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Bump the dependabot group in /AB_environments with 13 updates #1481

Closed dependabot[bot] closed 6 months ago

dependabot[bot] commented 6 months ago

Bumps the dependabot group in /AB_environments with 13 updates:

Package From To
numba 0.58.1 0.59.0
scikit-learn 1.3.2 1.4.1.post1
xarray 2023.12.0 2024.2.0
zarr 2.16.1 2.17.1
msgpack 1.0.7 1.0.8
tornado 6.3.3 6.4
toolz 0.12.0 0.12.1
xgboost 1.7.6 2.0.3
optuna 3.5.0 3.6.0
scipy 1.11.4 1.12.0
sqlalchemy 2.0.23 2.0.28
bokeh 3.3.2 3.4.0
duckdb 0.10.0 0.10.1

Updates numba from 0.58.1 to 0.59.0

Release notes

Sourced from numba's releases.

Numba 0.59.0

Major Numba release supporting Python 3.12

Commits
  • c6da269 Merge pull request #9411 from stuartarchibald/wip/version_tab_rel_notes_0590
  • 54d91fb Merge pull request #9405 from sklam/misc/rel59final
  • e76f945 Doc updates for 0.59.0 final.
  • 938e779 Merge pull request #9407 from sklam/fix/parfor_bug_from_9244
  • 4502c3d Merge pull request #9403 from sklam/fix/sigill
  • f911c19 Merge pull request #9402 from sklam/misc/doc_updates_59
  • 29529a0 Merge pull request #9404 from stuartarchibald/wip/fix_py3_12_1_update
  • 437b91b Merge pull request #9371 from sklam/misc/relese0.59prep
  • 35afde5 Flake8 fix
  • 74166ce Apply suggestions from code review
  • Additional commits viewable in compare view


Updates scikit-learn from 1.3.2 to 1.4.1.post1

Release notes

Sourced from scikit-learn's releases.

Scikit-learn 1.4.1.post1

We're happy to announce the 1.4.1.post1 release.

You can see the changelog here: https://scikit-learn.org/stable/whats_new/v1.4.html#version-1-4-1-post1

This version supports Python versions 3.9 to 3.12.

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

Scikit-learn 1.4.1

We're happy to announce the 1.4.1 release.

You can see the changelog here: https://scikit-learn.org/stable/whats_new/v1.4.html#version-1-4-1

This version supports Python versions 3.9 to 3.12.

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

Scikit-learn 1.4.0

We're happy to announce the 1.4.0 release.

You can read the release highlights under https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_4_0.html and the long version of the change log under https://scikit-learn.org/stable/whats_new/v1.4.html

This version supports Python versions 3.9 to 3.12.

You can upgrade with pip as usual:

pip install -U scikit-learn

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Commits
  • 719c0c6 [cd build][azure parallel] Trigger CI/CD
  • 05dc38f REL scikit-learn 1.4.1.post1 (#28423)
  • cf02653 [cd build][azure parallel] Trigger CI/CD
  • 0d00f7b FIX handle inconsistence between fill_value and X dtype in `SimpleImputer...
  • b012b04 DOC update changelog for 1.4.1 release (#28413)
  • d7e9734 bump to version 1.4.1
  • 6121cb4 remove numpy pin < 2
  • 3564f20 MNT Checking function _estimator_has also raises AttributeError (#28167)
  • b2e231e FIX EmptyRequest.get defaults to Bunch of METHODS (#28371)
  • e4d7d9a FIX handle properly missing value in MSE and Friedman-MSE children_impurity...
  • Additional commits viewable in compare view


Updates xarray from 2023.12.0 to 2024.2.0

Release notes

Sourced from xarray's releases.

v2024.02.0

This release brings size information to the text repr, changes to the accepted frequency strings, and various bug fixes.

Thanks to our 12 contributors:

Anderson Banihirwe, Deepak Cherian, Eivind Jahren, Etienne Schalk, Justus Magin, Marco Wolsza, Mathias Hauser, Matt Savoie, Maximilian Roos, Rambaud Pierrick, Tom Nicholas

v2024.01.1

This release is to fix a bug with the rendering of the documentation, but it also includes changes to the handling of pandas frequency strings.

What's Changed

New Contributors

Full Changelog: https://github.com/pydata/xarray/compare/v2024.01.0...v2024.01.1

v2024.01.0

This release brings support for weights in correlation and covariance functions, a new DataArray.cumulative aggregation, improvements to xr.map_blocks, an update to our minimum dependencies, and various bugfixes.

