First round of Pylint fixes.

deap/algorithms.py

@@ -74,9 +74,9 @@ def varAnd(population, toolbox, cxpb, mutpb):
                                                           offspring[i])
             del offspring[i - 1].fitness.values, offspring[i].fitness.values
 
-    for i in range(len(offspring)):
+    for i, off in enumerate(offspring):
         if random.random() < mutpb:
-            offspring[i], = toolbox.mutate(offspring[i])
+            offspring[i], = toolbox.mutate(off)
             del offspring[i].fitness.values
 
     return offspring

deap/base.py

@@ -298,9 +298,9 @@ def __le__(self, other):
 
         if self_violates_constraints and other_violates_constraints:
             return True
-        elif self_violates_constraints:
+        if self_violates_constraints:
             return True
-        elif other_violates_constraints:
+        if other_violates_constraints:
             return False
 
         return self.wvalues <= other.wvalues
@@ -311,9 +311,9 @@ def __lt__(self, other):
 
         if self_violates_constraints and other_violates_constraints:
             return False
-        elif self_violates_constraints:
+        if self_violates_constraints:
             return True
-        elif other_violates_constraints:
+        if other_violates_constraints:
             return False
 
         return self.wvalues < other.wvalues
@@ -324,9 +324,9 @@ def __eq__(self, other):
 
         if self_violates_constraints and other_violates_constraints:
             return True
-        elif self_violates_constraints:
+        if self_violates_constraints:
             return False
-        elif other_violates_constraints:
+        if other_violates_constraints:
             return False
 
         return self.wvalues == other.wvalues
@@ -340,9 +340,9 @@ def dominates(self, other):
 
         if self_violates_constraints and other_violates_constraints:
             return False
-        elif self_violates_constraints:
+        if self_violates_constraints:
             return False
-        elif other_violates_constraints:
+        if other_violates_constraints:
             return True
 
         return super(ConstrainedFitness, self).dominates(other)

deap/cma.py

@@ -406,7 +406,7 @@ def generate(self, ind_init):
                   of its parent.
         """
         arz = numpy.random.randn(self.lambda_, self.dim)
-        individuals = list()
+        individuals = []
 
         # Make sure every parent has a parent tag and index
         for i, p in enumerate(self.parents):
@@ -436,9 +436,9 @@ def _select(self, candidates):
 
         pareto_fronts = tools.sortLogNondominated(candidates, len(candidates))
 
-        chosen = list()
+        chosen = []
         mid_front = None
-        not_chosen = list()
+        not_chosen = []
 
         # Fill the next population (chosen) with the fronts until there is not enough space
         # When an entire front does not fit in the space left we rely on the hypervolume
@@ -641,7 +641,7 @@ def __init__(self, parent, sigma, steps, **kargs):
                                        < self.S_int)
 
         self.constraint_vecs = None
-        self.ancestors_fitness = list()
+        self.ancestors_fitness = []
 
     @property
     def lambda_(self):
@@ -703,7 +703,7 @@ def _integer_mutation(self):
         # the last best solution. We skip this last best solution part.
         if n_I_R == 0:
             return numpy.zeros((self.lambda_, self.dim))
-        elif n_I_R == self.dim:
+        if n_I_R == self.dim:
             p = self.lambda_ / 2.0 / self.lambda_
             # lambda_int = int(numpy.floor(self.lambda_ / 2))
         else:

deap/gp.py

@@ -211,8 +211,7 @@ def __eq__(self, other):
         if type(self) is type(other):
             return all(getattr(self, slot) == getattr(other, slot)
                        for slot in self.__slots__)
-        else:
-            return NotImplemented
+        return NotImplemented
 
 
 class Terminal(object):
@@ -238,8 +237,7 @@ def __eq__(self, other):
         if type(self) is type(other):
             return all(getattr(self, slot) == getattr(other, slot)
                        for slot in self.__slots__)
-        else:
-            return NotImplemented
+        return NotImplemented
 
 
 class MetaEphemeral(type):
@@ -296,7 +294,7 @@ def __init__(self, name, in_types, ret_type, prefix="ARG"):
         # being polluted by builtins function when evaluating
         # GP expression.
         self.context = {"__builtins__": None}
-        self.mapping = dict()
+        self.mapping = {}
         self.terms_count = 0
         self.prims_count = 0
 
