def __init__(self, distribution, num_components, num_sample_discard=1000,
num_samples_gmm=1000, num_samples_when_to_switch=40000, num_runs_em=1):
StandardMetropolis.__init__(self, distribution)
self.num_components = num_components
self.num_sample_discard = num_sample_discard
self.num_samples_gmm = num_samples_gmm
self.num_samples_when_to_switch = num_samples_when_to_switch
self.num_runs_em = num_runs_em
# start with empty proposal, is changed to something in adapt method
self.proposal = None
def construct_proposal(self, y):
# fixed proposal exists from a certain iteration, return std MH otherwise
# was created in adapt method
if self.proposal is not None:
return self.proposal
else:
return StandardMetropolis.construct_proposal(self, y)
def construct_proposal(self, y):
# fixed proposal exists from a certain iteration, return std MH otherwise
# was created in adapt method
if self.proposal is not None:
return self.proposal
else:
return StandardMetropolis.construct_proposal(self, y)
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 __init__(self,
distribution,
num_components,
num_sample_discard=1000,
num_samples_gmm=1000,
num_samples_when_to_switch=40000,
num_runs_em=1):
StandardMetropolis.__init__(self, distribution)
self.num_components = num_components
self.num_sample_discard = num_sample_discard
self.num_samples_gmm = num_samples_gmm
self.num_samples_when_to_switch = num_samples_when_to_switch
self.num_runs_em = num_runs_em
# start with empty proposal, is changed to something in adapt method
self.proposal = None
def __str__(self):
s = self.__class__.__name__ + "=["
s += "num_components=" + str(self.num_components)
s += ", num_sample_discard=" + str(self.num_sample_discard)
s += ", num_samples_gmm=" + str(self.num_samples_gmm)
s += ", num_runs_em=" + str(self.num_runs_em)
s += ", " + StandardMetropolis.__str__(self)
s += "]"
return s
def __str__(self):
s = self.__class__.__name__ + "=["
s += "num_components=" + str(self.num_components)
s += ", num_sample_discard=" + str(self.num_sample_discard)
s += ", num_samples_gmm=" + str(self.num_samples_gmm)
s += ", num_runs_em=" + str(self.num_runs_em)
s += ", " + StandardMetropolis.__str__(self)
s += "]"
return s
def main():
Log.set_loglevel(logging.DEBUG)
prior = Gaussian(Sigma=eye(2) * 100)
num_estimates = 1000
home = expanduser("~")
folder = os.sep.join([home, "sample_ozone_posterior_rr_sge"])
# cluster admin set project jump for me to exclusively allocate nodes
parameter_prefix = "" # #$ -P jump"
cluster_parameters = BatchClusterParameters(
foldername=folder,
memory=7.8,
loglevel=logging.DEBUG,
parameter_prefix=parameter_prefix,
max_walltime=60 * 60 * 24 - 1)
computation_engine = SGEComputationEngine(cluster_parameters,
check_interval=10)
rr_instance = RussianRoulette(1e-3, block_size=400)
posterior = OzonePosteriorRREngine(rr_instance=rr_instance,
computation_engine=computation_engine,
num_estimates=num_estimates,
prior=prior)
posterior.logdet_method = "shogun_estimate"
proposal_cov = diag([4.000000000000000e-05, 1.072091680000000e+02])
mcmc_sampler = StandardMetropolis(posterior, scale=1.0, cov=proposal_cov)
start = asarray([-11.55, -10.1])
mcmc_params = MCMCParams(start=start, num_iterations=5000)
chain = MCMCChain(mcmc_sampler, mcmc_params)
# chain.append_mcmc_output(PlottingOutput(None, plot_from=1, lag=1))
chain.append_mcmc_output(StatisticsOutput(print_from=1, lag=1))
store_chain_output = StoreChainOutput(folder, lag=1)
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 main():
Log.set_loglevel(logging.DEBUG)
modulename = "sample_ozone_posterior_average_slurm"
if not FileSystem.cmd_exists("sbatch"):
engine = SerialComputationEngine()
else:
johns_slurm_hack = "#SBATCH --partition=intel-ivy,wrkstn,compute"
johns_slurm_hack = "#SBATCH --partition=intel-ivy,compute"
folder = os.sep + os.sep.join(["nfs", "data3", "ucabhst", modulename])
batch_parameters = BatchClusterParameters(
foldername=folder,
max_walltime=24 * 60 * 60,
resubmit_on_timeout=False,
memory=3,
parameter_prefix=johns_slurm_hack)
engine = SlurmComputationEngine(batch_parameters,
check_interval=1,
do_clean_up=True)
prior = Gaussian(Sigma=eye(2) * 100)
num_estimates = 100
posterior = OzonePosteriorAverageEngine(computation_engine=engine,
num_estimates=num_estimates,
prior=prior)
posterior.logdet_method = "shogun_estimate"
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=2000)
chain = MCMCChain(mcmc_sampler, mcmc_params)
chain.append_mcmc_output(StatisticsOutput(print_from=1, lag=1))
home = expanduser("~")
folder = os.sep.join([home, modulename])
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 main():
Log.set_loglevel(logging.DEBUG)
prior = Gaussian(Sigma=eye(2) * 100)
num_estimates = 2
home = expanduser("~")
folder = os.sep.join([home, "sample_ozone_posterior_rr_sge"])
computation_engine = SerialComputationEngine()
rr_instance = RussianRoulette(1e-3, block_size=10)
posterior = OzonePosteriorRREngine(rr_instance=rr_instance,
computation_engine=computation_engine,
num_estimates=num_estimates,
prior=prior)
posterior.logdet_method = "shogun_estimate"
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=200)
chain = MCMCChain(mcmc_sampler, mcmc_params)
# chain.append_mcmc_output(PlottingOutput(None, plot_from=1, lag=1))
chain.append_mcmc_output(StatisticsOutput(print_from=1, lag=1))
store_chain_output = StoreChainOutput(folder, lag=50)
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 main():
distribution = Banana(dimension=2, bananicity=0.03, V=100.0)
mcmc_sampler = StandardMetropolis(distribution)
start = zeros(distribution.dimension)
start = asarray([0., -2.])
