Python deap.tools.initRepeat() Examples

def _setup_toolbox(self):
        with warnings.catch_warnings():
            warnings.simplefilter('ignore')
            creator.create('FitnessMulti', base.Fitness, weights=(-1.0, 1.0))
            creator.create('Individual', gp.PrimitiveTree, fitness=creator.FitnessMulti, statistics=dict)

        self._toolbox = base.Toolbox()
        self._toolbox.register('expr', self._gen_grow_safe, pset=self._pset, min_=self._min, max_=self._max)
        self._toolbox.register('individual', tools.initIterate, creator.Individual, self._toolbox.expr)
        self._toolbox.register('population', tools.initRepeat, list, self._toolbox.individual)
        self._toolbox.register('compile', self._compile_to_sklearn)
        self._toolbox.register('select', tools.selNSGA2)
        self._toolbox.register('mate', self._mate_operator)
        if self.tree_structure:
            self._toolbox.register('expr_mut', self._gen_grow_safe, min_=self._min, max_=self._max + 1)
        else:
            self._toolbox.register('expr_mut', self._gen_grow_safe, min_=self._min, max_=self._max)
        self._toolbox.register('mutate', self._random_mutation_operator) 

def getGlobalToolbox(representationModule):
    # GLOBAL FUNCTION TO GET GLOBAL TBX UNDER DEVELOPMENT;
    toolbox = base.Toolbox()
    creator.create("FitnessMax", base.Fitness, weights=(1.0,))
    creator.create(
        "Individual",
        list,
        fitness=creator.FitnessMax,
        PromoterMap=None,
        Strategy=genconf.Strategy,
    )
    toolbox.register("mate", representationModule.crossover)
    toolbox.register("mutate", representationModule.mutate)
    PromoterMap = initPromoterMap(Attributes)
    toolbox.register("newind", initInd, creator.Individual, PromoterMap)
    toolbox.register("population", tools.initRepeat, list, toolbox.newind)
    toolbox.register("constructPhenotype", representationModule.constructPhenotype)
    return toolbox 

def init_deap_functions(self):

        creator.create("Fitness", base.Fitness, weights=self.weights)
        creator.create("Individual", list, fitness=creator.Fitness)

        self.toolbox = base.Toolbox()

        self.toolbox.register("individual", tools.initIterate, creator.Individual, self.generate_ind)
        self.toolbox.register("population", tools.initRepeat, list, self.toolbox.individual)

        self.toolbox.register("evaluate", self.fit_func)

        if self.penalty != None:
            self.toolbox.decorate("evaluate", tools.DeltaPenality(self.feasible, self.inf_val)) 

def create_toolbox(self):
    """OptiGenAlgNsga2Deap method to create DEAP toolbox
    Parameters
    ----------
    self : OptiGenAlgNsga2Deap

    Returns
    -------
    self : OptiGenAlgNsga2Deap
        OptiGenAlgNsga2Deap with toolbox created 
    """

    # Create toolbox
    self.toolbox = base.Toolbox()

    # Create Fitness and individual
    creator.create(
        "FitnessMin", base.Fitness, weights=[-1 for _ in self.problem.design_var]
    )
    creator.create("Individual", list, typecode="d", fitness=creator.FitnessMin)

    self.toolbox.register("creator", creator.Individual)

    # Register individual and population
    self.toolbox.register(
        "individual",
        create_indiv,
        self.toolbox.creator,
        self.problem.output,
        self.problem.design_var,
    )

    self.toolbox.register("population", tools.initRepeat, list, self.toolbox.individual) 

def geneticAlgorithm(X, y, n_population, n_generation):
    """
    Deap global variables
    Initialize variables to use eaSimple
    """
    # create individual
    creator.create("FitnessMax", base.Fitness, weights=(1.0,))
    creator.create("Individual", list, fitness=creator.FitnessMax)

