https://github.com/black-swan-icl/pyrkhsstats
A Python package implementing a variety of statistical methods that rely on kernels (e.g. HSIC for independence testing).
Science Score: 10.0%
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Low similarity (7.6%) to scientific vocabulary
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Repository
A Python package implementing a variety of statistical methods that rely on kernels (e.g. HSIC for independence testing).
Basic Info
Statistics
- Stars: 14
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- Forks: 3
- Open Issues: 0
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Metadata Files
README.md
PyRKHSstats
A Python package implementing a variety of statistical/machine learning methods that rely on kernels (e.g. HSIC for independence testing).
Overview
- Independence testing with HSIC (Hilbert-Schmidt Independence Criterion), as introduced in A Kernel Statistical Test of Independence, A. Gretton, K. Fukumizu, C. Hui Teo, L. Song, B. Schölkopf, and A. Smola (NIPS 2007).
- Measurement of conditional independence with HSCIC (Hilbert-Schmidt Conditional Independence Criterion), as introduced in A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings, J. Park and K. Muandet (NeurIPS 2020).
- The Kernel-based Conditional Independence Test (KCIT), as introduced in Kernel-based Conditional Independence Test and Application in Causal Discovery, K. Zhang, J. Peters, D. Janzing, B. Schölkopf (UAI 2011).
- Two-sample testing (also known as homogeneity testing) with the MMD (Maximum Mean Discrepancy), as presented in A Fast, Consistent Kernel Two-Sample Test, A. Gretton, K. Fukumizu, Z. Harchaoui, and B. K. Sriperumbudur (NIPS 2009) and in A Kernel Two-Sample Test, A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola (JMLR, volume 13, 2012).
| Resource | Description | | :--- | :--- | | HSIC | For independence testing | | HSCIC | For the measurement of conditional independence | | KCIT | For conditional independence testing | | MMD | For two-sample testing |
Implementations available
The following table details the implementation schemes for the different resources available in the package.
| Resource | Implementation Scheme | Numpy based available | PyTorch based available | | :--- | :--- | :----: |:----: | | HSIC | Resampling (permuting the xi's but leaving the yi's unchanged) | Yes | No | | HSIC | Gamma approximation | Yes | No | | HSCIC | N/A | Yes | Yes | | KCIT | Gamma approximation | Yes | No | | KCIT | Monte Carlo simulation (weighted sum of χ2 random variables)| Yes | No | | MMD | Gram matrix spectrum | Yes | No |
[comment]: <> (| MMD | Permutation | Yes | No |)
In development
- Joint independence testing with dHSIC.
- Goodness-of-fit testing.
- Methods for time series models.
- Bayesian statistical kernel methods.
- Regression by independence maximisation.
Owner
- Login: Black-Swan-ICL
- Kind: user
- Repositories: 3
- Profile: https://github.com/Black-Swan-ICL
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- Total packages: 1
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Total downloads:
- pypi 65 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 5
- Total maintainers: 1
pypi.org: pyrkhsstats
A Python package for kernel methods in Statistics/ML.
- Homepage: https://github.com/Black-Swan-ICL/PyRKHSstats
- Documentation: https://pyrkhsstats.readthedocs.io/
- License: GNU General Public License v3.0
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Latest release: 2.1.0
published about 4 years ago
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Maintainers (1)
Dependencies
- GPy ==1.9.9
- matplotlib ==3.4.0
- numpy ==1.20.1
- pandas ==1.2.3
- pytest ==6.2.2
- pyyaml ==6.0
- scikit-learn ==0.24.1
- scipy ==1.6.2
- GPy *
- numpy *
- pandas *
- pyyaml *
- scikit-learn *
- scipy *