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Recent Releases of skada

skada - 0.4.0

Skada v0.4.0 Release Highlights

This update brings significant enhancements and new features: 1. New Shallow Methods: MongeAlignment and JCPOT 2. New Deep Methods: CAN, MCC, MDD, SPA, SourceOnly, and TargetOnly models. 3. Scorers: Introduced MixValScorer and improved scorer compatibility with deep models. 4. Subsampling Transformers: Added StratifiedDomainSubsampler and DomainSubsampler. 5. Deep Models: Enhanced batch handling, fixed predict_proba, stabilized MDD loss, and fixed Deep Coral. 6. Docs & Design: Added a contributor guide, new logo, and documentation updates.

What's Changed

  • Update README.md with zenodo badge by @rflamary in https://github.com/scikit-adaptation/skada/pull/216
  • [MRG] Add multi-domain Monge alignment and JCPOT Target shift method by @rflamary in https://github.com/scikit-adaptation/skada/pull/180
  • [MRG] Add a parameter base_criterion to deep models by @tgnassou in https://github.com/scikit-adaptation/skada/pull/217
  • [MRG] Add new scorer: MixValScorer by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/221
  • [MRG] Fix mixval by @antoinecollas in https://github.com/scikit-adaptation/skada/pull/222
  • [MRG] Fix batch issue when generating features + add sample_weight in deep models by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/220
  • [MRG] Allow model selection cv to handle nd inputs by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/225
  • [MRG] In DEV, reshape features to 2D instead of input by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/226
  • [MRG] Add utilities functions to the doc by @antoinecollas in https://github.com/scikit-adaptation/skada/pull/227
  • Add new logo! by @tgnassou in https://github.com/scikit-adaptation/skada/pull/223
  • Fix ImportanceWeightedScorer compatibility with deep learning models by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/232
  • [MRG] fix param for Deepjdot by @tgnassou in https://github.com/scikit-adaptation/skada/pull/234
  • [MRG] Add SourceOnly and TargetOnly models by @tgnassou in https://github.com/scikit-adaptation/skada/pull/233
  • [MRG] Fix docstring for the regulariation parameter of DA loss by @tgnassou in https://github.com/scikit-adaptation/skada/pull/230
  • [MRG] Fix order of feature acquisition for deep module by @tgnassou in https://github.com/scikit-adaptation/skada/pull/235
  • [MRG] Add recentering in DeepCoral by @tgnassou in https://github.com/scikit-adaptation/skada/pull/242
  • [MRG] Add DomainOnlySampler and DomainOnlyDataloader for SourceOnly ou TargetOnly deep methods by @tgnassou in https://github.com/scikit-adaptation/skada/pull/243
  • [MRG] Modify sampler to take the max of the two domains by @tgnassou in https://github.com/scikit-adaptation/skada/pull/241
  • Fix: Dev scorer wasn't working with SourceOnly and TargetOnly by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/244
  • [MRG] Fix deep coral by @antoinecollas in https://github.com/scikit-adaptation/skada/pull/246
  • [MRG] Harmonize fixtures by @antoinecollas in https://github.com/scikit-adaptation/skada/pull/248
  • [MRG] Bug fix when None in makedapipeline by @antoinecollas in https://github.com/scikit-adaptation/skada/pull/256
  • [MRG] Handle edge case Mixvalscorer by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/257
  • [MRG] Add CAN Method by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/251
  • [MRG] Uncomment MMDTarSReweightAdapter tests by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/260
  • [MRG] Enhancements to DomainAwareNet and Scorers to handle allow_source arg by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/258
  • [MRG] Subsampling transformer by @rflamary in https://github.com/scikit-adaptation/skada/pull/259
  • [MRG] Add MCC method by @tgnassou in https://github.com/scikit-adaptation/skada/pull/250
  • [MRG] Fix callback issue in CAN by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/265
  • [MRG] fix predict_proba for deep method by @tgnassou in https://github.com/scikit-adaptation/skada/pull/247
  • Batchnormfix2 by @antoinedemathelin in https://github.com/scikit-adaptation/skada/pull/266
  • [MRG] Handle scalar sample domain by @antoinecollas in https://github.com/scikit-adaptation/skada/pull/267
  • [MRG] Add DomainAndLabelStratifiedSubsampleTransformer + Fix DomainStratifiedSubsampleTransformer by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/268
  • [MRG] Check if sampledomain have only unique domains indexes in check*_domain by @apmellot in https://github.com/scikit-adaptation/skada/pull/261
  • [MRG] Add epsilon in MCC to prevent log(0) by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/270
  • [MRG] Handle edge case for DAN by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/271
  • [MRG] Handle edge cases for CAN by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/269
  • [MRG] Add MDD method by @ambroiseodt in https://github.com/scikit-adaptation/skada/pull/263
  • [MRG] Fix dissimilarities computations of Deep CAN by @antoinecollas in https://github.com/scikit-adaptation/skada/pull/274
  • [MRG] Remove redundant centroid computation in spherical k-means by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/275
  • [MRG] Fix mdd loss by @antoinecollas in https://github.com/scikit-adaptation/skada/pull/277
  • [MRG] Apply label smoothing to stabilize MDD by @antoinecollas in https://github.com/scikit-adaptation/skada/pull/279
  • [MRG] do not try to complete when X_source is empty by @antoinecollas in https://github.com/scikit-adaptation/skada/pull/280
  • [MRG] Add SPA method by @tgnassou in https://github.com/scikit-adaptation/skada/pull/276
  • [MRG] Add contributor guide by @tgnassou in https://github.com/scikit-adaptation/skada/pull/282

