推荐算法之矩阵分解实例

2024-06-23 22:08
文章标签 矩阵 实例 算法 分解 推荐

本文主要是介绍推荐算法之矩阵分解实例，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

矩阵分解的数据利用的上篇文章的数据，协同过滤

用到的知识

python的surprise
k折交叉验证
SVD
SVDpp
NMF

算法与结果可视化

# 可以使用上面提到的各种推荐系统算法
from surprise import SVD,SVDpp,NMF
from surprise import Dataset
from surprise import  print_perf
import os
from surprise import Reader, Dataset
from surprise.model_selection import cross_validate
from pandas import DataFrame 
import numpy as np
import pandas as pd##################SVD_noBiased 
## 指定文件路径
file_path = os.path.expanduser('./python_data.txt')
## 指定文件格式\n",
reader = Reader(line_format='user item rating timestamp', sep=',')
## 从文件读取数据
data = Dataset.load_from_file(file_path, reader=reader)# 在数据集上测试一下效果
#perf = evaluate(algo, data, measures=['RMSE', 'MAE'])
# Run 5-fold cross-validation and print results.
perf1 = cross_validate(SVD(n_factors=1,biased=False),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf2 = cross_validate(SVD(n_factors=3,biased=False),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf3 = cross_validate(SVD(n_factors=5,biased=False),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf4 = cross_validate(SVD(n_factors=7,biased=False),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf5 = cross_validate(SVD(n_factors=9,biased=False),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf6 = cross_validate(SVD(n_factors=11,biased=False),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf7 = cross_validate(SVD(n_factors=13,biased=False),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf8 = cross_validate(SVD(n_factors=15,biased=False),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf9 = cross_validate(SVD(n_factors=17,biased=False),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf10= cross_validate(SVD(n_factors=19,biased=False),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf11= cross_validate(SVD(n_factors=21,biased=False),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf12= cross_validate(SVD(n_factors=23,biased=False),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf13= cross_validate(SVD(n_factors=25,biased=False),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf14= cross_validate(SVD(n_factors=27,biased=False),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf15= cross_validate(SVD(n_factors=29,biased=False),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)perf_result=[]
for i in range(1,16):perf_result.append('perf'+ str(i)) 
MAE=[]
for perf in perf_result:MAE.append(round(np.mean(DataFrame(eval(perf)).iloc[:,1]),4))RMSE=[]
for perf in perf_result:RMSE.append(round(np.mean(DataFrame(eval(perf)).iloc[:,2]),4))FIT_TIME=[]
for perf in perf_result:FIT_TIME.append(round(np.mean(DataFrame(eval(perf)).iloc[:,0]),4))TEST_TIME=[]
for perf in perf_result:TEST_TIME.append(round(np.mean(DataFrame(eval(perf)).iloc[:,3]),4))MAE = DataFrame(MAE,columns=['MAE'])
RMSE = DataFrame(RMSE,columns=['RMSE'])
FIT_TIME = DataFrame(FIT_TIME,columns=['FIT_TIME'])
TEST_TIME = DataFrame(TEST_TIME,columns=['TEST_TIME'])
Factors = DataFrame([1,3,5,7,9,11,13,15,17,19,21,23,25,27,29],columns=['Factors'])SVD_noBaised_result = pd.concat([Factors,MAE,RMSE,FIT_TIME,TEST_TIME],axis=1)
SVD_noBaised_result.to_csv('./result_data/SVD_noBaised_result.csv',header=True,encoding='utf-8')
##################SVD_biased
