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Description:

pyparetoarchive 0.21

Archive of non-dominated points

Creating an archive 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.
COMPILATION
CYTHONIZE=1 python3 -m pip install --user py-paretoarchive

The package requires Cython module for its run. On Windows, you will need to have Microsoft Build Tools. Note that PyPi suggest you a link to the proper tool after this command.
Instalation from sources
pip3 install pytest Cython
make install-from-source
pytest

The package requires Cython module for its run. When Cython is correctly installed, the package should be platform independent.
USAGE
PyBspTreeArchive(objectives=3, minimizeObjective1=True, minimizeObjective2=True, minimizeObjective3=True, minimizeObjective4=True)

from paretoarchive import PyBspTreeArchive

a = PyBspTreeArchive(3, minimizeObjective1=False, minimizeObjective2=False, minimizeObjective3=True)
a.process([1,2,3])
# True (is non-dominated)
a.process([1,2,3])
# True (is non-dominated)
a.process([1,3,3])
# True (is non-dominated)
a.process([1,1,2])
# True (is non-dominated)
print(a.size()) # get the number of non-dominates solutions
# 2
print(a.points()) # get the non-dominated solutions
# [[1.0, 3.0, 3.0], [1.0, 1.0, 2.0]]

Single-line example:
print(PyBspTreeArchive(2, minimizeObjective1=True, minimizeObjective2=False).filter(
[[2,4], [3,1], [2,1], [1,1]], sortKey=lambda itm: itm[0]
) #process the array and sort the result by first objective (useful for plotting)
# [[1.0, 1.0], [2.0, 4.0]]

Return indexes of the elements:
a = PyBspTreeArchive(3, minimizeObjective1=True, minimizeObjective2=False)
print(a.process([1,3,3], returnId=True))
# (True, 0) (is non-dominated, received ID 0)
print(a.process([1,2,3], returnId=True))
# (False, 1) (is dominated, received ID 1)
print(a.process([1,1,2], returnId=True))
# (True, 2) (is non-dominated, received ID 2)
print(a.points(returnIds=True))
# [0,2] (item with ID 0 and 2 are non-dominated)
print(a.points())
# [[1.0, 3.0, 3.0], [1.0, 1.0, 2.0]]

Custom IDs:
a = PyBspTreeArchive(3, minimizeObjective1=True, minimizeObjective2=False)
print(a.process([1,2,3], customId=10))
# True
print(a.process([1,3,3], customId=20))
# True
print(a.process([1,1,2], customId=30))
# True
print(a.points(returnIds=True))
# [20,30]

Pruning of the set of non-dominated solutions specifying data resolution:
def resample(val, resolution=0.01):
return round(val / resolution)*resolution

pf = PyBspTreeArchive(4)
for i, x in enumerate(dataset):
pf.process( ( resample(math.log10(x['wce']),0.001), #resolution is 0.001
resample(x['area'],10), #values can be only multiples of 10
resample(x['pwr'],0.1), #resolution is 0.1
resample(x['time'],0.1) #resolution is 0.1
), customId=i) #customId may be omitted, there is an internal counter initialized to 0
indexes = pf.points(returnIds=True)

print([dataset[i]['wce'] for i in indexes])

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"], minimizeObjective2 = False)

or
from paretoarchive import PyBspTreeArchive
def pandas_pareto(data : pd.DataFrame, columns : list, **kwargs) -> pd.DataFrame:
filt = list(zip(*[data[c] for c in columns]))
ids = data.index.tolist()

sel = [ids[i] for i in PyBspTreeArchive(len(columns), **kwargs).filter(filt, returnIds=True)]
filt = [i in sel for i in ids]
return data[filt]

# example usage
par_df = pandas_pareto(df, ["area", "energy", "weight"], minimizeObjective2 = False)

Notes

The values are internally repesented as double numbers. For very large integers you may lose the precision.
Up to 15 objectives can be handled.

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