【读书笔记】《机器学习实战》 k近邻算法改进约会网站的配对效果

企业开发 2020-03-15 12:58:44 阅读次数: 0

【读书笔记】Python3 《机器学习实战》 k近邻算法改进约会网站的配对效果


书中用Python2实现。用Python3的话，一些命名方式，方法需要修改。

注释包括中英文，先英后中，中英之间空一行。

1. 创建py文件

首先创建一个python文件，命名为kNN.py，并输入以下代码来import这些模块。这些在之后的函数中会有用到。

from numpy import *
import operator
import matplotlib.pyplot as plt
from os import listdir

2. k-近邻算法函数

继续在kNN.py 文件中输入以下代码

与Python2 的主要不同：字典的.iteritems()方法在Python3中被删除，故用.items()替代。

参数说明（四个）：

  • in_x: 是一个vector。 需要进行分类的向量。

  • data_set: 是一个matrix。训练的样本数据集。如果其有m行n列，那么共有m个数据，每个数据有n个特征值。

  • labels: 是一个vector。为样本分类的标签向量，挨个顺序对应data_set, 所以其元素数量与data_set的行数相同。

  • k: 是一个scalar。用于选择近邻的数据的数量。

def classify0(in_x, data_set, labels, k):
	# 1 calculate distance
	
	# 1 计算距离
	
	
	## data_set rows number, which means the number of data, scalar
	
	## data_set 的行数，意味着有多少个数据，是标量
	data_set_size = data_set.shape[0]
	## copy "in_x" by "data_set_size" times on row,
	## and 1 time on column. Then get the difference between
	## "in_x" to each element in "data_set"
	
	## 把向量 "in_x" 在行方向上复制 "data_set_size" 次，
	## 在列方向上复制1次，然后计算 in_x 与 "data_set" 每个数据之间的差，
	## 用于之后的距离计算
	diff_mat = tile(in_x, (data_set_size, 1)) - data_set
	## below are the processes to calculate Euclidean distance
	
	## 下面是利用上边的差计算欧几里得距离的过程
	sq_diff_mat = diff_mat**2
	### "axis=1" means summing for each row
	
	### "axis=1" 意味着每一行内部自身求和
	sq_distances = sq_diff_mat.sum(axis=1) 
	distances = sq_distances**0.5
	# 2 get ascending sorted distance's index
	
	# 2 按升序把距离排序，排好序后显示的为索引值，非距离值
	sorted_dist_indices = distances.argsort()
	# 3 choose the first k smallest distances by using a dictionary to record
	
	# 3 选择前k个距离最近的点，通过字典来记录
	class_count = {}
	for i in range(k):
		vote_label = labels[sorted_dist_indices[i]]
		class_count[vote_label] = class_count.get(vote_label, 0) + 1
	# 4 sort labels by frequency by descending order
	
	# 4 按label出现的次数降序排序
	sorted_class_count = sorted(class_count.items(),
								## use operator.itemgetter(1) to sort 
								## by the second element,
								## which is the value in the dictionary

								## 使用operator.itemgetter(1)对第二个元素排序，
								## 也就是字典中的value
								key=operator.itemgetter(1),
								## sort by descending order
								
								##降序排序
								reverse=True)
	## return the label with the most appearence
	
	## 返回出现次数最多的标签，可能是标量，字符串等等
	return sorted_class_count[0][0]

3. 将txt文件内数据转换为Numpy格式数据的函数

同理，以下所有小节代码都在kNN.py 文件中输入，不再赘述

与书中代码不同的说明：因为之后要打开的两个文件的标签不一致，一个是数字标签，一个是文字标签，故与书中稍有不同。

参数说明（一个）：

  • filename: 是一个string。要打开的文件的全名，需要包含后缀名，例如: ‘datingTestSet2.txt’ 。该文件需要与kNN.py放在同一目录，才能在默认情况下打开。
def file2matrix(filename):
	fr = open(filename)
	# return a list which includes all lines
	
	# 返回列表，元素是每行的数据
	array_of_lines = fr.readlines()
	number_of_lines = len(array_of_lines)
	# create a matrix having "number_of_lines" rows and 3 columns,
	# whose elements are all zeros.
	
	# 创建一个元素全为0，"number_of_lines" 行，3列的矩阵
	return_mat = zeros(number_of_lines, 3))
	class_label_vector = []
	index = 0
	# below are some differences comparing codes in books,
	# which are illustrated at begining of this chapter
		
	# 下边的与书中的代码不同, 在本小节的开头有说明
	labels = {'didntLike': 1, 'smallDoses: 2, 'largeDoses': 3}
	for line in array_lines:
		# delete space on two side
		
		# 删除每行两边的空白符
		line = line.strip()
		# delete horizontal tabs between each element
		
		# 删除行内各元素间的横向制表符，并返回list
		list_from_line = line.split('\t')
		# copy the three features of each data
		
		# 复制每个数据的三个所有特征
		return_mat[index, :] = list_from_line[0:3]
 		if list_from_line[-1] not in ['1', '2', '3']:
 			class_label_vector.append(int(labels[list_from_line[-1]]))
 		else:
 			class_label_vector.append(int(list_from_line[-1]))
 		index += 1
 	# return two values, a numpy matrix with data features and a list with data labels
 	
 	# 返回两个值， 一个是数据特征值的numpy的矩阵，另一个是数据标签的的list
 	return return_mat, class_label_vector

4. 使用Matplotlib创建散点图，对数据有更直观印象

在 kNN.py 中创建 main 函数，输入如下代码：

def main():
	dating_data_mat, dating_labels = file2matrix('datingTestSet2.txt')
	# return a figure instance
	
	# 返回一个图形实例
	fig = plt.figure()
	# divide canvas into 1 row 1 1 column, 
	# draw the picture at the first row first column.
	