mcmc_params = MCMCParams(start=start, num_iterations=10000)
chain = MCMCChain(mcmc_sampler, mcmc_params)
chain.append_mcmc_output(StatisticsOutput(plot_times=True, lag=1000))
# chain.append_mcmc_output(PlottingOutput(distribution, plot_from=1, num_samples_plot=0,
# colour_by_likelihood=False))
chain.run()
f = open("std_metropolis_chain_gaussian.bin", 'w')
dump(chain, f)
f.close()
mean_est = zeros(distribution.dimension, dtype="float64")
cov_est = 1.0 * eye(distribution.dimension)
cov_est[0, 0] = distribution.V
mcmc_samplers.append(
AdaptiveMetropolisLearnScale(distribution,
mean_est=mean_est,
cov_est=cov_est))
mcmc_samplers.append(
AdaptiveMetropolis(distribution,
mean_est=mean_est,
cov_est=cov_est))
#
# num_eigen = distribution.dimension
# mcmc_samplers.append(AdaptiveMetropolisPCA(distribution, num_eigen=num_eigen, mean_est=mean_est, cov_est=cov_est))
#
mcmc_samplers.append(StandardMetropolis(distribution))
start = zeros(distribution.dimension, dtype="float64")
mcmc_params = MCMCParams(start=start,
num_iterations=num_iterations,
burnin=burnin)
mcmc_chains = [
MCMCChain(mcmc_sampler, mcmc_params)
for mcmc_sampler in mcmc_samplers
]
for mcmc_chain in mcmc_chains:
mcmc_chain.append_mcmc_output(StatisticsOutput())
experiments = [
SingleChainExperiment(mcmc_chain, experiment_dir)
os.sep)[-1].split(".")[0] + os.sep
distribution = Flower(amplitude=6,
frequency=6,
variance=1,
radius=10,
dimension=8)
sigma = 5
kernel = GaussianKernel(sigma=sigma)
burnin = 60000
num_iterations = 120000
#mcmc_sampler = KameleonWindowLearnScale(distribution, kernel, stop_adapt=burnin)
mean_est = zeros(distribution.dimension, dtype="float64")
cov_est = 1.0 * eye(distribution.dimension)
#mcmc_sampler = AdaptiveMetropolisLearnScale(distribution, mean_est=mean_est, cov_est=cov_est)
#mcmc_sampler = AdaptiveMetropolis(distribution, mean_est=mean_est, cov_est=cov_est)
mcmc_sampler = StandardMetropolis(distribution)
start = zeros(distribution.dimension, dtype="float64")
mcmc_params = MCMCParams(start=start,
num_iterations=num_iterations,
burnin=burnin)
mcmc_chain = MCMCChain(mcmc_sampler, mcmc_params)
mcmc_chain.append_mcmc_output(StatisticsOutput())
experiment = SingleChainExperiment(mcmc_chain, experiment_dir)
experiment.run()
num_iterations = 120000
mcmc_samplers.append(
KameleonWindowLearnScale(distribution, kernel, stop_adapt=burnin))
mean_est = zeros(distribution.dimension, dtype="float64")
cov_est = 1.0 * eye(distribution.dimension)
mcmc_samplers.append(
AdaptiveMetropolisLearnScale(distribution,
mean_est=mean_est,
cov_est=cov_est))
mcmc_samplers.append(
AdaptiveMetropolis(distribution,
mean_est=mean_est,
cov_est=cov_est))
mcmc_samplers.append(StandardMetropolis(distribution, cov=cov_est))
start = zeros(distribution.dimension, dtype="float64")
mcmc_params = MCMCParams(start=start,
num_iterations=num_iterations,
burnin=burnin)
mcmc_chains = [
MCMCChain(mcmc_sampler, mcmc_params)
for mcmc_sampler in mcmc_samplers
]
for mcmc_chain in mcmc_chains:
mcmc_chain.append_mcmc_output(StatisticsOutput())
experiments = [
SingleChainExperiment(mcmc_chain, experiment_dir)
dim = shape(data)[1]
# prior on theta and posterior target estimate
theta_prior = Gaussian(mu=0 * ones(dim), Sigma=eye(dim) * 5)
target=PseudoMarginalHyperparameterDistributionDiffusion(data, labels, \
n_importance=100, prior=theta_prior, \
ridge=1e-3)
# create sampler
burnin = 50000
num_iterations = burnin + 500000
kernel = GaussianKernel(sigma=23.0)
# sampler=KameleonWindowLearnScale(target, kernel, stop_adapt=burnin)
# sampler=AdaptiveMetropolisLearnScale(target)
sampler = StandardMetropolis(target)
# posterior mode derived by initial tests
start = zeros(target.dimension)
params = MCMCParams(start=start,
num_iterations=num_iterations,
burnin=burnin)
# create MCMC chain
chain = MCMCChain(sampler, params)
chain.append_mcmc_output(StatisticsOutput(print_from=0, lag=100))
#chain.append_mcmc_output(PlottingOutput(plot_from=0, lag=100))
# create experiment instance to store results
experiment_dir = str(os.path.abspath(sys.argv[0])).split(
os.sep)[-1].split(".")[0] + os.sep
