def setUp(self):
self.classifiers = [("BayesLearner", [], {}), ("TreeLearner", [], {}),
("kNNLearner", [], {
"k": 1
}), ("kNNLearner", [], {
"k": 3
}), ("TreeLearner", [], {})]
self.knn1 = OrangeClassifier(self.classifiers[2][0])
self.knn3 = OrangeClassifier(self.classifiers[3][0])
self.tree = OrangeClassifier(self.classifiers[4][0])
def setUp(self):
self.meta = simple_meta_attrs(['-', '+'])
self.cs = lambda i, v: Sample(
[i, self.meta[1].set_value(v)], self.meta, last_is_class=True)
self.classifier = OrangeClassifier('kNNLearner', k=1)
test_samples = '+++-++-+-+--+---'
N = len(test_samples)
train_samples = ('+' * (N / 2)) + ('-' * (N / 2))
self.test_samples, self.train_samples = ([
self.cs(i, v) for i, v in enumerate(samples)
] for samples in [test_samples, train_samples])
random.shuffle(self.test_samples)
self.classifier.train(self.train_samples)
def setUp(self):
self.meta = simple_meta_attrs(['-', '+'])
self.cs = lambda i, v: Sample([i, self.meta[1].set_value(v)], self.meta, last_is_class=True)
self.classifier = OrangeClassifier('kNNLearner', k=1)
test_samples = '+++-++-+-+--+---'
N = len(test_samples)
train_samples = ('+' * (N / 2)) + ('-' * (N / 2))
self.test_samples, self.train_samples = ([self.cs(i, v) for i, v in enumerate(samples)]
for samples in [test_samples, train_samples])
random.shuffle(self.test_samples)
self.classifier.train(self.train_samples)
def setUp(self):
self.classifiers = [("BayesLearner", [], {}), ("TreeLearner", [], {}),
("kNNLearner", [], {
"k": 1
}), ("kNNLearner", [], {
"k": 3
}), ("TreeLearner", [], {})]
self.knn1 = OrangeClassifier(self.classifiers[2][0])
self.knn3 = OrangeClassifier(self.classifiers[3][0])
self.tree = OrangeClassifier(self.classifiers[4][0])
self.cls_meta = NominalAttribute([0, 1])
self.meta = [NumericAttribute() for _ in xrange(3)]
self.train_set = [
Sample([0, 0, 0], self.meta, 0, self.cls_meta),
Sample([0, 1, 0], self.meta, 0, self.cls_meta),
Sample([0, 0, 1], self.meta, 0, self.cls_meta),
Sample([3, 0, 0], self.meta, 1, self.cls_meta),
Sample([3, 1, 0], self.meta, 1, self.cls_meta),
Sample([3, 0, 1], self.meta, 1, self.cls_meta),
]
class TestAUCROCStatistics(unittest.TestCase):
def setUp(self):
self.meta = simple_meta_attrs(['-', '+'])
self.cs = lambda i, v: Sample([i, self.meta[1].set_value(v)], self.meta, last_is_class=True)
self.classifier = OrangeClassifier('kNNLearner', k=1)
test_samples = '+++-++-+-+--+---'
N = len(test_samples)
train_samples = ('+' * (N / 2)) + ('-' * (N / 2))
self.test_samples, self.train_samples = ([self.cs(i, v) for i, v in enumerate(samples)]
for samples in [test_samples, train_samples])
random.shuffle(self.test_samples)
self.classifier.train(self.train_samples)
def _test_roc_eq(self):
roc = ROC(self.classifier, self.test_samples, '+')
middle = roc[1]
tpr, fpr = map(lambda f: f(self.classifier, self.test_samples, '+'), [TPR, FPR])
self.assertEqual(middle[1], tpr)
self.assertEqual(middle[0], fpr)
def test_ROC(self):
self._test_roc_eq()
def _test_auc_eq(self):
tpr, fpr = map(lambda f: f(self.classifier, self.test_samples, '+'), [TPR, FPR])
auc = AUCROC(self.classifier, self.test_samples)
expected_area = fpr * tpr / 2 + (1 - fpr) * (tpr + 1) / 2
expected_area_v2 = (1 + tpr - fpr) / 2.
