import pandas as pd
from common.bet_tools import compute_metrics
from common.bet_tools import calculate_no_vig_moneyline_power, moneyline_to_prob
from loguru import logger
from common.utils import timeit


def get_no_vig_prob(row) -> pd.Series:
    odds = [row["first_price"], row["second_price"]]
    no_vig_odds_power = calculate_no_vig_moneyline_power(odds)
    novig_probs_power = [moneyline_to_prob(o) for o in no_vig_odds_power]
    # 返回两个无水概率
    return pd.Series(
        {
            "first_no_vig_prob": novig_probs_power[0],
            "second_no_vig_prob": novig_probs_power[1],
        }
    )


@timeit
def calc_metrics(df: pd.DataFrame, cols: list) -> pd.DataFrame:
    data_list = []
    for cs in df[cols].drop_duplicates().values:
        tmp_df = df[cols + ["win_prob", "res"]].copy()
        for i, col in enumerate(cols):
            tmp_df = tmp_df[tmp_df[col] == cs[i]]
        # if len(tmp_df) < 10000:
        #     continue
        res = compute_metrics(df=tmp_df, include_draws=False)
        res["filter_cols"] = ",".join(cs)
        data_list.append(res)
    res_df = pd.DataFrame(data_list)
    res_df["reg_alpha"] = abs(res_df["reg_alpha"])
    res_df = res_df.sort_values(by=["brier", "logloss", "ece", "reg_alpha"])
    return res_df


if __name__ == "__main__":
    df = pd.read_csv(
        "/Users/aszer/Documents/vscode/bet/data/pinnical_1xbet_all_api.csv",
        encoding="utf-8-sig",
    )
    df = df[
        [
            "sportsbook",
            "sport",
            "league",
            "fixture_id",
            "game_id",
            "market",
            "first_price",
            "second_price",
            "market_width",
            "result",
        ]
    ]
    # 防止 SettingWithCopyWarning，推荐使用 .loc 显式分配
    df.loc[:, ["first_no_vig_prob", "second_no_vig_prob"]] = df[
        ["first_price", "second_price"]
    ].apply(get_no_vig_prob, axis=1)
    df["win_prob"] = df["first_no_vig_prob"]
    df["res"] = df["result"]
    cols = ["sportsbook", "sport"]
    res_df = calc_metrics(df, cols)
    logger.info(f"\n{res_df}")