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benchmark-methods.R
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#' @importFrom stats sd
plot.normConstCalc <- function(NormCalcList)
{
# Plot the progress
normC=countsS=0
curVariance <- sapply(c(1:length(NormCalcList$partitionFuncTempAll)), function(x) stats::sd(NormCalcList$partitionFuncTempAll[1:x])/sqrt(x))
lowBound <- NormCalcList$partitionFunc-curVariance
upBound <- NormCalcList$partitionFunc+curVariance
df <- data.frame(normC=NormCalcList$partitionFunc,countsS=c(1:length(NormCalcList$partitionFunc)),lowBound=lowBound,upBound=upBound,type="Cool core")
color_list <- '#EE6677'
ggplot(data=df, aes(y = normC, x = countsS))+
geom_line(aes(y = normC, x = countsS,color=color_list)) +
geom_ribbon(aes(ymin=lowBound, ymax=upBound, fill=color_list),alpha=0.5) +
theme(legend.position = "none") +
xlab("Sampling Step") +
ylab("Normalizing Constant")
}
#' @importFrom stats sd
#' @importFrom RColorBrewer brewer.pal
plot.samplingProgress <- function(sampleResults, exactValue = NULL)
{
# Plot the progress
normC=countsS=group=Method=0
lengthMethods <- length(sampleResults)
# set colors
if(lengthMethods>3){
N_colors <- lengthMethods
color_list <- brewer.pal(n = N_colors, name = 'RdYlBu')
}else{
N_colors <- 3
color_list <- brewer.pal(n = N_colors, name = 'RdYlBu')[1:lengthMethods]
}
# without Rcolorbrewer:
# color_list <- c("#EE6677", "#228833", "#4477AA")
dfAll <- NULL
Smethod <- NULL
for (t0 in 1:length(sampleResults)){
NormCalcList <- sampleResults[[t0]]
curVariance <- sapply(c(1:length(NormCalcList$partitionFuncTempAll)),
function(x) stats::sd(NormCalcList$partitionFuncTempAll[1:x])/sqrt(x))
lowBound <- NormCalcList$partitionFunc-curVariance
upBound <- NormCalcList$partitionFunc+curVariance
timeScale <- c(1:length(NormCalcList$partitionFunc))/length(NormCalcList$partitionFunc)*NormCalcList$elapsed_time[[1]]
df <- data.frame(normC=NormCalcList$partitionFunc,
countsS=timeScale,
lowBound=lowBound,upBound=upBound)
df$group <- t0
df$Method <- NormCalcList$method
Smethod <- c(Smethod,NormCalcList$method)
dfAll <- rbind(dfAll, df)
}
if(length(exactValue)==0){
ggplot(data=dfAll, aes(y = normC, x = countsS, group=group))+
geom_line(aes(y = normC, x = countsS,colour=Method)) +
geom_ribbon(aes(ymin=lowBound, ymax=upBound, fill=Method),alpha=0.5) +
scale_colour_manual(values=color_list) +
scale_fill_manual(values=color_list) +
# guides(fill = FALSE) +
guides(col = FALSE) +
xlab("Elapsed Time (s)") +
ylab("Normalizing Constant")
}else{
ggplot(data=dfAll, aes(y = normC, x = countsS, group=group))+
geom_line(aes(y = normC, x = countsS,colour=Method)) +
geom_ribbon(aes(ymin=lowBound, ymax=upBound, fill=Method),alpha=0.5) +
scale_colour_manual(values=color_list) +
scale_fill_manual(values=color_list) +
geom_hline(yintercept=exactValue, linetype="dashed", color = "black") +
# guides(fill = FALSE) +
guides(col = FALSE) +
xlab("Elapsed Time (s)") +
ylab("Normalizing Constant")
}
}
benchmark <- function(BayesNet = NULL, obs = NULL, methods = c("FS","GS", "SGS"), N_samples = 500, N_nodes = 30, exactValue = FALSE)
{
# benchmark different sampling schemes
# sample Bayesian network and observation if missing
if(length(BayesNet)==0){
N_nodes <- N_nodes #10
N_neighbours <- 2
nodeDim <- 2
BayesNet <- randomBN(N_nodes, N_neighbours, nodeDim, visualize = FALSE)
}
if(length(obs)==0){
N_obs <- as.integer(N_nodes/2)
obs <- list("observed.vars" = sample(1:N_nodes, N_obs), "observed.vals" = rep(1,N_obs))
}
# calc exact result
if(exactValue){
exactVal <- calcExactInference(BayesNet, obs)
}
# sample by method
sampleResults <- list()
for (b0 in 1:length(methods)){
# adjust the number of samples to the method as some sample faster
# (for plotting purposes only)
if(methods[b0]=="GS"){
N_samplesTemp <- as.integer(N_samples/2.5)
}else if(methods[b0]=="FS"){
N_samplesTemp <- N_samples*2
}else if(methods[b0]=="SGS"){
N_samplesTemp <- N_samples*3
}else{
N_samplesTemp <- N_samples
}
# perform sampling
start_time <- Sys.time() # measure computation time
sampleResults[[b0]] <- approxInference(BayesNet, obs, s_method = methods[b0], N_samples = N_samplesTemp, returnList=TRUE)
end_time <- Sys.time()
elapsed_time <- end_time - start_time
