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1 ##########
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2 args <- commandArgs(T)
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3 arg1 <- args[1]
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4 arg2 <- args[2]
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5 arg3 <- args[3]
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6 #source("~/galaxy-dist/tools/mpdstoolsV2/tool3/Preold.R")
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7 #pre(arg1,arg2,arg3)
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8
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9 pre <- function(args1,args2,args3){
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10 #args <- commandArgs(TRUE)
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11 nTrain <- read.csv(args1,row.names= 1, header = T) # example nTrain.csv file of unknown activity
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12 #save(nTrain,file = "nTrain.RData")
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13 #load("nTrain.RData")
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14 load(args2) # model generated from previous programn
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15 newdata <- nTrain
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16 modelFit <- Fit
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17 ###########
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18 # input csv file must contaion the exact same column as used in model building #
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19 # Also do pre-proccessing by means of centering and scaling
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20 ## problem in s4 object so first check that the given model has s4 object in
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21 ## >isS4(Fit$finalmodel) if it is s4 than add in with elseif loop
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22 ## eg . isS4(plsFit$finalModel) == TRUE
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23
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24 library(caret)
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25
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26 #if(as.character(!isS4(Fit$finalModel == "TRUE")))
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27 if(Fit$method != "svmRadial")
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28 {
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29 reqcol <- Fit$finalModel$xNames
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30 newdata <- newdata[,reqcol]
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31 newdata1 <- preProcess(newdata, method = c("center", "scale"))
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32 newdata11 <- predict(newdata1,newdata)
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33 ###########
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34 library(stats)
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35 testpredict <- predict(modelFit,newdata11)
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36 Label <- levels(testpredict)
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37 a1 <- Label[1]
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38 a2 <- Label[2]
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39 probpredict <- predict(modelFit,newdata11,type="prob")
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40 names <- as.data.frame(rownames(nTrain))
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41 colnames(names) <- "COMPOUND"
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42 activity <- as.data.frame(testpredict)
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43 colnames(activity) <- "PREDICTED ACTIVITY"
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44 colnames(probpredict) <- c(eval(a1),eval(a2))
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45 Prob <- as.data.frame(probpredict)
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46 dw <- format(cbind(names,Prob,activity),justify="centre")
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47 write.table(dw,file=args3,row.names=FALSE,sep="\t")
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48 }
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49 else if(Fit$method == "svmRadial")
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50 {
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51 library(stats)
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52 newdata1 <- preProcess(newdata, method = c("center", "scale"))
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53 newdata11 <- predict(newdata1,newdata)
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54 #library(stats)
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55 #testpredict <- predict(modelFit,newdata11)
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56 #names <- as.data.frame(rownames(nTrain))
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57 #colnames(names) <- "COMPOUND"
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58 #activity <- as.data.frame(testpredict)
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59 #colnames(activity) <- "ACTIVITY"
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60 #dw <- cbind(names,activity)
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61 #write.csv(dw,file=args3,row.names=FALSE)
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62 library(stats)
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63 testpredict <- predict(modelFit,newdata11)
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64 Label <- levels(testpredict)
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65 a1 <- Label[1]
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66 a2 <- Label[2]
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67 probpredict <- predict(modelFit,newdata11,type="prob")
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68 names <- as.data.frame(rownames(nTrain))
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69 colnames(names) <- "COMPOUND"
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70 activity <- as.data.frame(testpredict)
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71 colnames(activity) <- "PREDICTED ACTIVITY"
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72 colnames(probpredict) <- c(eval(a1),eval(a2))
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73 Prob <- as.data.frame(probpredict)
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74 dw <- format(cbind(names,Prob,activity),justify="centre")
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75 write.table(dw,file=args3,row.names=FALSE,sep="\t")
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76 }
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77 else {
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78 dw <- "There is something wrong in data or model"
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79 write.csv(dw,file=args3,row.names=FALSE)
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80
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81 }
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82
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83 }
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84 pre(arg1,arg2,arg3)
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