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.
* Make sure the task is initialized before construction of tree updater.
This is a quick fix meant to be backported to 1.6, for a full fix we should pass the model
param into tree updater by reference instead.
* Skip non-increasing test with external memory when subsample is used.
* Increase bin numbers for boost from prediction test. This mitigates the effect of
non-deterministic partitioning.
* 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.
* Generate column matrix from gHistIndex.
* Avoid synchronization with the sparse page once the cache is written.
* Cleanups: Remove member variables/functions, change the update routine to look like approx and gpu_hist.
* Remove pruner.
* Extract partitioner from hist.
* Implement categorical data support by passing the gradient index directly into the partitioner.
* Organize/update document.
* Remove code for negative hessian.
* Fix copy for cv. This prevents inserting default callbacks into the input list.
* Clarify the behavior of callbacks in training/cv.
* Fix typos in doc.
* Cleanup some pylint errors.
* Cleanup pylint errors in rabit modules.
* Make data iter an abstract class and cleanup private access.
* Cleanup no-self-use for booster.
- Mention standard install command for R package.
- Remove repeated "get source" step.
- Remove troubleshooting on Windows. It's outdated considering VS 2022 is already out.
* Implement `MaxCategory` in quantile.
* Implement partition-based split for GPU evaluation. Currently, it's based on the existing evaluation function.
* Extract an evaluator from GPU Hist to store the needed states.
* Added some CUDA stream/event utilities.
* Update document with references.
* Fixed a bug in approx evaluator where the number of data points is less than the number of categories.
Empty partition is different from empty dataset. For the former case, each worker has
non-empty dask collections, but each collection might contain empty partition.
This PR prepares the GHistIndexMatrix to host the column matrix which is used by the hist tree method by accepting sparse_threshold parameter.
Some cleanups are made to ensure the correct batch param is being passed into DMatrix along with some additional tests for correctness of SimpleDMatrix.
* Add a new utility for mapping function onto workers.
* Unify the type for feature names.
* Clean up the iterator.
* Fix prediction with DaskDMatrix worker specification.
* Fix base margin with DeviceQuantileDMatrix.
* Support vs 2022 in setup.py.
This is the one last PR for removing omp global variable.
* Add context object to the `DMatrix`. This bridges `DMatrix` with https://github.com/dmlc/xgboost/issues/7308 .
* Require context to be available at the construction time of booster.
* Add `n_threads` support for R csc DMatrix constructor.
* Remove `omp_get_max_threads` in R glue code.
* Remove threading utilities that rely on omp global variable.
- Add user configuration.
- Bring back to the logic of using scheduler address from dask. This was removed when we were trying to support GKE, now we bring it back and let xgboost try it if direct guess or host IP from user config failed.
Note that when cub inside CUDA is being used, XGBoost performs checks on input size
instead of using internal cub function to accept inputs larger than maximum integer.
* Implement ubjson.
This is a partial implementation of UBJSON with support for typed arrays. Some missing
features are `f64`, typed object, and the no-op.
This PR rewrites the approx tree method to use codebase from hist for better performance and code sharing.
The rewrite has many benefits:
- Support for both `max_leaves` and `max_depth`.
- Support for `grow_policy`.
- Support for mono constraint.
- Support for feature weights.
- Support for easier bin configuration (`max_bin`).
- Support for categorical data.
- Faster performance for most of the datasets. (many times faster)
- Support for prediction cache.
- Significantly better performance for external memory.
- Unites the code base between approx and hist.
Jiaming Yuan
