Commit History
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Phase 34-35: retire cross-fleet CV, re-screen 72 configs, champion unchanged
Thibaut Forest +1: Claude Fable 5No behaviour change to the shipped path. models_cv.py is untouched; the only code edits are docstrings and four unused imports (planner.py numpy/FEATURES, train.py Field, submit.py sys). Champion config verified in submit.py: logbag, ALPHA=ALPHA_STATIC=0.8, EC_MARGIN=0.8, EC_PMAX=25, SPLIT_DAYS=7, FLOOR=2.5/gate 20, CLIFF_SCALE=1.0 (off). all-48 = 1661.7, public 1661.45. Phase 34: cross-fleet CV retired after a second confident false positive. rankavg won it at -18.4 (27/32 folds, p=0.0002) and is +55.3 on the real full-fleet all-48 - losing on the panel alone (+23.9), so not a warm-up weighting artifact. Root cause: every fold trains on the full history, so the harness cannot reproduce cold-start scarcity. Also closed: warm-up physics prior (logbag already best at cold origins), optimizer budget (2x60 optimal), feature LOO (best removal +2/308). Phase 35: everything ever judged on a retired instrument re-run on the surviving one. 72 configs, base reproduced 1661.7 in every group, no…7d93fb8
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Phase 14: building day-splitting (sd7) + censoring-aware pricing adopted
Thibaut Forest +1: Claude Opus 5 (1M context)Panel 1679.2 vs 1702.0, sd7 selected in 100% of random and blocked splits; censoring-aware curves -32 on TRUNC=30 counterfactual, inert on covered windows. Hazard-WHEN and park_mobility rejected. All-48 Docker metric 2146.9 (was 2175.4). Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>2ba75d6
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Phase 11: price-adaptive break-even top-up (fresh-only, m0.8/pm25) + two-sided cost-curve fix
Thibaut Forest +1: Claude Opus 5 (1M context)Panel 1702.0 vs 1715.8, 63% random-split selection, never beaten in a decided blocked split. All-48 Docker metric 2175.4 (was 2202). Includes user's phase-10 CohortGate + merged-block protocol write-up. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>4fb1dc8 -
Adaptive runtime submission: per-scenario refits, bagging, stronger routing
Thibaut Forest +1: Claude Opus 5 (1M context)Uses the 30-minute runtime budget instead of a static pickle: - submit.py: refits the model at every scenario origin on the target split's own history pooled with packaged train features, with labels self-masked to windows closed before each origin (assertion-enforced; never reads future eol_times). Time-budget + mid-split deadline guards fall back to the static pickle per split. - logbag model: 15-fold bagged logistic (dev 2126 -> 2074, test 1440 -> 1398) - stronger routing search: merge moves + restarts, and within-day visits now sorted by building/room (the evaluator pays repeated building changes for interleaved rows) - tau_projected feature: master-curve inversion minus staleness, targeting devices that go dark and keep discharging (test 1476 -> 1439) - synthetic training origins every 4 days back to Feb-2025 (2.7x positives) Walk-forward: dev 2074, test 1398, honest all-48 train metric 2202 with mobility+limits at 242 vs current leaderboard leader's 280. Runtime: 2…fc9c286
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Logistic RUL model with adaptive top-sum planner (walk-forward CV selected)
Thibaut Forest +1: Claude Opus 5 (1M context)- features.py: cached (device, origin) feature table; leak-free per-origin slicing - models_cv.py: walk-forward CV harness; logistic/GBM/AFT/rank models, threshold/topsum/batching planners; dev s_8-s_23, test s_24-s_41 - planner.py: final TopSumPlanner (logistic p42, N = 0.8 * sum(p42), q05 day placement, building batching); refit on all closed windows - harness.py: baseline comparisons (defer-all, lowest-voltage-k, oracle) - best.pickle trained inside the competition Docker image (sklearn 1.7.2) to avoid version skew; verified via script.py + metric in the image Test-block walk-forward mean total_cost 1368 (voltage-15 2034, defer 2512, oracle 186); train split via official metric: 2150 vs 8673 for the example. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>b89e779