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diff --git a/python-cimcb.spec b/python-cimcb.spec new file mode 100644 index 0000000..66a71c4 --- /dev/null +++ b/python-cimcb.spec @@ -0,0 +1,368 @@ +%global _empty_manifest_terminate_build 0 +Name: python-cimcb +Version: 1.1.0 +Release: 1 +Summary: This is a pre-release. +License: http://www.apache.org/licenses/LICENSE-2.0.html +URL: https://github.com/KevinMMendez/cimcb +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/0f/48/1be2926978b5eecac0ae6c5427d8c9a20d8a07886659131d675e522d9bb3/cimcb-1.1.0.tar.gz +BuildArch: noarch + +Requires: python3-bokeh +Requires: python3-keras +Requires: python3-numpy +Requires: python3-pandas +Requires: python3-scipy +Requires: python3-scikit-learn +Requires: python3-statsmodels +Requires: python3-tensorflow +Requires: python3-tqdm +Requires: python3-xlrd +Requires: python3-joblib + +%description +<img src="cimcb_logo.png" alt="drawing" width="400"/> + +# cimcb +cimcb package containing the necessary tools for the statistical analysis of untargeted and targeted metabolomics data. + +## Installation + +### Dependencies +cimcb requires: +- Python (>=3.5) +- Bokeh (>=1.0.0) +- Keras +- NumPy (>=1.12) +- SciPy +- scikit-learn +- Statsmodels +- TensorFlow +- tqdm + +### User installation +The recommend way to install cimcb and dependencies is to using ``conda``: +```console +conda install -c cimcb cimcb +``` +or ``pip``: +```console +pip install cimcb +``` +Alternatively, to install directly from github: +```console +pip install https://github.com/KevinMMendez/cimcb/archive/master.zip +``` + +### Tutorial +Open with Binders: + +[](https://mybinder.org/v2/gh/KevinMMendez/BinderTutorial_Workflow/master?filepath=BinderTutorial_Workflow.ipynb) + +### API +For futher detail on the usage refer to the docstring. + +#### cimcb.model +- [PLS_SIMPLS](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/PLS_SIMPLS.py#L14-L36): Partial least-squares regression using the SIMPLS algorithm. +- [PCR](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/PCR.py#L8-L29): Principal component regression. +- [PCLR](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/PCLR.py#L8-L29): Principal component logistic regression. +- [RF](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/RF.py#L8-L9): Random forest. +- [SVM](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/SVM.py#L8-L9): Support Vector Machine. +- [RBF_NN](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/RBF_NN.py#L8-L9): Radial basis function neural network. +- [NN_LinearLinear](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/NN_LinearLinear.py#L7-L8): 2 Layer linear-linear neural network. +- [NN_LinearLogit](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/NN_LinearLogit.py#L7-L8): 2 Layer linear-logistic neural network. +- [NN_LogitLogit](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/NN_LogitLogit.py#L7-L8): 2 Layer logistic-logistic neural network. + +#### cimcb.plot +- [boxplot](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/boxplot.py#L8-L18): Creates a boxplot using Bokeh. +- [distribution](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/distribution.py#L6-L16): Creates a distribution plot using Bokeh. +- [pca](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/pca.py#L10-L17): Creates a PCA scores and loadings plot using Bokeh. +- [permutation_test](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/permutation_test.py#L13-L27): Creates permutation test plots using Bokeh. +- [roc_plot](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/roc.py#L11-L24): Creates a rocplot using Bokeh. +- [scatter](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/scatter.py#L6-L16): Creates a scatterplot using Bokeh. +- [scatterCI](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/scatterCI.py#L7-L14): Creates a scatterCI plot using Bokeh. + +#### cimcb.cross_val +- [kfold](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/cross_val/kfold.py#L14-L42): Exhaustitive search over param_dict calculating binary metrics. + +#### cimcb.bootstrap +- [Perc](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/bootstrap/Perc.py#L6-L35): Returns bootstrap confidence intervals using the percentile boostrap interval. +- [BC](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/bootstrap/BC.py#L8-L37): Returns bootstrap confidence intervals using the bias-corrected boostrap interval. +- [BCA](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/bootstrap/BCA.py#L8-L36): Returns bootstrap confidence intervals using the