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indicator.py
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indicator.py
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# Copyright (C) 2010 Simon Wessing
# TU Dortmund University
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import numpy
try:
# try importing the C version
from ._hypervolume import hv as hv
except ImportError:
# fallback on python version
from ._hypervolume import pyhv as hv
def hypervolume(front, **kargs):
"""Returns the index of the individual with the least the hypervolume
contribution. The provided *front* should be a set of non-dominated
individuals having each a :attr:`fitness` attribute.
"""
# Must use wvalues * -1 since hypervolume use implicit minimization
# And minimization in deap use max on -obj
wobj = numpy.array([ind.fitness.wvalues for ind in front]) * -1
ref = kargs.get("ref", None)
if ref is None:
ref = numpy.max(wobj, axis=0) + 1
def contribution(i):
# The contribution of point p_i in point set P
# is the hypervolume of P without p_i
return hv.hypervolume(numpy.concatenate((wobj[:i], wobj[i+1:])), ref)
# Parallelization note: Cannot pickle local function
contrib_values = list(map(contribution, list(range(len(front)))))
# Select the maximum hypervolume value (correspond to the minimum difference)
return numpy.argmax(contrib_values)
def additive_epsilon(front, **kargs):
"""Returns the index of the individual with the least the additive epsilon
contribution. The provided *front* should be a set of non-dominated
individuals having each a :attr:`fitness` attribute.
.. warning::
This function has not been tested.
"""
wobj = numpy.array([ind.fitness.wvalues for ind in front]) * -1
def contribution(i):
mwobj = numpy.ma.array(wobj)
mwobj[i] = numpy.ma.masked
return numpy.min(numpy.max(wobj[i] - mwobj, axis=1))
contrib_values = list(map(contribution, list(range(len(front)))))
# Select the minimum contribution value
return numpy.argmin(contrib_values)
def multiplicative_epsilon(front, **kargs):
"""Returns the index of the individual with the least the multiplicative epsilon
contribution. The provided *front* should be a set of non-dominated
individuals having each a :attr:`fitness` attribute.
.. warning::
This function has not been tested.
"""
wobj = numpy.array([ind.fitness.wvalues for ind in front]) * -1
def contribution(i):
mwobj = numpy.ma.array(wobj)
mwobj[i] = numpy.ma.masked
return numpy.min(numpy.max(wobj[i] / mwobj, axis=1))
contrib_values = list(map(contribution, list(range(len(front)))))
# Select the minimum contribution value
return numpy.argmin(contrib_values)
__all__ = ["hypervolume", "additive_epsilon", "multiplicative_epsilon"]