Merge pull request #3106 from eigenfoo/fix_div_warning

Fix grammar in divergence warning; refactor code

Author: fonnesbeck

RELEASE-NOTES.md

@@ -31,6 +31,7 @@
 
 ### Fixes
 
+- Fixed grammar in divergence warning, previously `There were 1 divergences ...` could be raised.
 - Fixed `KeyError` raised when only subset of variables are specified to be recorded in the trace.
 - Removed unused `repeat=None` arguments from all `random()` methods in distributions.
 - Deprecated the `sigma` argument in `MarginalSparse.marginal_likelihood` in favor of `noise`

pymc3/step_methods/hmc/base_hmc.py

@@ -169,17 +169,19 @@ def warnings(self):
         warnings = self._warnings[:]
 
         # Generate a global warning for divergences
+        message = ''
         n_divs = self._num_divs_sample
         if n_divs and self._samples_after_tune == n_divs:
-            msg = ('The chain contains only diverging samples. The model is '
-                   'probably misspecified.')
-            warning = SamplerWarning(
-                WarningType.DIVERGENCES, msg, 'error', None, None, None)
-            warnings.append(warning)
-        elif n_divs > 0:
+            message = ('The chain contains only diverging samples. The model '
+                       'is probably misspecified.')
+        elif n_divs == 1:
+            message = ('There was 1 divergence after tuning. Increase '
+                       '`target_accept` or reparameterize.')
+        elif n_divs > 1:
             message = ('There were %s divergences after tuning. Increase '
-                       '`target_accept` or reparameterize.'
-                       % n_divs)
+                       '`target_accept` or reparameterize.' % n_divs)
+
+        if message:
             warning = SamplerWarning(
                 WarningType.DIVERGENCES, message, 'error', None, None, None)
             warnings.append(warning)

pymc3/tests/test_step.py

@@ -446,6 +446,9 @@ def test_linalg(self, caplog):
             warns = [msg.msg for msg in caplog.records]
             assert np.any(trace['diverging'])
             assert (
+                any('divergence after tuning' in warn
+                    for warn in warns)
+                or
                 any('divergences after tuning' in warn
                     for warn in warns)
                 or