scorer.py

from dataclasses import dataclass
from typing import Dict, Any, List
import re

REQ_KEYS = [
    "inferred_tactical_rules",
    "rule_trigger_conditions",
    "rule_execution_latency",
    "rule_consistency_score",
    "dominant_rule_clusters",
]

@dataclass
class ScoreResult:
    score: float
    details: Dict[str, Any]

def _has_if_then(p: str) -> bool:
    return ("if " in p and " then " in p) or ("r" in p and ":" in p and "if" in p)

def _has_latency(p: str) -> bool:
    return bool(re.search(r"\b\d+(\.\d+)?\b", p)) and ("sec" in p or "s" in p or "latency" in p)

def _has_trigger_fields(p: str) -> bool:
    # Look for simple key=value trigger style
    return bool(re.search(r"\b[a-z_]+=[a-z0-9_]+\b", p))

def _has_cluster(p: str) -> bool:
    return any(w in p for w in [
        "counter_press",
        "rest_defense",
        "box_protection",
        "direct_defense",
        "shape_reset",
        "zone_press",
        "cluster",
    ])

def score(sample: Dict[str, Any], prediction: str) -> ScoreResult:
    p = (prediction or "").lower()

    words_ok = len(p.split()) <= 950
    hits = sum(1 for k in REQ_KEYS if k.lower() in p)

    has_rule_form = _has_if_then(p)
    has_latency = _has_latency(p)
    has_triggers = _has_trigger_fields(p)
    has_cluster = _has_cluster(p)

    raw = (
        0.20 * int(words_ok) +
        0.30 * (hits / len(REQ_KEYS)) +
        0.20 * int(has_rule_form) +
        0.15 * int(has_triggers) +
        0.10 * int(has_latency) +
        0.05 * int(has_cluster)
    )

    return ScoreResult(
        score=min(1.0, raw),
        details={
            "id": sample.get("id"),
            "hits": hits,
            "has_rule_form": has_rule_form,
            "has_triggers": has_triggers,
            "has_latency": has_latency,
        },
    )

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),
    }