def test_loo(self):
train, test = get_instance(Table('iris'), 0)
loocp = LOOClassifier(InverseProbability(LogisticRegressionLearner()),
train)
pred = loocp(test.x, 0.1)
self.assertEqual(pred, ['Iris-setosa'])
train, test = get_instance(Table('housing'), 0)
loocr = LOORegressor(AbsError(LinearRegressionLearner()), train)
lo, hi = loocr(test.x, 0.1)
self.assertLess(hi - lo, 20)
def test_inverse_probability(self):
tab = Table('iris')
train, test = get_instance(tab, 0)
tcp = TransductiveClassifier(InverseProbability(NaiveBayesLearner()),
train)
pred = tcp(test.x, 0.1)
self.assertEqual(pred, ['Iris-setosa'])
train, test = get_instance(tab, 0)
tcp = TransductiveClassifier(InverseProbability(NaiveBayesLearner()))
tcp.fit(train)
pred = tcp(test.x, 0.1)
self.assertEqual(pred, ['Iris-setosa'])
def test_validate_transductive(self):
tab = Table('iris')
eps = 0.1
correct, num, all = 0, 0, len(tab)
for i in range(all):
train, test = get_instance(tab, i)
tcp = TransductiveClassifier(
InverseProbability(LogisticRegressionLearner()), train)
pred = tcp(test.x, eps)
if test.get_class() in pred: correct += 1
num += len(pred)
self.assertAlmostEqual(correct / all, 1.0 - eps, delta=0.01)
def test_validate_cross_regression(self):
tab = shuffle_data(Table('housing'))
eps = 0.1
correct, num, all = 0, 0, len(tab)
for i in range(all):
train, test = get_instance(tab, i)
ccr = CrossRegressor(AbsError(LinearRegressionLearner()), 5,
shuffle_data(train))
y_min, y_max = ccr(test.x, eps)
if y_min <= test.y <= y_max: correct += 1
num += y_max - y_min
self.assertAlmostEqual(correct / all, 1.0 - eps, delta=0.02)
def test_validate_cross_classification(self):
tab = shuffle_data(Table('iris'))
eps = 0.1
correct, num, all = 0, 0, len(tab)
for i in range(all):
train, test = get_instance(tab, i)
ccp = CrossClassifier(InverseProbability(NaiveBayesLearner()), 5,
train)
pred = ccp(test.x, eps)
if test.get_class() in pred: correct += 1
num += len(pred)
self.assertAlmostEqual(correct / all, 1.0 - eps, delta=0.02)
def test_validate_regression(self):
tab = Table('housing')
eps = 0.1
correct, num, all = 0, 0, len(tab)
for i in range(all):
train, test = get_instance(tab, i)
train, calibrate = split_data(shuffle_data(train), 2, 1)
icr = InductiveRegressor(AbsError(LinearRegressionLearner()),
train, calibrate)
y_min, y_max = icr(test.x, eps)
if y_min <= test.y <= y_max: correct += 1
num += y_max - y_min
self.assertAlmostEqual(correct / all, 1.0 - eps, delta=0.02)
def test_validate_inductive(self):
eps = 0.1
correct, num, all = 0, 0, len(self.tab)
for i in range(all):
train, test = get_instance(self.tab, i)
train, calibrate = split_data(shuffle_data(train), 2, 1)
icp = InductiveClassifier(
InverseProbability(LogisticRegressionLearner()), train,
calibrate)
pred = icp(test.x, eps)
if test.get_class() in pred: correct += 1
num += len(pred)
self.assertAlmostEqual(correct / all, 1.0 - eps, delta=0.01)
def test_individual_classification(self):
train, test = get_instance(Table('iris'), 123) # borderline case
train = shuffle_data(train)
ccp = CrossClassifier(InverseProbability(LogisticRegressionLearner()),
3, train)
pred = ccp.predict(test.x)
cred, conf = pred.credibility(), pred.confidence()
self.assertLess(cred, 0.5)
d = 1e-6
self.assertEqual(len(ccp(test.x, 1 - (conf - d))), 1)
self.assertGreater(len(ccp(test.x, 1 - (conf + d))), 1)
self.assertEqual(len(ccp(test.x, (cred + d))), 0)
self.assertGreater(len(ccp(test.x, (cred - d))), 0)
def test_SVM(self):
iris = Table('iris')
tab = Table(iris.X[50:], iris.Y[50:] - 1) # versicolor, virginica
# clear cases
train, test = get_instance(tab, 30)
train, calibrate = next(RandomSampler(train, 2, 1))
icp = InductiveClassifier(SVMDistance(skl_svm.SVC()), train, calibrate)
pred = icp(test.x, 0.1)
self.assertEqual(pred, ['v1'])
train, test = get_instance(tab, 85)
train, calibrate = next(RandomSampler(train, 2, 1))
icp = InductiveClassifier(SVMDistance(skl_svm.SVC()), train, calibrate)
pred = icp(test.x, 0.1)
self.assertEqual(pred, ['v2'])
# border case
train, test = get_instance(tab, 27)
train, calibrate = next(RandomSampler(train, 2, 1))
icp = InductiveClassifier(SVMDistance(skl_svm.SVC()), train, calibrate)
pred = icp(test.x, 0.2)
self.assertEqual(pred, [])
pred = icp(test.x, 0.01)
self.assertEqual(pred, ['v1', 'v2'])
def test_cross_classification(self):
tab = Table('iris')
train, test = get_instance(tab, 0)
train = shuffle_data(train)
ccp = CrossClassifier(InverseProbability(LogisticRegressionLearner()),
3, train)
pred = ccp(test.x, 0.1)
self.assertEqual(pred, ['Iris-setosa'])
ccp = CrossClassifier(InverseProbability(LogisticRegressionLearner()),
3)
ccp.fit(train)
pred = ccp(test.x, 0.1)
self.assertEqual(pred, ['Iris-setosa'])
def setUp(self):
self.tab = Table('iris')
train, self.test = get_instance(self.tab, 0)
self.train, self.calibrate = split_data(shuffle_data(train), 2, 1)
def test_nearest_neighbours(self):
tab = Table('iris')
train, test = get_instance(tab, 0)
tcp = TransductiveClassifier(KNNDistance(Euclidean), train)
pred = tcp(test.x, 0.1)
self.assertEqual(pred, ['Iris-setosa'])
def setUp(self):
self.train, self.test = get_instance(Table('housing'), 0)
self.train, self.calibrate = split_data(shuffle_data(self.train), 2, 1)