    # create toolbox
    toolbox = base.Toolbox()
    toolbox.register("attr_bool", random.randint, 0, 1)
    toolbox.register("individual", tools.initRepeat,
                     creator.Individual, toolbox.attr_bool, len(X.columns))
    toolbox.register("population", tools.initRepeat, list,
                     toolbox.individual)
    toolbox.register("evaluate", getFitness, X=X, y=y)
    toolbox.register("mate", tools.cxOnePoint)
    toolbox.register("mutate", tools.mutFlipBit, indpb=0.05)
    toolbox.register("select", tools.selTournament, tournsize=3)

    # initialize parameters
    pop = toolbox.population(n=n_population)
    hof = tools.HallOfFame(n_population * n_generation)
    stats = tools.Statistics(lambda ind: ind.fitness.values)
    stats.register("avg", np.mean)
    stats.register("min", np.min)
    stats.register("max", np.max)

    # genetic algorithm
    pop, log = algorithms.eaSimple(pop, toolbox, cxpb=0.5, mutpb=0.2,
                                   ngen=n_generation, stats=stats, halloffame=hof,
                                   verbose=True)

    # return hall of fame
    return hof 

def create_toolbox(num_bits):
    creator.create("FitnessMax", base.Fitness, weights=(1.0,))
    creator.create("Individual", list, fitness=creator.FitnessMax)

    # Initialize the toolbox
    toolbox = base.Toolbox()

    # Generate attributes 
    toolbox.register("attr_bool", random.randint, 0, 1)

    # Initialize structures
    toolbox.register("individual", tools.initRepeat, creator.Individual, 
        toolbox.attr_bool, num_bits)

    # Define the population to be a list of individuals
    toolbox.register("population", tools.initRepeat, list, toolbox.individual)

    # Register the evaluation operator 
    toolbox.register("evaluate", eval_func)

    # Register the crossover operator
    toolbox.register("mate", tools.cxTwoPoint)

    # Register a mutation operator
    toolbox.register("mutate", tools.mutFlipBit, indpb=0.05)

    # Operator for selecting individuals for breeding
    toolbox.register("select", tools.selTournament, tournsize=3)
    
    return toolbox 

def create_toolbox():
    pset = gp.PrimitiveSet("MAIN", 1)
    pset.addPrimitive(operator.add, 2)
    pset.addPrimitive(operator.sub, 2)
    pset.addPrimitive(operator.mul, 2)
    pset.addPrimitive(division_operator, 2)
    pset.addPrimitive(operator.neg, 1)
    pset.addPrimitive(math.cos, 1)
    pset.addPrimitive(math.sin, 1)

    pset.addEphemeralConstant("rand101", lambda: random.randint(-1,1))

    pset.renameArguments(ARG0='x')

    creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
    creator.create("Individual", gp.PrimitiveTree, fitness=creator.FitnessMin)

    toolbox = base.Toolbox()

    toolbox.register("expr", gp.genHalfAndHalf, pset=pset, min_=1, max_=2)
    toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.expr)
    toolbox.register("population", tools.initRepeat, list, toolbox.individual)
    toolbox.register("compile", gp.compile, pset=pset)
    toolbox.register("evaluate", eval_func, points=[x/10. for x in range(-10,10)])
    toolbox.register("select", tools.selTournament, tournsize=3)
    toolbox.register("mate", gp.cxOnePoint)
    toolbox.register("expr_mut", gp.genFull, min_=0, max_=2)
    toolbox.register("mutate", gp.mutUniform, expr=toolbox.expr_mut, pset=pset)

    toolbox.decorate("mate", gp.staticLimit(key=operator.attrgetter("height"), max_value=17))
    toolbox.decorate("mutate", gp.staticLimit(key=operator.attrgetter("height"), max_value=17))

    return toolbox 

def create_toolbox():
    global robot, pset

    pset = gp.PrimitiveSet("MAIN", 0)
    pset.addPrimitive(robot.if_target_ahead, 2)
    pset.addPrimitive(Prog().prog2, 2)
    pset.addPrimitive(Prog().prog3, 3)
    pset.addTerminal(robot.move_forward)
    pset.addTerminal(robot.turn_left)
    pset.addTerminal(robot.turn_right)

    creator.create("FitnessMax", base.Fitness, weights=(1.0,))
    creator.create("Individual", gp.PrimitiveTree, fitness=creator.FitnessMax)

    toolbox = base.Toolbox()

