To run the algorithms and their grid search check the scripts in the bin/ folder.
To learn a default ensemble from the training set portion of the nltcs
data you can call:
ipython -- bin/expc_exp.py nltcs
To get an overview of the possible parameters use ipython -- bin/expc_exp.py -h :
-dim ENSEMBLE_DIMENSION [ENSEMBLE_DIMENSION ...], --ensemble-dimension ENSEMBLE_DIMENSION [ENSEMBLE_DIMENSION ...]
EXPC dimension
-sd STR_DEC_LEVEL [STR_DEC_LEVEL ...], --str-dec-level STR_DEC_LEVEL [STR_DEC_LEVEL ...]
0 for no-SD; 1 for no-SD EXPC with SD XPCs;
2 for SD EXPC
-det DETERMINISM [DETERMINISM ...], --determinism DETERMINISM [DETERMINISM ...]
0 for no-determinism; 1 for determinism
-m MIN_PARTITION_INSTANCES [MIN_PARTITION_INSTANCES ...], --min-partition-instances MIN_PARTITION_INSTANCES [MIN_PARTITION_INSTANCES ...]
Minimum number of instances per partition
-l CONJUNCTION_LENGTH [CONJUNCTION_LENGTH ...], --conjunction-length CONJUNCTION_LENGTH [CONJUNCTION_LENGTH ...]
Conjunction length
-a ARITY [ARITY ...], --arity ARITY [ARITY ...]
Maximum number of sum nodes children
-p MAX_PARTITIONS [MAX_PARTITIONS ...], --max-partitions MAX_PARTITIONS [MAX_PARTITIONS ...]
Maximum number of leaf partitions
-p SMOOTHING [SMOOTHING ...], --smoothing SMOOTHING [SMOOTHING ...]
Smoothing parameter alpha
-o [OUTPUT], --output [OUTPUT]
Output dir path
To run a grid search you can do:
ipython -- bin/expc_exp.py nltcs -dim 10 20 -sd 0 1 2 -det 0 1 -m 256 -l 3 -a 3 -p 200
@InProceedings{di-mauro_649,
title = {Random Probabilistic Circuits},
author = {Di Mauro, Nicola and Gala, Gennaro and Iannotta, Marco and Basile, Teresa M.A.},
booktitle = {37th Conference on Uncertainty in Artificial Intelligence (UAI) },
year = {2021}
}