- Rework the precision metric for both CPU and GPU.
- Mention it in the document.
- Cleanup old support code for GPU ranking metric.
- Deterministic GPU implementation.
* Drop support for classification.
* type.
* use batch shape.
* lint.
* cpu build.
* cpu build.
* lint.
* Tests.
* Fix.
* Cleanup error message.
* Support sklearn cross validation for ranker.
- Add a convention for X to include a special `qid` column.
sklearn utilities consider only `X`, `y` and `sample_weight` for supervised learning
algorithms, but we need an additional qid array for ranking.
It's important to be able to support the cross validation function in sklearn since all
other tuning functions like grid search are based on cross validation.
* Fix copy for cv. This prevents inserting default callbacks into the input list.
* Clarify the behavior of callbacks in training/cv.
* Fix typos in doc.
A new parameter `custom_metric` is added to `train` and `cv` to distinguish the behaviour from the old `feval`. And `feval` is deprecated. The new `custom_metric` receives transformed prediction when the built-in objective is used. This enables XGBoost to use cost functions from other libraries like scikit-learn directly without going through the definition of the link function.
`eval_metric` and `early_stopping_rounds` in sklearn interface are moved from `fit` to `__init__` and is now saved as part of the scikit-learn model. The old ones in `fit` function are now deprecated. The new `eval_metric` in `__init__` has the same new behaviour as `custom_metric`.
Added more detailed documents for the behaviour of custom objective and metric.
* Implement early stopping with training continuation.
* Add new C API for obtaining boosted rounds.
* Fix off by 1 in `save_best`.
Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
This PR is meant the end the confusion around best_ntree_limit and unify model slicing. We have multi-class and random forests, asking users to understand how to set ntree_limit is difficult and error prone.
* Implement the save_best option in early stopping.
Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
* Remove `learning_rates`.
It's been deprecated since we have callback.
* Set `before_iteration` of `reset_learning_rate` to False to preserve
the initial learning rate, and comply to the term "reset".
Closes#4709.
* Tests for various `tree_method`.
* Fix#3397: early_stop callback does not maximize metric of form NDCG@n-
Early stopping callback makes splits with '-' letter, which interferes
with metrics of form NDCG@n-. As a result, XGBoost tries to minimize
NDCG@n-, where it should be maximized instead.
Fix. Specify maxsplit=1.
* Python 2.x compatibility fix
verbose_eval docs claim it will log the last iteration (http://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.train). this is also consistent w/the behavior from 0.4. not a huge deal but I found it handy to see the last iter's result b/c my period is usually large.
this doesn't address logging the last stage found by early_stopping (as noted in docs) as I'm not sure how to do that.
* Allow using learning_rates parameter when doing CV
- Create a new `callback_cv` method working when called from `xgb.cv()`
- Rename existing `callback` into `callback_train` and make it the default callback
- Get the logic out of the callbacks and place it into a common helper
* Add a learning_rates parameter to cv()
* lint
* remove caller explicit reference
* callback is aware of its calling context
* remove caller argument
* remove learning_rates param
* restore learning_rates for training, but deprecated
* lint
* lint line too long
* quick example for predefined callbacks
*Fix 1439
*Fix python_wrapper when eval set name contain '-' will cause early_stop maximize variable con't set to True propely
Change-Id: Ib0595afd4ae7b445a84c00a3a8faeccc506c6d13
