lambda1,
lambda2,
reg,
Omega_0,
Theta_0=Theta_0,
eps=1e-3,
verbose=False,
measure=False)
Theta_sol = sol['Theta']
Omega_sol = sol['Omega']
# warm start
Omega_0 = Omega_sol.copy()
Theta_0 = Theta_sol.copy()
TPR[g1, g2] = discovery_rate(Theta_sol, Theta)['TPR']
FPR[g1, g2] = discovery_rate(Theta_sol, Theta)['FPR']
DTPR[g1, g2] = discovery_rate(Theta_sol, Theta)['TPR_DIFF']
DFPR[g1, g2] = discovery_rate(Theta_sol, Theta)['FPR_DIFF']
ERR[g1, g2] = error(Theta_sol, Theta)
AIC[g1, g2] = aic(S_train, Theta_sol, N_train)
BIC[g1, g2] = ebic(S_train, Theta_sol, N_train, gamma=0.1)
for l in np.arange(len(gammas)):
GAM[l, g1, g2] = ebic(S_train, Theta_sol, N_train, gamma=gammas[l])
# get optimal lambda
ix = np.unravel_index(np.nanargmin(BIC), BIC.shape)
ix2 = np.unravel_index(np.nanargmin(AIC), AIC.shape)
#%%
S, sample = sample_covariance_matrix(Sigma, vecN[j])
#start = time()
solA, infoA = ADMM_MGL(S,
l1[j],
l2[j],
reg,
Omega_0,
eps_admm=5e-5,
verbose=False,
measure=True)
#end = time()
#RT_ADMM[j] = end-start
iA[j] = infoA
TPR[j] = discovery_rate(solA['Theta'], Theta)['TPR']
FPR[j] = discovery_rate(solA['Theta'], Theta)['FPR']
#start = time()
solP, infoP = warmPPDNA(S,
l1[j],
l2[j],
reg,
Omega_0,
eps=5e-5,
eps_admm=1e-2,
verbose=False,
measure=True)
#end = time()
#RT_PPA[j] = end-start
iP[j] = infoP
for g1 in np.arange(grid1):
lambda1 = L1[g1,g2]
lambda2 = L2[g1,g2]
sol, info = ADMM_MGL(S, lambda1, lambda2, reg , Omega_0, Theta_0 = Theta_0, X_0 = X_0, tol = 1e-8, rtol = 1e-8, verbose = False, measure = False)
Theta_sol = sol['Theta']
Omega_sol = sol['Omega']
X_sol = sol['X']
# warm start
Omega_0 = Omega_sol.copy()
Theta_0 = Theta_sol.copy()
X_0 = X_sol.copy()
dr = discovery_rate(Theta_sol, Theta)
TPR[g1,g2] = dr['TPR']
FPR[g1,g2] = dr['FPR']
DTPR[g1,g2] = dr['TPR_DIFF']
DFPR[g1,g2] = dr['FPR_DIFF']
ERR[g1,g2] = error(Theta_sol, Theta)
AIC[g1,g2] = aic(S, Theta_sol, N)
BIC[g1,g2] = ebic(S, Theta_sol, N, gamma = 0.1)
# get optimal lambda
ix= np.unravel_index(np.nanargmin(BIC), BIC.shape)
ix2= np.unravel_index(np.nanargmin(AIC), AIC.shape)
l1opt = L1[ix]
Theta_0 = get_K_identity(K,p)
for g1 in np.arange(grid1):
for g2 in np.arange(grid2):
lambda1 = L1[g1,g2]
lambda2 = L2[g1,g2]
sol, info = warmPPDNA(S_train, lambda1, lambda2, reg, Omega_0, Theta_0 = Theta_0, eps = 1e-3, verbose = False, measure = False)
Theta_sol = sol['Theta']
Omega_sol = sol['Omega']
# warm start
Omega_0 = Omega_sol.copy()
Theta_0 = Theta_sol.copy()
TPR[g1,g2] = discovery_rate(Theta_sol, Theta)['TPR']
FPR[g1,g2] = discovery_rate(Theta_sol, Theta)['FPR']
DTPR[g1,g2] = discovery_rate(Theta_sol, Theta)['TPR_DIFF']
DFPR[g1,g2] = discovery_rate(Theta_sol, Theta)['FPR_DIFF']
ERR[g1,g2] = error(Theta_sol, Theta)
AIC[g1,g2] = aic(S_train, Theta_sol, N)
BIC[g1,g2] = ebic(S_train, Theta_sol, N, gamma = 0.1)
# get optimal lambda
ix= np.unravel_index(np.nanargmin(BIC), BIC.shape)
ix2= np.unravel_index(np.nanargmin(AIC), AIC.shape)
lambda1 = L1[ix]
lambda2 = L2[ix]
print("Optimal lambda values: (l1,l2) = ", (lambda1,lambda2))
plot_fpr_tpr(FPR.T, TPR.T, ix[::-1], ix2[::-1])
X_0=X_0,
tol=1e-8,
rtol=1e-8,
verbose=False,
measure=False)
Theta_sol = sol['Theta']
Omega_sol = sol['Omega']
X_sol = sol['X']
# warm start
Omega_0 = Omega_sol.copy()
Theta_0 = Theta_sol.copy()
X_0 = X_sol.copy()
dr = discovery_rate(Theta_sol, Theta)
TPR[g1, g2] = dr['TPR']
FPR[g1, g2] = dr['FPR']
DTPR[g1, g2] = dr['TPR_DIFF']
DFPR[g1, g2] = dr['FPR_DIFF']
ERR[g1, g2] = error(Theta_sol, Theta)
AIC[g1, g2] = aic(S, Theta_sol, N)
BIC[g1, g2] = ebic(S, Theta_sol, N, gamma=0.1)
# get optimal lambda
ix = np.unravel_index(np.nanargmin(BIC), BIC.shape)
ix2 = np.unravel_index(np.nanargmin(AIC), AIC.shape)
l1opt = L1[ix]
l2opt = L2[ix]