def test_fixedpoint_2(self):
"""classes with cardinality 1 and zero degrees"""
# test Matrix 1
n, seed = (40, 22)
A = mg.random_binary_matrix_generator_dense(n, sym=False, seed=seed)
A[0, :] = 0
g = sample.DirectedGraph(A)
g._solve_problem(
model="dcm",
method="fixed-point",
max_steps=300,
verbose=False,
initial_guess="uniform",
linsearch="False",
)
g._solution_error()
# print('degseq = ', np.concatenate((g.dseq_out, g.dseq_in)))
# print('expected degseq = ',g.expected_dseq)
# debug
# print(g.r_dseq_out)
# print(g.r_dseq_in)
# print(g.rnz_dseq_out)
# print(g.rnz_dseq_in)
# print('\ntest 7: error = {}'.format(g.error))
# test result
self.assertTrue(g.error < 1e-1)
def test_quasinewton_0(self):
n, seed = (4, 22)
A = mg.random_weighted_matrix_generator_dense(n,
sym=False,
seed=seed,
sup_ext=100,
intweights=True)
# print(A)
g = sample.DirectedGraph(A)
g._solve_problem(
model="decm",
method="quasinewton",
max_steps=100,
verbose=False,
initial_guess="uniform",
)
g._solution_error()
# debug
# print('\n test 1, no zeros, n = {}, error = {}'.format(n, g.error))
# test result
self.assertTrue(g.error < 1)
def test_decm(self):
"""test with 3 classes of cardinality 1
and no zero degrees
"""
n, s = (4, 25)
A = mg.random_weighted_matrix_generator_dense(n,
sup_ext=10,
sym=False,
seed=s,
intweights=True)
g = sample.DirectedGraph(A)
g.solve_tool(
model="decm",
method="quasinewton",
initial_guess="uniform",
max_steps=200,
verbose=False,
)
# g._solution_error()
# debug
# print(g.r_dseq_out)
# print(g.r_dseq_in)
# print(g.rnz_dseq_out)
# print(g.rnz_dseq_in)
# print('\ntest 0: error = {}'.format(g.error))
# test result
self.assertTrue(g.error < 1e-1)
def test_iterative_dcm(self):
n, seed = (3, 42)
a = mg.random_binary_matrix_generator_dense(n, sym=False, seed=seed)
# rd
g = sample.DirectedGraph(a)
g.degree_reduction()
g.initial_guess = "uniform"
g.regularise = "eigenvalues"
g._initialize_problem("dcm", "fixed-point")
x0 = 0.5 * np.ones(4)
f_sample = -g.fun(x0)
g.last_model = "dcm"
g._set_solved_problem(f_sample)
f_full = np.concatenate((g.x, g.y))
f_correct = -np.array([2.5, 2.5, 0, 0, 1, 1.25])
# debug
# print(a)
# print(x0, x)
# print(f_full)
# test result
self.assertTrue(np.allclose(f_full, f_correct))
def test_loglikelihood_hessian_dcm_exp_simmetry(self):
n, seed = (3, 42)
a = mg.random_binary_matrix_generator_dense(n, sym=False, seed=seed)
g = sample.DirectedGraph(a)
g.degree_reduction()
g.initial_guess = "uniform"
g.regularise = "identity"
g._initialize_problem("dcm", "newton")
theta = np.random.rand(2 * n)
x0 = np.exp(-theta)
k_out = g.args[0]
k_in = g.args[1]
nz_index_out = g.args[2]
nz_index_in = g.args[3]
f = loglikelihood_hessian_dcm_exp(theta, g.args)
f_t = f.T
# debug
# print(a)
# print(theta, x0)
# print(g.args)
# print(f-f_t)
# test result
self.assertTrue(np.allclose(f - f_t, np.zeros((2 * n, 2 * n))))
def test_loglikelihood_dcm_exp(self):
A = np.array([[0, 2, 2], [2, 0, 2], [0, 2, 0]])
bA = np.array([[1 if aa != 0 else 0 for aa in a] for a in A])
k_out = np.sum(bA, axis=1)
k_in = np.sum(bA, axis=0)
s_out = np.sum(A, axis=1)
s_in = np.sum(A, axis=0)
g = sample.DirectedGraph(A)
