git: e7ad0b44e51e - main - math/py-deap: Fix build with setuptools 58.0.0+
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Date: Mon, 07 Mar 2022 18:27:20 UTC
The branch main has been updated by sunpoet:
URL: https://cgit.FreeBSD.org/ports/commit/?id=e7ad0b44e51efdcae16d9b90944d00e763c77770
commit e7ad0b44e51efdcae16d9b90944d00e763c77770
Author: Po-Chuan Hsieh <sunpoet@FreeBSD.org>
AuthorDate: 2022-03-07 18:07:34 +0000
Commit: Po-Chuan Hsieh <sunpoet@FreeBSD.org>
CommitDate: 2022-03-07 18:13:20 +0000
math/py-deap: Fix build with setuptools 58.0.0+
With hat: python
---
math/py-deap/files/patch-2to3 | 1148 +++++++++++++++++++++++++++++++++++++++++
1 file changed, 1148 insertions(+)
diff --git a/math/py-deap/files/patch-2to3 b/math/py-deap/files/patch-2to3
new file mode 100644
index 000000000000..fe8e7b516557
--- /dev/null
+++ b/math/py-deap/files/patch-2to3
@@ -0,0 +1,1148 @@
+--- deap/algorithms.py.orig 2020-01-21 01:17:45 UTC
++++ deap/algorithms.py
+@@ -27,7 +27,7 @@ you really want them to do.
+
+ import random
+
+-import tools
++from . import tools
+
+
+ def varAnd(population, toolbox, cxpb, mutpb):
+@@ -157,7 +157,7 @@ def eaSimple(population, toolbox, cxpb, mutpb, ngen, s
+ record = stats.compile(population) if stats else {}
+ logbook.record(gen=0, nevals=len(invalid_ind), **record)
+ if verbose:
+- print logbook.stream
++ print(logbook.stream)
+
+ # Begin the generational process
+ for gen in range(1, ngen + 1):
+@@ -184,7 +184,7 @@ def eaSimple(population, toolbox, cxpb, mutpb, ngen, s
+ record = stats.compile(population) if stats else {}
+ logbook.record(gen=gen, nevals=len(invalid_ind), **record)
+ if verbose:
+- print logbook.stream
++ print(logbook.stream)
+
+ return population, logbook
+
+@@ -227,10 +227,10 @@ def varOr(population, toolbox, lambda_, cxpb, mutpb):
+ "or equal to 1.0.")
+
+ offspring = []
+- for _ in xrange(lambda_):
++ for _ in range(lambda_):
+ op_choice = random.random()
+ if op_choice < cxpb: # Apply crossover
+- ind1, ind2 = map(toolbox.clone, random.sample(population, 2))
++ ind1, ind2 = list(map(toolbox.clone, random.sample(population, 2)))
+ ind1, ind2 = toolbox.mate(ind1, ind2)
+ del ind1.fitness.values
+ offspring.append(ind1)
+@@ -308,7 +308,7 @@ def eaMuPlusLambda(population, toolbox, mu, lambda_, c
+ record = stats.compile(population) if stats is not None else {}
+ logbook.record(gen=0, nevals=len(invalid_ind), **record)
+ if verbose:
+- print logbook.stream
++ print(logbook.stream)
+
+ # Begin the generational process
+ for gen in range(1, ngen + 1):
+@@ -332,7 +332,7 @@ def eaMuPlusLambda(population, toolbox, mu, lambda_, c
+ record = stats.compile(population) if stats is not None else {}
+ logbook.record(gen=gen, nevals=len(invalid_ind), **record)
+ if verbose:
+- print logbook.stream
++ print(logbook.stream)
+
+ return population, logbook
+
+@@ -409,7 +409,7 @@ def eaMuCommaLambda(population, toolbox, mu, lambda_,
+ record = stats.compile(population) if stats is not None else {}
+ logbook.record(gen=0, nevals=len(invalid_ind), **record)
+ if verbose:
+- print logbook.stream
++ print(logbook.stream)
+
+ # Begin the generational process
+ for gen in range(1, ngen + 1):
+@@ -433,7 +433,7 @@ def eaMuCommaLambda(population, toolbox, mu, lambda_,
+ record = stats.compile(population) if stats is not None else {}
+ logbook.record(gen=gen, nevals=len(invalid_ind), **record)
+ if verbose:
+- print logbook.stream
++ print(logbook.stream)
+ return population, logbook
+
+
+@@ -477,7 +477,7 @@ def eaGenerateUpdate(toolbox, ngen, halloffame=None, s
+ logbook = tools.Logbook()
+ logbook.header = ['gen', 'nevals'] + (stats.fields if stats else [])
+
+- for gen in xrange(ngen):
++ for gen in range(ngen):
+ # Generate a new population
+ population = toolbox.generate()
+ # Evaluate the individuals
+@@ -494,6 +494,6 @@ def eaGenerateUpdate(toolbox, ngen, halloffame=None, s
+ record = stats.compile(population) if stats is not None else {}
+ logbook.record(gen=gen, nevals=len(population), **record)
+ if verbose:
+- print logbook.stream
++ print(logbook.stream)
+
+ return population, logbook
+--- deap/base.py.orig 2020-01-21 01:17:45 UTC
++++ deap/base.py
+@@ -189,12 +189,12 @@ class Fitness(object):
+ self.wvalues = tuple(map(mul, values, self.weights))
+ except TypeError:
+ _, _, traceback = sys.exc_info()
+- raise TypeError, ("Both weights and assigned values must be a "
++ raise TypeError("Both weights and assigned values must be a "
+ "sequence of numbers when assigning to values of "
+ "%r. Currently assigning value(s) %r of %r to a "
+ "fitness with weights %s."
