Metadata-Version: 2.1
Name: causal-lasso
Version: 1.0.0
Summary: Causal Lasso
Home-page: https://github.com/manon643/causal_lasso
Author: Manon Romain
Author-email: manon.romain@ens.fr
License: UNKNOWN
Description: # Causal Lasso
        
        This repository implements paper "A Bregman Method for Structure Learning on Sparse Directed Acyclic Graphs" by Manon Romain and Alexandre d'Aspremont.
        
        ### Requirements
        
        This package requires `numpy`, `scipy`, `tqdm`, `networkx` and `mosek`, installation can be done with `pip install -r docs/requirements.txt`.
        
        
        ### Solver 
        For now, the default solver used at each iteration is Mosek. We plan to provide an open source implementation in the near future. 
        
        MOSEK's license is free for academic use, first obtain your license [here](https://www.mosek.com/products/academic-licenses/) using institutional email and place the obtained file `mosek.lic` in a file called:
        ```
        %USERPROFILE%\mosek\mosek.lic           (Windows)
        $HOME/mosek/mosek.lic                   (Linux, MacOS)
        ``` 
        
        
        Further information available [here](https://docs.mosek.com/9.2/install/installation.html#setting-up-the-license).
        
        
        ### Use
        Minimal testing code is:
        ```
        import numpy as np
        import networkx as nx
        from causal_lasso.solver import CLSolver
        X = np.random.random((1000, 30)) # Replace with your data
        lasso = CLSolver()
        W_est = lasso.fit(X)
        nx.draw(nx.DiGraph(W_est))
        ```
        
        A more detailed tutorial is available in `examples/tutorial.ipynb`.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
