py-paretoarchive 1.0

Efficient incremental Pareto archive based on BSP

Archive of non-dominated points

Previous version (0.21) used to achive of all non-dominated points using Fast Incremental BSP Tree. This package provides a Python wrapper for code provided as a fast incremental BSP archive.

In order to not needing to install Cython, the package uses numpy implementation only

Debuging

python -m venv -r requirements.txt .venv
source .venv/bin/activate
pip install --upgrade pip

python -m pip install -e .[dev]

Testing

# Run tests
PYTHONPATH=src /usr/bin/python -m pytest tests/ -v  




# PYTHONPATH=src /usr/bin/python -m pytest tests/ -v

USAGE

obj1 = [1, 2, 3]
obj2 = [3, 2, 1]
from paretoarchive import np_pareto
dom = np_pareto(obj1, obj2, minimize=[False, True])
print(dom) # [True, False, True] (first and third points are non-dominated)

Using in Pandas

You can easily use the library to filter a Pandas DataFrame. Note that the selected columns cannot have a "NaN" values (you should use df.dropna(subset=["c1", "c2"]) function.

from paretoarchive.pandas import pareto
par_df = pareto(df, ["area", "energy", "weight"], minimize=[False, True, True])

SOURCE

• Tobias Glasmachers: A Fast Incremental BSP Tree Archive for Non-dominated Points
  • https://link.springer.com/chapter/10.1007/978-3-319-54157-0_18
  • https://www.ini.rub.de/PEOPLE/glasmtbl/code/ParetoArchive/index.html
• Similar problem (SKYLINE) https://github.com/sean-chester/SkyBench