%global _empty_manifest_terminate_build 0 Name: python-causalnex Version: 0.12.0 Release: 1 Summary: Toolkit for causal reasoning (Bayesian Networks / Inference) License: Apache Software License (Apache 2.0) URL: https://github.com/quantumblacklabs/causalnex Source0: https://mirrors.nju.edu.cn/pypi/web/packages/46/76/c58ddbd00cd7ed45ee92d5ad0624681cd2bfa1629228712f81155c0c89a3/causalnex-0.12.0.tar.gz BuildArch: noarch Requires: python3-ipython Requires: python3-networkx Requires: python3-numpy Requires: python3-pandas Requires: python3-pathos Requires: python3-pgmpy Requires: python3-pyvis Requires: python3-scipy Requires: python3-setuptools Requires: python3-torch Requires: python3-wheel Requires: python3-wrapt Requires: python3-scikit-learn Requires: python3-scikit-learn Requires: python3-scikit-learn Requires: python3-mdlp-discretization Requires: python3-mdlp-discretization %description | Theme | Status | |------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Latest Release | [![PyPI version](https://badge.fury.io/py/causalnex.svg)](https://pypi.org/project/causalnex/) | | Python Version | [![Python Version](https://img.shields.io/badge/python-3.8%20%7C%203.9%20%7C%203.10-blue.svg)](https://pypi.org/project/causalnex/) | | `master` Branch Build | [![CircleCI](https://circleci.com/gh/quantumblacklabs/causalnex/tree/master.svg?style=shield&circle-token=92ab70f03f3183655473dad16be641959cd31b83)](https://circleci.com/gh/quantumblacklabs/causalnex/tree/master) | | `develop` Branch Build | [![CircleCI](https://circleci.com/gh/quantumblacklabs/causalnex/tree/develop.svg?style=shield&circle-token=92ab70f03f3183655473dad16be641959cd31b83)](https://circleci.com/gh/quantumblacklabs/causalnex/tree/develop) | | Documentation Build | [![Documentation](https://readthedocs.org/projects/causalnex/badge/?version=latest)](https://causalnex.readthedocs.io/) | | License | [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) | | Code Style | [![Code Style: Black](https://img.shields.io/badge/code%20style-black-black.svg)](https://github.com/ambv/black) | ## What is CausalNex? > "A toolkit for causal reasoning with Bayesian Networks." CausalNex aims to become one of the leading libraries for causal reasoning and "what-if" analysis using Bayesian Networks. It helps to simplify the steps: - To learn causal structures, - To allow domain experts to augment the relationships, - To estimate the effects of potential interventions using data. ## Why CausalNex? CausalNex is built on our collective experience to leverage Bayesian Networks to identify causal relationships in data so that we can develop the right interventions from analytics. We developed CausalNex because: - We believe **leveraging Bayesian Networks** is more intuitive to describe causality compared to traditional machine learning methodology that are built on pattern recognition and correlation analysis. - Causal relationships are more accurate if we can easily **encode or augment domain expertise** in the graph model. - We can then use the graph model to **assess the impact** from changes to underlying features, i.e. counterfactual analysis, and **identify the right intervention**. In our experience, a data scientist generally has to use at least 3-4 different open-source libraries before arriving at the final step of finding the right intervention. CausalNex aims to simplify this end-to-end process for causality and counterfactual analysis. ## What are the main features of CausalNex? The main features of this library are: - Use state-of-the-art structure learning methods to understand conditional dependencies between variables - Allow domain knowledge to augment model relationship - Build predictive models based on structural relationships - Fit probability distribution of the Bayesian Networks - Evaluate model quality with standard statistical checks - Simplify how causality is understood in Bayesian Networks through visualisation - Analyse the impact of interventions using Do-calculus ## How do I install CausalNex? CausalNex is a Python package. To install it, simply run: ```bash pip install causalnex ``` Use `all` for a full installation of dependencies: ```bash pip install "causalnex[all]" ``` See more detailed installation instructions, including how to setup Python virtual environments, in our [installation guide](https://causalnex.readthedocs.io/en/latest/02_getting_started/02_install.html) and get started with our [tutorial](https://causalnex.readthedocs.io/en/latest/03_tutorial/01_first_tutorial.html). ## How do I use CausalNex? You can find the documentation for the latest stable release [here](https://causalnex.readthedocs.io/en/latest/). It explains: - An end-to-end [tutorial on how to use CausalNex](https://causalnex.readthedocs.io/en/latest/03_tutorial/01_first_tutorial.html) - The [main concepts and methods](https://causalnex.readthedocs.io/en/latest/04_user_guide/04_user_guide.html) in using Bayesian Networks for Causal Inference > Note: You can find the notebook and markdown files used to build the docs in [`docs/source`](docs/source). ## Can I contribute? Yes! We'd love you to join us and help us build CausalNex. Check out our [contributing](CONTRIBUTING.md) documentation. ## How do I upgrade CausalNex? We use [SemVer](http://semver.org/) for versioning. The best way to upgrade safely is to check our [release notes](RELEASE.md) for any notable breaking changes. ## How do I cite CausalNex? You may click "Cite this repository" under the "About" section of this repository to get the citation information in APA and BibTeX formats. ## What licence do you use? See our [LICENSE](LICENSE.md) for more detail. ## We're