This PR introduces a new parameter `extmem_concat_pages` to make the page concatenation optional for GPU hist. In addition, the document is updated for the new GPU-based external memory.
- Bump the Python requirement.
- Fix type hints.
- Use loky to avoid deadlock.
- Workaround cupy-numpy compatibility issue on Windows caused by the `safe` casting rule.
- Simplify the repartitioning logic to avoid dask errors.
- Implement a columnar adapter.
- Refactor Python pandas handling code to avoid converting into a single numpy array.
- Add support in R for transforming columns.
- Support R data.frame and factor type.
- Add `numBoostedRound` to jvm packages
- Remove rabit checkpoint version.
- Change the starting version of training continuation in JVM [breaking].
- Redefine the checkpoint version policy in jvm package. [breaking]
- Rename the Python check point callback parameter. [breaking]
- Unifies the checkpoint policy between Python and JVM.
- Use the `linalg::Matrix` for storing gradients.
- New API for the custom objective.
- Custom objective for multi-class/multi-target is now required to return the correct shape.
- Custom objective for Python can accept arrays with any strides. (row-major, column-major)
* Define `best_iteration` only if early stopping is used.
This is the behavior specified by the document but not honored in the actual code.
- Don't set the attributes if there's no early stopping.
- Clean up the code for callbacks, and replace assertions with proper exceptions.
- Assign the attributes when early stopping `save_best` is used.
- Turn the attributes into Python properties.
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Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
* Handle the new `device` parameter in dask and demos.
- Check no ordinal is specified in the dask interface.
- Update demos.
- Update dask doc.
- Update the condition for QDM.
Previously, we use `libsvm` as default when format is not specified. However, the dmlc
data parser is not particularly robust against errors, and the most common type of error
is undefined format.
Along with which, we will recommend users to use other data loader instead. We will
continue the maintenance of the parsers as it's currently used for many internal tests
including federated learning.
* Implement multi-target for hist.
- Add new hist tree builder.
- Move data fetchers for tests.
- Dispatch function calls in gbm base on the tree type.
* 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.
* 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.
* Replace all uses of deprecated function sklearn.datasets.load_boston
* More renaming
* Fix bad name
* Update assertion
* Fix n boosted rounds.
* Avoid over regularization.
* Rebase.
* Avoid over regularization.
* Whac-a-mole
Co-authored-by: fis <jm.yuan@outlook.com>
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.
* Add num target model parameter, which is configured from input labels.
* Change elementwise metric and indexing for weights.
* Add demo.
* Add tests.
