Metadata-Version: 2.1
Name: cfl
Version: 0.1.2
Summary: Causal Feature Learning (CFL) is an unsupervised algorithm designed to construct macro-variables from low-level data, while maintaining the causal relationships between these macro-variables. 
Home-page: https://github.com/eberharf/cfl
Author: Jenna Kahn and Iman Wahle
Author-email: iwahle@caltech.edu
License: UNKNOWN
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: Free for non-commercial use
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.7
Requires-Dist: tqdm
Requires-Dist: matplotlib
Requires-Dist: tensorflow (>=2.4.0)
Requires-Dist: numpy (>=1.19.2)
Requires-Dist: scikit-learn (>=0.23)
Requires-Dist: jupyter
Requires-Dist: ipykernel
Requires-Dist: joblib (>=0.16.0)

See cfl.readthedocs.io for a full description


