移除main

BetMetices.py

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+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}")