def mcmc_stats(runs, burnin, prob, batch):
"""
入力
runs: モンテカルロ標本
burnin: バーンインの回数
prob: 区間確率 (0 < prob < 1)
batch: 乱数系列の分割数
出力
事後統計量のデータフレーム
"""
traces = runs[burnin:, :]
n = traces.shape[0] // batch
k = traces.shape[1]
alpha = 100 * (1.0 - prob)
post_mean = np.mean(traces, axis=0)
post_median = np.median(traces, axis=0)
post_sd = np.std(traces, axis=0)
mc_err = [pm.mcse(traces[:, i].reshape((n, batch), order='F')).item(0) \
for i in range(k)]
ci_lower = np.percentile(traces, 0.5 * alpha, axis=0)
ci_upper = np.percentile(traces, 100 - 0.5 * alpha, axis=0)
hpdi = pm.hpd(traces, 1.0 - prob)
rhat = [pm.rhat(traces[:, i].reshape((n, batch), order='F')).item(0) \
for i in range(k)]
stats = np.vstack((post_mean, post_median, post_sd, mc_err, ci_lower,
ci_upper, hpdi.T, rhat)).T
stats_string = [
'平均', '中央値', '標準偏差', '近似誤差', '信用区間（下限）', '信用区間（上限）', 'HPDI（下限）',
'HPDI（上限）', '$\\hat R$'
]
param_string = ['$\\beta_{0:<d}$'.format(i + 1) for i in range(k - 1)]
param_string.append('$\\sigma^2$')
return pd.DataFrame(stats, index=param_string, columns=stats_string)
def test_Rhat(self):
rhat = pm.rhat(self.trace[self.burn :])
for var in rhat:
npt.assert_allclose(rhat[var], 1, rtol=0.01)
def _run_convergence_checks(self, trace, model):
if trace.nchains == 1:
msg = ("Only one chain was sampled, this makes it impossible to "
"run some convergence checks")
warn = SamplerWarning(WarningType.BAD_PARAMS, msg, 'info', None,
None, None)
self._add_warnings([warn])
return
from pymc3 import rhat, ess
valid_name = [rv.name for rv in model.free_RVs + model.deterministics]
varnames = []
for rv in model.free_RVs:
rv_name = rv.name
if is_transformed_name(rv_name):
rv_name2 = get_untransformed_name(rv_name)
rv_name = rv_name2 if rv_name2 in valid_name else rv_name
if rv_name in trace.varnames:
varnames.append(rv_name)
self._ess = ess = ess(trace, var_names=varnames)
self._rhat = rhat = rhat(trace, var_names=varnames)
warnings = []
rhat_max = max(val.max() for val in rhat.values())
if rhat_max > 1.4:
msg = ("The rhat statistic is larger than 1.4 for some "
"parameters. The sampler did not converge.")
warn = SamplerWarning(WarningType.CONVERGENCE, msg, 'error', None,
None, rhat)
warnings.append(warn)
elif rhat_max > 1.2:
msg = ("The rhat statistic is larger than 1.2 for some "
"parameters.")
warn = SamplerWarning(WarningType.CONVERGENCE, msg, 'warn', None,
None, rhat)
warnings.append(warn)
elif rhat_max > 1.05:
msg = ("The rhat statistic is larger than 1.05 for some "
"parameters. This indicates slight problems during "
"sampling.")
warn = SamplerWarning(WarningType.CONVERGENCE, msg, 'info', None,
None, rhat)
warnings.append(warn)
eff_min = min(val.min() for val in ess.values())
n_samples = len(trace) * trace.nchains
if eff_min < 200 and n_samples >= 500:
msg = ("The estimated number of effective samples is smaller than "
"200 for some parameters.")
warn = SamplerWarning(WarningType.CONVERGENCE, msg, 'error', None,
None, ess)
warnings.append(warn)
elif eff_min / n_samples < 0.1:
msg = ("The number of effective samples is smaller than "
"10% for some parameters.")
warn = SamplerWarning(WarningType.CONVERGENCE, msg, 'warn', None,
None, ess)
warnings.append(warn)
elif eff_min / n_samples < 0.25:
msg = ("The number of effective samples is smaller than "
"25% for some parameters.")
warn = SamplerWarning(WarningType.CONVERGENCE, msg, 'info', None,
None, ess)
warnings.append(warn)
self._add_warnings(warnings)