pyfracval.experimental.candidate_policies
=========================================

.. py:module:: pyfracval.experimental.candidate_policies

.. autoapi-nested-parse::

   Archived CCA candidate-pair ordering policies.

   ``leaf_soft`` (leaf-pairs-first ordering) and ``leaf_score``/``leaf_hybrid``
   (heuristic-scored ordering) were benchmarked against the unranked
   (shuffled) baseline ordering at N=512 in the hard regime (see
   ``docs/source/experiments.md``): both land on the same success rate as
   baseline. *How* you search for a contact pair doesn't matter much when the
   real question is *whether* a valid contact pair exists at all at the
   enforced ``gamma_pc``.

   Kept reachable via ``cca_candidate_policy`` for anyone who wants to try a
   different scoring heuristic later. Leaf classification and scoring
   themselves (``_candidate_leaf_class``/``_candidate_score`` in
   ``cca/candidates.py``) stay in production code since the per-attempt
   telemetry they feed is diagnostic instrumentation used regardless of
   policy, not part of what was benchmarked here.





Module Contents
---------------

.. py:function:: reorder_candidates_by_policy(candidate_policy, candidate_indices, leaf_mask_1, leaf_mask_2, coords1, radii1, cm1, coords2, radii2, cm2, gamma_pc, algorithm_config, candidate_leaf_class_fn, candidate_score_fn)

   Reorder shuffled candidate pairs according to an archived policy.