feat(m5): full eval pipeline — parse/metrics/judge/baselines/report (T5.1-T5.5)

- src/tsmm/eval/parse_answer.py: text→JSON segments→point scores (JSON-first,
  regex fallback, all-zero on fail).
- src/tsmm/eval/ts_metrics.py: VUS-PR (main, threshold-free; constant-score →
  prevalence, NOT inflated), AUC-PR, Point-F1 (no PA), PA-F1 (control only),
  self-contained Affiliation-F1.
- src/tsmm/eval/qa_judge.py: RuleJudge (anomaly IoU / describe+forecast numeric
  tolerance) + LLMJudge (stub backend = deterministic offline heuristic for the
  no-network host; openai backend = GPT-4o 0-5; pluggable callable).
- src/tsmm/eval/baselines.py: trivial (Random/Constant/AllReport) + ts_to_text
  + pure_llm_answer glue; Time-LLM/ChatTS marked not-reproduced (honest).
- src/tsmm/eval/report.py + scripts/run_eval.sh: trained model + all baselines
  through the SAME pipeline → reports/eval-YYYYMMDD.md (VUS-PR main, PA-F1
  explicitly labelled control, trivial baselines included).
- tests: 45 new (parse 13, metrics 12, judge 12, baselines 8). 145 passing.
- Smoke: run_eval on 40 samples produces valid markdown; PA-F1≈1.0 vs
  VUS-PR≈0.35 for all_report demonstrates why PA must not be the headline.

scripts/run_eval.sh

@@ -0,0 +1,17 @@
+#!/usr/bin/env bash
+# Full evaluation (T5.5): trained model + all baselines through the same
+# parse_answer + ts_metrics pipeline → reports/eval-YYYYMMDD.md
+#
+# Usage:
+#   scripts/run_eval.sh                   # trivial baselines only (CPU, fast)
+#   scripts/run_eval.sh --model           # include the trained model (GPU)
+set -euo pipefail
+cd "$(dirname "$0")/.."
+
+export PYTHONPATH=src
+.venv/bin/python -m tsmm.eval.report \
+  --synth data/eval_synth.jsonl \
+  --real data/eval_real.jsonl \
+  --report_dir reports \
+  --judge_backend "${JUDGE_BACKEND:-stub}" \
+  "$@"