Update python-machine-learning-libs #481
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This PR contains the following updates:
==4.6.0→==4.7.0==1.7.2→==1.8.0==0.49.1→==0.50.0==0.5.9.post2→==0.5.11Release Notes
optuna/optuna (optuna)
v4.7.0Compare Source
This is the release note of v4.7.0.
Highlights
Two New Multi-Objective Samplers Added to OptunaHub!
@hrntsm introduces two new multi-objective samplers—SPEA-II (Strength Pareto Evolutionary Algorithm 2) and HypE (Hypervolume Estimation Algorithm)—to OptunaHub. SPEA-II is an improved multi-objective evolutionary algorithm that differs from NSGA-II in its selection mechanism. HypE is a fast, hypervolume-based evolutionary algorithm designed for many-objective optimization problems. Please refer to the following pages for more details:
PedAnovaImportanceEvaluatorNow Supports Local Hyperparameter Importance ComputationThe
target_quantileandregion_quantilearguments have been introduced toPedAnovaImportanceEvaluator. This change allows you to investigate local hyperparameter importance rather than the global one withregion_quantile < 1.0. See the original paper for the technical details.Enhancements
JournalStoragelock acquisition is delayed (#6361)Bug Fixes
TPESampler(#6258)Documentation
SECURITY.md(#6317)-Woption on Sphinx build (#6373)Examples
minioversion to<=7.2.18to fix CI & stop daily CI running (optuna/optuna-examples#339)Tests
Code Fixes
.format()with f-string in_setup_studies(#6326, thanks @haitham404!)_upload.pyforTYPE_CHECKING(#6327, thanks @satyarth7srivastava!)optuna/samplers/_cmaes.py(#6331, thanks @swativdusane!).format()with f-string in_parallel_coordinate.py(#6333, thanks @satyarth7srivastava!).formatwith f-strings inoptuna/importance/_base(#6342, thanks @VihaanMotwani!)_terminator_improvement.pyforTYPE_CHECKING(#6343, thanks @satyarth7srivastava!).formatwith f-string in_param_importances.py(#6345, thanks @Harshadev-24!).formatwith f-string intutorial/20_recipes/004_cli.py(#6350, thanks @RektPunk!).formatwith f-string inoptuna/study/_optimize.py(#6351, thanks @RektPunk!)optuna/files with Ruff (#6352)tests/andtutorials/files with Ruff (#6360)_color_supported()check (#6363)StorageTestCaseclass inoptuna.testingpackage (#6369)optuna/pruners/_hyperband.py(#6370, thanks @eleannapapaio!)test_study.pyto use f-string instead of.format()(#6372, thanks @nepersoned!)_successive_halving.py(#6375, thanks @spenam!)optuna/trial/_frozen.py(#6377, thanks @spenam!)optuna/study/study.py(#6378, thanks @spenam!)tests/study_tests/test_study.py(#6379, thanks @spenam!)TYPE_CHECKINGintest_visualizations.py(#6380, thanks @Sip4818!)TYPE_CHECKINGin_constrained_optimization.py(#6381, thanks @Sip4818!)TYPE_CHECKINGin_multi_objective.py(#6385, thanks @Sip4818!)FrozenTrialimport underTYPE_CHECKINGfor_study_summary.pyfile (#6386, thanks @Sip4818!)TYPE_CHECKINGinoptuna/terminator/callback.py(#6388, thanks @Sip4818!).format()(#6389, thanks @Lakshman142!)optuna/_experimental.py(#6390, thanks @Rohan0497!)StorageTestCasescenarios involving trial state and values (#6391)type-hintimport insideType-Checkingblock inoptuna\terminator\erroreval.py(#6395, thanks @Sip4818!)type-checkimports toTYPE_CHECKINGinoptuna\terminator\improvement\emmr.py(#6396, thanks @Sip4818!)TYPE_CHECKINGinmatplotlib/_slice.py(#6399, thanks @kapishyadav!)logger.warninginstead ofoptuna_warnfor lock-acquisition delay notifications (#6400)optuna/samplers/_grid.py(#6401, thanks @kapishyadav!)storages/_in_memory.pyto use f-strings (#6404, thanks @jrings!)type-hintimports intotype-checkingblock inoptuna\terminator\improvement\evaluator.py(#6405, thanks @Sip4818!)type-hintimports intotype-checkingblock inmedian_erroreval.py(#6408, thanks @Sip4818!).format()with f-string in_rank.py(#6409, thanks @jwalith!)Continuous Integration
storage_tests/test_with_server.py(#6330)storage_tests/test_cached_storage.py(#6337)storage_tests/rdb_tests/test_storage.py(#6338)-Woption on Sphinx build (#6354)Other
v4.7.0.dev(#6325)formats.shand tidy upCONTRIBUTING.md(#6353)asvand the speed benchmark workflow (#6393)Thanks to All the Contributors!
This release was made possible by the authors and the people who participated in the reviews and discussions.
@Alnusjaponica, @Banjiola, @Harshadev-24, @HideakiImamura, @Kaichi-Irie, @Lakshman142, @Nayil97, @ParagEkbote, @Quant-Quasar, @RektPunk, @Rohan0497, @Sip4818, @VedantMadane, @VihaanMotwani, @c-bata, @eleannapapaio, @fritshermans, @fusawa-yugo, @gadmin7, @gen740, @haitham404, @jiayusu, @jrings, @jwalith, @kAIto47802, @kapishyadav, @nabenabe0928, @nepersoned, @not522, @nzw0301, @satyarth7srivastava, @sawa3030, @sotagg, @spenam, @swativdusane, @toshihikoyanase, @varundevr, @y0z
scikit-learn/scikit-learn (scikit-learn)
v1.8.0Compare Source
We're happy to announce the 1.8.0 release.
You can read the release highlights under https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_8_0.html and the long version of the change log under https://scikit-learn.org/stable/whats_new/v1.8.html
This version supports Python versions 3.11 to 3.14 and features support of free-threaded CPython.
You can upgrade with pip as usual:
The conda-forge builds can be installed using:
shap/shap (shap)
v0.50.0Compare Source
What's Changed
New Contributors
Full Changelog: shap/shap@v0.49.1...v0.50.0
lmcinnes/umap (umap-learn)
v0.5.11Compare Source
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