Buckets:
| # cwm3/eval/run_warning_head_final.sh — E7 (roadmap §四): the learned warning head on the FINAL main model's 3 seeds, CPU only. | |
| # bash eval/run_warning_head_final.sh <main_variant: all_minus_road2drv|all> ["0 1 2"] [n_boot=1000] | |
| # Per seed: train/train_warning_head.py --run I1_<main>_s<k>_final --demand P (feature sets current / future / both; heads lr / hgb / mlp, device-grouped 5-fold CV; | |
| # label curve n ∈ {0,100,300,979} x 20 subsamples; operating points at clean-val recall 0.5 AND at the DMS att@3s alert rate per regime; lead times) — | |
| # skipped when a results/warning_head/I1_<main>_s<k>_final_* dir with operating_dms_rate.csv already exists. | |
| # Then eval/warning_head_eval.py --item cwm3_final --runs <the 3 runs> --out-name warning_head_final.md (unchanged cwm2/eval/warning_gold.py, 509 gold rows, | |
| # device-cluster CIs, paired deltas vs DMS att@3s / cockpit / own W_direct, B4 / B5 by the pre-registered CV rule + the post-hoc fixed-HGB rows labelled). | |
| set -u | |
| cd "$(dirname "$0")/.." | |
| PY=python | |
| export OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 | |
| MAIN="$1"; SEEDS="${2:-0 1 2}"; NB="${3:-1000}" | |
| RUNS=() | |
| for s in $SEEDS; do | |
| R=I1_${MAIN}_s${s}_final; RUNS+=("$R") | |
| [[ -f results/runs/$R/PRED_DONE && -f results/runs/$R/pred_manual_train_labelrows.npz ]] || { echo "skip $R (not finished / no label-row packs)"; continue; } | |
| if ls results/warning_head/${R}_*/operating_dms_rate.csv >/dev/null 2>&1; then echo "have warning head for $R"; continue; fi | |
| echo "== train_warning_head $R" | |
| $PY train/train_warning_head.py --run "$R" --demand P --n-boot "$NB" 2>&1 | grep -E "DONE|Error|Traceback|selected|gold clean_test AUROC|DMS att@3s|@DMS rate" | cut -c1-220 | |
| done | |
| echo "== warning_head_eval --item cwm3_final" | |
| $PY eval/warning_head_eval.py --runs "${RUNS[@]}" --item cwm3_final --n-boot 2000 --out-name warning_head_final.md 2>&1 | grep -E "Verdict|B4|B5|written|Error|Traceback" | head -20 | |
| echo WH_FINAL_DONE | |
Xet Storage Details
- Size:
- 1.95 kB
- Xet hash:
- cfdb07e0c3021387317f55631d17915fd1cf2bb2204b21735bdf7e1c52d0680a
·
Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.