Small improvements to early nuts behaviour

pymc/step_methods/hmc/base_hmc.py

@@ -236,6 +236,7 @@ def astep(self, q0):
 
         stats.update(hmc_step.stats)
         stats.update(self.step_adapt.stats())
+        stats.update(self.potential.stats())
 
         return hmc_step.end.q, [stats]
 

pymc/step_methods/hmc/hmc.py

@@ -51,6 +51,8 @@ class HamiltonianMC(BaseHMC):
             "process_time_diff": np.float64,
             "perf_counter_diff": np.float64,
             "perf_counter_start": np.float64,
+            "largest_eigval": np.float64,
+            "smallest_eigval": np.float64,
         }
     ]
 
@@ -162,7 +164,16 @@ def _hamiltonian_step(self, start, p0, step_size):
             "model_logp": state.model_logp,
         }
         # Retrieve State q and p data from respective RaveledVars
-        end = State(end.q.data, end.p.data, end.v, end.q_grad, end.energy, end.model_logp)
+        end = State(
+            end.q.data,
+            end.p.data,
+            end.v,
+            end.q_grad,
+            end.energy,
+            end.model_logp,
+            end.index_in_trajectory,
+        )
+        stats.update(self.potential.stats())
         return HMCStepData(end, accept_stat, div_info, stats)
 
     @staticmethod

pymc/step_methods/hmc/integration.py

@@ -20,7 +20,7 @@
 
 from pymc.blocking import RaveledVars
 
-State = namedtuple("State", "q, p, v, q_grad, energy, model_logp")
+State = namedtuple("State", "q, p, v, q_grad, energy, model_logp, index_in_trajectory")
 
 
 class IntegrationError(RuntimeError):
@@ -49,7 +49,7 @@ def compute_state(self, q, p):
         v = self._potential.velocity(p.data)
         kinetic = self._potential.energy(p.data, velocity=v)
         energy = kinetic - logp
-        return State(q, p, v, dlogp, energy, logp)
+        return State(q, p, v, dlogp, energy, logp, 0)
 
     def step(self, epsilon, state):
         """Leapfrog integrator step.
@@ -114,4 +114,12 @@ def _step(self, epsilon, state):
         kinetic = pot.velocity_energy(p_new.data, v_new)
         energy = kinetic - logp
 
-        return State(q_new, p_new, v_new, q_new_grad, energy, logp)
+        return State(
+            q_new,
+            p_new,
+            v_new,
+            q_new_grad,
+            energy,
+            logp,
+            state.index_in_trajectory + int(np.sign(epsilon)),
+        )

pymc/step_methods/hmc/nuts.py

@@ -18,7 +18,7 @@
 
 from pymc.aesaraf import floatX
 from pymc.backends.report import SamplerWarning, WarningType
-from pymc.math import logbern, logdiffexp_numpy
+from pymc.math import logbern
 from pymc.step_methods.arraystep import Competence
 from pymc.step_methods.hmc.base_hmc import BaseHMC, DivergenceInfo, HMCStepData
 from pymc.step_methods.hmc.integration import IntegrationError
@@ -78,6 +78,12 @@ class NUTS(BaseHMC):
       by the python standard library `time.perf_counter` (wall time).
     - `perf_counter_start`: The value of `time.perf_counter` at the beginning
       of the computation of the draw.
+    - `index_in_trajectory`: This is usually only interesting for debugging
+      purposes. This indicates the position of the posterior draw in the
+      trajectory. Eg a -4 would indicate that the draw was the result of the
+      fourth leapfrog step in negative direction.
+    - `largest_eigval` and `smallest_eigval`: Experimental statistics for
+      some mass matrix adaptation algorithms. This is nan if it is not used.
 
     References
     ----------
@@ -105,6 +111,9 @@ class NUTS(BaseHMC):
             "process_time_diff": np.float64,
             "perf_counter_diff": np.float64,
             "perf_counter_start": np.float64,
+            "largest_eigval": np.float64,
+            "smallest_eigval": np.float64,
+            "index_in_trajectory": np.int64,
         }
     ]
 
@@ -219,12 +228,12 @@ def warnings(self):
 
 
 # A proposal for the next position
-Proposal = namedtuple("Proposal", "q, q_grad, energy, log_p_accept_weighted, logp")
+Proposal = namedtuple("Proposal", "q, q_grad, energy, logp, index_in_trajectory")
 
 # A subtree of the binary tree built by nuts.
 Subtree = namedtuple(
     "Subtree",
-    "left, right, p_sum, proposal, log_size, log_weighted_accept_sum, n_proposals",
+    "left, right, p_sum, proposal, log_size",
 )
 
