## Main
rm(list = ls())
gc()
dir <- "/Users/chengmingwang/Dropbox/Kaggle/Sales"
codedir <- paste(dir,"codes/ChW",sep="/")
datadir <- paste(dir,"data",sep="/")
outputdir <- paste(dir,"output",sep="/")
n.cores <- 3
load(file.path(datadir, 'pretreat/data.Rda'))
## Test a single function
if(FALSE) {
dat <- numeric(train.fulldata)[Store == 1,]
dat.fit <- dat[Date < '2015-1-1', ]
dat.tune <- dat[Date >= '2015-1-1', ]
model.result <- model.train.mlts(dat.fit, method = 'rf')
model.predict <- model.predict.mlts(dat.tune, model.result)
plot(dat.tune$Sales, model.predict)
}
if(FALSE) {
if(n.cores > detectCores()) {
if(detectCores() > 1) {
n.cores <- detectCores() - 1
} else {
n.cores <- 1
}
}
cl <- registerDoParallel(n.cores)
}
##foreach(i = seq(10)) %dopar% {
#i <- 1
for(i in seq(10)) {
dat <- numeric(train.fulldata)[Store == i,]
dat.fit <- dat[Date < '2015-1-1', ]
dat.tune <- dat[Date >= '2015-1-1', ]
result <- FitAndPredict(dat.fit, dat.tune)
pdf(file.path(datadir, paste('../output/ml_ts+tune/tune_store_', i, '.pdf', sep = "")))
print(ggplot() + geom_point(aes(x = dat.tune$Sales, y = result$predict)) + xlab("Sales") + ylab(paste("Pred",
"log=", result$log.transform,
"tri= ", result$add.tri,
"poly= ", result$add.poly,
"meth= ", result$method,
"ntrees= ",result$ntrees,
"maxdepth=", result$maxdepth)) +
ggtitle(paste("MSE =", round(result$mse, 0))) + geom_abline(intercept = 0, slope = 1))
dev.off()
}
## stopCluster(cl)
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