import numpy as np
from pymoo.util.misc import cdist
def default_attr(pop):
return pop.get("X")
class DuplicateElimination:
def __init__(self, func=None) -> None:
super().__init__()
self.func = func
if self.func is None:
self.func = default_attr
def do(self, pop, *args, return_indices=False, to_itself=True):
original = pop
if len(pop) == 0:
return pop
if to_itself:
pop = pop[~self._do(pop, None, np.full(len(pop), False))]
for arg in args:
if len(arg) > 0:
if len(pop) == 0:
break
elif len(arg) == 0:
continue
else:
pop = pop[~self._do(pop, arg, np.full(len(pop), False))]
if return_indices:
no_duplicate, is_duplicate = [], []
H = set(pop)
for i, ind in enumerate(original):
if ind in H:
no_duplicate.append(i)
else:
is_duplicate.append(i)
return pop, no_duplicate, is_duplicate
else:
return pop
def _do(self, pop, other, is_duplicate):
pass
class DefaultDuplicateElimination(DuplicateElimination):
def __init__(self, epsilon=1e-16, **kwargs) -> None:
super().__init__(**kwargs)
self.epsilon = epsilon
def calc_dist(self, pop, other=None):
X = self.func(pop)
if other is None:
D = cdist(X, X)
D[np.triu_indices(len(X))] = np.inf
else:
_X = self.func(other)
D = cdist(X, _X)
return D
def _do(self, pop, other, is_duplicate):
D = self.calc_dist(pop, other)
D[np.isnan(D)] = np.inf
is_duplicate[np.any(D < self.epsilon, axis=1)] = True
return is_duplicate
class ElementwiseDuplicateElimination(DefaultDuplicateElimination):
def __init__(self, cmp_func=None, **kwargs) -> None:
super().__init__(**kwargs)
self.cmp = cmp_func
if self.cmp is None:
self.cmp = lambda a, b: self.is_equal(a, b)
def is_equal(self, a, b):
pass
def _do(self, pop, other, is_duplicate):
def to_float(val):
if isinstance(val, bool) or isinstance(val, np.bool_):
return 0.0 if val else 1.0
else:
return val
if other is None:
for i in range(len(pop)):
for j in range(i + 1, len(pop)):
val = to_float(self.cmp(pop[i], pop[j]))
if val < self.epsilon:
is_duplicate[i] = True
break
else:
for i in range(len(pop)):
for j in range(len(other)):
val = to_float(self.cmp(pop[i], other[j]))
if val < self.epsilon:
is_duplicate[i] = True
break
return is_duplicate
def to_hash(x):
try:
h = hash(x)
except:
try:
h = hash(str(x))
except:
raise Exception("Hash could not be calculated. Please use another duplicate elimination.")
return h
class HashDuplicateElimination(DuplicateElimination):
def __init__(self, func=to_hash) -> None:
super().__init__()
self.func = func
def _do(self, pop, other, is_duplicate):
H = set()
if other is not None:
for o in other:
val = self.func(o)
H.add(self.func(val))
for i, ind in enumerate(pop):
val = self.func(ind)
h = self.func(val)
if h in H:
is_duplicate[i] = True
else:
H.add(h)
return is_duplicate
class NoDuplicateElimination(DuplicateElimination):
def do(self, pop, *args, **kwargs):
return pop
