Mercurial > repos > deepakjadmin > r_caret_test
view caret_future/tool3/test/tmp1/Preold_advance.R @ 0:68300206e90d draft default tip
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author | deepakjadmin |
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date | Thu, 05 Nov 2015 02:41:30 -0500 |
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########## args <- commandArgs(T) arg1 <- args[1] arg2 <- args[2] arg3 <- args[3] #source("~/galaxy-dist/tools/mpdstoolsV2/tool3/Preold.R") #pre(arg1,arg2,arg3) 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 library(caret) #if(as.character(!isS4(Fit$finalModel == "TRUE"))) if(Fit$method != "svmRadial") { reqcol <- Fit$finalModel$xNames newdata <- newdata[,reqcol] newdata1 <- preProcess(newdata, method = c("center", "scale")) newdata11 <- predict(newdata1,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") { library(stats) newdata1 <- preProcess(newdata, method = c("center", "scale")) newdata11 <- predict(newdata1,newdata) #library(stats) #testpredict <- predict(modelFit,newdata11) #names <- as.data.frame(rownames(nTrain)) #colnames(names) <- "COMPOUND" #activity <- as.data.frame(testpredict) #colnames(activity) <- "ACTIVITY" #dw <- cbind(names,activity) #write.csv(dw,file=args3,row.names=FALSE) 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 { dw <- "There is something wrong in data or model" write.csv(dw,file=args3,row.names=FALSE) } } pre(arg1,arg2,arg3)