hiring! Do you want to be part of the team that builds CausalNex and [other great products](https://www.mckinsey.com/capabilities/quantumblack/labs) at QuantumBlack? If so, you're in luck! QuantumBlack is currently hiring Machine Learning Engineers who love using data to drive their decisions. Take a look at [our open positions](https://www.mckinsey.com/capabilities/quantumblack/careers-and-community) and see if you're a fit. %package -n python3-causalnex Summary: Toolkit for causal reasoning (Bayesian Networks / Inference) Provides: python-causalnex BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-causalnex | Theme | Status | |------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Latest Release | [![PyPI version](https://badge.fury.io/py/causalnex.svg)](https://pypi.org/project/causalnex/) | | Python Version | [![Python Version](https://img.shields.io/badge/python-3.8%20%7C%203.9%20%7C%203.10-blue.svg)](https://pypi.org/project/causalnex/) | | `master` Branch Build | [![CircleCI](https://circleci.com/gh/quantumblacklabs/causalnex/tree/master.svg?style=shield&circle-token=92ab70f03f3183655473dad16be641959cd31b83)](https://circleci.com/gh/quantumblacklabs/causalnex/tree/master) | | `develop` Branch Build | [![CircleCI](https://circleci.com/gh/quantumblacklabs/causalnex/tree/develop.svg?style=shield&circle-token=92ab70f03f3183655473dad16be641959cd31b83)](https://circleci.com/gh/quantumblacklabs/causalnex/tree/develop) | | Documentation Build | [![Documentation](https://readthedocs.org/projects/causalnex/badge/?version=latest)](https://causalnex.readthedocs.io/) | | License | [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) | | Code Style | [![Code Style: Black](https://img.shields.io/badge/code%20style-black-black.svg)](https://github.com/ambv/black) | ## What is CausalNex? > "A toolkit for causal reasoning with Bayesian Networks." CausalNex aims to become one of the leading libraries for causal reasoning and "what-if" analysis using Bayesian Networks. It helps to simplify the steps: - To learn causal structures, - To allow domain experts to augment the relationships, - To estimate the effects of potential interventions using data. ## Why CausalNex? CausalNex is built on our collective experience to leverage Bayesian Networks to identify causal relationships in data so that we can develop the right interventions from analytics. We developed CausalNex because: - We believe **leveraging Bayesian Networks** is more intuitive to describe causality compared to traditional machine learning methodology that are built on pattern recognition and correlation analysis. - Causal relationships are more accurate if we can easily **encode or augment domain expertise** in the graph model. - We can then use the graph model to **assess the impact** from changes to underlying features, i.e. counterfactual analysis, and **identify the right intervention**. In our experience, a data scientist generally has to use at least 3-4 different open-source libraries before arriving at the final step of finding the right intervention. CausalNex aims to simplify this end-to-end process for causality and counterfactual analysis. ## What are the main features of CausalNex? The main features of this library are: - Use state-of-the-art structure learning methods to understand conditional dependencies between variables - Allow domain knowledge to augment model relationship - Build predictive models based on structural relationships - Fit probability distribution of the Bayesian Networks - Evaluate model quality with standard statistical checks - Simplify how causality is understood in Bayesian Networks through visualisation - Analyse the impact of interventions using Do-calculus ## How do I install CausalNex? CausalNex is a Python package. To install it, simply run: ```bash pip install causalnex ``` Use `all` for a full installation of dependencies: ```bash pip install "causalnex[all]" ``` See more detailed installation instructions, including how to setup Python virtual environments, in our [installation guide](https://causalnex.readthedocs.io/en/latest/02_getting_started/02_install.html) and get started with our [tutorial](https://causalnex.readthedocs.io/en/latest/03_tutorial/01_first_tutorial.html). ## How do I use CausalNex? You can find the documentation for the latest stable release [here](https://causalnex.readthedocs.io/en/latest/). It explains: - An end-to-end [tutorial on how to use CausalNex](https://causalnex.readthedocs.io/en/latest/03_tutorial/01_first_tutorial.html) - The [main concepts and methods](https://causalnex.readthedocs.io/en/latest/04_user_guide/04_user_guide.html) in using Bayesian Networks for Causal Inference > Note: You can find the notebook and markdown files used to build the docs in [`docs/source`](docs/source). ## Can I contribute? Yes! We'd love you to join us and help us build CausalNex. Check out our [contributing](CONTRIBUTING.md) documentation. ## How do I upgrade CausalNex? We use [SemVer](http://semver.org/) for versioning. The best way to upgrade safely is to check our [release notes](RELEASE.md) for any notable breaking changes. ## How do I cite CausalNex? You may click "Cite this repository" under the "About" section of this repository to get the citation information in APA and BibTeX formats. ## What licence do you use? See our [LICENSE](LICENSE.md) for more detail. ## We're hiring! Do you want to be part of the team that builds CausalNex and [other great products](https://www.mckinsey.com/capabilities/quantumblack/labs) at QuantumBlack? If so, you're in luck! QuantumBlack is currently hiring Machine Learning Engineers who love using data to drive their decisions. Take a look at [our open positions](https://www.mckinsey.com/capabilities/quantumblack/careers-and-community) and see if you're a fit. %package help Summary: Development documents and examples for causalnex Provides: python3-causalnex-doc %description help | Theme | Status | |------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Latest Release | [![PyPI version](https://badge.fury.io/py/causalnex.svg)](https://pypi.org/project/causalnex/) | | Python Version | [![Python Version](https://img.shields.io/badge/python-3.8%20%7C%203.9%20%7C%203.10-blue.svg)](https://pypi.org/project/causalnex/) | | `master` Branch Build | [![CircleCI](https://circleci.com/gh/quantumblacklabs/causalnex/tree/master.svg?style=shield&circle-token=92ab70f03f3183655473dad16be641959cd31b83)](https://circleci.com/gh/quantumblacklabs/causalnex/tree/master) | | `develop` Branch Build | [![CircleCI](https://circleci.com/gh/quantumblacklabs/causalnex/tree/develop.svg?style=shield&circle-token=92ab70f03f3183655473dad16be641959cd31b83)](https://circleci.com/gh/quantumblacklabs/causalnex/tree/develop) | | Documentation Build | [![Documentation](https://readthedocs.org/projects/causalnex/badge/?version=latest)](https://causalnex.readthedocs.io/) | | License | [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) | | Code Style | [![Code Style: Black](https://img.shields.io/badge/code%20style-black-black.svg)](https://github.com/ambv/black) | ## What is CausalNex? > "A toolkit for causal reasoning with Bayesian Networks." CausalNex aims to become one of the leading libraries for causal reasoning and "what-if" analysis using Bayesian Networks. It helps to simplify the steps: - To learn causal structures, - To allow domain experts to augment the relationships, - To estimate the effects of potential interventions using data. ## Why CausalNex? CausalNex is built on our collective experience to leverage Bayesian Networks to identify causal relationships in data so that we can develop the right interventions from analytics. We developed CausalNex because: - We believe **leveraging Bayesian Networks** is more intuitive to describe causality compared to traditional machine learning methodology that are built on pattern recognition and correlation analysis. - Causal relationships are more accurate if we can easily **encode or augment domain expertise** in the graph model. - We can then use the graph model to **assess the impact** from changes to underlying features, i.e. counterfactual analysis, and **identify the right intervention**. In our experience, a data scientist generally has to use at least 3-4 different open-source libraries before arriving at the final step of finding the right intervention. CausalNex aims to simplify this end-to-end process for causality and counterfactual analysis. ## What are the main features of CausalNex? The main features of this library are: - Use state-of-the-art structure learning methods to understand conditional dependencies between variables - Allow domain knowledge to augment model relationship - Build predictive models based on structural relationships - Fit probability distribution of the Bayesian Networks - Evaluate model quality with standard statistical checks - Simplify how causality is understood in Bayesian Networks through visualisation - Analyse the impact of interventions using Do-calculus ## How do I install CausalNex? CausalNex is a Python package. To install it, simply run: ```bash pip install causalnex ``` Use `all` for a full installation of dependencies: ```bash pip install "causalnex[all]" ``` See more detailed installation instructions, including how to setup Python virtual environments, in our [installation guide](https://causalnex.readthedocs.io/en/latest/02_getting_started/02_install.html) and get started with our [tutorial](https://causalnex.readthedocs.io/en/latest/03_tutorial/01_first_tutorial.html). ## How do I use CausalNex? You can find the documentation for the latest stable release [here](https://causalnex.readthedocs.io/en/latest/). It explains: - An end-to-end [tutorial on how to use CausalNex](https://causalnex.readthedocs.io/en/latest/03_tutorial/01_first_tutorial.html) - The [main concepts and methods](https://causalnex.readthedocs.io/en/latest/04_user_guide/04_user_guide.html) in using Bayesian Networks for Causal Inference > Note: You can find the notebook and markdown files used to build the docs in [`docs/source`](docs/source). ## Can I contribute? Yes! We'd love you to join us and help us build CausalNex. Check out our [contributing](CONTRIBUTING.md) documentation. ## How do I upgrade CausalNex? We use [SemVer](http://semver.org/) for versioning. The best way to upgrade safely is to check our [release notes](RELEASE.md) for any notable breaking changes. ## How do I cite CausalNex? You may click "Cite this repository" under the "About" section of this repository to get the citation information in APA and BibTeX formats. ## What licence do you use? See our [LICENSE](LICENSE.md) for more detail. ## We're hiring! Do you want to be part of the team that builds CausalNex and [other great products](https://www.mckinsey.com/capabilities/quantumblack/labs) at QuantumBlack? If so, you're in luck! QuantumBlack is currently hiring Machine Learning Engineers who love using data to drive their decisions. Take a look at [our open positions](https://www.mckinsey.com/capabilities/quantumblack/careers-and-community) and see if you're a fit. %prep %autosetup -n causalnex-0.12.0 %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-causalnex -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Fri May 05 2023 Python_Bot - 0.12.0-1 - Package Spec generated