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featureConstruction.R
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featureConstruction.R
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# TODO: Add comment
#
# Author: Ricardo Cachucho
###############################################################################
######################################################################################################
############Feature Construction using Search: Using corelation and Cross-Corelation##################
evoS.CCF.FC <- function(DData, IData, AIData, PParameter)
{
AFnames <- c("Mean","SD","Min","Max","RMS")
#AFnames <- c("Mean","Median","iqr","SD","Min","Max","RMS")
nDVariables <- length(AIData$Variables.Names)
nFeatures <- length(AFnames)
output.ncol <- (nDVariables-1)*nFeatures
anOutput <- matrix(data=NA, nrow=1, ncol=output.ncol)
ncol.names <- vector(mode= "character", length = output.ncol)
p <- 1
############just for experimental section purposes##########
nCount <- 0
for ( i in 2:nDVariables)
{
variableName = AIData$Variables.Names[i]
for (k in 1:nFeatures)
{
AFname <- AFnames[k]
#Initial Population
oTopCor <- 0
first.Generation <- sapply(1:PParameter$nOperations, function(i) Find_Max_CCF(y=DData,
x=eval(parse(text = paste(AFname,"(IData$",variableName,",AIData$Variables.nrow,PParameter$Jump,",PParameter$Size[i],")",sep=""))),
max=PParameter$nOperations)[1,1])
names(first.Generation) <- sapply(1:PParameter$nOperations,
function(i) c(paste(variableName, AFname, PParameter$Size[i], sep="_")))
NAcount <- length(which(is.na(as.vector(first.Generation)) == TRUE))
if(NAcount == length(first.Generation))
next
nTopCor <- max(abs(first.Generation), na.rm = TRUE)
positive <- list(which(first.Generation == nTopCor))
negative <- list(which(first.Generation == (-nTopCor)))
top.Feature <- mapply(c, positive, negative, SIMPLIFY=T)
top.Feature <- names(first.Generation[as.vector(top.Feature)])
nCount <- nCount + PParameter$nOperations
#evolutionary process
k <- 1
while(nTopCor > oTopCor)
{
if(nTopCor < 0.2)
break
oTopCor <- nTopCor
seed <- as.numeric(unlist(strsplit(top.Feature,split="_"))[3])
limits <- c(1, PParameter$Size[length(PParameter$Size)])
#refining the search should discussed with someone!!!
width <- round(seq(max(limits[1],(seed-PParameter$Jump/k)),min(limits[2],(seed+PParameter$Jump/k)),length.out=PParameter$nOperations))
offspring <- sapply(1:PParameter$nOperations, function(i) Find_Max_CCF(y=DData,
x=eval(parse(text = paste(AFname,"(IData$",variableName,",AIData$Variables.nrow,PParameter$Jump,",width[i],")",sep=""))),
max=PParameter$nOperations)[1,1])
names(offspring) <- sapply(1:PParameter$nOperations,
function(i) c(paste(variableName, AFname, width[i], sep="_")))
nTopCor <- max(abs(offspring), na.rm = TRUE)
positive <- list(which(offspring == nTopCor))
negative <- list(which(offspring == (-nTopCor)))
top.Feature <- mapply(c, positive, negative, SIMPLIFY=T)
top.Feature <- names(offspring[as.vector(top.Feature)])
k <- k + 1
nCount <- nCount + PParameter$nOperations
}
#saving the feature
if(nTopCor > 0.2)
{
optimal <- as.numeric(unlist(strsplit(top.Feature,split="_"))[3])
aCCF <- Find_Max_CCF(y=DData,x=eval(parse(text = paste(AFname,"(IData$",variableName,",AIData$Variables.nrow,PParameter$Jump,",
optimal,")",sep=""))),max=PParameter$nOperations)
anOutput[1,p]<- aCCF[1,1]
ncol.names[p] <- c(paste(variableName, AFname, optimal, aCCF[1,2], sep="_"))
p <- p + 1
}
}
}
ncol.names <- ncol.names[1:p-1]
anOutput <- anOutput[,1:p-1]
names(anOutput) <- ncol.names
alternativeOutput <- list(nCount, anOutput)