bias-corrected and accelerated boostrap interval. + +#### cimcb.utils +- [binary_metrics](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/binary_metrics.py#L5-L23): Return a dict of binary stats with the following metrics: R2, auc, accuracy, precision, sensitivity, specificity, and F1 score. +- [ci95_ellipse](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/ci95_ellipse.py#L6-L28): Construct a 95% confidence ellipse using PCA. +- [knnimpute](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/knnimpute.py#L7-L22): kNN missing value imputation using Euclidean distance. +- [load_dataXL](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/load_dataXL.py#L7-L29): Loads and validates the DataFile and PeakFile from an excel file. +- [nested_getattr](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/nested_getattr.py#L4-L5): getattr for nested attributes. +- [scale](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/scale.py#L4-L42): Scales x (which can include nans) with method: 'auto', 'pareto', 'vast', or 'level'. +- [table_check](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/table_check.py#L4-L17): Error checking for DataTable and PeakTable (used in load_dataXL). +- [univariate_2class](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/univariate_2class.py#L8-L35): Creates a table of univariate statistics (2 class). +- [wmean](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/wmean.py#L4-L19): Returns Weighted Mean. Ignores NaNs and handles infinite weights. + +### License +cimcb is licensed under the ___ license. + +### Authors +- Kevin Mendez +- [David Broadhurst](https://scholar.google.ca/citations?user=M3_zZwUAAAAJ&hl=en) + +### Correspondence +Professor David Broadhurst, Director of the Centre for Integrative Metabolomics & Computation Biology at Edith Cowan University. +E-mail: d.broadhurst@ecu.edu.au + +### Citation +If you would cite cimcb in a scientific publication, you can use the following: ___ + + + + +%package -n python3-cimcb +Summary: This is a pre-release. +Provides: python-cimcb +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-cimcb +<img src="cimcb_logo.png" alt="drawing" width="400"/> + +# cimcb +cimcb package containing the necessary tools for the statistical analysis of untargeted and targeted metabolomics data. + +## Installation + +### Dependencies +cimcb requires: +- Python (>=3.5) +- Bokeh (>=1.0.0) +- Keras +- NumPy (>=1.12) +- SciPy +- scikit-learn +- Statsmodels +- TensorFlow +- tqdm + +### User installation +The recommend way to install cimcb and dependencies is to using ``conda``: +```console +conda install -c cimcb cimcb +``` +or ``pip``: +```console +pip install cimcb +``` +Alternatively, to install directly from github: +```console +pip install https://github.com/KevinMMendez/cimcb/archive/master.zip +``` + +### Tutorial +Open with Binders: + +[](https://mybinder.org/v2/gh/KevinMMendez/BinderTutorial_Workflow/master?filepath=BinderTutorial_Workflow.ipynb) + +### API +For futher detail on the usage refer to the docstring. + +#### cimcb.model +- [PLS_SIMPLS](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/PLS_SIMPLS.py#L14-L36): Partial least-squares regression using the SIMPLS algorithm. +- [PCR](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/PCR.py#L8-L29): Principal component regression. +- [PCLR](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/PCLR.py#L8-L29): Principal component logistic regression. +- [RF](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/RF.py#L8-L9): Random forest. +- [SVM](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/SVM.py#L8-L9): Support Vector Machine. +- [RBF_NN](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/RBF_NN.py#L8-L9): Radial basis function neural network. +- [NN_LinearLinear](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/NN_LinearLinear.py#L7-L8): 2 Layer linear-linear neural network. +- [NN_LinearLogit](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/NN_LinearLogit.py#L7-L8): 2 Layer linear-logistic neural network. +- [NN_LogitLogit](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/NN_LogitLogit.py#L7-L8): 2 Layer logistic-logistic neural network. + +#### cimcb.plot +- [boxplot](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/boxplot.py#L8-L18): Creates a boxplot using Bokeh. +- [distribution](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/distribution.py#L6-L16): Creates a distribution plot using Bokeh. +- [pca](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/pca.py#L10-L17): Creates a PCA scores and loadings plot