def test_prototypes1d_init_with_int_data(self):
_ = prototypes.Prototypes1D(
num_classes=2,
prototypes_per_class=1,
prototype_initializer="stratified_mean",
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_inputdim_with_data(self):
with self.assertRaises(ValueError):
_ = prototypes.Prototypes1D(
input_dim=2,
nclasses=2,
prototypes_per_class=1,
prototype_initializer='stratified_mean',
data=[[[1.], [0.]], [1, 0]])
def test_prototypes1d_init_with_int_dtype(self):
with self.assertRaises(RuntimeError):
_ = prototypes.Prototypes1D(
nclasses=2,
prototypes_per_class=1,
prototype_initializer='stratified_mean',
data=[[[1], [0]], [1, 0]],
dtype=torch.int32)
def test_prototypes1d_proto_init_without_data(self):
with self.assertWarns(UserWarning):
_ = prototypes.Prototypes1D(
input_dim=3,
nclasses=2,
prototypes_per_class=1,
prototype_initializer='stratified_mean',
data=None)
def test_prototypes1d_forward(self):
p1 = prototypes.Prototypes1D(data=[self.x, self.y])
protos, _ = p1()
actual = protos.detach().numpy()
desired = torch.ones(2, 3)
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_prototypes1d_init_with_pdist(self):
p1 = prototypes.Prototypes1D(data=[self.x, self.y],
prototype_distribution=[6, 9],
prototype_initializer='zeros')
protos = p1.prototypes
actual = protos.detach().numpy()
desired = torch.zeros(15, 3)
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
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):
_ = prototypes.Prototypes1D(
input_dim=1,
nclasses=1,
prototypes_per_class=1,
prototype_initializer='stratified_mean',
data=[[[1.], [2.]], [1, 2]])
def test_prototypes1d_init_with_ppc(self):
p1 = prototypes.Prototypes1D(data=[self.x, self.y],
prototypes_per_class=2,
prototype_initializer="zeros")
protos = p1.prototypes
actual = protos.detach().numpy()
desired = torch.zeros(4, 3)
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
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_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_torch_pdist(self):
pdist = torch.tensor([2, 2])
p1 = prototypes.Prototypes1D(input_dim=3,
prototype_distribution=pdist,
prototype_initializer='zeros')
protos = p1.prototypes
actual = protos.detach().numpy()
desired = torch.zeros(4, 3)
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_prototypes1d_init_without_pdist(self):
p1 = prototypes.Prototypes1D(input_dim=6,
nclasses=2,
prototypes_per_class=4,
prototype_initializer='ones')
protos = p1.prototypes
actual = protos.detach().numpy()
desired = torch.ones(8, 6)
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
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,
num_classes=2,
prototypes_per_class=1,
prototype_initializer="stratified_mean",
data=([[0.0], [1.0]], [[0], [1]]),
one_hot_labels=True,
)
def test_prototypes1d_func_initializer(self):
def my_initializer(*args, **kwargs):
return torch.full((2, 99), 99.0), torch.tensor([0, 1])
p1 = prototypes.Prototypes1D(input_dim=99,
nclasses=2,
prototypes_per_class=1,
prototype_initializer=my_initializer)
protos = p1.prototypes
actual = protos.detach().numpy()
desired = 99 * torch.ones(2, 99)
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_prototypes1d_init_with_int_data(self):
_ = prototypes.Prototypes1D(nclasses=1,
prototypes_per_class=1,
prototype_initializer='stratified_mean',
data=[[[1]], [1]])
def test_prototypes1d_inputndim_with_data(self):
with self.assertRaises(ValueError):
_ = prototypes.Prototypes1D(input_dim=1,
nclasses=1,
prototypes_per_class=1,
data=[[1.], [1]])
def test_prototypes1d_init_without_inputdim_with_data(self):
_ = prototypes.Prototypes1D(nclasses=2,
prototypes_per_class=1,
prototype_initializer='stratified_mean',
data=[[[1.], [0.]], [1, 0]])
def test_prototypes1d_init_with_nclasses_1(self):
with self.assertWarns(UserWarning):
_ = prototypes.Prototypes1D(nclasses=1, input_dim=1)
def test_prototypes1d_validate_extra_repr_not_empty(self):
p1 = prototypes.Prototypes1D(input_dim=0, prototype_distribution=[0])
rep = p1.extra_repr()
self.assertNotEqual(rep, '')
def test_prototypes1d_dist_validate(self):
p1 = prototypes.Prototypes1D(input_dim=0, prototype_distribution=[0])
with self.assertWarns(UserWarning):
_ = p1._validate_prototype_distribution()
def test_prototypes1d_init_without_nclasses(self):
with self.assertRaises(NameError):
_ = prototypes.Prototypes1D(input_dim=1)
