build(deps): bump xgboost from 1.5.2 to 1.6.2

[dependabot]

Bumps xgboost from 1.5.2 to 1.6.2.

Release notes

Sourced from xgboost's releases.

1.6.1 Patch Release

v1.6.1 (2022 May 9)

This is a patch release for bug fixes and Spark barrier mode support. The R package is unchanged.

Experimental support for categorical data

• Fix segfault when the number of samples is smaller than the number of categories. (dmlc/xgboost#7853)
• Enable partition-based split for all model types. (dmlc/xgboost#7857)

JVM packages

We replaced the old parallelism tracker with spark barrier mode to improve the robustness of the JVM package and fix the GPU training pipeline.

• Fix GPU training pipeline quantile synchronization. (#7823, #7834)
• Use barrier model in spark package. (dmlc/xgboost#7836, dmlc/xgboost#7840, dmlc/xgboost#7845, dmlc/xgboost#7846)
• Fix shared object loading on some platforms. (dmlc/xgboost#7844)

Artifacts

You can verify the downloaded packages by running this on your Unix shell:

echo "<hash> <artifact>" | shasum -a 256 --check

2633f15e7be402bad0660d270e0b9a84ad6fcfd1c690a5d454efd6d55b4e395b  ./xgboost.tar.gz

Release 1.6.0 stable

v1.6.0 (2022 Apr 16)

After a long period of development, XGBoost v1.6.0 is packed with many new features and improvements. We summarize them in the following sections starting with an introduction to some major new features, then moving on to language binding specific changes including new features and notable bug fixes for that binding.

Development of categorical data support

This version of XGBoost features new improvements and full coverage of experimental categorical data support in Python and C package with tree model. Both hist, approx and gpu_hist now support training with categorical data. Also, partition-based categorical split is introduced in this release. This split type is first available in LightGBM in the context of gradient boosting. The previous XGBoost release supported one-hot split where the splitting criteria is of form x \in {c}, i.e. the categorical feature x is tested against a single candidate. The new release allows for more expressive conditions: x \in S where the categorical feature x is tested against multiple candidates. Moreover, it is now possible to use any tree algorithms (hist, approx, gpu_hist) when creating categorical splits. For more information, please see our tutorial on categorical data, along with examples linked on that page. (#7380, #7708, #7695, #7330, #7307, #7322, #7705, #7652, #7592, #7666, #7576, #7569, #7529, #7575, #7393, #7465, #7385, #7371, #7745, #7810)

In the future, we will continue to improve categorical data support with new features and optimizations. Also, we are looking forward to bringing the feature beyond Python binding, contributions and feedback are welcomed! Lastly, as a result of experimental status, the behavior might be subject to change, especially the default value of related

... (truncated)

Changelog

Sourced from xgboost's changelog.

XGBoost Change Log

This file records the changes in xgboost library in reverse chronological order.

v1.6.1 (2022 May 9)

This is a patch release for bug fixes and Spark barrier mode support. The R package is unchanged.

Experimental support for categorical data

• Fix segfault when the number of samples is smaller than the number of categories. (dmlc/xgboost#7853)
• Enable partition-based split for all model types. (dmlc/xgboost#7857)

JVM packages

We replaced the old parallelism tracker with spark barrier mode to improve the robustness of the JVM package and fix the GPU training pipeline.

• Fix GPU training pipeline quantile synchronization. (#7823, #7834)
• Use barrier model in spark package. (dmlc/xgboost#7836, dmlc/xgboost#7840, dmlc/xgboost#7845, dmlc/xgboost#7846)
• Fix shared object loading on some platforms. (dmlc/xgboost#7844)

v1.6.0 (2022 Apr 16)

After a long period of development, XGBoost v1.6.0 is packed with many new features and improvements. We summarize them in the following sections starting with an introduction to some major new features, then moving on to language binding specific changes including new features and notable bug fixes for that binding.

Development of categorical data support

This version of XGBoost features new improvements and full coverage of experimental categorical data support in Python and C package with tree model. Both hist, approx and gpu_hist now support training with categorical data. Also, partition-based categorical split is introduced in this release. This split type is first available in LightGBM in the context of gradient boosting. The previous XGBoost release supported one-hot split where the splitting criteria is of form x \in {c}, i.e. the categorical feature x is tested against a single candidate. The new release allows for more expressive conditions: x \in S where the categorical feature x is tested against multiple candidates. Moreover, it is now possible to use any tree algorithms (hist, approx, gpu_hist) when creating categorical splits. For more information, please see our tutorial on categorical data, along with examples linked on that page. (#7380, #7708, #7695, #7330, #7307, #7322, #7705, #7652, #7592, #7666, #7576, #7569, #7529, #7575, #7393, #7465, #7385, #7371, #7745, #7810)

In the future, we will continue to improve categorical data support with new features and optimizations. Also, we are looking forward to bringing the feature beyond Python binding, contributions and feedback are welcomed! Lastly, as a result of experimental status, the behavior might be subject to change, especially the default value of related hyper-parameters.

Experimental support for multi-output model

XGBoost 1.6 features initial support for the multi-output model, which includes multi-output regression and multi-label classification. Along with this, the XGBoost classifier has proper support for base margin without to need for the user to flatten the input. In this initial support, XGBoost builds one model for each target similar to the sklearn meta estimator, for more details, please see our quick introduction.

... (truncated)

Commits

• See full diff in compare view

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.

---

Dependabot commands and options

You can trigger Dependabot actions by commenting on this PR:

• @dependabot rebase will rebase this PR
• @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
• @dependabot merge will merge this PR after your CI passes on it
• @dependabot squash and merge will squash and merge this PR after your CI passes on it
• @dependabot cancel merge will cancel a previously requested merge and block automerging
• @dependabot reopen will reopen this PR if it is closed
• @dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
• @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
• @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
• @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)

[dependabot]

Superseded by #148.