xgboost中的访问训练和评估错误 [英] Access train and evaluation error in xgboost
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:
[09:17:37] src/tree/updater_prune.cc:74:树修剪结束，有1个根， 124个额外的节点，0个修剪的节点，max_depth = 6
[09:17:37] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 124 extra nodes, 0 pruned nodes, max_depth=6
[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' }).
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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',....]}}
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