* Add `XGBOOST_RABIT_TRACKER_IP_FOR_TEST` to set rabit tracker IP
* change spark and rabit tracker IP to 127.0.0.1on GitHub Action.
Co-authored-by: fis <jm.yuan@outlook.com>
* Re-implement ROC-AUC.
* Binary
* MultiClass
* LTR
* Add documents.
This PR resolves a few issues:
- Define a value when the dataset is invalid, which can happen if there's an
empty dataset, or when the dataset contains only positive or negative values.
- Define ROC-AUC for multi-class classification.
- Define weighted average value for distributed setting.
- A correct implementation for learning to rank task. Previous
implementation is just binary classification with averaging across groups,
which doesn't measure ordered learning to rank.
* Ensure RMM is 0.18 or later
* Add use_rmm flag to global configuration
* Modify XGBCachingDeviceAllocatorImpl to skip CUB when use_rmm=True
* Update the demo
* [CI] Pin NumPy to 1.19.4, since NumPy 1.19.5 doesn't work with latest Shap
* Save feature info in booster in JSON model.
* [breaking] Remove automatic feature name generation in `DMatrix`.
This PR is to enable reliable feature validation in Python package.
This PR changes predict and inplace_predict to accept a Future of model, to avoid sending models to workers repeatably.
* Document is updated to reflect functionality additions in recent changes.
* [dask] Use a 1 line sample to infer output shape.
This is for inferring shape with direct prediction (without DaskDMatrix).
There are a few things that requires known output shape before carrying out
actual prediction, including dask meta data, output dataframe columns.
* Infer output shape based on local prediction.
* Remove set param in predict function as it's not thread safe nor necessary as
we now let dask to decide the parallelism.
* Simplify prediction on `DaskDMatrix`.
* Add management functions for global configuration: XGBSetGlobalConfig(), XGBGetGlobalConfig().
* Add Python interface: set_config(), get_config(), and config_context().
* Add unit tests for Python
* Add R interface: xgb.set.config(), xgb.get.config()
* Add unit tests for R
Co-authored-by: Jiaming Yuan <jm.yuan@outlook.com>
* [CI] Add noLD test
* Make noLD test only trigger with a PR comment
* [CI] Don't install stringi
* Add the Titanic example as a unit test
* Document trigger
* add to index
* Clarify that it needs to be a review comment
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>
* Change DefaultEvalMetric of classification from error to logloss
* Change default binary metric in plugin/example/custom_obj.cc
* Set old error metric in python tests
* Set old error metric in R tests
* Fix missed eval metrics and typos in R tests
* Fix setting eval_metric twice in R tests
* Add warning for empty eval_metric for classification
* Fix Dask tests
Co-authored-by: Hyunsu Cho <chohyu01@cs.washington.edu>
