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inst/doc/fscaret.R
In fscaret: Automated Feature Selection from 'caret'
### R code from vignette source 'fscaret.Rnw'
###################################################
### code chunk number 1: install (eval = FALSE)
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## 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)
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## 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
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library(fscaret)
data(funcRegPred)
funcRegPred
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### code chunk number 7: funcClassPred_all
###################################################
library(fscaret)
data(funcClassPred)
funcClassPred
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### 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)
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fscaret documentation built on May 2, 2019, 10:15 a.m.
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### 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)
Try the fscaret package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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