def plotHistograms(labelsFilename):
for anomalyScore in ['gamma', 'zeta']:
for k in [10, 22, 30, 50]:
evaluator = RankingEvaluator(datasetDir, labelsFilename)
rankingName = 'termDocMatrixRanker_%s_%d.pickl' % (anomalyScore, k)
evaluator.generateHistograms(rankingName)
def plotGammaAndZeta(constantLengthDict, legend, labelsFilename):
for anomalyScore in ['gamma', 'zeta']:
for k in [10, 22, 30, 50]:
legend.append('%s, k=%d' % (anomalyScore, k))
evaluator = RankingEvaluator(datasetDir, labelsFilename)
rankingName = 'termDocMatrixRanker_%s_%d.pickl' % (anomalyScore, k)
# (X, Y) = evaluator.generateGlobalCDF(rankingName)
(X, Y) = evaluator.generateAverageCDF(rankingName, constantLengthDict=constantLengthDict)
pylab.plot(X, Y)
pylab.hold(True)
def plotCDFs(labelsFilename):
legend = []
constantLengthDict = False
pylab.hold(True)
# plotGammaAndZeta(constantLengthDict, legend, labelsFilename)
evaluator = RankingEvaluator(datasetDir)
(X,Y) = evaluator.generateAverageCDF('lengthRanking.pickl', reverse = True, constantLengthDict=constantLengthDict)
# (X,Y) = evaluator.generateGlobalCDF('lengthRanking.pickl', reverse = True, constantLengthDict=constantLengthDict)
pylab.plot(X, Y)
legend.append('len')
evaluator = RankingEvaluator(datasetDir)
(X,Y) = evaluator.generateAverageCDF('gammaCRanking.pickl', reverse = True, constantLengthDict=constantLengthDict)
# (X,Y) = evaluator.generateGlobalCDF('gammaCRanking.pickl', reverse = True, constantLengthDict=constantLengthDict)
pylab.plot(X, Y)
legend.append('gammaC')
pylab.plot([0, 100], [0,100])
legend.append('rand')
pylab.legend(legend)
pylab.xlabel('Percentage of the code base read')
pylab.ylabel('Percentage of vulnerabilities found')
