def test_bed_confusion_matrix_datasets(self):
'''
Test BED-based confusion matrix upon a dataset.
'''
hypothesis = HYPOTHESIS_STARGAZER
reference = HEARST_1997_STARGAZER
value = boundary_confusion_matrix(hypothesis, reference)
hyp_p = precision(value)
hyp_r = recall(value)
hyp_f = fmeasure(value)
# Precision
self.assertAlmostEquals(float(hyp_p['stargazer,h1,1']), 0.57142857)
self.assertAlmostEquals(float(hyp_p['stargazer,h2,1']), 0.41176470)
self.assertAlmostEquals(float(hyp_p['stargazer,h1,2']), 0.42857142)
self.assertAlmostEquals(float(hyp_p['stargazer,h2,2']), 0.33333333)
# Recall
self.assertAlmostEquals(float(hyp_r['stargazer,h1,1']), 0.57142857)
self.assertAlmostEquals(float(hyp_r['stargazer,h2,1']), 0.41176470)
self.assertAlmostEquals(float(hyp_r['stargazer,h1,2']), 0.42857142)
self.assertAlmostEquals(float(hyp_r['stargazer,h2,2']), 0.33333333)
# FMeasure
self.assertAlmostEquals(float(hyp_f['stargazer,h1,1']), 0.72727272)
self.assertAlmostEquals(float(hyp_f['stargazer,h2,1']), 0.58333333)
self.assertAlmostEquals(float(hyp_f['stargazer,h1,2']), 0.6)
self.assertAlmostEquals(float(hyp_f['stargazer,h2,2']), 0.5)
def test_bed_confusion_matrix_datasets(self):
'''
Test BED-based confusion matrix upon a dataset.
'''
hypothesis = HYPOTHESIS_STARGAZER
reference = HEARST_1997_STARGAZER
value = boundary_confusion_matrix(hypothesis, reference)
hyp_p = precision(value)
hyp_r = recall(value)
hyp_f = fmeasure(value)
# Precision
self.assertAlmostEquals(float(hyp_p['stargazer,h1,1']), 0.57142857)
self.assertAlmostEquals(float(hyp_p['stargazer,h2,1']), 0.41176470)
self.assertAlmostEquals(float(hyp_p['stargazer,h1,2']), 0.42857142)
self.assertAlmostEquals(float(hyp_p['stargazer,h2,2']), 0.33333333)
# Recall
self.assertAlmostEquals(float(hyp_r['stargazer,h1,1']), 0.57142857)
self.assertAlmostEquals(float(hyp_r['stargazer,h2,1']), 0.41176470)
self.assertAlmostEquals(float(hyp_r['stargazer,h1,2']), 0.42857142)
self.assertAlmostEquals(float(hyp_r['stargazer,h2,2']), 0.33333333)
# FMeasure
self.assertAlmostEquals(float(hyp_f['stargazer,h1,1']), 0.72727272)
self.assertAlmostEquals(float(hyp_f['stargazer,h2,1']), 0.58333333)
self.assertAlmostEquals(float(hyp_f['stargazer,h1,2']), 0.6)
self.assertAlmostEquals(float(hyp_f['stargazer,h2,2']), 0.5)
def test_bed_confusion_matrix(self):
'''
Test BED-based confusion matrix upon two segmentations.
'''
hypothesis = (5,5,5,5,1)
reference = HEARST_1997_STARGAZER['stargazer']['2']
value = boundary_confusion_matrix(hypothesis, reference)
self.assertAlmostEquals(float(precision(value)), 0.23076923)
self.assertAlmostEquals(float(recall(value)), 0.23076923)
self.assertAlmostEquals(float(fmeasure(value)), 0.375)
def test_bed_confusion_matrix(self):
'''
Test BED-based confusion matrix upon two segmentations.
'''
hypothesis = (5, 5, 5, 5, 1)
reference = HEARST_1997_STARGAZER['stargazer']['2']
value = boundary_confusion_matrix(hypothesis, reference)
self.assertAlmostEquals(float(precision(value)), 0.23076923)
self.assertAlmostEquals(float(recall(value)), 0.23076923)
self.assertAlmostEquals(float(fmeasure(value)), 0.375)
def example_in_paper_test():
gold = [2, 3, 6]
h_list = [[5, 6], [2, 2, 7], [2, 3, 3, 3], [1, 1, 3, 1, 5]]
for n, h in enumerate(h_list):
cm = boundary_confusion_matrix(h, gold)
print("第%d次实验" % int(n + 1))
# The P, R and F values are different from those in the normal method because it will correct the near missing.
print("P=%.4f, R=%.4f, F=%.4f" %
(precision(cm), recall(cm), fmeasure(cm)))
print("1-Pk=%.3f, 1-WD=%.3f, B=%.3f, S=%.3f" %
(pk(h, gold,
one_minus=True), WD(h, gold, one_minus=True,
window_size=2), B(h, gold), S(h, gold)))
def test_fmeasure(self):
'''
Test FMeasure.
