def plotVsMaxInDegree(data, prop, label):
pl.newFigure()
pl.plot(getLevel0Prop(data, "max in-degree"),
getLevel0Prop(data, prop),
style='k-o',
markerSize=4)
pl.labels("In-degree threshold", label)
def plotLevel0VsLevel1Stats(data, prop0, prop1, label0, label1):
d0 = getLevel0Prop(data, prop0)
m, std, sm = level1Stats(data, prop1)
for (v, p) in [(m, "mean"), (std, "standard deviation"), (sm, "sum")]:
pl.newFigure()
pl.plot(d0, v, style='ko-', xLog=False, yLog=False)
pl.labels(label0, label1 + " " + p)
def wordSimScatterPlot(set1, set2, label1, label2):
pl.newFigure()
sims1, sims2 = intersectionSimilarities(set1, set2)
pl.plot(sims1, sims2, style='ko', markerSize=4)
pl.labels(label1, label2)
print "Spearman correlation between", label1, "and", label2, ":", spearmanCorrelation(
sims1, sims2)
def similarityDistribution(data):
pl.newFigure()
for d in data["results"]:
hist = d["relative l1 norm"]["histogram"]
bins = [1.0 - b for [b, c] in hist]
counts = [float(c) for [t, c] in hist]
tot = sum(counts)
pl.plot(bins, [c / tot for c in counts],
style='-',
xLog=True,
yLog=True)
pl.labels("$1 - \mathrm{L_{1}}$", "Density")
pl.legend(getLevel0Prop(data, "max in-degree"))
def histograms(data, prop, label):
pl.newFigure()
for d in data["results"]:
hist = d[prop]["histogram"]
bins = [b for [b, c] in hist]
counts = [float(c) for [b, c] in hist]
deltaTot = sum(counts) * (bins[1] - bins[0])
pl.plot(bins, [c / deltaTot for c in counts],
style='-',
xLog=False,
yLog=False)
pl.labels(label, "Density")
pl.legend(getLevel0Prop(data, "max in-degree"))
def correlationsVsLevel0Prop(data, prop0, label0, benchmarks, benchLabels):
pl.newFigure()
l0Prop = getLevel0Prop(data, prop0)
l1Sims = selectedSimilarities(data)
for b in benchmarks:
def spearman(s1, s2):
sims1, sims2 = intersectionSimilarities(s1, s2)
return spearmanCorrelation(sims1, sims2)
correlations = [spearman(b, s1) for s1 in l1Sims]
pl.plot(l0Prop, correlations, style='o-', markerSize=4)
pl.labels(label0, "Spearman rank correlation coefficient")
pl.legend(benchLabels)
def plotVsMaxInDegree(data, prop, label):
pl.newFigure()
pl.plot(getLevel0Prop(data, "max in-degree"),
getLevel0Prop(data, prop),
style='k-o',
markerSize=4)
pl.labels("In-degree threshold", label)
def level0Plot(data, (prop1, label1), (prop2, label2)):
fig = pl.newFigure()
pl.plot(getLevel0Prop(data, prop1),
getLevel0Prop(data, prop2),
style='k-o',
markerSize=4)
pl.labels(label1, label2)
return fig
def histograms(data, prop, label):
pl.newFigure()
for d in data["results"]:
hist = d[prop]["histogram"]
bins = [b for [b, c] in hist]
counts = [float(c) for [b, c] in hist]
deltaTot = sum(counts) * (bins[1] - bins[0])
pl.plot(bins, [c / deltaTot for c in counts],
style='-',
xLog=False,
yLog=False)
pl.labels(label, "Density")