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Add test cases to test newly added features

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This commit is contained in:
blackfly 2020-04-27 12:49:54 +02:00
parent d17b9a3346
commit cf0659d881
3 changed files with 119 additions and 21 deletions
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4
RELEASE.md
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@ -3,8 +3,8 @@
## Release 0.1.1-dev0 ## Release 0.1.1-dev0
### Includes ### Includes
- Minor bugfixes. - Minor bugfixes.
- 100% line coverage. - 100% line coverage.
## Release 0.1.0-dev0 ## Release 0.1.0-dev0

55
tests/test_functions.py
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@ -126,6 +126,16 @@ class TestCompetitions(unittest.TestCase):
decimal=5) decimal=5)
self.assertIsNone(mismatch) self.assertIsNone(mismatch)
def test_stratified_min_simple(self):
d = torch.tensor([[0., 2., 3.], [8., 0, 1]])
labels = torch.tensor([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_knnc_k1(self): def test_knnc_k1(self):
d = torch.tensor([[2., 3., 1.99, 3.01], [2., 3., 2.01, 3.]]) d = torch.tensor([[2., 3., 1.99, 3.01], [2., 3., 2.01, 3.]])
labels = torch.tensor([0, 1, 2, 3]) labels = torch.tensor([0, 1, 2, 3])
@ -372,7 +382,7 @@ class TestInitializers(unittest.TestCase):
def test_stratified_mean_equal1(self): def test_stratified_mean_equal1(self):
pdist = torch.tensor([1, 1]) pdist = torch.tensor([1, 1])
actual, _ = initializers.stratified_mean(self.x, self.y, pdist) actual, _ = initializers.stratified_mean(self.x, self.y, pdist, False)
desired = torch.tensor([[5., 5., 5.], [1., 1., 1.]]) desired = torch.tensor([[5., 5., 5.], [1., 1., 1.]])
mismatch = np.testing.assert_array_almost_equal(actual, mismatch = np.testing.assert_array_almost_equal(actual,
desired, desired,
@ -381,7 +391,8 @@ class TestInitializers(unittest.TestCase):
def test_stratified_random_equal1(self): def test_stratified_random_equal1(self):
pdist = torch.tensor([1, 1]) pdist = torch.tensor([1, 1])
actual, _ = initializers.stratified_random(self.x, self.y, pdist) actual, _ = initializers.stratified_random(self.x, self.y, pdist,
False)
desired = torch.tensor([[0., -1., -2.], [0., 0., 0.]]) desired = torch.tensor([[0., -1., -2.], [0., 0., 0.]])
mismatch = np.testing.assert_array_almost_equal(actual, mismatch = np.testing.assert_array_almost_equal(actual,
desired, desired,
@ -390,7 +401,7 @@ class TestInitializers(unittest.TestCase):
def test_stratified_mean_equal2(self): def test_stratified_mean_equal2(self):
pdist = torch.tensor([2, 2]) pdist = torch.tensor([2, 2])
actual, _ = initializers.stratified_mean(self.x, self.y, pdist) actual, _ = initializers.stratified_mean(self.x, self.y, pdist, False)
desired = torch.tensor([[5., 5., 5.], [5., 5., 5.], [1., 1., 1.], desired = torch.tensor([[5., 5., 5.], [5., 5., 5.], [1., 1., 1.],
[1., 1., 1.]]) [1., 1., 1.]])
mismatch = np.testing.assert_array_almost_equal(actual, mismatch = np.testing.assert_array_almost_equal(actual,
@ -400,7 +411,8 @@ class TestInitializers(unittest.TestCase):
def test_stratified_random_equal2(self): def test_stratified_random_equal2(self):
pdist = torch.tensor([2, 2]) pdist = torch.tensor([2, 2])
actual, _ = initializers.stratified_random(self.x, self.y, pdist) actual, _ = initializers.stratified_random(self.x, self.y, pdist,
False)
desired = torch.tensor([[0., -1., -2.], [0., -1., -2.], [0., 0., 0.], desired = torch.tensor([[0., -1., -2.], [0., -1., -2.], [0., 0., 0.],
[0., 0., 0.]]) [0., 0., 0.]])
mismatch = np.testing.assert_array_almost_equal(actual, mismatch = np.testing.assert_array_almost_equal(actual,
@ -410,7 +422,7 @@ class TestInitializers(unittest.TestCase):
def test_stratified_mean_unequal(self): def test_stratified_mean_unequal(self):
pdist = torch.tensor([1, 3]) pdist = torch.tensor([1, 3])
actual, _ = initializers.stratified_mean(self.x, self.y, pdist) actual, _ = initializers.stratified_mean(self.x, self.y, pdist, False)
desired = torch.tensor([[5., 5., 5.], [1., 1., 1.], [1., 1., 1.], desired = torch.tensor([[5., 5., 5.], [1., 1., 1.], [1., 1., 1.],
[1., 1., 1.]]) [1., 1., 1.]])
