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load_dmc01.R
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load_dmc01.R
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# Test ------------------------------
#path <- "../Data/DMC01"
#temp <- load_dmc01(path)
# End Test --------------------------
library(data.table)
load_dmc01 <- function(folder){
data_train <- fread(paste0(path,"/dmc2001-train.txt"))
data_class <- fread(paste0(path,"/dmc2001-class.txt"))
y_class <- fread(paste0(path,"/dmc2001-trueclass.txt"))
data_class$AKTIV <- y_class$AKTIV[match(y_class$ID, data_class$ID)]
data <- rbind(data_train, data_class)
# Change target variable coding to 1 (purchase) and 0 (no future purchase)
data$AKTIV <- ifelse(data$AKTIV==1,0,1)
# Recode West/East variable
data[WO=="F", WO := NA]
# factor variables
for (var in c("WO","Regiotyp","Bebautyp", "Strtyp")){
set(data, j=var, value=paste("level",data[[var]],sep="_"))
#set(data, which(is.na(data[[var]])), var, "UNKNOWN")
set(data, j=var, value=factor(data[[var]]))
}
# Merge rare levels for robustness over cross validation
data[Bebautyp=="level_0", Bebautyp:="level_1"]
data[Bebautyp=="level_5", Bebautyp:="level_4"]
data[Strtyp=="level_5", Strtyp:="level_4"]
# Impute missing with median and include missing indicator
# Transform to factor
data[, "missing_habitation":=ifelse(data$Regiotyp == "level_NA", 1,0)]
for (var in c("WO","Regiotyp","Bebautyp", "Strtyp")){
set(data, i=which(data[[var]] == "level_NA"), j=var, value= names(which.max(table(data[[var]]))) )
set(data, j=var, value= factor(data[[var]]))
}
# Length of customer relationship
data[, customer_age := 2001-jahrstart]
data[customer_age>10,customer_age := 0] # Correct for no history
data[, jahrstart := NULL]
# Drop ID
data[, ID := NULL]
# Median imputation for missing values
for (var in colnames(data)[which(sapply(data, is.numeric))]){
temp_median <- median(data[[var]], na.rm=TRUE)
if(!is.integer(temp_median)){
set(data, j=var, value=as.numeric(data[[var]]))
}
set(data, i=which(is.na(data[[var]])), j=var, value=median(data[[var]], na.rm=TRUE))
}
# Drop due to inconsistent scaling between regions
data[, Kaufkraft := NULL]
# Rename target variable
setnames(data, "AKTIV", "y")
# Normalize non-ordinal
for(var in c("AnzHH", "AnzGew", "customer_age")){
set(data, j=var, value=scale(data[[var]]))
}
# One-hot encode data
data <- data.frame(model.matrix(~.-1,data))
return(data)
}