%global _empty_manifest_terminate_build 0 Name: python-PyRKHSstats Version: 2.1.0 Release: 1 Summary: A Python package for kernel methods in Statistics/ML. License: GNU General Public License v3.0 URL: https://github.com/Black-Swan-ICL/PyRKHSstats Source0: https://mirrors.aliyun.com/pypi/web/packages/68/7f/01cf21d15ef653abdd9c7bb00fb772467c8618cf19ba38944f833c4f224b/PyRKHSstats-2.1.0.tar.gz BuildArch: noarch Requires: python3-numpy Requires: python3-scipy Requires: python3-pandas Requires: python3-scikit-learn Requires: python3-GPy Requires: python3-pyyaml %description # 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](https://papers.nips.cc/paper/2007/hash/d5cfead94f5350c12c322b5b664544c1-Abstract.html), 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](https://papers.nips.cc/paper/2020/hash/f340f1b1f65b6df5b5e3f94d95b11daf-Abstract.html), 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](https://arxiv.org/abs/1202.3775), 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](https://papers.nips.cc/paper/2009/hash/9246444d94f081e3549803b928260f56-Abstract.html), A. Gretton, K. Fukumizu, Z. Harchaoui, and B. K. Sriperumbudur (NIPS 2009) and in [A Kernel Two-Sample Test](https://jmlr.org/papers/v13/gretton12a.html), 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. %package -n python3-PyRKHSstats Summary: A Python package for kernel methods in Statistics/ML. Provides: python-PyRKHSstats BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-PyRKHSstats # 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](https://papers.nips.cc/paper/2007/hash/d5cfead94f5350c12c322b5b664544c1-Abstract.html), 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](https://papers.nips.cc/paper/2020/hash/f340f1b1f65b6df5b5e3f94d95b11daf-Abstract.html), 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](https://arxiv.org/abs/1202.3775), 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](https://papers.nips.cc/paper/2009/hash/9246444d94f081e3549803b928260f56-Abstract.html), A. Gretton, K. Fukumizu, Z. Harchaoui, and B. K. Sriperumbudur (NIPS 2009) and in [A Kernel Two-Sample Test](https://jmlr.org/papers/v13/gretton12a.html), 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. %package help Summary: Development documents and examples for PyRKHSstats Provides: python3-PyRKHSstats-doc %description help # 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](https://papers.nips.cc/paper/2007/hash/d5cfead94f5350c12c322b5b664544c1-Abstract.html), 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](https://papers.nips.cc/paper/2020/hash/f340f1b1f65b6df5b5e3f94d95b11daf-Abstract.html), 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](https://arxiv.org/abs/1202.3775), 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](https://papers.nips.cc/paper/2009/hash/9246444d94f081e3549803b928260f56-Abstract.html), A. Gretton, K. Fukumizu, Z. Harchaoui, and B. K. Sriperumbudur (NIPS 2009) and in [A Kernel Two-Sample Test](https://jmlr.org/papers/v13/gretton12a.html), 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. %prep %autosetup -n PyRKHSstats-2.1.0 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-PyRKHSstats -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue Jun 20 2023 Python_Bot - 2.1.0-1 - Package Spec generated