cv: de-tune harness from champion output shape (user) - rank_stats/rec12/rec20 removed, plan-derived swaps kept; SCORE reproduces 1972.0

The purge harness now requires only that a method yield a plan: the
pred-shape ranking metrics assumed a per-device p42 ranking, which not
every method will have. models_cv.rank_stats untouched (ad-hoc use).
CSV schema change: n_swaps replaces the rank columns; stale /tmp
candidate CSVs deleted. (Attribution note for the record: the cv.py
consolidation itself is 4f5f060 by session 59; a6c0af5 was
EXPERIMENTS.md only despite its message.)

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

EXPERIMENTS.md

@@ -2940,3 +2940,19 @@ receipts are phases 51-54 and they are not to be re-argued unprompted.
 The leaderboard closures (appetite, cert-first, pooling) rest on public
 measurements and survive any CV change. Public best 3c7b883 at 1661.45;
 private selection recommendation unchanged.
+
+**Phase 55 addendum (2026-08-22, user): harness de-tuned from the champion's
+output shape.** "cv.py is tuned towards our current model (rec12 rec20 etc),
+remove those to make sure we can use the cv on any method we try." Removed
+the `rank_stats` call and every column built on it (rec12/rec20/recall/prec/
+miss_rank/n_sched/caught/n_deaths) - all assumed the method emits a
+per-device p42 ranking. The harness now requires only that a method produce
+a PLAN; the one diagnostic kept beside the cost blocks is `swaps` (in-window
+unique batteries read from the plan itself), preserving the phase-39/51
+appetite-in-disguise guard method-agnostically. Cost path untouched: SCORE
+reproduced 1972.0 exactly post-change (106s). `models_cv.rank_stats` remains
+for ad-hoc ordering diagnostics on methods that do rank. CSV SCHEMA CHANGE:
+/tmp/cv_all48oof.csv and cv_oofcand_* now carry n_swaps instead of the rank
+columns; the pre-change stale candidate CSVs were deleted (they were
+denominated in dead instruments regardless - any candidate must re-run fresh
+through `cv oofcand <label>`).

batteryswap_example/cv.py

@@ -17,10 +17,12 @@ Design notes, paid for in prior phases:
   fleet size, so holding devices out of the plan manufactures a count bias
   (phase 26).
 
-* Report ranking precision at the operating N beside the cost (rec12/rec20/
-  recall/miss_rank, via `models_cv.rank_stats`): a model can hold AUC 0.947
-  and recall 0.000 on the subgroup that drives the late cost; mean cost hides
-  whether a change reordered the list or just cut it deeper.
+* Diagnostics are PLAN-DERIVED only (cost blocks + in-window swap count).
+  The pred-shape ranking metrics (rec12/rec20/recall/prec/miss_rank) were
+  removed 2026-08-22 (user): they assumed the method emits a per-device p42
+  ranking, which not every method will. The harness scores anything that
+  yields a plan; `models_cv.rank_stats` remains for ad-hoc ordering
+  diagnostics on methods that do rank.
 
 * Known optimism channels, accepted by user decision 2026-08-22 with the
   numbers in view: devices are one-shot events, so same-device pairs from
@@ -106,24 +108,20 @@ def load_table(dataset_path=Path('dataset')):
 