@@ -639,7 +637,7 @@ def generate(pset, min_, max_, condition, type_=None):
                 raise IndexError("The gp.generate function tried to add "
                                  "a terminal of type '%s', but there is "
                                  "none available." % (type_,)).with_traceback(traceback)
-            if type(term) is MetaEphemeral:
+            if isinstance(term, MetaEphemeral):
                 term = term()
             expr.append(term)
         else:
@@ -772,7 +770,7 @@ def mutUniform(individual, expr, pset):
     slice_ = individual.searchSubtree(index)
     type_ = individual[index].ret
     individual[slice_] = expr(pset=pset, type_=type_)
-    return individual,
+    return (individual,)
 
 
 def mutNodeReplacement(individual, pset):
@@ -784,21 +782,21 @@ def mutNodeReplacement(individual, pset):
     :returns: A tuple of one tree.
     """
     if len(individual) < 2:
-        return individual,
+        return (individual,)
 
     index = random.randrange(1, len(individual))
     node = individual[index]
 
     if node.arity == 0:  # Terminal
         term = random.choice(pset.terminals[node.ret])
-        if type(term) is MetaEphemeral:
+        if isinstance(term, MetaEphemeral):
             term = term()
         individual[index] = term
     else:  # Primitive
         prims = [p for p in pset.primitives[node.ret] if p.args == node.args]
         individual[index] = random.choice(prims)
 
-    return individual,
+    return (individual,)
 
 
 def mutEphemeral(individual, mode):
@@ -826,7 +824,7 @@ def mutEphemeral(individual, mode):
         for i in ephemerals_idx:
             individual[i] = type(individual[i])()
 
-    return individual,
+    return (individual,)
 
 
 def mutInsert(individual, pset):
@@ -850,7 +848,7 @@ def mutInsert(individual, pset):
     primitives = [p for p in pset.primitives[node.ret] if node.ret in p.args]
 
     if len(primitives) == 0:
-        return individual,
+        return (individual,)
 
     new_node = choice(primitives)
     new_subtree = [None] * len(new_node.args)
@@ -866,7 +864,7 @@ def mutInsert(individual, pset):
     new_subtree[position:position + 1] = individual[slice_]
     new_subtree.insert(0, new_node)
     individual[slice_] = new_subtree
-    return individual,
+    return (individual,)
 
 
 def mutShrink(individual):
@@ -878,7 +876,7 @@ def mutShrink(individual):
     """
     # We don't want to "shrink" the root
     if len(individual) < 3 or individual.height <= 1:
-        return individual,
+        return (individual,)
 
     iprims = []
     for i, node in enumerate(individual[1:], 1):
@@ -897,7 +895,7 @@ def mutShrink(individual):
         slice_ = individual.searchSubtree(index)
         individual[slice_] = subtree
 
-    return individual,
+    return (individual,)
 
 
 ######################################
@@ -1057,8 +1055,7 @@ def _genpop(n, pickfrom=[], acceptfunc=lambda s: True, producesizes=False):
 
         if producesizes:
             return producedpop, producedpopsizes
-        else:
-            return producedpop
+        return producedpop
 
     def halflifefunc(x):
         return x * float(alpha) + beta
@@ -1210,8 +1207,8 @@ def graph(expr):
        out of order when using `NetworX <http://networkx.github.com/>`_.
     """
     nodes = list(range(len(expr)))
-    edges = list()
-    labels = dict()
+    edges = []
+    labels = {}
 
     stack = []
     for i, node in enumerate(expr):
@@ -1282,7 +1279,7 @@ def mutSemantic(individual, gen_func=genGrow, pset=None, ms=None, min=2, max=6):
     new_ind.extend(tr1)
     new_ind.extend(tr2)
 
-    return new_ind,
+    return (new_ind,)
 
 
 def cxSemantic(ind1, ind2, gen_func=genGrow, pset=None, min=2, max=6):

examples/bbob.py

@@ -43,7 +43,7 @@ def tupleize(func):
     when the evaluation function returns a single value.
     """
     def wrapper(*args, **kargs):
-        return func(*args, **kargs),
+        return (func(*args, **kargs),)
     return wrapper
 
 
@@ -71,7 +71,7 @@ def main(func, dim, maxfuncevals, ftarget=None):
 