# Run 5-fold cross-validation and print results.
perf01 = cross_validate(SVD(n_factors=1,biased=True),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf02 = cross_validate(SVD(n_factors=3,biased=True),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf03 = cross_validate(SVD(n_factors=5,biased=True),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf04 = cross_validate(SVD(n_factors=7,biased=True),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf05 = cross_validate(SVD(n_factors=9,biased=True),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf06 = cross_validate(SVD(n_factors=11,biased=True),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf07 = cross_validate(SVD(n_factors=13,biased=True),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf08 = cross_validate(SVD(n_factors=15,biased=True),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf09 = cross_validate(SVD(n_factors=17,biased=True),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf010= cross_validate(SVD(n_factors=19,biased=True),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf011= cross_validate(SVD(n_factors=21,biased=True),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf012= cross_validate(SVD(n_factors=23,biased=True),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf013= cross_validate(SVD(n_factors=25,biased=True),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf014= cross_validate(SVD(n_factors=27,biased=True),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf015= cross_validate(SVD(n_factors=29,biased=True),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)perf_result1=[]
for i in range(1,16):perf_result1.append('perf0'+ str(i)) 
MAE=[]
for perf in perf_result1:MAE.append(round(np.mean(DataFrame(eval(perf)).iloc[:,1]),4))RMSE=[]
for perf in perf_result1:RMSE.append(round(np.mean(DataFrame(eval(perf)).iloc[:,2]),4))FIT_TIME=[]
for perf in perf_result1:FIT_TIME.append(round(np.mean(DataFrame(eval(perf)).iloc[:,0]),4))TEST_TIME=[]
for perf in perf_result1:TEST_TIME.append(round(np.mean(DataFrame(eval(perf)).iloc[:,3]),4))MAE = DataFrame(MAE,columns=['MAE'])
RMSE = DataFrame(RMSE,columns=['RMSE'])
FIT_TIME = DataFrame(FIT_TIME,columns=['FIT_TIME'])
TEST_TIME = DataFrame(TEST_TIME,columns=['TEST_TIME'])
Factors = DataFrame([1,3,5,7,9,11,13,15,17,19,21,23,25,27,29],columns=['Factors'])SVD_baised_result = pd.concat([Factors,MAE,RMSE,FIT_TIME,TEST_TIME],axis=1)
SVD_baised_result.to_csv('./result_data/SVD_baised_result.csv',header=True,encoding='utf-8')##############SVD++# Run 5-fold cross-validation and print results.
perf001 = cross_validate(SVDpp(n_factors=1),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf002 = cross_validate(SVDpp(n_factors=3),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf003 = cross_validate(SVDpp(n_factors=5),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf004 = cross_validate(SVDpp(n_factors=7),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf005 = cross_validate(SVDpp(n_factors=9),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf006 = cross_validate(SVDpp(n_factors=11),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf007 = cross_validate(SVDpp(n_factors=13),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf008 = cross_validate(SVDpp(n_factors=15),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf009 = cross_validate(SVDpp(n_factors=17),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf0010= cross_validate(SVDpp(n_factors=19),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf0011= cross_validate(SVDpp(n_factors=21),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf0012= cross_validate(SVDpp(n_factors=23),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf0013= cross_validate(SVDpp(n_factors=25),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf0014= cross_validate(SVDpp(n_factors=27),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf0015= cross_validate(SVDpp(n_factors=29),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)perf_result2=[]