sampleResults[[b0]]$elapsed_time <- elapsed_time
sampleResults[[b0]]$method <- methods[b0]
# if(methods[b0]=="SGS"){
# exactValue <- sampleResults[[b0]]$partitionFunc[N_samples]
# }
}
# plot the results (i.e. sampling progress)
if(exactValue){
plot.samplingProgress(sampleResults, exactVal)
}else{
plot.samplingProgress(sampleResults)
}
}
#
# benchmarkExtensive <- function(BayesNet = NULL, obs = NULL, methods = c("FS","GS", "SGS"), N_samples = 500, N_nodes = 30)
# {
# # benchmark different sampling schemes
#
# # sample Bayesian network and observation if missing
# if(length(BayesNet)==0){
# N_nodes <- N_nodes #10
# N_neighbours <- 2
# nodeDim <- 2
# BayesNet <- randomBN(N_nodes, N_neighbours, nodeDim, visualize = FALSE)
# }
# exactValue <- NULL
# if(length(obs)==0){
# N_obs <- as.integer(N_nodes/2)
# obs <- list("observed.vars" = sample(1:N_nodes, N_obs), "observed.vals" = rep(1,N_obs))
# # exactValue <- (1/nodeDim)^N_obs
# }
#
# #
# sampleResults <- list()
# for (b0 in 1:length(methods)){
# # adjust the number of samples to the method as some sample faster
# # (for plotting purposes only)
# if(methods[b0]=="GS"){
# N_samplesTemp <- as.integer(N_samples/4)
# }else if(methods[b0]=="FS"){
# N_samplesTemp <- N_samples*2
# }else{
# N_samplesTemp <- N_samples
# }
#
# # perform sampling
# start_time <- Sys.time() # measure computation time
# sampleResults[[b0]] <- approxInference(BayesNet, obs, s_method = methods[b0], N_samples = N_samplesTemp, returnList=TRUE)
# end_time <- Sys.time()
# elapsed_time <- end_time - start_time
# sampleResults[[b0]]$elapsed_time <- elapsed_time
# sampleResults[[b0]]$method <- methods[b0]
#
# # if(methods[b0]=="SGS"){
# # exactValue <- sampleResults[[b0]]$partitionFunc[N_samples]
# # }
# }
#
# # plot the results (i.e. sampling progress)
# if(length(exactValue)==0){
# plot.samplingProgress(sampleResults)
# }else{
# plot.samplingProgress(sampleResults, exactValue)
# }
#
# }
benchmarkStudy <- function(BayesNet = NULL, obs = NULL, methods = c("FS","GS", "SGS"), N_samples = 500, N_nodes = 30)
{
# benchmark different sampling schemes
# sample Bayesian network and observation if missing
if(length(BayesNet)==0){
N_nodes <- N_nodes
N_neighbours <- 2
nodeDim <- 2
BayesNet <- randomBN(N_nodes, N_neighbours, nodeDim, visualize = FALSE)
}
exactValue <- NULL
if(length(obs)==0){
N_obs <- as.integer(N_nodes/2)
obs <- list("observed.vars" = sample(1:N_nodes, N_obs), "observed.vals" = rep(1,N_obs))
# exactValue <- (1/nodeDim)^N_obs
}
#
sampleResults <- list()
for (b0 in 1:length(methods)){
# adjust the number of samples to the method as some sample faster
# (for plotting purposes only)
if(methods[b0]=="GS"){
N_samplesTemp <- as.integer(N_samples/2)
}else if(methods[b0]=="FS"){
N_samplesTemp <- N_samples*2
}else if(methods[b0]=="SGS"){
N_samplesTemp <- N_samples*3
}else{
N_samplesTemp <- N_samples
}
# perform sampling
start_time <- Sys.time() # measure computation time
sampleResults[[b0]] <- approxInference(BayesNet, obs, s_method = methods[b0], N_samples = N_samplesTemp, returnList=TRUE)
end_time <- Sys.time()
elapsed_time <- end_time - start_time
sampleResults[[b0]]$elapsed_time <- elapsed_time
sampleResults[[b0]]$method <- methods[b0]
# if(methods[b0]=="SGS"){
# exactValue <- sampleResults[[b0]]$partitionFunc[N_samples]
# }
}
# # plot the results (i.e. sampling progress)
# if(length(exactValue)==0){
# plot.samplingProgress(sampleResults)
# }else{
# plot.samplingProgress(sampleResults, exactValue)
# }
return(sampleResults)
}
benchmarkStudyMain <- function(BayesNet = NULL, obs = NULL, methods = c("FS","GS", "SGS"), N_samples = 500, N_rep = 10, N_nodes = 30, exactValue = TRUE)
{
# repeat the benchmarking N_rep times
# calc exact result
if(exactValue){
exactVal <- exactInference(BayesNet, obs)
}
benchmarkResults <- list()
for (i in 1:N_rep){
tempRes <- benchmarkStudy(BayesNet, obs, methods, N_samples, N_nodes)
benchmarkResults[[i]] <- tempRes
}
if(exactValue){
myResults <- processAndPlot(benchmarkResults,exactVal)
myResults$simulationDetails <- list("BayesNet"=BayesNet, "obs"=obs, "methods"=methods, "N_samples"=N_samples, "N_rep"=N_rep)
return(myResults)
}#else{
# plot.samplingProgressStudy(benchmarkResults)
#}
}