mismatch = np.testing.assert_array_almost_equal(actual, mismatch = np.testing.assert_array_almost_equal(actual,
@ -420,7 +432,8 @@ class TestInitializers(unittest.TestCase):
def test_stratified_random_unequal(self): def test_stratified_random_unequal(self):
pdist = torch.tensor([1, 3]) pdist = torch.tensor([1, 3])
actual, _ = initializers.stratified_random(self.x, self.y, pdist) actual, _ = initializers.stratified_random(self.x, self.y, pdist,
False)
desired = torch.tensor([[0., -1., -2.], [0., 0., 0.], [0., 0., 0.], desired = torch.tensor([[0., -1., -2.], [0., 0., 0.], [0., 0., 0.],
[0., 0., 0.]]) [0., 0., 0.]])
mismatch = np.testing.assert_array_almost_equal(actual, mismatch = np.testing.assert_array_almost_equal(actual,
@ -428,6 +441,36 @@ class TestInitializers(unittest.TestCase):
decimal=5) decimal=5)
self.assertIsNone(mismatch) self.assertIsNone(mismatch)
def test_stratified_mean_unequal_one_hot(self):
pdist = torch.tensor([1, 3])
y = torch.eye(2)[self.y]
desired1 = torch.tensor([[5., 5., 5.], [1., 1., 1.], [1., 1., 1.],
[1., 1., 1.]])
actual1, actual2 = initializers.stratified_mean(self.x, y, pdist)
desired2 = torch.tensor([[1, 0], [0, 1], [0, 1], [0, 1]])
mismatch = np.testing.assert_array_almost_equal(actual1,
desired1,
decimal=5)
mismatch = np.testing.assert_array_almost_equal(actual2,
desired2,
decimal=5)
self.assertIsNone(mismatch)
def test_stratified_random_unequal_one_hot(self):
pdist = torch.tensor([1, 3])
y = torch.eye(2)[self.y]
actual1, actual2 = initializers.stratified_random(self.x, y, pdist)
desired1 = torch.tensor([[0., -1., -2.], [0., 0., 0.], [0., 0., 0.],
[0., 0., 0.]])
desired2 = torch.tensor([[1, 0], [0, 1], [0, 1], [0, 1]])
mismatch = np.testing.assert_array_almost_equal(actual1,
desired1,
decimal=5)
mismatch = np.testing.assert_array_almost_equal(actual2,
desired2,
decimal=5)
self.assertIsNone(mismatch)
def tearDown(self): def tearDown(self):
del self.x, self.y, self.gen del self.x, self.y, self.gen
_ = torch.seed() _ = torch.seed()

81
tests/test_modules.py
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@ -5,7 +5,7 @@ import unittest
import numpy as np import numpy as np
import torch import torch
from prototorch.modules import prototypes, losses from prototorch.modules import losses, prototypes
class TestPrototypes(unittest.TestCase): class TestPrototypes(unittest.TestCase):
@ -18,12 +18,16 @@ class TestPrototypes(unittest.TestCase):
def test_prototypes1d_init_without_input_dim(self): def test_prototypes1d_init_without_input_dim(self):
with self.assertRaises(NameError): with self.assertRaises(NameError):
_ = prototypes.Prototypes1D(nclasses=1) _ = prototypes.Prototypes1D(nclasses=2)
def test_prototypes1d_init_without_nclasses(self): def test_prototypes1d_init_without_nclasses(self):
with self.assertRaises(NameError): with self.assertRaises(NameError):
_ = prototypes.Prototypes1D(input_dim=1) _ = prototypes.Prototypes1D(input_dim=1)
def test_prototypes1d_init_with_nclasses_1(self):
with self.assertWarns(UserWarning):
_ = prototypes.Prototypes1D(nclasses=1, input_dim=1)
def test_prototypes1d_init_without_pdist(self): def test_prototypes1d_init_without_pdist(self):
p1 = prototypes.Prototypes1D(input_dim=6, p1 = prototypes.Prototypes1D(input_dim=6,
nclasses=2, nclasses=2,
@ -73,24 +77,72 @@ class TestPrototypes(unittest.TestCase):
self.assertIsNone(mismatch) self.assertIsNone(mismatch)
def test_prototypes1d_init_without_inputdim_with_data(self): def test_prototypes1d_init_without_inputdim_with_data(self):
_ = prototypes.Prototypes1D(nclasses=1, _ = prototypes.Prototypes1D(nclasses=2,
prototypes_per_class=1, prototypes_per_class=1,
prototype_initializer='stratified_mean', prototype_initializer='stratified_mean',
data=[[[1.]], [1]]) data=[[[1.], [0.]], [1, 0]])
def test_prototypes1d_init_with_int_data(self): def test_prototypes1d_init_with_int_data(self):
_ = prototypes.Prototypes1D(nclasses=1, _ = prototypes.Prototypes1D(nclasses=2,
prototypes_per_class=1, prototypes_per_class=1,
prototype_initializer='stratified_mean', prototype_initializer='stratified_mean',
data=[[[1]], [1]]) data=[[[1], [0]], [1, 0]])
def test_prototypes1d_init_one_hot_without_data(self):
_ = prototypes.Prototypes1D(input_dim=1,
nclasses=2,
prototypes_per_class=1,
prototype_initializer='stratified_mean',
data=None,
one_hot_labels=True)
def test_prototypes1d_init_one_hot_labels_false(self):
"""Test if ValueError is raised when `one_hot_labels` is set to `False`
but the provided `data` has one-hot encoded labels.
