Always add values from drawn dict to givens

RELEASE-NOTES.md

@@ -9,6 +9,7 @@
 + Enable documentation generation via ReadTheDocs for upcoming v3 releases. (see [#4805](https://github.com/pymc-devs/pymc3/pull/4805)).
 + Remove `float128` dtype support (see [#4834](https://github.com/pymc-devs/pymc3/pull/4834)).
 + Use `to_tuple` function in `pm.fast_sample_posterior_predictive` to pass shape assertions (see [#4927](https://github.com/pymc-devs/pymc3/pull/4927)).
++ Fixed [bug in `draw_values`](https://github.com/pymc-devs/pymc3/issues/3789), in which values that had been drawn in a separate `_DrawValuesContext` were not added to the `givens` dictionary and lead to `ValueError: Cannot resolve inputs for ...` exceptions (see [#3792](https://github.com/pymc-devs/pymc3/pull/3792)).
 
 ### New Features
 + Generalized BART, bounded distributions like Binomial and Poisson can now be used as likelihoods (see [#4675](https://github.com/pymc-devs/pymc3/pull/4675), [#4709](https://github.com/pymc-devs/pymc3/pull/4709) and

pymc3/distributions/dist_math.py

@@ -457,7 +457,7 @@ def incomplete_beta_cfe(a, b, x, small):
     qkm1 = one
     r = one
 
-    def _step(i, pkm1, pkm2, qkm1, qkm2, k1, k2, k3, k4, k5, k6, k7, k8, r):
+    def _step(_i, pkm1, pkm2, qkm1, qkm2, k1, k2, k3, k4, k5, k6, k7, k8, r):
         xk = -(x * k1 * k2) / (k3 * k4)
         pk = pkm1 + pkm2 * xk
         qk = qkm1 + qkm2 * xk

pymc3/distributions/distribution.py

@@ -754,8 +754,8 @@ def draw_values(params, point=None, size=None):
         while stack:
             next_ = stack.pop(0)
             if (next_, size) in drawn:
-                # If the node already has a givens value, skip it
-                continue
+                # If the node already has a givens value, add it to givens
+                givens[next_.name] = (next_, drawn[(next_, size)])
             elif isinstance(next_, (theano_constant, tt.sharedvar.SharedVariable)):
                 # If the node is a theano.tensor.TensorConstant or a
                 # theano.tensor.sharedvar.SharedVariable, its value will be
@@ -799,7 +799,7 @@ def draw_values(params, point=None, size=None):
                         [
                             node
                             for node in named_nodes_descendents[next_]
-                            if node is not None and (node, size) not in drawn
+                            if node is not None and getattr(node, "name", None) not in givens
                         ]
                     )
 
@@ -823,14 +823,6 @@ def draw_values(params, point=None, size=None):
                     evaluated[param_idx] = drawn[(param, size)]
                 else:
                     try:  # might evaluate in a bad order,
-                        # Sometimes _draw_value recurrently calls draw_values.
-                        # This may set values for certain nodes in the drawn
-                        # dictionary, but they don't get added to the givens
-                        # dictionary. Here, we try to fix that.
-                        if param in named_nodes_ancestors:
-                            for node in named_nodes_ancestors[param]:
-                                if node.name not in givens and (node, size) in drawn:
-                                    givens[node.name] = (node, drawn[(node, size)])
                         value = _draw_value(param, point=point, givens=givens.values(), size=size)
                         evaluated[param_idx] = drawn[(param, size)] = value
                         givens[param.name] = (param, value)

pymc3/tests/test_sampling.py

@@ -21,6 +21,7 @@
 import arviz as az
 import numpy as np
 import numpy.testing as npt
+import pandas as pd
 import pytest
 import theano
 import theano.tensor as tt
@@ -56,7 +57,7 @@ def test_parallel_sample_does_not_reuse_seed(self):
         random_numbers = []
         draws = []
         for _ in range(2):
-            np.random.seed(1)  # seeds in other processes don't effect main process
+            np.random.seed(1)  # seeds in other processes don't affect main process
             with self.model:
                 trace = pm.sample(100, tune=0, cores=cores, return_inferencedata=False)
             # numpy thread mentioned race condition.  might as well check none are equal
@@ -1108,6 +1109,38 @@ def test_potentials_warning(self):
                 pm.sample_prior_predictive(samples=5)
 
 
+def test_prior_sampling_mixture():
+    """
+    Added this test because the NormalMixture distribution did not support
+    component shape identification, causing prior predictive sampling to error out.
+    """
+    old_faithful_df = pd.read_csv(pm.get_data("old_faithful.csv"))
+    old_faithful_df["std_waiting"] = (
+        old_faithful_df.waiting - old_faithful_df.waiting.mean()
+    ) / old_faithful_df.waiting.std()
+    N = old_faithful_df.shape[0]
+    K = 30
+
+    def stick_breaking(beta):
+        portion_remaining = tt.concatenate([[1], tt.extra_ops.cumprod(1 - beta)[:-1]])
+        result = beta * portion_remaining
+        return result / tt.sum(result, axis=-1, keepdims=True)
+
+    with pm.Model() as model:
+        alpha = pm.Gamma("alpha", 1.0, 1.0)
+        beta = pm.Beta("beta", 1.0, alpha, shape=K)
+        w = pm.Deterministic("w", stick_breaking(beta))
+
+        tau = pm.Gamma("tau", 1.0, 1.0, shape=K)
+        lambda_ = pm.Gamma("lambda_", 10.0, 1.0, shape=K)
+        mu = pm.Normal("mu", 0, tau=lambda_ * tau, shape=K)
+        obs = pm.NormalMixture(
+            "obs", w, mu, tau=lambda_ * tau, observed=old_faithful_df.std_waiting.values
+        )
+
+        pm.sample_prior_predictive()
+
+
 class TestSamplePosteriorPredictive:
     def test_point_list_arg_bug_fspp(self, point_list_arg_bug_fixture):
         pmodel, trace = point_list_arg_bug_fixture