add diverging to extra_fields by default (#433)

README.md

@@ -55,7 +55,7 @@ Let us infer the values of the unknown parameters in our model by running MCMC u
 We can print the summary of the MCMC run, and examine if we observed any divergences during inference:
 
 ```python
-mcmc.print_summary()
+>>> mcmc.print_summary()
 
                 mean       std    median      5.0%     95.0%     n_eff     r_hat
         mu      3.94      2.81      3.16      0.03      9.28    114.51      1.06
@@ -69,8 +69,6 @@ mcmc.print_summary()
   theta[6]      5.74      4.67      4.34     -1.92     13.25     58.42      1.05
   theta[7]      4.29      4.63      3.23     -2.14     12.37    342.50      1.02
 
->>> print("Number of divergences: {}".format(sum(mcmc.get_extra_fields()['diverging'])))
-
 Number of divergences: 139
 ```
 
@@ -104,8 +102,6 @@ The values above 1 for the split Gelman Rubin diagnostic (`r_hat`) indicates tha
   theta[5]      3.92      4.43      4.06     -2.41     11.09   1179.74      1.00
   theta[6]      5.88      4.84      5.34     -1.45     13.11    881.38      1.00
   theta[7]      4.63      4.86      4.64     -3.57     11.80   1065.27      1.00
-
->>> print("Number of divergences: {}".format(sum(mcmc.get_extra_fields()['diverging'])))
 
 Number of divergences: 0
 ```

docs/source/diagnostics.rst

@@ -29,4 +29,4 @@ HPDI
 
 Summary
 -------
-.. autofunction:: numpyro.diagnostics.summary
+.. autofunction:: numpyro.diagnostics.print_summary

examples/neutra.py

@@ -14,7 +14,7 @@
 import numpyro
 from numpyro import optim
 from numpyro.contrib.autoguide import AutoContinuousELBO, AutoIAFNormal
-from numpyro.diagnostics import summary
+from numpyro.diagnostics import print_summary
 import numpyro.distributions as dist
 from numpyro.distributions import constraints
 from numpyro.infer import MCMC, NUTS, SVI
@@ -90,7 +90,7 @@ def main(args):
     zs = mcmc.get_samples()
     print("Transform samples into unwarped space...")
     samples = vmap(transformed_constrain_fn)(zs)
-    summary(tree_map(lambda x: x[None, ...], samples))
+    print_summary(tree_map(lambda x: x[None, ...], samples))
     samples = samples['x'].copy()
 
     # make plots

numpyro/diagnostics.py

@@ -16,7 +16,7 @@
     'gelman_rubin',
     'hpdi',
     'split_gelman_rubin',
-    'summary',
+    'print_summary',
 ]
 
 
@@ -212,7 +212,7 @@ def hpdi(x, prob=0.90, axis=0):
     return onp.concatenate([hpd_left, hpd_right], axis=axis)
 
 
-def summary(samples, prob=0.90, group_by_chain=True):
+def print_summary(samples, prob=0.90, group_by_chain=True):
     """
     Prints a summary table displaying diagnostics of ``samples`` from the
     posterior. The diagnostics displayed are mean, standard deviation, median,
@@ -241,7 +241,7 @@ def summary(samples, prob=0.90, group_by_chain=True):
     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()
     print(header_format.format(*columns))
 
     # XXX: consider to expose digits, depending on user requests
@@ -264,4 +264,4 @@ def summary(samples, prob=0.90, group_by_chain=True):
                 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')
+    print()

numpyro/infer/mcmc.py

@@ -13,7 +13,7 @@
 from jax.random import PRNGKey
 from jax.tree_util import tree_flatten, tree_map, tree_multimap
 
-from numpyro.diagnostics import summary
+from numpyro.diagnostics import print_summary
 from numpyro.infer.hmc_util import (
     IntegratorState,
     build_tree,
@@ -598,10 +598,10 @@ def _single_chain_mcmc(self, init, collect_fields=('z',), collect_warmup=False,
         if len(collect_fields) == 1:
             states = (states,)
         states = dict(zip(collect_fields, states))
-        states['z'] = vmap(constrain_fn)(states['z']) if len(tree_flatten(states)[0]) > 0 else states['z']
+        states['z'] = vmap(constrain_fn)(states['z']) if len(tree_flatten(states['z'])[0]) > 0 else states['z']
         return states
 
-    def run(self, rng_key, *args, extra_fields=(), collect_warmup=False, init_params=None, **kwargs):
+    def run(self, rng_key, *args, extra_fields=('diverging',), collect_warmup=False, init_params=None, **kwargs):
         """
         Run the MCMC samplers and collect samples.
 
@@ -693,4 +693,7 @@ def get_extra_fields(self, group_by_chain=False):
         return {k: v for k, v in states.items() if k != 'z'}
 
     def print_summary(self, prob=0.9):
-        summary(self._states['z'], prob=prob)
+        print_summary(self._states['z'], prob=prob)
+        extra_fields = self.get_extra_fields()
+        if 'diverging' in extra_fields:
+            print("Number of divergences: {}".format(np.sum(extra_fields['diverging'])))