dreamquark-ai / tabnet

PyTorch implementation of TabNet paper : https://arxiv.org/pdf/1908.07442.pdf
https://dreamquark-ai.github.io/tabnet/
MIT License
2.55k stars 470 forks source link

fix(deps): update dependency scikit_learn to v1.5.0 - autoclosed #464

Closed renovate[bot] closed 2 weeks ago

renovate[bot] commented 1 year ago

Mend Renovate

This PR contains the following updates:

Package Change Age Adoption Passing Confidence
scikit_learn (source, changelog) 1.0.2 -> 1.5.0 age adoption passing confidence

Release Notes

scikit-learn/scikit-learn (scikit_learn) ### [`v1.5.0`](https://togithub.com/scikit-learn/scikit-learn/releases/tag/1.5.0): Scikit-learn 1.5.0 [Compare Source](https://togithub.com/scikit-learn/scikit-learn/compare/1.4.2...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 ### [`v1.4.2`](https://togithub.com/scikit-learn/scikit-learn/releases/tag/1.4.2): Scikit-learn 1.4.2 [Compare Source](https://togithub.com/scikit-learn/scikit-learn/compare/1.4.1.post1...1.4.2) We're happy to announce the 1.4.2 release. This release only includes support for numpy 2. This version supports Python versions 3.9 to 3.12. You can upgrade with pip as usual: pip install -U scikit-learn ### [`v1.4.1.post1`](https://togithub.com/scikit-learn/scikit-learn/releases/tag/1.4.1.post1): Scikit-learn 1.4.1.post1 [Compare Source](https://togithub.com/scikit-learn/scikit-learn/compare/1.4.0...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 ### [`v1.4.0`](https://togithub.com/scikit-learn/scikit-learn/compare/1.3.2...1.4.0) [Compare Source](https://togithub.com/scikit-learn/scikit-learn/compare/1.3.2...1.4.0) ### [`v1.3.2`](https://togithub.com/scikit-learn/scikit-learn/releases/tag/1.3.2): Scikit-learn 1.3.2 [Compare Source](https://togithub.com/scikit-learn/scikit-learn/compare/1.3.1...1.3.2) We're happy to announce the 1.3.2 release. You can see the changelog here: https://scikit-learn.org/stable/whats_new/v1.3.html#version-1-3-2 This version supports Python versions 3.8 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 ### [`v1.3.1`](https://togithub.com/scikit-learn/scikit-learn/releases/tag/1.3.1): Scikit-learn 1.3.1 [Compare Source](https://togithub.com/scikit-learn/scikit-learn/compare/1.3.0...1.3.1) We're happy to announce the 1.3.1 release. You can see the changelog here: https://scikit-learn.org/stable/whats_new/v1.3.html#version-1-3-1 This version supports Python versions 3.8 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 ### [`v1.3.0`](https://togithub.com/scikit-learn/scikit-learn/releases/tag/1.3.0): Scikit-learn 1.3.0 [Compare Source](https://togithub.com/scikit-learn/scikit-learn/compare/1.2.2...1.3.0) We're happy to announce the 1.3.0 release. You can read the release highlights under https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights\_1\_3\_0.html and the long version of the change log under https://scikit-learn.org/stable/whats_new/v1.3.html This version supports Python versions 3.8 to 3.11. 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 ### [`v1.2.2`](https://togithub.com/scikit-learn/scikit-learn/releases/tag/1.2.2): Scikit-learn 1.2.2 [Compare Source](https://togithub.com/scikit-learn/scikit-learn/compare/1.2.1...1.2.2) We're happy to announce the 1.2.2 release. You can see the changelog here: https://scikit-learn.org/stable/whats_new/v1.2.html#version-1-2-2 You can upgrade with pip as usual: pip install -U scikit-learn The conda-forge builds will be available shortly, which you can then install using: conda install -c conda-forge scikit-learn ### [`v1.2.1`](https://togithub.com/scikit-learn/scikit-learn/releases/tag/1.2.1): scikit-learn 1.2.1 [Compare Source](https://togithub.com/scikit-learn/scikit-learn/compare/1.2.0...1.2.1) We're happy to announce the 1.2.1 release. You can see the changelog here: https://scikit-learn.org/stable/whats_new/v1.2.html#version-1-2-1 You can upgrade with pip as usual: pip install -U scikit-learn The conda-forge builds will be available shortly, which you can then install using: conda install -c conda-forge scikit-learn ### [`v1.2.0`](https://togithub.com/scikit-learn/scikit-learn/releases/tag/1.2.0): Scikit-learn 1.2.0 [Compare Source](https://togithub.com/scikit-learn/scikit-learn/compare/1.1.3...1.2.0) We're happy to announce the 1.2.0 release. You can read the release highlights under https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights\_1\_2\_0.html and the long version of the change log under https://scikit-learn.org/stable/whats_new/v1.2.html This version supports Python versions 3.8 to 3.11. ### [`v1.1.3`](https://togithub.com/scikit-learn/scikit-learn/releases/tag/1.1.3): scikit-learn 1.1.3 [Compare Source](https://togithub.com/scikit-learn/scikit-learn/compare/1.1.2...1.1.3) We're happy to announce the 1.1.3 release. This bugfix release only includes fixes for compatibility with the latest SciPy release >= 1.9.2 and wheels for Python 3.11. Note that support for 32-bit Python on Windows has been dropped in this release. This is due to the fact that SciPy 1.9.2 also dropped the support for that platform. Windows users are advised to install the 64-bit version of Python instead. You can see the changelog here: https://scikit-learn.org/dev/whats_new/v1.1.html#version-1-1-3 You can upgrade with pip as usual: pip install -U scikit-learn The conda-forge builds will be available shortly, which you can then install using: conda install -c conda-forge scikit-learn ### [`v1.1.2`](https://togithub.com/scikit-learn/scikit-learn/releases/tag/1.1.2): scikit-learn 1.1.2 [Compare Source](https://togithub.com/scikit-learn/scikit-learn/compare/1.1.1...1.1.2) We're happy to announce the 1.1.2 release with several bugfixes: You can see the changelog here: https://scikit-learn.org/dev/whats_new/v1.1.html#version-1-1-2 You can upgrade with pip as usual: pip install -U scikit-learn The conda-forge builds will be available shortly, which you can then install using: conda install -c conda-forge scikit-learn ### [`v1.1.1`](https://togithub.com/scikit-learn/scikit-learn/releases/tag/1.1.1): scikit-learn 1.1.1 [Compare Source](https://togithub.com/scikit-learn/scikit-learn/compare/1.1.0...1.1.1) We're happy to announce the 1.1.1 release with several bugfixes: You can see the changelog here: https://scikit-learn.org/dev/whats_new/v1.1.html#version-1-1-1 You can upgrade with pip as usual: pip install -U scikit-learn The conda-forge builds will be available shortly, which you can then install using: conda install -c conda-forge scikit-learn ### [`v1.1.0`](https://togithub.com/scikit-learn/scikit-learn/releases/tag/1.1.0): scikit-learn 1.1.0 [Compare Source](https://togithub.com/scikit-learn/scikit-learn/compare/1.0.2...1.1.0) We're happy to announce the 1.1.0 release. You can read the release highlights under https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights\_1\_1\_0.html and the long version of the change log under https://scikit-learn.org/stable/whats_new/v1.1.html#changes-1-1 This version supports Python versions 3.8 to 3.10.

Configuration

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