#' @importFrom RColorBrewer brewer.pal
#' @importFrom ggpubr ggarrange
processAndPlot <- function(benchmarkResults, exactVal)
{
normC=countsS=group=Method=0
# Plot the progress
lengthMethods <- length(benchmarkResults[[1]])
lengthRes <- length(benchmarkResults)
# set colors
if(lengthMethods>3){
N_colors <- lengthMethods
color_list <- brewer.pal(n = N_colors, name = 'RdYlBu')
}else{
N_colors <- 3
color_list <- brewer.pal(n = N_colors, name = 'RdYlBu')[1:lengthMethods]
}
# without Rcolorbrewer:
# color_list <- c("#EE6677", "#228833", "#4477AA")
dfAll <- NULL
df2All <- NULL
Smethod <- NULL
# process results for plot
for (t0 in 1:lengthMethods){
# NormCalcList <- benchmarkResults[[tt0]][[t0]]
curMean <- rowMeans(sapply(1:lengthRes, function(x) benchmarkResults[[x]][[t0]]$partitionFunc))
timeMean <- mean(sapply(1:lengthRes, function(x) benchmarkResults[[x]][[t0]]$elapsed_time[[1]]))
curSTD <- sqrt(RowVar(sapply(1:lengthRes, function(x) benchmarkResults[[x]][[t0]]$partitionFunc)))
MSE <- rowMeans(sapply(1:lengthRes, function(x) (benchmarkResults[[x]][[t0]]$partitionFunc-exactVal)^2))
# RMSE <- sqrt(rowMeans(sapply(1:lengthRes, function(x) (benchmarkResults[[x]][[t0]]$partitionFunc-exactVal)^2)))
NRMSE <- sqrt(rowMeans(sapply(1:lengthRes, function(x) (benchmarkResults[[x]][[t0]]$partitionFunc-exactVal)^2)))/exactVal
# curVariance <- sapply(c(1:length(NormCalcList$partitionFuncTempAll)),
# function(x) sd(NormCalcList$partitionFuncTempAll[1:x])/sqrt(x))
lowBound <- curMean-curSTD
upBound <- curMean+curSTD
timeScale <- c(1:length(curMean))/length(curMean)*timeMean
df <- data.frame(normC=curMean, stdEr=curSTD, MSE=MSE, NRMSE=NRMSE,
countsS=timeScale,
lowBound=lowBound,upBound=upBound)
df$group <- t0
df$Method <- benchmarkResults[[1]][[t0]]$method
timeOfMeasurmentForBoxPlot <- 0.2 # set the fixed time
timePointForBoxPlotRes <- which.min(abs(timeScale-timeOfMeasurmentForBoxPlot))
BoxPlotRes <- sapply(1:lengthRes, function(x) (benchmarkResults[[x]][[t0]]$partitionFunc[timePointForBoxPlotRes]-exactVal)^2)
BoxPlotResNRMS <- sapply(1:lengthRes, function(x) sqrt((benchmarkResults[[x]][[t0]]$partitionFunc[timePointForBoxPlotRes]-exactVal)^2)/exactVal )
df2 <- data.frame(BoxPlotRes=BoxPlotRes, BoxPlotResNRMS=BoxPlotResNRMS)
df2$group <- t0
df2$Method <- benchmarkResults[[1]][[t0]]$method
df2$timePoint <- timePointForBoxPlotRes
df2$timePointInSec <- timeOfMeasurmentForBoxPlot
Smethod <- c(Smethod,df$Method)
dfAll <- rbind(dfAll, df)
df2All <- rbind(df2All, df2)
}
# plot
p1 <- ggplot(data=dfAll, aes(y = normC, x = countsS, group=group))+
geom_line(aes(y = normC, x = countsS,colour=Method)) +
geom_ribbon(aes(ymin=lowBound, ymax=upBound, fill=Method),alpha=0.5) +
scale_colour_manual(values=color_list) +
scale_fill_manual(values=color_list) +
geom_hline(yintercept=exactVal, linetype="dashed", color = "black") +
# guides(fill = FALSE) +
guides(col = FALSE) +
xlab("Elapsed Time (s)") +
ylab("Normalizing Constant")
# plot the MSE
p2 <- ggplot(data=dfAll, aes(y = MSE, x = countsS, group=group))+
geom_line(aes(y = MSE, x = countsS,colour=Method)) +
scale_colour_manual(values=color_list) +
# guides(fill = FALSE) +
# guides(col = FALSE) +
xlab("Elapsed Time (s)") +
ylab("MSE")
# make a boxplot of the results
p3 <- ggplot(data=df2All, aes(x=Method, y=BoxPlotRes, colour=Method)) +
geom_boxplot()+
geom_jitter(position=position_jitter(0.8))+
scale_colour_manual(values=color_list)+
theme(legend.position = "none")+
ylab(paste0("MSE (", timeOfMeasurmentForBoxPlot, "s)"))
p4 <- ggarrange(
p2, # First row with line plot
# Second row with box and dot plots
ggarrange(p1, p3, ncol = 2, labels = c("B", "C")),
nrow = 2,
labels = "A" # Label of the line plot
)
# plot the NRMSE
p5 <- ggplot(data=dfAll, aes(y = NRMSE, x = countsS, group=group))+
geom_line(aes(y = NRMSE, x = countsS,colour=Method)) +
scale_colour_manual(values=color_list) +
# guides(fill = FALSE) +
# guides(col = FALSE) +
xlab("Elapsed Time (s)") +
ylab("NRMSE")
# make a boxplot of the results
p6 <- ggplot(data=df2All, aes(x=Method, y=BoxPlotResNRMS, colour=Method)) +
geom_boxplot()+
geom_jitter(position=position_jitter(0.8))+
scale_colour_manual(values=color_list)+
theme(legend.position = "none")+
ylab(paste0("NRMSE (", timeOfMeasurmentForBoxPlot, "s)"))
