[机器学习] 用KNN识别MNIST手写字符实战

Hi, 好久不见,粉丝涨了不少,我要再不更新,估计要掉粉了,今天有时间把最近做的一些工作做个总结,我用KNN来识别MNIST手写字符,主要是代码部分,全部纯手写,没有借助机器学习的框架,希望对大家理解KNN有帮助。

https://github.com/Alvin2580du/KNN_mnist

-------------------------------------------------

首先介绍一些KNN的原理,KNN也叫K近邻分类算法,说到底它也是用来做分类任务的,所以我们只要明白它分类的依据是什么就差不多了。换句话说,给定一个样本,他是怎么(原理)把这个样本分到第一类还是第二类的,以此类推。K近邻里面的K是一个可调的参数,一般取正整数,比如K=3,4,5,6,...。我们举个栗子,比如当K=10的时候,即选择距离待分类样本距离最近的10个样本,这10个样本里面有3个第一类,7个第二类,那么就把这个待分类样本划分到第二类。是不是很简单?

然后介绍下数据,MNIST数据集是一个比较著名的数据了,做机器学习的应该都知道,只是我们今天用的数据稍微有点特殊,他是把MNIST数据集图像二值化以后得到的,即黑色的地方取0,白色的地方取1。原始数据是训练集,测试集,和预测集在三个文件中,首先把这三个数据集拆开,每个样本独立一个文件中,这样做的目的是为了便于后续的读取,其应该不分开也可以做,只是这样看起来更清楚一点吧。

import os
import math
from functools import reduce
import numpy as np
from collections import Counter
import pandas as pd
from datetime import datetime
def applyfuns(inputs):
    if len(inputs) > 10:
        return "data"
    else:
        return inputs.strip()


def split_datasets(filename="./datasets/knn/digit-training.txt"):
    # 将原始数据分拆开,一个样本保存到一个文件中
    dir_name = filename.split("/")[-1].split(".")[0].split("-")[1]
    save_path = './datasets/knn/{}'.format(dir_name)
    if not os.path.exists(save_path):
        os.makedirs(save_path)
    data = pd.read_csv(filename, header=None)

    datacopy = data.copy()
    datacopy['labels'] = data[0].apply(applyfuns)
    label = datacopy[~datacopy['labels'].isin(['data'])]
    label.columns = ['0', '1']
    train = datacopy[datacopy['labels'].isin(['data'])][0]
    k = 0
    index = 0
    limit = 32
    save = []
    for y in train:
        save.append(y)
        k += 1
        if k >= limit:
            df = pd.DataFrame(save)
            df.to_csv("./datasets/knn/{}/{}_{}.txt".
                      format(dir_name, index, label['1'].values[index]),
                      index=None,
                      header=None)
            save = []
            k = 0
            index += 1

就得到了下面这样的数据,我截图示例:

这样一个数据就代表了一个样本,然后训练集我们有942个,测试集有195个,最后留下8个样本用来预测。下面使我们的数据的目录结构。

下面直接开始上代码

首先我们需要有一个方法,来实现对一个样本数据变成一个矩阵向量,即

def img2vectorV1(filename):
    # get data
    rows = 32
    cols = 32
    imgVector = []
    fileIn = open(filename)
    for row in range(rows):
        lineStr = fileIn.readline()
        for col in range(cols):
            imgVector.append(int(lineStr[col]))
    return imgVector

首先打开文件,按行去读取,然后遍历每一行,并把字符型转换为整型。

def vector_subtract(v, w):
    # 向量相减
    return [v_i - w_i for v_i, w_i in zip(v, w)]


def distance(v, w):
    # 计算距离函数
    s = vector_subtract(v, w)
    return math.sqrt(sum_of_squares(s))
def get_dict_min(lis, k):
    #  找到距离最近的k个样本,然后找到出现次数最多的那一类样本
    gifts = lis[:k]
    save = []
    for g in gifts:
        res = g[1]
        save.append(res)
    return Counter(save).most_common(1)[0][0]
def knnclassifiy(k=3):
    # 用来统计训练集中没类样本总数
    k0, k1, k2, k3, k4, k5, k6, k7, k8, k9 = 0, 0, 0, 0, 0, 0, 0, 0, 0, 0

