Fix recognition of tuning stat in .to_inferencedata() method

mcbackend/__init__.py

@@ -12,7 +12,7 @@
 except ModuleNotFoundError:
     pass
 
-__version__ = "0.5.1"
+__version__ = "0.5.2"
 __all__ = [
     "NumPyBackend",
     "Backend",

mcbackend/core.py

@@ -23,6 +23,9 @@
 _log = logging.getLogger(__file__)
 
 
+__all__ = ("is_rigid", "chain_id", "Chain", "Run", "Backend")
+
+
 def is_rigid(nshape: Optional[Shape]):
     """Determines wheather the shape is constant.
 
@@ -133,6 +136,20 @@ def sample_stats(self) -> Dict[str, Variable]:
         return {var.name: var for var in self.rmeta.sample_stats}
 
 
+def get_tune_mask(chain: Chain, slc: slice = slice(None)) -> numpy.ndarray:
+    """Load the tuning mask from either a ``"tune"``, or a ``"*__tune"`` stat.
+
+    Raises
+    ------
+    KeyError
+        When no matching stat is found.
+    """
+    for sname in chain.sample_stats:
+        if sname.endswith("__tune") or sname == "tune":
+            return chain.get_stats(sname, slc).astype(bool)
+    raise KeyError("No tune stat found.")
+
+
 class Run:
     """A handle on one MCMC run."""
 
@@ -231,14 +248,15 @@ def to_inferencedata(self, *, equalize_chain_lengths: bool = True, **kwargs) ->
                 slc = slice(0, min_clen)
 
             # Obtain a mask by which draws can be split into warmup/posterior
-            if "tune" in chain.sample_stats:
-                tune = chain.get_stats("tune", slc).astype(bool)
-            else:
+            try:
+                # Use the same slice to avoid shape issues in case the chain is still active
+                tune = get_tune_mask(chain, slc)
+            except KeyError:
                 if c == 0:
                     _log.warning(
                         "No 'tune' stat found. Assuming all iterations are posterior draws."
                     )
-                tune = numpy.repeat((chain_lengths[chain.cid],), False)
+                tune = numpy.full((slc.stop,), False)
 
             # Split all variables draws into warmup/posterior
             for var in variables:

mcbackend/test_utils.py

@@ -277,11 +277,12 @@ def test__get_chains(self):
             assert len(chain) == 1
         pass
 
-    def test__to_inferencedata(self):
+    @pytest.mark.parametrize("tstatname", ["tune", "sampler__tune", "nottune"])
+    def test__to_inferencedata(self, tstatname, caplog):
         rmeta = make_runmeta(
             flexibility=False,
             sample_stats=[
-                Variable("tune", "bool"),
+                Variable(tstatname, "bool"),
                 Variable("sampler_0__logp", "float32"),
                 Variable("warning", "str"),
             ],
@@ -294,15 +295,22 @@ def test__to_inferencedata(self):
         draws = [make_draw(rmeta.variables) for _ in range(n)]
         stats = [make_draw(rmeta.sample_stats) for _ in range(n)]
         for i, (d, s) in enumerate(zip(draws, stats)):
-            s["tune"] = i < 4
+            s[tstatname] = i < 4
             chain.append(d, s)
 
         idata = run.to_inferencedata()
         assert isinstance(idata, arviz.InferenceData)
         assert idata.warmup_posterior.dims["chain"] == 1
-        assert idata.warmup_posterior.dims["draw"] == 4
         assert idata.posterior.dims["chain"] == 1
-        assert idata.posterior.dims["draw"] == 6
+        if tstatname == "nottune":
+            # Splitting into warmup/posterior requires a tune stat!
+            assert any("No 'tune' stat" in r.message for r in caplog.records)
+            assert idata.warmup_posterior.dims["draw"] == 0
+            assert idata.posterior.dims["draw"] == 10
+        else:
+            assert idata.warmup_posterior.dims["draw"] == 4
+            assert idata.posterior.dims["draw"] == 6
+
         for var in rmeta.variables:
             assert var.name in set(idata.posterior.keys())
         for svar in rmeta.sample_stats: