# HG changeset patch
# User anmoljh
# Date 1497273501 14400
# Node ID 7d5a86725094365cc44277b52f55ab3356971d92
planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
diff -r 000000000000 -r 7d5a86725094 predict_activity.R
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/predict_activity.R Mon Jun 12 09:18:21 2017 -0400
@@ -0,0 +1,157 @@
+##########
+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(1)
+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) | is.nan(x) | is.infinite(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(as.character(!isS4(Fit$finalModel == "TRUE")))
+if(exists('ppInfo')){
+#if(as.character(!isS4(Fit$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)
diff -r 000000000000 -r 7d5a86725094 predict_activity.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/predict_activity.xml Mon Jun 12 09:18:21 2017 -0400
@@ -0,0 +1,72 @@
+
+
+ used to predict activity based on given model
+
+
+ R
+ carettools
+
+
+
+
+
+ predict_activity.R $file1 $model $output1 2>/dev/null
+
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+
+.. class:: infomark
+
+Make sure this file **must** contain **all** or **more features** than **input** "csv file" used for **model building**
+
+----------
+
+**Input "csv file" must be as follows**
+
+----------
+
+
+Example file:-
+
+
+
+# example.csv
+
+ feature1,feature2,feature3,..,featureN
+
+ro1 234,2.3,34,7,..,0.9
+
+ro2 432,3.4,23.1,12,..,0.12
+
+ro3 692,23,12.2,19,..,0.14
+
+
+-----------
+
+**MODEL**
+
+Choose model file received from model building step.
+
+Model file has "data" file format can be seen by
+
+clicking on output files shown in history .
+
+
+
+
diff -r 000000000000 -r 7d5a86725094 tool_dependencies.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/tool_dependencies.xml Mon Jun 12 09:18:21 2017 -0400
@@ -0,0 +1,9 @@
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