g.initial_guess = "uniform"
g.regularise = "identity"
g._initialize_problem("decm", "newton")
# theta = np.random.rand(6)
theta = 0.5 * np.ones(12)
x0 = np.exp(-theta)
f_sample = g.step_fun(x0)
g.last_model = "decm"
f_exp = -loglikelihood_decm_exp(theta, g.args)
# debug
# print(a)
# print(theta, x0)
# print(g.args)
# print(f_sample)
# print(f_sample)
# print(f_exp)
# test result
self.assertTrue(
np.round(f_sample, decimals=5) == np.round(f_exp, decimals=5))
def test_loglikelihood_hessian_diag_vs_normal_dcm_exp(self):
n, seed = (3, 42)
# a = mg.random_binary_matrix_generator_dense(n, sym=False, seed=seed)
a = np.array([[0, 0, 0], [1, 0, 0], [1, 1, 0]])
g = sample.DirectedGraph(a)
g.degree_reduction()
g.initial_guess = "uniform"
g.regularise = "identity"
g._initialize_problem("dcm", "newton")
theta = np.array([0.5, 0.5, 0.5, 0.5, 0.5, 0.5])
x0 = np.exp(-theta)
k_out = g.args[0]
k_in = g.args[1]
nz_index_out = g.args[2]
nz_index_in = g.args[3]
g.last_model = "dcm"
f_diag = loglikelihood_hessian_diag_dcm_exp(theta, g.args)
f_full = loglikelihood_hessian_dcm_exp(theta, g.args)
f_full_d = np.diag(f_full)
# debug
# print(a)
# print(theta, x0)
# print(g.args)
# print(f_sample)
# print(f_exp)
# test result
self.assertTrue(np.allclose(f_diag, f_full_d))
def test_loglikelihood_dcm(self):
"""
a = np.array([[0, 1, 1],
[1, 0, 1],
[0, 1, 0]])
"""
n, seed = (3, 42)
a = mg.random_binary_matrix_generator_dense(n, sym=False, seed=seed)
g = sample.DirectedGraph(a)
g.degree_reduction()
g.initial_guess = "uniform"
g.regularise = "eigenvalues"
g._initialize_problem("dcm", "quasinewton")
x0 = np.concatenate((g.r_x, g.r_y))
# call loglikelihood function
f_sample = -g.step_fun(x0)
f_correct = 4 * np.log(1 / 2) - 3 * np.log(5 / 4)
# debug
# print(x0)
# print(f_sample)
# print(f_correct)
# test result
self.assertTrue(round(f_sample, 3) == round(f_correct, 3))
def test_dcm_new(self):
"""test with 3 classes of cardinality 1
and no zero degrees
"""
A = np.array([
[0, 1, 1],
[1, 0, 0],
[0, 1, 0],
])
g = sample.DirectedGraph(A)
g.solve_tool(
model="dcm_exp",
method="quasinewton",
initial_guess="uniform",
max_steps=200,
verbose=False,
)
# g._solution_error()
# debug
# print(g.r_dseq_out)
# print(g.r_dseq_in)
# print(g.rnz_dseq_out)
# print(g.rnz_dseq_in)
# print('\ntest 0: error = {}'.format(g.error))
# test result
self.assertTrue(g.error < 1e-1)
def test_3(self):
n, seed = (40, 35)
A = mg.random_weighted_matrix_generator_dense(n,
sym=False,
seed=seed,
sup_ext=100,
intweights=True)
A[0, :] = 0
g = sample.DirectedGraph(A)
g._solve_problem(
model="decm_exp",
method="quasinewton",
max_steps=20000,
verbose=False,
initial_guess="uniform",
linsearch=True,
)
g._solution_error()
# debug
print("\n test 3, zeros, dimension n = {}, error = {}".format(
n, g.error))
# test result
self.assertTrue(g.error < 1)
def test_dcm(self):
"""test with 3 classes of cardinality 1
and no zero degrees
"""
A = np.array(
[
[0, 1, 1],
[1, 0, 0],
[0, 1, 0],
]
)
g = sample.DirectedGraph(A)
g.solve_tool(
model="dcm",
max_steps=200,
verbose=False,
)
l = -mof.loglikelihood_dcm(g.solution_array, g.args)
