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mistral-io-datasets
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Better plotting.

This commit is contained in:
Julian M. Kunkel 2020-08-20 12:11:35 +01:00
parent 70739e74d5
commit 58709e01e6
3 changed files with 19 additions and 16 deletions
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10
scripts/analyse-all.sh
View File

@ -1,10 +1,10 @@
#!/bin/bash #!/bin/bash
# call me from parent directory # call me from the parent directory
for I in job_similarities_*.csv ; do for I in job_similarities_*.csv ; do
./scripts/plot.R $I > description.txt ./scripts/plot.R $I > description.txt
mkdir $I.out OUT=${I%%.csv}-out
rm $I.out/* mkdir $OUT
mv *.png *.pdf description.txt $I.out rm $OUT/*
mv *.png *.pdf description.txt $OUT
done done

8
scripts/plot-single-job.py
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@ -10,6 +10,8 @@ import matplotlib.cm as cm
jobs = sys.argv[1].split(",") jobs = sys.argv[1].split(",")
prefix = sys.argv[2].split(",") prefix = sys.argv[2].split(",")
fileformat = ".png"
print("Plotting the job: " + str(jobs)) print("Plotting the job: " + str(jobs))
# Color map # Color map
@ -81,7 +83,7 @@ def plot(prefix, header, row):
fsize = (8, 1 + 1.5 * len(labels)) fsize = (8, 1 + 1.5 * len(labels))
fsizeFixed = (8, 2) fsizeFixed = (8, 2)
pyplot.close('all') pyplot.close('all')
if len(labels) < 4 : if len(labels) < 4 :
@ -93,7 +95,7 @@ def plot(prefix, header, row):
ax[i].set_ylabel(l) ax[i].set_ylabel(l)
pyplot.xlabel("Segment number") pyplot.xlabel("Segment number")
pyplot.savefig(prefix + "timeseries" + jobid + ".pdf", bbox_inches='tight') pyplot.savefig(prefix + "timeseries" + jobid + fileformat, bbox_inches='tight')
# Plot first 30 segments # Plot first 30 segments
if len(timeseries) <= 50: if len(timeseries) <= 50:
@ -109,7 +111,7 @@ def plot(prefix, header, row):
ax[i].set_ylabel(l) ax[i].set_ylabel(l)
pyplot.xlabel("Segment number") pyplot.xlabel("Segment number")
pyplot.savefig(prefix + "timeseries" + jobid + "-30.pdf", bbox_inches='tight') pyplot.savefig(prefix + "timeseries" + jobid + "-30" + fileformat, bbox_inches='tight')
### end plotting function ### end plotting function

17
scripts/plot.R
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@ -23,16 +23,17 @@ cat("Job count:")
cat(nrow(data)) cat(nrow(data))
# empirical cummulative density function (ECDF) # empirical cummulative density function (ECDF)
ggplot(data, aes(similarity, color=alg_name, group=alg_name)) + stat_ecdf(geom = "step") + xlab("SIM") + ylab("Fraction of jobs") + theme(legend.position="bottom") + scale_color_brewer(palette = "Set2") 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")
ggsave("ecdf.png") ggsave("ecdf.png", width=8, height=3)
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)
ggsave("ecdf-0.5.png", width=8, height=3)
e = data %>% filter(similarity >= 0.5) e = data %>% filter(similarity >= 0.5)
ggplot(e, aes(similarity, color=alg_name, group=alg_name)) + stat_ecdf(geom = "step") + xlab("SIM") + ylab("Fraction of jobs") + theme(legend.position="bottom") + scale_color_brewer(palette = "Set2")
print(summary(e)) print(summary(e))
ggsave("ecdf-0.5.png")
# histogram for the jobs # histogram for the jobs
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") 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))
ggsave("hist-sim.png") ggsave("hist-sim.png")
# load job information, i.e., the time series per job # load job information, i.e., the time series per job
@ -51,8 +52,8 @@ plotJobs = function(jobs){
# print the job timelines # print the job timelines
r = e[ordered, ] r = e[ordered, ]
prefix = do.call("sprintf", list("%s-%.0f-", level, r$similarity)) #prefix = do.call("sprintf", list("%s-%.0f-", level, r$similarity))
system(sprintf("scripts/plot-single-job.py %s %s", paste(r$jobid, collapse=","), paste(prefix, collapse=","))) #system(sprintf("scripts/plot-single-job.py %s %s", paste(r$jobid, collapse=","), paste(prefix, collapse=",")))
} }
# Store the job ids in a table, each column is one algorithm # Store the job ids in a table, each column is one algorithm
@ -94,7 +95,7 @@ print(res.intersect)
# Plot heatmap about intersection # Plot heatmap about intersection
ggplot(tbl.intersect, aes(first, second, fill=intersect)) + geom_tile() + geom_text(aes(label = round(intersect, 1))) + scale_fill_gradientn(colours = rev(plotcolors)) ggplot(tbl.intersect, aes(first, second, fill=intersect)) + geom_tile() + geom_text(aes(label = round(intersect, 1))) + scale_fill_gradientn(colours = rev(plotcolors))
ggsave("intersection-heatmap.png") ggsave("intersection-heatmap.png", width=6, height=5)
# Collect the metadata of all jobs in a new table # Collect the metadata of all jobs in a new table
res.jobs = tibble() res.jobs = tibble()