return(alternativeOutput)
#return(anOutput)
}
#########################Cross Correlations: Enabling Lag Regressions#################################
Find_Max_CCF<- function(x,y,max)
{
d <- ccf(x, y, lag.max=max, plot = FALSE, na.action = na.pass)
cor = d$acf[,,1][1:max+1]
lag = d$lag[,,1][1:max+1]
cor_max = which.max(abs(cor))
res = data.frame(cor,lag)
res_max = res[cor_max,]
return(res_max)
}
evoS.cor.FC <- function(DData, IData, AIData, PParameter)
{
AFnames <- c("Mean","SD","Min","Max","RMS")
#AFnames <- c("Mean","Median","iqr","SD","Min","Max","RMS")
nDVariables <- length(AIData$Variables.Names)
nFeatures <- length(AFnames)
output.ncol <- (nDVariables-1)*nFeatures
anOutput <- matrix(data=NA, nrow=1, ncol=output.ncol)
ncol.names <- vector(mode= "character", length = output.ncol)
p <- 1
for ( i in 2:nDVariables)
{
variableName = AIData$Variables.Names[i]
for (k in 1:nFeatures)
{
AFname <- AFnames[k]
#Initial Population
oTopCor <- 0
first.Generation <- sapply(1:PParameter$nOperations, function(i) cor(y=DData,
x=eval(parse(text = paste(AFname,"(IData$",variableName,",AIData$Variables.nrow,PParameter$Jump,",PParameter$Size[i],")",sep="")))))
names(first.Generation) <- sapply(1:PParameter$nOperations,
function(i) c(paste(variableName, AFname, PParameter$Size[i], sep="_")))
NAcount <- length(which(is.na(as.vector(first.Generation)) == TRUE))
if(NAcount == length(first.Generation))
next
nTopCor <- max(abs(first.Generation), na.rm = TRUE)
positive <- list(which(first.Generation == nTopCor))
negative <- list(which(first.Generation == (-nTopCor)))
top.Feature <- mapply(c, positive, negative, SIMPLIFY=T)
top.Feature <- names(first.Generation[as.vector(top.Feature)])
#evolutionary process
k <- 1
while(nTopCor > oTopCor)
{
if(nTopCor < 0.2)
break
oTopCor <- nTopCor
seed <- as.numeric(unlist(strsplit(top.Feature, split="_"))[3])
limits <- c(1, PParameter$Size[length(PParameter$Size)])
#refining the search should discussed with someone!!!
width <- round(seq(max(limits[1],(seed-PParameter$Jump/k)),min(limits[2],(seed+PParameter$Jump/k)),length.out=PParameter$nOperations))
offspring <- sapply(1:PParameter$nOperations, function(i) cor(y=DData,
x=eval(parse(text = paste(AFname,"(IData$",variableName,",AIData$Variables.nrow,PParameter$Jump,",width[i],")",sep="")))))
names(offspring) <- sapply(1:PParameter$nOperations,
function(i) c(paste(variableName, AFname, width[i], sep="_")))
nTopCor <- max(abs(offspring), na.rm = TRUE)
positive <- list(which(offspring == nTopCor))
negative <- list(which(offspring == (-nTopCor)))
top.Feature <- mapply(c, positive, negative, SIMPLIFY=T)
top.Feature <- names(offspring[as.vector(top.Feature)])
k <- k + 1
}
#saving the feature
if(nTopCor > 0.2)
{
optimal <- as.numeric(unlist(strsplit(top.Feature, split="_"))[3])
anOutput[1,p]<- cor(y=DData,x=eval(parse(text = paste(AFname,"(IData$",variableName,",AIData$Variables.nrow,PParameter$Jump,",
optimal,")",sep=""))))
ncol.names[p] <- c(paste(variableName, AFname, optimal, sep="_"))
p <- p + 1
}
}
}
ncol.names <- ncol.names[1:p-1]
anOutput <- anOutput[,1:p-1]
names(anOutput) <- ncol.names
return(anOutput)
}
evoS.GainRatio.FC <- function(DData, IData, AIData, PParameter, dataToUse)
{
if(missing(dataToUse)) { # 'missing()' is used to test whether a value was specified as an argument
index <- seq(1,length(DData),1)
} else {
index <- dataToUse
}
AFnames <- c("Mean","SD","Min","Max","RMS")
#AFnames <- c("Mean","Median","iqr","SD","Min","Max","RMS")
nDVariables <- length(AIData$Variables.Names)
nFeatures <- length(AFnames)
