The dataset of 1000 Guillotinable and 1000 Braam rectangle packing problems with 20 tiles can be found in problems.

pip3 install requirements.txt

To run experiments, you will need to have a PostgreSQL database running.

After installing the PostgreSQL database you can create one by logging into the psql shell and running:

postgres=# create role binpack_user;
CREATE ROLE
postgres=# create database binpack;
CREATE DATABASE
postgres=# grant all on database binpack to binpack_user;
GRANT
postgres=# alter role binpack_user with login;
postgres=# alter user binpack_user with password 'password';

Copy the file visapi/secrets.json.local to visapi/secrets.json:

cp visapi/secrets.json.template visaip/secrets.json

Then edit it with a secret key and the details to connect to the database:

The database values meanings are in order:
HOST, DATABASE_NAME, USER, PASSWORD, PORT

For the example provided, the contents of the file would be:

{
    "SECRET_KEY": "something_random",
    "DATABASE": ["localhost", "binpack", "binpack_user", "password", ""]
}

To create a database schema, you will need to run:

python3 manage.py migrate

All experiments results will be saved to the database, in the table affinity.

Access them using the django shell:

python3 manage.py shell_plus

And then:

Result.objects.all()

See Django ORM reference.

Or access them using postgresql shell:

select * from affinity;

All the problem results will be available in the database with all the metrics needed to finish them (number of tiles placed etc.), and their scores (success / failure).

Run:

PYTHONPATH=engine/ python3 manage.py run_mcts

to see all the options for the runner.

Example:

Run dynamically generated problems:

PYTHONPATH=engine/ python3 manage.py run_mcts 20 11 11

From file:

PYTHONPATH=engine/ python3 manage.py run_mcts 20 11 11 --from_file

This algorithm performs only one tile placement. To get the final solution calls the NN predict multiple times.

To train the neural network you can use:

PYTHONPATH=engine/ python3 manage.py just_train