'''
matrix = cm()
matrix['p']['p'] += 1
matrix['p']['f'] += 1
self.assertAlmostEqual(
__fmeasure__(matrix, 'p'), Decimal('0.66666'), 4)
self.assertEqual(__fmeasure__(matrix, 'f'), Decimal('0'))
self.assertAlmostEqual(fmeasure(matrix, version=Average.micro),
Decimal('0.66666'), 4)
self.assertAlmostEqual(fmeasure(matrix, version=Average.macro),
Decimal('0.33333'), 4)
self.assertAlmostEqual(fmeasure(matrix, classification='p'),
Decimal('0.66666'), 4)
matrix = cm()
matrix['p']['p'] += 1
matrix['p']['f'] += 3
matrix['f']['p'] += 1
self.assertAlmostEqual(fmeasure(matrix, version=Average.micro),
Decimal('0.33333'), 4)
self.assertAlmostEqual(fmeasure(matrix, version=Average.macro),
Decimal('0.16666'), 4)
self.assertAlmostEqual(__fmeasure__(matrix, 'p'),
Decimal('0.33333'), 4)
self.assertAlmostEqual(fmeasure(matrix, classification='p'),
Decimal('0.33333'), 4)
self.assertEqual(__fmeasure__(matrix, 'f'), Decimal('0'))
matrix = cm()
matrix['p']['p'] += 5
matrix['p']['f'] += 2
matrix['f']['p'] += 1
matrix['f']['f'] += 2
self.assertAlmostEqual(fmeasure(matrix, version=Average.micro),
Decimal('0.68421'), 4)
self.assertAlmostEqual(fmeasure(matrix, version=Average.macro),
Decimal('0.67032'), 4)
self.assertAlmostEqual(__fmeasure__(matrix, 'p'),
Decimal('0.76923'), 4)
self.assertAlmostEqual(__fmeasure__(matrix, 'f'),
Decimal('0.57142'), 4)
matrix = cm()
matrix['p']['f'] += 2
self.assertEqual(fmeasure(matrix), Decimal('0'))
self.assertEqual(__fmeasure__(matrix, 'p'), Decimal('0'))
self.assertEqual(__fmeasure__(matrix, 'f'), Decimal('0'))
matrix = cm()
matrix['p']['p'] += 2
self.assertEqual(fmeasure(matrix), Decimal('1'))
self.assertEqual(__fmeasure__(matrix, 'p'), Decimal('1'))
self.assertEqual(__fmeasure__(matrix, 'f'), Decimal('0'))
matrix = cm()
self.assertEqual(fmeasure(matrix), Decimal('0'))
self.assertEqual(__fmeasure__(matrix, 'p'), Decimal('0'))
self.assertEqual(__fmeasure__(matrix, 'f'), Decimal('0'))
def test_fmeasure(self):
'''
Test FMeasure.
'''
matrix = cm()
matrix['p']['p'] += 1
matrix['p']['f'] += 1
self.assertAlmostEqual(__fmeasure__(matrix, 'p'), Decimal('0.66666'),
4)
self.assertEqual(__fmeasure__(matrix, 'f'), Decimal('0'))
self.assertAlmostEqual(fmeasure(matrix, version=Average.micro),
Decimal('0.66666'), 4)
self.assertAlmostEqual(fmeasure(matrix, version=Average.macro),
Decimal('0.33333'), 4)
self.assertAlmostEqual(fmeasure(matrix, classification='p'),
Decimal('0.66666'), 4)
matrix = cm()
matrix['p']['p'] += 1
matrix['p']['f'] += 3
matrix['f']['p'] += 1
self.assertAlmostEqual(fmeasure(matrix, version=Average.micro),
Decimal('0.33333'), 4)
self.assertAlmostEqual(fmeasure(matrix, version=Average.macro),
Decimal('0.16666'), 4)
self.assertAlmostEqual(__fmeasure__(matrix, 'p'), Decimal('0.33333'),
4)
self.assertAlmostEqual(fmeasure(matrix, classification='p'),
Decimal('0.33333'), 4)
self.assertEqual(__fmeasure__(matrix, 'f'), Decimal('0'))
matrix = cm()
matrix['p']['p'] += 5
matrix['p']['f'] += 2
matrix['f']['p'] += 1
matrix['f']['f'] += 2
self.assertAlmostEqual(fmeasure(matrix, version=Average.micro),
Decimal('0.68421'), 4)
self.assertAlmostEqual(fmeasure(matrix, version=Average.macro),
Decimal('0.67032'), 4)
self.assertAlmostEqual(__fmeasure__(matrix, 'p'), Decimal('0.76923'),
4)
self.assertAlmostEqual(__fmeasure__(matrix, 'f'), Decimal('0.57142'),
4)
matrix = cm()
matrix['p']['f'] += 2
self.assertEqual(fmeasure(matrix), Decimal('0'))
self.assertEqual(__fmeasure__(matrix, 'p'), Decimal('0'))
self.assertEqual(__fmeasure__(matrix, 'f'), Decimal('0'))
matrix = cm()
matrix['p']['p'] += 2
self.assertEqual(fmeasure(matrix), Decimal('1'))
self.assertEqual(__fmeasure__(matrix, 'p'), Decimal('1'))
self.assertEqual(__fmeasure__(matrix, 'f'), Decimal('0'))
matrix = cm()
self.assertEqual(fmeasure(matrix), Decimal('0'))
self.assertEqual(__fmeasure__(matrix, 'p'), Decimal('0'))
self.assertEqual(__fmeasure__(matrix, 'f'), Decimal('0'))
