bluepyopt/deapext/hype.py
"""
Copyright (c) 2016-2022, EPFL/Blue Brain Project
This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>
This library is free software; you can redistribute it and/or modify it under
the terms of the GNU Lesser General Public License version 3.0 as published
by the Free Software Foundation.
This library 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 Lesser General Public License for more
details.
You should have received a copy of the GNU Lesser General Public License
along with this library; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
"""
import numpy
def hypesub(la, A, actDim, bounds, pvec, alpha, k):
"""HypE algorithm sub function"""
h = numpy.zeros(la)
i = numpy.argsort(A[:, actDim - 1])
S = A[i]
pvec = pvec[i]
for i in range(1, S.shape[0] + 1):
if i < S.shape[0]:
extrusion = S[i, actDim - 1] - S[i - 1, actDim - 1]
else:
extrusion = bounds[actDim - 1] - S[i - 1, actDim - 1]
if actDim == 1:
if i > k:
break
if alpha[i - 1] >= 0:
h[pvec[0:i]] += extrusion * alpha[i - 1]
elif extrusion > 0.0:
h += extrusion * hypesub(
la, S[0:i, :], actDim - 1, bounds, pvec[0:i], alpha, k
)
return h
def hypeIndicatorExact(points, bounds, k):
"""HypE algorithm. Python implementation of the Matlab code available at
https://sop.tik.ee.ethz.ch/download/supplementary/hype/
Args:
points(array): 2D array containing the objective values of the
population
bounds(array): 1D array containing the reference point from which to
compute the hyper-volume
k(int): HypE parameter
"""
Ps = points.shape[0]
if k < 0:
k = Ps
actDim = points.shape[1]
pvec = numpy.arange(points.shape[0])
alpha = []
for i in range(1, k + 1):
j = numpy.arange(1, i)
alpha.append(numpy.prod((k - j) / (Ps - j) / i))
alpha = numpy.asarray(alpha)
return hypesub(points.shape[0], points, actDim, bounds, pvec, alpha, k)
def hypeIndicatorSampled(points, bounds, k, nrOfSamples):
"""Monte-Carlo approximation of the HypE algorithm. Python implementation
of the Matlab code available at
https://sop.tik.ee.ethz.ch/download/supplementary/hype/
Args:
points(array): 2D array containing the objective values of the
population
bounds(array): 1D array containing the reference point from which to
compute the hyper-volume
k(int): HypE parameter
nrOfSamples(int): number of random samples to use for the
Monte-Carlo approximation
"""
nrP = points.shape[0]
dim = points.shape[1]
F = numpy.zeros(nrP)
BoxL = numpy.min(points, axis=0)
alpha = []
for i in range(1, k + 1):
j = numpy.arange(1, i)
alpha.append(numpy.prod((k - j) / (nrP - j) / i))
alpha = numpy.asarray(alpha + [0.0] * nrP)
S = numpy.random.uniform(low=BoxL, high=bounds, size=(nrOfSamples, dim))
dominated = numpy.zeros(nrOfSamples, dtype="uint")
for j in range(1, nrP + 1):
B = S - points[j - 1]
ind = numpy.sum(B >= 0, axis=1) == dim
dominated[ind] += 1
for j in range(1, nrP + 1):
B = S - points[j - 1]
ind = numpy.sum(B >= 0, axis=1) == dim
x = dominated[ind]
F[j - 1] = numpy.sum(alpha[x - 1])
F = F * numpy.prod(bounds - BoxL) / nrOfSamples
return F