def test_missing(self):
iris = data.Table('iris')
learn = majority_.MajorityLearner()
for e in iris[:len(iris) // 2:2]:
e.set_class("?")
clf = learn(iris)
y = clf(iris)
self.assertTrue((y == 2).all())
iris = data.Table('iris')
learn = majority_.MajorityLearner()
for e in iris:
e.set_class("?")
clf = learn(iris)
y = clf(iris)
self.assertTrue((y == 0).all())
def test_majority(self):
nrows = 1000
ncols = 10
x = np.random.random_integers(1, 3, (nrows, ncols))
y = np.random.random_integers(1, 3, (nrows, 1)) // 2
y[0] = 4
t = data.Table(x, y)
learn = majority_.MajorityLearner()
clf = learn(t)
x2 = np.random.random_integers(1, 3, (nrows, ncols))
y2 = clf(x2)
self.assertTrue((y2 == 1).all())
def test_weights(self):
nrows = 100
ncols = 10
x = np.random.random_integers(1, 3, (nrows, ncols))
y = np.random.random_integers(1, 5, (nrows, 1))
heavy = 3
w = (y == heavy) * 123 + 1
t = data.Table(x, y, W=w)
learn = majority_.MajorityLearner()
clf = learn(t)
x2 = np.random.random_integers(1, 3, (nrows, ncols))
y2 = clf(x2)
self.assertTrue((y2 == heavy).all())
def test_continuous(self):
autompg = data.Table('auto-mpg')
learn = majority_.MajorityLearner()
self.assertRaises(ValueError, learn, autompg)
def test_empty(self):
iris = data.Table('iris')
learn = majority_.MajorityLearner()
clf = learn(iris[:0])
y = clf(iris[0], clf.Probs)
self.assertTrue(np.allclose(y, y.sum() / y.size))
class TestMajorityLearnerWithLoessEstimator(testing.LearnerTestCase):
LEARNER = majority.MajorityLearner(estimator_constructor=\
Orange.core.ProbabilityEstimatorConstructor_loess())
class TestMajorityLearner(testing.LearnerTestCase):
LEARNER = majority.MajorityLearner()