    # Attribute generator
    toolbox.register("expr_init", gp.genFull, pset=pset, min_=1, max_=2)

    # Structure initializers
    toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.expr_init)
    toolbox.register("population", tools.initRepeat, list, toolbox.individual)

    toolbox.register("evaluate", eval_func)
    toolbox.register("select", tools.selTournament, tournsize=7)
    toolbox.register("mate", gp.cxOnePoint)
    toolbox.register("expr_mut", gp.genFull, min_=0, max_=2)
    toolbox.register("mutate", gp.mutUniform, expr=toolbox.expr_mut, pset=pset)

    return toolbox 

def getToolbox(Strategy, genconf, Attributes):
    toolbox = base.Toolbox()
    creator = Creator.init(base.Fitness, {'Strategy': Strategy})
    toolbox.register("newind", initInd, creator.Individual, Attributes)
    toolbox.register("population", tools.initRepeat, list, toolbox.newind)
    toolbox.register("mate", tools.cxTwoPoint)
    toolbox.register("mutate", tools.mutUniformInt, low=10, up=10, indpb=0.2)
    toolbox.register("constructPhenotype", constructPhenotype, Attributes)
    return toolbox 

def getToolbox(Strategy, genconf, Attributes):
    toolbox = base.Toolbox()
    creator = Creator.init(base.Fitness, {'promoterMap': None, 'Strategy': Strategy})
    # creator.create("FitnessMax", base.Fitness, weights=(1.0, 3))
    toolbox.register("mate", pachytene)
    toolbox.register("mutate", mutate)
    PromoterMap = initPromoterMap(Attributes)
    toolbox.register(
        "newind", initInd, creator.Individual, PromoterMap, genconf.chromosome
    )
    toolbox.register("population", tools.initRepeat, list, toolbox.newind)
    toolbox.register(
        "constructPhenotype", constructPhenotype, Attributes, genconf.chromosome
    )
    return toolbox 

