* Add a new API function for predicting on `DMatrix`. This function aligns
with rest of the `XGBoosterPredictFrom*` functions on semantic of function
arguments.
* Purge `ntree_limit` from libxgboost, use iteration instead.
* [dask] Use `inplace_predict` by default for dask sklearn models.
* [dask] Run prediction shape inference on worker instead of client.
The breaking change is in the Python sklearn `apply` function, I made it to be
consistent with other prediction functions where `best_iteration` is used by
default.
* Accept array interface for csr and array.
* Accept an optional proxy dmatrix for metainfo.
This constructs an explicit `_ProxyDMatrix` type in Python.
* Remove unused doc.
* Add strict output.
* 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.
* 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>
* Make external memory data partitioning deterministic.
* Change the meaning of `page_size` from bytes to number of rows.
* Design a data pool.
* Note for external memory.
* Enable unity build on Windows CI.
* Force garbage collect on test.
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>
* Removed some warnings
* Rebase with master
* Solved C++ Google Tests errors made by refactoring in order to remove warnings
* Undo renaming path -> path_
* Fix style check
Co-authored-by: Hyunsu Cho <chohyu01@cs.washington.edu>
* Disable JSON serialization for now.
* Multi-class classification is checkpointing for each iteration.
This brings significant overhead.
Revert: 90355b4f007ae
* Set R tests to use binary.
* 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>
* Supply `-G;-src-in-ptx` when `USE_DEVICE_DEBUG` is set and debug mode is selected.
* Refactor CMake script to gather all CUDA configuration.
* Use CMAKE_CUDA_ARCHITECTURES. Close#6029.
* Add compute 80. Close#5999
* fixed some endian issues
* Use dmlc::ByteSwap() to simplify code
* Fix lint check
* [CI] Add test for s390x
* Download latest CMake on s390x
* Fix a bug in my code
* Save magic number in dmatrix with byteswap on big-endian machine
* Save version in binary with byteswap on big-endian machine
* Load scalar with byteswap in MetaInfo
* Add a debugging message
* Handle arrays correctly when byteswapping
* EOF can also be 255
* Handle magic number in MetaInfo carefully
* Skip Tree.Load test for big-endian, since the test manually builds little-endian binary model
* Handle missing packages in Python tests
* Don't use boto3 in model compatibility tests
* Add s390 Docker file for local testing
* Add model compatibility tests
* Add R compatibility test
* Revert "Add R compatibility test"
This reverts commit c2d2bdcb7dbae133cbb927fcd20f7e83ee2b18a8.
Co-authored-by: Qi Zhang <q.zhang@ibm.com>
Co-authored-by: Hyunsu Cho <chohyu01@cs.washington.edu>
