def test_log_det_ozone_shogun_exact(self):
o = OzonePosterior()
kappa = 2 ** (-13.1)
Q = o.create_Q_matrix(kappa)
self.assertAlmostEqual(OzonePosterior.log_det_shogun_exact(Q),
2.317769370813052e+06,
delta=1)
def test_log_likelihood_scikits_exact(self):
tau = 2 ** (-11.35)
kappa = 2 ** (-13.1)
o = OzonePosterior(prior=None, logdet_method="scikits",
solve_method="scikits")
self.assertAlmostEqual(o.log_likelihood(tau, kappa),
- 9.336375798558606e+05,
delta=0.001)
def main():
Log.set_loglevel(logging.DEBUG)
prior = Gaussian(Sigma=eye(2) * 100)
posterior = OzonePosterior(prior,
logdet_alg="scikits",
solve_method="scikits")
proposal_cov = diag([4.000000000000000e-05, 1.072091680000000e+02])
mcmc_sampler = StandardMetropolis(posterior, scale=1.0, cov=proposal_cov)
start = asarray([-11.35, -13.1])
mcmc_params = MCMCParams(start=start, num_iterations=5000)
chain = MCMCChain(mcmc_sampler, mcmc_params)
chain.append_mcmc_output(StatisticsOutput(print_from=1, lag=1))
home = expanduser("~")
folder = os.sep.join([home, "sample_ozone_posterior_average_serial"])
store_chain_output = StoreChainOutput(folder)
chain.append_mcmc_output(store_chain_output)
loaded = store_chain_output.load_last_stored_chain()
if loaded is None:
logging.info("Running chain from scratch")
else:
logging.info("Running chain from iteration %d" % loaded.iteration)
chain = loaded
chain.run()
f = open(folder + os.sep + "final_chain", "w")
dump(chain, f)
f.close()
def test_log_det_exact_toy_large_shogun(self):
n = 1e6
d = abs(randn(n))
Q = spdiags(d, 0, n, n)
self.assertAlmostEqual(OzonePosterior.log_det_shogun_exact(Q), sum(log(d)),
delta=1e-5)
def test_log_det_exact_toy_small_scikits(self):
n = 3
d = abs(randn(n))
Q = spdiags(d, 0, n, n)
self.assertAlmostEqual(OzonePosterior.log_det_scikits(Q), sum(log(d)),
delta=1e-15)
def __init__(self, aggregator, ozone_posterior, tau, kappa):
IndependentJob.__init__(self, aggregator)
self.ozone_posterior = OzonePosterior(ozone_posterior.prior,
ozone_posterior.logdet_method,
ozone_posterior.solve_method)
self.tau = tau
self.kappa = kappa
def compute(self):
logging.debug("Entering")
# needed for both matrices
Q = self.ozone_posterior.create_Q_matrix(self.kappa);
if self.matrix_type == "Q":
logging.info("Matrix type Q")
logdet = self.ozone_posterior.log_det_method(Q)
elif self.matrix_type == "M":
logging.info("Matrix type M")
_, A = OzonePosterior.load_ozone_data()
AtA = A.T.dot(A)
M = Q + self.tau * AtA;
logdet = self.ozone_posterior.log_det_method(M)
else:
raise ValueError("Unknown matrix type: %s" % self.matrix_type)
result = ScalarResult(logdet)
self.aggregator.submit_result(result)
logging.debug("Leaving")
def compute(self):
logging.debug("Entering")
# needed for both matrices
Q = self.ozone_posterior.create_Q_matrix(self.kappa)
if self.matrix_type == "Q":
logging.info("Matrix type Q")
logdet = self.ozone_posterior.log_det_method(Q)
elif self.matrix_type == "M":
logging.info("Matrix type M")
_, A = OzonePosterior.load_ozone_data()
AtA = A.T.dot(A)
M = Q + self.tau * AtA
logdet = self.ozone_posterior.log_det_method(M)
else:
raise ValueError("Unknown matrix type: %s" % self.matrix_type)
result = ScalarResult(logdet)
self.aggregator.submit_result(result)
logging.debug("Leaving")
def precompute_likelihood_estimates(self, tau, kappa):
logging.debug("Entering")
# submit all jobs for log-determinant Q
aggregators_Q = []
for _ in range(self.num_estimates):
job = OzoneLogDetJob(ScalarResultAggregator(), self, tau, kappa,
"Q")
aggregators_Q.append(self.computation_engine.submit_job(job))
# submit all jobs for log-determinant M
aggregators_M = []
for _ in range(self.num_estimates):
job = OzoneLogDetJob(ScalarResultAggregator(), self, tau, kappa,
"M")
aggregators_M.append(self.computation_engine.submit_job(job))
# submit job for remainder of likelihood
job = OzoneLikelihoodWithoutLogDetJob(ScalarResultAggregator(), self,
tau, kappa)
aggregator_remainder = self.computation_engine.submit_job(job)
# grab a coffee
self.computation_engine.wait_for_all()
# collect results from all aggregators
log_dets_Q = zeros(self.num_estimates)
log_dets_M = zeros(self.num_estimates)
for i in range(self.num_estimates):
