largest_eafdiff#

moocore.largest_eafdiff(x, /, ref, *, maximise=False, intervals=5, ideal=None)[source]#

Identify largest EAF differences.

Given a list of datasets, return the indexes of the pair with the largest EAF differences according to the method proposed by Diaz and López-Ibáñez[1].

Warning

The current implementation only supports 2 objectives.

Parameters:

  • x (list) – A list of matrices with at least 3 columns (last column indicates the set).

  • ref (ArrayLike) – Reference point as a 1D vector. Must be same length as a single point in the input data.

  • maximise (bool | list[bool], default: False) – Whether the objectives must be maximised instead of minimised. Either a single boolean value that applies to all objectives or a list of booleans, with one value per objective. Also accepts a 1D numpy array with value 0/1 for each objective

  • intervals (int, default: 5) – The absolute range of the differences \([0, 1]\) is partitioned into the number of intervals provided.

  • ideal (ArrayLike | None, default: None) – Ideal point as a vector of numerical values. If None, it is calculated as minimum (or maximum if maximising that objective) of each objective in the input data.

Returns:

  • pair (tuple[int,int]) – Pair of indexes into the list that give the largest EAF difference.

  • value (float) – Value of the largest difference.

See also

eafdiff, whv_rect

References

Examples

>>> import pandas as pd
>>> df = pd.read_csv(moocore.get_dataset_path("tpls50x20_1_MWT.csv"))
>>> df
     algorithm  Makespan  WeightedTardiness   run
0         1to2    4280.0            10231.0   1.0
1         1to2    4238.0            10999.0   1.0
2         1to2    4137.0            11737.0   1.0
3         1to2    4024.0            14871.0   1.0
4         1to2    4014.0            17825.0   1.0
...        ...       ...                ...   ...
1506    double    4048.0            14755.0  15.0
1507    double    3923.0            25507.0  15.0
1508    double    3890.0            29567.0  15.0
1509    double    3862.0            31148.0  15.0
1510    double    4413.0             9894.0  15.0

[1511 rows x 4 columns]
>>> nadir = df.iloc[:, 1:3].max().to_numpy()
>>> # Split by column 'algorithm'
>>> x = [
...     g.drop("algorithm", axis=1) for i, g in df.groupby("algorithm", sort=False)
... ]
>>> moocore.largest_eafdiff(x, ref=nadir)
((2, 5), 777017.0)
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