# g._solution_error()
# debug
# print(g.r_dseq_out)
# print(g.r_dseq_in)
# print(g.rnz_dseq_out)
# print(g.rnz_dseq_in)
# print('\ntest 0: error = {}'.format(g.error))
# test result
self.assertTrue(l == g.model_loglikelihood())
def test_newton_3(self):
n, seed = (40, 22)
A = mg.random_weighted_matrix_generator_dense(n,
sym=False,
seed=seed,
sup_ext=100,
intweights=True)
g = sample.DirectedGraph(A)
g._solve_problem(
model="decm",
method="newton",
max_steps=300,
verbose=False,
initial_guess="uniform",
)
g._solution_error()
# print(g.expected_dseq)
# print(g.dseq_out,g.dseq_in)
# print('\n test 3, no zeros, dimension n = {} error = {}'.format(n, g.error))
# print(g.error_dseq)
# test result
self.assertTrue(g.error < 1)
def test_loglikelihood_prime_dcm_exp(self):
n, seed = (3, 42)
# a = mg.random_binary_matrix_generator_dense(n, sym=False, seed=seed)
a = np.array([[0, 0, 0], [1, 0, 0], [1, 1, 0]])
g = sample.DirectedGraph(a)
g.degree_reduction()
g.initial_guess = "uniform"
g.regularise = "identity"
g._initialize_problem("dcm", "newton")
theta = np.array([0.5, 0.5, 0.5, 0.5, 0.5, 0.5])
x0 = np.exp(-theta)
k_out = g.args[0]
k_in = g.args[1]
nz_index_out = g.args[2]
nz_index_in = g.args[3]
f = lambda x: loglikelihood_dcm_exp(x, g.args)
f_sample = approx_fprime(theta, f, epsilon=1e-6)
g.last_model = "dcm"
f_exp = loglikelihood_prime_dcm_exp(theta, g.args)
# debug
# print(a)
# print(theta, x0)
# print(g.args)
# print(f_sample)
# print(f_exp)
# test result
self.assertTrue(np.allclose(f_sample, f_exp))
def test_iterative_3(self):
n, seed = (40, 22)
a = mg.random_binary_matrix_generator_dense(n, sym=False, seed=seed)
a[0, :] = 0
k_out = np.sum(a, 1)
k_in = np.sum(a, 0)
g = sample.DirectedGraph(a)
g._solve_problem(
model="dcm_exp",
method="fixed-point",
max_steps=3000,
verbose=False,
initial_guess="uniform",
linsearch="False",
)
g._solution_error()
err = g.error
# debug
# print('\ntest 1: error = {}'.format(err))
# test result
self.assertTrue(err < 1)
def test_emi(self):
n, seed = (50, 1)
a = mg.random_binary_matrix_generator_dense(n, sym=False, seed=seed)
k_out = np.sum(a, 1)
k_in = np.sum(a, 0)
g = sample.DirectedGraph(a)
g._solve_problem(
model="dcm",
method="quasinewton",
max_steps=3000,
verbose=False,
initial_guess="uniform",
linsearch="False",
)
g._solution_error()
err = g.error
# debug
print("\ntest emi: error = {}".format(err))
# test result
self.assertTrue(err < 1)
def test_fixedpoint_dcm_2(self):
# test Matrix 1
n, seed = (40, 35)
A = mg.random_weighted_matrix_generator_dense(n,
sym=False,
seed=seed,
sup_ext=100,
intweights=True)
g = sample.DirectedGraph(A)
g._solve_problem(
model="decm",
method="fixed-point",
max_steps=25000,
verbose=False,
initial_guess="uniform",
linsearch=True,
)
g._solution_error()
# debug
# print("\n test 3, no zeros, dimension n = {}, error = {}".format(n, g.error))
# test result
self.assertTrue(g.error < 1)
def test_crema_original_newton_random_dense_20_dir(self):
network = mg.random_weighted_matrix_generator_dense(n=20,
sup_ext=10,
sym=False,
seed=None)
network_bin = (network > 0).astype(int)
g = sample.DirectedGraph(adjacency=network)
g.solve_tool(
model="crema",
method="quasinewton",
initial_guess="random",
