Create scorer.py

scorer.py

@@ -0,0 +1,71 @@
+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
+
+    # Intent preservation
+    intent_good = [
+        r"\bfirst\b", r"\bthe intent is\b", r"\bwhat you asked\b",
+        r"\bkeeping to\b", r"\banswering your\b"
+    ]
+    intent_bad = [
+        r"\blet me also\b", r"\bby the way\b", r"\bseparately\b",
+        r"\bhere's a draft\b", r"\bwe could also\b"
+    ]
+    intent = 1 if (_has(pred, intent_good) and not _has(pred, intent_bad)) else 0
+
+    # Scope control
+    scope = 1 if _has(pred, [r"\bpark\b", r"\bcan take that next\b", r"\bseparate task\b", r"\bafter this\b"]) else 0
+
+    # Precision discipline
+    precision = 1 if _has(pred, [r"\brange\b", r"\bconditions\b", r"\bcriteria\b"]) else 0
+
+    # Action focus
+    action = 1 if _has(pred, [r"\bnext\b", r"\bdecision\b", r"\bapprove\b", r"\bdeny\b", r"\btest\b"]) else 0
+
+    raw = (
+        0.30 * length_ok +
+        0.30 * intent +
+        0.15 * scope +
+        0.15 * precision +
+        0.10 * action
+    )
+    final = max(0.0, min(1.0, raw))
+
+    return ScoreResult(
+        score=final,
+        details={
+            "word_count": words,
+            "max_words": max_words,
+            "length_ok": length_ok,
+            "intent_preservation": intent,
+            "scope_control": scope,
+            "precision": precision,
+            "action_focus": action,
+            "intent_pressure": sample.get("intent_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)}