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 test_slurm_engine_max_waiting_time(self):
if not FileSystem.cmd_exists("sbatch"):
raise SkipTest
home = expanduser("~")
folder = os.sep.join([home, "unit_test_dummy_slurm_result_max_wait"])
try:
shutil.rmtree(folder)
except OSError:
pass
batch_parameters = BatchClusterParameters(foldername=folder)
engine = SlurmComputationEngine(batch_parameters, check_interval=1)
sleep_times = [2, -1]
self.engine_helper(engine, sleep_times)
def test_slurm_engine_no_clean_up(self):
if not FileSystem.cmd_exists("sbatch"):
raise SkipTest
home = expanduser("~")
folder = os.sep.join([home, "unit_test_slurm_dummy_result"])
try:
shutil.rmtree(folder)
except OSError:
pass
batch_parameters = BatchClusterParameters(foldername=folder)
engine = SlurmComputationEngine(batch_parameters,
check_interval=1,
do_clean_up=False)
num_submissions = 3
sleep_times = randint(0, 3, num_submissions)
self.engine_helper(engine, sleep_times)
def run_dataset(prob_label):
"""Run the experiment"""
sample_source, n = get_sample_source(prob_label)
# /////// submit jobs //////////
# create folder name string
home = os.path.expanduser("~")
foldername = os.path.join(home, "freqopttest_slurm", 'e%d' % ex)
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,
job_name_base="e%d_" % ex,
parameter_prefix="")
# Use the following line if Slurm queue is not used.
#engine = SerialComputationEngine()
engine = SlurmComputationEngine(batch_parameters, do_clean_up=True)
n_methods = len(method_job_funcs)
# repetitions x #methods
aggregators = np.empty((reps, n_methods), dtype=object)
d = sample_source.dim()
for r in range(reps):
for mi, f in enumerate(method_job_funcs):
# name used to save the result
func_name = f.__name__
fname = '%s-%s-J%d_r%d_d%d_a%.3f_trp%.2f.p' \
%(prob_label, func_name, J, r, d, alpha, tr_proportion)
if not is_rerun and glo.ex_file_exists(ex, prob_label, fname):
logger.info('%s exists. Load and return.' % fname)
test_result = glo.ex_load_result(ex, prob_label, fname)
sra = SingleResultAggregator()
sra.submit_result(SingleResult(test_result))
aggregators[r, mi] = sra
else:
# result not exists or rerun
job = Ex5Job(SingleResultAggregator(), prob_label, r, n, f)
agg = engine.submit_job(job)
aggregators[r, mi] = agg
# let the engine finish its business
logger.info("Wait for all call in engine")
engine.wait_for_all()
# ////// collect the results ///////////
logger.info("Collecting results")
test_results = np.empty((reps, n_methods), dtype=object)
for r in range(reps):
for mi, f in enumerate(method_job_funcs):
logger.info("Collecting result (%s, r=%d)" % (f.__name__, r))
# let the aggregator finalize things
aggregators[r, mi].finalize()
# aggregators[i].get_final_result() returns a SingleResult instance,
# which we need to extract the actual result
test_result = aggregators[r, mi].get_final_result().result
test_results[r, mi] = test_result
func_name = f.__name__
fname = '%s-%s-J%d_r%d_d%d_a%.3f_trp%.2f.p' \
%(prob_label, func_name, J, r, d, alpha, tr_proportion)
glo.ex_save_result(ex, test_result, prob_label, fname)
func_names = [f.__name__ for f in method_job_funcs]
func2labels = exglobal.get_func2label_map()
method_labels = [func2labels[f] for f in func_names if f in func2labels]
# save results
results = {
'results': test_results,
'n': n,
'data_fname': label2fname[prob_label],
'alpha': alpha,
'J': J,
'sample_source': sample_source,
'tr_proportion': tr_proportion,
'method_job_funcs': method_job_funcs,
'prob_label': prob_label,
'method_labels': method_labels
}
# class name
fname = 'ex%d-%s-me%d_J%d_rs%d_nma%d_d%d_a%.3f_trp%.2f.p' \
%(ex, prob_label, n_methods, J, reps, n, d, alpha, tr_proportion)
glo.ex_save_result(ex, results, fname)
logger.info('Saved aggregated results to %s' % fname)
num_warmup = 200
compute_local = False
if not FileSystem.cmd_exists("sbatch") or compute_local:
engine = SerialComputationEngine()
else:
johns_slurm_hack = "#SBATCH --partition=intel-ivy,wrkstn,compute"
folder = os.sep + os.sep.join(["nfs", "data3", "ucabhst", modulename])
batch_parameters = BatchClusterParameters(
foldername=folder,
resubmit_on_timeout=False,
parameter_prefix=johns_slurm_hack)
engine = SlurmComputationEngine(batch_parameters,
check_interval=1,
do_clean_up=True)
engine.max_jobs_in_queue = 1000
engine.store_fire_and_forget = True
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):
def run_dataset(prob_label):
"""Run the experiment"""
sample_source, n = get_sample_source(prob_label)
# /////// submit jobs //////////
# create folder name string
home = os.path.expanduser("~")
foldername = os.path.join(home, "freqopttest_slurm", 'e%d'%ex)
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, job_name_base="e%d_"%ex, parameter_prefix="")
