source("RSquared.R")

# rm(LiblineaR)
library(LiblineaR)

set.seed(1)
N=10
# Note that 1st record is negative
df=data.frame(x1 = (1:N)/N*10 + 2*rnorm(N), x2 = (1:N)/N*10 + 2*rnorm(N), x3 = (1:N)/N*10 + 2*rnorm(N))
df$y.regr = apply(as.matrix(df),1,mean) + 2*rnorm(N)
df$y.logical = df$y.regr > 5.5
df$y.int = ifelse(df$y.logical, 1L, -1L)
df$y.double = as.double(df$y.int)
df$y.char = as.character(df$y.int)
df$y.factor = factor(df$y.int)
df$y.factorRev = factor(df$y.int, levels=rev(levels(df$y.factor)))
df$y.factorExtra = factor(df$y.int, levels=c(-1,1,99), labels=c("no","yes","maybe"))
df$y.multiclass = cut(df$y.regr, breaks=c(-99,4,7,99))
# Expected: good classification performance, negative bias and positive sum of weights 

regrTargets = "y.regr"
classifTargets = setdiff(grep("^y",colnames(df), value=TRUE), regrTargets)
binTargets = setdiff(classifTargets,"y.multiclass")

testClassif = function(rev,yy,weighted,tt) {
	cat("Testing",rev,yy,weighted,tt,"\n")
	if(rev)
		is=1:nrow(df)
	else
		is=nrow(df):1
	nis = which(!df[is,"y.logical"])
	
	if(weighted) # class weights giving more importance to negative classes
		wi=c("1"=2,"TRUE"=2,"yes"=2, "(7,99]"=1, 
				 "(4,7]"=50,
				 "-1"=100,"FALSE"=100,"no"=100,"(-99,4]"=150)
	else
		wi=NULL
	
	y = df[is,yy]
	x = df[is,1:3]
	m = LiblineaR(x, y, type = tt, wi=wi)
	p = predict(m, newx = x)
	res=c(
		type=tt,
		target=yy,
		weighted=weighted,
		y1 = as.character(y[1]),
		perf=(mean(as.character(y)==as.character(p$predictions))), # >= 0.75
		perfNeg=(mean(as.character(y[nis])==as.character(p$predictions[nis]))), # >= 0.75
		dimW=paste(dim(m$W), collapse = " "),
		sumW=sum(m$W[,1:3]),
		biasW=m$W[1,][["Bias"]],
		classNames=paste(m$ClassNames, collapse = " "),
		yLev=paste(levels(y), collapse=" "),
		predLev=paste(levels(p$predictions), collapse=" "),
		yClass=class(y),
		predClass=class(p$predictions),
		weights=paste(colnames(m$W),"=",round(m$W[1,],3),collapse = " ; ")
	)
	
	#table(true=as.character(y),pred=as.character(p$predictions))
	
	return(res)
}

testRegr = function(rev,yy,tt) {
	cat("Testing",rev,yy,tt,"\n")
	
	if(rev)
		is=1:nrow(df)
	else
		is=nrow(df):1
	
	y = df[is,yy]
	x = df[is,1:3]
	m = LiblineaR(x, y, type = tt, svr_eps=.1)
	p = predict(m, newx = x)
	res=c(
		type=tt,
		target=yy,
		weighted=FALSE,
		y1 = as.character(y[1]),
		perf=RSquared(p$predictions, y), # >= 0.75
		perfNeg=0,
		dimW=paste(dim(m$W), collapse = " "),
		sumW=sum(m$W[,1:3]),
		biasW=m$W[1,][["Bias"]],
		classNames=paste(m$ClassNames, collapse = " "),
		yLev=paste(levels(y), collapse=" "),
		predLev=paste(levels(p$predictions), collapse=" "),
		yClass=class(y),
		predClass=class(p$predictions),
		weights=paste(colnames(m$W),"=",round(m$W[1,],3),collapse = " ; ")
	)
	
	return(res)
}


allRes=NULL
for(tt in 0:7) {
	for(weighted in c(FALSE,TRUE)) {
		for(rev in c(FALSE,TRUE)) {
			for (yy in classifTargets) {
				res = testClassif(rev,yy,weighted,tt)
				allRes=rbind(allRes,res)
			}
		}
	}
}

