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.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'])
# 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,
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
metric_functions = [
noisy_predictive_logjoint_loglike_metric(num_steps_ahead=3,
observations=Y_test,
tqdm=tqdm),
]
sample_functions = sample_function_parameters(['pi', 'logit_pi', 'mu', 'R'])
# Evaluate SGRLD samples
sgrld_evaluator = OfflineEvaluator(
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(sgrld_evaluator)
plot_metrics(sgrld_evaluator)
plot_metrics(sgrld_evaluator, x='time')
# EOF