diff caret_future/tool3/test/tmp1/Preold_advance.R @ 0:68300206e90d draft default tip

Uploaded

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/caret_future/tool3/test/tmp1/Preold_advance.R	Thu Nov 05 02:41:30 2015 -0500
@@ -0,0 +1,84 @@
+##########
+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)