Published February 24, 2025
| Version v1
Dataset
Open
100 samples of 5000 instances of categorical BNs from bnlearn's Bayesian Network Repository
Description
We provide 100 samples, each containing 5000 instances, of discrete Bayesian Networks from bnlearn's Bayesian Network Repository. Specifically, the BNs, along with their characteristics, are:
| NETWORK | #NODES | #EDGES | #PARAMETERS | MAX. PARENTS | MEAN DEGREE |
| Cancer | 5 | 4 | 10 | 2 | 2.00 |
| Earthquake | 5 | 4 | 10 | 2 | 2.00 |
| Survey | 6 | 6 | 21 | 2 | 2.00 |
| Asia | 8 | 8 | 18 | 2 | 2.00 |
| Sachs | 11 | 17 | 178 | 3 | 3.09 |
| Child | 20 | 25 | 230 | 4 | 2.50 |
| Insurance | 27 | 52 | 230 | 4 | 2.50 |
| Water | 32 | 66 | 10083 | 5 | 4.12 |
| Mildew | 35 | 46 | 540150 | 3 | 2.63 |
| Alarm | 37 | 46 | 509 | 5 | 2.49 |
| Barley | 48 | 84 | 114005 | 4 | 3.50 |
| Hailfinder | 56 | 66 | 2656 | 4 | 2.36 |
| Hepar2 | 70 | 123 | 1453 | 6 | 3.51 |
| Win95pts | 76 | 112 | 574 | 7 | 2.95 |
| Pathfinder | 109 | 195 | 72079 | 5 | 3.58 |
| Munin1 | 186 | 273 | 15622 | 3 | 2.94 |
| Andes | 223 | 338 | 1157 | 6 | 3.03 |
| Diabetes | 413 | 602 | 429409 | 2 | 2.92 |
| Pigs | 441 | 592 | 5618 | 2 | 2.68 |
| Link | 724 | 1125 | 14211 | 3 | 3.11 |
| Munin2 | 1003 | 1244 | 69431 | 3 | 2.48 |
| Munin4 | 1038 | 1388 | 80352 | 3 | 2.67 |
| Munin3 | 1041 | 1306 | 71059 | 3 | 2.51 |
| Munin | 1041 | 1397 | 80592 | 3 | 2.68 |
Each dataset is sampled using Python and the bnlearn package. The BN structure is loaded from the .bif file using bif = bnlearn.import_DAG(path), and samples are generated with bnlearn.sampling(bif, n=5000, methodtype='bayes'). Post-processing is then applied to replace the generated numerical values with those categorical specified in the .bif structure file.
Additionally, ten extra old database samples of most BNs can be found on OpenML.
Files
alarm.zip
Files
(1.2 GB)
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Additional details
References
- Scutari M (2010). "Learning Bayesian Networks with the bnlearn R Package." Journal of Statistical Software, 35(3), 1–22. doi:10.18637/jss.v035.i03