Create scorer.py

scorer.py

@@ -0,0 +1,74 @@
+from dataclasses import dataclass
+from typing import Dict, Any, List
+import re
+
+REQ = [
+    "decoherence_onset_timestamp",
+    "coherence_drop_delta",
+    "affected_modalities",
+    "narrative_conflict_flag",
+    "onset_confidence",
+    "early_warning_score",
+]
+
+@dataclass
+class ScoreResult:
+    score: float
+    details: Dict[str, Any]
+
+def _time_ok(p: str):
+    # accepts t=6.2s or 6.2s
+    m = re.search(r"decoherence_onset_timestamp\s*[:=]\s*(t\s*=\s*)?([0-9]+(\.[0-9]+)?)\s*s", p)
+    if not m:
+        return None
+    return float(m.group(2))
+
+def _f(p: str, key: str):
+    m = re.search(rf"{key}\s*[:=]\s*(0\.\d+|1\.0)\b", p)
+    return float(m.group(1)) if m else None
+
+def _i(p: str, key: str):
+    m = re.search(rf"{key}\s*[:=]\s*(\d+)\b", p)
+    return int(m.group(1)) if m else None
+
+def score(sample: Dict[str, Any], prediction: str) -> ScoreResult:
+    p = (prediction or "").lower()
+    words_ok = len(p.split()) <= 900
+
+    hits = sum(1 for k in REQ if k in p)
+
+    t = _time_ok(p)
+    delta = _f(p, "coherence_drop_delta")
+    conf = _f(p, "onset_confidence")
+    warn = _f(p, "early_warning_score")
+    flag = _i(p, "narrative_conflict_flag")
+
+    numeric_ok = int(
+        t is not None and 0.0 <= t <= 120.0 and
+        delta is not None and 0.0 <= delta <= 1.0 and
+        conf is not None and 0.0 <= conf <= 1.0 and
+        warn is not None and 0.0 <= warn <= 1.0 and
+        flag is not None and flag in [0, 1]
+    )
+
+    mods_ok = int("affected_modalities" in p and len(p) > 70)
+
+    # optional sanity: if delta is high, warning should tend high
+    sanity = 0
+    if delta is not None and warn is not None:
+        sanity = int(warn + 0.15 >= delta)
+
+    raw = (
+        0.15 * int(words_ok) +
+        0.45 * (hits / len(REQ)) +
+        0.20 * numeric_ok +
+        0.10 * mods_ok +
+        0.10 * sanity
+    )
+
+    return ScoreResult(score=min(1.0, raw), details={"id": sample.get("id"), "hits": hits, "sanity": sanity})
+
+def aggregate(results: List[ScoreResult]) -> Dict[str, Any]:
+    if not results:
+        return {"mean": 0.0, "n": 0}
+    return {"mean": sum(r.score for r in results)/len(results), "n": len(results)}