Bump xgboost from 1.6.2 to 1.7.4

[dependabot]

Bumps xgboost from 1.6.2 to 1.7.4.

Release notes

Sourced from xgboost's releases.

1.7.4 Patch Release

1.7.4 (2023 Feb 16)

This is a patch release for bug fixes.

• [R] Fix OpenMP detection on macOS. #8684
• [Python] Make sure input numpy array is aligned. #8690
• Fix feature interaction with column sampling in gpu_hist evaluator. #8754
• Fix GPU L1 error. #8749
• [PySpark] Fix feature types param #8772
• Fix ranking with quantile dmatrix and group weight. #8762
• Fix CPU bin compression with categorical data. #8809

Artifacts

xgboost_r_gpu_win64_1.7.4.tar.gz: Download

1.7.3 Patch Release

1.7.3 (2023 Jan 6)

This is a patch release for bug fixes.

• [Breaking] XGBoost Sklearn estimator method get_params no longer returns internally configured values. (#8634)
• Fix linalg iterator, which may crash the L1 error. (#8603)
• Fix loading pickled GPU sklearn estimator with a CPU-only XGBoost build. (#8632)
• Fix inference with unseen categories with categorical features. (#8591, #8602)
• CI fixes. (#8620, #8631, #8579)

Artifacts

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

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

0b6aa86b93aec2b3e7ec6f53a696f8bbb23e21a03b369dc5a332c55ca57bc0c4  xgboost.tar.gz

1.7.2 Patch Release

v1.7.2 (2022 Dec 8)

This is a patch release for bug fixes.

• Work with newer thrust and libcudacxx (#8432)

• Support null value in CUDA array interface namespace. (#8486)

• Use getsockname instead of SO_DOMAIN on AIX. (#8437)

• [pyspark] Make QDM optional based on a cuDF check (#8471)

• [pyspark] sort qid for SparkRanker. (#8497)

• [dask] Properly await async method client.wait_for_workers. (#8558)

• [R] Fix CRAN test notes. (#8428)

• [doc] Fix outdated document [skip ci]. (#8527)

• [CI] Fix github action mismatched glibcxx. (#8551)

... (truncated)

Changelog

Sourced from xgboost's changelog.

XGBoost Change Log

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

1.7.3 (2023 Jan 6)

This is a patch release for bug fixes.

• [Breaking] XGBoost Sklearn estimator method get_params no longer returns internally configured values. (#8634)
• Fix linalg iterator, which may crash the L1 error. (#8603)
• Fix loading pickled GPU model with a CPU-only XGBoost build. (#8632)
• Fix inference with unseen categories with categorical features. (#8591, #8602)
• CI fixes. (#8620, #8631, #8579)

v1.7.2 (2022 Dec 8)

This is a patch release for bug fixes.

• Work with newer thrust and libcudacxx (#8432)

• Support null value in CUDA array interface namespace. (#8486)

• Use getsockname instead of SO_DOMAIN on AIX. (#8437)

• [pyspark] Make QDM optional based on a cuDF check (#8471)

• [pyspark] sort qid for SparkRanker. (#8497)

• [dask] Properly await async method client.wait_for_workers. (#8558)

• [R] Fix CRAN test notes. (#8428)

• [doc] Fix outdated document [skip ci]. (#8527)

• [CI] Fix github action mismatched glibcxx. (#8551)

v1.7.1 (2022 Nov 3)

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

... (truncated)

Commits

• 36ad160 Bump version to 1.7.4. (#8805)
• c22f6db [backport] Fix CPU bin compression with categorical data. (#8809) (#8810)
• f15a6d2 [backport] Fix ranking with quantile dmatrix and group weight. (#8762) (#8800)
• 08a547f [backport] Fix feature types param (#8772) (#8801)
• 60303db [backport] Fix GPU L1 error. (#8749) (#8770)
• df984f9 [backport] Fix different number of features in gpu_hist evaluator. (#8754) (#...
• 2f22f8d [backport] Make sure input numpy array is aligned. (#8690) (#8696) (#8734)
• 68d8633 [backport] [R] fix OpenMP detection on macOS (#8684) (#8732)
• 76bdca0 [R] Fix threads used to create DMatrix in predict. (#8681) (#8682)
• 021e6a8 [backport] [R] Get CXX flags from R CMD config. (#8669) (#8680)
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