seyeon/01 at main · DaconPrediction/seyeon · GitHub

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library(lubridate) #날짜 연산 package
library(psych)#산포도와 상관관계, 히스토그램 등을 모두 보여주는 함수
library(MASS)
library(e1071)
library(kernlab)

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
regression <- function(n){
  #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))
  tr_date <- matrix(rep(0,len=4030),nrow=31,ncol=130) #날짜data포함
  tr_date <- as.data.frame(tr_date)
  colnames(tr_date) <- date
  #train할 날짜데이터를 가지는 DF->tr_date
  for(i in 1:130){
    a <- c()
    day <- c()
    day <- date[i]
    a <- date[i]+7 #예측할 label값(1주뒤)
    a <- c(a,date[i])
    a <- c(a,day-years(1)) #1년전 데이터
    a <- c(a, day-seq(1,28,1))
    tr_date[,i] <- a
  }
  rownames(tr_date) <- c('label','today','1year',1:28)
  tr_date <- t(tr_date)
  tr_date <- as.data.frame(tr_date)
  tr_price <- matrix(rep(0,len=4030),nrow=130,ncol=31) #가격데이터를 담은 DF
  tr_price <- as.data.frame(tr_price)
  #train할 가격데이터를 가지는 DF->tr_price
  for(i in 1:130){
    for(j in 1:31){
      tr_price[i,j] <- tr[tr_date[i,j]==tr$date,1+2*n]
    }
  }
  subset <- subset(tr,tr[,1+2*n]!=0)
  laplace <- mean(subset[,1+2*n])
  for(i in 1:130){
    for(j in 1:31){
      #0을 포함하는 행을 제거하기 위해 행번호 수집
      if(tr_price[i,j]==0){
        tr_price[i,j] <- laplace
      }
    }
  }
  colnames(tr_price) <-c('label','today','1year',1:28)
  rownames(tr_price) <- date


  # tune.svm(label~., data=tr_price, gamma=2^(-1:1), cost=2^(2:4))

  model <- svm(label~., data=tr_price, cost=2, kernel="linear")
  # model <- ksvm(label~., data=tr_price, kernal="stringdot")

  pred <- predict(model,tr_price[,2:31])
  print(cor(pred,tr_price$label))

  plot(tr_price$label)
  points(pred, col="blue")

  # model <- lm(label~., data=tr_price) #다중 선형 회귀 모델 생성
  # m <- stepAIC(model)


  return(model)
}

######test데이터 준비######

date2 <- seq(as.Date('2019/09/28','%Y/%m/%d'), #test할 데이터의 날짜
             as.Date('2019/11/04','%Y/%m/%d'),1)
prediction <- function(n){
  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 #예측할 label값(1주뒤)
    b <- c(b,date2[i])
    b <- c(b,day-years(1)) #1년 전 데이터
    b <- c(b, day-seq(1,28,1))
    ts_date[,i] <- b
  }
  rownames(ts_date) <- c('label','today','1year',1:28)
  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 1:31){
      ts_price[i,j] <- tr[ts_date[i,j]==tr$date,1+2*n]
    }
  }
  subset <- subset(tr,tr[,1+2*n]!=0)
  laplace <- mean(subset[,1+2*n])
  for(i in 1:38){
    for(j in 1:31){
      #0을 포함하는 행을 제거하기 위해 행번호 수집
      if(ts_price[i,j]==0){
        ts_price[i,j] <- laplace
      }
    }
  }

  colnames(ts_price) <-c('label','today','1year',1:28)
  rownames(ts_price) <- date2
  ts_price
  ml <- regression(n)#선형 회귀 모델

  pred <- predict(ml,ts_price[2:31])

  plot(ts_price$label)
  points(pred, col="blue")

  return(pred)
} #예측값을 반환해주는 함수

mx <- matrix(rep(0,len=798),ncol=38,nrow=21)
mx <- as.data.frame(mx)
for(i in 1:21){
  mx[i,] <- prediction(i)
}
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


real_price <- subset(tr, date %in% seq(as.Date('2019/10/05','%Y/%m/%d'),
                                       as.Date('2019/11/11','%Y/%m/%d'),1)) #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)