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predict_validation.Rmd
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---
title: "predict_validation"
output: html_document
---
```{r,echo=FALSE, results='hide',message=FALSE,warning=FALSE}
###loading libraries
suppressWarnings(library(dplyr))
suppressWarnings(library(purrr))
```
```{r,echo=FALSE, results='hide',message=FALSE,warning=FALSE}
###loading dataset
load("paintings_train.Rdata")
train <- paintings_train
```
```{r,echo=FALSE, results='hide',message=FALSE,warning=FALSE}
#recode
train <- train %>%
mutate(shape_recode = ifelse(Shape == "", "Not Available",
ifelse(Shape == "ovale", "oval",
ifelse(Shape == "ronde", "round",
ifelse(Shape == "octogon", "octagon", Shape)))))
train <- train %>%
mutate(mat_recode = ifelse(mat %in% c("a", "bc", "c","br"), "metal",
ifelse(mat %in% c("al", "ar", "m"), "stone",
ifelse(mat %in% c("co", "bt", "t","h","ta"), "canvas",
ifelse(mat %in% c("p", "ca"), "paper",
ifelse(mat %in% c("b"), "wood",
ifelse(mat %in% c("o", "e","v","mi","pa","g"), "other",
ifelse(mat %in% c("n/a", ""), "uncertain", NA))))))))
train <- train%>%
mutate(fig_mention = ifelse(nfigures ==0, "no figures", "figures"))
train <- train%>%
mutate(artist_living_notliving = ifelse(artistliving==0, "not living", "living"))
train <- train%>%
mutate(history_nohistory = ifelse(history==0, "no history", "history"))
train <- train%>%
mutate(mytho_nomytho = ifelse(mytho==0, "no mytho", "mytho"))
train <- train%>%
mutate(finished_nofinished = ifelse(finished==0, "no finished", "finished"))
train <- train%>%
mutate(LF = ifelse(lrgfont==0, "no LF", "LF"))
#change price into numeric
train$price <- as.numeric(gsub(",","",train$price))
#create a new variable "famous_author"
author_price <- train %>%
group_by(authorstandard) %>%
summarise(mean_price = mean(price)) %>%
ungroup() %>%
arrange(desc(mean_price))
train <- train %>%
mutate(famous_author = ifelse(authorstandard %in%
author_price$authorstandard[which(author_price$mean_price >= 3000)],1,0))
#Another variable "dealer_author"
train$dealer_author <-0
train <- within(train, dealer_author[dealer == 'R' & origin_author == 'D/FL'] <- 1)
train <- within(train, dealer_author[dealer == 'R' & origin_author == 'S'] <- 1)
#Another variable "dealer_school_pntg"
train$dealer_school <-0
train <- within(train, dealer_school[dealer == 'R' & school_pntg == 'D/FL'] <- 1)
train <- within(train, dealer_school[dealer == 'R' & school_pntg == 'S'] <- 1)
#Another variable "endbuyer_paired"
train$endbuyer_paired <-0
train <- within(train, endbuyer_paired[endbuyer == 'B' & paired == 0] <- 1)
train <- within(train, endbuyer_paired[endbuyer == 'C' & paired == 0] <- 1)
#Another variable "endbuyer_history"
train$endbuyer_history <-0
train <- within(train, endbuyer_history[endbuyer == 'C' & history == 1] <- 1)
#remove completely descriptive (thus irrelevant) variables
newTrain <- train[,!names(train) %in% c("sale","lot","position","logprice","subject",
"authorstyle","authorstandard","author",
"winningbidder",
"Interm","Height_in","Width_in","Surface_Rect",
"Diam_in","Surface_Rnd","material","mat",
"lands_sc","lands_elem","lands_figs","lands_ment")]
#change all character strings to factors
character_vars <- lapply(newTrain, class) == "character"
newTrain[, character_vars] <- lapply(newTrain[, character_vars], as.factor)
#change the level of factor
levels(newTrain$type_intermed)[levels(newTrain$type_intermed)==""] <- "NONE"
levels(newTrain$winningbiddertype)[levels(newTrain$winningbiddertype)==""] <- "NONE"
levels(newTrain$endbuyer)[levels(newTrain$endbuyer)==""] <- "NONE"
levels(newTrain$materialCat)[levels(newTrain$materialCat)==""] <- "NONE"
levels(newTrain$school_pntg)[levels(newTrain$school_pntg)=="S"] <- "OTHER"
levels(newTrain$winningbiddertype)[levels(newTrain$winningbiddertype)=="EBC"] <- "OTHER"
#simple method to predict the NA in surface
newTrain$Surface[which(is.na(newTrain$Surface)==TRUE)] <- mean(newTrain$Surface, na.rm = TRUE)
```
```{r,echo=FALSE, results='hide',message=FALSE,warning=FALSE}
###testing dataset