+ % (self.__class__, values, type(values),
+- self.weights)), traceback
++ self.weights)).with_traceback(traceback)
+
+ def delValues(self):
+ self.wvalues = ()
+--- deap/benchmarks/__init__.py.orig 2020-01-21 01:17:45 UTC
++++ deap/benchmarks/__init__.py
+@@ -489,7 +489,7 @@ def dtlz1(individual, obj):
+ """
+ g = 100 * (len(individual[obj-1:]) + sum((xi-0.5)**2 - cos(20*pi*(xi-0.5)) for xi in individual[obj-1:]))
+ f = [0.5 * reduce(mul, individual[:obj-1], 1) * (1 + g)]
+- f.extend(0.5 * reduce(mul, individual[:m], 1) * (1 - individual[m]) * (1 + g) for m in reversed(xrange(obj-1)))
++ f.extend(0.5 * reduce(mul, individual[:m], 1) * (1 - individual[m]) * (1 + g) for m in reversed(range(obj-1)))
+ return f
+
+ def dtlz2(individual, obj):
+@@ -588,7 +588,7 @@ def dtlz5(ind, n_objs):
+ theta = lambda x: pi / (4.0 * (1 + gval)) * (1 + 2 * gval * x)
+ fit = [(1 + gval) * cos(pi / 2.0 * ind[0]) * reduce(lambda x,y: x*y, [cos(theta(a)) for a in ind[1:]])]
+
+- for m in reversed(range(1, n_objs)):
++ for m in reversed(list(range(1, n_objs))):
+ if m == 1:
+ fit.append((1 + gval) * sin(pi / 2.0 * ind[0]))
+ else:
+@@ -608,7 +608,7 @@ def dtlz6(ind, n_objs):
+ fit = [(1 + gval) * cos(pi / 2.0 * ind[0]) *
+ reduce(lambda x,y: x*y, [cos(theta(a)) for a in ind[1:]])]
+
+- for m in reversed(range(1, n_objs)):
++ for m in reversed(list(range(1, n_objs))):
+ if m == 1:
+ fit.append((1 + gval) * sin(pi / 2.0 * ind[0]))
+ else:
+--- deap/benchmarks/binary.py.orig 2020-01-21 01:17:45 UTC
++++ deap/benchmarks/binary.py
+@@ -13,7 +13,7 @@
+ # You should have received a copy of the GNU Lesser General Public
+ # License along with DEAP. If not, see <http://www.gnu.org/licenses/>.
+
+-from __future__ import division
++
+ from functools import wraps
+ import math
+
+@@ -29,7 +29,7 @@ def bin2float(min_, max_, nbits):
+ # User must take care to make nelem an integer.
+ nelem = len(individual)//nbits
+ decoded = [0] * nelem
+- for i in xrange(nelem):
++ for i in range(nelem):
+ gene = int("".join(map(str,
+ individual[i*nbits:i*nbits+nbits])),
+ 2)
+@@ -68,10 +68,10 @@ def chuang_f1(individual):
+ """
+ total = 0
+ if individual[-1] == 0:
+- for i in xrange(0, len(individual)-1, 4):
++ for i in range(0, len(individual)-1, 4):
+ total += inv_trap(individual[i:i+4])
+ else:
+- for i in xrange(0, len(individual)-1, 4):
++ for i in range(0, len(individual)-1, 4):
+ total += trap(individual[i:i+4])
+ return total,
+
+@@ -85,16 +85,16 @@ def chuang_f2(individual):
+ """
+ total = 0
+ if individual[-2] == 0 and individual[-1] == 0:
+- for i in xrange(0, len(individual)-2, 8):
++ for i in range(0, len(individual)-2, 8):
+ total += inv_trap(individual[i:i+4]) + inv_trap(individual[i+4:i+8])
+ elif individual[-2] == 0 and individual[-1] == 1:
+- for i in xrange(0, len(individual)-2, 8):
++ for i in range(0, len(individual)-2, 8):
+ total += inv_trap(individual[i:i+4]) + trap(individual[i+4:i+8])
+ elif individual[-2] == 1 and individual[-1] == 0:
+- for i in xrange(0, len(individual)-2, 8):
++ for i in range(0, len(individual)-2, 8):
+ total += trap(individual[i:i+4]) + inv_trap(individual[i+4:i+8])
+ else:
+- for i in xrange(0, len(individual)-2, 8):
++ for i in range(0, len(individual)-2, 8):
+ total += trap(individual[i:i+4]) + trap(individual[i+4:i+8])
+ return total,
+
+@@ -108,10 +108,10 @@ def chuang_f3(individual):
+ """
+ total = 0
+ if individual[-1] == 0:
+- for i in xrange(0, len(individual)-1, 4):
++ for i in range(0, len(individual)-1, 4):
+ total += inv_trap(individual[i:i+4])
+ else:
+- for i in xrange(2, len(individual)-3, 4):
++ for i in range(2, len(individual)-3, 4):
+ total += inv_trap(individual[i:i+4])
+ total += trap(individual[-2:]+individual[:2])
+ return total,
+@@ -125,7 +125,7 @@ def royal_road1(individual, order):
+ nelem = len(individual) // order
+ max_value = int(2**order - 1)
+ total = 0
+- for i in xrange(nelem):
++ for i in range(nelem):
+ value = int("".join(map(str, individual[i*order:i*order+order])), 2)
+ total += int(order) * int(value/max_value)
+ return total,
+--- deap/benchmarks/movingpeaks.py.orig 2020-01-21 01:17:45 UTC
++++ deap/benchmarks/movingpeaks.py
+@@ -393,6 +393,6 @@ def diversity(population):
+
+ if __name__ == "__main__":
+ mpb = MovingPeaks(dim=2, npeaks=[1,1,10], number_severity=0.1)
+- print mpb.maximums()
++ print(mpb.maximums())
+ mpb.changePeaks()
+- print mpb.maximums()
++ print(mpb.maximums())
+--- deap/benchmarks/tools.py.orig 2020-01-21 01:17:45 UTC
++++ deap/benchmarks/tools.py
+@@ -285,7 +285,7 @@ def convergence(first_front, optimal_front):
+ distances.append(float("inf"))
+ for opt_ind in optimal_front:
+ dist = 0.
