The old (before fix) best_ntree_limit ignores the num_class parameters, which is incorrect. In before we workarounded it in c++ layer to avoid possible breaking changes on other language bindings. But the Python interpretation stayed incorrect. The PR fixed that in Python to consider num_class, but didn't remove the old workaround, so tree calculation in predictor is incorrect, see PredictBatch in CPUPredictor.
* Initial support for distributed LTR using dask.
* Support `qid` in libxgboost.
* Refactor `predict` and `n_features_in_`, `best_[score/iteration/ntree_limit]`
to avoid duplicated code.
* Define `DaskXGBRanker`.
The dask ranker doesn't support group structure, instead it uses query id and
convert to group ptr internally.
* For sklearn:
- Handles user defined objective function.
- Handles `softmax`.
* For dask:
- Use the implementation from sklearn, the previous implementation doesn't perform any extra handling.
* 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>
* 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>
Normal prediction with DMatrix is now thread safe with locks. Added inplace prediction is lock free thread safe.
When data is on device (cupy, cudf), the returned data is also on device.
* Implementation for numpy, csr, cudf and cupy.
* Implementation for dask.
* Remove sync in simple dmatrix.
* Don't set_params at the end of set_state.
* Also fix another issue found in dask prediction.
* Add note about prediction.
Don't support other prediction modes at the moment.
