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Add Script Computing DTP Betweenness Centrality Plot for all Case for…
… "The Maximum Transmission Switching Flow Problem" (#93) * Add script computing the DTP betweenness centrality plot for all case in one plot, * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci. Publication: * "The Maximum Transmission Switching Flow Problem", 2018, ACM e-Energy, doi:10.1145/3208903.3208910. Changes to be committed: * new file: evaluations/2018_ACM_The-Maximum-Transmission-Switching-Flow-Problem/statistics/plot_dtp_betweenness_centrality_all.R
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...imum-Transmission-Switching-Flow-Problem/statistics/plot_dtp_betweenness_centrality_all.R
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| #! /opt/local/bin/Rscript | ||
| # | ||
| # plot_dtp_betweenness_centrality_all.R | ||
| # | ||
| # Created on: - | ||
| # Author: Franziska Wegner | ||
| # | ||
| # This script computes the DTP betweenness centrality plot with all cases | ||
| # "The Maximum Transmission Switching Flow Problem", 2018, ACM e-Energy, | ||
| # doi:10.1145/3208903.3208910. | ||
| # | ||
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| # R include | ||
| source("../Helper/plotting.R", chdir=T) | ||
| source("../Helper/colors.R", chdir=T) | ||
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| ## https://www.bioconductor.org/packages/release/bioc/html/BiSeq.html | ||
| ## try http:// if https:// URLs are not supported | ||
| # source("https://bioconductor.org/biocLite.R") | ||
| # biocLite("BiSeq") | ||
| ## Documentation | ||
| # browseVignettes("BiSeq") | ||
| library(BiSeq) | ||
| # require(BiSeq) | ||
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| # Commandline arguments | ||
| args <- commandArgs(trailingOnly = TRUE) | ||
| argument.path <- args[1] | ||
| argument.output <- args[2] | ||
| argument.standAlone <- args[3] | ||
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| argument.lowerQuantile <- args[4] | ||
| argument.avgQuantile <- args[5] | ||
| argument.upperQuantile <- args[6] | ||
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| argument.latex_path <- args[7] | ||
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| # Path to the data files | ||
| if ( is.na(argument.path) ) { | ||
| argument.path <- "/Users/fwegner/Documents/work/paper/MTSF-ACM-e-Energy/02-ACM/data/results/DTP-Betweenness/" | ||
| } | ||
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| # Output path | ||
| if ( is.na(argument.output) ) { | ||
| argument.output <- "/Users/fwegner/Documents/work/paper/MTSF-ACM-e-Energy/02-ACM/plots/plot_dtp_betweenness_centrality_all_quantiles" | ||
| } | ||
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| # Standalone | ||
| if ( is.na(argument.standAlone) ) { | ||
| argument.standAlone <- FALSE | ||
| } | ||
| if ( argument.standAlone == "TRUE" ) { | ||
| argument.standAlone <- TRUE | ||
| } else { | ||
| argument.standAlone <- FALSE | ||
| } | ||
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| if ( is.na(argument.lowerQuantile) ) { | ||
| argument.lowerQuantile <- 0.32#0.31#0.32 | ||
| } | ||
| if ( is.na(argument.avgQuantile) ) { | ||
| argument.avgQuantile <- 0.35#0.35#0.36 | ||
| } | ||
| if ( is.na(argument.upperQuantile) ) { | ||
| argument.upperQuantile <- 0.75 | ||
| } | ||
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| if ( !is.na(argument.latex_path) ) { | ||
| options(latexcmd = argument.latex_path) | ||
| } else { | ||
| argument.latex_path <- "/opt/local/bin/pdflatex" | ||
| options(latexcmd = "/opt/local/bin/pdflatex") | ||
| } | ||
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| # Extract necessary files | ||
| files <- NULL | ||
| files <- list.files(path = argument.path, pattern = "\\-dtpbc-mpf.csv$") | ||
| files <- files[files != ""] | ||
| filename <- "DTP_BC-AllNestaCases_And_Quantiles" | ||
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| lfontsize <- 1.0 | ||
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| totaldata = NULL | ||
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| # ------- READING IN DATA FROM A CSV ------------------------------------------- | ||
