def summary(samples, prob=0.9, group_by_chain=True):
"""
Returns a summary table displaying diagnostics of ``samples`` from the
posterior. The diagnostics displayed are mean, standard deviation, median,
the 90% Credibility Interval, :func:`~pyro.ops.stats.effective_sample_size`,
:func:`~pyro.ops.stats.split_gelman_rubin`.
:param dict samples: dictionary of samples keyed by site name.
:param float prob: the probability mass of samples within the credibility interval.
:param bool group_by_chain: If True, each variable in `samples`
will be treated as having shape `num_chains x num_samples x sample_shape`.
Otherwise, the corresponding shape will be `num_samples x sample_shape`
(i.e. without chain dimension).
"""
if not group_by_chain:
samples = {k: v.unsqueeze(0) for k, v in samples.items()}
summary_dict = {}
for name, value in samples.items():
value_flat = torch.reshape(value, (-1,) + value.shape[2:])
mean = value_flat.mean(dim=0)
std = value_flat.std(dim=0)
median = value_flat.median(dim=0)[0]
hpdi = stats.hpdi(value_flat, prob=prob)
n_eff = _safe(stats.effective_sample_size)(value)
r_hat = stats.split_gelman_rubin(value)
hpd_lower = '{:.1f}%'.format(50 * (1 - prob))
hpd_upper = '{:.1f}%'.format(50 * (1 + prob))
summary_dict[name] = OrderedDict([("mean", mean), ("std", std), ("median", median),
(hpd_lower, hpdi[0]), (hpd_upper, hpdi[1]),
("n_eff", n_eff), ("r_hat", r_hat)])
return summary_dict
def summary(samples, prob=0.9, group_by_chain=True):
"""
Prints a summary table displaying diagnostics of ``samples`` from the
posterior. The diagnostics displayed are mean, standard deviation, median,
the 90% Credibility Interval, :func:`~pyro.ops.stats.effective_sample_size`,
:func:`~pyro.ops.stats.split_gelman_rubin`.
:param dict samples: dictionary of samples keyed by site name.
:param float prob: the probability mass of samples within the credibility interval.
:param bool group_by_chain: If True, each variable in `samples`
will be treated as having shape `num_chains x num_samples x sample_shape`.
Otherwise, the corresponding shape will be `num_samples x sample_shape`
(i.e. without chain dimension).
"""
if not group_by_chain:
samples = {k: v.unsqueeze(0) for k, v in samples.items()}
row_names = {
k: k + '[' + ','.join(map(lambda x: str(x - 1), v.shape[2:])) + ']'
for k, v in samples.items()
}
max_len = max(max(map(lambda x: len(x), row_names.values())), 10)
name_format = '{:>' + str(max_len) + '}'
header_format = name_format + ' {:>9} {:>9} {:>9} {:>9} {:>9} {:>9} {:>9}'
columns = [
'', 'mean', 'std', 'median', '{:.1f}%'.format(50 * (1 - prob)),
'{:.1f}%'.format(50 * (1 + prob)), 'n_eff', 'r_hat'
]
print('\n')
print(header_format.format(*columns))
row_format = name_format + ' {:>9.2f} {:>9.2f} {:>9.2f} {:>9.2f} {:>9.2f} {:>9.2f} {:>9.2f}'
for name, value in samples.items():
value_flat = torch.reshape(value, (-1, ) + value.shape[2:])
mean = value_flat.mean(dim=0)
sd = value_flat.std(dim=0)
median = value_flat.median(dim=0)[0]
hpd = stats.hpdi(value_flat, prob=prob)
n_eff = _safe(stats.effective_sample_size)(value)
r_hat = stats.split_gelman_rubin(value)
shape = value_flat.shape[1:]
if len(shape) == 0:
print(
row_format.format(name, mean, sd, median, hpd[0], hpd[1],
n_eff, r_hat))
else:
for idx in product(*map(range, shape)):
idx_str = '[{}]'.format(','.join(map(str, idx)))
print(
row_format.format(name + idx_str, mean[idx], sd[idx],
median[idx], hpd[0][idx], hpd[1][idx],
n_eff[idx], r_hat[idx]))
print('\n')
def diagnostics(self):
if self._diagnostics:
return self._diagnostics
for site in self.sites:
site_stats = OrderedDict()
try:
site_stats["n_eff"] = stats.effective_sample_size(
self.support()[site])
except NotImplementedError:
site_stats["n_eff"] = torch.tensor(float('nan'))
site_stats["r_hat"] = stats.split_gelman_rubin(
self.support()[site])
self._diagnostics[site] = site_stats
return self._diagnostics
def diagnostics(samples, group_by_chain=True):
"""
Gets diagnostics statistics such as effective sample size and
split Gelman-Rubin using the samples drawn from the posterior
distribution.
:param dict samples: dictionary of samples keyed by site name.
:param bool group_by_chain: If True, each variable in `samples`
will be treated as having shape `num_chains x num_samples x sample_shape`.
Otherwise, the corresponding shape will be `num_samples x sample_shape`
(i.e. without chain dimension).
:return: dictionary of diagnostic stats for each sample site.
"""
diagnostics = {}
for site, support in samples.items():
if not group_by_chain:
support = support.unsqueeze(0)
site_stats = OrderedDict()
site_stats["n_eff"] = _safe(stats.effective_sample_size)(support)
site_stats["r_hat"] = stats.split_gelman_rubin(support)
diagnostics[site] = site_stats
return diagnostics
def test_split_gelman_rubin_agree_with_gelman_rubin():
x = torch.rand(2, 10)
r_hat1 = gelman_rubin(x.reshape(2, 2, 5).reshape(4, 5))
r_hat2 = split_gelman_rubin(x)
assert_equal(r_hat1, r_hat2)
