def plot_comparison(samplers, params, distribution):
""" Plot a comparison between samplers and params
:param samplers: list of samplers to test
:param params: respective list of parameters for each sampler
:param distribution: distribution to compare on
:returns: None
:rtype: None
"""
for sampler, hparams in zip(samplers, params):
_, n_grad_evals, _, autocor, _ = obj_func_helper(
sampler, distribution.reset(), False, hparams)
plt.plot(n_grad_evals, autocor,
label="B: {}, eps: {}, M: {}".format(hparams['beta'],
hparams['epsilon'],
hparams['num_leapfrog_steps']))
plt.legend()
plt.savefig('comparison.pdf')
def plot_all_best(custom_params=None):
""" Creates a plot with the autocorrelation and fit for each distribution and sampler
:param custom_params: dictionary of custom params will be used on all distributions
and samplers. if None uses the current best params for each
:returns: None
:rtype: None
"""
distributions = [
RoughWell(nbatch=200)
# Gaussian(ndims=10, nbatch=200),
# MultimodalGaussian(ndims=5, separation=1)
]
samplers = [
ControlHMC,
MarkovJumpHMC
]
with PdfPages("validation.pdf") as pdf:
for distribution in distributions:
# [control, mjhmc, lahmc]
if custom_params is None:
params = load_params(distribution)
else:
params = [custom_params] * 3
active_params = params[:-1]
for sampler, hparams in zip(samplers, active_params):
print "Now running for {} on {}".format(sampler, distribution)
cos_coef, n_grad_evals, exp_coef, autocor, _ = obj_func_helper(
sampler, distribution.reset(), False, hparams)
fig = plot_fit(n_grad_evals,
autocor,
exp_coef,
cos_coef,
'validation',
hparams,
save=False
)
pdf.savefig(fig)