# Use the following line if Slurm queue is not used.
#engine = SerialComputationEngine()
engine = SlurmComputationEngine(batch_parameters, do_clean_up=True)
n_methods = len(method_job_funcs)
# repetitions x #methods
aggregators = np.empty((reps, n_methods ), dtype=object)
d = sample_source.dim()
for r in range(reps):
for mi, f in enumerate(method_job_funcs):
# name used to save the result
func_name = f.__name__
fname = '%s-%s-J%d_r%d_d%d_a%.3f_trp%.2f.p' \
%(prob_label, func_name, J, r, d, alpha, tr_proportion)
if not is_rerun and glo.ex_file_exists(ex, prob_label, fname):
logger.info('%s exists. Load and return.'%fname)
test_result = glo.ex_load_result(ex, prob_label, fname)
sra = SingleResultAggregator()
sra.submit_result(SingleResult(test_result))
aggregators[r, mi] = sra
else:
# result not exists or rerun
job = Ex5Job(SingleResultAggregator(), prob_label, r, n, f)
agg = engine.submit_job(job)
aggregators[r, mi] = agg
# let the engine finish its business
logger.info("Wait for all call in engine")
engine.wait_for_all()
# ////// collect the results ///////////
logger.info("Collecting results")
test_results = np.empty((reps, n_methods), dtype=object)
for r in range(reps):
for mi, f in enumerate(method_job_funcs):
logger.info("Collecting result (%s, r=%d)" % (f.__name__, r ))
# let the aggregator finalize things
aggregators[r, mi].finalize()
# aggregators[i].get_final_result() returns a SingleResult instance,
# which we need to extract the actual result
test_result = aggregators[r, mi].get_final_result().result
test_results[r, mi] = test_result
func_name = f.__name__
fname = '%s-%s-J%d_r%d_d%d_a%.3f_trp%.2f.p' \
%(prob_label, func_name, J, r, d, alpha, tr_proportion)
glo.ex_save_result(ex, test_result, prob_label, fname)
func_names = [f.__name__ for f in method_job_funcs]
func2labels = exglobal.get_func2label_map()
method_labels = [func2labels[f] for f in func_names if f in func2labels]
# save results
results = {'results': test_results, 'n': n, 'data_fname':label2fname[prob_label],
'alpha': alpha, 'J': J, 'sample_source': sample_source,
'tr_proportion': 0.5, 'method_job_funcs': method_job_funcs,
'prob_label': prob_label, 'method_labels': method_labels}
# class name
fname = 'ex%d-%s-me%d_J%d_rs%d_nma%d_d%d_a%.3f_trp%.2f.p' \
%(ex, prob_label, n_methods, J, reps, n, d, alpha, tr_proportion)
glo.ex_save_result(ex, results, fname)
logger.info('Saved aggregated results to %s'%fname)
def run_problem(prob_label):
"""Run the experiment"""
L = get_pqsource_list(prob_label)
prob_params, ps, data_sources = zip(*L)
# make them lists
prob_params = list(prob_params)
ps = list(ps)
data_sources = list(data_sources)
# /////// submit jobs //////////
# create folder name string
#result_folder = glo.result_folder()
from kgof.config import expr_configs
tmp_dir = expr_configs['scratch_path']
foldername = os.path.join(tmp_dir, 'kgof_slurm', 'e%d' % ex)
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,
job_name_base="e%d_" % ex,
parameter_prefix="")