for i in range(1,16):perf_result2.append('perf00'+ str(i)) 
MAE=[]
for perf in perf_result2:MAE.append(round(np.mean(DataFrame(eval(perf)).iloc[:,1]),4))RMSE=[]
for perf in perf_result2:RMSE.append(round(np.mean(DataFrame(eval(perf)).iloc[:,2]),4))FIT_TIME=[]
for perf in perf_result2:FIT_TIME.append(round(np.mean(DataFrame(eval(perf)).iloc[:,0]),4))TEST_TIME=[]
for perf in perf_result2:TEST_TIME.append(round(np.mean(DataFrame(eval(perf)).iloc[:,3]),4))MAE = DataFrame(MAE,columns=['MAE'])
RMSE = DataFrame(RMSE,columns=['RMSE'])
FIT_TIME = DataFrame(FIT_TIME,columns=['FIT_TIME'])
TEST_TIME = DataFrame(TEST_TIME,columns=['TEST_TIME'])
Factors = DataFrame([1,3,5,7,9,11,13,15,17,19,21,23,25,27,29],columns=['Factors'])SVDpp_result = pd.concat([Factors,MAE,RMSE,FIT_TIME,TEST_TIME],axis=1)
SVDpp_result.to_csv('./result_data/SVDpp_result.csv',header=True,encoding='utf-8')######## NMF# Run 5-fold cross-validation and print results.
perf0001 = cross_validate(NMF(n_factors=1,biased=False),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf0002 = cross_validate(NMF(n_factors=3,biased=False),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf0003 = cross_validate(NMF(n_factors=5,biased=False),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf0004 = cross_validate(NMF(n_factors=7,biased=False),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf0005 = cross_validate(NMF(n_factors=9,biased=False),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf0006 = cross_validate(NMF(n_factors=11,biased=False),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf0007 = cross_validate(NMF(n_factors=13,biased=False),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf0008 = cross_validate(NMF(n_factors=15,biased=False),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf0009 = cross_validate(NMF(n_factors=17,biased=False),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf00010= cross_validate(NMF(n_factors=19,biased=False),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf00011= cross_validate(NMF(n_factors=21,biased=False),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf00012= cross_validate(NMF(n_factors=23,biased=False),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf00013= cross_validate(NMF(n_factors=25,biased=False),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf00014= cross_validate(NMF(n_factors=27,biased=False),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
perf00015= cross_validate(NMF(n_factors=29,biased=False),data, measures=['RMSE', 'MAE'], cv=5, verbose=True)perf_result3=[]
for i in range(1,16):perf_result3.append('perf000'+ str(i)) 
MAE=[]
for perf in perf_result3:MAE.append(round(np.mean(DataFrame(eval(perf)).iloc[:,1]),4))RMSE=[]
for perf in perf_result3:RMSE.append(round(np.mean(DataFrame(eval(perf)).iloc[:,2]),4))FIT_TIME=[]
for perf in perf_result3:FIT_TIME.append(round(np.mean(DataFrame(eval(perf)).iloc[:,0]),4))TEST_TIME=[]
for perf in perf_result3:TEST_TIME.append(round(np.mean(DataFrame(eval(perf)).iloc[:,3]),4))MAE = DataFrame(MAE,columns=['MAE'])
RMSE = DataFrame(RMSE,columns=['RMSE'])
FIT_TIME = DataFrame(FIT_TIME,columns=['FIT_TIME'])
TEST_TIME = DataFrame(TEST_TIME,columns=['TEST_TIME'])
Factors = DataFrame([1,3,5,7,9,11,13,15,17,19,21,23,25,27,29],columns=['Factors'])NMF_result = pd.concat([Factors,MAE,RMSE,FIT_TIME,TEST_TIME],axis=1)
NMF_result.to_csv('./result_data/NMF_result.csv',header=True,encoding='utf-8')
#########################################################################################################################

