scorer.py

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

REQ = [
    "crowded_agent_type",
    "capacity_utilization_score",
    "reversal_risk_score",
    "unwind_trigger_conditions",
    "time_to_capacity_event_hours",
]

AGENTS = ["cta_trend", "options_hedging", "distressed_seller", "market_maker", "fundamental_growth"]

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

def _has_float_0_1(p: str) -> bool:
    return bool(re.search(r"\b0\.\d+\b", p)) or "1.0" in p

def _has_agent(p: str) -> bool:
    return any(a in p for a in AGENTS)

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

def score(sample: Dict[str, Any], prediction: str) -> ScoreResult:
    p = (prediction or "").lower()
    words_ok = len(p.split()) <= 900

    hits = sum(1 for k in REQ if k in p)
    has_nums = _has_float_0_1(p)
    has_agent = _has_agent(p)
    has_time = _has_time(p)

    raw = (
        0.20 * int(words_ok) +
        0.60 * (hits / len(REQ)) +
        0.10 * int(has_nums) +
        0.05 * int(has_agent) +
        0.05 * int(has_time)
    )

    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)}