Metadata-Version: 2.1
Name: xaddpy
Version: 0.1.6
Summary: XADD package in Python
Home-page: https://github.com/jihwan-jeong/xaddpy
Download-URL: https://github.com/jihwan-jeong/xaddpy/archive/refs/tags/0.1.6.tar.gz
Author: Jihwan Jeong
Author-email: Jihwan Jeong <jiihwan.jeong@gmail.com>
License: MIT License
Project-URL: Homepage, https://github.com/jihwan-jeong/xaddpy
Project-URL: Bug Tracker, https://github.com/jihwan-jeong/xaddpy/issues
Keywords: xadd,xadd python,symbolic diagram
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: sympy
Requires-Dist: pulp
Requires-Dist: psutil

### Python Implementation of XADD

This repository implements the Python version of XADD (eXtended Algebraic Decision Diagrams) which was first introduced in [Sanner et al. (2011)](https://arxiv.org/pdf/1202.3762.pdf); you can find the original Java implementation from [here](https://github.com/ssanner/xadd-inference). 

Our Python XADD code uses [Sympy](https://github.com/sympy/sympy) for symbolically maintaining all variables and related operations, and [PULP](https://github.com/coin-or/pulp) is used for pruning unreachable paths.  Note that we only check linear conditionals.  If you have Gurobi installed and configured in the conda environment, then PULP will use Gurobi for solving (MI)LPs; otherwise, the default solver ([CBC](https://github.com/coin-or/Cbc)) is going to be used.

Note that the implementation for [EMSPO](https://proceedings.mlr.press/v162/jeong22a/jeong22a.pdf) --- Exact symbolic reduction of linear Smart Predict+Optimize to MILP (Jeong et al., ICML-22) --- has been moved to the branch [emspo](https://github.com/jihwan-jeong/xaddpy/tree/emspo). 

### Installation

#### Load your Python virtual environment then type the following commands for package installation

```shell
pip install xaddpy

# Optional: if you want to use Gurobi for the 'reduce_lp' method that prunes out unreachable partitions using LP solvers
pip install gurobipy    # If you have a license
```