+- for i in xrange(len(opt_ind)):
++ for i in range(len(opt_ind)):
+ dist += (ind.fitness.values[i] - opt_ind[i])**2
+ if dist < distances[-1]:
+ distances[-1] = dist
+--- deap/cma.py.orig 2020-01-21 01:17:45 UTC
++++ deap/cma.py
+@@ -24,7 +24,7 @@ import copy
+ from math import sqrt, log, exp
+ import numpy
+
+-import tools
++from . import tools
+
+
+ class Strategy(object):
+@@ -118,7 +118,7 @@ class Strategy(object):
+ """
+ arz = numpy.random.standard_normal((self.lambda_, self.dim))
+ arz = self.centroid + self.sigma * numpy.dot(arz, self.BD.T)
+- return map(ind_init, arz)
++ return list(map(ind_init, arz))
+
+ def update(self, population):
+ """Update the current covariance matrix strategy from the
+@@ -286,7 +286,7 @@ class StrategyOnePlusLambda(object):
+ # self.y = numpy.dot(self.A, numpy.random.standard_normal(self.dim))
+ arz = numpy.random.standard_normal((self.lambda_, self.dim))
+ arz = self.parent + self.sigma * numpy.dot(arz, self.A.T)
+- return map(ind_init, arz)
++ return list(map(ind_init, arz))
+
+ def update(self, population):
+ """Update the current covariance matrix strategy from the
+--- deap/creator.py.orig 2020-01-21 01:17:45 UTC
++++ deap/creator.py
+@@ -142,7 +142,7 @@ def create(name, base, **kargs):
+
+ dict_inst = {}
+ dict_cls = {}
+- for obj_name, obj in kargs.iteritems():
++ for obj_name, obj in kargs.items():
+ if isinstance(obj, type):
+ dict_inst[obj_name] = obj
+ else:
+@@ -161,7 +161,7 @@ def create(name, base, **kargs):
+ """Replace the __init__ function of the new type, in order to
+ add attributes that were defined with **kargs to the instance.
+ """
+- for obj_name, obj in dict_inst.iteritems():
++ for obj_name, obj in dict_inst.items():
+ setattr(self, obj_name, obj())
+ if base.__init__ is not object.__init__:
+ base.__init__(self, *args, **kargs)
+--- deap/gp.py.orig 2020-01-21 01:17:45 UTC
++++ deap/gp.py
+@@ -31,7 +31,7 @@ from functools import partial, wraps
+ from inspect import isclass
+ from operator import eq, lt
+
+-import tools # Needed by HARM-GP
++from . import tools # Needed by HARM-GP
+
+ ######################################
+ # GP Data structure #
+@@ -197,7 +197,7 @@ class Primitive(object):
+ self.arity = len(args)
+ self.args = args
+ self.ret = ret
+- args = ", ".join(map("{{{0}}}".format, range(self.arity)))
++ args = ", ".join(map("{{{0}}}".format, list(range(self.arity))))
+ self.seq = "{name}({args})".format(name=self.name, args=args)
+
+ def format(self, *args):
+@@ -298,7 +298,7 @@ class PrimitiveSetTyped(object):
+ def addType(dict_, ret_type):
+ if ret_type not in dict_:
+ new_list = []
+- for type_, list_ in dict_.items():
++ for type_, list_ in list(dict_.items()):
+ if issubclass(type_, ret_type):
+ for item in list_:
+ if item not in new_list:
+@@ -478,11 +478,11 @@ def compile(expr, pset):
+ return eval(code, pset.context, {})
+ except MemoryError:
+ _, _, traceback = sys.exc_info()
+- raise MemoryError, ("DEAP : Error in tree evaluation :"
++ raise MemoryError("DEAP : Error in tree evaluation :"
+ " Python cannot evaluate a tree higher than 90. "
+ "To avoid this problem, you should use bloat control on your "
+ "operators. See the DEAP documentation for more information. "
+- "DEAP will now abort."), traceback
++ "DEAP will now abort.").with_traceback(traceback)
+
+
+ def compileADF(expr, psets):
+@@ -504,7 +504,7 @@ def compileADF(expr, psets):
+ """
+ adfdict = {}
+ func = None
+- for pset, subexpr in reversed(zip(psets, expr)):
++ for pset, subexpr in reversed(list(zip(psets, expr))):
+ pset.context.update(adfdict)
+ func = compile(subexpr, pset)
+ adfdict.update({pset.name: func})
+@@ -613,9 +613,9 @@ def generate(pset, min_, max_, condition, type_=None):
+ term = random.choice(pset.terminals[type_])
+ except IndexError:
+ _, _, traceback = sys.exc_info()
+- raise IndexError, "The gp.generate function tried to add " \
++ raise IndexError("The gp.generate function tried to add " \
+ "a terminal of type '%s', but there is " \
+- "none available." % (type_,), traceback
++ "none available." % (type_,)).with_traceback(traceback)
+ if isclass(term):
+ term = term()
+ expr.append(term)
+@@ -624,9 +624,9 @@ def generate(pset, min_, max_, condition, type_=None):
+ prim = random.choice(pset.primitives[type_])
+ except IndexError:
+ _, _, traceback = sys.exc_info()