aggregators_Q[i].finalize()
aggregators_M[i].finalize()
log_dets_Q[i] = aggregators_Q[i].get_final_result().result
log_dets_M[i] = aggregators_M[i].get_final_result().result
aggregators_Q[i].clean_up()
aggregators_M[i].clean_up()
aggregator_remainder.finalize()
result_remainder = aggregator_remainder.get_final_result().result
aggregator_remainder.clean_up()
# load n since needed for likelihood
y, _ = OzonePosterior.load_ozone_data()
n = len(y)
# construct all likelihood estimates
log_det_parts = 0.5 * log_dets_Q + 0.5 * n * log(
tau) - 0.5 * log_dets_M
estimates = log_det_parts + result_remainder
# crude check for an overflow to print error details
limit = 1e100
indices = where(abs(estimates) > limit)[0]
if len(indices) > 0:
logging.info(
"Log-likelihood estimates overflow occured at the following indices:"
)
for idx in indices:
logging.info("At index %d. Details are: " % idx)
logging.info("log-det Q: " + aggregators_Q[idx].job_name +
". Result is %f" % log_dets_Q[idx])
logging.info("log-det M: " + aggregators_M[idx].job_name +
". Result is %f" % log_dets_M[idx])
logging.info("log-lik-without-log-det: " +
aggregator_remainder.job_name +
". Result is %f" % result_remainder[idx])
logging.info("Removing mentioned estimates from list")
estimates = estimates[abs(estimates) < limit]
logging.info("New number of estimates is %d, old was %d" %
(len(estimates), self.num_estimates))
logging.debug("Leaving")
return estimates
def precompute_likelihood_estimates(self, tau, kappa):
logging.debug("Entering")
# submit all jobs for log-determinant Q
aggregators_Q = []
for _ in range(self.num_estimates):
job = OzoneLogDetJob(ScalarResultAggregator(), self, tau, kappa, "Q")
aggregators_Q.append(self.computation_engine.submit_job(job))
# submit all jobs for log-determinant M
aggregators_M = []
for _ in range(self.num_estimates):
job = OzoneLogDetJob(ScalarResultAggregator(), self, tau, kappa, "M")
aggregators_M.append(self.computation_engine.submit_job(job))
# submit job for remainder of likelihood
job = OzoneLikelihoodWithoutLogDetJob(ScalarResultAggregator(), self, tau, kappa)
aggregator_remainder = self.computation_engine.submit_job(job)
# grab a coffee
self.computation_engine.wait_for_all()
# collect results from all aggregators
log_dets_Q = zeros(self.num_estimates)
log_dets_M = zeros(self.num_estimates)
for i in range(self.num_estimates):
aggregators_Q[i].finalize()
aggregators_M[i].finalize()
log_dets_Q[i] = aggregators_Q[i].get_final_result().result
log_dets_M[i] = aggregators_M[i].get_final_result().result
aggregators_Q[i].clean_up()
aggregators_M[i].clean_up()
aggregator_remainder.finalize()
result_remainder = aggregator_remainder.get_final_result().result
aggregator_remainder.clean_up()
# load n since needed for likelihood
y, _ = OzonePosterior.load_ozone_data()
n = len(y)
# construct all likelihood estimates
log_det_parts = 0.5 * log_dets_Q + 0.5 * n * log(tau) - 0.5 * log_dets_M
estimates = log_det_parts + result_remainder
# crude check for an overflow to print error details
limit = 1e100
indices = where(abs(estimates) > limit)[0]
if len(indices) > 0:
logging.info("Log-likelihood estimates overflow occured at the following indices:")
for idx in indices:
logging.info("At index %d. Details are: " % idx)
logging.info("log-det Q: " + aggregators_Q[idx].job_name +
". Result is %f" % log_dets_Q[idx])
logging.info("log-det M: " + aggregators_M[idx].job_name +
". Result is %f" % log_dets_M[idx])
logging.info("log-lik-without-log-det: " +
aggregator_remainder.job_name + ". Result is %f" % result_remainder[idx])
logging.info("Removing mentioned estimates from list")
estimates = estimates[abs(estimates) < limit]
logging.info("New number of estimates is %d, old was %d" %
(len(estimates), self.num_estimates))
logging.debug("Leaving")
return estimates
def test_load_data(self):
OzonePosterior()
def __init__(self, num_estimates, prior):
OzonePosterior.__init__(self, prior)
self.num_estimates = num_estimates
def precompute_likelihood_estimates(self, tau, kappa):
logging.debug("Entering")
estimates = asarray([OzonePosterior.log_likelihood(self, tau, kappa) for _ in range(self.num_estimates)])
logging.debug("Leaving")
return estimates