return(list("RAWbenchmarkResults"=benchmarkResults,"results_byTime"=dfAll, "results_final"=df2All, "plot.MSE"=p2, "plot.NC"=p1, "plot.boxplotMSE"=p3, "plot.summary"=p4, "plot.NRMSE"=p5, "plot.boxplotNRMSE"=p6))
}
# plot.samplingProgressStudy <- function(benchmarkResults, exactValue = NULL)
# {
# # Plot the progress
#
# lengthMethods <- length(benchmarkResults[[1]])
# lengthRes <- length(benchmarkResults)
#
# # set colors
# if(lengthMethods>3){
# N_colors <- lengthMethods
# color_list <- brewer.pal(n = N_colors, name = 'RdYlBu')
# }else{
# N_colors <- 3
# color_list <- brewer.pal(n = N_colors, name = 'RdYlBu')[1:lengthMethods]
# }
# # without Rcolorbrewer:
# # color_list <- c("#EE6677", "#228833", "#4477AA")
#
# dfAll <- NULL
# df2All <- NULL
# Smethod <- NULL
#
# # process results for plot
# for (t0 in 1:lengthMethods){
# # NormCalcList <- benchmarkResults[[tt0]][[t0]]
# curMean <- rowMeans(sapply(1:lengthRes, function(x) benchmarkResults[[x]][[t0]]$partitionFunc))
# timeMean <- mean(sapply(1:lengthRes, function(x) benchmarkResults[[x]][[t0]]$elapsed_time[[1]]))
# curSTD <- sqrt(RowVar(sapply(1:lengthRes, function(x) benchmarkResults[[x]][[t0]]$partitionFunc)))
# # curVariance <- sapply(c(1:length(NormCalcList$partitionFuncTempAll)),
# # function(x) sd(NormCalcList$partitionFuncTempAll[1:x])/sqrt(x))
# lowBound <- curMean-curSTD
# upBound <- curMean+curSTD
# timeScale <- c(1:length(curMean))/length(curMean)*timeMean
#
# df <- data.frame(normC=curMean, stdEr=curSTD,
# countsS=timeScale,
# lowBound=lowBound,upBound=upBound)
# df$group <- t0
# df$Method <- benchmarkResults[[1]][[t0]]$method
#
# timePointForBoxPlotRes <- which.min(abs(timeScale-0.1))
#
# BoxPlotRes <- sapply(1:lengthRes, function(x) benchmarkResults[[x]][[t0]]$partitionFunc[timePointForBoxPlotRes])
#
# df2 <- data.frame(BoxPlotRes=BoxPlotRes)
#
# df2$group <- t0
# df2$Method <- benchmarkResults[[1]][[t0]]$method
# df2$timePoint <- timePointForBoxPlotRes
#
# Smethod <- c(Smethod,df$Method)
# dfAll <- rbind(dfAll, df)
# df2All <- rbind(df2All, df2)
# }
#
# # plot
# if(length(exactValue)==0){
# p1 <- ggplot(data=dfAll, aes(y = normC, x = countsS, group=group))+
# geom_line(aes(y = normC, x = countsS,colour=Method)) +
# geom_ribbon(aes(ymin=lowBound, ymax=upBound, fill=Method),alpha=0.5) +
# scale_colour_manual(values=color_list) +
# scale_fill_manual(values=color_list) +
# # guides(fill = FALSE) +
# guides(col = FALSE) +
# xlab("Elapsed Time (s)") +
# ylab("Normalizing Constant")
# }else{
# ggplot(data=dfAll, aes(y = normC, x = countsS, group=group))+
# geom_line(aes(y = normC, x = countsS,colour=Method)) +
# geom_ribbon(aes(ymin=lowBound, ymax=upBound, fill=Method),alpha=0.5) +
# scale_colour_manual(values=color_list) +
# scale_fill_manual(values=color_list) +
# geom_hline(yintercept=exactValue, linetype="dashed", color = "black") +
# # guides(fill = FALSE) +
# guides(col = FALSE) +
# xlab("Elapsed Time (s)") +
# ylab("Normalizing Constant")
# }
#
# # plot
# if(length(exactValue)==0){
# p2 <- ggplot(data=dfAll, aes(y = stdEr, x = countsS, group=group))+
# geom_line(aes(y = stdEr, x = countsS,colour=Method)) +
# scale_colour_manual(values=color_list) +
# # guides(fill = FALSE) +
# # guides(col = FALSE) +
# xlab("Elapsed Time (s)") +
# ylab("Standard Error")
# }else{
# ggplot(data=dfAll, aes(y = normC, x = countsS, group=group))+
# geom_line(aes(y = normC, x = countsS,colour=Method)) +
# geom_ribbon(aes(ymin=lowBound, ymax=upBound, fill=Method),alpha=0.5) +
# scale_colour_manual(values=color_list) +
# scale_fill_manual(values=color_list) +
# geom_hline(yintercept=exactValue, linetype="dashed", color = "black") +
# # guides(fill = FALSE) +
# guides(col = FALSE) +
# xlab("Elapsed Time (s)") +
# ylab("Standard Error")
# }
#
# # make a boxplot of the results
#
# p3 <- ggplot(df2All, aes(x=Method, y=BoxPlotRes, colour=Method)) +
# geom_boxplot()+
# scale_colour_manual(values=color_list)+
# theme(legend.position = "none")
#
# ggarrange(
# p2, # First row with line plot
# # Second row with box and dot plots
# ggarrange(p1, p3, ncol = 2, labels = c("B", "C")),
# nrow = 2,
# labels = "A" # Label of the line plot
# )
#
# # ggarrange(
# # p2, # First row with line plot
# # # Second row with box and dot plots
# # ggarrange(p1, p3, ncol = 2),
# # nrow = 2
# # )
#
# }
RowVar <- function(x, ...) {
rowSums((x - rowMeans(x, ...))^2, ...)/(dim(x)[2] - 1)
}
plot.BNwithObs <- function(myBN, myObs){
# plot the Bayes net with observations in grey
nodeCol <- rep('white',num.nodes(myBN))
nodeCol[myObs$observed.vars] <- rep('grey',length(myObs$observed.vars))
plot.BN(myBN, node.col = nodeCol)
}
#' @importFrom RColorBrewer brewer.pal
#' @importFrom graphics par
plot.BNwithSubGroups <- function(myBN, myObs, visualizeAll=FALSE){
# plot BN with subgroups in color and corresponding observations in grey
mySubGroups <- get.allSubGroups(myBN@dag, myObs$observed.vars)
N_colors <- length(mySubGroups$allChiEvidence)
color_list <- brewer.pal(n = (N_colors+2), name = 'Greys')[2:(N_colors+1)]
color_list2 <- brewer.pal(n = N_colors, name = 'RdYlBu')
nodeCol <- rep('white',num.nodes(myBN))
for (yy in 1:length(mySubGroups$allSubGroups)){
nodeCol[mySubGroups$allSubGroups[[yy]]] <- rep(color_list2[yy],length(mySubGroups$allSubGroups[[yy]]))
}
for (yy in 1:length(mySubGroups$allChiEvidence)){
nodeCol[mySubGroups$allChiEvidence[[yy]]] <- rep(color_list[yy],length(mySubGroups$allChiEvidence[[yy]]))
}
# nodeCol[myObs$observed.vars] <- rep('grey',length(myObs$observed.vars))
plot.BN(myBN, node.col = nodeCol)
if(visualizeAll){
dim2 <- ceiling(sqrt(length(mySubGroups$allSubGroups)))
dim1 <- ceiling(length(mySubGroups$allSubGroups)/dim2)
storeMar <- par("mar")
par(mfrow=c(dim1,dim2),mar=c(1,1,1,1))
for (jj in 1:length(mySubGroups$allSubGroups)){
subBN <- get.subBN(myBN, c(mySubGroups$allEvidenceSubGroups[[jj]],mySubGroups$allSubGroups[[jj]]))
plot.BN(subBN)
}
par(mfrow = c(1,1))
par(mar=storeMar)
}
}
#' Benchmark Inference Methods
#'
#' Outputs benchmark results of different inference methods
#'
#' @param N_var number of variables
#' @param N_Obs number of observations
#' @param N_nets number of Bayes nets
#' @param N_rep number of repetitions
#' @param N_samples number of samples
#' @param samplingMethods benchmarked sampling methods
#' @param DAGmethod DAG method
#' @param N_neighbours number of neighbours
#' @param nodeDim category size
#' @return benchmark results
#' @export
benchmarkMultipleNets <- function(N_var, N_Obs, N_nets, N_rep, N_samples=100, samplingMethods=c("GS","SGS", "LBP"),DAGmethod="er", N_neighbours=2, nodeDim=2){
# benchmark multiple Bayes nets of same size
resultTable <<- list()
for (o1 in 1:N_nets){
print(paste0("Benchmarking Bayes net #", o1, " of ", N_nets))
start_time <- Sys.time() # measure computation time
# Simulate a random BN and observations
myBN <- randomBN(N_var, method=DAGmethod, N_neighbours=N_neighbours, nodeDim=nodeDim)#, N_neighbours=2) #, uniformCPTs = FALSE)
myObs <- list("observed.vars" = sample(1:N_var, N_Obs), "observed.vals" = rep(1,N_Obs))
# perform benchmark
resultTable[[o1]] <- benchmarkStudyMain(myBN,myObs,methods=samplingMethods,N_rep=N_rep,N_samples=N_samples)
end_time <- Sys.time()
elapsed_time <- end_time - start_time
print(elapsed_time)
}
return(resultTable)
}
# ProcessResultTable <- function(myResultTable){
#
# averageResults_byTime <- myResultTable[[1]]$results_byTime
# averageResults_final <- myResultTable[[1]]$results_final
#
# lengthMethods <- length(myResultTable[[1]]$simulationDetails$methods)
#
# averageResults_byTime$MSE <- rowMeans(sapply(1:length(myResultTable), function(x) myResultTable[[x]]$results_byTime$MSE))
# averageResults_byTime$countsS <- rowMeans(sapply(1:length(myResultTable), function(x) myResultTable[[x]]$results_byTime$countsS))
# averageResults_final$MSE <- rowMeans(sapply(1:length(myResultTable), function(x) myResultTable[[x]]$results_final$BoxPlotRes))
#
# # set colors
# if(lengthMethods>3){
# N_colors <- lengthMethods
# color_list <- brewer.pal(n = N_colors, name = 'RdYlBu')
# }else{
# N_colors <- 3
# color_list <- brewer.pal(n = N_colors, name = 'RdYlBu')[1:lengthMethods]
# }
#
# # plot the MSE
# p2 <- ggplot(data=averageResults_byTime, aes(y = MSE, x = countsS, group=group))+
# geom_line(aes(y = MSE, x = countsS,colour=Method)) +
# scale_colour_manual(values=color_list) +
# # guides(fill = FALSE) +
# # guides(col = FALSE) +
# xlab("Elapsed Time (s)") +
