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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}
}
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