def test_majority_class_is_set_on_test_instances(self):
path = datasetsDir(self) + 'test_phones' + SEP + 'phoney'
zeror = z.ZeroR(training(path), attributes(path), klass(path))
zeror.train()
zeror.test(test(path))
i = 0
for i in range(4):
self.assertEqual('b', zeror.test_instances[i].classified_klass)
self.assertEqual(None, zeror.test_instances[i].klass_value)
def test_zeroR_verifies_validity_of_training_data(self):
try:
path = datasetsDir(
self) + 'test_faulty' + SEP + 'invalid_attributes'
classifier = z.ZeroR(training(path), attributes(path), klass(path))
classifier.train()
self.fail('should throw invalid data error')
except inv.InvalidDataError:
pass
def test_verify_returns_correct_confusion_matrix(self):
path = datasetsDir(self) + 'minigolf' + SEP + 'weather'
klasses = klass(path)
zeror = z.ZeroR(training(path), attributes(path), klasses)
zeror.train()
confusion_matrix = zeror.verify(gold(path))
self.assertEqual(0.75, confusion_matrix.accuracy())
self.assertEqual(0.25, confusion_matrix.error())
self.assertEqual(1, confusion_matrix.tpr())
self.assertEqual(0, confusion_matrix.tnr())
self.assertEqual(1, confusion_matrix.fpr())
self.assertEqual(0.75, confusion_matrix.precision())
self.assertEqual(1, confusion_matrix.recall())
self.assertAlmostEqual(0.85714286, confusion_matrix.fscore(), 8)
def test_can_classify_data_having_continuous_attributes(self):
path = datasetsDir(self) + 'numerical' + SEP + 'weather'
zeror = z.ZeroR(training(path), attributes(path), klass(path))
zeror.train()
zeror.verify(gold(path))
def test_majority_class(self):
path = datasetsDir(self) + 'test_phones' + SEP + 'phoney'
classifier = z.ZeroR(training(path), attributes(path), klass(path))
self.assertEqual('b', classifier.majority_class())
def test_zeroR_instance_is_created_with_training_data(self):
path = datasetsDir(self) + 'test_phones' + SEP + 'phoney'
classifier = z.ZeroR(training(path), attributes(path), klass(path))
self.assertEqual(
training(datasetsDir(self) + 'test_phones' + SEP + 'phoney'),
classifier.training, 'should have created training instances')
