Bump xgboost from 0.72.1 to 1.7.3 in /xgboost_ames_housing/seldon_serve

[dependabot]

Bumps xgboost from 0.72.1 to 1.7.3.

Release notes

Sourced from xgboost's releases.

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

R packages Win64: Download Linux: Download

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
880a54e83e52c38ebada183254f55dc2bb9411bc1ff229a29f00ef39451c118c  xgboost_r_gpu_linux_1.7.3.tar.gz
76ad3c07da8adea531ab0643ed532eae38b3d1f7bc338f3b0f18620c2901092b  xgboost_r_gpu_win64_1.7.3.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• [doc] Fix outdated document [skip ci].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)

Artifacts

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

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

15be5a96e86c3c539112a2052a5be585ab9831119cd6bc3db7048f7e3d356bac  xgboost_r_gpu_linux_1.7.2.tar.gz
</tr></table> 

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Changelog

Sourced from xgboost's changelog.

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

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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