Rename property name in NUTS to better reflect the computation

Author: junpenglao

pymc3/step_methods/hmc/nuts.py

@@ -206,11 +206,12 @@ def warnings(self):
 
 
 # A proposal for the next position
-Proposal = namedtuple("Proposal", "q, q_grad, energy, log_p_accept, logp")
+Proposal = namedtuple("Proposal", "q, q_grad, energy, log_p_accept_weighted, logp")
 
 # A subtree of the binary tree built by nuts.
 Subtree = namedtuple(
-    "Subtree", "left, right, p_sum, proposal, log_size, log_accept_sum, n_proposals"
+    "Subtree",
+    "left, right, p_sum, proposal, log_size, log_weighted_accept_sum, n_proposals",
 )
 
 
@@ -243,7 +244,7 @@ def __init__(self, ndim, integrator, start, step_size, Emax):
         )
         self.depth = 0
         self.log_size = 0
-        self.log_accept_sum = -np.inf
+        self.log_weighted_accept_sum = -np.inf
         self.mean_tree_accept = 0.0
         self.n_proposals = 0
         self.p_sum = start.p.copy()
@@ -291,7 +292,9 @@ def extend(self, direction):
             self.proposal = tree.proposal
 
         self.log_size = np.logaddexp(self.log_size, tree.log_size)
-        self.log_accept_sum = np.logaddexp(self.log_accept_sum, tree.log_accept_sum)
+        self.log_weighted_accept_sum = np.logaddexp(
+            self.log_weighted_accept_sum, tree.log_weighted_accept_sum
+        )
         self.p_sum[:] += tree.p_sum
 
         # Additional turning check only when tree depth > 0 to avoid redundant work
@@ -331,13 +334,17 @@ def _single_step(self, left, epsilon):
                 # e^{H(q_0, p_0) - H(q_n, p_n)} max(1, e^{H(q_0, p_0) - H(q_n, p_n)})
                 # Saturated Metropolis accept probability with Boltzmann weight
                 # if h - H0 < 0
-                log_p_accept = -energy_change + min(0.0, -energy_change)
+                log_p_accept_weighted = -energy_change + min(0.0, -energy_change)
                 log_size = -energy_change
                 proposal = Proposal(
-                    right.q, right.q_grad, right.energy, log_p_accept, right.model_logp
+                    right.q,
+                    right.q_grad,
+                    right.energy,
+                    log_p_accept_weighted,
+                    right.model_logp,
                 )
                 tree = Subtree(
-                    right, right, right.p, proposal, log_size, log_p_accept, 1
+                    right, right, right.p, proposal, log_size, log_p_accept_weighted, 1
                 )
                 return tree, None, False
             else:
@@ -377,21 +384,23 @@ def _build_subtree(self, left, depth, epsilon):
                 turning = turning | turning1 | turning2
 
             log_size = np.logaddexp(tree1.log_size, tree2.log_size)
-            log_accept_sum = np.logaddexp(tree1.log_accept_sum, tree2.log_accept_sum)
+            log_weighted_accept_sum = np.logaddexp(
+                tree1.log_weighted_accept_sum, tree2.log_weighted_accept_sum
+            )
             if logbern(tree2.log_size - log_size):
                 proposal = tree2.proposal
             else:
                 proposal = tree1.proposal
         else:
             p_sum = tree1.p_sum
             log_size = tree1.log_size
-            log_accept_sum = tree1.log_accept_sum
+            log_weighted_accept_sum = tree1.log_weighted_accept_sum
             proposal = tree1.proposal
 
         n_proposals = tree1.n_proposals + tree2.n_proposals
 
         tree = Subtree(
-            left, right, p_sum, proposal, log_size, log_accept_sum, n_proposals
+            left, right, p_sum, proposal, log_size, log_weighted_accept_sum, n_proposals
         )
         return tree, diverging, turning
 
@@ -401,7 +410,9 @@ def stats(self):
             # Remove contribution from initial state which is always a perfect
             # accept
             log_sum_weight = logdiffexp_numpy(self.log_size, 0.0)
-            self.mean_tree_accept = np.exp(self.log_accept_sum - log_sum_weight)
+            self.mean_tree_accept = np.exp(
+                self.log_weighted_accept_sum - log_sum_weight
+            )
         return {
             "depth": self.depth,
             "mean_tree_accept": self.mean_tree_accept,