def template_admm_vs_ppdna(p=50, K=3, N=1000, reg="GGL"):
M = 5 # M should be divisor of p
if reg == 'GGL':
Sigma, Theta = group_power_network(p, K, M)
elif reg == 'FGL':
Sigma, Theta = time_varying_power_network(p, K, M)
S, samples = sample_covariance_matrix(Sigma, N)
lambda1 = 0.05
lambda2 = 0.01
Omega_0 = get_K_identity(K, p)
sol, info = ADMM_MGL(S,
lambda1,
lambda2,
reg,
Omega_0,
stopping_criterion='kkt',
tol=1e-6,
rtol=1e-5,
verbose=True,
latent=False)
sol2, info2 = warmPPDNA(S,
lambda1,
lambda2,
reg,
Omega_0,
eps=1e-6,
verbose=False,
measure=True)
sol3, info3 = PPDNA(S,
lambda1,
lambda2,
reg,
Omega_0,
eps_ppdna=1e-6,
verbose=True,
measure=True)
assert_array_almost_equal(sol['Theta'], sol2['Theta'], 2)
assert_array_almost_equal(sol2['Theta'], sol3['Theta'], 2)
return
def template_ADMM_MGL(p=100, K=5, N=1000, reg='GGL', latent=False):
"""
template for test for ADMM MGL solver with conforming variables
"""
M = 10 # M should be divisor of p
if reg == 'GGL':
Sigma, Theta = group_power_network(p, K, M)
elif reg == 'FGL':
Sigma, Theta = time_varying_power_network(p, K, M)
S, samples = sample_covariance_matrix(Sigma, N)
lambda1 = 0.05
lambda2 = 0.01
Omega_0 = get_K_identity(K, p)
sol, info = ADMM_MGL(S,
lambda1,
lambda2,
reg,
Omega_0,
tol=1e-5,
rtol=1e-5,
verbose=True,
measure=True,
latent=latent,
mu1=0.01)
assert info['status'] == 'optimal'
_, info2 = ADMM_MGL(S,
lambda1,
lambda2,
reg,
Omega_0,
tol=1e-20,
rtol=1e-20,
max_iter=2,
latent=latent,
mu1=0.01)
assert info2['status'] == 'max iterations reached'
return
def test_FGL_latent():
Sigma, Theta = time_varying_power_network(p, K, M)
S, samples = sample_covariance_matrix(Sigma, N)
template_problem_MGL(S, N, reg = 'FGL', latent = True)
return
from gglasso.solver.latent_admm_solver import latent_ADMM_GGL
from gglasso.helper.data_generation import time_varying_power_network, group_power_network, sample_covariance_matrix
from gglasso.helper.experiment_helper import get_K_identity
from gglasso.helper.model_selection import grid_search, single_range_search
p = 100
K = 5
N = 1000
M = 2
reg = 'GGL'
if reg == 'GGL':
Sigma, Theta = group_power_network(p, K, M)
elif reg == 'FGL':
Sigma, Theta = time_varying_power_network(p, K, M)
#np.linalg.norm(np.eye(p) - Sigma@Theta)
S, samples = sample_covariance_matrix(Sigma, N)
#S = get_K_identity(K,p)
lambda1 = 0.05
lambda2 = 0.05
L = np.logspace(-1, -3, 5)
#%%
grid_search(ADMM_MGL, S, N, p, reg, L, method='eBIC', l2=L)
est_uniform, est_indv, range_stats = single_range_search(S,
L,
from gglasso.solver.admm_solver import ADMM_MGL
from gglasso.helper.data_generation import time_varying_power_network, sample_covariance_matrix
from gglasso.helper.experiment_helper import lambda_grid, discovery_rate, error
from gglasso.helper.utils import get_K_identity
from gglasso.helper.experiment_helper import plot_evolution, plot_deviation, surface_plot, single_heatmap_animation
from gglasso.helper.model_selection import aic, ebic
p = 100
K = 10
N = 5000
M = 5
L = int(p / M)
reg = 'FGL'
Sigma, Theta = time_varying_power_network(p, K, M, scale=False, nxseed=2340)
S, sample = sample_covariance_matrix(Sigma, N)
results = {}
results['truth'] = {'Theta': Theta}
#%%
# Animate precision matrix over time
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
# colored squares represent non-zero entries
#
anim = single_heatmap_animation(Theta)
# %%