+- raise IndexError, "The gp.generate function tried to add " \
++ raise IndexError("The gp.generate function tried to add " \
+ "a primitive of type '%s', but there is " \
+- "none available." % (type_,), traceback
++ "none available." % (type_,)).with_traceback(traceback)
+ expr.append(prim)
+ for arg in reversed(prim.args):
+ stack.append((depth + 1, arg))
+@@ -654,8 +654,8 @@ def cxOnePoint(ind1, ind2):
+ types2 = defaultdict(list)
+ if ind1.root.ret == __type__:
+ # Not STGP optimization
+- types1[__type__] = xrange(1, len(ind1))
+- types2[__type__] = xrange(1, len(ind2))
++ types1[__type__] = range(1, len(ind1))
++ types2[__type__] = range(1, len(ind2))
+ common_types = [__type__]
+ else:
+ for idx, node in enumerate(ind1[1:], 1):
+@@ -1009,8 +1009,8 @@ def harm(population, toolbox, cxpb, mutpb, ngen,
+ opRandom = random.random()
+ if opRandom < cxpb:
+ # Crossover
+- aspirant1, aspirant2 = toolbox.mate(*map(toolbox.clone,
+- toolbox.select(population, 2)))
++ aspirant1, aspirant2 = toolbox.mate(*list(map(toolbox.clone,
++ toolbox.select(population, 2))))
+ del aspirant1.fitness.values, aspirant2.fitness.values
+ if acceptfunc(len(aspirant1)):
+ producedpop.append(aspirant1)
+@@ -1058,7 +1058,7 @@ def harm(population, toolbox, cxpb, mutpb, ngen,
+ record = stats.compile(population) if stats else {}
+ logbook.record(gen=0, nevals=len(invalid_ind), **record)
+ if verbose:
+- print logbook.stream
++ print(logbook.stream)
+
+ # Begin the generational process
+ for gen in range(1, ngen + 1):
+@@ -1125,7 +1125,7 @@ def harm(population, toolbox, cxpb, mutpb, ngen,
+ record = stats.compile(population) if stats else {}
+ logbook.record(gen=gen, nevals=len(invalid_ind), **record)
+ if verbose:
+- print logbook.stream
++ print(logbook.stream)
+
+ return population, logbook
+
+@@ -1186,7 +1186,7 @@ def graph(expr):
+ <http://networkx.lanl.gov/pygraphviz/>`_ as the nodes might be plotted
+ out of order when using `NetworX <http://networkx.github.com/>`_.
+ """
+- nodes = range(len(expr))
++ nodes = list(range(len(expr)))
+ edges = list()
+ labels = dict()
+
+--- deap/tests/test_benchmarks.py.orig 2020-01-21 01:17:45 UTC
++++ deap/tests/test_benchmarks.py
+@@ -34,7 +34,7 @@ class BenchmarkTest(unittest.TestCase):
+ full_individual = creator.Individual([1] * 10)
+ two_individiual = creator.Individual(8*[0] + [1, 0])
+ population = [zero_individual, full_individual, two_individiual]
+- fitnesses = map(self.toolbox.evaluate, population)
++ fitnesses = list(map(self.toolbox.evaluate, population))
+ for ind, fit in zip(population, fitnesses):
+ ind.fitness.values = fit
+ assert population[0].fitness.values == (0.0, )
+@@ -46,7 +46,7 @@ class BenchmarkTest(unittest.TestCase):
+ wrong_population = [wrong_size_individual]
+ # It is up the user to make sure that bin2float gets an individual with
+ # an adequate length; no exceptions are raised.
+- fitnesses = map(self.toolbox.evaluate, wrong_population)
++ fitnesses = list(map(self.toolbox.evaluate, wrong_population))
+ for ind, fit in zip(wrong_population, fitnesses):
+ # In python 2.7 operator.mul works in a different way than in
+ # python3. Thus an error occurs in python2.7 but an assignment is
+--- deap/tests/test_init.py.orig 2020-01-21 01:17:45 UTC
++++ deap/tests/test_init.py
+@@ -8,6 +8,6 @@ from deap import tools
+ class LogbookTest(unittest.TestCase):
+ def test_statistics_compile(self):
+ l = 10
+- gen_idx = partial(random.sample, range(l), l)
++ gen_idx = partial(random.sample, list(range(l)), l)
+ i = tools.initIterate(list, gen_idx)
+ self.assertSetEqual(set(i), set(range(l)))
+--- deap/tests/test_logbook.py.orig 2020-01-21 01:17:45 UTC
++++ deap/tests/test_logbook.py
+@@ -7,7 +7,7 @@ class LogbookTest(unittest.TestCase):
+
+ def setUp(self):
+ self.logbook = tools.Logbook()
+- print
++ print()
+
+ def test_multi_chapters(self):
+ self.logbook.record(gen=0, evals=100, fitness={'obj 1' : {'avg' : 1.0, 'max' : 10},
+@@ -18,23 +18,23 @@ class LogbookTest(unittest.TestCase):
+ 'obj 2' : {'avg' : 1.0, 'max' : 10}},
+ length={'avg' : 1.0, 'max' : 30},
+ test={'avg' : 1.0, 'max' : 20})
+- print(self.logbook.stream)
++ print((self.logbook.stream))
+
+
+ def test_one_chapter(self):
+ self.logbook.record(gen=0, evals=100, fitness={'avg' : 1.0, 'max' : 10})
+ self.logbook.record(gen=0, evals=100, fitness={'avg' : 1.0, 'max' : 10})