Full Changelog: https://github.com/scikit-adaptation/skada/compare/0.3.0...0.4.0

- Python
Published by tgnassou about 1 year ago

skada - 0.3.0

First release of SKADA!

The following algorithms are currently implemented.

Domain adaptation algorithms

  • Sample reweighting methods (Gaussian [1], Discriminant [2], KLIEPReweight [3], DensityRatio [4], TarS [21], KMMReweight [23])
  • Sample mapping methods (CORAL [5], Optimal Transport DA OTDA [6], LinearMonge [7], LS-ConS [21])
  • Subspace methods (SubspaceAlignment [8], TCA [9], Transfer Subspace Learning [27])
  • Other methods (JDOT [10], DASVM [11], OT Label Propagation [28])

Any methods that can be cast as an adaptation of the input data can be used in one of two ways: - a scikit-learn transformer (Adapter) which provides both a full Classifier/Regressor estimator - or an Adapter that can be used in a DA pipeline with make_da_pipeline. Refer to the examples below and visit the gallery for more details.

Deep learning domain adaptation algorithms

  • Deep Correlation alignment (DeepCORAL [12])
  • Deep joint distribution optimal (DeepJDOT [13])
  • Divergence minimization (MMD/DAN [14])
  • Adversarial/discriminator based DA (DANN [15], CDAN [16])

DA metrics

  • Importance Weighted [17]
  • Prediction entropy [18]
  • Soft neighborhood density [19]
  • Deep Embedded Validation (DEV) [20]
  • Circular Validation [11]

References

[1] Shimodaira Hidetoshi. "Improving predictive inference under covariate shift by weighting the log-likelihood function." Journal of statistical planning and inference 90, no. 2 (2000): 227-244.

[2] Sugiyama Masashi, Taiji Suzuki, and Takafumi Kanamori. "Density-ratio matching under the Bregman divergence: a unified framework of density-ratio estimation." Annals of the Institute of Statistical Mathematics 64 (2012): 1009-1044.

[3] Sugiyama Masashi, Taiji Suzuki, Shinichi Nakajima, Hisashi Kashima, Paul Von Bünau, and Motoaki Kawanabe. "Direct importance estimation for covariate shift adaptation." Annals of the Institute of Statistical Mathematics 60 (2008): 699-746.

[4] Sugiyama Masashi, and Klaus-Robert Müller. "Input-dependent estimation of generalization error under covariate shift." (2005): 249-279.

[5] Sun Baochen, Jiashi Feng, and Kate Saenko. "Correlation alignment for unsupervised domain adaptation." Domain adaptation in computer vision applications (2017): 153-171.

[6] Courty Nicolas, Flamary Rémi, Tuia Devis, and Alain Rakotomamonjy. "Optimal transport for domain adaptation." IEEE Trans. Pattern Anal. Mach. Intell 1, no. 1-40 (2016): 2.

