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Add test cases to test recently added features

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This commit is contained in:
blackfly 2020-04-14 19:53:51 +02:00
parent 88cbe0a126
commit a0f20a40f6
2 changed files with 66 additions and 7 deletions
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57
tests/test_functions.py
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@ -85,6 +85,16 @@ class TestCompetitions(unittest.TestCase):
decimal=5)
self.assertIsNone(mismatch)
def test_wtac_unequal_dist(self):
d = torch.tensor([[2., 3., 4.], [2., 3., 1.]])
labels = torch.tensor([0, 1, 1])
actual = competitions.wtac(d, labels)
desired = torch.tensor([0, 1])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_wtac_one_hot(self):
d = torch.tensor([[1.99, 3.01], [3., 2.01]])
labels = torch.tensor([[0, 1], [1, 0]])
@ -95,6 +105,27 @@ class TestCompetitions(unittest.TestCase):
decimal=5)
self.assertIsNone(mismatch)
def test_stratified_min(self):
d = torch.tensor([[1., 0., 2., 3.], [9., 8., 0, 1]])
labels = torch.tensor([0, 0, 1, 2])
actual = competitions.stratified_min(d, labels)
desired = torch.tensor([[0., 2., 3.], [8., 0., 1.]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_stratified_min_one_hot(self):
d = torch.tensor([[1., 0., 2., 3.], [9., 8., 0, 1]])
labels = torch.tensor([0, 0, 1, 2])
labels = torch.eye(3)[labels]
actual = competitions.stratified_min(d, labels)
desired = torch.tensor([[0., 2., 3.], [8., 0., 1.]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_knnc_k1(self):
d = torch.tensor([[2., 3., 1.99, 3.01], [2., 3., 2.01, 3.]])
labels = torch.tensor([0, 1, 2, 3])
@ -351,7 +382,7 @@ class TestInitializers(unittest.TestCase):
def test_stratified_random_equal1(self):
pdist = torch.tensor([1, 1])
actual, _ = initializers.stratified_random(self.x, self.y, pdist)
desired = torch.tensor([[0., -1., -2.], [2., 2., 2.]])
desired = torch.tensor([[0., -1., -2.], [0., 0., 0.]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
@ -367,6 +398,16 @@ class TestInitializers(unittest.TestCase):
decimal=5)
self.assertIsNone(mismatch)
def test_stratified_random_equal2(self):
pdist = torch.tensor([2, 2])
actual, _ = initializers.stratified_random(self.x, self.y, pdist)
desired = torch.tensor([[0., -1., -2.], [0., -1., -2.], [0., 0., 0.],
[0., 0., 0.]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_stratified_mean_unequal(self):
pdist = torch.tensor([1, 3])
actual, _ = initializers.stratified_mean(self.x, self.y, pdist)
@ -380,7 +421,7 @@ class TestInitializers(unittest.TestCase):
def test_stratified_random_unequal(self):
pdist = torch.tensor([1, 3])
actual, _ = initializers.stratified_random(self.x, self.y, pdist)
desired = torch.tensor([[0., -1., -2.], [2., 2., 2.], [0., 0., 0.],
desired = torch.tensor([[0., -1., -2.], [0., 0., 0.], [0., 0., 0.],
[0., 0., 0.]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
@ -417,5 +458,17 @@ class TestLosses(unittest.TestCase):
loss_value = torch.sum(batch_loss, dim=0)
self.assertEqual(loss_value, -100)
def test_glvq_loss_one_hot_unequal(self):
dlist = [torch.ones(100), torch.zeros(100), torch.zeros(100)]
d = torch.stack(dlist, dim=1)
labels = torch.tensor([[0, 1], [1, 0], [1, 0]])
wl = torch.tensor([1, 0])
targets = torch.stack([wl for _ in range(100)], dim=0)
batch_loss = losses.glvq_loss(distances=d,
target_labels=targets,
prototype_labels=labels)
loss_value = torch.sum(batch_loss, dim=0)
self.assertEqual(loss_value, -100)
def tearDown(self):
pass

16
tests/test_modules.py
View File

@ -51,7 +51,7 @@ class TestPrototypes(unittest.TestCase):
self.assertIsNone(mismatch)
def test_prototypes1d_proto_init_without_data(self):
with self.assertWarns(Warning):
with self.assertWarns(UserWarning):
_ = prototypes.Prototypes1D(
input_dim=3,
nclasses=2,
@ -168,6 +168,16 @@ class TestPrototypes(unittest.TestCase):
decimal=5)
self.assertIsNone(mismatch)
def test_prototypes1d_dist_check(self):
p1 = prototypes.Prototypes1D(input_dim=0, prototype_distribution=[0])
with self.assertWarns(UserWarning):
_ = p1._check_prototype_distribution()
def test_prototypes1d_check_extra_repr_not_empty(self):
p1 = prototypes.Prototypes1D(input_dim=0, prototype_distribution=[0])
rep = p1.extra_repr()
self.assertNotEqual(rep, '')
def tearDown(self):
del self.x, self.y, self.gen
_ = torch.seed()
@ -194,7 +204,3 @@ class TestLosses(unittest.TestCase):
def tearDown(self):
pass
if __name__ == '__main__':
unittest.main()