aggs = []
for i in range(num_repetitions):
job = rw_generator_isotropic(num_warmup, thin_step)
logger.info("Repetition %d/%d, %s" %
(i + 1, num_repetitions, job.get_parameter_fname_suffix()))
aggs += [engine.submit_job(job)]
engine.wait_for_all()
for i, agg in enumerate(aggs):
agg.finalize()
result = agg.get_final_result()
agg.clean_up()
if isinstance(engine, SerialComputationEngine):
plot_mcmc_result(result, D1=0, D2=1)
agg.store_fire_and_forget_result(folder="", job_name="")
# print some summary stats
accepted = result.accepted
avg_ess = result.posterior_statistics["avg_ess"]
time = result.time_taken_sampling
time_set_up = result.time_taken_set_up
logger.info("Repetition %d" % i)
logger.info("Average acceptance probability: %.2f" % np.mean(accepted))
logger.info("Average ESS: %.2f" % avg_ess)
logger.info("Total time: %.2f" % (time + time_set_up))
accepted = result.accepted
avg_ess = result.posterior_statistics["avg_ess"]
min_ess = result.posterior_statistics["min_ess"]
time = result.time_taken_sampling
time_set_up = result.time_taken_set_up
logger.info("Average acceptance probability: %.2f" % np.mean(accepted))
logger.info("Average ESS: %.2f" % avg_ess)
logger.info("Minimum ESS: %.2f" % min_ess)
logger.info("Total time: %.2f" % (time + time_set_up))
logger.info("Average ESS/s: %.2f" % (avg_ess / (time + time_set_up)))
logger.info("Minimum ESS/s: %.2f" % (min_ess / (time + time_set_up)))
if False:
plot_mcmc_result(result, D1=1, D2=6)
inds = np.arange(warmup, result.num_iterations, step=thin)
for i in inds:
samples += [result.samples[i]]
samples = np.array(samples)
samples = samples[np.random.permutation(len(samples))]
logger.info("Extracted %d samples in dimension %d" % (samples.shape[0], samples.shape[1]))
if True:
with open("benchmark_samples.arr", 'w+') as f:
np.save(f, samples)
D_pairs = []
for D1 in range(9):