[7] Flamary, R., Lounici, K., & Ferrari, A. (2019). Concentration bounds for linear monge mapping estimation and optimal transport domain adaptation. arXiv preprint arXiv:1905.10155.

[8] Fernando, B., Habrard, A., Sebban, M., & Tuytelaars, T. (2013). Unsupervised visual domain adaptation using subspace alignment. In Proceedings of the IEEE international conference on computer vision (pp. 2960-2967).

[9] Pan, S. J., Tsang, I. W., Kwok, J. T., & Yang, Q. (2010). Domain adaptation via transfer component analysis. IEEE transactions on neural networks, 22(2), 199-210.

[10] Courty, N., Flamary, R., Habrard, A., & Rakotomamonjy, A. (2017). Joint distribution optimal transportation for domain adaptation. Advances in neural information processing systems, 30.

[11] Bruzzone, L., & Marconcini, M. (2009). Domain adaptation problems: A DASVM classification technique and a circular validation strategy. IEEE transactions on pattern analysis and machine intelligence, 32(5), 770-787.

[12] Sun, B., & Saenko, K. (2016). Deep coral: Correlation alignment for deep domain adaptation. In Computer Vision–ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part III 14 (pp. 443-450). Springer International Publishing.

[13] Damodaran, B. B., Kellenberger, B., Flamary, R., Tuia, D., & Courty, N. (2018). Deepjdot: Deep joint distribution optimal transport for unsupervised domain adaptation. In Proceedings of the European conference on computer vision (ECCV) (pp. 447-463).

[14] Long, M., Cao, Y., Wang, J., & Jordan, M. (2015, June). Learning transferable features with deep adaptation networks. In International conference on machine learning (pp. 97-105). PMLR.

[15] Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., ... & Lempitsky, V. (2016). Domain-adversarial training of neural networks. Journal of machine learning research, 17(59), 1-35.

[16] Long, M., Cao, Z., Wang, J., & Jordan, M. I. (2018). Conditional adversarial domain adaptation. Advances in neural information processing systems, 31.

[17] Sugiyama, M., Krauledat, M., & Müller, K. R. (2007). Covariate shift adaptation by importance weighted cross validation. Journal of Machine Learning Research, 8(5).

[18] Morerio, P., Cavazza, J., & Murino, V. (2017). Minimal-entropy correlation alignment for unsupervised deep domain adaptation. arXiv preprint arXiv:1711.10288.

[19] Saito, K., Kim, D., Teterwak, P., Sclaroff, S., Darrell, T., & Saenko, K. (2021). Tune it the right way: Unsupervised validation of domain adaptation via soft neighborhood density. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 9184-9193).

[20] You, K., Wang, X., Long, M., & Jordan, M. (2019, May). Towards accurate model selection in deep unsupervised domain adaptation. In International Conference on Machine Learning (pp. 7124-7133). PMLR.

[21] Zhang, K., Schölkopf, B., Muandet, K., Wang, Z. (2013). Domain Adaptation under Target and Conditional Shift. In International Conference on Machine Learning (pp. 819-827). PMLR.

[22] Loog, M. (2012). Nearest neighbor-based importance weighting. In 2012 IEEE International Workshop on Machine Learning for Signal Processing, pages 1–6. IEEE (https://arxiv.org/pdf/2102.02291.pdf)

[23] Domain Adaptation Problems: A DASVM ClassificationTechnique and a Circular Validation StrategyLorenzo Bruzzone, Fellow, IEEE, and Mattia Marconcini, Member, IEEE (https://rslab.disi.unitn.it/papers/R82-PAMI.pdf)

[24] Loog, M. (2012). Nearest neighbor-based importance weighting. In 2012 IEEE International Workshop on Machine Learning for Signal Processing, pages 1–6. IEEE (https://arxiv.org/pdf/2102.02291.pdf)

[25] J. Huang, A. Gretton, K. Borgwardt, B. Schölkopf and A. J. Smola. Correcting sample selection bias by unlabeled data. In NIPS, 2007. (https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=07117994f0971b2fc2df95adb373c31c3d313442)

[26] Long, M., Wang, J., Ding, G., Sun, J., and Yu, P. (2014). Transfer joint matching for unsupervised domain adaptation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1410–1417

[27] S. Si, D. Tao and B. Geng. In IEEE Transactions on Knowledge and Data Engineering, (2010) Bregman Divergence-Based Regularization for Transfer Subspace Learning

[28] Solomon, J., Rustamov, R., Guibas, L., & Butscher, A. (2014, January). Wasserstein propagation for semi-supervised learning. In International Conference on Machine Learning (pp. 306-314). PMLR.

