=====
PySDD
=====
Python wrapper package to interactively use `Sentential Decision Diagrams (SDD) <http://reasoning.cs.ucla.edu/sdd/>`_.
Full documentation available on http://pysdd.readthedocs.io.
------------
Installation
------------
.. code-block:: shell
$ pip install PySDD
--------------
Python package
--------------
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``.
.. code-block:: python
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:
.. image:: https://people.cs.kuleuven.be/wannes.meert/pysdd/sdd.png
.. image:: https://people.cs.kuleuven.be/wannes.meert/pysdd/vtree.png
More examples are available in the ``examples`` directory.
An interactive Jupyter notebook is available in
`notebooks/examples.ipynb <notebooks/examples.ipynb>`_
----------------------
Command Line Interface
----------------------
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)
.. code-block:: shell
$ 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".
-----------------
Memory management
-----------------
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.
-----------------------
Compilation from source
-----------------------
To install from source, make sure to have the correct development tools installed:
* C compiler (see `Installing Cython <https://cython.readthedocs.io/en/latest/src/quickstart/install.html>`_)
* The Python development version that includes Python header files and static library (e.g. libpython3-dev, python-dev, ...)
The build process will download Cython and numpy in an isolated environment.
Then run:
.. code-block:: shell
$ pip install build
$ python -m build
To install the main branch:
.. code-block:: shell
$ pip install git+https://github.com/wannesm/PySDD.git#egg=PySDD
----------
References
----------
This package is inspired by the SDD wrapper used in the probabilistic
programming language `ProbLog <https://dtai.cs.kuleuven.be/problog/>`_.
References:
* Wannes Meert & Arthur Choi, PySDD,
in `Recent Trends in Knowledge Compilation
<http://drops.dagstuhl.de/opus/volltexte/2018/8589/pdf/dagrep_v007_i009_p062_17381.pdf>`_,
Report from Dagstuhl Seminar 17381, Sep 2017.
Eds. A. Darwiche, P. Marquis, D. Suciu, S. Szeider.
Other languages:
* C: http://reasoning.cs.ucla.edu/sdd/
* Java: https://github.com/jessa/JSDD
-------
Contact
-------
* Wannes Meert, KU Leuven, https://people.cs.kuleuven.be/wannes.meert
* Arthur Choi, UCLA, http://web.cs.ucla.edu/~aychoi/
-------
License
-------
Python SDD wrapper:
Copyright 2017-2024, 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.
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"description": "=====\nPySDD\n=====\n\nPython wrapper package to interactively use `Sentential Decision Diagrams (SDD) <http://reasoning.cs.ucla.edu/sdd/>`_.\n\nFull documentation available on http://pysdd.readthedocs.io.\n\n------------\nInstallation\n------------\n\n.. code-block:: shell\n\n $ pip install PySDD\n\n\n--------------\nPython package\n--------------\n\nThe wrapper can be used as a Python package and allows for interactive use.\n\nThe following example builds an SDD for the formula ``a\u2227b \u2228 b\u2227c \u2228 c\u2227d``.\n\n.. code-block:: python\n\n from pysdd.sdd import SddManager, Vtree, WmcManager\n vtree = Vtree(var_count=4, var_order=[2,1,4,3], vtree_type=\"balanced\")\n sdd = SddManager.from_vtree(vtree)\n a, b, c, d = sdd.vars\n\n # Build SDD for formula\n formula = (a & b) | (b & c) | (c & d)\n\n # Model Counting\n wmc = formula.wmc(log_mode=False)\n print(f\"Model Count: {wmc.propagate()}\")\n wmc.set_literal_weight(a, 0.5)\n print(f\"Weighted Model Count: {wmc.propagate()}\")\n\n # Visualize SDD and Vtree\n with open(\"output/sdd.dot\", \"w\") as out:\n print(formula.dot(), file=out)\n with open(\"output/vtree.dot\", \"w\") as out:\n print(vtree.dot(), file=out)\n\nThe SDD and Vtree are visualized using Graphviz DOT:\n\n.. image:: https://people.cs.kuleuven.be/wannes.meert/pysdd/sdd.png\n.. image:: https://people.cs.kuleuven.be/wannes.meert/pysdd/vtree.png\n\n\n\nMore examples are available in the ``examples`` directory.