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_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='likelihood',
projection=True,
**{'L_eig_dec': (eig_vals, eig_vecs)})
dpp.compute_L()
L = (eig_vecs * eig_vals).dot(eig_vecs.T)
self.assertTrue(np.allclose(dpp.L, L))
def test_instanciation_from_kernel(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_vecs * eig_vals).dot(eig_vecs.T)})
dpp.compute_L()
L = (eig_vecs * (eig_vals / (1.0 - eig_vals))).dot(eig_vecs.T)
self.assertTrue(np.allclose(dpp.L, L))
def test_computation_of_likehood_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_L()
if 'L' in param:
L = param['L']
elif 'L_eig_dec' in param:
e_vals, e_vecs = param['L_eig_dec']
L = (e_vecs * e_vals).dot(e_vecs.T)
elif 'L_gram_factor' in param:
L = param['L_gram_factor'].T.dot(param['L_gram_factor'])
self.assertTrue(np.allclose(dpp.L, L))
def test_proj_dpp_sampler_as_kDPP_with_likelihood_kernel(self):
""" Test whether projection DPP sampled as a k-DPP with k=rank(K) generates samples with the right 1 and 2 points inclusion probabilities when DPP defined by orthogonal projection likelihood kernel L from its eigendecomposition
"""
eig_vals = np.ones(self.rank)
eig_vecs, _ = qr(rndm.randn(self.N, self.rank), mode='economic')
dpp = FiniteDPP(kernel_type='likelihood',
projection=True,
**{'L': (eig_vecs * eig_vals).dot(eig_vecs.T)})
dpp.flush_samples()
for _ in range(self.nb_samples):
dpp.sample_exact_k_dpp(self.rank)
dpp.compute_L()
dpp.K = dpp.L
self.assertTrue(self.singleton_adequation(dpp, dpp.list_of_samples))
self.assertTrue(self.doubleton_adequation(dpp, dpp.list_of_samples))
def test_computation_of_likehood_kernel_should_raise_warning_with_L_eval(
self):
def eval_L_linear(X, Y=None):
if Y is None:
return X.dot(X.T)
else:
return X.dot(Y.T)
X_data = rndm.rand(100, 6)
dpp = FiniteDPP(kernel_type='likelihood',
projection=False,
**{'L_eval_X_data': (eval_L_linear, X_data)})
# raise warning when projection not set to True
# https://docs.python.org/3/library/warnings.html
with warnings.catch_warnings(record=True) as w:
dpp.compute_L()
self.assertIn('Weird setting', str(w[-1].message))
def test_proj_dpp_sampler_as_kDPP_with_likelihood_kernel_eig_proj_false(
self):
""" Test whether projection DPP sampled as a k-DPP with k=rank(K) generates samples with the right 1 and 2 points inclusion probabilities when DPP defined by orthogonal projection likelihood kernel L from its eigendecomposition and projection is set to False in order to go through the computation of elementary symmetric polynomials etc
"""
eig_vals = np.zeros(self.N)
eig_vals[:self.rank] = 1.0
eig_vecs, _ = qr(rndm.randn(self.N, self.N), mode='economic')
dpp = FiniteDPP(kernel_type='likelihood',
projection=False,
**{'L_eig_dec': (eig_vals, eig_vecs)})
dpp.flush_samples()
for _ in range(self.nb_samples):
dpp.sample_exact_k_dpp(self.rank)
dpp.compute_L()
dpp.K = dpp.L
self.assertTrue(self.singleton_adequation(dpp, dpp.list_of_samples))
self.assertTrue(self.doubleton_adequation(dpp, dpp.list_of_samples))
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_geq_0(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_dec': (eig_vals, eig_vecs)})
dpp.compute_L()
L = (eig_vecs * eig_vals).dot(eig_vecs.T)
self.assertTrue(np.allclose(dpp.L, L))
dpp = FiniteDPP(kernel_type='likelihood',
projection=False,
**{'L_eig_dec': (eig_vals, eig_vecs)})
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_likehood_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)
try:
dpp.compute_L()
if 'K' in param:
e_vals, e_vecs = la.eigh(param['K'])
elif 'K_eig_dec' in param:
e_vals, e_vecs = param['K_eig_dec']
elif 'A_zono' in param:
e_vals = np.ones(param['A_zono'].shape[0])
e_vecs, _ = la.qr(param['A_zono'], mode='economic')
L = (e_vecs * (e_vals / (1.0 - e_vals))).dot(e_vecs.T)
self.assertTrue(np.allclose(dpp.L, L))
except (ValueError, FloatingPointError) as e:
self.assertIn('cannot be computed', str(e.args))