# Use the following line if Slurm queue is not used.
#engine = SerialComputationEngine()
engine = SlurmComputationEngine(batch_parameters)
#engine = SlurmComputationEngine(batch_parameters, partition='wrkstn,compute')
n_methods = len(method_job_funcs)
# repetitions x len(prob_params) x #methods
aggregators = np.empty((reps, len(prob_params), n_methods), dtype=object)
for r in range(reps):
for pi, param in enumerate(prob_params):
for mi, f in enumerate(method_job_funcs):
# name used to save the result
func_name = f.__name__
fname = '%s-%s-n%d_r%d_p%g_a%.3f_trp%.2f.p' \
%(prob_label, func_name, sample_size, r, param, alpha,
tr_proportion)
if not is_rerun and glo.ex_file_exists(ex, prob_label, fname):
logger.info('%s exists. Load and return.' % fname)
job_result = glo.ex_load_result(ex, prob_label, fname)
sra = SingleResultAggregator()
sra.submit_result(SingleResult(job_result))
aggregators[r, pi, mi] = sra
else:
# result not exists or rerun
# p: an UnnormalizedDensity object
p = ps[pi]
job = Ex2Job(SingleResultAggregator(), p, data_sources[pi],
prob_label, r, f, param)
agg = engine.submit_job(job)
aggregators[r, pi, mi] = agg
# let the engine finish its business
logger.info("Wait for all call in engine")
engine.wait_for_all()
# ////// collect the results ///////////
logger.info("Collecting results")
job_results = np.empty((reps, len(prob_params), n_methods), dtype=object)
for r in range(reps):
for pi, param in enumerate(prob_params):
for mi, f in enumerate(method_job_funcs):
logger.info("Collecting result (%s, r=%d, param=%.3g)" %
(f.__name__, r, param))
# let the aggregator finalize things
aggregators[r, pi, mi].finalize()
# aggregators[i].get_final_result() returns a SingleResult instance,
# which we need to extract the actual result
job_result = aggregators[r, pi, mi].get_final_result().result
job_results[r, pi, mi] = job_result
#func_names = [f.__name__ for f in method_job_funcs]
#func2labels = exglobal.get_func2label_map()
#method_labels = [func2labels[f] for f in func_names if f in func2labels]
# save results
results = {
'job_results': job_results,
'prob_params': prob_params,
'alpha': alpha,
'repeats': reps,
'ps': ps,
'list_data_source': data_sources,
'tr_proportion': tr_proportion,
'method_job_funcs': method_job_funcs,
'prob_label': prob_label,
'sample_size': sample_size,
}
# class name
fname = 'ex%d-%s-me%d_n%d_rs%d_pmi%g_pma%g_a%.3f_trp%.2f.p' \
%(ex, prob_label, n_methods, sample_size, reps, min(prob_params),
max(prob_params), alpha, tr_proportion)
glo.ex_save_result(ex, results, fname)
logger.info('Saved aggregated results to %s' % fname)
def run_problem(folder_path, prob_label):
"""Run the experiment"""
pl = exglo.parse_prob_label(prob_label)
is_h0 = pl['is_h0']
n = pl['n']
# /////// submit jobs //////////
# create folder name string
#result_folder = glo.result_folder()
#tmp_dir = tempfile.gettempdir()
from fsic.config import expr_configs
tmp_dir = expr_configs['scratch_dir']
foldername = os.path.join(tmp_dir, 'wj_slurm', 'e%d' % ex)
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,
job_name_base="e%d_" % ex,
parameter_prefix="")
