* Support more input types for categorical data.
* Shorten the type name from "categorical" to "c".
* Tests for np/cp array and scipy csr/csc/coo.
* Specify the type for feature info.
* Add hessian to batch param in preparation of new approx impl.
* Extract a push method for gradient index matrix.
* Use span instead of vector ref for hessian in sketching.
* Create a binary format for gradient index.
* Work around a segfault observed in SparsePage::Push()
* Revert "Work around a segfault observed in SparsePage::Push()"
This reverts commit 30934844d00908750a5442082eb4769b1489f6a9.
* Don't call vector::resize() inside OpenMP block
* Set GITHUB_PAT env var to fix R tests
* Use built-in GITHUB_TOKEN
* Use type aliases for discard iterators
* update to include host_vector as thrust 1.12 doesn't bring it in as a side-effect
* cub::DispatchRadixSort requires signed offset types
- Reduce dependency on dmlc parsers and provide an interface for users to load data by themselves.
- Remove use of threaded iterator and IO queue.
- Remove `page_size`.
- Make sure the number of pages in memory is bounded.
- Make sure the cache can not be violated.
- Provide an interface for internal algorithms to process data asynchronously.
* Support categorical data for dask functional interface and DQM.
* Implement categorical data support for GPU GK-merge.
* Add support for dask functional interface.
* Add support for DQM.
* Get newer cupy.
* Change C API name.
* Test for all primitive types from array.
* Add native support for CPU 128 float.
* Convert boolean and float16 in Python.
* Fix dask version for now.
* 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.
* 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.
* 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.
