"""XGBoost 推論 API 套件。"""

from __future__ import annotations

from dataclasses import dataclass
from pathlib import Path
from typing import Any

import numpy as np
from xgboost import Booster, DMatrix


@dataclass(frozen=True)
class XGBoostPrediction:
    home_win: float
    draw: float
    away_win: float


def _safe_probability(x: float) -> float:
    return float(max(0.0, min(1.0, x)))


class XGBoostPredictor:
    """XGBoost 預測器：輸入特徵 => 輸出 1x2 機率。"""

    def __init__(
        self,
        model_path: str | None = None,
        *,
        feature_columns: list[str] | None = None,
    ) -> None:
        self.feature_columns = feature_columns or []
        self.model_path = model_path
        self.model = self._load_model(model_path) if model_path else None

    def _load_model(self, model_path: str | None) -> Booster | None:
        if not model_path:
            return None
        path = Path(model_path)
        if not path.exists():
            return None
        model = Booster()
        model.load_model(str(path))
        return model

    def predict_match_outcome(self, features: dict[str, float]) -> dict[str, float]:
        """輸出主勝/平/客勝機率。"""

        if self.model is None:
            # fallback: 均分
            return {'home': 1 / 3, 'draw': 1 / 3, 'away': 1 / 3}

        ordered_values = [float(features.get(col, 0.0)) for col in self.feature_columns]
        dmatrix = DMatrix(np.array([ordered_values]), feature_names=self.feature_columns)
        probs = self.model.predict(dmatrix)

        if probs.ndim == 1:
            probs = probs.reshape(1, -1)
        arr = probs[0]
        if arr.size < 3:
            raise ValueError('模型輸出維度不足 3')

        raw = np.array(arr[:3], dtype=float)
        raw = np.maximum(raw, 0.0)
        s = raw.sum()
        if s <= 0:
            raise ValueError('模型輸出總和異常為 0')
        norm = raw / s

        return {'home': _safe_probability(norm[0]), 'draw': _safe_probability(norm[1]), 'away': _safe_probability(norm[2])}

    def find_model_edge(
        self,
        ml_probs: dict[str, float],
        bookmaker_implied_probs: dict[str, float],
    ) -> list[dict[str, Any]]:
        """回傳模型超越莊家 4% 以上的投注選項。"""

        mapping = [('home', 'home'), ('draw', 'draw'), ('away', 'away')]
        outputs: list[dict[str, Any]] = []

        for model_key, book_key in mapping:
            ml_v = float(ml_probs.get(model_key, 0.0))
            book_v = float(bookmaker_implied_probs.get(book_key, 0.0))
            edge = ml_v - book_v

            if edge >= 0.04:
                outputs.append(
                    {
                        'selection': model_key,
                        'ml_prob': round(ml_v, 6),
                        'bookmaker_implied_prob': round(book_v, 6),
                        'edge': round(edge, 6),
                        'label': 'Strong Buy',
                    },
                )

        return outputs