python呼叫R語言,關聯規則視覺化
阿新 • • 發佈:2018-12-10
首先當然要配置r語言環境變數什麼的
D:\R-3.5.1\bin\x64;
D:\R-3.5.1\bin\x64\R.dll;
D:\R-3.5.1;
D:\ProgramData\Anaconda3\Lib\site-packages\rpy2;
本來用python也可以實現關聯規則,雖然沒包,但是視覺化挺麻煩的
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還要下載兩個R的包
import rpy2.robjects as robjects b=(''' install.packages("arules") install.packages("arulesViz") ''') robjects.r(b)
然後就是主程式碼了
import rpy2.robjects as robjects a=('''Encoding("UTF-8") setwd("F:/goverment/Aprior") all_data<-read.csv("F:/goverment/Aprior/NewData.csv",header = T,#將資料轉化為因子型 colClasses=c("factor","factor","factor","factor","factor","factor","factor","factor","factor","factor","factor","factor")) library(arules) rule=apriori(data=all_data[,c(1,4,5,6,7,8,9,10,12)], parameter = list(support=0.05,confidence=0.7,minlen=2,maxlen=10)) ''') robjects.r(a) robjects.r(''' rule.subset<-subset(rule,lift>1) #inspect(rule.subset) rules.sorted<-sort(rule.subset,by="lift") subset.matrix<-is.subset(rules.sorted,rules.sorted) lower.tri(subset.matrix,diag=T) subset.matrix[lower.tri(subset.matrix,diag = T)]<-NA redundant<-colSums(subset.matrix,na.rm = T)>=1 #這五條就是去冗餘(感興趣可以去網上搜),我雖然這裡寫了,但我沒有去冗餘,我的去了以後一個規則都沒了 which(redundant) rules.pruned<-rules.sorted[!redundant] #inspect(rules.pruned) #輸出去冗餘後的規則 ''') c=(''' library(arulesViz)#掉包 jpeg(file="plot1.jpg") #inspect(rule.subset) plt<-plot(rule.subset,shading = "lift")#畫散點圖 dev.off() subrules<-head(sort(rule.subset,by="lift"),50) #jpeg(file="plot2.jpg") plot(subrules,method = "graph")#畫圖 #dev.off() rule.sorted <- sort(rule.subset, decreasing=TRUE, by="lift") #按提升度排序 rules.write<-as(rule.sorted,"data.frame") #將規則轉化為data型別 write.csv(rules.write,"F:/goverment/Aprior/NewRules.csv",fileEncoding="UTF-8") ''') robjects.r(c) #取出儲存的規則,放到一個列表中 from pandas import read_csv data_set = read_csv("F:/goverment/Aprior/NewRules.csv") data = data_set.values[:, :] rul = [] for line in data: ls = [] for j in line: try : j=float(j) if j>0 and j<=1: j=str(round(j*100,2))+"%" ls.append(j) else: ls.append(round(j,2)) except: ls.append(j) rul.append(ls) for line in rul: print(line)