def to_kanga(self, keys=None):
keys = set(keys or self.vals.keys()) & set(
['sample', 'target_val', 'grad_val', 'accepted'])
vals = {}
for key in keys:
if key == 'sample':
vals[key] = self.get_samples().detach().cpu().numpy()
elif key == 'target_val':
vals[key] = self.get_target_vals().detach().cpu().numpy()
elif key == 'grad_val':
vals[key] = self.get_grad_vals().detach().cpu().numpy()
elif key == 'accepted':
vals[key] = np.array(self.vals['accepted'])
return ChainArray(vals)
# %% Import packages
import kanga.plots as ps
from kanga.chains import ChainArray
from bnn_mcmc_examples.examples.mlp.pima.setting2.constants import diagnostic_iter_thres
from bnn_mcmc_examples.examples.mlp.pima.setting2.metropolis_hastings.constants import sampler_output_pilot_path
from bnn_mcmc_examples.examples.mlp.pima.setting2.model import model
# %% Load chain array
chain_array = ChainArray.from_file(keys=['sample', 'accepted'],
path=sampler_output_pilot_path)
# %% Drop burn-in samples
chain_array.vals['sample'] = chain_array.vals['sample'][
diagnostic_iter_thres:, :]
chain_array.vals['accepted'] = chain_array.vals['accepted'][
diagnostic_iter_thres:]
# %% Plot traces of simulated chain
for i in range(model.num_params()):
ps.trace(chain_array.get_param(i),
title=r'Traceplot of $\theta_{{{}}}$'.format(i + 1),
xlabel='Iteration',
ylabel='Parameter value')
# %% Plot running means of simulated chain