HiDimStat: High-dimensional statistical inference tool for Python

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The HiDimStat package provides statistical inference methods to solve the problem of support recovery in the context of high-dimensional and spatially structured data.

Installation

HiDimStat working only with Python 3, ideally Python 3.6+. For installation, run the following from terminal:

pip install hidimstat

Or if you want the latest version available (for example to contribute to the development of this project):

git clone https://github.com/mind-inria/hidimstat.git
cd hidimstat
pip install -e .

Dependencies

HiDimStat depends on the following packages:

joblib
numpy
scipy
scikit-learn

To run examples it is neccessary to install matplotlib, and to run tests it is also needed to install pytest.

Documentation & Examples

Documentation about the main HiDimStat functions is available here and examples are available here.

As of now, there are three different examples (Python scripts) that illustrate how to use the main HiDimStat functions. In each example we handle a different kind of dataset: plot_2D_simulation_example.py handles a simulated dataset with a 2D spatial structure, plot_fmri_data_example.py solves the decoding problem on Haxby fMRI dataset, plot_meg_data_example.py tackles the source localization problem on several MEG/EEG datasets.

# For example run the following command in terminal
python plot_2D_simulation_example.py

Build the documentation

To build the documentation you will need to run:

pip install -U sphinx_gallery sphinx_bootstrap_theme
cd doc
make html

References

The algorithms developed in this package have been detailed in several conference/journal articles that can be downloaded at https://mind-inria.github.io/.

Main references

Ensemble of Clustered desparsified Lasso (ECDL):

  • Chevalier, J. A., Salmon, J., & Thirion, B. (2018). Statistical inference with ensemble of clustered desparsified lasso. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 638-646). Springer, Cham.

  • Chevalier, J. A., Nguyen, T. B., Thirion, B., & Salmon, J. (2021). Spatially relaxed inference on high-dimensional linear models. arXiv preprint arXiv:2106.02590.

Aggregation of multiple Knockoffs (AKO):

  • Nguyen T.-B., Chevalier J.-A., Thirion B., & Arlot S. (2020). Aggregation of Multiple Knockoffs. In Proceedings of the 37th International Conference on Machine Learning, Vienna, Austria, PMLR 119.

Application to decoding (fMRI data):

  • Chevalier, J. A., Nguyen T.-B., Salmon, J., Varoquaux, G. & Thirion, B. (2021). Decoding with confidence: Statistical control on decoder maps. In NeuroImage, 234, 117921.

Application to source localization (MEG/EEG data):

  • Chevalier, J. A., Gramfort, A., Salmon, J., & Thirion, B. (2020). Statistical control for spatio-temporal MEG/EEG source imaging with desparsified multi-task Lasso. In Proceedings of the 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada.

Single/Group statistically validated importance using conditional permutations:

  • Chamma, A., Thirion, B., & Engemann, D. (2024). **Variable importance in

high-dimensional settings requires grouping**. In Proceedings of the 38th Conference of the Association for the Advancement of Artificial Intelligence(AAAI 2024), Vancouver, Canada.

  • Chamma, A., Engemann, D., & Thirion, B. (2023). **Statistically Valid Variable

Importance Assessment through Conditional Permutations**. In Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS 2023), New Orleans, USA.

If you use our packages, we would appreciate citations to the relevant aforementioned papers.

Other useful references

For de-sparsified(or de-biased) Lasso:

  • Javanmard, A., & Montanari, A. (2014). Confidence intervals and hypothesis testing for high-dimensional regression. The Journal of Machine Learning Research, 15(1), 2869-2909.

  • Zhang, C. H., & Zhang, S. S. (2014). Confidence intervals for low dimensional parameters in high dimensional linear models. Journal of the Royal Statistical Society: Series B: Statistical Methodology, 217-242.

  • Van de Geer, S., Bühlmann, P., Ritov, Y. A., & Dezeure, R. (2014). On asymptotically optimal confidence regions and tests for high-dimensional models. The Annals of Statistics, 42(3), 1166-1202.

For Knockoffs Inference:

  • Barber, R. F; Candès, E. J. (2015). Controlling the false discovery rate via knockoffs. Annals of Statistics. 43 , no. 5, 2055–2085. doi:10.1214/15-AOS1337. https://projecteuclid.org/euclid.aos/1438606853

  • Candès, E., Fan, Y., Janson, L., & Lv, J. (2018). Panning for gold: Model-X knockoffs for high dimensional controlled variable selection. Journal of the Royal Statistical Society Series B, 80(3), 551-577.

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