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机器学习实战(一)kNN

时间:2016-11-28 07:40:29      阅读:305      评论:0      收藏:0      [点我收藏+]

标签:key   文件名   还需   readline   0.00   mat   ica   分类器   max   

 $k$-近邻算法(kNN)的工作原理:存在一个训练样本集,样本集中的每个数据都存在标签,即我们知道样本集中每一数据与所属分类的对于关系。输入没有标签的新数据后,将新数据的每一个特征与样本集中数据对应的特征进行比较,然后算法提取样本集中特征最相似数据(最近邻)的分类标签。一般来说,我们只选择样本数据集中前 $k$ 个最相似的数据,这就是$k$-近邻算法中$k$的出处,通常$k$是不大于20的整数。最后,选择$k$个最相似的数据中出现次数最多的分类,作为新数据的分类。

 

1.  Putting the kNN classification algorithm into action

For every point in our dataset:
    calculate the distance between inX and the current point
    sort the distances in increasing order
    take k items with lowest distances to inX
    find the majority class among these items
    return the majority class as our prediction for the class of inX

一个简单的例子:kNN.py

# coding=utf-8
from numpy import *
import operator

def createDataSet():
    group = array([[1.0,1.1], [1.0,1.0], [0,0], [0,0.1]])
    labels = [A, A, B, B]
    return group, labels

def classify0(inX, dataSet, labels, k):
    dataSetSize = dataSet.shape[0]
    diffMat = tile(inX, (dataSetSize,1)) - dataSet
    sqDiffMat = diffMat**2
    sqDistances = sqDiffMat.sum(axis=1)  # 按行求和
    distances = sqDistances**0.5
    sortedDistIndicies = distances.argsort() # 将索引按照距离从小到大顺序排列
    classCount={}  # 以dict形式存储
    for i in range(k):
        voteIlabel = labels[sortedDistIndicies[i]]  # 第i最靠近的样本的标签
        # dict是按照key-value的形式构成的,classCount.get(voteIlabel,0)是取出classCount中key是voteIlabel的value,如果key不存在,则定义返回0
        classCount[voteIlabel] = classCount.get(voteIlabel,0) +1
    # classCount.items()以(key, value) tuple 形式返回list
    sortedClassCount = sorted(classCount.items(),
                              key=operator.itemgetter(1), reverse=True)
    return sortedClassCount[0][0]

group, labels = createDataSet()
label = classify0([0.2,0.2], group, labels, 3)
print label

createDataSet() 函数为我们准备了四个简单的训练数据。

classify0() 函数是一个简单的$k$-近邻算法实现,函数有四个参数:待预测样本的输入特征inX,训练样本集特征集合dataSet,训练样本集标签向量labels,最近邻数目$k$

2. Example: improving matches from a dating site with kNN

Example: using kNN on results from a dating site
1. Collect: Text file provided.
2. Prepare: Parse a text file in Python.
3. Analyze: Use Matplotlib to make 2D plots of our data.
4. Train: Doesn’t apply to the kNN algorithm.
5. Test: Write a function to use some portion of the data Hellen gave us as test examples. The test examples are classified against the non-test examples. If the predicted class doesn’t match the real class, we’ll count that as an error.
6. Use: Build a simple command-line program Hellen can use to predict whether she’ll like someone based on a few inputs.

2.1 Prepare: parsing data from a text file

所有训练数据存放在文本文件datingTestSet.txt中,样本容量大小为1000。样本主要包含以下三种特征:

■ Number of frequent flyer miles earned per year
■ Percentage of time spent playing video games
■ Liters of ice cream consumed per week

设计分类器之前,我们首先要把原始数据读入到python中。在kNN.py中创建名为file2matrix的函数,以此来处理原始数据。该函数的输入为文件名字符串,输出为训练样本矩阵和类标签向量。

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48111    9.134528    0.728045    largeDoses
43757    7.882601    1.332446    largeDoses
View Code

这里我们还需要先把标签替换成数字型的以便识别,largeDoses -> 3, smallDoses -> 2, didntLike -> 1

def file2matrix(filename):
    fr = open(filename)
    arrayOLines = fr.readlines()
    numberOfLines = len(arrayOLines)
    returnMat = zeros((numberOfLines,3))
    classLabelVector = []
    index = 0;
    for line in arrayOLines:
        line = line.strip() # 去掉所有的回车符
        listFromLine = line.split(\t) # 按照tab字符分割成list
        returnMat[index,:] = listFromLine[0:3]
        # 我们必须明确地将标签值转换成整型,否则python语言会将其当做字符串处理
        classLabelVector.append(int(listFromLine[-1]))
        index += 1
    return returnMat, classLabelVector

接下来我们可以采用图形化的方式直观的展示数据。

2.2 Analyze: creating scatter plots with Matplotlib

我们在cmd中定位到源码所在路径,然后绘制原始数据的散点图:

>>> import kNN
>>> datingDataMat, datingLabels = kNN.file2matrix(datingTestSet2.txt)
>>> import matplotlib
>>> import matplotlib.pyplot as plt
>>> fig = plt.figure()
>>> ax = fig.add_subplot(111)
>>> ax.scatter(datingDataMat[:,1], datingDataMat[:,2])
>>> plt.show()

技术分享

由于没有使用样本分类的label,我们很难从上图看到任何有用的数据模式信息。为了更好的理解数据信息,我们可以使用色彩或者其他记号来区别标记

datingDataMat, datingLabels = file2matrix(datingTestSet2.txt)
from matplotlib.font_manager import FontProperties
import matplotlib.pyplot as plt
zhfont1 = FontProperties(fname=C:\Windows\Fonts\simkai.ttf,size=16)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(datingDataMat[:,1], datingDataMat[:,2], 15.0*array(datingLabels), 15.0*array(datingLabels))
plt.xlabel(u玩游戏所耗时间百分比, fontproperties=zhfont1)
plt.ylabel(u每周消费的冰淇淋公升数, fontproperties=zhfont1)
plt.show()

