%global _empty_manifest_terminate_build 0 Name: python-dtreeviz Version: 2.2.1 Release: 1 Summary: A Python 3 library for sci-kit learn, XGBoost, LightGBM, Spark, and TensorFlow decision tree visualization License: MIT URL: https://github.com/parrt/dtreeviz Source0: https://mirrors.nju.edu.cn/pypi/web/packages/47/63/288c0bd461c82b34e4b9ecf18e144e0e3e0ae4d014f7dea2ad733be3c3c8/dtreeviz-2.2.1.tar.gz BuildArch: noarch Requires: python3-graphviz Requires: python3-pandas Requires: python3-numpy Requires: python3-scikit-learn Requires: python3-matplotlib Requires: python3-colour Requires: python3-pytest Requires: python3-xgboost Requires: python3-pyspark Requires: python3-lightgbm Requires: python3-tensorflow-decision-forests Requires: python3-lightgbm Requires: python3-pyspark Requires: python3-tensorflow-decision-forests Requires: python3-xgboost %description A python library for decision tree visualization and model interpretation. Decision trees are the fundamental building block of [gradient boosting machines](http://explained.ai/gradient-boosting/index.html) and [Random Forests](https://en.wikipedia.org/wiki/Random_forest)(tm), probably the two most popular machine learning models for structured data. Visualizing decision trees is a tremendous aid when learning how these models work and when interpreting models. The visualizations are inspired by an educational animation by [R2D3](http://www.r2d3.us/); [A visual introduction to machine learning](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/). Please see [How to visualize decision trees](http://explained.ai/decision-tree-viz/index.html) for deeper discussion of our decision tree visualization library and the visual design decisions we made. Currently dtreeviz supports: [scikit-learn](https://scikit-learn.org/stable), [XGBoost](https://xgboost.readthedocs.io/en/latest), [Spark MLlib](https://spark.apache.org/mllib/), [LightGBM](https://lightgbm.readthedocs.io/en/latest/), and [Tensorflow](https://www.tensorflow.org/decision_forests). See [Installation instructions](README.md#Installation). %package -n python3-dtreeviz Summary: A Python 3 library for sci-kit learn, XGBoost, LightGBM, Spark, and TensorFlow decision tree visualization Provides: python-dtreeviz BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-dtreeviz A python library for decision tree visualization and model interpretation. Decision trees are the fundamental building block of [gradient boosting machines](http://explained.ai/gradient-boosting/index.html) and [Random Forests](https://en.wikipedia.org/wiki/Random_forest)(tm), probably the two most popular machine learning models for structured data. Visualizing decision trees is a tremendous aid when learning how these models work and when interpreting models. The visualizations are inspired by an educational animation by [R2D3](http://www.r2d3.us/); [A visual introduction to machine learning](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/). Please see [How to visualize decision trees](http://explained.ai/decision-tree-viz/index.html) for deeper discussion of our decision tree visualization library and the visual design decisions we made. Currently dtreeviz supports: [scikit-learn](https://scikit-learn.org/stable), [XGBoost](https://xgboost.readthedocs.io/en/latest), [Spark MLlib](https://spark.apache.org/mllib/), [LightGBM](https://lightgbm.readthedocs.io/en/latest/), and [Tensorflow](https://www.tensorflow.org/decision_forests). See [Installation instructions](README.md#Installation). %package help Summary: Development documents and examples for dtreeviz Provides: python3-dtreeviz-doc %description help A python library for decision tree visualization and model interpretation. Decision trees are the fundamental building block of [gradient boosting machines](http://explained.ai/gradient-boosting/index.html) and [Random Forests](https://en.wikipedia.org/wiki/Random_forest)(tm), probably the two most popular machine learning models for structured data. Visualizing decision trees is a tremendous aid when learning how these models work and when interpreting models. The visualizations are inspired by an educational animation by [R2D3](http://www.r2d3.us/); [A visual introduction to machine learning](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/). Please see [How to visualize decision trees](http://explained.ai/decision-tree-viz/index.html) for deeper discussion of our decision tree visualization library and the visual design decisions we made. Currently dtreeviz supports: [scikit-learn](https://scikit-learn.org/stable), [XGBoost](https://xgboost.readthedocs.io/en/latest), [Spark MLlib](https://spark.apache.org/mllib/), [LightGBM](https://lightgbm.readthedocs.io/en/latest/), and [Tensorflow](https://www.tensorflow.org/decision_forests). See [Installation instructions](README.md#Installation). %prep %autosetup -n dtreeviz-2.2.1 %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-dtreeviz -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Fri Apr 21 2023 Python_Bot - 2.2.1-1 - Package Spec generated