    hwLabels = []
    trainingFileList = os.listdir(dataSetDir + "training")  # load training data
    m = len(trainingFileList)
    trainingMat = np.zeros((m, 1024))
    for i in range(m):
        fileNameStr = trainingFileList[i]
        fileStr = fileNameStr.split('.')[0]
        classNumStr = int(fileStr.split('.')[0].split("_")[1])

        if classNumStr == 0:
            k0 += 1
        elif classNumStr == 1:
            k1 += 1
        elif classNumStr == 2:
            k2 += 1
        elif classNumStr == 3:
            k3 += 1
        elif classNumStr == 4:
            k4 += 1
        elif classNumStr == 5:
            k5 += 1
        elif classNumStr == 6:
            k6 += 1
        elif classNumStr == 7:
            k7 += 1
        elif classNumStr == 8:
            k8 += 1
        else:  # 9
            k9 += 1
        hwLabels.append(classNumStr)
        trainingMat[i, :] = img2vectorV1(dataSetDir + 'training/%s' % fileNameStr) 
        

    testFileList = os.listdir(dataSetDir + 'testing')
    # 用来统计测试集的样本总数
    tkp0, tkp1, tkp2, tkp3, tkp4, tkp5, tkp6, tkp7, tkp8, tkp9 = 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
    # 用来统计分类正确的样本数
    tk0, tk1, tk2, tk3, tk4, tk5, tk6, tk7, tk8, tk9 = 0, 0, 0, 0, 0, 0, 0, 0, 0, 0

    C = 0.0
    mTest = len(testFileList)
    for i in range(mTest):
        fileNameStr = testFileList[i]
        fileStr = fileNameStr.split('.')[0]
        TestclassNumStr = int(fileStr.split('.')[0].split("_")[1])
        if TestclassNumStr == 0:
            tkp0 += 1
        elif TestclassNumStr == 1:
            tkp1 += 1
        elif TestclassNumStr == 2:
            tkp2 += 1
        elif TestclassNumStr == 3:
            tkp3 += 1
        elif TestclassNumStr == 4:
            tkp4 += 1
        elif TestclassNumStr == 5:
            tkp5 += 1
        elif TestclassNumStr == 6:
            tkp6 += 1
        elif TestclassNumStr == 7:
            tkp7 += 1
        elif TestclassNumStr == 8:
            tkp8 += 1
        else:  # 9
            tkp9 += 1
        data_file_name = dataSetDir + 'testing/%s' % fileNameStr
        vectorUnderTest = img2vectorV1(data_file_name)
        distaces_list = {}
        for j in range(m):
            distaces = distance(vectorUnderTest, trainingMat[j])  # 计算距离
            distaces_list[distaces] = hwLabels[j]
        sorted_distance_list = sorted(distaces_list.items(),
                                      key=lambda e: e[0],
                                      reverse=False)  
        # 对距离进行排序
        gifts = get_dict_min(sorted_distance_list, k) 
        # 获得距离最近的K个样本中,出现次数最多的那个样本
        if TestclassNumStr == gifts:
             C += 1

