TAcq - Framework for Learning Compact Representations of Constraint Networks

A framework for learning compact representations of constraint networks. This project is based on the paper “Learning Compact Representations of Constraint Networks” to be published.

The code is organized in the following way:

• src/: Contains the code for the experiments and the models.
• data/: Contains data used for some experiments.

You need to have the following dependencies installed:

• "languageFreeAcq~=0.0.7"
• "typing_extensions~=4.12.2"
• "ortools~=9.11.4210"

You can install them using pip:

pip install ortools
pip install tqdm
pip install languageFreeAcq

Use the TAcq framework in your code

You can install the TAcq framework using pip:

pip install tacq

Then, you can use it in your code as follows:

from tacq import TemplateAcquisition

ta = TemplateAcquisition()
ta.learn_from_file("examtimetabling_2.csv", 300, 1000)
print(ta.get_network())
print(ta.get_template())

Running using CLI

To run an experiment using CLI, you may use the following command:

python src/main.py <nb_examples> <examples_file> <timeout_solver> <verbose> <max_cpu>

Where <nb_examples> is the number of examples to use as training set, <examples_file> is the file containing the examples (some are provided in the data/ directory), <max_cpu> is the maximum number of CPU cores, <timeout_solver> is the timeout for each call to the solver in seconds, <verbose> is a flag to enable verbose mode (1 for true, 0 for false),