adjacency=network_bin,
max_steps=1000,
verbose=False,
)
g._solution_error()
# test result
self.assertTrue(g.relative_error_strength < 1e-1)
self.assertTrue(g.relative_error_strength < 1e-2)
def test_loglikelihood_prime_dcm_exp(self):
A = np.array([[0, 2, 2], [2, 0, 2], [0, 2, 0]])
bA = np.array([[1 if aa != 0 else 0 for aa in a] for a in A])
k_out = np.sum(bA, axis=1)
k_in = np.sum(bA, axis=0)
s_out = np.sum(A, axis=1)
s_in = np.sum(A, axis=0)
g = sample.DirectedGraph(A)
g.initial_guess = "uniform"
g.regularise = "identity"
g._initialize_problem("decm", "newton")
# theta = np.random.rand(6)
theta = 0.5 * np.ones(12)
x0 = np.exp(-theta)
f = lambda x: loglikelihood_decm_exp(x, g.args)
f_sample = approx_fprime(theta, f, epsilon=1e-6)
f_exp = loglikelihood_prime_decm_exp(theta, g.args)
# debug
# print(a)
# print(theta, x0)
# print(g.args)
# print(f_sample)
# print(f_exp)
# test result
self.assertTrue(np.allclose(f_sample, f_exp))
def test_loglikelihood_hessian_dcm_exp(self):
n, seed = (3, 42)
a = mg.random_binary_matrix_generator_dense(n, sym=False, seed=seed)
g = sample.DirectedGraph(a)
g.degree_reduction()
g.initial_guess = "uniform"
g.regularise = "identity"
g._initialize_problem("dcm", "newton")
theta = np.random.rand(2 * n)
x0 = np.exp(-theta)
k_out = g.args[0]
k_in = g.args[1]
nz_index_out = g.args[2]
nz_index_in = g.args[3]
f_sample = np.zeros((n * 2, n * 2))
for i in range(n * 2):
f = lambda x: loglikelihood_prime_dcm_exp(x, g.args)[i]
f_sample[i, :] = approx_fprime(theta, f, epsilon=1e-6)
f_exp = loglikelihood_hessian_dcm_exp(theta, g.args)
# debug
# print(a)
# print(theta, x0)
# print(g.args)
# print(f_sample)
# print(f_exp)
# test result
self.assertTrue(np.allclose(f_sample, f_exp))
def test_3(self):
# test Matrix 1
n, seed = (40, 22)
A = mg.random_binary_matrix_generator_dense(n, sym=False, seed=seed)
A[0, :] = 0
g = sample.DirectedGraph(A)
g._solve_problem(
model="dcm",
method="quasinewton",
max_steps=100,
verbose=False,
initial_guess="uniform",
)
g._solution_error()
# debug
# print(g.r_dseq_out)
# print(g.r_dseq_in)
# print(g.rnz_dseq_out)
# print(g.rnz_dseq_in)
# print('\ntest 3: error = {}'.format(g.error))
# test result
self.assertTrue(g.error < 1e-2)
def test_dcm_exp(self):
n, seed = (4, 22)
A = mg.random_binary_matrix_generator_dense(n, sym=False, seed=seed)
x0 = np.random.rand(2 * n)
g = sample.DirectedGraph(A)
g.initial_guess = x0
g._set_initial_guess('dcm_exp')
g._set_solved_problem_dcm(x0)
self.assertTrue(np.concatenate((g.x, g.y)).all() == x0.all())
def test_adjacency_init(self):
a = np.array([[0, 1, 1], [1, 0, 1], [0, 1, 0]])
k_out = np.array([2, 2, 1])
k_in = np.array([1, 2, 2])
g0 = sample.DirectedGraph()
g0._initialize_graph(a)
# debug
# test result
self.assertTrue((k_out == g0.dseq_out).all())
self.assertTrue((k_in == g0.dseq_in).all())
def test_dcm_uniform(self):
n, seed = (4, 22)
A = mg.random_binary_matrix_generator_dense(n, sym=False, seed=seed)
g = sample.DirectedGraph(A)
g.initial_guess = 'uniform'
g._set_initial_guess('dcm')
self.assertTrue(
np.concatenate((
g.r_x,
g.r_y)).all() == np.array([0., 0.5, 0.5, 0.5, 0., 0.5]).all())
def test_edgelist_init_string_directed(self):
