def match_convergence_test(v_fun,seed,correct_mean,correct_cov):
mcmc_meta_double = mcmc_sampler_settings_dict(mcmc_id=0, samples_per_chain=2000, num_chains=4, num_cpu=4, thin=1,
tune_l_per_chain=1000,
warmup_per_chain=1100, is_float=False, isstore_to_disk=False,
allow_restart=True, seed=seed)
mcmc_meta_float = mcmc_sampler_settings_dict(mcmc_id=0, samples_per_chain=2000, num_chains=4, num_cpu=4, thin=1,
tune_l_per_chain=1000,
warmup_per_chain=1100, is_float=True, isstore_to_disk=False,
allow_restart=True, seed=seed)
input_dict = {"v_fun": [v_fun], "epsilon": ["dual"], "second_order": [False], "cov": ["adapt"],
"metric_name": ["diag_e"], "dynamic": [True], "windowed": [False],
"criterion": ["gnuts"]}
ep_dual_metadata_argument = {"name": "epsilon", "target": 0.8, "gamma": 0.05, "t_0": 10,
"kappa": 0.75, "obj_fun": "accept_rate", "par_type": "fast"}
dim = len(v_fun(precision_type="torch.DoubleTensor").flattened_tensor)
adapt_cov_arguments = [adapt_cov_default_arguments(par_type="slow", dim=dim)]
dual_args_list = [ep_dual_metadata_argument]
other_arguments = other_default_arguments()
tune_settings_dict = tuning_settings(dual_args_list, [], adapt_cov_arguments, other_arguments)
tune_dict = tuneinput_class(input_dict).singleton_tune_dict()
sampler_double = mcmc_sampler(tune_dict=tune_dict, mcmc_settings_dict=mcmc_meta_double,
tune_settings_dict=tune_settings_dict)
sampler_float = mcmc_sampler(tune_dict=tune_dict, mcmc_settings_dict=mcmc_meta_float,
tune_settings_dict=tune_settings_dict)
sampler_double.start_sampling()
sampler_float.start_sampling()
sampler_double.remove_failed_chains()
sampler_float.remove_failed_chains()
float_samples = sampler_float.get_samples(permuted=False)
double_samples = sampler_double.get_samples(permuted=False)
short_diagnostics_float = check_mean_var_stan(mcmc_samples=float_samples,
correct_mean=correct_mean.astype(numpy.float32),
correct_cov=correct_cov.astype(numpy.float32))
short_diagnostics_double = check_mean_var_stan(mcmc_samples=double_samples,correct_mean=correct_mean,correct_cov=correct_cov)
samples_double_cast_to_float = double_samples.astype(numpy.float32)
# samples_float = output_float["samples"]
# combined_samples = torch.cat([samples_double_cast_to_float,float_samples],dim=0)
combined_samples = numpy.concatenate([samples_double_cast_to_float, float_samples], axis=0)
short_diagnostics_combined = check_mean_var_stan(mcmc_samples=combined_samples,correct_mean=correct_mean.astype(numpy.float32),
correct_cov=correct_cov.astype(numpy.float32))
out = {"diag_combined_mean": short_diagnostics_combined["pc_of_mean"], "diag_float_mean": short_diagnostics_float["pc_of_mean"],
"diag_double_mean": short_diagnostics_double["pc_of_mean"],"diag_combined_cov":short_diagnostics_combined["pc_of_cov"],
"diag_float_cov":short_diagnostics_float["pc_of_cov"],"diag_double_cov":short_diagnostics_double["pc_of_cov"]}
return(out)
dual_args_list = [ep_dual_metadata_argument]
other_arguments = other_default_arguments()
tune_dict = tuneinput_class(input_dict).singleton_tune_dict()
tune_settings_dict = tuning_settings(dual_args_list, [], [], other_arguments)
sampler1 = mcmc_sampler(tune_dict=tune_dict,
mcmc_settings_dict=mcmc_meta,
tune_settings_dict=tune_settings_dict)
store_name = 'standard_sampler.pkl'
sampled = False
if sampled:
sampler1 = pickle.load(open(store_name, 'rb'))
else:
sampler1.start_sampling()
with open(store_name, 'wb') as f:
pickle.dump(sampler1, f)
mcmc_samples1 = sampler1.get_samples(permuted=False)
out = check_mean_var_stan(mcmc_samples=mcmc_samples1,
correct_mean=correct_mean,
correct_cov=correct_cov,
diag_only=False)
mean_check, cov_check = out["mcmc_mean"], out["mcmc_Cov"]
pc_mean, pc_cov = out["pc_of_mean"], out["pc_of_cov"]
print(mean_check)
print(cov_check)
print(pc_mean)
print(pc_cov)
"obj_fun": "accept_rate",
"par_type": "fast"
}
#
adapt_cov_arguments = [
adapt_cov_default_arguments(
par_type="slow",
dim=v_generator(precision_type="torch.DoubleTensor").get_model_dim())
]
dual_args_list = [ep_dual_metadata_argument]
other_arguments = other_default_arguments()
tune_settings_dict = tuning_settings(dual_args_list, [], adapt_cov_arguments,
other_arguments)
tune_dict = tuneinput_class(input_dict).singleton_tune_dict()
sampler1 = mcmc_sampler(tune_dict=tune_dict,
mcmc_settings_dict=mcmc_meta,
tune_settings_dict=tune_settings_dict)
sampler1.start_sampling()
mcmc_samples = sampler1.get_samples(permuted=False)
correct_mean = numpy.zeros(dim)
correct_cov = numpy.linalg.inv(input_data["input"])
check_result = check_mean_var_stan(mcmc_samples=mcmc_samples,
correct_mean=correct_mean,
correct_cov=correct_cov)
print(check_result)
save_name = "check_result_{}.npz".format("mvn_{}".format(100))
numpy.savez(save_name, **check_result)