+- print(self.logbook.stream)
++ print((self.logbook.stream))
+
+ def test_one_big_chapter(self):
+ self.logbook.record(gen=0, evals=100, fitness={'obj 1' : {'avg' : 1.0, 'max' : 10}, 'obj 2' : {'avg' : 1.0, 'max' : 10}})
+ self.logbook.record(gen=0, evals=100, fitness={'obj 1' : {'avg' : 1.0, 'max' : 10}, 'obj 2' : {'avg' : 1.0, 'max' : 10}})
+- print(self.logbook.stream)
++ print((self.logbook.stream))
+
+ def test_no_chapters(self):
+ self.logbook.record(gen=0, evals=100, **{'avg' : 1.0, 'max' : 10})
+ self.logbook.record(gen=0, evals=100, **{'avg' : 1.0, 'max' : 10})
+- print(self.logbook.stream)
++ print((self.logbook.stream))
+
+
+
+--- deap/tools/_hypervolume/pyhv.py.orig 2020-01-21 01:17:45 UTC
++++ deap/tools/_hypervolume/pyhv.py
+@@ -64,7 +64,7 @@ class _HyperVolume:
+ """
+
+ def weaklyDominates(point, other):
+- for i in xrange(len(point)):
++ for i in range(len(point)):
+ if point[i] > other[i]:
+ return False
+ return True
+@@ -152,7 +152,7 @@ class _HyperVolume:
+ hvol = qPrevDimIndex.volume[dimIndex] + qPrevDimIndex.area[dimIndex] * (qCargo[dimIndex] - qPrevDimIndex.cargo[dimIndex])
+ else:
+ qArea[0] = 1
+- qArea[1:dimIndex+1] = [qArea[i] * -qCargo[i] for i in xrange(dimIndex)]
++ qArea[1:dimIndex+1] = [qArea[i] * -qCargo[i] for i in range(dimIndex)]
+ q.volume[dimIndex] = hvol
+ if q.ignore >= dimIndex:
+ qArea[dimIndex] = qPrevDimIndex.area[dimIndex]
+@@ -184,7 +184,7 @@ class _HyperVolume:
+ dimensions = len(self.referencePoint)
+ nodeList = _MultiList(dimensions)
+ nodes = [_MultiList.Node(dimensions, point) for point in front]
+- for i in xrange(dimensions):
++ for i in range(dimensions):
+ self.sortByDimension(nodes, i)
+ nodeList.extend(nodes, i)
+ self.list = nodeList
+@@ -239,7 +239,7 @@ class _MultiList:
+
+ def __str__(self):
+ strings = []
+- for i in xrange(self.numberLists):
++ for i in range(self.numberLists):
+ currentList = []
+ node = self.sentinel.next[i]
+ while node != self.sentinel:
+@@ -292,7 +292,7 @@ class _MultiList:
+
+ def remove(self, node, index, bounds):
+ """Removes and returns 'node' from all lists in [0, 'index'[."""
+- for i in xrange(index):
++ for i in range(index):
+ predecessor = node.prev[i]
+ successor = node.next[i]
+ predecessor.next[i] = successor
+@@ -309,7 +309,7 @@ class _MultiList:
+ nodes of the node that is reinserted are in the list.
+
+ """
+- for i in xrange(index):
++ for i in range(index):
+ node.prev[i].next[i] = node
+ node.next[i].prev[i] = node
+ if bounds[i] > node.cargo[i]:
+@@ -329,8 +329,8 @@ if __name__ == "__main__":
+ pointset = [(a, a) for a in numpy.arange(1, 0, -0.01)]
+ ref = numpy.array([2, 2])
+
+- print("Python version: %f" % hypervolume(pointset, ref))
++ print(("Python version: %f" % hypervolume(pointset, ref)))
+ if hv:
+- print("C version: %f" % hv.hypervolume(pointset, ref))
+- print("Approximated: %f" % hypervolume_approximation(pointset, ref))
++ print(("C version: %f" % hv.hypervolume(pointset, ref)))
++ print(("Approximated: %f" % hypervolume_approximation(pointset, ref)))
+
+--- deap/tools/constraint.py.orig 2020-01-21 01:17:45 UTC
++++ deap/tools/constraint.py
+@@ -172,6 +172,6 @@ if __name__ == "__main__":
+ toolbox.decorate("evaluate", ClosestValidPenalty(valid, closest_feasible, 1.0e-6, distance))
+
+ ind1 = creator.Individual((-5.6468535666e-01,2.2483050478e+00,-1.1087909644e+00,-1.2710112861e-01,1.1682438733e+00,-1.3642007438e+00,-2.1916417835e-01,-5.9137308999e-01,-1.0870160336e+00,6.0515070232e-01,2.1532075914e+00,-2.6164718271e-01,1.5244071578e+00,-1.0324305612e+00,1.2858152343e+00,-1.2584683962e+00,1.2054392372e+00,-1.7429571973e+00,-1.3517256013e-01,-2.6493429355e+00,-1.3051320798e-01,2.2641961090e+00,-2.5027232340e+00,-1.2844874148e+00,1.9955852925e+00,-1.2942218834e+00,3.1340109155e+00,1.6440111097e+00,-1.7750105857e+00,7.7610242710e-01))
+- print(toolbox.evaluate(ind1))
+- print("Individuals is valid: %s" % ("True" if valid(ind1) else "False"))
++ print((toolbox.evaluate(ind1)))
++ print(("Individuals is valid: %s" % ("True" if valid(ind1) else "False")))
+
+--- deap/tools/crossover.py.orig 2020-01-21 01:17:45 UTC
++++ deap/tools/crossover.py
+@@ -1,4 +1,4 @@
+-from __future__ import division
++
+ import random
+ import warnings
+
+@@ -84,7 +84,7 @@ def cxUniform(ind1, ind2, indpb):
+ :mod:`random` module.