Thanks to our 17 contributors to this release:

Abel Aoun, Deepak Cherian, Illviljan, Johan Mathe, Justus Magin, Kai Mühlbauer, Llorenç Lledó, Mark Harfouche, Markel, Mathias Hauser, Maximilian Roos, Michael Niklas, Niclas Rieger, Sébastien Celles, Tom Nicholas, Trinh Quoc Anh, and crusaderky.

Commits


Updates zarr from 2.16.1 to 2.17.1

Release notes

Sourced from zarr's releases.

v2.17.1

See release notes: https://zarr.readthedocs.io/en/stable/release.html#release-2-17-1

What's Changed

Full Changelog: https://github.com/zarr-developers/zarr-python/compare/v2.17.0...v2.17.1

v2.17.0

See release notes: https://zarr.readthedocs.io/en/stable/release.html#release-2-17-0

What's Changed

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Changelog

Sourced from zarr's changelog.

2.17.1

Enhancements


* Change occurrences of % and format() to f-strings.
  By :user:`Dimitri Papadopoulos Orfanos <DimitriPapadopoulos>` :issue:`1423`.

    

  • 

    Proper argument for numpy.reshape.
By :user:Dimitri Papadopoulos Orfanos &lt;DmitriPapadopoulos&gt; :issue:1425.
    

    • 

  • 

    Add typing to dimension separator arguments.
By :user:David Stansby &lt;dstansby&gt; :issue:1620.
    

    • 



Docs



* ZIP related tweaks.
  By :user:`Davis Bennett &lt;d-v-b&gt;` :issue:`1641`.

Maintenance



    

  • 

    Update config.yml with Zulip.
By :user:Josh Moore &lt;joshmoore&gt;.
    

    • 

  • 

    Replace Gitter with the new Zulip Chat link.
By :user:Sanket Verma &lt;msankeys963&gt; :issue:1685.
    

    • 

  • 

    Fix RTD build.
By :user:Sanket Verma &lt;msankeys963&gt; :issue:1694.
    

    • 



.. _release_2.17.0:


2.17.0


Enhancements

  • Added type hints to zarr.creation.create(). By :user:David Stansby <dstansby> :issue:1536.

  • Pyodide support: Don't require fasteners on Emscripten. By :user:Hood Chatham <hoodmane> :issue:1663.

Docs


</tr></table>

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Commits


Updates msgpack from 1.0.7 to 1.0.8

Release notes

Sourced from msgpack's releases.

v1.0.8

What's Changed

Full Changelog: https://github.com/msgpack/msgpack-python/compare/v1.0.7...v1.0.8

Changelog

Sourced from msgpack's changelog.

1.0.8

Release Date: 2024-03-01

  • Update Cython to 3.0.8. This fixes memory leak when iterating Unpacker object on Python 3.12.
  • Do not include C/Cython files in binary wheels.
Commits


Updates tornado from 6.3.3 to 6.4

Changelog

Sourced from tornado's changelog.

Release notes

.. toctree:: :maxdepth: 2

releases/v6.4.0 releases/v6.3.3 releases/v6.3.2 releases/v6.3.1 releases/v6.3.0 releases/v6.2.0 releases/v6.1.0 releases/v6.0.4 releases/v6.0.3 releases/v6.0.2 releases/v6.0.1 releases/v6.0.0 releases/v5.1.1 releases/v5.1.0 releases/v5.0.2 releases/v5.0.1 releases/v5.0.0 releases/v4.5.3 releases/v4.5.2 releases/v4.5.1 releases/v4.5.0 releases/v4.4.3 releases/v4.4.2 releases/v4.4.1 releases/v4.4.0 releases/v4.3.0 releases/v4.2.1 releases/v4.2.0 releases/v4.1.0 releases/v4.0.2 releases/v4.0.1 releases/v4.0.0 releases/v3.2.2 releases/v3.2.1 releases/v3.2.0 releases/v3.1.1 releases/v3.1.0 releases/v3.0.2 releases/v3.0.1 releases/v3.0.0 releases/v2.4.1 releases/v2.4.0 releases/v2.3.0 releases/v2.2.1

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Commits
  • b3f2a4b Merge pull request #3352 from bdarnell/master
  • 451419c Set version to 6.4 final
  • 5a87723 Merge pull request #3348 from bdarnell/iostream-hostname-test
  • 2da0a99 iostream_test: Don't require server-side log on windows
  • 06e1a65 iostream_test: Test check_hostname functionality.
  • a6dfd70 Merge pull request #3341 from bdarnell/more-utcnow
  • c60d80c web,demos: Remove more uses of deprecated datetime utc methods
  • 55db80e Merge pull request #3339 from tornadoweb/dependabot/pip/urllib3-1.26.18
  • ec59fa0 Merge pull request #3332 from bdarnell/selector-thread-atexit
  • dcc6e59 build(deps): bump urllib3 from 1.26.17 to 1.26.18
  • Additional commits viewable in compare view


Updates toolz from 0.12.0 to 0.12.1

Release notes

Sourced from toolz's releases.