using Bokeh. +- [permutation_test](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/permutation_test.py#L13-L27): Creates permutation test plots using Bokeh. +- [roc_plot](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/roc.py#L11-L24): Creates a rocplot using Bokeh. +- [scatter](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/scatter.py#L6-L16): Creates a scatterplot using Bokeh. +- [scatterCI](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/scatterCI.py#L7-L14): Creates a scatterCI plot using Bokeh. + +#### cimcb.cross_val +- [kfold](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/cross_val/kfold.py#L14-L42): Exhaustitive search over param_dict calculating binary metrics. + +#### cimcb.bootstrap +- [Perc](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/bootstrap/Perc.py#L6-L35): Returns bootstrap confidence intervals using the percentile boostrap interval. +- [BC](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/bootstrap/BC.py#L8-L37): Returns bootstrap confidence intervals using the bias-corrected boostrap interval. +- [BCA](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/bootstrap/BCA.py#L8-L36): Returns bootstrap confidence intervals using the bias-corrected and accelerated boostrap interval. + +#### cimcb.utils +- [binary_metrics](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/binary_metrics.py#L5-L23): Return a dict of binary stats with the following metrics: R2, auc, accuracy, precision, sensitivity, specificity, and F1 score. +- [ci95_ellipse](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/ci95_ellipse.py#L6-L28): Construct a 95% confidence ellipse using PCA. +- [knnimpute](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/knnimpute.py#L7-L22): kNN missing value imputation using Euclidean distance. +- [load_dataXL](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/load_dataXL.py#L7-L29): Loads and validates the DataFile and PeakFile from an excel file. +- [nested_getattr](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/nested_getattr.py#L4-L5): getattr for nested attributes. +- [scale](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/scale.py#L4-L42): Scales x (which can include nans) with method: 'auto', 'pareto', 'vast', or 'level'. +- [table_check](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/table_check.py#L4-L17): Error checking for DataTable and PeakTable (used in load_dataXL). +- [univariate_2class](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/univariate_2class.py#L8-L35): Creates a table of univariate statistics (2 class). +- [wmean](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/wmean.py#L4-L19): Returns Weighted Mean. Ignores NaNs and handles infinite weights. + +### License +cimcb is licensed under the ___ license. + +### Authors +- Kevin Mendez +- [David Broadhurst](https://scholar.google.ca/citations?user=M3_zZwUAAAAJ&hl=en) + +### Correspondence +Professor David Broadhurst, Director of the Centre for Integrative Metabolomics & Computation Biology at Edith Cowan University. +E-mail: d.broadhurst@ecu.edu.au + +### Citation +If you would cite cimcb in a scientific publication, you can use the following: ___ + + + + +%package help +Summary: Development documents and examples for cimcb +Provides: python3-cimcb-doc +%description help +<img src="cimcb_logo.png" alt="drawing" width="400"/> + +# cimcb +cimcb package containing the necessary tools for the statistical analysis of untargeted and targeted metabolomics data. + +## Installation + +### Dependencies +cimcb requires: +- Python (>=3.5) +- Bokeh (>=1.0.0) +- Keras +- NumPy (>=1.12) +- SciPy +- scikit-learn +- Statsmodels +- TensorFlow +- tqdm + +### User installation +The recommend way to install cimcb and dependencies is to using ``conda``: +```console +conda install -c cimcb cimcb +``` +or ``pip``: +```console +pip install cimcb +``` +Alternatively, to install directly from github: +```console +pip install https://github.com/KevinMMendez/cimcb/archive/master.zip +``` + +### Tutorial +Open with Binders: + +[](https://mybinder.org/v2/gh/KevinMMendez/BinderTutorial_Workflow/master?filepath=BinderTutorial_Workflow.ipynb) + +### API +For futher detail on the usage refer to the docstring. + +#### cimcb.model +- [PLS_SIMPLS](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/PLS_SIMPLS.py#L14-L36): Partial least-squares regression using the SIMPLS algorithm. +- [PCR](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/PCR.py#L8-L29): Principal component regression. +- [PCLR](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/PCLR.py#L8-L29): Principal