用贝叶斯优化绘制xgboost评估指标

如何解决用贝叶斯优化绘制xgboost评估指标

我正在使用这段代码通过贝叶斯优化来优化和训练XGBoost。我想针对时代绘制对数损失，但是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','domain': (0,1)},{'name': 'alpha',{'name': 'lmbd',50)},{'name': 'subsample','domain': (0.50,0.99)},{'name': 'colsample_bytree','domain': (0.40,0.70)},{'name': 'colsample_bylevel',{'name': 'learning_rate',{'name': 'max_delta_step',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)

这是我发现的获取每个时期图的对数损失的代码，但这只是一个简单的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()

我认为最后一部分代码可能可以用于贝叶斯优化，但是我不知道该怎么做。有没有人做过或类似的事情？