%global _empty_manifest_terminate_build 0 Name: python-kmapper Version: 2.0.1 Release: 1 Summary: Python implementation of Mapper algorithm for Topological Data Analysis. License: MIT URL: http://kepler-mapper.scikit-tda.org Source0: https://mirrors.nju.edu.cn/pypi/web/packages/72/7e/d3ff347d053fd60f13c42522e7264b8bc3003c3e389cf61745274447a0d1/kmapper-2.0.1.tar.gz BuildArch: noarch Requires: python3-scikit-learn Requires: python3-numpy Requires: python3-scipy Requires: python3-Jinja2 Requires: python3-sktda-docs-config Requires: python3-pandas Requires: python3-sphinx-gallery Requires: python3-networkx Requires: python3-matplotlib Requires: python3-igraph Requires: python3-plotly Requires: python3-ipywidgets Requires: python3-pytest Requires: python3-networkx Requires: python3-matplotlib Requires: python3-igraph Requires: python3-plotly Requires: python3-ipywidgets %description [![PyPI version](https://badge.fury.io/py/kmapper.svg)](https://badge.fury.io/py/kmapper) [![Downloads](https://pypip.in/download/kmapper/badge.svg)](https://pypi.python.org/pypi/kmapper/) [![Build Status](https://travis-ci.org/scikit-tda/kepler-mapper.svg?branch=master)](https://travis-ci.org/scikit-tda/kepler-mapper) [![Codecov](https://codecov.io/gh/scikit-tda/kepler-mapper/branch/master/graph/badge.svg)](https://codecov.io/gh/scikit-tda/kepler-mapper) [![DOI](https://joss.theoj.org/papers/10.21105/joss.01315/status.svg)](https://doi.org/10.21105/joss.01315) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1002377.svg)](https://doi.org/10.5281/zenodo.1002377) # KeplerMapper > Nature uses as little as possible of anything. - Johannes Kepler This is a Python implementation of the TDA Mapper algorithm for visualization of high-dimensional data. For complete documentation, see [https://kepler-mapper.scikit-tda.org](https://kepler-mapper.scikit-tda.org). KeplerMapper employs approaches based on the Mapper algorithm (Singh et al.) as first described in the paper "Topological Methods for the Analysis of High Dimensional Data Sets and 3D Object Recognition". KeplerMapper can make use of Scikit-Learn API compatible cluster and scaling algorithms. ## Install ### Dependencies KeplerMapper requires: - Python (>= 3.6) - NumPy - Scikit-learn Using the plotly visualizations requires a few extra libraries: - Python-Igraph - Plotly - Ipywidgets Additionally, running some of the examples requires: - matplotlib - umap-learn ### Installation Install KeplerMapper with pip: ``` pip install kmapper ``` To install from source: ``` git clone https://github.com/MLWave/kepler-mapper cd kepler-mapper pip install -e . ``` ## Usage KeplerMapper adopts the scikit-learn API as much as possible, so it should feel very familiar to anyone who has used these libraries. ### Python code ```python # Import the class import kmapper as km # Some sample data from sklearn import datasets data, labels = datasets.make_circles(n_samples=5000, noise=0.03, factor=0.3) # Initialize mapper = km.KeplerMapper(verbose=1) # Fit to and transform the data projected_data = mapper.fit_transform(data, projection=[0,1]) # X-Y axis # Create dictionary called 'graph' with nodes, edges and meta-information graph = mapper.map(projected_data, data, cover=km.Cover(n_cubes=10)) # Visualize it mapper.visualize(graph, path_html="make_circles_keplermapper_output.html", title="make_circles(n_samples=5000, noise=0.03, factor=0.3)") ``` ## Disclaimer Standard MIT disclaimer applies, see `DISCLAIMER.md` for full text. Development status is Alpha. ## How to cite To credit KeplerMapper in your work: https://kepler-mapper.scikit-tda.org/en/latest/#citations %package -n python3-kmapper Summary: Python implementation of Mapper algorithm for Topological Data Analysis. Provides: python-kmapper BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-kmapper [![PyPI version](https://badge.fury.io/py/kmapper.svg)](https://badge.fury.io/py/kmapper) [![Downloads](https://pypip.in/download/kmapper/badge.svg)](https://pypi.python.org/pypi/kmapper/) [![Build Status](https://travis-ci.org/scikit-tda/kepler-mapper.svg?branch=master)](https://travis-ci.org/scikit-tda/kepler-mapper) [![Codecov](https://codecov.io/gh/scikit-tda/kepler-mapper/branch/master/graph/badge.svg)](https://codecov.io/gh/scikit-tda/kepler-mapper) [![DOI](https://joss.theoj.org/papers/10.21105/joss.01315/status.svg)](https://doi.org/10.21105/joss.01315) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1002377.svg)](https://doi.org/10.5281/zenodo.1002377) # KeplerMapper > Nature uses as little as possible of anything. - Johannes Kepler This is a Python implementation of the TDA Mapper algorithm for visualization of high-dimensional data. For complete documentation, see [https://kepler-mapper.scikit-tda.org](https://kepler-mapper.scikit-tda.org). KeplerMapper employs