+ """
+ size = min(len(ind1), len(ind2))
+- for i in xrange(size):
++ for i in range(size):
+ if random.random() < indpb:
+ ind1[i], ind2[i] = ind2[i], ind1[i]
+
+@@ -115,7 +115,7 @@ def cxPartialyMatched(ind1, ind2):
+ p1, p2 = [0] * size, [0] * size
+
+ # Initialize the position of each indices in the individuals
+- for i in xrange(size):
++ for i in range(size):
+ p1[ind1[i]] = i
+ p2[ind2[i]] = i
+ # Choose crossover points
+@@ -127,7 +127,7 @@ def cxPartialyMatched(ind1, ind2):
+ cxpoint1, cxpoint2 = cxpoint2, cxpoint1
+
+ # Apply crossover between cx points
+- for i in xrange(cxpoint1, cxpoint2):
++ for i in range(cxpoint1, cxpoint2):
+ # Keep track of the selected values
+ temp1 = ind1[i]
+ temp2 = ind2[i]
+@@ -166,11 +166,11 @@ def cxUniformPartialyMatched(ind1, ind2, indpb):
+ p1, p2 = [0] * size, [0] * size
+
+ # Initialize the position of each indices in the individuals
+- for i in xrange(size):
++ for i in range(size):
+ p1[ind1[i]] = i
+ p2[ind2[i]] = i
+
+- for i in xrange(size):
++ for i in range(size):
+ if random.random() < indpb:
+ # Keep track of the selected values
+ temp1 = ind1[i]
+@@ -209,7 +209,7 @@ def cxOrdered(ind1, ind2):
+ optimization and machine learning. Addison Wesley, 1989
+ """
+ size = min(len(ind1), len(ind2))
+- a, b = random.sample(xrange(size), 2)
++ a, b = random.sample(range(size), 2)
+ if a > b:
+ a, b = b, a
+
+@@ -321,7 +321,7 @@ def cxSimulatedBinaryBounded(ind1, ind2, eta, low, up)
+ elif len(up) < size:
+ raise IndexError("up must be at least the size of the shorter individual: %d < %d" % (len(up), size))
+
+- for i, xl, xu in zip(xrange(size), low, up):
++ for i, xl, xu in zip(range(size), low, up):
+ if random.random() <= 0.5:
+ # This epsilon should probably be changed for 0 since
+ # floating point arithmetic in Python is safer
+--- deap/tools/emo.py.orig 2020-01-21 01:17:45 UTC
++++ deap/tools/emo.py
+@@ -1,4 +1,4 @@
+-from __future__ import division
++
+ import bisect
+ from collections import defaultdict, namedtuple
+ from itertools import chain
+@@ -74,7 +74,7 @@ def sortNondominated(individuals, k, first_front_only=
+ map_fit_ind = defaultdict(list)
+ for ind in individuals:
+ map_fit_ind[ind.fitness].append(ind)
+- fits = map_fit_ind.keys()
++ fits = list(map_fit_ind.keys())
+
+ current_front = []
+ next_front = []
+@@ -129,7 +129,7 @@ def assignCrowdingDist(individuals):
+
+ nobj = len(individuals[0].fitness.values)
+
+- for i in xrange(nobj):
++ for i in range(nobj):
+ crowd.sort(key=lambda element: element[0][i])
+ distances[crowd[0][1]] = float("inf")
+ distances[crowd[-1][1]] = float("inf")
+@@ -184,7 +184,7 @@ def selTournamentDCD(individuals, k):
+ individuals_2 = random.sample(individuals, len(individuals))
+
+ chosen = []
+- for i in xrange(0, k, 4):
++ for i in range(0, k, 4):
+ chosen.append(tourn(individuals_1[i], individuals_1[i+1]))
+ chosen.append(tourn(individuals_1[i+2], individuals_1[i+3]))
+ chosen.append(tourn(individuals_2[i], individuals_2[i+1]))
+@@ -248,7 +248,7 @@ def sortLogNondominated(individuals, k, first_front_on
+
+ #Launch the sorting algorithm
+ obj = len(individuals[0].fitness.wvalues)-1
+- fitnesses = unique_fits.keys()
++ fitnesses = list(unique_fits.keys())
+ front = dict.fromkeys(fitnesses, 0)
+
+ # Sort the fitnesses lexicographically.
