print(evaluator.samples)
## Run a few ADA_GRAD sampler steps
for _ in range(10):
evaluator.evaluate_sampler_step(
['step_adagrad', 'project_parameters'],
[dict(epsilon=0.1, subsequence_length=10, buffer_length=5), {}],
)
print(evaluator.metrics)
print(evaluator.samples)
## Run a few SGRLD Steps
for _ in range(10):
evaluator.evaluate_sampler_step(
['sample_sgrld', 'project_parameters'],
[dict(preconditioner=preconditioner,
epsilon=0.1, subsequence_length=10, buffer_length=5), {}],
)
print(evaluator.metrics)
print(evaluator.samples)
from sgmcmc_ssm.plotting_utils import plot_metrics, plot_trace_plot
plot_metrics(evaluator)
plot_trace_plot(evaluator, single_variables=['C', 'LRinv', 'R', 'Rinv'])
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',
)
#
print(evaluator.metrics)
print(evaluator.samples)
## Run a few ADA_GRAD sampler steps
for _ in tqdm(range(100)):
evaluator.evaluate_sampler_step(
['step_adagrad', 'project_parameters'],
[dict(kind='pf', N=1000,
epsilon=0.1, subsequence_length=10, buffer_length=5), {}],
)
print(evaluator.metrics)
print(evaluator.samples)
from sgmcmc_ssm.plotting_utils import plot_metrics, plot_trace_plot
plot_metrics(evaluator)
plot_trace_plot(evaluator)
### Run a few SGLD Steps
#for _ in range(10):
# evaluator.evaluate_sampler_step(
# ['sample_sgld', 'project_parameters'],
# [dict(kind='pf', N=1000,
# epsilon=0.1, subsequence_length=10, buffer_length=5), {}],
# )
#print(evaluator.metrics)
#print(evaluator.samples)
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')
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
# Offline Evaluation
from sgmcmc_ssm.evaluator import OfflineEvaluator
evaluator = OfflineEvaluator(
sampler=sampler,
parameters_list=parameters_list,
metric_functions=metric_functions,
sample_functions=sample_functions,
)
evaluator.evaluate(num_to_eval=40, tqdm=tqdm)
print(evaluator.get_metrics())
print(evaluator.get_samples())
# Plot Results
from sgmcmc_ssm.plotting_utils import plot_metrics, plot_trace_plot
plot_metrics(evaluator, burnin=10)
plot_trace_plot(evaluator, burnin=10)
###############################################################################
# Compare Multiple Inference Methods
###############################################################################
init = sampler.prior_init()
sampler = LGSSMSampler(n=2, m=2, observations=data['observations'])
max_time = 60
## Fit Gibbs saving sample every second
gibbs_parameters, gibbs_time = sampler.fit_timed(
iter_type='Gibbs',
init_parameters=init,
max_time=max_time,
min_save_time=1,
tqdm=tqdm,
}
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, "lgssm_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])
fig.suptitle(key)
fig.savefig(os.path.join(path_to_save,
"{0}_trace.png".format(key)))
plt.close('all')
