SKADA - Domain Adaptation with scikit-learn and PyTorch

SKADA is a library for domain adaptation (DA) with a scikit-learn and PyTorch/skorch compatible API with the following features:

• DA estimators and transformers with a scikit-learn compatible API (fit, transform, predict).
• PyTorch/skorch API for deep learning DA algorithms.
• Classifier/Regressor and data Adapter DA algorithms compatible with scikit-learn pipelines.
• Compatible with scikit-learn validation loops (crossvalscore, GridSearchCV, etc).

Citation: If you use this library in your research, please cite the following reference:

Gnassounou T., Kachaiev O., Flamary R., Collas A., Lalou Y., de Mathelin A., Gramfort A., Bueno R., Michel F., Mellot A., Loison V., Odonnat A., Moreau T. (2024). SKADA : Scikit Adaptation (version 0.3.0). URL: https://scikit-adaptation.github.io/

or in Bibtex format :

bibtex @misc{gnassounou2024skada, author = {Gnassounou, Théo and Kachaiev, Oleksii and Flamary, Rémi and Collas, Antoine and Lalou, Yanis and de Mathelin, Antoine and Gramfort, Alexandre and Bueno, Ruben and Michel, Florent and Mellot, Apolline and Loison, Virginie and Odonnat, Ambroise and Moreau, Thomas}, month = {7}, title = {SKADA : Scikit Adaptation}, url = {https://scikit-adaptation.github.io/}, year = {2024} }

Implemented algorithms

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 galleryfor 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]

Installation

The library is not yet available on PyPI. You can install it from the source code. python pip install git+https://github.com/scikit-adaptation/skada

Short examples

We provide here a few examples to illustrate the use of the library. For more details, please refer to this example, the quick start guide and the gallery.

First, the DA data in the SKADA API is stored in the following format:

python X, y, sample_domain

Where X is the input data, y is the target labels and sample_domain is the domain labels (positive for source and negative for target domains). We provide below an example ho how to fit a DA estimator:

```python from skada import CORAL

da = CORAL() da.fit(X, y, sampledomain=sampledomain) # sample_domain passed by name

ypred = da.predict(Xt) # predict on test data ```

One can also use Adapter classes to create a full pipeline with DA:

```python from skada import CORALAdapter, makedapipeline from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LogisticRegression

pipe = makedapipeline(StandardScaler(), CORALAdapter(), LogisticRegression())

pipe.fit(X, y, sampledomain=sampledomain) # sample_domain passed by name ```

Please note that for Adapter classes that implement sample reweighting, the subsequent classifier/regressor must require sampleweights as input. This is done with the `setfitrequiresmethod. For instance, withLogisticRegression, you would useLogisticRegression().setfitrequires('sampleweight')`:

python from skada import GaussianReweightAdapter, make_da_pipeline pipe = make_da_pipeline(GaussianReweightAdapter(), LogisticRegression().set_fit_request(sample_weight=True))

Finally SKADA can be used for cross validation scores estimation and hyperparameter selection :

```python from sklearn.modelselection import crossvalscore, GridSearchCV from sklearn.preprocessing import StandardScaler from sklearn.linearmodel import LogisticRegression

from skada import CORALAdapter, makedapipeline from skada.model_selection import SourceTargetShuffleSplit from skada.metrics import PredictionEntropyScorer

make pipeline

pipe = makedapipeline(StandardScaler(), CORALAdapter(), LogisticRegression())

split and score

cv = SourceTargetShuffleSplit() scorer = PredictionEntropyScorer()

cross val score

scores = crossvalscore(pipe, X, y, params={'sampledomain': sampledomain}, cv=cv, scoring=scorer)

grid search

paramgrid = {'coraladapterreg': [0.1, 0.5, 0.9]} gridsearch = GridSearchCV(estimator=pipe, paramgrid=paramgrid, cv=cv, scoring=scorer)

gridsearch.fit(X, y, sampledomain=sample_domain) ```

Acknowledgements

This toolbox has been created and is maintained by the SKADA team that includes the following members:

• Théo Gnassounou
• Oleksii Kachaiev
• Rémi Flamary
• Antoine Collas
• Yanis Lalou
• Antoine de Mathelin
• Ruben Bueno

SKADA has benefited from the financing or manpower from the following partners:

License

The library is distributed under the 3-Clause BSD license.

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.

[29] Montesuma, Eduardo Fernandes, and Fred Maurice Ngole Mboula. "Wasserstein barycenter for multi-source domain adaptation." In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 16785-16793. 2021.

[30] Gnassounou, Theo, Rémi Flamary, and Alexandre Gramfort. "Convolution Monge Mapping Normalization for learning on sleep data." Advances in Neural Information Processing Systems 36 (2024).

[31] Redko, Ievgen, Nicolas Courty, Rémi Flamary, and Devis Tuia. "Optimal transport for multi-source domain adaptation under target shift." In The 22nd International Conference on artificial intelligence and statistics, pp. 849-858. PMLR, 2019.

[32] Hu, D., Liang, J., Liew, J. H., Xue, C., Bai, S., & Wang, X. (2023). Mixed Samples as Probes for Unsupervised Model Selection in Domain Adaptation. Advances in Neural Information Processing Systems 36 (2024).

[33] Kang, G., Jiang, L., Yang, Y., & Hauptmann, A. G. (2019). Contrastive Adaptation Network for Unsupervised Domain Adaptation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 4893-4902).

[34] Jin, Ying, Wang, Ximei, Long, Mingsheng, Wang, Jianmin. Minimum Class Confusion for Versatile Domain Adaptation. ECCV, 2020.

[35] Zhang, Y., Liu, T., Long, M., & Jordan, M. I. (2019). Bridging Theory and Algorithm for Domain Adaptation. In Proceedings of the 36th International Conference on Machine Learning, (pp. 7404-7413).

[36] Xiao, Zhiqing, Wang, Haobo, Jin, Ying, Feng, Lei, Chen, Gang, Huang, Fei, Zhao, Junbo.SPA: A Graph Spectral Alignment Perspective for Domain Adaptation. In Neurips, 2023.

[37] Xie, Renchunzi, Odonnat, Ambroise, Feofanov, Vasilii, Deng, Weijian, Zhang, Jianfeng and An, Bo. MaNo: Exploiting Matrix Norm for Unsupervised Accuracy Estimation Under Distribution Shifts. In NeurIPS, 2024.