inst/oldFiles/ipredInterfaces.R

In MLInterfaces: Uniform interfaces to R machine learning procedures for data in Bioconductor containers

#####################
# PACKAGE: ipred
#####################
#
#####################
# title: baggingB
# description: interface to bagging {ipred} 
# arguments:
#	exprObj		ExpressionSet
#	classifLab	character string specifying what covariate data 
#			to use for classification
#	metric		for distance matrix 
# value:
# 	object of class "classifPred"
# note:
# 	aggregation	argument specifying how to combine results for prediction 
# example:
# train <- c(sample(1:47, 23), sample(48:72, 12))
# hg <- baggingB(golubMerge[100:200,], "ALL.AML", train)
#####################

setGeneric("baggingB", function(exprObj, classifLab, trainInd, subset, aggregation="majority", metric="euclidean", ...){
		standardGeneric("baggingB")
})

setMethod("baggingB", c("ExpressionSet", "character", "integer", "ANY", "ANY", "ANY"), 
		function(exprObj, classifLab, trainInd, subset, aggregation, metric, ...){

		if(missing(subset)){ subset <- NULL }

		cl <- pData(exprObj)[[classifLab]][trainInd]		
		trainDat <- data.frame(y=cl, t(exprs(exprObj)[,trainInd]))
		testDat <- data.frame(t(exprs(exprObj)[,-trainInd]))
		dis <- dist(testDat, method=metric)
library(ipred)
		tmp <- bagging(y~., data=trainDat, subset=subset, ...)
		out <- predict(tmp, newdata=testDat)
                new("classifOutput", method="bagging",
                        predLabels=newPredClass(as.character(out)),
			trainInds=trainInd, allClass=as.character(pData(exprObj)[[classifLab]]),
#                        predScores=newQualScore(attr(out,"prob")),
                        RObject=tmp, call=match.call(), distMat=dis)
})

#####################
# title: ipredknnB
# description: interface to ipredknn {ipred} 
# arguments:
#	exprObj		ExpressionSet
#	trainInd	vector of indices for the columns to be 
#			included in the training set
#	classifLab	character string specifying what covariate data 
#			to use for classification
#	metric		for distance matrix 
# value:
# 	object of class "classifPred"
# example:
# train <- c(sample(1:47, 23), sample(48:72, 12))
# hg <- ipredknnB(golubMerge[100:200,], "ALL.AML", train)
# note: would be great if we could get prob and class predictions
#####################

setGeneric("ipredknnB", function(exprObj, classifLab, trainInd, na.action, k=5, metric="euclidean", ...){
		standardGeneric("ipredknnB")
})

setMethod("ipredknnB", c("ExpressionSet", "character", "integer", "ANY", "ANY", "ANY"), 
		function(exprObj, classifLab, trainInd, na.action, k, metric, ...){
				
		if(missing(na.action)){ na.action <- NULL }
		cl <- pData(exprObj)[[classifLab]][trainInd]		
		trainDat <- data.frame(y = cl, t(exprs(exprObj)[,trainInd]))
		testDat <- data.frame(t(exprs(exprObj)[,-trainInd]))
		dis <- dist(testDat, method=metric)
library(ipred)
		tmp <- ipredknn(y~., data=trainDat, na.action=na.action, k=k, ...)
		out <- predict(tmp, newdata=testDat, type="class")
		prob <- predict(tmp, newdata=testDat, type="prob")
                new("classifOutput", method="ipredknn",
                        predLabels=newPredClass(as.character(out)),
			trainInds=trainInd, allClass=as.character(pData(exprObj)[[classifLab]]),
                        predScores=newQualScore(prob),
                        RObject=tmp, call=match.call(), distMat=dis)
})

