library(lubridate) #날짜 연산 package library(kernlab) library(e1071) library(xgboost) setwd("c:/AI/public_data") submission <- read.csv("sample_submission.csv",header=TRUE) tr <- read.csv("train.csv") tr <- tr[,-2] tr$date <- as.Date(tr$date)

#nmae측정 함수 nm <- function(prediction, answer){ nmae <- abs((prediction-answer)/answer) out <- c() #Inf인 값들의 index for(i in 1:38){ if(nmae[i]==Inf) { out <- c(out, i) } } nmae <- nmae[-c(out)] score <- rowMeans(nmae) return(score) }

######train데이터 준비###### #n=1~21-> 품목 개수 (1주뒤 예측) 모델 return xgB <- function(n,wk){ #train할 날짜 date <- c(seq(as.Date('2017/09/28','%Y/%m/%d'), as.Date('2017/12/01','%Y/%m/%d'),1), seq(as.Date('2018/09/28','%Y/%m/%d'), as.Date('2018/12/01','%Y/%m/%d'),1), seq(as.Date('2019/09/28','%Y/%m/%d'), as.Date('2019/12/01','%Y/%m/%d'),1)) tr_date <- matrix(rep(0,len=6045),nrow=31,ncol=195) #날짜data포함 tr_date <- as.data.frame(tr_date) colnames(tr_date) <- date #train할 날짜데이터를 가지는 DF->tr_date for(i in 1:195){ a <- c() day <- c() day <- date[i] a <- date[i]+7wk #예측할 label값(wk주뒤) a <- c(a,day-years(1)) #1년전 데이터 a <- c(a, day-seq(56,84,1)) tr_date[,i] <- a } rownames(tr_date) <- c('label','1year',56:84) tr_date <- t(tr_date) tr_date <- as.data.frame(tr_date) tr_price <- matrix(rep(0,len=6045),nrow=195,ncol=31) #가격데이터를 담은 DF tr_price <- as.data.frame(tr_price) #train할 가격데이터를 가지는 DF->tr_price for(i in 1:195){ for(j in 1:31){ tr_price[i,j] <- tr[tr_date[i,j]==tr$date,1+2n] } } subset <- subset(tr,tr[,1+2n]!=0) laplace <- mean(subset[,1+2n]) #원하는 농작물행의 평균값 for(i in 1:195){ for(j in 1:31){ #0인 값들을 각 농작물의 평균으로 대체 if(tr_price[i,j]==0){ tr_price[i,j] <- laplace } } } colnames(tr_price) <-c('label','1year',56:84) rownames(tr_price) <- date

subset1 <- subset(tr_price$label,tr_price$label!=0) laplace1 <- mean(subset1) for(i in 1:length(tr_price$label)){ if(tr_price$label[i]==0){ tr_price$label[i] <- laplace1 } } m <- lm(label ~ ., data = ts_price) model <- stepAIC(m) return(model) }

######test데이터 준비###### library(psych)#산포도와 상관관계, 히스토그램 등을 모두 보여주는 함수 library(MASS) date2 <- seq(as.Date('2020/09/29','%Y/%m/%d'), #test할 데이터의 날짜 as.Date('2020/11/05','%Y/%m/%d'),1) prediction <- function(n,wk){ ts_date <- matrix(rep(0,len=1178),nrow=31,ncol=38) #날짜data포함 ts_date <- as.data.frame(ts_date) colnames(ts_date) <- date2 #train할 날짜데이터를 가지는 DF for(i in 1:38){ b <- c() day <- c() day <- date2[i] b <- date2[i]+7*wk #예측할 label값(wk주뒤) b <- c(b,day-years(1)) #1년 전 데이터 b <- c(b, day-seq(56,84,1)) ts_date[,i] <- b } rownames(ts_date) <- c('label','1year',56:84) ts_date <- t(ts_date)

ts_price <- matrix(rep(0,len=1178),nrow=38,ncol=31) #가격데이터를 담은 DF ts_price <- as.data.frame(ts_price) for(i in 1:38){ for(j in 2:31){ ts_price[i,j] <- tr[ts_date[i,j]==tr$date,1+2n] } } subset <- subset(tr,tr[,1+2n]!=0) laplace <- mean(subset[,1+2*n]) for(i in 1:38){ for(j in 1:31){ #값이 0인 부분을 평균값으로 대체 if(ts_price[i,j]==0){ ts_price[i,j] <- laplace } } }

colnames(ts_price) <-c('label','1year',56:84) rownames(ts_price) <- date2 ts_price test.data <- xgb.DMatrix(data=as.matrix(ts_price[,2:31]), label=ts_price$label) ml <- xgB(n,wk)#xgBoost모델 pred <- predict(ml,test.data) return(pred) } #예측값을 반환해주는 함수

######제출 데이터 생성###### #mx <- matrix(rep(0,len=798),ncol=38,nrow=21) #mx <- as.data.frame(mx) #rownames(mx) <- c("배추","무","양파","건고추","마늘"

,"대파","얼갈이배추","양배추","깻잎"

,"시금치","미나리","당근","파프리카","새송이"

,"팽이버섯","토마토","청상추","백다다기"

,"애호박","캠벨얼리","샤인마스캇")

#colnames(mx) <- date2+7 #for(i in 1:21){

mx[i,] <- prediction(i,1)

#}

for(a in 1:21){ actual <- prediction(a,1) #1주뒤 a번째 농산물의 예측 가격들 for(b in 1:38){ submission[3b-2,a+1] <- actual[b] } actual <- prediction(a,2) #2주뒤 a번째 농산물의 예측 가격들 for(b in 1:38){ submission[3b-1,a+1] <- actual[b] } actual <- prediction(a,3) #2주뒤 a번째 농산물의 예측 가격들 for(b in 1:38){ submission[3*b,a+1] <- actual[b] } } colnames(submission) <-c("예측대상일자","배추_가격(원/kg)","무_가격(원/kg)", "양파_가격(원/kg)","건고추_가격(원/kg)","마늘_가격(원/kg)", "대파_가격(원/kg)","얼갈이배추_가격(원/kg)","양배추_가격(원/kg)", "깻잎_가격(원/kg)","시금치_가격(원/kg)","미나리_가격(원/kg)", "당근_가격(원/kg)","파프리카_가격(원/kg)","새송이_가격(원/kg)", "팽이버섯_가격(원/kg)","토마토_가격(원/kg)","청상추_가격(원/kg)", "백다다기_가격(원/kg)","애호박_가격(원/kg)","캠벨얼리_가격(원/kg)", "샤인마스캇_가격(원/kg)")

write.csv(submission,"Submission.csv") ######NMAE값 돌려보기###### real_date <- date2+7 #1주일 뒤 음식의 실제 가격 real_price <- subset(tr, date %in% real_date) #기간내의 음식들의 실제 가격 NM <- c() #nmae값 담기 for(i in 1:21){ NM <- c(NM,nm(mx[i,],real_price[,2*i+1])) } mean(NM)