output.ncol <- (nDVariables-1)*nFeatures
anOutput <- matrix(data=NA, nrow=1, ncol=output.ncol)
ncol.names <- vector(mode= "character", length = output.ncol)
p <- 1
for ( i in 2:nDVariables)
{
variableName = AIData$Variables.Names[i]
for (k in 1:nFeatures)
{
AFname <- AFnames[k]
#Initial Candidates
oTop <- 0
FeatMatrix <- matrix(data = NA, nrow = length(DData), ncol = PParameter$nOperations)
FeatMatrix <- sapply(1:PParameter$nOperations, function(i)
eval(parse(text = paste(AFname,"(IData$",variableName,",AIData$Variables.nrow,PParameter$Jump,",PParameter$Size[i],")",sep=""))))
colnames(FeatMatrix) <- sapply(1:PParameter$nOperations,
function(i) c(paste(variableName, AFname, PParameter$Size[i], sep="_")))
DataFrame <- data.frame(FeatMatrix,DData)
DataFrame <- DataFrame[index,]
colErase <- c()
for(i in 1:PParameter$nOperations)
{
if(length(unique(DataFrame[,i])) == 1)
colErase <- c(colErase,i)
}
if(length(colErase) == ncol(DataFrame)-1)
break
if(length(colErase) >= 1)
DataFrame <- DataFrame[,-colErase]
first.Generation <- attrEval(DData ~ ., DataFrame, estimator="GainRatio")
#Slecting the most promissing feature
nTop <- max(abs(first.Generation))
positive <- list(which(first.Generation == nTop))
negative <- list(which(first.Generation == (-nTop)))
top.Feature <- mapply(c, positive, negative, SIMPLIFY=T)
#evolutionary process
k <- 1
while(nTop > oTop)
{
#if(nTop < 0.2)
# break
oTop <- nTop
ifelse(length(top.Feature)==1, seed <- as.numeric(unlist(strsplit(names(top.Feature),split="_"))[3]),seed <- as.numeric(unlist(strsplit(rownames(top.Feature)[1],split="_"))[3]))
#seed <- as.numeric(unlist(strsplit(rownames(top.Feature)[1],split="_"))[3])
limits <- c(1, PParameter$Size[length(PParameter$Size)])
#refining the search should discussed with someone!!!
width <- round(seq(max(limits[1],(seed-PParameter$Jump/k)),min(limits[2],(seed+PParameter$Jump/k)),length.out=PParameter$nOperations))
FeatMatrix <- matrix(data = NA, nrow = length(DData), ncol = length(width))
FeatMatrix <- sapply(1:PParameter$nOperations, function(i)
eval(parse(text = paste(AFname,"(IData$",variableName,",AIData$Variables.nrow,PParameter$Jump,",width[i],")",sep=""))))
colnames(FeatMatrix) <- sapply(1:PParameter$nOperations,
function(i) c(paste(variableName, AFname, width[i], sep="_")))
DataFrame <- data.frame(FeatMatrix,DData)
DataFrame <- DataFrame[index,]
colErase <- c()
for(i in 1:PParameter$nOperations)
{
if(length(unique(DataFrame[,i])) == 1)
colErase <- c(colErase,i)
}
if(length(colErase) == ncol(DataFrame)-1)
break
if(length(colErase) >= 1)
DataFrame <- DataFrame[,-colErase]
offspring <- attrEval(DData ~ ., DataFrame, estimator="GainRatio")
nTop <- max(abs(offspring))
positive <- list(which(offspring == nTop))
negative <- list(which(offspring == (-nTop)))
top.Feature <- mapply(c, positive, negative, SIMPLIFY=T)
k <- k + 1
}
#saving the feature
if(nTop > 0.5)
{
ifelse(length(top.Feature)==1, optimal <- as.numeric(unlist(strsplit(names(top.Feature),split="_"))[3]),optimal <- as.numeric(unlist(strsplit(rownames(top.Feature)[1],split="_"))[3]))
#optimal <- as.numeric(unlist(strsplit(names(top.Feature[1]),split="_"))[3])
x <- eval(parse(text = paste(AFname,"(IData$",variableName,",AIData$Variables.nrow,PParameter$Jump,",optimal,")",sep="")))
DataFrame <- data.frame(x,DData)
DataFrame <- DataFrame[index,]
anOutput[1,p] <- attrEval(DData ~ ., DataFrame, estimator="GainRatio")
ncol.names[p] <- c(paste(variableName, AFname, optimal, sep="_"))
p <- p + 1
}
}
}
ncol.names <- ncol.names[1:p-1]
anOutput <- anOutput[,1:p-1]
names(anOutput) <- ncol.names
return(anOutput)
}