# ylab("MSE") # + xlim(-0.01, 0.2) + ylim(-0.1e-11, 0.75e-11)
#
# # make a boxplot of the results
# p3 <- ggplot(data=averageResults_final, aes(x=Method, y=BoxPlotRes, colour=Method)) +
# geom_boxplot()+
# scale_colour_manual(values=color_list)+
# theme(legend.position = "none")+
# ylab(paste0("MSE (", averageResults_final$timePointInSec, "s)"))
#
# p4 <- ggplot(data=averageResults_final, aes(x=Method, y=BoxPlotRes, colour=Method)) +
# geom_boxplot()+
# scale_colour_manual(values=tail(color_list,2))+
# theme(legend.position = "none")+
# ylab(paste0("MSE (", averageResults_final$timePointInSec, "s)"))+
# scale_x_discrete(limits=c("LBP","SGS"))
#
# print(p2)
#
# print(p3)
#
# print(p4)
# }
#' @importFrom utils tail
ProcessResultTableNRMSE <- function(myResultTable, returnResults=FALSE){
NRMSE=countsS=group=Method=BoxPlotResNRMS=0
averageResults_byTime <- myResultTable[[1]]$results_byTime
averageResults_final <- myResultTable[[1]]$results_final
lengthMethods <- length(myResultTable[[1]]$simulationDetails$methods)
averageResults_byTime$NRMSE <- rowMeans(sapply(1:length(myResultTable), function(x) myResultTable[[x]]$results_byTime$NRMSE))
averageResults_byTime$countsS <- rowMeans(sapply(1:length(myResultTable), function(x) myResultTable[[x]]$results_byTime$countsS))
averageResults_final <- myResultTable[[1]]$results_final
for (h1 in 2:length(myResultTable)){
averageResults_final <- rbind(averageResults_final,myResultTable[[h1]]$results_final)
}
# averageResults_final$BoxPlotResNRMS <- sapply(1:length(myResultTable), function(x) myResultTable[[x]]$results_final$BoxPlotResNRMS)
# set colors
if(lengthMethods>3){
N_colors <- lengthMethods
color_list <- brewer.pal(n = N_colors, name = 'RdYlBu')
}else{
N_colors <- 3
color_list <- brewer.pal(n = N_colors, name = 'RdYlBu')[1:lengthMethods]
}
if(returnResults==TRUE){
return(list("averageResults_byTime"=averageResults_byTime, "averageResults_final"=averageResults_final))
}
# plot the MSE
p2 <- ggplot(data=averageResults_byTime, aes(y = NRMSE, x = countsS, group=group))+
geom_line(aes(y = NRMSE, x = countsS,colour=Method)) +
scale_colour_manual(values=color_list) +
# guides(fill = FALSE) +
# guides(col = FALSE) +
xlab("Elapsed Time (s)") +
ylab("NRMSE") # + xlim(-0.01, 0.2) + ylim(-0.1e-11, 0.75e-11)
# make a boxplot of the results
p3 <- ggplot(data=averageResults_final, aes(x=Method, y=BoxPlotResNRMS, colour=Method)) +
geom_boxplot()+
geom_jitter(position=position_jitter(0.8))+
scale_colour_manual(values=color_list)+
theme(legend.position = "none")+
ylab(paste0("NRMSE (", averageResults_final$timePointInSec, "s)"))
p4 <- ggplot(data=averageResults_final, aes(x=Method, y=BoxPlotResNRMS, colour=Method)) +
geom_boxplot()+
geom_jitter(position=position_jitter(0.8))+
scale_colour_manual(values=utils::tail(color_list,2))+
theme(legend.position = "none")+
ylab(paste0("NRMSE (", averageResults_final$timePointInSec, "s)"))+
scale_x_discrete(limits=c("LBP","SGS"))
print(p2)
print(p3)
print(p4)
}
# pathfinderNet <- readRDS("/Users/frbayer/Downloads/pathfinder.rds")
#
# hailfinder <- readRDS("/Users/frbayer/Downloads/hailfinder.rds")
#
#
# pathfinderNet
#
# tempBN <- convert_bnlearn(hailfinder)
# convert_bnlearn <- function(BNconvert){
# # convert Bayes net of type "bnlearn" to type "SubGroupSeparation"
#
# lengthBNC <- length(BNconvert)
#
# # create new BN
# tempBN <- BN()
# name(tempBN) <- "converted Bayes net"
# num.nodes(tempBN) <- lengthBNC
# variables(tempBN) <- names(BNconvert)
# discreteness(tempBN) <- rep(TRUE, lengthBNC)
# node.sizes(tempBN) <- sapply(1:lengthBNC, function(x) unname(dim(BNconvert[[x]]$prob)[1]))
# cpts(tempBN) <- sapply(1:lengthBNC, function(x) BNconvert[[x]]$prob)
# dag(tempBN) <- amat(BNconvert)
# wpdag(tempBN) <- matrix(0, lengthBNC, lengthBNC)
#
# return(tempBN)
# }
#' Visualize benchmark results
#'
#' Outputs plots of benchmark results for different inference methods
#'
#' @param resultTable1 result table 1 of function benchmarkMultipleNets()
#' @param resultTable2 result table 2 of function benchmarkMultipleNets()
#' @param resultTable3 result table 3 of function benchmarkMultipleNets()
#' @param resultTable4 result table 4 of function benchmarkMultipleNets()
#' @param labelBP label
