张宗平
40ec960cf8
Critical eval-pipeline bugs found while running the stage-2 model:
1. parse_anomaly_segments only recognized {"segments\:[[s,e]]}; the training
data (data/instruct.py) emits {"anomaly_regions":[{"start","end"}]}.
The model learned the correct format but scored 0 → VUS-PR=0. Now accepts
both schemas + a balanced-brace JSON scanner (the flat regex could not
match nested objects the model emits).
2. qa_judge._extract_json had the same nesting bug — reuse the scanner.
3. Generation truncated at 64-96 tokens (too short for stats JSON) → bumped
to 192 in report + instruct_check.
Report enhancements:
- wire the pure-LLM baseline (series-as-text, no TS modality) so design §5.4
'model vs pure LLM' is actually computed. Heavy predictors built/evaluated
one at a time with GPU cleanup (single 12GB card).
- add QA-judge table across all 6 categories (design §6.5 问答均分).
- add --pure_llm / --max_per_cat flags.
Results (reports/eval-2026-06-30.md): model QA-judge mean 2.45 vs pure_llm
0.88 (2.8x); anomaly localization (VUS-PR) is the weak point — neither
model nor pure_llm localize well; TS-modality ablation change_rate 0.98.
38 lines
1.1 KiB
Bash
Executable File
38 lines
1.1 KiB
Bash
Executable File
#!/usr/bin/env bash
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# M4 + M5 exit-validation eval (T4.2 / T4.3 / T5.6).
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#
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# Runs (1) the instruction-following check (T4.2) and (2) the full eval report
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# (T5.5/5.6) including the trained model + trivial baselines, on the stage-2
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# checkpoint. Requires a free GPU (so run AFTER training finishes).
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#
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# Usage:
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# scripts/run_m4_m5_eval.sh [stage2_ckpt]
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set -euo pipefail
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cd "$(dirname "$0")/.."
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export PYTHONPATH=src
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CKPT="${1:-checkpoints/stage2/stage2_final.pt}"
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if [ ! -f "$CKPT" ]; then
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echo "[run_m4_m5_eval] checkpoint $CKPT not found"; exit 1
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fi
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echo "=== T4.2 instruction-following check (6 cats × 10) ==="
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.venv/bin/python -m tsmm.eval.instruct_check \
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--data data/eval_synth.jsonl \
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--per_cat 10 \
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--stage2_ckpt "$CKPT" \
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--judge_backend stub \
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--out reports/instruct_check.json
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echo
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echo "=== T5.5/T5.6 full eval report (model + trivial baselines) ==="
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.venv/bin/python -m tsmm.eval.report \
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--synth data/eval_synth.jsonl \
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--model \
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--stage2_ckpt "$CKPT" \
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--judge_backend stub \
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--report_dir reports
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echo
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echo "[run_m4_m5_eval] done. See reports/."
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