What's Changed

  • Update previously used dataset fixture by @kachayev in https://github.com/scikit-adaptation/skada/pull/117
  • Remove masked inputs only if estimator does not accept sample_domain by @kachayev in https://github.com/scikit-adaptation/skada/pull/123
  • Fix DiscriminatorReweightDensity and ReweightDensity by @antoinedemathelin in https://github.com/scikit-adaptation/skada/pull/118
  • [TOREVIEW] _findy_type return enum by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/125
  • [MRG] Implement CircularValidation as a scorer by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/124
  • No need for mark-as-final operation by @kachayev in https://github.com/scikit-adaptation/skada/pull/128
  • [MRG] Add TarS method by @antoinecollas in https://github.com/scikit-adaptation/skada/pull/93
  • Selector to avoid filtering out masked samples when fitting transformer by @kachayev in https://github.com/scikit-adaptation/skada/pull/129
  • [MRG] Fix tests random seed and TarS by @antoinecollas in https://github.com/scikit-adaptation/skada/pull/131
  • [MRG] DA deep methods with new API by @tgnassou in https://github.com/scikit-adaptation/skada/pull/45
  • [MRG] Add LS-ConS method by @antoinecollas in https://github.com/scikit-adaptation/skada/pull/103
  • Precommit with ruff+codespell by @agramfort in https://github.com/scikit-adaptation/skada/pull/130
  • Ignore deep/* tests when torch is not installed by @kachayev in https://github.com/scikit-adaptation/skada/pull/133
  • [MRG] Separate Lint and Tests + codecov configuration by @rflamary in https://github.com/scikit-adaptation/skada/pull/135
  • SelectSource and SelectTarget selectors by @kachayev in https://github.com/scikit-adaptation/skada/pull/142
  • SelectSourceTarget selector by @kachayev in https://github.com/scikit-adaptation/skada/pull/145
  • [MRG] Implementation of 1NN reweighting and reweighting example implementation by @BuenoRuben in https://github.com/scikit-adaptation/skada/pull/108
  • [MRG] Add predict_proba for jdot by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/153
  • Update readme and add unique references in docstring by @ambroiseodt in https://github.com/scikit-adaptation/skada/pull/154
  • [MRG] CircularValidation: Re-encode y labels before training the estimator on ysourcepred by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/155
  • [MRG] Fix Tars & MMDSConS by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/156
  • [MRG] Add the auto/scale mode in KLIEP by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/157
  • [TO_REVIEW] Make DeepEmbeddedValidation scorer to work with the new API by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/47
  • [MRG] Add predictproba to DASVM by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/158
  • [MRG] Update How-to with advanced pipelines examples by @rflamary in https://github.com/scikit-adaptation/skada/pull/144
  • [MRG] Propagate adaptation output through multiple steps by @kachayev in https://github.com/scikit-adaptation/skada/pull/149
  • [MRG] Implementation of the TJM method by @BuenoRuben in https://github.com/scikit-adaptation/skada/pull/140
  • [TOREVIEW] Small bug fix checkXydomain() + sourcetargetmerge() by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/165
  • Add frank-wolfe solver (v2) by @antoinedemathelin in https://github.com/scikit-adaptation/skada/pull/167
  • [MRG] Add kwargs to DASVM predict/predict_proba + add score func by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/160