#####################
# title: sldaB
# description: interface to slda {ipred} 
# arguments:
#	exprObj		ExpressionSet
#	trainInd	vector of indices for the columns to be 
#			included in the training set
#	classifLab	character string specifying what covariate data 
#			to use for classification
#	metric		for distance matrix 
# value:
# 	object of class "classif3Output"
# example:
# train <- c(sample(1:47, 23), sample(48:72, 12))
# hg <- sldaB(golubMerge[100:200,], "ALL.AML", train)
#####################

setGeneric("sldaB", function(exprObj, classifLab, trainInd, subset, na.action=na.rpart, metric="euclidean", ...){
		standardGeneric("sldaB")
})

setMethod("sldaB", c("ExpressionSet", "character", "integer", "ANY", "ANY", "ANY"), 
		function(exprObj, classifLab, trainInd, subset, na.action, metric, ...){

		if(missing(subset)){ subset <- NULL }
		cl <- pData(exprObj)[[classifLab]][trainInd]		
		trainDat <- data.frame(y=cl, t(exprs(exprObj)[,trainInd]))			
		dat <- data.frame(t(exprs(exprObj)[,trainInd]))
		testDat <- data.frame(t(exprs(exprObj)[,-trainInd]))
		dis <- dist(testDat, method=metric)

library(ipred)
		tmp <- slda(y~., data=trainDat, na.action=na.action, ...)
		out <- predict(tmp, newdata=testDat)
                new("classifOutput", method="slda",
                        predLabels=newPredClass(as.character(out$class)),
			trainInds=trainInd, allClass=as.character(pData(exprObj)[[classifLab]]),
                        predScores=newProbMat(out$posterior),
                        RObject=tmp, call=match.call(), distMat=dis)
})