        if gifts == 0:
            tk0 += 1
        elif gifts == 1:
            tk1 += 1
        elif gifts == 2:
            tk2 += 1
        elif gifts == 3:
            tk3 += 1
        elif gifts == 4:
            tk4 += 1
        elif gifts == 5:
            tk5 += 1
        elif gifts == 6:
            tk6 += 1
        elif gifts == 7:
            tk7 += 1
        elif gifts == 8:
            tk8 += 1
        else:  # 9
            tk9 += 1
    print("- " * 20)
    print('              Training info                 ')
    print("  {}  =  {}".format("0", k0))
    print("  {}  =  {}  ".format("1", k1))
    print("  {}  =  {} ".format("2", k2))
    print("  {}  =  {} ".format("3", k3))
    print("              {}  =  {}               ".format("4", k4))
    print("              {}  =  {}               ".format("5", k5))
    print("              {}  =  {}               ".format("6", k6))
    print("              {}  =  {}               ".format("7", k7))
    print("              {}  =  {}               ".format("8", k8))
    print("              {}  =  {}               ".format("9", k9))
    print("- " * 20)
    print("     Total Sample = {} ".format(m))
    print()
    print("- " * 20)
    print('              Testing info                 ')
    print("- " * 20)
    print(" {}  =  {},   {},   {:0.2f}%  ".
          format("0", tkp0, abs(tkp0 - tk0), 1-abs(tkp0 - tk0)/tkp0))
    print(" {}  =  {},   {},   {:0.2f}% ".
          format("1", tkp1, abs(tkp1 - tk1), 1-abs(tkp1 - tk1)/tkp1))
    print(" {}  =  {},   {},   {:0.2f}%  ".
          format("2", tkp2, abs(tkp2 - tk2), 1-abs(tkp2 - tk2)/tkp2))
    print(" {}  =  {},   {},   {:0.2f}%  ".
          format("3", tkp3, abs(tkp3 - tk3), 1-abs(tkp3 - tk3)/tkp3))
    print(" {}  =  {},   {},   {:0.2f}%  ".
          format("4", tkp4, abs(tkp4 - tk4), 1-abs(tkp4 - tk4)/tkp4))
    print(" {}  =  {},   {},   {:0.2f}%  ".
          format("5", tkp5, abs(tkp5 - tk5), 1-abs(tkp5 - tk5)/tkp5))
    print(" {}  =  {},   {},   {:0.2f}%  ".
          format("6", tkp6, abs(tkp6 - tk6), 1-abs(tkp6 - tk6)/tkp6))
    print(" {}  =  {},   {},   {:0.2f}% ".
          format("7", tkp7, abs(tkp7 - tk7), 1-abs(tkp7 - tk7)/tkp7))
    print(" {}  =  {},   {},   {:0.2f}% ".
          format("8", tkp8, abs(tkp8 - tk8), 1-abs(tkp8 - tk8)/tkp8))
    print(" {}  =  {},   {},   {:0.2f}%  ".
          format("9", tkp9, abs(tkp9 - tk9), 1-abs(tkp9 - tk9)/tkp9))
    print("- " * 20)
    print(" Accuracy = {:0.2f}%".format(C / float(mTest)))
    print("Correct/Total = {}/{}".format(int(C), mTest))
    print(" End of Training @ {} ".
          format(datetime.now().strftime("%Y-%m-%d %H:%M:%S")))


def build_knnclassifier():
    # 这里对不同的k进行分类,找到最合适的K。
    ks = [3, 5, 7, 9]
    for k in ks:
        print(" Beginning of Training @ {} ".
              format(datetime.now().strftime("%Y-%m-%d %H:%M:%S")))
        knnclassifiy(k)
        print()

最后是根据上一步训练找到最合适的K,然后进行预测。

def buildPredict(k=7):
    hwLabels = []
    trainingFileList = os.listdir(dataSetDir + "training")  # 加载测试数据

    m = len(trainingFileList)
    trainingMat = np.zeros((m, 1024))
    for i in range(m):
        fileNameStr = trainingFileList[i]
        fileStr = fileNameStr.split('.')[0]
        classNumStr = int(fileStr.split('.')[0].split("_")[1])  # return 1
        hwLabels.append(classNumStr)
        trainingMat[i, :] = img2vectorV1(dataSetDir + 'training/%s' % fileNameStr)

    predictFileList = os.listdir(dataSetDir + 'predict')  # load the testing set
    mTest = len(predictFileList)
    for i in range(mTest):
        fileNameStr = predictFileList[i]
        data_file_name = dataSetDir + 'predict/%s' % fileNameStr
        vectorUnderTest = img2vectorV1(data_file_name)
        distaces_list = {}
        for j in range(m):
            distaces = distance(vectorUnderTest, trainingMat[j])
            distaces_list[distaces] = hwLabels[j]
        sorted_distance_list = sorted(distaces_list.items(), 
                                      key=lambda e: e[0], 
                                      reverse=False)
        gifts = get_dict_min(sorted_distance_list, k)
        print(gifts)

最后执行 上面的代码, 这里只要修改 method 的值,执行下面的对应的方法就可以了。

if __name__ == '__main__':

    method = 'build_knnclassifier'

    if method == 'split_datasets':
        dataname = ['./datasets/knn/digit-training.txt', './datasets/knn/digit-testing.txt',
                    './datasets/knn/digit-predict.txt']
        for n in dataname:
            split_datasets(n)

    if method == 'build_knnclassifier':
        build_knnclassifier()

    if method == 'buildPredict':
        buildPredict(k=7)

