CSAI-probabilistic-programming Assignment

Installing

The requirements are listed in requirements.txt and can be installed using pip:

pip install -r requirements.txt

pySdd may need to be compiled seperatly, the source can be found at: https://github.com/wannesm/PySDD

For creating the vtree MiniC2D is used. It can be found at: http://reasoning.cs.ucla.edu/minic2d/ After compiling the path to the miniC2D executable must be specified in the MINIC2D_PATH variable in pipeline.py

For model counting using cachet a custom version must be used that supports both negative and positive weights. It can be found at: https://github.com/timower/cachet-fix

Usage

usage: pipeline.py [-h] [--bif-file BIF_FILE] [--pl-file PL_FILE] [--enc ENC]
                   [--cnf-type CNF_TYPE] [--verbose]
                   cnf_file

problog pipeline

positional arguments:
  cnf_file              The output cnf file

optional arguments:
  -h, --help            show this help message and exit
  --bif-file BIF_FILE, -b BIF_FILE
                        The input bayesian network
  --pl-file PL_FILE, -p PL_FILE
                        The input problog file
  --enc ENC, -e ENC     The enc type 1 or 2
  --cnf-type CNF_TYPE, -c CNF_TYPE
                        The type of cnf file to output (c2d for minic2d or
                        cachet)
  --verbose, -v         Verbose output

The pipeline needs either a bif Bayesian network or a problog file as input using the -b or -p parameters. For Bayesian networks the encoding can be specified using the -e parameter.

The output cnf file needs to be specified as the last parameter. The format of the weights encoded in cnf can be specified using the -c paramter. This is either c2d for miniC2D and SDD or cachet.

If MiniC2D is used as output format (the default), MiniC2D is executed automaticly to generate a vtree saved as (cnf_file_name.vtree). Weighted model counting using pySDD is also performed.

The verbose option can be used to get more information about parsing and weight generation.

Examples

Compute the enc1 encoding of the cancer Bayesian network:

./pipeline.py -e 1 -c c2d -b ./bif/cancer.bif output.cnf

Compute the enc2 encoding of the cancer network for cachet:

./pipeline.py -e 2 -c cachet -b ./bif/cancer.bif output.cnf

Compute the cnf encoding and queries of the monyhall problog file:

./pipeline.py -p ./problog/montyhall.pl output.cnf