scorer.py

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

SYSTEMS = {
    "sleep_circadian",
    "autonomic",
    "immune_inflammatory",
    "metabolic",
    "neurocognitive",
    "gut_microbiome",
    "behavior_load",
    "subjective_experience",
}

VECTOR_KEYS = ["direction=", "magnitude=", "velocity=", "coupling_loss=", "onset_time=", "cross_modal_consensus="]

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

def _ints(text: str) -> List[int]:
    return [int(x) for x in re.findall(r"\b\d{1,3}\b", text) if 0 <= int(x) <= 100]

def score(sample: Dict[str, Any], prediction: str) -> ScoreResult:
    p = (prediction or "").lower().strip()
    words_ok = len(p.split()) <= 360

    keys_ok = sum(1 for k in VECTOR_KEYS if k in p) >= 4
    sys_ok = any(s in p for s in SYSTEMS)

    nums = _ints(p)
    has_sev = len(nums) >= 1

    evidence_ref = any(k in p for k in ["envelope", "beyond", "break", "coupling", "predicts", "decouples"])

    raw = (
        0.20 * int(words_ok) +
        0.30 * int(keys_ok) +
        0.20 * int(sys_ok) +
        0.20 * int(has_sev) +
        0.10 * int(evidence_ref)
    )
    return ScoreResult(score=min(1.0, raw), details={"id": sample.get("id"), "keys_ok": keys_ok, "sys_ok": sys_ok})

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)}