\nAn interactive Jupyter notebook is available in\n`notebooks/examples.ipynb <notebooks/examples.ipynb>`_\n\n\n----------------------\nCommand Line Interface\n----------------------\n\nA Python CLI application is installed if you use pip, ``pysdd``. Or it can be used\ndirectly from the source directory where it is called ``pysdd-cli.py``.\nThis script mimicks the original sdd binary and adds additional features (e.g. weighted model counting)\n\n.. code-block:: shell\n\n $ pysdd -h\n $ ./pysdd-cli.py -h\n usage: pysdd-cli.py [-h] [-c FILE | -d FILE | -s FILE] [-v FILE] [-W FILE]\n [-V FILE] [-R FILE] [-S FILE] [-m] [-t TYPE] [-r K] [-q]\n [-p] [--log_mode]\n\n Sentential Decision Diagram, Compiler\n\n optional arguments:\n -h, --help show this help message and exit\n -c FILE set input CNF file\n -d FILE set input DNF file\n -s FILE set input SDD file\n -v FILE set input VTREE file\n -W FILE set output VTREE file\n -V FILE set output VTREE (dot) file\n -R FILE set output SDD file\n -S FILE set output SDD (dot) file\n -m minimize the cardinality of compiled sdd\n -t TYPE set initial vtree type (left/right/vertical/balanced/random)\n -r K if K>0: invoke vtree search every K clauses. If K=0: disable\n vtree search. By default (no -r option), dynamic vtree search is\n enabled\n -q perform post-compilation vtree search\n -p verbose output\n --log_mode weights in log\n\n Weighted Model Counting is performed if the NNF file containts a line\n formatted as follows: \"c weights PW_1 NW_1 ... PW_n NW_n\".\n\n\n-----------------\nMemory management\n-----------------\n\nPython's memory management is not used for the internal datastructures.\nUse the SDD library's garbage collection commands (e.g. ref, deref) to\nperform memory management.\n\n\n-----------------------\nCompilation from source\n-----------------------\n\nTo install from source, make sure to have the correct development tools installed:\n\n* C compiler (see `Installing Cython <https://cython.readthedocs.io/en/latest/src/quickstart/install.html>`_)\n* The Python development version that includes Python header files and static library (e.g. libpython3-dev, python-dev, ...)\n\nThe build process will download Cython and numpy in an isolated environment.\n\nThen run:\n\n.. code-block:: shell\n\n $ pip install build\n $ python -m build\n\n\nTo install the main branch:\n\n.. code-block:: shell\n\n $ pip install git+https://github.com/wannesm/PySDD.git#egg=PySDD\n\n\n\n----------\nReferences\n----------\n\nThis package is inspired by the SDD wrapper used in the probabilistic\nprogramming language `ProbLog <https://dtai.cs.kuleuven.be/problog/>`_.\n\nReferences:\n\n* Wannes Meert & Arthur Choi, PySDD,\n in `Recent Trends in Knowledge Compilation\n <http://drops.dagstuhl.de/opus/volltexte/2018/8589/pdf/dagrep_v007_i009_p062_17381.pdf>`_,\n Report from Dagstuhl Seminar 17381, Sep 2017.\n Eds. A. Darwiche, P. Marquis, D. Suciu, S. Szeider.\n\nOther languages:\n\n* C: http://reasoning.cs.ucla.edu/sdd/\n* Java: https://github.com/jessa/JSDD\n\n\n-------\nContact\n-------\n\n* Wannes Meert, KU Leuven, https://people.cs.kuleuven.be/wannes.meert\n* Arthur Choi, UCLA, http://web.cs.ucla.edu/~aychoi/\n\n\n-------\nLicense\n-------\n\nPython SDD wrapper:\n\nCopyright 2017-2024, KU Leuven and Regents of the University of California.\nLicensed under the Apache License, Version 2.0.\n\n\nSDD package:\n\nCopyright 2013-2018, Regents of the University of California\nLicensed under the Apache License, Version 2.0.\n",
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