2to3 and parameter specific mutation rate

deap/algorithms.py

@@ -27,7 +27,7 @@
 
 import random
 
-import tools
+from . import tools
 
 
 def varAnd(population, toolbox, cxpb, mutpb):
@@ -157,7 +157,7 @@ def eaSimple(population, toolbox, cxpb, mutpb, ngen, stats=None,
     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, stats=None,
         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_, cxpb, mutpb, ngen,
     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_, cxpb, mutpb, ngen,
         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_, cxpb, mutpb, ngen,
     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_, cxpb, mutpb, ngen,
         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
 
 
@@ -481,7 +481,7 @@ def eaGenerateUpdate(toolbox, ngen, halloffame=None, stats=None,
     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
@@ -498,6 +498,6 @@ def eaGenerateUpdate(toolbox, ngen, halloffame=None, stats=None,
         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

@@ -190,12 +190,11 @@ def setValues(self, values):
             self.wvalues = tuple(map(mul, values, self.weights))
         except TypeError:
             _, _, traceback = sys.exc_info()
-            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
+            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))
 
     def delValues(self):
         self.wvalues = ()

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

@@ -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 wrapped_function(individual, *args, **kargs):
             # 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

@@ -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

@@ -287,7 +287,7 @@ 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

@@ -24,7 +24,7 @@
 from math import sqrt, log, exp
 import numpy
 
-import tools
+from . import tools
 
 
 class Strategy(object):
@@ -118,7 +118,7 @@ def generate(self, ind_init):
         """
         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 @@ def generate(self, ind_init):
         # 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

@@ -142,7 +142,7 @@ def __init__(self):
 
     dict_inst = {}
     dict_cls = {}
-    for obj_name, obj in kargs.iteritems():
+    for obj_name, obj in list(kargs.items()):
         if isinstance(obj, type):
             dict_inst[obj_name] = obj
         else:
@@ -161,7 +161,7 @@ def initType(self, *args, **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 list(dict_inst.items()):
             setattr(self, obj_name, obj())
         if base.__init__ is not object.__init__:
             base.__init__(self, *args, **kargs)

deap/gp.py

@@ -31,7 +31,7 @@
 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                  #
@@ -199,7 +199,7 @@ def __init__(self, name, args, ret):
         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):
@@ -300,7 +300,7 @@ def _add(self, prim):
         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:
@@ -480,11 +480,11 @@ 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):
@@ -506,7 +506,7 @@ 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})
@@ -618,9 +618,9 @@ 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)
@@ -629,9 +629,9 @@ 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))
@@ -659,8 +659,8 @@ 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):
@@ -1014,8 +1014,8 @@ def _genpop(n, pickfrom=[], acceptfunc=lambda s: True, producesizes=False):
                 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)
@@ -1063,7 +1063,7 @@ def halflifefunc(x):
     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):
@@ -1130,7 +1130,7 @@ def acceptfunc(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
 
@@ -1191,7 +1191,7 @@ 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()