Parallel Computation for Create_Matrix 'RTextTools' package - r

Question

I am creating a DocumentTermMatrix using create_matrix() from RTextTools and create container and model based on that. It is for extremely large datasets.
I do this for each category (factor levels). So for each category it has to run matrix, container and model. When I run the below code in (say 16 core / 64 gb) - it runs only in one core and memory used is less than 10%.
Is there way I can speedup this process? Perhaps using doparallel & foreach? Any information would certainly help.
#import the required libraries
library("RTextTools")
library("hash")
library(tm)
for ( n in 1:length(folderaddress)){
#Initialize the variables
traindata = list()
matrix = list()
container = list()
models = list()
trainingdata = list()
results = list()
classifiermodeldiv = 0.80`
#Create the directory to place the models and the output files
pradd = paste(combinedmodelsaveaddress[n],"SelftestClassifierModels",sep="")
if (!file.exists(pradd)){
dir.create(file.path(pradd))
}
Data$CATEGORY <- as.factor(Data$CATEGORY)
#Read the training files
X <- split(Data, Data$CATEGORY)
data <- lapply(seq_along(X), function(x) as.data.frame(X[[x]])[,5])
names(data) <- levels(Data$CATEGORY)
list2env(data, envir = .GlobalEnv)
files=as.matrix(names(data))
fileno=length(files)
fileno=as.integer(fileno)
print(fileno)
#For all the files in the training folder(the number of files in the training folder = Number of categories in Taxonomy)
for(i in 1:fileno){
filename = as.character(files[i,1])
data1 = as.data.frame(data[i])
data1 = as.matrix(data1)
filenamechanged = gsub ("\\.[[:alnum:]]+","",filename)
type = matrix(data = as.character(filenamechanged),nrow = length(data1[,1]),ncol=1 )
data1 = cbind(data1,type)
traindata[[i]] = data1
print(i)
}
for(i in 1:fileno){
#Obtain the unique classified data from the train files for one category
trainingdata1 = as.data.frame(traindata[[i]][,1])
uniquetraintweet = hash()
typetrain1 = matrix(data=as.character(traindata[[i]][1,2]), ncol =1, nrow = length(trainingdata1[,1]))
#If the training data is less than 10 records for a category, do not create a model
#The model created based on a smaller set of data will not be accurate
if (length(trainingdata1[,1])<200){
matrix[[i]] = NULL
next
}
#Obtain the unique classified data from the train files of all the other category except that is considered as training category
trainingdata2=matrix(data="",nrow=0,ncol=1)
for (j in 1:fileno){
if ( j==i) next
trainingdata2dummy = as.data.frame(traindata[[j]][,1])
length(trainingdata1[,1])
colnames(trainingdata2)="feedbacks"
colnames(trainingdata2dummy)="feedbacks"
trainingdata2 = rbind(trainingdata2,trainingdata2dummy)
}
#Consider one category as training set and make the remaining categories as Others
typetrain2 = matrix(data="ZZOther",nrow=length(trainingdata2[,1]),ncol=1)
colnames(trainingdata1)="feedbacks"
trainingdata[[i]]=rbind(trainingdata1,trainingdata2)
colnames(typetrain1)="type"
colnames(typetrain2)="type"
type=rbind(typetrain1,typetrain2)
trainingdata[[i]] = cbind(trainingdata[[i]],type)
trainingdata[[i]]=trainingdata[[i]][sample(nrow(trainingdata[[i]])),]
#Input the training set and other set to the classifier
mindoc = max(1,floor(min(0.001*length(trainingdata[[i]][,1]),3)))
#Create Matrix
matrix[[i]] <- create_matrix(trainingdata[[i]][,1], language="english",
removeNumbers=FALSE, stemWords=FALSE,weighting=weightTf,minWordLength=3, minDocFreq=mindoc, maxDocFreq=floor(0.5*(length(trainingdata[[i]][,1]))))
#rowTotals <- apply(matrix[[i]] , 1, sum) #Find the sum of words in each Document
#matrix[[i]] <- matrix[[i]][rowTotals> 0,]
print(i)
#Create Container
container[[i]] <- create_container(matrix[[i]],trainingdata[[i]][,2],trainSize=1:length(trainingdata[[i]][,1]),virgin=FALSE)
print(i)
#Create Models
models[[i]] <- train_models(container[[i]], algorithms=c("SVM"))
print(i)
}
save(matrix, file = paste(pradd,"/Matrix",sep=""))
save(models, file = paste(pradd,"/Models",sep=""))
}

Answer 1

Here is an example of working with RTextTools in parallel. I created the dummy function using information to be found here.
The function myFun follows the introduction in the above link - at the end it writes a csv file (no directory is specified) containing the analytics/summary. Afterwards it is straight forward application of the base R package parallel in order to run myFun in parallel.
library(parallel)
library(RTextTools)
# I. A dummy function
# Uses RTextTools
myFun <- function (trainMethod) {
library(RTextTools)
data(USCongress)
# Create the document-term matrix
doc_matrix <- create_matrix(USCongress$text, language="english", removeNumbers=TRUE,
stemWords=TRUE, removeSparseTerms=.998)
container <- create_container(doc_matrix, USCongress$major, trainSize=1:4000,
testSize=4001:4449, virgin=FALSE)
# Train
model <- train_model(container,trainMethod)
classify <- classify_model(container, model)
# Analytics
analytics <- create_analytics(container,
cbind(classify))
summary(analytics)
# Saving
nameToSave <- paste(trainMethod, 'DocumentSummary.csv', sep = '_')
write.csv(analytics#document_summary, nameToSave)
}
# II. Parallel Processing
#
# 1. Vector for parallelization & number of cores available
trainMethods <- c('SVM','GLMNET','MAXENT','SLDA','BOOSTING')
num_cores <- detectCores() - 1L
# 2. Start a cluster
cl <- makeCluster(num_cores)
# 3. Export Variables needed to the cluster
# specifying exactly which variables should be exported
clusterExport(cl, varlist = c('myFun', 'trainMethods'))
# 4. do in parallel
parLapply(cl, seq_along(trainMethods), function (n) myFun(trainMethod = trainMethods[n]))
# stop the cluster
stopCluster(cl)
In your case, you'd have to turn your code into a function myFun (n, ...) with n being an element of seq_along(folderaddress) and of course substitute seq_along(trainMethods) for seq_along(folderaddress) in parLapply.
Of course chances are there exist ways besides parallelization to enhance your code. The problem is without sample data, any suggested improvement is but conjecture.