### R code from vignette source 'fscaret.Rnw' ################################################### ### code chunk number 1: install (eval = FALSE) ################################################### ## install.packages("fscaret", dependencies = c("Depends", "Suggests")) ################################################### ### code chunk number 2: install (eval = FALSE) ################################################### ## install.packages("fscaret", dependencies = c("Depends")) ################################################### ### code chunk number 3: load_data (eval = FALSE) ################################################### ## basename_file <- "My_database" ## file_name <- paste(basename_file,".csv",sep="") ################################################### ### code chunk number 4: load_data (eval = FALSE) ################################################### ## matrixTrain <- read.csv(file_name,header=TRUE,sep="\t", ## strip.white = TRUE, na.strings = c("NA","")) ################################################### ### code chunk number 5: load_data (eval = FALSE) ################################################### ## matrixTrain <- as.data.frame(matrixTrain) ################################################### ### code chunk number 6: funcRegPred_all ################################################### library(fscaret) data(funcRegPred) funcRegPred ################################################### ### code chunk number 7: funcClassPred_all ################################################### library(fscaret) data(funcClassPred) funcClassPred ################################################### ### code chunk number 8: fscaret_example (eval = FALSE) ################################################### ## my_res_foba <- myFS$VarImp$model$foba ## my_res_foba <- structure(my_res_foba,class="train") ################################################### ### code chunk number 9: fscaret_example (eval = FALSE) ################################################### ## ## library(fscaret) ## data(dataset.train) ## data(dataset.test) ## ## trainDF <- dataset.train ## testDF <- dataset.test ## ## myFS<-fscaret(trainDF, testDF, myTimeLimit = 5, preprocessData=TRUE, ## Used.funcRegPred=c("pcr","pls"), with.labels=TRUE, ## supress.output=TRUE, no.cores=1) ## myRES_tab <- myFS$VarImp$matrixVarImp.MSE[1:10,] ## myRES_tab <- subset(myRES_tab, select=c("pcr","pls","SUM%","ImpGrad","Input_no")) ## myRES_rawMSE <- myFS$VarImp$rawMSE ## myRES_PPlabels <- myFS$PPlabels ################################################### ### code chunk number 10: fscaret_example ################################################### library(fscaret) # if((Sys.info()['sysname'])=="SunOS"){ myRES_tab <- data.frame(pcr = c(5.862841e+01, 1.567799e+01, 1.916511e+01, 2.519981e-01, 1.872058e-02, 2.880832e-04, 5.880416e-04, 7.190168e-05, 1.570926e-06, 1.081909e-06), pls = c(5.227714e+01, 2.741963e+01, 1.995465e+01, 3.161112e-01, 3.079973e-02, 1.324904e-03, 2.781880e-04, 6.894892e-05, 2.697715e-06, 1.078743e-06), "SUM" = c(1.000000e+02, 3.885975e+01, 3.527303e+01, 5.122461e-01, 4.465089e-02, 1.454379e-03, 7.810516e-04, 1.270005e-04, 3.848898e-06, 1.948191e-06), ImpGrad=c(0.000000, 61.140253, 9.229896, 98.547769, 91.283313, 96.742777, 46.296556, 83.739807, 96.969384, 49.383143), Input_no=c("4","5","22","23","2","13","9","1","17","21")) names(myRES_tab)[length(myRES_tab)-2]<-"SUM%" myRES_rawMSE <- data.frame(pcr = c(716.6597), pls = c(671.8195)) myRES_PPlabels <- data.frame("Orig Input No"=c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22, 23, 24, 25, 26, 27, 29), Labels = c("Balaban.index", "Dreiding.energy", "Fused.aromatic.ring.count", "Hyper.wiener.index", "Szeged.index", "Ring.count.of.atom", "pI", "Quaternary_structure", "PLGA_Mw", "La_to_Gly", "PVA_conc_inner_phase", "PVA_conc_outer_phase", "PVA_Mw", "Inner_phase_volume", "Encaps_rate", "PLGA_conc", "PLGA_to_Placticizer", "diss_pH", "diss_add", "Prod_method", "Asymmetric.atom.count.1", "Hyper.wiener.index.1", "Szeged.index.1", "count", "pH_14_logd", "bpKa2", "Cyclomatic.number.2")) # } else { # data(dataset.train) # data(dataset.test) # trainDF <- dataset.train # testDF <- dataset.test # myFS<-fscaret(trainDF, testDF, myTimeLimit = 5, preprocessData=TRUE,regPred=TRUE, # Used.funcRegPred=c("pcr","pls"), with.labels=TRUE, # supress.output=TRUE, no.cores=1, saveModel=FALSE) # myRES_tab <- myFS$VarImp$matrixVarImp.MSE[1:10,] # myRES_rawMSE <- myFS$VarImp$rawMSE # myRES_PPlabels <- myFS$PPlabels # } myRES_tab <- subset(myRES_tab, select=c("pcr","pls","SUM%","ImpGrad","Input_no")) ################################################### ### code chunk number 11: fscaret_example_class ################################################### # library(MASS) # # # make testing set # data(Pima.te) # # Pima.te[,8] <- as.numeric(Pima.te[,8])-1 # # myDF <- Pima.te # # myFS.class<-fscaret(myDF, myDF, myTimeLimit = 20, preprocessData=FALSE, with.labels=TRUE, classPred=TRUE, regPred=FALSE, Used.funcClassPred=c("knn","rpart"), # supress.output=FALSE, no.cores=1) # # print(myFS.class) # myRES.class_tab <- myFS.class$VarImp$matrixVarImp.MeasureError[,] # myRES.class_tab <- subset(myRES.class_tab, select=c("knn","rpart","SUM%","ImpGrad","Input_no")) # myRES.class_rawError <- myFS.class$VarImp$rawMeasureError ################################################### ### code chunk number 12: fscaret_example_class (eval = FALSE) ################################################### ## library(MASS) ## ## # make testing set ## data(Pima.te) ## ## Pima.te[,8] <- as.numeric(Pima.te[,8])-1 ## ## myDF <- Pima.te ## ## myFS.class<-fscaret(myDF, myDF, myTimeLimit = 5, preprocessData=FALSE, ## with.labels=TRUE, classPred=TRUE,regPred=FALSE, ## Used.funcClassPred=c("knn","rpart"), supress.output=TRUE, no.cores=1) ## myRES.class_tab <- myFS.class$VarImp$matrixVarImp.MeasureError ## myRES.class_tab <- subset(myRES.class_tab, select=c("knn","rpart","SUM%","ImpGrad","Input_no")) ## myRES.class_rawError <- myFS.class$VarImp$rawMeasureError ################################################### ### code chunk number 13: fscaret_example ################################################### # Print out the Variable importance results for MSE scaling print(myRES_tab) ################################################### ### code chunk number 14: fscaret_example ################################################### # Print out the generalization error for models print(myRES_rawMSE) ################################################### ### code chunk number 15: fscaret_example ################################################### # Print out the reduced number of inputs after preprocessing print(myRES_PPlabels) ################################################### ### code chunk number 16: barPlot ################################################### # Present variable importance on barplot a=0.9 b=0.7 c=2 # if((Sys.info()['sysname'])=="SunOS"){ myFS <- NULL myFS$VarImp$matrixVarImp.MSE <- myRES_tab # } lk_row.mse=nrow(myFS$VarImp$matrixVarImp.MSE) setEPS() barplot1 <- barplot(myFS$VarImp$matrixVarImp.MSE$"SUM%"[1:(a*lk_row.mse)], cex.names=b, las = c, xlab="Variables", ylab="Importance Sum%", names.arg=c(myFS$VarImp$matrixVarImp.MSE$Input_no[1:(a*lk_row.mse)])) lines(x = barplot1, y = myFS$VarImp$matrixVarImp.MSE$"SUM%"[1:(a*lk_row.mse)]) points(x = barplot1, y = myFS$VarImp$matrixVarImp.MSE$"SUM%"[1:(a*lk_row.mse)]) ################################################### ### code chunk number 17: fscaret_example_class (eval = FALSE) ################################################### ## # Print out the Variable importance results for F-measure scaling ## print(myRES.class_tab) ################################################### ### code chunk number 18: fscaret_example_class (eval = FALSE) ################################################### ## # Print out the generalization error for models ## print(myRES.class_rawError) ################################################### ### code chunk number 19: fscaret_issue (eval = FALSE) ################################################### ## library(fscaret) ## myFuncRegPred <- funcRegPred[which(funcRegPred!="partDSA")] ## ## print(funcRegPred) ## ## myFS<-fscaret(trainDF, testDF, myTimeLimit = 12*60*60, preprocessData=TRUE,regPred=TRUE, ## Used.funcRegPred=myFuncRegPred, with.labels=TRUE, ## supress.output=TRUE, no.cores=NULL, saveModel=FALSE)