问题描述
我开始使用python xgboost
支持.有没有一种方法可以在每个训练时期获得训练和验证错误?我在文档
I started using python xgboost
backage. Is there a way to get training and validation errors at each training epoch? I can't find one in the documentation
已经训练了一个简单的模型并获得了输出:
Have trained a simple model and got output:
[0]评估均方根:0.407474火车均方根:0.346349 [09:17:37] src/tree/updater_prune.cc:74:树修剪结束,有1个根,额外有116个 节点,0个修剪的节点,max_depth = 6
[0] eval-rmse:0.407474 train-rmse:0.346349 [09:17:37] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 116 extra nodes, 0 pruned nodes, max_depth=6
1 eval-rmse:0.410902 train-rmse: 0.339925 [09:17:38] src/tree/updater_prune.cc:74:树修剪结束,有1个根,额外有124个 节点,0个修剪的节点,max_depth = 6
1 eval-rmse:0.410902 train-rmse:0.339925 [09:17:38] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 124 extra nodes, 0 pruned nodes, max_depth=6
[2]评估均方根:0.413563火车均方根:0.335941 [09:17:38] src/tree/updater_prune.cc:74:修剪树结束,1个根,额外126个 节点,0个修剪的节点,max_depth = 6
[2] eval-rmse:0.413563 train-rmse:0.335941 [09:17:38] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 126 extra nodes, 0 pruned nodes, max_depth=6
[3]评估均方根:0.418412火车均方根:0.333071 [09:17:38] src/tree/updater_prune.cc:74:树修剪结束,1个根,额外114个 节点,0个修剪的节点,max_depth = 6
[3] eval-rmse:0.418412 train-rmse:0.333071 [09:17:38] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 114 extra nodes, 0 pruned nodes, max_depth=6
但是我需要在代码中进一步传递这些eval-rmse
和train-rmse
,或者至少绘制这些曲线.
However I need to pass these eval-rmse
and train-rmse
further in code or at least plot these curves.
推荐答案
保存中间结果的一种方法是将evals_result
参数传递给xgb.train
方法.
One way to save your intermediate results is by passing evals_result
argument to xgb.train
method.
假设您已经创建了XGB格式的train
和eval
矩阵,并为XGBoost初始化了一些参数params
(在我的情况下为params = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic' }
).
Let's say you have created a train
and an eval
matrix in XGB format, and have initialized some parameters params
for XGBoost (In my case, params = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic' }
).
-
创建一个空的字典
Create an empty dict
progress = dict()
创建一个监视列表,(考虑到您正在打印train-rmse,我想您已经有了它)
Create a watchlist, (I guess you already have it given that you are printing train-rmse)
watchlist = [(train,'train-rmse'), (eval, 'eval-rmse')]
将这些传递给xgb.train
bst = xgb.train(param, train, 10, watchlist, evals_result=progress)
在迭代结束时,progress
词典将包含所需的训练/验证错误
At the end of iteration, the progress
dictionary will contain the desired train/validation errors
> print progress
{'train-rmse': {'error': ['0.50000', ....]}, 'eval-rmse': { 'error': ['0.5000',....]}}
这篇关于xgboost中的访问训练和评估错误的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持!