Using multiple self-defined metrics in LightGBM

Question

Given that we could use self-defined metric in LightGBM and use parameter 'feval' to call it during training. And for given metric, we could define it in the parameter dict like metric:(l1, l2) My question is that how call several self-defined metric at the same time? I cannot use feval=(my_metric1, my_metric2) to get the result

Answer 1

params = {}
params['learning_rate'] = 0.003
params['boosting_type'] = 'goss'
params['objective'] = 'multiclassova'
params['metric'] = ['multi_error', 'multi_logloss']
params['sub_feature'] = 0.8
params['num_leaves'] = 15
params['min_data'] = 600
params['tree_learner'] = 'voting'
params['bagging_freq'] = 3
params['num_class'] = 3
params['max_depth'] = -1
params['max_bin'] = 512
params['verbose'] = -1
params['is_unbalance'] = True
evals_result = {}
aa = lgb.train(params,
                   d_train,
                   valid_sets=[d_train, d_dev],
                   evals_result=evals_result,
                   num_boost_round=4500,
                   feature_name=f_names,
                   verbose_eval=10,
                   categorical_feature = f_names,
                   learning_rates=lambda iter: (1 / (1 + decay_rate * iter)) * params['learning_rate'])

Lets' discuss on the code I share here. d_train is my training set. d_dev is my validation set (I have a different test set.) evals_result will record our multi_error and multi_logloss per iteration as a list. verbose_eval = 10 will make LightGBM print multi_error and multi_logloss of both training set and validation set at every 10 iterations. If you want to plot multi_error and multi_logloss as a graph:

lgb.plot_metric(evals_result, metric='multi_error')
plt.show()

lgb.plot_metric(evals_result, metric='multi_logloss')
plt.show()

You can find other useful functions from LightGBM documentation. If you can't find what you need, go to XGBoost documentation, a simple trick. If there is something missing, please do not hesitate to ask more.