scorer.py

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

REQ = [
    "platform_coherence_score",
    "zone_pressure_asymmetry_index",
    "vortex_system_integrity_flags",
    "localized_collapse_zones",
    "balance_shift_risk_score",
]

ZONE_HINT = ["floor_right", "floor_left", "diffuser", "front_wing", "none"]
FLAG_HINT = ["ok", "fail", "weak", "stall", "transient"]

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

def _has_float(p: str) -> bool:
    return bool(re.search(r"\b0\.\d+\b", p)) or "1.0" in p

def _has_zone(p: str) -> bool:
    return any(z in p for z in ZONE_HINT)

def _has_flag(p: str) -> bool:
    return any(f in p for f in FLAG_HINT)

def score(sample: Dict[str, Any], prediction: str) -> ScoreResult:
    p = (prediction or "").lower()
    words_ok = len(p.split()) <= 1000

    hits = sum(1 for k in REQ if k in p)
    has_float = _has_float(p)
    has_zone = _has_zone(p)
    has_flag = _has_flag(p)

    raw = (
        0.20 * int(words_ok) +
        0.60 * (hits / len(REQ)) +
        0.10 * int(has_float) +
        0.05 * int(has_zone) +
        0.05 * int(has_flag)
    )

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

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)}