This is the source code for the experiments reported in papers "On Pruning for Score-Based Bayesian Network Structure Learning" [1] and "An Experimental Study of Prior Dependence in Bayesian Network Structure Learning" [2]. If this code has been useful to you, consider citing the corresponding paper (see references at the bottom).
The main code for both papers is contained in scoring.py. There are, however, two different scripts to run the experiments of each paper.
pruning.py concerns the experiments in [1] to compare different upper bounds for BDeu structure learning. The command line accepts 6 arguments:
- data (-d): the name of the file containing the dataset (should include the extension .csv). We assume all such files are in a Datasets folder in the same path as pruning.py
- child (-c): the name or index of the child If 'all' is passed, the program will run over all variables in the dataset
- bound or (-b): the type of bound to be used. Either 'f', 'g', 'h', 'min' or 'all'. The latter sets the program to compute all three bounds. See [1] for details on each bound.
- timeout (-t): the number of time (secs) the program waits before exiting
- alpha (-a): the equivalent sample size (ESS).
- palim (-p): the maximum number of parents per variable.
The program outputs a csv file which is saved to a folder named after data (the dataset name). The csv contains the best parents and the number of scores computed for each pair (child variable, palim).
vary_alpha.py runs the experiments in [2] where the influence of the equivalent sample size (ESS) is studied. The command line accept similar parameters.
- data (-d), child (-c) and timeout (-t) as above.
- n_runs (-n): How many times the experiment is run. Each run corresponds to a different (random) topological ordering of the variables.
The program outputs a csv file which is saved to a folder named after alpha_data (the dataset name). The csv contains the best parents for each pair (child, palim, alpha).
[1] Correia, AHC, Cussens J, de Campos, CP. "On Pruning for Score-Based Bayesian Network Structure Learning." In the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020), 2020.
[2] Correia, AHC, Campos, CP, Gaag, LC. "An Experimental Study of Prior Dependence in Bayesian Network Structure Learning." In International Symposium on Imprecise Probabilities: Theories and Applications, pp. 78-81, 2019.