chore(deps): bump the py-dependencies group across 3 directories with 3 updates

[dependabot]

Updates the requirements on matplotlib, xgboost and numpy to permit the latest version.
Updates matplotlib to 3.9.3

Release notes

Sourced from matplotlib's releases.

REL: 3.9.3

This is the third bugfix release of the 3.9.x series.

This release contains several bug-fixes and adjustments:

• Fix axline with extremely small slopes
• Fix axline with non-linear axis scales
• Fix minimumSizeHint with Qt backend
• Fix config directory usage when it's behind a symlink
• Fix draggable legend when blitting is enabled
• Fix high CPU utilization in the macosx backend
• Fix multiple hatch edgecolors passed to contourf
• Improve compatibility with pytest 8.2.0

Commits

• 3ac0aea REL: 3.9.3
• 3f7adbd Merge branch 'v3.9.2-doc' into v3.9.x
• 4ca8d68 DOC: Create release notes for 3.9.3
• 0cabfe2 Merge pull request #29195 from meeseeksmachine/auto-backport-of-pr-29191-on-v...
• 562d458 Backport PR #29191: ci: Simplify 3.13t test setup
• 0586854 Merge pull request #29176 from meeseeksmachine/auto-backport-of-pr-29148-on-v...
• 84f2ae2 Merge pull request #29178 from meeseeksmachine/auto-backport-of-pr-29163-on-v...
• dd57772 Backport PR #29163: ci: Remove outdated pkg-config package on macOS
• c4bfd54 Backport PR #29148: Don't fail on equal-but-differently-named cmaps in qt fig...
• d71ff49 Backport PR #29153: Bump codecov/codecov-action from 4 to 5 in the actions gr...
• Additional commits viewable in compare view

Updates xgboost to 2.1.3

Release notes

Sourced from xgboost's releases.

2.1.3 Patch release

The 2.1.3 patch release makes the following bug fixes:

• [pyspark] Support large model size (#10984).
• Fix rng for the column sampler (#10998).
• Handle cudf.pandas proxy objects properly (#11014).

Additional artifacts:

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

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

90b1b7b770803299b337dd9b9206760d9c16f418403c77acce74b350c6427667  xgboost-2.1.3.tar.gz
96b41da84769920408c5733d05fa2d56b53feeefd209e3d96842cf9c266e27ea  xgboost_r_gpu_linux_2.1.3.tar.gz

Experimental binary packages for R with CUDA enabled

• xgboost_r_gpu_linux_2.1.3.tar.gz: Download

Source tarball

• xgboost.tar.gz: Download

Changelog

Sourced from xgboost's changelog.

XGBoost Change Log

Starting from 2.1.0, release note is recorded in the documentation.

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

2.0.0 (2023 Aug 16)

We are excited to announce the release of XGBoost 2.0. This note will begin by covering some overall changes and then highlight specific updates to the package.

Initial work on multi-target trees with vector-leaf outputs

We have been working on vector-leaf tree models for multi-target regression, multi-label classification, and multi-class classification in version 2.0. Previously, XGBoost would build a separate model for each target. However, with this new feature that's still being developed, XGBoost can build one tree for all targets. The feature has multiple benefits and trade-offs compared to the existing approach. It can help prevent overfitting, produce smaller models, and build trees that consider the correlation between targets. In addition, users can combine vector leaf and scalar leaf trees during a training session using a callback. Please note that the feature is still a working in progress, and many parts are not yet available. See #9043 for the current status. Related PRs: (#8538, #8697, #8902, #8884, #8895, #8898, #8612, #8652, #8698, #8908, #8928, #8968, #8616, #8922, #8890, #8872, #8889, #9509) Please note that, only the hist (default) tree method on CPU can be used for building vector leaf trees at the moment.

New device parameter.

A new device parameter is set to replace the existing gpu_id, gpu_hist, gpu_predictor, cpu_predictor, gpu_coord_descent, and the PySpark specific parameter use_gpu. Onward, users need only the device parameter to select which device to run along with the ordinal of the device. For more information, please see our document page (https://xgboost.readthedocs.io/en/stable/parameter.html#general-parameters) . For example, with device="cuda", tree_method="hist", XGBoost will run the hist tree method on GPU. (#9363, #8528, #8604, #9354, #9274, #9243, #8896, #9129, #9362, #9402, #9385, #9398, #9390, #9386, #9412, #9507, #9536). The old behavior of gpu_hist is preserved but deprecated. In addition, the predictor parameter is removed.

hist is now the default tree method

Starting from 2.0, the hist tree method will be the default. In previous versions, XGBoost chooses approx or exact depending on the input data and training environment. The new default can help XGBoost train models more efficiently and consistently. (#9320, #9353)

GPU-based approx tree method

There's initial support for using the approx tree method on GPU. The performance of the approx is not yet well optimized but is feature complete except for the JVM packages. It can be accessed through the use of the parameter combination device="cuda", tree_method="approx". (#9414, #9399, #9478). Please note that the Scala-based Spark interface is not yet supported.

Optimize and bound the size of the histogram on CPU, to control memory footprint

XGBoost has a new parameter max_cached_hist_node for users to limit the CPU cache size for histograms. It can help prevent XGBoost from caching histograms too aggressively. Without the cache, performance is likely to decrease. However, the size of the cache grows exponentially with the depth of the tree. The limit can be crucial when growing deep trees. In most cases, users need not configure this parameter as it does not affect the model's accuracy. (#9455, #9441, #9440, #9427, #9400).

