Strip heuristic input features and add selectivity diagnostics

Audit of the Deep CFR information-state encoding identified two tiers of
non-pure features and removed both:

- Tier 3 (judgment): is_bad_open_candidate, open_risk_score,
  is_safe_continuation. Same heuristic family used to label bad_open in
  evaluation, embedded as model input.
- Tier 2 (projection): recoverable_score_no_bonus,
  recoverable_margin_no_bonus, min_needed_to_break_even,
  cards_needed_for_bonus, has_bonus_path. Mechanical but assumption-laden
  ("commit and play all currently-playable cards"). The no_bonus form is
  asymmetric: it amplifies the immediate -20 penalty while truncating the
  +20 bonus upside, biasing the model toward the same "don't open" basin
  the diagnostics already flagged.

Input dim 365 -> 297. DERIVED_PLAYABILITY_PER_COLOR 19 -> 15;
SLOT_AWARE_PLAYABILITY_PER_SLOT 12 -> 6. Test shape assertions updated.

Also adds selectivity diagnostic infrastructure used to reach this point:
- traversal.outcome_unsampled_first_open_prior_alpha config field with
  signed-prior overlay on unsampled first-open advantage targets (A1).
- analyze_first_open_counterfactual.py --post-policy to swap the
  policy_player rollout policy and isolate selection bias (D1).
- analyze_first_open_followup.py to inspect post-forced-open behavior
  (E2): same-color play vs discard counts, other-open rate, terminal
  hand composition.

Findings recorded in docs/plans/deep-cfr-selectivity.md sections 3-6.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

scripts/analyze_first_open_targets.py

@@ -345,6 +345,9 @@ def analyze_checkpoint(
         outcome_sampling_epsilon=cfg.traversal.outcome_sampling_epsilon,
         outcome_sampling_value_clip=cfg.traversal.outcome_sampling_value_clip,
         outcome_unsampled_regret=cfg.traversal.outcome_unsampled_regret,
+        outcome_unsampled_first_open_prior_alpha=getattr(
+            cfg.traversal, "outcome_unsampled_first_open_prior_alpha", 0.0
+        ),
         max_depth=cfg.traversal.max_depth,
         max_nodes=cfg.traversal.max_nodes_per_traversal,
         strategy_sample_interval=cfg.traversal.strategy_sample_interval,