| while ( length(files) != 0 ) { | ||
| # CSV access | ||
| data <- NULL | ||
| data <- read.csv(paste(argument.path, files[1], sep = ""), header=TRUE, sep=",") | ||
| data <- data[order(data$BetweennessNormalizedValue, decreasing = TRUE),] | ||
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| # Normalize opt of mpf -> y-axis | ||
| data$OptimumSolutions <- data$OptimumSolutions / max(data$OptimumSolutions) | ||
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| # Add and normalize edge ids -> x-axis | ||
| data$EdgeIndex <- seq.int(nrow(data)) | ||
| data$EdgeIndex <- data$EdgeIndex/data$NumberOfEdges | ||
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| # Next file in the list | ||
| files[1] <- "" | ||
| files <- files[files != ""] | ||
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| # Data frame of all data sets | ||
| totaldata <- rbind(totaldata,data) | ||
| } # while files | ||
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| # ------- PROCESS DATA --------------------------------------------------------- | ||
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| # Prepare data -> rounding, sorting and replace NA by 0 (not solvable instances) | ||
| totaldata$EdgeIndex <- round( totaldata$EdgeIndex, digits = 2 ) | ||
| totaldata <- totaldata[ order( totaldata$EdgeIndex, decreasing = FALSE), ] | ||
| totaldata$OptimumSolutions <- replace( totaldata$OptimumSolutions, is.na(totaldata$OptimumSolutions), 0 ) | ||
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| # Initialize | ||
| forSeq <- c( seq(from=0,to=1,by=0.01) ) | ||
| quantileData <- NULL | ||
| quantileData <- data.frame( matrix(ncol = 4, nrow = 0) ) | ||
| # totaldata <- subset(totaldata, totaldata$OptimumSolutions > 0) | ||
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| # Get quantiles for similar edge ids | ||
| for ( i in forSeq ) { | ||
| subtotaldata <- subset(totaldata, EdgeIndex %in% c(i) ) | ||
| quantileData <- rbind(quantileData, c( i, quantile(subtotaldata$OptimumSolutions, probs=c(argument.lowerQuantile, argument.avgQuantile, argument.upperQuantile), type=5) )) | ||
| } | ||
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| # Postprocess quantile data | ||
| colnames(quantileData) <- c("EdgeIndex", "lowerQuantile", "avgQuantile", "upperQuantile") | ||
| quantileData <- quantileData[complete.cases(quantileData), ] | ||
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| library(stringr) | ||
| totaldata$Name <- gsub("\t", "", totaldata$Name) | ||
| totaldata$Name <- gsub("dc_mpf_nesta_case", "", totaldata$Name) | ||
| #totaldata$Name = paste( "\\\\tablecase\\{", str_pad(gsub("\\D*", "", totaldata$Name ), 4, pad = "0"),str_replace(totaldata$Name,"^.[0-9]*",""), "\\}" ,sep = "") | ||
| totaldata$Name <- paste( "\\\\tablecase\\{nestacase", str_pad(gsub("\\D*", "", totaldata$Name ), 4, pad = "0"), gsub("_", "", str_replace(totaldata$Name,"^.[0-9]*","") ), "\\}" ,sep = "") | ||
| totaldata$Name <- gsub("_", "\\\\_",totaldata$Name) | ||
| totaldata <- totaldata[ order(totaldata$NumberOfVertices, totaldata$EdgeIndex) , ] | ||
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| # Sample the data set using sample(data, nr, replace = FALSE, prob = NULL) | ||
| # 10 % sample produced | ||
| # if(argument.sample == TRUE){ | ||
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| totaldata[!duplicated(totaldata[,c('EdgeIndex', 'OptimumSolutions')]),]#EdgeIndex, OptimumSolutions | ||
| #removed | ||
| #noSamples <- nrow(totaldata)/22#3#22#23 | ||
| #totaldata <- filterBySharedRegions(object = totaldata, perc.samples = 1) | ||
| #totaldata <- totaldata[sample(nrow(totaldata), noSamples, replace = TRUE),] | ||
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| #require(plotrix) | ||
| #cluster.overplot(x,y) | ||
| #plot(cluster.overplot(x, y ), main=”Using cluster.overplot”) | ||
| # } | ||
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| # ------- PLOT DATA USING TIKZ ----------------------------------------------------- | ||
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| if ( argument.standAlone == TRUE ) { # Standalone tex | ||
