Top-level scripts

The top-level scripts have the following dependency hierarchy:

    setup

    clean

    test

    grid_run  compare
       |      |    |
       |      |    |
       v      v    |
    grid_search    |
     |    |  \     |
     |    |   \    |
     v    v    v   v
    dag  tree  experiment
     |    /
     |   /
     v  v
    learn

Quick explanation of the top-level scripts:

    setup.py            compile Cython code
    clean               delete the code generated by Cython
    test.py             test spnss package
    grid_run.py         run grid-search in multiple processes (one process per dataset)
    grid_search.py      train/evaluate DAG-SPN and Tree-SPN models using a grid of hyperparameters
    dag.py              train a DAG-SPN
    tree.py             train a Tree-SPN
    learn.py            various functions used by dag.py and tree.py
    experiment.py       save/load experiment result files
    compare.py          analyze/summarize experiment results

Datasets

The datasets/ directory contains two zip files (gens.domingos.zip and permanent.zip) that need to be decompressed before running experiments.

Compiling, testing, and running

To compile the Cython source code, run python setup.py build_ext --inplace

To test the spnss package, run python test.py

Running python NAME --help (where NAME is grid_search.py, dag.py, or tree.py) prints out information about the options available when running these scripts.

Example

Running python dag.py nltcs -t 0.01 -cp 10 trains a DAG-structured SPN on the NLTCS dataset using a threshold of 0.01 and a cluster penalty of 10. On my system this resulted in the following output:

WARNING:root:   ================ 0 =============== 
WARNING:root:   size: 3439  vld: -6.024147  thresh: 0.010000
nltcs   t: 0.01000  cp:  10.0   time:11.1   dag  1288 va: -6.0011 te: -6.0595

It may also print a line reading WARNING:root: ran out of steps to take.

The last line shows the dataset, threshold, cluster-penalty, training time, size of the final SPN, validation likelihood, and test likelihood.

Citing

If you use this code for research, please cite:

Dennis, Aaron, and Ventura, Dan. "Greedy structure search for sum-product networks." In Proceedings of the 24th International Joint Conference on Artificial Intelligence. AAAI Press, 2015.

For your convenience, here is a BibTeX entry:

@inproceedings{Dennis2015,
    title={Greedy Structure Search for Sum-Product Networks},
    author={Dennis, Aaron and Ventura, Dan},
    booktitle={Proceedings of the 24th International Joint Conference on Artificial Intelligence},
    year={2015},
    organization={AAAI Press}
}