Python wrapper package to interactively use Sententical Decision Diagrams (SDD).
Full documentation available on http://pysdd.readthedocs.io.
- Python >=3.6
- Cython
Optional:
- cysignals
- numpy
Make sure to have the correct development tools installed:
- C compiler (see Installing Cython)
- The Python development version that includes Python header files and static library (e.g. libpython3-dev, python-dev, ...)
$ pip install PySDD
The wrapper can be used as a Python package and allows for interactive use.
The following example builds an SDD for the formula a∧b ∨ b∧c ∨ c∧d
.
from pysdd.sdd import SddManager, Vtree, WmcManager
vtree = Vtree(var_count=4, var_order=[2,1,4,3], vtree_type="balanced")
sdd = SddManager.from_vtree(vtree)
a, b, c, d = sdd.vars
# Build SDD for formula
formula = (a & b) | (b & c) | (c & d)
# Model Counting
wmc = formula.wmc(log_mode=False)
print(f"Model Count: {wmc.propagate()}")
wmc.set_literal_weight(a, 0.5)
print(f"Weighted Model Count: {wmc.propagate()}")
# Visualize SDD and Vtree
with open("output/sdd.dot", "w") as out:
print(formula.dot(), file=out)
with open("output/vtree.dot", "w") as out:
print(vtree.dot(), file=out)
The SDD and Vtree are visualized using Graphviz DOT:
More examples are available in the examples
directory.
An interactive Jupyter notebook is available in
notebooks/examples.ipynb
A Python CLI application is installed if you use pip, pysdd
. Or it can be used
directly from the source directory where it is called pysdd-cli.py
.
This script mimicks the original sdd binary and adds additional features (e.g. weighted model counting)
$ pysdd -h
$ ./pysdd-cli.py -h
usage: pysdd-cli.py [-h] [-c FILE | -d FILE | -s FILE] [-v FILE] [-W FILE]
[-V FILE] [-R FILE] [-S FILE] [-m] [-t TYPE] [-r K] [-q]
[-p] [--log_mode]
Sentential Decision Diagram, Compiler
optional arguments:
-h, --help show this help message and exit
-c FILE set input CNF file
-d FILE set input DNF file
-s FILE set input SDD file
-v FILE set input VTREE file
-W FILE set output VTREE file
-V FILE set output VTREE (dot) file
-R FILE set output SDD file
-S FILE set output SDD (dot) file
-m minimize the cardinality of compiled sdd
-t TYPE set initial vtree type (left/right/vertical/balanced/random)
-r K if K>0: invoke vtree search every K clauses. If K=0: disable
vtree search. By default (no -r option), dynamic vtree search is
enabled
-q perform post-compilation vtree search
-p verbose output
--log_mode weights in log
Weighted Model Counting is performed if the NNF file containts a line
formatted as follows: "c weights PW_1 NW_1 ... PW_n NW_n".
Python's memory management is not used for the internal datastructures. Use the SDD library's garbage collection commands (e.g. ref, deref) to perform memory management.
$ pip install git+https://github.com/wannesm/PySDD.git#egg=PySDD
The repository should contain all the required files and libraries (unless you use Windows). If you want to compile from source, note that some c-source files from the SDD package have been updated to work with this wrapper and are included in this repository. Do not overwrite these new files with the original files.
- Download the SDD package from http://reasoning.cs.ucla.edu/sdd/.
- Install the SDD package in the PySDD package in directories
pysdd/lib/sdd-2.0
andpysdd/lib/sddlib-2.0
without overwriting the already available files. - Run
python3 setup.py build_ext --inplace
ormake build
to compile the library in the current directory. If you want to install the library such that the library is available for your local installation or in your virtual environment, usepython3 setup.py install
.
For some Linux platforms, it might be necessary to recompile the libsdd-2.0 code with
the gcc option -fPIC
and replace the pysdd/lib/sdd-2.0/lib/Linux/libsdd.a
library with your newly compiled version.
The Windows platform is not supported. There is some initial support but we cannot offer guarantees or detailed instructions (but are happy to accept pull requests).
This package is inspired by the SDD wrapper used in the probabilistic programming language ProbLog.
References:
- Wannes Meert & Arthur Choi, PySDD, in Recent Trends in Knowledge Compilation, Report from Dagstuhl Seminar 17381, Sep 2017. Eds. A. Darwiche, P. Marquis, D. Suciu, S. Szeider.
Other languages:
- Wannes Meert, KU Leuven, https://people.cs.kuleuven.be/wannes.meert
- Arthur Choi, UCLA, http://web.cs.ucla.edu/~aychoi/
Python SDD wrapper:
Copyright 2017-2018, KU Leuven and Regents of the University of California. Licensed under the Apache License, Version 2.0.
SDD package:
Copyright 2013-2018, Regents of the University of California Licensed under the Apache License, Version 2.0.