| tex_output_file <- paste(argument.output, "-standalone", ".tex",sep = "") | ||
| tikzDevice::tikz(tex_output_file, | ||
| width = 6.1, | ||
| height = 3.75, | ||
| pointsize = 8, | ||
| standAlone = TRUE) | ||
| } else { # Tikz include graphic | ||
| tex_output_file <- paste(argument.output, ".tex",sep = "") | ||
| tikzDevice::tikz(tex_output_file, | ||
| width = 6.1, | ||
| height = 3.75, | ||
| pointsize = 8, | ||
| standAlone = FALSE) | ||
| } | ||
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| # create extra margin room on the right for an axis | ||
| # mar=c(unten, links, oben, rechts) | ||
| par(mar=c(4,5,3.5,5.3)+.1, mgp = c(4, 0.8, 0), xpd=TRUE) | ||
| par(las=1, cex=lfontsize) # make label text perpendicular to axis | ||
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| g <- ggplot( ) | ||
| g + theme_gray(base_size = 14 | ||
| ) + theme(legend.position = "top", | ||
| axis.text.x = element_text(size=13), | ||
| axis.text.y = element_text(size=13) | ||
| # Vertical line at 0 to mark values with highest centrality | ||
| ) + geom_vline( xintercept = 0, | ||
| color = KITcyanblue70, | ||
| size = 2.2, | ||
| alpha = 0.7 | ||
| # Vertical line at 1 to mark values with lowest centrality | ||
| ) + geom_vline( xintercept = 1, | ||
| color = KITorange70, | ||
| size = 2.2, | ||
| alpha = 0.7 | ||
| # Plot background as lines | ||
| # ) + geom_line( aes(EdgeIndex, OptimumSolutions, column = Name), | ||
| # totaldata, | ||
| # colour = KITblack15 | ||
| # Plot background as points | ||
| ) + geom_jitter(alpha = 0.5, | ||
| aes(EdgeIndex, OptimumSolutions), #, column = Name | ||
| totaldata, | ||
| color = KITblack25, | ||
| position = position_jitter(width = 0.01) | ||
| # Coloring between the green curve and the blue one | ||
| ) + geom_ribbon( data = subset(quantileData, 0 <= quantileData$EdgeIndex & quantileData$EdgeIndex <= 1), | ||
| aes( x = EdgeIndex, | ||
| ymin = lowerQuantile, | ||
| ymax = upperQuantile | ||
| ), | ||
| fill = KITgreen15, | ||
| alpha = "0.7" | ||
| # Coloring between the red curve and the blue one | ||
| ) + geom_ribbon( data = subset(quantileData, 0 <= quantileData$EdgeIndex & quantileData$EdgeIndex <= 1), | ||
| aes( x = EdgeIndex, | ||
| ymin = avgQuantile, | ||
| ymax = upperQuantile | ||
| ), | ||
| fill = KITred30, | ||
| alpha = "0.7" | ||
| # 40 % quantile line | ||
| ) + geom_line( aes(EdgeIndex, lowerQuantile, col=paste(argument.lowerQuantile*100, "\\%~quantile", sep = "") ), | ||
| quantileData, | ||
| size = 0.8 | ||
| # 45 % quantile line | ||
| ) + geom_line( aes(EdgeIndex, avgQuantile, col=paste(argument.avgQuantile*100, "\\%~quantile", sep = "") ), | ||
| quantileData, | ||
| size = 0.8 | ||
| # 75 % quantile line | ||
| ) + geom_line( aes(EdgeIndex, upperQuantile, col=paste(argument.upperQuantile*100, "\\%~quantile", sep = "") ), | ||
| quantileData, | ||
| size = 0.6 | ||
| # Colors used for the lines | ||
| ) + scale_color_manual(values=c(KITgreen50, KITred50, KITseablue70, KITblack02, KITblack03, KITblack04, KITblack05, KITblack06, KITblack07, KITblack08, KITblack09, | ||
| KITblack10, KITblack11, KITblack12, KITblack13, KITblack14, KITblack15, KITblack16, KITblack17,KITblack18, KITblack19, KITblack20, | ||
| KITblack21, KITblack23, KITblack25, KITblack26, KITblack27, KITblack28, KITblack29, KITblack30, KITblack31, KITblack32) | ||
| # Labels | ||
| ) + labs( x = "Edges normalized by factor~$|E|$", | ||
| y = "Normalized~$\\mathsf{MPF}$ in~$\\mathrm{MW}$", | ||
| color = "NESTA Cases \\& Quantiles\n" | ||
| ) + guides( col = guide_legend(title = NULL) | ||
| # Text telling that "High c-DTPBC" | ||
| ) + annotate("text", x = 0.093, | ||
| y = 1.06, | ||
| label = "\\bf High $c_{\\mathsf{DTPBC}}$", | ||
| parse = FALSE, | ||
| color = KITcyanblue | ||
| # Text telling that "Low c-DTPBC" | ||
| ) + annotate("text", x = 0.91, | ||
| y = 1.06, | ||
| label = "\\bf Low $c_{\\mathsf{DTPBC}}$", | ||
| parse = FALSE, | ||
| color = KITorange | ||
| ) | ||
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| # Print output path | ||
| print(paste("File written to: ",tex_output_file, sep = "")) | ||
| par(font=1) | ||
| dev.off() |