 
@@ -252,10 +261,10 @@ def __init__(self, ndim, integrator, start, step_size, Emax):
         self.start_energy = np.array(start.energy)
 
         self.left = self.right = start
-        self.proposal = Proposal(start.q.data, start.q_grad, start.energy, 1.0, start.model_logp)
+        self.proposal = Proposal(start.q.data, start.q_grad, start.energy, start.model_logp, 0)
         self.depth = 0
         self.log_size = 0
-        self.log_weighted_accept_sum = -np.inf
+        self.log_accept_sum = -np.inf
         self.mean_tree_accept = 0.0
         self.n_proposals = 0
         self.p_sum = start.p.data.copy()
@@ -279,7 +288,7 @@ def extend(self, direction):
             )
             leftmost_begin, leftmost_end = self.left, self.right
             rightmost_begin, rightmost_end = tree.left, tree.right
-            leftmost_p_sum = self.p_sum
+            leftmost_p_sum = self.p_sum.copy()
             rightmost_p_sum = tree.p_sum
             self.right = tree.right
         else:
@@ -289,11 +298,10 @@ def extend(self, direction):
             leftmost_begin, leftmost_end = tree.right, tree.left
             rightmost_begin, rightmost_end = self.left, self.right
             leftmost_p_sum = tree.p_sum
-            rightmost_p_sum = self.p_sum
+            rightmost_p_sum = self.p_sum.copy()
             self.left = tree.right
 
         self.depth += 1
-        self.n_proposals += tree.n_proposals
 
         if diverging or turning:
             return diverging, turning
@@ -303,9 +311,6 @@ def extend(self, direction):
             self.proposal = tree.proposal
 
         self.log_size = np.logaddexp(self.log_size, tree.log_size)
-        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
@@ -336,30 +341,30 @@ def _single_step(self, left, epsilon):
             if np.isnan(energy_change):
                 energy_change = np.inf
 
+            self.log_accept_sum = np.logaddexp(self.log_accept_sum, min(0, -energy_change))
+
             if np.abs(energy_change) > np.abs(self.max_energy_change):
                 self.max_energy_change = energy_change
-            if np.abs(energy_change) < self.Emax:
+            if energy_change < self.Emax:
                 # Acceptance statistic
                 # 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_weighted = -energy_change + min(0.0, -energy_change)
                 log_size = -energy_change
                 proposal = Proposal(
                     right.q.data,
                     right.q_grad,
                     right.energy,
-                    log_p_accept_weighted,
                     right.model_logp,
+                    right.index_in_trajectory,
                 )
-                tree = Subtree(
-                    right, right, right.p.data, proposal, log_size, log_p_accept_weighted, 1
-                )
+                tree = Subtree(right, right, right.p.data, proposal, log_size)
                 return tree, None, False
             else:
                 error_msg = f"Energy change in leapfrog step is too large: {energy_change}."
                 error = None
-        tree = Subtree(None, None, None, None, -np.inf, -np.inf, 1)
+        finally:
+            self.n_proposals += 1
+        tree = Subtree(None, None, None, None, -np.inf)
         divergance_info = DivergenceInfo(error_msg, error, left, right)
         return tree, divergance_info, False
 
@@ -387,31 +392,20 @@ def _build_subtree(self, left, depth, epsilon):
                 turning = turning | turning1 | turning2
 
             log_size = np.logaddexp(tree1.log_size, tree2.log_size)
-            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_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_weighted_accept_sum, n_proposals)
+        tree = Subtree(left, right, p_sum, proposal, log_size)
         return tree, diverging, turning
 
     def stats(self):
-        # Update accept stat if any subtrees were accepted
-        if self.log_size > 0:
-            # 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_weighted_accept_sum - log_sum_weight)
+        self.mean_tree_accept = np.exp(self.log_accept_sum) / self.n_proposals
         return {
             "depth": self.depth,
             "mean_tree_accept": self.mean_tree_accept,
@@ -420,4 +414,5 @@ def stats(self):
             "tree_size": self.n_proposals,
             "max_energy_error": self.max_energy_change,
             "model_logp": self.proposal.logp,
+            "index_in_trajectory": self.proposal.index_in_trajectory,
         }

pymc/step_methods/hmc/quadpotential.py

@@ -136,6 +136,9 @@ def raise_ok(self, map_info=None):
     def reset(self):
         pass
 
+    def stats(self):
+        return {"largest_eigval": np.nan, "smallest_eigval": np.nan}
+
 
 def isquadpotential(value):
     """Check whether an object might be a QuadPotential object."""
@@ -254,6 +257,7 @@ def random(self):
 
     def _update_from_weightvar(self, weightvar):
         weightvar.current_variance(out=self._var)
+        self._var = np.clip(self._var, 1e-12, 1e12)
         np.sqrt(self._var, out=self._stds)
         np.divide(1, self._stds, out=self._inv_stds)
         self._var_aesara.set_value(self._var)

pymc/tests/test_step.py

@@ -595,6 +595,9 @@ def test_sampler_stats(self):
             "perf_counter_diff",
             "perf_counter_start",
             "process_time_diff",
+            "index_in_trajectory",
+            "largest_eigval",
+            "smallest_eigval",
         }
         assert trace.stat_names == expected_stat_names
         for varname in trace.stat_names: