def post_process(name, date, dt_max, burn=500):
N = 12
model = NetworkPoisson(N, dt_max)
sample_path = '/Volumes/datasets/ITCH/samples/large2007_dt_max={}.hdf5'.format(
dt_max)
# Import samples
samples = import_samples(sample_path, name, date, burn)
if samples is not None:
# Unpack samples
lambda0, W, mu, tau = samples
# Compute point estimates
lambda0 = np.median(lambda0, axis=1)
W = np.median(W, axis=2).reshape(N * N) # row major
mu = np.median(mu, axis=2).reshape(N * N) # row major
tau = np.median(tau, axis=2).reshape(N * N) # row major
estimates = [name, date] + list(np.concatenate([lambda0, W, mu, tau]))
# Check stability
model.lamb = lambda0
model.W = W.reshape((N, N))
model.mu = mu.reshape((N, N))
model.tau = tau.reshape((N, N))
_, maxeig = model.check_stability(return_value=True)
eigenvalue = [name, date, maxeig]
return estimates, eigenvalue
def check_stability(lambda0, W, mu, tau, dt_max):
"""Check if the model is stable for given parameter estimates."""
N, _ = W.shape
model = NetworkPoisson(N=N, dt_max=dt_max)
model.lamb = lambda0
model.W = W
model.mu = mu
model.tau = tau
return model.check_stability(return_value=True)
read_path = '/Volumes/datasets/ITCH/samples/large2007_dt_max=60.hdf5'
write_path = '/Users/colinswaney/Desktop/threshold_name={}_date={}_M={}_norm={}.txt'.format(
name, event_date, M, norm)
with h5.File(read_path, 'r') as hdf:
start = time.time()
try:
lambda0, W, mu, tau = import_samples(read_path, name, mcmc_date)
except:
print('Unable to import samples; skipping')
for threshold in np.arange(-8, 1, .5):
lambda0_, W_, mu_, tau_ = get_estimates((lambda0, W, mu, tau),
threshold=threshold,
log=log,
norm=norm)
events, T = import_events(name, event_date)
model_net.lamb = lambda0_
model_net.W = W_
model_net.mu = mu_
model_net.tau = tau_
model_hom.lambda0 = np.median(model_hom.sample(events, T, size=2500),
axis=0)
print(
'Computing likelihood on random subsamples (threshold={}, M={})...'
.format(threshold, M))
start_sub = time.time()
for i in np.arange(L):
sample, T = sample_events(events, size=M)
ll_net, _ = model_net.compute_likelihood(sample, T)
ll_hom, _ = model_hom.compute_likelihood(sample, T)
bits = (ll_net - ll_hom) / M
df.append([name, threshold, ll_net, ll_hom, bits, T])