技术分享技术分享

 

 2.3 Prepare: normalizing numeric values

newValue = (oldValue-min)/(max-min)
def autoNorm(dataSet):
    minVals = dataSet.min(0)  # 比较每行获得特征矩阵最小值
    maxVals = dataSet.max(0)  # 比较每行获得特征矩阵最大值
    ranges = maxVals - minVals
    normDataSet = zeros(shape(dataSet))
    m = dataSet.shape[0]     # 特征向量行数
    normDataSet = dataSet - tile(minVals, (m,1)) # tile函数将变量内容复制成dataSet同样大小的矩阵
    normDataSet = normDataSet/tile(ranges, (m,1))
    return normDataSet, ranges, minVals

2.4 Test: testing the classifier as a whole program

机器学习算法一个很重要的工作就是评估算法的正确率,通常我们只提供已有数据的90%作为训练样本来训练分类器,而使用其余的10%数据去测试分类器,检测分类器的正确率。

这里我们可以随机选择这10%的测试样本,也可以顺序选择。

最终我们可以选择错误率来检测分类器的性能。

def datingClassTest():
    hoRatio = 0.10 # 测试集所占比例
    datingDataMat, datingLabels = file2matrix(datingTestSet2.txt) # 读入样本集
    normMat ,ranges, minVals = autoNorm(datingDataMat) # 特征归一化
    m = normMat.shape[0]
    numTestVecs = int(m*hoRatio)
    errorCount = 0.0
    # 对于每个测试样本,测试结果,并统计错误次数
    for i in range(numTestVecs):
        classifierResult = classify0(normMat[i,:], normMat[numTestVecs:m,:], datingLabels[numTestVecs:m], 3)
        print "the classifier came back with: %d, the real answer is: %d" % (classifierResult, datingLabels[i])
        if classifierResult != datingLabels[i]:
            errorCount += 1.0
    print "the total error rate is: %f" % (errorCount/float(numTestVecs))

2.5 Use: putting together a useful system

def classifyPerson():
    resultList = [not at all, in small doses, in large doses]
    percentTats = float(raw_input("percentage of time spent playing video games?"))
    ffMiles = float(raw_input("ferquent fliter miles earned per year?"))
    iceCream = float(raw_input("liters of ice cream consumed per year?"))
    datingDataMat, datingLabels = file2matrix(datingTestSet2.txt)  # 读入样本集
    normMat, ranges, minVals = autoNorm(datingDataMat)  # 特征归一化
    inArr = array([ffMiles, percentTats, iceCream]) # 预测样本特征
    classifierResult = classify0((inArr-minVals)/ranges, normMat, datingLabels, 3)
    print "You will probably like the person: ", resultList[classifierResult-1]

到此为止,一个简单的约会对象匹配算法就完成了!


3. Example: a handwriting recognition system

3.1 Prepare: converting images into test vectors

为了简单起见,这里构造的系统只能识别数字0-9。需要识别的数字已经使用图形处理软件,处理成具有相同的色彩和大小:宽高是32像素$\times$32像素的黑白图像。尽管采用文本格式存储图形不能有效地利用内存空间,但是为了方便理解,我们还是将图像转换为文本格式。

def img2vector(filename):
    returnVect = zeros((1,1024))
    fr = open(filename)
    for i in range(32):
        lineStr = fr.readline() # 读取一行数据
        for j in range(32):
            returnVect[0,32*i+j] = int(lineStr[j]) # 数据默认都是以字符形式读入,需要强制转换
    return returnVect

testVector = img2vector(testDigits/0_13.txt)
print testVector[0,0:31]

这样我们就可以借助于之前的kNN算法代码测试了

Test: kNN on handwritten digits

from os import listdir
def handwritingClasstest():
    hwLabels = []
    trainingFileList = listdir(trainingDigits) # 获得路径下所有文件名
    m = len(trainingFileList) # 训练样本数
    trainingMat = zeros((m,1024))
    for i in range(m):
        fileNameStr = trainingFileList[i]
        fileStr = fileNameStr.split(.)[0]
        classNumStr = int(fileStr.split(_)[0]) # 训练样本标签
        hwLabels.append(classNumStr)
        trainingMat[i,:] = img2vector(trainingDigits/%s % fileNameStr) # 读取样本
    testFileList = listdir(testDigits)
    errorCount = 0.0
    mTest = len(testFileList) # 测试样本数
    for i in range(mTest):
        fileNameStr = testFileList[i]
        fileStr = fileNameStr.split(.)[0]
        classNumStr = int(fileStr.split(_)[0]) # 测试样本标签
        vectorUnderTest = img2vector(testDigits/%s % fileNameStr)
        classifierResult = classify0(vectorUnderTest, trainingMat, hwLabels, 3) # 预测样本类别
        print "the classifier came back with: %d, the real answer is: %d" % (classifierResult, classNumStr)
        if classifierResult != classNumStr:
            errorCount += 1.0 # 统计错误分类数
    print "\nthe total number of errors is: %d" % errorCount
    print "\nthe total error rate is: %f" % (errorCount/mTest)

handwritingClasstest()

实际使用这个算法是,算法的执行效率并不高。因为预测时,测试样本要和所有样本做距离计算。后面我们会使用一种$k$决策树算法来节省计算开销。









机器学习实战(一)kNN

标签:key   文件名   还需   readline   0.00   mat   ica   分类器   max   

原文地址:http://www.cnblogs.com/xuanyuyt/p/6106409.html

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