def test_equal_estimates(self):
Log.set_loglevel(logging.DEBUG)
rr = RussianRoulette(1e-5, block_size=100)
log_estimates=randn(1000)
log_estimates=ones(1000)*(-942478.011941)
print rr.exponential(log_estimates)
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 prepare_engine(submit_type='local',
duration_job_min=60*4):
# ---------------------
Log.set_loglevel(20)
logger.info("Start")
foldername = expanduser("~")+'/slurm_jobs'
if not os.path.exists(foldername):
os.makedirs(foldername)
logger.info("Setting engine folder to %s" % foldername)
logger.info("Creating batch parameter instance")
johns_slurm_hack = "#SBATCH --partition=intel-ivy,wrkstn,compute"
timestr = time.strftime("%Y%m%d-%H%M%S")
batch_parameters = BatchClusterParameters(max_walltime=duration_job_min,
foldername=foldername,
job_name_base="sim_"+timestr+"_",
parameter_prefix=johns_slurm_hack)
if submit_type =='slurm':
logger.info("Creating slurm engine instance")
engine = SlurmComputationEngine(batch_parameters)
elif submit_type == "local":
logger.info("Creating serial engine instance")
engine = SerialComputationEngine()
# ---------------------
return engine
def test_equal_estimates(self):
Log.set_loglevel(logging.DEBUG)
rr = RussianRoulette(1e-5, block_size=100)
log_estimates = randn(1000)
log_estimates = ones(1000) * (-942478.011941)
print rr.exponential(log_estimates)
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()
if samples is not None:
ax.scatter(samples[:, 0], samples[:, 1], c='r', s=1)
if __name__ == '__main__':
"""
Example that just sends out jobs that store their result to a file when done;
there is no control over the job after it has been submitted.
No aggregators are stored and results can be picked up from disc when ready.
This script also illustrates a typical use case in scientific computing:
Run the same function with different parameters a certain number of times.
Make sure to read the minimal example first.
"""
Log.set_loglevel(10)
# filename of the result database
home = expanduser("~")
foldername = os.path.join(home, "test")
db_fname = os.path.join(foldername, "test.txt")
batch_parameters = BatchClusterParameters(foldername=foldername)
engine = SerialComputationEngine()
# engine = SlurmComputationEngine(batch_parameters)
# here are some example parameters for jobs
# we here create all combinations and then shuffle them
# this randomizes the runs over the parameter space
params_x = np.linspace(-3, 3, num=25)
params_y = np.linspace(-2, 2, num=12)
if samples is not None:
ax.scatter(samples[:, 0], samples[:, 1], c='r', s=1);
if __name__ == '__main__':
"""
Example that just sends out jobs that store their result to a file when done;
there is no control over the job after it has been submitted.
No aggregators are stored and results can be picked up from disc when ready.
This script also illustrates a typical use case in scientific computing:
Run the same function with different parameters a certain number of times.
Make sure to read the minimal example first.
"""
Log.set_loglevel(10)
# filename of the result database
home = expanduser("~")
foldername = os.path.join(home, "test")
db_fname = os.path.join(foldername, "test.txt")
batch_parameters = BatchClusterParameters(foldername=foldername)
engine = SerialComputationEngine()
# engine = SlurmComputationEngine(batch_parameters)
# here are some example parameters for jobs
# we here create all combinations and then shuffle them
# this randomizes the runs over the parameter space
params_x = np.linspace(-3, 3, num=25)
params_y = np.linspace(-2, 2, num=12)
from independent_jobs.aggregators.ScalarResultAggregator import ScalarResultAggregator
from independent_jobs.engines.BatchClusterParameters import BatchClusterParameters
from independent_jobs.engines.SGEComputationEngine import SGEComputationEngine
from independent_jobs.engines.SerialComputationEngine import SerialComputationEngine
from independent_jobs.examples.MyJob import MyJob
from independent_jobs.tools.Log import Log
from independent_jobs.tools.Log import logger
import numpy as np
# See other file for implementation of MyJob
# Since we are using ScalarResult, we can use the already implemented aggregator
# ScalarResultAggregator
if __name__ == '__main__':
Log.set_loglevel(logger.info)
logger.info("Start")
# create an instance of the SGE engine, with certain parameters
# create folder name string
home = expanduser("~")
foldername = os.sep.join([home, "minimal_example"])
logger.info("Setting engine folder to %s" % foldername)
# create parameter instance that is needed for any batch computation engine
logger.info("Creating batch parameter instance")
batch_parameters = BatchClusterParameters(foldername=foldername)
# possibly create SGE engine instance, which can be used to submit jobs to
# there are more engines available.
# logger.info("creating SGE engine instance")