load("paintings_test.Rdata")
test <- paintings_test
#recode
test <- test %>%
mutate(mat_recode = ifelse(mat %in% c("a", "bc", "c","br"), "metal",
ifelse(mat %in% c("al", "ar", "m"), "stone",
ifelse(mat %in% c("co", "bt", "t","h","ta"), "canvas",
ifelse(mat %in% c("p", "ca"), "paper",
ifelse(mat %in% c("b"), "wood",
ifelse(mat %in% c("o", "e","v","mi","pa","g"), "other",
ifelse(mat %in% c("n/a", ""), "uncertain", NA))))))))
test <- test%>%
mutate(fig_mention = ifelse(nfigures ==0, "no figures", "figures"))
test <- test%>%
mutate(artist_living_notliving = ifelse(artistliving==0, "not living", "living"))
test <- test%>%
mutate(history_nohistory = ifelse(history==0, "no history", "history"))
test <- test%>%
mutate(mytho_nomytho = ifelse(mytho==0, "no mytho", "mytho"))
test <- test%>%
mutate(finished_nofinished = ifelse(finished==0, "no finished", "finished"))
test <- test%>%
mutate(LF = ifelse(lrgfont==0, "no LF", "LF"))
#change price into numeric
test$price <- as.numeric(gsub(",","",test$price))
#create a new variable "famous_author"
author_price <- train %>%
group_by(authorstandard) %>%
summarise(mean_price = mean(price)) %>%
ungroup() %>%
arrange(desc(mean_price))
test <- test %>%
mutate(famous_author = ifelse(authorstandard %in%
author_price$authorstandard[which(author_price$mean_price >= 3000)],1,0))
#Another variable "dealer_author"
test$dealer_author <-0
test <- within(test, dealer_author[dealer == 'R' & school_pntg == 'D/FL'] <- 1)
test <- within(test, dealer_author[dealer == 'R' & school_pntg == 'S'] <- 1)
#Another variable "dealer_school_pntg"
test$dealer_school <-0
test <- within(test, dealer_school[dealer == 'R' & origin_author == 'D/FL'] <- 1)
test <- within(test, dealer_school[dealer == 'R' & origin_author == 'S'] <- 1)
#Another variable "endbuyer_paired"
test$endbuyer_paired <-0
test <- within(test, endbuyer_paired[endbuyer == 'B' & paired == 0] <- 1)
test <- within(test, endbuyer_paired[endbuyer == 'C' & paired == 0] <- 1)
#Another variable "endbuyer_paired"
test$endbuyer_history <-0
test <- within(test, endbuyer_history[endbuyer == 'C' & history == 1] <- 1)
#Professor's recoding
test <- test %>%
mutate(shape_recode = ifelse(Shape == "", "Not Available",
ifelse(Shape == "ovale", "oval",
ifelse(Shape == "ronde", "round",
ifelse(Shape == "octogon", "octagon", Shape)))))
#remove descriptive variables
newTest <- test[,!names(test) %in% c("sale","lot","position","logprice","subject",
"authorstyle","authorstandard","author",
"winningbidder",
"Interm","Height_in","Width_in","Surface_Rect",
"Diam_in","Surface_Rnd","material","mat",
"lands_sc","lands_elem","lands_figs","lands_ment")]
#change all character strings to factors
character_vars <- lapply(newTest, class) == "character"
newTest[, character_vars] <- lapply(newTest[, character_vars], as.factor)
#change the level of factors
levels(newTest$type_intermed)[levels(newTest$type_intermed)==""] <- "NONE"
levels(newTest$winningbiddertype)[levels(newTest$winningbiddertype)==""] <- "NONE"
levels(newTest$endbuyer)[levels(newTest$endbuyer)==""] <- "NONE"
levels(newTest$materialCat)[levels(newTest$materialCat)==""] <- "NONE"
levels(newTest$school_pntg)[levels(newTest$school_pntg)=="A"] <- "OTHER"
levels(newTest$school_pntg)[levels(newTest$school_pntg)=="G"] <- "OTHER"
levels(newTest$winningbiddertype)[levels(newTest$winningbiddertype)=="EBC"] <- "OTHER"
levels(newTest$winningbiddertype)[levels(newTest$winningbiddertype)=="BB"] <- "OTHER"
levels(newTest$winningbiddertype)[levels(newTest$winningbiddertype)=="ED"] <- "OTHER"
#simple method to predict the NA in surface
newTest$Surface[which(is.na(newTest$Surface)==TRUE)] <- mean(newTest$Surface, na.rm = TRUE)
```
```{r}
combined <- rbind(newTrain,newTest)
```
```{r}
load("paintings_validation.Rdata")
valid <- paintings_validation
#recode
valid <- valid %>%
mutate(mat_recode = ifelse(mat %in% c("a", "bc", "c","br"), "metal",
ifelse(mat %in% c("al", "ar", "m"), "stone",
ifelse(mat %in% c("co", "bt", "t","h","ta"), "canvas",
ifelse(mat %in% c("p", "ca"), "paper",
ifelse(mat %in% c("b"), "wood",
