🔥 remove vars from sample_posterior_predictive, other random refactorings

Author: MarcoGorelli

RELEASE-NOTES.md

@@ -8,7 +8,7 @@ This is the first release to support Python3.9 and to drop Python3.6.
 - Fixed bug whereby partial traces returns after keyboard interrupt during parallel sampling had fewer draws than would've been available [#4318](https://github.com/pymc-devs/pymc3/pull/4318)
 - Make `sample_shape` same across all contexts in `draw_values` (see [#4305](https://github.com/pymc-devs/pymc3/pull/4305)).
 - Removed `theanof.set_theano_config` because it illegally touched Theano's privates (see [#4329](https://github.com/pymc-devs/pymc3/pull/4329)).
-
+- In `sample_posterior_predictive` the `vars` kwarg was removed in favor of `var_names` (see [#4343](https://github.com/pymc-devs/pymc3/pull/4343)).
 
 ## PyMC3 3.10.0 (7 December 2020)
 

pymc3/sampling.py

@@ -33,7 +33,6 @@
 
 from arviz import InferenceData
 from fastprogress.fastprogress import progress_bar
-from theano.tensor import Tensor
 
 import pymc3 as pm
 
@@ -561,12 +560,11 @@ def sample(
             _log.debug("Pickling error:", exec_info=True)
             parallel = False
         except AttributeError as e:
-            if str(e).startswith("AttributeError: Can't pickle"):
-                _log.warning("Could not pickle model, sampling singlethreaded.")
-                _log.debug("Pickling error:", exec_info=True)
-                parallel = False
-            else:
+            if not str(e).startswith("AttributeError: Can't pickle"):
                 raise
+            _log.warning("Could not pickle model, sampling singlethreaded.")
+            _log.debug("Pickling error:", exec_info=True)
+            parallel = False
     if not parallel:
         if has_population_samplers:
             has_demcmc = np.any(
@@ -1602,7 +1600,6 @@ def sample_posterior_predictive(
     trace,
     samples: Optional[int] = None,
     model: Optional[Model] = None,
-    vars: Optional[Iterable[Tensor]] = None,
     var_names: Optional[List[str]] = None,
     size: Optional[int] = None,
     keep_size: Optional[bool] = False,
@@ -1696,14 +1693,9 @@ def sample_posterior_predictive(
     model = modelcontext(model)
 
     if var_names is not None:
-        if vars is not None:
-            raise IncorrectArgumentsError("Should not specify both vars and var_names arguments.")
-        else:
-            vars = [model[x] for x in var_names]
-    elif vars is not None:  # var_names is None, and vars is not.
-        warnings.warn("vars argument is deprecated in favor of var_names.", DeprecationWarning)
-    if vars is None:
-        vars = model.observed_RVs
+        vars_ = [model[x] for x in var_names]
+    else:
+        vars_ = model.observed_RVs
 
     if random_seed is not None:
         np.random.seed(random_seed)
@@ -1729,8 +1721,8 @@ def sample_posterior_predictive(
             else:
                 param = _trace[idx % len_trace]
 
-            values = draw_values(vars, point=param, size=size)
-            for k, v in zip(vars, values):
+            values = draw_values(vars_, point=param, size=size)
+            for k, v in zip(vars_, values):
                 ppc_trace_t.insert(k.name, v, idx)
     except KeyboardInterrupt:
         pass
@@ -1809,7 +1801,7 @@ def sample_posterior_predictive_w(
         raise ValueError("The number of models and weights should be the same")
 
     length_morv = len(models[0].observed_RVs)
-    if not all(len(i.observed_RVs) == length_morv for i in models):
+    if any(len(i.observed_RVs) != length_morv for i in models):
         raise ValueError("The number of observed RVs should be the same for all models")
 
     weights = np.asarray(weights)

pymc3/tests/test_sampling.py

@@ -406,8 +406,7 @@ def test_normal_scalar(self):
             ppc0 = pm.sample_posterior_predictive([model.test_point], samples=10)
             ppc0 = pm.fast_sample_posterior_predictive([model.test_point], samples=10)
             # deprecated argument is not introduced to fast version [2019/08/20:rpg]
-            with pytest.warns(DeprecationWarning):
-                ppc = pm.sample_posterior_predictive(trace, vars=[a])
+            ppc = pm.sample_posterior_predictive(trace, var_names=["a"])
             # test empty ppc
             ppc = pm.sample_posterior_predictive(trace, var_names=[])
             assert len(ppc) == 0
@@ -518,8 +517,6 @@ def test_exceptions(self, caplog):
             # Not for fast_sample_posterior_predictive
             with pytest.raises(IncorrectArgumentsError):
                 ppc = pm.sample_posterior_predictive(trace, size=4, keep_size=True)
-            with pytest.raises(IncorrectArgumentsError):
-                ppc = pm.sample_posterior_predictive(trace, vars=[a], var_names=["a"])
             # test wrong type argument
             bad_trace = {"mu": stats.norm.rvs(size=1000)}
             with pytest.raises(TypeError):
@@ -653,16 +650,7 @@ def test_deterministic_of_observed(self):
 
             trace = pm.sample(100, chains=nchains)
             np.random.seed(0)
-            with pytest.warns(DeprecationWarning):
-                ppc = pm.sample_posterior_predictive(
-                    model=model,
-                    trace=trace,
-                    samples=len(trace) * nchains,
-                    vars=(model.deterministics + model.basic_RVs),
-                )
-
             rtol = 1e-5 if theano.config.floatX == "float64" else 1e-4
-            npt.assert_allclose(ppc["in_1"] + ppc["in_2"], ppc["out"], rtol=rtol)
 
             np.random.seed(0)
             ppc = pm.sample_posterior_predictive(