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توضیحات

causal-learn Python Package

ویژگی	مقدار
سیستم عامل	OS Independent
نام فایل	causal-learn-0.1.3.0
نام	causal-learn
نسخه کتابخانه	0.1.3.0
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	-
ایمیل نویسنده	-
آدرس صفحه اصلی	https://github.com/cmu-phil/causal-learn
آدرس اینترنتی	https://pypi.org/project/causal-learn/
مجوز	-

# causal-learn: Causal Discovery for Python Causal-learn is a python package for causal discovery that implements both classical and state-of-the-art causal discovery algorithms, which is a Python translation and extension of [Tetrad](https://github.com/cmu-phil/tetrad). The package is actively being developed. Feedbacks (issues, suggestions, etc.) are highly encouraged. # Package Overview Our causal-learn implements methods for causal discovery: * Constraint-based causal discovery methods. * Score-based causal discovery methods. * Causal discovery methods based on constrained functional causal models. * Hidden causal representation learning. * Permutation-based causal discovery methods. * Granger causality. * Multiple utilities for building your own method, such as independence tests, score functions, graph operations, and evaluations. # Install Causal-learn needs the following packages to be installed beforehand: * python 3 * numpy * networkx * pandas * scipy * scikit-learn * statsmodels * pydot (For visualization) * matplotlib * graphviz To use causal-learn, we could install it using [pip](https://pypi.org/project/causal-learn/): ``` pip install causal-learn ``` # Documentation Please kindly refer to [causal-learn Doc](https://causal-learn.readthedocs.io/en/latest/) for detailed tutorials and usages. # Running examples For search methods in causal discovery, there are various running examples in the **‘tests’** directory, such as TestPC.py and TestGES.py. For the implemented modules, such as (conditional) independent test methods, we provide unit tests for the convenience of developing your own methods. # Benchmarks For the convenience of our community, [CMU-CLeaR](https://www.cmu.edu/dietrich/causality) group maintains a list of benchmark datasets including real-world scenarios and various learning tasks. Please refer to the following links: * [https://github.com/cmu-phil/example-causal-datasets](https://github.com/cmu-phil/example-causal-datasets) (maintained by Joseph Ramsey) * [https://www.cmu.edu/dietrich/causality/projects/causal_learn_benchmarks](https://www.cmu.edu/dietrich/causality/projects/causal_learn_benchmarks) Please feel free to let us know if you have any recommendation regarding causal datasets with high-quality. We are grateful for any effort that benefits the development of causality community. # Contribution Please feel free to open an issue if you find anything unexpected. And please create pull requests, perhaps after passing unittests in 'tests/', if you would like to contribute to causal-learn. We are always targeting to make our community better!

نیازمندی

مقدار	نام
-	numpy
-	scipy
-	scikit-learn
-	graphviz
-	statsmodels
-	pandas
-	matplotlib
-	networkx
-	pydot
-	tqdm

زبان مورد نیاز

مقدار	نام
>=3.6	Python

نحوه نصب

نصب پکیج whl causal-learn-0.1.3.0:

pip install causal-learn-0.1.3.0.whl

نصب پکیج tar.gz causal-learn-0.1.3.0:

pip install causal-learn-0.1.3.0.tar.gz