118 lines
4.6 KiB
R
Executable File
118 lines
4.6 KiB
R
Executable File
#!/usr/bin/env Rscript
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library(ggplot2)
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library(dplyr)
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require(scales)
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#library(hrbrthemes)
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file = "job_similarities_5024292.csv"
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file = "job_similarities_7488914.csv"
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# Color scheme
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plotcolors <- c("#CC0000", "#FFA500", "#FFFF00", "#008000", "#9999ff", "#000066")
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# Parse job from command line
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args = commandArgs(trailingOnly = TRUE)
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file = args[1]
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data = read.csv(file)
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# Columns are: jobid alg_id alg_name similarity
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data$alg_id = as.factor(data$alg_id)
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cat("Job count:")
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cat(nrow(data))
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# empirical cummulative density function (ECDF)
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ggplot(data, aes(similarity, color=alg_name, group=alg_name)) + stat_ecdf(geom = "step") + xlab("SIM") + ylab("Fraction of jobs") + theme(legend.position=c(0.9, 0.4)) + scale_color_brewer(palette = "Set2")
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ggsave("ecdf.png", width=8, height=3)
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ggplot(data, aes(similarity, color=alg_name, group=alg_name)) + stat_ecdf(geom = "step") + xlab("SIM") + ylab("Fraction of jobs") + theme(legend.position=c(0.9, 0.4)) + scale_color_brewer(palette = "Set2") + xlim(0.5, 1.0)
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ggsave("ecdf-0.5.png", width=8, height=3)
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e = data %>% filter(similarity >= 0.5)
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print(summary(e))
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# histogram for the jobs
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ggplot(data, aes(similarity), group=alg_name) + geom_histogram(color="black", binwidth=0.025) + aes(fill = alg_name) + facet_grid(alg_name ~ ., switch = 'y') + scale_y_continuous(limits=c(0, 100), oob=squish) + scale_color_brewer(palette = "Set2") + ylab("Count (cropped at 100)") + theme(legend.position = "none") + stat_bin(binwidth=0.025, geom="text", angle = 90, colour="black", size=3, aes(label=..count.., y=0*(..count..)+20))
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ggsave("hist-sim.png")
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# load job information, i.e., the time series per job
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jobData = read.csv("job-io-datasets/datasets/job_codings.csv")
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metadata = read.csv("job-io-datasets/datasets/job_metadata.csv")
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metadata$user_id = as.factor(metadata$user_id)
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metadata$group_id = as.factor(metadata$group_id)
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plotJobs = function(jobs){
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# plot details about the jobs of a given algorithm
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tbl = jobData[jobData$jobid %in% jobs,]
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print(summary(tbl))
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#print(tbl)
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md = metadata[metadata$jobid %in% jobs,]
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print(summary(md))
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# print the job timelines
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r = e[ordered, ]
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#prefix = do.call("sprintf", list("%s-%.0f-", level, r$similarity))
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#system(sprintf("scripts/plot-single-job.py %s %s", paste(r$jobid, collapse=","), paste(prefix, collapse=",")))
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}
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# Store the job ids in a table, each column is one algorithm
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dim = length(levels(data$alg_name))
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count = 100
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result = matrix(1:(dim*count), nrow=count, ncol=dim)
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colnames(result) = levels(data$alg_name)
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# Extract the 100 most similar jobs into the table
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for (level in levels(data$alg_name)){
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e = data %>% filter(alg_name == level)
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print(level)
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print(summary(e))
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ordered = order(e$similarity, decreasing=TRUE)[1:count]
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print(e[ordered,])
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# Extract the data for the jobs
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jobs = e[ordered,"jobid"]
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result[, level] = jobs
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plotJobs(jobs)
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}
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# Compute intersection in a new table
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res.intersect = matrix(1:(dim*dim), nrow=dim, ncol=dim)
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colnames(res.intersect) = levels(data$alg_name)
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rownames(res.intersect) = levels(data$alg_name)
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tbl.intersect = expand.grid(first=levels(data$alg_name), second=levels(data$alg_name))
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tbl.intersect$intersect = 0
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for (l1 in levels(data$alg_name)){
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for (l2 in levels(data$alg_name)){
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res = length(intersect(result[,l1], result[,l2]))
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res.intersect[l1,l2] = res
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tbl.intersect[tbl.intersect$first == l1 & tbl.intersect$second == l2, ]$intersect = res
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}
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}
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print(res.intersect)
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# Plot heatmap about intersection
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ggplot(tbl.intersect, aes(first, second, fill=intersect)) + geom_tile() + geom_text(aes(label = round(intersect, 1))) + scale_fill_gradientn(colours = rev(plotcolors))
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ggsave("intersection-heatmap.png", width=6, height=5)
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# Collect the metadata of all jobs in a new table
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res.jobs = tibble()
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for (alg_name in levels(data$alg_name)){
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res.jobs = rbind(res.jobs, cbind(alg_name, metadata[metadata$jobid %in% result[, alg_name],]))
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}
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ggplot(res.jobs, aes(alg_name, total_nodes, fill=alg_name)) + geom_boxplot() + scale_y_continuous(trans = log2_trans(), breaks = trans_breaks("log2", function(x) 2^x), labels = trans_format("log2", math_format(2^.x)))
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ggsave("jobs-nodes.png")
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ggplot(res.jobs, aes(alg_name, elapsed, fill=alg_name)) + geom_boxplot() + scale_y_continuous(trans = log2_trans(), breaks = trans_breaks("log10", function(x) 10^x), labels = trans_format("log10", math_format(10^.x))) + ylab("Runtime in s") + xlab("Algorithm")
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ggsave("jobs-elapsed.png")
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# scale_y_continuous(trans = log2_trans(), breaks = trans_breaks("log2", function(x) 2^x), labels = trans_format("log2", math_format(2^.x)))
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# stat_summary(aes(linetype = alg_id), fun.y=mean, geom="line")
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