Add test cases to test newly added features
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blackfly
parent
d17b9a3346
commit
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@ -3,8 +3,8 @@
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## Release 0.1.1-dev0
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### Includes
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- Minor bugfixes.
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- 100% line coverage.
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- Minor bugfixes.
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- 100% line coverage.
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## Release 0.1.0-dev0
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@ -126,6 +126,16 @@ class TestCompetitions(unittest.TestCase):
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decimal=5)
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self.assertIsNone(mismatch)
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def test_stratified_min_simple(self):
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d = torch.tensor([[0., 2., 3.], [8., 0, 1]])
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labels = torch.tensor([0, 1, 2])
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actual = competitions.stratified_min(d, labels)
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desired = torch.tensor([[0., 2., 3.], [8., 0., 1.]])
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mismatch = np.testing.assert_array_almost_equal(actual,
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desired,
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decimal=5)
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self.assertIsNone(mismatch)
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def test_knnc_k1(self):
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d = torch.tensor([[2., 3., 1.99, 3.01], [2., 3., 2.01, 3.]])
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labels = torch.tensor([0, 1, 2, 3])
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@ -372,7 +382,7 @@ class TestInitializers(unittest.TestCase):
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def test_stratified_mean_equal1(self):
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pdist = torch.tensor([1, 1])
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actual, _ = initializers.stratified_mean(self.x, self.y, pdist)
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actual, _ = initializers.stratified_mean(self.x, self.y, pdist, False)
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desired = torch.tensor([[5., 5., 5.], [1., 1., 1.]])
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mismatch = np.testing.assert_array_almost_equal(actual,
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desired,
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@ -381,7 +391,8 @@ class TestInitializers(unittest.TestCase):
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def test_stratified_random_equal1(self):
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pdist = torch.tensor([1, 1])
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actual, _ = initializers.stratified_random(self.x, self.y, pdist)
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actual, _ = initializers.stratified_random(self.x, self.y, pdist,
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False)
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desired = torch.tensor([[0., -1., -2.], [0., 0., 0.]])
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mismatch = np.testing.assert_array_almost_equal(actual,
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desired,
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@ -390,7 +401,7 @@ class TestInitializers(unittest.TestCase):
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def test_stratified_mean_equal2(self):
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pdist = torch.tensor([2, 2])
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actual, _ = initializers.stratified_mean(self.x, self.y, pdist)
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actual, _ = initializers.stratified_mean(self.x, self.y, pdist, False)
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desired = torch.tensor([[5., 5., 5.], [5., 5., 5.], [1., 1., 1.],
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[1., 1., 1.]])
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mismatch = np.testing.assert_array_almost_equal(actual,
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@ -400,7 +411,8 @@ class TestInitializers(unittest.TestCase):
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def test_stratified_random_equal2(self):
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pdist = torch.tensor([2, 2])
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actual, _ = initializers.stratified_random(self.x, self.y, pdist)
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actual, _ = initializers.stratified_random(self.x, self.y, pdist,
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False)
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desired = torch.tensor([[0., -1., -2.], [0., -1., -2.], [0., 0., 0.],
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[0., 0., 0.]])
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mismatch = np.testing.assert_array_almost_equal(actual,
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@ -410,7 +422,7 @@ class TestInitializers(unittest.TestCase):
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def test_stratified_mean_unequal(self):
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pdist = torch.tensor([1, 3])
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actual, _ = initializers.stratified_mean(self.x, self.y, pdist)
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actual, _ = initializers.stratified_mean(self.x, self.y, pdist, False)
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desired = torch.tensor([[5., 5., 5.], [1., 1., 1.], [1., 1., 1.],
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[1., 1., 1.]])
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mismatch = np.testing.assert_array_almost_equal(actual,
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@ -420,7 +432,8 @@ class TestInitializers(unittest.TestCase):
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def test_stratified_random_unequal(self):
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pdist = torch.tensor([1, 3])
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actual, _ = initializers.stratified_random(self.x, self.y, pdist)
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actual, _ = initializers.stratified_random(self.x, self.y, pdist,
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False)
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desired = torch.tensor([[0., -1., -2.], [0., 0., 0.], [0., 0., 0.],
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[0., 0., 0.]])
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mismatch = np.testing.assert_array_almost_equal(actual,
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@ -428,6 +441,36 @@ class TestInitializers(unittest.TestCase):
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decimal=5)
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self.assertIsNone(mismatch)
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def test_stratified_mean_unequal_one_hot(self):
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pdist = torch.tensor([1, 3])
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y = torch.eye(2)[self.y]
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desired1 = torch.tensor([[5., 5., 5.], [1., 1., 1.], [1., 1., 1.],
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[1., 1., 1.]])
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actual1, actual2 = initializers.stratified_mean(self.x, y, pdist)
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desired2 = torch.tensor([[1, 0], [0, 1], [0, 1], [0, 1]])
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mismatch = np.testing.assert_array_almost_equal(actual1,
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desired1,
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decimal=5)
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mismatch = np.testing.assert_array_almost_equal(actual2,
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desired2,
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decimal=5)
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self.assertIsNone(mismatch)
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def test_stratified_random_unequal_one_hot(self):
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pdist = torch.tensor([1, 3])
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y = torch.eye(2)[self.y]
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actual1, actual2 = initializers.stratified_random(self.x, y, pdist)
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desired1 = torch.tensor([[0., -1., -2.], [0., 0., 0.], [0., 0., 0.],
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[0., 0., 0.]])
