Merge pull request #3841 from rpgoldman/iss3840

Fix computation of samples argument in sample_posterior_predictive
Solves #3840 

pymc3/sampling.py

@@ -1568,7 +1568,13 @@ def sample_posterior_predictive(
         raise IncorrectArgumentsError("Should not specify both keep_size and size argukments")
 
     if samples is None:
-        samples = sum(len(v) for v in trace._straces.values())
+        if isinstance(trace, MultiTrace):
+            samples = sum(len(v) for v in trace._straces.values())
+        elif isinstance(trace, list) and all((isinstance(x, dict) for x in trace)):
+            # this is a list of points
+            samples = len(trace)
+        else:
+            raise ValueError("Do not know how to compute number of samples for trace argument of type %s"%type(trace))
 
     if samples < len_trace * nchain:
         warnings.warn(

pymc3/tests/test_sampling.py

@@ -13,6 +13,7 @@
 #   limitations under the License.
 
 from itertools import combinations
+from typing import Tuple
 import numpy as np
 
 try:
@@ -693,6 +694,16 @@ def test_exec_nuts_init(method):
         assert "a" in start[0] and "b_log__" in start[0]
 
 
+@pytest.fixture(scope="class")
+def point_list_arg_bug_fixture() -> Tuple[pm.Model, pm.backends.base.MultiTrace]:
+    with pm.Model() as pmodel:
+        n = pm.Normal('n')
+        trace = pm.sample()
+
+    with pmodel:
+        d = pm.Deterministic('d', n * 4)
+    return pmodel, trace
+
 class TestSamplePriorPredictive(SeededTest):
     def test_ignores_observed(self):
         observed = np.random.normal(10, 1, size=200)
@@ -851,3 +862,21 @@ class TestSamplePriorPredictive(SeededTest):
         with model:
             prior_trace = pm.sample_prior_predictive(5)
             assert prior_trace["x"].shape == (5, 3, 1)
+
+class TestSamplePosteriorPredictive:
+    def test_point_list_arg_bug_fspp(self, point_list_arg_bug_fixture):
+        pmodel, trace = point_list_arg_bug_fixture
+        with pmodel:
+            pp = pm.fast_sample_posterior_predictive(
+                [trace[15]],
+                var_names=['d']
+            )
+
+    def test_point_list_arg_bug_spp(self, point_list_arg_bug_fixture):
+        pmodel, trace = point_list_arg_bug_fixture
+        with pmodel:
+            pp = pm.sample_posterior_predictive(
+                [trace[15]],
+                var_names=['d']
+            )
+