def get_toolbox(self, predictors, response, pset, variable_type_indices, variable_names):
        subset_size = int(math.floor(predictors.shape[0] * self.subset_proportion))
        creator.create("Error", base.Fitness, weights=(-1.0,))
        creator.create("Individual", sp.SimpleParametrizedPrimitiveTree, fitness=creator.Error, age=int)
        toolbox = base.Toolbox()
        toolbox.register("expr", sp.generate_parametrized_expression,
                         partial(gp.genHalfAndHalf, pset=pset, min_=self.min_depth_init, max_=self.max_depth_init),
                         variable_type_indices, variable_names)
        toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.expr)
        toolbox.register("population", tools.initRepeat, list, toolbox.individual)
        toolbox.register("compile", gp.compile, pset=pset)
        toolbox.register("grow", sp.generate_parametrized_expression,
                         partial(gp.genGrow, pset=pset, min_=self.min_gen_grow, max_=self.max_gen_grow),
                         variable_type_indices, variable_names)
        toolbox.register("mutate", operators.mutation_biased, expr=toolbox.grow,
                         node_selector=operators.uniform_depth_node_selector)
        toolbox.decorate("mutate", operators.static_limit(key=operator.attrgetter("height"), max_value=self.max_height))
        toolbox.decorate("mutate", operators.static_limit(key=len, max_value=self.max_size))
        # self.history = tools.History()
        # toolbox.decorate("mutate", self.history.decorator)
        toolbox.register("error_func", self.error_function)
        expression_dict = cachetools.LRUCache(maxsize=1000)
        subset_selection_archive = subset_selection.RandomSubsetSelectionArchive(frequency=self.subset_change_frequency,
                                                                                 predictors=predictors,
                                                                                 response=response,
                                                                                 subset_size=subset_size,
                                                                                 expression_dict=expression_dict)
        evaluate_function = partial(subset_selection.fast_numpy_evaluate_subset,
                                    get_node_semantics=sp.get_node_semantics,
                                    context=pset.context,
                                    subset_selection_archive=subset_selection_archive,
                                    error_function=toolbox.error_func,
                                    expression_dict=expression_dict)
        toolbox.register("evaluate_error", evaluate_function)
        self.multi_archive = utils.get_archive(100)
        if self.log_mutate:
            mutation_stats_archive = archive.MutationStatsArchive(evaluate_function)
            toolbox.decorate("mutate", operators.stats_collector(archive=mutation_stats_archive))
            self.multi_archive.archives.append(mutation_stats_archive)
        self.multi_archive.archives.append(subset_selection_archive)
        self.mstats = reports.configure_parametrized_inf_protected_stats()
        self.pop = toolbox.population(n=self.pop_size)
        toolbox.register("run", truncation_with_elite.optimize, population=self.pop, toolbox=toolbox,
                         ngen=self.ngen, stats=self.mstats, archive=self.multi_archive, verbose=False,
                         history=None)
                         # history=self.history)
        toolbox.register("save", reports.save_log_to_csv)
        toolbox.decorate("save", reports.save_archive(self.multi_archive))
        return toolbox 

def runOptGenetic():
    '''

    @return:
    @rtype:
    '''
    # COULDDO parametrisation

    creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
    creator.create("Individual", list, fitness=creator.FitnessMin)

    IND_SIZE = num_slots * num_evs
    POP_SIZE = 30

    toolbox = base.Toolbox()
    toolbox.register("attr_float", rd.random)  # COULDDO heuristic init
    toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_float, n=IND_SIZE)
    toolbox.register("population", tools.initRepeat, list, toolbox.individual, n=POP_SIZE)
    toolbox.register("evaluate", evaluate)
    toolbox.decorate("evaluate", tools.DeltaPenalty(feasible, 0.0, distance))
    toolbox.register("mate", tools.cxTwoPoint)
    toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=0.5, indpb=0.5)
    toolbox.register("select", tools.selTournament, tournsize=3)

    stats = tools.Statistics(key=lambda ind: ind.fitness.values)
    stats.register("avg", np.mean)
    stats.register("std", np.std)
    stats.register("min", np.min)
    stats.register("max", np.max)

    hof = tools.HallOfFame(1)
    population = toolbox.population()

# if no of-the-shelf algorithm used...
#     fits = toolbox.map(toolbox.evaluate, population)
#     for fit, ind in zip(fits, population):
#             ind.fitness.values = fit

    population, logbook = algorithms.eaSimple(population, toolbox, cxpb=0.5, mutpb=0.3, ngen=5, stats=stats, verbose=True, halloffame=hof)

    sorted_pop = sorted(population, key=lambda ind: ind.fitness)

    ev_schedules = np.asarray(best).reshape((num_evs, num_slots))
    schedules = np.zeros((num_households, num_slots)).tolist()

    for i in range(num_evs):
        schedules[evs[i].position] = ev_schedules[i].tolist()
        evs[i].schedule = schedules[evs[i].position]

    return schedules

# *****************************************************************************************************
# * Metaheuristics Side Functions
# *****************************************************************************************************

