Rename bot family, curate analyze plots, tier evaluation cadence

Three coordinated hygiene changes; none target the diagnosed
selection-bias bottleneck. They make the codebase honestly reflect the
pure-self-play stance and reduce dashboard noise.

Bot rename (drop the unhelpful safe_ prefix; suffixes describe behaviour):
- safe_heuristic_loose -> heuristic_aggressive
- safe_heuristic       -> heuristic_balanced
- safe_heuristic_strict -> heuristic_cautious
- noisy_safe           -> heuristic_noisy
- passive_discard      -> discard_only

Class renames in bots/: SafeHeuristicBot -> HeuristicBot,
SafeHeuristicParams -> HeuristicParams, PassiveDiscardBot -> DiscardOnlyBot,
plus loose/strict parameter constants. Backwards compatibility was dropped
intentionally per user instruction; no aliases. Active configs, docs,
scripts, tests updated. Archive directories (configs/archive,
docs/archive, runs/archive) left intact and may still reference old
names per their read-only policy. The src/.../bots/passive.py module was
renamed to discard_only.py via git mv.

Analyze plot curation (deep_cfr/analyze.py):
- Added analysis_00_core.png as the canonical daily dashboard with 10
  heuristic-free metrics (loss/{advantage,strategy}; vs heuristic_cautious:
  avg_score_diff0, win_rate0, avg_opened_colors, positive_expedition_rate,
  bonus_expedition_rate, score_per_opened_color, policy_entropy; vs random:
  win_rate0).
- Removed analysis_05_open_quality.png (bad/weak/good open rates,
  recoverable score) and analysis_07_calibration.png (calibration gap,
  recoverable mean) - both relied on the heuristic recoverable_score
  classifier already dropped from inputs.
- Removed SELECTIVITY_PLOTS and plot_selectivity (heuristic-laden).
- SUMMARY_EVAL_METRICS no longer includes bad_open_rate or
  calibration_gap.
- PlotSpec gained an opponents allowlist so the new core section can pin
  a specific opponent per panel without restructuring plot_section.

Tiered evaluation cadence (EvaluationConfig):
- Added extended_opponents and extended_eval_every (default 0 = disabled).
- opponents_for_iteration(iteration) returns the core list every
  eval_every and appends extended_opponents (de-duplicated) when
  iteration is also a multiple of extended_eval_every.
- default.yaml now uses 3 core opponents (random, discard_only,
  heuristic_cautious) every 5 iterations and 3 extended opponents
  (heuristic_balanced, heuristic_aggressive, heuristic_noisy) every 50
  iterations. random is the floor sanity. discard_only is the
  zero-pit detector / absolute-score reference (its score is always 0,
  so eval/discard_only/avg_score_diff0 directly equals the model's raw
  average score). heuristic_cautious is the ceiling and the
  archive-comparable benchmark used in the prior diagnostic sections.

Net eval cost reduction: roughly 50% (3 opponents x every 5 iter, plus
6 opponents x every 50 iter, vs the prior 6 x every 5).

Documented in docs/plans/deep-cfr-selectivity.md section 9.

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

docs/plans/model_size_experiment.md

@@ -39,7 +39,7 @@ The experiment must produce either a recommended new `network` config or a docum
 
 ## Success criteria
 
-1. At least one tested config produces win-rate trajectories vs `safe_heuristic_strict` that are **clearly outside seed noise** compared to the current baseline at iteration 200 — OR a clear documented null result (no size in the tested range improves the curve).
+1. At least one tested config produces win-rate trajectories vs `heuristic_cautious` that are **clearly outside seed noise** compared to the current baseline at iteration 200 — OR a clear documented null result (no size in the tested range improves the curve).
 2. `iteration_seconds`, `traversal_seconds`, `advantage_train_seconds`, `strategy_train_seconds`, and `policy_network_seconds` (eval) are captured for each tested size and written to `docs/performance.md`.
 3. A recommended `network` config emerges from the data, OR the experiment documents why the current size should be kept, with specific rationale.
 
@@ -72,7 +72,7 @@ configs/deep_cfr/model-size-1536x8.yaml
   - `traversal_seconds` — traversal phase.
   - `advantage_train_seconds` — advantage network optimization.
   - `strategy_train_seconds` — strategy network optimization.
-  - At eval iterations {50, 100, 150, 200}: `eval/<opponent>/win_rate` for all opponents, with special attention to `safe_heuristic_strict`.
+  - At eval iterations {50, 100, 150, 200}: `eval/<opponent>/win_rate` for all opponents, with special attention to `heuristic_cautious`.
   - At eval iterations: `eval/<opponent>/policy_network_seconds` — needed for the AMP/TRT prerequisite check.
 - **Memory monitoring:** watch GPU VRAM during the 1024x6 and 1536x8 runs. If a run OOMs or VRAM > 20 GB, reduce `optimization.advantage_batch_size` and `optimization.strategy_batch_size` by half (1024 → 512) and note the change in the results table. Do not adjust traversal settings.
 
@@ -259,8 +259,8 @@ if non_eval:
 eval_rows = {r['iteration']: r for r in rows if r.get('evaluation_seconds')}
 for it in [50, 100, 150, 200]:
     if it in eval_rows:
-        wr = eval_rows[it].get('eval/safe_heuristic_strict/win_rate', 'n/a')
-        print(f'  iter={it} safe_heuristic_strict win_rate={wr}')
+        wr = eval_rows[it].get('eval/heuristic_cautious/win_rate', 'n/a')
+        print(f'  iter={it} heuristic_cautious win_rate={wr}')
 "
 done
 ```
@@ -269,7 +269,7 @@ done
 
 After the grid completes, append a date-stamped experiment subsection to `docs/performance.md` under the "Experiments" heading. The subsection must include:
 
-- A results table with `iteration_seconds` mean (non-eval) and win-rate vs `safe_heuristic_strict` at {50, 100, 150, 200} for each config.
+- A results table with `iteration_seconds` mean (non-eval) and win-rate vs `heuristic_cautious` at {50, 100, 150, 200} for each config.
 - A `policy_network_seconds` column from eval rows — this is the key data for the AMP/compile/TRT prerequisite check.
 - The recommendation that follows from the decision tree below.
 
@@ -302,7 +302,7 @@ Apply this logic after the grid completes:
 
 ### Branch A — a size unlocks the curve AND iter time is acceptable
 
-**Condition:** at least one config at or above 768x4 shows win-rate trajectories vs `safe_heuristic_strict` that are clearly outside seed noise vs 512x3 baseline at iteration 200, AND `iteration_seconds` at that size is ≤ 3× the baseline (i.e., ≤ ~54s/iter).
+**Condition:** at least one config at or above 768x4 shows win-rate trajectories vs `heuristic_cautious` that are clearly outside seed noise vs 512x3 baseline at iteration 200, AND `iteration_seconds` at that size is ≤ 3× the baseline (i.e., ≤ ~54s/iter).
 
 **Action:**
 1. Recommend that config as the new `network` default.