def test_apply_reduce_num_components(self):
"""
Checks whether PCA performs dimensionality reduction
"""
sut = PCA(self.num_components - 1, whiten=True)
sut.apply(self.dataset, True)
assert self.dataset.get_design_matrix().shape[1] ==\
self.num_components - 1
def test_apply_reduce_num_components(self):
"""
Checks whether PCA performs dimensionality reduction
"""
sut = PCA(self.num_components - 1, whiten=True)
sut.apply(self.dataset, True)
assert self.dataset.get_design_matrix().shape[1] ==\
self.num_components - 1
def test_apply_whiten(self):
"""
Confirms that PCA has decorrelated the input dataset and
variance is the same along all principal components and equal to one
"""
sut = PCA(self.num_components, whiten=True)
sut.apply(self.dataset, True)
cm = np.cov(self.dataset.get_design_matrix().T) # covariance matrix
# testing whether the covariance matrix is a diagonal one
np.testing.assert_almost_equal(cm * (np.ones(cm.shape[0]) -
np.eye(cm.shape[0])),
np.zeros((cm.shape[0], cm.shape[0])))
# testing whether the eigenvalues are all ones
np.testing.assert_almost_equal(np.diag(cm), np.ones(cm.shape[0]))
def test_apply_whiten(self):
"""
Confirms that PCA has decorrelated the input dataset and
variance is the same along all principal components and equal to one
"""
sut = PCA(self.num_components, whiten=True)
sut.apply(self.dataset, True)
cm = np.cov(self.dataset.get_design_matrix().T) # covariance matrix
# testing whether the covariance matrix is a diagonal one
np.testing.assert_almost_equal(
cm * (np.ones(cm.shape[0]) - np.eye(cm.shape[0])),
np.zeros((cm.shape[0], cm.shape[0])))
# testing whether the eigenvalues are all ones
np.testing.assert_almost_equal(np.diag(cm), np.ones(cm.shape[0]))
def test_apply_no_whiten(self):
"""
Confirms that PCA has decorrelated the input dataset and
principal components are arranged in decreasing order by variance
"""
# sut is an abbreviation for System Under Test
sut = PCA(self.num_components)
sut.apply(self.dataset, True)
cm = np.cov(self.dataset.get_design_matrix().T) # covariance matrix
# testing whether the covariance matrix is a diagonal one
np.testing.assert_almost_equal(cm * (np.ones(cm.shape[0]) -
np.eye(cm.shape[0])),
np.zeros((cm.shape[0], cm.shape[0])))
# testing whether the eigenvalues are in decreasing order
assert (np.diag(cm)[:-1] > np.diag(cm)[1:]).all()
def test_apply_no_whiten(self):
"""
Confirms that PCA has decorrelated the input dataset and
principal components are arranged in decreasing order by variance
"""
# sut is an abbreviation for System Under Test
sut = PCA(self.num_components)
sut.apply(self.dataset, True)
cm = np.cov(self.dataset.get_design_matrix().T) # covariance matrix
# testing whether the covariance matrix is a diagonal one
np.testing.assert_almost_equal(
cm * (np.ones(cm.shape[0]) - np.eye(cm.shape[0])),
np.zeros((cm.shape[0], cm.shape[0])))
# testing whether the eigenvalues are in decreasing order
assert (np.diag(cm)[:-1] > np.diag(cm)[1:]).all()