- Define a new data type, the proto file is copied for now.
- Merge client and communicator into `FederatedColl`.
- Define CUDA variant.
- Migrate tests for CPU, add tests for CUDA.
Support adaptive tree, a feature supported by both sklearn and lightgbm. The tree leaf is recomputed based on residue of labels and predictions after construction.
For l1 error, the optimal value is the median (50 percentile).
This is marked as experimental support for the following reasons:
- The value is not well defined for distributed training, where we might have empty leaves for local workers. Right now I just use the original leaf value for computing the average with other workers, which might cause significant errors.
- Some follow-ups are required, for exact, pruner, and optimization for quantile function. Also, we need to calculate the initial estimation.
* 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.
* Added plugin with DPC++-based predictor and objective function
* Update CMakeLists.txt
* Update regression_obj_oneapi.cc
* Added README.md for OneAPI plugin
* Added OneAPI predictor support to gbtree
* Update README.md
* Merged kernels in gradient computation. Enabled multiple loss functions with DPC++ backend
* Aligned plugin CMake files with latest master changes. Fixed whitespace typos
* Removed debug output
* [CI] Make oneapi_plugin a CMake target
* Added tests for OneAPI plugin for predictor and obj. functions
* Temporarily switched to default selector for device dispacthing in OneAPI plugin to enable execution in environments without gpus
* Updated readme file.
* Fixed USM usage in predictor
* Removed workaround with explicit templated names for DPC++ kernels
* Fixed warnings in plugin tests
* Fix CMake build of gtest
Co-authored-by: Hyunsu Cho <chohyu01@cs.washington.edu>
* Set default dtor for SimpleDMatrix to initialize default copy ctor, which is
deleted due to unique ptr.
* Remove commented code.
* Remove warning for calling host function (std::max).
* Remove warning for initialization order.
* Remove warning for unused variables.
* Only define `gpu_id` and `n_gpus` in `LearnerTrainParam`
* Pass LearnerTrainParam through XGBoost vid factory method.
* Disable all GPU usage when GPU related parameters are not specified (fixes XGBoost choosing GPU over aggressively).
* Test learner train param io.
* Fix gpu pickling.
* Enable running objectives with 0 GPU.
* Enable 0 GPU for objectives.
* Add doc for GPU objectives.
* Fix some objectives defaulted to running on all GPUs.
* Implement Transform class.
* Add tests for softmax.
* Use Transform in regression, softmax and hinge objectives, except for Cox.
* Mark old gpu objective functions deprecated.
* static_assert for softmax.
* Split up multi-gpu tests.
