Optimal Decision Diagrams

Description

This repository implements the code for the paper Optimal Decision Diagrams for Classification, presented and published at AAAI 2023.

Requirements

This project requires Python3 and the Gurobi solver, which has a free academic license available.

Documentation

Full documentation is available in optimal-decision-diagrams.readthedocs.io, including information about the paper experiments.

Getting started

Installation

First, clone the repository.

$ git clone https://https://github.com/pedrosbmartins/optimal-decision-graphs.git

Then, create and activate a python environment (for instance, using virtualenv).

$ virtualenv -p python3 env
$ source env/bin/activate

Finally, install required packages.

$ pip install -r requirements.txt
$ python -m pip install -i https://pypi.gurobi.com gurobipy

Basic usage

Note: make sure the src directory is present in your sys.path.

from dataset import Dataset
from topology import Topology
from heuristic import Heuristic
from optimizer import Optimizer
from visualizer import Visualizer

dataset = "datasets/processed/[dataset name].csv"
skeleton = [1,2,4,4,4]
alpha = 0.1

data = Dataset(dataset, train_validation_test_split=[0.5, 0.25, 0.25])
topology = Topology(skeleton, data)

heuristic = Heuristic(data, topology, alpha=alpha)
print('Heuristic', heuristic.solution.training_accuracy(), heuristic.solution.test_accuracy())

optimized = Optimizer(data, topology, alpha=alpha, initial_solution=heuristic.solution)
print('Optimized', optimized.solution.training_accuracy(), optimized.solution.test_accuracy())

optimized_viz = Visualizer(data, optimized.solution)
optimized_viz.view()

Issues

If you encounter any issues, please open an issue ticket.