Create scorer.py

scorer.py

@@ -0,0 +1,76 @@
+import re
+from dataclasses import dataclass
+from typing import Dict, Any, List
+
+@dataclass
+class ScoreResult:
+    score: float
+    details: Dict[str, Any]
+
+def _has(text: str, pats: List[str]) -> bool:
+    t = (text or "").lower()
+    return any(re.search(p, t) for p in pats)
+
+def score(sample: Dict[str, Any], prediction: str) -> ScoreResult:
+    pred = (prediction or "").strip()
+    words = len([w for w in re.split(r"\s+", pred) if w])
+
+    max_words = 200
+    m = re.search(r"under\s+(\d+)\s+words", (sample.get("constraints") or "").lower())
+    if m:
+        max_words = int(m.group(1))
+    length_ok = 1 if words <= max_words else 0
+
+    # State preservation
+    state_good = [
+        r"\bas (approved|logged|declared|active)\b",
+        r"\bremains\b",
+        r"\bstill\b",
+        r"\bper record\b",
+        r"\bper policy\b"
+    ]
+    state_bad = [
+        r"\bapproved now\b",
+        r"\ball clear\b",
+        r"\bwe agreed\b",
+        r"\bfinalized\b"
+    ]
+    state = 1 if (_has(pred, state_good) and not _has(pred, state_bad)) else 0
+
+    # Restatement of prior decision or status
+    restate = 1 if _has(pred, [r"\bwas approved\b", r"\bwas denied\b", r"\bongoing\b", r"\bpending\b", r"\bfrozen\b"]) else 0
+
+    # Uncertainty discipline
+    uncertainty = 1 if _has(pred, [r"\bunknown\b", r"\bpending\b", r"\bawaiting\b"]) else 0
+
+    # Next step clarity
+    next_step = 1 if _has(pred, [r"\bnext\b", r"\bby\b.*\b(today|tomorrow|\d+)\b", r"\bwill\b.*\bupdate\b"]) else 0
+
+    raw = (
+        0.30 * length_ok +
+        0.30 * state +
+        0.20 * restate +
+        0.10 * uncertainty +
+        0.10 * next_step
+    )
+    final = max(0.0, min(1.0, raw))
+
+    return ScoreResult(
+        score=final,
+        details={
+            "word_count": words,
+            "max_words": max_words,
+            "length_ok": length_ok,
+            "state_preservation": state,
+            "restate": restate,
+            "uncertainty": uncertainty,
+            "next_step": next_step,
+            "context_pressure": sample.get("context_pressure"),
+            "domain": sample.get("domain"),
+        },
+    )
+
+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)}