+@@ -284,7 +284,7 @@ def sortNDHelperA(fitnesses, obj, front):
+ front[s2] = max(front[s2], front[s1] + 1)
+ elif obj == 1:
+ sweepA(fitnesses, front)
+- elif len(frozenset(map(itemgetter(obj), fitnesses))) == 1:
++ elif len(frozenset(list(map(itemgetter(obj), fitnesses)))) == 1:
+ #All individuals for objective M are equal: go to objective M-1
+ sortNDHelperA(fitnesses, obj-1, front)
+ else:
+@@ -615,7 +615,7 @@ def associate_to_niche(fitnesses, reference_points, be
+
+ # Retrieve min distance niche index
+ niches = numpy.argmin(distances, axis=1)
+- distances = distances[range(niches.shape[0]), niches]
++ distances = distances[list(range(niches.shape[0])), niches]
+ return niches, distances
+
+
+@@ -705,7 +705,7 @@ def selSPEA2(individuals, k):
+ K = math.sqrt(N)
+ strength_fits = [0] * N
+ fits = [0] * N
+- dominating_inds = [list() for i in xrange(N)]
++ dominating_inds = [list() for i in range(N)]
+
+ for i, ind_i in enumerate(individuals):
+ for j, ind_j in enumerate(individuals[i+1:], i+1):
+@@ -716,19 +716,19 @@ def selSPEA2(individuals, k):
+ strength_fits[j] += 1
+ dominating_inds[i].append(j)
+
+- for i in xrange(N):
++ for i in range(N):
+ for j in dominating_inds[i]:
+ fits[i] += strength_fits[j]
+
+ # Choose all non-dominated individuals
+- chosen_indices = [i for i in xrange(N) if fits[i] < 1]
++ chosen_indices = [i for i in range(N) if fits[i] < 1]
+
+ if len(chosen_indices) < k: # The archive is too small
+- for i in xrange(N):
++ for i in range(N):
+ distances = [0.0] * N
+- for j in xrange(i + 1, N):
++ for j in range(i + 1, N):
+ dist = 0.0
+- for l in xrange(L):
++ for l in range(L):
+ val = individuals[i].fitness.values[l] - \
+ individuals[j].fitness.values[l]
+ dist += val * val
+@@ -737,7 +737,7 @@ def selSPEA2(individuals, k):
+ density = 1.0 / (kth_dist + 2.0)
+ fits[i] += density
+
+- next_indices = [(fits[i], i) for i in xrange(N)
++ next_indices = [(fits[i], i) for i in range(N)
+ if not i in chosen_indices]
+ next_indices.sort()
+ #print next_indices
+@@ -745,12 +745,12 @@ def selSPEA2(individuals, k):
+
+ elif len(chosen_indices) > k: # The archive is too large
+ N = len(chosen_indices)
+- distances = [[0.0] * N for i in xrange(N)]
+- sorted_indices = [[0] * N for i in xrange(N)]
+- for i in xrange(N):
+- for j in xrange(i + 1, N):
++ distances = [[0.0] * N for i in range(N)]
++ sorted_indices = [[0] * N for i in range(N)]
++ for i in range(N):
++ for j in range(i + 1, N):
+ dist = 0.0
+- for l in xrange(L):
++ for l in range(L):
+ val = individuals[chosen_indices[i]].fitness.values[l] - \
+ individuals[chosen_indices[j]].fitness.values[l]
+ dist += val * val
+@@ -759,8 +759,8 @@ def selSPEA2(individuals, k):
+ distances[i][i] = -1
+
+ # Insert sort is faster than quick sort for short arrays
+- for i in xrange(N):
+- for j in xrange(1, N):
++ for i in range(N):
++ for j in range(1, N):
+ l = j
+ while l > 0 and distances[i][j] < distances[i][sorted_indices[i][l - 1]]:
+ sorted_indices[i][l] = sorted_indices[i][l - 1]
+@@ -772,8 +772,8 @@ def selSPEA2(individuals, k):
+ while size > k:
+ # Search for minimal distance
+ min_pos = 0
+- for i in xrange(1, N):
+- for j in xrange(1, size):
++ for i in range(1, N):
++ for j in range(1, size):
+ dist_i_sorted_j = distances[i][sorted_indices[i][j]]
+ dist_min_sorted_j = distances[min_pos][sorted_indices[min_pos][j]]
+
+@@ -784,11 +784,11 @@ def selSPEA2(individuals, k):
+ break
+
+ # Remove minimal distance from sorted_indices
+- for i in xrange(N):
++ for i in range(N):
+ distances[i][min_pos] = float("inf")
+ distances[min_pos][i] = float("inf")
+
+- for j in xrange(1, size - 1):
++ for j in range(1, size - 1):
+ if sorted_indices[i][j] == min_pos:
+ sorted_indices[i][j] = sorted_indices[i][j + 1]
+ sorted_indices[i][j + 1] = min_pos
+--- deap/tools/indicator.py.orig 2020-01-21 01:17:45 UTC
++++ deap/tools/indicator.py
+@@ -41,7 +41,7 @@ def hypervolume(front, **kargs):
+ return hv.hypervolume(numpy.concatenate((wobj[:i], wobj[i+1:])), ref)
+
+ # Parallelization note: Cannot pickle local function
+- contrib_values = map(contribution, range(len(front)))
++ contrib_values = list(map(contribution, list(range(len(front)))))
+
+ # Select the maximum hypervolume value (correspond to the minimum difference)
+ return numpy.argmax(contrib_values)
+@@ -62,7 +62,7 @@ def additive_epsilon(front, **kargs):
+ mwobj[i] = numpy.ma.masked
+ return numpy.min(numpy.max(wobj[i] - mwobj, axis=1))
+
+- contrib_values = map(contribution, range(len(front)))
++ contrib_values = list(map(contribution, list(range(len(front)))))
+
+ # Select the minimum contribution value
+ return numpy.argmin(contrib_values)
+@@ -84,7 +84,7 @@ def multiplicative_epsilon(front, **kargs):
+ mwobj[i] = numpy.ma.masked
+ return numpy.min(numpy.max(wobj[i] / mwobj, axis=1))
+
+- contrib_values = map(contribution, range(len(front)))
++ contrib_values = list(map(contribution, list(range(len(front)))))
+
+ # Select the minimum contribution value
+ return numpy.argmin(contrib_values)
+--- deap/tools/init.py.orig 2020-01-21 01:17:45 UTC
++++ deap/tools/init.py
+@@ -1,5 +1,5 @@
+-from __future__ import division
+
++
+ def initRepeat(container, func, n):
+ """Call the function *container* with a generator function corresponding
+ to the calling *n* times the function *func*.
+@@ -22,7 +22,7 @@ def initRepeat(container, func, n):
+
+ See the :ref:`list-of-floats` and :ref:`population` tutorials for more examples.
