Remove Prototypes1D and its tests
This commit is contained in:
Alexander Engelsberger
@@ -1,297 +0,0 @@
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"""ProtoTorch modules test suite."""
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import unittest
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import numpy as np
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import torch
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from prototorch.modules import losses, prototypes
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class TestPrototypes(unittest.TestCase):
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def setUp(self):
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self.x = torch.tensor(
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[[0, -1, -2], [10, 11, 12], [0, 0, 0], [2, 2, 2]],
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dtype=torch.float32)
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self.y = torch.tensor([0, 0, 1, 1])
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self.gen = torch.manual_seed(42)
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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(num_classes=2)
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def test_prototypes1d_init_without_num_classes(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_num_classes_1(self):
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with self.assertWarns(UserWarning):
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_ = prototypes.Prototypes1D(num_classes=1, input_dim=1)
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def test_prototypes1d_init_without_pdist(self):
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p1 = prototypes.Prototypes1D(
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input_dim=6,
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num_classes=2,
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prototypes_per_class=4,
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prototype_initializer="ones",
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)
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protos = p1.prototypes
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actual = protos.detach().numpy()
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desired = torch.ones(8, 6)
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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_prototypes1d_init_without_data(self):
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pdist = [2, 2]
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p1 = prototypes.Prototypes1D(input_dim=3,
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prototype_distribution=pdist,
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prototype_initializer="zeros")
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protos = p1.prototypes
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actual = protos.detach().numpy()
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desired = torch.zeros(4, 3)
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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_prototypes1d_proto_init_without_data(self):
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with self.assertWarns(UserWarning):
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_ = prototypes.Prototypes1D(
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input_dim=3,
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num_classes=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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)
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def test_prototypes1d_init_torch_pdist(self):
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pdist = torch.tensor([2, 2])
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p1 = prototypes.Prototypes1D(input_dim=3,
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prototype_distribution=pdist,
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prototype_initializer="zeros")
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protos = p1.prototypes
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actual = protos.detach().numpy()
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desired = torch.zeros(4, 3)
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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_prototypes1d_init_without_inputdim_with_data(self):
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_ = prototypes.Prototypes1D(
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num_classes=2,
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prototypes_per_class=1,
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prototype_initializer="stratified_mean",
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data=[[[1.0], [0.0]], [1, 0]],
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)
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def test_prototypes1d_init_with_int_data(self):
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_ = prototypes.Prototypes1D(
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num_classes=2,
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prototypes_per_class=1,
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prototype_initializer="stratified_mean",
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data=[[[1], [0]], [1, 0]],
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)
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def test_prototypes1d_init_one_hot_without_data(self):
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_ = prototypes.Prototypes1D(
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input_dim=1,
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num_classes=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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)
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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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num_classes=2,
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prototypes_per_class=1,
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prototype_initializer="stratified_mean",
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data=([[0.0], [1.0]], [[0, 1], [1, 0]]),
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one_hot_labels=False,
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)
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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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num_classes=2,
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prototypes_per_class=1,
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prototype_initializer="stratified_mean",
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data=([[0.0], [1.0]], [0, 1]),
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one_hot_labels=True,
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)
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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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num_classes=2,
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prototypes_per_class=1,
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prototype_initializer="stratified_mean",
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data=([[0.0], [1.0]], [[0], [1]]),
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one_hot_labels=True,
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)
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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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num_classes=2,
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prototypes_per_class=1,
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prototype_initializer="stratified_mean",
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data=[[[1], [0]], [1, 0]],
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dtype=torch.int32,
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)
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def test_prototypes1d_inputndim_with_data(self):
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with self.assertRaises(ValueError):
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_ = prototypes.Prototypes1D(input_dim=1,
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num_classes=1,
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prototypes_per_class=1,
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data=[[1.0], [1]])
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def test_prototypes1d_inputdim_with_data(self):
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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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num_classes=2,
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prototypes_per_class=1,
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prototype_initializer="stratified_mean",
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data=[[[1.0], [0.0]], [1, 0]],
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)
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def test_prototypes1d_num_classes_with_data(self):
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"""Test ValueError raise if provided `num_classes` 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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num_classes=1,
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prototypes_per_class=1,
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prototype_initializer="stratified_mean",
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data=[[[1.0], [2.0]], [1, 2]],
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)
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def test_prototypes1d_init_with_ppc(self):
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p1 = prototypes.Prototypes1D(data=[self.x, self.y],
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prototypes_per_class=2,
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prototype_initializer="zeros")
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protos = p1.prototypes
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actual = protos.detach().numpy()
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desired = torch.zeros(4, 3)
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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_prototypes1d_init_with_pdist(self):
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p1 = prototypes.Prototypes1D(
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data=[self.x, self.y],
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prototype_distribution=[6, 9],
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prototype_initializer="zeros",
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)
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protos = p1.prototypes
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actual = protos.detach().numpy()
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desired = torch.zeros(15, 3)
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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_prototypes1d_func_initializer(self):
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def my_initializer(*args, **kwargs):
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return torch.full((2, 99), 99.0), torch.tensor([0, 1])
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p1 = prototypes.Prototypes1D(
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input_dim=99,
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num_classes=2,
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prototypes_per_class=1,
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prototype_initializer=my_initializer,
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)
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protos = p1.prototypes
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actual = protos.detach().numpy()
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desired = 99 * torch.ones(2, 99)
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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_prototypes1d_forward(self):
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p1 = prototypes.Prototypes1D(data=[self.x, self.y])
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protos, _ = p1()
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actual = protos.detach().numpy()
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desired = torch.ones(2, 3)
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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_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._validate_prototype_distribution()
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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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def tearDown(self):
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del self.x, self.y, self.gen
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_ = torch.seed()
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class TestLosses(unittest.TestCase):
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def setUp(self):
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pass
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def test_glvqloss_init(self):
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_ = losses.GLVQLoss(0, "swish_beta", beta=20)
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def test_glvqloss_forward_1ppc(self):
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criterion = losses.GLVQLoss(margin=0,
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squashing="sigmoid_beta",
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beta=100)
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d = torch.stack([torch.ones(100), torch.zeros(100)], dim=1)
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labels = torch.tensor([0, 1])
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targets = torch.ones(100)
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outputs = [d, labels]
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loss = criterion(outputs, targets)
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loss_value = loss.item()
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self.assertAlmostEqual(loss_value, 0.0)
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def test_glvqloss_forward_2ppc(self):
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criterion = losses.GLVQLoss(margin=0,
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squashing="sigmoid_beta",
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beta=100)
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d = torch.stack([
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torch.ones(100),
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torch.ones(100),
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torch.zeros(100),
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torch.ones(100)
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],
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dim=1)
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labels = torch.tensor([0, 0, 1, 1])
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targets = torch.ones(100)
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outputs = [d, labels]
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loss = criterion(outputs, targets)
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loss_value = loss.item()
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self.assertAlmostEqual(loss_value, 0.0)
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def tearDown(self):
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pass
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