# Use the following line if Slurm queue is not used.
#engine = SerialComputationEngine()
engine = SlurmComputationEngine(batch_parameters)
n_methods = len(method_job_funcs)
# repetitions x sample_sizes x #methods
aggregators = np.empty((reps, n_methods), dtype=object)
for r in range(reps):
for mi, f in enumerate(method_job_funcs):
# name used to save the result
func_name = f.__name__
fname = '%s-%s-r%d_a%.3f_trp%.2f.p' \
%(prob_label, func_name, r, alpha, tr_proportion)
if not is_rerun and glo.ex_file_exists(ex, prob_label, fname):
logger.info('%s exists. Load and return.' % fname)
job_result = glo.ex_load_result(ex, prob_label, fname)
sra = SingleResultAggregator()
sra.submit_result(SingleResult(job_result))
aggregators[r, mi] = sra
else:
# result not exists or rerun
job = Ex4Job(SingleResultAggregator(), folder_path, prob_label,
r, f)
agg = engine.submit_job(job)
aggregators[r, mi] = agg
# let the engine finish its business
logger.info("Wait for all call in engine")
engine.wait_for_all()
# ////// collect the results ///////////
logger.info("Collecting results")
job_results = np.empty((reps, n_methods), dtype=object)
for r in range(reps):
for mi, f in enumerate(method_job_funcs):
logger.info("Collecting result (%s, r=%d, n=%d)" %
(f.__name__, r, n))
# let the aggregator finalize things
aggregators[r, mi].finalize()
# aggregators[i].get_final_result() returns a SingleResult instance,
# which we need to extract the actual result
job_result = aggregators[r, mi].get_final_result().result
job_results[r, mi] = job_result
#func_names = [f.__name__ for f in method_job_funcs]
#func2labels = exglobal.get_func2label_map()
#method_labels = [func2labels[f] for f in func_names if f in func2labels]
# save results
# - Do not store PairedSource because it can be very big.
results = {
'job_results': job_results,
'n': n,
'is_h0': is_h0,
'alpha': alpha,
'repeats': reps,
'tr_proportion': tr_proportion,
'method_job_funcs': method_job_funcs,
'prob_label': prob_label,
}
# class name
fname = 'ex%d-%s-me%d_rs%d_a%.3f_trp%.2f.p' \
%(ex, prob_label, n_methods, reps, alpha, tr_proportion)
glo.ex_save_result(ex, results, fname)
logger.info('Saved aggregated results to %s' % fname)
def run_problem(prob_label):
"""Run the experiment"""
# /////// submit jobs //////////
# create folder name string
#result_folder = glo.result_folder()
from kmod.config import expr_configs
tmp_dir = expr_configs['scratch_path']
foldername = os.path.join(tmp_dir, 'kmod_slurm', 'e%d' % ex)
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,
job_name_base="e%d_" % ex,
parameter_prefix="")
# Use the following line if Slurm queue is not used.
#engine = SerialComputationEngine()
partitions = expr_configs['slurm_partitions']
if partitions is None:
engine = SlurmComputationEngine(batch_parameters)
else:
engine = SlurmComputationEngine(batch_parameters, partition=partitions)
n_methods = len(method_funcs)
# problem setting
ns, P, Q, ds, = get_ns_pqrsource(prob_label)
# repetitions x len(ns) x #methods
aggregators = np.empty((reps, len(ns), n_methods), dtype=object)
for r in range(reps):
for ni, n in enumerate(ns):
for mi, f in enumerate(method_funcs):
# name used to save the result
func_name = f.__name__
fname = '%s-%s-n%d_r%d_a%.3f.p' \
%(prob_label, func_name, n, r, alpha,)
if not is_rerun and glo.ex_file_exists(ex, prob_label, fname):
logger.info('%s exists. Load and return.' % fname)
job_result = glo.ex_load_result(ex, prob_label, fname)
sra = SingleResultAggregator()
sra.submit_result(SingleResult(job_result))
aggregators[r, ni, mi] = sra
else:
# result not exists or rerun
job = Ex1Job(SingleResultAggregator(), P, Q, ds,
prob_label, r, f, n)
agg = engine.submit_job(job)
aggregators[r, ni, mi] = agg
# let the engine finish its business
logger.info("Wait for all call in engine")
engine.wait_for_all()
# ////// collect the results ///////////
logger.info("Collecting results")
job_results = np.empty((reps, len(ns), n_methods), dtype=object)
for r in range(reps):
for ni, n in enumerate(ns):
for mi, f in enumerate(method_funcs):