  • Allow multi dim inputs through the pipeline by @kachayev in https://github.com/scikit-adaptation/skada/pull/170
  • Fix sign for source/targets in the HowTo docs by @kachayev in https://github.com/scikit-adaptation/skada/pull/172
  • Make deep DA methods compatible with GPU by @Florent-Michel in https://github.com/scikit-adaptation/skada/pull/174
  • [MRG] Make make_da_pipeline work with deep methods by @tgnassou in https://github.com/scikit-adaptation/skada/pull/159
  • Fix issue 171 on deepcoralloss by @Florent-Michel in https://github.com/scikit-adaptation/skada/pull/182
  • Update documentation by @apmellot in https://github.com/scikit-adaptation/skada/pull/176
  • Fix CDAN input to domain_classifier and gpu compatibility by @Florent-Michel in https://github.com/scikit-adaptation/skada/pull/178
  • [MRG] Fix issue of the circular validation with deep models by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/169
  • [MRG] Major update of Adapter API by @kachayev in https://github.com/scikit-adaptation/skada/pull/184
  • [MRG] Add TransferSubspaceLearning method by @antoinecollas in https://github.com/scikit-adaptation/skada/pull/181
  • [MRG] OT Label propagation methods (classical and target shift) by @rflamary in https://github.com/scikit-adaptation/skada/pull/195
  • [MRG] Fix pack when y is a string by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/197
  • [MRG] Add officehome dataset by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/196
  • [MRG] Add Amazon review dataset to skada by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/185
  • [TOREVIEW] Add nneighbours as an arg of NN RW by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/199
  • [MRG][FIX] Add allo_source parameter to JDOTClassifier by @rflamary in https://github.com/scikit-adaptation/skada/pull/202
  • [MRG] Fix Circular validation for NODATARGET_ONLY by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/201
  • [MRG] MMDLS handle case where we have Xsource and no ysource by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/200
  • [MRG] Clean datasets references by @ambroiseodt in https://github.com/scikit-adaptation/skada/pull/203
  • [MRG] Incorporate sklearn 1.5.0 changes that are incompatible by @kachayev in https://github.com/scikit-adaptation/skada/pull/208
  • [MRG] Fix subspace alignment by @antoinecollas in https://github.com/scikit-adaptation/skada/pull/206
  • [MRG] Fix Density Reweight + Deep embedded validation + ImportanceWeightedScorer by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/205
  • FIX test collection don't run examples by @tomMoral in https://github.com/scikit-adaptation/skada/pull/209
  • Add a covshiftcenter parameter to move the center of the covariate shift by @antoinedemathelin in https://github.com/scikit-adaptation/skada/pull/210
  • [WIP] Center data when using transform of coral by @antoinecollas in https://github.com/scikit-adaptation/skada/pull/211
  • Add nitermax to OTLabelProp by @antoinecollas in https://github.com/scikit-adaptation/skada/pull/212
  • Fix test_cv np.bincount error by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/213
  • [FIX][ENH] Fix and add more explanation to "DA validation procedures" examples by @apmellot in https://github.com/scikit-adaptation/skada/pull/183
  • [FIX][ENH] Improve "DA methods" examples by @vloison in https://github.com/scikit-adaptation/skada/pull/188
  • [MRG] Release 0.3.0, pyproject.toml an Citations files by @rflamary in https://github.com/scikit-adaptation/skada/pull/215