component logistic regression. +- [RF](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/RF.py#L8-L9): Random forest. +- [SVM](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/SVM.py#L8-L9): Support Vector Machine. +- [RBF_NN](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/RBF_NN.py#L8-L9): Radial basis function neural network. +- [NN_LinearLinear](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/NN_LinearLinear.py#L7-L8): 2 Layer linear-linear neural network. +- [NN_LinearLogit](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/NN_LinearLogit.py#L7-L8): 2 Layer linear-logistic neural network. +- [NN_LogitLogit](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/NN_LogitLogit.py#L7-L8): 2 Layer logistic-logistic neural network. + +#### cimcb.plot +- [boxplot](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/boxplot.py#L8-L18): Creates a boxplot using Bokeh. +- [distribution](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/distribution.py#L6-L16): Creates a distribution plot using Bokeh. +- [pca](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/pca.py#L10-L17): Creates a PCA scores and loadings plot using Bokeh. +- [permutation_test](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/permutation_test.py#L13-L27): Creates permutation test plots using Bokeh. +- [roc_plot](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/roc.py#L11-L24): Creates a rocplot using Bokeh. +- [scatter](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/scatter.py#L6-L16): Creates a scatterplot using Bokeh. +- [scatterCI](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/scatterCI.py#L7-L14): Creates a scatterCI plot using Bokeh. + +#### cimcb.cross_val +- [kfold](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/cross_val/kfold.py#L14-L42): Exhaustitive search over param_dict calculating binary metrics. + +#### cimcb.bootstrap +- [Perc](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/bootstrap/Perc.py#L6-L35): Returns bootstrap confidence intervals using the percentile boostrap interval. +- [BC](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/bootstrap/BC.py#L8-L37): Returns bootstrap confidence intervals using the bias-corrected boostrap interval. +- [BCA](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/bootstrap/BCA.py#L8-L36): Returns bootstrap confidence intervals using the bias-corrected and accelerated boostrap interval. + +#### cimcb.utils +- [binary_metrics](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/binary_metrics.py#L5-L23): Return a dict of binary stats with the following metrics: R2, auc, accuracy, precision, sensitivity, specificity, and F1 score. +- [ci95_ellipse](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/ci95_ellipse.py#L6-L28): Construct a 95% confidence ellipse using PCA. +- [knnimpute](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/knnimpute.py#L7-L22): kNN missing value imputation using Euclidean distance. +- [load_dataXL](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/load_dataXL.py#L7-L29): Loads and validates the DataFile and PeakFile from an excel file. +- [nested_getattr](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/nested_getattr.py#L4-L5): getattr for nested attributes. +- [scale](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/scale.py#L4-L42): Scales x (which can include nans) with method: 'auto', 'pareto', 'vast', or 'level'. +- [table_check](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/table_check.py#L4-L17): Error checking for DataTable and PeakTable (used in load_dataXL). +- [univariate_2class](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/univariate_2class.py#L8-L35): Creates a table of univariate statistics (2 class). +- [wmean](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/wmean.py#L4-L19): Returns Weighted Mean. Ignores NaNs and handles infinite weights. + +### License +cimcb is licensed under the ___ license. + +### Authors +- Kevin Mendez +- [David Broadhurst](https://scholar.google.ca/citations?user=M3_zZwUAAAAJ&hl=en) + +### Correspondence +Professor David Broadhurst, Director of the Centre for Integrative Metabolomics & Computation Biology at Edith Cowan University. +E-mail: d.broadhurst@ecu.edu.au + +### Citation +If you would cite cimcb in a scientific publication, you can use the following: ___ + + + + +%prep +%autosetup -n cimcb-1.1.0 + +%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-cimcb -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 1.1.0-1 +- Package Spec generated |