下面是我得到的实验结果,准确率达到了95%,这个准确率其实也不算高 。

TRAINING

Beginning of Training @ 2018-05-06 23:08:16

- - - - - - - - - - - - - - - - - - - -

Training info

0 = 100

1 = 94

2 = 93

3 = 105

4 = 87

5 = 81

6 = 95

7 = 90

8 = 109

9 = 89

- - - - - - - - - - - - - - - - - - - -

Total Sample = 943

- - - - - - - - - - - - - - - - - - - -

TESTING

- - - - - - - - - - - - - - - - - - - -

Testing info

- - - - - - - - - - - - - - - - - - - -

0 = 20, 1, 0.95%

1 = 20, 2, 0.90%

2 = 25, 0, 1.00%

3 = 18, 1, 0.94%

4 = 25, 2, 0.92%

5 = 16, 0, 1.00%

6 = 16, 1, 0.94%

7 = 19, 0, 1.00%

8 = 17, 1, 0.94%

9 = 20, 2, 0.90%

- - - - - - - - - - - - - - - - - - - -

Accuracy = 0.95%

Correct/Total = 187.0/196

Endof Training @ 2018-05-06 23:09:48

TRAINING

Beginning of Training @ 2018-05-06 23:09:48

- - - - - - - - - - - - - - - - - - - -

Training info

0 = 100

1 = 94

2 = 93

3 = 105

4 = 87

5 = 81

6 = 95

7 = 90

8 = 109

9 = 89

- - - - - - - - - - - - - - - - - - - -

Total Sample = 943

- - - - - - - - - - - - - - - - - - - -

TESTING

- - - - - - - - - - - - - - - - - - - -

Testing info

- - - - - - - - - - - - - - - - - - - -

0 = 20, 1, 0.95%

1 = 20, 4, 0.80%

2 = 25, 0, 1.00%

3 = 18, 1, 0.94%

4 = 25, 5, 0.80%

5 = 16, 0, 1.00%

6 = 16, 1, 0.94%

7 = 19, 0, 1.00%

8 = 17, 3, 0.82%

9 = 20, 5, 0.75%

- - - - - - - - - - - - - - - - - - - -

Accuracy = 0.94%

Correct/Total = 185.0/196

Endof Training @ 2018-05-06 23:11:20

TRAINING

Beginning of Training @ 2018-05-06 23:11:20

- - - - - - - - - - - - - - - - - - - -

Training info

0 = 100

1 = 94

2 = 93

3 = 105

4 = 87

5 = 81

6 = 95

7 = 90

8 = 109

9 = 89

- - - - - - - - - - - - - - - - - - - -

Total Sample = 943

- - - - - - - - - - - - - - - - - - - -

TESTING

- - - - - - - - - - - - - - - - - - - -

Testing info

- - - - - - - - - - - - - - - - - - - -

0 = 20, 1, 0.95%

1 = 20, 4, 0.80%

2 = 25, 0, 1.00%

3 = 18, 0, 1.00%

4 = 25, 4, 0.84%

5 = 16, 0, 1.00%

6 = 16, 1, 0.94%

7 = 19, 0, 1.00%

8 = 17, 3, 0.82%

9 = 20, 3, 0.85%

- - - - - - - - - - - - - - - - - - - -

Accuracy = 0.95%

Correct/Total = 187.0/196

Endof Training @ 2018-05-06 23:12:45

TRAINING

Beginning of Training @ 2018-05-06 23:12:45

- - - - - - - - - - - - - - - - - - - -

Training info

0 = 100

1 = 94

2 = 93

3 = 105

4 = 87

5 = 81

6 = 95

7 = 90

8 = 109

9 = 89

- - - - - - - - - - - - - - - - - - - -

Total Sample = 943

TESTING

- - - - - - - - - - - - - - - - - - - -

Testing info

- - - - - - - - - - - - - - - - - - - -

0 = 20, 1, 0.95%

1 = 20, 4, 0.80%

2 = 25, 0, 1.00%

3 = 18, 0, 1.00%

4 = 25, 4, 0.84%

5 = 16, 0, 1.00%

6 = 16, 1, 0.94%

7 = 19, 0, 1.00%

8 = 17, 3, 0.82%

9 = 20, 3, 0.85%

- - - - - - - - - - - - - - - - - - - -

Accuracy = 0.94%

Correct/Total = 185.0/196

Endof Training @ 2018-05-06 23:14:10

PREDICTION

5

2

1

8

2

9

9

5

本文分享自微信公众号 - 机器学习和数学(ML_And_Maths)

原文出处及转载信息见文内详细说明,如有侵权,请联系 yunjia_community@tencent.com 删除。

原始发表时间:2018-05-13

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