"""
with self.assertRaises(ValueError):
_ = prototypes.Prototypes1D(
input_dim=1,
nclasses=2,
prototypes_per_class=1,
prototype_initializer='stratified_mean',
data=([[0.], [1.]], [[0, 1], [1, 0]]),
one_hot_labels=False)
def test_prototypes1d_init_1d_y_data_one_hot_labels_true(self):
"""Test if ValueError is raised when `one_hot_labels` is set to `True`
but the provided `data` does not contain one-hot encoded labels.
"""
with self.assertRaises(ValueError):
_ = prototypes.Prototypes1D(
input_dim=1,
nclasses=2,
prototypes_per_class=1,
prototype_initializer='stratified_mean',
data=([[0.], [1.]], [0, 1]),
one_hot_labels=True)
def test_prototypes1d_init_one_hot_labels_true(self):
"""Test if ValueError is raised when `one_hot_labels` is set to `True`
but the provided `data` contains 2D targets but
does not contain one-hot encoded labels.
"""
with self.assertRaises(ValueError):
_ = prototypes.Prototypes1D(
input_dim=1,
nclasses=2,
prototypes_per_class=1,
prototype_initializer='stratified_mean',
data=([[0.], [1.]], [[0], [1]]),
one_hot_labels=True)
def test_prototypes1d_init_with_int_dtype(self): def test_prototypes1d_init_with_int_dtype(self):
with self.assertRaises(RuntimeError): with self.assertRaises(RuntimeError):
_ = prototypes.Prototypes1D( _ = prototypes.Prototypes1D(
nclasses=1, nclasses=2,
prototypes_per_class=1, prototypes_per_class=1,
prototype_initializer='stratified_mean', prototype_initializer='stratified_mean',
data=[[[1]], [1]], data=[[[1], [0]], [1, 0]],
dtype=torch.int32) dtype=torch.int32)
def test_prototypes1d_inputndim_with_data(self): def test_prototypes1d_inputndim_with_data(self):
@ -104,12 +156,15 @@ class TestPrototypes(unittest.TestCase):
with self.assertRaises(ValueError): with self.assertRaises(ValueError):
_ = prototypes.Prototypes1D( _ = prototypes.Prototypes1D(
input_dim=2, input_dim=2,
nclasses=1, nclasses=2,
prototypes_per_class=1, prototypes_per_class=1,
prototype_initializer='stratified_mean', prototype_initializer='stratified_mean',
data=[[[1.]], [1]]) data=[[[1.], [0.]], [1, 0]])
def test_prototypes1d_nclasses_with_data(self): def test_prototypes1d_nclasses_with_data(self):
"""Test ValueError raise if provided `nclasses` is not the same
as the one computed from the provided `data`.
"""
with self.assertRaises(ValueError): with self.assertRaises(ValueError):
_ = prototypes.Prototypes1D( _ = prototypes.Prototypes1D(
input_dim=1, input_dim=1,
@ -168,12 +223,12 @@ class TestPrototypes(unittest.TestCase):
decimal=5) decimal=5)
self.assertIsNone(mismatch) self.assertIsNone(mismatch)
def test_prototypes1d_dist_check(self): def test_prototypes1d_dist_validate(self):
p1 = prototypes.Prototypes1D(input_dim=0, prototype_distribution=[0]) p1 = prototypes.Prototypes1D(input_dim=0, prototype_distribution=[0])
with self.assertWarns(UserWarning): with self.assertWarns(UserWarning):
_ = p1._check_prototype_distribution() _ = p1._validate_prototype_distribution()
def test_prototypes1d_check_extra_repr_not_empty(self): def test_prototypes1d_validate_extra_repr_not_empty(self):
p1 = prototypes.Prototypes1D(input_dim=0, prototype_distribution=[0]) p1 = prototypes.Prototypes1D(input_dim=0, prototype_distribution=[0])
rep = p1.extra_repr() rep = p1.extra_repr()
self.assertNotEqual(rep, '') self.assertNotEqual(rep, '')