approaches based on the Mapper algorithm (Singh et al.) as first described in the paper "Topological Methods for the Analysis of High Dimensional Data Sets and 3D Object Recognition". KeplerMapper can make use of Scikit-Learn API compatible cluster and scaling algorithms. ## Install ### Dependencies KeplerMapper requires: - Python (>= 3.6) - NumPy - Scikit-learn Using the plotly visualizations requires a few extra libraries: - Python-Igraph - Plotly - Ipywidgets Additionally, running some of the examples requires: - matplotlib - umap-learn ### Installation Install KeplerMapper with pip: ``` pip install kmapper ``` To install from source: ``` git clone https://github.com/MLWave/kepler-mapper cd kepler-mapper pip install -e . ``` ## Usage KeplerMapper adopts the scikit-learn API as much as possible, so it should feel very familiar to anyone who has used these libraries. ### Python code ```python # Import the class import kmapper as km # Some sample data from sklearn import datasets data, labels = datasets.make_circles(n_samples=5000, noise=0.03, factor=0.3) # Initialize mapper = km.KeplerMapper(verbose=1) # Fit to and transform the data projected_data = mapper.fit_transform(data, projection=[0,1]) # X-Y axis # Create dictionary called 'graph' with nodes, edges and meta-information graph = mapper.map(projected_data, data, cover=km.Cover(n_cubes=10)) # Visualize it mapper.visualize(graph, path_html="make_circles_keplermapper_output.html", title="make_circles(n_samples=5000, noise=0.03, factor=0.3)") ``` ## Disclaimer Standard MIT disclaimer applies, see `DISCLAIMER.md` for full text. Development status is Alpha. ## How to cite To credit KeplerMapper in your work: https://kepler-mapper.scikit-tda.org/en/latest/#citations %package help Summary: Development documents and examples for kmapper Provides: python3-kmapper-doc %description help [![PyPI version](https://badge.fury.io/py/kmapper.svg)](https://badge.fury.io/py/kmapper) [![Downloads](https://pypip.in/download/kmapper/badge.svg)](https://pypi.python.org/pypi/kmapper/) [![Build Status](https://travis-ci.org/scikit-tda/kepler-mapper.svg?branch=master)](https://travis-ci.org/scikit-tda/kepler-mapper) [![Codecov](https://codecov.io/gh/scikit-tda/kepler-mapper/branch/master/graph/badge.svg)](https://codecov.io/gh/scikit-tda/kepler-mapper) [![DOI](https://joss.theoj.org/papers/10.21105/joss.01315/status.svg)](https://doi.org/10.21105/joss.01315) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1002377.svg)](https://doi.org/10.5281/zenodo.1002377) # KeplerMapper > Nature uses as little as possible of anything. - Johannes Kepler This is a Python implementation of the TDA Mapper algorithm for visualization of high-dimensional data. For complete documentation, see [https://kepler-mapper.scikit-tda.org](https://kepler-mapper.scikit-tda.org). KeplerMapper employs approaches based on the Mapper algorithm (Singh et al.) as first described in the paper "Topological Methods for the Analysis of High Dimensional Data Sets and 3D Object Recognition". KeplerMapper can make use of Scikit-Learn API compatible cluster and scaling algorithms. ## Install ### Dependencies KeplerMapper requires: - Python (>= 3.6) - NumPy - Scikit-learn Using the plotly visualizations requires a few extra libraries: - Python-Igraph - Plotly - Ipywidgets Additionally, running some of the examples requires: - matplotlib - umap-learn ### Installation Install KeplerMapper with pip: ``` pip install kmapper ``` To install from source: ``` git clone https://github.com/MLWave/kepler-mapper cd kepler-mapper pip install -e . ``` ## Usage KeplerMapper adopts the scikit-learn API as much as possible, so it should feel very familiar to anyone who has used these libraries. ### Python code ```python # Import the class import kmapper as km # Some sample data from sklearn import datasets data, labels = datasets.make_circles(n_samples=5000, noise=0.03, factor=0.3) # Initialize mapper = km.KeplerMapper(verbose=1) # Fit to and transform the data projected_data = mapper.fit_transform(data, projection=[0,1]) # X-Y axis # Create dictionary called 'graph' with nodes, edges and meta-information graph = mapper.map(projected_data, data, cover=km.Cover(n_cubes=10)) # Visualize it mapper.visualize(graph, path_html="make_circles_keplermapper_output.html", title="make_circles(n_samples=5000, noise=0.03, factor=0.3)") ``` ## Disclaimer Standard MIT disclaimer applies, see `DISCLAIMER.md` for full text. Development status is Alpha. ## How to cite To credit KeplerMapper in your work: https://kepler-mapper.scikit-tda.org/en/latest/#citations %prep %autosetup -n kmapper-2.0.1 %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-kmapper -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Fri May 05 2023 Python_Bot - 2.0.1-1 - Package Spec generated