####################################对SVD的可视化SVD_noBaised_result <- read.csv('SVD_noBaised_result1.csv',encoding = 'utf-8')
SVD_baised_result <- read.csv('SVD_baised_result1.csv',encoding = 'utf-8')
SVDpp_result <- read.csv('SVDpp_result1.csv',encoding = 'utf-8')
NMF_result <- read.csv('NMF_result1.csv',encoding = 'utf-8')SVD_noBiased_result <- as.data.table(SVD_noBaised_result)
SVD_biased_result <- as.data.table(SVD_baised_result)
SVDpp_result <- as.data.table(SVDpp_result)
NMF_result <- as.data.table(NMF_result)SVD_noBiased_result <- SVD_noBiased_result[,SVD_class:='SVD_noBiased']
SVD_biased_result <- SVD_biased_result[,SVD_class:='SVD_biased']
SVDpp_result <- SVDpp_result[,SVD_class:='SVD++']
NMF_result <- NMF_result[,SVD_class:='NMF']merge_SVD_result <- rbind(SVD_noBiased_result,SVD_biased_result,SVDpp_result,NMF_result)
merge_SVD_result <- merge_SVD_result[,-1]# plot resultcolour <- c('#34495e','#3498db','#2ecc71','#f1c40f','#e74c3c','#9b59b6','#1abc9c')
mycol <- define_palette(swatch = colour,gradient = c(lower=colour[1L],upper=colour[2L]))
ggthemr(mycol)p01 <- ggplot(data= merge_SVD_result, aes(x=Factors, y= MAE,group=SVD_class, shape=SVD_class, color=SVD_class)) +geom_line()+geom_point()+theme(legend.position="top",axis.text.x = element_text(angle = 50, hjust = 0.5, vjust = 0.5),text = element_text(color = "black", size = 12))p02 <- ggplot(data= merge_SVD_result, aes(x=Factors, y= RMSE,group=SVD_class, shape=SVD_class, color=SVD_class)) +geom_line()+geom_point()+theme(legend.position="top",axis.text.x = element_text(angle = 50, hjust = 0.5, vjust = 0.5),text = element_text(color = "black", size = 12))p1 <- ggplot(data= merge_SVD_result, aes(x=Factors, y= FIT_TIME,group=SVD_class, shape=SVD_class, color=SVD_class)) +geom_line()+geom_point()+theme(legend.position="top",axis.text.x = element_text(angle = 50, hjust = 0.5, vjust = 0.5),text = element_text(color = "black", size = 12))p2 <- ggplot(data= merge_SVD_result, aes(x=Factors, y= TEST_TIME,group=SVD_class, shape=SVD_class, color=SVD_class)) +geom_line()+geom_point()+theme(legend.position="top",axis.text.x = element_text(angle = 50, hjust = 0.5, vjust = 0.5),text = element_text(color = "black", size = 12))p3 <- ggplot(data= filter(merge_SVD_result,SVD_class!='SVD++'), aes(x=Factors, y= FIT_TIME,group=SVD_class, shape=SVD_class, color=SVD_class)) +geom_line()+geom_point()+theme(legend.position="none",axis.text.x = element_text(angle = 50, hjust = 0.5, vjust = 0.5),text = element_text(color = "black", size = 12))p4 <- ggplot(data= filter(merge_SVD_result,SVD_class!='SVD++'), aes(x=Factors, y= TEST_TIME,group=SVD_class, shape=SVD_class, color=SVD_class)) +geom_line()+geom_point()+theme(legend.position="none",axis.text.x = element_text(angle = 50, hjust = 0.5, vjust = 0.5),text = element_text(color = "black", size = 12))x11()
ggplot2.multiplot(p01,p02,cols = 2)
x11()
ggplot2.multiplot(p1,p2,p3,p4,cols = 2)

这里写图片描述

这里写图片描述

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在一台机器上一个MySQL服务器运行多个MySQL实例有什么好处？这里我先入为主给大家介绍这样做至少存在两个好处（看完这篇文章后理解会更透彻）： （1）减轻服务器链接负担 （2）为不同的用户提供不同的mysqld服务器的访问权限以方便这些用户进行自我管理。   下面我介绍具体的实现过程： 一、准备工作     台式机一台、Windows系统、MySQL服务器（我安装的版本是MySQL
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Python分解多重列表对象，isinstance实现

Python分解多重列表对象，isinstance实现

“”“待打印的字符串列表:['ft','bt',['ad',['bm','dz','rc'],'mzd']]分析可知，该列表内既有字符对象，又有列表对象（Python允许列表对象不一致）现将所有字符依次打印并组成新的列表”“”a=['ft','bt',['ad',['bm','dz','rc'],'mzd']]x=[]def func(y):for i in y:if isinst
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