logger.info("Collecting result (%s, r=%d, n=%d)" %
(f.__name__, r, n))
# let the aggregator finalize things
aggregators[r, ni, mi].finalize()
# aggregators[i].get_final_result() returns a SingleResult instance,
# which we need to extract the actual result
job_result = aggregators[r, ni, mi].get_final_result().result
job_results[r, ni, mi] = job_result
#func_names = [f.__name__ for f in method_funcs]
#func2labels = exglobal.get_func2label_map()
#method_labels = [func2labels[f] for f in func_names if f in func2labels]
# save results
results = {
'job_results': job_results,
'P': P,
'Q': Q,
'data_source': ds,
'alpha': alpha,
'repeats': reps,
'ns': ns,
'method_funcs': method_funcs,
'prob_label': prob_label,
}
# class name
fname = 'ex%d-%s-me%d_rs%d_nmi%d_nma%d_a%.3f.p' \
%(ex, prob_label, n_methods, reps, min(ns), max(ns), alpha,)
glo.ex_save_result(ex, results, fname)
logger.info('Saved aggregated results to %s' % fname)
def run_problem(prob_label):
"""Run the experiment"""
ns, p, ds = get_ns_pqsource(prob_label)
# /////// submit jobs //////////
# create folder name string
# result_folder = glo.result_folder()
from sbibm.third_party.kgof.config import expr_configs
tmp_dir = expr_configs["scratch_path"]
foldername = os.path.join(tmp_dir, "kgof_slurm", "e%d" % ex)
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,
job_name_base="e%d_" % ex,
parameter_prefix="")
# Use the following line if Slurm queue is not used.
# engine = SerialComputationEngine()
# engine = SlurmComputationEngine(batch_parameters, partition='wrkstn,compute')
engine = SlurmComputationEngine(batch_parameters)
n_methods = len(method_job_funcs)
# repetitions x len(ns) x #methods
aggregators = np.empty((reps, len(ns), n_methods), dtype=object)
for r in range(reps):
for ni, n in enumerate(ns):
for mi, f in enumerate(method_job_funcs):
# name used to save the result
func_name = f.__name__
fname = "%s-%s-n%d_r%d_a%.3f_trp%.2f.p" % (
prob_label,
func_name,
n,
r,
alpha,
tr_proportion,
)
if not is_rerun and glo.ex_file_exists(ex, prob_label, fname):
logger.info("%s exists. Load and return." % fname)
job_result = glo.ex_load_result(ex, prob_label, fname)
sra = SingleResultAggregator()
sra.submit_result(SingleResult(job_result))
aggregators[r, ni, mi] = sra
else:
# result not exists or rerun
# p: an UnnormalizedDensity object
job = Ex1Job(SingleResultAggregator(), p, ds, prob_label,
r, f, n)
agg = engine.submit_job(job)
aggregators[r, ni, mi] = agg
# let the engine finish its business
logger.info("Wait for all call in engine")
engine.wait_for_all()
# ////// collect the results ///////////
logger.info("Collecting results")
job_results = np.empty((reps, len(ns), n_methods), dtype=object)
for r in range(reps):
for ni, n in enumerate(ns):
for mi, f in enumerate(method_job_funcs):
logger.info("Collecting result (%s, r=%d, n=%rd)" %
(f.__name__, r, n))
# let the aggregator finalize things
aggregators[r, ni, mi].finalize()
# aggregators[i].get_final_result() returns a SingleResult instance,
# which we need to extract the actual result
job_result = aggregators[r, ni, mi].get_final_result().result
job_results[r, ni, mi] = job_result
# func_names = [f.__name__ for f in method_job_funcs]
# func2labels = exglobal.get_func2label_map()
# method_labels = [func2labels[f] for f in func_names if f in func2labels]
# save results
results = {
"job_results": job_results,
"data_source": ds,
"alpha": alpha,
"repeats": reps,
"ns": ns,
"p": p,
"tr_proportion": tr_proportion,
"method_job_funcs": method_job_funcs,
"prob_label": prob_label,
}
# class name
fname = "ex%d-%s-me%d_rs%d_nmi%d_nma%d_a%.3f_trp%.2f.p" % (
ex,
prob_label,
n_methods,
reps,
min(ns),
max(ns),
alpha,
tr_proportion,
)
glo.ex_save_result(ex, results, fname)
logger.info("Saved aggregated results to %s" % fname)
sample_source, n = get_sample_source(prob_label)
# /////// submit jobs //////////
# create folder name string
home = os.path.expanduser("~")
foldername = os.path.join(home, "freqopttest_slurm", 'e%d'%ex)
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, job_name_base="e%d_"%ex, parameter_prefix="")
