line.strip().split('\t')
lineArr = []
for i in range(len(currLine)-1):
lineArr.append(float(currLine[i]))
trainingSet.append(lineArr)
trainingLabels.append(float(currLine[-1]))
for line in frTest.readlines():
currLine = line.strip().split('\t')
lineArr =[]
for i in range(len(currLine)-1):
lineArr.append(float(currLine[i]))
testSet.append(lineArr)
testLabels.append(float(currLine[-1]))
classifier = LogisticRegression(solver = 'sag',max_iter = 5000).fit(trainingSet, trainingLabels)
test_accurcy = classifier.score(testSet, testLabels) * 100
print('正确率:%f%%' % test_accurcy)
if __name__ == '__main__':
colicSklearn()