Releases: koaning/scikit-lego
v0.9.5
v0.9.4
This version adds proper support for scikit-learn 1.6. There were a lot of small hiccups but we are now vendoring https://sklearn-compat.readthedocs.io/en/latest/ to aid with compatibility.
v0.9.3
Version v0.9.2
What's Changed
Enhancement
- feat: enable
GroupedTransformer.set_output(..)by @FBruzzesi in #697 - feat: bump narwhals and adapt to support pyarrow by @FBruzzesi in #694
- Multi output support for TrainOnlyTransformerMixin by @CarloLepelaars in #700
Bug fix
- patch: fix estimator kwargs in signature by @FBruzzesi in #693
- Fix flaky tests by @koaning in #698
Documentation
- docs:
LowessRegressionusage docstring by @david26694 in #688 - docs: first docstrings for linear model by @david26694 in #691
- docs: PCA and UMAP OutlierDetection docstring examples by @anopsy in #654
- docs: automatic
this.mdgeneration by @FBruzzesi in #705
Other
- ci: remove pre flag for optional deps by @FBruzzesi in #702
Full Changelog: v0.9.1...v0.9.2
Version v0.9.1
What's Changed
New features
- feat: support polars lazyframe in add_lags by @MarcoGorelli in #675
- feat:
ZeroInflatedRegressor.score_samples(...)by @FBruzzesi in #680
Fixes
- patch:
.drop(columns=...)test hotfix by @FBruzzesi in #678 - patch: ci/cd hotfix by @FBruzzesi in #683
- patch:
strict=Falsein polars constructor by @FBruzzesi in #684 - patch:
FairClassifierregularization and fit intercept by @FBruzzesi in #679
Others
- perf: make sure X.schema is only calculated once per dataframe in TypeSelector by @MarcoGorelli in #676
- chore: use Narwhals stable api by @MarcoGorelli in #685
- version 0.9.1 by @koaning in #686
Full Changelog: v0.9.0...v0.9.1
Version v0.9.0
What's Changed
Disclaimer
This is a large one, with a lot of improvements, new features and bug fixes!
TL;DR
- narwhals support for dataframe agnostic codebase
- numpy 2.0 support
- cvxpy 1.5 support
- improvements in scikit-learn intergration
New Features ✨
-
Narwhals for dataframe-agnostic codebase by @FBruzzesi in #671
This was a very large effort with the help of many to introduce narwhals:- #655 by @MarcoGorelli
- #659 by @anopsy
- #661 by @MarcoGorelli
- #665 by @MarcoGorelli
- #667 by @FBruzzesi
- #669 by @DeaMariaLeon
- #670 by @MarcoGorelli
-
numpy 2.0rc support by @FBruzzesi in #651
Supports of numpy 2.0 for base library (not including cvxpy and umap)
Bug fixes 🐞
- cvxpy 1.5.0 support by @FBruzzesi in #663
ZeroInflatedRegressorfix training indices by @FBruzzesi in #666
Internals ⚒️
- Improving sklearn compatibility via
parametrize_with_checksby @FBruzzesi in #660
Full Changelog: v0.8.2...v0.9.0
Version v0.8.2
What's Changed
- Support for python 3.12 by @FBruzzesi in #628
- Fix broken links on home page by @iuliaferoli in #634
- Fix typos by @k-moun in #638, #640, #641
- Docstring example for
OutlierRemoverby @anopsy in #639 - Add bayesian methods to GMM density page by @mkalimeri in #642
- Adopt UV in GHA by @FBruzzesi in #629
- Docs rendering by @FBruzzesi in #645
ClusterFoldValidationnow with correct spelling by @koaning in #636- Docstring example for
DictMapperandOutlierClassifierby @anopsy in #646 - Contributing docs section by @FBruzzesi in #644
- Update this.py by @FBruzzesi in #647
ClusterFoldValidationdocs api section by @FBruzzesi in #649- Docstrings example for
FormulaicTransformer,IdentityTransformer,InformationFilter,PandasTypeSelectorandRepeatingBasisFunctionby @anopsy in #648
New Contributors
- @iuliaferoli made their first contribution in #634
- @k-moun made their first contribution in #638
- @anopsy made their first contribution in #639
- @mkalimeri made their first contribution in #642
Full Changelog: v0.8.0...v0.8.2
0.8.0
Description
✨ New Features
GroupedClassifierandGroupedRegressor#619 by @FBruzzesiMaximumRelevanceMinimumRedundancyfeature selector #622 by @fabioscantamburloHierarchicalPredictor,HierarchicalClassifierandHierarchicalRegressor#623 by @FBruzzesi
🐞 Bug Fix
GroupedTransformerallows transformers that use y #624 by @FBruzzesi- Raise early for
GroupedPredictorwith shrinkage in non-regression tasks #619 by @FBruzzesi
⚠️ Breaking Changes
0.7.4
0.7.0
@FBruzzesi created a new OrdinalClassifier meta model. While somewhat experimental ... we can totally see how it may be nice for some folks. Enjoy!