# Use the following line if Slurm queue is not used.
#engine = SerialComputationEngine()
engine = SlurmComputationEngine(batch_parameters)
n_methods = len(method_job_funcs)
# repetitions x #methods
aggregators = np.empty((reps, n_methods ), dtype=object)
d = sample_source.dim()
for r in range(reps):
for mi, f in enumerate(method_job_funcs):
# name used to save the result
func_name = f.__name__
fname = '%s-%s-J%d_r%d_d%d_a%.3f_trp%.2f.p' \
%(prob_label, func_name, J, r, d, alpha, tr_proportion)
if not is_rerun and glo.ex_file_exists(ex, prob_label, fname):
logger.info('%s exists. Load and return.'%fname)
test_result = glo.ex_load_result(ex, prob_label, fname)
sra = SingleResultAggregator()
def compute(fname_base,
job_generator,
Ds,
Ns,
num_repetitions,
num_steps,
step_size,
max_steps=None,
compute_local=False):
if not FileSystem.cmd_exists("sbatch") or compute_local:
engine = SerialComputationEngine()
else:
johns_slurm_hack = "#SBATCH --partition=intel-ivy,wrkstn,compute"
folder = os.sep + os.sep.join(["nfs", "data3", "ucabhst", fname_base])
batch_parameters = BatchClusterParameters(
foldername=folder,
resubmit_on_timeout=False,
parameter_prefix=johns_slurm_hack)
engine = SlurmComputationEngine(batch_parameters,
check_interval=1,
do_clean_up=True)
engine.max_jobs_in_queue = 1000
engine.store_fire_and_forget = True
# fixed order of aggregators
aggregators = []
for D in Ds:
for N in Ns:
for j in range(num_repetitions):
logger.info("%s trajectory, D=%d/%d, N=%d/%d repetition %d/%d" % \
(str(job_generator), D, np.max(Ds), N, np.max(Ns), j + 1, num_repetitions))
job = job_generator(D, N, N)
aggregators += [engine.submit_job(job)]
time.sleep(0.1)
# block until all done
engine.wait_for_all()
avg_accept = np.zeros((num_repetitions, len(Ds), len(Ns)))
avg_accept_est = np.zeros((num_repetitions, len(Ds), len(Ns)))
log_dets = np.zeros((num_repetitions, len(Ds), len(Ns)))
log_dets_est = np.zeros((num_repetitions, len(Ds), len(Ns)))
avg_steps_taken = np.zeros((num_repetitions, len(Ds), len(Ns)))
agg_counter = 0
for i in range(len(Ds)):
for k in range(len(Ns)):
for j in range(num_repetitions):
agg = aggregators[agg_counter]
agg_counter += 1
agg.finalize()
result = agg.get_final_result()
agg.clean_up()
avg_accept[j, i, k] = result.acc_mean
avg_accept_est[j, i, k] = result.acc_est_mean
log_dets[j, i, k] = result.vol
log_dets_est[j, i, k] = result.vol_est
avg_steps_taken[j, i, k] = result.steps_taken
with open(fname_base + ".csv", 'a+') as f:
line = np.array([
Ds[i],
Ns[k],
avg_accept[j, i, k],
avg_accept_est[j, i, k],
log_dets[j, i, k],
log_dets_est[j, i, k],
avg_steps_taken[j, i, k],
])
f.write(" ".join(map(str, line)) + os.linesep)