	# 将画布分割成1行1列，图像画在从左到右从上到下第1块
	ax = fig.add_subplot(111)
	# draw points by using the second and third features, 
	# whose sizes and colours are depending on its label
	
	# 把第二列和第三列的特征值画到画布上。根据数据的标签，决定点的颜色和大小。
	ax.scatter(dating_data_mat[:, 1], dating_data_mat[:, 2],
			   15.0*array(dating_labels),15*array(dating_labels))
	# show the figure
	
	# 展示图像
	plt.show()


if __name__ == "__main__":
	main()

然后运行kNN.py,得到如下图像：在这里插入图片描述
换成第一列和第二列属性，获得更好的展示效果，把上边代码中ax.scatter() 的方法改写成如下：

ax.scatter(dating_data_mat[:, 0], dating_data_mat[:, 1],
           15.0*array(dating_labels), 15.0*array(dating_labels))

保存后运行kNN.py，得到如下图：[

5. 归一化特征值函数

参数说明（一个）：

  • data_set: 是个matrix。需要进行归一处理的特征值矩阵。
def auto_norm(data_set):
	# min value in each column
	
	# 取得每列的最小值，返回一个向量
	min_values = data_set.min(0)
	# max value in each column
	
	# 同理，取得每列的最大值。
	max_values = data_set.max(0)
	ranges = max_values - min_values
	# number of rows
	
	# 返回行数
	m = data_set.shape[0]
	norm_data_set = data_set - tile(min_values, (m, 1))
	norm_data_set = norm_data_set/tile(ranges, (m, 1))
	# return three values, normalised data set matrix, ranges vector
	# and minimal values vector
	
	# 返回三个值，归一化处理后的数据集矩阵，各特征范围向量，各特征最小值向量
	return norm_data_set, ranges, min_values

6. 测试函数

参数说明：无

def dating_class_test():
	# test percentage
	
	# 测试集占的比例
	ho_ratio = 0.10
	# get data features matix and labels vector
	
	# 获得数据特征值矩阵和标签向量
	dating_data_mat, dating_labels = file2matrix('datingTestSet.txt')
	# normalise data set features
	
	# 归一化特征值
	norm-mat, ranges, min_values = auto_norm(dating_data_mat)
	# get number of rows(datas) in data set
	
	# 获得数据集的行数（数据的个数）
	m = norm_mat.shape[0]
	# test data numbers
	
	# 测试数据的数量
	num_test_vectors = int(m*ho_ratio)
	# error rate
	
	# 错误率
	error_count = 0.0
	# input each test data to get result, record error rate
		
	# 输入每个测试数据并获得结果, 记录错误率
	for i in range(num_test_vectors):
		classifier_result = classify0(norm_mat[i, :],
									  norm_mat[num_test_vector:m, :],
									  dating_labels[num_test_vectors:m],
									  3)
		print("the classifier came back with: %d, the real answer is: %d"
		 	  % (classifier_result, dating_labels[i]))
		if classifier_result != dating_labels[i]:
			error_count += 1.0
	print("the total error rate is: %f" % (error_count/float(num_test_vectors)))

运行kNN.py 文件,在 Shell 输入以下代码：

>>> dating_class_test()

按回车，得到结果如下输出结果：

the classifier came back with: 3, the real answer is: 3
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 3, the real answer is: 2
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 3, the real answer is: 1
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 3, the real answer is: 1
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 2, the real answer is: 3
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 3, the real answer is: 1
the total error rate is: 0.050000
>>>

可知错误率为5%

7. 使用算法，约会网站预测函数，构建完整可用系统

参数说明：无

def classify_person():
	result_list = ['not at all', 'in small doses', 'in large doses']
	percent_tats = float(input("percentage of time spent playing video game?"))
	ff_miles = float(input("frequent flier miles earned per year?"))
	ice_cream = float(input("liters of ice cream consumed per year?"))
	dating_data_mat, dating_labels = file2matrix('datingTestSet2.txt')
	norm_mat, ranges, min_values = auto_norm(dating_data_mat)
	# create input numpy array
	
	# 创建输入numpy向量
	in_arr = array([ff_miles, percent_tats, ice_cream])
	classifier_result = classify0((in_arr-min_values)/ranges,
								  norm_mat, dating_labels, 3)
	print("You will probably like this person: ",
		  result_list[classifier_result - 1])

运行kNN.py 文件，输入以下代码：

>>> classify_person()

并按回车，会出现问题，并按着如下示例数据依次输入，获得最后的预测结果 “in small doses”

percentage of time spent playing video games?10
frequent flier miles earned per year?10000
liters of ice cream consumed per year?0.5
You will probably like this person:  in small doses
>>>
Honghao Wang
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