tqdm=tqdm, tqdm_iter=True,
)
print(sampler.noisy_logjoint(kind='pf', pf='paris', N=1000,
return_loglike=True, tqdm=tqdm))
###############################################################################
# Evaluate Fit
###############################################################################
from sgmcmc_ssm.evaluator import OfflineEvaluator, half_average_parameters_list
from sgmcmc_ssm.metric_functions import (
sample_function_parameters,
noisy_logjoint_loglike_metric,
)
# Evaluate Loglikelihood on Training Set
metric_functions=[
noisy_logjoint_loglike_metric(tqdm=tqdm, kind='pf', pf='paris', N=1000),
]
sample_functions=sample_function_parameters(['A', 'Q', 'R'])
# Evaluate SGLD samples
sgld_evaluator = OfflineEvaluator(sampler,
parameters_list=sgld_parameters,
parameters_times=sgld_time,
metric_functions = metric_functions,
sample_functions = sample_functions,
)
sgld_evaluator.evaluate(40, tqdm=tqdm)
ld_evaluator = OfflineEvaluator(sampler,
parameters_list=ld_parameters,
parameters_times=ld_time,
sampler.parameters = sampler.prior.sample_prior()
print(sampler.parameters)
for _ in range(5):
print(sampler.sample_sgrld(epsilon=0.1, preconditioner=preconditioner).project_parameters())
## Using Evaluator
from sgmcmc_ssm import SamplerEvaluator
from sgmcmc_ssm.metric_functions import (
sample_function_parameters,
noisy_logjoint_loglike_metric,
metric_function_parameters,
)
metric_functions = [
noisy_logjoint_loglike_metric(),
metric_function_parameters(
parameter_names=['A', 'Q', 'C', 'R'],
target_values=[parameters.A, parameters.Q,
parameters.C, parameters.R],
metric_names = ['mse', 'mse', 'mse', 'mse'],
)
]
sample_functions = sample_function_parameters(
['A', 'Q', 'LQinv', 'C', 'R', 'LRinv'],
)
sampler = SLDSSampler(**parameters.dim)
sampler.setup(data['observations'], prior)
sampler.init_sample_latent() ## THIS IS IMPORTANT
for _ in range(5):
print(sampler.sample_sgld(kind='pf', N=1000,
epsilon=0.1, subsequence_length=10, buffer_length=5
).project_parameters())
## Using Evaluator
from sgmcmc_ssm import SamplerEvaluator
from sgmcmc_ssm.metric_functions import (
sample_function_parameters,
noisy_logjoint_loglike_metric,
metric_function_parameters,
)
metric_functions = [
noisy_logjoint_loglike_metric(kind='pf', N=1000),
metric_function_parameters(
parameter_names=['A', 'LQinv', 'LRinv'],
target_values=[parameters.A, parameters.LQinv, parameters.LRinv],
metric_names = ['mse', 'mse', 'mse'],
)
]
sample_functions = sample_function_parameters(
['A', 'Q', 'LQinv', 'R', 'LRinv'],
)
sampler = SVMSampler(**parameters.dim)
sampler.setup(data['observations'], prior)
evaluator = SamplerEvaluator(
sampler=sampler,
for parameter_name in parameter_names2
],
metric_names=['mse' for parameter_name in parameter_names2],
criteria=[min for parameter_name in parameter_names2],
),
metric_function_parameters(
parameter_names3,
target_values=[
getattr(my_data['parameters'], parameter_name)
for parameter_name in parameter_names3
],
metric_names=['mse' for parameter_name in parameter_names2],
),
# metric_compare_x(my_data['latent_vars']['x']),
# metric_compare_z(my_data['latent_vars']['z']),
noisy_logjoint_loglike_metric(subsequence_length=50, buffer_length=10),
]
my_sample_functions = [
sample_function_parameters(parameter_names2 + ['LRinv', 'LQinv']),
]
my_evaluators = {
"{0}_{1}".format(*key):
SamplerEvaluator(sampler,
my_metric_functions,
my_sample_functions,
sampler_name="{0}_{1}".format(*key))
for key, sampler in my_samplers.items()
}
keys = my_evaluators.keys()
for step in tqdm(range(1000)):
parameters_list = sampler.fit(
num_iters=1000,
iter_type='SGRLD',
epsilon=0.1,
subsequence_length=16,
buffer_length=4,
tqdm=tqdm,
output_all=True,
)
## Specify Metric Functions
metric_functions = []
### Loglikelihood and Logjoint
from sgmcmc_ssm.metric_functions import noisy_logjoint_loglike_metric
metric_functions += [noisy_logjoint_loglike_metric()]
### log10 MSE at recovering X
from sgmcmc_ssm.metric_functions import metric_compare_x
metric_functions += [metric_compare_x(data['latent_vars'])]
### log10 MSE at recovering parameters
from sgmcmc_ssm.metric_functions import metric_function_parameters
metric_functions += [
metric_function_parameters(
parameter_names=['A', 'Q', 'R'],
target_values=[parameters.A, parameters.Q, parameters.R],
metric_names=['logmse', 'logmse', 'logmse'],
)
]
fig.suptitle('Final SGRLD Fit')
################################################################################
# Sampler Evaluation
################################################################################
from sgmcmc_ssm.evaluator import OfflineEvaluator, half_average_parameters_list
from sgmcmc_ssm.metric_functions import (
sample_function_parameters,
noisy_logjoint_loglike_metric,
noisy_predictive_logjoint_loglike_metric,
)
# Evaluate Loglikelihood on Training and Predictive Loglikelihood on Test
metric_functions=[
noisy_logjoint_loglike_metric(tqdm=tqdm, observations=Y),
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 Gibbs samples
gibbs_evaluator = OfflineEvaluator(sampler,
parameters_list=half_average_parameters_list(gibbs_parameters),
parameters_times=gibbs_time,
metric_functions = metric_functions,
sample_functions = sample_functions,
)
gibbs_evaluator.evaluate(16, tqdm=tqdm)