#####################
# title: inbaggB
# description: interface to inbagg {ipred} 
# arguments:
#	exprObj		ExpressionSet
#	classifLab	character string specifying what covariate data 
#			to use for classification
#	trainInd	vector of indices for the columns to be 
#			included in the training set
# 	intLab 		character string or vector of character strings 
#			representing names of the intermediate variables
#	metric		for distance matrix 
# value:
# 	object of class "classifPred"
# example:
# train <- c(sample(1:47, 23), sample(48:72, 12))
# a <- inbaggB(golubMerge[100:110,], "ALL.AML", train, "PS")
#####################
#
#setGeneric("inbaggB", function(exprObj, classifLab, trainInd, intLab, pFUN, cFUN, metric="euclidean", ...){
#		standardGeneric("inbaggB")
#})
#
#setMethod("inbaggB", c("ExpressionSet", "character", "integer", "character", "ANY", "ANY", "ANY", "ANY"), 
#		function(exprObj, classifLab, trainInd, intLab, pFUN, cFUN, metric, ...){
#
##		if(missing(pFUN)){ pFUN <- list(list(model=lm)) }
#		if(missing(pFUN)){ pFUN <- NULL }
#		if(missing(cFUN)){ cFUN <- NULL }
#
#		cl <- pData(exprObj)[[classifLab]]
#		
#		if(length(intLab) > 1){ 
#			intDat <- pData(exprObj)[[intLab[1]]]
#			for( v in intLab[-1] ){
#				intDat <- cbind(intDat, pData(exprObj)[[v]])
#			}
#
#			intNam <- paste("i", ".", 1:length(intLab), sep="")
#			colnames(intDat) <- intNam
#			trainDat <- data.frame(intVar=intDat[trainInd,], y=cl[trainInd], t(exprs(exprObj)[,trainInd]))
#			testDat <- data.frame(intVar=intDat[-trainInd,], y=cl[-trainInd], t(exprs(exprObj)[,-trainInd]))
#
#			intEqu <- paste(colnames(trainDat)[1:length(intLab)], collapse="+")
#			equ <- paste("y", intEqu, ".", sep="~")
#			equ <- as.formula(equ)
#		}
#
#		else{
#			intDat <- pData(exprObj)[[intLab]]
#			trainDat <- data.frame(intVar=intDat[trainInd], y=cl[trainInd], t(exprs(exprObj)[,trainInd]))
#			testDat <- data.frame(intVar=intDat[-trainInd], y=cl[-trainInd], t(exprs(exprObj)[,-trainInd]))
#			
#			equ <- as.formula(y ~ intVar ~ .)
#		}
#
#		dis <- dist(t(exprs(exprObj)[,-trainInd]), method=metric)
#library(ipred)
#		tmp <- inbagg(equ, pFun=pFun, cFun=cFun, data=trainDat,  ...)
#		out <- predict(tmp, newdata=testDat)
#                new("classifOutput", method="inbagg",
#                        predLabels=newPredClass(as.character(out)),
#			trainInds=trainInd, allClass=as.character(pData(exprObj)[[classifLab]]),
#                        predScores=newProbMat(out$posterior),
#                        RObject=tmp, call=match.call(), distMat=dis)
#})
#
######################
## title: inclassB
## description: interface to inclass {ipred} 
## arguments:
##	exprObj		ExpressionSet
##	classifLab	character string specifying what covariate data 
##			to use for classification
##	trainInd	
## 	intLab 		single label or a vector of labels
##			representing names of the factors for intermediate variables
##	metric		for distance matrix 
## value:
## 	object of class "classifPred"
## note:
## 	cFUN argument requires a function object which assigns
##	the response labels using the intermediate variable(s).
##	This function should have a single input argument "newdata".
##	Internally inclass applies this cFUN function to new data 
##	that is stored as a data frame in such a way that the 
##	intermediate variables occupy the last columns of the data
##	frame.
##	The example below uses a single intermediate variable ("PS" 
##	values) and samples with PS values > 0.75 are
##	classified as ALL (this is all made up, it's intended to illustrate
##	the function usage), otherwise AML. 
## example:
## train <- c(sample(1:47, 23), sample(48:72, 12))
## psclassify <- function(newdata){
##		xx <- ifelse((newdata[,ncol(newdata)] > 0.75), "ALL", "AML")
##		as.factor(xx)
## }
## a <- inclassB(golubMerge[100:110,], "ALL.AML", train, "PS", cFUN=psclassify)
######################
#
#setGeneric("inclassB", function(exprObj, classifLab, trainInd, intLab, pFUN, cFUN, metric="euclidean", ...){
#		standardGeneric("inclassB")
#}) 
#
#setMethod("inclassB", c("ExpressionSet", "character", "integer", "character", "ANY", "ANY", "ANY", "ANY"), 
#			function(exprObj, classifLab, trainInd, intLab, pFUN, cFUN, metric, ...){
#
#		if(missing(pFUN)){ pFUN <- list(list(model=lm)) }
#	
#		cl <- pData(exprObj)[[classifLab]]
#	
#		if(length(intLab) > 1){ 
#
#			intDat <- pData(exprObj)[[intLab[1]]]
#			for( v in intLab[-1] ){
#				intDat <- cbind(intDat, pData(exprObj)[[v]])
#			}
#
#			intNam <- paste("i", ".", 1:length(intLab), sep="")
#			colnames(intDat) <- intNam
#
#			trainDat <- data.frame(intVar=intDat[trainInd,], y=cl[trainInd], t(exprs(exprObj)[,trainInd]))
#			testDat <- data.frame(intVar=intDat[-trainInd,], y=cl[-trainInd], t(exprs(exprObj)[,-trainInd]))
#	
#			intEqu <- paste(colnames(trainDat)[1:length(intLab)], collapse="+")
#			equ <- paste("y", intEqu, ".", sep="~")
#			equ <- as.formula(equ)
#		}
#
#		else{
#			intDat <- pData(exprObj)[[intLab]]
#			trainDat <- data.frame(intVar=intDat[trainInd], y=cl[trainInd], t(exprs(exprObj)[,trainInd]))
#			testDat <- data.frame(intVar=intDat[-trainInd], y=cl[-trainInd], t(exprs(exprObj)[,-trainInd]))
#			equ <- as.formula(y ~ intVar ~ .)
#		}
#
#		dis <- dist(t(exprs(exprObj)[,-trainInd]), method=metric)
#		out <- ipred::inclass(equ, pFUN=pFUN, cFUN=cFUN, data=trainDat, ...)
#		new("classifPred", sampLabels=predict(out, testDat, ...), distMat=dis, classifObj=out)
#})