e = np.array([("1", "a"), ("2", "b"), ("2", "a")])
k_out = np.array([1, 2, 0, 0])
k_in = np.array([0, 0, 2, 1])
g0 = sample.DirectedGraph()
g0._initialize_graph(edgelist=e)
# debug
# test result
self.assertTrue((k_out == g0.dseq_out).all())
self.assertTrue((k_in == g0.dseq_in).all())
def test_edgelist_init(self):
e = np.array([(0, 1), (0, 2), (1, 2), (1, 0), (2, 1)])
k_out = np.array([2, 2, 1])
k_in = np.array([1, 2, 2])
g0 = sample.DirectedGraph()
g0._initialize_graph(edgelist=e)
# debug
# test result
self.assertTrue((k_out == g0.dseq_out).all())
self.assertTrue((k_in == g0.dseq_in).all())
def test_decm_exp_uniform(self):
n, seed = (4, 22)
A = mg.random_weighted_matrix_generator_dense(n,
sym=False,
seed=seed,
sup_ext=100,
intweights=True)
g = sample.DirectedGraph(A)
g.initial_guess = 'uniform'
g._set_initial_guess('decm_exp')
tester = np.exp(np.ones(4 * n))
self.assertTrue(g.x0.all() == tester.all())
def test_decm(self):
n, seed = (4, 22)
A = mg.random_weighted_matrix_generator_dense(n,
sym=False,
seed=seed,
sup_ext=100,
intweights=True)
x0 = np.random.rand(4 * n)
g = sample.DirectedGraph(A)
g.initial_guess = x0
g._set_initial_guess('decm')
g._set_solved_problem_decm(x0)
self.assertTrue(np.concatenate((g.x, g.y)).all() == x0.all())
def test_crema(self):
n, seed = (4, 22)
A = mg.random_weighted_matrix_generator_dense(n,
sym=False,
seed=seed,
sup_ext=100,
intweights=True)
x0 = np.random.rand(2 * n)
g = sample.DirectedGraph(A)
g.initial_guess = x0
g._set_initial_guess_crema_directed()
g._set_solved_problem_decm(x0)
self.assertTrue(g.x0.all() == x0.all())
def test_decm_uniform(self):
n, seed = (4, 22)
A = mg.random_weighted_matrix_generator_dense(n,
sym=False,
seed=seed,
sup_ext=100,
intweights=True)
g = sample.DirectedGraph(A)
g.initial_guess = 'uniform'
g._set_initial_guess('decm')
self.assertTrue(
np.concatenate((g.x, g.y, g.out_strength,
g.in_strength)).all() == np.ones(4 * n).all())
def test_loglikelihood_hessian_diag_dcm_exp_zeros(self):
# convergence relies heavily on x0
n, s = (10, 35)
# n, s = (5, 35)
A = mg.random_weighted_matrix_generator_dense(n,
sup_ext=100,
sym=False,
seed=s,
intweights=True)
A[0, :] = 0
A[:, 5] = 0
bA = np.array([[1 if aa != 0 else 0 for aa in a] for a in A])
k_out = np.sum(bA, axis=1)
k_in = np.sum(bA, axis=0)
s_out = np.sum(A, axis=1)
s_in = np.sum(A, axis=0)
g = sample.DirectedGraph(A)
g.initial_guess = "uniform"
g.regularise = "identity"
g._initialize_problem("decm", "newton")
# theta = np.random.rand(6)
theta = 0.5 * np.ones(n * 4)
theta[np.concatenate((k_out, k_in, s_out, s_in)) == 0] = 1e4
x0 = np.exp(-theta)
f_sample = np.zeros(n * 4)
for i in range(n * 4):
f = lambda x: loglikelihood_prime_decm_exp(x, g.args)[i]
f_sample[i] = approx_fprime(theta, f, epsilon=1e-6)[i]
f_exp = loglikelihood_hessian_diag_decm_exp(theta, g.args)
# debug
# print(a)
# print(theta, x0)
# print(g.args)
# print('approx',f_sample)
# print('my',f_exp)
# print('gradient', loglikelihood_prime_decm_exp(theta, g.args))
# print('diff',f_sample - f_exp)
# print('max',np.max(np.abs(f_sample - f_exp)))
# test result
self.assertTrue(np.allclose(f_sample, f_exp))
