def test_instanciation_from_eig_vals_equal_01(self):
rank, N = 6, 10
eig_vals = np.ones(rank)
eig_vecs, _ = qr(rndm.randn(N, rank), mode='economic')
dpp = FiniteDPP(kernel_type='correlation',
projection=True,
**{'K_eig_dec': (eig_vals, eig_vecs)})
dpp.compute_K()
K = (eig_vecs * eig_vals).dot(eig_vecs.T)
self.assertTrue(np.allclose(dpp.K, K))
# If projection=True
with self.assertRaises(ValueError) as context:
dpp.compute_L()
self.assertTrue('cannot be computed' in str(context.exception))
# If projection=False
dpp = FiniteDPP(kernel_type='correlation',
projection=False,
**{'K_eig_dec': (eig_vals, eig_vecs)})
with self.assertRaises(FloatingPointError) as context:
dpp.compute_L()
self.assertTrue('cannot be computed' in str(context.exception))
def test_instanciation_from_A_zono(self):
rank, N = 6, 10
A = rndm.randn(rank, N)
dpp = FiniteDPP(kernel_type='correlation',
projection=True,
**{'A_zono': A})
dpp.compute_K()
K = A.T.dot(np.linalg.inv(A.dot(A.T))).dot(A)
self.assertTrue(np.allclose(dpp.K, K))
# raise warning when projection not set to True
# https://docs.python.org/3/library/warnings.html
with warnings.catch_warnings(record=True) as w:
FiniteDPP(kernel_type='correlation',
projection=False,
**{'A_zono': rndm.randn(rank, N)})
self.assertTrue('Weird setting' in str(w[-1].message))
# Raise error when not full row rank
with self.assertRaises(ValueError) as context:
FiniteDPP(kernel_type='correlation',
projection=True,
**{'A_zono': rndm.randn(N, rank)})
self.assertTrue('not full row rank' in str(context.exception))
def test_instanciation_from_kernel(self):
rank, N = 6, 10
eig_vals = 1 + rndm.geometric(p=0.5, size=rank)
eig_vecs, _ = qr(rndm.randn(N, rank), mode='economic')
dpp = FiniteDPP(kernel_type='likelihood',
projection=False,
**{'L': (eig_vecs * eig_vals).dot(eig_vecs.T)})
dpp.compute_K()
K = (eig_vecs * (eig_vals / (1.0 + eig_vals))).dot(eig_vecs.T)
self.assertTrue(np.allclose(dpp.K, K))
def test_computation_of_correlation_kernel_from_valid_parameters(self):
kernel_type = 'correlation'
for idx, (proj, param) in enumerate(self.list_of_valid_params):
with self.subTest(index=idx, projection=proj, param=param.keys()):
dpp = FiniteDPP(kernel_type, projection=proj, **param)
dpp.compute_K()
if 'K' in param:
K = param['K']
elif 'K_eig_dec' in param:
e_vals, e_vecs = param['K_eig_dec']
K = (e_vecs * e_vals).dot(e_vecs.T)
elif 'A_zono' in param:
e_vals = np.ones(param['A_zono'].shape[0])
e_vecs, _ = la.qr(param['A_zono'].T, mode='economic')
K = (e_vecs * e_vals).dot(e_vecs.T)
self.assertTrue(np.allclose(dpp.K, K))
def test_computation_of_correlation_kernel_from_valid_parameters(self):
kernel_type = 'likelihood'
for idx, (proj, param) in enumerate(self.list_of_valid_params):
with self.subTest(index=idx, projection=proj, param=param.keys()):
dpp = FiniteDPP(kernel_type, projection=proj, **param)
dpp.compute_K()
if 'L' in param:
e_vals, e_vecs = la.eigh(param['L'])
elif 'L_eig_dec' in param:
e_vals, e_vecs = param['L_eig_dec']
elif 'L_gram_factor' in param:
L = param['L_gram_factor'].T.dot(param['L_gram_factor'])
e_vals, e_vecs = la.eigh(L)
K = (e_vecs * (e_vals / (1.0 + e_vals))).dot(e_vecs.T)
self.assertTrue(np.allclose(dpp.K, K))
def test_instanciation_from_L_gram_factor_tall(self):
rank, N = 6, 10
phi = rndm.randn(rank, N).T
dpp = FiniteDPP(kernel_type='likelihood',
projection=False,
**{'L_gram_factor': phi})
dpp.compute_L()
L = phi.T.dot(phi)
self.assertTrue(np.allclose(dpp.L, L))
dpp = FiniteDPP(kernel_type='likelihood',
projection=False,
**{'L_gram_factor': phi})
dpp.compute_K()
eig_vals, eig_vecs = eigh(L)
K = (eig_vecs * (eig_vals / (1.0 + eig_vals))).dot(eig_vecs.T)
self.assertTrue(np.allclose(dpp.K, K))
def test_instanciation_from_eig_vals_in_01(self):
rank, N = 6, 10
eig_vals = rndm.rand(rank)
eig_vecs, _ = qr(rndm.randn(N, rank), mode='economic')
dpp = FiniteDPP(kernel_type='correlation',
projection=False,
**{'K_eig_dec': (eig_vals, eig_vecs)})
dpp.compute_K()
K = (eig_vecs * eig_vals).dot(eig_vecs.T)
self.assertTrue(np.allclose(dpp.K, K))
dpp = FiniteDPP(kernel_type='correlation',
projection=False,
**{'K_eig_dec': (eig_vals, eig_vecs)})
dpp.compute_L()
L = (eig_vecs * (eig_vals / (1.0 - eig_vals))).dot(eig_vecs.T)
self.assertTrue(np.allclose(dpp.L, L))