     # Evolve until ftarget is reached or the number of evaluation
     # is exhausted (maxfuncevals)
-    for g in range(1, maxfuncevals):
+    for _ in range(1, maxfuncevals):
         toolbox.update(worst, best, sigma)
         worst.fitness.values = toolbox.evaluate(worst)
         if best.fitness <= worst.fitness:

setup.py

@@ -11,7 +11,7 @@
 
 import deap
 
-warnings = list()
+warnings = []
 
 try:
     from setuptools import setup, Extension, find_packages
@@ -59,7 +59,7 @@ def build_extension(self, ext):
 def run_setup(build_ext):
     extra_modules = None
     if build_ext:
-        extra_modules = list()
+        extra_modules = []
 
         hv_module = Extension("deap.tools._hypervolume.hv", sources=["deap/tools/_hypervolume/_hv.c", "deap/tools/_hypervolume/hv.cpp"])
         extra_modules.append(hv_module)

tests/test_algorithms.py

@@ -100,7 +100,7 @@ def test_nsga2(setup_teardown_multi_obj):
         ind.fitness.values = fit
 
     pop = toolbox.select(pop, len(pop))
-    for gen in range(1, NGEN):
+    for _ in range(1, NGEN):
         offspring = tools.selTournamentDCD(pop, len(pop))
         offspring = [toolbox.clone(ind) for ind in offspring]
 
@@ -169,7 +169,7 @@ def valid(individual):
     toolbox.register("generate", strategy.generate, creator.__dict__[INDCLSNAME])
     toolbox.register("update", strategy.update)
 
-    for gen in range(NGEN):
+    for _ in range(NGEN):
         # Generate a new population
         population = toolbox.generate()
 
@@ -222,7 +222,7 @@ def test_nsga3(setup_teardown_multi_obj):
 
     pop = toolbox.select(pop, len(pop))
     # Begin the generational process
-    for gen in range(1, NGEN):
+    for _ in range(1, NGEN):
         offspring = algorithms.varAnd(pop, toolbox, 1.0, 1.0)
 
         # Evaluate the individuals with an invalid fitness

tests/test_convergence.py

@@ -82,7 +82,7 @@ def test_cma_mixed_integer_1_p_1_no_constraint(self):
 
         best = None
 
-        for gen in range(NGEN):
+        for _ in range(NGEN):
             # Generate a new population
             population = toolbox.generate()
 
@@ -118,7 +118,7 @@ def test_cma_mixed_integer_1_p_20_no_constraint(self):
 
         best = None
 
-        for gen in range(NGEN):
+        for _ in range(NGEN):
             # Generate a new population
             population = toolbox.generate()
 
@@ -174,7 +174,7 @@ def c2(individual):
 
             best = None
 
-            for gen in range(NGEN):
+            for _ in range(NGEN):
                 # Generate a new population
                 population = toolbox.generate()
 
@@ -234,7 +234,7 @@ def c2(individual):
 
             best = None
 
-            for gen in range(NGEN):
+            for _ in range(NGEN):
                 # Generate a new population
                 population = toolbox.generate()
 
@@ -291,7 +291,7 @@ def test_nsga2(self):
             ind.fitness.values = fit
 
         pop = toolbox.select(pop, len(pop))
-        for gen in range(1, NGEN):
+        for _ in range(1, NGEN):
             offspring = tools.selTournamentDCD(pop, len(pop))
             offspring = [toolbox.clone(ind) for ind in offspring]
 
@@ -344,7 +344,7 @@ def test_nsga3(self):
 
         pop = toolbox.select(pop, len(pop))
         # Begin the generational process
-        for gen in range(1, NGEN):
+        for _ in range(1, NGEN):
             # Vary the individuals
             offspring = list(islice(algorithms.varAnd(pop, toolbox, 1.0, 1.0), len(pop)))
 
@@ -414,7 +414,7 @@ def valid(individual):
         toolbox.register("generate", strategy.generate, creator.__dict__[INDCLSNAME])
         toolbox.register("update", strategy.update)
 
-        for gen in range(NGEN):
+        for _ in range(NGEN):
             # Generate a new population
             population = toolbox.generate()
 

tests/test_multiproc.py

@@ -5,7 +5,7 @@
 
 
 def _evalOneMax(individual):
-    return sum(individual),
+    return (sum(individual),)
 
 
 def test_multiproc():