* Prepare for improving Windows networking compatibility.
* Include dmlc filesystem indirectly as dmlc/filesystem.h includes windows.h, which
conflicts with winsock2.h
* Define `NOMINMAX` conditionally.
* Link the winsock library when mysys32 is used.
* Add config file for read the doc.
* Use the name `Context`.
* Pass a context object into `SetInfo`.
* Add context to proxy matrix.
* Add context to iterative DMatrix.
This is to remove the use of the default number of threads during `SetInfo` as a follow-up on
removing the global omp variable while preparing for CUDA stream semantic. Currently, XGBoost
uses the legacy CUDA stream, we will gradually remove them in the future in favor of non-blocking streams.
* Add a new ctor to tensor for `initilizer_list`.
* Change labels from host device vector to tensor.
* Rename the field from `labels_` to `labels` since it's a public member.
This PR changes base_margin into a 3-dim array, with one of them being reserved for multi-target classification. Also, a breaking change is made for binary serialization due to extra dimension along with a fix for saving the feature weights. Lastly, it unifies the prediction initialization between CPU and GPU. After this PR, the meta info setter in Python will be based on array interface.
This is already partially supported but never properly tested. So the only possible way to use it is calling `numpy.ndarray.flatten` with `base_margin` before passing it into XGBoost. This PR adds proper support
for most of the data types along with tests.
- 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.
* 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 thread local return entry for DMatrix.
* Save feature name and feature type in binary file.
Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
* Group aware GPU weighted sketching.
* Distribute group weights to each data point.
* Relax the test.
* Validate input meta info.
* Fix metainfo copy ctor.
* Turn xgboost::DataType into C++11 enum class
* New binary serialization format for DMatrix::MetaInfo
* Fix clang-tidy
* Fix c++ test
* Implement new format proposal
* Move helper functions to anonymous namespace; remove unneeded field
* Fix lint
* Add shape.
* Keep only roundtrip test.
* Fix test.
* various fixes
* Update data.cc
Co-authored-by: Jiaming Yuan <jm.yuan@outlook.com>
* Extract interaction constraints from split evaluator.
The reason for doing so is mostly for model IO, where num_feature and interaction_constraints are copied in split evaluator. Also interaction constraint by itself is a feature selector, acting like column sampler and it's inefficient to bury it deep in the evaluator chain. Lastly removing one another copied parameter is a win.
* Enable inc for approx tree method.
As now the implementation is spited up from evaluator class, it's also enabled for approx method.
* Removing obsoleted code in colmaker.
They are never documented nor actually used in real world. Also there isn't a single test for those code blocks.
* Unifying the types used for row and column.
As the size of input dataset is marching to billion, incorrect use of int is subject to overflow, also singed integer overflow is undefined behaviour. This PR starts the procedure for unifying used index type to unsigned integers. There's optimization that can utilize this undefined behaviour, but after some testings I don't see the optimization is beneficial to XGBoost.
* Use CMake config file for representing version.
* Generate c and Python version file with CMake.
The generated file is written into source tree. But unless XGBoost upgrades
its version, there will be no actual modification. This retains compatibility
with Makefiles for R.
* Add XGBoost version the DMatrix binaries.
* Simplify prefetch detection in CMakeLists.txt
* DMatrix refactor 2
* Remove buffered rowset usage where possible
* Transition to c++11 style iterators for row access
* Transition column iterators to C++ 11
* Replaced std::vector with HostDeviceVector in MetaInfo and SparsePage.
- added distributions to HostDeviceVector
- using HostDeviceVector for labels, weights and base margings in MetaInfo
- using HostDeviceVector for offset and data in SparsePage
- other necessary refactoring
* Added const version of HostDeviceVector API calls.
- const versions added to calls that can trigger data transfers, e.g. DevicePointer()
- updated the code that uses HostDeviceVector
- objective functions now accept const HostDeviceVector<bst_float>& for predictions
* Updated src/linear/updater_gpu_coordinate.cu.
* Added read-only state for HostDeviceVector sync.
- this means no copies are performed if both host and devices access
the HostDeviceVector read-only
* Fixed linter and test errors.
- updated the lz4 plugin
- added ConstDeviceSpan to HostDeviceVector
- using device % dh::NVisibleDevices() for the physical device number,
e.g. in calls to cudaSetDevice()
* Fixed explicit template instantiation errors for HostDeviceVector.
- replaced HostDeviceVector<unsigned int> with HostDeviceVector<int>
* Fixed HostDeviceVector tests that require multiple GPUs.
- added a mock set device handler; when set, it is called instead of cudaSetDevice()
* add qid for https://github.com/dmlc/xgboost/issues/2748
* change names
* change spaces
* change qid to bst_uint type
* change qid type to size_t
* change qid first to SIZE_MAX
* change qid type from size_t to uint64_t
* update dmlc-core
* fix qids name error
* fix group_ptr_ error
* Style fix
* Add qid handling logic to SparsePage
* New MetaInfo format + backward compatibility fix
Old MetaInfo format (1.0) doesn't contain qid field. We still want to be able
to read from MetaInfo files saved in old format. Also, define a new format
(2.0) that contains the qid field. This way, we can distinguish files that
contain qid and those that do not.
* Update MetaInfo test
* Simply group assignment logic
* Explicitly set qid=nullptr in NativeDataIter
NativeDataIter's callback does not support qid field. Users of NativeDataIter
will need to call setGroup() function separately to set group information.
* Save qids_ in SaveBinary()
* Upgrade dmlc-core submodule
* Add a test for reading qid
* Add contributor
* Check the size of qids_
* Document qid format
