Mercurial > repos > deepakjadmin > caret_tool3

changeset 2:e6d9c3fac3c4 draft

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author deepakjadmin
date Wed, 09 Nov 2016 07:33:34 -0500
parents 4b54bb0e5958
children cccab5660509
files Preold_advance.R
diffstat 1 files changed, 0 insertions(+), 156 deletions(-) [+]
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line diff
--- a/Preold_advance.R	Tue Sep 27 06:44:59 2016 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,156 +0,0 @@
-##########
-args <- commandArgs(T)
-arg1 <- args[1]
-arg2 <- args[2]
-arg3 <- args[3]
-#source("~/galaxy-dist/tools/mpdstoolsV2/tool3/Preold.R")
-#pre(arg1,arg2,arg3)
-set.seed(1234)
-
-pre <- function(args1,args2,args3){
-#args <- commandArgs(TRUE)
-nTrain <- read.csv(args1,row.names= 1, header = T) # example nTrain.csv file of unknown activity
-#save(nTrain,file = "nTrain.RData")
-#load("nTrain.RData")
-load(args2) # model generated from  previous programn  
-newdata <- nTrain
-modelFit <- Fit
-###########
-# input csv file must contaion the exact same column as used in model building #
-# Also do pre-proccessing by means of centering and scaling 
-## problem in s4 object so first check that the given model has s4 object in
-## >isS4(Fit$finalmodel) if it is s4 than add in with elseif loop 
-## eg . isS4(plsFit$finalModel) == TRUE
-f=function(x){
-   x<-as.numeric(as.character(x)) #first convert each column into numeric if it is from factor
-   x[is.na(x)] =median(x, na.rm=TRUE) #convert the item with NA to median value from the column
-   x #display the column
-}
-
-f2=function(x){
-               all(is.na(x))
-                }
-
-
-fop <- apply(newdata,2,f2)
-allcolumnmissing <- which(fop)
-if (length(allcolumnmissing) > 0){
-newdata[,allcolumnmissing] <- 0
-newdata[,allcolumnmissing] <- newdata[,allcolumnmissing] + runif(3,0,0.00001) ### add noise}
-}
-
-library(caret)
-if(exists('ppInfo')){
-#if(as.character(!isS4(Fit$finalModel == "TRUE")))
-if((Fit$method != "svmRadial") && (Fit$method != "svmLinear"))
-{
-        reqcol <- Fit$finalModel$xNames
-        newdata <- newdata[,reqcol]
-        newdata <- apply(newdata,2,f)
-        newdata <- newdata + runif(3,0,0.0001) ### add noise to overcome from NZV error
-        #newdata1 <- preProcess(newdata, method = c("center", "scale"))
-        #newdata1 <- preProcess(newdata, ppInfo)
-        newdata11 <- predict(ppInfo,newdata)
-###########
-        library(stats)
-        testpredict <- predict(modelFit,newdata11)
-        Label <- levels(testpredict)
-        a1 <- Label[1]
-        a2 <- Label[2]
-        probpredict <- predict(modelFit,newdata11,type="prob")
-        names <- as.data.frame(rownames(nTrain))
-        colnames(names) <- "COMPOUND"
-        activity <- as.data.frame(testpredict)
-        colnames(activity) <- "PREDICTED ACTIVITY"
-        colnames(probpredict) <- c(eval(a1),eval(a2))
-        Prob <- as.data.frame(probpredict)
-        dw <- format(cbind(names,Prob,activity),justify="centre")
-        write.table(dw,file=args3,row.names=FALSE,sep="\t")
-        
-        
-        
-} else if((Fit$method == "svmRadial") | (Fit$method == "svmLinear")){       
-        library(stats)
-        reqcol <- colnames(Fit$trainingData)
-        reqcol <- reqcol[1:length(reqcol)-1]
-        newdata <- newdata[,reqcol]
-
-        newdata <- apply(newdata,2,f)
-        newdata <- newdata + runif(3,0,0.0001) ### add little noise to overcome from NZV problem
-        #newdata1 <- preProcess(newdata, method = c("center", "scale"))
-        #newdata1 <- preProcess(newdata,ppInfo)
-        newdata11 <- predict(ppInfo,newdata)
-        testpredict <- predict(modelFit,newdata11)
-        Label <- levels(testpredict)
-        a1 <- Label[1]
-        a2 <- Label[2]
-        probpredict <- predict(modelFit,newdata11,type="prob")
-        names <- as.data.frame(rownames(nTrain))
-        colnames(names) <- "COMPOUND"
-        activity <- as.data.frame(testpredict)
-        colnames(activity) <- "PREDICTED ACTIVITY"
-        colnames(probpredict) <- c(eval(a1),eval(a2))
-        Prob <- as.data.frame(probpredict)
-        dw <- format(cbind(names,Prob,activity),justify="centre")
-        write.table(dw,file=args3,row.names=FALSE,sep="\t")
-}else {
-      dw <- "There is something wrong in data or model"
-	 write.csv(dw,file=args3,row.names=FALSE)
-}
-} else{
-
-#if(as.character(!isS4(Fit$finalModel == "TRUE")))
-if((Fit$method != "svmRadial") && (Fit$method != "svmLinear"))
-{
-        reqcol <- Fit$finalModel$xNames
-        newdata <- newdata[,reqcol]
-        newdata <- apply(newdata,2,f)
-        newdata <- newdata + runif(3,0,0.0001) ### add noise to overcome from NZV error
-       
-###########
-        library(stats)
-        testpredict <- predict(modelFit,newdata)
-        Label <- levels(testpredict)
-        a1 <- Label[1]
-        a2 <- Label[2]
-        probpredict <- predict(modelFit,newdata,type="prob")
-        names <- as.data.frame(rownames(nTrain))
-        colnames(names) <- "COMPOUND"
-        activity <- as.data.frame(testpredict)
-        colnames(activity) <- "PREDICTED ACTIVITY"
-        colnames(probpredict) <- c(eval(a1),eval(a2))
-        Prob <- as.data.frame(probpredict)
-        dw <- format(cbind(names,Prob,activity),justify="centre")
-        write.table(dw,file=args3,row.names=FALSE,sep="\t")
-        
-        
-        
-} else if((Fit$method == "svmRadial") | (Fit$method == "svmLinear")){       
-        library(stats)
-        reqcol <- colnames(Fit$trainingData)
-        reqcol <- reqcol[1:length(reqcol)-1]
-        newdata <- newdata[,reqcol]
-
-        newdata <- apply(newdata,2,f)
-        newdata <- newdata + runif(3,0,0.0001) ### add little noise to overcome from NZV problem
-              
-        testpredict <- predict(modelFit,newdata)
-        Label <- levels(testpredict)
-        a1 <- Label[1]
-        a2 <- Label[2]
-        probpredict <- predict(modelFit,newdata,type="prob")
-        names <- as.data.frame(rownames(nTrain))
-        colnames(names) <- "COMPOUND"
-        activity <- as.data.frame(testpredict)
-        colnames(activity) <- "PREDICTED ACTIVITY"
-        colnames(probpredict) <- c(eval(a1),eval(a2))
-        Prob <- as.data.frame(probpredict)
-        dw <- format(cbind(names,Prob,activity),justify="centre")
-        write.table(dw,file=args3,row.names=FALSE,sep="\t")
-}else {
-      dw <- "There is something wrong in data or model"
-	 write.csv(dw,file=args3,row.names=FALSE)
-}
-}
-}
-pre(arg1,arg2,arg3)