self.assertEqual(expected_area, expected_area_v2)
# ^^^ just checking my math :)
self.assertEqual(auc, expected_area)
def test_AUC(self):
self._test_auc_eq()
def test_multiplerandom_test(self):
N = len(self.test_samples)
def gen_test_case():
schema = ''.join(['+' if random.random() >= 0.5 else '-' for _ in xrange(N)])
return [self.cs(i, v) for i, v in enumerate(schema)]
for _ in xrange(200):
train = gen_test_case()
self.classifier.train(train)
self._test_roc_eq()
self._test_auc_eq()
def test_avg_auc_roc_with_splited_cv(self):
sets = split_data_cv(self.test_samples)
def tmp(train, test):
self.classifier.train(train)
return AUCROC(self.classifier, test)
aucs = [tmp(train, test) for train, test in sets]
max_auc, min_auc = (f(aucs) for f in (max, min))
avg_auc = aucroc_avg_classifier_performance(self.classifier, sets)
self.assertTrue(min_auc <= avg_auc[0] <= max_auc)
def test_classifier_creation(self):
""" Proper classifier creation """
for (c, args, kargs) in self.classifiers:
classifier = OrangeClassifier(c, *args, **kargs)
self.assertEqual(getattr(orange, c), type(classifier.classifier))
class TestAUCROCStatistics(unittest.TestCase):
def setUp(self):
self.meta = simple_meta_attrs(['-', '+'])
self.cs = lambda i, v: Sample(
[i, self.meta[1].set_value(v)], self.meta, last_is_class=True)
self.classifier = OrangeClassifier('kNNLearner', k=1)
test_samples = '+++-++-+-+--+---'
N = len(test_samples)
train_samples = ('+' * (N / 2)) + ('-' * (N / 2))
self.test_samples, self.train_samples = ([
self.cs(i, v) for i, v in enumerate(samples)
] for samples in [test_samples, train_samples])
random.shuffle(self.test_samples)
self.classifier.train(self.train_samples)
def _test_roc_eq(self):
roc = ROC(self.classifier, self.test_samples, '+')
middle = roc[1]
tpr, fpr = map(lambda f: f(self.classifier, self.test_samples, '+'),
[TPR, FPR])
self.assertEqual(middle[1], tpr)
self.assertEqual(middle[0], fpr)
def test_ROC(self):
self._test_roc_eq()
def _test_auc_eq(self):
tpr, fpr = map(lambda f: f(self.classifier, self.test_samples, '+'),
[TPR, FPR])
auc = AUCROC(self.classifier, self.test_samples)
expected_area = fpr * tpr / 2 + (1 - fpr) * (tpr + 1) / 2
expected_area_v2 = (1 + tpr - fpr) / 2.
self.assertEqual(expected_area, expected_area_v2)
# ^^^ just checking my math :)
self.assertEqual(auc, expected_area)
def test_AUC(self):
self._test_auc_eq()
def test_multiplerandom_test(self):
N = len(self.test_samples)
def gen_test_case():
schema = ''.join(
['+' if random.random() >= 0.5 else '-' for _ in xrange(N)])
return [self.cs(i, v) for i, v in enumerate(schema)]
for _ in xrange(200):
train = gen_test_case()
self.classifier.train(train)
self._test_roc_eq()
self._test_auc_eq()
def test_avg_auc_roc_with_splited_cv(self):
sets = split_data_cv(self.test_samples)
def tmp(train, test):
self.classifier.train(train)
return AUCROC(self.classifier, test)
aucs = [tmp(train, test) for train, test in sets]
max_auc, min_auc = (f(aucs) for f in (max, min))
avg_auc = aucroc_avg_classifier_performance(self.classifier, sets)
self.assertTrue(min_auc <= avg_auc[0] <= max_auc)
