def AUCROC_weighting(gbc):
N = math.fsum([len(samples) for _, samples in gbc.iteritems()])
N2 = N * N
waucs = [(len(samples1) * len(samples2) / N2, calc_auc(_ROC(samples1 + samples2, cls1)))
for (cls1, samples1), (cls2, samples2) in combinations(gbc.items(), 2)]
wsum = math.fsum((x[0] for x in waucs))
# TODO can wsum be 0? what if?
return math.fsum(map(lambda x: x[0] * x[1], waucs)) / wsum
def test_auc(self):
test_data = [
([(0, 0), (1, 2), (2, 0)], 2.),
([(0, 1), (1, 1)], 1),
([(0., 0.5), (1, 2), (2, 2.)], 1.25 + 2.)
]
for curve, expected_auc in test_data:
self.assertEqual(expected_auc, calc_auc(curve))
def AUCROC_weighting(gbc):
N = math.fsum([len(samples) for _, samples in gbc.iteritems()])
N2 = N * N
waucs = [(len(samples1) * len(samples2) / N2,
calc_auc(_ROC(samples1 + samples2, cls1)))
for (cls1, samples1), (cls2,
samples2) in combinations(gbc.items(), 2)]
wsum = math.fsum((x[0] for x in waucs))
# TODO can wsum be 0? what if?
return math.fsum(map(lambda x: x[0] * x[1], waucs)) / wsum
def AUCROC_nonweighting(gbc):
return avg([calc_auc(_ROC(samples1 + samples2, cls1))
for (cls1, samples1), (cls2, samples2) in combinations(gbc.items(), 2)])
def AUCROC_nonweighting(gbc):
return avg([
calc_auc(_ROC(samples1 + samples2, cls1))
for (cls1, samples1), (cls2, samples2) in combinations(gbc.items(), 2)
])
def test_auc(self):
test_data = [([(0, 0), (1, 2), (2, 0)], 2.), ([(0, 1), (1, 1)], 1),
([(0., 0.5), (1, 2), (2, 2.)], 1.25 + 2.)]
for curve, expected_auc in test_data:
self.assertEqual(expected_auc, calc_auc(curve))
