Hi
I have a problem with getting stuck in the number of rows that the first
data group has when looping.
Let me explain the program:
I want to run randomForest on 200 stocks and get scores of each of them.
First I shall point at the training data set file(with data from 200 stocks)
then I shall point at the predict dataset(with 200 rows of data from the 200
stocks with unknown target).
At the end I shall point at a place to save the file with the score for each
stock.
This works almost. The problem is that the number of rows of data from the
stocks in the training file differs on every stock. One time it is 50 rows
for one stock and for another stock it can be 100 rows of data. When I run
this code the number of rows from the first stock is used on all stocks. For
example if stock 1 has 50 rows the calculation on stock2 also will use 50
rows.
So the score in the result file differs from if I do the calculations one
stock at the time.
What am I doing wrong?
Kind regards
Rolf
The data in the files looks like this:
Train file:
STOCK.NAME
Indicator1
Indicator2
Indicator3
Indicator4
Indicator5
Indicator6
Indicator7
Indicator8
Indicator9
Indicator10
Action
Stock.1
0.53464
0.809136
0.090641
0.212288
0.817402
0.976926
0.383471
0.119862
0.369533
0.374066
Buy
Stock.1
0.907586
0.421417
0.292742
0.78914
0.263374
0.597003
0.420898
0.582622
0.666901
0.71218
Notbuy
Stock.1
0.682471
0.501301
0.160167
0.753329
0.426113
0.874266
0.752404
0.535917
0.26929
0.30212
Notbuy
Stock.1
0.156847
0.057765
0.345092
0.148373
0.79769
0.927548
0.797175
0.4544
0.135831
0.767282
Buy
Stock.2
0.177951
0.506193
0.075647
0.719628
0.52613
0.131471
0.140883
0.926419
0.393547
0.292262
Notbuy
Stock.2
0.525604
0.152735
0.033175
0.780946
0.037649
0.733622
0.128549
0.763801
0.493194
0.008631
Buy
Predict file:
STOCK.NAME
Indicator1
Indicator2
Indicator3
Indicator4
Indicator5
Indicator6
Indicator7
Indicator8
Indicator9
Indicator10
Action
Stock.1
0.53464
0.809136
0.090641
0.212288
0.817402
0.976926
0.383471
0.119862
0.369533
0.374066
Stock.2
0.907586
0.421417
0.292742
0.78914
0.263374
0.597003
0.420898
0.582622
0.666901
0.71218
rm(list=ls())
require(randomForest, quietly=TRUE)
#Reading the TRAINING data...
dtot=read.csv(choose.files(caption="Choose the TRAINING data..."))
#Reading the NEW data...
newtot=read.csv(choose.files(caption="Choose the NEW data..."))
stk=names(table(dtot[,1]))
date=paste(
strsplit(as.character(Sys.Date()),"-")[[1]][1],
strsplit(as.character(Sys.Date()),"-")[[1]][2],
strsplit(as.character(Sys.Date()),"-")[[1]][3],
sep="")
res=matrix(0,length(stk),2)
for (i in 1:length(stk))
{
d=dtot[which(dtot[,1]==stk[i]),-1]
#write.csv(d,paste(stk[i],".csv",sep=""),row.names=F)
#write.csv(newtot[i,-c(1,12)],paste(stk[i],"_PRED.csv",sep=""),row.names=F)
#}
# Build the Random Forest model.
set.seed(42)
rf <- randomForest(Action ~ .,
data=d,
ntree=500,
mtry=3,
importance=TRUE,
na.action=na.roughfix,
replace=FALSE)
test=rbind(dtot[nrow(dtot),-c(1,12)],newtot[i,-c(1,12)])
# Obtain probability scores for the Random Forest model
res[i,2]=predict(rf, test, type="prob")[2,2]
}
res=data.frame(res)
names(res)=c("Stockname","Score")
res[,1]=stk
# Output the combined data.
setwd(choose.dir(caption="Choose the FOLDER where you want the send the
results..."))
write.csv(res, file=paste("Stock1_newdata_score_all_",date,".csv",sep=""),
row.names=FALSE)
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