Hi R Helpers,
I am using the Neural Net build in the CARET package and it produces a large
amount of output that I don't need to see and interferes with my ability to
get to the output that I want to see. I am using the nnet.trace=FALSE setting,
but still getting a disproportionate amount of output from this one procedure.
Is there another option setting that will turn off this output?
Reproducible example is below. It has a little extra complication in it because
I hacked it from a post. Let me know if I need to do anything to it to make it
more use-able.
Many thanks.
--John Sparks
library('caret')
set.seed(1)
data<-read.csv(url('https://datahack-prod.s3.ap-south-1.amazonaws.com/train_file/train_u6lujuX_CVtuZ9i.csv'))
#Imputing missing values using median
preProcValues <- preProcess(data, method =
c("medianImpute","center","scale"))
library('RANN')
data_processed <- predict(preProcValues, data)
index <- createDataPartition(data_processed$Loan_Status, p=0.75, list=FALSE)
trainSet <- data_processed[ index,]
testSet <- data_processed[-index,]
fitControl <- trainControl(method = "cv",number = 5,savePredictions
= 'final',classProbs = T)
trainSet<-subset(trainSet,select=-c(Loan_ID))
outcomeName<-"Loan_Status"
predictors<-names(trainSet)[!names(trainSet) %in% outcomeName]
NN<-train(trainSet[,predictors],trainSet[,outcomeName],method='nnet',trControl=fitControl,tuneLength=5,nnet.trace=FALSE)
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You can use capture.output to store all that tracing information in a character vector instead of having it printed. You can look at it to diagnose problems or just throw it away. NN.text <- capture.output(NN<-train(trainSet[,predictors],trainSet[,outcomeName],method='nnet',trControl=fitControl,tuneLength=5,nnet.trace=FALSE)) Bill Dunlap TIBCO Software wdunlap tibco.com On Fri, Aug 17, 2018 at 10:34 AM, Sparks, John <jspark4 at uic.edu> wrote:> Hi R Helpers, > > > I am using the Neural Net build in the CARET package and it produces a > large amount of output that I don't need to see and interferes with my > ability to get to the output that I want to see. I am using the > nnet.trace=FALSE setting, but still getting a disproportionate amount of > output from this one procedure. > > > Is there another option setting that will turn off this output? > > > Reproducible example is below. It has a little extra complication in it > because I hacked it from a post. Let me know if I need to do anything to > it to make it more use-able. > > > Many thanks. > > --John Sparks > > > library('caret') > set.seed(1) > > data<-read.csv(url('https://datahack-prod.s3.ap-south-1. > amazonaws.com/train_file/train_u6lujuX_CVtuZ9i.csv')) > > #Imputing missing values using median > preProcValues <- preProcess(data, method = c("medianImpute","center"," > scale")) > library('RANN') > data_processed <- predict(preProcValues, data) > index <- createDataPartition(data_processed$Loan_Status, p=0.75, > list=FALSE) > trainSet <- data_processed[ index,] > testSet <- data_processed[-index,] > fitControl <- trainControl(method = "cv",number = 5,savePredictions > 'final',classProbs = T) > > trainSet<-subset(trainSet,select=-c(Loan_ID)) > outcomeName<-"Loan_Status" > predictors<-names(trainSet)[!names(trainSet) %in% outcomeName] > > NN<-train(trainSet[,predictors],trainSet[,outcomeName],method='nnet', > trControl=fitControl,tuneLength=5,nnet.trace=FALSE) > > > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/ > posting-guide.html > and provide commented, minimal, self-contained, reproducible code. >[[alternative HTML version deleted]]
Terrific! Thanks for the speedy and informative reply.
--JJS
________________________________
From: William Dunlap <wdunlap at tibco.com>
Sent: Friday, August 17, 2018 12:45 PM
To: Sparks, John
Cc: r-help at r-project.org
Subject: Re: [R] CARET NN Too Much Output Even with Trace=False
You can use capture.output to store all that tracing information in a character
vector instead of having it printed. You can look at it to diagnose problems or
just throw it away.
NN.text <-
capture.output(NN<-train(trainSet[,predictors],trainSet[,outcomeName],method='nnet',trControl=fitControl,tuneLength=5,nnet.trace=FALSE))
Bill Dunlap
TIBCO Software
wdunlap tibco.com<http://tibco.com>
On Fri, Aug 17, 2018 at 10:34 AM, Sparks, John <jspark4 at
uic.edu<mailto:jspark4 at uic.edu>> wrote:
Hi R Helpers,
I am using the Neural Net build in the CARET package and it produces a large
amount of output that I don't need to see and interferes with my ability to
get to the output that I want to see. I am using the nnet.trace=FALSE setting,
but still getting a disproportionate amount of output from this one procedure.
Is there another option setting that will turn off this output?
Reproducible example is below. It has a little extra complication in it because
I hacked it from a post. Let me know if I need to do anything to it to make it
more use-able.
Many thanks.
--John Sparks
library('caret')
set.seed(1)
data<-read.csv(url('https://datahack-prod.s3.ap-south-1.amazonaws.com/train_file/train_u6lujuX_CVtuZ9i.csv'))
#Imputing missing values using median
preProcValues <- preProcess(data, method =
c("medianImpute","center","scale"))
library('RANN')
data_processed <- predict(preProcValues, data)
index <- createDataPartition(data_processed$Loan_Status, p=0.75, list=FALSE)
trainSet <- data_processed[ index,]
testSet <- data_processed[-index,]
fitControl <- trainControl(method = "cv",number = 5,savePredictions
= 'final',classProbs = T)
trainSet<-subset(trainSet,select=-c(Loan_ID))
outcomeName<-"Loan_Status"
predictors<-names(trainSet)[!names(trainSet) %in% outcomeName]
NN<-train(trainSet[,predictors],trainSet[,outcomeName],method='nnet',trControl=fitControl,tuneLength=5,nnet.trace=FALSE)
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______________________________________________
R-help at r-project.org<mailto:R-help at r-project.org> mailing list -- To
UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
[[alternative HTML version deleted]]