More robust tune stat fetching and type hints

The mcmc module relies on the `"tune"` stat to figure out the number of
tune/draw iterations post sampling.
These changes remove reliance on any weird squeeze-cat magic.

Author: michaelosthege

pymc/backends/base.py

@@ -58,7 +58,7 @@ class IBaseTrace(ABC, Sized):
     varnames: List[str]
     """Names of tracked variables."""
 
-    sampler_vars: List[Dict[str, type]]
+    sampler_vars: List[Dict[str, Union[type, np.dtype]]]
     """Sampler stats for each sampler."""
 
     def __len__(self):
@@ -79,23 +79,27 @@ def get_values(self, varname: str, burn=0, thin=1) -> np.ndarray:
         """
         raise NotImplementedError()
 
-    def get_sampler_stats(self, stat_name: str, sampler_idx: Optional[int] = None, burn=0, thin=1):
+    def get_sampler_stats(
+        self, stat_name: str, sampler_idx: Optional[int] = None, burn=0, thin=1
+    ) -> np.ndarray:
         """Get sampler statistics from the trace.
 
         Parameters
         ----------
-        stat_name: str
-        sampler_idx: int or None
-        burn: int
-        thin: int
+        stat_name : str
+            Name of the stat to fetch.
+        sampler_idx : int or None
+            Index of the sampler to get the stat from.
+        burn : int
+            Draws to skip from the start.
+        thin : int
+            Stepsize for the slice.
 
         Returns
         -------
-        If the `sampler_idx` is specified, return the statistic with
-        the given name in a numpy array. If it is not specified and there
-        is more than one sampler that provides this statistic, return
-        a numpy array of shape (m, n), where `m` is the number of
-        such samplers, and `n` is the number of samples.
+        stats : np.ndarray
+            If `sampler_idx` was specified, the shape should be `(draws, samplers)`.
+            Otherwise, the shape should be `(draws,)`.
         """
         raise NotImplementedError()
 
@@ -220,23 +224,31 @@ def __getitem__(self, idx):
         except (ValueError, TypeError):  # Passed variable or variable name.
             raise ValueError("Can only index with slice or integer")
 
-    def get_sampler_stats(self, stat_name, sampler_idx=None, burn=0, thin=1):
+    def get_sampler_stats(
+        self, stat_name: str, sampler_idx: Optional[int] = None, burn=0, thin=1
+    ) -> np.ndarray:
         """Get sampler statistics from the trace.
 
+        Note: This implementation attempts to squeeze object arrays into a consistent dtype,
+        #     which can change their shape in hard-to-predict ways.
+        #     See https://github.com/pymc-devs/pymc/issues/6207
+
         Parameters
         ----------
-        stat_name: str
-        sampler_idx: int or None
-        burn: int
-        thin: int
+        stat_name : str
+            Name of the stat to fetch.
+        sampler_idx : int or None
+            Index of the sampler to get the stat from.
+        burn : int
+            Draws to skip from the start.
+        thin : int
+            Stepsize for the slice.
 
         Returns
         -------
-        If the `sampler_idx` is specified, return the statistic with
-        the given name in a numpy array. If it is not specified and there
-        is more than one sampler that provides this statistic, return
-        a numpy array of shape (m, n), where `m` is the number of
-        such samplers, and `n` is the number of samples.
+        stats : np.ndarray
+            If `sampler_idx` was specified, the shape should be `(draws, samplers)`.
+            Otherwise, the shape should be `(draws,)`.
         """
         if sampler_idx is not None:
             return self._get_sampler_stats(stat_name, sampler_idx, burn, thin)
@@ -254,14 +266,16 @@ def get_sampler_stats(self, stat_name, sampler_idx=None, burn=0, thin=1):
 
         if vals.dtype == np.dtype(object):
             try:
-                vals = np.vstack(vals)
+                vals = np.vstack(list(vals))
             except ValueError:
                 # Most likely due to non-identical shapes. Just stick with the object-array.
                 pass
 
         return vals
 
-    def _get_sampler_stats(self, stat_name, sampler_idx, burn, thin):
+    def _get_sampler_stats(
+        self, stat_name: str, sampler_idx: int, burn: int, thin: int
+    ) -> np.ndarray:
         """Get sampler statistics."""
         raise NotImplementedError()
 
@@ -476,23 +490,34 @@ def get_sampler_stats(
         combine: bool = True,
         chains: Optional[Union[int, Sequence[int]]] = None,
         squeeze: bool = True,
-    ):
+    ) -> Union[List[np.ndarray], np.ndarray]:
         """Get sampler statistics from the trace.
 
+        Note: This implementation attempts to squeeze object arrays into a consistent dtype,
+        #     which can change their shape in hard-to-predict ways.
+        #     See https://github.com/pymc-devs/pymc/issues/6207
+
         Parameters
         ----------
-        stat_name: str
-        sampler_idx: int or None
-        burn: int
-        thin: int
+        stat_name : str
+            Name of the stat to fetch.
+        sampler_idx : int or None
+            Index of the sampler to get the stat from.
+        burn : int
+            Draws to skip from the start.
+        thin : int
+            Stepsize for the slice.
+        combine : bool
+            If True, results from `chains` will be concatenated.
+        squeeze : bool
+            Return a single array element if the resulting list of
+            values only has one element. If False, the result will
+            always be a list of arrays, even if `combine` is True.
 
         Returns
         -------
-        If the `sampler_idx` is specified, return the statistic with
-        the given name in a numpy array. If it is not specified and there
-        is more than one sampler that provides this statistic, return
-        a numpy array of shape (m, n), where `m` is the number of
-        such samplers, and `n` is the number of samples.
+        stats : np.ndarray
+            List or ndarray depending on parameters.
         """
         if stat_name not in self.stat_names:
             raise KeyError("Unknown sampler statistic %s" % stat_name)
@@ -543,7 +568,7 @@ def points(self, chains=None):
         return itl.chain.from_iterable(self._straces[chain] for chain in chains)
 
 
-def _squeeze_cat(results, combine, squeeze):
+def _squeeze_cat(results, combine: bool, squeeze: bool):
     """Squeeze and concatenate the results depending on values of
     `combine` and `squeeze`."""
     if combine:

pymc/backends/ndarray.py

@@ -119,7 +119,9 @@ def record(self, point, sampler_stats=None) -> None:
                     data[key][self.draw_idx] = val
         self.draw_idx += 1
 
-    def _get_sampler_stats(self, varname, sampler_idx, burn, thin):
+    def _get_sampler_stats(
+        self, varname: str, sampler_idx: int, burn: int, thin: int
+    ) -> np.ndarray:
         return self._stats[sampler_idx][varname][burn::thin]
 
     def close(self):

pymc/sampling/mcmc.py

@@ -580,10 +580,8 @@ def sample(
     # count the number of tune/draw iterations that happened
     # ideally via the "tune" statistic, but not all samplers record it!
     if "tune" in mtrace.stat_names:
-        stat = mtrace.get_sampler_stats("tune", chains=0)
-        # when CompoundStep is used, the stat is 2 dimensional!
-        if len(stat.shape) == 2:
-            stat = stat[:, 0]
+        # Get the tune stat directly from chain 0, sampler 0
+        stat = mtrace._straces[0].get_sampler_stats("tune", sampler_idx=0)
         stat = tuple(stat)
         n_tune = stat.count(True)
         n_draws = stat.count(False)