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chore(deps): bump the py-dependencies group across 3 directories with 4 updates #26

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Updates the requirements on matplotlib, scikit-learn, xgboost and numpy to permit the latest version.
Updates matplotlib to 3.10.0

Release notes

Sourced from matplotlib's releases.

REL: v3.10.0

Highlights of this release include:

- Preliminary support for free-threaded CPython 3.13
- New more-accessible color cycle
- Dark-mode diverging colormaps
- Exception handling control
- InsetIndicator artist
- FillBetweenPolyCollection
- Fill between 3D lines
- Data in 3D plots can now be dynamically clipped to the axes view limits
- Rotating 3d plots with the mouse
- Increased Figure limits with Agg renderer
- Subfigures no longer provisional
- Subfigures are now added in row-major order
Commits
  • 8d64f03 REL: v3.10.0 release
  • d9dfee8 [doc] Fix dead links
  • 87a603f Update release notes for 3.10.0
  • cdecf97 Update github stats for 3.10
  • b8d19bd Merge pull request #29306 from meeseeksmachine/auto-backport-of-pr-29242-on-v...
  • a42d0ed Backport PR #29242: DOC: Add kwdoc list to scatter() docstring
  • 1900640 Merge pull request #29299 from QuLogic/merge-v39x
  • 815389c Merge branch 'v3.9.x' into v3.10.x
  • 73873c0 DOC: Create release notes for 3.9.4
  • 9d17a2b DOC: Add Zenodo DOI for 3.9.4
  • Additional commits viewable in compare view

Updates scikit-learn to 1.6.0

Release notes

Sourced from scikit-learn's releases.

Scikit-learn 1.6.0

We're happy to announce the 1.6.0 release.

You can read the release highlights under https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_6_0.html and the long version of the change log under https://scikit-learn.org/stable/whats_new/v1.6.html

This version supports Python versions 3.9 to 3.13 and features an experimental support of free-threaded CPython.

You can upgrade with pip as usual:

pip install -U scikit-learn

The conda-forge builds can be installed using:

conda install -c conda-forge scikit-learn
Commits

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

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

Updates matplotlib from 3.9.2 to 3.10.0

Release notes

Sourced from matplotlib's releases.

REL: v3.10.0

Highlights of this release include:

- Preliminary support for free-threaded CPython 3.13
- New more-accessible color cycle
- Dark-mode diverging colormaps
- Exception handling control
- InsetIndicator artist
- FillBetweenPolyCollection
- Fill between 3D lines
- Data in 3D plots can now be dynamically clipped to the axes view limits
- Rotating 3d plots with the mouse
- Increased Figure limits with Agg renderer
- Subfigures no longer provisional
- Subfigures are now added in row-major order
Commits
  • 8d64f03 REL: v3.10.0 release
  • d9dfee8 [doc] Fix dead links
  • 87a603f Update release notes for 3.10.0
  • cdecf97 Update github stats for 3.10
  • b8d19bd Merge pull request #29306 from meeseeksmachine/auto-backport-of-pr-29242-on-v...
  • a42d0ed Backport PR #29242: DOC: Add kwdoc list to scatter() docstring
  • 1900640 Merge pull request #29299 from QuLogic/merge-v39x
  • 815389c Merge branch 'v3.9.x' into v3.10.x
  • 73873c0 DOC: Create release notes for 3.9.4
  • 9d17a2b DOC: Add Zenodo DOI for 3.9.4
  • Additional commits viewable in compare view

Updates scikit-learn from 1.5.2 to 1.6.0

Release notes

Sourced from scikit-learn's releases.

Scikit-learn 1.6.0

We're happy to announce the 1.6.0 release.

You can read the release highlights under https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_6_0.html and the long version of the change log under https://scikit-learn.org/stable/whats_new/v1.6.html

This version supports Python versions 3.9 to 3.13 and features an experimental support of free-threaded CPython.

You can upgrade with pip as usual:

pip install -U scikit-learn

The conda-forge builds can be installed using:

conda install -c conda-forge scikit-learn
Commits

Updates numpy from 2.1.2 to 2.2.1

Release notes

Sourced from numpy's releases.

2.2.1 (DEC 21, 2024)

NumPy 2.2.1 Release Notes

NumPy 2.2.1 is a patch release following 2.2.0. It fixes bugs found after the 2.2.0 release and has several maintenance pins to work around upstream changes.

There was some breakage in downstream projects following the 2.2.0 release due to updates to NumPy typing. Because of problems due to MyPy defects, we recommend using basedpyright for type checking, it can be installed from PyPI. The Pylance extension for Visual Studio Code is also based on Pyright. Problems that persist when using basedpyright should be reported as issues on the NumPy github site.