#' @param labelHead label head
#' @param fileName file name
#' @param width figure width
#' @param height figure height
#' @param niceBoxPlot if TRUE, only a fraction of points (150) are plotted in boxplot
#' @param retP if TRUE, a plot is returned for summary plot, otherwise no ruturn
#' @return plot of benchmark results
#'
#' @import ggplot2
#' @importFrom RColorBrewer brewer.pal
#' @importFrom gridExtra grid.arrange
#' @importFrom grDevices cairo_pdf
#' @importFrom utils tail
#'
#' @export
makeAllPlots <- function(resultTable1, resultTable2, resultTable3, resultTable4, labelBP = c("1","2","3","4"), labelHead = c("1","2","3","4"), fileName = "Test", width = 7, height = 4.1, niceBoxPlot=FALSE, retP=FALSE){
# function to create the final plots from the benchmark studies
dimBN=BoxPlotResNRMS=Method=NRMSE=countsS=group=NULL
## FinalPlots:
restuls1 <- ProcessResultTableNRMSE(resultTable1, returnResults = TRUE)
restuls2 <- ProcessResultTableNRMSE(resultTable2, returnResults = TRUE)
restuls3 <- ProcessResultTableNRMSE(resultTable3, returnResults = TRUE)
restuls4 <- ProcessResultTableNRMSE(resultTable4, returnResults = TRUE)
restuls1$averageResults_final$dimBN <- labelBP[1]
restuls2$averageResults_final$dimBN <- labelBP[2]
restuls3$averageResults_final$dimBN <- labelBP[3]
restuls4$averageResults_final$dimBN <- labelBP[4]
resAllDims <- rbind(restuls1$averageResults_final,restuls2$averageResults_final,restuls3$averageResults_final,restuls4$averageResults_final)
# if FS is removed
resAllDims2 <- resAllDims[!resAllDims$Method=='FS',]
# resAllDims2$Method[resAllDims2$Method=="LBP (IS)"] <- "LBP"
# set colors
color_list <- brewer.pal(n = 4, name = 'RdYlBu')
# change LBP name for plotting
resAllDims2$Method[resAllDims2$Method=="LBP"] <- "LBP-IS"
# # create subset of data for plotting with "geom_jitter"
numb_points <- 150
if (niceBoxPlot==TRUE){
dftest1 = resAllDims2[resAllDims2$Method=="LBP-IS"&resAllDims2$dimBN==labelBP[1],][1:numb_points,]
dftest2 = resAllDims2[resAllDims2$Method=="SGS"&resAllDims2$dimBN==labelBP[1],][1:numb_points,]
dftest3 = resAllDims2[resAllDims2$Method=="GS"&resAllDims2$dimBN==labelBP[1],][1:numb_points,]
resAllDims2_subset1 <- merge(merge(dftest1,dftest2, all = TRUE),dftest3, all = TRUE)
dftest1 = resAllDims2[resAllDims2$Method=="LBP-IS"&resAllDims2$dimBN==labelBP[2],][1:numb_points,]
dftest2 = resAllDims2[resAllDims2$Method=="SGS"&resAllDims2$dimBN==labelBP[2],][1:numb_points,]
dftest3 = resAllDims2[resAllDims2$Method=="GS"&resAllDims2$dimBN==labelBP[2],][1:numb_points,]
resAllDims2_subset2 <- merge(merge(dftest1,dftest2, all = TRUE),dftest3, all = TRUE)
dftest1 = resAllDims2[resAllDims2$Method=="LBP-IS"&resAllDims2$dimBN==labelBP[3],][1:numb_points,]
dftest2 = resAllDims2[resAllDims2$Method=="SGS"&resAllDims2$dimBN==labelBP[3],][1:numb_points,]
dftest3 = resAllDims2[resAllDims2$Method=="GS"&resAllDims2$dimBN==labelBP[3],][1:numb_points,]
resAllDims2_subset3 <- merge(merge(dftest1,dftest2, all = TRUE),dftest3, all = TRUE)
dftest1 = resAllDims2[resAllDims2$Method=="LBP-IS"&resAllDims2$dimBN==labelBP[4],][1:numb_points,]
dftest2 = resAllDims2[resAllDims2$Method=="SGS"&resAllDims2$dimBN==labelBP[4],][1:numb_points,]
dftest3 = resAllDims2[resAllDims2$Method=="GS"&resAllDims2$dimBN==labelBP[4],][1:numb_points,]
resAllDims2_subset4 <- merge(merge(dftest1,dftest2, all = TRUE),dftest3, all = TRUE)
resAllDims2_subset <- merge(merge(merge(resAllDims2_subset1,resAllDims2_subset2, all = TRUE),resAllDims2_subset3,all = TRUE), resAllDims2_subset4, all = TRUE)
}
dodge <- position_dodge(width = 0.4)
if(niceBoxPlot==TRUE){
# make a boxplot of the results
p1 <- ggplot(data=resAllDims2, aes(x=factor(dimBN, levels = c(labelBP[1],labelBP[2],labelBP[3],labelBP[4])), y=BoxPlotResNRMS, fill=Method, colour=Method), alpha = 0.3) +
geom_boxplot(outlier.shape = NA, alpha = 0.3) + ## THIS IS FOR JITTER, otherwise geom_boxplot()+
# geom_jitter(position=position_jitter(0.42), cex=0.4, data=resAllDims2_subset, position = dodge)+
geom_point(pch = 21,data=resAllDims2_subset, position = position_jitterdodge(0.15),cex=0.4, alpha = 0.3)+ ## THIS IS FOR JITTER
scale_colour_manual(values=color_list[c(1,3,4)])+
scale_fill_manual(values=color_list[c(1,3,4)])+ # values=c("white","white","white"))+