New Contributors

  • @antoinecollas made their first contribution in https://github.com/scikit-adaptation/skada/pull/93
  • @tgnassou made their first contribution in https://github.com/scikit-adaptation/skada/pull/45
  • @agramfort made their first contribution in https://github.com/scikit-adaptation/skada/pull/130
  • @ambroiseodt made their first contribution in https://github.com/scikit-adaptation/skada/pull/154
  • @Florent-Michel made their first contribution in https://github.com/scikit-adaptation/skada/pull/174
  • @apmellot made their first contribution in https://github.com/scikit-adaptation/skada/pull/176
  • @tomMoral made their first contribution in https://github.com/scikit-adaptation/skada/pull/209
  • @vloison made their first contribution in https://github.com/scikit-adaptation/skada/pull/188

Full Changelog: https://github.com/scikit-adaptation/skada/compare/0.2.3...0.3.0

- Python
Published by rflamary over 1 year ago

skada - 0.2.3

Documentatrion pre release

- Python
Published by rflamary almost 2 years ago

skada - 0.2.1

Documentation update tag

- Python
Published by rflamary almost 2 years ago

skada - 0.2

This is a first tag for SKADA. The library is still under heavy development and should not be used in production. API will definitely change in the future.

What's Changed

  • Fix merge conflic in losses module by @kachayev in https://github.com/scikit-adaptation/skada/pull/23
  • Follow github block-quote markdown syntax for the README by @kachayev in https://github.com/scikit-adaptation/skada/pull/22
  • [MRG] CirleCI documentation by @rflamary in https://github.com/scikit-adaptation/skada/pull/26
  • [MRG] Doc circleCI by @rflamary in https://github.com/scikit-adaptation/skada/pull/30
  • Pipeline to respect default_selector parameters by @kachayev in https://github.com/scikit-adaptation/skada/pull/29
  • [MRG] Debug examples by @rflamary in https://github.com/scikit-adaptation/skada/pull/31
  • The selector to pass the params to the base estimator by @kachayev in https://github.com/scikit-adaptation/skada/pull/35
  • Add code cells markup for dataset examples to make them interactive by @kachayev in https://github.com/scikit-adaptation/skada/pull/39
  • Fix label masking for training dataset by @kachayev in https://github.com/scikit-adaptation/skada/pull/40
  • make_da_pipeline helper to allow named estimators by @kachayev in https://github.com/scikit-adaptation/skada/pull/37
  • Rename pack_flatten to pack_for_lodo by @kachayev in https://github.com/scikit-adaptation/skada/pull/27
  • [DOC] Corrections README and string for DomainAware dataset by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/42
  • Rename Bunch keys by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/44
  • [DOC] Small README fix by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/52
  • [WIP] Add testing with minimal install and update it to use pip by @rflamary in https://github.com/scikit-adaptation/skada/pull/48
  • [FIX] Switch NotImplementedError to ValueError + New test cases by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/50
  • Flake8 correction for samplesgenerator.py by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/54
  • Additional flake8 fixes by @kachayev in https://github.com/scikit-adaptation/skada/pull/55
  • Remove version for POT by @kachayev in https://github.com/scikit-adaptation/skada/pull/57
  • DomainAwareDataset str repr edge case handling by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/49
  • Switch from dev0 to stable sklearn 1.4.0 by @kachayev in https://github.com/scikit-adaptation/skada/pull/60
  • [FIX] Unwrap expliticly given selector before generating the name for the pipeline by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/51
  • [MRG] Make sure all API methods accept sample_domain as None by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/53
  • Fix flake8 errors by @kachayev in https://github.com/scikit-adaptation/skada/pull/62
  • Doc fix by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/66
  • [MRG] Add test cases for the Reweight class by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/70
  • [TO_REVIEW] Using global variables instead of number by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/68
  • [TOREVIEW] Switch allowsource to True by default by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/64
  • [Fix] Update flake8.yaml to actually run! by @rflamary in https://github.com/scikit-adaptation/skada/pull/72
  • Fix flake8 for utils and tests by @kachayev in https://github.com/scikit-adaptation/skada/pull/73
  • [MRG] Regression label for 2d classification data generation by @BuenoRuben in https://github.com/scikit-adaptation/skada/pull/69
  • [WIP] Update test suite for base selector functionality by @kachayev in https://github.com/scikit-adaptation/skada/pull/74
  • Fix masked inputs filtering in the base selector for regression tasks by @kachayev in https://github.com/scikit-adaptation/skada/pull/86
  • [MRG] JDOT Regressor by @rflamary in https://github.com/scikit-adaptation/skada/pull/76
  • Properly process sample_weight when using reweight adapters by @kachayev in https://github.com/scikit-adaptation/skada/pull/90
  • Remove target labels in the method comparison example by @antoinedemathelin in https://github.com/scikit-adaptation/skada/pull/92
  • [MRG] Modification sourcetargetmerge function behaviours by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/71
  • [MRG] PredictionEntropyScorer output negative scores by @YanisLalou in https://github.com/scikit-adaptation/skada/pull/63

New Contributors

  • @kachayev made their first contribution in https://github.com/scikit-adaptation/skada/pull/23
  • @rflamary made their first contribution in https://github.com/scikit-adaptation/skada/pull/26
  • @YanisLalou made their first contribution in https://github.com/scikit-adaptation/skada/pull/42
  • @BuenoRuben made their first contribution in https://github.com/scikit-adaptation/skada/pull/69
  • @antoinedemathelin made their first contribution in https://github.com/scikit-adaptation/skada/pull/92

Full Changelog: https://github.com/scikit-adaptation/skada/commits/0.2

- Python
Published by rflamary about 2 years ago