def test_converts_input_domain_if_needed(self):
self.create_normal_dataset()
projector = linear.Pca(variance_covered=.99)(self.dataset)
new_examples = data.Table(data.Domain(self.dataset.domain.features[:5]), [[1.,2.,3.,4.,5.]])
projector(new_examples)
def test_pca_with_max_components(self):
max_components = 3
self.create_dataset(ncomponents = max_components + 3)
pca = linear.Pca(max_components=max_components)(self.dataset)
nvectors, vector_dimension = pca.projection.shape
self.assertEqual(nvectors, max_components)
def test_pca_with_variance_covered(self):
ncomponents = 3
self.create_dataset(ncomponents=ncomponents)
pca = linear.Pca(variance_covered=.99)(self.dataset)
nvectors, vector_dimension = pca.projection.shape
self.assertEqual(nvectors, ncomponents)
def test_projected_domain_can_convert_data_without_class(self):
self.create_normal_dataset()
projector = linear.Pca(variance_covered=.99)(self.dataset)
projected_data = projector(self.dataset)
converted_data = data.Table(projected_data.domain, self.dataset)
self.assertItemsEqual(projected_data, converted_data)
def test_total_variance_remains_the_same(self):
for m in (10, 250):
self.create_dataset(m=m)
pca = linear.Pca()(self.dataset)
self.assertAlmostEqual(pca.variance_sum, pca.variances.sum())
self.assertAlmostEqual(pca.variance_sum, (self.principal_components != 0).sum())
def test_pca_with_standardization(self):
self.create_dataset(ncomponents=1)
pca = linear.Pca(standardize=True)(self.dataset)
projection = pca.projection[0]
non_zero_elements = projection[projection.nonzero()]
# since values in all dimensions are normally distributed, dimensions should be treated as equally important
self.assertAlmostEqual(non_zero_elements.min(), non_zero_elements.max())
def test_pca(self):
for m in (10, 250):
self.create_dataset(m=m)
pca = linear.Pca(standardize=False)(self.dataset)
self.assertInCorrectSpace(pca.projection[pca.variances > 0.01, :])
for v in pca.projection:
# projections vectors should be normalized
self.assertAlmostEqual(np.linalg.norm(v), 1.)
# Components should have decreasing variants
self.assertListEqual(pca.variances.tolist(), sorted(pca.variances, reverse=True))
def test_projected_domain_contains_class_vars(self):
self.create_dataset_with_classes()
projector = linear.Pca(variance_covered=.99)(self.dataset)
projected_data = projector(self.dataset)
self.assertIn(self.dataset.domain.class_var, projected_data.domain)
for class_ in self.dataset.domain.class_vars:
self.assertIn(class_, projected_data.domain)
for ex1, ex2 in zip(self.dataset, projected_data):
self.assertEqual(ex1.get_class(), ex2.get_class())
for v1, v2 in zip(ex1.get_classes(), ex2.get_classes()):
self.assertEqual(v2, v2)
def test_projects_data_table(self):
self.create_normal_dataset()
projector = linear.Pca(variance_covered=.99)(self.dataset)
projector(self.dataset)
def test_pca_on_only_constant_features(self):
self.create_constant_dataset()
with self.assertRaises(ValueError):
linear.Pca()(self.dataset)
def test_pca_on_empty_data(self):
self.create_empty_dataset()
with self.assertRaises(ValueError):
linear.Pca()(self.dataset)
def test_pca_handles_unknowns(self):
self.create_dataset_with_unknowns()
linear.Pca()(self.dataset)
