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 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 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 = 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(): 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, 1.072091680000000e02]) 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()