Python 使用贝叶斯优化绘制xgboost评估指标

我正在使用这段代码对XGBoost进行贝叶斯优化调优和训练。我想根据时代来绘制日志损失，但我还没有找到一种方法

这是我的XGBoost代码：

def bayes_fun(parameters):
    '''
    Function that that sets paramters and performs cross-validation for Bayesian Optimisation
    '''
    parameters = parameters[0]                                # setting regressor parameters
    reg = xgb.XGBClassifier(objective = 'multi:softmax',       # objective function (target variable follows poisson distribution)
                               num_class = 3,
                               eval_metric = 'mlogloss',          # eval metric for poisson distribution
                               base_score = 1,                    # specific to scoring 
                               learning_rate = [8]               # learning rate
                               random_state = 1234,               # set seed
                               max_depth = int(parameters[0]),    # maximum depth trees can grow
                               min_child_weight=parameters[1],    # amount of weight for a tree to produce a child
                               gamma = parameters[2],             # controls minimum loss reduction
                               reg_alpha = parameters[3],         # L1 regularisation term on weights
                               reg_lambda = parameters[4],        # L2 regularisation term on weights
                               subsample = parameters[5],         # ratio of the training instances
                               colsample_bytree = parameters[6],  # ratio of number of features used when constructing a tree 
                               colsample_bylevel = parameters[7],
                               max_delta_step =parameters[9]) # ratio of number of features used at each node of each tree
    
    reg_param = reg.get_xgb_params() # getting parameters from regressor
    
    # cross validation
    bst = xgb.cv(params = reg_param,          # setting parameters
                 dtrain = DTrain,             # training data 
                 folds = folds_list,          # list of folds to use
                 num_boost_round = 9999,      # number of iterations
                 early_stopping_rounds = 30,  # early stopping (reduce overfitting)
                 verbose_eval = False)        # don\t show output text
    
    score = -100 * bst['test-mlogloss-mean'].iloc[-1] # if you are using negative log-likelyhood use minus
    return score

# set parameter ranges
domains = [{'name': 'max_depth', 'type': 'discrete', 'domain': (2, 10)},
           {'name': 'minchild', 'type': 'continuous', 'domain': (5, 500)},
           {'name': 'gamma', 'type': 'continuous', 'domain': (0, 1)},
           {'name': 'alpha', 'type': 'continuous', 'domain': (0, 10)},
           {'name': 'lmbd', 'type': 'continuous', 'domain': (0, 50)},
           {'name': 'subsample', 'type': 'continuous', 'domain': (0.50, 0.99)},
           {'name': 'colsample_bytree', 'type': 'continuous', 'domain': (0.40, 0.70)},
           {'name': 'colsample_bylevel', 'type': 'continuous', 'domain': (0.40, 0.70)},
           {'name': 'learning_rate', 'type': 'continuous', 'domain': (0,1)},
           {'name': 'max_delta_step', 'type': 'continuous', 'domain': (0,1)}]

# Bayesian Optmisation (check package notes to adjust parameters to suit your data)
optimizer = BayesianOptimization(f=bayes_fun, domain=domains, model_type='GP', acquisition_type='LCB', initial_design_type = 'latin', initial_design_numdata=5, exact_feval=True, maximize=False)

# run Bayesian for 15 rounds
optimizer.run_optimization(max_iter=200, verbosity=True)

下面是我找到的一段代码，用于获取每个历元图的logloss，但它只是通过一个简单的

XGBClassifier（）

调用：

model.fit(X, data_nobands2.categoric, eval_metric=['merror','mlogloss'], eval_set=eval_s)

results = model.evals_result()
epochs = len(results['validation_0']['merror'])

import matplotlib.pyplot as pyplot
x_axis = range(0, epochs)
# plot log loss
fig, ax = pyplot.subplots()
ax.plot(x_axis, results['validation_0']['mlogloss'], label='Train')
ax.plot(x_axis, results['validation_1']['mlogloss'], label='Test')
ax.legend()
pyplot.ylabel('Log Loss')
pyplot.title('XGBoost Log Loss')
pyplot.show()

我认为最后一段代码中可能有一部分可以用于贝叶斯优化，但我不这么认为；我不知道怎么做。有没有人这样做过，或者类似的事情