for(tt in 11:13) {
	for(rev in c(FALSE,TRUE)) {
		for (yy in regrTargets) {
			res = testRegr(rev,yy,tt)
			allRes=rbind(allRes,res)
		}
	}
}

allRes = as.data.frame(allRes,stringsAsFactors = F)
# Simple tests
allRes$dimOK=(allRes$type=="4" | allRes$target=="y.multiclass" | allRes$dimW=="1 4")
allRes$perfOK=(allRes$target=="y.multiclass" & allRes$perf>=.6 | 
							 	ifelse(allRes$weighted, allRes$perfNeg>=.9, allRes$perf>=.75))
allRes$sumOK=(!allRes$target%in%c("y.int","y.double") | allRes$type=="4" | as.numeric(allRes$sumW)>0)
allRes$biasOK=(!allRes$target%in%c("y.int","y.double") | allRes$type=="4" | as.numeric(allRes$biasW)<0)
allRes$levelsOK=(allRes$target%in%c("y.char","y.double") | (allRes$yLev==allRes$predLev & allRes$yClass==allRes$predClass))

# View(allRes)
# library(reshape2)
# print(dcast(allRes, dimOK + perfOK + levelsOK + sumOK + biasOK ~ .))

#===============================================================================
# Testing sparse matrices of various formats

# Sparsifying the iris dataset:
iS=apply(iris[,1:4],2,function(a){a[a<quantile(a,probs=c(0.25))]=0;return(a)})
y=iris[,5]

# Sparse matrix of class matrix.csr, matrix.csc, matrix.coo from SparseM package :
if(require(SparseM)){
  cat("Testing sparse matrices of class matrix.csr from package SparseM.\n")
  irisSparse<-as.matrix.csr(iS)
  acc=LiblineaR(data=irisSparse,target=y,type=0,cost=0.1,bias=1,verbose=FALSE,cross = 5)
  cat("Accuracy:",acc,"\n",sep=" ")
  
  cat("Testing sparse matrices of class matrix.csc from package SparseM.\n")
  irisSparse<-as.matrix.csc(iS)
  acc=LiblineaR(data=irisSparse,target=y,type=0,cost=0.1,bias=1,verbose=FALSE,cross = 5)
  cat("Accuracy:",acc,"\n",sep=" ")
  
  cat("Testing sparse matrices of class matrix.coo from package SparseM.\n")
  irisSparse<-as.matrix.coo(iS)
  acc=LiblineaR(data=irisSparse,target=y,type=0,cost=0.1,bias=1,verbose=FALSE,cross = 5)
  cat("Accuracy:",acc,"\n",sep=" ")
  
}

# Sparse matrix of class dgRMatrix, dgCMatrix, dgTMatrix from Matrix package :
if(require(Matrix)){
  cat("Testing sparse matrices of class dgCMatrix from package Matrix.\n")
  irisSparse<-as(iS,"sparseMatrix")
  acc=LiblineaR(data=irisSparse,target=y,type=0,cost=0.1,bias=1,verbose=FALSE,cross=5)
  cat("Accuracy:",acc,"\n",sep=" ")
  
  cat("Testing sparse matrices of class dgRMatrix from package Matrix.\n")
  irisSparse<-as(iS,"sparseMatrix")
  irisSparse<-as(as(irisSparse,"matrix"),"dgRMatrix")
  acc=LiblineaR(data=irisSparse,target=y,type=0,cost=0.1,bias=1,verbose=FALSE,cross=5)
  cat("Accuracy:",acc,"\n",sep=" ")
  
  cat("Testing sparse matrices of class dgTMatrix from package Matrix.\n")
  irisSparse<-as(iS,"sparseMatrix")
  irisSparse<-as(as(irisSparse,"matrix"),"dgTMatrix")
  acc=LiblineaR(data=irisSparse,target=y,type=0,cost=0.1,bias=1,verbose=FALSE,cross=5)
  cat("Accuracy:",acc,"\n",sep=" ")
  
}
#' 
#' #############################################