This release supports Python 3.10-3.13.

Contributors

A total of 9 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

  • Charles Harris
  • Joren Hammudoglu
  • Matti Picus
  • Nathan Goldbaum
  • Peter Hawkins
  • Simon Altrogge
  • Thomas A Caswell
  • Warren Weckesser
  • Yang Wang +

Pull requests merged

A total of 12 pull requests were merged for this release.

  • #27935: MAINT: Prepare 2.2.x for further development
  • #27950: TEST: cleanups
  • #27958: BUG: fix use-after-free error in npy_hashtable.cpp (#27955)
  • #27959: BLD: add missing include
  • #27982: BUG:fix compile error libatomic link test to meson.build
  • #27990: TYP: Fix falsely rejected value types in ndarray.__setitem__
  • #27991: MAINT: Don't wrap #include <Python.h> with extern "C"
  • #27993: BUG: Fix segfault in stringdtype lexsort
  • #28006: MAINT: random: Tweak module code in mtrand.pyx to fix a Cython...
  • #28007: BUG: Cython API was missing NPY_UINTP.
  • #28021: CI: pin scipy-doctest to 1.5.1
  • #28044: TYP: allow None in operand sequence of nditer

Checksums

... (truncated)

Commits
  • 7469245 Merge pull request #28047 from charris/prepare-2.2.1
  • acb051e REL: Prepare for the NumPy 2.2.1 release [wheel build]
  • 28a091a Merge pull request #28044 from charris/backport-28039
  • 723605b TST: Add test for allowing None in operand sequence passed to nditer
  • 554739e TYP: allow None in operand sequence of nditer
  • 31bc4c8 Merge pull request #28021 from charris/backport-28020
  • 32f52a3 CI: pin scipy-doctest to 1.5.1 (#28020)
  • 6219aeb Merge pull request #28007 from charris/backport-28005
  • eb7071c Merge pull request #28006 from charris/backport-28003
  • 4f82c32 BUG: Cython API was missing NPY_UINTP.
  • Additional commits viewable in compare view

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@dependabot
chore(deps): bump the py-dependencies group across 3 directories with…
4621b58 
… 4 updates

Updates the requirements on [matplotlib](https://github.com/matplotlib/matplotlib), [scikit-learn](https://github.com/scikit-learn/scikit-learn), [xgboost](https://github.com/dmlc/xgboost) and [numpy](https://github.com/numpy/numpy) to permit the latest version.

Updates `matplotlib` to 3.10.0
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](matplotlib/matplotlib@v3.9.2...v3.10.0)

Updates `scikit-learn` to 1.6.0
- [Release notes](https://github.com/scikit-learn/scikit-learn/releases)
- [Commits](scikit-learn/scikit-learn@1.5.2...1.6.0)

Updates `xgboost` to 2.1.3
- [Release notes](https://github.com/dmlc/xgboost/releases)
- [Changelog](https://github.com/dmlc/xgboost/blob/master/NEWS.md)
- [Commits](dmlc/xgboost@v2.1.2...v2.1.3)

Updates `matplotlib` from 3.9.2 to 3.10.0
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](matplotlib/matplotlib@v3.9.2...v3.10.0)

Updates `scikit-learn` from 1.5.2 to 1.6.0
- [Release notes](https://github.com/scikit-learn/scikit-learn/releases)
- [Commits](scikit-learn/scikit-learn@1.5.2...1.6.0)

Updates `numpy` from 2.1.2 to 2.2.1
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst)
- [Commits](numpy/numpy@v2.1.2...v2.2.1)

---
updated-dependencies:
- dependency-name: matplotlib
  dependency-type: direct:production
  dependency-group: py-dependencies
- dependency-name: scikit-learn
  dependency-type: direct:production
  dependency-group: py-dependencies
- dependency-name: xgboost
  dependency-type: direct:production
  dependency-group: py-dependencies
- dependency-name: matplotlib
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: py-dependencies
- dependency-name: scikit-learn
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: py-dependencies
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: py-dependencies
...

Signed-off-by: dependabot[bot] <support@github.com>
@dependabot dependabot bot added dependencies Pull requests that update a dependency file python Pull requests that update Python code labels Jan 1, 2025
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dependabot bot commented on behalf of github Feb 1, 2025

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

@dependabot dependabot bot closed this Feb 1, 2025
@dependabot dependabot bot deleted the dependabot/pip/covid19/py-dependencies-838b6bd7ac branch February 1, 2025 10:18
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