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aeon-toolkit / aeon

A toolkit for machine learning from time series
https://aeon-toolkit.org/
BSD 3-Clause "New" or "Revised" License
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[MNT] Bump the python-packages group across 1 directory with 3 updates #1689

Closed dependabot[bot] closed 2 days ago

dependabot[bot] commented 2 weeks ago

Updates the requirements on numba, numpy and scikit-learn to permit the latest version. Updates numba to 0.60.0

Release notes

Sourced from numba's releases.

0.60.0

Major Numba release.

Commits
  • 53e976f Merge pull request #9620 from esc/cherry-pick/rc-bugfixes-for-0.60.0-final
  • 6cee88e add remaining PRs that were cherry-picked
  • 38beff8 update change log
  • 5e6270a update version support table with release date
  • ff2e495 Merge pull request #9603 from sklam/fix/avx512nocona
  • 48faddf Merge pull request #9602 from sklam/fix/np2compat
  • 7d1976f Merge pull request #9586 from sklam/fix/bug9581
  • b3dc3df Merge pull request #9596 from kc611/import-issue
  • 056012a Added tests
  • 08bfe07 Added inline_closurecall as an import during registry loading
  • Additional commits viewable in compare view


Updates numpy to 2.0.0

Release notes

Sourced from numpy's releases.

v2.0.0

NumPy 2.0.0 Release Notes

NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs.

This major release includes breaking changes that could not happen in a regular minor (feature) release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0, in addition to these release notes, include:

Highlights

Highlights of this release include:

  • New features:
    • A new variable-length string dtype, numpy.dtypes.StringDType and a new numpy.strings namespace with performant ufuncs for string operations,
    • Support for float32 and longdouble in all numpy.fft functions,
    • Support for the array API standard in the main numpy namespace.
  • Performance improvements:
    • Sorting functions sort, argsort, partition, argpartition have been accelerated through the use of the Intel x86-simd-sort and Google Highway libraries, and may see large (hardware-specific) speedups,
    • macOS Accelerate support and binary wheels for macOS >=14, with significant performance improvements for linear algebra operations on macOS, and wheels that are about 3 times smaller,
    • numpy.char fixed-length string operations have been accelerated by implementing ufuncs that also support numpy.dtypes.StringDType in addition to the fixed-length string dtypes,
    • A new tracing and introspection API, numpy.lib.introspect.opt_func_info, to determine which hardware-specific kernels are available and will be dispatched to.
    • numpy.save now uses pickle protocol version 4 for saving arrays with object dtype, which allows for pickle objects larger than 4GB and improves saving speed by about 5% for large arrays.
  • Python API improvements:

... (truncated)

Commits
  • 1d49c7f Merge pull request #26698 from charris/prepare-2.0.0
  • 2103511 DOC: Remove duplicate in author list.
  • db8030e BUG: Change cibuildwheel version [wheel build]
  • 1a68264 REL: Prepare for the NumPy 2.0.0 release [wheel build]
  • c8665ba Merge pull request #26696 from charris/backport-26582
  • 103f4dd Merge pull request #26697 from charris/backport-25963
  • c193dcd Merge pull request #26695 from charris/backport-26667
  • 8fa8191 BUG: Fix bug in numpy.pad() (#25963)
  • ece3559 BUG: weighted nanpercentile, nanquantile and multi-dim q (#26582)
  • b31e195 BUG: Adds asanyarray to start of linalg.cross (#26667)
  • Additional commits viewable in compare view


Updates scikit-learn to 1.5.0

Release notes

Sourced from scikit-learn's releases.

Scikit-learn 1.5.0

We're happy to announce the 1.5.0 release.

You can read the release highlights under https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_5_0.html and the long version of the change log under https://scikit-learn.org/stable/whats_new/v1.5.html

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
Commits


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dependabot[bot] commented 2 days ago

Superseded by #1740.