def test_more_klass_values(self):
c = cm.ConfusionMatrix(['1', '2', '3', '4', '5', 'U'])
c.count('1', '2')
c.count('2', '2')
c.count('2', '3')
c.count('3', '3')
c.count('4', '2')
c.count('5', '5')
c.count('2', '2')
c.count('3', '2')
#matrix values for class '1'
self.assertEqual(0, c.tp(0))
self.assertEqual(7, c.tn(0))
self.assertEqual(1, c.fn(0))
self.assertEqual(0, c.fp(0))
#matrix values for class 'U'
self.assertEqual(0, c.tp(5))
self.assertEqual(8, c.tn(5))
self.assertEqual(0, c.fn(5))
self.assertEqual(0, c.fp(5))
self.assertEqual(
0.875, c.accuracy()) #accuracy for class '1'(default accuracy)
self.assertEqual(1, c.accuracy(index=5)) #accuracy for 'U'
self.assertEqual(0, c.fscore()) #f-score for '1'(default f-score)
self.assertEqual(0, c.fscore(index=5)) #f-score for 'U'
self.assertEqual(0.5, c.fscore(index=1)) #f-score for '2'
def confusion_matrix(self, klass):
for i in self.data:
if i.classified_klass == None:
raise system.SystemError('Cannot calculate accuracy as one or more instance(s) are not classified')
matrix = cm.ConfusionMatrix(klass)
for i in self.data:
matrix.count(i.klass_value, i.classified_klass)
return matrix
def setUp(self):
self.c = cm.ConfusionMatrix(['yes', 'no'])
self.pos = 'yes'
self.neg = 'no'