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desired2 = torch.tensor([[1, 0], [0, 1], [0, 1], [0, 1]])
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mismatch = np.testing.assert_array_almost_equal(actual1,
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desired1,
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decimal=5)
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mismatch = np.testing.assert_array_almost_equal(actual2,
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desired2,
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decimal=5)
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self.assertIsNone(mismatch)
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def tearDown(self):
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del self.x, self.y, self.gen
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_ = torch.seed()
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@ -5,7 +5,7 @@ import unittest
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import numpy as np
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import torch
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from prototorch.modules import prototypes, losses
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from prototorch.modules import losses, prototypes
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class TestPrototypes(unittest.TestCase):
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@ -18,12 +18,16 @@ class TestPrototypes(unittest.TestCase):
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def test_prototypes1d_init_without_input_dim(self):
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with self.assertRaises(NameError):
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_ = prototypes.Prototypes1D(nclasses=1)
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_ = prototypes.Prototypes1D(nclasses=2)
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def test_prototypes1d_init_without_nclasses(self):
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with self.assertRaises(NameError):
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_ = prototypes.Prototypes1D(input_dim=1)
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def test_prototypes1d_init_with_nclasses_1(self):
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with self.assertWarns(UserWarning):
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_ = prototypes.Prototypes1D(nclasses=1, input_dim=1)
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def test_prototypes1d_init_without_pdist(self):
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p1 = prototypes.Prototypes1D(input_dim=6,
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nclasses=2,
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@ -73,24 +77,72 @@ class TestPrototypes(unittest.TestCase):
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self.assertIsNone(mismatch)
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def test_prototypes1d_init_without_inputdim_with_data(self):
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_ = prototypes.Prototypes1D(nclasses=1,
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_ = prototypes.Prototypes1D(nclasses=2,
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prototypes_per_class=1,
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prototype_initializer='stratified_mean',
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data=[[[1.]], [1]])
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data=[[[1.], [0.]], [1, 0]])
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def test_prototypes1d_init_with_int_data(self):
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_ = prototypes.Prototypes1D(nclasses=1,
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_ = prototypes.Prototypes1D(nclasses=2,
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prototypes_per_class=1,
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prototype_initializer='stratified_mean',
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data=[[[1]], [1]])
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data=[[[1], [0]], [1, 0]])
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def test_prototypes1d_init_one_hot_without_data(self):
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_ = prototypes.Prototypes1D(input_dim=1,
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nclasses=2,
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prototypes_per_class=1,
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prototype_initializer='stratified_mean',
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data=None,
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one_hot_labels=True)
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def test_prototypes1d_init_one_hot_labels_false(self):
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"""Test if ValueError is raised when `one_hot_labels` is set to `False`
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but the provided `data` has one-hot encoded labels.
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"""
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with self.assertRaises(ValueError):
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_ = prototypes.Prototypes1D(
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input_dim=1,
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nclasses=2,
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prototypes_per_class=1,
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prototype_initializer='stratified_mean',
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data=([[0.], [1.]], [[0, 1], [1, 0]]),
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one_hot_labels=False)
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def test_prototypes1d_init_1d_y_data_one_hot_labels_true(self):
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"""Test if ValueError is raised when `one_hot_labels` is set to `True`
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but the provided `data` does not contain one-hot encoded labels.
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"""
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with self.assertRaises(ValueError):
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_ = prototypes.Prototypes1D(
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input_dim=1,
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nclasses=2,
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prototypes_per_class=1,
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prototype_initializer='stratified_mean',
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data=([[0.], [1.]], [0, 1]),
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one_hot_labels=True)
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def test_prototypes1d_init_one_hot_labels_true(self):
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"""Test if ValueError is raised when `one_hot_labels` is set to `True`
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but the provided `data` contains 2D targets but
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does not contain one-hot encoded labels.
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"""
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with self.assertRaises(ValueError):
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_ = prototypes.Prototypes1D(
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input_dim=1,
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nclasses=2,
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prototypes_per_class=1,
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prototype_initializer='stratified_mean',
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data=([[0.], [1.]], [[0], [1]]),
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one_hot_labels=True)
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def test_prototypes1d_init_with_int_dtype(self):
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with self.assertRaises(RuntimeError):
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_ = prototypes.Prototypes1D(
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nclasses=1,
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nclasses=2,
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prototypes_per_class=1,
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prototype_initializer='stratified_mean',
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data=[[[1]], [1]],
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data=[[[1], [0]], [1, 0]],
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dtype=torch.int32)
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def test_prototypes1d_inputndim_with_data(self):
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@ -104,12 +156,15 @@ class TestPrototypes(unittest.TestCase):
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with self.assertRaises(ValueError):
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_ = prototypes.Prototypes1D(
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input_dim=2,
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nclasses=1,
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nclasses=2,
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prototypes_per_class=1,
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prototype_initializer='stratified_mean',
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data=[[[1.]], [1]])
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data=[[[1.], [0.]], [1, 0]])
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def test_prototypes1d_nclasses_with_data(self):
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"""Test ValueError raise if provided `nclasses` is not the same
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as the one computed from the provided `data`.
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"""
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with self.assertRaises(ValueError):
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_ = prototypes.Prototypes1D(
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input_dim=1,
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@ -168,12 +223,12 @@ class TestPrototypes(unittest.TestCase):
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decimal=5)
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self.assertIsNone(mismatch)
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def test_prototypes1d_dist_check(self):
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def test_prototypes1d_dist_validate(self):
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p1 = prototypes.Prototypes1D(input_dim=0, prototype_distribution=[0])
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with self.assertWarns(UserWarning):
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_ = p1._check_prototype_distribution()
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_ = p1._validate_prototype_distribution()
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def test_prototypes1d_check_extra_repr_not_empty(self):
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def test_prototypes1d_validate_extra_repr_not_empty(self):
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p1 = prototypes.Prototypes1D(input_dim=0, prototype_distribution=[0])
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rep = p1.extra_repr()
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self.assertNotEqual(rep, '')
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