Release 0.12.1

  • Add support for Python 3.12 and PyPy 3.10
  • Drop support for Python 3.5 and 3.6
  • Fix typos (#565, #568)
  • Use codecov for coverage instead of coveralls

Pre-release 0.12.1a0

This is a pre-release

  • Support Python 3.12
  • Drop support for Python 3.5 and 3.6
Commits
  • 7a0382e Prepare for 0.12.1 release
  • 0c601d5 Oops update to pypa/gh-action-pypi-publish@v1.8.1
  • 4057679 Merge pull request #574 from eriknw/py312
  • d70d110 Minimal maintenance to support Python 3.12 (and drop 3.5 and 3.6)
  • 85be7ad Merge pull request #565 from wenhoujx/master
  • e78db44 Merge pull request #568 from LLyaudet/patch-typo-Syndey
  • 47d62a2 LL : typo "Syndey" -> "Sydney"
  • c4fc6b0 fix doc, add a blank line to show code snippet correctly
  • 9fef85a Merge pull request #555 from groutr/py311
  • 6b25d6b Update for released versions of 3.11
  • Additional commits viewable in compare view


Updates xgboost from 1.7.6 to 2.0.3

Release notes

Sourced from xgboost's releases.

2.0.3 Patch Release

The 2.0.3 patch release make the following bug fixes:

Full Changelog: https://github.com/dmlc/xgboost/compare/v2.0.2...v2.0.3

Additional artifacts:

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

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

7c4bd1cf6162d335fd20a8168a54dd11508342f82fbf381a80c02ac57be0bce4  xgboost-2.0.3.tar.gz
d0c3499504133a8ea0043da2974c51cc71aae792f0719080bc227d7add8fb881  xgboost_r_gpu_win64_2.0.3.tar.gz
ee47da5b21231965b1f054d191a5418543377f4ba0d0615a593a6f99d1832ca1  xgboost_r_gpu_linux_2.0.3.tar.gz

Experimental binary packages for R with CUDA enabled

  • xgboost_r_gpu_linux_2.0.3.tar.gz: Download
  • xgboost_r_gpu_win64_2.0.3.tar.gz: Download

2.0.2 Patch Release

The 2.0.2 patch releases make the following bug fixes:

  • [jvm-packages] Add Scala version suffix to xgboost-jvm package (#9776). The JVM packages had incorrect metadata, and the 2.0.2 patch version fixes the metadata.
  • [backport] Fix using categorical data with the ranker. (#9753)

2.0.1 Patch Release

This is a patch release for bug fixes.

Bug fixes

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Changelog

Sourced from xgboost's changelog.

XGBoost Change Log

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

In the previous version, base_score was a constant that could be set as a training parameter. In the new version, XGBoost can automatically estimate this parameter based on input labels for optimal accuracy. (#8539, #8498, #8272, #8793, #8607)

... (truncated)

Commits


Updates optuna from 3.5.0 to 3.6.0

Release notes

Sourced from optuna's releases.

v3.6.0

This is the release note of v3.6.0.

Highlights

Optuna 3.6 newly supports the following new features. See our release blog for more detailed information.

  • Wilcoxon Pruner: New Pruner Based on Wilcoxon Signed-Rank Test
  • Lightweight Gaussian Process (GP)-Based Sampler
  • Speeding up Importance Evaluation with PED-ANOVA
  • Stricter Verification Logic for FrozenTrial
  • Refactoring the Optuna Dashboard
  • Migration to Optuna Integration

Breaking Changes

  • Implement optuna.terminator using optuna._gp (#5241)

These migration-related PRs do not break the backward compatibility as long as optuna-integration v3.6.0 or later is installed in your environment.

New F... _Description has been truncated_

dependabot[bot] commented 6 months ago

This pull request was built based on a group rule. Closing it will not ignore any of these versions in future pull requests.

dependabot[bot] commented 6 months ago

This pull request was built based on a group rule. Closing it will not ignore any of these versions in future pull requests.