This is a patch release to incorporate the following hotfix:
Add back xgboost.rabit for backwards compatibility (#8411)
v1.7.0 (2022 Oct 20)
We are excited to announce the feature packed XGBoost 1.7 release. The release note will walk through some of the major new features first, then make a summary for other improvements and language-binding-specific changes.
PySpark
XGBoost 1.7 features initial support for PySpark integration. The new interface is adapted from the existing PySpark XGBoost interface developed by databricks with additional features like QuantileDMatrix and the rapidsai plugin (GPU pipeline) support. The new Spark XGBoost Python estimators not only benefit from PySpark ml facilities for powerful distributed computing but also enjoy the rest of the Python ecosystem. Users can define a custom objective, callbacks, and metrics in Python and use them with this interface on distributed clusters. The support is labeled as experimental with more features to come in future releases. For a brief introduction please visit the tutorial on XGBoost's document page. (#8355, #8344, #8335, #8284, #8271, #8283, #8250, #8231, #8219, #8245, #8217, #8200, #8173, #8172, #8145, #8117, #8131, #8088, #8082, #8085, #8066, #8068, #8067, #8020, #8385)
Due to its initial support status, the new interface has some limitations; categorical features and multi-output models are not yet supported.
Development of categorical data support
More progress on the experimental support for categorical features. In 1.7, XGBoost can handle missing values in categorical features and features a new parameter max_cat_threshold, which limits the number of categories that can be used in the split evaluation. The parameter is enabled when the partitioning algorithm is used and helps prevent over-fitting. Also, the sklearn interface can now accept the feature_types parameter to use data types other than dataframe for categorical features. (#8280, #7821, #8285, #8080, #7948, #7858, #7853, #8212, #7957, #7937, #7934)
Experimental support for federated learning and new communication collective
An exciting addition to XGBoost is the experimental federated learning support. The federated learning is implemented with a gRPC federated server that aggregates allreduce calls, and federated clients that train on local data and use existing tree methods (approx, hist, gpu_hist). Currently, this only supports horizontal federated learning (samples are split across participants, and each participant has all the features and labels). Future plans include vertical federated learning (features split across participants), and stronger privacy guarantees with homomorphic encryption and differential privacy. See Demo with NVFlare integration for example usage with nvflare.
As part of the work, XGBoost 1.7 has replaced the old rabit module with the new collective module as the network communication interface with added support for runtime backend selection. In previous versions, the backend is defined at compile time and can not be changed once built. In this new release, users can choose between rabit and federated. (#8029, #8351, #8350, #8342, #8340, #8325, #8279, #8181, #8027, #7958, #7831, #7879, #8257, #8316, #8242, #8057, #8203, #8038, #7965, #7930, #7911)
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Updates the requirements on xgboost to permit the latest version.
Release notes
Sourced from xgboost's releases.
Changelog
Sourced from xgboost's changelog.
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Commits
ccf43d4
Bump R package version to 1.7.3. (#8649)dd58c2a
Bump version to 1.7.3. (#8646)899e4c8
[backport] Do not return internal value forget_params
. (#8634) (#8642)a2085bf
[backport] Fix loading GPU pickle with a CPU-only xgboost distribution. (#863...067b704
[backport] Fix inference with categorical feature. (#8591) (#8602) (#8638)1a834b2
Fix linalg iterator. (#8603) (#8639)162b48a
[backport] [CI] Disable gtest with RMM (#8620) (#8640)83a078b
[backport] Fix sklearn test that calls a removed field (#8579) (#8636)575fba6
[backport] [CI] Fix CI with updated dependencies. (#8631) (#8635)62ed8b5
Bump release version to 1.7.2. (#8569)Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting
@dependabot rebase
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