Experiment a bit

Author: zaxtax

pymc/sampling.py

@@ -2032,8 +2032,6 @@ def sample_posterior_predictive_w(
         weighted models (default), or a dictionary with variable names as keys, and samples as
         numpy arrays.
     """
-    raise NotImplementedError(f"sample_posterior_predictive_w has not yet been ported to PyMC 4.0.")
-
     if isinstance(traces[0], InferenceData):
         n_samples = [
             trace.posterior.sizes["chain"] * trace.posterior.sizes["draw"] for trace in traces
@@ -2140,13 +2138,13 @@ def sample_posterior_predictive_w(
             # TODO sample_posterior_predictive_w is currently only work for model with
             # one observed.
             # XXX: This needs to be refactored
-            # ppc[var.name].append(draw_values([var], point=param, size=size[idx])[0])
-            raise NotImplementedError()
+            ppcl[var.name].append(draw([var])[0])
 
     except KeyboardInterrupt:
         pass
     else:
         ppcd = {k: np.asarray(v) for k, v in ppcl.items()}
+        return ppcd
         if not return_inferencedata:
             return ppcd
         ikwargs: Dict[str, Any] = dict(model=models)

pymc/tests/test_sampling.py

@@ -935,9 +935,6 @@ def test_deterministics_out_of_idata(self, multitrace):
         assert np.all(np.abs(ppc.posterior_predictive.c + 4) <= 0.1)
 
 
-@pytest.mark.xfail(
-    reason="sample_posterior_predictive_w not refactored for v4", raises=NotImplementedError
-)
 class TestSamplePPCW(SeededTest):
     def test_sample_posterior_predictive_w(self):
         data0 = np.random.normal(0, 1, size=50)