+ """
+- return container(func() for _ in xrange(n))
++ return container(func() for _ in range(n))
+
+ def initIterate(container, generator):
+ """Call the function *container* with an iterable as
+@@ -72,7 +72,7 @@ def initCycle(container, seq_func, n=1):
+
+ See the :ref:`funky` tutorial for an example.
+ """
+- return container(func() for _ in xrange(n) for func in seq_func)
++ return container(func() for _ in range(n) for func in seq_func)
+
+ __all__ = ['initRepeat', 'initIterate', 'initCycle']
+
+--- deap/tools/migration.py.orig 2020-01-21 01:17:45 UTC
++++ deap/tools/migration.py
+@@ -1,6 +1,6 @@
+-from __future__ import division
+
+
++
+ def migRing(populations, k, selection, replacement=None, migarray=None):
+ """Perform a ring migration between the *populations*. The migration first
+ select *k* emigrants from each population using the specified *selection*
+@@ -31,12 +31,12 @@ def migRing(populations, k, selection, replacement=Non
+ """
+ nbr_demes = len(populations)
+ if migarray is None:
+- migarray = range(1, nbr_demes) + [0]
++ migarray = list(range(1, nbr_demes)) + [0]
+
+- immigrants = [[] for i in xrange(nbr_demes)]
+- emigrants = [[] for i in xrange(nbr_demes)]
++ immigrants = [[] for i in range(nbr_demes)]
++ emigrants = [[] for i in range(nbr_demes)]
+
+- for from_deme in xrange(nbr_demes):
++ for from_deme in range(nbr_demes):
+ emigrants[from_deme].extend(selection(populations[from_deme], k))
+ if replacement is None:
+ # If no replacement strategy is selected, replace those who migrate
+--- deap/tools/mutation.py.orig 2020-01-21 01:17:45 UTC
++++ deap/tools/mutation.py
+@@ -1,4 +1,4 @@
+-from __future__ import division
++
+ import math
+ import random
+
+@@ -41,7 +41,7 @@ def mutGaussian(individual, mu, sigma, indpb):
+ elif len(sigma) < size:
+ raise IndexError("sigma must be at least the size of individual: %d < %d" % (len(sigma), size))
+
+- for i, m, s in zip(xrange(size), mu, sigma):
++ for i, m, s in zip(range(size), mu, sigma):
+ if random.random() < indpb:
+ individual[i] += random.gauss(m, s)
+
+@@ -72,7 +72,7 @@ def mutPolynomialBounded(individual, eta, low, up, ind
+ elif len(up) < size:
+ raise IndexError("up must be at least the size of individual: %d < %d" % (len(up), size))
+
+- for i, xl, xu in zip(xrange(size), low, up):
++ for i, xl, xu in zip(range(size), low, up):
+ if random.random() <= indpb:
+ x = individual[i]
+ delta_1 = (x - xl) / (xu - xl)
+@@ -110,7 +110,7 @@ def mutShuffleIndexes(individual, indpb):
+ functions from the python base :mod:`random` module.
+ """
+ size = len(individual)
+- for i in xrange(size):
++ for i in range(size):
+ if random.random() < indpb:
+ swap_indx = random.randint(0, size - 2)
+ if swap_indx >= i:
+@@ -135,7 +135,7 @@ def mutFlipBit(individual, indpb):
+ This function uses the :func:`~random.random` function from the python base
+ :mod:`random` module.
+ """
+- for i in xrange(len(individual)):
++ for i in range(len(individual)):
+ if random.random() < indpb:
+ individual[i] = type(individual[i])(not individual[i])
+
+@@ -166,7 +166,7 @@ def mutUniformInt(individual, low, up, indpb):
+ elif len(up) < size:
+ raise IndexError("up must be at least the size of individual: %d < %d" % (len(up), size))
+
+- for i, xl, xu in zip(xrange(size), low, up):
++ for i, xl, xu in zip(range(size), low, up):
+ if random.random() < indpb:
+ individual[i] = random.randint(xl, xu)
+
+@@ -207,7 +207,7 @@ def mutESLogNormal(individual, c, indpb):
+ n = random.gauss(0, 1)
+ t0_n = t0 * n
+
+- for indx in xrange(size):
++ for indx in range(size):
+ if random.random() < indpb:
+ individual.strategy[indx] *= math.exp(t0_n + t * random.gauss(0, 1))
+ individual[indx] += individual.strategy[indx] * random.gauss(0, 1)
+--- deap/tools/selection.py.orig 2020-01-21 01:17:45 UTC
++++ deap/tools/selection.py
+@@ -1,4 +1,4 @@
+-from __future__ import division
++
+ import random
+ import numpy as np
+
+@@ -21,7 +21,7 @@ def selRandom(individuals, k):
+ This function uses the :func:`~random.choice` function from the
+ python base :mod:`random` module.
+ """
+- return [random.choice(individuals) for i in xrange(k)]
++ return [random.choice(individuals) for i in range(k)]
+
+
+ def selBest(individuals, k, fit_attr="fitness"):
+@@ -63,7 +63,7 @@ def selTournament(individuals, k, tournsize, fit_attr=
+ :mod:`random` module.
+ """
+ chosen = []
+- for i in xrange(k):
++ for i in range(k):
+ aspirants = selRandom(individuals, tournsize)
+ chosen.append(max(aspirants, key=attrgetter(fit_attr)))
+ return chosen
+@@ -90,7 +90,7 @@ def selRoulette(individuals, k, fit_attr="fitness"):
+ s_inds = sorted(individuals, key=attrgetter(fit_attr), reverse=True)
+ sum_fits = sum(getattr(ind, fit_attr).values[0] for ind in individuals)
+ chosen = []
+- for i in xrange(k):
++ for i in range(k):
*** 184 LINES SKIPPED ***