labs(y = "NRMSE", x="Dimensions")+
ylim(0,1.5)+
theme_minimal()+
theme(legend.position = "bottom")
}else{
# make a boxplot of the results
p1 <- ggplot(data=resAllDims2, aes(x=factor(dimBN, level = c(labelBP[1],labelBP[2],labelBP[3],labelBP[4])), y=BoxPlotResNRMS, fill=Method, colour=Method), alpha = 0.3) +
geom_boxplot(outlier.shape = NA, alpha = 0.3) + ## THIS IS FOR JITTER, otherwise geom_boxplot()+
# geom_jitter(position=position_jitter(0.42), cex=0.4, data=resAllDims2_subset, position = dodge)+
geom_point(pch = 21,data=resAllDims2, position = position_jitterdodge(0.15),cex=0.4, alpha = 0.3)+ ## THIS IS FOR JITTER
scale_colour_manual(values=color_list[c(1,3,4)])+
scale_fill_manual(values=color_list[c(1,3,4)])+ # values=c("white","white","white"))+
labs(y = "NRMSE", x="Dimensions")+
ylim(0,1.5)+
theme_minimal()+
theme(legend.position = "bottom")
}
print(p1)
# if FS is removed
plotData <- restuls1$averageResults_byTime
plotData <- plotData[!plotData$Method=='FS',]
plotData$Method[plotData$Method=="LBP"] <- "LBP-IS"
minLims <- min(c(tail(plotData$countsS[plotData$Method=="LBP-IS"],1),tail(plotData$countsS[plotData$Method=="GS"],1), tail(plotData$countsS[plotData$Method=="SGS"],1)))
p2 <- ggplot(data=plotData, aes(y = NRMSE, x = countsS, group=group))+
geom_line(aes(y = NRMSE, x = countsS,colour=Method)) +
scale_colour_manual(values=color_list[c(1,3,4)]) +
lims(x=c(0,minLims))+
# guides(fill = FALSE) +
# guides(col = FALSE) +
xlab("Elapsed Time") +
theme_minimal()+
theme(legend.direction = "horizontal")+
ylab("NRMSE") # + xlim(-0.01, 0.2) + ylim(-0.1e-11, 0.75e-11)
print(p2)
p2s <- ggplot(data=plotData, aes(y = NRMSE, x = countsS, group=group))+
geom_line(aes(y = NRMSE*sqrt(countsS), x = countsS,colour=Method)) +
scale_colour_manual(values=color_list[c(1,3,4)]) +
lims(x=c(0,minLims))+
# guides(fill = FALSE) +
# guides(col = FALSE) +
xlab("Elapsed Time") +
theme_minimal()+
ylab(expression(NRMSE*sqrt(t))) # + xlim(-0.01, 0.2) + ylim(-0.1e-11, 0.75e-11)
print(p2s)
p2ss <- ggplot(data=plotData, aes(y = NRMSE, x = countsS, group=group))+
geom_line(aes(y = log(NRMSE), x = log(countsS),colour=Method)) +
scale_colour_manual(values=color_list[c(1,3,4)]) +
# lims(x=c(0,minLims))+
# guides(fill = FALSE) +
# guides(col = FALSE) +
# scale_x_continuous(trans='log2') +
# scale_y_continuous(trans='log2') +
xlab("log(Elapsed Time)") +
theme_minimal()+
ylab("log(NRMSE)") # + xlim(-0.01, 0.2) + ylim(-0.1e-11, 0.75e-11)
print(p2ss)
# if FS is removed
plotData <- restuls2$averageResults_byTime
plotData <- plotData[!plotData$Method=='FS',]
plotData$Method[plotData$Method=="LBP"] <- "LBP-IS"
minLims <- min(c(tail(plotData$countsS[plotData$Method=="LBP-IS"],1),tail(plotData$countsS[plotData$Method=="GS"],1), tail(plotData$countsS[plotData$Method=="SGS"],1)))
p3 <- ggplot(data=plotData, aes(y = NRMSE, x = countsS, group=group))+
geom_line(aes(y = NRMSE, x = countsS,colour=Method)) +
scale_colour_manual(values=color_list[c(1,3,4)]) +
lims(x=c(0,minLims))+
# guides(fill = FALSE) +
# guides(col = FALSE) +
xlab("Elapsed Time") +
theme_minimal()+
ylab("NRMSE") # + xlim(-0.01, 0.2) + ylim(-0.1e-11, 0.75e-11)
print(p3)
p3s <- ggplot(data=plotData, aes(y = NRMSE*sqrt(countsS), x = countsS, group=group))+
geom_line(aes(y = NRMSE*sqrt(countsS), x = countsS,colour=Method)) +
scale_colour_manual(values=color_list[c(1,3,4)]) +
lims(x=c(0,minLims))+
# guides(fill = FALSE) +
# guides(col = FALSE) +
xlab("Elapsed Time") +
theme_minimal()+
ylab(expression(NRMSE*sqrt(t))) # + xlim(-0.01, 0.2) + ylim(-0.1e-11, 0.75e-11)
print(p3s)
p3ss <- ggplot(data=plotData, aes(y = NRMSE, x = countsS, group=group))+
geom_line(aes(y = log(NRMSE), x = log(countsS),colour=Method)) +
scale_colour_manual(values=color_list[c(1,3,4)]) +
# lims(x=c(0,minLims))+
# guides(fill = FALSE) +
# guides(col = FALSE) +
# scale_x_continuous(trans='log2') +
# scale_y_continuous(trans='log2') +
xlab("log(Elapsed Time)") +
theme_minimal()+
ylab("log(NRMSE)") # + xlim(-0.01, 0.2) + ylim(-0.1e-11, 0.75e-11)
print(p3ss)
# if FS is removed
plotData <- restuls3$averageResults_byTime
plotData <- plotData[!plotData$Method=='FS',]
plotData$Method[plotData$Method=="LBP"] <- "LBP-IS"