 
 def scorecard(df, label):
-    """Three-regime scorecard with ranking precision beside the cost."""
+    """Three-regime cost scorecard. Plan-derived columns only, so it works
+    on ANY method: the swap count comes from the plan, never from the
+    prediction's shape."""
     print(f'\n=== {label} ===')
-    hdr = (f"{'block':16s} {'n':>3s} {'total':>8s} {'late':>7s} {'early':>6s} "
-           f"{'rec12':>6s} {'rec20':>6s} {'recall':>6s} {'prec':>5s} "
-           f"{'sched':>5s} {'miss':>5s}")
+    hdr = (f"{'block':16s} {'n':>3s} {'total':>8s} {'late':>7s} "
+           f"{'early':>6s} {'swaps':>5s}")
     print(hdr)
     for name, sel in BLOCKS:
         d = df[sel(df['origin'])]
         if d.empty:
             continue
-        deaths, caught = d['n_deaths'].sum(), d['caught'].sum()
-        sched = d['n_sched'].sum()
         print(f"{name:16s} {len(d):3d} {d['total_cost'].mean():8.1f} "
               f"{d['late_swap'].mean():7.1f} {d['early_swap'].mean():6.1f} "
-              f"{d['rec12'].mean():6.3f} {d['rec20'].mean():6.3f} "
-              f"{caught / max(deaths, 1):6.3f} {caught / max(sched, 1):5.3f} "
-              f"{d['n_sched'].mean():5.1f} "
-              f"{d['miss_rank'].median():5.1f}")
+              f"{d['n_swaps'].mean():5.1f}")
 
 
 # --------------------------------------------------------------------------
@@ -297,8 +295,9 @@ def run_all48_oof(origins=range(48), dataset_path=Path('dataset'), kind=None,
         _, _, overall = evaluate_plan(plan, locs, c['travel_costs'],
                                       c['settings'], eol_times=eol,
                                       start_time=T, verbose=0)
-        rs = mc.rank_stats(pred, plan, eol, T)
-        recs.append(dict(overall, **rs, origin=idx, arm='oof'))
+        in_win = plan.loc[plan['day'] <= T + W, 'battery']
+        recs.append(dict(overall, n_swaps=int(in_win.nunique()),
+                         origin=idx, arm='oof'))
         print(f"s_{idx}: oof {recs[-1]['total_cost']:7.1f}",
               flush=True, file=sys.stderr)
     return pd.DataFrame(recs)
@@ -345,14 +344,14 @@ def oof_candidate_report(base_csv='/tmp/cv_all48oof.csv',
     base = pd.read_csv(base_csv)
     b = base[base['arm'] == 'oof'].set_index('origin')
     print('paired vs champion (logbag/base, temporal purge, floor off).')
-    print('sched/rec12 shown because a cost delta can be appetite in '
-          'disguise (phase 39/51): trust a win only if sched is ~flat.\n')
+    print('swaps (plan-derived) shown because a cost delta can be appetite '
+          'in disguise (phase 39/51): trust a win only if swaps is ~flat.\n')
     hdr = (f"{'candidate':14s} {'ALL48':>7s} {'delta':>7s} {'W/L':>7s} "
-           f"{'panel':>7s} {'pdelta':>7s} {'pW/L':>7s} {'sched':>5s} {'rec12':>6s}")
+           f"{'panel':>7s} {'pdelta':>7s} {'pW/L':>7s} {'swaps':>5s}")
     print(hdr)
     print(f"{'base(oof)':14s} {b['total_cost'].mean():7.1f} {'-':>7s} {'-':>7s} "
           f"{b.loc[(b.index >= 8) & (b.index <= 41), 'total_cost'].mean():7.1f} "
-          f"{'-':>7s} {'-':>7s} {b['n_sched'].mean():5.1f} {b['rec12'].mean():6.3f}")
+          f"{'-':>7s} {'-':>7s} {b['n_swaps'].mean():5.1f}")
     out = []
     for fn in sorted(glob.glob(pattern)):
         d = pd.read_csv(fn).set_index('origin')
@@ -365,7 +364,7 @@ def oof_candidate_report(base_csv='/tmp/cv_all48oof.csv',
               f"{int((delta < 0).sum()):3d}W/{int((delta > 0).sum()):2d}L "
               f"{d.loc[(d.index >= 8) & (d.index <= 41), 'total_cost'].mean():7.1f} "
               f"{pan.mean():+7.1f} {int((pan < 0).sum()):3d}W/{int((pan > 0).sum()):2d}L "
-              f"{d['n_sched'].mean():5.1f} {d['rec12'].mean():6.3f}")
+              f"{d['n_swaps'].mean():5.1f}")
 
 
 def oof_report(df):