# UNUSED 

def test_nsga2():
    NDIM = 5
    BOUND_LOW, BOUND_UP = 0.0, 1.0
    MU = 16
    NGEN = 100

    toolbox = base.Toolbox()
    toolbox.register("attr_float", random.uniform, BOUND_LOW, BOUND_UP)
    toolbox.register("individual", tools.initRepeat, creator.__dict__[INDCLSNAME], toolbox.attr_float, NDIM)
    toolbox.register("population", tools.initRepeat, list, toolbox.individual)

    toolbox.register("evaluate", benchmarks.zdt1)
    toolbox.register("mate", tools.cxSimulatedBinaryBounded, low=BOUND_LOW, up=BOUND_UP, eta=20.0)
    toolbox.register("mutate", tools.mutPolynomialBounded, low=BOUND_LOW, up=BOUND_UP, eta=20.0, indpb=1.0/NDIM)
    toolbox.register("select", tools.selNSGA2)

    pop = toolbox.population(n=MU)
    fitnesses = toolbox.map(toolbox.evaluate, pop)
    for ind, fit in zip(pop, fitnesses):
        ind.fitness.values = fit

    pop = toolbox.select(pop, len(pop))
    for gen in range(1, NGEN):
        offspring = tools.selTournamentDCD(pop, len(pop))
        offspring = [toolbox.clone(ind) for ind in offspring]

        for ind1, ind2 in zip(offspring[::2], offspring[1::2]):
            if random.random() <= 0.9:
                toolbox.mate(ind1, ind2)

            toolbox.mutate(ind1)
            toolbox.mutate(ind2)
            del ind1.fitness.values, ind2.fitness.values

        invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
        fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
        for ind, fit in zip(invalid_ind, fitnesses):
            ind.fitness.values = fit

        pop = toolbox.select(pop + offspring, MU)

    hv = hypervolume(pop, [11.0, 11.0])
    # hv = 120.777 # Optimal value

    assert hv > HV_THRESHOLD, "Hypervolume is lower than expected %f < %f" % (hv, HV_THRESHOLD)

    for ind in pop:
        assert not (any(numpy.asarray(ind) < BOUND_LOW) or any(numpy.asarray(ind) > BOUND_UP)) 

def test_nsga3():
    NDIM = 5
    BOUND_LOW, BOUND_UP = 0.0, 1.0
    MU = 16
    NGEN = 100

    ref_points = tools.uniform_reference_points(2, p=12)

    toolbox = base.Toolbox()
    toolbox.register("attr_float", random.uniform, BOUND_LOW, BOUND_UP)
    toolbox.register("individual", tools.initRepeat, creator.__dict__[INDCLSNAME], toolbox.attr_float, NDIM)
    toolbox.register("population", tools.initRepeat, list, toolbox.individual)

    toolbox.register("evaluate", benchmarks.zdt1)
    toolbox.register("mate", tools.cxSimulatedBinaryBounded, low=BOUND_LOW, up=BOUND_UP, eta=20.0)
    toolbox.register("mutate", tools.mutPolynomialBounded, low=BOUND_LOW, up=BOUND_UP, eta=20.0, indpb=1.0/NDIM)
    toolbox.register("select", tools.selNSGA3, ref_points=ref_points)

    pop = toolbox.population(n=MU)
    fitnesses = toolbox.map(toolbox.evaluate, pop)
    for ind, fit in zip(pop, fitnesses):
        ind.fitness.values = fit

    pop = toolbox.select(pop, len(pop))
     # Begin the generational process
    for gen in range(1, NGEN):
        offspring = algorithms.varAnd(pop, toolbox, 1.0, 1.0)

        # Evaluate the individuals with an invalid fitness
        invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
        fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
        for ind, fit in zip(invalid_ind, fitnesses):
            ind.fitness.values = fit

        # Select the next generation population
        pop = toolbox.select(pop + offspring, MU)

    hv = hypervolume(pop, [11.0, 11.0])
    # hv = 120.777 # Optimal value

    assert hv > HV_THRESHOLD, "Hypervolume is lower than expected %f < %f" % (hv, HV_THRESHOLD)

    for ind in pop:
        assert not (any(numpy.asarray(ind) < BOUND_LOW) or any(numpy.asarray(ind) > BOUND_UP))