xges 0.1.0

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Extremely Greedy Equivalence Search

This is the code for the paper "Extremely Greedy Equivalence Search".

We recommend checking any updated code at the official repository.

Reproducing the experiments

The experiments can be reproduced by running the evaluation/benchmarks.py script. The figures are generated in the notebook evaluation/paper.ipynb.

Building the code

Use the CMakeLists.txt file to build the code.

Running the code

We recommend checking the simple Python wrapper evaluation/benchmarks.py to see how to call the xges executable.

The code can be run with the following command:

xges --input data.npy --output out.csv --stats stats.csv -v1

The input file should be a numpy file with the data matrix. The output file will contain the CPDAG. The stats file will contain some statistics collected during the execution of the algorithm. -v1 is the verbosity level. It can be set to 0, 1, or 2.

More options can be found by running xges --help.

Citing

If you use this code, please cite the following paper:

@inproceedings{nazaret2021extremely,
  title={Extremely Greedy Equivalence Search},
  author={Nazaret, Achille and Blei, David},
  booktitle={Uncertainty in Artificial Intelligence},
  year={2024}
}