贝叶斯优化xgboost回归模型进行超参数寻优，训练好最优模型，并调用训练好的模型对预测集Dataset for Predict TC.CSV进行预测，给出详细代码

好的，以下是实现的详细代码：

import pandas as pd
import numpy as np
import xgboost as xgb
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from bayes_opt import BayesianOptimization

#读取数据集
df = pd.read_csv('Dataset for Train TC.CSV', index_col=0)

#划分训练集和验证集
train, valid = train_test_split(df, test_size=0.2, random_state=42)

#定义目标函数，即xgboost回归模型的交叉验证指标
def xgb_cv(max_depth, gamma, min_child_weight, subsample, colsample_bytree):
    params = {
        'nthread': -1,
        'max_depth': int(max_depth),
        'gamma': gamma,
        'eta': 0.1,
        'min_child_weight': min_child_weight,
        'subsample': max(min(subsample, 1), 0),
        'colsample_bytree': max(min(colsample_bytree, 1), 0),
        'objective': 'reg:squarederror',
        'eval_metric': 'rmse',
        'seed': 42,
    }
    dtrain = xgb.DMatrix(train.drop('TC', axis=1), label=train['TC'])
    cv_result = xgb.cv(params, dtrain, num_boost_round=1000, nfold=5, early_stopping_rounds=50)
    return -cv_result['test-rmse-mean'].iloc[-1]

#定义超参数搜索空间
pbounds = {
    'max_depth': (3, 7),
    'gamma': (0, 1),
    'min_child_weight': (1, 10),
    'subsample': (0.5, 1),
    'colsample_bytree': (0.5, 1),
}

#使用贝叶斯优化进行超参数寻优
optimizer = BayesianOptimization(f=xgb_cv,