import numpy as np
from pymoo.model.decision_making import DecisionMaking, normalize, NeighborFinder, find_outliers_upper_tail
class HighTradeoffPointsInverted(DecisionMaking):
def __init__(self, **kwargs) -> None:
super().__init__(**kwargs)
def _do(self, F, **kwargs):
n, m = F.shape
if self.normalize:
F = normalize(F, self.ideal_point, self.nadir_point, estimate_bounds_if_none=True)
neighbors_finder = NeighborFinder(F, n_min_neigbors="auto")
mu = np.full(n, - np.inf)
# for each solution in the set calculate the least amount of improvement per unit deterioration
for i in range(n):
# neighbors to the current point
neighbors = neighbors_finder.find(i)
# calculate the trade-off to all neighbours
diff = F[neighbors] - F[i]
# calculate sacrifice and gain
sacrifice = np.maximum(0, diff).sum(axis=1)
gain = np.maximum(0, -diff).sum(axis=1)
np.warnings.filterwarnings('ignore')
tradeoff = sacrifice / gain
# otherwise find the one with the smalled one
mu[i] = np.nanmean(tradeoff)
return find_outliers_upper_tail(mu)
if __name__ == '__main__':
F = np.array([[2, 13], [3, 11], [7, 9], [9, 4], [12, 3]])
#_, w = PseudoWeights(np.array([0.5, 0.5])).do(F, return_pseudo_weights=True)
HighTradeoffPointsInverted(normalize=False).do(F)
