parameters_list=ld_parameters,
parameters_times=ld_time,
metric_functions = metric_functions,
sample_functions = sample_functions,
)
ld_evaluator.evaluate(40, tqdm=tqdm)
# Plot Traces, Metrics, and Compare
from sgmcmc_ssm.plotting_utils import (
plot_trace_plot,
plot_metrics,
compare_metrics,
)
plot_trace_plot(sgld_evaluator)
plot_metrics(sgld_evaluator)
plot_trace_plot(ld_evaluator)
plot_metrics(ld_evaluator)
compare_metrics(dict(
SGLD=sgld_evaluator,
LD=ld_evaluator,
),
x='time',
)
#
sampler,
parameters_list=half_average_parameters_list(sgrld_parameters),
parameters_times=sgrld_time,
metric_functions=metric_functions,
sample_functions=sample_functions,
)
sgrld_evaluator.evaluate(16, tqdm=tqdm)
# Plot Traces, Metrics, and Compare
from sgmcmc_ssm.plotting_utils import (
plot_trace_plot,
plot_metrics,
compare_metrics,
)
plot_trace_plot(gibbs_evaluator)
plot_metrics(gibbs_evaluator)
plot_trace_plot(sgrld_evaluator)
plot_metrics(sgrld_evaluator)
compare_metrics(
dict(
Gibbs=gibbs_evaluator,
SGRLD=sgrld_evaluator,
),
x='time',
)
# EOF
keys = my_evaluators.keys()
for step in tqdm(range(1000)):
for ii, key in enumerate(keys):
my_evaluators[key].evaluate_sampler_step(
*sampler_steps[key.split("_")[0]])
if (step % 25) == 0:
logging.info("============= CHECKPOINT ================")
if not os.path.isdir(path_to_save):
os.makedirs(path_to_save)
joblib.dump(
{
key: evaluator.get_state()
for key, evaluator in my_evaluators.items()
}, os.path.join(path_to_save, "slds_demo.p"))
g = compare_metrics(my_evaluators)
g.savefig(os.path.join(path_to_save, "metrics_compare.png"))
if step > 50:
g = compare_metrics(my_evaluators, full_trace=False)
g.savefig(os.path.join(path_to_save, "metrics_compare_zoom.png"))
for key in my_evaluators.keys():
sampler = my_evaluators[key].sampler
fig, axes = plot_trace_plot(
my_evaluators[key],
single_variables=['C', 'LRinv', 'R', 'Rinv'])
fig.suptitle(key)
fig.savefig(os.path.join(path_to_save,
"{0}_trace.png".format(key)))
plt.close('all')
evaluators = {}
from sgmcmc_ssm.evaluator import half_average_parameters_list
evaluators['Gibbs'] = OfflineEvaluator(
sampler,
parameters_list=half_average_parameters_list(gibbs_parameters),
parameters_times=gibbs_time,
metric_functions=metric_functions,
sample_functions=sample_functions,
)
evaluators['SGRLD No Buffer'] = OfflineEvaluator(
sampler,
parameters_list=half_average_parameters_list(nobuffer_parameters),
parameters_times=nobuffer_time,
metric_functions=metric_functions,
sample_functions=sample_functions,
)
evaluators['SGRLD Buffer'] = OfflineEvaluator(
sampler,
parameters_list=half_average_parameters_list(buffer_parameters),
parameters_times=buffer_time,
metric_functions=metric_functions,
sample_functions=sample_functions,
)
for evaluator in tqdm(evaluators.values()):
evaluator.evaluate(40, tqdm=tqdm)
# Plot Results
from sgmcmc_ssm.plotting_utils import compare_metrics
compare_metrics(evaluators, x='time')