Along with the cache limit, XGBoost also reduces the memory usage of the hist and approx tree method on distributed systems by cutting the size of the cache by half. (#9433)

Improved external memory support

There is some exciting development around external memory support in XGBoost. It's still an experimental feature, but the performance has been significantly improved with the default hist tree method. We replaced the old file IO logic with memory map. In addition to performance, we have reduced CPU memory usage and added extensive documentation. Beginning from 2.0.0, we encourage users to try it with the hist tree method when the memory saving by QuantileDMatrix is not sufficient. (#9361, #9317, #9282, #9315, #8457)

Learning to rank

We created a brand-new implementation for the learning-to-rank task. With the latest version, XGBoost gained a set of new features for ranking task including:

• A new parameter lambdarank_pair_method for choosing the pair construction strategy.
• A new parameter lambdarank_num_pair_per_sample for controlling the number of samples for each group.
• An experimental implementation of unbiased learning-to-rank, which can be accessed using the lambdarank_unbiased parameter.
• Support for custom gain function with NDCG using the ndcg_exp_gain parameter.
• Deterministic GPU computation for all objectives and metrics.
• NDCG is now the default objective function.
• Improved performance of metrics using caches.
• Support scikit-learn utilities for XGBRanker.
• Extensive documentation on how learning-to-rank works with XGBoost.

For more information, please see the tutorial. Related PRs: (#8771, #8692, #8783, #8789, #8790, #8859, #8887, #8893, #8906, #8931, #9075, #9015, #9381, #9336, #8822, #9222, #8984, #8785, #8786, #8768)

Automatically estimated intercept

... (truncated)

Commits

• 600be4d Bump version to 2.1.3. (#11019)
• 6676f56 [bp] Handle cudf.pandas proxy objects properly (#11014) (#11018)
• 7b675da [bp] Fix rng for the column sampler. (#10998) (#11004)
• 5973d60 [bp][spark] Make xgboost spark support large model size (#10984) (#11005)
• See full diff in compare view

Updates matplotlib from 3.9.2 to 3.9.3

Release notes

Sourced from matplotlib's releases.

REL: 3.9.3

This is the third bugfix release of the 3.9.x series.

This release contains several bug-fixes and adjustments:

• Fix axline with extremely small slopes
• Fix axline with non-linear axis scales
• Fix minimumSizeHint with Qt backend
• Fix config directory usage when it's behind a symlink
• Fix draggable legend when blitting is enabled
• Fix high CPU utilization in the macosx backend
• Fix multiple hatch edgecolors passed to contourf
• Improve compatibility with pytest 8.2.0

Commits

• 3ac0aea REL: 3.9.3
• 3f7adbd Merge branch 'v3.9.2-doc' into v3.9.x
• 4ca8d68 DOC: Create release notes for 3.9.3
• 0cabfe2 Merge pull request #29195 from meeseeksmachine/auto-backport-of-pr-29191-on-v...
• 562d458 Backport PR #29191: ci: Simplify 3.13t test setup
• 0586854 Merge pull request #29176 from meeseeksmachine/auto-backport-of-pr-29148-on-v...
• 84f2ae2 Merge pull request #29178 from meeseeksmachine/auto-backport-of-pr-29163-on-v...
• dd57772 Backport PR #29163: ci: Remove outdated pkg-config package on macOS
• c4bfd54 Backport PR #29148: Don't fail on equal-but-differently-named cmaps in qt fig...
• d71ff49 Backport PR #29153: Bump codecov/codecov-action from 4 to 5 in the actions gr...
• Additional commits viewable in compare view

Updates numpy from 2.1.2 to 2.1.3

Release notes

Sourced from numpy's releases.

2.1.3 (Nov 2, 2024)

NumPy 2.1.3 Release Notes

NumPy 2.1.3 is a maintenance release that fixes bugs and regressions discovered after the 2.1.2 release. This release also adds support for free threaded Python 3.13 on Windows.

The Python versions supported by this release are 3.10-3.13.

Improvements

• Fixed a number of issues around promotion for string ufuncs with StringDType arguments. Mixing StringDType and the fixed-width DTypes using the string ufuncs should now generate much more uniform results.

  (gh-27636)

Changes

• numpy.fix now won't perform casting to a floating data-type for integer and boolean data-type input arrays.

  (gh-26766)

Contributors

A total of 15 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

• Abhishek Kumar +
• Austin +
• Benjamin A. Beasley +
• Charles Harris
• Christian Lorentzen
• Marcel Telka +
• Matti Picus
• Michael Davidsaver +
• Nathan Goldbaum
• Peter Hawkins
• Raghuveer Devulapalli
• Ralf Gommers
• Sebastian Berg
• dependabot[bot]
• kp2pml30 +

Pull requests merged

A total of 21 pull requests were merged for this release.

... (truncated)

Commits

• 98464cc Merge pull request #27690 from charris/prepare-2.1.3
• cbda85b REL: Prepare for the NumPy 2.1.3 release [wheel build]
• daa8699 Merge pull request #27672 from charris/backport-27666
• 614ca19 Merge pull request #27673 from charris/backport-27636
• e6b02d7 DOC: add release note
• 54fd729 BUG: substantially simplify and fix issue with justification promoter
• a90fe7c BUG: fix more issues with string ufunc promotion
• a121864 BUG: fixes for StringDType/unicode promoters
• f055fb9 BUG: Fix a reference count leak in npy_find_descr_for_scalar.
• 5895c02 Merge pull request #27669 from charris/backport-27663
• Additional commits viewable in compare view

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[dependabot]

Looks like these dependencies are updatable in another way, so this is no longer needed.