ifelse(mat %in% c("o", "e","v","mi","pa","g"), "other",
ifelse(mat %in% c("n/a", ""), "uncertain", NA))))))))
valid <- valid%>%
mutate(fig_mention = ifelse(nfigures ==0, "no figures", "figures"))
valid <- valid%>%
mutate(artist_living_notliving = ifelse(artistliving==0, "not living", "living"))
valid <- valid%>%
mutate(history_nohistory = ifelse(history==0, "no history", "history"))
valid <- valid%>%
mutate(mytho_nomytho = ifelse(mytho==0, "no mytho", "mytho"))
valid <- valid%>%
mutate(finished_nofinished = ifelse(finished==0, "no finished", "finished"))
valid <- valid%>%
mutate(LF = ifelse(lrgfont==0, "no LF", "LF"))
#change price into numeric
valid$price <- as.numeric(gsub(",","",valid$price))
prelim_combined <- rbind(train,test)
#create a new variable "famous_author"
author_price <- prelim_combined %>%
group_by(authorstandard) %>%
summarise(mean_price = mean(price)) %>%
ungroup() %>%
arrange(desc(mean_price))
valid <- valid %>%
mutate(famous_author = ifelse(authorstandard %in%
author_price$authorstandard[which(author_price$mean_price >= 3000)],1,0))
#Another variable "dealer_author"
valid$dealer_author <-0
valid <- within(valid, dealer_author[dealer == 'R' & school_pntg == 'D/FL'] <- 1)
valid <- within(valid, dealer_author[dealer == 'R' & school_pntg == 'S'] <- 1)
#Another variable "dealer_school_pntg"
valid$dealer_school <-0
valid <- within(valid, dealer_school[dealer == 'R' & origin_author == 'D/FL'] <- 1)
valid <- within(valid, dealer_school[dealer == 'R' & origin_author == 'S'] <- 1)
#Another variable "endbuyer_paired"
valid$endbuyer_paired <-0
valid <- within(valid, endbuyer_paired[endbuyer == 'B' & paired == 0] <- 1)
valid <- within(valid, endbuyer_paired[endbuyer == 'C' & paired == 0] <- 1)
#Another variable "endbuyer_paired"
valid$endbuyer_history <-0
valid <- within(valid, endbuyer_history[endbuyer == 'C' & history == 1] <- 1)
#Professor's recoding
valid <- valid %>%
mutate(shape_recode = ifelse(Shape == "", "Not Available",
ifelse(Shape == "ovale", "oval",
ifelse(Shape == "ronde", "round",
ifelse(Shape == "octogon", "octagon", Shape)))))
#remove descriptive variables
newValid <- valid[,!names(valid) %in% c("sale","lot","position","logprice","subject",
"authorstyle","authorstandard","author",
"winningbidder",
"Interm","Height_in","Width_in","Surface_Rect",
"Diam_in","Surface_Rnd","material","mat",
"lands_sc","lands_elem","lands_figs","lands_ment")]
#change all character strings to factors
character_vars <- lapply(newValid, class) == "character"
newValid[, character_vars] <- lapply(newValid[, character_vars], as.factor)
#change the level of factors
levels(newValid$type_intermed)[levels(newValid$type_intermed)==""] <- "NONE"
levels(newValid$winningbiddertype)[levels(newValid$winningbiddertype)==""] <- "NONE"
levels(newValid$endbuyer)[levels(newValid$endbuyer)==""] <- "NONE"
levels(newValid$materialCat)[levels(newValid$materialCat)==""] <- "NONE"
levels(newValid$school_pntg)[levels(newValid$school_pntg)=="A"] <- "OTHER"
levels(newValid$school_pntg)[levels(newValid$school_pntg)=="G"] <- "OTHER"
levels(newValid$school_pntg)[levels(newValid$school_pntg)=="S"] <- "OTHER"
levels(newValid$winningbiddertype)[levels(newValid$winningbiddertype)=="EBC"] <- "OTHER"
levels(newValid$winningbiddertype)[levels(newValid$winningbiddertype)=="BB"] <- "OTHER"
levels(newValid$winningbiddertype)[levels(newValid$winningbiddertype)=="ED"] <- "OTHER"
levels(newValid$mat_recode)[levels(newValid$mat_recode)=="stone"] <- "other"
#simple method to predict the NA in surface
newValid$Surface[which(is.na(newValid$Surface)==TRUE)] <- mean(newValid$Surface, na.rm = TRUE)
```
```{r}
m <- lm(formula = log(price) ~ dealer + year + school_pntg + diff_origin +
artistliving + winningbiddertype + Surface + engraved +
prevcoll + paired + finished + lrgfont + othgenre +
discauth + winningbiddertype:prevcoll +
Surface:mat_recode + famous_author:Surface, data = combined)
predictions = as.data.frame(
exp(predict(m, newdata=newValid,
interval = "pred")))
save(predictions, file="predict-validation.Rdata")
```