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diff --git a/python-cwgp.spec b/python-cwgp.spec new file mode 100644 index 0000000..98763bd --- /dev/null +++ b/python-cwgp.spec @@ -0,0 +1,368 @@ +%global _empty_manifest_terminate_build 0 +Name: python-CWGP +Version: 2.0.7 +Release: 1 +Summary: CWGP +License: MIT License +URL: https://github.com/andy971022/CWGP +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/2c/ef/7ed0587f6d2bfbb6fda1b2bbb28bf6cb063f1371cb726d5242914a4e9ebc/CWGP-2.0.7.tar.gz +BuildArch: noarch + +Requires: python3-autograd +Requires: python3-scipy +Requires: python3-tqdm +Requires: python3-numpy +Requires: python3-gpy + +%description +# Compositionally Warped Gaussian Processes +This package is dedicated to realizing methods used in this [paper](https://arxiv.org/abs/1906.09665). + + +## TLDR; +A package that transforms anything to a Gaussian distribution. + +## Tutorial + +Visit [here](./examples/cwgp_beta.ipynb) + +## Installation +`pip install CWGP` + +## Quick Start + +Let's randomly generate 100 numbers following an exponential distribution. +``` python +import numpy as np +import seaborn as sns +import matplotlib.pyplot as plt + +exp = np.random.exponential(scale=5, size=50) +idx = np.arange(50) +``` + + +We now instantiate a CWGP class consisting of 3 Sinh-Arcsinh transformations. +``` python +from cwgp.cwgp import CWGP + +compgp = CWGP(["sa","box_cox"]) +``` + +We then fit our data into the model. This minimizes the negative log likelihood function and stores the corresponding parameters for us. +``` python +compgp.fit(exp, idx) +``` + +To get the parameters, we do +``` python +params = compgp.phi.res.x +``` + +We then transform the data via + +``` python +t_exp, d = compgp.phi.comp_phi(params, exp) +sns.distplot(t_exp) +plt.show() +``` + + +Let's make a QQ-plot and see how Gaussian it is. +``` python +from scipy import stats + +stats.probplot(t_exp, dist="norm", plot=plt) +plt.show() +``` + + +The inverse function is also implemented. +``` python +inv_t_exp = compgp.phi.inv_comp_phi(params, t_exp) +``` + +``` python +fig, ax = plt.subplots(1, 2) +sns.distplot(inv_t_exp, ax=ax[0]) +sns.distplot(exp, ax=ax[1]) +plt.show() +``` + +The one on the left is the one being transformed and transformed-back, and the one on the right is the original distribution. +They should be exactly the same. + + +## Transformations + +### Sinh-Arcsinh (sa) + +`from cwgp.transformations import sa` + +### Arcsinh (asinh) + +`from cwgp.transformations import asinh` + +### Box-Cox (box_cox) + +`from cwgp.transformations import box_cox` + +### Sinh-Arcsinh and Affine (SAL) + +`from cwgp.transformations import sal` + + + + +%package -n python3-CWGP +Summary: CWGP +Provides: python-CWGP +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-CWGP +# Compositionally Warped Gaussian Processes +This package is dedicated to realizing methods used in this [paper](https://arxiv.org/abs/1906.09665). + + +## TLDR; +A package that transforms anything to a Gaussian distribution. + +## Tutorial + +Visit [here](./examples/cwgp_beta.ipynb) + +## Installation +`pip install CWGP` + +## Quick Start + +Let's randomly generate 100 numbers following an exponential distribution. +``` python +import numpy as np +import seaborn as sns +import matplotlib.pyplot as plt + +exp = np.random.exponential(scale=5, size=50) +idx = np.arange(50) +``` + + +We now instantiate a CWGP class consisting of 3 Sinh-Arcsinh transformations. +``` python +from cwgp.cwgp import CWGP + +compgp = CWGP(["sa","box_cox"]) +``` + +We then fit our data into the model. This minimizes the negative log likelihood function and stores the corresponding parameters for us. +``` python +compgp.fit(exp, idx) +``` + +To get the parameters, we do +``` python +params = compgp.phi.res.x +``` + +We then transform the data via + +``` python +t_exp, d = compgp.phi.comp_phi(params, exp) +sns.distplot(t_exp) +plt.show() +``` + + +Let's make a QQ-plot and see how Gaussian it is. +``` python +from scipy import stats + +stats.probplot(t_exp, dist="norm", plot=plt) +plt.show() +``` + + +The inverse function is also implemented. +``` python +inv_t_exp = compgp.phi.inv_comp_phi(params, t_exp) +``` + +``` python +fig, ax = plt.subplots(1, 2) +sns.distplot(inv_t_exp, ax=ax[0]) +sns.distplot(exp, ax=ax[1]) +plt.show() +``` + +The one on the left is the one being transformed and transformed-back, and the one on the right is the original distribution. +They should be exactly the same. + + +## Transformations + +### Sinh-Arcsinh (sa) + +`from cwgp.transformations import sa` + +### Arcsinh (asinh) + +`from cwgp.transformations import asinh` + +### Box-Cox (box_cox) + +`from cwgp.transformations import box_cox` + +### Sinh-Arcsinh and Affine (SAL) + +`from cwgp.transformations import sal` + + + + +%package help +Summary: Development documents and examples for CWGP +Provides: python3-CWGP-doc +%description help +# Compositionally Warped Gaussian Processes +This package is dedicated to realizing methods used in this [paper](https://arxiv.org/abs/1906.09665). + + +## TLDR; +A package that transforms anything to a Gaussian distribution. + +## Tutorial + +Visit [here](./examples/cwgp_beta.ipynb) + +## Installation +`pip install CWGP` + +## Quick Start + +Let's randomly generate 100 numbers following an exponential distribution. +``` python +import numpy as np +import seaborn as sns +import matplotlib.pyplot as plt + +exp = np.random.exponential(scale=5, size=50) +idx = np.arange(50) +``` + + +We now instantiate a CWGP class consisting of 3 Sinh-Arcsinh transformations. +``` python +from cwgp.cwgp import CWGP + +compgp = CWGP(["sa","box_cox"]) +``` + +We then fit our data into the model. This minimizes the negative log likelihood function and stores the corresponding parameters for us. +``` python +compgp.fit(exp, idx) +``` + +To get the parameters, we do +``` python +params = compgp.phi.res.x +``` + +We then transform the data via + +``` python +t_exp, d = compgp.phi.comp_phi(params, exp) +sns.distplot(t_exp) +plt.show() +``` + + +Let's make a QQ-plot and see how Gaussian it is. +``` python +from scipy import stats + +stats.probplot(t_exp, dist="norm", plot=plt) +plt.show() +``` + + +The inverse function is also implemented. +``` python +inv_t_exp = compgp.phi.inv_comp_phi(params, t_exp) +``` + +``` python +fig, ax = plt.subplots(1, 2) +sns.distplot(inv_t_exp, ax=ax[0]) +sns.distplot(exp, ax=ax[1]) +plt.show() +``` + +The one on the left is the one being transformed and transformed-back, and the one on the right is the original distribution. +They should be exactly the same. + + +## Transformations + +### Sinh-Arcsinh (sa) + +`from cwgp.transformations import sa` + +### Arcsinh (asinh) + +`from cwgp.transformations import asinh` + +### Box-Cox (box_cox) + +`from cwgp.transformations import box_cox` + +### Sinh-Arcsinh and Affine (SAL) + +`from cwgp.transformations import sal` + + + + +%prep +%autosetup -n CWGP-2.0.7 + +%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-CWGP -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 2.0.7-1 +- Package Spec generated |