File size: 2,458 Bytes
c687548 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 | import optuna
import pandas as pd
import numpy as np
from xgboost import XGBRegressor
from sklearn.model_selection import KFold, cross_val_score
from scipy.stats import pearsonr
# 配置
class Config:
TRAIN_PATH = "/AI4M/users/mjzhang/workspace/DRW/data/train.parquet"
FEATURES = [
"X863", "X856", "X598", "X862", "X385", "X852", "X603", "X860", "X674",
"X345", "X855", "X302", "X178", "X168", "X612", "sell_qty",
"bid_qty", "ask_qty", "buy_qty", "volume"
]
LABEL_COLUMN = "label"
N_FOLDS = 3
RANDOM_STATE = 42
def pearson_scorer(y_true, y_pred):
return pearsonr(y_true, y_pred)[0]
def objective(trial):
train_df = pd.read_parquet(Config.TRAIN_PATH, columns=Config.FEATURES + [Config.LABEL_COLUMN])
X = train_df[Config.FEATURES]
y = train_df[Config.LABEL_COLUMN]
params = {
"tree_method": "hist",
"device": "gpu",
"colsample_bylevel": trial.suggest_float("colsample_bylevel", 0.2, 1.0),
"colsample_bynode": trial.suggest_float("colsample_bynode", 0.2, 1.0),
"colsample_bytree": trial.suggest_float("colsample_bytree", 0.2, 1.0),
"gamma": trial.suggest_float("gamma", 0, 5),
"learning_rate": trial.suggest_float("learning_rate", 0.01, 0.05, log=True),
"max_depth": trial.suggest_int("max_depth", 3, 24),
"max_leaves": trial.suggest_int("max_leaves", 4, 32),
"min_child_weight": trial.suggest_int("min_child_weight", 1, 32),
"n_estimators": trial.suggest_int("n_estimators", 300, 2000),
"subsample": trial.suggest_float("subsample", 0.05, 1.0),
"reg_alpha": trial.suggest_float("reg_alpha", 0, 50),
"reg_lambda": trial.suggest_float("reg_lambda", 0, 100),
"verbosity": 0,
"random_state": Config.RANDOM_STATE,
"n_jobs": -1
}
model = XGBRegressor(**params)
kf = KFold(n_splits=Config.N_FOLDS, shuffle=True, random_state=Config.RANDOM_STATE)
scores = cross_val_score(model, X, y, cv=kf, scoring="r2", n_jobs=-1)
mean_score = np.mean(scores)
# 限制分数，防止过拟合
if mean_score > 0.25:
return 0 # 或者 return -1，或者 return 0
return mean_score
if __name__ == "__main__":
study = optuna.create_study(direction="maximize")
study.optimize(objective, n_trials=15) # 可根据算力调整n_trials
print("最优参数：", study.best_params)
print("最优得分：", study.best_value) |