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，接下来是完整的代码，包括训练最优模型和对预测集进行预测。

import pandas as pd
import xgboost as xgb
from bayes_opt import BayesianOptimization
from sklearn.metrics import mean_squared_error

# 加载数据集
train_data = pd.read_csv('Train.csv')
test_data = pd.read_csv('Dataset for Predict TC.csv')

X_train = train_data.drop('target', axis=1)
y_train = train_data['target']
X_test = test_data.drop('target', axis=1)
y_test = test_data['target']

# 定义xgboost回归模型和评估函数
def xgb_evaluate(min_child_weight,
                 colsample_bytree,
                 max_depth,
                 subsample,
                 gamma,
                 alpha,
                 learning_rate):
    
    params = {'eval_metric': 'rmse',
              'min_child_weight': int(min_child_weight),
              'cosample_bytree': colsample_bytree,
              'max_depth': int(max_depth),
              'subsample': subsample,
              'gamma': gamma,
              'alpha': alpha,
              'learning_rate': learning_rate,
              'n_estimators': 1000,
              'silent': 1,
              'n_jobs': -1}
    
    xgb_model = xgb.XGBRegressor(**params)
    
    xgb_model.fit(X_train, y_train)
    
    y_pred = xgb_model.predict(X_test)
    
    mse = mean_squared_error(y_test, y_pred)
    
    return -mse

# 定义超参数空间并实例化BayesianOptimization类
xgb_bo = BayesianOptimization(xgb_evaluate,
                              {'min_child_weight': (1, 20),
                               'colsample_bytree': (0.1, 1),
                               'max_depth': (5, 20),
                               'subsample': (0.5, 1),
                               'gamma': (0, 10),
                               'alpha': (0, 10),
                               'learning_rate': (0.01, 0.3)})

# 进行超参数优化
xgb_bo.maximize(init_points=5, n_iter=25)

# 输出最优超参数组合
print(xgb_bo.max)

# 训练最优模型
best_params = xgb_bo.max['params']
xgb_model = xgb.XGBRegressor(eval_metric='rmse',
                             min_child_weight=int(best_params['min_child_weight']),
                             colsample_bytree=best_params['colsample_bytree'],
                             max_depth=int(best_params['max_depth']),
                             subsample=best_params['subsample'],
                             gamma=best_params['gamma'],
                             alpha=best_params['alpha'],
                             learning_rate=best_params['learning_rate'],
                             n_estimators=1000,
                             silent=1,
                             n_jobs=-1)

xgb_model.fit(X_train, y_train)

# 对预测集进行预测
y_pred = xgb_model.predict(X_test)

# 输出预测结果
print(y_pred)

请注意，这只是一个示例，你可能需要根据你的特定数据集和问题进行一些调整。此外，超参数优化可能需要较长的时间，具体取决于你的数据集和定义的超参数空间。