# pylab.ylabel('True positive rate')
# pylab.xlabel('False positive rate')
# pylab.axis([0, 1, 0, 1])
# pylab.savefig('9-results/roc.png')
# Initialize
radius = 25
radiusInfos = []
length = 50
lengthInfos = []
scanInformationByPath = loadInformations('probabilities', '.')
# For each scanInformation,
for scanFolderPath, scanInformation in scanInformationByPath.iteritems():
# Initialize
scanPath = os.path.join(scanFolderPath, folder_store.fileNameByFolderName['probabilities'])
# Evaluate with radius
radiusInfos.append(evaluation_process.evaluateWindowsByRadius(scanPath, radius))
# Evaluate with length
lengthInfos.append(evaluation_process.evaluateWindowsByLength(scanPath, length))
# Plot with radius
pylab.figure()
pylab.plot([x['recall'] for x in radiusInfos], [x['precision'] for x in radiusInfos], '*')
pylab.title('Evaluation of scan using relative circles (circle radius of %s meters)' % radius)
pylab.ylabel('Precision (Percent of predicted that are actual)')
pylab.xlabel('Recall (Percent of actual that are predicted)')
pylab.axis([0, 1, 0, 1])
pylab.savefig(expandPath('scanEvaluationByRadius.png'))
# Plot with length
pylab.figure()
pylab.plot([x['false positive rate'] for x in lengthInfos], [x['true positive rate'] for x in lengthInfos], '*')
pylab.title('Evaluation of scan using absolute rectangles (rectangle length of %s meters)' % length)
pylab.ylabel('True positive rate')